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Bump mypy-protobuf in sync_tensorflow script and improve generation scripts (#11740)

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This commit is contained in:
Avasam
2024-04-10 09:12:07 -04:00
committed by GitHub
parent efad2fb315
commit fe02cba606
94 changed files with 2408 additions and 2116 deletions
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scripts/generate_proto_stubs.sh
+12 -5
View File
@@ -35,6 +35,7 @@ echo "Working in $TMP_DIR"
wget "$PROTOC_URL"
mkdir protoc_install
unzip "$PROTOC_FILENAME" -d protoc_install
protoc_install/bin/protoc --version
# Fetch protoc-python (which contains all the .proto files)
wget "$PYTHON_PROTOBUF_URL"
@@ -67,16 +68,22 @@ PROTO_FILES=$(grep "GenProto.*google" $PYTHON_PROTOBUF_DIR/python/setup.py | \
# And regenerate!
# shellcheck disable=SC2086
protoc_install/bin/protoc --proto_path="$PYTHON_PROTOBUF_DIR/src" --mypy_out="relax_strict_optional_primitives:$REPO_ROOT/stubs/protobuf" $PROTO_FILES
protoc_install/bin/protoc \
--proto_path="$PYTHON_PROTOBUF_DIR/src" \
--mypy_out="relax_strict_optional_primitives:$REPO_ROOT/stubs/protobuf" \
$PROTO_FILES
PYTHON_PROTOBUF_VERSION=$(jq -r '.[] | .languages.python' "$PYTHON_PROTOBUF_DIR/version.json")
# Cleanup after ourselves, this is a temp dir, but it can still grow fast if run multiple times
rm -rf "$TMP_DIR"
# Must be in a git repository to run pre-commit
cd "$REPO_ROOT"
sed --in-place="" \
"s/extra_description = .*$/extra_description = \"Generated using [mypy-protobuf==$MYPY_PROTOBUF_VERSION](https:\/\/github.com\/nipunn1313\/mypy-protobuf\/tree\/v$MYPY_PROTOBUF_VERSION) on [protobuf v$PROTOBUF_VERSION](https:\/\/github.com\/protocolbuffers\/protobuf\/releases\/tag\/v$PROTOBUF_VERSION) (python protobuf==$PYTHON_PROTOBUF_VERSION)\"/" \
"$REPO_ROOT/stubs/protobuf/METADATA.toml"
stubs/protobuf/METADATA.toml
# Must be run in a git repository
cd "$REPO_ROOT"
# use `|| true` so the script still continues even if a pre-commit hook
# applies autofixes (which will result in a nonzero exit code)
pre-commit run --files $(git ls-files -- "$REPO_ROOT/stubs/protobuf/**_pb2.pyi") || true
pre-commit run --files $(git ls-files -- "stubs/protobuf/**_pb2.pyi") || true
scripts/sync_tensorflow_protobuf_stubs.sh
+79 -61
View File
@@ -1,79 +1,97 @@
#!/bin/bash
set -euxo pipefail
# Partly based on scripts/generate_proto_stubs.sh.
# Based on scripts/generate_proto_stubs.sh.
# Generates the protobuf stubs for the given tensorflow version using mypy-protobuf.
# Generally, new minor versions are a good time to update the stubs.
REPO_ROOT="$(realpath "$(dirname "${BASH_SOURCE[0]}")"/..)"
# This version should be consistent with the version in tensorflow's METADATA.toml.
set -euxo pipefail
# Need protoc >= 3.15 for explicit optional
PROTOBUF_VERSION=25.3 # 4.25.3
# Whenever you update TENSORFLOW_VERSION here, version should be updated
# in stubs/tensorflow/METADATA.toml and vice-versa.
TENSORFLOW_VERSION=2.12.1
# Latest mypy-protobuf has dependency on protobuf >4, which is incompatible at runtime
# with tensorflow. However, the stubs produced do still work with tensorflow. So after
# installing mypy-protobuf, before running stubtest on tensorflow you should downgrade
# protobuf<4.
MYPY_PROTOBUF_VERSION=3.5.0
MYPY_PROTOBUF_VERSION=3.6.0
pip install pre-commit mypy-protobuf=="$MYPY_PROTOBUF_VERSION"
if uname -a | grep Darwin; then
# brew install coreutils wget
PLAT=osx
else
PLAT=linux
fi
REPO_ROOT="$(realpath "$(dirname "${BASH_SOURCE[0]}")"/..)"
TMP_DIR="$(mktemp -d)"
TENSORFLOW_FILENAME="v$TENSORFLOW_VERSION.zip"
PROTOC_FILENAME="protoc-$PROTOBUF_VERSION-$PLAT-x86_64.zip"
PROTOC_URL="https://github.com/protocolbuffers/protobuf/releases/download/v$PROTOBUF_VERSION/$PROTOC_FILENAME"
TENSORFLOW_URL="https://github.com/tensorflow/tensorflow/archive/refs/tags/$TENSORFLOW_FILENAME"
cd "$(dirname "$0")" > /dev/null
cd ../stubs/tensorflow
mkdir -p repository
pushd repository &> /dev/null
# If the script fails halfway, it's nice to be able to re-run it immediately
if [ ! -d "tensorflow" ] ; then
git clone --depth 1 --branch v"$TENSORFLOW_VERSION" https://github.com/tensorflow/tensorflow.git
fi
pushd tensorflow &> /dev/null
# Folders here cover the more commonly used protobufs externally and
# their dependencies. Tensorflow has more protobufs and can be added if requested.
protoc --mypy_out "relax_strict_optional_primitives:$REPO_ROOT/stubs/tensorflow" \
tensorflow/compiler/xla/*.proto \
tensorflow/compiler/xla/service/*.proto \
tensorflow/core/example/*.proto \
tensorflow/core/framework/*.proto \
tensorflow/core/protobuf/*.proto \
tensorflow/core/protobuf/tpu/*.proto \
tensorflow/core/util/*.proto \
tensorflow/python/keras/protobuf/*.proto \
tensorflow/tsl/protobuf/*.proto
popd &> /dev/null
popd &> /dev/null
cd "$TMP_DIR"
echo "Working in $TMP_DIR"
# Install protoc
wget "$PROTOC_URL"
mkdir protoc_install
unzip "$PROTOC_FILENAME" -d protoc_install
protoc_install/bin/protoc --version
# Fetch tensorflow (which contains all the .proto files)
wget "$TENSORFLOW_URL"
unzip "$TENSORFLOW_FILENAME"
TENSORFLOW_DIR="tensorflow-$TENSORFLOW_VERSION"
# Prepare virtualenv
python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install pre-commit mypy-protobuf=="$MYPY_PROTOBUF_VERSION"
# Remove existing pyi
find "$REPO_ROOT/stubs/tensorflow/" -name "*_pb2.pyi" -delete
# Folders here cover the more commonly used protobufs externally and
# their dependencies. Tensorflow has more protobufs and can be added if requested.
protoc_install/bin/protoc \
--proto_path="$TENSORFLOW_DIR" \
--mypy_out "relax_strict_optional_primitives:$REPO_ROOT/stubs/tensorflow" \
$TENSORFLOW_DIR/tensorflow/compiler/xla/*.proto \
$TENSORFLOW_DIR/tensorflow/compiler/xla/service/*.proto \
$TENSORFLOW_DIR/tensorflow/core/example/*.proto \
$TENSORFLOW_DIR/tensorflow/core/framework/*.proto \
$TENSORFLOW_DIR/tensorflow/core/protobuf/*.proto \
$TENSORFLOW_DIR/tensorflow/core/protobuf/tpu/*.proto \
$TENSORFLOW_DIR/tensorflow/core/util/*.proto \
$TENSORFLOW_DIR/tensorflow/python/keras/protobuf/*.proto \
$TENSORFLOW_DIR/tensorflow/tsl/protobuf/*.proto \
# Cleanup after ourselves, this is a temp dir, but it can still grow fast if run multiple times
rm -rf "$TMP_DIR"
# Must be in a git repository to run pre-commit
cd "$REPO_ROOT"
# These protos exist in a folder with protos used in python, but are not
# included in the python wheel. They are likely only used for other
# language builds. stubtest was used to identify them by looking for
# ModuleNotFoundError.
rm tensorflow/compiler/xla/service/hlo_execution_profile_data_pb2.pyi \
tensorflow/compiler/xla/service/hlo_profile_printer_data_pb2.pyi \
tensorflow/compiler/xla/service/test_compilation_environment_pb2.pyi \
tensorflow/compiler/xla/xla_pb2.pyi \
tensorflow/core/protobuf/autotuning_pb2.pyi \
tensorflow/core/protobuf/conv_autotuning_pb2.pyi \
tensorflow/core/protobuf/critical_section_pb2.pyi \
tensorflow/core/protobuf/eager_service_pb2.pyi \
tensorflow/core/protobuf/master_pb2.pyi \
tensorflow/core/protobuf/master_service_pb2.pyi \
tensorflow/core/protobuf/replay_log_pb2.pyi \
tensorflow/core/protobuf/tpu/compile_metadata_pb2.pyi \
tensorflow/core/protobuf/worker_pb2.pyi \
tensorflow/core/protobuf/worker_service_pb2.pyi \
tensorflow/core/util/example_proto_fast_parsing_test_pb2.pyi
rm \
stubs/tensorflow/tensorflow/compiler/xla/service/hlo_execution_profile_data_pb2.pyi \
stubs/tensorflow/tensorflow/compiler/xla/service/hlo_profile_printer_data_pb2.pyi \
stubs/tensorflow/tensorflow/compiler/xla/service/test_compilation_environment_pb2.pyi \
stubs/tensorflow/tensorflow/compiler/xla/xla_pb2.pyi \
stubs/tensorflow/tensorflow/core/protobuf/autotuning_pb2.pyi \
stubs/tensorflow/tensorflow/core/protobuf/conv_autotuning_pb2.pyi \
stubs/tensorflow/tensorflow/core/protobuf/critical_section_pb2.pyi \
stubs/tensorflow/tensorflow/core/protobuf/eager_service_pb2.pyi \
stubs/tensorflow/tensorflow/core/protobuf/master_pb2.pyi \
stubs/tensorflow/tensorflow/core/protobuf/master_service_pb2.pyi \
stubs/tensorflow/tensorflow/core/protobuf/replay_log_pb2.pyi \
stubs/tensorflow/tensorflow/core/protobuf/tpu/compile_metadata_pb2.pyi \
stubs/tensorflow/tensorflow/core/protobuf/worker_pb2.pyi \
stubs/tensorflow/tensorflow/core/protobuf/worker_service_pb2.pyi \
stubs/tensorflow/tensorflow/core/util/example_proto_fast_parsing_test_pb2.pyi \
sed --in-place="" \
"s/extra_description = .*$/extra_description = \"Partially generated using [mypy-protobuf==$MYPY_PROTOBUF_VERSION](https:\/\/github.com\/nipunn1313\/mypy-protobuf\/tree\/v$MYPY_PROTOBUF_VERSION) on tensorflow==$TENSORFLOW_VERSION\"/" \
"$REPO_ROOT/stubs/tensorflow/METADATA.toml"
stubs/tensorflow/METADATA.toml
# Cleanup last. If the script fails halfway, it's nice to be able to re-run it immediately
rm -rf repository/
# Must be run in a git repository
cd $REPO_ROOT
# use `|| true` so the script still continues even if a pre-commit hook
# applies autofixes (which will result in a nonzero exit code)
pre-commit run --files $(git ls-files -- "$REPO_ROOT/stubs/tensorflow/tensorflow") || true
# Ruff takes two passes to fix everything, re-running all of pre-commit is *slow*
# and we don't need --unsafe-fixes to remove imports
ruff check "$REPO_ROOT/stubs/tensorflow/tensorflow" --fix --exit-zero
pre-commit run --files $(git ls-files -- "stubs/tensorflow/**_pb2.pyi") || true
stubs/tensorflow/METADATA.toml
+1 -1
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@@ -2,7 +2,7 @@ version = "2.15.*"
upstream_repository = "https://github.com/tensorflow/tensorflow"
# requires a version of numpy with a `py.typed` file
requires = ["numpy>=1.20", "types-protobuf", "types-requests"]
extra_description = "Partially generated using [mypy-protobuf==3.5.0](https://github.com/nipunn1313/mypy-protobuf/tree/v3.5.0) on tensorflow==2.12.1"
extra_description = "Partially generated using [mypy-protobuf==3.6.0](https://github.com/nipunn1313/mypy-protobuf/tree/v3.6.0) on tensorflow==2.12.1"
partial_stub = true
[tool.stubtest]
stubs/tensorflow/tensorflow/compiler/xla/autotune_results_pb2.pyi
+8 -6
View File
@@ -16,9 +16,10 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -27,7 +28,7 @@ import tensorflow.tsl.protobuf.autotuning_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class AutotuneResults(google.protobuf.message.Message):
"""A collection of algorithms for particular dot/convs. Usually this is "the
best" algorithm for the particular dot/conv, although that's not strictly
@@ -43,7 +44,7 @@ class AutotuneResults(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Entry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -60,6 +61,7 @@ class AutotuneResults(google.protobuf.message.Message):
algorithms returned by cublasLt. Different version of cublasLt ->
different list of algos -> different interpretation of results!
"""
def __init__(
self,
*,
@@ -67,8 +69,8 @@ class AutotuneResults(google.protobuf.message.Message):
hlo: builtins.str | None = ...,
result: tensorflow.tsl.protobuf.autotuning_pb2.AutotuneResult | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["result", b"result"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["device", b"device", "hlo", b"hlo", "result", b"result"]) -> None: ...
def HasField(self, field_name: typing.Literal["result", b"result"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["device", b"device", "hlo", b"hlo", "result", b"result"]) -> None: ...
VERSION_FIELD_NUMBER: builtins.int
DOTS_FIELD_NUMBER: builtins.int
@@ -85,6 +87,6 @@ class AutotuneResults(google.protobuf.message.Message):
dots: collections.abc.Iterable[global___AutotuneResults.Entry] | None = ...,
convs: collections.abc.Iterable[global___AutotuneResults.Entry] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["convs", b"convs", "dots", b"dots", "version", b"version"]) -> None: ...
def ClearField(self, field_name: typing.Literal["convs", b"convs", "dots", b"dots", "version", b"version"]) -> None: ...
global___AutotuneResults = AutotuneResults
stubs/tensorflow/tensorflow/compiler/xla/service/hlo_pb2.pyi
+243 -202
View File
@@ -14,6 +14,7 @@ Unlike most protos, you can't safely change the names of fields, even if you
keep the numeric ids the same. This is because we sometimes serialize these
protos as JSON, which includes the field names in the serialization.
"""
import builtins
import collections.abc
import sys
@@ -214,7 +215,7 @@ MUST_ALIAS: Kind.ValueType # 2
"""The buffers must alias at runtime."""
global___Kind = Kind
@typing_extensions.final
@typing.final
class HloInstructionProto(google.protobuf.message.Message):
"""Serialization of HloInstruction.
Next ID: 81
@@ -222,7 +223,7 @@ class HloInstructionProto(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class SliceDimensions(google.protobuf.message.Message):
"""Describes the [begin, end) index range and stride for slices."""
@@ -241,7 +242,7 @@ class HloInstructionProto(google.protobuf.message.Message):
limit: builtins.int | None = ...,
stride: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["limit", b"limit", "start", b"start", "stride", b"stride"]) -> None: ...
def ClearField(self, field_name: typing.Literal["limit", b"limit", "start", b"start", "stride", b"stride"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
OPCODE_FIELD_NUMBER: builtins.int
@@ -313,51 +314,21 @@ class HloInstructionProto(google.protobuf.message.Message):
ASYNC_EXECUTION_THREAD_FIELD_NUMBER: builtins.int
name: builtins.str
opcode: builtins.str
@property
def shape(self) -> tensorflow.compiler.xla.xla_data_pb2.ShapeProto: ...
@property
def metadata(self) -> tensorflow.compiler.xla.xla_data_pb2.OpMetadata: ...
@property
def literal(self) -> tensorflow.compiler.xla.xla_data_pb2.LiteralProto:
"""Literal, only present for kConstant."""
parameter_number: builtins.int
"""Parameter number is only present for kParameter."""
fusion_kind: builtins.str
"""Fusion state, only present for kFusion."""
tuple_index: builtins.int
"""Index for kGetTupleElement."""
@property
def dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Dimensions present for some operations that require reshaping or
broadcasting, including Reshape, Reduce, ReduceWindow, and Reverse.
"""
@property
def window(self) -> tensorflow.compiler.xla.xla_data_pb2.Window:
"""Describes the window in a windowed operation such as convolution."""
@property
def convolution_dimension_numbers(self) -> tensorflow.compiler.xla.xla_data_pb2.ConvolutionDimensionNumbers:
"""Describes the dimension numbers used for a convolution."""
feature_group_count: builtins.int
"""The number of feature groups. Used for a convolution. Must be a divisor of
the input feature dimension and output feature dimension. If not specified,
it will use a default value of 1.
"""
batch_group_count: builtins.int
@property
def slice_dimensions(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___HloInstructionProto.SliceDimensions]: ...
exponent_bits: builtins.int
"""The bit sizes for a reduce-precision operation."""
mantissa_bits: builtins.int
@property
def dynamic_slice_sizes(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Describes the [start, start + size) range size for a dynamic slice
('start' is specified dynamically in the second operand of the operation).
"""
@property
def padding_config(self) -> tensorflow.compiler.xla.xla_data_pb2.PaddingConfig:
"""The padding configuration that describes the edge padding and interior
padding of this pad instruction. Only set for pad instructions.
"""
outfeed_config: builtins.bytes
"""Outfeed configuration information, only present for kOutfeed."""
distribution: tensorflow.compiler.xla.xla_data_pb2.RandomDistribution.ValueType
@@ -385,39 +356,14 @@ class HloInstructionProto(google.protobuf.message.Message):
"""Name of a external target (eg, global symbol) to call, only present for
kCustomCall.
"""
@property
def outfeed_shape(self) -> tensorflow.compiler.xla.xla_data_pb2.ShapeProto:
"""Shape of outfeed request."""
@property
def dot_dimension_numbers(self) -> tensorflow.compiler.xla.xla_data_pb2.DotDimensionNumbers:
"""Describes the dimension numbers used for a dot operation"""
fft_type: tensorflow.compiler.xla.xla_data_pb2.FftType.ValueType
"""FFT type (FFT, IFFT, etc)."""
@property
def fft_length(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""FFT length."""
comparison_direction: builtins.str
"""Comparison direction only used for kCompare."""
@property
def gather_dimension_numbers(self) -> tensorflow.compiler.xla.xla_data_pb2.GatherDimensionNumbers:
"""Gather dimension numbers."""
@property
def gather_slice_sizes(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
id: builtins.int
"""The id of this instruction."""
@property
def operand_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def control_predecessor_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def called_computation_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def sharding(self) -> tensorflow.compiler.xla.xla_data_pb2.OpSharding: ...
backend_config: builtins.bytes
"""Backend configuration for the instruction. Has backend-specific meaning."""
@property
def replica_groups(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.compiler.xla.xla_data_pb2.ReplicaGroup]:
"""Cross replica op fields."""
all_reduce_id: builtins.int
"""Deprecated, but keeping it for backward compatibility. Use channel_id.
Non-positive all_reduce_id is equivalent to no all_reduce_id.
@@ -433,45 +379,16 @@ class HloInstructionProto(google.protobuf.message.Message):
"""
is_stable: builtins.bool
"""Whether this Sort instruction should be stable."""
@property
def scatter_dimension_numbers(self) -> tensorflow.compiler.xla.xla_data_pb2.ScatterDimensionNumbers: ...
@property
def precision_config(self) -> tensorflow.compiler.xla.xla_data_pb2.PrecisionConfig:
"""Precision configuration for the instruction. Has backend-specific meaning."""
@property
def source_target_pairs(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.compiler.xla.xla_data_pb2.SourceTarget]:
"""Collective permute field."""
@property
def domain_entry_sharding(self) -> tensorflow.compiler.xla.xla_data_pb2.OpSharding:
"""Sharding for kDomain instructions."""
@property
def domain_exit_sharding(self) -> tensorflow.compiler.xla.xla_data_pb2.OpSharding: ...
constrain_layout: builtins.bool
"""For custom call this indicates that the layouts are constrained. If
constrain_layout is true then the 'shape' field must contain a layout, and
'operand_shapes_with_layout' must contain a shape with layout for each
operand.
"""
@property
def operand_shapes_with_layout(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.compiler.xla.xla_data_pb2.ShapeProto]: ...
@property
def triangular_solve_options(self) -> tensorflow.compiler.xla.xla_data_pb2.TriangularSolveOptions:
"""Options for TriangularSolve"""
@property
def cholesky_options(self) -> tensorflow.compiler.xla.xla_data_pb2.CholeskyOptions:
"""Options for Cholesky"""
@property
def parameter_replication(self) -> tensorflow.compiler.xla.xla_data_pb2.ParameterReplication:
"""Describes how parameters behave with regards to replicas."""
custom_call_has_side_effect: builtins.bool
"""Whether the kCustomCall instruction has side-effects, only present for
kCustomCall.
"""
@property
def output_operand_aliasing(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.compiler.xla.xla_data_pb2.OutputOperandAliasing]:
"""A list of OutputOperandAliasing pairs that specifies aliasing buffers
between output and operands for kCustomCall and kFusion.
"""
custom_call_schedule: global___CustomCallSchedule.ValueType
"""Specifies the desired schedule for the custom-call. The field is only
present for custom-call.
@@ -482,9 +399,6 @@ class HloInstructionProto(google.protobuf.message.Message):
"""Specifies if the gather/scatter indices are guaranteed to be sorted by the
caller.
"""
@property
def frontend_attributes(self) -> tensorflow.compiler.xla.xla_data_pb2.FrontendAttributes:
"""Frontend attributes to pass to the XLA backend."""
unique_indices: builtins.bool
"""Specifies if all elements updated are guaranteed to be unique by
the caller.
@@ -516,6 +430,112 @@ class HloInstructionProto(google.protobuf.message.Message):
Each HLO module may contain a main thread and one or more parallel threads.
Empty async_execution_thread is equivalent to main thread.
"""
@property
def shape(self) -> tensorflow.compiler.xla.xla_data_pb2.ShapeProto: ...
@property
def metadata(self) -> tensorflow.compiler.xla.xla_data_pb2.OpMetadata: ...
@property
def literal(self) -> tensorflow.compiler.xla.xla_data_pb2.LiteralProto:
"""Literal, only present for kConstant."""
@property
def dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Dimensions present for some operations that require reshaping or
broadcasting, including Reshape, Reduce, ReduceWindow, and Reverse.
"""
@property
def window(self) -> tensorflow.compiler.xla.xla_data_pb2.Window:
"""Describes the window in a windowed operation such as convolution."""
@property
def convolution_dimension_numbers(self) -> tensorflow.compiler.xla.xla_data_pb2.ConvolutionDimensionNumbers:
"""Describes the dimension numbers used for a convolution."""
@property
def slice_dimensions(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___HloInstructionProto.SliceDimensions]: ...
@property
def dynamic_slice_sizes(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Describes the [start, start + size) range size for a dynamic slice
('start' is specified dynamically in the second operand of the operation).
"""
@property
def padding_config(self) -> tensorflow.compiler.xla.xla_data_pb2.PaddingConfig:
"""The padding configuration that describes the edge padding and interior
padding of this pad instruction. Only set for pad instructions.
"""
@property
def outfeed_shape(self) -> tensorflow.compiler.xla.xla_data_pb2.ShapeProto:
"""Shape of outfeed request."""
@property
def dot_dimension_numbers(self) -> tensorflow.compiler.xla.xla_data_pb2.DotDimensionNumbers:
"""Describes the dimension numbers used for a dot operation"""
@property
def fft_length(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""FFT length."""
@property
def gather_dimension_numbers(self) -> tensorflow.compiler.xla.xla_data_pb2.GatherDimensionNumbers:
"""Gather dimension numbers."""
@property
def gather_slice_sizes(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def operand_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def control_predecessor_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def called_computation_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def sharding(self) -> tensorflow.compiler.xla.xla_data_pb2.OpSharding: ...
@property
def replica_groups(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.compiler.xla.xla_data_pb2.ReplicaGroup]:
"""Cross replica op fields."""
@property
def scatter_dimension_numbers(self) -> tensorflow.compiler.xla.xla_data_pb2.ScatterDimensionNumbers: ...
@property
def precision_config(self) -> tensorflow.compiler.xla.xla_data_pb2.PrecisionConfig:
"""Precision configuration for the instruction. Has backend-specific meaning."""
@property
def source_target_pairs(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.compiler.xla.xla_data_pb2.SourceTarget]:
"""Collective permute field."""
@property
def domain_entry_sharding(self) -> tensorflow.compiler.xla.xla_data_pb2.OpSharding:
"""Sharding for kDomain instructions."""
@property
def domain_exit_sharding(self) -> tensorflow.compiler.xla.xla_data_pb2.OpSharding: ...
@property
def operand_shapes_with_layout(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.compiler.xla.xla_data_pb2.ShapeProto]: ...
@property
def triangular_solve_options(self) -> tensorflow.compiler.xla.xla_data_pb2.TriangularSolveOptions:
"""Options for TriangularSolve"""
@property
def cholesky_options(self) -> tensorflow.compiler.xla.xla_data_pb2.CholeskyOptions:
"""Options for Cholesky"""
@property
def parameter_replication(self) -> tensorflow.compiler.xla.xla_data_pb2.ParameterReplication:
"""Describes how parameters behave with regards to replicas."""
@property
def output_operand_aliasing(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.compiler.xla.xla_data_pb2.OutputOperandAliasing]:
"""A list of OutputOperandAliasing pairs that specifies aliasing buffers
between output and operands for kCustomCall and kFusion.
"""
@property
def frontend_attributes(self) -> tensorflow.compiler.xla.xla_data_pb2.FrontendAttributes:
"""Frontend attributes to pass to the XLA backend."""
def __init__(
self,
*,
@@ -588,13 +608,13 @@ class HloInstructionProto(google.protobuf.message.Message):
async_group_id: builtins.int | None = ...,
async_execution_thread: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["cholesky_options", b"cholesky_options", "convolution_dimension_numbers", b"convolution_dimension_numbers", "cross_program_prefetch_index", b"cross_program_prefetch_index", "domain_entry_sharding", b"domain_entry_sharding", "domain_exit_sharding", b"domain_exit_sharding", "dot_dimension_numbers", b"dot_dimension_numbers", "frontend_attributes", b"frontend_attributes", "gather_dimension_numbers", b"gather_dimension_numbers", "literal", b"literal", "metadata", b"metadata", "optional_cross_program_prefetch_index", b"optional_cross_program_prefetch_index", "outfeed_shape", b"outfeed_shape", "padding_config", b"padding_config", "parameter_replication", b"parameter_replication", "precision_config", b"precision_config", "scatter_dimension_numbers", b"scatter_dimension_numbers", "shape", b"shape", "sharding", b"sharding", "triangular_solve_options", b"triangular_solve_options", "window", b"window"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["all_reduce_id", b"all_reduce_id", "async_execution_thread", b"async_execution_thread", "async_group_id", b"async_group_id", "backend_config", b"backend_config", "batch_group_count", b"batch_group_count", "called_computation_ids", b"called_computation_ids", "channel_id", b"channel_id", "cholesky_options", b"cholesky_options", "comparison_direction", b"comparison_direction", "comparison_type", b"comparison_type", "constrain_layout", b"constrain_layout", "control_predecessor_ids", b"control_predecessor_ids", "convolution_dimension_numbers", b"convolution_dimension_numbers", "cross_program_prefetch_index", b"cross_program_prefetch_index", "custom_call_api_version", b"custom_call_api_version", "custom_call_has_side_effect", b"custom_call_has_side_effect", "custom_call_schedule", b"custom_call_schedule", "custom_call_target", b"custom_call_target", "delta", b"delta", "dimensions", b"dimensions", "distribution", b"distribution", "domain_entry_sharding", b"domain_entry_sharding", "domain_exit_sharding", b"domain_exit_sharding", "dot_dimension_numbers", b"dot_dimension_numbers", "dynamic_slice_sizes", b"dynamic_slice_sizes", "epsilon", b"epsilon", "exponent_bits", b"exponent_bits", "feature_group_count", b"feature_group_count", "feature_index", b"feature_index", "fft_length", b"fft_length", "fft_type", b"fft_type", "frontend_attributes", b"frontend_attributes", "fusion_kind", b"fusion_kind", "gather_dimension_numbers", b"gather_dimension_numbers", "gather_slice_sizes", b"gather_slice_sizes", "id", b"id", "indices_are_sorted", b"indices_are_sorted", "infeed_config", b"infeed_config", "is_cross_program_prefetch", b"is_cross_program_prefetch", "is_host_transfer", b"is_host_transfer", "is_stable", b"is_stable", "literal", b"literal", "mantissa_bits", b"mantissa_bits", "metadata", b"metadata", "name", b"name", "opcode", b"opcode", "operand_ids", b"operand_ids", "operand_shapes_with_layout", b"operand_shapes_with_layout", "optional_cross_program_prefetch_index", b"optional_cross_program_prefetch_index", "outfeed_config", b"outfeed_config", "outfeed_shape", b"outfeed_shape", "output_operand_aliasing", b"output_operand_aliasing", "padding_config", b"padding_config", "padding_type", b"padding_type", "parameter_number", b"parameter_number", "parameter_replication", b"parameter_replication", "precision_config", b"precision_config", "replica_groups", b"replica_groups", "rng_algorithm", b"rng_algorithm", "scatter_dimension_numbers", b"scatter_dimension_numbers", "shape", b"shape", "sharding", b"sharding", "slice_dimensions", b"slice_dimensions", "source_target_pairs", b"source_target_pairs", "triangular_solve_options", b"triangular_solve_options", "tuple_index", b"tuple_index", "unique_indices", b"unique_indices", "use_global_device_ids", b"use_global_device_ids", "window", b"window"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_cross_program_prefetch_index", b"optional_cross_program_prefetch_index"]) -> typing_extensions.Literal["cross_program_prefetch_index"] | None: ...
def HasField(self, field_name: typing.Literal["cholesky_options", b"cholesky_options", "convolution_dimension_numbers", b"convolution_dimension_numbers", "cross_program_prefetch_index", b"cross_program_prefetch_index", "domain_entry_sharding", b"domain_entry_sharding", "domain_exit_sharding", b"domain_exit_sharding", "dot_dimension_numbers", b"dot_dimension_numbers", "frontend_attributes", b"frontend_attributes", "gather_dimension_numbers", b"gather_dimension_numbers", "literal", b"literal", "metadata", b"metadata", "optional_cross_program_prefetch_index", b"optional_cross_program_prefetch_index", "outfeed_shape", b"outfeed_shape", "padding_config", b"padding_config", "parameter_replication", b"parameter_replication", "precision_config", b"precision_config", "scatter_dimension_numbers", b"scatter_dimension_numbers", "shape", b"shape", "sharding", b"sharding", "triangular_solve_options", b"triangular_solve_options", "window", b"window"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["all_reduce_id", b"all_reduce_id", "async_execution_thread", b"async_execution_thread", "async_group_id", b"async_group_id", "backend_config", b"backend_config", "batch_group_count", b"batch_group_count", "called_computation_ids", b"called_computation_ids", "channel_id", b"channel_id", "cholesky_options", b"cholesky_options", "comparison_direction", b"comparison_direction", "comparison_type", b"comparison_type", "constrain_layout", b"constrain_layout", "control_predecessor_ids", b"control_predecessor_ids", "convolution_dimension_numbers", b"convolution_dimension_numbers", "cross_program_prefetch_index", b"cross_program_prefetch_index", "custom_call_api_version", b"custom_call_api_version", "custom_call_has_side_effect", b"custom_call_has_side_effect", "custom_call_schedule", b"custom_call_schedule", "custom_call_target", b"custom_call_target", "delta", b"delta", "dimensions", b"dimensions", "distribution", b"distribution", "domain_entry_sharding", b"domain_entry_sharding", "domain_exit_sharding", b"domain_exit_sharding", "dot_dimension_numbers", b"dot_dimension_numbers", "dynamic_slice_sizes", b"dynamic_slice_sizes", "epsilon", b"epsilon", "exponent_bits", b"exponent_bits", "feature_group_count", b"feature_group_count", "feature_index", b"feature_index", "fft_length", b"fft_length", "fft_type", b"fft_type", "frontend_attributes", b"frontend_attributes", "fusion_kind", b"fusion_kind", "gather_dimension_numbers", b"gather_dimension_numbers", "gather_slice_sizes", b"gather_slice_sizes", "id", b"id", "indices_are_sorted", b"indices_are_sorted", "infeed_config", b"infeed_config", "is_cross_program_prefetch", b"is_cross_program_prefetch", "is_host_transfer", b"is_host_transfer", "is_stable", b"is_stable", "literal", b"literal", "mantissa_bits", b"mantissa_bits", "metadata", b"metadata", "name", b"name", "opcode", b"opcode", "operand_ids", b"operand_ids", "operand_shapes_with_layout", b"operand_shapes_with_layout", "optional_cross_program_prefetch_index", b"optional_cross_program_prefetch_index", "outfeed_config", b"outfeed_config", "outfeed_shape", b"outfeed_shape", "output_operand_aliasing", b"output_operand_aliasing", "padding_config", b"padding_config", "padding_type", b"padding_type", "parameter_number", b"parameter_number", "parameter_replication", b"parameter_replication", "precision_config", b"precision_config", "replica_groups", b"replica_groups", "rng_algorithm", b"rng_algorithm", "scatter_dimension_numbers", b"scatter_dimension_numbers", "shape", b"shape", "sharding", b"sharding", "slice_dimensions", b"slice_dimensions", "source_target_pairs", b"source_target_pairs", "triangular_solve_options", b"triangular_solve_options", "tuple_index", b"tuple_index", "unique_indices", b"unique_indices", "use_global_device_ids", b"use_global_device_ids", "window", b"window"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_cross_program_prefetch_index", b"optional_cross_program_prefetch_index"]) -> typing.Literal["cross_program_prefetch_index"] | None: ...
global___HloInstructionProto = HloInstructionProto
@typing_extensions.final
@typing.final
class HloComputationProto(google.protobuf.message.Message):
"""Serialization of HloComputation."""
@@ -608,14 +628,6 @@ class HloComputationProto(google.protobuf.message.Message):
IS_FUSION_COMPUTATION_FIELD_NUMBER: builtins.int
EXECUTION_THREAD_FIELD_NUMBER: builtins.int
name: builtins.str
@property
def instructions(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___HloInstructionProto]:
"""The array of instructions is always in a valid dependency order, where
operands appear before their users.
"""
@property
def program_shape(self) -> tensorflow.compiler.xla.xla_data_pb2.ProgramShapeProto:
"""The program shape (with layout) of this computation."""
id: builtins.int
"""The id of this computation."""
root_id: builtins.int
@@ -627,6 +639,16 @@ class HloComputationProto(google.protobuf.message.Message):
"""
execution_thread: builtins.str
"""The name of execution thread this computation belongs to."""
@property
def instructions(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___HloInstructionProto]:
"""The array of instructions is always in a valid dependency order, where
operands appear before their users.
"""
@property
def program_shape(self) -> tensorflow.compiler.xla.xla_data_pb2.ProgramShapeProto:
"""The program shape (with layout) of this computation."""
def __init__(
self,
*,
@@ -638,12 +660,12 @@ class HloComputationProto(google.protobuf.message.Message):
is_fusion_computation: builtins.bool | None = ...,
execution_thread: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["program_shape", b"program_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["execution_thread", b"execution_thread", "id", b"id", "instructions", b"instructions", "is_fusion_computation", b"is_fusion_computation", "name", b"name", "program_shape", b"program_shape", "root_id", b"root_id"]) -> None: ...
def HasField(self, field_name: typing.Literal["program_shape", b"program_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["execution_thread", b"execution_thread", "id", b"id", "instructions", b"instructions", "is_fusion_computation", b"is_fusion_computation", "name", b"name", "program_shape", b"program_shape", "root_id", b"root_id"]) -> None: ...
global___HloComputationProto = HloComputationProto
@typing_extensions.final
@typing.final
class HloScheduleProto(google.protobuf.message.Message):
"""Serialization of an HLO schedule. An HLO schedule contains a total order of
instructions for each non-fusion computation in the module.
@@ -651,7 +673,7 @@ class HloScheduleProto(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class InstructionSequence(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -663,9 +685,9 @@ class HloScheduleProto(google.protobuf.message.Message):
*,
instruction_ids: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["instruction_ids", b"instruction_ids"]) -> None: ...
def ClearField(self, field_name: typing.Literal["instruction_ids", b"instruction_ids"]) -> None: ...
@typing_extensions.final
@typing.final
class SequencesEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -680,27 +702,28 @@ class HloScheduleProto(google.protobuf.message.Message):
key: builtins.int | None = ...,
value: global___HloScheduleProto.InstructionSequence | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
SEQUENCES_FIELD_NUMBER: builtins.int
@property
def sequences(self) -> google.protobuf.internal.containers.MessageMap[builtins.int, global___HloScheduleProto.InstructionSequence]:
"""Map from computation id to sequence."""
def __init__(
self,
*,
sequences: collections.abc.Mapping[builtins.int, global___HloScheduleProto.InstructionSequence] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["sequences", b"sequences"]) -> None: ...
def ClearField(self, field_name: typing.Literal["sequences", b"sequences"]) -> None: ...
global___HloScheduleProto = HloScheduleProto
@typing_extensions.final
@typing.final
class HloInputOutputAliasProto(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class AliasEntryProto(google.protobuf.message.Message):
"""The following proto describes a pair of aliased an input
(described by parameter number and a ShapeIndex of the parameter)
@@ -723,16 +746,18 @@ class HloInputOutputAliasProto(google.protobuf.message.Message):
PARAMETER_NUMBER_FIELD_NUMBER: builtins.int
PARAMETER_SHAPE_INDEX_FIELD_NUMBER: builtins.int
KIND_FIELD_NUMBER: builtins.int
parameter_number: builtins.int
"""Number of the parameter in entry computation."""
kind: global___Kind.ValueType
"""The kind of alias to be setup."""
@property
def output_shape_index(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""ShapeIndex of the root hlo."""
parameter_number: builtins.int
"""Number of the parameter in entry computation."""
@property
def parameter_shape_index(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""ShapeIndex of the parameter instruction."""
kind: global___Kind.ValueType
"""The kind of alias to be setup."""
def __init__(
self,
*,
@@ -741,7 +766,7 @@ class HloInputOutputAliasProto(google.protobuf.message.Message):
parameter_shape_index: collections.abc.Iterable[builtins.int] | None = ...,
kind: global___Kind.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["kind", b"kind", "output_shape_index", b"output_shape_index", "parameter_number", b"parameter_number", "parameter_shape_index", b"parameter_shape_index"]) -> None: ...
def ClearField(self, field_name: typing.Literal["kind", b"kind", "output_shape_index", b"output_shape_index", "parameter_number", b"parameter_number", "parameter_shape_index", b"parameter_shape_index"]) -> None: ...
ENTRIES_FIELD_NUMBER: builtins.int
@property
@@ -751,15 +776,15 @@ class HloInputOutputAliasProto(google.protobuf.message.Message):
*,
entries: collections.abc.Iterable[global___HloInputOutputAliasProto.AliasEntryProto] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["entries", b"entries"]) -> None: ...
def ClearField(self, field_name: typing.Literal["entries", b"entries"]) -> None: ...
global___HloInputOutputAliasProto = HloInputOutputAliasProto
@typing_extensions.final
@typing.final
class DynamicParameterBindingProto(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Binding(google.protobuf.message.Message):
"""A list of bindings which indicates that the `target_param_dim_num` in
the subshape `target_param_index` of parameter `target_param_num`
@@ -794,12 +819,12 @@ class DynamicParameterBindingProto(google.protobuf.message.Message):
TARGET_PARAM_INDEX_FIELD_NUMBER: builtins.int
TARGET_PARAM_DIM_NUM_FIELD_NUMBER: builtins.int
dynamic_param_num: builtins.int
target_param_num: builtins.int
target_param_dim_num: builtins.int
@property
def dynamic_param_index(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
target_param_num: builtins.int
@property
def target_param_index(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
target_param_dim_num: builtins.int
def __init__(
self,
*,
@@ -809,7 +834,7 @@ class DynamicParameterBindingProto(google.protobuf.message.Message):
target_param_index: collections.abc.Iterable[builtins.int] | None = ...,
target_param_dim_num: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["dynamic_param_index", b"dynamic_param_index", "dynamic_param_num", b"dynamic_param_num", "target_param_dim_num", b"target_param_dim_num", "target_param_index", b"target_param_index", "target_param_num", b"target_param_num"]) -> None: ...
def ClearField(self, field_name: typing.Literal["dynamic_param_index", b"dynamic_param_index", "dynamic_param_num", b"dynamic_param_num", "target_param_dim_num", b"target_param_dim_num", "target_param_index", b"target_param_index", "target_param_num", b"target_param_num"]) -> None: ...
ENTRIES_FIELD_NUMBER: builtins.int
@property
@@ -819,11 +844,11 @@ class DynamicParameterBindingProto(google.protobuf.message.Message):
*,
entries: collections.abc.Iterable[global___DynamicParameterBindingProto.Binding] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["entries", b"entries"]) -> None: ...
def ClearField(self, field_name: typing.Literal["entries", b"entries"]) -> None: ...
global___DynamicParameterBindingProto = DynamicParameterBindingProto
@typing_extensions.final
@typing.final
class CrossProgramPrefetch(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -831,9 +856,9 @@ class CrossProgramPrefetch(google.protobuf.message.Message):
INDEX_FIELD_NUMBER: builtins.int
OFFSET_FIELD_NUMBER: builtins.int
parameter: builtins.int
offset: builtins.int
@property
def index(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
offset: builtins.int
def __init__(
self,
*,
@@ -841,11 +866,11 @@ class CrossProgramPrefetch(google.protobuf.message.Message):
index: collections.abc.Iterable[builtins.int] | None = ...,
offset: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["index", b"index", "offset", b"offset", "parameter", b"parameter"]) -> None: ...
def ClearField(self, field_name: typing.Literal["index", b"index", "offset", b"offset", "parameter", b"parameter"]) -> None: ...
global___CrossProgramPrefetch = CrossProgramPrefetch
@typing_extensions.final
@typing.final
class HloModuleProto(google.protobuf.message.Message):
"""Serialization of HloModule."""
@@ -872,7 +897,7 @@ class HloModuleProto(google.protobuf.message.Message):
LAYOUT: HloModuleProto.ProfileType.ValueType # 3
DOT: HloModuleProto.ProfileType.ValueType # 4
@typing_extensions.final
@typing.final
class ProfileInfo(google.protobuf.message.Message):
"""Information about the optimization profile that this module contains."""
@@ -898,7 +923,7 @@ class HloModuleProto(google.protobuf.message.Message):
profile_source: tensorflow.compiler.xla.xla_data_pb2.ProfileSource.ValueType | None = ...,
compilation_event: tensorflow.compiler.xla.xla_data_pb2.CompilationEvent.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["compilation_event", b"compilation_event", "profile_source", b"profile_source", "profile_type", b"profile_type", "relative_speedup", b"relative_speedup"]) -> None: ...
def ClearField(self, field_name: typing.Literal["compilation_event", b"compilation_event", "profile_source", b"profile_source", "profile_type", b"profile_type", "relative_speedup", b"relative_speedup"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
ENTRY_COMPUTATION_NAME_FIELD_NUMBER: builtins.int
@@ -919,40 +944,46 @@ class HloModuleProto(google.protobuf.message.Message):
name: builtins.str
entry_computation_name: builtins.str
entry_computation_id: builtins.int
id: builtins.int
"""The id of this module."""
is_dynamic: builtins.bool
"""True if the module contains dynamic computation."""
use_auto_spmd_partitioning: builtins.bool
"""Uses AutoSharding pass or not."""
@property
def computations(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___HloComputationProto]:
"""The array of computations is always in a valid dependency order, where
callees appear before their callers.
"""
@property
def host_program_shape(self) -> tensorflow.compiler.xla.xla_data_pb2.ProgramShapeProto:
"""The host program shape (with layout) of the entry computation."""
id: builtins.int
"""The id of this module."""
@property
def schedule(self) -> global___HloScheduleProto:
"""The schedule for this module."""
@property
def input_output_alias(self) -> global___HloInputOutputAliasProto:
"""Describes alias information between inputs and outputs."""
@property
def dynamic_parameter_binding(self) -> global___DynamicParameterBindingProto: ...
@property
def cross_program_prefetches(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___CrossProgramPrefetch]: ...
is_dynamic: builtins.bool
"""True if the module contains dynamic computation."""
@property
def spmd_output_sharding(self) -> tensorflow.compiler.xla.xla_data_pb2.OpSharding: ...
@property
def spmd_parameters_shardings(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.compiler.xla.xla_data_pb2.OpSharding]: ...
use_auto_spmd_partitioning: builtins.bool
"""Uses AutoSharding pass or not."""
@property
def profile_info(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___HloModuleProto.ProfileInfo]:
"""Profile information for the HLO module."""
@property
def device_assignment(self) -> tensorflow.compiler.xla.xla_data_pb2.DeviceAssignmentProto:
"""DeviceAssignment object information."""
def __init__(
self,
*,
@@ -973,18 +1004,18 @@ class HloModuleProto(google.protobuf.message.Message):
profile_info: collections.abc.Iterable[global___HloModuleProto.ProfileInfo] | None = ...,
device_assignment: tensorflow.compiler.xla.xla_data_pb2.DeviceAssignmentProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["device_assignment", b"device_assignment", "dynamic_parameter_binding", b"dynamic_parameter_binding", "host_program_shape", b"host_program_shape", "input_output_alias", b"input_output_alias", "schedule", b"schedule", "spmd_output_sharding", b"spmd_output_sharding"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["computations", b"computations", "cross_program_prefetches", b"cross_program_prefetches", "device_assignment", b"device_assignment", "dynamic_parameter_binding", b"dynamic_parameter_binding", "entry_computation_id", b"entry_computation_id", "entry_computation_name", b"entry_computation_name", "host_program_shape", b"host_program_shape", "id", b"id", "input_output_alias", b"input_output_alias", "is_dynamic", b"is_dynamic", "name", b"name", "profile_info", b"profile_info", "schedule", b"schedule", "spmd_output_sharding", b"spmd_output_sharding", "spmd_parameters_shardings", b"spmd_parameters_shardings", "use_auto_spmd_partitioning", b"use_auto_spmd_partitioning"]) -> None: ...
def HasField(self, field_name: typing.Literal["device_assignment", b"device_assignment", "dynamic_parameter_binding", b"dynamic_parameter_binding", "host_program_shape", b"host_program_shape", "input_output_alias", b"input_output_alias", "schedule", b"schedule", "spmd_output_sharding", b"spmd_output_sharding"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["computations", b"computations", "cross_program_prefetches", b"cross_program_prefetches", "device_assignment", b"device_assignment", "dynamic_parameter_binding", b"dynamic_parameter_binding", "entry_computation_id", b"entry_computation_id", "entry_computation_name", b"entry_computation_name", "host_program_shape", b"host_program_shape", "id", b"id", "input_output_alias", b"input_output_alias", "is_dynamic", b"is_dynamic", "name", b"name", "profile_info", b"profile_info", "schedule", b"schedule", "spmd_output_sharding", b"spmd_output_sharding", "spmd_parameters_shardings", b"spmd_parameters_shardings", "use_auto_spmd_partitioning", b"use_auto_spmd_partitioning"]) -> None: ...
global___HloModuleProto = HloModuleProto
@typing_extensions.final
@typing.final
class LogicalBufferProto(google.protobuf.message.Message):
"""Serialization of LogicalBuffer."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Location(google.protobuf.message.Message):
"""Location represents an instruction and its shape index, which uniquely
identifies a point where a buffer is needed.
@@ -1007,7 +1038,7 @@ class LogicalBufferProto(google.protobuf.message.Message):
instruction_id: builtins.int | None = ...,
shape_index: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["instruction_id", b"instruction_id", "instruction_name", b"instruction_name", "shape_index", b"shape_index"]) -> None: ...
def ClearField(self, field_name: typing.Literal["instruction_id", b"instruction_id", "instruction_name", b"instruction_name", "shape_index", b"shape_index"]) -> None: ...
ID_FIELD_NUMBER: builtins.int
SIZE_FIELD_NUMBER: builtins.int
@@ -1015,10 +1046,11 @@ class LogicalBufferProto(google.protobuf.message.Message):
COLOR_FIELD_NUMBER: builtins.int
id: builtins.int
size: builtins.int
color: builtins.int
@property
def defined_at(self) -> global___LogicalBufferProto.Location:
"""The location where the buffer is defined."""
color: builtins.int
def __init__(
self,
*,
@@ -1027,18 +1059,18 @@ class LogicalBufferProto(google.protobuf.message.Message):
defined_at: global___LogicalBufferProto.Location | None = ...,
color: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["defined_at", b"defined_at"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["color", b"color", "defined_at", b"defined_at", "id", b"id", "size", b"size"]) -> None: ...
def HasField(self, field_name: typing.Literal["defined_at", b"defined_at"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["color", b"color", "defined_at", b"defined_at", "id", b"id", "size", b"size"]) -> None: ...
global___LogicalBufferProto = LogicalBufferProto
@typing_extensions.final
@typing.final
class BufferAllocationProto(google.protobuf.message.Message):
"""Serialization of BufferAllocation."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Assigned(google.protobuf.message.Message):
"""Assigned represents a single LogicalBuffer that is assigned to this
BufferAllocation.
@@ -1059,7 +1091,7 @@ class BufferAllocationProto(google.protobuf.message.Message):
offset: builtins.int | None = ...,
size: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["logical_buffer_id", b"logical_buffer_id", "offset", b"offset", "size", b"size"]) -> None: ...
def ClearField(self, field_name: typing.Literal["logical_buffer_id", b"logical_buffer_id", "offset", b"offset", "size", b"size"]) -> None: ...
INDEX_FIELD_NUMBER: builtins.int
SIZE_FIELD_NUMBER: builtins.int
@@ -1079,11 +1111,11 @@ class BufferAllocationProto(google.protobuf.message.Message):
is_entry_computation_parameter: builtins.bool
is_constant: builtins.bool
parameter_number: builtins.int
@property
def parameter_shape_index(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
maybe_live_out: builtins.bool
color: builtins.int
@property
def parameter_shape_index(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def assigned(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___BufferAllocationProto.Assigned]: ...
def __init__(
self,
@@ -1100,17 +1132,17 @@ class BufferAllocationProto(google.protobuf.message.Message):
color: builtins.int | None = ...,
assigned: collections.abc.Iterable[global___BufferAllocationProto.Assigned] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["assigned", b"assigned", "color", b"color", "index", b"index", "is_constant", b"is_constant", "is_entry_computation_parameter", b"is_entry_computation_parameter", "is_thread_local", b"is_thread_local", "is_tuple", b"is_tuple", "maybe_live_out", b"maybe_live_out", "parameter_number", b"parameter_number", "parameter_shape_index", b"parameter_shape_index", "size", b"size"]) -> None: ...
def ClearField(self, field_name: typing.Literal["assigned", b"assigned", "color", b"color", "index", b"index", "is_constant", b"is_constant", "is_entry_computation_parameter", b"is_entry_computation_parameter", "is_thread_local", b"is_thread_local", "is_tuple", b"is_tuple", "maybe_live_out", b"maybe_live_out", "parameter_number", b"parameter_number", "parameter_shape_index", b"parameter_shape_index", "size", b"size"]) -> None: ...
global___BufferAllocationProto = BufferAllocationProto
@typing_extensions.final
@typing.final
class HeapSimulatorTrace(google.protobuf.message.Message):
"""A trace of a HeapSimulator run."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Event(google.protobuf.message.Message):
"""The trace includes a list of events, where each event describes one action
performed by the heap simulator.
@@ -1176,15 +1208,15 @@ class HeapSimulatorTrace(google.protobuf.message.Message):
instruction_name: builtins.str | None = ...,
share_with_canonical_id: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["buffer_id", b"buffer_id", "computation_name", b"computation_name", "instruction_name", b"instruction_name", "kind", b"kind", "share_with_canonical_id", b"share_with_canonical_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["buffer_id", b"buffer_id", "computation_name", b"computation_name", "instruction_name", b"instruction_name", "kind", b"kind", "share_with_canonical_id", b"share_with_canonical_id"]) -> None: ...
EVENTS_FIELD_NUMBER: builtins.int
WHOLE_MODULE_SIMULATION_FIELD_NUMBER: builtins.int
BUFFER_ALLOCATION_INDEX_FIELD_NUMBER: builtins.int
@property
def events(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___HeapSimulatorTrace.Event]: ...
whole_module_simulation: builtins.bool
buffer_allocation_index: builtins.int
@property
def events(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___HeapSimulatorTrace.Event]: ...
def __init__(
self,
*,
@@ -1192,11 +1224,11 @@ class HeapSimulatorTrace(google.protobuf.message.Message):
whole_module_simulation: builtins.bool | None = ...,
buffer_allocation_index: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["buffer_allocation_index", b"buffer_allocation_index", "events", b"events", "whole_module_simulation", b"whole_module_simulation"]) -> None: ...
def ClearField(self, field_name: typing.Literal["buffer_allocation_index", b"buffer_allocation_index", "events", b"events", "whole_module_simulation", b"whole_module_simulation"]) -> None: ...
global___HeapSimulatorTrace = HeapSimulatorTrace
@typing_extensions.final
@typing.final
class HloModuleGroupProto(google.protobuf.message.Message):
"""An abstraction representing a set of HLO module built to run concurrently
across different devices.
@@ -1215,17 +1247,17 @@ class HloModuleGroupProto(google.protobuf.message.Message):
name: builtins.str | None = ...,
hlo_modules: collections.abc.Iterable[global___HloModuleProto] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["hlo_modules", b"hlo_modules", "name", b"name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["hlo_modules", b"hlo_modules", "name", b"name"]) -> None: ...
global___HloModuleGroupProto = HloModuleGroupProto
@typing_extensions.final
@typing.final
class BufferAssignmentProto(google.protobuf.message.Message):
"""Serialization of BufferAssignment."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class BufferAlias(google.protobuf.message.Message):
"""Alias represents a source LogicalBuffer, and the buffer location that
aliases it.
@@ -1244,8 +1276,8 @@ class BufferAssignmentProto(google.protobuf.message.Message):
source_buffer_id: builtins.int | None = ...,
location: global___LogicalBufferProto.Location | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["location", b"location"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["location", b"location", "source_buffer_id", b"source_buffer_id"]) -> None: ...
def HasField(self, field_name: typing.Literal["location", b"location"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["location", b"location", "source_buffer_id", b"source_buffer_id"]) -> None: ...
LOGICAL_BUFFERS_FIELD_NUMBER: builtins.int
BUFFER_ALIASES_FIELD_NUMBER: builtins.int
@@ -1267,11 +1299,11 @@ class BufferAssignmentProto(google.protobuf.message.Message):
buffer_allocations: collections.abc.Iterable[global___BufferAllocationProto] | None = ...,
heap_simulator_traces: collections.abc.Iterable[global___HeapSimulatorTrace] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["buffer_aliases", b"buffer_aliases", "buffer_allocations", b"buffer_allocations", "heap_simulator_traces", b"heap_simulator_traces", "logical_buffers", b"logical_buffers"]) -> None: ...
def ClearField(self, field_name: typing.Literal["buffer_aliases", b"buffer_aliases", "buffer_allocations", b"buffer_allocations", "heap_simulator_traces", b"heap_simulator_traces", "logical_buffers", b"logical_buffers"]) -> None: ...
global___BufferAssignmentProto = BufferAssignmentProto
@typing_extensions.final
@typing.final
class HloProto(google.protobuf.message.Message):
"""Grouping message that contains all of the information above."""
@@ -1289,12 +1321,12 @@ class HloProto(google.protobuf.message.Message):
hlo_module: global___HloModuleProto | None = ...,
buffer_assignment: global___BufferAssignmentProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["buffer_assignment", b"buffer_assignment", "hlo_module", b"hlo_module"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["buffer_assignment", b"buffer_assignment", "hlo_module", b"hlo_module"]) -> None: ...
def HasField(self, field_name: typing.Literal["buffer_assignment", b"buffer_assignment", "hlo_module", b"hlo_module"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["buffer_assignment", b"buffer_assignment", "hlo_module", b"hlo_module"]) -> None: ...
global___HloProto = HloProto
@typing_extensions.final
@typing.final
class HloSnapshot(google.protobuf.message.Message):
"""Encapsulates HloProto together with the arguments, result, and
execution_platform. This message is used for purposes such as
@@ -1307,17 +1339,20 @@ class HloSnapshot(google.protobuf.message.Message):
ARGUMENTS_FIELD_NUMBER: builtins.int
RESULT_FIELD_NUMBER: builtins.int
EXECUTION_PLATFORM_FIELD_NUMBER: builtins.int
execution_platform: builtins.str
"""The name of the platform used to run the graph."""
@property
def hlo(self) -> global___HloProto:
"""The hlo graph."""
@property
def arguments(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.compiler.xla.xla_data_pb2.LiteralProto]:
"""The arguments passed to the graph."""
@property
def result(self) -> tensorflow.compiler.xla.xla_data_pb2.LiteralProto:
"""The result of the graph."""
execution_platform: builtins.str
"""The name of the platform used to run the graph."""
def __init__(
self,
*,
@@ -1326,12 +1361,12 @@ class HloSnapshot(google.protobuf.message.Message):
result: tensorflow.compiler.xla.xla_data_pb2.LiteralProto | None = ...,
execution_platform: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["hlo", b"hlo", "result", b"result"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["arguments", b"arguments", "execution_platform", b"execution_platform", "hlo", b"hlo", "result", b"result"]) -> None: ...
def HasField(self, field_name: typing.Literal["hlo", b"hlo", "result", b"result"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["arguments", b"arguments", "execution_platform", b"execution_platform", "hlo", b"hlo", "result", b"result"]) -> None: ...
global___HloSnapshot = HloSnapshot
@typing_extensions.final
@typing.final
class HloModuleMetadataProto(google.protobuf.message.Message):
"""Metadata for an HLO module. Dumped after HLO passes and before LLO lowering
with filename module_####.metadata.textproto, where #### is
@@ -1362,9 +1397,11 @@ class HloModuleMetadataProto(google.protobuf.message.Message):
"""The canonical module ids of the modules that this one is partitioned into,
if applicable.
"""
@property
def pass_metadata(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___HloPassMetadata]:
"""Metadata for the HLO passes that are run on the module."""
def __init__(
self,
*,
@@ -1374,11 +1411,11 @@ class HloModuleMetadataProto(google.protobuf.message.Message):
partitioned_module_ids: collections.abc.Iterable[builtins.int] | None = ...,
pass_metadata: collections.abc.Iterable[global___HloPassMetadata] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["canonical_module_id", b"canonical_module_id", "module_group_name", b"module_group_name", "original_module_id", b"original_module_id", "partitioned_module_ids", b"partitioned_module_ids", "pass_metadata", b"pass_metadata"]) -> None: ...
def ClearField(self, field_name: typing.Literal["canonical_module_id", b"canonical_module_id", "module_group_name", b"module_group_name", "original_module_id", b"original_module_id", "partitioned_module_ids", b"partitioned_module_ids", "pass_metadata", b"pass_metadata"]) -> None: ...
global___HloModuleMetadataProto = HloModuleMetadataProto
@typing_extensions.final
@typing.final
class HloPassMetadata(google.protobuf.message.Message):
"""Metadata for one run of an HLO pass on a module. Provides more information
when processing debug dumps of HloProtos about the order of HLO passes and
@@ -1407,12 +1444,6 @@ class HloPassMetadata(google.protobuf.message.Message):
"""
pass_name: builtins.str
pipeline_name: builtins.str
@property
def dump_filenames(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Filenames of the dumps of the module after this pass ran. Module may be
dumped in multiple formats, and the order of formats in this field will
stay consistent across passes.
"""
module_changed: builtins.bool
"""Return value of pass.Run(). True if this pass changed the module, or, in
the case where the module was run through this pass as part of a module
@@ -1423,14 +1454,22 @@ class HloPassMetadata(google.protobuf.message.Message):
the canonical_module_id of the HloModuleMetadata that this HloPassMetadata
is inside.
"""
start_timestamp_usec: builtins.int
"""Timestamp before and after the pass is run. Note they may be equal."""
end_timestamp_usec: builtins.int
@property
def dump_filenames(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Filenames of the dumps of the module after this pass ran. Module may be
dumped in multiple formats, and the order of formats in this field will
stay consistent across passes.
"""
@property
def module_group_module_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""If the module went through this pass as part of a module group, this is
set as the ids of all the modules in the module group. Empty otherwise.
"""
start_timestamp_usec: builtins.int
"""Timestamp before and after the pass is run. Note they may be equal."""
end_timestamp_usec: builtins.int
def __init__(
self,
*,
@@ -1444,17 +1483,17 @@ class HloPassMetadata(google.protobuf.message.Message):
start_timestamp_usec: builtins.int | None = ...,
end_timestamp_usec: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["dump_filenames", b"dump_filenames", "end_timestamp_usec", b"end_timestamp_usec", "module_changed", b"module_changed", "module_group_module_ids", b"module_group_module_ids", "module_id", b"module_id", "pass_id", b"pass_id", "pass_name", b"pass_name", "pipeline_name", b"pipeline_name", "start_timestamp_usec", b"start_timestamp_usec"]) -> None: ...
def ClearField(self, field_name: typing.Literal["dump_filenames", b"dump_filenames", "end_timestamp_usec", b"end_timestamp_usec", "module_changed", b"module_changed", "module_group_module_ids", b"module_group_module_ids", "module_id", b"module_id", "pass_id", b"pass_id", "pass_name", b"pass_name", "pipeline_name", b"pipeline_name", "start_timestamp_usec", b"start_timestamp_usec"]) -> None: ...
global___HloPassMetadata = HloPassMetadata
@typing_extensions.final
@typing.final
class EntryFunctionAttributes(google.protobuf.message.Message):
"""Encodes attributes for an entry function."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class ShapeIndex(google.protobuf.message.Message):
"""Acts as the underlying container for an xla::ShapeIndex."""
@@ -1468,9 +1507,9 @@ class EntryFunctionAttributes(google.protobuf.message.Message):
*,
indices: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["indices", b"indices"]) -> None: ...
def ClearField(self, field_name: typing.Literal["indices", b"indices"]) -> None: ...
@typing_extensions.final
@typing.final
class BufferParameterAttributes(google.protobuf.message.Message):
"""Encodes attributes for a single buffer parameter."""
@@ -1488,16 +1527,18 @@ class EntryFunctionAttributes(google.protobuf.message.Message):
"""TODO(hanbinyoon): Deprecate when optional fields are available in proto3
(Protocol Buffers v3.15.0).
"""
@property
def lmhlo_param_shape_index(self) -> global___EntryFunctionAttributes.ShapeIndex:
"""Represents an lmhlo.param_shape_index function argument attribute."""
lmhlo_constant_name: builtins.str
"""Represents an lmhlo.constant_name function argument attribute."""
lmhlo_must_alias: builtins.bool
"""Represents an lmhlo.must_alias function argument attribute."""
@property
def lmhlo_param_shape_index(self) -> global___EntryFunctionAttributes.ShapeIndex:
"""Represents an lmhlo.param_shape_index function argument attribute."""
@property
def lmhlo_output_index(self) -> global___EntryFunctionAttributes.ShapeIndex:
"""Represents an lmhlo.params function argument attribute."""
def __init__(
self,
*,
@@ -1508,26 +1549,26 @@ class EntryFunctionAttributes(google.protobuf.message.Message):
lmhlo_must_alias: builtins.bool | None = ...,
lmhlo_output_index: global___EntryFunctionAttributes.ShapeIndex | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["lmhlo_output_index", b"lmhlo_output_index", "lmhlo_param_shape_index", b"lmhlo_param_shape_index"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["lmhlo_constant_name", b"lmhlo_constant_name", "lmhlo_must_alias", b"lmhlo_must_alias", "lmhlo_output_index", b"lmhlo_output_index", "lmhlo_param_shape_index", b"lmhlo_param_shape_index", "lmhlo_params", b"lmhlo_params", "lmhlo_params_present", b"lmhlo_params_present"]) -> None: ...
def HasField(self, field_name: typing.Literal["lmhlo_output_index", b"lmhlo_output_index", "lmhlo_param_shape_index", b"lmhlo_param_shape_index"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["lmhlo_constant_name", b"lmhlo_constant_name", "lmhlo_must_alias", b"lmhlo_must_alias", "lmhlo_output_index", b"lmhlo_output_index", "lmhlo_param_shape_index", b"lmhlo_param_shape_index", "lmhlo_params", b"lmhlo_params", "lmhlo_params_present", b"lmhlo_params_present"]) -> None: ...
BUFFERS_FIELD_NUMBER: builtins.int
RESULT_XLA_SHAPE_FIELD_NUMBER: builtins.int
@property
def buffers(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___EntryFunctionAttributes.BufferParameterAttributes]: ...
result_xla_shape: builtins.str
"""xla::Shape in string format."""
@property
def buffers(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___EntryFunctionAttributes.BufferParameterAttributes]: ...
def __init__(
self,
*,
buffers: collections.abc.Iterable[global___EntryFunctionAttributes.BufferParameterAttributes] | None = ...,
result_xla_shape: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["buffers", b"buffers", "result_xla_shape", b"result_xla_shape"]) -> None: ...
def ClearField(self, field_name: typing.Literal["buffers", b"buffers", "result_xla_shape", b"result_xla_shape"]) -> None: ...
global___EntryFunctionAttributes = EntryFunctionAttributes
@typing_extensions.final
@typing.final
class XlaRuntimeExecutableProto(google.protobuf.message.Message):
"""Encodes the underlying Xla runtime executable compiled from the XLA module."""
@@ -1536,8 +1577,6 @@ class XlaRuntimeExecutableProto(google.protobuf.message.Message):
HLO_MODULE_PROTO_FIELD_NUMBER: builtins.int
OBJ_FILE_FIELD_NUMBER: builtins.int
MLIR_MODULE_FIELD_NUMBER: builtins.int
@property
def hlo_module_proto(self) -> global___HloModuleProto: ...
obj_file: builtins.bytes
"""TODO(b/232263665)): Serialized executable has to know what APIs it has to
be linked with, including the version. For example Gpu executable must be
@@ -1547,6 +1586,8 @@ class XlaRuntimeExecutableProto(google.protobuf.message.Message):
"""
mlir_module: builtins.str
"""Serialized MLIR module corresponding to compiled object file."""
@property
def hlo_module_proto(self) -> global___HloModuleProto: ...
def __init__(
self,
*,
@@ -1554,7 +1595,7 @@ class XlaRuntimeExecutableProto(google.protobuf.message.Message):
obj_file: builtins.bytes | None = ...,
mlir_module: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["hlo_module_proto", b"hlo_module_proto"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["hlo_module_proto", b"hlo_module_proto", "mlir_module", b"mlir_module", "obj_file", b"obj_file"]) -> None: ...
def HasField(self, field_name: typing.Literal["hlo_module_proto", b"hlo_module_proto"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["hlo_module_proto", b"hlo_module_proto", "mlir_module", b"mlir_module", "obj_file", b"obj_file"]) -> None: ...
global___XlaRuntimeExecutableProto = XlaRuntimeExecutableProto
stubs/tensorflow/tensorflow/compiler/xla/service/metrics_pb2.pyi
+10 -7
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import sys
import typing
@@ -19,7 +20,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class CompilationLogEntry(google.protobuf.message.Message):
"""Defines XLA compilation metrics."""
@@ -50,16 +51,18 @@ class CompilationLogEntry(google.protobuf.message.Message):
STAGE_FIELD_NUMBER: builtins.int
DURATION_FIELD_NUMBER: builtins.int
TASK_INDEX_FIELD_NUMBER: builtins.int
stage: global___CompilationLogEntry.CompilationStage.ValueType
"""Compilation stage recorded by this log entry."""
task_index: builtins.int
"""Task index from which this log entry was recorded."""
@property
def timestamp(self) -> google.protobuf.timestamp_pb2.Timestamp:
"""Time when the event captured by this log entry occurred."""
stage: global___CompilationLogEntry.CompilationStage.ValueType
"""Compilation stage recorded by this log entry."""
@property
def duration(self) -> google.protobuf.duration_pb2.Duration:
"""Duration of the given compilation stage."""
task_index: builtins.int
"""Task index from which this log entry was recorded."""
def __init__(
self,
*,
@@ -68,7 +71,7 @@ class CompilationLogEntry(google.protobuf.message.Message):
duration: google.protobuf.duration_pb2.Duration | None = ...,
task_index: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["duration", b"duration", "timestamp", b"timestamp"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["duration", b"duration", "stage", b"stage", "task_index", b"task_index", "timestamp", b"timestamp"]) -> None: ...
def HasField(self, field_name: typing.Literal["duration", b"duration", "timestamp", b"timestamp"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["duration", b"duration", "stage", b"stage", "task_index", b"task_index", "timestamp", b"timestamp"]) -> None: ...
global___CompilationLogEntry = CompilationLogEntry
stubs/tensorflow/tensorflow/compiler/xla/xla_data_pb2.pyi
+184 -146
View File
@@ -16,6 +16,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================
"""
import builtins
import collections.abc
import sys
@@ -377,7 +378,7 @@ RNG_PHILOX: RandomAlgorithm.ValueType # 2
"""Next: 2"""
global___RandomAlgorithm = RandomAlgorithm
@typing_extensions.final
@typing.final
class PaddingConfig(google.protobuf.message.Message):
"""Describes the padding configuration for Pad operation. The padding amount on
both edges as well as between the elements are specified for each dimension.
@@ -385,7 +386,7 @@ class PaddingConfig(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class PaddingConfigDimension(google.protobuf.message.Message):
"""Describes the padding configuration for a dimension."""
@@ -409,22 +410,23 @@ class PaddingConfig(google.protobuf.message.Message):
edge_padding_high: builtins.int | None = ...,
interior_padding: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["edge_padding_high", b"edge_padding_high", "edge_padding_low", b"edge_padding_low", "interior_padding", b"interior_padding"]) -> None: ...
def ClearField(self, field_name: typing.Literal["edge_padding_high", b"edge_padding_high", "edge_padding_low", b"edge_padding_low", "interior_padding", b"interior_padding"]) -> None: ...
DIMENSIONS_FIELD_NUMBER: builtins.int
@property
def dimensions(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___PaddingConfig.PaddingConfigDimension]:
"""The padding configuration for all dimensions."""
def __init__(
self,
*,
dimensions: collections.abc.Iterable[global___PaddingConfig.PaddingConfigDimension] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["dimensions", b"dimensions"]) -> None: ...
def ClearField(self, field_name: typing.Literal["dimensions", b"dimensions"]) -> None: ...
global___PaddingConfig = PaddingConfig
@typing_extensions.final
@typing.final
class TileProto(google.protobuf.message.Message):
"""Describes a tile used in tiling-based layout. Refer to
g3doc/third_party/tensorflow/compiler/xla/g3doc/tiled_layout.md for
@@ -441,16 +443,17 @@ class TileProto(google.protobuf.message.Message):
The dimensions correspond to a suffix of the dimensions of the shape being
tiled.
"""
def __init__(
self,
*,
dimensions: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["dimensions", b"dimensions"]) -> None: ...
def ClearField(self, field_name: typing.Literal["dimensions", b"dimensions"]) -> None: ...
global___TileProto = TileProto
@typing_extensions.final
@typing.final
class LayoutProto(google.protobuf.message.Message):
"""A layout describes how the array is placed in (1D) memory space. This
includes the minor-to-major ordering of dimensions within a shape.
@@ -476,35 +479,6 @@ class LayoutProto(google.protobuf.message.Message):
POINTER_PRIMITIVE_TYPE_FIELD_NUMBER: builtins.int
PHYSICAL_SHAPE_FIELD_NUMBER: builtins.int
DYNAMIC_SHAPE_METADATA_PREFIX_BYTES_FIELD_NUMBER: builtins.int
@property
def dim_level_types(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[global___DimLevelType.ValueType]:
"""The dimension level type list for this array, specifying the way in which
each array dimension is represented in memory. If this list is empty, the
array is assumed to be dense.
"""
@property
def dim_unique(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bool]:
"""Whether each dimension is unique or ordered. Each of the following lists
must be empty, or have one entry for each entry of dim_level_types. If
either list is empty, all dimensions are assumed to be unique and ordered,
respectively. Entries in this list may not be false for some DimLevelType
values (such as DIM_DENSE in particular).
"""
@property
def dim_ordered(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bool]: ...
@property
def minor_to_major(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Sequence of dimension numbers, from minor (fastest varying index) to major
(slowest varying index). This field is required.
"""
@property
def tiles(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TileProto]:
"""A sequence of tiles, starting from the tile that's applied first to the
Shape.
TODO(b/119839262): implement tiling in each backend or add Unimplemented
error.
"""
memory_space: builtins.int
"""Memory space where this array resides. The integer field is interpreted in
a backend-specific manner.
@@ -518,6 +492,44 @@ class LayoutProto(google.protobuf.message.Message):
supported devices).
"""
pointer_primitive_type: global___PrimitiveType.ValueType
dynamic_shape_metadata_prefix_bytes: builtins.int
"""The dynamic shape metadata size in bytes in front of the shape data. The
field may be non-zero for a static shape whose associated buffer is for a
dynamic shape, e.g. a result of SliceToDynamic.
"""
@property
def dim_level_types(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[global___DimLevelType.ValueType]:
"""The dimension level type list for this array, specifying the way in which
each array dimension is represented in memory. If this list is empty, the
array is assumed to be dense.
"""
@property
def dim_unique(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bool]:
"""Whether each dimension is unique or ordered. Each of the following lists
must be empty, or have one entry for each entry of dim_level_types. If
either list is empty, all dimensions are assumed to be unique and ordered,
respectively. Entries in this list may not be false for some DimLevelType
values (such as DIM_DENSE in particular).
"""
@property
def dim_ordered(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bool]: ...
@property
def minor_to_major(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Sequence of dimension numbers, from minor (fastest varying index) to major
(slowest varying index). This field is required.
"""
@property
def tiles(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TileProto]:
"""A sequence of tiles, starting from the tile that's applied first to the
Shape.
TODO(b/119839262): implement tiling in each backend or add Unimplemented
error.
"""
@property
def physical_shape(self) -> global___ShapeProto:
"""The physical, on-device shape used to represent the shape this layout
@@ -525,11 +537,7 @@ class LayoutProto(google.protobuf.message.Message):
The layout(s) contained within the physical shape should not also contain
a physical shape.
"""
dynamic_shape_metadata_prefix_bytes: builtins.int
"""The dynamic shape metadata size in bytes in front of the shape data. The
field may be non-zero for a static shape whose associated buffer is for a
dynamic shape, e.g. a result of SliceToDynamic.
"""
def __init__(
self,
*,
@@ -544,12 +552,12 @@ class LayoutProto(google.protobuf.message.Message):
physical_shape: global___ShapeProto | None = ...,
dynamic_shape_metadata_prefix_bytes: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["physical_shape", b"physical_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["dim_level_types", b"dim_level_types", "dim_ordered", b"dim_ordered", "dim_unique", b"dim_unique", "dynamic_shape_metadata_prefix_bytes", b"dynamic_shape_metadata_prefix_bytes", "index_primitive_type", b"index_primitive_type", "memory_space", b"memory_space", "minor_to_major", b"minor_to_major", "physical_shape", b"physical_shape", "pointer_primitive_type", b"pointer_primitive_type", "tiles", b"tiles"]) -> None: ...
def HasField(self, field_name: typing.Literal["physical_shape", b"physical_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["dim_level_types", b"dim_level_types", "dim_ordered", b"dim_ordered", "dim_unique", b"dim_unique", "dynamic_shape_metadata_prefix_bytes", b"dynamic_shape_metadata_prefix_bytes", "index_primitive_type", b"index_primitive_type", "memory_space", b"memory_space", "minor_to_major", b"minor_to_major", "physical_shape", b"physical_shape", "pointer_primitive_type", b"pointer_primitive_type", "tiles", b"tiles"]) -> None: ...
global___LayoutProto = LayoutProto
@typing_extensions.final
@typing.final
class ShapeProto(google.protobuf.message.Message):
"""A shape describes the number of dimensions in the array, the size of each
dimension, and the primitive component type.
@@ -582,12 +590,15 @@ class ShapeProto(google.protobuf.message.Message):
If the respective element in 'is_dimension_dynamic' is true then the value
in this field represents an upper bound on the size of the dimension.
"""
@property
def tuple_shapes(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___ShapeProto]:
"""For tuples only, the shapes of constituent shapes in the tuple sequence."""
@property
def layout(self) -> global___LayoutProto:
"""The layout used to back this shape."""
@property
def is_dynamic_dimension(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bool]:
"""For arrays, this indicates whether or not each dimension is
@@ -595,6 +606,7 @@ class ShapeProto(google.protobuf.message.Message):
zero (indicating that no dimensions are dynamic) or equal to the number of
elements in the 'dimensions' field.
"""
def __init__(
self,
*,
@@ -604,12 +616,12 @@ class ShapeProto(google.protobuf.message.Message):
layout: global___LayoutProto | None = ...,
is_dynamic_dimension: collections.abc.Iterable[builtins.bool] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["layout", b"layout"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["dimensions", b"dimensions", "element_type", b"element_type", "is_dynamic_dimension", b"is_dynamic_dimension", "layout", b"layout", "tuple_shapes", b"tuple_shapes"]) -> None: ...
def HasField(self, field_name: typing.Literal["layout", b"layout"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["dimensions", b"dimensions", "element_type", b"element_type", "is_dynamic_dimension", b"is_dynamic_dimension", "layout", b"layout", "tuple_shapes", b"tuple_shapes"]) -> None: ...
global___ShapeProto = ShapeProto
@typing_extensions.final
@typing.final
class ProgramShapeProto(google.protobuf.message.Message):
"""Shape of the parameters and output of a computation (like a traditional
function signature).
@@ -633,12 +645,12 @@ class ProgramShapeProto(google.protobuf.message.Message):
result: global___ShapeProto | None = ...,
parameter_names: collections.abc.Iterable[builtins.str] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["result", b"result"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["parameter_names", b"parameter_names", "parameters", b"parameters", "result", b"result"]) -> None: ...
def HasField(self, field_name: typing.Literal["result", b"result"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["parameter_names", b"parameter_names", "parameters", b"parameters", "result", b"result"]) -> None: ...
global___ProgramShapeProto = ProgramShapeProto
@typing_extensions.final
@typing.final
class ComputationStats(google.protobuf.message.Message):
"""Statistics of a computation."""
@@ -656,11 +668,11 @@ class ComputationStats(google.protobuf.message.Message):
flop_count: builtins.float | None = ...,
transcendental_count: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["flop_count", b"flop_count", "transcendental_count", b"transcendental_count"]) -> None: ...
def ClearField(self, field_name: typing.Literal["flop_count", b"flop_count", "transcendental_count", b"transcendental_count"]) -> None: ...
global___ComputationStats = ComputationStats
@typing_extensions.final
@typing.final
class OpMetadata(google.protobuf.message.Message):
"""Symbolization metadata for HLO Instructions.
@@ -670,7 +682,7 @@ class OpMetadata(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class ProfileInfo(google.protobuf.message.Message):
"""Information about the optimization profile that this operation contains."""
@@ -680,9 +692,6 @@ class OpMetadata(google.protobuf.message.Message):
RELATIVE_SPEEDUP_FIELD_NUMBER: builtins.int
PROFILE_SOURCE_FIELD_NUMBER: builtins.int
COMPILATION_EVENT_FIELD_NUMBER: builtins.int
@property
def profile_type(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[global___ProfileType.ValueType]:
"""The type of optimization profiles that this operation contains."""
relative_speedup: builtins.float
"""Speedup of tuned config compared to default config.
TODO(b/203817882) Set the relative_speedup.
@@ -691,6 +700,10 @@ class OpMetadata(google.protobuf.message.Message):
"""The source of the optimization profiles that this operation contains."""
compilation_event: global___CompilationEvent.ValueType
"""The compilation event that triggered the use of the profiles."""
@property
def profile_type(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[global___ProfileType.ValueType]:
"""The type of optimization profiles that this operation contains."""
def __init__(
self,
*,
@@ -699,7 +712,7 @@ class OpMetadata(google.protobuf.message.Message):
profile_source: global___ProfileSource.ValueType | None = ...,
compilation_event: global___CompilationEvent.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["compilation_event", b"compilation_event", "profile_source", b"profile_source", "profile_type", b"profile_type", "relative_speedup", b"relative_speedup"]) -> None: ...
def ClearField(self, field_name: typing.Literal["compilation_event", b"compilation_event", "profile_source", b"profile_source", "profile_type", b"profile_type", "relative_speedup", b"relative_speedup"]) -> None: ...
OP_TYPE_FIELD_NUMBER: builtins.int
OP_NAME_FIELD_NUMBER: builtins.int
@@ -732,9 +745,6 @@ class OpMetadata(google.protobuf.message.Message):
e.g. it could be the file and line of user code that generated the op.
"""
source_line: builtins.int
@property
def profile_type(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[global___ProfileType.ValueType]:
"""Deprecated, use [ProfileInfo][profile_type] instead."""
creation_pass_id: builtins.int
"""HloPassMetadata.pass_id of the pass that created this HLO instruction
object. Should never be copied between HLO instructions. Zero if unset and
@@ -752,9 +762,14 @@ class OpMetadata(google.protobuf.message.Message):
"""The size of the working set, i.e., the amount of memory, used by the
instruction in a compiler-managed fast device memory.
"""
@property
def profile_type(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[global___ProfileType.ValueType]:
"""Deprecated, use [ProfileInfo][profile_type] instead."""
@property
def profile_info(self) -> global___OpMetadata.ProfileInfo:
"""Profile information for the Op."""
def __init__(
self,
*,
@@ -769,12 +784,12 @@ class OpMetadata(google.protobuf.message.Message):
size_of_memory_working_set_in_bytes: builtins.int | None = ...,
profile_info: global___OpMetadata.ProfileInfo | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["profile_info", b"profile_info"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["creation_pass_id", b"creation_pass_id", "logical_creation_pass_id", b"logical_creation_pass_id", "op_name", b"op_name", "op_type", b"op_type", "profile_info", b"profile_info", "profile_type", b"profile_type", "size_of_generated_code_in_bytes", b"size_of_generated_code_in_bytes", "size_of_memory_working_set_in_bytes", b"size_of_memory_working_set_in_bytes", "source_file", b"source_file", "source_line", b"source_line"]) -> None: ...
def HasField(self, field_name: typing.Literal["profile_info", b"profile_info"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["creation_pass_id", b"creation_pass_id", "logical_creation_pass_id", b"logical_creation_pass_id", "op_name", b"op_name", "op_type", b"op_type", "profile_info", b"profile_info", "profile_type", b"profile_type", "size_of_generated_code_in_bytes", b"size_of_generated_code_in_bytes", "size_of_memory_working_set_in_bytes", b"size_of_memory_working_set_in_bytes", "source_file", b"source_file", "source_line", b"source_line"]) -> None: ...
global___OpMetadata = OpMetadata
@typing_extensions.final
@typing.final
class ExecutionProfile(google.protobuf.message.Message):
"""Profile data from the execution of a computation."""
@@ -824,11 +839,11 @@ class ExecutionProfile(google.protobuf.message.Message):
executable_size_in_bytes: builtins.int | None = ...,
profile_cache_hit: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["compilation_cache_hit", b"compilation_cache_hit", "compile_time_ms", b"compile_time_ms", "compute_and_transfer_time_ns", b"compute_and_transfer_time_ns", "compute_cycle_count", b"compute_cycle_count", "compute_time_ns", b"compute_time_ns", "executable_size_in_bytes", b"executable_size_in_bytes", "profile_cache_hit", b"profile_cache_hit"]) -> None: ...
def ClearField(self, field_name: typing.Literal["compilation_cache_hit", b"compilation_cache_hit", "compile_time_ms", b"compile_time_ms", "compute_and_transfer_time_ns", b"compute_and_transfer_time_ns", "compute_cycle_count", b"compute_cycle_count", "compute_time_ns", b"compute_time_ns", "executable_size_in_bytes", b"executable_size_in_bytes", "profile_cache_hit", b"profile_cache_hit"]) -> None: ...
global___ExecutionProfile = ExecutionProfile
@typing_extensions.final
@typing.final
class ExecutionHandle(google.protobuf.message.Message):
"""Handle given to a user that represents an execution that the user launched
asynchronously on the device.
@@ -843,11 +858,11 @@ class ExecutionHandle(google.protobuf.message.Message):
*,
handle: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["handle", b"handle"]) -> None: ...
def ClearField(self, field_name: typing.Literal["handle", b"handle"]) -> None: ...
global___ExecutionHandle = ExecutionHandle
@typing_extensions.final
@typing.final
class GlobalDataHandle(google.protobuf.message.Message):
"""Handle given to a user that represents a globally accessible allocation.
Contrast this against a ComputationDataHandle, which is not globally
@@ -863,11 +878,11 @@ class GlobalDataHandle(google.protobuf.message.Message):
*,
handle: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["handle", b"handle"]) -> None: ...
def ClearField(self, field_name: typing.Literal["handle", b"handle"]) -> None: ...
global___GlobalDataHandle = GlobalDataHandle
@typing_extensions.final
@typing.final
class DeviceHandle(google.protobuf.message.Message):
"""Handle given to a user that represents a replicated virtual device. Each
replicated device represents N physical devices for execution where N is the
@@ -889,11 +904,11 @@ class DeviceHandle(google.protobuf.message.Message):
handle: builtins.int | None = ...,
device_count: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["device_count", b"device_count", "handle", b"handle"]) -> None: ...
def ClearField(self, field_name: typing.Literal["device_count", b"device_count", "handle", b"handle"]) -> None: ...
global___DeviceHandle = DeviceHandle
@typing_extensions.final
@typing.final
class ChannelHandle(google.protobuf.message.Message):
"""Handle given to a user to represent a channel between two computations
via a Send and Recv instruction pair. Channels are unbuffered, so Send
@@ -945,11 +960,11 @@ class ChannelHandle(google.protobuf.message.Message):
handle: builtins.int | None = ...,
type: global___ChannelHandle.ChannelType.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["handle", b"handle", "type", b"type"]) -> None: ...
def ClearField(self, field_name: typing.Literal["handle", b"handle", "type", b"type"]) -> None: ...
global___ChannelHandle = ChannelHandle
@typing_extensions.final
@typing.final
class DeviceAssignmentProto(google.protobuf.message.Message):
"""DeviceAssignmentProto is a serialized form of DeviceAssignment class, which
represents the device ids assigned to a set of replicated computations.
@@ -958,7 +973,7 @@ class DeviceAssignmentProto(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class ComputationDevice(google.protobuf.message.Message):
"""Each logical computation runs on replica_count physical devices.
ComputationDevice represents the device ids assinged to the replicas.
@@ -974,7 +989,7 @@ class DeviceAssignmentProto(google.protobuf.message.Message):
*,
replica_device_ids: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["replica_device_ids", b"replica_device_ids"]) -> None: ...
def ClearField(self, field_name: typing.Literal["replica_device_ids", b"replica_device_ids"]) -> None: ...
REPLICA_COUNT_FIELD_NUMBER: builtins.int
COMPUTATION_COUNT_FIELD_NUMBER: builtins.int
@@ -990,11 +1005,11 @@ class DeviceAssignmentProto(google.protobuf.message.Message):
computation_count: builtins.int | None = ...,
computation_devices: collections.abc.Iterable[global___DeviceAssignmentProto.ComputationDevice] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["computation_count", b"computation_count", "computation_devices", b"computation_devices", "replica_count", b"replica_count"]) -> None: ...
def ClearField(self, field_name: typing.Literal["computation_count", b"computation_count", "computation_devices", b"computation_devices", "replica_count", b"replica_count"]) -> None: ...
global___DeviceAssignmentProto = DeviceAssignmentProto
@typing_extensions.final
@typing.final
class LiteralProto(google.protobuf.message.Message):
"""Literals are used when the server and client need to exchange materialized
data / results. Literals are also used to describe constants used in
@@ -1026,12 +1041,19 @@ class LiteralProto(google.protobuf.message.Message):
F8E5M2S_FIELD_NUMBER: builtins.int
F8E4M3FNS_FIELD_NUMBER: builtins.int
SPARSE_INDICES_FIELD_NUMBER: builtins.int
s8s: builtins.bytes
u8s: builtins.bytes
f16s: builtins.bytes
"""The F16s, BF16s, U16s and S16s are encoded in little endian byte order"""
bf16s: builtins.bytes
u16s: builtins.bytes
s16s: builtins.bytes
f8e5m2s: builtins.bytes
f8e4m3fns: builtins.bytes
@property
def shape(self) -> global___ShapeProto: ...
@property
def preds(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bool]: ...
s8s: builtins.bytes
u8s: builtins.bytes
@property
def s32s(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
@@ -1047,21 +1069,17 @@ class LiteralProto(google.protobuf.message.Message):
@property
def c64s(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]:
"""Stored as interleaved real, imag floats."""
@property
def c128s(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]:
"""Stored as interleaved real, imag doubles."""
@property
def tuple_literals(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___LiteralProto]: ...
f16s: builtins.bytes
"""The F16s, BF16s, U16s and S16s are encoded in little endian byte order"""
bf16s: builtins.bytes
u16s: builtins.bytes
s16s: builtins.bytes
f8e5m2s: builtins.bytes
f8e4m3fns: builtins.bytes
@property
def sparse_indices(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Next = 21"""
def __init__(
self,
*,
@@ -1086,12 +1104,12 @@ class LiteralProto(google.protobuf.message.Message):
f8e4m3fns: builtins.bytes | None = ...,
sparse_indices: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["bf16s", b"bf16s", "c128s", b"c128s", "c64s", b"c64s", "f16s", b"f16s", "f32s", b"f32s", "f64s", b"f64s", "f8e4m3fns", b"f8e4m3fns", "f8e5m2s", b"f8e5m2s", "preds", b"preds", "s16s", b"s16s", "s32s", b"s32s", "s64s", b"s64s", "s8s", b"s8s", "shape", b"shape", "sparse_indices", b"sparse_indices", "tuple_literals", b"tuple_literals", "u16s", b"u16s", "u32s", b"u32s", "u64s", b"u64s", "u8s", b"u8s"]) -> None: ...
def HasField(self, field_name: typing.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["bf16s", b"bf16s", "c128s", b"c128s", "c64s", b"c64s", "f16s", b"f16s", "f32s", b"f32s", "f64s", b"f64s", "f8e4m3fns", b"f8e4m3fns", "f8e5m2s", b"f8e5m2s", "preds", b"preds", "s16s", b"s16s", "s32s", b"s32s", "s64s", b"s64s", "s8s", b"s8s", "shape", b"shape", "sparse_indices", b"sparse_indices", "tuple_literals", b"tuple_literals", "u16s", b"u16s", "u32s", b"u32s", "u64s", b"u64s", "u8s", b"u8s"]) -> None: ...
global___LiteralProto = LiteralProto
@typing_extensions.final
@typing.final
class WindowDimension(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -1153,11 +1171,11 @@ class WindowDimension(google.protobuf.message.Message):
base_dilation: builtins.int | None = ...,
window_reversal: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["base_dilation", b"base_dilation", "padding_high", b"padding_high", "padding_low", b"padding_low", "size", b"size", "stride", b"stride", "window_dilation", b"window_dilation", "window_reversal", b"window_reversal"]) -> None: ...
def ClearField(self, field_name: typing.Literal["base_dilation", b"base_dilation", "padding_high", b"padding_high", "padding_low", b"padding_low", "size", b"size", "stride", b"stride", "window_dilation", b"window_dilation", "window_reversal", b"window_reversal"]) -> None: ...
global___WindowDimension = WindowDimension
@typing_extensions.final
@typing.final
class Window(google.protobuf.message.Message):
"""Describes the windowing in an operation such as convolution.
@@ -1176,11 +1194,11 @@ class Window(google.protobuf.message.Message):
*,
dimensions: collections.abc.Iterable[global___WindowDimension] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["dimensions", b"dimensions"]) -> None: ...
def ClearField(self, field_name: typing.Literal["dimensions", b"dimensions"]) -> None: ...
global___Window = Window
@typing_extensions.final
@typing.final
class GatherDimensionNumbers(google.protobuf.message.Message):
"""Describes the dimension numbers for a gather operation.
@@ -1194,6 +1212,10 @@ class GatherDimensionNumbers(google.protobuf.message.Message):
COLLAPSED_SLICE_DIMS_FIELD_NUMBER: builtins.int
START_INDEX_MAP_FIELD_NUMBER: builtins.int
INDEX_VECTOR_DIM_FIELD_NUMBER: builtins.int
index_vector_dim: builtins.int
"""The dimension in the start_indices input that contains the starting
indices.
"""
@property
def offset_dims(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
""""Window indices" is a term for a set of indices that index into the
@@ -1210,6 +1232,7 @@ class GatherDimensionNumbers(google.protobuf.message.Message):
then 0
else Out[offset_dims[i++]]
"""
@property
def collapsed_slice_dims(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
@@ -1218,10 +1241,7 @@ class GatherDimensionNumbers(google.protobuf.message.Message):
transforms the gather index looked up from the start_indices tensor into
the starting index in the input space.
"""
index_vector_dim: builtins.int
"""The dimension in the start_indices input that contains the starting
indices.
"""
def __init__(
self,
*,
@@ -1230,11 +1250,11 @@ class GatherDimensionNumbers(google.protobuf.message.Message):
start_index_map: collections.abc.Iterable[builtins.int] | None = ...,
index_vector_dim: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["collapsed_slice_dims", b"collapsed_slice_dims", "index_vector_dim", b"index_vector_dim", "offset_dims", b"offset_dims", "start_index_map", b"start_index_map"]) -> None: ...
def ClearField(self, field_name: typing.Literal["collapsed_slice_dims", b"collapsed_slice_dims", "index_vector_dim", b"index_vector_dim", "offset_dims", b"offset_dims", "start_index_map", b"start_index_map"]) -> None: ...
global___GatherDimensionNumbers = GatherDimensionNumbers
@typing_extensions.final
@typing.final
class ScatterDimensionNumbers(google.protobuf.message.Message):
"""Describes the dimension numbers for a scatter operation.
@@ -1248,15 +1268,17 @@ class ScatterDimensionNumbers(google.protobuf.message.Message):
INSERTED_WINDOW_DIMS_FIELD_NUMBER: builtins.int
SCATTER_DIMS_TO_OPERAND_DIMS_FIELD_NUMBER: builtins.int
INDEX_VECTOR_DIM_FIELD_NUMBER: builtins.int
index_vector_dim: builtins.int
@property
def update_window_dims(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The set of dimensions in the updates shape that are window dimensions."""
@property
def inserted_window_dims(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The set of window dimensions that must be inserted into the updates shape."""
@property
def scatter_dims_to_operand_dims(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
index_vector_dim: builtins.int
def __init__(
self,
*,
@@ -1265,11 +1287,11 @@ class ScatterDimensionNumbers(google.protobuf.message.Message):
scatter_dims_to_operand_dims: collections.abc.Iterable[builtins.int] | None = ...,
index_vector_dim: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["index_vector_dim", b"index_vector_dim", "inserted_window_dims", b"inserted_window_dims", "scatter_dims_to_operand_dims", b"scatter_dims_to_operand_dims", "update_window_dims", b"update_window_dims"]) -> None: ...
def ClearField(self, field_name: typing.Literal["index_vector_dim", b"index_vector_dim", "inserted_window_dims", b"inserted_window_dims", "scatter_dims_to_operand_dims", b"scatter_dims_to_operand_dims", "update_window_dims", b"update_window_dims"]) -> None: ...
global___ScatterDimensionNumbers = ScatterDimensionNumbers
@typing_extensions.final
@typing.final
class ConvolutionDimensionNumbers(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -1286,11 +1308,6 @@ class ConvolutionDimensionNumbers(google.protobuf.message.Message):
"""The number of the dimension that represents batch in the input."""
input_feature_dimension: builtins.int
"""The number of the dimension that represents features in the input."""
@property
def input_spatial_dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The dimension numbers for the spatial dimensions that the window
moves through in the input.
"""
kernel_input_feature_dimension: builtins.int
"""The number of the dimension that represents input features in the
convolutional kernel (rhs).
@@ -1299,21 +1316,29 @@ class ConvolutionDimensionNumbers(google.protobuf.message.Message):
"""The number of the dimension that represents output features in
the convolutional kernel (rhs).
"""
output_batch_dimension: builtins.int
"""The number of the dimension that represents batch in the output."""
output_feature_dimension: builtins.int
"""The number of the dimension that represents features in the output."""
@property
def input_spatial_dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The dimension numbers for the spatial dimensions that the window
moves through in the input.
"""
@property
def kernel_spatial_dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The dimension numbers for the spatial dimensions that the window
moves through in the kernel (rhs). window.strides(0) is the
stride in the kernel_spatial_dimensions(0) dimension.
"""
output_batch_dimension: builtins.int
"""The number of the dimension that represents batch in the output."""
output_feature_dimension: builtins.int
"""The number of the dimension that represents features in the output."""
@property
def output_spatial_dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The dimension numbers for the spatial dimensions that the window
moves through in the output.
"""
def __init__(
self,
*,
@@ -1327,11 +1352,11 @@ class ConvolutionDimensionNumbers(google.protobuf.message.Message):
output_feature_dimension: builtins.int | None = ...,
output_spatial_dimensions: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["input_batch_dimension", b"input_batch_dimension", "input_feature_dimension", b"input_feature_dimension", "input_spatial_dimensions", b"input_spatial_dimensions", "kernel_input_feature_dimension", b"kernel_input_feature_dimension", "kernel_output_feature_dimension", b"kernel_output_feature_dimension", "kernel_spatial_dimensions", b"kernel_spatial_dimensions", "output_batch_dimension", b"output_batch_dimension", "output_feature_dimension", b"output_feature_dimension", "output_spatial_dimensions", b"output_spatial_dimensions"]) -> None: ...
def ClearField(self, field_name: typing.Literal["input_batch_dimension", b"input_batch_dimension", "input_feature_dimension", b"input_feature_dimension", "input_spatial_dimensions", b"input_spatial_dimensions", "kernel_input_feature_dimension", b"kernel_input_feature_dimension", "kernel_output_feature_dimension", b"kernel_output_feature_dimension", "kernel_spatial_dimensions", b"kernel_spatial_dimensions", "output_batch_dimension", b"output_batch_dimension", "output_feature_dimension", b"output_feature_dimension", "output_spatial_dimensions", b"output_spatial_dimensions"]) -> None: ...
global___ConvolutionDimensionNumbers = ConvolutionDimensionNumbers
@typing_extensions.final
@typing.final
class DotDimensionNumbers(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -1342,15 +1367,19 @@ class DotDimensionNumbers(google.protobuf.message.Message):
@property
def lhs_contracting_dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The dimension numbers that represent the 'lhs' contracting dimensions."""
@property
def rhs_contracting_dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The dimension numbers that represent the 'rhs' contracting dimensions."""
@property
def lhs_batch_dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The dimension numbers that represent the 'lhs' batch dimensions."""
@property
def rhs_batch_dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The dimension numbers that represent the 'rhs' batch dimensions."""
def __init__(
self,
*,
@@ -1359,11 +1388,11 @@ class DotDimensionNumbers(google.protobuf.message.Message):
lhs_batch_dimensions: collections.abc.Iterable[builtins.int] | None = ...,
rhs_batch_dimensions: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["lhs_batch_dimensions", b"lhs_batch_dimensions", "lhs_contracting_dimensions", b"lhs_contracting_dimensions", "rhs_batch_dimensions", b"rhs_batch_dimensions", "rhs_contracting_dimensions", b"rhs_contracting_dimensions"]) -> None: ...
def ClearField(self, field_name: typing.Literal["lhs_batch_dimensions", b"lhs_batch_dimensions", "lhs_contracting_dimensions", b"lhs_contracting_dimensions", "rhs_batch_dimensions", b"rhs_batch_dimensions", "rhs_contracting_dimensions", b"rhs_contracting_dimensions"]) -> None: ...
global___DotDimensionNumbers = DotDimensionNumbers
@typing_extensions.final
@typing.final
class TriangularSolveOptions(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -1411,11 +1440,11 @@ class TriangularSolveOptions(google.protobuf.message.Message):
unit_diagonal: builtins.bool | None = ...,
transpose_a: global___TriangularSolveOptions.Transpose.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["left_side", b"left_side", "lower", b"lower", "transpose_a", b"transpose_a", "unit_diagonal", b"unit_diagonal"]) -> None: ...
def ClearField(self, field_name: typing.Literal["left_side", b"left_side", "lower", b"lower", "transpose_a", b"transpose_a", "unit_diagonal", b"unit_diagonal"]) -> None: ...
global___TriangularSolveOptions = TriangularSolveOptions
@typing_extensions.final
@typing.final
class CholeskyOptions(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -1429,11 +1458,11 @@ class CholeskyOptions(google.protobuf.message.Message):
*,
lower: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["lower", b"lower"]) -> None: ...
def ClearField(self, field_name: typing.Literal["lower", b"lower"]) -> None: ...
global___CholeskyOptions = CholeskyOptions
@typing_extensions.final
@typing.final
class FrontendAttributes(google.protobuf.message.Message):
"""Generic map of attributes used to pass hints / configuration options from
the Python frontend to the XLA backend.
@@ -1441,7 +1470,7 @@ class FrontendAttributes(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class MapEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -1455,7 +1484,7 @@ class FrontendAttributes(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
MAP_FIELD_NUMBER: builtins.int
@property
@@ -1465,11 +1494,11 @@ class FrontendAttributes(google.protobuf.message.Message):
*,
map: collections.abc.Mapping[builtins.str, builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["map", b"map"]) -> None: ...
def ClearField(self, field_name: typing.Literal["map", b"map"]) -> None: ...
global___FrontendAttributes = FrontendAttributes
@typing_extensions.final
@typing.final
class OpSharding(google.protobuf.message.Message):
"""LINT.IfChange"""
@@ -1521,20 +1550,28 @@ class OpSharding(google.protobuf.message.Message):
METADATA_FIELD_NUMBER: builtins.int
LAST_TILE_DIMS_FIELD_NUMBER: builtins.int
type: global___OpSharding.Type.ValueType
replicate_on_last_tile_dim: builtins.bool
"""Only used for OTHER type. If true, data is sharded according to other
dimensions of tile_assignment(), but replicated across devices along the
last dimension. (Experimental)
"""
@property
def tile_shape(self) -> global___ShapeProto:
"""The shape of the sharded tile."""
@property
def tile_assignment_dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The shape of the tile assignment tensor - this must be the same rank as
tile_shape and the product of its dimensions must equal
tile_assignment_devices.size().
"""
@property
def tile_assignment_devices(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Flattened list of device IDs. The order of flattening is the same as used
by IndexUtil::MultiToLinearIndex(tile_assignment_shape).
"""
@property
def tuple_shardings(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___OpSharding]:
"""If type == TUPLE, the sub-shardings, one per leaf node in the tuple shape,
@@ -1544,11 +1581,7 @@ class OpSharding(google.protobuf.message.Message):
applied, this is inferred from the instruction this sharding gets attached
to.
"""
replicate_on_last_tile_dim: builtins.bool
"""Only used for OTHER type. If true, data is sharded according to other
dimensions of tile_assignment(), but replicated across devices along the
last dimension. (Experimental)
"""
@property
def metadata(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___OpMetadata]:
"""This field is used to track the source of this sharding, usually derived
@@ -1557,6 +1590,7 @@ class OpSharding(google.protobuf.message.Message):
type == TUPLE and instead metadata should be set on individual tuple
elements.
"""
@property
def last_tile_dims(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[global___OpSharding.Type.ValueType]:
"""This field is used to represented the sharding type of each subgroup.
@@ -1565,6 +1599,7 @@ class OpSharding(google.protobuf.message.Message):
in [2,2,2,2] represents a subgrouping in replicate, manual,
unreduced sharding type respectively.
"""
def __init__(
self,
*,
@@ -1577,12 +1612,12 @@ class OpSharding(google.protobuf.message.Message):
metadata: collections.abc.Iterable[global___OpMetadata] | None = ...,
last_tile_dims: collections.abc.Iterable[global___OpSharding.Type.ValueType] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["tile_shape", b"tile_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["last_tile_dims", b"last_tile_dims", "metadata", b"metadata", "replicate_on_last_tile_dim", b"replicate_on_last_tile_dim", "tile_assignment_devices", b"tile_assignment_devices", "tile_assignment_dimensions", b"tile_assignment_dimensions", "tile_shape", b"tile_shape", "tuple_shardings", b"tuple_shardings", "type", b"type"]) -> None: ...
def HasField(self, field_name: typing.Literal["tile_shape", b"tile_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["last_tile_dims", b"last_tile_dims", "metadata", b"metadata", "replicate_on_last_tile_dim", b"replicate_on_last_tile_dim", "tile_assignment_devices", b"tile_assignment_devices", "tile_assignment_dimensions", b"tile_assignment_dimensions", "tile_shape", b"tile_shape", "tuple_shardings", b"tuple_shardings", "type", b"type"]) -> None: ...
global___OpSharding = OpSharding
@typing_extensions.final
@typing.final
class ReplicaGroup(google.protobuf.message.Message):
"""Describes the replica groups in a cross replica op (e.g., all-reduce and
all-to-all).
@@ -1596,16 +1631,17 @@ class ReplicaGroup(google.protobuf.message.Message):
"""The ids of the replicas that belongs to the same group. The ordering of the
ids matters in some ops (e.g., all-to-all).
"""
def __init__(
self,
*,
replica_ids: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["replica_ids", b"replica_ids"]) -> None: ...
def ClearField(self, field_name: typing.Literal["replica_ids", b"replica_ids"]) -> None: ...
global___ReplicaGroup = ReplicaGroup
@typing_extensions.final
@typing.final
class SourceTarget(google.protobuf.message.Message):
"""Describes the source target pair in the collective permute op."""
@@ -1621,11 +1657,11 @@ class SourceTarget(google.protobuf.message.Message):
source: builtins.int | None = ...,
target: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["source", b"source", "target", b"target"]) -> None: ...
def ClearField(self, field_name: typing.Literal["source", b"source", "target", b"target"]) -> None: ...
global___SourceTarget = SourceTarget
@typing_extensions.final
@typing.final
class PrecisionConfig(google.protobuf.message.Message):
"""Used to indicate the precision configuration. It has backend specific
meaning.
@@ -1660,11 +1696,11 @@ class PrecisionConfig(google.protobuf.message.Message):
*,
operand_precision: collections.abc.Iterable[global___PrecisionConfig.Precision.ValueType] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["operand_precision", b"operand_precision"]) -> None: ...
def ClearField(self, field_name: typing.Literal["operand_precision", b"operand_precision"]) -> None: ...
global___PrecisionConfig = PrecisionConfig
@typing_extensions.final
@typing.final
class ParameterReplication(google.protobuf.message.Message):
"""Describes whether all data-parallelism replicas will receive the same
parameter data at each buffer.
@@ -1682,16 +1718,17 @@ class ParameterReplication(google.protobuf.message.Message):
number of elements in this field must match the number of leaf buffers in
the HLO instruction's shape.
"""
def __init__(
self,
*,
replicated_at_leaf_buffers: collections.abc.Iterable[builtins.bool] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["replicated_at_leaf_buffers", b"replicated_at_leaf_buffers"]) -> None: ...
def ClearField(self, field_name: typing.Literal["replicated_at_leaf_buffers", b"replicated_at_leaf_buffers"]) -> None: ...
global___ParameterReplication = ParameterReplication
@typing_extensions.final
@typing.final
class WhileLoopBackendConfig(google.protobuf.message.Message):
"""A backend-config for kWhile loops that stores the loop's trip count, if it is
known.
@@ -1705,7 +1742,7 @@ class WhileLoopBackendConfig(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class KnownTripCount(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -1716,7 +1753,7 @@ class WhileLoopBackendConfig(google.protobuf.message.Message):
*,
n: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["n", b"n"]) -> None: ...
def ClearField(self, field_name: typing.Literal["n", b"n"]) -> None: ...
KNOWN_TRIP_COUNT_FIELD_NUMBER: builtins.int
@property
@@ -1724,17 +1761,18 @@ class WhileLoopBackendConfig(google.protobuf.message.Message):
"""This indirection lets us distinguish between known-trip-count == 0 and
unknown-trip-count.
"""
def __init__(
self,
*,
known_trip_count: global___WhileLoopBackendConfig.KnownTripCount | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["known_trip_count", b"known_trip_count"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["known_trip_count", b"known_trip_count"]) -> None: ...
def HasField(self, field_name: typing.Literal["known_trip_count", b"known_trip_count"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["known_trip_count", b"known_trip_count"]) -> None: ...
global___WhileLoopBackendConfig = WhileLoopBackendConfig
@typing_extensions.final
@typing.final
class OutputOperandAliasing(google.protobuf.message.Message):
"""Specifies a pair of output/operand buffers that alias each other for
kCustomCall and kFusion
@@ -1745,9 +1783,9 @@ class OutputOperandAliasing(google.protobuf.message.Message):
OUTPUT_SHAPE_INDEX_FIELD_NUMBER: builtins.int
OPERAND_INDEX_FIELD_NUMBER: builtins.int
OPERAND_SHAPE_INDEX_FIELD_NUMBER: builtins.int
operand_index: builtins.int
@property
def output_shape_index(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
operand_index: builtins.int
@property
def operand_shape_index(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
def __init__(
@@ -1757,6 +1795,6 @@ class OutputOperandAliasing(google.protobuf.message.Message):
operand_index: builtins.int | None = ...,
operand_shape_index: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["operand_index", b"operand_index", "operand_shape_index", b"operand_shape_index", "output_shape_index", b"output_shape_index"]) -> None: ...
def ClearField(self, field_name: typing.Literal["operand_index", b"operand_index", "operand_shape_index", b"operand_shape_index", "output_shape_index", b"output_shape_index"]) -> None: ...
global___OutputOperandAliasing = OutputOperandAliasing
stubs/tensorflow/tensorflow/core/example/example_parser_configuration_pb2.pyi
+17 -16
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
Protocol messages for describing the configuration of the ExampleParserOp."""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -15,7 +16,7 @@ import tensorflow.core.framework.types_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class VarLenFeatureProto(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -35,11 +36,11 @@ class VarLenFeatureProto(google.protobuf.message.Message):
indices_output_tensor_name: builtins.str | None = ...,
shapes_output_tensor_name: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["dtype", b"dtype", "indices_output_tensor_name", b"indices_output_tensor_name", "shapes_output_tensor_name", b"shapes_output_tensor_name", "values_output_tensor_name", b"values_output_tensor_name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["dtype", b"dtype", "indices_output_tensor_name", b"indices_output_tensor_name", "shapes_output_tensor_name", b"shapes_output_tensor_name", "values_output_tensor_name", b"values_output_tensor_name"]) -> None: ...
global___VarLenFeatureProto = VarLenFeatureProto
@typing_extensions.final
@typing.final
class FixedLenFeatureProto(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -48,11 +49,11 @@ class FixedLenFeatureProto(google.protobuf.message.Message):
DEFAULT_VALUE_FIELD_NUMBER: builtins.int
VALUES_OUTPUT_TENSOR_NAME_FIELD_NUMBER: builtins.int
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
values_output_tensor_name: builtins.str
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: ...
@property
def default_value(self) -> tensorflow.core.framework.tensor_pb2.TensorProto: ...
values_output_tensor_name: builtins.str
def __init__(
self,
*,
@@ -61,12 +62,12 @@ class FixedLenFeatureProto(google.protobuf.message.Message):
default_value: tensorflow.core.framework.tensor_pb2.TensorProto | None = ...,
values_output_tensor_name: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["default_value", b"default_value", "shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["default_value", b"default_value", "dtype", b"dtype", "shape", b"shape", "values_output_tensor_name", b"values_output_tensor_name"]) -> None: ...
def HasField(self, field_name: typing.Literal["default_value", b"default_value", "shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["default_value", b"default_value", "dtype", b"dtype", "shape", b"shape", "values_output_tensor_name", b"values_output_tensor_name"]) -> None: ...
global___FixedLenFeatureProto = FixedLenFeatureProto
@typing_extensions.final
@typing.final
class FeatureConfiguration(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -82,17 +83,17 @@ class FeatureConfiguration(google.protobuf.message.Message):
fixed_len_feature: global___FixedLenFeatureProto | None = ...,
var_len_feature: global___VarLenFeatureProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["config", b"config", "fixed_len_feature", b"fixed_len_feature", "var_len_feature", b"var_len_feature"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["config", b"config", "fixed_len_feature", b"fixed_len_feature", "var_len_feature", b"var_len_feature"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["config", b"config"]) -> typing_extensions.Literal["fixed_len_feature", "var_len_feature"] | None: ...
def HasField(self, field_name: typing.Literal["config", b"config", "fixed_len_feature", b"fixed_len_feature", "var_len_feature", b"var_len_feature"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["config", b"config", "fixed_len_feature", b"fixed_len_feature", "var_len_feature", b"var_len_feature"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["config", b"config"]) -> typing.Literal["fixed_len_feature", "var_len_feature"] | None: ...
global___FeatureConfiguration = FeatureConfiguration
@typing_extensions.final
@typing.final
class ExampleParserConfiguration(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class FeatureMapEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -107,8 +108,8 @@ class ExampleParserConfiguration(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___FeatureConfiguration | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
FEATURE_MAP_FIELD_NUMBER: builtins.int
@property
@@ -118,6 +119,6 @@ class ExampleParserConfiguration(google.protobuf.message.Message):
*,
feature_map: collections.abc.Mapping[builtins.str, global___FeatureConfiguration] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["feature_map", b"feature_map"]) -> None: ...
def ClearField(self, field_name: typing.Literal["feature_map", b"feature_map"]) -> None: ...
global___ExampleParserConfiguration = ExampleParserConfiguration
stubs/tensorflow/tensorflow/core/example/example_pb2.pyi
+8 -7
View File
@@ -4,8 +4,9 @@ isort:skip_file
Protocol messages for describing input data Examples for machine learning
model training or inference.
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
@@ -13,7 +14,7 @@ import tensorflow.core.example.feature_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class Example(google.protobuf.message.Message):
"""An Example is a mostly-normalized data format for storing data for
training and inference. It contains a key-value store (features); where
@@ -99,12 +100,12 @@ class Example(google.protobuf.message.Message):
*,
features: tensorflow.core.example.feature_pb2.Features | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["features", b"features"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["features", b"features"]) -> None: ...
def HasField(self, field_name: typing.Literal["features", b"features"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["features", b"features"]) -> None: ...
global___Example = Example
@typing_extensions.final
@typing.final
class SequenceExample(google.protobuf.message.Message):
"""A SequenceExample is an Example representing one or more sequences, and
some context. The context contains features which apply to the entire
@@ -327,7 +328,7 @@ class SequenceExample(google.protobuf.message.Message):
context: tensorflow.core.example.feature_pb2.Features | None = ...,
feature_lists: tensorflow.core.example.feature_pb2.FeatureLists | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["context", b"context", "feature_lists", b"feature_lists"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["context", b"context", "feature_lists", b"feature_lists"]) -> None: ...
def HasField(self, field_name: typing.Literal["context", b"context", "feature_lists", b"feature_lists"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["context", b"context", "feature_lists", b"feature_lists"]) -> None: ...
global___SequenceExample = SequenceExample
stubs/tensorflow/tensorflow/core/example/feature_pb2.pyi
+26 -23
View File
@@ -55,9 +55,10 @@ Example Features for a movie recommendation application:
}}
}
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -65,7 +66,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class BytesList(google.protobuf.message.Message):
"""LINT.IfChange
Containers to hold repeated fundamental values.
@@ -81,11 +82,11 @@ class BytesList(google.protobuf.message.Message):
*,
value: collections.abc.Iterable[builtins.bytes] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["value", b"value"]) -> None: ...
global___BytesList = BytesList
@typing_extensions.final
@typing.final
class FloatList(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -97,11 +98,11 @@ class FloatList(google.protobuf.message.Message):
*,
value: collections.abc.Iterable[builtins.float] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["value", b"value"]) -> None: ...
global___FloatList = FloatList
@typing_extensions.final
@typing.final
class Int64List(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -113,11 +114,11 @@ class Int64List(google.protobuf.message.Message):
*,
value: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["value", b"value"]) -> None: ...
global___Int64List = Int64List
@typing_extensions.final
@typing.final
class Feature(google.protobuf.message.Message):
"""Containers for non-sequential data."""
@@ -139,17 +140,17 @@ class Feature(google.protobuf.message.Message):
float_list: global___FloatList | None = ...,
int64_list: global___Int64List | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["bytes_list", b"bytes_list", "float_list", b"float_list", "int64_list", b"int64_list", "kind", b"kind"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["bytes_list", b"bytes_list", "float_list", b"float_list", "int64_list", b"int64_list", "kind", b"kind"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["kind", b"kind"]) -> typing_extensions.Literal["bytes_list", "float_list", "int64_list"] | None: ...
def HasField(self, field_name: typing.Literal["bytes_list", b"bytes_list", "float_list", b"float_list", "int64_list", b"int64_list", "kind", b"kind"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["bytes_list", b"bytes_list", "float_list", b"float_list", "int64_list", b"int64_list", "kind", b"kind"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["kind", b"kind"]) -> typing.Literal["bytes_list", "float_list", "int64_list"] | None: ...
global___Feature = Feature
@typing_extensions.final
@typing.final
class Features(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class FeatureEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -164,23 +165,24 @@ class Features(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___Feature | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
FEATURE_FIELD_NUMBER: builtins.int
@property
def feature(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___Feature]:
"""Map from feature name to feature."""
def __init__(
self,
*,
feature: collections.abc.Mapping[builtins.str, global___Feature] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["feature", b"feature"]) -> None: ...
def ClearField(self, field_name: typing.Literal["feature", b"feature"]) -> None: ...
global___Features = Features
@typing_extensions.final
@typing.final
class FeatureList(google.protobuf.message.Message):
"""Containers for sequential data.
@@ -201,15 +203,15 @@ class FeatureList(google.protobuf.message.Message):
*,
feature: collections.abc.Iterable[global___Feature] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["feature", b"feature"]) -> None: ...
def ClearField(self, field_name: typing.Literal["feature", b"feature"]) -> None: ...
global___FeatureList = FeatureList
@typing_extensions.final
@typing.final
class FeatureLists(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class FeatureListEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -224,18 +226,19 @@ class FeatureLists(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___FeatureList | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
FEATURE_LIST_FIELD_NUMBER: builtins.int
@property
def feature_list(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___FeatureList]:
"""Map from feature name to feature list."""
def __init__(
self,
*,
feature_list: collections.abc.Mapping[builtins.str, global___FeatureList] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["feature_list", b"feature_list"]) -> None: ...
def ClearField(self, field_name: typing.Literal["feature_list", b"feature_list"]) -> None: ...
global___FeatureLists = FeatureLists
stubs/tensorflow/tensorflow/core/framework/allocation_description_pb2.pyi
+4 -3
View File
@@ -2,15 +2,16 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class AllocationDescription(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -42,6 +43,6 @@ class AllocationDescription(google.protobuf.message.Message):
has_single_reference: builtins.bool | None = ...,
ptr: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["allocated_bytes", b"allocated_bytes", "allocation_id", b"allocation_id", "allocator_name", b"allocator_name", "has_single_reference", b"has_single_reference", "ptr", b"ptr", "requested_bytes", b"requested_bytes"]) -> None: ...
def ClearField(self, field_name: typing.Literal["allocated_bytes", b"allocated_bytes", "allocation_id", b"allocation_id", "allocator_name", b"allocator_name", "has_single_reference", b"has_single_reference", "ptr", b"ptr", "requested_bytes", b"requested_bytes"]) -> None: ...
global___AllocationDescription = AllocationDescription
stubs/tensorflow/tensorflow/core/framework/api_def_pb2.pyi
+27 -24
View File
@@ -4,6 +4,7 @@ isort:skip_file
Defines the text format for including per-op API definition and
overrides for client language op code generators.
"""
import builtins
import collections.abc
import sys
@@ -22,7 +23,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class ApiDef(google.protobuf.message.Message):
"""Used to specify and override the default API & behavior in the
generated code for client languages, from what you would get from
@@ -81,7 +82,7 @@ class ApiDef(google.protobuf.message.Message):
is appropriate in the target language).
"""
@typing_extensions.final
@typing.final
class Endpoint(google.protobuf.message.Message):
"""If you specify any endpoint, this will replace all of the
inherited endpoints. The first endpoint should be the
@@ -116,9 +117,9 @@ class ApiDef(google.protobuf.message.Message):
deprecated: builtins.bool | None = ...,
deprecation_version: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["deprecated", b"deprecated", "deprecation_version", b"deprecation_version", "name", b"name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["deprecated", b"deprecated", "deprecation_version", b"deprecation_version", "name", b"name"]) -> None: ...
@typing_extensions.final
@typing.final
class Arg(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -143,9 +144,9 @@ class ApiDef(google.protobuf.message.Message):
rename_to: builtins.str | None = ...,
description: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["description", b"description", "name", b"name", "rename_to", b"rename_to"]) -> None: ...
def ClearField(self, field_name: typing.Literal["description", b"description", "name", b"name", "rename_to", b"rename_to"]) -> None: ...
@typing_extensions.final
@typing.final
class Attr(google.protobuf.message.Message):
"""Description of the graph-construction-time configuration of this
Op. That is to say, this describes the attr fields that will
@@ -164,6 +165,10 @@ class ApiDef(google.protobuf.message.Message):
is used in the GraphDef. Note that these names in `backticks`
will also be replaced in the summary & description fields.
"""
description: builtins.str
"""Note: this will replace any inherited attr doc, there is no current
way of modifying attr descriptions as can be done with op descriptions.
"""
@property
def default_value(self) -> tensorflow.core.framework.attr_value_pb2.AttrValue:
"""Specify a new default value to use for this attr. This default
@@ -171,10 +176,7 @@ class ApiDef(google.protobuf.message.Message):
default in the OpDef, which will be used when interpreting old
GraphDefs.
"""
description: builtins.str
"""Note: this will replace any inherited attr doc, there is no current
way of modifying attr descriptions as can be done with op descriptions.
"""
def __init__(
self,
*,
@@ -183,8 +185,8 @@ class ApiDef(google.protobuf.message.Message):
default_value: tensorflow.core.framework.attr_value_pb2.AttrValue | None = ...,
description: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["default_value", b"default_value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["default_value", b"default_value", "description", b"description", "name", b"name", "rename_to", b"rename_to"]) -> None: ...
def HasField(self, field_name: typing.Literal["default_value", b"default_value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["default_value", b"default_value", "description", b"description", "name", b"name", "rename_to", b"rename_to"]) -> None: ...
GRAPH_OP_NAME_FIELD_NUMBER: builtins.int
DEPRECATION_MESSAGE_FIELD_NUMBER: builtins.int
@@ -212,6 +214,15 @@ class ApiDef(google.protobuf.message.Message):
deprecated in versions before that.
"""
visibility: global___ApiDef.Visibility.ValueType
summary: builtins.str
"""One-line human-readable description of what the Op does."""
description: builtins.str
"""Additional, longer human-readable description of what the Op does."""
description_prefix: builtins.str
"""Modify an existing/inherited description by adding text to the beginning
or end.
"""
description_suffix: builtins.str
@property
def endpoint(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___ApiDef.Endpoint]: ...
@property
@@ -224,17 +235,9 @@ class ApiDef(google.protobuf.message.Message):
Length of arg_order should be either empty to keep current order
or match size of in_arg.
"""
@property
def attr(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___ApiDef.Attr]: ...
summary: builtins.str
"""One-line human-readable description of what the Op does."""
description: builtins.str
"""Additional, longer human-readable description of what the Op does."""
description_prefix: builtins.str
"""Modify an existing/inherited description by adding text to the beginning
or end.
"""
description_suffix: builtins.str
def __init__(
self,
*,
@@ -252,11 +255,11 @@ class ApiDef(google.protobuf.message.Message):
description_prefix: builtins.str | None = ...,
description_suffix: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["arg_order", b"arg_order", "attr", b"attr", "deprecation_message", b"deprecation_message", "deprecation_version", b"deprecation_version", "description", b"description", "description_prefix", b"description_prefix", "description_suffix", b"description_suffix", "endpoint", b"endpoint", "graph_op_name", b"graph_op_name", "in_arg", b"in_arg", "out_arg", b"out_arg", "summary", b"summary", "visibility", b"visibility"]) -> None: ...
def ClearField(self, field_name: typing.Literal["arg_order", b"arg_order", "attr", b"attr", "deprecation_message", b"deprecation_message", "deprecation_version", b"deprecation_version", "description", b"description", "description_prefix", b"description_prefix", "description_suffix", b"description_suffix", "endpoint", b"endpoint", "graph_op_name", b"graph_op_name", "in_arg", b"in_arg", "out_arg", b"out_arg", "summary", b"summary", "visibility", b"visibility"]) -> None: ...
global___ApiDef = ApiDef
@typing_extensions.final
@typing.final
class ApiDefs(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -268,6 +271,6 @@ class ApiDefs(google.protobuf.message.Message):
*,
op: collections.abc.Iterable[global___ApiDef] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["op", b"op"]) -> None: ...
def ClearField(self, field_name: typing.Literal["op", b"op"]) -> None: ...
global___ApiDefs = ApiDefs
stubs/tensorflow/tensorflow/core/framework/attr_value_pb2.pyi
+41 -28
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -15,7 +16,7 @@ import tensorflow.core.framework.types_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class AttrValue(google.protobuf.message.Message):
"""Protocol buffer representing the value for an attr used to configure an Op.
Comment indicates the corresponding attr type. Only the field matching the
@@ -24,7 +25,7 @@ class AttrValue(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class ListValue(google.protobuf.message.Message):
"""LINT.IfChange"""
@@ -41,27 +42,35 @@ class AttrValue(google.protobuf.message.Message):
@property
def s(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bytes]:
""""list(string)" """
@property
def i(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
""""list(int)" """
@property
def f(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]:
""""list(float)" """
@property
def b(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bool]:
""""list(bool)" """
@property
def type(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[tensorflow.core.framework.types_pb2.DataType.ValueType]:
""""list(type)" """
@property
def shape(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto]:
""""list(shape)" """
@property
def tensor(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.tensor_pb2.TensorProto]:
""""list(tensor)" """
@property
def func(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___NameAttrList]:
""""list(attr)" """
def __init__(
self,
*,
@@ -74,7 +83,7 @@ class AttrValue(google.protobuf.message.Message):
tensor: collections.abc.Iterable[tensorflow.core.framework.tensor_pb2.TensorProto] | None = ...,
func: collections.abc.Iterable[global___NameAttrList] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["b", b"b", "f", b"f", "func", b"func", "i", b"i", "s", b"s", "shape", b"shape", "tensor", b"tensor", "type", b"type"]) -> None: ...
def ClearField(self, field_name: typing.Literal["b", b"b", "f", b"f", "func", b"func", "i", b"i", "s", b"s", "shape", b"shape", "tensor", b"tensor", "type", b"type"]) -> None: ...
S_FIELD_NUMBER: builtins.int
I_FIELD_NUMBER: builtins.int
@@ -96,22 +105,6 @@ class AttrValue(google.protobuf.message.Message):
""""bool" """
type: tensorflow.core.framework.types_pb2.DataType.ValueType
""""type" """
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto:
""""shape" """
@property
def tensor(self) -> tensorflow.core.framework.tensor_pb2.TensorProto:
""""tensor" """
@property
def list(self) -> global___AttrValue.ListValue:
"""any "list(...)" """
@property
def func(self) -> global___NameAttrList:
""""func" represents a function. func.name is a function's name or
a primitive op's name. func.attr.first is the name of an attr
defined for that function. func.attr.second is the value for
that attr in the instantiation.
"""
placeholder: builtins.str
"""This is a placeholder only used in nodes defined inside a
function. It indicates the attr value will be supplied when
@@ -121,6 +114,26 @@ class AttrValue(google.protobuf.message.Message):
set to "bar", the instantiated node N's attr A will have been
given the value "bar".
"""
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto:
""""shape" """
@property
def tensor(self) -> tensorflow.core.framework.tensor_pb2.TensorProto:
""""tensor" """
@property
def list(self) -> global___AttrValue.ListValue:
"""any "list(...)" """
@property
def func(self) -> global___NameAttrList:
""""func" represents a function. func.name is a function's name or
a primitive op's name. func.attr.first is the name of an attr
defined for that function. func.attr.second is the value for
that attr in the instantiation.
"""
def __init__(
self,
*,
@@ -135,13 +148,13 @@ class AttrValue(google.protobuf.message.Message):
func: global___NameAttrList | None = ...,
placeholder: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["b", b"b", "f", b"f", "func", b"func", "i", b"i", "list", b"list", "placeholder", b"placeholder", "s", b"s", "shape", b"shape", "tensor", b"tensor", "type", b"type", "value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["b", b"b", "f", b"f", "func", b"func", "i", b"i", "list", b"list", "placeholder", b"placeholder", "s", b"s", "shape", b"shape", "tensor", b"tensor", "type", b"type", "value", b"value"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["value", b"value"]) -> typing_extensions.Literal["s", "i", "f", "b", "type", "shape", "tensor", "list", "func", "placeholder"] | None: ...
def HasField(self, field_name: typing.Literal["b", b"b", "f", b"f", "func", b"func", "i", b"i", "list", b"list", "placeholder", b"placeholder", "s", b"s", "shape", b"shape", "tensor", b"tensor", "type", b"type", "value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["b", b"b", "f", b"f", "func", b"func", "i", b"i", "list", b"list", "placeholder", b"placeholder", "s", b"s", "shape", b"shape", "tensor", b"tensor", "type", b"type", "value", b"value"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["value", b"value"]) -> typing.Literal["s", "i", "f", "b", "type", "shape", "tensor", "list", "func", "placeholder"] | None: ...
global___AttrValue = AttrValue
@typing_extensions.final
@typing.final
class NameAttrList(google.protobuf.message.Message):
"""A list of attr names and their values. The whole list is attached
with a string name. E.g., MatMul[T=float].
@@ -149,7 +162,7 @@ class NameAttrList(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class AttrEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -164,8 +177,8 @@ class NameAttrList(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___AttrValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
ATTR_FIELD_NUMBER: builtins.int
@@ -178,6 +191,6 @@ class NameAttrList(google.protobuf.message.Message):
name: builtins.str | None = ...,
attr: collections.abc.Mapping[builtins.str, global___AttrValue] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["attr", b"attr", "name", b"name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["attr", b"attr", "name", b"name"]) -> None: ...
global___NameAttrList = NameAttrList
stubs/tensorflow/tensorflow/core/framework/cost_graph_pb2.pyi
+21 -19
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -14,15 +15,15 @@ import tensorflow.core.framework.types_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class CostGraphDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Node(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class InputInfo(google.protobuf.message.Message):
"""Inputs of this node. They must be executed before this node can be
executed. An input is a particular output of another node, specified
@@ -41,9 +42,9 @@ class CostGraphDef(google.protobuf.message.Message):
preceding_node: builtins.int | None = ...,
preceding_port: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["preceding_node", b"preceding_node", "preceding_port", b"preceding_port"]) -> None: ...
def ClearField(self, field_name: typing.Literal["preceding_node", b"preceding_node", "preceding_port", b"preceding_port"]) -> None: ...
@typing_extensions.final
@typing.final
class OutputInfo(google.protobuf.message.Message):
"""Outputs of this node."""
@@ -59,9 +60,9 @@ class CostGraphDef(google.protobuf.message.Message):
may itself be an alias. The algorithm will therefore need to follow
those pointers.
"""
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: ...
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
def __init__(
self,
*,
@@ -70,8 +71,8 @@ class CostGraphDef(google.protobuf.message.Message):
shape: tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto | None = ...,
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["alias_input_port", b"alias_input_port", "dtype", b"dtype", "shape", b"shape", "size", b"size"]) -> None: ...
def HasField(self, field_name: typing.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["alias_input_port", b"alias_input_port", "dtype", b"dtype", "shape", b"shape", "size", b"size"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
DEVICE_FIELD_NUMBER: builtins.int
@@ -97,10 +98,6 @@ class CostGraphDef(google.protobuf.message.Message):
"""
id: builtins.int
"""The id of the node. Node ids are only unique inside a partition."""
@property
def input_info(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___CostGraphDef.Node.InputInfo]: ...
@property
def output_info(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___CostGraphDef.Node.OutputInfo]: ...
temporary_memory_size: builtins.int
"""Temporary memory used by this node."""
persistent_memory_size: builtins.int
@@ -122,11 +119,16 @@ class CostGraphDef(google.protobuf.message.Message):
"""If true, the output is permanent: it can't be discarded, because this
node is part of the "final output". Nodes may depend on final nodes.
"""
inaccurate: builtins.bool
"""Are the costs inaccurate?"""
@property
def input_info(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___CostGraphDef.Node.InputInfo]: ...
@property
def output_info(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___CostGraphDef.Node.OutputInfo]: ...
@property
def control_input(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Ids of the control inputs for this node."""
inaccurate: builtins.bool
"""Are the costs inaccurate?"""
def __init__(
self,
*,
@@ -147,9 +149,9 @@ class CostGraphDef(google.protobuf.message.Message):
control_input: collections.abc.Iterable[builtins.int] | None = ...,
inaccurate: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["compute_cost", b"compute_cost", "compute_time", b"compute_time", "control_input", b"control_input", "device", b"device", "device_persistent_memory_size", b"device_persistent_memory_size", "device_temp_memory_size", b"device_temp_memory_size", "host_temp_memory_size", b"host_temp_memory_size", "id", b"id", "inaccurate", b"inaccurate", "input_info", b"input_info", "is_final", b"is_final", "memory_time", b"memory_time", "name", b"name", "output_info", b"output_info", "persistent_memory_size", b"persistent_memory_size", "temporary_memory_size", b"temporary_memory_size"]) -> None: ...
def ClearField(self, field_name: typing.Literal["compute_cost", b"compute_cost", "compute_time", b"compute_time", "control_input", b"control_input", "device", b"device", "device_persistent_memory_size", b"device_persistent_memory_size", "device_temp_memory_size", b"device_temp_memory_size", "host_temp_memory_size", b"host_temp_memory_size", "id", b"id", "inaccurate", b"inaccurate", "input_info", b"input_info", "is_final", b"is_final", "memory_time", b"memory_time", "name", b"name", "output_info", b"output_info", "persistent_memory_size", b"persistent_memory_size", "temporary_memory_size", b"temporary_memory_size"]) -> None: ...
@typing_extensions.final
@typing.final
class AggregatedCost(google.protobuf.message.Message):
"""Total cost of this graph, typically used for balancing decisions."""
@@ -167,7 +169,7 @@ class CostGraphDef(google.protobuf.message.Message):
cost: builtins.float | None = ...,
dimension: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["cost", b"cost", "dimension", b"dimension"]) -> None: ...
def ClearField(self, field_name: typing.Literal["cost", b"cost", "dimension", b"dimension"]) -> None: ...
NODE_FIELD_NUMBER: builtins.int
COST_FIELD_NUMBER: builtins.int
@@ -181,6 +183,6 @@ class CostGraphDef(google.protobuf.message.Message):
node: collections.abc.Iterable[global___CostGraphDef.Node] | None = ...,
cost: collections.abc.Iterable[global___CostGraphDef.AggregatedCost] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["cost", b"cost", "node", b"node"]) -> None: ...
def ClearField(self, field_name: typing.Literal["cost", b"cost", "node", b"node"]) -> None: ...
global___CostGraphDef = CostGraphDef
stubs/tensorflow/tensorflow/core/framework/dataset_metadata_pb2.pyi
+4 -3
View File
@@ -2,15 +2,16 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class Metadata(google.protobuf.message.Message):
"""next: 2"""
@@ -23,6 +24,6 @@ class Metadata(google.protobuf.message.Message):
*,
name: builtins.bytes | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["name", b"name"]) -> None: ...
global___Metadata = Metadata
stubs/tensorflow/tensorflow/core/framework/dataset_options_pb2.pyi
+47 -42
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import sys
import typing
@@ -88,7 +89,7 @@ POLICY_IGNORE: ExternalStatePolicy.ValueType # 1
POLICY_FAIL: ExternalStatePolicy.ValueType # 2
global___ExternalStatePolicy = ExternalStatePolicy
@typing_extensions.final
@typing.final
class AutotuneOptions(google.protobuf.message.Message):
"""next: 5"""
@@ -110,20 +111,20 @@ class AutotuneOptions(google.protobuf.message.Message):
ram_budget: builtins.int | None = ...,
autotune_algorithm: tensorflow.core.framework.model_pb2.AutotuneAlgorithm.ValueType | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["autotune_algorithm", b"autotune_algorithm", "cpu_budget", b"cpu_budget", "enabled", b"enabled", "optional_autotune_algorithm", b"optional_autotune_algorithm", "optional_cpu_budget", b"optional_cpu_budget", "optional_enabled", b"optional_enabled", "optional_ram_budget", b"optional_ram_budget", "ram_budget", b"ram_budget"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["autotune_algorithm", b"autotune_algorithm", "cpu_budget", b"cpu_budget", "enabled", b"enabled", "optional_autotune_algorithm", b"optional_autotune_algorithm", "optional_cpu_budget", b"optional_cpu_budget", "optional_enabled", b"optional_enabled", "optional_ram_budget", b"optional_ram_budget", "ram_budget", b"ram_budget"]) -> None: ...
def HasField(self, field_name: typing.Literal["autotune_algorithm", b"autotune_algorithm", "cpu_budget", b"cpu_budget", "enabled", b"enabled", "optional_autotune_algorithm", b"optional_autotune_algorithm", "optional_cpu_budget", b"optional_cpu_budget", "optional_enabled", b"optional_enabled", "optional_ram_budget", b"optional_ram_budget", "ram_budget", b"ram_budget"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["autotune_algorithm", b"autotune_algorithm", "cpu_budget", b"cpu_budget", "enabled", b"enabled", "optional_autotune_algorithm", b"optional_autotune_algorithm", "optional_cpu_budget", b"optional_cpu_budget", "optional_enabled", b"optional_enabled", "optional_ram_budget", b"optional_ram_budget", "ram_budget", b"ram_budget"]) -> None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_autotune_algorithm", b"optional_autotune_algorithm"]) -> typing_extensions.Literal["autotune_algorithm"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_autotune_algorithm", b"optional_autotune_algorithm"]) -> typing.Literal["autotune_algorithm"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_cpu_budget", b"optional_cpu_budget"]) -> typing_extensions.Literal["cpu_budget"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_cpu_budget", b"optional_cpu_budget"]) -> typing.Literal["cpu_budget"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_enabled", b"optional_enabled"]) -> typing_extensions.Literal["enabled"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_enabled", b"optional_enabled"]) -> typing.Literal["enabled"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_ram_budget", b"optional_ram_budget"]) -> typing_extensions.Literal["ram_budget"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_ram_budget", b"optional_ram_budget"]) -> typing.Literal["ram_budget"] | None: ...
global___AutotuneOptions = AutotuneOptions
@typing_extensions.final
@typing.final
class CardinalityOptions(google.protobuf.message.Message):
"""next: 2"""
@@ -169,11 +170,11 @@ class CardinalityOptions(google.protobuf.message.Message):
*,
compute_level: global___CardinalityOptions.ComputeLevel.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["compute_level", b"compute_level"]) -> None: ...
def ClearField(self, field_name: typing.Literal["compute_level", b"compute_level"]) -> None: ...
global___CardinalityOptions = CardinalityOptions
@typing_extensions.final
@typing.final
class DistributeOptions(google.protobuf.message.Message):
"""next: 3"""
@@ -189,13 +190,13 @@ class DistributeOptions(google.protobuf.message.Message):
auto_shard_policy: global___AutoShardPolicy.ValueType | None = ...,
num_devices: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["num_devices", b"num_devices", "optional_num_devices", b"optional_num_devices"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["auto_shard_policy", b"auto_shard_policy", "num_devices", b"num_devices", "optional_num_devices", b"optional_num_devices"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_num_devices", b"optional_num_devices"]) -> typing_extensions.Literal["num_devices"] | None: ...
def HasField(self, field_name: typing.Literal["num_devices", b"num_devices", "optional_num_devices", b"optional_num_devices"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["auto_shard_policy", b"auto_shard_policy", "num_devices", b"num_devices", "optional_num_devices", b"optional_num_devices"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_num_devices", b"optional_num_devices"]) -> typing.Literal["num_devices"] | None: ...
global___DistributeOptions = DistributeOptions
@typing_extensions.final
@typing.final
class OptimizationOptions(google.protobuf.message.Message):
"""next: 20"""
@@ -238,34 +239,34 @@ class OptimizationOptions(google.protobuf.message.Message):
filter_parallelization: builtins.bool | None = ...,
inject_prefetch: builtins.bool | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["apply_default_optimizations", b"apply_default_optimizations", "filter_fusion", b"filter_fusion", "filter_parallelization", b"filter_parallelization", "inject_prefetch", b"inject_prefetch", "map_and_batch_fusion", b"map_and_batch_fusion", "map_and_filter_fusion", b"map_and_filter_fusion", "map_fusion", b"map_fusion", "map_parallelization", b"map_parallelization", "noop_elimination", b"noop_elimination", "optional_apply_default_optimizations", b"optional_apply_default_optimizations", "optional_filter_fusion", b"optional_filter_fusion", "optional_filter_parallelization", b"optional_filter_parallelization", "optional_inject_prefetch", b"optional_inject_prefetch", "optional_map_and_batch_fusion", b"optional_map_and_batch_fusion", "optional_map_and_filter_fusion", b"optional_map_and_filter_fusion", "optional_map_fusion", b"optional_map_fusion", "optional_map_parallelization", b"optional_map_parallelization", "optional_noop_elimination", b"optional_noop_elimination", "optional_parallel_batch", b"optional_parallel_batch", "optional_shuffle_and_repeat_fusion", b"optional_shuffle_and_repeat_fusion", "parallel_batch", b"parallel_batch", "shuffle_and_repeat_fusion", b"shuffle_and_repeat_fusion"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["apply_default_optimizations", b"apply_default_optimizations", "filter_fusion", b"filter_fusion", "filter_parallelization", b"filter_parallelization", "inject_prefetch", b"inject_prefetch", "map_and_batch_fusion", b"map_and_batch_fusion", "map_and_filter_fusion", b"map_and_filter_fusion", "map_fusion", b"map_fusion", "map_parallelization", b"map_parallelization", "noop_elimination", b"noop_elimination", "optional_apply_default_optimizations", b"optional_apply_default_optimizations", "optional_filter_fusion", b"optional_filter_fusion", "optional_filter_parallelization", b"optional_filter_parallelization", "optional_inject_prefetch", b"optional_inject_prefetch", "optional_map_and_batch_fusion", b"optional_map_and_batch_fusion", "optional_map_and_filter_fusion", b"optional_map_and_filter_fusion", "optional_map_fusion", b"optional_map_fusion", "optional_map_parallelization", b"optional_map_parallelization", "optional_noop_elimination", b"optional_noop_elimination", "optional_parallel_batch", b"optional_parallel_batch", "optional_shuffle_and_repeat_fusion", b"optional_shuffle_and_repeat_fusion", "parallel_batch", b"parallel_batch", "shuffle_and_repeat_fusion", b"shuffle_and_repeat_fusion"]) -> None: ...
def HasField(self, field_name: typing.Literal["apply_default_optimizations", b"apply_default_optimizations", "filter_fusion", b"filter_fusion", "filter_parallelization", b"filter_parallelization", "inject_prefetch", b"inject_prefetch", "map_and_batch_fusion", b"map_and_batch_fusion", "map_and_filter_fusion", b"map_and_filter_fusion", "map_fusion", b"map_fusion", "map_parallelization", b"map_parallelization", "noop_elimination", b"noop_elimination", "optional_apply_default_optimizations", b"optional_apply_default_optimizations", "optional_filter_fusion", b"optional_filter_fusion", "optional_filter_parallelization", b"optional_filter_parallelization", "optional_inject_prefetch", b"optional_inject_prefetch", "optional_map_and_batch_fusion", b"optional_map_and_batch_fusion", "optional_map_and_filter_fusion", b"optional_map_and_filter_fusion", "optional_map_fusion", b"optional_map_fusion", "optional_map_parallelization", b"optional_map_parallelization", "optional_noop_elimination", b"optional_noop_elimination", "optional_parallel_batch", b"optional_parallel_batch", "optional_shuffle_and_repeat_fusion", b"optional_shuffle_and_repeat_fusion", "parallel_batch", b"parallel_batch", "shuffle_and_repeat_fusion", b"shuffle_and_repeat_fusion"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["apply_default_optimizations", b"apply_default_optimizations", "filter_fusion", b"filter_fusion", "filter_parallelization", b"filter_parallelization", "inject_prefetch", b"inject_prefetch", "map_and_batch_fusion", b"map_and_batch_fusion", "map_and_filter_fusion", b"map_and_filter_fusion", "map_fusion", b"map_fusion", "map_parallelization", b"map_parallelization", "noop_elimination", b"noop_elimination", "optional_apply_default_optimizations", b"optional_apply_default_optimizations", "optional_filter_fusion", b"optional_filter_fusion", "optional_filter_parallelization", b"optional_filter_parallelization", "optional_inject_prefetch", b"optional_inject_prefetch", "optional_map_and_batch_fusion", b"optional_map_and_batch_fusion", "optional_map_and_filter_fusion", b"optional_map_and_filter_fusion", "optional_map_fusion", b"optional_map_fusion", "optional_map_parallelization", b"optional_map_parallelization", "optional_noop_elimination", b"optional_noop_elimination", "optional_parallel_batch", b"optional_parallel_batch", "optional_shuffle_and_repeat_fusion", b"optional_shuffle_and_repeat_fusion", "parallel_batch", b"parallel_batch", "shuffle_and_repeat_fusion", b"shuffle_and_repeat_fusion"]) -> None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_apply_default_optimizations", b"optional_apply_default_optimizations"]) -> typing_extensions.Literal["apply_default_optimizations"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_apply_default_optimizations", b"optional_apply_default_optimizations"]) -> typing.Literal["apply_default_optimizations"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_filter_fusion", b"optional_filter_fusion"]) -> typing_extensions.Literal["filter_fusion"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_filter_fusion", b"optional_filter_fusion"]) -> typing.Literal["filter_fusion"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_filter_parallelization", b"optional_filter_parallelization"]) -> typing_extensions.Literal["filter_parallelization"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_filter_parallelization", b"optional_filter_parallelization"]) -> typing.Literal["filter_parallelization"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_inject_prefetch", b"optional_inject_prefetch"]) -> typing_extensions.Literal["inject_prefetch"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_inject_prefetch", b"optional_inject_prefetch"]) -> typing.Literal["inject_prefetch"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_map_and_batch_fusion", b"optional_map_and_batch_fusion"]) -> typing_extensions.Literal["map_and_batch_fusion"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_map_and_batch_fusion", b"optional_map_and_batch_fusion"]) -> typing.Literal["map_and_batch_fusion"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_map_and_filter_fusion", b"optional_map_and_filter_fusion"]) -> typing_extensions.Literal["map_and_filter_fusion"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_map_and_filter_fusion", b"optional_map_and_filter_fusion"]) -> typing.Literal["map_and_filter_fusion"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_map_fusion", b"optional_map_fusion"]) -> typing_extensions.Literal["map_fusion"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_map_fusion", b"optional_map_fusion"]) -> typing.Literal["map_fusion"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_map_parallelization", b"optional_map_parallelization"]) -> typing_extensions.Literal["map_parallelization"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_map_parallelization", b"optional_map_parallelization"]) -> typing.Literal["map_parallelization"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_noop_elimination", b"optional_noop_elimination"]) -> typing_extensions.Literal["noop_elimination"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_noop_elimination", b"optional_noop_elimination"]) -> typing.Literal["noop_elimination"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_parallel_batch", b"optional_parallel_batch"]) -> typing_extensions.Literal["parallel_batch"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_parallel_batch", b"optional_parallel_batch"]) -> typing.Literal["parallel_batch"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_shuffle_and_repeat_fusion", b"optional_shuffle_and_repeat_fusion"]) -> typing_extensions.Literal["shuffle_and_repeat_fusion"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_shuffle_and_repeat_fusion", b"optional_shuffle_and_repeat_fusion"]) -> typing.Literal["shuffle_and_repeat_fusion"] | None: ...
global___OptimizationOptions = OptimizationOptions
@typing_extensions.final
@typing.final
class ThreadingOptions(google.protobuf.message.Message):
"""next: 3"""
@@ -281,16 +282,16 @@ class ThreadingOptions(google.protobuf.message.Message):
max_intra_op_parallelism: builtins.int | None = ...,
private_threadpool_size: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["max_intra_op_parallelism", b"max_intra_op_parallelism", "optional_max_intra_op_parallelism", b"optional_max_intra_op_parallelism", "optional_private_threadpool_size", b"optional_private_threadpool_size", "private_threadpool_size", b"private_threadpool_size"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["max_intra_op_parallelism", b"max_intra_op_parallelism", "optional_max_intra_op_parallelism", b"optional_max_intra_op_parallelism", "optional_private_threadpool_size", b"optional_private_threadpool_size", "private_threadpool_size", b"private_threadpool_size"]) -> None: ...
def HasField(self, field_name: typing.Literal["max_intra_op_parallelism", b"max_intra_op_parallelism", "optional_max_intra_op_parallelism", b"optional_max_intra_op_parallelism", "optional_private_threadpool_size", b"optional_private_threadpool_size", "private_threadpool_size", b"private_threadpool_size"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["max_intra_op_parallelism", b"max_intra_op_parallelism", "optional_max_intra_op_parallelism", b"optional_max_intra_op_parallelism", "optional_private_threadpool_size", b"optional_private_threadpool_size", "private_threadpool_size", b"private_threadpool_size"]) -> None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_max_intra_op_parallelism", b"optional_max_intra_op_parallelism"]) -> typing_extensions.Literal["max_intra_op_parallelism"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_max_intra_op_parallelism", b"optional_max_intra_op_parallelism"]) -> typing.Literal["max_intra_op_parallelism"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_private_threadpool_size", b"optional_private_threadpool_size"]) -> typing_extensions.Literal["private_threadpool_size"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_private_threadpool_size", b"optional_private_threadpool_size"]) -> typing.Literal["private_threadpool_size"] | None: ...
global___ThreadingOptions = ThreadingOptions
@typing_extensions.final
@typing.final
class Options(google.protobuf.message.Message):
"""Message stored with Dataset objects to control how datasets are processed and
optimized.
@@ -309,21 +310,25 @@ class Options(google.protobuf.message.Message):
EXTERNAL_STATE_POLICY_FIELD_NUMBER: builtins.int
SYMBOLIC_CHECKPOINT_FIELD_NUMBER: builtins.int
deterministic: builtins.bool
slack: builtins.bool
external_state_policy: global___ExternalStatePolicy.ValueType
symbolic_checkpoint: builtins.bool
@property
def autotune_options(self) -> global___AutotuneOptions:
"""The distribution strategy options associated with the dataset."""
@property
def distribute_options(self) -> global___DistributeOptions:
"""The distribution strategy options associated with the dataset."""
@property
def optimization_options(self) -> global___OptimizationOptions:
"""The optimization options associated with the dataset."""
slack: builtins.bool
@property
def threading_options(self) -> global___ThreadingOptions:
"""The threading options associated with the dataset."""
external_state_policy: global___ExternalStatePolicy.ValueType
symbolic_checkpoint: builtins.bool
def __init__(
self,
*,
@@ -336,15 +341,15 @@ class Options(google.protobuf.message.Message):
external_state_policy: global___ExternalStatePolicy.ValueType | None = ...,
symbolic_checkpoint: builtins.bool | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["autotune_options", b"autotune_options", "deterministic", b"deterministic", "distribute_options", b"distribute_options", "external_state_policy", b"external_state_policy", "optimization_options", b"optimization_options", "optional_deterministic", b"optional_deterministic", "optional_external_state_policy", b"optional_external_state_policy", "optional_slack", b"optional_slack", "optional_symbolic_checkpoint", b"optional_symbolic_checkpoint", "slack", b"slack", "symbolic_checkpoint", b"symbolic_checkpoint", "threading_options", b"threading_options"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["autotune_options", b"autotune_options", "deterministic", b"deterministic", "distribute_options", b"distribute_options", "external_state_policy", b"external_state_policy", "optimization_options", b"optimization_options", "optional_deterministic", b"optional_deterministic", "optional_external_state_policy", b"optional_external_state_policy", "optional_slack", b"optional_slack", "optional_symbolic_checkpoint", b"optional_symbolic_checkpoint", "slack", b"slack", "symbolic_checkpoint", b"symbolic_checkpoint", "threading_options", b"threading_options"]) -> None: ...
def HasField(self, field_name: typing.Literal["autotune_options", b"autotune_options", "deterministic", b"deterministic", "distribute_options", b"distribute_options", "external_state_policy", b"external_state_policy", "optimization_options", b"optimization_options", "optional_deterministic", b"optional_deterministic", "optional_external_state_policy", b"optional_external_state_policy", "optional_slack", b"optional_slack", "optional_symbolic_checkpoint", b"optional_symbolic_checkpoint", "slack", b"slack", "symbolic_checkpoint", b"symbolic_checkpoint", "threading_options", b"threading_options"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["autotune_options", b"autotune_options", "deterministic", b"deterministic", "distribute_options", b"distribute_options", "external_state_policy", b"external_state_policy", "optimization_options", b"optimization_options", "optional_deterministic", b"optional_deterministic", "optional_external_state_policy", b"optional_external_state_policy", "optional_slack", b"optional_slack", "optional_symbolic_checkpoint", b"optional_symbolic_checkpoint", "slack", b"slack", "symbolic_checkpoint", b"symbolic_checkpoint", "threading_options", b"threading_options"]) -> None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_deterministic", b"optional_deterministic"]) -> typing_extensions.Literal["deterministic"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_deterministic", b"optional_deterministic"]) -> typing.Literal["deterministic"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_external_state_policy", b"optional_external_state_policy"]) -> typing_extensions.Literal["external_state_policy"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_external_state_policy", b"optional_external_state_policy"]) -> typing.Literal["external_state_policy"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_slack", b"optional_slack"]) -> typing_extensions.Literal["slack"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_slack", b"optional_slack"]) -> typing.Literal["slack"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_symbolic_checkpoint", b"optional_symbolic_checkpoint"]) -> typing_extensions.Literal["symbolic_checkpoint"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_symbolic_checkpoint", b"optional_symbolic_checkpoint"]) -> typing.Literal["symbolic_checkpoint"] | None: ...
global___Options = Options
stubs/tensorflow/tensorflow/core/framework/dataset_pb2.pyi
+15 -11
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -15,7 +16,7 @@ import tensorflow.core.framework.types_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class CompressedComponentMetadata(google.protobuf.message.Message):
"""This file contains protocol buffers for working with tf.data Datasets.
@@ -32,6 +33,7 @@ class CompressedComponentMetadata(google.protobuf.message.Message):
@property
def tensor_shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto:
"""The shape of the component tensor."""
@property
def uncompressed_bytes(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The amount of uncompressed tensor data.
@@ -40,6 +42,7 @@ class CompressedComponentMetadata(google.protobuf.message.Message):
- For all other tensors, there is a single element indicating the size of
the tensor.
"""
def __init__(
self,
*,
@@ -47,12 +50,12 @@ class CompressedComponentMetadata(google.protobuf.message.Message):
tensor_shape: tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto | None = ...,
uncompressed_bytes: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["tensor_shape", b"tensor_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["dtype", b"dtype", "tensor_shape", b"tensor_shape", "uncompressed_bytes", b"uncompressed_bytes"]) -> None: ...
def HasField(self, field_name: typing.Literal["tensor_shape", b"tensor_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["dtype", b"dtype", "tensor_shape", b"tensor_shape", "uncompressed_bytes", b"uncompressed_bytes"]) -> None: ...
global___CompressedComponentMetadata = CompressedComponentMetadata
@typing_extensions.final
@typing.final
class CompressedElement(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -61,9 +64,6 @@ class CompressedElement(google.protobuf.message.Message):
VERSION_FIELD_NUMBER: builtins.int
data: builtins.bytes
"""Compressed tensor bytes for all components of the element."""
@property
def component_metadata(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___CompressedComponentMetadata]:
"""Metadata for the components of the element."""
version: builtins.int
"""Version of the CompressedElement. CompressedElements may be stored on disk
and read back by later versions of code, so we store a version number to
@@ -71,6 +71,10 @@ class CompressedElement(google.protobuf.message.Message):
field to this proto, you need to increment kCompressedElementVersion in
tensorflow/core/data/compression_utils.cc.
"""
@property
def component_metadata(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___CompressedComponentMetadata]:
"""Metadata for the components of the element."""
def __init__(
self,
*,
@@ -78,11 +82,11 @@ class CompressedElement(google.protobuf.message.Message):
component_metadata: collections.abc.Iterable[global___CompressedComponentMetadata] | None = ...,
version: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["component_metadata", b"component_metadata", "data", b"data", "version", b"version"]) -> None: ...
def ClearField(self, field_name: typing.Literal["component_metadata", b"component_metadata", "data", b"data", "version", b"version"]) -> None: ...
global___CompressedElement = CompressedElement
@typing_extensions.final
@typing.final
class UncompressedElement(google.protobuf.message.Message):
"""An uncompressed dataset element."""
@@ -96,6 +100,6 @@ class UncompressedElement(google.protobuf.message.Message):
*,
components: collections.abc.Iterable[tensorflow.core.framework.tensor_pb2.TensorProto] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["components", b"components"]) -> None: ...
def ClearField(self, field_name: typing.Literal["components", b"components"]) -> None: ...
global___UncompressedElement = UncompressedElement
stubs/tensorflow/tensorflow/core/framework/device_attributes_pb2.pyi
+19 -16
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,7 +13,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class InterconnectLink(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -29,11 +30,11 @@ class InterconnectLink(google.protobuf.message.Message):
type: builtins.str | None = ...,
strength: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["device_id", b"device_id", "strength", b"strength", "type", b"type"]) -> None: ...
def ClearField(self, field_name: typing.Literal["device_id", b"device_id", "strength", b"strength", "type", b"type"]) -> None: ...
global___InterconnectLink = InterconnectLink
@typing_extensions.final
@typing.final
class LocalLinks(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -45,11 +46,11 @@ class LocalLinks(google.protobuf.message.Message):
*,
link: collections.abc.Iterable[global___InterconnectLink] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["link", b"link"]) -> None: ...
def ClearField(self, field_name: typing.Literal["link", b"link"]) -> None: ...
global___LocalLinks = LocalLinks
@typing_extensions.final
@typing.final
class DeviceLocality(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -65,6 +66,7 @@ class DeviceLocality(google.protobuf.message.Message):
@property
def links(self) -> global___LocalLinks:
"""Optional local interconnect links to other devices."""
def __init__(
self,
*,
@@ -72,12 +74,12 @@ class DeviceLocality(google.protobuf.message.Message):
numa_node: builtins.int | None = ...,
links: global___LocalLinks | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["links", b"links"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["bus_id", b"bus_id", "links", b"links", "numa_node", b"numa_node"]) -> None: ...
def HasField(self, field_name: typing.Literal["links", b"links"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["bus_id", b"bus_id", "links", b"links", "numa_node", b"numa_node"]) -> None: ...
global___DeviceLocality = DeviceLocality
@typing_extensions.final
@typing.final
class DeviceAttributes(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -94,11 +96,6 @@ class DeviceAttributes(google.protobuf.message.Message):
"""String representation of device_type."""
memory_limit: builtins.int
"""Memory capacity of device in bytes."""
@property
def locality(self) -> global___DeviceLocality:
"""Platform-specific data about device that may be useful
for supporting efficient data transfers.
"""
incarnation: builtins.int
"""A device is assigned a global unique number each time it is
initialized. "incarnation" should never be 0.
@@ -110,6 +107,12 @@ class DeviceAttributes(google.protobuf.message.Message):
clients in a multi-client setup. Set to -1 if unavailable, non-negative
otherwise.
"""
@property
def locality(self) -> global___DeviceLocality:
"""Platform-specific data about device that may be useful
for supporting efficient data transfers.
"""
def __init__(
self,
*,
@@ -121,7 +124,7 @@ class DeviceAttributes(google.protobuf.message.Message):
physical_device_desc: builtins.str | None = ...,
xla_global_id: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["locality", b"locality"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["device_type", b"device_type", "incarnation", b"incarnation", "locality", b"locality", "memory_limit", b"memory_limit", "name", b"name", "physical_device_desc", b"physical_device_desc", "xla_global_id", b"xla_global_id"]) -> None: ...
def HasField(self, field_name: typing.Literal["locality", b"locality"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["device_type", b"device_type", "incarnation", b"incarnation", "locality", b"locality", "memory_limit", b"memory_limit", "name", b"name", "physical_device_desc", b"physical_device_desc", "xla_global_id", b"xla_global_id"]) -> None: ...
global___DeviceAttributes = DeviceAttributes
stubs/tensorflow/tensorflow/core/framework/full_type_pb2.pyi
+7 -6
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -573,7 +574,7 @@ not a subtype of LEGACY_VARIANT.
"""
global___FullTypeId = FullTypeId
@typing_extensions.final
@typing.final
class FullTypeDef(google.protobuf.message.Message):
"""Highly experimental and very likely to change.
This encoding uses tags instead of dedicated messages for regularity. In
@@ -592,11 +593,11 @@ class FullTypeDef(google.protobuf.message.Message):
(Tensor, Dataset) a type variable (used for dependent types) a type
symbol (Any, Union). See FullTypeId for details.
"""
@property
def args(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___FullTypeDef]: ...
s: builtins.str
i: builtins.int
"""TODO(mdan): list/tensor, map? Need to reconcile with TFT_RECORD, etc."""
@property
def args(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___FullTypeDef]: ...
def __init__(
self,
*,
@@ -605,8 +606,8 @@ class FullTypeDef(google.protobuf.message.Message):
s: builtins.str | None = ...,
i: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["attr", b"attr", "i", b"i", "s", b"s"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["args", b"args", "attr", b"attr", "i", b"i", "s", b"s", "type_id", b"type_id"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["attr", b"attr"]) -> typing_extensions.Literal["s", "i"] | None: ...
def HasField(self, field_name: typing.Literal["attr", b"attr", "i", b"i", "s", b"s"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["args", b"args", "attr", b"attr", "i", b"i", "s", b"s", "type_id", b"type_id"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["attr", b"attr"]) -> typing.Literal["s", "i"] | None: ...
global___FullTypeDef = FullTypeDef
stubs/tensorflow/tensorflow/core/framework/function_pb2.pyi
+34 -27
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -15,7 +16,7 @@ import tensorflow.core.framework.op_def_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class FunctionDefLibrary(google.protobuf.message.Message):
"""A library is a set of named functions."""
@@ -37,11 +38,11 @@ class FunctionDefLibrary(google.protobuf.message.Message):
gradient: collections.abc.Iterable[global___GradientDef] | None = ...,
registered_gradients: collections.abc.Iterable[global___RegisteredGradient] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["function", b"function", "gradient", b"gradient", "registered_gradients", b"registered_gradients"]) -> None: ...
def ClearField(self, field_name: typing.Literal["function", b"function", "gradient", b"gradient", "registered_gradients", b"registered_gradients"]) -> None: ...
global___FunctionDefLibrary = FunctionDefLibrary
@typing_extensions.final
@typing.final
class FunctionDef(google.protobuf.message.Message):
"""A function can be instantiated when the runtime can bind every attr
with a value. When a GraphDef has a call to a function, it must
@@ -53,7 +54,7 @@ class FunctionDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class AttrEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -68,10 +69,10 @@ class FunctionDef(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: tensorflow.core.framework.attr_value_pb2.AttrValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class ArgAttrs(google.protobuf.message.Message):
"""Attributes for function arguments. These attributes are the same set of
valid attributes as to _Arg nodes.
@@ -79,7 +80,7 @@ class FunctionDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class AttrEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -94,8 +95,8 @@ class FunctionDef(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: tensorflow.core.framework.attr_value_pb2.AttrValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
ATTR_FIELD_NUMBER: builtins.int
@property
@@ -105,9 +106,9 @@ class FunctionDef(google.protobuf.message.Message):
*,
attr: collections.abc.Mapping[builtins.str, tensorflow.core.framework.attr_value_pb2.AttrValue] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["attr", b"attr"]) -> None: ...
def ClearField(self, field_name: typing.Literal["attr", b"attr"]) -> None: ...
@typing_extensions.final
@typing.final
class ArgAttrEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -122,10 +123,10 @@ class FunctionDef(google.protobuf.message.Message):
key: builtins.int | None = ...,
value: global___FunctionDef.ArgAttrs | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class ResourceArgUniqueIdEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -139,9 +140,9 @@ class FunctionDef(google.protobuf.message.Message):
key: builtins.int | None = ...,
value: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class RetEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -155,9 +156,9 @@ class FunctionDef(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class ControlRetEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -171,7 +172,7 @@ class FunctionDef(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
SIGNATURE_FIELD_NUMBER: builtins.int
ATTR_FIELD_NUMBER: builtins.int
@@ -185,9 +186,11 @@ class FunctionDef(google.protobuf.message.Message):
"""The definition of the function's name, arguments, return values,
attrs etc.
"""
@property
def attr(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, tensorflow.core.framework.attr_value_pb2.AttrValue]:
"""Attributes specific to this function definition."""
@property
def arg_attr(self) -> google.protobuf.internal.containers.MessageMap[builtins.int, global___FunctionDef.ArgAttrs]: ...
@property
@@ -202,6 +205,7 @@ class FunctionDef(google.protobuf.message.Message):
When instantiated, the unique IDs will be attached to the _Arg nodes'
"_resource_arg_unique_id" attribute.
"""
@property
def node_def(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.node_def_pb2.NodeDef]:
"""The body of the function. Unlike the NodeDefs in a GraphDef, attrs
@@ -212,16 +216,19 @@ class FunctionDef(google.protobuf.message.Message):
user-defined library first. If not resolved, "func" is assumed to
be a builtin op.
"""
@property
def ret(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]:
"""A mapping from the output arg names from `signature` to the
outputs from `node_def` that should be returned by the function.
"""
@property
def control_ret(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]:
"""A mapping from control output names from `signature` to node names in
`node_def` which should be control outputs of this function.
"""
def __init__(
self,
*,
@@ -233,12 +240,12 @@ class FunctionDef(google.protobuf.message.Message):
ret: collections.abc.Mapping[builtins.str, builtins.str] | None = ...,
control_ret: collections.abc.Mapping[builtins.str, builtins.str] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["signature", b"signature"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["arg_attr", b"arg_attr", "attr", b"attr", "control_ret", b"control_ret", "node_def", b"node_def", "resource_arg_unique_id", b"resource_arg_unique_id", "ret", b"ret", "signature", b"signature"]) -> None: ...
def HasField(self, field_name: typing.Literal["signature", b"signature"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["arg_attr", b"arg_attr", "attr", b"attr", "control_ret", b"control_ret", "node_def", b"node_def", "resource_arg_unique_id", b"resource_arg_unique_id", "ret", b"ret", "signature", b"signature"]) -> None: ...
global___FunctionDef = FunctionDef
@typing_extensions.final
@typing.final
class GradientDef(google.protobuf.message.Message):
"""GradientDef defines the gradient function of a function defined in
a function library.
@@ -274,11 +281,11 @@ class GradientDef(google.protobuf.message.Message):
function_name: builtins.str | None = ...,
gradient_func: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["function_name", b"function_name", "gradient_func", b"gradient_func"]) -> None: ...
def ClearField(self, field_name: typing.Literal["function_name", b"function_name", "gradient_func", b"gradient_func"]) -> None: ...
global___GradientDef = GradientDef
@typing_extensions.final
@typing.final
class RegisteredGradient(google.protobuf.message.Message):
"""RegisteredGradient stores a gradient function that is registered in the
gradients library and used in the ops of a function in the function library.
@@ -300,6 +307,6 @@ class RegisteredGradient(google.protobuf.message.Message):
gradient_func: builtins.str | None = ...,
registered_op_type: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["gradient_func", b"gradient_func", "registered_op_type", b"registered_op_type"]) -> None: ...
def ClearField(self, field_name: typing.Literal["gradient_func", b"gradient_func", "registered_op_type", b"registered_op_type"]) -> None: ...
global___RegisteredGradient = RegisteredGradient
stubs/tensorflow/tensorflow/core/framework/graph_pb2.pyi
+12 -9
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -15,7 +16,7 @@ import tensorflow.core.framework.versions_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class GraphDef(google.protobuf.message.Message):
"""Represents the graph of operations"""
@@ -25,6 +26,11 @@ class GraphDef(google.protobuf.message.Message):
VERSIONS_FIELD_NUMBER: builtins.int
VERSION_FIELD_NUMBER: builtins.int
LIBRARY_FIELD_NUMBER: builtins.int
version: builtins.int
"""Deprecated single version field; use versions above instead. Since all
GraphDef changes before "versions" was introduced were forward
compatible, this field is entirely ignored.
"""
@property
def node(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.node_def_pb2.NodeDef]: ...
@property
@@ -33,11 +39,7 @@ class GraphDef(google.protobuf.message.Message):
history. The GraphDef version is distinct from the TensorFlow version, and
each release of TensorFlow will support a range of GraphDef versions.
"""
version: builtins.int
"""Deprecated single version field; use versions above instead. Since all
GraphDef changes before "versions" was introduced were forward
compatible, this field is entirely ignored.
"""
@property
def library(self) -> tensorflow.core.framework.function_pb2.FunctionDefLibrary:
""""library" provides user-defined functions.
@@ -67,6 +69,7 @@ class GraphDef(google.protobuf.message.Message):
consumer does not start until all return values of the callee
function are ready.
"""
def __init__(
self,
*,
@@ -75,7 +78,7 @@ class GraphDef(google.protobuf.message.Message):
version: builtins.int | None = ...,
library: tensorflow.core.framework.function_pb2.FunctionDefLibrary | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["library", b"library", "versions", b"versions"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["library", b"library", "node", b"node", "version", b"version", "versions", b"versions"]) -> None: ...
def HasField(self, field_name: typing.Literal["library", b"library", "versions", b"versions"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["library", b"library", "node", b"node", "version", b"version", "versions", b"versions"]) -> None: ...
global___GraphDef = GraphDef
stubs/tensorflow/tensorflow/core/framework/graph_transfer_info_pb2.pyi
+24 -22
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -20,7 +21,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class GraphTransferNodeInput(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -34,11 +35,11 @@ class GraphTransferNodeInput(google.protobuf.message.Message):
node_id: builtins.int | None = ...,
output_port: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["node_id", b"node_id", "output_port", b"output_port"]) -> None: ...
def ClearField(self, field_name: typing.Literal["node_id", b"node_id", "output_port", b"output_port"]) -> None: ...
global___GraphTransferNodeInput = GraphTransferNodeInput
@typing_extensions.final
@typing.final
class GraphTransferNodeInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -67,11 +68,11 @@ class GraphTransferNodeInfo(google.protobuf.message.Message):
input_count: builtins.int | None = ...,
output_count: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["input_count", b"input_count", "name", b"name", "node_id", b"node_id", "output_count", b"output_count", "padding_id", b"padding_id", "soc_op_id", b"soc_op_id", "type_name", b"type_name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["input_count", b"input_count", "name", b"name", "node_id", b"node_id", "output_count", b"output_count", "padding_id", b"padding_id", "soc_op_id", b"soc_op_id", "type_name", b"type_name"]) -> None: ...
global___GraphTransferNodeInfo = GraphTransferNodeInfo
@typing_extensions.final
@typing.final
class GraphTransferConstNodeInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -82,10 +83,10 @@ class GraphTransferConstNodeInfo(google.protobuf.message.Message):
DTYPE_FIELD_NUMBER: builtins.int
name: builtins.str
node_id: builtins.int
@property
def shape(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
data: builtins.bytes
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
@property
def shape(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
def __init__(
self,
*,
@@ -95,11 +96,11 @@ class GraphTransferConstNodeInfo(google.protobuf.message.Message):
data: builtins.bytes | None = ...,
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["data", b"data", "dtype", b"dtype", "name", b"name", "node_id", b"node_id", "shape", b"shape"]) -> None: ...
def ClearField(self, field_name: typing.Literal["data", b"data", "dtype", b"dtype", "name", b"name", "node_id", b"node_id", "shape", b"shape"]) -> None: ...
global___GraphTransferConstNodeInfo = GraphTransferConstNodeInfo
@typing_extensions.final
@typing.final
class GraphTransferNodeInputInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -114,11 +115,11 @@ class GraphTransferNodeInputInfo(google.protobuf.message.Message):
node_id: builtins.int | None = ...,
node_input: collections.abc.Iterable[global___GraphTransferNodeInput] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["node_id", b"node_id", "node_input", b"node_input"]) -> None: ...
def ClearField(self, field_name: typing.Literal["node_id", b"node_id", "node_input", b"node_input"]) -> None: ...
global___GraphTransferNodeInputInfo = GraphTransferNodeInputInfo
@typing_extensions.final
@typing.final
class GraphTransferNodeOutputInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -133,11 +134,11 @@ class GraphTransferNodeOutputInfo(google.protobuf.message.Message):
node_id: builtins.int | None = ...,
max_byte_size: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["max_byte_size", b"max_byte_size", "node_id", b"node_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["max_byte_size", b"max_byte_size", "node_id", b"node_id"]) -> None: ...
global___GraphTransferNodeOutputInfo = GraphTransferNodeOutputInfo
@typing_extensions.final
@typing.final
class GraphTransferGraphInputNodeInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -145,9 +146,9 @@ class GraphTransferGraphInputNodeInfo(google.protobuf.message.Message):
SHAPE_FIELD_NUMBER: builtins.int
DTYPE_FIELD_NUMBER: builtins.int
name: builtins.str
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
@property
def shape(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
def __init__(
self,
*,
@@ -155,11 +156,11 @@ class GraphTransferGraphInputNodeInfo(google.protobuf.message.Message):
shape: collections.abc.Iterable[builtins.int] | None = ...,
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["dtype", b"dtype", "name", b"name", "shape", b"shape"]) -> None: ...
def ClearField(self, field_name: typing.Literal["dtype", b"dtype", "name", b"name", "shape", b"shape"]) -> None: ...
global___GraphTransferGraphInputNodeInfo = GraphTransferGraphInputNodeInfo
@typing_extensions.final
@typing.final
class GraphTransferGraphOutputNodeInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -167,9 +168,9 @@ class GraphTransferGraphOutputNodeInfo(google.protobuf.message.Message):
SHAPE_FIELD_NUMBER: builtins.int
DTYPE_FIELD_NUMBER: builtins.int
name: builtins.str
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
@property
def shape(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
def __init__(
self,
*,
@@ -177,11 +178,11 @@ class GraphTransferGraphOutputNodeInfo(google.protobuf.message.Message):
shape: collections.abc.Iterable[builtins.int] | None = ...,
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["dtype", b"dtype", "name", b"name", "shape", b"shape"]) -> None: ...
def ClearField(self, field_name: typing.Literal["dtype", b"dtype", "name", b"name", "shape", b"shape"]) -> None: ...
global___GraphTransferGraphOutputNodeInfo = GraphTransferGraphOutputNodeInfo
@typing_extensions.final
@typing.final
class GraphTransferInfo(google.protobuf.message.Message):
"""Protocol buffer representing a handle to a tensorflow resource. Handles are
not valid across executions, but can be serialized back and forth from within
@@ -210,6 +211,8 @@ class GraphTransferInfo(google.protobuf.message.Message):
GRAPH_INPUT_NODE_INFO_FIELD_NUMBER: builtins.int
GRAPH_OUTPUT_NODE_INFO_FIELD_NUMBER: builtins.int
DESTINATION_FIELD_NUMBER: builtins.int
destination: global___GraphTransferInfo.Destination.ValueType
"""Destination of graph transfer"""
@property
def node_info(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___GraphTransferNodeInfo]: ...
@property
@@ -221,10 +224,9 @@ class GraphTransferInfo(google.protobuf.message.Message):
@property
def graph_input_node_info(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___GraphTransferGraphInputNodeInfo]:
"""Input Node parameters of transferred graph"""
@property
def graph_output_node_info(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___GraphTransferGraphOutputNodeInfo]: ...
destination: global___GraphTransferInfo.Destination.ValueType
"""Destination of graph transfer"""
def __init__(
self,
*,
@@ -236,6 +238,6 @@ class GraphTransferInfo(google.protobuf.message.Message):
graph_output_node_info: collections.abc.Iterable[global___GraphTransferGraphOutputNodeInfo] | None = ...,
destination: global___GraphTransferInfo.Destination.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["const_node_info", b"const_node_info", "destination", b"destination", "graph_input_node_info", b"graph_input_node_info", "graph_output_node_info", b"graph_output_node_info", "node_info", b"node_info", "node_input_info", b"node_input_info", "node_output_info", b"node_output_info"]) -> None: ...
def ClearField(self, field_name: typing.Literal["const_node_info", b"const_node_info", "destination", b"destination", "graph_input_node_info", b"graph_input_node_info", "graph_output_node_info", b"graph_output_node_info", "node_info", b"node_info", "node_input_info", b"node_input_info", "node_output_info", b"node_output_info"]) -> None: ...
global___GraphTransferInfo = GraphTransferInfo
stubs/tensorflow/tensorflow/core/framework/kernel_def_pb2.pyi
+18 -15
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -13,11 +14,11 @@ import tensorflow.core.framework.attr_value_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class KernelDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class AttrConstraint(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -30,14 +31,15 @@ class KernelDef(google.protobuf.message.Message):
"""A list of values that this kernel supports for this attr.
Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops.
"""
def __init__(
self,
*,
name: builtins.str | None = ...,
allowed_values: tensorflow.core.framework.attr_value_pb2.AttrValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["allowed_values", b"allowed_values"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["allowed_values", b"allowed_values", "name", b"name"]) -> None: ...
def HasField(self, field_name: typing.Literal["allowed_values", b"allowed_values"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["allowed_values", b"allowed_values", "name", b"name"]) -> None: ...
OP_FIELD_NUMBER: builtins.int
DEVICE_TYPE_FIELD_NUMBER: builtins.int
@@ -49,13 +51,6 @@ class KernelDef(google.protobuf.message.Message):
"""Must match the name of an Op."""
device_type: builtins.str
"""Type of device this kernel runs on."""
@property
def constraint(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___KernelDef.AttrConstraint]: ...
@property
def host_memory_arg(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Names of the Op's input_/output_args that reside in host memory
instead of device memory.
"""
label: builtins.str
"""This allows experimental kernels to be registered for an op that
won't be used unless the user specifies a "_kernel" attr with
@@ -66,6 +61,14 @@ class KernelDef(google.protobuf.message.Message):
priority is 0. The higher the priority the better. By default (i.e. if
this is not set), we prefer GPU kernels over CPU.
"""
@property
def constraint(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___KernelDef.AttrConstraint]: ...
@property
def host_memory_arg(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Names of the Op's input_/output_args that reside in host memory
instead of device memory.
"""
def __init__(
self,
*,
@@ -76,11 +79,11 @@ class KernelDef(google.protobuf.message.Message):
label: builtins.str | None = ...,
priority: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["constraint", b"constraint", "device_type", b"device_type", "host_memory_arg", b"host_memory_arg", "label", b"label", "op", b"op", "priority", b"priority"]) -> None: ...
def ClearField(self, field_name: typing.Literal["constraint", b"constraint", "device_type", b"device_type", "host_memory_arg", b"host_memory_arg", "label", b"label", "op", b"op", "priority", b"priority"]) -> None: ...
global___KernelDef = KernelDef
@typing_extensions.final
@typing.final
class KernelList(google.protobuf.message.Message):
"""A collection of KernelDefs"""
@@ -94,6 +97,6 @@ class KernelList(google.protobuf.message.Message):
*,
kernel: collections.abc.Iterable[global___KernelDef] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["kernel", b"kernel"]) -> None: ...
def ClearField(self, field_name: typing.Literal["kernel", b"kernel"]) -> None: ...
global___KernelList = KernelList
stubs/tensorflow/tensorflow/core/framework/log_memory_pb2.pyi
+18 -15
View File
@@ -2,8 +2,9 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
@@ -11,7 +12,7 @@ import tensorflow.core.framework.tensor_description_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class MemoryLogStep(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -27,11 +28,11 @@ class MemoryLogStep(google.protobuf.message.Message):
step_id: builtins.int | None = ...,
handle: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["handle", b"handle", "step_id", b"step_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["handle", b"handle", "step_id", b"step_id"]) -> None: ...
global___MemoryLogStep = MemoryLogStep
@typing_extensions.final
@typing.final
class MemoryLogTensorAllocation(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -47,6 +48,7 @@ class MemoryLogTensorAllocation(google.protobuf.message.Message):
@property
def tensor(self) -> tensorflow.core.framework.tensor_description_pb2.TensorDescription:
"""Allocated tensor details."""
def __init__(
self,
*,
@@ -54,12 +56,12 @@ class MemoryLogTensorAllocation(google.protobuf.message.Message):
kernel_name: builtins.str | None = ...,
tensor: tensorflow.core.framework.tensor_description_pb2.TensorDescription | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["tensor", b"tensor"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["kernel_name", b"kernel_name", "step_id", b"step_id", "tensor", b"tensor"]) -> None: ...
def HasField(self, field_name: typing.Literal["tensor", b"tensor"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["kernel_name", b"kernel_name", "step_id", b"step_id", "tensor", b"tensor"]) -> None: ...
global___MemoryLogTensorAllocation = MemoryLogTensorAllocation
@typing_extensions.final
@typing.final
class MemoryLogTensorDeallocation(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -77,11 +79,11 @@ class MemoryLogTensorDeallocation(google.protobuf.message.Message):
allocation_id: builtins.int | None = ...,
allocator_name: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["allocation_id", b"allocation_id", "allocator_name", b"allocator_name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["allocation_id", b"allocation_id", "allocator_name", b"allocator_name"]) -> None: ...
global___MemoryLogTensorDeallocation = MemoryLogTensorDeallocation
@typing_extensions.final
@typing.final
class MemoryLogTensorOutput(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -100,6 +102,7 @@ class MemoryLogTensorOutput(google.protobuf.message.Message):
@property
def tensor(self) -> tensorflow.core.framework.tensor_description_pb2.TensorDescription:
"""Output tensor details."""
def __init__(
self,
*,
@@ -108,12 +111,12 @@ class MemoryLogTensorOutput(google.protobuf.message.Message):
index: builtins.int | None = ...,
tensor: tensorflow.core.framework.tensor_description_pb2.TensorDescription | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["tensor", b"tensor"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["index", b"index", "kernel_name", b"kernel_name", "step_id", b"step_id", "tensor", b"tensor"]) -> None: ...
def HasField(self, field_name: typing.Literal["tensor", b"tensor"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["index", b"index", "kernel_name", b"kernel_name", "step_id", b"step_id", "tensor", b"tensor"]) -> None: ...
global___MemoryLogTensorOutput = MemoryLogTensorOutput
@typing_extensions.final
@typing.final
class MemoryLogRawAllocation(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -147,11 +150,11 @@ class MemoryLogRawAllocation(google.protobuf.message.Message):
allocation_id: builtins.int | None = ...,
allocator_name: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["allocation_id", b"allocation_id", "allocator_name", b"allocator_name", "num_bytes", b"num_bytes", "operation", b"operation", "ptr", b"ptr", "step_id", b"step_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["allocation_id", b"allocation_id", "allocator_name", b"allocator_name", "num_bytes", b"num_bytes", "operation", b"operation", "ptr", b"ptr", "step_id", b"step_id"]) -> None: ...
global___MemoryLogRawAllocation = MemoryLogRawAllocation
@typing_extensions.final
@typing.final
class MemoryLogRawDeallocation(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -183,6 +186,6 @@ class MemoryLogRawDeallocation(google.protobuf.message.Message):
allocator_name: builtins.str | None = ...,
deferred: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["allocation_id", b"allocation_id", "allocator_name", b"allocator_name", "deferred", b"deferred", "operation", b"operation", "step_id", b"step_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["allocation_id", b"allocation_id", "allocator_name", b"allocator_name", "deferred", b"deferred", "operation", b"operation", "step_id", b"step_id"]) -> None: ...
global___MemoryLogRawDeallocation = MemoryLogRawDeallocation
stubs/tensorflow/tensorflow/core/framework/model_pb2.pyi
+25 -21
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -67,7 +68,7 @@ MAX_PARALLELISM: AutotuneAlgorithm.ValueType # 3
STAGE_BASED: AutotuneAlgorithm.ValueType # 4
global___AutotuneAlgorithm = AutotuneAlgorithm
@typing_extensions.final
@typing.final
class ModelProto(google.protobuf.message.Message):
"""Protocol buffer representing the data used by the autotuning modeling
framework.
@@ -75,13 +76,13 @@ class ModelProto(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Node(google.protobuf.message.Message):
"""General representation of a node in the model."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Parameter(google.protobuf.message.Message):
"""Represents a node parameter."""
@@ -117,7 +118,7 @@ class ModelProto(google.protobuf.message.Message):
max: builtins.float | None = ...,
tunable: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["max", b"max", "min", b"min", "name", b"name", "state_value", b"state_value", "tunable", b"tunable", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["max", b"max", "min", b"min", "name", b"name", "state_value", b"state_value", "tunable", b"tunable", "value", b"value"]) -> None: ...
ID_FIELD_NUMBER: builtins.int
NAME_FIELD_NUMBER: builtins.int
@@ -158,15 +159,9 @@ class ModelProto(google.protobuf.message.Message):
"""An indication whether this node records metrics about produced and
consumed elements.
"""
@property
def parameters(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___ModelProto.Node.Parameter]:
"""Parameters of this node."""
input_processing_time_sum: builtins.float
"""Statistic of inputs processing time history."""
input_processing_time_count: builtins.int
@property
def inputs(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""IDs of inputs of this node."""
node_class: global___NodeClass.ValueType
"""Class of this node."""
ratio: builtins.float
@@ -177,6 +172,14 @@ class ModelProto(google.protobuf.message.Message):
"""Ratio identifies how many parallelism calls are introduced by one
buffered element. This is only used by ASYNC_KNOWN_RATIO nodes.
"""
@property
def parameters(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___ModelProto.Node.Parameter]:
"""Parameters of this node."""
@property
def inputs(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""IDs of inputs of this node."""
def __init__(
self,
*,
@@ -198,9 +201,9 @@ class ModelProto(google.protobuf.message.Message):
ratio: builtins.float | None = ...,
memory_ratio: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["autotune", b"autotune", "buffered_bytes", b"buffered_bytes", "buffered_elements", b"buffered_elements", "bytes_consumed", b"bytes_consumed", "bytes_produced", b"bytes_produced", "id", b"id", "input_processing_time_count", b"input_processing_time_count", "input_processing_time_sum", b"input_processing_time_sum", "inputs", b"inputs", "memory_ratio", b"memory_ratio", "name", b"name", "node_class", b"node_class", "num_elements", b"num_elements", "parameters", b"parameters", "processing_time", b"processing_time", "ratio", b"ratio", "record_metrics", b"record_metrics"]) -> None: ...
def ClearField(self, field_name: typing.Literal["autotune", b"autotune", "buffered_bytes", b"buffered_bytes", "buffered_elements", b"buffered_elements", "bytes_consumed", b"bytes_consumed", "bytes_produced", b"bytes_produced", "id", b"id", "input_processing_time_count", b"input_processing_time_count", "input_processing_time_sum", b"input_processing_time_sum", "inputs", b"inputs", "memory_ratio", b"memory_ratio", "name", b"name", "node_class", b"node_class", "num_elements", b"num_elements", "parameters", b"parameters", "processing_time", b"processing_time", "ratio", b"ratio", "record_metrics", b"record_metrics"]) -> None: ...
@typing_extensions.final
@typing.final
class NodesEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -215,10 +218,10 @@ class ModelProto(google.protobuf.message.Message):
key: builtins.int | None = ...,
value: global___ModelProto.Node | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class OptimizationParams(google.protobuf.message.Message):
"""Contains parameters of the model autotuning optimization."""
@@ -246,19 +249,20 @@ class ModelProto(google.protobuf.message.Message):
ram_budget: builtins.int | None = ...,
model_input_time: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["algorithm", b"algorithm", "cpu_budget", b"cpu_budget", "model_input_time", b"model_input_time", "ram_budget", b"ram_budget"]) -> None: ...
def ClearField(self, field_name: typing.Literal["algorithm", b"algorithm", "cpu_budget", b"cpu_budget", "model_input_time", b"model_input_time", "ram_budget", b"ram_budget"]) -> None: ...
NODES_FIELD_NUMBER: builtins.int
OUTPUT_FIELD_NUMBER: builtins.int
ID_COUNTER_FIELD_NUMBER: builtins.int
OPTIMIZATION_PARAMS_FIELD_NUMBER: builtins.int
@property
def nodes(self) -> google.protobuf.internal.containers.MessageMap[builtins.int, global___ModelProto.Node]:
"""Map of node IDs to nodes of this model."""
output: builtins.int
"""ID of the output node of this model."""
id_counter: builtins.int
"""Counter for node IDs of this model."""
@property
def nodes(self) -> google.protobuf.internal.containers.MessageMap[builtins.int, global___ModelProto.Node]:
"""Map of node IDs to nodes of this model."""
@property
def optimization_params(self) -> global___ModelProto.OptimizationParams: ...
def __init__(
@@ -269,7 +273,7 @@ class ModelProto(google.protobuf.message.Message):
id_counter: builtins.int | None = ...,
optimization_params: global___ModelProto.OptimizationParams | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["optimization_params", b"optimization_params"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["id_counter", b"id_counter", "nodes", b"nodes", "optimization_params", b"optimization_params", "output", b"output"]) -> None: ...
def HasField(self, field_name: typing.Literal["optimization_params", b"optimization_params"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["id_counter", b"id_counter", "nodes", b"nodes", "optimization_params", b"optimization_params", "output", b"output"]) -> None: ...
global___ModelProto = ModelProto
stubs/tensorflow/tensorflow/core/framework/node_def_pb2.pyi
+24 -17
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -14,11 +15,11 @@ import tensorflow.core.framework.full_type_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class NodeDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class AttrEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -33,10 +34,10 @@ class NodeDef(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: tensorflow.core.framework.attr_value_pb2.AttrValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class ExperimentalDebugInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -52,6 +53,7 @@ class NodeDef(google.protobuf.message.Message):
be {A, B}. This information can be used to map errors originating at the
current node to some top level source code.
"""
@property
def original_func_names(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""This is intended to store the list of names of the functions from the
@@ -62,13 +64,14 @@ class NodeDef(google.protobuf.message.Message):
`original_node_names` can be used to map errors originating at the
current ndoe to some top level source code.
"""
def __init__(
self,
*,
original_node_names: collections.abc.Iterable[builtins.str] | None = ...,
original_func_names: collections.abc.Iterable[builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["original_func_names", b"original_func_names", "original_node_names", b"original_node_names"]) -> None: ...
def ClearField(self, field_name: typing.Literal["original_func_names", b"original_func_names", "original_node_names", b"original_node_names"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
OP_FIELD_NUMBER: builtins.int
@@ -86,14 +89,6 @@ class NodeDef(google.protobuf.message.Message):
"""The operation name. There may be custom parameters in attrs.
Op names starting with an underscore are reserved for internal use.
"""
@property
def input(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Each input is "node:src_output" with "node" being a string name and
"src_output" indicating which output tensor to use from "node". If
"src_output" is 0 the ":0" suffix can be omitted. Regular inputs
may optionally be followed by control inputs that have the format
"^node".
"""
device: builtins.str
"""A (possibly partial) specification for the device on which this
node should be placed.
@@ -116,6 +111,15 @@ class NodeDef(google.protobuf.message.Message):
field is empty or not present), the runtime will attempt to
choose a device automatically.
"""
@property
def input(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Each input is "node:src_output" with "node" being a string name and
"src_output" indicating which output tensor to use from "node". If
"src_output" is 0 the ":0" suffix can be omitted. Regular inputs
may optionally be followed by control inputs that have the format
"^node".
"""
@property
def attr(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, tensorflow.core.framework.attr_value_pb2.AttrValue]:
"""Operation-specific graph-construction-time configuration.
@@ -131,9 +135,11 @@ class NodeDef(google.protobuf.message.Message):
attr's type field.
TODO(josh11b): Add some examples here showing best practices.
"""
@property
def experimental_debug_info(self) -> global___NodeDef.ExperimentalDebugInfo:
"""This stores debug information associated with the node."""
@property
def experimental_type(self) -> tensorflow.core.framework.full_type_pb2.FullTypeDef:
"""The complete type of this node. Experimental and subject to change.
@@ -141,6 +147,7 @@ class NodeDef(google.protobuf.message.Message):
extend in the future to contain the entire signature of the node, as a
function type.
"""
def __init__(
self,
*,
@@ -152,7 +159,7 @@ class NodeDef(google.protobuf.message.Message):
experimental_debug_info: global___NodeDef.ExperimentalDebugInfo | None = ...,
experimental_type: tensorflow.core.framework.full_type_pb2.FullTypeDef | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["experimental_debug_info", b"experimental_debug_info", "experimental_type", b"experimental_type"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["attr", b"attr", "device", b"device", "experimental_debug_info", b"experimental_debug_info", "experimental_type", b"experimental_type", "input", b"input", "name", b"name", "op", b"op"]) -> None: ...
def HasField(self, field_name: typing.Literal["experimental_debug_info", b"experimental_debug_info", "experimental_type", b"experimental_type"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["attr", b"attr", "device", b"device", "experimental_debug_info", b"experimental_debug_info", "experimental_type", b"experimental_type", "input", b"input", "name", b"name", "op", b"op"]) -> None: ...
global___NodeDef = NodeDef
stubs/tensorflow/tensorflow/core/framework/op_def_pb2.pyi
+47 -38
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -16,7 +17,7 @@ import tensorflow.core.framework.types_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class OpDef(google.protobuf.message.Message):
"""Defines an operation. A NodeDef in a GraphDef specifies an Op by
using the "op" field which should match the name of a OpDef.
@@ -25,7 +26,7 @@ class OpDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class ArgDef(google.protobuf.message.Message):
"""For describing inputs and outputs."""
@@ -64,14 +65,15 @@ class OpDef(google.protobuf.message.Message):
"""If specified, attr must have type "list(type)", and none of
type, type_attr, and number_attr may be specified.
"""
@property
def handle_data(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.resource_handle_pb2.ResourceHandleProto.DtypeAndShape]:
"""The handle data for resource inputs."""
is_ref: builtins.bool
"""For inputs: if true, the inputs are required to be refs.
By default, inputs can be either refs or non-refs.
For outputs: if true, outputs are refs, otherwise they are not.
"""
@property
def handle_data(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.resource_handle_pb2.ResourceHandleProto.DtypeAndShape]:
"""The handle data for resource inputs."""
@property
def experimental_full_type(self) -> tensorflow.core.framework.full_type_pb2.FullTypeDef:
"""Experimental. Full type declaration for this argument.
@@ -84,6 +86,7 @@ class OpDef(google.protobuf.message.Message):
entire OpDef as a single type: a callable. In that context, this field is
just the type of a single argument.
"""
def __init__(
self,
*,
@@ -97,10 +100,10 @@ class OpDef(google.protobuf.message.Message):
is_ref: builtins.bool | None = ...,
experimental_full_type: tensorflow.core.framework.full_type_pb2.FullTypeDef | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["experimental_full_type", b"experimental_full_type"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["description", b"description", "experimental_full_type", b"experimental_full_type", "handle_data", b"handle_data", "is_ref", b"is_ref", "name", b"name", "number_attr", b"number_attr", "type", b"type", "type_attr", b"type_attr", "type_list_attr", b"type_list_attr"]) -> None: ...
def HasField(self, field_name: typing.Literal["experimental_full_type", b"experimental_full_type"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["description", b"description", "experimental_full_type", b"experimental_full_type", "handle_data", b"handle_data", "is_ref", b"is_ref", "name", b"name", "number_attr", b"number_attr", "type", b"type", "type_attr", b"type_attr", "type_list_attr", b"type_list_attr"]) -> None: ...
@typing_extensions.final
@typing.final
class AttrDef(google.protobuf.message.Message):
"""Description of the graph-construction-time configuration of this
Op. That is to say, this describes the attr fields that will
@@ -125,11 +128,6 @@ class OpDef(google.protobuf.message.Message):
"""One of the type names from attr_value.proto ("string", "list(string)",
"int", etc.).
"""
@property
def default_value(self) -> tensorflow.core.framework.attr_value_pb2.AttrValue:
"""A reasonable default for this attribute if the user does not supply
a value. If not specified, the user must supply a value.
"""
description: builtins.str
"""Human-readable description."""
has_minimum: builtins.bool
@@ -141,6 +139,12 @@ class OpDef(google.protobuf.message.Message):
types, this is the minimum length.
"""
minimum: builtins.int
@property
def default_value(self) -> tensorflow.core.framework.attr_value_pb2.AttrValue:
"""A reasonable default for this attribute if the user does not supply
a value. If not specified, the user must supply a value.
"""
@property
def allowed_values(self) -> tensorflow.core.framework.attr_value_pb2.AttrValue:
"""The set of allowed values. Has type that is the "list" version
@@ -150,6 +154,7 @@ class OpDef(google.protobuf.message.Message):
If type == "string" or "list(string)", then the "s" field of
"allowed_values.list" has the set of allowed strings.
"""
def __init__(
self,
*,
@@ -161,8 +166,8 @@ class OpDef(google.protobuf.message.Message):
minimum: builtins.int | None = ...,
allowed_values: tensorflow.core.framework.attr_value_pb2.AttrValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["allowed_values", b"allowed_values", "default_value", b"default_value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["allowed_values", b"allowed_values", "default_value", b"default_value", "description", b"description", "has_minimum", b"has_minimum", "minimum", b"minimum", "name", b"name", "type", b"type"]) -> None: ...
def HasField(self, field_name: typing.Literal["allowed_values", b"allowed_values", "default_value", b"default_value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["allowed_values", b"allowed_values", "default_value", b"default_value", "description", b"description", "has_minimum", b"has_minimum", "minimum", b"minimum", "name", b"name", "type", b"type"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
INPUT_ARG_FIELD_NUMBER: builtins.int
@@ -181,22 +186,6 @@ class OpDef(google.protobuf.message.Message):
"""Op names starting with an underscore are reserved for internal use.
Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9>_]*".
"""
@property
def input_arg(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___OpDef.ArgDef]:
"""Description of the input(s)."""
@property
def output_arg(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___OpDef.ArgDef]:
"""Description of the output(s)."""
@property
def control_output(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Named control outputs for this operation. Useful only for composite
operations (i.e. functions) which want to name different control outputs.
"""
@property
def attr(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___OpDef.AttrDef]: ...
@property
def deprecation(self) -> global___OpDeprecation:
"""Optional deprecation based on GraphDef versions."""
summary: builtins.str
"""One-line human-readable description of what the Op does."""
description: builtins.str
@@ -250,6 +239,26 @@ class OpDef(google.protobuf.message.Message):
If True, the op is allowed to return errors for network disconnection and
trigger TF network failure handling logics.
"""
@property
def input_arg(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___OpDef.ArgDef]:
"""Description of the input(s)."""
@property
def output_arg(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___OpDef.ArgDef]:
"""Description of the output(s)."""
@property
def control_output(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Named control outputs for this operation. Useful only for composite
operations (i.e. functions) which want to name different control outputs.
"""
@property
def attr(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___OpDef.AttrDef]: ...
@property
def deprecation(self) -> global___OpDeprecation:
"""Optional deprecation based on GraphDef versions."""
def __init__(
self,
*,
@@ -267,12 +276,12 @@ class OpDef(google.protobuf.message.Message):
allows_uninitialized_input: builtins.bool | None = ...,
is_distributed_communication: builtins.bool | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["deprecation", b"deprecation"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["allows_uninitialized_input", b"allows_uninitialized_input", "attr", b"attr", "control_output", b"control_output", "deprecation", b"deprecation", "description", b"description", "input_arg", b"input_arg", "is_aggregate", b"is_aggregate", "is_commutative", b"is_commutative", "is_distributed_communication", b"is_distributed_communication", "is_stateful", b"is_stateful", "name", b"name", "output_arg", b"output_arg", "summary", b"summary"]) -> None: ...
def HasField(self, field_name: typing.Literal["deprecation", b"deprecation"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["allows_uninitialized_input", b"allows_uninitialized_input", "attr", b"attr", "control_output", b"control_output", "deprecation", b"deprecation", "description", b"description", "input_arg", b"input_arg", "is_aggregate", b"is_aggregate", "is_commutative", b"is_commutative", "is_distributed_communication", b"is_distributed_communication", "is_stateful", b"is_stateful", "name", b"name", "output_arg", b"output_arg", "summary", b"summary"]) -> None: ...
global___OpDef = OpDef
@typing_extensions.final
@typing.final
class OpDeprecation(google.protobuf.message.Message):
"""Information about version-dependent deprecation of an op"""
@@ -290,11 +299,11 @@ class OpDeprecation(google.protobuf.message.Message):
version: builtins.int | None = ...,
explanation: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["explanation", b"explanation", "version", b"version"]) -> None: ...
def ClearField(self, field_name: typing.Literal["explanation", b"explanation", "version", b"version"]) -> None: ...
global___OpDeprecation = OpDeprecation
@typing_extensions.final
@typing.final
class OpList(google.protobuf.message.Message):
"""A collection of OpDefs"""
@@ -308,6 +317,6 @@ class OpList(google.protobuf.message.Message):
*,
op: collections.abc.Iterable[global___OpDef] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["op", b"op"]) -> None: ...
def ClearField(self, field_name: typing.Literal["op", b"op"]) -> None: ...
global___OpList = OpList
stubs/tensorflow/tensorflow/core/framework/optimized_function_graph_pb2.pyi
+12 -8
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -14,7 +15,7 @@ import tensorflow.core.framework.types_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class OptimizedFunctionGraph(google.protobuf.message.Message):
"""Optimized function graph after instantiation-related graph optimization
passes (up till before graph partitioning). The first half of the proto is
@@ -24,7 +25,7 @@ class OptimizedFunctionGraph(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class NodeNameToControlRetEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -38,7 +39,7 @@ class OptimizedFunctionGraph(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
FUNCTION_GRAPH_FIELD_NUMBER: builtins.int
@@ -49,21 +50,24 @@ class OptimizedFunctionGraph(google.protobuf.message.Message):
"""Function name. It can be a human-readable SignatureDef's method name, or a
FunctionDef name.
"""
num_return_nodes: builtins.int
"""Number of return nodes. This is an output of graph preprocessing."""
@property
def function_graph(self) -> tensorflow.core.framework.graph_pb2.GraphDef:
"""Optimized function graph."""
@property
def node_name_to_control_ret(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]:
"""Maps from node name to control ret. This is an output from running TF/XLA
bridge.
"""
@property
def ret_types(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[tensorflow.core.framework.types_pb2.DataType.ValueType]:
"""Return node types of the function. This is an output of graph
preprocessing.
"""
num_return_nodes: builtins.int
"""Number of return nodes. This is an output of graph preprocessing."""
def __init__(
self,
*,
@@ -73,7 +77,7 @@ class OptimizedFunctionGraph(google.protobuf.message.Message):
ret_types: collections.abc.Iterable[tensorflow.core.framework.types_pb2.DataType.ValueType] | None = ...,
num_return_nodes: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["function_graph", b"function_graph"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["function_graph", b"function_graph", "name", b"name", "node_name_to_control_ret", b"node_name_to_control_ret", "num_return_nodes", b"num_return_nodes", "ret_types", b"ret_types"]) -> None: ...
def HasField(self, field_name: typing.Literal["function_graph", b"function_graph"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["function_graph", b"function_graph", "name", b"name", "node_name_to_control_ret", b"node_name_to_control_ret", "num_return_nodes", b"num_return_nodes", "ret_types", b"ret_types"]) -> None: ...
global___OptimizedFunctionGraph = OptimizedFunctionGraph
stubs/tensorflow/tensorflow/core/framework/reader_base_pb2.pyi
+4 -3
View File
@@ -2,15 +2,16 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class ReaderBaseState(google.protobuf.message.Message):
"""For serializing and restoring the state of ReaderBase, see
reader_base.h for details.
@@ -34,6 +35,6 @@ class ReaderBaseState(google.protobuf.message.Message):
num_records_produced: builtins.int | None = ...,
current_work: builtins.bytes | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["current_work", b"current_work", "num_records_produced", b"num_records_produced", "work_finished", b"work_finished", "work_started", b"work_started"]) -> None: ...
def ClearField(self, field_name: typing.Literal["current_work", b"current_work", "num_records_produced", b"num_records_produced", "work_finished", b"work_finished", "work_started", b"work_started"]) -> None: ...
global___ReaderBaseState = ReaderBaseState
stubs/tensorflow/tensorflow/core/framework/resource_handle_pb2.pyi
+8 -6
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -14,7 +15,7 @@ import tensorflow.core.framework.types_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class ResourceHandleProto(google.protobuf.message.Message):
"""Protocol buffer representing a handle to a tensorflow resource. Handles are
not valid across executions, but can be serialized back and forth from within
@@ -23,7 +24,7 @@ class ResourceHandleProto(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class DtypeAndShape(google.protobuf.message.Message):
"""Protocol buffer representing a pair of (data type, tensor shape)."""
@@ -40,8 +41,8 @@ class ResourceHandleProto(google.protobuf.message.Message):
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ...,
shape: tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["dtype", b"dtype", "shape", b"shape"]) -> None: ...
def HasField(self, field_name: typing.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["dtype", b"dtype", "shape", b"shape"]) -> None: ...
DEVICE_FIELD_NUMBER: builtins.int
CONTAINER_FIELD_NUMBER: builtins.int
@@ -66,6 +67,7 @@ class ResourceHandleProto(google.protobuf.message.Message):
@property
def dtypes_and_shapes(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___ResourceHandleProto.DtypeAndShape]:
"""Data types and shapes for the underlying resource."""
def __init__(
self,
*,
@@ -76,6 +78,6 @@ class ResourceHandleProto(google.protobuf.message.Message):
maybe_type_name: builtins.str | None = ...,
dtypes_and_shapes: collections.abc.Iterable[global___ResourceHandleProto.DtypeAndShape] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["container", b"container", "device", b"device", "dtypes_and_shapes", b"dtypes_and_shapes", "hash_code", b"hash_code", "maybe_type_name", b"maybe_type_name", "name", b"name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["container", b"container", "device", b"device", "dtypes_and_shapes", b"dtypes_and_shapes", "hash_code", b"hash_code", "maybe_type_name", b"maybe_type_name", "name", b"name"]) -> None: ...
global___ResourceHandleProto = ResourceHandleProto
stubs/tensorflow/tensorflow/core/framework/step_stats_pb2.pyi
+35 -32
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -14,7 +15,7 @@ import tensorflow.core.framework.tensor_description_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class AllocationRecord(google.protobuf.message.Message):
"""An allocation/de-allocation operation performed by the allocator."""
@@ -32,11 +33,11 @@ class AllocationRecord(google.protobuf.message.Message):
alloc_micros: builtins.int | None = ...,
alloc_bytes: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["alloc_bytes", b"alloc_bytes", "alloc_micros", b"alloc_micros"]) -> None: ...
def ClearField(self, field_name: typing.Literal["alloc_bytes", b"alloc_bytes", "alloc_micros", b"alloc_micros"]) -> None: ...
global___AllocationRecord = AllocationRecord
@typing_extensions.final
@typing.final
class AllocatorMemoryUsed(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -52,13 +53,14 @@ class AllocatorMemoryUsed(google.protobuf.message.Message):
peak_bytes: builtins.int
live_bytes: builtins.int
"""The bytes that are not deallocated."""
@property
def allocation_records(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___AllocationRecord]:
"""The allocation and deallocation timeline."""
allocator_bytes_in_use: builtins.int
"""These are snapshots of the overall allocator memory stats.
The number of live bytes currently allocated by the allocator.
"""
@property
def allocation_records(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___AllocationRecord]:
"""The allocation and deallocation timeline."""
def __init__(
self,
*,
@@ -69,11 +71,11 @@ class AllocatorMemoryUsed(google.protobuf.message.Message):
allocation_records: collections.abc.Iterable[global___AllocationRecord] | None = ...,
allocator_bytes_in_use: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["allocation_records", b"allocation_records", "allocator_bytes_in_use", b"allocator_bytes_in_use", "allocator_name", b"allocator_name", "live_bytes", b"live_bytes", "peak_bytes", b"peak_bytes", "total_bytes", b"total_bytes"]) -> None: ...
def ClearField(self, field_name: typing.Literal["allocation_records", b"allocation_records", "allocator_bytes_in_use", b"allocator_bytes_in_use", "allocator_name", b"allocator_name", "live_bytes", b"live_bytes", "peak_bytes", b"peak_bytes", "total_bytes", b"total_bytes"]) -> None: ...
global___AllocatorMemoryUsed = AllocatorMemoryUsed
@typing_extensions.final
@typing.final
class NodeOutput(google.protobuf.message.Message):
"""Output sizes recorded for a single execution of a graph node."""
@@ -90,12 +92,12 @@ class NodeOutput(google.protobuf.message.Message):
slot: builtins.int | None = ...,
tensor_description: tensorflow.core.framework.tensor_description_pb2.TensorDescription | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["tensor_description", b"tensor_description"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["slot", b"slot", "tensor_description", b"tensor_description"]) -> None: ...
def HasField(self, field_name: typing.Literal["tensor_description", b"tensor_description"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["slot", b"slot", "tensor_description", b"tensor_description"]) -> None: ...
global___NodeOutput = NodeOutput
@typing_extensions.final
@typing.final
class MemoryStats(google.protobuf.message.Message):
"""For memory tracking."""
@@ -109,11 +111,11 @@ class MemoryStats(google.protobuf.message.Message):
DEVICE_PERSISTENT_TENSOR_ALLOC_IDS_FIELD_NUMBER: builtins.int
temp_memory_size: builtins.int
persistent_memory_size: builtins.int
@property
def persistent_tensor_alloc_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
device_temp_memory_size: builtins.int
device_persistent_memory_size: builtins.int
@property
def persistent_tensor_alloc_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def device_persistent_tensor_alloc_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
def __init__(
self,
@@ -125,11 +127,11 @@ class MemoryStats(google.protobuf.message.Message):
device_persistent_memory_size: builtins.int | None = ...,
device_persistent_tensor_alloc_ids: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["device_persistent_memory_size", b"device_persistent_memory_size", "device_persistent_tensor_alloc_ids", b"device_persistent_tensor_alloc_ids", "device_temp_memory_size", b"device_temp_memory_size", "persistent_memory_size", b"persistent_memory_size", "persistent_tensor_alloc_ids", b"persistent_tensor_alloc_ids", "temp_memory_size", b"temp_memory_size"]) -> None: ...
def ClearField(self, field_name: typing.Literal["device_persistent_memory_size", b"device_persistent_memory_size", "device_persistent_tensor_alloc_ids", b"device_persistent_tensor_alloc_ids", "device_temp_memory_size", b"device_temp_memory_size", "persistent_memory_size", b"persistent_memory_size", "persistent_tensor_alloc_ids", b"persistent_tensor_alloc_ids", "temp_memory_size", b"temp_memory_size"]) -> None: ...
global___MemoryStats = MemoryStats
@typing_extensions.final
@typing.final
class NodeExecStats(google.protobuf.message.Message):
"""Time/size stats recorded for a single execution of a graph node."""
@@ -162,22 +164,22 @@ class NodeExecStats(google.protobuf.message.Message):
op_start_rel_micros: builtins.int
op_end_rel_micros: builtins.int
all_end_rel_micros: builtins.int
@property
def memory(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___AllocatorMemoryUsed]: ...
@property
def output(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___NodeOutput]: ...
timeline_label: builtins.str
scheduled_micros: builtins.int
thread_id: builtins.int
@property
def referenced_tensor(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.allocation_description_pb2.AllocationDescription]: ...
@property
def memory_stats(self) -> global___MemoryStats: ...
all_start_nanos: builtins.int
op_start_rel_nanos: builtins.int
op_end_rel_nanos: builtins.int
all_end_rel_nanos: builtins.int
scheduled_nanos: builtins.int
@property
def memory(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___AllocatorMemoryUsed]: ...
@property
def output(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___NodeOutput]: ...
@property
def referenced_tensor(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.allocation_description_pb2.AllocationDescription]: ...
@property
def memory_stats(self) -> global___MemoryStats: ...
def __init__(
self,
*,
@@ -199,16 +201,16 @@ class NodeExecStats(google.protobuf.message.Message):
all_end_rel_nanos: builtins.int | None = ...,
scheduled_nanos: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["memory_stats", b"memory_stats"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["all_end_rel_micros", b"all_end_rel_micros", "all_end_rel_nanos", b"all_end_rel_nanos", "all_start_micros", b"all_start_micros", "all_start_nanos", b"all_start_nanos", "memory", b"memory", "memory_stats", b"memory_stats", "node_name", b"node_name", "op_end_rel_micros", b"op_end_rel_micros", "op_end_rel_nanos", b"op_end_rel_nanos", "op_start_rel_micros", b"op_start_rel_micros", "op_start_rel_nanos", b"op_start_rel_nanos", "output", b"output", "referenced_tensor", b"referenced_tensor", "scheduled_micros", b"scheduled_micros", "scheduled_nanos", b"scheduled_nanos", "thread_id", b"thread_id", "timeline_label", b"timeline_label"]) -> None: ...
def HasField(self, field_name: typing.Literal["memory_stats", b"memory_stats"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["all_end_rel_micros", b"all_end_rel_micros", "all_end_rel_nanos", b"all_end_rel_nanos", "all_start_micros", b"all_start_micros", "all_start_nanos", b"all_start_nanos", "memory", b"memory", "memory_stats", b"memory_stats", "node_name", b"node_name", "op_end_rel_micros", b"op_end_rel_micros", "op_end_rel_nanos", b"op_end_rel_nanos", "op_start_rel_micros", b"op_start_rel_micros", "op_start_rel_nanos", b"op_start_rel_nanos", "output", b"output", "referenced_tensor", b"referenced_tensor", "scheduled_micros", b"scheduled_micros", "scheduled_nanos", b"scheduled_nanos", "thread_id", b"thread_id", "timeline_label", b"timeline_label"]) -> None: ...
global___NodeExecStats = NodeExecStats
@typing_extensions.final
@typing.final
class DeviceStepStats(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class ThreadNamesEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -222,7 +224,7 @@ class DeviceStepStats(google.protobuf.message.Message):
key: builtins.int | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
DEVICE_FIELD_NUMBER: builtins.int
NODE_STATS_FIELD_NUMBER: builtins.int
@@ -233,6 +235,7 @@ class DeviceStepStats(google.protobuf.message.Message):
@property
def thread_names(self) -> google.protobuf.internal.containers.ScalarMap[builtins.int, builtins.str]:
"""Its key is thread id."""
def __init__(
self,
*,
@@ -240,11 +243,11 @@ class DeviceStepStats(google.protobuf.message.Message):
node_stats: collections.abc.Iterable[global___NodeExecStats] | None = ...,
thread_names: collections.abc.Mapping[builtins.int, builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["device", b"device", "node_stats", b"node_stats", "thread_names", b"thread_names"]) -> None: ...
def ClearField(self, field_name: typing.Literal["device", b"device", "node_stats", b"node_stats", "thread_names", b"thread_names"]) -> None: ...
global___DeviceStepStats = DeviceStepStats
@typing_extensions.final
@typing.final
class StepStats(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -256,6 +259,6 @@ class StepStats(google.protobuf.message.Message):
*,
dev_stats: collections.abc.Iterable[global___DeviceStepStats] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["dev_stats", b"dev_stats"]) -> None: ...
def ClearField(self, field_name: typing.Literal["dev_stats", b"dev_stats"]) -> None: ...
global___StepStats = StepStats
stubs/tensorflow/tensorflow/core/framework/summary_pb2.pyi
+26 -22
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -69,7 +70,7 @@ have `tensor` set to a rank-1 tensor of bytestring dtype.
"""
global___DataClass = DataClass
@typing_extensions.final
@typing.final
class SummaryDescription(google.protobuf.message.Message):
"""Metadata associated with a series of Summary data"""
@@ -85,11 +86,11 @@ class SummaryDescription(google.protobuf.message.Message):
*,
type_hint: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["type_hint", b"type_hint"]) -> None: ...
def ClearField(self, field_name: typing.Literal["type_hint", b"type_hint"]) -> None: ...
global___SummaryDescription = SummaryDescription
@typing_extensions.final
@typing.final
class SummaryMetadata(google.protobuf.message.Message):
"""A SummaryMetadata encapsulates information on which plugins are able to make
use of a certain summary value.
@@ -97,7 +98,7 @@ class SummaryMetadata(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class PluginData(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -115,15 +116,12 @@ class SummaryMetadata(google.protobuf.message.Message):
plugin_name: builtins.str | None = ...,
content: builtins.bytes | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["content", b"content", "plugin_name", b"plugin_name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["content", b"content", "plugin_name", b"plugin_name"]) -> None: ...
PLUGIN_DATA_FIELD_NUMBER: builtins.int
DISPLAY_NAME_FIELD_NUMBER: builtins.int
SUMMARY_DESCRIPTION_FIELD_NUMBER: builtins.int
DATA_CLASS_FIELD_NUMBER: builtins.int
@property
def plugin_data(self) -> global___SummaryMetadata.PluginData:
"""Data that associates a summary with a certain plugin."""
display_name: builtins.str
"""Display name for viewing in TensorBoard."""
summary_description: builtins.str
@@ -134,6 +132,10 @@ class SummaryMetadata(google.protobuf.message.Message):
imposes constraints on the dtype and shape of the corresponding tensor
values. See `DataClass` docs for details.
"""
@property
def plugin_data(self) -> global___SummaryMetadata.PluginData:
"""Data that associates a summary with a certain plugin."""
def __init__(
self,
*,
@@ -142,12 +144,12 @@ class SummaryMetadata(google.protobuf.message.Message):
summary_description: builtins.str | None = ...,
data_class: global___DataClass.ValueType | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["plugin_data", b"plugin_data"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["data_class", b"data_class", "display_name", b"display_name", "plugin_data", b"plugin_data", "summary_description", b"summary_description"]) -> None: ...
def HasField(self, field_name: typing.Literal["plugin_data", b"plugin_data"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["data_class", b"data_class", "display_name", b"display_name", "plugin_data", b"plugin_data", "summary_description", b"summary_description"]) -> None: ...
global___SummaryMetadata = SummaryMetadata
@typing_extensions.final
@typing.final
class Summary(google.protobuf.message.Message):
"""A Summary is a set of named values to be displayed by the
visualizer.
@@ -159,7 +161,7 @@ class Summary(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Image(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -191,9 +193,9 @@ class Summary(google.protobuf.message.Message):
colorspace: builtins.int | None = ...,
encoded_image_string: builtins.bytes | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["colorspace", b"colorspace", "encoded_image_string", b"encoded_image_string", "height", b"height", "width", b"width"]) -> None: ...
def ClearField(self, field_name: typing.Literal["colorspace", b"colorspace", "encoded_image_string", b"encoded_image_string", "height", b"height", "width", b"width"]) -> None: ...
@typing_extensions.final
@typing.final
class Audio(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -222,9 +224,9 @@ class Summary(google.protobuf.message.Message):
encoded_audio_string: builtins.bytes | None = ...,
content_type: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["content_type", b"content_type", "encoded_audio_string", b"encoded_audio_string", "length_frames", b"length_frames", "num_channels", b"num_channels", "sample_rate", b"sample_rate"]) -> None: ...
def ClearField(self, field_name: typing.Literal["content_type", b"content_type", "encoded_audio_string", b"encoded_audio_string", "length_frames", b"length_frames", "num_channels", b"num_channels", "sample_rate", b"sample_rate"]) -> None: ...
@typing_extensions.final
@typing.final
class Value(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -244,6 +246,8 @@ class Summary(google.protobuf.message.Message):
are often organized by scope (which contains slashes to convey
hierarchy). For example: foo/bar/0
"""
simple_value: builtins.float
obsolete_old_style_histogram: builtins.bytes
@property
def metadata(self) -> global___SummaryMetadata:
"""Contains metadata on the summary value such as which plugins may use it.
@@ -252,8 +256,7 @@ class Summary(google.protobuf.message.Message):
value with a certain tag for each tag. TensorBoard then remembers which
tags are associated with which plugins. This saves space.
"""
simple_value: builtins.float
obsolete_old_style_histogram: builtins.bytes
@property
def image(self) -> global___Summary.Image: ...
@property
@@ -275,19 +278,20 @@ class Summary(google.protobuf.message.Message):
audio: global___Summary.Audio | None = ...,
tensor: tensorflow.core.framework.tensor_pb2.TensorProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["audio", b"audio", "histo", b"histo", "image", b"image", "metadata", b"metadata", "obsolete_old_style_histogram", b"obsolete_old_style_histogram", "simple_value", b"simple_value", "tensor", b"tensor", "value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["audio", b"audio", "histo", b"histo", "image", b"image", "metadata", b"metadata", "node_name", b"node_name", "obsolete_old_style_histogram", b"obsolete_old_style_histogram", "simple_value", b"simple_value", "tag", b"tag", "tensor", b"tensor", "value", b"value"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["value", b"value"]) -> typing_extensions.Literal["simple_value", "obsolete_old_style_histogram", "image", "histo", "audio", "tensor"] | None: ...
def HasField(self, field_name: typing.Literal["audio", b"audio", "histo", b"histo", "image", b"image", "metadata", b"metadata", "obsolete_old_style_histogram", b"obsolete_old_style_histogram", "simple_value", b"simple_value", "tensor", b"tensor", "value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["audio", b"audio", "histo", b"histo", "image", b"image", "metadata", b"metadata", "node_name", b"node_name", "obsolete_old_style_histogram", b"obsolete_old_style_histogram", "simple_value", b"simple_value", "tag", b"tag", "tensor", b"tensor", "value", b"value"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["value", b"value"]) -> typing.Literal["simple_value", "obsolete_old_style_histogram", "image", "histo", "audio", "tensor"] | None: ...
VALUE_FIELD_NUMBER: builtins.int
@property
def value(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___Summary.Value]:
"""Set of values for the summary."""
def __init__(
self,
*,
value: collections.abc.Iterable[global___Summary.Value] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["value", b"value"]) -> None: ...
global___Summary = Summary
stubs/tensorflow/tensorflow/core/framework/tensor_description_pb2.pyi
+7 -4
View File
@@ -2,8 +2,9 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
@@ -13,7 +14,7 @@ import tensorflow.core.framework.types_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class TensorDescription(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -25,9 +26,11 @@ class TensorDescription(google.protobuf.message.Message):
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto:
"""Shape of the tensor."""
@property
def allocation_description(self) -> tensorflow.core.framework.allocation_description_pb2.AllocationDescription:
"""Information about the size and allocator used for the data"""
def __init__(
self,
*,
@@ -35,7 +38,7 @@ class TensorDescription(google.protobuf.message.Message):
shape: tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto | None = ...,
allocation_description: tensorflow.core.framework.allocation_description_pb2.AllocationDescription | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["allocation_description", b"allocation_description", "shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["allocation_description", b"allocation_description", "dtype", b"dtype", "shape", b"shape"]) -> None: ...
def HasField(self, field_name: typing.Literal["allocation_description", b"allocation_description", "shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["allocation_description", b"allocation_description", "dtype", b"dtype", "shape", b"shape"]) -> None: ...
global___TensorDescription = TensorDescription
stubs/tensorflow/tensorflow/core/framework/tensor_pb2.pyi
+29 -13
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -15,7 +16,7 @@ import tensorflow.core.framework.types_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class TensorProto(google.protobuf.message.Message):
"""Protocol buffer representing a tensor."""
@@ -40,9 +41,6 @@ class TensorProto(google.protobuf.message.Message):
UINT64_VAL_FIELD_NUMBER: builtins.int
FLOAT8_VAL_FIELD_NUMBER: builtins.int
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
@property
def tensor_shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto:
"""Shape of the tensor. TODO(touts): sort out the 0-rank issues."""
version_number: builtins.int
"""Only one of the representations below is set, one of "tensor_contents" and
the "xxx_val" attributes. We are not using oneof because as oneofs cannot
@@ -61,6 +59,14 @@ class TensorProto(google.protobuf.message.Message):
reduce serialization overhead during RPC call by avoiding serialization of
many repeated small items.
"""
float8_val: builtins.bytes
"""DT_FLOAT8_*, use variable-sized set of bytes
(i.e. the equivalent of repeated uint8, if such a thing existed).
"""
@property
def tensor_shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto:
"""Shape of the tensor. TODO(touts): sort out the 0-rank issues."""
@property
def half_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Type specific representations that make it easy to create tensor protos in
@@ -71,50 +77,59 @@ class TensorProto(google.protobuf.message.Message):
DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
have some pointless zero padding for each value here.
"""
@property
def float_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]:
"""DT_FLOAT."""
@property
def double_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]:
"""DT_DOUBLE."""
@property
def int_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""DT_INT32, DT_INT16, DT_UINT16, DT_INT8, DT_UINT8."""
@property
def string_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bytes]:
"""DT_STRING"""
@property
def scomplex_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]:
"""DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
and imaginary parts of i-th single precision complex.
"""
@property
def int64_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""DT_INT64"""
@property
def bool_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bool]:
"""DT_BOOL"""
@property
def dcomplex_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]:
"""DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
and imaginary parts of i-th double precision complex.
"""
@property
def resource_handle_val(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.resource_handle_pb2.ResourceHandleProto]:
"""DT_RESOURCE"""
@property
def variant_val(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___VariantTensorDataProto]:
"""DT_VARIANT"""
@property
def uint32_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""DT_UINT32"""
@property
def uint64_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""DT_UINT64"""
float8_val: builtins.bytes
"""DT_FLOAT8_*, use variable-sized set of bytes
(i.e. the equivalent of repeated uint8, if such a thing existed).
"""
def __init__(
self,
*,
@@ -137,12 +152,12 @@ class TensorProto(google.protobuf.message.Message):
uint64_val: collections.abc.Iterable[builtins.int] | None = ...,
float8_val: builtins.bytes | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["tensor_shape", b"tensor_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["bool_val", b"bool_val", "dcomplex_val", b"dcomplex_val", "double_val", b"double_val", "dtype", b"dtype", "float8_val", b"float8_val", "float_val", b"float_val", "half_val", b"half_val", "int64_val", b"int64_val", "int_val", b"int_val", "resource_handle_val", b"resource_handle_val", "scomplex_val", b"scomplex_val", "string_val", b"string_val", "tensor_content", b"tensor_content", "tensor_shape", b"tensor_shape", "uint32_val", b"uint32_val", "uint64_val", b"uint64_val", "variant_val", b"variant_val", "version_number", b"version_number"]) -> None: ...
def HasField(self, field_name: typing.Literal["tensor_shape", b"tensor_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["bool_val", b"bool_val", "dcomplex_val", b"dcomplex_val", "double_val", b"double_val", "dtype", b"dtype", "float8_val", b"float8_val", "float_val", b"float_val", "half_val", b"half_val", "int64_val", b"int64_val", "int_val", b"int_val", "resource_handle_val", b"resource_handle_val", "scomplex_val", b"scomplex_val", "string_val", b"string_val", "tensor_content", b"tensor_content", "tensor_shape", b"tensor_shape", "uint32_val", b"uint32_val", "uint64_val", b"uint64_val", "variant_val", b"variant_val", "version_number", b"version_number"]) -> None: ...
global___TensorProto = TensorProto
@typing_extensions.final
@typing.final
class VariantTensorDataProto(google.protobuf.message.Message):
"""Protocol buffer representing the serialization format of DT_VARIANT tensors."""
@@ -158,6 +173,7 @@ class VariantTensorDataProto(google.protobuf.message.Message):
@property
def tensors(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TensorProto]:
"""Tensors contained within objects being serialized."""
def __init__(
self,
*,
@@ -165,6 +181,6 @@ class VariantTensorDataProto(google.protobuf.message.Message):
metadata: builtins.bytes | None = ...,
tensors: collections.abc.Iterable[global___TensorProto] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["metadata", b"metadata", "tensors", b"tensors", "type_name", b"type_name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["metadata", b"metadata", "tensors", b"tensors", "type_name", b"type_name"]) -> None: ...
global___VariantTensorDataProto = VariantTensorDataProto
stubs/tensorflow/tensorflow/core/framework/tensor_shape_pb2.pyi
+11 -9
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
Protocol buffer representing the shape of tensors."""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,13 +13,13 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class TensorShapeProto(google.protobuf.message.Message):
"""Dimensions of a tensor."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Dim(google.protobuf.message.Message):
"""One dimension of the tensor."""
@@ -41,10 +42,15 @@ class TensorShapeProto(google.protobuf.message.Message):
size: builtins.int | None = ...,
name: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "size", b"size"]) -> None: ...
def ClearField(self, field_name: typing.Literal["name", b"name", "size", b"size"]) -> None: ...
DIM_FIELD_NUMBER: builtins.int
UNKNOWN_RANK_FIELD_NUMBER: builtins.int
unknown_rank: builtins.bool
"""If true, the number of dimensions in the shape is unknown.
If true, "dim.size()" must be 0.
"""
@property
def dim(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TensorShapeProto.Dim]:
"""Dimensions of the tensor, such as {"input", 30}, {"output", 40}
@@ -61,17 +67,13 @@ class TensorShapeProto(google.protobuf.message.Message):
If "dim.size()" > 0, "unknown_rank" must be false.
"""
unknown_rank: builtins.bool
"""If true, the number of dimensions in the shape is unknown.
If true, "dim.size()" must be 0.
"""
def __init__(
self,
*,
dim: collections.abc.Iterable[global___TensorShapeProto.Dim] | None = ...,
unknown_rank: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["dim", b"dim", "unknown_rank", b"unknown_rank"]) -> None: ...
def ClearField(self, field_name: typing.Literal["dim", b"dim", "unknown_rank", b"unknown_rank"]) -> None: ...
global___TensorShapeProto = TensorShapeProto
stubs/tensorflow/tensorflow/core/framework/tensor_slice_pb2.pyi
+9 -7
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
Protocol buffer representing slices of a tensor"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,13 +13,13 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class TensorSliceProto(google.protobuf.message.Message):
"""Can only be interpreted if you know the corresponding TensorShape."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Extent(google.protobuf.message.Message):
"""Extent of the slice in one dimension.
Either both or no attributes must be set. When no attribute is set
@@ -38,9 +39,9 @@ class TensorSliceProto(google.protobuf.message.Message):
start: builtins.int | None = ...,
length: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["has_length", b"has_length", "length", b"length"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["has_length", b"has_length", "length", b"length", "start", b"start"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["has_length", b"has_length"]) -> typing_extensions.Literal["length"] | None: ...
def HasField(self, field_name: typing.Literal["has_length", b"has_length", "length", b"length"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["has_length", b"has_length", "length", b"length", "start", b"start"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["has_length", b"has_length"]) -> typing.Literal["length"] | None: ...
EXTENT_FIELD_NUMBER: builtins.int
@property
@@ -51,11 +52,12 @@ class TensorSliceProto(google.protobuf.message.Message):
slice belongs to. The order of sizes is the same as the order of
dimensions in the TensorShape.
"""
def __init__(
self,
*,
extent: collections.abc.Iterable[global___TensorSliceProto.Extent] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["extent", b"extent"]) -> None: ...
def ClearField(self, field_name: typing.Literal["extent", b"extent"]) -> None: ...
global___TensorSliceProto = TensorSliceProto
stubs/tensorflow/tensorflow/core/framework/types_pb2.pyi
+3 -2
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import sys
import typing
@@ -177,7 +178,7 @@ DT_FLOAT8_E5M2_REF: DataType.ValueType # 124
DT_FLOAT8_E4M3FN_REF: DataType.ValueType # 125
global___DataType = DataType
@typing_extensions.final
@typing.final
class SerializedDType(google.protobuf.message.Message):
"""Represents a serialized tf.dtypes.Dtype"""
@@ -190,6 +191,6 @@ class SerializedDType(google.protobuf.message.Message):
*,
datatype: global___DataType.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["datatype", b"datatype"]) -> None: ...
def ClearField(self, field_name: typing.Literal["datatype", b"datatype"]) -> None: ...
global___SerializedDType = SerializedDType
stubs/tensorflow/tensorflow/core/framework/variable_pb2.pyi
+13 -8
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -109,7 +110,7 @@ global step counter.
"""
global___VariableAggregation = VariableAggregation
@typing_extensions.final
@typing.final
class VariableDef(google.protobuf.message.Message):
"""Protocol buffer representing a Variable."""
@@ -132,9 +133,6 @@ class VariableDef(google.protobuf.message.Message):
"""Name of the initializer op."""
snapshot_name: builtins.str
"""Name of the snapshot tensor."""
@property
def save_slice_info_def(self) -> global___SaveSliceInfoDef:
"""Support for saving variables as slices of a larger variable."""
is_resource: builtins.bool
"""Whether to represent this as a ResourceVariable."""
trainable: builtins.bool
@@ -143,6 +141,10 @@ class VariableDef(google.protobuf.message.Message):
"""Indicates when a distributed variable will be synced."""
aggregation: global___VariableAggregation.ValueType
"""Indicates how a distributed variable will be aggregated."""
@property
def save_slice_info_def(self) -> global___SaveSliceInfoDef:
"""Support for saving variables as slices of a larger variable."""
def __init__(
self,
*,
@@ -156,12 +158,12 @@ class VariableDef(google.protobuf.message.Message):
synchronization: global___VariableSynchronization.ValueType | None = ...,
aggregation: global___VariableAggregation.ValueType | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["save_slice_info_def", b"save_slice_info_def"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["aggregation", b"aggregation", "initial_value_name", b"initial_value_name", "initializer_name", b"initializer_name", "is_resource", b"is_resource", "save_slice_info_def", b"save_slice_info_def", "snapshot_name", b"snapshot_name", "synchronization", b"synchronization", "trainable", b"trainable", "variable_name", b"variable_name"]) -> None: ...
def HasField(self, field_name: typing.Literal["save_slice_info_def", b"save_slice_info_def"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["aggregation", b"aggregation", "initial_value_name", b"initial_value_name", "initializer_name", b"initializer_name", "is_resource", b"is_resource", "save_slice_info_def", b"save_slice_info_def", "snapshot_name", b"snapshot_name", "synchronization", b"synchronization", "trainable", b"trainable", "variable_name", b"variable_name"]) -> None: ...
global___VariableDef = VariableDef
@typing_extensions.final
@typing.final
class SaveSliceInfoDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -174,12 +176,15 @@ class SaveSliceInfoDef(google.protobuf.message.Message):
@property
def full_shape(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Shape of the full variable."""
@property
def var_offset(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Offset of this variable into the full variable."""
@property
def var_shape(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Shape of this variable."""
def __init__(
self,
*,
@@ -188,6 +193,6 @@ class SaveSliceInfoDef(google.protobuf.message.Message):
var_offset: collections.abc.Iterable[builtins.int] | None = ...,
var_shape: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["full_name", b"full_name", "full_shape", b"full_shape", "var_offset", b"var_offset", "var_shape", b"var_shape"]) -> None: ...
def ClearField(self, field_name: typing.Literal["full_name", b"full_name", "full_shape", b"full_shape", "var_offset", b"var_offset", "var_shape", b"var_shape"]) -> None: ...
global___SaveSliceInfoDef = SaveSliceInfoDef
stubs/tensorflow/tensorflow/core/framework/versions_pb2.pyi
+5 -3
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,7 +13,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class VersionDef(google.protobuf.message.Message):
"""Version information for a piece of serialized data
@@ -40,6 +41,7 @@ class VersionDef(google.protobuf.message.Message):
@property
def bad_consumers(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Specific consumer versions which are disallowed (e.g. due to bugs)."""
def __init__(
self,
*,
@@ -47,6 +49,6 @@ class VersionDef(google.protobuf.message.Message):
min_consumer: builtins.int | None = ...,
bad_consumers: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["bad_consumers", b"bad_consumers", "min_consumer", b"min_consumer", "producer", b"producer"]) -> None: ...
def ClearField(self, field_name: typing.Literal["bad_consumers", b"bad_consumers", "min_consumer", b"min_consumer", "producer", b"producer"]) -> None: ...
global___VersionDef = VersionDef
stubs/tensorflow/tensorflow/core/protobuf/bfc_memory_map_pb2.pyi
+1
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import google.protobuf.descriptor
from tensorflow.tsl.protobuf.bfc_memory_map_pb2 import (
BinSummary as BinSummary,
stubs/tensorflow/tensorflow/core/protobuf/cluster_pb2.pyi
+10 -7
View File
@@ -16,9 +16,10 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -26,7 +27,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class JobDef(google.protobuf.message.Message):
"""This file contains protos to be used when defining a TensorFlow
cluster.
@@ -74,7 +75,7 @@ class JobDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class TasksEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -88,7 +89,7 @@ class JobDef(google.protobuf.message.Message):
key: builtins.int | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
TASKS_FIELD_NUMBER: builtins.int
@@ -102,17 +103,18 @@ class JobDef(google.protobuf.message.Message):
mapping from 7 to "example.org:2222", then the device prefix
"/job:worker/task:7" will be assigned to "example.org:2222".
"""
def __init__(
self,
*,
name: builtins.str | None = ...,
tasks: collections.abc.Mapping[builtins.int, builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "tasks", b"tasks"]) -> None: ...
def ClearField(self, field_name: typing.Literal["name", b"name", "tasks", b"tasks"]) -> None: ...
global___JobDef = JobDef
@typing_extensions.final
@typing.final
class ClusterDef(google.protobuf.message.Message):
"""Defines a TensorFlow cluster as a set of jobs."""
@@ -122,11 +124,12 @@ class ClusterDef(google.protobuf.message.Message):
@property
def job(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___JobDef]:
"""The jobs that comprise the cluster."""
def __init__(
self,
*,
job: collections.abc.Iterable[global___JobDef] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["job", b"job"]) -> None: ...
def ClearField(self, field_name: typing.Literal["job", b"job"]) -> None: ...
global___ClusterDef = ClusterDef
stubs/tensorflow/tensorflow/core/protobuf/composite_tensor_variant_pb2.pyi
+5 -4
View File
@@ -2,8 +2,9 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
@@ -11,7 +12,7 @@ import tensorflow.core.protobuf.struct_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class CompositeTensorVariantMetadata(google.protobuf.message.Message):
"""Metadata for CompositeTensorVariant, used when serializing as Variant.
@@ -30,7 +31,7 @@ class CompositeTensorVariantMetadata(google.protobuf.message.Message):
*,
type_spec_proto: tensorflow.core.protobuf.struct_pb2.TypeSpecProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["type_spec_proto", b"type_spec_proto"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["type_spec_proto", b"type_spec_proto"]) -> None: ...
def HasField(self, field_name: typing.Literal["type_spec_proto", b"type_spec_proto"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["type_spec_proto", b"type_spec_proto"]) -> None: ...
global___CompositeTensorVariantMetadata = CompositeTensorVariantMetadata
stubs/tensorflow/tensorflow/core/protobuf/config_pb2.pyi
+188 -158
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -27,15 +28,15 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class GPUOptions(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class Experimental(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class VirtualDevices(google.protobuf.message.Message):
"""Configuration for breaking down a visible GPU into multiple "virtual"
devices.
@@ -57,6 +58,7 @@ class GPUOptions(google.protobuf.message.Message):
For the concept of "visible" and "virtual" GPU, see the comments for
"visible_device_list" above for more information.
"""
@property
def priority(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Priority values to use with the virtual devices. Use the cuda function
@@ -70,6 +72,7 @@ class GPUOptions(google.protobuf.message.Message):
created with the default. If this field has values set, then the size
of this must match with the above memory_limit_mb.
"""
@property
def device_ordinal(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Virtual Device ordinal number determines the device ID of the device.
@@ -77,6 +80,7 @@ class GPUOptions(google.protobuf.message.Message):
smaller device id. The phyiscal device id and location in the
virtual device list is used to break ties.
"""
def __init__(
self,
*,
@@ -84,7 +88,7 @@ class GPUOptions(google.protobuf.message.Message):
priority: collections.abc.Iterable[builtins.int] | None = ...,
device_ordinal: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["device_ordinal", b"device_ordinal", "memory_limit_mb", b"memory_limit_mb", "priority", b"priority"]) -> None: ...
def ClearField(self, field_name: typing.Literal["device_ordinal", b"device_ordinal", "memory_limit_mb", b"memory_limit_mb", "priority", b"priority"]) -> None: ...
VIRTUAL_DEVICES_FIELD_NUMBER: builtins.int
USE_UNIFIED_MEMORY_FIELD_NUMBER: builtins.int
@@ -99,47 +103,6 @@ class GPUOptions(google.protobuf.message.Message):
DISALLOW_RETRY_ON_ALLOCATION_FAILURE_FIELD_NUMBER: builtins.int
GPU_HOST_MEM_LIMIT_IN_MB_FIELD_NUMBER: builtins.int
GPU_HOST_MEM_DISALLOW_GROWTH_FIELD_NUMBER: builtins.int
@property
def virtual_devices(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___GPUOptions.Experimental.VirtualDevices]:
"""The multi virtual device settings. If empty (not set), it will create
single virtual device on each visible GPU, according to the settings
in "visible_device_list" above. Otherwise, the number of elements in the
list must be the same as the number of visible GPUs (after
"visible_device_list" filtering if it is set), and the string represented
device names (e.g. /device:GPU:<id>) will refer to the virtual
devices and have the <id> field assigned sequentially starting from 0,
according to the order of the virtual devices determined by
device_ordinal and the location in the virtual device list.
For example,
visible_device_list = "1,0"
virtual_devices { memory_limit: 1GB memory_limit: 2GB }
virtual_devices { memory_limit: 3GB memory_limit: 4GB }
will create 4 virtual devices as:
/device:GPU:0 -> visible GPU 1 with 1GB memory
/device:GPU:1 -> visible GPU 1 with 2GB memory
/device:GPU:2 -> visible GPU 0 with 3GB memory
/device:GPU:3 -> visible GPU 0 with 4GB memory
but
visible_device_list = "1,0"
virtual_devices { memory_limit: 1GB memory_limit: 2GB
device_ordinal: 10 device_ordinal: 20}
virtual_devices { memory_limit: 3GB memory_limit: 4GB
device_ordinal: 10 device_ordinal: 20}
will create 4 virtual devices as:
/device:GPU:0 -> visible GPU 1 with 1GB memory (ordinal 10)
/device:GPU:1 -> visible GPU 0 with 3GB memory (ordinal 10)
/device:GPU:2 -> visible GPU 1 with 2GB memory (ordinal 20)
/device:GPU:3 -> visible GPU 0 with 4GB memory (ordinal 20)
NOTE:
1. It's invalid to set both this and "per_process_gpu_memory_fraction"
at the same time.
2. Currently this setting is per-process, not per-session. Using
different settings in different sessions within same process will
result in undefined behavior.
"""
use_unified_memory: builtins.bool
"""If true, uses CUDA unified memory for memory allocations. If
per_process_gpu_memory_fraction option is greater than 1.0, then unified
@@ -222,6 +185,48 @@ class GPUOptions(google.protobuf.message.Message):
gpu_host_mem_limit_in_mb, because the default GPU host memory limit is
quite high.
"""
@property
def virtual_devices(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___GPUOptions.Experimental.VirtualDevices]:
"""The multi virtual device settings. If empty (not set), it will create
single virtual device on each visible GPU, according to the settings
in "visible_device_list" above. Otherwise, the number of elements in the
list must be the same as the number of visible GPUs (after
"visible_device_list" filtering if it is set), and the string represented
device names (e.g. /device:GPU:<id>) will refer to the virtual
devices and have the <id> field assigned sequentially starting from 0,
according to the order of the virtual devices determined by
device_ordinal and the location in the virtual device list.
For example,
visible_device_list = "1,0"
virtual_devices { memory_limit: 1GB memory_limit: 2GB }
virtual_devices { memory_limit: 3GB memory_limit: 4GB }
will create 4 virtual devices as:
/device:GPU:0 -> visible GPU 1 with 1GB memory
/device:GPU:1 -> visible GPU 1 with 2GB memory
/device:GPU:2 -> visible GPU 0 with 3GB memory
/device:GPU:3 -> visible GPU 0 with 4GB memory
but
visible_device_list = "1,0"
virtual_devices { memory_limit: 1GB memory_limit: 2GB
device_ordinal: 10 device_ordinal: 20}
virtual_devices { memory_limit: 3GB memory_limit: 4GB
device_ordinal: 10 device_ordinal: 20}
will create 4 virtual devices as:
/device:GPU:0 -> visible GPU 1 with 1GB memory (ordinal 10)
/device:GPU:1 -> visible GPU 0 with 3GB memory (ordinal 10)
/device:GPU:2 -> visible GPU 1 with 2GB memory (ordinal 20)
/device:GPU:3 -> visible GPU 0 with 4GB memory (ordinal 20)
NOTE:
1. It's invalid to set both this and "per_process_gpu_memory_fraction"
at the same time.
2. Currently this setting is per-process, not per-session. Using
different settings in different sessions within same process will
result in undefined behavior.
"""
def __init__(
self,
*,
@@ -239,7 +244,7 @@ class GPUOptions(google.protobuf.message.Message):
gpu_host_mem_limit_in_mb: builtins.float | None = ...,
gpu_host_mem_disallow_growth: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["collective_ring_order", b"collective_ring_order", "disallow_retry_on_allocation_failure", b"disallow_retry_on_allocation_failure", "gpu_host_mem_disallow_growth", b"gpu_host_mem_disallow_growth", "gpu_host_mem_limit_in_mb", b"gpu_host_mem_limit_in_mb", "internal_fragmentation_fraction", b"internal_fragmentation_fraction", "kernel_tracker_max_bytes", b"kernel_tracker_max_bytes", "kernel_tracker_max_interval", b"kernel_tracker_max_interval", "kernel_tracker_max_pending", b"kernel_tracker_max_pending", "num_dev_to_dev_copy_streams", b"num_dev_to_dev_copy_streams", "timestamped_allocator", b"timestamped_allocator", "use_cuda_malloc_async", b"use_cuda_malloc_async", "use_unified_memory", b"use_unified_memory", "virtual_devices", b"virtual_devices"]) -> None: ...
def ClearField(self, field_name: typing.Literal["collective_ring_order", b"collective_ring_order", "disallow_retry_on_allocation_failure", b"disallow_retry_on_allocation_failure", "gpu_host_mem_disallow_growth", b"gpu_host_mem_disallow_growth", "gpu_host_mem_limit_in_mb", b"gpu_host_mem_limit_in_mb", "internal_fragmentation_fraction", b"internal_fragmentation_fraction", "kernel_tracker_max_bytes", b"kernel_tracker_max_bytes", "kernel_tracker_max_interval", b"kernel_tracker_max_interval", "kernel_tracker_max_pending", b"kernel_tracker_max_pending", "num_dev_to_dev_copy_streams", b"num_dev_to_dev_copy_streams", "timestamped_allocator", b"timestamped_allocator", "use_cuda_malloc_async", b"use_cuda_malloc_async", "use_unified_memory", b"use_unified_memory", "virtual_devices", b"virtual_devices"]) -> None: ...
PER_PROCESS_GPU_MEMORY_FRACTION_FIELD_NUMBER: builtins.int
ALLOW_GROWTH_FIELD_NUMBER: builtins.int
@@ -336,6 +341,7 @@ class GPUOptions(google.protobuf.message.Message):
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
"""
def __init__(
self,
*,
@@ -349,12 +355,12 @@ class GPUOptions(google.protobuf.message.Message):
force_gpu_compatible: builtins.bool | None = ...,
experimental: global___GPUOptions.Experimental | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["experimental", b"experimental"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["allocator_type", b"allocator_type", "allow_growth", b"allow_growth", "deferred_deletion_bytes", b"deferred_deletion_bytes", "experimental", b"experimental", "force_gpu_compatible", b"force_gpu_compatible", "per_process_gpu_memory_fraction", b"per_process_gpu_memory_fraction", "polling_active_delay_usecs", b"polling_active_delay_usecs", "polling_inactive_delay_msecs", b"polling_inactive_delay_msecs", "visible_device_list", b"visible_device_list"]) -> None: ...
def HasField(self, field_name: typing.Literal["experimental", b"experimental"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["allocator_type", b"allocator_type", "allow_growth", b"allow_growth", "deferred_deletion_bytes", b"deferred_deletion_bytes", "experimental", b"experimental", "force_gpu_compatible", b"force_gpu_compatible", "per_process_gpu_memory_fraction", b"per_process_gpu_memory_fraction", "polling_active_delay_usecs", b"polling_active_delay_usecs", "polling_inactive_delay_msecs", b"polling_inactive_delay_msecs", "visible_device_list", b"visible_device_list"]) -> None: ...
global___GPUOptions = GPUOptions
@typing_extensions.final
@typing.final
class OptimizerOptions(google.protobuf.message.Message):
"""Options passed to the graph optimizer"""
@@ -466,11 +472,11 @@ class OptimizerOptions(google.protobuf.message.Message):
global_jit_level: global___OptimizerOptions.GlobalJitLevel.ValueType | None = ...,
cpu_global_jit: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["cpu_global_jit", b"cpu_global_jit", "do_common_subexpression_elimination", b"do_common_subexpression_elimination", "do_constant_folding", b"do_constant_folding", "do_function_inlining", b"do_function_inlining", "global_jit_level", b"global_jit_level", "max_folded_constant_in_bytes", b"max_folded_constant_in_bytes", "opt_level", b"opt_level"]) -> None: ...
def ClearField(self, field_name: typing.Literal["cpu_global_jit", b"cpu_global_jit", "do_common_subexpression_elimination", b"do_common_subexpression_elimination", "do_constant_folding", b"do_constant_folding", "do_function_inlining", b"do_function_inlining", "global_jit_level", b"global_jit_level", "max_folded_constant_in_bytes", b"max_folded_constant_in_bytes", "opt_level", b"opt_level"]) -> None: ...
global___OptimizerOptions = OptimizerOptions
@typing_extensions.final
@typing.final
class GraphOptions(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -487,9 +493,6 @@ class GraphOptions(google.protobuf.message.Message):
"""If true, use control flow to schedule the activation of Recv nodes.
(Currently ignored.)
"""
@property
def optimizer_options(self) -> global___OptimizerOptions:
"""Options controlling how graph is optimized."""
build_cost_model: builtins.int
"""The number of steps to run before returning a cost model detailing
the memory usage and performance of each node of the graph. 0 means
@@ -518,12 +521,17 @@ class GraphOptions(google.protobuf.message.Message):
"""If > 0, record a timeline every this many steps.
EXPERIMENTAL: This currently has no effect in MasterSession.
"""
@property
def optimizer_options(self) -> global___OptimizerOptions:
"""Options controlling how graph is optimized."""
@property
def rewrite_options(self) -> tensorflow.core.protobuf.rewriter_config_pb2.RewriterConfig:
"""Options that control the type and amount of graph rewriting.
Not currently configurable via the public Python API (i.e. there is no API
stability guarantee if you import RewriterConfig explicitly).
"""
def __init__(
self,
*,
@@ -537,12 +545,12 @@ class GraphOptions(google.protobuf.message.Message):
timeline_step: builtins.int | None = ...,
rewrite_options: tensorflow.core.protobuf.rewriter_config_pb2.RewriterConfig | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["optimizer_options", b"optimizer_options", "rewrite_options", b"rewrite_options"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["build_cost_model", b"build_cost_model", "build_cost_model_after", b"build_cost_model_after", "enable_bfloat16_sendrecv", b"enable_bfloat16_sendrecv", "enable_recv_scheduling", b"enable_recv_scheduling", "infer_shapes", b"infer_shapes", "optimizer_options", b"optimizer_options", "place_pruned_graph", b"place_pruned_graph", "rewrite_options", b"rewrite_options", "timeline_step", b"timeline_step"]) -> None: ...
def HasField(self, field_name: typing.Literal["optimizer_options", b"optimizer_options", "rewrite_options", b"rewrite_options"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["build_cost_model", b"build_cost_model", "build_cost_model_after", b"build_cost_model_after", "enable_bfloat16_sendrecv", b"enable_bfloat16_sendrecv", "enable_recv_scheduling", b"enable_recv_scheduling", "infer_shapes", b"infer_shapes", "optimizer_options", b"optimizer_options", "place_pruned_graph", b"place_pruned_graph", "rewrite_options", b"rewrite_options", "timeline_step", b"timeline_step"]) -> None: ...
global___GraphOptions = GraphOptions
@typing_extensions.final
@typing.final
class ThreadPoolOptionProto(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -577,11 +585,11 @@ class ThreadPoolOptionProto(google.protobuf.message.Message):
num_threads: builtins.int | None = ...,
global_name: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["global_name", b"global_name", "num_threads", b"num_threads"]) -> None: ...
def ClearField(self, field_name: typing.Literal["global_name", b"global_name", "num_threads", b"num_threads"]) -> None: ...
global___ThreadPoolOptionProto = ThreadPoolOptionProto
@typing_extensions.final
@typing.final
class SessionMetadata(google.protobuf.message.Message):
"""Metadata about the session.
@@ -606,11 +614,11 @@ class SessionMetadata(google.protobuf.message.Message):
name: builtins.str | None = ...,
version: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "version", b"version"]) -> None: ...
def ClearField(self, field_name: typing.Literal["name", b"name", "version", b"version"]) -> None: ...
global___SessionMetadata = SessionMetadata
@typing_extensions.final
@typing.final
class ConfigProto(google.protobuf.message.Message):
"""Session configuration parameters.
The system picks appropriate values for fields that are not set.
@@ -618,7 +626,7 @@ class ConfigProto(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class DeviceCountEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -632,9 +640,9 @@ class ConfigProto(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class Experimental(google.protobuf.message.Message):
"""Everything inside Experimental is subject to change and is not subject
to API stability guarantees in
@@ -747,16 +755,6 @@ class ConfigProto(google.protobuf.message.Message):
"""This was promoted to a non-experimental API. Please use
ConfigProto.share_cluster_devices_in_session instead.
"""
@property
def session_metadata(self) -> global___SessionMetadata:
"""Metadata about the session.
If set, this can be used by the runtime and the Ops for debugging,
monitoring, etc.
NOTE: This is currently used and propagated only by the direct session
and EagerContext.
"""
optimize_for_static_graph: builtins.bool
"""If true, the session may treat the graph as being static for optimization
purposes.
@@ -812,9 +810,6 @@ class ConfigProto(google.protobuf.message.Message):
"""Provides a hint to XLA auto clustering to prefer forming a single large
cluster that encompases most of the graph.
"""
@property
def coordination_config(self) -> tensorflow.tsl.protobuf.coordination_config_pb2.CoordinationServiceConfig:
"""Distributed coordination service configurations."""
disable_optimize_for_static_graph: builtins.bool
"""If true, the session will treat the graph as being non-static for
optimization purposes.
@@ -826,6 +821,21 @@ class ConfigProto(google.protobuf.message.Message):
This option is meant to replace `optimize_for_static_graph` and it
aims to negate its value.
"""
@property
def session_metadata(self) -> global___SessionMetadata:
"""Metadata about the session.
If set, this can be used by the runtime and the Ops for debugging,
monitoring, etc.
NOTE: This is currently used and propagated only by the direct session
and EagerContext.
"""
@property
def coordination_config(self) -> tensorflow.tsl.protobuf.coordination_config_pb2.CoordinationServiceConfig:
"""Distributed coordination service configurations."""
def __init__(
self,
*,
@@ -851,8 +861,8 @@ class ConfigProto(google.protobuf.message.Message):
coordination_config: tensorflow.tsl.protobuf.coordination_config_pb2.CoordinationServiceConfig | None = ...,
disable_optimize_for_static_graph: builtins.bool | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["coordination_config", b"coordination_config", "session_metadata", b"session_metadata"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["collective_deterministic_sequential_execution", b"collective_deterministic_sequential_execution", "collective_group_leader", b"collective_group_leader", "collective_nccl", b"collective_nccl", "coordination_config", b"coordination_config", "disable_functional_ops_lowering", b"disable_functional_ops_lowering", "disable_optimize_for_static_graph", b"disable_optimize_for_static_graph", "disable_output_partition_graphs", b"disable_output_partition_graphs", "disable_thread_spinning", b"disable_thread_spinning", "enable_mlir_bridge", b"enable_mlir_bridge", "enable_mlir_graph_optimization", b"enable_mlir_graph_optimization", "executor_type", b"executor_type", "mlir_bridge_rollout", b"mlir_bridge_rollout", "optimize_for_static_graph", b"optimize_for_static_graph", "recv_buf_max_chunk", b"recv_buf_max_chunk", "session_metadata", b"session_metadata", "share_cluster_devices_in_session", b"share_cluster_devices_in_session", "share_session_state_in_clusterspec_propagation", b"share_session_state_in_clusterspec_propagation", "use_numa_affinity", b"use_numa_affinity", "use_tfrt", b"use_tfrt", "xla_fusion_autotuner_thresh", b"xla_fusion_autotuner_thresh", "xla_prefer_single_graph_cluster", b"xla_prefer_single_graph_cluster"]) -> None: ...
def HasField(self, field_name: typing.Literal["coordination_config", b"coordination_config", "session_metadata", b"session_metadata"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["collective_deterministic_sequential_execution", b"collective_deterministic_sequential_execution", "collective_group_leader", b"collective_group_leader", "collective_nccl", b"collective_nccl", "coordination_config", b"coordination_config", "disable_functional_ops_lowering", b"disable_functional_ops_lowering", "disable_optimize_for_static_graph", b"disable_optimize_for_static_graph", "disable_output_partition_graphs", b"disable_output_partition_graphs", "disable_thread_spinning", b"disable_thread_spinning", "enable_mlir_bridge", b"enable_mlir_bridge", "enable_mlir_graph_optimization", b"enable_mlir_graph_optimization", "executor_type", b"executor_type", "mlir_bridge_rollout", b"mlir_bridge_rollout", "optimize_for_static_graph", b"optimize_for_static_graph", "recv_buf_max_chunk", b"recv_buf_max_chunk", "session_metadata", b"session_metadata", "share_cluster_devices_in_session", b"share_cluster_devices_in_session", "share_session_state_in_clusterspec_propagation", b"share_session_state_in_clusterspec_propagation", "use_numa_affinity", b"use_numa_affinity", "use_tfrt", b"use_tfrt", "xla_fusion_autotuner_thresh", b"xla_fusion_autotuner_thresh", "xla_prefer_single_graph_cluster", b"xla_prefer_single_graph_cluster"]) -> None: ...
DEVICE_COUNT_FIELD_NUMBER: builtins.int
INTRA_OP_PARALLELISM_THREADS_FIELD_NUMBER: builtins.int
@@ -871,13 +881,6 @@ class ConfigProto(google.protobuf.message.Message):
ISOLATE_SESSION_STATE_FIELD_NUMBER: builtins.int
SHARE_CLUSTER_DEVICES_IN_SESSION_FIELD_NUMBER: builtins.int
EXPERIMENTAL_FIELD_NUMBER: builtins.int
@property
def device_count(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.int]:
"""Map from device type name (e.g., "CPU" or "GPU" ) to maximum
number of devices of that type to use. If a particular device
type is not found in the map, the system picks an appropriate
number.
"""
intra_op_parallelism_threads: builtins.int
"""The execution of an individual op (for some op types) can be
parallelized on a pool of intra_op_parallelism_threads.
@@ -916,6 +919,46 @@ class ConfigProto(google.protobuf.message.Message):
session_inter_op_thread_pool to have one element, whose num_threads equals
inter_op_parallelism_threads.
"""
placement_period: builtins.int
"""Assignment of Nodes to Devices is recomputed every placement_period
steps until the system warms up (at which point the recomputation
typically slows down automatically).
"""
allow_soft_placement: builtins.bool
"""Whether soft placement is allowed. If allow_soft_placement is true,
an op will be placed on CPU if
1. there's no GPU implementation for the OP
or
2. no GPU devices are known or registered
or
3. need to co-locate with reftype input(s) which are from CPU.
"""
log_device_placement: builtins.bool
"""Whether device placements should be logged."""
operation_timeout_in_ms: builtins.int
"""Global timeout for all blocking operations in this session. If non-zero,
and not overridden on a per-operation basis, this value will be used as the
deadline for all blocking operations.
"""
isolate_session_state: builtins.bool
"""If true, any resources such as Variables used in the session will not be
shared with other sessions. However, when clusterspec propagation is
enabled, this field is ignored and sessions are always isolated.
"""
share_cluster_devices_in_session: builtins.bool
"""When true, WorkerSessions are created with device attributes from the
full cluster.
This is helpful when a worker wants to partition a graph
(for example during a PartitionedCallOp).
"""
@property
def device_count(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.int]:
"""Map from device type name (e.g., "CPU" or "GPU" ) to maximum
number of devices of that type to use. If a particular device
type is not found in the map, the system picks an appropriate
number.
"""
@property
def session_inter_op_thread_pool(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___ThreadPoolOptionProto]:
"""This option is experimental - it may be replaced with a different mechanism
@@ -938,56 +981,30 @@ class ConfigProto(google.protobuf.message.Message):
run the non-low-priority work, even across sessions, in a single large
pool.
"""
placement_period: builtins.int
"""Assignment of Nodes to Devices is recomputed every placement_period
steps until the system warms up (at which point the recomputation
typically slows down automatically).
"""
@property
def device_filters(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""When any filters are present sessions will ignore all devices which do not
match the filters. Each filter can be partially specified, e.g. "/job:ps"
"/job:worker/replica:3", etc.
"""
@property
def gpu_options(self) -> global___GPUOptions:
"""Options that apply to all GPUs."""
allow_soft_placement: builtins.bool
"""Whether soft placement is allowed. If allow_soft_placement is true,
an op will be placed on CPU if
1. there's no GPU implementation for the OP
or
2. no GPU devices are known or registered
or
3. need to co-locate with reftype input(s) which are from CPU.
"""
log_device_placement: builtins.bool
"""Whether device placements should be logged."""
@property
def graph_options(self) -> global___GraphOptions:
"""Options that apply to all graphs."""
operation_timeout_in_ms: builtins.int
"""Global timeout for all blocking operations in this session. If non-zero,
and not overridden on a per-operation basis, this value will be used as the
deadline for all blocking operations.
"""
@property
def rpc_options(self) -> tensorflow.tsl.protobuf.rpc_options_pb2.RPCOptions:
"""Options that apply when this session uses the distributed runtime."""
@property
def cluster_def(self) -> tensorflow.core.protobuf.cluster_pb2.ClusterDef:
"""Optional list of all workers to use in this session."""
isolate_session_state: builtins.bool
"""If true, any resources such as Variables used in the session will not be
shared with other sessions. However, when clusterspec propagation is
enabled, this field is ignored and sessions are always isolated.
"""
share_cluster_devices_in_session: builtins.bool
"""When true, WorkerSessions are created with device attributes from the
full cluster.
This is helpful when a worker wants to partition a graph
(for example during a PartitionedCallOp).
"""
@property
def experimental(self) -> global___ConfigProto.Experimental: ...
def __init__(
@@ -1011,12 +1028,12 @@ class ConfigProto(google.protobuf.message.Message):
share_cluster_devices_in_session: builtins.bool | None = ...,
experimental: global___ConfigProto.Experimental | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["cluster_def", b"cluster_def", "experimental", b"experimental", "gpu_options", b"gpu_options", "graph_options", b"graph_options", "rpc_options", b"rpc_options"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["allow_soft_placement", b"allow_soft_placement", "cluster_def", b"cluster_def", "device_count", b"device_count", "device_filters", b"device_filters", "experimental", b"experimental", "gpu_options", b"gpu_options", "graph_options", b"graph_options", "inter_op_parallelism_threads", b"inter_op_parallelism_threads", "intra_op_parallelism_threads", b"intra_op_parallelism_threads", "isolate_session_state", b"isolate_session_state", "log_device_placement", b"log_device_placement", "operation_timeout_in_ms", b"operation_timeout_in_ms", "placement_period", b"placement_period", "rpc_options", b"rpc_options", "session_inter_op_thread_pool", b"session_inter_op_thread_pool", "share_cluster_devices_in_session", b"share_cluster_devices_in_session", "use_per_session_threads", b"use_per_session_threads"]) -> None: ...
def HasField(self, field_name: typing.Literal["cluster_def", b"cluster_def", "experimental", b"experimental", "gpu_options", b"gpu_options", "graph_options", b"graph_options", "rpc_options", b"rpc_options"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["allow_soft_placement", b"allow_soft_placement", "cluster_def", b"cluster_def", "device_count", b"device_count", "device_filters", b"device_filters", "experimental", b"experimental", "gpu_options", b"gpu_options", "graph_options", b"graph_options", "inter_op_parallelism_threads", b"inter_op_parallelism_threads", "intra_op_parallelism_threads", b"intra_op_parallelism_threads", "isolate_session_state", b"isolate_session_state", "log_device_placement", b"log_device_placement", "operation_timeout_in_ms", b"operation_timeout_in_ms", "placement_period", b"placement_period", "rpc_options", b"rpc_options", "session_inter_op_thread_pool", b"session_inter_op_thread_pool", "share_cluster_devices_in_session", b"share_cluster_devices_in_session", "use_per_session_threads", b"use_per_session_threads"]) -> None: ...
global___ConfigProto = ConfigProto
@typing_extensions.final
@typing.final
class RunOptions(google.protobuf.message.Message):
"""Options for a single Run() call."""
@@ -1043,7 +1060,7 @@ class RunOptions(google.protobuf.message.Message):
HARDWARE_TRACE: RunOptions.TraceLevel.ValueType # 2
FULL_TRACE: RunOptions.TraceLevel.ValueType # 3
@typing_extensions.final
@typing.final
class Experimental(google.protobuf.message.Message):
"""Everything inside Experimental is subject to change and is not subject
to API stability guarantees in
@@ -1052,7 +1069,7 @@ class RunOptions(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class RunHandlerPoolOptions(google.protobuf.message.Message):
"""Options for run handler thread pool."""
@@ -1068,7 +1085,7 @@ class RunOptions(google.protobuf.message.Message):
*,
priority: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["priority", b"priority"]) -> None: ...
def ClearField(self, field_name: typing.Literal["priority", b"priority"]) -> None: ...
COLLECTIVE_GRAPH_KEY_FIELD_NUMBER: builtins.int
USE_RUN_HANDLER_POOL_FIELD_NUMBER: builtins.int
@@ -1094,8 +1111,8 @@ class RunOptions(google.protobuf.message.Message):
use_run_handler_pool: builtins.bool | None = ...,
run_handler_pool_options: global___RunOptions.Experimental.RunHandlerPoolOptions | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["run_handler_pool_options", b"run_handler_pool_options"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["collective_graph_key", b"collective_graph_key", "run_handler_pool_options", b"run_handler_pool_options", "use_run_handler_pool", b"use_run_handler_pool"]) -> None: ...
def HasField(self, field_name: typing.Literal["run_handler_pool_options", b"run_handler_pool_options"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["collective_graph_key", b"collective_graph_key", "run_handler_pool_options", b"run_handler_pool_options", "use_run_handler_pool", b"use_run_handler_pool"]) -> None: ...
TRACE_LEVEL_FIELD_NUMBER: builtins.int
TIMEOUT_IN_MS_FIELD_NUMBER: builtins.int
@@ -1119,9 +1136,6 @@ class RunOptions(google.protobuf.message.Message):
"""Whether the partition graph(s) executed by the executor(s) should be
outputted via RunMetadata.
"""
@property
def debug_options(self) -> tensorflow.core.protobuf.debug_pb2.DebugOptions:
"""EXPERIMENTAL. Options used to initialize DebuggerState, if enabled."""
report_tensor_allocations_upon_oom: builtins.bool
"""When enabled, causes tensor allocation information to be included in
the error message when the Run() call fails because the allocator ran
@@ -1129,6 +1143,10 @@ class RunOptions(google.protobuf.message.Message):
Enabling this option can slow down the Run() call.
"""
@property
def debug_options(self) -> tensorflow.core.protobuf.debug_pb2.DebugOptions:
"""EXPERIMENTAL. Options used to initialize DebuggerState, if enabled."""
@property
def experimental(self) -> global___RunOptions.Experimental: ...
def __init__(
@@ -1142,18 +1160,18 @@ class RunOptions(google.protobuf.message.Message):
report_tensor_allocations_upon_oom: builtins.bool | None = ...,
experimental: global___RunOptions.Experimental | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["debug_options", b"debug_options", "experimental", b"experimental"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["debug_options", b"debug_options", "experimental", b"experimental", "inter_op_thread_pool", b"inter_op_thread_pool", "output_partition_graphs", b"output_partition_graphs", "report_tensor_allocations_upon_oom", b"report_tensor_allocations_upon_oom", "timeout_in_ms", b"timeout_in_ms", "trace_level", b"trace_level"]) -> None: ...
def HasField(self, field_name: typing.Literal["debug_options", b"debug_options", "experimental", b"experimental"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["debug_options", b"debug_options", "experimental", b"experimental", "inter_op_thread_pool", b"inter_op_thread_pool", "output_partition_graphs", b"output_partition_graphs", "report_tensor_allocations_upon_oom", b"report_tensor_allocations_upon_oom", "timeout_in_ms", b"timeout_in_ms", "trace_level", b"trace_level"]) -> None: ...
global___RunOptions = RunOptions
@typing_extensions.final
@typing.final
class RunMetadata(google.protobuf.message.Message):
"""Metadata output (i.e., non-Tensor) for a single Run() call."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class FunctionGraphs(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -1163,6 +1181,7 @@ class RunMetadata(google.protobuf.message.Message):
@property
def partition_graphs(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.graph_pb2.GraphDef]:
"""TODO(nareshmodi): Include some sort of function/cache-key identifier?"""
@property
def pre_optimization_graph(self) -> tensorflow.core.framework.graph_pb2.GraphDef: ...
@property
@@ -1174,8 +1193,8 @@ class RunMetadata(google.protobuf.message.Message):
pre_optimization_graph: tensorflow.core.framework.graph_pb2.GraphDef | None = ...,
post_optimization_graph: tensorflow.core.framework.graph_pb2.GraphDef | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["post_optimization_graph", b"post_optimization_graph", "pre_optimization_graph", b"pre_optimization_graph"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["partition_graphs", b"partition_graphs", "post_optimization_graph", b"post_optimization_graph", "pre_optimization_graph", b"pre_optimization_graph"]) -> None: ...
def HasField(self, field_name: typing.Literal["post_optimization_graph", b"post_optimization_graph", "pre_optimization_graph", b"pre_optimization_graph"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["partition_graphs", b"partition_graphs", "post_optimization_graph", b"post_optimization_graph", "pre_optimization_graph", b"pre_optimization_graph"]) -> None: ...
STEP_STATS_FIELD_NUMBER: builtins.int
COST_GRAPH_FIELD_NUMBER: builtins.int
@@ -1188,12 +1207,15 @@ class RunMetadata(google.protobuf.message.Message):
"RunOptions" proto.
EXPERIMENTAL: The format and set of events may change in future versions.
"""
@property
def cost_graph(self) -> tensorflow.core.framework.cost_graph_pb2.CostGraphDef:
"""The cost graph for the computation defined by the run call."""
@property
def partition_graphs(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.graph_pb2.GraphDef]:
"""Graphs of the partitions executed by executors."""
@property
def function_graphs(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___RunMetadata.FunctionGraphs]:
"""This is only populated for graphs that are run as functions in TensorFlow
@@ -1207,9 +1229,11 @@ class RunMetadata(google.protobuf.message.Message):
level idea of what the built graph looks like (since the various graph
optimization passes might change the structure of the graph significantly).
"""
@property
def session_metadata(self) -> global___SessionMetadata:
"""Metadata about the session."""
def __init__(
self,
*,
@@ -1219,12 +1243,12 @@ class RunMetadata(google.protobuf.message.Message):
function_graphs: collections.abc.Iterable[global___RunMetadata.FunctionGraphs] | None = ...,
session_metadata: global___SessionMetadata | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["cost_graph", b"cost_graph", "session_metadata", b"session_metadata", "step_stats", b"step_stats"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["cost_graph", b"cost_graph", "function_graphs", b"function_graphs", "partition_graphs", b"partition_graphs", "session_metadata", b"session_metadata", "step_stats", b"step_stats"]) -> None: ...
def HasField(self, field_name: typing.Literal["cost_graph", b"cost_graph", "session_metadata", b"session_metadata", "step_stats", b"step_stats"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["cost_graph", b"cost_graph", "function_graphs", b"function_graphs", "partition_graphs", b"partition_graphs", "session_metadata", b"session_metadata", "step_stats", b"step_stats"]) -> None: ...
global___RunMetadata = RunMetadata
@typing_extensions.final
@typing.final
class TensorConnection(google.protobuf.message.Message):
"""Defines a connection between two tensors in a `GraphDef`."""
@@ -1246,11 +1270,11 @@ class TensorConnection(google.protobuf.message.Message):
from_tensor: builtins.str | None = ...,
to_tensor: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["from_tensor", b"from_tensor", "to_tensor", b"to_tensor"]) -> None: ...
def ClearField(self, field_name: typing.Literal["from_tensor", b"from_tensor", "to_tensor", b"to_tensor"]) -> None: ...
global___TensorConnection = TensorConnection
@typing_extensions.final
@typing.final
class CallableOptions(google.protobuf.message.Message):
"""Defines a subgraph in another `GraphDef` as a set of feed points and nodes
to be fetched or executed.
@@ -1260,7 +1284,7 @@ class CallableOptions(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class FeedDevicesEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -1274,9 +1298,9 @@ class CallableOptions(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class FetchDevicesEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -1290,7 +1314,7 @@ class CallableOptions(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
FEED_FIELD_NUMBER: builtins.int
FETCH_FIELD_NUMBER: builtins.int
@@ -1300,29 +1324,46 @@ class CallableOptions(google.protobuf.message.Message):
FEED_DEVICES_FIELD_NUMBER: builtins.int
FETCH_DEVICES_FIELD_NUMBER: builtins.int
FETCH_SKIP_SYNC_FIELD_NUMBER: builtins.int
fetch_skip_sync: builtins.bool
"""By default, RunCallable() will synchronize the GPU stream before returning
fetched tensors on a GPU device, to ensure that the values in those tensors
have been produced. This simplifies interacting with the tensors, but
potentially incurs a performance hit.
If this options is set to true, the caller is responsible for ensuring
that the values in the fetched tensors have been produced before they are
used. The caller can do this by invoking `Device::Sync()` on the underlying
device(s), or by feeding the tensors back to the same Session using
`feed_devices` with the same corresponding device name.
"""
@property
def feed(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Tensors to be fed in the callable. Each feed is the name of a tensor."""
@property
def fetch(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Fetches. A list of tensor names. The caller of the callable expects a
tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
order of specified fetches does not change the execution order.
"""
@property
def target(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Target Nodes. A list of node names. The named nodes will be run by the
callable but their outputs will not be returned.
"""
@property
def run_options(self) -> global___RunOptions:
"""Options that will be applied to each run."""
@property
def tensor_connection(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TensorConnection]:
"""Tensors to be connected in the callable. Each TensorConnection denotes
a pair of tensors in the graph, between which an edge will be created
in the callable.
"""
@property
def feed_devices(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]:
"""The Tensor objects fed in the callable and fetched from the callable
@@ -1373,20 +1414,9 @@ class CallableOptions(google.protobuf.message.Message):
CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
cuStreamSynchronize()).
"""
@property
def fetch_devices(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ...
fetch_skip_sync: builtins.bool
"""By default, RunCallable() will synchronize the GPU stream before returning
fetched tensors on a GPU device, to ensure that the values in those tensors
have been produced. This simplifies interacting with the tensors, but
potentially incurs a performance hit.
If this options is set to true, the caller is responsible for ensuring
that the values in the fetched tensors have been produced before they are
used. The caller can do this by invoking `Device::Sync()` on the underlying
device(s), or by feeding the tensors back to the same Session using
`feed_devices` with the same corresponding device name.
"""
def __init__(
self,
*,
@@ -1399,7 +1429,7 @@ class CallableOptions(google.protobuf.message.Message):
fetch_devices: collections.abc.Mapping[builtins.str, builtins.str] | None = ...,
fetch_skip_sync: builtins.bool | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["run_options", b"run_options"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["feed", b"feed", "feed_devices", b"feed_devices", "fetch", b"fetch", "fetch_devices", b"fetch_devices", "fetch_skip_sync", b"fetch_skip_sync", "run_options", b"run_options", "target", b"target", "tensor_connection", b"tensor_connection"]) -> None: ...
def HasField(self, field_name: typing.Literal["run_options", b"run_options"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["feed", b"feed", "feed_devices", b"feed_devices", "fetch", b"fetch", "fetch_devices", b"fetch_devices", "fetch_skip_sync", b"fetch_skip_sync", "run_options", b"run_options", "target", b"target", "tensor_connection", b"tensor_connection"]) -> None: ...
global___CallableOptions = CallableOptions
stubs/tensorflow/tensorflow/core/protobuf/control_flow_pb2.pyi
+33 -24
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,7 +13,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class ValuesDef(google.protobuf.message.Message):
"""Control flow context related protocol buffers.
@@ -21,7 +22,7 @@ class ValuesDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class ExternalValuesEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -35,27 +36,29 @@ class ValuesDef(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
VALUES_FIELD_NUMBER: builtins.int
EXTERNAL_VALUES_FIELD_NUMBER: builtins.int
@property
def values(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Value names that have been seen in this context."""
@property
def external_values(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]:
"""Value names referenced by but external to this context."""
def __init__(
self,
*,
values: collections.abc.Iterable[builtins.str] | None = ...,
external_values: collections.abc.Mapping[builtins.str, builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["external_values", b"external_values", "values", b"values"]) -> None: ...
def ClearField(self, field_name: typing.Literal["external_values", b"external_values", "values", b"values"]) -> None: ...
global___ValuesDef = ValuesDef
@typing_extensions.final
@typing.final
class ControlFlowContextDef(google.protobuf.message.Message):
"""Container for any kind of control flow context. Any other control flow
contexts that are added below should also be added here.
@@ -75,13 +78,13 @@ class ControlFlowContextDef(google.protobuf.message.Message):
cond_ctxt: global___CondContextDef | None = ...,
while_ctxt: global___WhileContextDef | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["cond_ctxt", b"cond_ctxt", "ctxt", b"ctxt", "while_ctxt", b"while_ctxt"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["cond_ctxt", b"cond_ctxt", "ctxt", b"ctxt", "while_ctxt", b"while_ctxt"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["ctxt", b"ctxt"]) -> typing_extensions.Literal["cond_ctxt", "while_ctxt"] | None: ...
def HasField(self, field_name: typing.Literal["cond_ctxt", b"cond_ctxt", "ctxt", b"ctxt", "while_ctxt", b"while_ctxt"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["cond_ctxt", b"cond_ctxt", "ctxt", b"ctxt", "while_ctxt", b"while_ctxt"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["ctxt", b"ctxt"]) -> typing.Literal["cond_ctxt", "while_ctxt"] | None: ...
global___ControlFlowContextDef = ControlFlowContextDef
@typing_extensions.final
@typing.final
class CondContextDef(google.protobuf.message.Message):
"""Protocol buffer representing a CondContext object."""
@@ -104,9 +107,11 @@ class CondContextDef(google.protobuf.message.Message):
@property
def values_def(self) -> global___ValuesDef:
"""Values and external values in control flow context."""
@property
def nested_contexts(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___ControlFlowContextDef]:
"""Contexts contained inside this context (e.g. nested conds)."""
def __init__(
self,
*,
@@ -117,12 +122,12 @@ class CondContextDef(google.protobuf.message.Message):
values_def: global___ValuesDef | None = ...,
nested_contexts: collections.abc.Iterable[global___ControlFlowContextDef] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["values_def", b"values_def"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["branch", b"branch", "context_name", b"context_name", "nested_contexts", b"nested_contexts", "pivot_name", b"pivot_name", "pred_name", b"pred_name", "values_def", b"values_def"]) -> None: ...
def HasField(self, field_name: typing.Literal["values_def", b"values_def"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["branch", b"branch", "context_name", b"context_name", "nested_contexts", b"nested_contexts", "pivot_name", b"pivot_name", "pred_name", b"pred_name", "values_def", b"values_def"]) -> None: ...
global___CondContextDef = CondContextDef
@typing_extensions.final
@typing.final
class WhileContextDef(google.protobuf.message.Message):
"""Protocol buffer representing a WhileContext object."""
@@ -154,20 +159,24 @@ class WhileContextDef(google.protobuf.message.Message):
"""Name of the pivot_for_pred tensor."""
pivot_for_body_name: builtins.str
"""Name of the pivot_for_body tensor."""
@property
def loop_exit_names(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""List of names for exit tensors."""
@property
def loop_enter_names(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""List of names for enter tensors."""
@property
def values_def(self) -> global___ValuesDef:
"""Values and external values in control flow context."""
maximum_iterations_name: builtins.str
"""Optional name of the maximum_iterations tensor."""
@property
def loop_exit_names(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""List of names for exit tensors."""
@property
def loop_enter_names(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""List of names for enter tensors."""
@property
def values_def(self) -> global___ValuesDef:
"""Values and external values in control flow context."""
@property
def nested_contexts(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___ControlFlowContextDef]:
"""Contexts contained inside this context (e.g. nested whiles)."""
def __init__(
self,
*,
@@ -184,7 +193,7 @@ class WhileContextDef(google.protobuf.message.Message):
maximum_iterations_name: builtins.str | None = ...,
nested_contexts: collections.abc.Iterable[global___ControlFlowContextDef] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["values_def", b"values_def"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["back_prop", b"back_prop", "context_name", b"context_name", "loop_enter_names", b"loop_enter_names", "loop_exit_names", b"loop_exit_names", "maximum_iterations_name", b"maximum_iterations_name", "nested_contexts", b"nested_contexts", "parallel_iterations", b"parallel_iterations", "pivot_for_body_name", b"pivot_for_body_name", "pivot_for_pred_name", b"pivot_for_pred_name", "pivot_name", b"pivot_name", "swap_memory", b"swap_memory", "values_def", b"values_def"]) -> None: ...
def HasField(self, field_name: typing.Literal["values_def", b"values_def"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["back_prop", b"back_prop", "context_name", b"context_name", "loop_enter_names", b"loop_enter_names", "loop_exit_names", b"loop_exit_names", "maximum_iterations_name", b"maximum_iterations_name", "nested_contexts", b"nested_contexts", "parallel_iterations", b"parallel_iterations", "pivot_for_body_name", b"pivot_for_body_name", "pivot_for_pred_name", b"pivot_for_pred_name", "pivot_name", b"pivot_name", "swap_memory", b"swap_memory", "values_def", b"values_def"]) -> None: ...
global___WhileContextDef = WhileContextDef
stubs/tensorflow/tensorflow/core/protobuf/coordination_config_pb2.pyi
-108
View File
@@ -1,108 +0,0 @@
"""
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import google.protobuf.descriptor
import google.protobuf.internal.containers
import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
class CoordinatedJob(google.protobuf.message.Message):
"""Represents a job type and the number of tasks under this job.
For example, ("worker", 20) implies that there will be 20 worker tasks.
"""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
NAME_FIELD_NUMBER: builtins.int
NUM_TASKS_FIELD_NUMBER: builtins.int
name: builtins.str
num_tasks: builtins.int
def __init__(
self,
*,
name: builtins.str | None = ...,
num_tasks: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "num_tasks", b"num_tasks"]) -> None: ...
global___CoordinatedJob = CoordinatedJob
@typing_extensions.final
class CoordinationServiceConfig(google.protobuf.message.Message):
"""Coordination service configuration parameters.
The system picks appropriate values for fields that are not set.
"""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
SERVICE_TYPE_FIELD_NUMBER: builtins.int
SERVICE_LEADER_FIELD_NUMBER: builtins.int
ENABLE_HEALTH_CHECK_FIELD_NUMBER: builtins.int
CLUSTER_REGISTER_TIMEOUT_IN_MS_FIELD_NUMBER: builtins.int
HEARTBEAT_TIMEOUT_IN_MS_FIELD_NUMBER: builtins.int
COORDINATED_JOB_LIST_FIELD_NUMBER: builtins.int
SHUTDOWN_BARRIER_TIMEOUT_IN_MS_FIELD_NUMBER: builtins.int
AGENT_DESTRUCTION_WITHOUT_SHUTDOWN_FIELD_NUMBER: builtins.int
RECOVERABLE_JOBS_FIELD_NUMBER: builtins.int
service_type: builtins.str
"""Type of coordination service implementation to enable.
For example, setting the service type as "standalone" starts a service
instance on the leader task to provide the coordination services such as
heartbeats and consistent key-value store.
"""
service_leader: builtins.str
"""Address where the coordination service instance is hosted."""
enable_health_check: builtins.bool
"""Whether to enable the health check mechanism."""
cluster_register_timeout_in_ms: builtins.int
"""Maximum wait time for all members in the cluster to be registered."""
heartbeat_timeout_in_ms: builtins.int
"""Heartbeat timeout, if a task does not record heartbeat in this time
window, it will be considered disconnected.
Note: This is also used as a grace period to accept any heartbeats after
the agent has disconnected, to account for the lag time between the service
recording the state change and the agent stopping heartbeats.
"""
@property
def coordinated_job_list(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___CoordinatedJob]: ...
shutdown_barrier_timeout_in_ms: builtins.int
"""Denotes how long to wait for all coordination agents to reach the barriers
(after the first shutdown request) before disconnecting together. If
set to 0, no barrier is imposed upon shutdown and each worker can
disconnect individually.
"""
agent_destruction_without_shutdown: builtins.bool
"""If set, agents do not make an explicit Shutdown() call. Service will only
find out about the disconnecte agent via stale heartbeats. Used for
testing.
"""
@property
def recoverable_jobs(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""The list of jobs which are recoverable. If a task in this list fails,
it will not propagate error to other tasks.
If empty, no jobs will be recoverable and every task failure will cause
error propagation to other tasks.
"""
def __init__(
self,
*,
service_type: builtins.str | None = ...,
service_leader: builtins.str | None = ...,
enable_health_check: builtins.bool | None = ...,
cluster_register_timeout_in_ms: builtins.int | None = ...,
heartbeat_timeout_in_ms: builtins.int | None = ...,
coordinated_job_list: collections.abc.Iterable[global___CoordinatedJob] | None = ...,
shutdown_barrier_timeout_in_ms: builtins.int | None = ...,
agent_destruction_without_shutdown: builtins.bool | None = ...,
recoverable_jobs: collections.abc.Iterable[builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["agent_destruction_without_shutdown", b"agent_destruction_without_shutdown", "cluster_register_timeout_in_ms", b"cluster_register_timeout_in_ms", "coordinated_job_list", b"coordinated_job_list", "enable_health_check", b"enable_health_check", "heartbeat_timeout_in_ms", b"heartbeat_timeout_in_ms", "recoverable_jobs", b"recoverable_jobs", "service_leader", b"service_leader", "service_type", b"service_type", "shutdown_barrier_timeout_in_ms", b"shutdown_barrier_timeout_in_ms"]) -> None: ...
global___CoordinationServiceConfig = CoordinationServiceConfig
stubs/tensorflow/tensorflow/core/protobuf/core_platform_payloads_pb2.pyi
+3 -2
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import sys
import typing
@@ -17,7 +18,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class ErrorSourceProto(google.protobuf.message.Message):
"""If included as a payload, this message contains the error source information
where the error was raised.
@@ -62,6 +63,6 @@ class ErrorSourceProto(google.protobuf.message.Message):
*,
error_source: global___ErrorSourceProto.ErrorSource.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["error_source", b"error_source"]) -> None: ...
def ClearField(self, field_name: typing.Literal["error_source", b"error_source"]) -> None: ...
global___ErrorSourceProto = ErrorSourceProto
stubs/tensorflow/tensorflow/core/protobuf/data_service_pb2.pyi
+11 -10
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import sys
import typing
@@ -47,7 +48,7 @@ hosts.
"""
global___DeploymentMode = DeploymentMode
@typing_extensions.final
@typing.final
class ProcessingModeDef(google.protobuf.message.Message):
"""Next tag: 2"""
@@ -145,11 +146,11 @@ class ProcessingModeDef(google.protobuf.message.Message):
*,
sharding_policy: global___ProcessingModeDef.ShardingPolicy.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["sharding_policy", b"sharding_policy"]) -> None: ...
def ClearField(self, field_name: typing.Literal["sharding_policy", b"sharding_policy"]) -> None: ...
global___ProcessingModeDef = ProcessingModeDef
@typing_extensions.final
@typing.final
class DataServiceMetadata(google.protobuf.message.Message):
"""Metadata related to tf.data service datasets.
Next tag: 4
@@ -191,13 +192,13 @@ class DataServiceMetadata(google.protobuf.message.Message):
compression: global___DataServiceMetadata.Compression.ValueType | None = ...,
cardinality: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["element_spec", b"element_spec", "optional_element_spec", b"optional_element_spec"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["cardinality", b"cardinality", "compression", b"compression", "element_spec", b"element_spec", "optional_element_spec", b"optional_element_spec"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_element_spec", b"optional_element_spec"]) -> typing_extensions.Literal["element_spec"] | None: ...
def HasField(self, field_name: typing.Literal["element_spec", b"element_spec", "optional_element_spec", b"optional_element_spec"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["cardinality", b"cardinality", "compression", b"compression", "element_spec", b"element_spec", "optional_element_spec", b"optional_element_spec"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_element_spec", b"optional_element_spec"]) -> typing.Literal["element_spec"] | None: ...
global___DataServiceMetadata = DataServiceMetadata
@typing_extensions.final
@typing.final
class CrossTrainerCacheOptions(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -208,11 +209,11 @@ class CrossTrainerCacheOptions(google.protobuf.message.Message):
*,
trainer_id: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["trainer_id", b"trainer_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["trainer_id", b"trainer_id"]) -> None: ...
global___CrossTrainerCacheOptions = CrossTrainerCacheOptions
@typing_extensions.final
@typing.final
class DataServiceConfig(google.protobuf.message.Message):
"""Data service config available to the client through GetDataServiceConfig RPC.
Next tag: 2
@@ -227,6 +228,6 @@ class DataServiceConfig(google.protobuf.message.Message):
*,
deployment_mode: global___DeploymentMode.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["deployment_mode", b"deployment_mode"]) -> None: ...
def ClearField(self, field_name: typing.Literal["deployment_mode", b"deployment_mode"]) -> None: ...
global___DataServiceConfig = DataServiceConfig
stubs/tensorflow/tensorflow/core/protobuf/debug_event_pb2.pyi
+87 -65
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -130,7 +131,7 @@ REDUCE_INF_NAN_THREE_SLOTS: TensorDebugMode.ValueType # 8
"""
global___TensorDebugMode = TensorDebugMode
@typing_extensions.final
@typing.final
class DebugEvent(google.protobuf.message.Message):
"""An Event related to the debugging of a TensorFlow program."""
@@ -151,40 +152,48 @@ class DebugEvent(google.protobuf.message.Message):
"""Timestamp in seconds (with microsecond precision)."""
step: builtins.int
"""Step of training (if available)."""
@property
def debug_metadata(self) -> global___DebugMetadata:
"""Metadata related to this debugging data."""
@property
def source_file(self) -> global___SourceFile:
"""The content of a source file."""
@property
def stack_frame_with_id(self) -> global___StackFrameWithId:
"""A stack frame (filename, line number and column number, function name and
code string) with ID.
"""
@property
def graph_op_creation(self) -> global___GraphOpCreation:
"""The creation of an op within a graph (e.g., a FuncGraph compiled from
a Python function).
"""
@property
def debugged_graph(self) -> global___DebuggedGraph:
"""Information about a debugged graph."""
@property
def execution(self) -> global___Execution:
"""Execution of an op or a Graph (e.g., a tf.function)."""
@property
def graph_execution_trace(self) -> global___GraphExecutionTrace:
"""A graph execution trace: Contains information about the intermediate
tensors computed during the graph execution.
"""
graph_id: builtins.str
"""The ID of the graph (i.e., FuncGraph) executed here: applicable only
to the execution of a FuncGraph.
"""
@property
def debug_metadata(self) -> global___DebugMetadata:
"""Metadata related to this debugging data."""
@property
def source_file(self) -> global___SourceFile:
"""The content of a source file."""
@property
def stack_frame_with_id(self) -> global___StackFrameWithId:
"""A stack frame (filename, line number and column number, function name and
code string) with ID.
"""
@property
def graph_op_creation(self) -> global___GraphOpCreation:
"""The creation of an op within a graph (e.g., a FuncGraph compiled from
a Python function).
"""
@property
def debugged_graph(self) -> global___DebuggedGraph:
"""Information about a debugged graph."""
@property
def execution(self) -> global___Execution:
"""Execution of an op or a Graph (e.g., a tf.function)."""
@property
def graph_execution_trace(self) -> global___GraphExecutionTrace:
"""A graph execution trace: Contains information about the intermediate
tensors computed during the graph execution.
"""
@property
def debugged_device(self) -> global___DebuggedDevice:
"""A device on which debugger-instrumented ops and/or tensors reside."""
def __init__(
self,
*,
@@ -200,13 +209,13 @@ class DebugEvent(google.protobuf.message.Message):
graph_id: builtins.str | None = ...,
debugged_device: global___DebuggedDevice | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["debug_metadata", b"debug_metadata", "debugged_device", b"debugged_device", "debugged_graph", b"debugged_graph", "execution", b"execution", "graph_execution_trace", b"graph_execution_trace", "graph_id", b"graph_id", "graph_op_creation", b"graph_op_creation", "source_file", b"source_file", "stack_frame_with_id", b"stack_frame_with_id", "what", b"what"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["debug_metadata", b"debug_metadata", "debugged_device", b"debugged_device", "debugged_graph", b"debugged_graph", "execution", b"execution", "graph_execution_trace", b"graph_execution_trace", "graph_id", b"graph_id", "graph_op_creation", b"graph_op_creation", "source_file", b"source_file", "stack_frame_with_id", b"stack_frame_with_id", "step", b"step", "wall_time", b"wall_time", "what", b"what"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["what", b"what"]) -> typing_extensions.Literal["debug_metadata", "source_file", "stack_frame_with_id", "graph_op_creation", "debugged_graph", "execution", "graph_execution_trace", "graph_id", "debugged_device"] | None: ...
def HasField(self, field_name: typing.Literal["debug_metadata", b"debug_metadata", "debugged_device", b"debugged_device", "debugged_graph", b"debugged_graph", "execution", b"execution", "graph_execution_trace", b"graph_execution_trace", "graph_id", b"graph_id", "graph_op_creation", b"graph_op_creation", "source_file", b"source_file", "stack_frame_with_id", b"stack_frame_with_id", "what", b"what"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["debug_metadata", b"debug_metadata", "debugged_device", b"debugged_device", "debugged_graph", b"debugged_graph", "execution", b"execution", "graph_execution_trace", b"graph_execution_trace", "graph_id", b"graph_id", "graph_op_creation", b"graph_op_creation", "source_file", b"source_file", "stack_frame_with_id", b"stack_frame_with_id", "step", b"step", "wall_time", b"wall_time", "what", b"what"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["what", b"what"]) -> typing.Literal["debug_metadata", "source_file", "stack_frame_with_id", "graph_op_creation", "debugged_graph", "execution", "graph_execution_trace", "graph_id", "debugged_device"] | None: ...
global___DebugEvent = DebugEvent
@typing_extensions.final
@typing.final
class DebugMetadata(google.protobuf.message.Message):
"""Metadata about the debugger and the debugged TensorFlow program."""
@@ -234,11 +243,11 @@ class DebugMetadata(google.protobuf.message.Message):
file_version: builtins.str | None = ...,
tfdbg_run_id: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["file_version", b"file_version", "tensorflow_version", b"tensorflow_version", "tfdbg_run_id", b"tfdbg_run_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["file_version", b"file_version", "tensorflow_version", b"tensorflow_version", "tfdbg_run_id", b"tfdbg_run_id"]) -> None: ...
global___DebugMetadata = DebugMetadata
@typing_extensions.final
@typing.final
class SourceFile(google.protobuf.message.Message):
"""Content of a source file involved in the execution of the debugged TensorFlow
program.
@@ -256,6 +265,7 @@ class SourceFile(google.protobuf.message.Message):
@property
def lines(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Line-by-line content of the file."""
def __init__(
self,
*,
@@ -263,11 +273,11 @@ class SourceFile(google.protobuf.message.Message):
host_name: builtins.str | None = ...,
lines: collections.abc.Iterable[builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["file_path", b"file_path", "host_name", b"host_name", "lines", b"lines"]) -> None: ...
def ClearField(self, field_name: typing.Literal["file_path", b"file_path", "host_name", b"host_name", "lines", b"lines"]) -> None: ...
global___SourceFile = SourceFile
@typing_extensions.final
@typing.final
class StackFrameWithId(google.protobuf.message.Message):
"""A stack frame with ID."""
@@ -283,18 +293,19 @@ class StackFrameWithId(google.protobuf.message.Message):
regarding the file name, line number, function name, code content
of the line, and column number (if available).
"""
def __init__(
self,
*,
id: builtins.str | None = ...,
file_line_col: tensorflow.core.protobuf.graph_debug_info_pb2.GraphDebugInfo.FileLineCol | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["file_line_col", b"file_line_col"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["file_line_col", b"file_line_col", "id", b"id"]) -> None: ...
def HasField(self, field_name: typing.Literal["file_line_col", b"file_line_col"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["file_line_col", b"file_line_col", "id", b"id"]) -> None: ...
global___StackFrameWithId = StackFrameWithId
@typing_extensions.final
@typing.final
class CodeLocation(google.protobuf.message.Message):
"""Code location information: A stack trace with host-name information.
Instead of encoding the detailed stack trace, this proto refers to IDs of
@@ -313,17 +324,18 @@ class CodeLocation(google.protobuf.message.Message):
by a unique ID. The ordering of the frames is consistent with Python's
`traceback.extract_tb()`.
"""
def __init__(
self,
*,
host_name: builtins.str | None = ...,
stack_frame_ids: collections.abc.Iterable[builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["host_name", b"host_name", "stack_frame_ids", b"stack_frame_ids"]) -> None: ...
def ClearField(self, field_name: typing.Literal["host_name", b"host_name", "stack_frame_ids", b"stack_frame_ids"]) -> None: ...
global___CodeLocation = CodeLocation
@typing_extensions.final
@typing.final
class GraphOpCreation(google.protobuf.message.Message):
"""The creation of an op in a TensorFlow Graph (e.g., FuncGraph in TF2)."""
@@ -350,17 +362,20 @@ class GraphOpCreation(google.protobuf.message.Message):
"""
device_name: builtins.str
"""Name of the device that the op is assigned to (if available)."""
@property
def input_names(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Names of the input tensors to the op."""
num_outputs: builtins.int
"""Number of output tensors emitted by the op."""
@property
def input_names(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Names of the input tensors to the op."""
@property
def code_location(self) -> global___CodeLocation:
"""The unique ID for code location (stack trace) of the op's creation."""
@property
def output_tensor_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Unique IDs for the output tensors of this op."""
def __init__(
self,
*,
@@ -374,12 +389,12 @@ class GraphOpCreation(google.protobuf.message.Message):
code_location: global___CodeLocation | None = ...,
output_tensor_ids: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["code_location", b"code_location"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["code_location", b"code_location", "device_name", b"device_name", "graph_id", b"graph_id", "graph_name", b"graph_name", "input_names", b"input_names", "num_outputs", b"num_outputs", "op_name", b"op_name", "op_type", b"op_type", "output_tensor_ids", b"output_tensor_ids"]) -> None: ...
def HasField(self, field_name: typing.Literal["code_location", b"code_location"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["code_location", b"code_location", "device_name", b"device_name", "graph_id", b"graph_id", "graph_name", b"graph_name", "input_names", b"input_names", "num_outputs", b"num_outputs", "op_name", b"op_name", "op_type", b"op_type", "output_tensor_ids", b"output_tensor_ids"]) -> None: ...
global___GraphOpCreation = GraphOpCreation
@typing_extensions.final
@typing.final
class DebuggedGraph(google.protobuf.message.Message):
"""A debugger-instrumented graph."""
@@ -397,11 +412,6 @@ class DebuggedGraph(google.protobuf.message.Message):
"""
graph_name: builtins.str
"""Name of the graph (if available)."""
@property
def instrumented_ops(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Names of the instrumented ops. This can be used to look up op name
based on the numeric-summary tensors (2nd column).
"""
original_graph_def: builtins.bytes
"""Original (uninstrumented) GraphDef (if available)."""
instrumented_graph_def: builtins.bytes
@@ -410,6 +420,12 @@ class DebuggedGraph(google.protobuf.message.Message):
"""
outer_context_id: builtins.str
"""IDs of the immediate enclosing context (graph), if any."""
@property
def instrumented_ops(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Names of the instrumented ops. This can be used to look up op name
based on the numeric-summary tensors (2nd column).
"""
def __init__(
self,
*,
@@ -420,11 +436,11 @@ class DebuggedGraph(google.protobuf.message.Message):
instrumented_graph_def: builtins.bytes | None = ...,
outer_context_id: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["graph_id", b"graph_id", "graph_name", b"graph_name", "instrumented_graph_def", b"instrumented_graph_def", "instrumented_ops", b"instrumented_ops", "original_graph_def", b"original_graph_def", "outer_context_id", b"outer_context_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["graph_id", b"graph_id", "graph_name", b"graph_name", "instrumented_graph_def", b"instrumented_graph_def", "instrumented_ops", b"instrumented_ops", "original_graph_def", b"original_graph_def", "outer_context_id", b"outer_context_id"]) -> None: ...
global___DebuggedGraph = DebuggedGraph
@typing_extensions.final
@typing.final
class DebuggedDevice(google.protobuf.message.Message):
"""A device on which ops and/or tensors are instrumented by the debugger."""
@@ -446,11 +462,11 @@ class DebuggedDevice(google.protobuf.message.Message):
device_name: builtins.str | None = ...,
device_id: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["device_id", b"device_id", "device_name", b"device_name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["device_id", b"device_id", "device_name", b"device_name"]) -> None: ...
global___DebuggedDevice = DebuggedDevice
@typing_extensions.final
@typing.final
class Execution(google.protobuf.message.Message):
"""Data relating to the eager execution of an op or a Graph.
For a op that generates N output tensors (N >= 0), only one
@@ -478,30 +494,35 @@ class Execution(google.protobuf.message.Message):
"""The graph that's executed: applicable only to the eager
execution of a FuncGraph.
"""
tensor_debug_mode: global___TensorDebugMode.ValueType
"""Type of the tensor value encapsulated in this proto."""
@property
def input_tensor_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""IDs of the input tensors (if available)."""
@property
def output_tensor_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""IDs of the output tensors (if availbable).
If specified, must have the same length as tensor_protos.
"""
tensor_debug_mode: global___TensorDebugMode.ValueType
"""Type of the tensor value encapsulated in this proto."""
@property
def tensor_protos(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.tensor_pb2.TensorProto]:
"""Output Tensor values in the type described by `tensor_value_type`.
The length of this should match `num_outputs`.
"""
@property
def code_location(self) -> global___CodeLocation:
"""Stack trace of the eager execution."""
@property
def output_tensor_device_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Debugged-generated IDs of the devices on which the output tensors reside.
To look up details about the device (e.g., name), cross-reference this
field with the DebuggedDevice messages.
"""
def __init__(
self,
*,
@@ -515,12 +536,12 @@ class Execution(google.protobuf.message.Message):
code_location: global___CodeLocation | None = ...,
output_tensor_device_ids: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["code_location", b"code_location"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["code_location", b"code_location", "graph_id", b"graph_id", "input_tensor_ids", b"input_tensor_ids", "num_outputs", b"num_outputs", "op_type", b"op_type", "output_tensor_device_ids", b"output_tensor_device_ids", "output_tensor_ids", b"output_tensor_ids", "tensor_debug_mode", b"tensor_debug_mode", "tensor_protos", b"tensor_protos"]) -> None: ...
def HasField(self, field_name: typing.Literal["code_location", b"code_location"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["code_location", b"code_location", "graph_id", b"graph_id", "input_tensor_ids", b"input_tensor_ids", "num_outputs", b"num_outputs", "op_type", b"op_type", "output_tensor_device_ids", b"output_tensor_device_ids", "output_tensor_ids", b"output_tensor_ids", "tensor_debug_mode", b"tensor_debug_mode", "tensor_protos", b"tensor_protos"]) -> None: ...
global___Execution = Execution
@typing_extensions.final
@typing.final
class GraphExecutionTrace(google.protobuf.message.Message):
"""Data relating to an execution of a Graph (e.g., an eager execution of a
FuncGraph).
@@ -552,14 +573,15 @@ class GraphExecutionTrace(google.protobuf.message.Message):
"""
tensor_debug_mode: global___TensorDebugMode.ValueType
"""Type of the tensor value encapsulated in this proto."""
device_name: builtins.str
"""Name of the device that the op belongs to."""
@property
def tensor_proto(self) -> tensorflow.core.framework.tensor_pb2.TensorProto:
"""Tensor value in the type described by `tensor_value_type`.
This tensor may summarize the value of a single intermediate op of the
graph, or those of multiple intermediate tensors.
"""
device_name: builtins.str
"""Name of the device that the op belongs to."""
def __init__(
self,
*,
@@ -570,7 +592,7 @@ class GraphExecutionTrace(google.protobuf.message.Message):
tensor_proto: tensorflow.core.framework.tensor_pb2.TensorProto | None = ...,
device_name: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["tensor_proto", b"tensor_proto"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["device_name", b"device_name", "op_name", b"op_name", "output_slot", b"output_slot", "tensor_debug_mode", b"tensor_debug_mode", "tensor_proto", b"tensor_proto", "tfdbg_context_id", b"tfdbg_context_id"]) -> None: ...
def HasField(self, field_name: typing.Literal["tensor_proto", b"tensor_proto"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["device_name", b"device_name", "op_name", b"op_name", "output_slot", b"output_slot", "tensor_debug_mode", b"tensor_debug_mode", "tensor_proto", b"tensor_proto", "tfdbg_context_id", b"tfdbg_context_id"]) -> None: ...
global___GraphExecutionTrace = GraphExecutionTrace
stubs/tensorflow/tensorflow/core/protobuf/debug_pb2.pyi
+22 -16
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,7 +13,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class DebugTensorWatch(google.protobuf.message.Message):
"""Option for watching a node in TensorFlow Debugger (tfdbg)."""
@@ -35,12 +36,17 @@ class DebugTensorWatch(google.protobuf.message.Message):
Other negative values of output_slot are invalid and will lead to
errors currently.
"""
tolerate_debug_op_creation_failures: builtins.bool
"""Do not error out if debug op creation fails (e.g., due to dtype
incompatibility). Instead, just log the failure.
"""
@property
def debug_ops(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Name(s) of the debugging op(s).
One or more than one probes on a tensor.
e.g., {"DebugIdentity", "DebugNanCount"}
"""
@property
def debug_urls(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""URL(s) for debug targets(s).
@@ -64,10 +70,7 @@ class DebugTensorWatch(google.protobuf.message.Message):
among the invocations.
TODO(cais): More visible documentation of this in g3docs.
"""
tolerate_debug_op_creation_failures: builtins.bool
"""Do not error out if debug op creation fails (e.g., due to dtype
incompatibility). Instead, just log the failure.
"""
def __init__(
self,
*,
@@ -77,11 +80,11 @@ class DebugTensorWatch(google.protobuf.message.Message):
debug_urls: collections.abc.Iterable[builtins.str] | None = ...,
tolerate_debug_op_creation_failures: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["debug_ops", b"debug_ops", "debug_urls", b"debug_urls", "node_name", b"node_name", "output_slot", b"output_slot", "tolerate_debug_op_creation_failures", b"tolerate_debug_op_creation_failures"]) -> None: ...
def ClearField(self, field_name: typing.Literal["debug_ops", b"debug_ops", "debug_urls", b"debug_urls", "node_name", b"node_name", "output_slot", b"output_slot", "tolerate_debug_op_creation_failures", b"tolerate_debug_op_creation_failures"]) -> None: ...
global___DebugTensorWatch = DebugTensorWatch
@typing_extensions.final
@typing.final
class DebugOptions(google.protobuf.message.Message):
"""Options for initializing DebuggerState in TensorFlow Debugger (tfdbg)."""
@@ -90,9 +93,6 @@ class DebugOptions(google.protobuf.message.Message):
DEBUG_TENSOR_WATCH_OPTS_FIELD_NUMBER: builtins.int
GLOBAL_STEP_FIELD_NUMBER: builtins.int
RESET_DISK_BYTE_USAGE_FIELD_NUMBER: builtins.int
@property
def debug_tensor_watch_opts(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___DebugTensorWatch]:
"""Debugging options"""
global_step: builtins.int
"""Caller-specified global step count.
Note that this is distinct from the session run count and the executor
@@ -104,6 +104,10 @@ class DebugOptions(google.protobuf.message.Message):
such as the local CLI ones to indicate that the dumped tensors
are cleaned up from the disk after each Session.run.
"""
@property
def debug_tensor_watch_opts(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___DebugTensorWatch]:
"""Debugging options"""
def __init__(
self,
*,
@@ -111,11 +115,11 @@ class DebugOptions(google.protobuf.message.Message):
global_step: builtins.int | None = ...,
reset_disk_byte_usage: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["debug_tensor_watch_opts", b"debug_tensor_watch_opts", "global_step", b"global_step", "reset_disk_byte_usage", b"reset_disk_byte_usage"]) -> None: ...
def ClearField(self, field_name: typing.Literal["debug_tensor_watch_opts", b"debug_tensor_watch_opts", "global_step", b"global_step", "reset_disk_byte_usage", b"reset_disk_byte_usage"]) -> None: ...
global___DebugOptions = DebugOptions
@typing_extensions.final
@typing.final
class DebuggedSourceFile(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -135,6 +139,7 @@ class DebuggedSourceFile(google.protobuf.message.Message):
@property
def lines(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Line-by-line content of the source code file."""
def __init__(
self,
*,
@@ -144,11 +149,11 @@ class DebuggedSourceFile(google.protobuf.message.Message):
bytes: builtins.int | None = ...,
lines: collections.abc.Iterable[builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["bytes", b"bytes", "file_path", b"file_path", "host", b"host", "last_modified", b"last_modified", "lines", b"lines"]) -> None: ...
def ClearField(self, field_name: typing.Literal["bytes", b"bytes", "file_path", b"file_path", "host", b"host", "last_modified", b"last_modified", "lines", b"lines"]) -> None: ...
global___DebuggedSourceFile = DebuggedSourceFile
@typing_extensions.final
@typing.final
class DebuggedSourceFiles(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -156,11 +161,12 @@ class DebuggedSourceFiles(google.protobuf.message.Message):
@property
def source_files(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___DebuggedSourceFile]:
"""A collection of source code files."""
def __init__(
self,
*,
source_files: collections.abc.Iterable[global___DebuggedSourceFile] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["source_files", b"source_files"]) -> None: ...
def ClearField(self, field_name: typing.Literal["source_files", b"source_files"]) -> None: ...
global___DebuggedSourceFiles = DebuggedSourceFiles
stubs/tensorflow/tensorflow/core/protobuf/device_filters_pb2.pyi
+12 -10
View File
@@ -16,9 +16,10 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -26,7 +27,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class TaskDeviceFilters(google.protobuf.message.Message):
"""This file contains protos to be used when defining a TensorFlow
cluster.
@@ -71,17 +72,17 @@ class TaskDeviceFilters(google.protobuf.message.Message):
*,
device_filters: collections.abc.Iterable[builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["device_filters", b"device_filters"]) -> None: ...
def ClearField(self, field_name: typing.Literal["device_filters", b"device_filters"]) -> None: ...
global___TaskDeviceFilters = TaskDeviceFilters
@typing_extensions.final
@typing.final
class JobDeviceFilters(google.protobuf.message.Message):
"""Defines the device filters for tasks in a job."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class TasksEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -96,8 +97,8 @@ class JobDeviceFilters(google.protobuf.message.Message):
key: builtins.int | None = ...,
value: global___TaskDeviceFilters | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
TASKS_FIELD_NUMBER: builtins.int
@@ -106,17 +107,18 @@ class JobDeviceFilters(google.protobuf.message.Message):
@property
def tasks(self) -> google.protobuf.internal.containers.MessageMap[builtins.int, global___TaskDeviceFilters]:
"""Mapping from task ID to task device filters."""
def __init__(
self,
*,
name: builtins.str | None = ...,
tasks: collections.abc.Mapping[builtins.int, global___TaskDeviceFilters] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "tasks", b"tasks"]) -> None: ...
def ClearField(self, field_name: typing.Literal["name", b"name", "tasks", b"tasks"]) -> None: ...
global___JobDeviceFilters = JobDeviceFilters
@typing_extensions.final
@typing.final
class ClusterDeviceFilters(google.protobuf.message.Message):
"""Defines the device filters for jobs in a cluster."""
@@ -130,6 +132,6 @@ class ClusterDeviceFilters(google.protobuf.message.Message):
*,
jobs: collections.abc.Iterable[global___JobDeviceFilters] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["jobs", b"jobs"]) -> None: ...
def ClearField(self, field_name: typing.Literal["jobs", b"jobs"]) -> None: ...
global___ClusterDeviceFilters = ClusterDeviceFilters
stubs/tensorflow/tensorflow/core/protobuf/device_properties_pb2.pyi
+15 -13
View File
@@ -16,9 +16,10 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -26,11 +27,11 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class DeviceProperties(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class EnvironmentEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -44,7 +45,7 @@ class DeviceProperties(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
TYPE_FIELD_NUMBER: builtins.int
VENDOR_FIELD_NUMBER: builtins.int
@@ -69,11 +70,6 @@ class DeviceProperties(google.protobuf.message.Message):
"""Core Frequency in Mhz"""
num_cores: builtins.int
"""Number of cores"""
@property
def environment(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]:
"""Version of the tools and libraries used with this device (e.g. gcc 4.9,
cudnn 5.1)
"""
num_registers: builtins.int
"""Number of registers per core."""
l1_cache_size: builtins.int
@@ -90,6 +86,12 @@ class DeviceProperties(google.protobuf.message.Message):
"""Memory size in bytes"""
bandwidth: builtins.int
"""Memory bandwidth in KB/s"""
@property
def environment(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]:
"""Version of the tools and libraries used with this device (e.g. gcc 4.9,
cudnn 5.1)
"""
def __init__(
self,
*,
@@ -107,11 +109,11 @@ class DeviceProperties(google.protobuf.message.Message):
memory_size: builtins.int | None = ...,
bandwidth: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["bandwidth", b"bandwidth", "environment", b"environment", "frequency", b"frequency", "l1_cache_size", b"l1_cache_size", "l2_cache_size", b"l2_cache_size", "l3_cache_size", b"l3_cache_size", "memory_size", b"memory_size", "model", b"model", "num_cores", b"num_cores", "num_registers", b"num_registers", "shared_memory_size_per_multiprocessor", b"shared_memory_size_per_multiprocessor", "type", b"type", "vendor", b"vendor"]) -> None: ...
def ClearField(self, field_name: typing.Literal["bandwidth", b"bandwidth", "environment", b"environment", "frequency", b"frequency", "l1_cache_size", b"l1_cache_size", "l2_cache_size", b"l2_cache_size", "l3_cache_size", b"l3_cache_size", "memory_size", b"memory_size", "model", b"model", "num_cores", b"num_cores", "num_registers", b"num_registers", "shared_memory_size_per_multiprocessor", b"shared_memory_size_per_multiprocessor", "type", b"type", "vendor", b"vendor"]) -> None: ...
global___DeviceProperties = DeviceProperties
@typing_extensions.final
@typing.final
class NamedDevice(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -126,7 +128,7 @@ class NamedDevice(google.protobuf.message.Message):
name: builtins.str | None = ...,
properties: global___DeviceProperties | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["properties", b"properties"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "properties", b"properties"]) -> None: ...
def HasField(self, field_name: typing.Literal["properties", b"properties"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["name", b"name", "properties", b"properties"]) -> None: ...
global___NamedDevice = NamedDevice
stubs/tensorflow/tensorflow/core/protobuf/distributed_runtime_payloads_pb2.pyi
-81
View File
@@ -1,81 +0,0 @@
"""
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import google.protobuf.descriptor
import google.protobuf.internal.containers
import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
class GrpcPayloadContainer(google.protobuf.message.Message):
"""Used to serialize and transmit tensorflow::Status payloads through
grpc::Status `error_details` since grpc::Status lacks payload API.
TODO(b/204231601): Use GRPC API once supported.
"""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
class PayloadsEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
KEY_FIELD_NUMBER: builtins.int
VALUE_FIELD_NUMBER: builtins.int
key: builtins.str
value: builtins.bytes
def __init__(
self,
*,
key: builtins.str | None = ...,
value: builtins.bytes | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
PAYLOADS_FIELD_NUMBER: builtins.int
@property
def payloads(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.bytes]: ...
def __init__(
self,
*,
payloads: collections.abc.Mapping[builtins.str, builtins.bytes] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["payloads", b"payloads"]) -> None: ...
global___GrpcPayloadContainer = GrpcPayloadContainer
@typing_extensions.final
class GrpcPayloadsLost(google.protobuf.message.Message):
"""If included as a payload, this message flags the Status to have lost payloads
during the GRPC transmission.
URI: "type.googleapis.com/tensorflow.distributed_runtime.GrpcPayloadsLost"
"""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
def __init__(
self,
) -> None: ...
global___GrpcPayloadsLost = GrpcPayloadsLost
@typing_extensions.final
class WorkerPossiblyRestarted(google.protobuf.message.Message):
"""If included as a payload, this message flags the Status to be a possible
outcome of a worker restart.
URI:
"type.googleapis.com/tensorflow.distributed_runtime.WorkerPossiblyRestarted"
"""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
def __init__(
self,
) -> None: ...
global___WorkerPossiblyRestarted = WorkerPossiblyRestarted
stubs/tensorflow/tensorflow/core/protobuf/error_codes_pb2.pyi
+1
View File
@@ -6,6 +6,7 @@ core/lib/core/error_codes.proto, or having tensorflow.error, like
tsl/protobuf/error_codes.proto, results in name collision errors in generated
code for some users that use JS through J2CL.
"""
import google.protobuf.descriptor
from tensorflow.tsl.protobuf.error_codes_pb2 import (
ABORTED as ABORTED,
stubs/tensorflow/tensorflow/core/protobuf/fingerprint_pb2.pyi
+6 -4
View File
@@ -2,8 +2,9 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
@@ -11,7 +12,7 @@ import tensorflow.core.framework.versions_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class FingerprintDef(google.protobuf.message.Message):
"""Protocol buffer representing a SavedModel Fingerprint.
@@ -40,6 +41,7 @@ class FingerprintDef(google.protobuf.message.Message):
@property
def version(self) -> tensorflow.core.framework.versions_pb2.VersionDef:
"""Version specification of the fingerprint."""
def __init__(
self,
*,
@@ -50,7 +52,7 @@ class FingerprintDef(google.protobuf.message.Message):
checkpoint_hash: builtins.int | None = ...,
version: tensorflow.core.framework.versions_pb2.VersionDef | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["version", b"version"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["checkpoint_hash", b"checkpoint_hash", "graph_def_program_hash", b"graph_def_program_hash", "saved_model_checksum", b"saved_model_checksum", "saved_object_graph_hash", b"saved_object_graph_hash", "signature_def_hash", b"signature_def_hash", "version", b"version"]) -> None: ...
def HasField(self, field_name: typing.Literal["version", b"version"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["checkpoint_hash", b"checkpoint_hash", "graph_def_program_hash", b"graph_def_program_hash", "saved_model_checksum", b"saved_model_checksum", "saved_object_graph_hash", b"saved_object_graph_hash", "signature_def_hash", b"signature_def_hash", "version", b"version"]) -> None: ...
global___FingerprintDef = FingerprintDef
stubs/tensorflow/tensorflow/core/protobuf/graph_debug_info_pb2.pyi
+14 -10
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,11 +13,11 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class GraphDebugInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class FileLineCol(google.protobuf.message.Message):
"""This represents a file/line location in the source code."""
@@ -48,9 +49,9 @@ class GraphDebugInfo(google.protobuf.message.Message):
func: builtins.str | None = ...,
code: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["code", b"code", "col", b"col", "file_index", b"file_index", "func", b"func", "line", b"line"]) -> None: ...
def ClearField(self, field_name: typing.Literal["code", b"code", "col", b"col", "file_index", b"file_index", "func", b"func", "line", b"line"]) -> None: ...
@typing_extensions.final
@typing.final
class StackTrace(google.protobuf.message.Message):
"""This represents a stack trace which is a ordered list of `FileLineCol`."""
@@ -60,14 +61,15 @@ class GraphDebugInfo(google.protobuf.message.Message):
@property
def file_line_cols(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___GraphDebugInfo.FileLineCol]:
"""Each line in the stack trace."""
def __init__(
self,
*,
file_line_cols: collections.abc.Iterable[global___GraphDebugInfo.FileLineCol] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["file_line_cols", b"file_line_cols"]) -> None: ...
def ClearField(self, field_name: typing.Literal["file_line_cols", b"file_line_cols"]) -> None: ...
@typing_extensions.final
@typing.final
class TracesEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -82,8 +84,8 @@ class GraphDebugInfo(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___GraphDebugInfo.StackTrace | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
FILES_FIELD_NUMBER: builtins.int
TRACES_FIELD_NUMBER: builtins.int
@@ -92,6 +94,7 @@ class GraphDebugInfo(google.protobuf.message.Message):
"""This stores all the source code file names and can be indexed by the
`file_index`.
"""
@property
def traces(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___GraphDebugInfo.StackTrace]:
"""This maps a node name to a stack trace in the source code.
@@ -105,12 +108,13 @@ class GraphDebugInfo(google.protobuf.message.Message):
It would be preferable to avoid mangling and use a tuple key of (op.name,
func_name), but this is not supported with protocol buffers.
"""
def __init__(
self,
*,
files: collections.abc.Iterable[builtins.str] | None = ...,
traces: collections.abc.Mapping[builtins.str, global___GraphDebugInfo.StackTrace] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["files", b"files", "traces", b"traces"]) -> None: ...
def ClearField(self, field_name: typing.Literal["files", b"files", "traces", b"traces"]) -> None: ...
global___GraphDebugInfo = GraphDebugInfo
stubs/tensorflow/tensorflow/core/protobuf/meta_graph_pb2.pyi
+97 -78
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.any_pb2
import google.protobuf.descriptor
@@ -20,7 +21,7 @@ import tensorflow.core.protobuf.struct_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class MetaGraphDef(google.protobuf.message.Message):
"""Protocol buffer containing the following which are necessary to restart
training, run inference. It can be used to serialize/de-serialize memory
@@ -37,7 +38,7 @@ class MetaGraphDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class MetaInfoDef(google.protobuf.message.Message):
"""Meta information regarding the graph to be exported. To be used by users
of this protocol buffer to encode information regarding their meta graph.
@@ -45,7 +46,7 @@ class MetaGraphDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class FunctionAliasesEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -59,7 +60,7 @@ class MetaGraphDef(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
META_GRAPH_VERSION_FIELD_NUMBER: builtins.int
STRIPPED_OP_LIST_FIELD_NUMBER: builtins.int
@@ -73,25 +74,6 @@ class MetaGraphDef(google.protobuf.message.Message):
"""User specified Version string. Can be the name of the model and revision,
steps this model has been trained to, etc.
"""
@property
def stripped_op_list(self) -> tensorflow.core.framework.op_def_pb2.OpList:
"""A copy of the OpDefs used by the producer of this graph_def.
Descriptions and Ops not used in graph_def are stripped out.
"""
@property
def any_info(self) -> google.protobuf.any_pb2.Any:
"""A serialized protobuf. Can be the time this meta graph is created, or
modified, or name of the model.
"""
@property
def tags(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""User supplied tag(s) on the meta_graph and included graph_def.
MetaGraphDefs should be tagged with their capabilities or use-cases.
Examples: "train", "serve", "gpu", "tpu", etc.
These tags enable loaders to access the MetaGraph(s) appropriate for a
specific use-case or runtime environment.
"""
tensorflow_version: builtins.str
"""The __version__ string of the tensorflow build used to write this graph.
This will be populated by the framework, which will overwrite any user
@@ -106,9 +88,32 @@ class MetaGraphDef(google.protobuf.message.Message):
"""A flag to denote whether default-valued attrs have been stripped from
the nodes in this graph_def.
"""
@property
def stripped_op_list(self) -> tensorflow.core.framework.op_def_pb2.OpList:
"""A copy of the OpDefs used by the producer of this graph_def.
Descriptions and Ops not used in graph_def are stripped out.
"""
@property
def any_info(self) -> google.protobuf.any_pb2.Any:
"""A serialized protobuf. Can be the time this meta graph is created, or
modified, or name of the model.
"""
@property
def tags(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""User supplied tag(s) on the meta_graph and included graph_def.
MetaGraphDefs should be tagged with their capabilities or use-cases.
Examples: "train", "serve", "gpu", "tpu", etc.
These tags enable loaders to access the MetaGraph(s) appropriate for a
specific use-case or runtime environment.
"""
@property
def function_aliases(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]:
"""FunctionDef name to aliases mapping."""
def __init__(
self,
*,
@@ -121,10 +126,10 @@ class MetaGraphDef(google.protobuf.message.Message):
stripped_default_attrs: builtins.bool | None = ...,
function_aliases: collections.abc.Mapping[builtins.str, builtins.str] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["any_info", b"any_info", "stripped_op_list", b"stripped_op_list"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["any_info", b"any_info", "function_aliases", b"function_aliases", "meta_graph_version", b"meta_graph_version", "stripped_default_attrs", b"stripped_default_attrs", "stripped_op_list", b"stripped_op_list", "tags", b"tags", "tensorflow_git_version", b"tensorflow_git_version", "tensorflow_version", b"tensorflow_version"]) -> None: ...
def HasField(self, field_name: typing.Literal["any_info", b"any_info", "stripped_op_list", b"stripped_op_list"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["any_info", b"any_info", "function_aliases", b"function_aliases", "meta_graph_version", b"meta_graph_version", "stripped_default_attrs", b"stripped_default_attrs", "stripped_op_list", b"stripped_op_list", "tags", b"tags", "tensorflow_git_version", b"tensorflow_git_version", "tensorflow_version", b"tensorflow_version"]) -> None: ...
@typing_extensions.final
@typing.final
class CollectionDefEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -139,10 +144,10 @@ class MetaGraphDef(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___CollectionDef | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class SignatureDefEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -157,8 +162,8 @@ class MetaGraphDef(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___SignatureDef | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
META_INFO_DEF_FIELD_NUMBER: builtins.int
GRAPH_DEF_FIELD_NUMBER: builtins.int
@@ -172,25 +177,31 @@ class MetaGraphDef(google.protobuf.message.Message):
@property
def graph_def(self) -> tensorflow.core.framework.graph_pb2.GraphDef:
"""GraphDef."""
@property
def saver_def(self) -> tensorflow.core.protobuf.saver_pb2.SaverDef:
"""SaverDef."""
@property
def collection_def(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___CollectionDef]:
"""collection_def: Map from collection name to collections.
See CollectionDef section for details.
"""
@property
def signature_def(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___SignatureDef]:
"""signature_def: Map from user supplied key for a signature to a single
SignatureDef.
"""
@property
def asset_file_def(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___AssetFileDef]:
"""Asset file def to be used with the defined graph."""
@property
def object_graph_def(self) -> tensorflow.core.protobuf.saved_object_graph_pb2.SavedObjectGraph:
"""Extra information about the structure of functions and stateful objects."""
def __init__(
self,
*,
@@ -202,12 +213,12 @@ class MetaGraphDef(google.protobuf.message.Message):
asset_file_def: collections.abc.Iterable[global___AssetFileDef] | None = ...,
object_graph_def: tensorflow.core.protobuf.saved_object_graph_pb2.SavedObjectGraph | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["graph_def", b"graph_def", "meta_info_def", b"meta_info_def", "object_graph_def", b"object_graph_def", "saver_def", b"saver_def"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["asset_file_def", b"asset_file_def", "collection_def", b"collection_def", "graph_def", b"graph_def", "meta_info_def", b"meta_info_def", "object_graph_def", b"object_graph_def", "saver_def", b"saver_def", "signature_def", b"signature_def"]) -> None: ...
def HasField(self, field_name: typing.Literal["graph_def", b"graph_def", "meta_info_def", b"meta_info_def", "object_graph_def", b"object_graph_def", "saver_def", b"saver_def"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["asset_file_def", b"asset_file_def", "collection_def", b"collection_def", "graph_def", b"graph_def", "meta_info_def", b"meta_info_def", "object_graph_def", b"object_graph_def", "saver_def", b"saver_def", "signature_def", b"signature_def"]) -> None: ...
global___MetaGraphDef = MetaGraphDef
@typing_extensions.final
@typing.final
class CollectionDef(google.protobuf.message.Message):
"""CollectionDef should cover most collections.
To add a user-defined collection, do one of the following:
@@ -275,7 +286,7 @@ class CollectionDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class NodeList(google.protobuf.message.Message):
"""NodeList is used for collecting nodes in graph. For example
collection_def {
@@ -299,9 +310,9 @@ class CollectionDef(google.protobuf.message.Message):
*,
value: collections.abc.Iterable[builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class BytesList(google.protobuf.message.Message):
"""BytesList is used for collecting strings and serialized protobufs. For
example:
@@ -328,9 +339,9 @@ class CollectionDef(google.protobuf.message.Message):
*,
value: collections.abc.Iterable[builtins.bytes] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class Int64List(google.protobuf.message.Message):
"""Int64List is used for collecting int, int64 and long values."""
@@ -344,9 +355,9 @@ class CollectionDef(google.protobuf.message.Message):
*,
value: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class FloatList(google.protobuf.message.Message):
"""FloatList is used for collecting float values."""
@@ -360,9 +371,9 @@ class CollectionDef(google.protobuf.message.Message):
*,
value: collections.abc.Iterable[builtins.float] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class AnyList(google.protobuf.message.Message):
"""AnyList is used for collecting Any protos."""
@@ -376,7 +387,7 @@ class CollectionDef(google.protobuf.message.Message):
*,
value: collections.abc.Iterable[google.protobuf.any_pb2.Any] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["value", b"value"]) -> None: ...
NODE_LIST_FIELD_NUMBER: builtins.int
BYTES_LIST_FIELD_NUMBER: builtins.int
@@ -402,19 +413,19 @@ class CollectionDef(google.protobuf.message.Message):
float_list: global___CollectionDef.FloatList | None = ...,
any_list: global___CollectionDef.AnyList | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["any_list", b"any_list", "bytes_list", b"bytes_list", "float_list", b"float_list", "int64_list", b"int64_list", "kind", b"kind", "node_list", b"node_list"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["any_list", b"any_list", "bytes_list", b"bytes_list", "float_list", b"float_list", "int64_list", b"int64_list", "kind", b"kind", "node_list", b"node_list"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["kind", b"kind"]) -> typing_extensions.Literal["node_list", "bytes_list", "int64_list", "float_list", "any_list"] | None: ...
def HasField(self, field_name: typing.Literal["any_list", b"any_list", "bytes_list", b"bytes_list", "float_list", b"float_list", "int64_list", b"int64_list", "kind", b"kind", "node_list", b"node_list"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["any_list", b"any_list", "bytes_list", b"bytes_list", "float_list", b"float_list", "int64_list", b"int64_list", "kind", b"kind", "node_list", b"node_list"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["kind", b"kind"]) -> typing.Literal["node_list", "bytes_list", "int64_list", "float_list", "any_list"] | None: ...
global___CollectionDef = CollectionDef
@typing_extensions.final
@typing.final
class TensorInfo(google.protobuf.message.Message):
"""Information about a Tensor necessary for feeding or retrieval."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class CooSparse(google.protobuf.message.Message):
"""For sparse tensors, The COO encoding stores a triple of values, indices,
and shape.
@@ -442,9 +453,9 @@ class TensorInfo(google.protobuf.message.Message):
indices_tensor_name: builtins.str | None = ...,
dense_shape_tensor_name: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["dense_shape_tensor_name", b"dense_shape_tensor_name", "indices_tensor_name", b"indices_tensor_name", "values_tensor_name", b"values_tensor_name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["dense_shape_tensor_name", b"dense_shape_tensor_name", "indices_tensor_name", b"indices_tensor_name", "values_tensor_name", b"values_tensor_name"]) -> None: ...
@typing_extensions.final
@typing.final
class CompositeTensor(google.protobuf.message.Message):
"""Generic encoding for composite tensors."""
@@ -455,17 +466,19 @@ class TensorInfo(google.protobuf.message.Message):
@property
def type_spec(self) -> tensorflow.core.protobuf.struct_pb2.TypeSpecProto:
"""The serialized TypeSpec for the composite tensor."""
@property
def components(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TensorInfo]:
"""A TensorInfo for each flattened component tensor."""
def __init__(
self,
*,
type_spec: tensorflow.core.protobuf.struct_pb2.TypeSpecProto | None = ...,
components: collections.abc.Iterable[global___TensorInfo] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["type_spec", b"type_spec"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["components", b"components", "type_spec", b"type_spec"]) -> None: ...
def HasField(self, field_name: typing.Literal["type_spec", b"type_spec"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["components", b"components", "type_spec", b"type_spec"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
COO_SPARSE_FIELD_NUMBER: builtins.int
@@ -474,6 +487,7 @@ class TensorInfo(google.protobuf.message.Message):
TENSOR_SHAPE_FIELD_NUMBER: builtins.int
name: builtins.str
"""For dense `Tensor`s, the name of the tensor in the graph."""
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
@property
def coo_sparse(self) -> global___TensorInfo.CooSparse:
"""There are many possible encodings of sparse matrices
@@ -481,16 +495,18 @@ class TensorInfo(google.protobuf.message.Message):
uses only the COO encoding. This is supported and documented in the
SparseTensor Python class.
"""
@property
def composite_tensor(self) -> global___TensorInfo.CompositeTensor:
"""Generic encoding for CompositeTensors."""
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
@property
def tensor_shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto:
"""The static shape should be recorded here, to the extent that it can
be known in advance. In the case of a SparseTensor, this field describes
the logical shape of the represented tensor (aka dense_shape).
"""
def __init__(
self,
*,
@@ -500,13 +516,13 @@ class TensorInfo(google.protobuf.message.Message):
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ...,
tensor_shape: tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["composite_tensor", b"composite_tensor", "coo_sparse", b"coo_sparse", "encoding", b"encoding", "name", b"name", "tensor_shape", b"tensor_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["composite_tensor", b"composite_tensor", "coo_sparse", b"coo_sparse", "dtype", b"dtype", "encoding", b"encoding", "name", b"name", "tensor_shape", b"tensor_shape"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["encoding", b"encoding"]) -> typing_extensions.Literal["name", "coo_sparse", "composite_tensor"] | None: ...
def HasField(self, field_name: typing.Literal["composite_tensor", b"composite_tensor", "coo_sparse", b"coo_sparse", "encoding", b"encoding", "name", b"name", "tensor_shape", b"tensor_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["composite_tensor", b"composite_tensor", "coo_sparse", b"coo_sparse", "dtype", b"dtype", "encoding", b"encoding", "name", b"name", "tensor_shape", b"tensor_shape"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["encoding", b"encoding"]) -> typing.Literal["name", "coo_sparse", "composite_tensor"] | None: ...
global___TensorInfo = TensorInfo
@typing_extensions.final
@typing.final
class SignatureDef(google.protobuf.message.Message):
"""SignatureDef defines the signature of a computation supported by a TensorFlow
graph.
@@ -569,7 +585,7 @@ class SignatureDef(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class InputsEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -584,10 +600,10 @@ class SignatureDef(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___TensorInfo | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
@typing_extensions.final
@typing.final
class OutputsEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -602,18 +618,12 @@ class SignatureDef(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___TensorInfo | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
INPUTS_FIELD_NUMBER: builtins.int
OUTPUTS_FIELD_NUMBER: builtins.int
METHOD_NAME_FIELD_NUMBER: builtins.int
@property
def inputs(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___TensorInfo]:
"""Named input parameters."""
@property
def outputs(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___TensorInfo]:
"""Named output parameters."""
method_name: builtins.str
"""Extensible method_name information enabling third-party users to mark a
SignatureDef as supporting a particular method. This enables producers and
@@ -624,6 +634,14 @@ class SignatureDef(google.protobuf.message.Message):
method_name. This is commonly used to support multi-headed computation,
where a single graph computation may return multiple results.
"""
@property
def inputs(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___TensorInfo]:
"""Named input parameters."""
@property
def outputs(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___TensorInfo]:
"""Named output parameters."""
def __init__(
self,
*,
@@ -631,11 +649,11 @@ class SignatureDef(google.protobuf.message.Message):
outputs: collections.abc.Mapping[builtins.str, global___TensorInfo] | None = ...,
method_name: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["inputs", b"inputs", "method_name", b"method_name", "outputs", b"outputs"]) -> None: ...
def ClearField(self, field_name: typing.Literal["inputs", b"inputs", "method_name", b"method_name", "outputs", b"outputs"]) -> None: ...
global___SignatureDef = SignatureDef
@typing_extensions.final
@typing.final
class AssetFileDef(google.protobuf.message.Message):
"""An asset file def for a single file or a set of sharded files with the same
name.
@@ -645,21 +663,22 @@ class AssetFileDef(google.protobuf.message.Message):
TENSOR_INFO_FIELD_NUMBER: builtins.int
FILENAME_FIELD_NUMBER: builtins.int
@property
def tensor_info(self) -> global___TensorInfo:
"""The tensor to bind the asset filename to."""
filename: builtins.str
"""The filename within an assets directory. Note: does not include the path
prefix, i.e. directories. For an asset at /tmp/path/vocab.txt, the filename
would be "vocab.txt".
"""
@property
def tensor_info(self) -> global___TensorInfo:
"""The tensor to bind the asset filename to."""
def __init__(
self,
*,
tensor_info: global___TensorInfo | None = ...,
filename: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["tensor_info", b"tensor_info"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["filename", b"filename", "tensor_info", b"tensor_info"]) -> None: ...
def HasField(self, field_name: typing.Literal["tensor_info", b"tensor_info"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["filename", b"filename", "tensor_info", b"tensor_info"]) -> None: ...
global___AssetFileDef = AssetFileDef
stubs/tensorflow/tensorflow/core/protobuf/named_tensor_pb2.pyi
+6 -4
View File
@@ -2,8 +2,9 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
@@ -11,7 +12,7 @@ import tensorflow.core.framework.tensor_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class NamedTensorProto(google.protobuf.message.Message):
"""A pair of tensor name and tensor values."""
@@ -30,13 +31,14 @@ class NamedTensorProto(google.protobuf.message.Message):
filled tensor fields (float_val, int_val, etc.) or encoded in a
compact form in tensor.tensor_content.
"""
def __init__(
self,
*,
name: builtins.str | None = ...,
tensor: tensorflow.core.framework.tensor_pb2.TensorProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["tensor", b"tensor"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "tensor", b"tensor"]) -> None: ...
def HasField(self, field_name: typing.Literal["tensor", b"tensor"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["name", b"name", "tensor", b"tensor"]) -> None: ...
global___NamedTensorProto = NamedTensorProto
stubs/tensorflow/tensorflow/core/protobuf/queue_runner_pb2.pyi
+9 -6
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -13,7 +14,7 @@ import tensorflow.tsl.protobuf.error_codes_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class QueueRunnerDef(google.protobuf.message.Message):
"""Protocol buffer representing a QueueRunner."""
@@ -26,18 +27,20 @@ class QueueRunnerDef(google.protobuf.message.Message):
QUEUE_CLOSED_EXCEPTION_TYPES_FIELD_NUMBER: builtins.int
queue_name: builtins.str
"""Queue name."""
@property
def enqueue_op_name(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""A list of enqueue operations."""
close_op_name: builtins.str
"""The operation to run to close the queue."""
cancel_op_name: builtins.str
"""The operation to run to cancel the queue."""
@property
def enqueue_op_name(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""A list of enqueue operations."""
@property
def queue_closed_exception_types(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[tensorflow.tsl.protobuf.error_codes_pb2.Code.ValueType]:
"""A list of exception types considered to signal a safely closed queue
if raised during enqueue operations.
"""
def __init__(
self,
*,
@@ -47,6 +50,6 @@ class QueueRunnerDef(google.protobuf.message.Message):
cancel_op_name: builtins.str | None = ...,
queue_closed_exception_types: collections.abc.Iterable[tensorflow.tsl.protobuf.error_codes_pb2.Code.ValueType] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["cancel_op_name", b"cancel_op_name", "close_op_name", b"close_op_name", "enqueue_op_name", b"enqueue_op_name", "queue_closed_exception_types", b"queue_closed_exception_types", "queue_name", b"queue_name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["cancel_op_name", b"cancel_op_name", "close_op_name", b"close_op_name", "enqueue_op_name", b"enqueue_op_name", "queue_closed_exception_types", b"queue_closed_exception_types", "queue_name", b"queue_name"]) -> None: ...
global___QueueRunnerDef = QueueRunnerDef
stubs/tensorflow/tensorflow/core/protobuf/remote_tensor_handle_pb2.pyi
+8 -6
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -14,7 +15,7 @@ import tensorflow.core.framework.types_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class ResourceDtypeAndShape(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -29,12 +30,12 @@ class ResourceDtypeAndShape(google.protobuf.message.Message):
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ...,
shape: tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["dtype", b"dtype", "shape", b"shape"]) -> None: ...
def HasField(self, field_name: typing.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["dtype", b"dtype", "shape", b"shape"]) -> None: ...
global___ResourceDtypeAndShape = ResourceDtypeAndShape
@typing_extensions.final
@typing.final
class RemoteTensorHandle(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -61,6 +62,7 @@ class RemoteTensorHandle(google.protobuf.message.Message):
@property
def resource_dtypes_and_shapes(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___ResourceDtypeAndShape]:
"""Optional data types and shapes of a remote resource variable."""
def __init__(
self,
*,
@@ -71,6 +73,6 @@ class RemoteTensorHandle(google.protobuf.message.Message):
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ...,
resource_dtypes_and_shapes: collections.abc.Iterable[global___ResourceDtypeAndShape] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["device", b"device", "dtype", b"dtype", "op_device", b"op_device", "op_id", b"op_id", "output_num", b"output_num", "resource_dtypes_and_shapes", b"resource_dtypes_and_shapes"]) -> None: ...
def ClearField(self, field_name: typing.Literal["device", b"device", "dtype", b"dtype", "op_device", b"op_device", "op_id", b"op_id", "output_num", b"output_num", "resource_dtypes_and_shapes", b"resource_dtypes_and_shapes"]) -> None: ...
global___RemoteTensorHandle = RemoteTensorHandle
stubs/tensorflow/tensorflow/core/protobuf/rewriter_config_pb2.pyi
+24 -17
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -21,7 +22,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class AutoParallelOptions(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -35,11 +36,11 @@ class AutoParallelOptions(google.protobuf.message.Message):
enable: builtins.bool | None = ...,
num_replicas: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["enable", b"enable", "num_replicas", b"num_replicas"]) -> None: ...
def ClearField(self, field_name: typing.Literal["enable", b"enable", "num_replicas", b"num_replicas"]) -> None: ...
global___AutoParallelOptions = AutoParallelOptions
@typing_extensions.final
@typing.final
class ScopedAllocatorOptions(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -47,16 +48,17 @@ class ScopedAllocatorOptions(google.protobuf.message.Message):
@property
def enable_op(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""If present, only perform optimization for these ops."""
def __init__(
self,
*,
enable_op: collections.abc.Iterable[builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["enable_op", b"enable_op"]) -> None: ...
def ClearField(self, field_name: typing.Literal["enable_op", b"enable_op"]) -> None: ...
global___ScopedAllocatorOptions = ScopedAllocatorOptions
@typing_extensions.final
@typing.final
class RewriterConfig(google.protobuf.message.Message):
"""Graph rewriting is experimental and subject to change, not covered by any
API stability guarantees.
@@ -207,13 +209,13 @@ class RewriterConfig(google.protobuf.message.Message):
HEURISTICS: RewriterConfig.MemOptType.ValueType # 3
"""Use any combination of swapping and recomputation heuristics."""
@typing_extensions.final
@typing.final
class CustomGraphOptimizer(google.protobuf.message.Message):
"""Message to describe custom graph optimizer and its parameters"""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class ParameterMapEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -228,8 +230,8 @@ class RewriterConfig(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: tensorflow.core.framework.attr_value_pb2.AttrValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
PARAMETER_MAP_FIELD_NUMBER: builtins.int
@@ -242,7 +244,7 @@ class RewriterConfig(google.protobuf.message.Message):
name: builtins.str | None = ...,
parameter_map: collections.abc.Mapping[builtins.str, tensorflow.core.framework.attr_value_pb2.AttrValue] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "parameter_map", b"parameter_map"]) -> None: ...
def ClearField(self, field_name: typing.Literal["name", b"name", "parameter_map", b"parameter_map"]) -> None: ...
CPU_LAYOUT_CONVERSION_FIELD_NUMBER: builtins.int
LAYOUT_OPTIMIZER_FIELD_NUMBER: builtins.int
@@ -403,16 +405,17 @@ class RewriterConfig(google.protobuf.message.Message):
timing out. If less than or equal to 0 (default value) the optimizer will
never time out.
"""
@property
def auto_parallel(self) -> global___AutoParallelOptions:
"""Configures AutoParallel optimization passes either through the
meta-optimizer or when manually specified through the optimizers field.
"""
fail_on_optimizer_errors: builtins.bool
"""If true, any optimization pass failing will cause the MetaOptimizer to
stop with an error. By default - or when set to false, failing passes are
skipped silently.
"""
@property
def auto_parallel(self) -> global___AutoParallelOptions:
"""Configures AutoParallel optimization passes either through the
meta-optimizer or when manually specified through the optimizers field.
"""
@property
def scoped_allocator_opts(self) -> global___ScopedAllocatorOptions: ...
@property
@@ -430,17 +433,21 @@ class RewriterConfig(google.protobuf.message.Message):
Custom optimizers (see custom_optimizers) that are not part of this
schedule will be run after - in the order that they were specified.
"""
@property
def custom_optimizers(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___RewriterConfig.CustomGraphOptimizer]:
"""list of CustomGraphOptimizers to apply."""
@property
def inter_optimizer_verifier_config(self) -> tensorflow.core.protobuf.verifier_config_pb2.VerifierConfig:
"""VerifierConfig specifying the verifiers to be run after every optimizer."""
@property
def post_optimization_verifier_config(self) -> tensorflow.core.protobuf.verifier_config_pb2.VerifierConfig:
"""VerifierConfig specifying the verifiers to be run at the end, after all
optimizers have run.
"""
def __init__(
self,
*,
@@ -481,7 +488,7 @@ class RewriterConfig(google.protobuf.message.Message):
inter_optimizer_verifier_config: tensorflow.core.protobuf.verifier_config_pb2.VerifierConfig | None = ...,
post_optimization_verifier_config: tensorflow.core.protobuf.verifier_config_pb2.VerifierConfig | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["auto_parallel", b"auto_parallel", "inter_optimizer_verifier_config", b"inter_optimizer_verifier_config", "post_optimization_verifier_config", b"post_optimization_verifier_config", "scoped_allocator_opts", b"scoped_allocator_opts"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["arithmetic_optimization", b"arithmetic_optimization", "auto_mixed_precision", b"auto_mixed_precision", "auto_mixed_precision_cpu", b"auto_mixed_precision_cpu", "auto_mixed_precision_mkl", b"auto_mixed_precision_mkl", "auto_mixed_precision_onednn_bfloat16", b"auto_mixed_precision_onednn_bfloat16", "auto_parallel", b"auto_parallel", "common_subgraph_elimination", b"common_subgraph_elimination", "constant_folding", b"constant_folding", "cpu_layout_conversion", b"cpu_layout_conversion", "custom_optimizers", b"custom_optimizers", "debug_stripper", b"debug_stripper", "dependency_optimization", b"dependency_optimization", "disable_meta_optimizer", b"disable_meta_optimizer", "disable_model_pruning", b"disable_model_pruning", "experimental_conditional_code_motion", b"experimental_conditional_code_motion", "experimental_disable_compressed_tensor_optimization", b"experimental_disable_compressed_tensor_optimization", "experimental_disable_folding_quantization_emulation", b"experimental_disable_folding_quantization_emulation", "fail_on_optimizer_errors", b"fail_on_optimizer_errors", "function_optimization", b"function_optimization", "implementation_selector", b"implementation_selector", "inter_optimizer_verifier_config", b"inter_optimizer_verifier_config", "layout_optimizer", b"layout_optimizer", "loop_optimization", b"loop_optimization", "memory_optimization", b"memory_optimization", "memory_optimizer_target_node_name_scope", b"memory_optimizer_target_node_name_scope", "meta_optimizer_iterations", b"meta_optimizer_iterations", "meta_optimizer_timeout_ms", b"meta_optimizer_timeout_ms", "min_graph_nodes", b"min_graph_nodes", "optimizers", b"optimizers", "pin_to_host_optimization", b"pin_to_host_optimization", "post_optimization_verifier_config", b"post_optimization_verifier_config", "remapping", b"remapping", "scoped_allocator_optimization", b"scoped_allocator_optimization", "scoped_allocator_opts", b"scoped_allocator_opts", "shape_optimization", b"shape_optimization", "use_plugin_optimizers", b"use_plugin_optimizers"]) -> None: ...
def HasField(self, field_name: typing.Literal["auto_parallel", b"auto_parallel", "inter_optimizer_verifier_config", b"inter_optimizer_verifier_config", "post_optimization_verifier_config", b"post_optimization_verifier_config", "scoped_allocator_opts", b"scoped_allocator_opts"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["arithmetic_optimization", b"arithmetic_optimization", "auto_mixed_precision", b"auto_mixed_precision", "auto_mixed_precision_cpu", b"auto_mixed_precision_cpu", "auto_mixed_precision_mkl", b"auto_mixed_precision_mkl", "auto_mixed_precision_onednn_bfloat16", b"auto_mixed_precision_onednn_bfloat16", "auto_parallel", b"auto_parallel", "common_subgraph_elimination", b"common_subgraph_elimination", "constant_folding", b"constant_folding", "cpu_layout_conversion", b"cpu_layout_conversion", "custom_optimizers", b"custom_optimizers", "debug_stripper", b"debug_stripper", "dependency_optimization", b"dependency_optimization", "disable_meta_optimizer", b"disable_meta_optimizer", "disable_model_pruning", b"disable_model_pruning", "experimental_conditional_code_motion", b"experimental_conditional_code_motion", "experimental_disable_compressed_tensor_optimization", b"experimental_disable_compressed_tensor_optimization", "experimental_disable_folding_quantization_emulation", b"experimental_disable_folding_quantization_emulation", "fail_on_optimizer_errors", b"fail_on_optimizer_errors", "function_optimization", b"function_optimization", "implementation_selector", b"implementation_selector", "inter_optimizer_verifier_config", b"inter_optimizer_verifier_config", "layout_optimizer", b"layout_optimizer", "loop_optimization", b"loop_optimization", "memory_optimization", b"memory_optimization", "memory_optimizer_target_node_name_scope", b"memory_optimizer_target_node_name_scope", "meta_optimizer_iterations", b"meta_optimizer_iterations", "meta_optimizer_timeout_ms", b"meta_optimizer_timeout_ms", "min_graph_nodes", b"min_graph_nodes", "optimizers", b"optimizers", "pin_to_host_optimization", b"pin_to_host_optimization", "post_optimization_verifier_config", b"post_optimization_verifier_config", "remapping", b"remapping", "scoped_allocator_optimization", b"scoped_allocator_optimization", "scoped_allocator_opts", b"scoped_allocator_opts", "shape_optimization", b"shape_optimization", "use_plugin_optimizers", b"use_plugin_optimizers"]) -> None: ...
global___RewriterConfig = RewriterConfig
stubs/tensorflow/tensorflow/core/protobuf/rpc_options_pb2.pyi
+1
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import google.protobuf.descriptor
from tensorflow.tsl.protobuf.rpc_options_pb2 import RPCOptions as RPCOptions
stubs/tensorflow/tensorflow/core/protobuf/saved_model_pb2.pyi
+5 -3
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -13,7 +14,7 @@ import tensorflow.core.protobuf.meta_graph_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class SavedModel(google.protobuf.message.Message):
"""SavedModel is the high level serialization format for TensorFlow Models.
See [todo: doc links, similar to session_bundle] for more information.
@@ -31,12 +32,13 @@ class SavedModel(google.protobuf.message.Message):
@property
def meta_graphs(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.protobuf.meta_graph_pb2.MetaGraphDef]:
"""One or more MetaGraphs."""
def __init__(
self,
*,
saved_model_schema_version: builtins.int | None = ...,
meta_graphs: collections.abc.Iterable[tensorflow.core.protobuf.meta_graph_pb2.MetaGraphDef] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["meta_graphs", b"meta_graphs", "saved_model_schema_version", b"saved_model_schema_version"]) -> None: ...
def ClearField(self, field_name: typing.Literal["meta_graphs", b"meta_graphs", "saved_model_schema_version", b"saved_model_schema_version"]) -> None: ...
global___SavedModel = SavedModel
stubs/tensorflow/tensorflow/core/protobuf/saved_object_graph_pb2.pyi
+87 -71
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -26,7 +27,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class SavedObjectGraph(google.protobuf.message.Message):
"""SavedObjectGraph shares some structure with TrackableObjectGraph, but
SavedObjectGraph belongs to the MetaGraph and contains pointers to functions
@@ -36,7 +37,7 @@ class SavedObjectGraph(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class ConcreteFunctionsEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -51,8 +52,8 @@ class SavedObjectGraph(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___SavedConcreteFunction | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
NODES_FIELD_NUMBER: builtins.int
CONCRETE_FUNCTIONS_FIELD_NUMBER: builtins.int
@@ -63,26 +64,28 @@ class SavedObjectGraph(google.protobuf.message.Message):
The position of the object in this list indicates its id.
Nodes[0] is considered the root node.
"""
@property
def concrete_functions(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___SavedConcreteFunction]:
"""Information about captures and output structures in concrete functions.
Referenced from SavedBareConcreteFunction and SavedFunction.
"""
def __init__(
self,
*,
nodes: collections.abc.Iterable[global___SavedObject] | None = ...,
concrete_functions: collections.abc.Mapping[builtins.str, global___SavedConcreteFunction] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["concrete_functions", b"concrete_functions", "nodes", b"nodes"]) -> None: ...
def ClearField(self, field_name: typing.Literal["concrete_functions", b"concrete_functions", "nodes", b"nodes"]) -> None: ...
global___SavedObjectGraph = SavedObjectGraph
@typing_extensions.final
@typing.final
class SavedObject(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class SaveableObjectsEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -97,8 +100,8 @@ class SavedObject(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___SaveableObject | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
CHILDREN_FIELD_NUMBER: builtins.int
DEPENDENCIES_FIELD_NUMBER: builtins.int
@@ -115,6 +118,28 @@ class SavedObject(google.protobuf.message.Message):
REGISTERED_NAME_FIELD_NUMBER: builtins.int
SERIALIZED_USER_PROTO_FIELD_NUMBER: builtins.int
REGISTERED_SAVER_FIELD_NUMBER: builtins.int
registered_name: builtins.str
"""The fields below are filled when the user serializes a registered Trackable
class or an object with a registered saver function.
Registered classes may save additional metadata and supersede the
default loading process where nodes are recreated from the proto.
If the registered class cannot be found, then the object will load as one
one of the default trackable objects: Autotrackable (a class similar to
tf.Module), tf.function, or tf.Variable.
Unlike SaveableObjects, which store the functions for saving and restoring
from tensors, registered savers allow Trackables to write checkpoint shards
directly (e.g. for performance or coordination reasons).
*All registered savers must be available when loading the SavedModel.*
The name of the registered class of the form "{package}.{class_name}".
This field is used to search for the registered class at loading time.
"""
registered_saver: builtins.str
"""String name of the registered saver. At most one of `saveable_objects` or
`registered_saver` is defined for each SavedObject.
"""
@property
def children(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.protobuf.trackable_object_graph_pb2.TrackableObjectGraph.TrackableObject.ObjectReference]:
"""Objects which this object depends on: named edges in the dependency
@@ -123,12 +148,14 @@ class SavedObject(google.protobuf.message.Message):
Note: All kinds of SavedObject may have children, except
"constant" and "captured_tensor".
"""
@property
def dependencies(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.protobuf.trackable_object_graph_pb2.TrackableObjectGraph.TrackableObject.ObjectReference]:
"""Ordered list of dependencies that must be loaded before this object.
SavedModel loads with the bottom-up approach, by first creating all objects
(in the order defined by the dependencies), then connecting the edges.
"""
@property
def slot_variables(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.protobuf.trackable_object_graph_pb2.TrackableObjectGraph.TrackableObject.SlotVariableReference]:
"""Slot variables owned by this object. This describes the three-way
@@ -137,6 +164,7 @@ class SavedObject(google.protobuf.message.Message):
Note: currently only valid if kind == "user_object".
"""
@property
def user_object(self) -> global___SavedUserObject: ...
@property
@@ -160,34 +188,14 @@ class SavedObject(google.protobuf.message.Message):
See the comment below for the difference between SaveableObject and
registered savers.
"""
registered_name: builtins.str
"""The fields below are filled when the user serializes a registered Trackable
class or an object with a registered saver function.
Registered classes may save additional metadata and supersede the
default loading process where nodes are recreated from the proto.
If the registered class cannot be found, then the object will load as one
one of the default trackable objects: Autotrackable (a class similar to
tf.Module), tf.function, or tf.Variable.
Unlike SaveableObjects, which store the functions for saving and restoring
from tensors, registered savers allow Trackables to write checkpoint shards
directly (e.g. for performance or coordination reasons).
*All registered savers must be available when loading the SavedModel.*
The name of the registered class of the form "{package}.{class_name}".
This field is used to search for the registered class at loading time.
"""
@property
def serialized_user_proto(self) -> google.protobuf.any_pb2.Any:
"""The user-generated proto storing metadata for this object, to be passed to
the registered classes's _deserialize_from_proto method when this object is
loaded from the SavedModel.
"""
registered_saver: builtins.str
"""String name of the registered saver. At most one of `saveable_objects` or
`registered_saver` is defined for each SavedObject.
"""
def __init__(
self,
*,
@@ -207,13 +215,13 @@ class SavedObject(google.protobuf.message.Message):
serialized_user_proto: google.protobuf.any_pb2.Any | None = ...,
registered_saver: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["asset", b"asset", "bare_concrete_function", b"bare_concrete_function", "captured_tensor", b"captured_tensor", "constant", b"constant", "function", b"function", "kind", b"kind", "resource", b"resource", "serialized_user_proto", b"serialized_user_proto", "user_object", b"user_object", "variable", b"variable"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["asset", b"asset", "bare_concrete_function", b"bare_concrete_function", "captured_tensor", b"captured_tensor", "children", b"children", "constant", b"constant", "dependencies", b"dependencies", "function", b"function", "kind", b"kind", "registered_name", b"registered_name", "registered_saver", b"registered_saver", "resource", b"resource", "saveable_objects", b"saveable_objects", "serialized_user_proto", b"serialized_user_proto", "slot_variables", b"slot_variables", "user_object", b"user_object", "variable", b"variable"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["kind", b"kind"]) -> typing_extensions.Literal["user_object", "asset", "function", "variable", "bare_concrete_function", "constant", "resource", "captured_tensor"] | None: ...
def HasField(self, field_name: typing.Literal["asset", b"asset", "bare_concrete_function", b"bare_concrete_function", "captured_tensor", b"captured_tensor", "constant", b"constant", "function", b"function", "kind", b"kind", "resource", b"resource", "serialized_user_proto", b"serialized_user_proto", "user_object", b"user_object", "variable", b"variable"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["asset", b"asset", "bare_concrete_function", b"bare_concrete_function", "captured_tensor", b"captured_tensor", "children", b"children", "constant", b"constant", "dependencies", b"dependencies", "function", b"function", "kind", b"kind", "registered_name", b"registered_name", "registered_saver", b"registered_saver", "resource", b"resource", "saveable_objects", b"saveable_objects", "serialized_user_proto", b"serialized_user_proto", "slot_variables", b"slot_variables", "user_object", b"user_object", "variable", b"variable"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["kind", b"kind"]) -> typing.Literal["user_object", "asset", "function", "variable", "bare_concrete_function", "constant", "resource", "captured_tensor"] | None: ...
global___SavedObject = SavedObject
@typing_extensions.final
@typing.final
class SavedUserObject(google.protobuf.message.Message):
"""A SavedUserObject is an object (in the object-oriented language of the
TensorFlow program) of some user- or framework-defined class other than
@@ -230,9 +238,6 @@ class SavedUserObject(google.protobuf.message.Message):
METADATA_FIELD_NUMBER: builtins.int
identifier: builtins.str
"""Corresponds to a registration of the type to use in the loading program."""
@property
def version(self) -> tensorflow.core.framework.versions_pb2.VersionDef:
"""Version information from the producer of this SavedUserObject."""
metadata: builtins.str
"""Metadata for deserializing this object.
@@ -240,6 +245,10 @@ class SavedUserObject(google.protobuf.message.Message):
field, and its saving and loading code will be updated shortly.
Please save your application-specific metadata to a separate file.
"""
@property
def version(self) -> tensorflow.core.framework.versions_pb2.VersionDef:
"""Version information from the producer of this SavedUserObject."""
def __init__(
self,
*,
@@ -247,12 +256,12 @@ class SavedUserObject(google.protobuf.message.Message):
version: tensorflow.core.framework.versions_pb2.VersionDef | None = ...,
metadata: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["version", b"version"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["identifier", b"identifier", "metadata", b"metadata", "version", b"version"]) -> None: ...
def HasField(self, field_name: typing.Literal["version", b"version"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["identifier", b"identifier", "metadata", b"metadata", "version", b"version"]) -> None: ...
global___SavedUserObject = SavedUserObject
@typing_extensions.final
@typing.final
class SavedAsset(google.protobuf.message.Message):
"""A SavedAsset points to an asset in the MetaGraph.
@@ -275,11 +284,11 @@ class SavedAsset(google.protobuf.message.Message):
*,
asset_file_def_index: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["asset_file_def_index", b"asset_file_def_index"]) -> None: ...
def ClearField(self, field_name: typing.Literal["asset_file_def_index", b"asset_file_def_index"]) -> None: ...
global___SavedAsset = SavedAsset
@typing_extensions.final
@typing.final
class SavedFunction(google.protobuf.message.Message):
"""A function with multiple signatures, possibly with non-Tensor arguments."""
@@ -297,12 +306,12 @@ class SavedFunction(google.protobuf.message.Message):
concrete_functions: collections.abc.Iterable[builtins.str] | None = ...,
function_spec: global___FunctionSpec | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["function_spec", b"function_spec"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["concrete_functions", b"concrete_functions", "function_spec", b"function_spec"]) -> None: ...
def HasField(self, field_name: typing.Literal["function_spec", b"function_spec"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["concrete_functions", b"concrete_functions", "function_spec", b"function_spec"]) -> None: ...
global___SavedFunction = SavedFunction
@typing_extensions.final
@typing.final
class CapturedTensor(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -318,11 +327,11 @@ class CapturedTensor(google.protobuf.message.Message):
name: builtins.str | None = ...,
concrete_function: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["concrete_function", b"concrete_function", "name", b"name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["concrete_function", b"concrete_function", "name", b"name"]) -> None: ...
global___CapturedTensor = CapturedTensor
@typing_extensions.final
@typing.final
class SavedConcreteFunction(google.protobuf.message.Message):
"""Stores low-level information about a concrete function. Referenced in either
a SavedFunction or a SavedBareConcreteFunction.
@@ -340,12 +349,14 @@ class SavedConcreteFunction(google.protobuf.message.Message):
"""Input in canonicalized form that was received to create this concrete
function.
"""
@property
def output_signature(self) -> tensorflow.core.protobuf.struct_pb2.StructuredValue:
"""Output that was the return value of this function after replacing all
Tensors with TensorSpecs. This can be an arbitrary nested function and will
be used to reconstruct the full structure from pure tensors.
"""
def __init__(
self,
*,
@@ -353,12 +364,12 @@ class SavedConcreteFunction(google.protobuf.message.Message):
canonicalized_input_signature: tensorflow.core.protobuf.struct_pb2.StructuredValue | None = ...,
output_signature: tensorflow.core.protobuf.struct_pb2.StructuredValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["canonicalized_input_signature", b"canonicalized_input_signature", "output_signature", b"output_signature"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["bound_inputs", b"bound_inputs", "canonicalized_input_signature", b"canonicalized_input_signature", "output_signature", b"output_signature"]) -> None: ...
def HasField(self, field_name: typing.Literal["canonicalized_input_signature", b"canonicalized_input_signature", "output_signature", b"output_signature"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["bound_inputs", b"bound_inputs", "canonicalized_input_signature", b"canonicalized_input_signature", "output_signature", b"output_signature"]) -> None: ...
global___SavedConcreteFunction = SavedConcreteFunction
@typing_extensions.final
@typing.final
class SavedBareConcreteFunction(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -368,11 +379,12 @@ class SavedBareConcreteFunction(google.protobuf.message.Message):
FUNCTION_SPEC_FIELD_NUMBER: builtins.int
concrete_function_name: builtins.str
"""Identifies a SavedConcreteFunction."""
allowed_positional_arguments: builtins.int
"""The prefix of `argument_keywords` which may be identified by position."""
@property
def argument_keywords(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""A sequence of unique strings, one per Tensor argument."""
allowed_positional_arguments: builtins.int
"""The prefix of `argument_keywords` which may be identified by position."""
@property
def function_spec(self) -> global___FunctionSpec:
"""The spec of the function that this ConcreteFunction is traced from. This
@@ -382,6 +394,7 @@ class SavedBareConcreteFunction(google.protobuf.message.Message):
TODO(b/169361281): support calling saved ConcreteFunction with structured
inputs in C++ SavedModel API.
"""
def __init__(
self,
*,
@@ -390,12 +403,12 @@ class SavedBareConcreteFunction(google.protobuf.message.Message):
allowed_positional_arguments: builtins.int | None = ...,
function_spec: global___FunctionSpec | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["function_spec", b"function_spec"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["allowed_positional_arguments", b"allowed_positional_arguments", "argument_keywords", b"argument_keywords", "concrete_function_name", b"concrete_function_name", "function_spec", b"function_spec"]) -> None: ...
def HasField(self, field_name: typing.Literal["function_spec", b"function_spec"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["allowed_positional_arguments", b"allowed_positional_arguments", "argument_keywords", b"argument_keywords", "concrete_function_name", b"concrete_function_name", "function_spec", b"function_spec"]) -> None: ...
global___SavedBareConcreteFunction = SavedBareConcreteFunction
@typing_extensions.final
@typing.final
class SavedConstant(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -407,11 +420,11 @@ class SavedConstant(google.protobuf.message.Message):
*,
operation: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["operation", b"operation"]) -> None: ...
def ClearField(self, field_name: typing.Literal["operation", b"operation"]) -> None: ...
global___SavedConstant = SavedConstant
@typing_extensions.final
@typing.final
class SavedVariable(google.protobuf.message.Message):
"""Represents a Variable that is initialized by loading the contents from the
checkpoint.
@@ -428,14 +441,14 @@ class SavedVariable(google.protobuf.message.Message):
DEVICE_FIELD_NUMBER: builtins.int
EXPERIMENTAL_DISTRIBUTED_VARIABLE_COMPONENTS_FIELD_NUMBER: builtins.int
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: ...
trainable: builtins.bool
synchronization: tensorflow.core.framework.variable_pb2.VariableSynchronization.ValueType
aggregation: tensorflow.core.framework.variable_pb2.VariableAggregation.ValueType
name: builtins.str
device: builtins.str
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: ...
@property
def experimental_distributed_variable_components(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___SavedVariable]:
"""List of component variables for a distributed variable.
@@ -444,6 +457,7 @@ class SavedVariable(google.protobuf.message.Message):
This is only supported by experimental loaders at the moment.
"""
def __init__(
self,
*,
@@ -456,12 +470,12 @@ class SavedVariable(google.protobuf.message.Message):
device: builtins.str | None = ...,
experimental_distributed_variable_components: collections.abc.Iterable[global___SavedVariable] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["aggregation", b"aggregation", "device", b"device", "dtype", b"dtype", "experimental_distributed_variable_components", b"experimental_distributed_variable_components", "name", b"name", "shape", b"shape", "synchronization", b"synchronization", "trainable", b"trainable"]) -> None: ...
def HasField(self, field_name: typing.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["aggregation", b"aggregation", "device", b"device", "dtype", b"dtype", "experimental_distributed_variable_components", b"experimental_distributed_variable_components", "name", b"name", "shape", b"shape", "synchronization", b"synchronization", "trainable", b"trainable"]) -> None: ...
global___SavedVariable = SavedVariable
@typing_extensions.final
@typing.final
class FunctionSpec(google.protobuf.message.Message):
"""Represents `FunctionSpec` used in `Function`. This represents a
function that has been wrapped as a TensorFlow `Function`.
@@ -498,15 +512,17 @@ class FunctionSpec(google.protobuf.message.Message):
IS_METHOD_FIELD_NUMBER: builtins.int
INPUT_SIGNATURE_FIELD_NUMBER: builtins.int
JIT_COMPILE_FIELD_NUMBER: builtins.int
is_method: builtins.bool
"""Whether this represents a class method."""
jit_compile: global___FunctionSpec.JitCompile.ValueType
@property
def fullargspec(self) -> tensorflow.core.protobuf.struct_pb2.StructuredValue:
"""Full arg spec from inspect.getfullargspec()."""
is_method: builtins.bool
"""Whether this represents a class method."""
@property
def input_signature(self) -> tensorflow.core.protobuf.struct_pb2.StructuredValue:
"""The input signature, if specified."""
jit_compile: global___FunctionSpec.JitCompile.ValueType
def __init__(
self,
*,
@@ -515,12 +531,12 @@ class FunctionSpec(google.protobuf.message.Message):
input_signature: tensorflow.core.protobuf.struct_pb2.StructuredValue | None = ...,
jit_compile: global___FunctionSpec.JitCompile.ValueType | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["fullargspec", b"fullargspec", "input_signature", b"input_signature"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["fullargspec", b"fullargspec", "input_signature", b"input_signature", "is_method", b"is_method", "jit_compile", b"jit_compile"]) -> None: ...
def HasField(self, field_name: typing.Literal["fullargspec", b"fullargspec", "input_signature", b"input_signature"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["fullargspec", b"fullargspec", "input_signature", b"input_signature", "is_method", b"is_method", "jit_compile", b"jit_compile"]) -> None: ...
global___FunctionSpec = FunctionSpec
@typing_extensions.final
@typing.final
class SavedResource(google.protobuf.message.Message):
"""A SavedResource represents a TF object that holds state during its lifetime.
An object of this type can have a reference to a:
@@ -540,11 +556,11 @@ class SavedResource(google.protobuf.message.Message):
*,
device: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["device", b"device"]) -> None: ...
def ClearField(self, field_name: typing.Literal["device", b"device"]) -> None: ...
global___SavedResource = SavedResource
@typing_extensions.final
@typing.final
class SaveableObject(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -561,6 +577,6 @@ class SaveableObject(google.protobuf.message.Message):
save_function: builtins.int | None = ...,
restore_function: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["restore_function", b"restore_function", "save_function", b"save_function"]) -> None: ...
def ClearField(self, field_name: typing.Literal["restore_function", b"restore_function", "save_function", b"save_function"]) -> None: ...
global___SaveableObject = SaveableObject
stubs/tensorflow/tensorflow/core/protobuf/saver_pb2.pyi
+3 -2
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import sys
import typing
@@ -17,7 +18,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class SaverDef(google.protobuf.message.Message):
"""Protocol buffer representing the configuration of a Saver."""
@@ -87,6 +88,6 @@ class SaverDef(google.protobuf.message.Message):
keep_checkpoint_every_n_hours: builtins.float | None = ...,
version: global___SaverDef.CheckpointFormatVersion.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["filename_tensor_name", b"filename_tensor_name", "keep_checkpoint_every_n_hours", b"keep_checkpoint_every_n_hours", "max_to_keep", b"max_to_keep", "restore_op_name", b"restore_op_name", "save_tensor_name", b"save_tensor_name", "sharded", b"sharded", "version", b"version"]) -> None: ...
def ClearField(self, field_name: typing.Literal["filename_tensor_name", b"filename_tensor_name", "keep_checkpoint_every_n_hours", b"keep_checkpoint_every_n_hours", "max_to_keep", b"max_to_keep", "restore_op_name", b"restore_op_name", "save_tensor_name", b"save_tensor_name", "sharded", b"sharded", "version", b"version"]) -> None: ...
global___SaverDef = SaverDef
stubs/tensorflow/tensorflow/core/protobuf/service_config_pb2.pyi
+22 -19
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -13,7 +14,7 @@ import tensorflow.core.protobuf.data_service_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class DispatcherConfig(google.protobuf.message.Message):
"""Configuration for a tf.data service DispatchServer.
Next id: 11
@@ -45,13 +46,6 @@ class DispatcherConfig(google.protobuf.message.Message):
"""Whether to run in fault tolerant mode, where dispatcher state is saved
across restarts. Requires that `work_dir` is nonempty.
"""
@property
def worker_addresses(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""(Optional.) If the job uses auto-sharding, it needs to specify a fixed list
of worker addresses that will register with the dispatcher. The worker
addresses should be in the format "host" or "host:port", where "port" is an
integer, named port, or %port% to match any port.
"""
deployment_mode: tensorflow.core.protobuf.data_service_pb2.DeploymentMode.ValueType
"""(Optional.) tf.data service deployment mode. Supported values are "REMOTE",
"COLOCATED", and "HYBRID". If unspecified, it is assumed to be "REMOTE".
@@ -76,6 +70,14 @@ class DispatcherConfig(google.protobuf.message.Message):
"""How long to wait for a worker to heartbeat before considering it missing.
A value of 0 indicates that the timeout should be left to the runtime.
"""
@property
def worker_addresses(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""(Optional.) If the job uses auto-sharding, it needs to specify a fixed list
of worker addresses that will register with the dispatcher. The worker
addresses should be in the format "host" or "host:port", where "port" is an
integer, named port, or %port% to match any port.
"""
def __init__(
self,
*,
@@ -90,11 +92,11 @@ class DispatcherConfig(google.protobuf.message.Message):
client_timeout_ms: builtins.int | None = ...,
worker_timeout_ms: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["client_timeout_ms", b"client_timeout_ms", "deployment_mode", b"deployment_mode", "fault_tolerant_mode", b"fault_tolerant_mode", "job_gc_check_interval_ms", b"job_gc_check_interval_ms", "job_gc_timeout_ms", b"job_gc_timeout_ms", "port", b"port", "protocol", b"protocol", "work_dir", b"work_dir", "worker_addresses", b"worker_addresses", "worker_timeout_ms", b"worker_timeout_ms"]) -> None: ...
def ClearField(self, field_name: typing.Literal["client_timeout_ms", b"client_timeout_ms", "deployment_mode", b"deployment_mode", "fault_tolerant_mode", b"fault_tolerant_mode", "job_gc_check_interval_ms", b"job_gc_check_interval_ms", "job_gc_timeout_ms", b"job_gc_timeout_ms", "port", b"port", "protocol", b"protocol", "work_dir", b"work_dir", "worker_addresses", b"worker_addresses", "worker_timeout_ms", b"worker_timeout_ms"]) -> None: ...
global___DispatcherConfig = DispatcherConfig
@typing_extensions.final
@typing.final
class WorkerConfig(google.protobuf.message.Message):
"""Configuration for a tf.data service WorkerServer.
Next id: 12
@@ -126,13 +128,6 @@ class WorkerConfig(google.protobuf.message.Message):
will be replaced with the worker's bound port. This is useful when the port
is set to `0`.
"""
@property
def worker_tags(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Tags attached to the worker. This allows reading from selected workers.
For example, by applying a "COLOCATED" tag, tf.data service is able to read
from the local tf.data worker if one exists, then from off-TF-host workers,
to avoid cross-TF-host reads.
"""
heartbeat_interval_ms: builtins.int
"""How often the worker should heartbeat to the master. A value of 0 indicates
that the decision should be left up to the runtime.
@@ -158,6 +153,14 @@ class WorkerConfig(google.protobuf.message.Message):
process the final requests. This is used to achieve clean shutdown in unit
tests.
"""
@property
def worker_tags(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Tags attached to the worker. This allows reading from selected workers.
For example, by applying a "COLOCATED" tag, tf.data service is able to read
from the local tf.data worker if one exists, then from off-TF-host workers,
to avoid cross-TF-host reads.
"""
def __init__(
self,
*,
@@ -173,6 +176,6 @@ class WorkerConfig(google.protobuf.message.Message):
cross_trainer_cache_size_bytes: builtins.int | None = ...,
shutdown_quiet_period_ms: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["cross_trainer_cache_size_bytes", b"cross_trainer_cache_size_bytes", "data_transfer_address", b"data_transfer_address", "data_transfer_protocol", b"data_transfer_protocol", "dispatcher_address", b"dispatcher_address", "dispatcher_timeout_ms", b"dispatcher_timeout_ms", "heartbeat_interval_ms", b"heartbeat_interval_ms", "port", b"port", "protocol", b"protocol", "shutdown_quiet_period_ms", b"shutdown_quiet_period_ms", "worker_address", b"worker_address", "worker_tags", b"worker_tags"]) -> None: ...
def ClearField(self, field_name: typing.Literal["cross_trainer_cache_size_bytes", b"cross_trainer_cache_size_bytes", "data_transfer_address", b"data_transfer_address", "data_transfer_protocol", b"data_transfer_protocol", "dispatcher_address", b"dispatcher_address", "dispatcher_timeout_ms", b"dispatcher_timeout_ms", "heartbeat_interval_ms", b"heartbeat_interval_ms", "port", b"port", "protocol", b"protocol", "shutdown_quiet_period_ms", b"shutdown_quiet_period_ms", "worker_address", b"worker_address", "worker_tags", b"worker_tags"]) -> None: ...
global___WorkerConfig = WorkerConfig
stubs/tensorflow/tensorflow/core/protobuf/snapshot_pb2.pyi
+19 -17
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -15,7 +16,7 @@ import tensorflow.core.framework.types_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class SnapshotRecord(google.protobuf.message.Message):
"""Each SnapshotRecord represents one batch of pre-processed input data. A batch
consists of a list of tensors that we encode as TensorProtos. This message
@@ -32,11 +33,11 @@ class SnapshotRecord(google.protobuf.message.Message):
*,
tensor: collections.abc.Iterable[tensorflow.core.framework.tensor_pb2.TensorProto] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["tensor", b"tensor"]) -> None: ...
def ClearField(self, field_name: typing.Literal["tensor", b"tensor"]) -> None: ...
global___SnapshotRecord = SnapshotRecord
@typing_extensions.final
@typing.final
class SnapshotMetadataRecord(google.protobuf.message.Message):
"""This stores the metadata information present in each snapshot record."""
@@ -59,12 +60,13 @@ class SnapshotMetadataRecord(google.protobuf.message.Message):
"""Time when we started creating this snapshot."""
version: builtins.int
"""Version of the snapshot data file format."""
@property
def dtype(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[tensorflow.core.framework.types_pb2.DataType.ValueType]:
"""A list of tensor dtype corresponding to each element of the snapshot."""
num_elements: builtins.int
"""The number of elements in the snapshot."""
finalized: builtins.bool
@property
def dtype(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[tensorflow.core.framework.types_pb2.DataType.ValueType]:
"""A list of tensor dtype corresponding to each element of the snapshot."""
def __init__(
self,
*,
@@ -76,11 +78,11 @@ class SnapshotMetadataRecord(google.protobuf.message.Message):
num_elements: builtins.int | None = ...,
finalized: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["creation_timestamp", b"creation_timestamp", "dtype", b"dtype", "finalized", b"finalized", "graph_hash", b"graph_hash", "num_elements", b"num_elements", "run_id", b"run_id", "version", b"version"]) -> None: ...
def ClearField(self, field_name: typing.Literal["creation_timestamp", b"creation_timestamp", "dtype", b"dtype", "finalized", b"finalized", "graph_hash", b"graph_hash", "num_elements", b"num_elements", "run_id", b"run_id", "version", b"version"]) -> None: ...
global___SnapshotMetadataRecord = SnapshotMetadataRecord
@typing_extensions.final
@typing.final
class TensorMetadata(google.protobuf.message.Message):
"""Metadata for a single tensor in the Snapshot Record."""
@@ -88,22 +90,22 @@ class TensorMetadata(google.protobuf.message.Message):
TENSOR_SHAPE_FIELD_NUMBER: builtins.int
TENSOR_SIZE_BYTES_FIELD_NUMBER: builtins.int
@property
def tensor_shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: ...
tensor_size_bytes: builtins.int
"""Number of uncompressed bytes used to store the tensor representation."""
@property
def tensor_shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: ...
def __init__(
self,
*,
tensor_shape: tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto | None = ...,
tensor_size_bytes: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["tensor_shape", b"tensor_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["tensor_shape", b"tensor_shape", "tensor_size_bytes", b"tensor_size_bytes"]) -> None: ...
def HasField(self, field_name: typing.Literal["tensor_shape", b"tensor_shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["tensor_shape", b"tensor_shape", "tensor_size_bytes", b"tensor_size_bytes"]) -> None: ...
global___TensorMetadata = TensorMetadata
@typing_extensions.final
@typing.final
class SnapshotTensorMetadata(google.protobuf.message.Message):
"""Metadata for all the tensors in a Snapshot Record."""
@@ -117,11 +119,11 @@ class SnapshotTensorMetadata(google.protobuf.message.Message):
*,
tensor_metadata: collections.abc.Iterable[global___TensorMetadata] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["tensor_metadata", b"tensor_metadata"]) -> None: ...
def ClearField(self, field_name: typing.Literal["tensor_metadata", b"tensor_metadata"]) -> None: ...
global___SnapshotTensorMetadata = SnapshotTensorMetadata
@typing_extensions.final
@typing.final
class DistributedSnapshotMetadata(google.protobuf.message.Message):
"""Metadata for a `tf.data.Dataset` distributed snapshot."""
@@ -142,6 +144,6 @@ class DistributedSnapshotMetadata(google.protobuf.message.Message):
element_spec: builtins.bytes | None = ...,
compression: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["compression", b"compression", "element_spec", b"element_spec"]) -> None: ...
def ClearField(self, field_name: typing.Literal["compression", b"compression", "element_spec", b"element_spec"]) -> None: ...
global___DistributedSnapshotMetadata = DistributedSnapshotMetadata
stubs/tensorflow/tensorflow/core/protobuf/struct_pb2.pyi
+50 -39
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -22,7 +23,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class StructuredValue(google.protobuf.message.Message):
"""`StructuredValue` represents a dynamically typed value representing various
data structures that are inspired by Python data structures typically used in
@@ -66,9 +67,6 @@ class StructuredValue(google.protobuf.message.Message):
TUPLE_VALUE_FIELD_NUMBER: builtins.int
DICT_VALUE_FIELD_NUMBER: builtins.int
NAMED_TUPLE_VALUE_FIELD_NUMBER: builtins.int
@property
def none_value(self) -> global___NoneValue:
"""Represents None."""
float64_value: builtins.float
"""Represents a double-precision floating-point value (a Python `float`)."""
int64_value: builtins.int
@@ -85,32 +83,44 @@ class StructuredValue(google.protobuf.message.Message):
"""
bool_value: builtins.bool
"""Represents a boolean value."""
@property
def tensor_shape_value(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto:
"""Represents a TensorShape."""
tensor_dtype_value: tensorflow.core.framework.types_pb2.DataType.ValueType
"""Represents an enum value for dtype."""
@property
def none_value(self) -> global___NoneValue:
"""Represents None."""
@property
def tensor_shape_value(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto:
"""Represents a TensorShape."""
@property
def tensor_spec_value(self) -> global___TensorSpecProto:
"""Represents a value for tf.TensorSpec."""
@property
def type_spec_value(self) -> global___TypeSpecProto:
"""Represents a value for tf.TypeSpec."""
@property
def bounded_tensor_spec_value(self) -> global___BoundedTensorSpecProto:
"""Represents a value for tf.BoundedTensorSpec."""
@property
def list_value(self) -> global___ListValue:
"""Represents a list of `Value`."""
@property
def tuple_value(self) -> global___TupleValue:
"""Represents a tuple of `Value`."""
@property
def dict_value(self) -> global___DictValue:
"""Represents a dict `Value`."""
@property
def named_tuple_value(self) -> global___NamedTupleValue:
"""Represents Python's namedtuple."""
def __init__(
self,
*,
@@ -129,13 +139,13 @@ class StructuredValue(google.protobuf.message.Message):
dict_value: global___DictValue | None = ...,
named_tuple_value: global___NamedTupleValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["bool_value", b"bool_value", "bounded_tensor_spec_value", b"bounded_tensor_spec_value", "dict_value", b"dict_value", "float64_value", b"float64_value", "int64_value", b"int64_value", "kind", b"kind", "list_value", b"list_value", "named_tuple_value", b"named_tuple_value", "none_value", b"none_value", "string_value", b"string_value", "tensor_dtype_value", b"tensor_dtype_value", "tensor_shape_value", b"tensor_shape_value", "tensor_spec_value", b"tensor_spec_value", "tuple_value", b"tuple_value", "type_spec_value", b"type_spec_value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["bool_value", b"bool_value", "bounded_tensor_spec_value", b"bounded_tensor_spec_value", "dict_value", b"dict_value", "float64_value", b"float64_value", "int64_value", b"int64_value", "kind", b"kind", "list_value", b"list_value", "named_tuple_value", b"named_tuple_value", "none_value", b"none_value", "string_value", b"string_value", "tensor_dtype_value", b"tensor_dtype_value", "tensor_shape_value", b"tensor_shape_value", "tensor_spec_value", b"tensor_spec_value", "tuple_value", b"tuple_value", "type_spec_value", b"type_spec_value"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["kind", b"kind"]) -> typing_extensions.Literal["none_value", "float64_value", "int64_value", "string_value", "bool_value", "tensor_shape_value", "tensor_dtype_value", "tensor_spec_value", "type_spec_value", "bounded_tensor_spec_value", "list_value", "tuple_value", "dict_value", "named_tuple_value"] | None: ...
def HasField(self, field_name: typing.Literal["bool_value", b"bool_value", "bounded_tensor_spec_value", b"bounded_tensor_spec_value", "dict_value", b"dict_value", "float64_value", b"float64_value", "int64_value", b"int64_value", "kind", b"kind", "list_value", b"list_value", "named_tuple_value", b"named_tuple_value", "none_value", b"none_value", "string_value", b"string_value", "tensor_dtype_value", b"tensor_dtype_value", "tensor_shape_value", b"tensor_shape_value", "tensor_spec_value", b"tensor_spec_value", "tuple_value", b"tuple_value", "type_spec_value", b"type_spec_value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["bool_value", b"bool_value", "bounded_tensor_spec_value", b"bounded_tensor_spec_value", "dict_value", b"dict_value", "float64_value", b"float64_value", "int64_value", b"int64_value", "kind", b"kind", "list_value", b"list_value", "named_tuple_value", b"named_tuple_value", "none_value", b"none_value", "string_value", b"string_value", "tensor_dtype_value", b"tensor_dtype_value", "tensor_shape_value", b"tensor_shape_value", "tensor_spec_value", b"tensor_spec_value", "tuple_value", b"tuple_value", "type_spec_value", b"type_spec_value"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["kind", b"kind"]) -> typing.Literal["none_value", "float64_value", "int64_value", "string_value", "bool_value", "tensor_shape_value", "tensor_dtype_value", "tensor_spec_value", "type_spec_value", "bounded_tensor_spec_value", "list_value", "tuple_value", "dict_value", "named_tuple_value"] | None: ...
global___StructuredValue = StructuredValue
@typing_extensions.final
@typing.final
class NoneValue(google.protobuf.message.Message):
"""Represents None."""
@@ -147,7 +157,7 @@ class NoneValue(google.protobuf.message.Message):
global___NoneValue = NoneValue
@typing_extensions.final
@typing.final
class ListValue(google.protobuf.message.Message):
"""Represents a Python list."""
@@ -161,11 +171,11 @@ class ListValue(google.protobuf.message.Message):
*,
values: collections.abc.Iterable[global___StructuredValue] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["values", b"values"]) -> None: ...
def ClearField(self, field_name: typing.Literal["values", b"values"]) -> None: ...
global___ListValue = ListValue
@typing_extensions.final
@typing.final
class TupleValue(google.protobuf.message.Message):
"""Represents a Python tuple."""
@@ -179,11 +189,11 @@ class TupleValue(google.protobuf.message.Message):
*,
values: collections.abc.Iterable[global___StructuredValue] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["values", b"values"]) -> None: ...
def ClearField(self, field_name: typing.Literal["values", b"values"]) -> None: ...
global___TupleValue = TupleValue
@typing_extensions.final
@typing.final
class DictValue(google.protobuf.message.Message):
"""Represents a Python dict keyed by `str`.
The comment on Unicode from Value.string_value applies analogously.
@@ -191,7 +201,7 @@ class DictValue(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class FieldsEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -206,8 +216,8 @@ class DictValue(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___StructuredValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
FIELDS_FIELD_NUMBER: builtins.int
@property
@@ -217,11 +227,11 @@ class DictValue(google.protobuf.message.Message):
*,
fields: collections.abc.Mapping[builtins.str, global___StructuredValue] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["fields", b"fields"]) -> None: ...
def ClearField(self, field_name: typing.Literal["fields", b"fields"]) -> None: ...
global___DictValue = DictValue
@typing_extensions.final
@typing.final
class PairValue(google.protobuf.message.Message):
"""Represents a (key, value) pair."""
@@ -238,12 +248,12 @@ class PairValue(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___StructuredValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
global___PairValue = PairValue
@typing_extensions.final
@typing.final
class NamedTupleValue(google.protobuf.message.Message):
"""Represents Python's namedtuple."""
@@ -260,11 +270,11 @@ class NamedTupleValue(google.protobuf.message.Message):
name: builtins.str | None = ...,
values: collections.abc.Iterable[global___PairValue] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "values", b"values"]) -> None: ...
def ClearField(self, field_name: typing.Literal["name", b"name", "values", b"values"]) -> None: ...
global___NamedTupleValue = NamedTupleValue
@typing_extensions.final
@typing.final
class TensorSpecProto(google.protobuf.message.Message):
"""A protobuf to represent tf.TensorSpec."""
@@ -274,9 +284,9 @@ class TensorSpecProto(google.protobuf.message.Message):
SHAPE_FIELD_NUMBER: builtins.int
DTYPE_FIELD_NUMBER: builtins.int
name: builtins.str
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: ...
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
def __init__(
self,
*,
@@ -284,12 +294,12 @@ class TensorSpecProto(google.protobuf.message.Message):
shape: tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto | None = ...,
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["dtype", b"dtype", "name", b"name", "shape", b"shape"]) -> None: ...
def HasField(self, field_name: typing.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["dtype", b"dtype", "name", b"name", "shape", b"shape"]) -> None: ...
global___TensorSpecProto = TensorSpecProto
@typing_extensions.final
@typing.final
class BoundedTensorSpecProto(google.protobuf.message.Message):
"""A protobuf to represent tf.BoundedTensorSpec."""
@@ -301,9 +311,9 @@ class BoundedTensorSpecProto(google.protobuf.message.Message):
MINIMUM_FIELD_NUMBER: builtins.int
MAXIMUM_FIELD_NUMBER: builtins.int
name: builtins.str
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: ...
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
@property
def minimum(self) -> tensorflow.core.framework.tensor_pb2.TensorProto: ...
@property
@@ -317,12 +327,12 @@ class BoundedTensorSpecProto(google.protobuf.message.Message):
minimum: tensorflow.core.framework.tensor_pb2.TensorProto | None = ...,
maximum: tensorflow.core.framework.tensor_pb2.TensorProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["maximum", b"maximum", "minimum", b"minimum", "shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["dtype", b"dtype", "maximum", b"maximum", "minimum", b"minimum", "name", b"name", "shape", b"shape"]) -> None: ...
def HasField(self, field_name: typing.Literal["maximum", b"maximum", "minimum", b"minimum", "shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["dtype", b"dtype", "maximum", b"maximum", "minimum", b"minimum", "name", b"name", "shape", b"shape"]) -> None: ...
global___BoundedTensorSpecProto = BoundedTensorSpecProto
@typing_extensions.final
@typing.final
class TypeSpecProto(google.protobuf.message.Message):
"""Represents a tf.TypeSpec"""
@@ -392,9 +402,6 @@ class TypeSpecProto(google.protobuf.message.Message):
TYPE_SPEC_CLASS_NAME_FIELD_NUMBER: builtins.int
NUM_FLAT_COMPONENTS_FIELD_NUMBER: builtins.int
type_spec_class: global___TypeSpecProto.TypeSpecClass.ValueType
@property
def type_state(self) -> global___StructuredValue:
"""The value returned by TypeSpec._serialize()."""
type_spec_class_name: builtins.str
"""The name of the TypeSpec class.
* If type_spec_class == REGISTERED_TYPE_SPEC, the TypeSpec class is
@@ -407,6 +414,10 @@ class TypeSpecProto(google.protobuf.message.Message):
"""
num_flat_components: builtins.int
"""The number of flat tensor components required by this TypeSpec."""
@property
def type_state(self) -> global___StructuredValue:
"""The value returned by TypeSpec._serialize()."""
def __init__(
self,
*,
@@ -415,7 +426,7 @@ class TypeSpecProto(google.protobuf.message.Message):
type_spec_class_name: builtins.str | None = ...,
num_flat_components: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["type_state", b"type_state"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["num_flat_components", b"num_flat_components", "type_spec_class", b"type_spec_class", "type_spec_class_name", b"type_spec_class_name", "type_state", b"type_state"]) -> None: ...
def HasField(self, field_name: typing.Literal["type_state", b"type_state"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["num_flat_components", b"num_flat_components", "type_spec_class", b"type_spec_class", "type_spec_class_name", b"type_spec_class_name", "type_state", b"type_state"]) -> None: ...
global___TypeSpecProto = TypeSpecProto
stubs/tensorflow/tensorflow/core/protobuf/tensor_bundle_pb2.pyi
+11 -8
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -23,7 +24,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class BundleHeaderProto(google.protobuf.message.Message):
"""Protos used in the tensor bundle module (tf/core/util/tensor_bundle/).
@@ -67,6 +68,7 @@ class BundleHeaderProto(google.protobuf.message.Message):
@property
def version(self) -> tensorflow.core.framework.versions_pb2.VersionDef:
"""Versioning of the tensor bundle format."""
def __init__(
self,
*,
@@ -74,12 +76,12 @@ class BundleHeaderProto(google.protobuf.message.Message):
endianness: global___BundleHeaderProto.Endianness.ValueType | None = ...,
version: tensorflow.core.framework.versions_pb2.VersionDef | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["version", b"version"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["endianness", b"endianness", "num_shards", b"num_shards", "version", b"version"]) -> None: ...
def HasField(self, field_name: typing.Literal["version", b"version"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["endianness", b"endianness", "num_shards", b"num_shards", "version", b"version"]) -> None: ...
global___BundleHeaderProto = BundleHeaderProto
@typing_extensions.final
@typing.final
class BundleEntryProto(google.protobuf.message.Message):
"""Describes the metadata related to a checkpointed tensor."""
@@ -94,8 +96,6 @@ class BundleEntryProto(google.protobuf.message.Message):
SLICES_FIELD_NUMBER: builtins.int
dtype: tensorflow.core.framework.types_pb2.DataType.ValueType
"""The tensor dtype and shape."""
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: ...
shard_id: builtins.int
"""The binary content of the tensor lies in:
File "shard_id": bytes [offset, offset + size).
@@ -105,6 +105,8 @@ class BundleEntryProto(google.protobuf.message.Message):
crc32c: builtins.int
"""The CRC32C checksum of the tensor bytes."""
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: ...
@property
def slices(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.tensor_slice_pb2.TensorSliceProto]:
"""Iff present, this entry represents a partitioned tensor. The previous
fields are interpreted as follows:
@@ -114,6 +116,7 @@ class BundleEntryProto(google.protobuf.message.Message):
These information for each slice can be looked up in their own
BundleEntryProto, keyed by each "slice_name".
"""
def __init__(
self,
*,
@@ -125,7 +128,7 @@ class BundleEntryProto(google.protobuf.message.Message):
crc32c: builtins.int | None = ...,
slices: collections.abc.Iterable[tensorflow.core.framework.tensor_slice_pb2.TensorSliceProto] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["crc32c", b"crc32c", "dtype", b"dtype", "offset", b"offset", "shape", b"shape", "shard_id", b"shard_id", "size", b"size", "slices", b"slices"]) -> None: ...
def HasField(self, field_name: typing.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["crc32c", b"crc32c", "dtype", b"dtype", "offset", b"offset", "shape", b"shape", "shard_id", b"shard_id", "size", b"size", "slices", b"slices"]) -> None: ...
global___BundleEntryProto = BundleEntryProto
stubs/tensorflow/tensorflow/core/protobuf/tensorflow_server_pb2.pyi
+14 -10
View File
@@ -16,8 +16,9 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
@@ -27,7 +28,7 @@ import tensorflow.core.protobuf.device_filters_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class ServerDef(google.protobuf.message.Message):
"""Defines the configuration of a single TensorFlow server."""
@@ -40,9 +41,6 @@ class ServerDef(google.protobuf.message.Message):
PROTOCOL_FIELD_NUMBER: builtins.int
PORT_FIELD_NUMBER: builtins.int
CLUSTER_DEVICE_FILTERS_FIELD_NUMBER: builtins.int
@property
def cluster(self) -> tensorflow.core.protobuf.cluster_pb2.ClusterDef:
"""The cluster of which this server is a member."""
job_name: builtins.str
"""The name of the job of which this server is a member.
@@ -55,9 +53,6 @@ class ServerDef(google.protobuf.message.Message):
NOTE: The `cluster` field must contain a `JobDef` with a matching `name`
and a mapping in its `tasks` field for this index.
"""
@property
def default_session_config(self) -> tensorflow.core.protobuf.config_pb2.ConfigProto:
"""The default configuration for sessions that run on this server."""
protocol: builtins.str
"""The protocol to be used by this server.
@@ -65,11 +60,20 @@ class ServerDef(google.protobuf.message.Message):
"""
port: builtins.int
"""The server port. If not set, then we identify the port from the job_name."""
@property
def cluster(self) -> tensorflow.core.protobuf.cluster_pb2.ClusterDef:
"""The cluster of which this server is a member."""
@property
def default_session_config(self) -> tensorflow.core.protobuf.config_pb2.ConfigProto:
"""The default configuration for sessions that run on this server."""
@property
def cluster_device_filters(self) -> tensorflow.core.protobuf.device_filters_pb2.ClusterDeviceFilters:
"""Device filters for remote tasks in the cluster.
NOTE: This is an experimental feature and only effective in TensorFlow 2.x.
"""
def __init__(
self,
*,
@@ -81,7 +85,7 @@ class ServerDef(google.protobuf.message.Message):
port: builtins.int | None = ...,
cluster_device_filters: tensorflow.core.protobuf.device_filters_pb2.ClusterDeviceFilters | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["cluster", b"cluster", "cluster_device_filters", b"cluster_device_filters", "default_session_config", b"default_session_config"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["cluster", b"cluster", "cluster_device_filters", b"cluster_device_filters", "default_session_config", b"default_session_config", "job_name", b"job_name", "port", b"port", "protocol", b"protocol", "task_index", b"task_index"]) -> None: ...
def HasField(self, field_name: typing.Literal["cluster", b"cluster", "cluster_device_filters", b"cluster_device_filters", "default_session_config", b"default_session_config"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["cluster", b"cluster", "cluster_device_filters", b"cluster_device_filters", "default_session_config", b"default_session_config", "job_name", b"job_name", "port", b"port", "protocol", b"protocol", "task_index", b"task_index"]) -> None: ...
global___ServerDef = ServerDef
stubs/tensorflow/tensorflow/core/protobuf/tpu/compilation_result_pb2.pyi
+5 -3
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -21,7 +22,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class CompilationResultProto(google.protobuf.message.Message):
"""Describes the result of a TPU compilation. This is also used as TPU
compilation result status payload.
@@ -50,10 +51,11 @@ class CompilationResultProto(google.protobuf.message.Message):
status_code: tensorflow.tsl.protobuf.error_codes_pb2.Code.ValueType
"""The error message, if any, returned during compilation."""
status_error_message: builtins.str
error_code: global___CompilationResultProto.ErrorCode.ValueType
@property
def hlo_protos(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.compiler.xla.service.hlo_pb2.HloProto]:
"""HLO proto."""
error_code: global___CompilationResultProto.ErrorCode.ValueType
def __init__(
self,
*,
@@ -62,6 +64,6 @@ class CompilationResultProto(google.protobuf.message.Message):
hlo_protos: collections.abc.Iterable[tensorflow.compiler.xla.service.hlo_pb2.HloProto] | None = ...,
error_code: global___CompilationResultProto.ErrorCode.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["error_code", b"error_code", "hlo_protos", b"hlo_protos", "status_code", b"status_code", "status_error_message", b"status_error_message"]) -> None: ...
def ClearField(self, field_name: typing.Literal["error_code", b"error_code", "hlo_protos", b"hlo_protos", "status_code", b"status_code", "status_error_message", b"status_error_message"]) -> None: ...
global___CompilationResultProto = CompilationResultProto
stubs/tensorflow/tensorflow/core/protobuf/tpu/dynamic_padding_pb2.pyi
+4 -3
View File
@@ -2,15 +2,16 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class PaddingMap(google.protobuf.message.Message):
"""A mapping between the dynamic shape dimension of an input and the arg that
represents the real shape.
@@ -36,6 +37,6 @@ class PaddingMap(google.protobuf.message.Message):
shape_index: builtins.int | None = ...,
padding_arg_index: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["arg_index", b"arg_index", "padding_arg_index", b"padding_arg_index", "shape_index", b"shape_index"]) -> None: ...
def ClearField(self, field_name: typing.Literal["arg_index", b"arg_index", "padding_arg_index", b"padding_arg_index", "shape_index", b"shape_index"]) -> None: ...
global___PaddingMap = PaddingMap
stubs/tensorflow/tensorflow/core/protobuf/tpu/optimization_parameters_pb2.pyi
+90 -81
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import sys
import typing
@@ -19,7 +20,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class ClippingLimits(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -28,21 +29,23 @@ class ClippingLimits(google.protobuf.message.Message):
@property
def lower(self) -> google.protobuf.wrappers_pb2.FloatValue:
"""-inf if not set"""
@property
def upper(self) -> google.protobuf.wrappers_pb2.FloatValue:
"""+inf if not set"""
def __init__(
self,
*,
lower: google.protobuf.wrappers_pb2.FloatValue | None = ...,
upper: google.protobuf.wrappers_pb2.FloatValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["lower", b"lower", "upper", b"upper"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["lower", b"lower", "upper", b"upper"]) -> None: ...
def HasField(self, field_name: typing.Literal["lower", b"lower", "upper", b"upper"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["lower", b"lower", "upper", b"upper"]) -> None: ...
global___ClippingLimits = ClippingLimits
@typing_extensions.final
@typing.final
class SimulatedQuantization(google.protobuf.message.Message):
"""Configuration for simulated quantization; simulated quantization is used to
reduce training/serving skew when the serving variables are quantized. The
@@ -66,11 +69,12 @@ class SimulatedQuantization(google.protobuf.message.Message):
NUM_BUCKETS_FIELD_NUMBER: builtins.int
enabled: builtins.bool
"""Whether simulated quantization is enabled."""
num_buckets: builtins.int
"""Number of possible quantized values."""
@property
def clipping_limits(self) -> global___ClippingLimits:
"""Minimum and maximum values of the range used for quantization."""
num_buckets: builtins.int
"""Number of possible quantized values."""
def __init__(
self,
*,
@@ -78,12 +82,12 @@ class SimulatedQuantization(google.protobuf.message.Message):
clipping_limits: global___ClippingLimits | None = ...,
num_buckets: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["clipping_limits", b"clipping_limits"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["clipping_limits", b"clipping_limits", "enabled", b"enabled", "num_buckets", b"num_buckets"]) -> None: ...
def HasField(self, field_name: typing.Literal["clipping_limits", b"clipping_limits"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["clipping_limits", b"clipping_limits", "enabled", b"enabled", "num_buckets", b"num_buckets"]) -> None: ...
global___SimulatedQuantization = SimulatedQuantization
@typing_extensions.final
@typing.final
class DynamicLearningRate(google.protobuf.message.Message):
"""Dynamic learning rate specification in the TPUEmbeddingConfiguration. The
actual learning rates are provided as a scalar input list to the
@@ -127,11 +131,11 @@ class DynamicLearningRate(google.protobuf.message.Message):
*,
tag: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["tag", b"tag"]) -> None: ...
def ClearField(self, field_name: typing.Literal["tag", b"tag"]) -> None: ...
global___DynamicLearningRate = DynamicLearningRate
@typing_extensions.final
@typing.final
class LearningRate(google.protobuf.message.Message):
"""Source of learning rate to use."""
@@ -148,13 +152,13 @@ class LearningRate(google.protobuf.message.Message):
constant: builtins.float | None = ...,
dynamic: global___DynamicLearningRate | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["constant", b"constant", "dynamic", b"dynamic", "learning_rate", b"learning_rate"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["constant", b"constant", "dynamic", b"dynamic", "learning_rate", b"learning_rate"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["learning_rate", b"learning_rate"]) -> typing_extensions.Literal["constant", "dynamic"] | None: ...
def HasField(self, field_name: typing.Literal["constant", b"constant", "dynamic", b"dynamic", "learning_rate", b"learning_rate"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["constant", b"constant", "dynamic", b"dynamic", "learning_rate", b"learning_rate"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["learning_rate", b"learning_rate"]) -> typing.Literal["constant", "dynamic"] | None: ...
global___LearningRate = LearningRate
@typing_extensions.final
@typing.final
class AdagradParameters(google.protobuf.message.Message):
"""Each optimizer's parameter proto has a link to its documentation and CPU
implementation (if available) for user reference.
@@ -171,7 +175,7 @@ class AdagradParameters(google.protobuf.message.Message):
global___AdagradParameters = AdagradParameters
@typing_extensions.final
@typing.final
class AdagradMomentumParameters(google.protobuf.message.Message):
"""This optimizer combines the Adagrad and Momentum update rules.
accum(new) = beta2 == 1.0 ?
@@ -212,11 +216,11 @@ class AdagradMomentumParameters(google.protobuf.message.Message):
beta2: builtins.float | None = ...,
epsilon: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["beta2", b"beta2", "epsilon", b"epsilon", "exponent", b"exponent", "momentum", b"momentum", "use_nesterov", b"use_nesterov"]) -> None: ...
def ClearField(self, field_name: typing.Literal["beta2", b"beta2", "epsilon", b"epsilon", "exponent", b"exponent", "momentum", b"momentum", "use_nesterov", b"use_nesterov"]) -> None: ...
global___AdagradMomentumParameters = AdagradMomentumParameters
@typing_extensions.final
@typing.final
class BoundedAdagradParameters(google.protobuf.message.Message):
"""Algorithm in http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf."""
@@ -245,11 +249,11 @@ class BoundedAdagradParameters(google.protobuf.message.Message):
max_var_update: builtins.float | None = ...,
max_accumulator: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["max_accumulator", b"max_accumulator", "max_var_update", b"max_var_update", "update_accumulator_first", b"update_accumulator_first"]) -> None: ...
def ClearField(self, field_name: typing.Literal["max_accumulator", b"max_accumulator", "max_var_update", b"max_var_update", "update_accumulator_first", b"update_accumulator_first"]) -> None: ...
global___BoundedAdagradParameters = BoundedAdagradParameters
@typing_extensions.final
@typing.final
class StochasticGradientDescentParameters(google.protobuf.message.Message):
"""https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/SGD
https://github.com/tensorflow/tensorflow/blob/6b6471f3ffb7f1fefe42d814aa5fb9ab7a535b58/tensorflow/core/kernels/training_ops.cc#L629
@@ -263,7 +267,7 @@ class StochasticGradientDescentParameters(google.protobuf.message.Message):
global___StochasticGradientDescentParameters = StochasticGradientDescentParameters
@typing_extensions.final
@typing.final
class FtrlParameters(google.protobuf.message.Message):
"""https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Ftrl
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41159.pdf
@@ -313,11 +317,11 @@ class FtrlParameters(google.protobuf.message.Message):
multiply_linear_by_lr: builtins.bool | None = ...,
allow_zero_accumulator: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["allow_zero_accumulator", b"allow_zero_accumulator", "beta", b"beta", "l1", b"l1", "l2", b"l2", "lr_power", b"lr_power", "multiply_linear_by_lr", b"multiply_linear_by_lr"]) -> None: ...
def ClearField(self, field_name: typing.Literal["allow_zero_accumulator", b"allow_zero_accumulator", "beta", b"beta", "l1", b"l1", "l2", b"l2", "lr_power", b"lr_power", "multiply_linear_by_lr", b"multiply_linear_by_lr"]) -> None: ...
global___FtrlParameters = FtrlParameters
@typing_extensions.final
@typing.final
class AdamParameters(google.protobuf.message.Message):
"""The Adam optimizer does not implement hyper-parameter update due to hardware
limitations; use the dynamic learning rate feature instead, setting the
@@ -362,11 +366,11 @@ class AdamParameters(google.protobuf.message.Message):
use_non_lazy_adam: builtins.bool | None = ...,
use_sum_inside_sqrt: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["beta1", b"beta1", "beta2", b"beta2", "epsilon", b"epsilon", "use_non_lazy_adam", b"use_non_lazy_adam", "use_sum_inside_sqrt", b"use_sum_inside_sqrt"]) -> None: ...
def ClearField(self, field_name: typing.Literal["beta1", b"beta1", "beta2", b"beta2", "epsilon", b"epsilon", "use_non_lazy_adam", b"use_non_lazy_adam", "use_sum_inside_sqrt", b"use_sum_inside_sqrt"]) -> None: ...
global___AdamParameters = AdamParameters
@typing_extensions.final
@typing.final
class MomentumParameters(google.protobuf.message.Message):
"""https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/SGD
https://github.com/tensorflow/tensorflow/blob/6b6471f3ffb7f1fefe42d814aa5fb9ab7a535b58/tensorflow/core/kernels/training_ops.cc#L3068
@@ -384,11 +388,11 @@ class MomentumParameters(google.protobuf.message.Message):
momentum: builtins.float | None = ...,
use_nesterov: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["momentum", b"momentum", "use_nesterov", b"use_nesterov"]) -> None: ...
def ClearField(self, field_name: typing.Literal["momentum", b"momentum", "use_nesterov", b"use_nesterov"]) -> None: ...
global___MomentumParameters = MomentumParameters
@typing_extensions.final
@typing.final
class RmsPropParameters(google.protobuf.message.Message):
"""https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/RMSprop
https://github.com/tensorflow/tensorflow/blob/6b6471f3ffb7f1fefe42d814aa5fb9ab7a535b58/tensorflow/core/kernels/training_ops.cc#L4229
@@ -409,11 +413,11 @@ class RmsPropParameters(google.protobuf.message.Message):
momentum: builtins.float | None = ...,
epsilon: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["epsilon", b"epsilon", "momentum", b"momentum", "rho", b"rho"]) -> None: ...
def ClearField(self, field_name: typing.Literal["epsilon", b"epsilon", "momentum", b"momentum", "rho", b"rho"]) -> None: ...
global___RmsPropParameters = RmsPropParameters
@typing_extensions.final
@typing.final
class CenteredRmsPropParameters(google.protobuf.message.Message):
"""https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/RMSprop
https://github.com/tensorflow/tensorflow/blob/6b6471f3ffb7f1fefe42d814aa5fb9ab7a535b58/tensorflow/core/kernels/training_ops.cc#L4358
@@ -434,11 +438,11 @@ class CenteredRmsPropParameters(google.protobuf.message.Message):
momentum: builtins.float | None = ...,
epsilon: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["epsilon", b"epsilon", "momentum", b"momentum", "rho", b"rho"]) -> None: ...
def ClearField(self, field_name: typing.Literal["epsilon", b"epsilon", "momentum", b"momentum", "rho", b"rho"]) -> None: ...
global___CenteredRmsPropParameters = CenteredRmsPropParameters
@typing_extensions.final
@typing.final
class MdlAdagradLightParameters(google.protobuf.message.Message):
"""Variant of algorithm in http://proceedings.mlr.press/v44/shamir15.pdf"""
@@ -484,11 +488,11 @@ class MdlAdagradLightParameters(google.protobuf.message.Message):
hard_limit_min_benefit: builtins.bool | None = ...,
mdl_regularize: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["benefit_revisit_scale", b"benefit_revisit_scale", "hard_limit_min_benefit", b"hard_limit_min_benefit", "l2", b"l2", "lr_power", b"lr_power", "max_event_benefit", b"max_event_benefit", "max_total_benefit", b"max_total_benefit", "mdl_benefit_rampup_coeff", b"mdl_benefit_rampup_coeff", "mdl_hard_limit", b"mdl_hard_limit", "mdl_min_weight", b"mdl_min_weight", "mdl_mix_in_margin", b"mdl_mix_in_margin", "mdl_regularize", b"mdl_regularize", "min_servable_mdl_benefit", b"min_servable_mdl_benefit"]) -> None: ...
def ClearField(self, field_name: typing.Literal["benefit_revisit_scale", b"benefit_revisit_scale", "hard_limit_min_benefit", b"hard_limit_min_benefit", "l2", b"l2", "lr_power", b"lr_power", "max_event_benefit", b"max_event_benefit", "max_total_benefit", b"max_total_benefit", "mdl_benefit_rampup_coeff", b"mdl_benefit_rampup_coeff", "mdl_hard_limit", b"mdl_hard_limit", "mdl_min_weight", b"mdl_min_weight", "mdl_mix_in_margin", b"mdl_mix_in_margin", "mdl_regularize", b"mdl_regularize", "min_servable_mdl_benefit", b"min_servable_mdl_benefit"]) -> None: ...
global___MdlAdagradLightParameters = MdlAdagradLightParameters
@typing_extensions.final
@typing.final
class AdadeltaParameters(google.protobuf.message.Message):
"""https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adadelta
https://github.com/tensorflow/tensorflow/blob/6b6471f3ffb7f1fefe42d814aa5fb9ab7a535b58/tensorflow/core/kernels/training_ops.cc#L933
@@ -506,11 +510,11 @@ class AdadeltaParameters(google.protobuf.message.Message):
rho: builtins.float | None = ...,
epsilon: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["epsilon", b"epsilon", "rho", b"rho"]) -> None: ...
def ClearField(self, field_name: typing.Literal["epsilon", b"epsilon", "rho", b"rho"]) -> None: ...
global___AdadeltaParameters = AdadeltaParameters
@typing_extensions.final
@typing.final
class ProximalAdagradParameters(google.protobuf.message.Message):
"""https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/ProximalAdagradOptimizer
https://github.com/tensorflow/tensorflow/blob/6b6471f3ffb7f1fefe42d814aa5fb9ab7a535b58/tensorflow/core/kernels/training_ops.cc#L1961
@@ -528,11 +532,11 @@ class ProximalAdagradParameters(google.protobuf.message.Message):
l1: builtins.float | None = ...,
l2: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["l1", b"l1", "l2", b"l2"]) -> None: ...
def ClearField(self, field_name: typing.Literal["l1", b"l1", "l2", b"l2"]) -> None: ...
global___ProximalAdagradParameters = ProximalAdagradParameters
@typing_extensions.final
@typing.final
class OnlineYogiParameters(google.protobuf.message.Message):
"""The online Yogi optimizer does not implement hyper-parameter update; use the
dynamic learning rate feature instead, setting the learning rate to:
@@ -563,11 +567,11 @@ class OnlineYogiParameters(google.protobuf.message.Message):
l2: builtins.float | None = ...,
beta2: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["beta2", b"beta2", "l1", b"l1", "l2", b"l2"]) -> None: ...
def ClearField(self, field_name: typing.Literal["beta2", b"beta2", "l1", b"l1", "l2", b"l2"]) -> None: ...
global___OnlineYogiParameters = OnlineYogiParameters
@typing_extensions.final
@typing.final
class ProximalYogiParameters(google.protobuf.message.Message):
"""The online Yogi optimizer does not implement hyper-parameter update; use the
dynamic learning rate feature instead, setting the learning rate to:
@@ -606,11 +610,11 @@ class ProximalYogiParameters(google.protobuf.message.Message):
beta2: builtins.float | None = ...,
epsilon: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["beta1", b"beta1", "beta2", b"beta2", "epsilon", b"epsilon", "l1", b"l1", "l2", b"l2"]) -> None: ...
def ClearField(self, field_name: typing.Literal["beta1", b"beta1", "beta2", b"beta2", "epsilon", b"epsilon", "l1", b"l1", "l2", b"l2"]) -> None: ...
global___ProximalYogiParameters = ProximalYogiParameters
@typing_extensions.final
@typing.final
class FrequencyEstimatorParameters(google.protobuf.message.Message):
"""Estimator for the frequency of updates to a lookup table. It maintains an
array (tf.Variable) D, where each element records the average number of
@@ -664,11 +668,11 @@ class FrequencyEstimatorParameters(google.protobuf.message.Message):
outlier_threshold: builtins.float | None = ...,
weight_exponent: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["max_delta", b"max_delta", "outlier_threshold", b"outlier_threshold", "tau", b"tau", "weight_exponent", b"weight_exponent"]) -> None: ...
def ClearField(self, field_name: typing.Literal["max_delta", b"max_delta", "outlier_threshold", b"outlier_threshold", "tau", b"tau", "weight_exponent", b"weight_exponent"]) -> None: ...
global___FrequencyEstimatorParameters = FrequencyEstimatorParameters
@typing_extensions.final
@typing.final
class UserDefinedProgramParameters(google.protobuf.message.Message):
"""A user-defined optimizer.
The contained HLO program must take the following arguments in the following
@@ -701,12 +705,12 @@ class UserDefinedProgramParameters(google.protobuf.message.Message):
*,
program: tensorflow.compiler.xla.service.hlo_pb2.HloModuleProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["program", b"program"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["program", b"program"]) -> None: ...
def HasField(self, field_name: typing.Literal["program", b"program"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["program", b"program"]) -> None: ...
global___UserDefinedProgramParameters = UserDefinedProgramParameters
@typing_extensions.final
@typing.final
class AssignParameters(google.protobuf.message.Message):
"""Optimizer that just sets the variable to the value of the gradient. To be
correct, this requires either gradient accumulation (to sum the values of a
@@ -722,7 +726,7 @@ class AssignParameters(google.protobuf.message.Message):
global___AssignParameters = AssignParameters
@typing_extensions.final
@typing.final
class GradientAccumulationStatus(google.protobuf.message.Message):
"""Status of using gradient accumulation (doing two passes over the input
gradients: one to accumulate them into a temporary array and another to apply
@@ -755,7 +759,7 @@ class GradientAccumulationStatus(google.protobuf.message.Message):
global___GradientAccumulationStatus = GradientAccumulationStatus
@typing_extensions.final
@typing.final
class LowDimensionalPackingStatus(google.protobuf.message.Message):
"""Whether to optimize the packing of low-dimensional embedding tables in HBM
(high bandwidth memory). TPUs access HBM at 32-byte (8-float) granularity.
@@ -832,7 +836,7 @@ class LowDimensionalPackingStatus(google.protobuf.message.Message):
global___LowDimensionalPackingStatus = LowDimensionalPackingStatus
@typing_extensions.final
@typing.final
class HotIdReplicationConfiguration(google.protobuf.message.Message):
"""Configuration proto for hot ID optimization. This is an experimental feature
that is currently disabled (by default).
@@ -870,11 +874,11 @@ class HotIdReplicationConfiguration(google.protobuf.message.Message):
*,
status: global___HotIdReplicationConfiguration.Status.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["status", b"status"]) -> None: ...
def ClearField(self, field_name: typing.Literal["status", b"status"]) -> None: ...
global___HotIdReplicationConfiguration = HotIdReplicationConfiguration
@typing_extensions.final
@typing.final
class OptimizationParameters(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -904,19 +908,6 @@ class OptimizationParameters(google.protobuf.message.Message):
FREQUENCY_ESTIMATOR_FIELD_NUMBER: builtins.int
USER_DEFINED_PROGRAM_FIELD_NUMBER: builtins.int
ASSIGN_FIELD_NUMBER: builtins.int
@property
def learning_rate(self) -> global___LearningRate:
"""Learning rate used for updating the embedding layer parameters."""
@property
def clipping_limits(self) -> global___ClippingLimits:
"""Limits to which to clip the weight values after the backward pass; not
present means no limits are applied.
"""
@property
def gradient_clipping_limits(self) -> global___ClippingLimits:
"""Limits to which to clip the backward pass gradient before using it for
updates; not present means no limits are applied.
"""
weight_decay_factor: builtins.float
"""Amount of weight decay to apply; see weight_decay_optimizers.py for
details. All optimizers except MDL Adagrad Light are supported with this
@@ -929,13 +920,6 @@ class OptimizationParameters(google.protobuf.message.Message):
before use; this is to match the note in DecoupledWeightDecayExtension in
weight_decay_optimizers.py.
"""
@property
def simulated_quantization(self) -> global___SimulatedQuantization:
"""Configuration for simulated quantization which is used to reduce
training/serving skew when the serving variables are quantized. The same
quantization operations are executed during training to minimize
differences with serving.
"""
gradient_accumulation_status: global___GradientAccumulationStatus.Status.ValueType
"""Status of using gradient accumulation (doing two passes over the input
gradients: one to accumulate them into a temporary array and another to
@@ -946,11 +930,36 @@ class OptimizationParameters(google.protobuf.message.Message):
whether to optimize the packing of 1-dimensional, 2-dimensional, and
4-dimensional embedding tables in memory.
"""
@property
def learning_rate(self) -> global___LearningRate:
"""Learning rate used for updating the embedding layer parameters."""
@property
def clipping_limits(self) -> global___ClippingLimits:
"""Limits to which to clip the weight values after the backward pass; not
present means no limits are applied.
"""
@property
def gradient_clipping_limits(self) -> global___ClippingLimits:
"""Limits to which to clip the backward pass gradient before using it for
updates; not present means no limits are applied.
"""
@property
def simulated_quantization(self) -> global___SimulatedQuantization:
"""Configuration for simulated quantization which is used to reduce
training/serving skew when the serving variables are quantized. The same
quantization operations are executed during training to minimize
differences with serving.
"""
@property
def hot_id_replication_configuration(self) -> global___HotIdReplicationConfiguration:
"""Configuration proto for hot ID replication. This is an experimental
feature that is currently disabled (by default).
"""
@property
def adagrad(self) -> global___AdagradParameters: ...
@property
@@ -1015,13 +1024,13 @@ class OptimizationParameters(google.protobuf.message.Message):
user_defined_program: global___UserDefinedProgramParameters | None = ...,
assign: global___AssignParameters | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["adadelta", b"adadelta", "adagrad", b"adagrad", "adagrad_momentum", b"adagrad_momentum", "adam", b"adam", "assign", b"assign", "bounded_adagrad", b"bounded_adagrad", "centered_rms_prop", b"centered_rms_prop", "clipping_limits", b"clipping_limits", "frequency_estimator", b"frequency_estimator", "ftrl", b"ftrl", "gradient_clipping_limits", b"gradient_clipping_limits", "hot_id_replication_configuration", b"hot_id_replication_configuration", "learning_rate", b"learning_rate", "mdl_adagrad_light", b"mdl_adagrad_light", "momentum", b"momentum", "online_yogi", b"online_yogi", "parameters", b"parameters", "proximal_adagrad", b"proximal_adagrad", "proximal_yogi", b"proximal_yogi", "rms_prop", b"rms_prop", "simulated_quantization", b"simulated_quantization", "stochastic_gradient_descent", b"stochastic_gradient_descent", "user_defined_program", b"user_defined_program"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["adadelta", b"adadelta", "adagrad", b"adagrad", "adagrad_momentum", b"adagrad_momentum", "adam", b"adam", "assign", b"assign", "bounded_adagrad", b"bounded_adagrad", "centered_rms_prop", b"centered_rms_prop", "clipping_limits", b"clipping_limits", "frequency_estimator", b"frequency_estimator", "ftrl", b"ftrl", "gradient_accumulation_status", b"gradient_accumulation_status", "gradient_clipping_limits", b"gradient_clipping_limits", "hot_id_replication_configuration", b"hot_id_replication_configuration", "learning_rate", b"learning_rate", "low_dimensional_packing_status", b"low_dimensional_packing_status", "mdl_adagrad_light", b"mdl_adagrad_light", "momentum", b"momentum", "multiply_weight_decay_factor_by_learning_rate", b"multiply_weight_decay_factor_by_learning_rate", "online_yogi", b"online_yogi", "parameters", b"parameters", "proximal_adagrad", b"proximal_adagrad", "proximal_yogi", b"proximal_yogi", "rms_prop", b"rms_prop", "simulated_quantization", b"simulated_quantization", "stochastic_gradient_descent", b"stochastic_gradient_descent", "user_defined_program", b"user_defined_program", "weight_decay_factor", b"weight_decay_factor"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["parameters", b"parameters"]) -> typing_extensions.Literal["adagrad", "adagrad_momentum", "bounded_adagrad", "stochastic_gradient_descent", "ftrl", "adam", "momentum", "rms_prop", "centered_rms_prop", "mdl_adagrad_light", "adadelta", "proximal_adagrad", "online_yogi", "proximal_yogi", "frequency_estimator", "user_defined_program", "assign"] | None: ...
def HasField(self, field_name: typing.Literal["adadelta", b"adadelta", "adagrad", b"adagrad", "adagrad_momentum", b"adagrad_momentum", "adam", b"adam", "assign", b"assign", "bounded_adagrad", b"bounded_adagrad", "centered_rms_prop", b"centered_rms_prop", "clipping_limits", b"clipping_limits", "frequency_estimator", b"frequency_estimator", "ftrl", b"ftrl", "gradient_clipping_limits", b"gradient_clipping_limits", "hot_id_replication_configuration", b"hot_id_replication_configuration", "learning_rate", b"learning_rate", "mdl_adagrad_light", b"mdl_adagrad_light", "momentum", b"momentum", "online_yogi", b"online_yogi", "parameters", b"parameters", "proximal_adagrad", b"proximal_adagrad", "proximal_yogi", b"proximal_yogi", "rms_prop", b"rms_prop", "simulated_quantization", b"simulated_quantization", "stochastic_gradient_descent", b"stochastic_gradient_descent", "user_defined_program", b"user_defined_program"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["adadelta", b"adadelta", "adagrad", b"adagrad", "adagrad_momentum", b"adagrad_momentum", "adam", b"adam", "assign", b"assign", "bounded_adagrad", b"bounded_adagrad", "centered_rms_prop", b"centered_rms_prop", "clipping_limits", b"clipping_limits", "frequency_estimator", b"frequency_estimator", "ftrl", b"ftrl", "gradient_accumulation_status", b"gradient_accumulation_status", "gradient_clipping_limits", b"gradient_clipping_limits", "hot_id_replication_configuration", b"hot_id_replication_configuration", "learning_rate", b"learning_rate", "low_dimensional_packing_status", b"low_dimensional_packing_status", "mdl_adagrad_light", b"mdl_adagrad_light", "momentum", b"momentum", "multiply_weight_decay_factor_by_learning_rate", b"multiply_weight_decay_factor_by_learning_rate", "online_yogi", b"online_yogi", "parameters", b"parameters", "proximal_adagrad", b"proximal_adagrad", "proximal_yogi", b"proximal_yogi", "rms_prop", b"rms_prop", "simulated_quantization", b"simulated_quantization", "stochastic_gradient_descent", b"stochastic_gradient_descent", "user_defined_program", b"user_defined_program", "weight_decay_factor", b"weight_decay_factor"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["parameters", b"parameters"]) -> typing.Literal["adagrad", "adagrad_momentum", "bounded_adagrad", "stochastic_gradient_descent", "ftrl", "adam", "momentum", "rms_prop", "centered_rms_prop", "mdl_adagrad_light", "adadelta", "proximal_adagrad", "online_yogi", "proximal_yogi", "frequency_estimator", "user_defined_program", "assign"] | None: ...
global___OptimizationParameters = OptimizationParameters
@typing_extensions.final
@typing.final
class StateVariableSpecification(google.protobuf.message.Message):
"""Specification of an optimization algorithm's state variables (both the main
value vector and any extra accumulators, etc.). This proto is only used
@@ -1030,7 +1039,7 @@ class StateVariableSpecification(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class UserDefined(google.protobuf.message.Message):
"""A normal state variable that should be saved and restored in checkpoints
and used as an input or output to non-debug TensorFlow ops.
@@ -1042,7 +1051,7 @@ class StateVariableSpecification(google.protobuf.message.Message):
self,
) -> None: ...
@typing_extensions.final
@typing.final
class FillWithConstant(google.protobuf.message.Message):
"""A state variable that should be filled with a constant and normally hidden
from users (used for intermediate gradients being accumulated, for
@@ -1058,7 +1067,7 @@ class StateVariableSpecification(google.protobuf.message.Message):
*,
initial_value: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["initial_value", b"initial_value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["initial_value", b"initial_value"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
USER_DEFINED_FIELD_NUMBER: builtins.int
@@ -1076,8 +1085,8 @@ class StateVariableSpecification(google.protobuf.message.Message):
user_defined: global___StateVariableSpecification.UserDefined | None = ...,
fill_with_constant: global___StateVariableSpecification.FillWithConstant | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["fill_with_constant", b"fill_with_constant", "usage", b"usage", "user_defined", b"user_defined"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["fill_with_constant", b"fill_with_constant", "name", b"name", "usage", b"usage", "user_defined", b"user_defined"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["usage", b"usage"]) -> typing_extensions.Literal["user_defined", "fill_with_constant"] | None: ...
def HasField(self, field_name: typing.Literal["fill_with_constant", b"fill_with_constant", "usage", b"usage", "user_defined", b"user_defined"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["fill_with_constant", b"fill_with_constant", "name", b"name", "usage", b"usage", "user_defined", b"user_defined"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["usage", b"usage"]) -> typing.Literal["user_defined", "fill_with_constant"] | None: ...
global___StateVariableSpecification = StateVariableSpecification
stubs/tensorflow/tensorflow/core/protobuf/tpu/topology_pb2.pyi
+13 -9
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -19,7 +20,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class TPUHardwareFeature(google.protobuf.message.Message):
"""Describes features of a tpu."""
@@ -67,11 +68,11 @@ class TPUHardwareFeature(google.protobuf.message.Message):
*,
embedding_feature: global___TPUHardwareFeature.EmbeddingFeature.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["embedding_feature", b"embedding_feature"]) -> None: ...
def ClearField(self, field_name: typing.Literal["embedding_feature", b"embedding_feature"]) -> None: ...
global___TPUHardwareFeature = TPUHardwareFeature
@typing_extensions.final
@typing.final
class TopologyProto(google.protobuf.message.Message):
"""Describes the geometry of a TPU mesh."""
@@ -82,6 +83,10 @@ class TopologyProto(google.protobuf.message.Message):
NUM_TPU_DEVICES_PER_TASK_FIELD_NUMBER: builtins.int
DEVICE_COORDINATES_FIELD_NUMBER: builtins.int
TPU_HARDWARE_FEATURE_FIELD_NUMBER: builtins.int
num_tasks: builtins.int
"""Number of TensorFlow tasks in the cluster."""
num_tpu_devices_per_task: builtins.int
"""Number of TPU devices per task."""
@property
def mesh_shape(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""The dimensions of the TPU topology, in cores. Typically, this is a 4D
@@ -89,10 +94,7 @@ class TopologyProto(google.protobuf.message.Message):
chips, and the minor dimension describes the number of cores on a multicore
chip.
"""
num_tasks: builtins.int
"""Number of TensorFlow tasks in the cluster."""
num_tpu_devices_per_task: builtins.int
"""Number of TPU devices per task."""
@property
def device_coordinates(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""A flattened rank 3 int32 array with shape
@@ -102,9 +104,11 @@ class TopologyProto(google.protobuf.message.Message):
in the TPU mesh topology. Each entry [task, device, axis] gives the
`axis`-th coordinate in the topology of a task/device pair.
"""
@property
def tpu_hardware_feature(self) -> global___TPUHardwareFeature:
"""TPU supported features."""
def __init__(
self,
*,
@@ -114,7 +118,7 @@ class TopologyProto(google.protobuf.message.Message):
device_coordinates: collections.abc.Iterable[builtins.int] | None = ...,
tpu_hardware_feature: global___TPUHardwareFeature | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["tpu_hardware_feature", b"tpu_hardware_feature"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["device_coordinates", b"device_coordinates", "mesh_shape", b"mesh_shape", "num_tasks", b"num_tasks", "num_tpu_devices_per_task", b"num_tpu_devices_per_task", "tpu_hardware_feature", b"tpu_hardware_feature"]) -> None: ...
def HasField(self, field_name: typing.Literal["tpu_hardware_feature", b"tpu_hardware_feature"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["device_coordinates", b"device_coordinates", "mesh_shape", b"mesh_shape", "num_tasks", b"num_tasks", "num_tpu_devices_per_task", b"num_tpu_devices_per_task", "tpu_hardware_feature", b"tpu_hardware_feature"]) -> None: ...
global___TopologyProto = TopologyProto
stubs/tensorflow/tensorflow/core/protobuf/tpu/tpu_embedding_configuration_pb2.pyi
+17 -13
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -20,7 +21,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class TPUEmbeddingConfiguration(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -72,7 +73,7 @@ class TPUEmbeddingConfiguration(google.protobuf.message.Message):
DIV_DEFAULT: TPUEmbeddingConfiguration.ShardingStrategy.ValueType # 0
MOD: TPUEmbeddingConfiguration.ShardingStrategy.ValueType # 1
@typing_extensions.final
@typing.final
class TableDescriptor(google.protobuf.message.Message):
"""Description of the various embedding tables."""
@@ -96,6 +97,7 @@ class TPUEmbeddingConfiguration(google.protobuf.message.Message):
"""Details of the learning algorithm used to update the embedding
parameters.
"""
def __init__(
self,
*,
@@ -105,10 +107,10 @@ class TPUEmbeddingConfiguration(google.protobuf.message.Message):
num_features: builtins.int | None = ...,
optimization_parameters: tensorflow.core.protobuf.tpu.optimization_parameters_pb2.OptimizationParameters | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["optimization_parameters", b"optimization_parameters"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["dimension", b"dimension", "name", b"name", "num_features", b"num_features", "optimization_parameters", b"optimization_parameters", "vocabulary_size", b"vocabulary_size"]) -> None: ...
def HasField(self, field_name: typing.Literal["optimization_parameters", b"optimization_parameters"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["dimension", b"dimension", "name", b"name", "num_features", b"num_features", "optimization_parameters", b"optimization_parameters", "vocabulary_size", b"vocabulary_size"]) -> None: ...
@typing_extensions.final
@typing.final
class FeatureDescriptor(google.protobuf.message.Message):
"""Description of different input features."""
@@ -131,6 +133,7 @@ class TPUEmbeddingConfiguration(google.protobuf.message.Message):
the reduction axis) and the embedding dimension is d, the output received
at the TensorCore will have shape [m, n, k, d].
"""
def __init__(
self,
*,
@@ -138,9 +141,9 @@ class TPUEmbeddingConfiguration(google.protobuf.message.Message):
table_id: builtins.int | None = ...,
input_shape: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["input_shape", b"input_shape", "name", b"name", "table_id", b"table_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["input_shape", b"input_shape", "name", b"name", "table_id", b"table_id"]) -> None: ...
@typing_extensions.final
@typing.final
class SpmdSharding(google.protobuf.message.Message):
"""SPMD (Single Program Multiple Data) sharding configuration for
TPUEmbedding. When model parallelism is used on the TensorCore, the number
@@ -162,7 +165,7 @@ class TPUEmbeddingConfiguration(google.protobuf.message.Message):
enabled: builtins.bool | None = ...,
num_cores_per_replica: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["enabled", b"enabled", "num_cores_per_replica", b"num_cores_per_replica"]) -> None: ...
def ClearField(self, field_name: typing.Literal["enabled", b"enabled", "num_cores_per_replica", b"num_cores_per_replica"]) -> None: ...
TABLE_DESCRIPTOR_FIELD_NUMBER: builtins.int
MODE_FIELD_NUMBER: builtins.int
@@ -174,8 +177,6 @@ class TPUEmbeddingConfiguration(google.protobuf.message.Message):
PROFILE_DATA_DIRECTORY_FIELD_NUMBER: builtins.int
FEATURE_DESCRIPTOR_FIELD_NUMBER: builtins.int
SPMD_SHARDING_FIELD_NUMBER: builtins.int
@property
def table_descriptor(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TPUEmbeddingConfiguration.TableDescriptor]: ...
mode: global___TPUEmbeddingConfiguration.Mode.ValueType
batch_size_per_tensor_core: builtins.int
"""Number of samples in each batch of embedding layer activations sent to
@@ -231,11 +232,14 @@ class TPUEmbeddingConfiguration(google.protobuf.message.Message):
models to reuse embedding lookup statistics.
"""
@property
def table_descriptor(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TPUEmbeddingConfiguration.TableDescriptor]: ...
@property
def feature_descriptor(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TPUEmbeddingConfiguration.FeatureDescriptor]:
"""If the feature_descriptor field is populated, the model should NOT populate
TableDescriptor.num_features and batch_size_per_tensor_core. These two
fields will be auto-populated by the TPUEmbedding rewrite passes.
"""
@property
def spmd_sharding(self) -> global___TPUEmbeddingConfiguration.SpmdSharding: ...
def __init__(
@@ -252,12 +256,12 @@ class TPUEmbeddingConfiguration(google.protobuf.message.Message):
feature_descriptor: collections.abc.Iterable[global___TPUEmbeddingConfiguration.FeatureDescriptor] | None = ...,
spmd_sharding: global___TPUEmbeddingConfiguration.SpmdSharding | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["spmd_sharding", b"spmd_sharding"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["batch_size_per_tensor_core", b"batch_size_per_tensor_core", "feature_descriptor", b"feature_descriptor", "mode", b"mode", "num_hosts", b"num_hosts", "num_tensor_cores", b"num_tensor_cores", "pipeline_execution_with_tensor_core", b"pipeline_execution_with_tensor_core", "profile_data_directory", b"profile_data_directory", "sharding_strategy", b"sharding_strategy", "spmd_sharding", b"spmd_sharding", "table_descriptor", b"table_descriptor"]) -> None: ...
def HasField(self, field_name: typing.Literal["spmd_sharding", b"spmd_sharding"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["batch_size_per_tensor_core", b"batch_size_per_tensor_core", "feature_descriptor", b"feature_descriptor", "mode", b"mode", "num_hosts", b"num_hosts", "num_tensor_cores", b"num_tensor_cores", "pipeline_execution_with_tensor_core", b"pipeline_execution_with_tensor_core", "profile_data_directory", b"profile_data_directory", "sharding_strategy", b"sharding_strategy", "spmd_sharding", b"spmd_sharding", "table_descriptor", b"table_descriptor"]) -> None: ...
global___TPUEmbeddingConfiguration = TPUEmbeddingConfiguration
@typing_extensions.final
@typing.final
class TPUEmbeddingError(google.protobuf.message.Message):
"""A placeholder message that is used to define a unique Status payload
URL for TPU embedding errors.
stubs/tensorflow/tensorflow/core/protobuf/trackable_object_graph_pb2.pyi
+20 -14
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -13,7 +14,7 @@ import google.protobuf.wrappers_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class TrackableObjectGraph(google.protobuf.message.Message):
"""A TensorBundle addition which saves extra information about the objects which
own variables, allowing for more robust checkpoint loading into modified
@@ -22,11 +23,11 @@ class TrackableObjectGraph(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class TrackableObject(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class ObjectReference(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -44,9 +45,9 @@ class TrackableObjectGraph(google.protobuf.message.Message):
node_id: builtins.int | None = ...,
local_name: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["local_name", b"local_name", "node_id", b"node_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["local_name", b"local_name", "node_id", b"node_id"]) -> None: ...
@typing_extensions.final
@typing.final
class SerializedTensor(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -73,9 +74,9 @@ class TrackableObjectGraph(google.protobuf.message.Message):
full_name: builtins.str | None = ...,
checkpoint_key: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["checkpoint_key", b"checkpoint_key", "full_name", b"full_name", "name", b"name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["checkpoint_key", b"checkpoint_key", "full_name", b"full_name", "name", b"name"]) -> None: ...
@typing_extensions.final
@typing.final
class SlotVariableReference(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -99,7 +100,7 @@ class TrackableObjectGraph(google.protobuf.message.Message):
slot_name: builtins.str | None = ...,
slot_variable_node_id: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["original_variable_node_id", b"original_variable_node_id", "slot_name", b"slot_name", "slot_variable_node_id", b"slot_variable_node_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["original_variable_node_id", b"original_variable_node_id", "slot_name", b"slot_name", "slot_variable_node_id", b"slot_variable_node_id"]) -> None: ...
CHILDREN_FIELD_NUMBER: builtins.int
ATTRIBUTES_FIELD_NUMBER: builtins.int
@@ -109,17 +110,21 @@ class TrackableObjectGraph(google.protobuf.message.Message):
@property
def children(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TrackableObjectGraph.TrackableObject.ObjectReference]:
"""Objects which this object depends on."""
@property
def attributes(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TrackableObjectGraph.TrackableObject.SerializedTensor]:
"""Serialized data specific to this object."""
@property
def slot_variables(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TrackableObjectGraph.TrackableObject.SlotVariableReference]:
"""Slot variables owned by this object."""
@property
def registered_saver(self) -> global___RegisteredSaver:
"""The registered saver used to save this object. If this saver is not
present when loading the checkpoint, then loading will fail.
"""
@property
def has_checkpoint_values(self) -> google.protobuf.wrappers_pb2.BoolValue:
"""Whether this object has checkpoint values or descendants with checkpoint
@@ -127,6 +132,7 @@ class TrackableObjectGraph(google.protobuf.message.Message):
object graph proto when restoring (which also has to traverse the live
object graph).
"""
def __init__(
self,
*,
@@ -136,8 +142,8 @@ class TrackableObjectGraph(google.protobuf.message.Message):
registered_saver: global___RegisteredSaver | None = ...,
has_checkpoint_values: google.protobuf.wrappers_pb2.BoolValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["has_checkpoint_values", b"has_checkpoint_values", "registered_saver", b"registered_saver"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["attributes", b"attributes", "children", b"children", "has_checkpoint_values", b"has_checkpoint_values", "registered_saver", b"registered_saver", "slot_variables", b"slot_variables"]) -> None: ...
def HasField(self, field_name: typing.Literal["has_checkpoint_values", b"has_checkpoint_values", "registered_saver", b"registered_saver"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["attributes", b"attributes", "children", b"children", "has_checkpoint_values", b"has_checkpoint_values", "registered_saver", b"registered_saver", "slot_variables", b"slot_variables"]) -> None: ...
NODES_FIELD_NUMBER: builtins.int
@property
@@ -147,11 +153,11 @@ class TrackableObjectGraph(google.protobuf.message.Message):
*,
nodes: collections.abc.Iterable[global___TrackableObjectGraph.TrackableObject] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["nodes", b"nodes"]) -> None: ...
def ClearField(self, field_name: typing.Literal["nodes", b"nodes"]) -> None: ...
global___TrackableObjectGraph = TrackableObjectGraph
@typing_extensions.final
@typing.final
class RegisteredSaver(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -167,6 +173,6 @@ class RegisteredSaver(google.protobuf.message.Message):
name: builtins.str | None = ...,
object_name: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "object_name", b"object_name"]) -> None: ...
def ClearField(self, field_name: typing.Literal["name", b"name", "object_name", b"object_name"]) -> None: ...
global___RegisteredSaver = RegisteredSaver
stubs/tensorflow/tensorflow/core/protobuf/transport_options_pb2.pyi
+4 -3
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,7 +13,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class RecvBufRespExtra(google.protobuf.message.Message):
"""Extra data needed on a non-RDMA RecvBufResponse."""
@@ -26,6 +27,6 @@ class RecvBufRespExtra(google.protobuf.message.Message):
*,
tensor_content: collections.abc.Iterable[builtins.bytes] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["tensor_content", b"tensor_content"]) -> None: ...
def ClearField(self, field_name: typing.Literal["tensor_content", b"tensor_content"]) -> None: ...
global___RecvBufRespExtra = RecvBufRespExtra
stubs/tensorflow/tensorflow/core/protobuf/verifier_config_pb2.pyi
+3 -2
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import sys
import typing
@@ -17,7 +18,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class VerifierConfig(google.protobuf.message.Message):
"""The config for graph verifiers."""
@@ -52,6 +53,6 @@ class VerifierConfig(google.protobuf.message.Message):
verification_timeout_in_ms: builtins.int | None = ...,
structure_verifier: global___VerifierConfig.Toggle.ValueType | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["structure_verifier", b"structure_verifier", "verification_timeout_in_ms", b"verification_timeout_in_ms"]) -> None: ...
def ClearField(self, field_name: typing.Literal["structure_verifier", b"structure_verifier", "verification_timeout_in_ms", b"verification_timeout_in_ms"]) -> None: ...
global___VerifierConfig = VerifierConfig
stubs/tensorflow/tensorflow/core/util/event_pb2.pyi
+30 -24
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -70,7 +71,7 @@ WAIT_FOR_COORDINATOR: WorkerShutdownMode.ValueType # 2
SHUTDOWN_AFTER_TIMEOUT: WorkerShutdownMode.ValueType # 3
global___WorkerShutdownMode = WorkerShutdownMode
@typing_extensions.final
@typing.final
class Event(google.protobuf.message.Message):
"""Protocol buffer representing an event that happened during
the execution of a Brain model.
@@ -100,28 +101,33 @@ class Event(google.protobuf.message.Message):
"""
graph_def: builtins.bytes
"""An encoded version of a GraphDef."""
meta_graph_def: builtins.bytes
"""An encoded version of a MetaGraphDef."""
@property
def summary(self) -> tensorflow.core.framework.summary_pb2.Summary:
"""A summary was generated."""
@property
def log_message(self) -> global___LogMessage:
"""The user output a log message. This was theoretically used by the defunct
tensorboard_logging module, which has since been removed; this field is
now deprecated and should not be used.
"""
@property
def session_log(self) -> global___SessionLog:
"""The state of the session which can be used for restarting after crashes."""
@property
def tagged_run_metadata(self) -> global___TaggedRunMetadata:
"""The metadata returned by running a session.run() call."""
meta_graph_def: builtins.bytes
"""An encoded version of a MetaGraphDef."""
@property
def source_metadata(self) -> global___SourceMetadata:
"""Information of the source that writes the events, this is only logged in
the very first event along with the `file_version` field.
"""
def __init__(
self,
*,
@@ -136,13 +142,13 @@ class Event(google.protobuf.message.Message):
meta_graph_def: builtins.bytes | None = ...,
source_metadata: global___SourceMetadata | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["file_version", b"file_version", "graph_def", b"graph_def", "log_message", b"log_message", "meta_graph_def", b"meta_graph_def", "session_log", b"session_log", "source_metadata", b"source_metadata", "summary", b"summary", "tagged_run_metadata", b"tagged_run_metadata", "what", b"what"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["file_version", b"file_version", "graph_def", b"graph_def", "log_message", b"log_message", "meta_graph_def", b"meta_graph_def", "session_log", b"session_log", "source_metadata", b"source_metadata", "step", b"step", "summary", b"summary", "tagged_run_metadata", b"tagged_run_metadata", "wall_time", b"wall_time", "what", b"what"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["what", b"what"]) -> typing_extensions.Literal["file_version", "graph_def", "summary", "log_message", "session_log", "tagged_run_metadata", "meta_graph_def"] | None: ...
def HasField(self, field_name: typing.Literal["file_version", b"file_version", "graph_def", b"graph_def", "log_message", b"log_message", "meta_graph_def", b"meta_graph_def", "session_log", b"session_log", "source_metadata", b"source_metadata", "summary", b"summary", "tagged_run_metadata", b"tagged_run_metadata", "what", b"what"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["file_version", b"file_version", "graph_def", b"graph_def", "log_message", b"log_message", "meta_graph_def", b"meta_graph_def", "session_log", b"session_log", "source_metadata", b"source_metadata", "step", b"step", "summary", b"summary", "tagged_run_metadata", b"tagged_run_metadata", "wall_time", b"wall_time", "what", b"what"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["what", b"what"]) -> typing.Literal["file_version", "graph_def", "summary", "log_message", "session_log", "tagged_run_metadata", "meta_graph_def"] | None: ...
global___Event = Event
@typing_extensions.final
@typing.final
class SourceMetadata(google.protobuf.message.Message):
"""Holds the information of the source that writes the events."""
@@ -158,11 +164,11 @@ class SourceMetadata(google.protobuf.message.Message):
*,
writer: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["writer", b"writer"]) -> None: ...
def ClearField(self, field_name: typing.Literal["writer", b"writer"]) -> None: ...
global___SourceMetadata = SourceMetadata
@typing_extensions.final
@typing.final
class LogMessage(google.protobuf.message.Message):
"""Protocol buffer used for logging messages to the events file.
@@ -213,11 +219,11 @@ class LogMessage(google.protobuf.message.Message):
level: global___LogMessage.Level.ValueType | None = ...,
message: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["level", b"level", "message", b"message"]) -> None: ...
def ClearField(self, field_name: typing.Literal["level", b"level", "message", b"message"]) -> None: ...
global___LogMessage = LogMessage
@typing_extensions.final
@typing.final
class SessionLog(google.protobuf.message.Message):
"""Protocol buffer used for logging session state."""
@@ -254,11 +260,11 @@ class SessionLog(google.protobuf.message.Message):
checkpoint_path: builtins.str | None = ...,
msg: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["checkpoint_path", b"checkpoint_path", "msg", b"msg", "status", b"status"]) -> None: ...
def ClearField(self, field_name: typing.Literal["checkpoint_path", b"checkpoint_path", "msg", b"msg", "status", b"status"]) -> None: ...
global___SessionLog = SessionLog
@typing_extensions.final
@typing.final
class TaggedRunMetadata(google.protobuf.message.Message):
"""For logging the metadata output for a single session.run() call."""
@@ -278,11 +284,11 @@ class TaggedRunMetadata(google.protobuf.message.Message):
tag: builtins.str | None = ...,
run_metadata: builtins.bytes | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["run_metadata", b"run_metadata", "tag", b"tag"]) -> None: ...
def ClearField(self, field_name: typing.Literal["run_metadata", b"run_metadata", "tag", b"tag"]) -> None: ...
global___TaggedRunMetadata = TaggedRunMetadata
@typing_extensions.final
@typing.final
class WatchdogConfig(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -293,11 +299,11 @@ class WatchdogConfig(google.protobuf.message.Message):
*,
timeout_ms: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["timeout_ms", b"timeout_ms"]) -> None: ...
def ClearField(self, field_name: typing.Literal["timeout_ms", b"timeout_ms"]) -> None: ...
global___WatchdogConfig = WatchdogConfig
@typing_extensions.final
@typing.final
class RequestedExitCode(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -308,11 +314,11 @@ class RequestedExitCode(google.protobuf.message.Message):
*,
exit_code: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["exit_code", b"exit_code"]) -> None: ...
def ClearField(self, field_name: typing.Literal["exit_code", b"exit_code"]) -> None: ...
global___RequestedExitCode = RequestedExitCode
@typing_extensions.final
@typing.final
class WorkerHeartbeatRequest(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -331,12 +337,12 @@ class WorkerHeartbeatRequest(google.protobuf.message.Message):
watchdog_config: global___WatchdogConfig | None = ...,
exit_code: global___RequestedExitCode | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["exit_code", b"exit_code", "watchdog_config", b"watchdog_config"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["exit_code", b"exit_code", "shutdown_mode", b"shutdown_mode", "watchdog_config", b"watchdog_config"]) -> None: ...
def HasField(self, field_name: typing.Literal["exit_code", b"exit_code", "watchdog_config", b"watchdog_config"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["exit_code", b"exit_code", "shutdown_mode", b"shutdown_mode", "watchdog_config", b"watchdog_config"]) -> None: ...
global___WorkerHeartbeatRequest = WorkerHeartbeatRequest
@typing_extensions.final
@typing.final
class WorkerHeartbeatResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -344,9 +350,9 @@ class WorkerHeartbeatResponse(google.protobuf.message.Message):
WORKER_LOG_FIELD_NUMBER: builtins.int
HOSTNAME_FIELD_NUMBER: builtins.int
health_status: global___WorkerHealth.ValueType
hostname: builtins.str
@property
def worker_log(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___Event]: ...
hostname: builtins.str
def __init__(
self,
*,
@@ -354,6 +360,6 @@ class WorkerHeartbeatResponse(google.protobuf.message.Message):
worker_log: collections.abc.Iterable[global___Event] | None = ...,
hostname: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["health_status", b"health_status", "hostname", b"hostname", "worker_log", b"worker_log"]) -> None: ...
def ClearField(self, field_name: typing.Literal["health_status", b"health_status", "hostname", b"hostname", "worker_log", b"worker_log"]) -> None: ...
global___WorkerHeartbeatResponse = WorkerHeartbeatResponse
stubs/tensorflow/tensorflow/core/util/memmapped_file_system_pb2.pyi
+6 -5
View File
@@ -16,9 +16,10 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -26,7 +27,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class MemmappedFileSystemDirectoryElement(google.protobuf.message.Message):
"""A message that describes one region of memmapped file."""
@@ -45,11 +46,11 @@ class MemmappedFileSystemDirectoryElement(google.protobuf.message.Message):
name: builtins.str | None = ...,
length: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["length", b"length", "name", b"name", "offset", b"offset"]) -> None: ...
def ClearField(self, field_name: typing.Literal["length", b"length", "name", b"name", "offset", b"offset"]) -> None: ...
global___MemmappedFileSystemDirectoryElement = MemmappedFileSystemDirectoryElement
@typing_extensions.final
@typing.final
class MemmappedFileSystemDirectory(google.protobuf.message.Message):
"""A directory of regions in a memmapped file."""
@@ -63,6 +64,6 @@ class MemmappedFileSystemDirectory(google.protobuf.message.Message):
*,
element: collections.abc.Iterable[global___MemmappedFileSystemDirectoryElement] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["element", b"element"]) -> None: ...
def ClearField(self, field_name: typing.Literal["element", b"element"]) -> None: ...
global___MemmappedFileSystemDirectory = MemmappedFileSystemDirectory
stubs/tensorflow/tensorflow/core/util/saved_tensor_slice_pb2.pyi
+25 -16
View File
@@ -15,9 +15,10 @@ ordered code that encodes the name of the tensor and the slice
information. The name is also stored in the SaveSlice message for ease of
debugging and manual examination.
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -30,7 +31,7 @@ import tensorflow.core.framework.versions_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class SavedSliceMeta(google.protobuf.message.Message):
"""Metadata describing the set of slices of the same tensor saved in a
checkpoint file.
@@ -44,14 +45,16 @@ class SavedSliceMeta(google.protobuf.message.Message):
SLICE_FIELD_NUMBER: builtins.int
name: builtins.str
"""Name of the tensor."""
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto:
"""Shape of the tensor"""
type: tensorflow.core.framework.types_pb2.DataType.ValueType
"""Type of the tensor"""
@property
def shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto:
"""Shape of the tensor"""
@property
def slice(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.tensor_slice_pb2.TensorSliceProto]:
"""Explicit list of slices saved in the checkpoint file."""
def __init__(
self,
*,
@@ -60,12 +63,12 @@ class SavedSliceMeta(google.protobuf.message.Message):
type: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ...,
slice: collections.abc.Iterable[tensorflow.core.framework.tensor_slice_pb2.TensorSliceProto] | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "shape", b"shape", "slice", b"slice", "type", b"type"]) -> None: ...
def HasField(self, field_name: typing.Literal["shape", b"shape"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["name", b"name", "shape", b"shape", "slice", b"slice", "type", b"type"]) -> None: ...
global___SavedSliceMeta = SavedSliceMeta
@typing_extensions.final
@typing.final
class SavedTensorSliceMeta(google.protobuf.message.Message):
"""Metadata describing the set of tensor slices saved in a checkpoint file.
It is always stored at the beginning of each checkpoint file.
@@ -78,23 +81,25 @@ class SavedTensorSliceMeta(google.protobuf.message.Message):
@property
def tensor(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___SavedSliceMeta]:
"""Each SavedSliceMeta describes the slices for one tensor."""
@property
def versions(self) -> tensorflow.core.framework.versions_pb2.VersionDef:
"""Compatibility version of this checkpoint. See core/public/version.h
for version history.
"""
def __init__(
self,
*,
tensor: collections.abc.Iterable[global___SavedSliceMeta] | None = ...,
versions: tensorflow.core.framework.versions_pb2.VersionDef | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["versions", b"versions"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["tensor", b"tensor", "versions", b"versions"]) -> None: ...
def HasField(self, field_name: typing.Literal["versions", b"versions"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["tensor", b"tensor", "versions", b"versions"]) -> None: ...
global___SavedTensorSliceMeta = SavedTensorSliceMeta
@typing_extensions.final
@typing.final
class SavedSlice(google.protobuf.message.Message):
"""Saved tensor slice: it stores the name of the tensors, the slice, and the
raw data.
@@ -114,11 +119,13 @@ class SavedSlice(google.protobuf.message.Message):
"""Extent of the slice. Must have one entry for each of the dimension of the
tensor that this slice belongs to.
"""
@property
def data(self) -> tensorflow.core.framework.tensor_pb2.TensorProto:
"""The raw data of the slice is stored as a TensorProto. Only raw data are
stored (we don't fill in fields such as dtype or tensor_shape).
"""
def __init__(
self,
*,
@@ -126,12 +133,12 @@ class SavedSlice(google.protobuf.message.Message):
slice: tensorflow.core.framework.tensor_slice_pb2.TensorSliceProto | None = ...,
data: tensorflow.core.framework.tensor_pb2.TensorProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["data", b"data", "slice", b"slice"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["data", b"data", "name", b"name", "slice", b"slice"]) -> None: ...
def HasField(self, field_name: typing.Literal["data", b"data", "slice", b"slice"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["data", b"data", "name", b"name", "slice", b"slice"]) -> None: ...
global___SavedSlice = SavedSlice
@typing_extensions.final
@typing.final
class SavedTensorSlices(google.protobuf.message.Message):
"""Each record in a v3 checkpoint file is a serialized SavedTensorSlices
message.
@@ -146,16 +153,18 @@ class SavedTensorSlices(google.protobuf.message.Message):
"""This is only present at the first item of each checkpoint file and serves
as a table of contents, listing all the tensor slices saved in this file.
"""
@property
def data(self) -> global___SavedSlice:
"""This exists in all but the first item of each checkpoint file."""
def __init__(
self,
*,
meta: global___SavedTensorSliceMeta | None = ...,
data: global___SavedSlice | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["data", b"data", "meta", b"meta"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["data", b"data", "meta", b"meta"]) -> None: ...
def HasField(self, field_name: typing.Literal["data", b"data", "meta", b"meta"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["data", b"data", "meta", b"meta"]) -> None: ...
global___SavedTensorSlices = SavedTensorSlices
stubs/tensorflow/tensorflow/core/util/test_log_pb2.pyi
+1
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
Protocol messages for describing the results of benchmarks and unit tests."""
import google.protobuf.descriptor
from tensorflow.tsl.protobuf.test_log_pb2 import (
AvailableDeviceInfo as AvailableDeviceInfo,
stubs/tensorflow/tensorflow/python/keras/protobuf/projector_config_pb2.pyi
+17 -14
View File
@@ -5,9 +5,10 @@ This file is a copy of the TensorBoard ProjectorConfig proto.
Keep this file in sync with the source proto definition at
https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/projector/projector_config.proto
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -15,7 +16,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class SpriteMetadata(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -25,17 +26,18 @@ class SpriteMetadata(google.protobuf.message.Message):
@property
def single_image_dim(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""[width, height] of a single image in the sprite."""
def __init__(
self,
*,
image_path: builtins.str | None = ...,
single_image_dim: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["image_path", b"image_path", "single_image_dim", b"single_image_dim"]) -> None: ...
def ClearField(self, field_name: typing.Literal["image_path", b"image_path", "single_image_dim", b"single_image_dim"]) -> None: ...
global___SpriteMetadata = SpriteMetadata
@typing_extensions.final
@typing.final
class EmbeddingInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -48,17 +50,18 @@ class EmbeddingInfo(google.protobuf.message.Message):
tensor_name: builtins.str
metadata_path: builtins.str
bookmarks_path: builtins.str
tensor_path: builtins.str
"""Path to the TSV file holding the tensor values. If missing, the tensor
is assumed to be stored in the model checkpoint.
"""
@property
def tensor_shape(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Shape of the 2D tensor [N x D]. If missing, it will be inferred from the
model checkpoint.
"""
@property
def sprite(self) -> global___SpriteMetadata: ...
tensor_path: builtins.str
"""Path to the TSV file holding the tensor values. If missing, the tensor
is assumed to be stored in the model checkpoint.
"""
def __init__(
self,
*,
@@ -69,12 +72,12 @@ class EmbeddingInfo(google.protobuf.message.Message):
sprite: global___SpriteMetadata | None = ...,
tensor_path: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["sprite", b"sprite"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["bookmarks_path", b"bookmarks_path", "metadata_path", b"metadata_path", "sprite", b"sprite", "tensor_name", b"tensor_name", "tensor_path", b"tensor_path", "tensor_shape", b"tensor_shape"]) -> None: ...
def HasField(self, field_name: typing.Literal["sprite", b"sprite"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["bookmarks_path", b"bookmarks_path", "metadata_path", b"metadata_path", "sprite", b"sprite", "tensor_name", b"tensor_name", "tensor_path", b"tensor_path", "tensor_shape", b"tensor_shape"]) -> None: ...
global___EmbeddingInfo = EmbeddingInfo
@typing_extensions.final
@typing.final
class ProjectorConfig(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -83,12 +86,12 @@ class ProjectorConfig(google.protobuf.message.Message):
MODEL_CHECKPOINT_DIR_FIELD_NUMBER: builtins.int
model_checkpoint_path: builtins.str
"""Path to the checkpoint file. Use either this or model_checkpoint_dir."""
@property
def embeddings(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___EmbeddingInfo]: ...
model_checkpoint_dir: builtins.str
"""Path to the checkpoint directory. The directory will be scanned for the
latest checkpoint file.
"""
@property
def embeddings(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___EmbeddingInfo]: ...
def __init__(
self,
*,
@@ -96,6 +99,6 @@ class ProjectorConfig(google.protobuf.message.Message):
embeddings: collections.abc.Iterable[global___EmbeddingInfo] | None = ...,
model_checkpoint_dir: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["embeddings", b"embeddings", "model_checkpoint_dir", b"model_checkpoint_dir", "model_checkpoint_path", b"model_checkpoint_path"]) -> None: ...
def ClearField(self, field_name: typing.Literal["embeddings", b"embeddings", "model_checkpoint_dir", b"model_checkpoint_dir", "model_checkpoint_path", b"model_checkpoint_path"]) -> None: ...
global___ProjectorConfig = ProjectorConfig
stubs/tensorflow/tensorflow/python/keras/protobuf/saved_metadata_pb2.pyi
+9 -6
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
Protobuf containing the metadata for each Keras object saved in a SavedModel."""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -13,7 +14,7 @@ import tensorflow.python.keras.protobuf.versions_pb2
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class SavedMetadata(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -23,16 +24,17 @@ class SavedMetadata(google.protobuf.message.Message):
"""Nodes represent trackable objects in the SavedModel. The data for every
Keras object is stored.
"""
def __init__(
self,
*,
nodes: collections.abc.Iterable[global___SavedObject] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["nodes", b"nodes"]) -> None: ...
def ClearField(self, field_name: typing.Literal["nodes", b"nodes"]) -> None: ...
global___SavedMetadata = SavedMetadata
@typing_extensions.final
@typing.final
class SavedObject(google.protobuf.message.Message):
"""Metadata of an individual Keras object."""
@@ -61,6 +63,7 @@ class SavedObject(google.protobuf.message.Message):
@property
def version(self) -> tensorflow.python.keras.protobuf.versions_pb2.VersionDef:
"""Version defined by the code serializing this Keras object."""
def __init__(
self,
*,
@@ -70,7 +73,7 @@ class SavedObject(google.protobuf.message.Message):
metadata: builtins.str | None = ...,
version: tensorflow.python.keras.protobuf.versions_pb2.VersionDef | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["version", b"version"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["identifier", b"identifier", "metadata", b"metadata", "node_id", b"node_id", "node_path", b"node_path", "version", b"version"]) -> None: ...
def HasField(self, field_name: typing.Literal["version", b"version"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["identifier", b"identifier", "metadata", b"metadata", "node_id", b"node_id", "node_path", b"node_path", "version", b"version"]) -> None: ...
global___SavedObject = SavedObject
stubs/tensorflow/tensorflow/python/keras/protobuf/versions_pb2.pyi
+5 -3
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,7 +13,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class VersionDef(google.protobuf.message.Message):
"""This file is a copy of the TensorFlow Versions proto.
Keep this file in sync with the source proto definition at
@@ -46,6 +47,7 @@ class VersionDef(google.protobuf.message.Message):
@property
def bad_consumers(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Specific consumer versions which are disallowed (e.g. due to bugs)."""
def __init__(
self,
*,
@@ -53,6 +55,6 @@ class VersionDef(google.protobuf.message.Message):
min_consumer: builtins.int | None = ...,
bad_consumers: collections.abc.Iterable[builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["bad_consumers", b"bad_consumers", "min_consumer", b"min_consumer", "producer", b"producer"]) -> None: ...
def ClearField(self, field_name: typing.Literal["bad_consumers", b"bad_consumers", "min_consumer", b"min_consumer", "producer", b"producer"]) -> None: ...
global___VersionDef = VersionDef
stubs/tensorflow/tensorflow/tsl/protobuf/autotuning_pb2.pyi
+27 -25
View File
@@ -7,6 +7,7 @@ operations.
They are in proto format because we want to log them structured. They offer
tremendous statistical, testing, and debugging value.
"""
import builtins
import collections.abc
import sys
@@ -27,7 +28,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class CudnnVersion(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -44,11 +45,11 @@ class CudnnVersion(google.protobuf.message.Message):
minor: builtins.int | None = ...,
patch: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["major", b"major", "minor", b"minor", "patch", b"patch"]) -> None: ...
def ClearField(self, field_name: typing.Literal["major", b"major", "minor", b"minor", "patch", b"patch"]) -> None: ...
global___CudnnVersion = CudnnVersion
@typing_extensions.final
@typing.final
class ComputeCapability(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -62,11 +63,11 @@ class ComputeCapability(google.protobuf.message.Message):
major: builtins.int | None = ...,
minor: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["major", b"major", "minor", b"minor"]) -> None: ...
def ClearField(self, field_name: typing.Literal["major", b"major", "minor", b"minor"]) -> None: ...
global___ComputeCapability = ComputeCapability
@typing_extensions.final
@typing.final
class AutotuneResult(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -93,7 +94,7 @@ class AutotuneResult(google.protobuf.message.Message):
DISQUALIFIED: AutotuneResult.FailureKind.ValueType # 3
"""Algorithm was rejected for failing to run or for known bugs."""
@typing_extensions.final
@typing.final
class FailureResult(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -106,6 +107,7 @@ class AutotuneResult(google.protobuf.message.Message):
BUFFER_ADDRESS_FIELD_NUMBER: builtins.int
kind: global___AutotuneResult.FailureKind.ValueType
msg: builtins.str
buffer_address: builtins.int
@property
def reference_conv(self) -> global___AutotuneResult.ConvKey: ...
@property
@@ -114,7 +116,6 @@ class AutotuneResult(google.protobuf.message.Message):
def reference_cuda_conv_plan(self) -> global___AutotuneResult.CudaConvPlanKey: ...
@property
def reference_algorithm(self) -> tensorflow.tsl.protobuf.dnn_pb2.AlgorithmProto: ...
buffer_address: builtins.int
def __init__(
self,
*,
@@ -126,11 +127,11 @@ class AutotuneResult(google.protobuf.message.Message):
reference_algorithm: tensorflow.tsl.protobuf.dnn_pb2.AlgorithmProto | None = ...,
buffer_address: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["key", b"key", "reference_algorithm", b"reference_algorithm", "reference_conv", b"reference_conv", "reference_cuda_conv_plan", b"reference_cuda_conv_plan", "reference_gemm", b"reference_gemm"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["buffer_address", b"buffer_address", "key", b"key", "kind", b"kind", "msg", b"msg", "reference_algorithm", b"reference_algorithm", "reference_conv", b"reference_conv", "reference_cuda_conv_plan", b"reference_cuda_conv_plan", "reference_gemm", b"reference_gemm"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["key", b"key"]) -> typing_extensions.Literal["reference_conv", "reference_gemm", "reference_cuda_conv_plan", "reference_algorithm"] | None: ...
def HasField(self, field_name: typing.Literal["key", b"key", "reference_algorithm", b"reference_algorithm", "reference_conv", b"reference_conv", "reference_cuda_conv_plan", b"reference_cuda_conv_plan", "reference_gemm", b"reference_gemm"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["buffer_address", b"buffer_address", "key", b"key", "kind", b"kind", "msg", b"msg", "reference_algorithm", b"reference_algorithm", "reference_conv", b"reference_conv", "reference_cuda_conv_plan", b"reference_cuda_conv_plan", "reference_gemm", b"reference_gemm"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["key", b"key"]) -> typing.Literal["reference_conv", "reference_gemm", "reference_cuda_conv_plan", "reference_algorithm"] | None: ...
@typing_extensions.final
@typing.final
class ConvKey(google.protobuf.message.Message):
"""Legacy and unused in new data; superseded by AlgorithmProto."""
@@ -146,9 +147,9 @@ class AutotuneResult(google.protobuf.message.Message):
algorithm: builtins.int | None = ...,
tensor_ops_enabled: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["algorithm", b"algorithm", "tensor_ops_enabled", b"tensor_ops_enabled"]) -> None: ...
def ClearField(self, field_name: typing.Literal["algorithm", b"algorithm", "tensor_ops_enabled", b"tensor_ops_enabled"]) -> None: ...
@typing_extensions.final
@typing.final
class GemmKey(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -159,9 +160,9 @@ class AutotuneResult(google.protobuf.message.Message):
*,
algorithm: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["algorithm", b"algorithm"]) -> None: ...
def ClearField(self, field_name: typing.Literal["algorithm", b"algorithm"]) -> None: ...
@typing_extensions.final
@typing.final
class CudaConvPlanKey(google.protobuf.message.Message):
"""Legacy and unused in new data; superseded by AlgorithmProto."""
@@ -174,7 +175,7 @@ class AutotuneResult(google.protobuf.message.Message):
*,
exec_plan_id: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["exec_plan_id", b"exec_plan_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["exec_plan_id", b"exec_plan_id"]) -> None: ...
SCRATCH_BYTES_FIELD_NUMBER: builtins.int
RUN_TIME_FIELD_NUMBER: builtins.int
@@ -207,13 +208,13 @@ class AutotuneResult(google.protobuf.message.Message):
cuda_conv_plan: global___AutotuneResult.CudaConvPlanKey | None = ...,
algorithm: tensorflow.tsl.protobuf.dnn_pb2.AlgorithmProto | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["algorithm", b"algorithm", "conv", b"conv", "cuda_conv_plan", b"cuda_conv_plan", "failure", b"failure", "gemm", b"gemm", "key", b"key", "run_time", b"run_time"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["algorithm", b"algorithm", "conv", b"conv", "cuda_conv_plan", b"cuda_conv_plan", "failure", b"failure", "gemm", b"gemm", "key", b"key", "run_time", b"run_time", "scratch_bytes", b"scratch_bytes"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["key", b"key"]) -> typing_extensions.Literal["conv", "gemm", "cuda_conv_plan", "algorithm"] | None: ...
def HasField(self, field_name: typing.Literal["algorithm", b"algorithm", "conv", b"conv", "cuda_conv_plan", b"cuda_conv_plan", "failure", b"failure", "gemm", b"gemm", "key", b"key", "run_time", b"run_time"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["algorithm", b"algorithm", "conv", b"conv", "cuda_conv_plan", b"cuda_conv_plan", "failure", b"failure", "gemm", b"gemm", "key", b"key", "run_time", b"run_time", "scratch_bytes", b"scratch_bytes"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["key", b"key"]) -> typing.Literal["conv", "gemm", "cuda_conv_plan", "algorithm"] | None: ...
global___AutotuneResult = AutotuneResult
@typing_extensions.final
@typing.final
class AutotuningLog(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -223,18 +224,19 @@ class AutotuningLog(google.protobuf.message.Message):
COMPUTE_CAPABILITY_FIELD_NUMBER: builtins.int
DEVICE_PCI_BUS_ID_FIELD_NUMBER: builtins.int
BLAS_VERSION_FIELD_NUMBER: builtins.int
device_pci_bus_id: builtins.str
"""stream_executor::DeviceDescription::pci_bus_id."""
blas_version: builtins.str
@property
def instr(self) -> google.protobuf.any_pb2.Any: ...
@property
def results(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___AutotuneResult]:
"""Records all auto-tuning results per algorithm."""
@property
def cudnn_version(self) -> global___CudnnVersion: ...
@property
def compute_capability(self) -> global___ComputeCapability: ...
device_pci_bus_id: builtins.str
"""stream_executor::DeviceDescription::pci_bus_id."""
blas_version: builtins.str
def __init__(
self,
*,
@@ -245,7 +247,7 @@ class AutotuningLog(google.protobuf.message.Message):
device_pci_bus_id: builtins.str | None = ...,
blas_version: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["compute_capability", b"compute_capability", "cudnn_version", b"cudnn_version", "instr", b"instr"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["blas_version", b"blas_version", "compute_capability", b"compute_capability", "cudnn_version", b"cudnn_version", "device_pci_bus_id", b"device_pci_bus_id", "instr", b"instr", "results", b"results"]) -> None: ...
def HasField(self, field_name: typing.Literal["compute_capability", b"compute_capability", "cudnn_version", b"cudnn_version", "instr", b"instr"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["blas_version", b"blas_version", "compute_capability", b"compute_capability", "cudnn_version", b"cudnn_version", "device_pci_bus_id", b"device_pci_bus_id", "instr", b"instr", "results", b"results"]) -> None: ...
global___AutotuningLog = AutotuningLog
stubs/tensorflow/tensorflow/tsl/protobuf/bfc_memory_map_pb2.pyi
+13 -12
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,7 +13,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class MemAllocatorStats(google.protobuf.message.Message):
"""Some of the data from AllocatorStats"""
@@ -37,11 +38,11 @@ class MemAllocatorStats(google.protobuf.message.Message):
largest_alloc_size: builtins.int | None = ...,
fragmentation_metric: builtins.float | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["bytes_in_use", b"bytes_in_use", "fragmentation_metric", b"fragmentation_metric", "largest_alloc_size", b"largest_alloc_size", "num_allocs", b"num_allocs", "peak_bytes_in_use", b"peak_bytes_in_use"]) -> None: ...
def ClearField(self, field_name: typing.Literal["bytes_in_use", b"bytes_in_use", "fragmentation_metric", b"fragmentation_metric", "largest_alloc_size", b"largest_alloc_size", "num_allocs", b"num_allocs", "peak_bytes_in_use", b"peak_bytes_in_use"]) -> None: ...
global___MemAllocatorStats = MemAllocatorStats
@typing_extensions.final
@typing.final
class MemChunk(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -76,11 +77,11 @@ class MemChunk(google.protobuf.message.Message):
in_use: builtins.bool | None = ...,
step_id: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["action_count", b"action_count", "address", b"address", "bin", b"bin", "freed_at_count", b"freed_at_count", "in_use", b"in_use", "op_name", b"op_name", "requested_size", b"requested_size", "size", b"size", "step_id", b"step_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["action_count", b"action_count", "address", b"address", "bin", b"bin", "freed_at_count", b"freed_at_count", "in_use", b"in_use", "op_name", b"op_name", "requested_size", b"requested_size", "size", b"size", "step_id", b"step_id"]) -> None: ...
global___MemChunk = MemChunk
@typing_extensions.final
@typing.final
class BinSummary(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -103,11 +104,11 @@ class BinSummary(google.protobuf.message.Message):
total_chunks_in_use: builtins.int | None = ...,
total_chunks_in_bin: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["bin", b"bin", "total_bytes_in_bin", b"total_bytes_in_bin", "total_bytes_in_use", b"total_bytes_in_use", "total_chunks_in_bin", b"total_chunks_in_bin", "total_chunks_in_use", b"total_chunks_in_use"]) -> None: ...
def ClearField(self, field_name: typing.Literal["bin", b"bin", "total_bytes_in_bin", b"total_bytes_in_bin", "total_bytes_in_use", b"total_bytes_in_use", "total_chunks_in_bin", b"total_chunks_in_bin", "total_chunks_in_use", b"total_chunks_in_use"]) -> None: ...
global___BinSummary = BinSummary
@typing_extensions.final
@typing.final
class SnapShot(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -121,11 +122,11 @@ class SnapShot(google.protobuf.message.Message):
action_count: builtins.int | None = ...,
size: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["action_count", b"action_count", "size", b"size"]) -> None: ...
def ClearField(self, field_name: typing.Literal["action_count", b"action_count", "size", b"size"]) -> None: ...
global___SnapShot = SnapShot
@typing_extensions.final
@typing.final
class MemoryDump(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -152,7 +153,7 @@ class MemoryDump(google.protobuf.message.Message):
snap_shot: collections.abc.Iterable[global___SnapShot] | None = ...,
stats: global___MemAllocatorStats | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["stats", b"stats"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["allocator_name", b"allocator_name", "bin_summary", b"bin_summary", "chunk", b"chunk", "snap_shot", b"snap_shot", "stats", b"stats"]) -> None: ...
def HasField(self, field_name: typing.Literal["stats", b"stats"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["allocator_name", b"allocator_name", "bin_summary", b"bin_summary", "chunk", b"chunk", "snap_shot", b"snap_shot", "stats", b"stats"]) -> None: ...
global___MemoryDump = MemoryDump
stubs/tensorflow/tensorflow/tsl/protobuf/coordination_config_pb2.pyi
+9 -7
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,7 +13,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class CoordinatedJob(google.protobuf.message.Message):
"""Represents a job type and the number of tasks under this job.
For example, ("worker", 20) implies that there will be 20 worker tasks.
@@ -30,11 +31,11 @@ class CoordinatedJob(google.protobuf.message.Message):
name: builtins.str | None = ...,
num_tasks: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["name", b"name", "num_tasks", b"num_tasks"]) -> None: ...
def ClearField(self, field_name: typing.Literal["name", b"name", "num_tasks", b"num_tasks"]) -> None: ...
global___CoordinatedJob = CoordinatedJob
@typing_extensions.final
@typing.final
class CoordinationServiceConfig(google.protobuf.message.Message):
"""Coordination service configuration parameters.
The system picks appropriate values for fields that are not set.
@@ -70,8 +71,6 @@ class CoordinationServiceConfig(google.protobuf.message.Message):
the agent has disconnected, to account for the lag time between the service
recording the state change and the agent stopping heartbeats.
"""
@property
def coordinated_job_list(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___CoordinatedJob]: ...
shutdown_barrier_timeout_in_ms: builtins.int
"""Denotes how long to wait for all coordination agents to reach the barriers
(after the first shutdown request) before disconnecting together. If
@@ -84,12 +83,15 @@ class CoordinationServiceConfig(google.protobuf.message.Message):
testing.
"""
@property
def coordinated_job_list(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___CoordinatedJob]: ...
@property
def recoverable_jobs(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""The list of jobs which are recoverable. If a task in this list fails,
it will not propagate error to other tasks.
If empty, no jobs will be recoverable and every task failure will cause
error propagation to other tasks.
"""
def __init__(
self,
*,
@@ -103,6 +105,6 @@ class CoordinationServiceConfig(google.protobuf.message.Message):
agent_destruction_without_shutdown: builtins.bool | None = ...,
recoverable_jobs: collections.abc.Iterable[builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["agent_destruction_without_shutdown", b"agent_destruction_without_shutdown", "cluster_register_timeout_in_ms", b"cluster_register_timeout_in_ms", "coordinated_job_list", b"coordinated_job_list", "enable_health_check", b"enable_health_check", "heartbeat_timeout_in_ms", b"heartbeat_timeout_in_ms", "recoverable_jobs", b"recoverable_jobs", "service_leader", b"service_leader", "service_type", b"service_type", "shutdown_barrier_timeout_in_ms", b"shutdown_barrier_timeout_in_ms"]) -> None: ...
def ClearField(self, field_name: typing.Literal["agent_destruction_without_shutdown", b"agent_destruction_without_shutdown", "cluster_register_timeout_in_ms", b"cluster_register_timeout_in_ms", "coordinated_job_list", b"coordinated_job_list", "enable_health_check", b"enable_health_check", "heartbeat_timeout_in_ms", b"heartbeat_timeout_in_ms", "recoverable_jobs", b"recoverable_jobs", "service_leader", b"service_leader", "service_type", b"service_type", "shutdown_barrier_timeout_in_ms", b"shutdown_barrier_timeout_in_ms"]) -> None: ...
global___CoordinationServiceConfig = CoordinationServiceConfig
stubs/tensorflow/tensorflow/tsl/protobuf/coordination_service_pb2.pyi
+86 -79
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import sys
@@ -52,7 +53,7 @@ TASKSTATE_CONNECTED: CoordinatedTaskState.ValueType # 3
TASKSTATE_ERROR: CoordinatedTaskState.ValueType # 4
global___CoordinatedTaskState = CoordinatedTaskState
@typing_extensions.final
@typing.final
class CoordinatedTask(google.protobuf.message.Message):
"""Represents a remote worker task, specified by job name and task id."""
@@ -68,11 +69,11 @@ class CoordinatedTask(google.protobuf.message.Message):
job_name: builtins.str | None = ...,
task_id: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["job_name", b"job_name", "task_id", b"task_id"]) -> None: ...
def ClearField(self, field_name: typing.Literal["job_name", b"job_name", "task_id", b"task_id"]) -> None: ...
global___CoordinatedTask = CoordinatedTask
@typing_extensions.final
@typing.final
class CoordinationServiceError(google.protobuf.message.Message):
"""Status payload for all coordination service errors.
Note: an empty proto may be set if the error is triggered by the task's own
@@ -92,18 +93,19 @@ class CoordinationServiceError(google.protobuf.message.Message):
"""Denotes which task hit the error. If unset, the error originated from the
same task that is processing this error.
"""
def __init__(
self,
*,
is_reported_error: builtins.bool | None = ...,
source_task: global___CoordinatedTask | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["is_reported_error", b"is_reported_error", "source_task", b"source_task"]) -> None: ...
def HasField(self, field_name: typing.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["is_reported_error", b"is_reported_error", "source_task", b"source_task"]) -> None: ...
global___CoordinationServiceError = CoordinationServiceError
@typing_extensions.final
@typing.final
class CoordinatedTaskStateInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -112,12 +114,12 @@ class CoordinatedTaskStateInfo(google.protobuf.message.Message):
ERROR_CODE_FIELD_NUMBER: builtins.int
ERROR_MESSAGE_FIELD_NUMBER: builtins.int
ERROR_PAYLOAD_FIELD_NUMBER: builtins.int
@property
def task(self) -> global___CoordinatedTask: ...
state: global___CoordinatedTaskState.ValueType
error_code: builtins.int
error_message: builtins.str
@property
def task(self) -> global___CoordinatedTask: ...
@property
def error_payload(self) -> global___CoordinationServiceError: ...
def __init__(
self,
@@ -128,12 +130,12 @@ class CoordinatedTaskStateInfo(google.protobuf.message.Message):
error_message: builtins.str | None = ...,
error_payload: global___CoordinationServiceError | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["error_payload", b"error_payload", "task", b"task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["error_code", b"error_code", "error_message", b"error_message", "error_payload", b"error_payload", "state", b"state", "task", b"task"]) -> None: ...
def HasField(self, field_name: typing.Literal["error_payload", b"error_payload", "task", b"task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["error_code", b"error_code", "error_message", b"error_message", "error_payload", b"error_payload", "state", b"state", "task", b"task"]) -> None: ...
global___CoordinatedTaskStateInfo = CoordinatedTaskStateInfo
@typing_extensions.final
@typing.final
class DeviceInfo(google.protobuf.message.Message):
"""Placeholder message to be extended by other runtimes' device representations."""
@@ -147,11 +149,11 @@ class DeviceInfo(google.protobuf.message.Message):
*,
device: collections.abc.Iterable[google.protobuf.any_pb2.Any] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["device", b"device"]) -> None: ...
def ClearField(self, field_name: typing.Literal["device", b"device"]) -> None: ...
global___DeviceInfo = DeviceInfo
@typing_extensions.final
@typing.final
class RegisterTaskRequest(google.protobuf.message.Message):
"""Request and response messages for registering a task to the cluster leader.
A task is uniquely represented by its `job_name`, `task_id` and
@@ -172,12 +174,12 @@ class RegisterTaskRequest(google.protobuf.message.Message):
incarnation: builtins.int | None = ...,
source_task: global___CoordinatedTask | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["incarnation", b"incarnation", "source_task", b"source_task"]) -> None: ...
def HasField(self, field_name: typing.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["incarnation", b"incarnation", "source_task", b"source_task"]) -> None: ...
global___RegisterTaskRequest = RegisterTaskRequest
@typing_extensions.final
@typing.final
class RegisterTaskResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -188,11 +190,11 @@ class RegisterTaskResponse(google.protobuf.message.Message):
*,
leader_incarnation: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["leader_incarnation", b"leader_incarnation"]) -> None: ...
def ClearField(self, field_name: typing.Literal["leader_incarnation", b"leader_incarnation"]) -> None: ...
global___RegisterTaskResponse = RegisterTaskResponse
@typing_extensions.final
@typing.final
class HeartbeatRequest(google.protobuf.message.Message):
"""Request and response messages for sending heartbeats."""
@@ -209,12 +211,12 @@ class HeartbeatRequest(google.protobuf.message.Message):
incarnation: builtins.int | None = ...,
source_task: global___CoordinatedTask | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["incarnation", b"incarnation", "source_task", b"source_task"]) -> None: ...
def HasField(self, field_name: typing.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["incarnation", b"incarnation", "source_task", b"source_task"]) -> None: ...
global___HeartbeatRequest = HeartbeatRequest
@typing_extensions.final
@typing.final
class HeartbeatResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -228,11 +230,11 @@ class HeartbeatResponse(google.protobuf.message.Message):
*,
leader_incarnation: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["leader_incarnation", b"leader_incarnation"]) -> None: ...
def ClearField(self, field_name: typing.Literal["leader_incarnation", b"leader_incarnation"]) -> None: ...
global___HeartbeatResponse = HeartbeatResponse
@typing_extensions.final
@typing.final
class WaitForAllTasksRequest(google.protobuf.message.Message):
"""Request and response messages for waiting for all tasks."""
@@ -245,18 +247,19 @@ class WaitForAllTasksRequest(google.protobuf.message.Message):
@property
def device_info(self) -> global___DeviceInfo:
"""All local device attributes on the request sender;"""
def __init__(
self,
*,
source_task: global___CoordinatedTask | None = ...,
device_info: global___DeviceInfo | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["device_info", b"device_info", "source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["device_info", b"device_info", "source_task", b"source_task"]) -> None: ...
def HasField(self, field_name: typing.Literal["device_info", b"device_info", "source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["device_info", b"device_info", "source_task", b"source_task"]) -> None: ...
global___WaitForAllTasksRequest = WaitForAllTasksRequest
@typing_extensions.final
@typing.final
class WaitForAllTasksResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -266,18 +269,19 @@ class WaitForAllTasksResponse(google.protobuf.message.Message):
@property
def device_info(self) -> global___DeviceInfo:
"""All devices in the cluster."""
def __init__(
self,
*,
leader_incarnation: builtins.int | None = ...,
device_info: global___DeviceInfo | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["device_info", b"device_info"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["device_info", b"device_info", "leader_incarnation", b"leader_incarnation"]) -> None: ...
def HasField(self, field_name: typing.Literal["device_info", b"device_info"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["device_info", b"device_info", "leader_incarnation", b"leader_incarnation"]) -> None: ...
global___WaitForAllTasksResponse = WaitForAllTasksResponse
@typing_extensions.final
@typing.final
class ShutdownTaskRequest(google.protobuf.message.Message):
"""Request and response messages for disconnecting a task from the service."""
@@ -291,12 +295,12 @@ class ShutdownTaskRequest(google.protobuf.message.Message):
*,
source_task: global___CoordinatedTask | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["source_task", b"source_task"]) -> None: ...
def HasField(self, field_name: typing.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["source_task", b"source_task"]) -> None: ...
global___ShutdownTaskRequest = ShutdownTaskRequest
@typing_extensions.final
@typing.final
class ShutdownTaskResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -306,7 +310,7 @@ class ShutdownTaskResponse(google.protobuf.message.Message):
global___ShutdownTaskResponse = ShutdownTaskResponse
@typing_extensions.final
@typing.final
class ResetTaskRequest(google.protobuf.message.Message):
"""Request and response messages for resetting a task state in the service."""
@@ -320,12 +324,12 @@ class ResetTaskRequest(google.protobuf.message.Message):
*,
source_task: global___CoordinatedTask | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["source_task", b"source_task"]) -> None: ...
def HasField(self, field_name: typing.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["source_task", b"source_task"]) -> None: ...
global___ResetTaskRequest = ResetTaskRequest
@typing_extensions.final
@typing.final
class ResetTaskResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -335,7 +339,7 @@ class ResetTaskResponse(google.protobuf.message.Message):
global___ResetTaskResponse = ResetTaskResponse
@typing_extensions.final
@typing.final
class ReportErrorToTaskRequest(google.protobuf.message.Message):
"""Request and response messages for reporting errors to task."""
@@ -355,12 +359,12 @@ class ReportErrorToTaskRequest(google.protobuf.message.Message):
error_message: builtins.str | None = ...,
error_payload: global___CoordinationServiceError | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["error_payload", b"error_payload"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["error_code", b"error_code", "error_message", b"error_message", "error_payload", b"error_payload"]) -> None: ...
def HasField(self, field_name: typing.Literal["error_payload", b"error_payload"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["error_code", b"error_code", "error_message", b"error_message", "error_payload", b"error_payload"]) -> None: ...
global___ReportErrorToTaskRequest = ReportErrorToTaskRequest
@typing_extensions.final
@typing.final
class ReportErrorToTaskResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -370,7 +374,7 @@ class ReportErrorToTaskResponse(google.protobuf.message.Message):
global___ReportErrorToTaskResponse = ReportErrorToTaskResponse
@typing_extensions.final
@typing.final
class ReportErrorToServiceRequest(google.protobuf.message.Message):
"""Request and response messages for reporting errors to service instance."""
@@ -390,12 +394,12 @@ class ReportErrorToServiceRequest(google.protobuf.message.Message):
error_message: builtins.str | None = ...,
error_origin: global___CoordinatedTask | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["error_origin", b"error_origin"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["error_code", b"error_code", "error_message", b"error_message", "error_origin", b"error_origin"]) -> None: ...
def HasField(self, field_name: typing.Literal["error_origin", b"error_origin"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["error_code", b"error_code", "error_message", b"error_message", "error_origin", b"error_origin"]) -> None: ...
global___ReportErrorToServiceRequest = ReportErrorToServiceRequest
@typing_extensions.final
@typing.final
class ReportErrorToServiceResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -405,7 +409,7 @@ class ReportErrorToServiceResponse(google.protobuf.message.Message):
global___ReportErrorToServiceResponse = ReportErrorToServiceResponse
@typing_extensions.final
@typing.final
class GetTaskStateRequest(google.protobuf.message.Message):
"""Request and response messages for getting state of a remote task."""
@@ -419,11 +423,11 @@ class GetTaskStateRequest(google.protobuf.message.Message):
*,
source_task: collections.abc.Iterable[global___CoordinatedTask] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["source_task", b"source_task"]) -> None: ...
def ClearField(self, field_name: typing.Literal["source_task", b"source_task"]) -> None: ...
global___GetTaskStateRequest = GetTaskStateRequest
@typing_extensions.final
@typing.final
class GetTaskStateResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -435,11 +439,11 @@ class GetTaskStateResponse(google.protobuf.message.Message):
*,
task_state: collections.abc.Iterable[global___CoordinatedTaskStateInfo] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["task_state", b"task_state"]) -> None: ...
def ClearField(self, field_name: typing.Literal["task_state", b"task_state"]) -> None: ...
global___GetTaskStateResponse = GetTaskStateResponse
@typing_extensions.final
@typing.final
class KeyValueEntry(google.protobuf.message.Message):
"""Message for configuration key value.
Key is structured like Unix file system, with multiple levels of directory
@@ -458,11 +462,11 @@ class KeyValueEntry(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.bytes | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
global___KeyValueEntry = KeyValueEntry
@typing_extensions.final
@typing.final
class InsertKeyValueRequest(google.protobuf.message.Message):
"""Request and response messages for inserting configuration key-value data."""
@@ -476,12 +480,12 @@ class InsertKeyValueRequest(google.protobuf.message.Message):
*,
kv: global___KeyValueEntry | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["kv", b"kv"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["kv", b"kv"]) -> None: ...
def HasField(self, field_name: typing.Literal["kv", b"kv"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["kv", b"kv"]) -> None: ...
global___InsertKeyValueRequest = InsertKeyValueRequest
@typing_extensions.final
@typing.final
class InsertKeyValueResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -491,7 +495,7 @@ class InsertKeyValueResponse(google.protobuf.message.Message):
global___InsertKeyValueResponse = InsertKeyValueResponse
@typing_extensions.final
@typing.final
class GetKeyValueRequest(google.protobuf.message.Message):
"""Request and response messages for getting configuration key-value data."""
@@ -504,11 +508,11 @@ class GetKeyValueRequest(google.protobuf.message.Message):
*,
key: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key"]) -> None: ...
global___GetKeyValueRequest = GetKeyValueRequest
@typing_extensions.final
@typing.final
class GetKeyValueResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -520,12 +524,12 @@ class GetKeyValueResponse(google.protobuf.message.Message):
*,
kv: global___KeyValueEntry | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["kv", b"kv"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["kv", b"kv"]) -> None: ...
def HasField(self, field_name: typing.Literal["kv", b"kv"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["kv", b"kv"]) -> None: ...
global___GetKeyValueResponse = GetKeyValueResponse
@typing_extensions.final
@typing.final
class TryGetKeyValueRequest(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -536,11 +540,11 @@ class TryGetKeyValueRequest(google.protobuf.message.Message):
*,
key: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key"]) -> None: ...
global___TryGetKeyValueRequest = TryGetKeyValueRequest
@typing_extensions.final
@typing.final
class TryGetKeyValueResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -552,12 +556,12 @@ class TryGetKeyValueResponse(google.protobuf.message.Message):
*,
kv: global___KeyValueEntry | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["kv", b"kv"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["kv", b"kv"]) -> None: ...
def HasField(self, field_name: typing.Literal["kv", b"kv"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["kv", b"kv"]) -> None: ...
global___TryGetKeyValueResponse = TryGetKeyValueResponse
@typing_extensions.final
@typing.final
class GetKeyValueDirRequest(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -568,11 +572,11 @@ class GetKeyValueDirRequest(google.protobuf.message.Message):
*,
directory_key: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["directory_key", b"directory_key"]) -> None: ...
def ClearField(self, field_name: typing.Literal["directory_key", b"directory_key"]) -> None: ...
global___GetKeyValueDirRequest = GetKeyValueDirRequest
@typing_extensions.final
@typing.final
class GetKeyValueDirResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -587,11 +591,11 @@ class GetKeyValueDirResponse(google.protobuf.message.Message):
directory_key: builtins.str | None = ...,
kv: collections.abc.Iterable[global___KeyValueEntry] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["directory_key", b"directory_key", "kv", b"kv"]) -> None: ...
def ClearField(self, field_name: typing.Literal["directory_key", b"directory_key", "kv", b"kv"]) -> None: ...
global___GetKeyValueDirResponse = GetKeyValueDirResponse
@typing_extensions.final
@typing.final
class DeleteKeyValueRequest(google.protobuf.message.Message):
"""Request and response messages for deleting configuration key-value data.
When is_directory is true, delete key-values recursively under `key`.
@@ -609,11 +613,11 @@ class DeleteKeyValueRequest(google.protobuf.message.Message):
key: builtins.str | None = ...,
is_directory: builtins.bool | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["is_directory", b"is_directory", "key", b"key"]) -> None: ...
def ClearField(self, field_name: typing.Literal["is_directory", b"is_directory", "key", b"key"]) -> None: ...
global___DeleteKeyValueRequest = DeleteKeyValueRequest
@typing_extensions.final
@typing.final
class DeleteKeyValueResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -623,7 +627,7 @@ class DeleteKeyValueResponse(google.protobuf.message.Message):
global___DeleteKeyValueResponse = DeleteKeyValueResponse
@typing_extensions.final
@typing.final
class BarrierRequest(google.protobuf.message.Message):
"""Request and response messages for generic sync barriers."""
@@ -640,9 +644,11 @@ class BarrierRequest(google.protobuf.message.Message):
"""Denotes list of tasks that will wait for the barrier. If unspecified, it
implies that the entire cluster is participating in the barrier.
"""
@property
def source_task(self) -> global___CoordinatedTask:
"""Task that is making the request."""
def __init__(
self,
*,
@@ -651,12 +657,12 @@ class BarrierRequest(google.protobuf.message.Message):
tasks: collections.abc.Iterable[global___CoordinatedTask] | None = ...,
source_task: global___CoordinatedTask | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["barrier_id", b"barrier_id", "barrier_timeout_in_ms", b"barrier_timeout_in_ms", "source_task", b"source_task", "tasks", b"tasks"]) -> None: ...
def HasField(self, field_name: typing.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["barrier_id", b"barrier_id", "barrier_timeout_in_ms", b"barrier_timeout_in_ms", "source_task", b"source_task", "tasks", b"tasks"]) -> None: ...
global___BarrierRequest = BarrierRequest
@typing_extensions.final
@typing.final
class BarrierResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -666,7 +672,7 @@ class BarrierResponse(google.protobuf.message.Message):
global___BarrierResponse = BarrierResponse
@typing_extensions.final
@typing.final
class CancelBarrierRequest(google.protobuf.message.Message):
"""Request and response messages for cancelling generic sync barriers."""
@@ -678,18 +684,19 @@ class CancelBarrierRequest(google.protobuf.message.Message):
@property
def source_task(self) -> global___CoordinatedTask:
"""Task that is making the request."""
def __init__(
self,
*,
barrier_id: builtins.str | None = ...,
source_task: global___CoordinatedTask | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["barrier_id", b"barrier_id", "source_task", b"source_task"]) -> None: ...
def HasField(self, field_name: typing.Literal["source_task", b"source_task"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["barrier_id", b"barrier_id", "source_task", b"source_task"]) -> None: ...
global___CancelBarrierRequest = CancelBarrierRequest
@typing_extensions.final
@typing.final
class CancelBarrierResponse(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
stubs/tensorflow/tensorflow/tsl/protobuf/distributed_runtime_payloads_pb2.pyi
+8 -7
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,7 +13,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class GrpcPayloadContainer(google.protobuf.message.Message):
"""Used to serialize and transmit tensorflow::Status payloads through
grpc::Status `error_details` since grpc::Status lacks payload API.
@@ -21,7 +22,7 @@ class GrpcPayloadContainer(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class PayloadsEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -35,7 +36,7 @@ class GrpcPayloadContainer(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.bytes | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
PAYLOADS_FIELD_NUMBER: builtins.int
@property
@@ -45,11 +46,11 @@ class GrpcPayloadContainer(google.protobuf.message.Message):
*,
payloads: collections.abc.Mapping[builtins.str, builtins.bytes] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["payloads", b"payloads"]) -> None: ...
def ClearField(self, field_name: typing.Literal["payloads", b"payloads"]) -> None: ...
global___GrpcPayloadContainer = GrpcPayloadContainer
@typing_extensions.final
@typing.final
class GrpcPayloadsLost(google.protobuf.message.Message):
"""If included as a payload, this message flags the Status to have lost payloads
during the GRPC transmission.
@@ -64,7 +65,7 @@ class GrpcPayloadsLost(google.protobuf.message.Message):
global___GrpcPayloadsLost = GrpcPayloadsLost
@typing_extensions.final
@typing.final
class WorkerPossiblyRestarted(google.protobuf.message.Message):
"""If included as a payload, this message flags the Status to be a possible
outcome of a worker restart.
stubs/tensorflow/tensorflow/tsl/protobuf/dnn_pb2.pyi
+30 -28
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
LINT: LEGACY_NAMES"""
import builtins
import collections.abc
import sys
@@ -250,7 +251,7 @@ BACKWARD_DATA: ConvolutionKind.ValueType # 3
FORWARD_BIAS_ACTIVATION: ConvolutionKind.ValueType # 4
global___ConvolutionKind = ConvolutionKind
@typing_extensions.final
@typing.final
class TensorDescriptorProto(google.protobuf.message.Message):
"""Generic tensor representation."""
@@ -260,11 +261,11 @@ class TensorDescriptorProto(google.protobuf.message.Message):
DATA_TYPE_FIELD_NUMBER: builtins.int
DATA_LAYOUT_FIELD_NUMBER: builtins.int
FILTER_LAYOUT_FIELD_NUMBER: builtins.int
@property
def dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
data_type: global___DataType.ValueType
data_layout: global___DataLayout.ValueType
filter_layout: global___FilterLayout.ValueType
@property
def dimensions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
def __init__(
self,
*,
@@ -273,13 +274,13 @@ class TensorDescriptorProto(google.protobuf.message.Message):
data_layout: global___DataLayout.ValueType | None = ...,
filter_layout: global___FilterLayout.ValueType | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["data_layout", b"data_layout", "filter_layout", b"filter_layout", "layout_oneof", b"layout_oneof"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["data_layout", b"data_layout", "data_type", b"data_type", "dimensions", b"dimensions", "filter_layout", b"filter_layout", "layout_oneof", b"layout_oneof"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["layout_oneof", b"layout_oneof"]) -> typing_extensions.Literal["data_layout", "filter_layout"] | None: ...
def HasField(self, field_name: typing.Literal["data_layout", b"data_layout", "filter_layout", b"filter_layout", "layout_oneof", b"layout_oneof"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["data_layout", b"data_layout", "data_type", b"data_type", "dimensions", b"dimensions", "filter_layout", b"filter_layout", "layout_oneof", b"layout_oneof"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["layout_oneof", b"layout_oneof"]) -> typing.Literal["data_layout", "filter_layout"] | None: ...
global___TensorDescriptorProto = TensorDescriptorProto
@typing_extensions.final
@typing.final
class AlgorithmProto(google.protobuf.message.Message):
"""Generic algorithm representation."""
@@ -304,7 +305,7 @@ class AlgorithmProto(google.protobuf.message.Message):
See cuDNN's documentation for CUDNN_TENSOR_OP_MATH.
"""
@typing_extensions.final
@typing.final
class TuningKnobsEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -318,7 +319,7 @@ class AlgorithmProto(google.protobuf.message.Message):
key: builtins.int | None = ...,
value: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
ALGO_ID_FIELD_NUMBER: builtins.int
MATH_TYPE_FIELD_NUMBER: builtins.int
@@ -327,8 +328,6 @@ class AlgorithmProto(google.protobuf.message.Message):
WORKSPACE_SIZE_FIELD_NUMBER: builtins.int
algo_id: builtins.int
math_type: global___AlgorithmProto.MathType.ValueType
@property
def tuning_knobs(self) -> google.protobuf.internal.containers.ScalarMap[builtins.int, builtins.int]: ...
is_cudnn_frontend: builtins.bool
"""Legacy algorithm enums and cuDNN Frontend engine numbers need to coexist in
the same proto medium-term, until we can be confident of no longer needing
@@ -336,6 +335,8 @@ class AlgorithmProto(google.protobuf.message.Message):
stop producing legacy algorithm enums and remove this field.
"""
@property
def tuning_knobs(self) -> google.protobuf.internal.containers.ScalarMap[builtins.int, builtins.int]: ...
@property
def workspace_size(self) -> google.protobuf.wrappers_pb2.UInt64Value:
"""For ROCm only, it's impossible to re-query the required workspace size
after running the algorithm search, so we must store the workspace size
@@ -347,6 +348,7 @@ class AlgorithmProto(google.protobuf.message.Message):
0 workspace size from unknown workspace size in an old message, so this is
a message field.
"""
def __init__(
self,
*,
@@ -356,12 +358,12 @@ class AlgorithmProto(google.protobuf.message.Message):
is_cudnn_frontend: builtins.bool | None = ...,
workspace_size: google.protobuf.wrappers_pb2.UInt64Value | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["workspace_size", b"workspace_size"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["algo_id", b"algo_id", "is_cudnn_frontend", b"is_cudnn_frontend", "math_type", b"math_type", "tuning_knobs", b"tuning_knobs", "workspace_size", b"workspace_size"]) -> None: ...
def HasField(self, field_name: typing.Literal["workspace_size", b"workspace_size"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["algo_id", b"algo_id", "is_cudnn_frontend", b"is_cudnn_frontend", "math_type", b"math_type", "tuning_knobs", b"tuning_knobs", "workspace_size", b"workspace_size"]) -> None: ...
global___AlgorithmProto = AlgorithmProto
@typing_extensions.final
@typing.final
class AlgorithmConfigProto(google.protobuf.message.Message):
"""Proto definition of AlgorithmConfig in "dnn.h".
TODO(ruochengw): After cl/380702564 is submitted, add support for algorithm
@@ -373,11 +375,11 @@ class AlgorithmConfigProto(google.protobuf.message.Message):
ALGORITHM_FIELD_NUMBER: builtins.int
ALGORITHM_NO_SCRATCH_FIELD_NUMBER: builtins.int
SCRATCH_SIZE_FIELD_NUMBER: builtins.int
scratch_size: builtins.int
@property
def algorithm(self) -> global___AlgorithmProto: ...
@property
def algorithm_no_scratch(self) -> global___AlgorithmProto: ...
scratch_size: builtins.int
def __init__(
self,
*,
@@ -385,18 +387,18 @@ class AlgorithmConfigProto(google.protobuf.message.Message):
algorithm_no_scratch: global___AlgorithmProto | None = ...,
scratch_size: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["algorithm", b"algorithm", "algorithm_no_scratch", b"algorithm_no_scratch", "optional_algorithm", b"optional_algorithm", "optional_algorithm_no_scratch", b"optional_algorithm_no_scratch", "optional_scratch_size", b"optional_scratch_size", "scratch_size", b"scratch_size"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["algorithm", b"algorithm", "algorithm_no_scratch", b"algorithm_no_scratch", "optional_algorithm", b"optional_algorithm", "optional_algorithm_no_scratch", b"optional_algorithm_no_scratch", "optional_scratch_size", b"optional_scratch_size", "scratch_size", b"scratch_size"]) -> None: ...
def HasField(self, field_name: typing.Literal["algorithm", b"algorithm", "algorithm_no_scratch", b"algorithm_no_scratch", "optional_algorithm", b"optional_algorithm", "optional_algorithm_no_scratch", b"optional_algorithm_no_scratch", "optional_scratch_size", b"optional_scratch_size", "scratch_size", b"scratch_size"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["algorithm", b"algorithm", "algorithm_no_scratch", b"algorithm_no_scratch", "optional_algorithm", b"optional_algorithm", "optional_algorithm_no_scratch", b"optional_algorithm_no_scratch", "optional_scratch_size", b"optional_scratch_size", "scratch_size", b"scratch_size"]) -> None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_algorithm", b"optional_algorithm"]) -> typing_extensions.Literal["algorithm"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_algorithm", b"optional_algorithm"]) -> typing.Literal["algorithm"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_algorithm_no_scratch", b"optional_algorithm_no_scratch"]) -> typing_extensions.Literal["algorithm_no_scratch"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_algorithm_no_scratch", b"optional_algorithm_no_scratch"]) -> typing.Literal["algorithm_no_scratch"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_scratch_size", b"optional_scratch_size"]) -> typing_extensions.Literal["scratch_size"] | None: ...
def WhichOneof(self, oneof_group: typing.Literal["optional_scratch_size", b"optional_scratch_size"]) -> typing.Literal["scratch_size"] | None: ...
global___AlgorithmConfigProto = AlgorithmConfigProto
@typing_extensions.final
@typing.final
class ConvolutionDescriptorProto(google.protobuf.message.Message):
"""Convolution-specific parameters."""
@@ -409,12 +411,6 @@ class ConvolutionDescriptorProto(google.protobuf.message.Message):
GROUP_COUNT_FIELD_NUMBER: builtins.int
CONVOLUTION_MODE_FIELD_NUMBER: builtins.int
NAME_FIELD_NUMBER: builtins.int
@property
def paddings(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def strides(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def dilations(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
compute_mode: global___DataType.ValueType
"""The "accumulator" type. For example, use F32 as an accumulator for F16
convolutions.
@@ -425,6 +421,12 @@ class ConvolutionDescriptorProto(google.protobuf.message.Message):
convolution_mode: global___ConvolutionMode.ValueType
name: builtins.str
"""Tensorflow node name, same as in NodeDef, for debugging purposes."""
@property
def paddings(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def strides(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
@property
def dilations(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
def __init__(
self,
*,
@@ -436,6 +438,6 @@ class ConvolutionDescriptorProto(google.protobuf.message.Message):
convolution_mode: global___ConvolutionMode.ValueType | None = ...,
name: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["compute_mode", b"compute_mode", "convolution_mode", b"convolution_mode", "dilations", b"dilations", "group_count", b"group_count", "name", b"name", "paddings", b"paddings", "strides", b"strides"]) -> None: ...
def ClearField(self, field_name: typing.Literal["compute_mode", b"compute_mode", "convolution_mode", b"convolution_mode", "dilations", b"dilations", "group_count", b"group_count", "name", b"name", "paddings", b"paddings", "strides", b"strides"]) -> None: ...
global___ConvolutionDescriptorProto = ConvolutionDescriptorProto
stubs/tensorflow/tensorflow/tsl/protobuf/error_codes_pb2.pyi
+1
View File
@@ -4,6 +4,7 @@ isort:skip_file
TODO(b/247876220): Change package and java_package once we figure out how to
migrate.
"""
import builtins
import sys
import typing
stubs/tensorflow/tensorflow/tsl/protobuf/histogram_pb2.pyi
+5 -3
View File
@@ -2,9 +2,10 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.internal.containers
@@ -12,7 +13,7 @@ import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class HistogramProto(google.protobuf.message.Message):
"""Serialization format for histogram module in
tsl/lib/histogram/histogram.h
@@ -40,6 +41,7 @@ class HistogramProto(google.protobuf.message.Message):
i == 0: -DBL_MAX .. bucket_limit(0)
i != 0: bucket_limit(i-1) .. bucket_limit(i)
"""
@property
def bucket(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: ...
def __init__(
@@ -53,6 +55,6 @@ class HistogramProto(google.protobuf.message.Message):
bucket_limit: collections.abc.Iterable[builtins.float] | None = ...,
bucket: collections.abc.Iterable[builtins.float] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["bucket", b"bucket", "bucket_limit", b"bucket_limit", "max", b"max", "min", b"min", "num", b"num", "sum", b"sum", "sum_squares", b"sum_squares"]) -> None: ...
def ClearField(self, field_name: typing.Literal["bucket", b"bucket", "bucket_limit", b"bucket_limit", "max", b"max", "min", b"min", "num", b"num", "sum", b"sum", "sum_squares", b"sum_squares"]) -> None: ...
global___HistogramProto = HistogramProto
stubs/tensorflow/tensorflow/tsl/protobuf/rpc_options_pb2.pyi
+4 -3
View File
@@ -2,15 +2,16 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import typing as typing_extensions
import typing
import google.protobuf.descriptor
import google.protobuf.message
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class RPCOptions(google.protobuf.message.Message):
"""RPC options for distributed runtime."""
@@ -63,6 +64,6 @@ class RPCOptions(google.protobuf.message.Message):
disable_session_connection_sharing: builtins.bool | None = ...,
num_channels_per_target: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["cache_rpc_response", b"cache_rpc_response", "compression_algorithm", b"compression_algorithm", "compression_level", b"compression_level", "disable_session_connection_sharing", b"disable_session_connection_sharing", "num_channels_per_target", b"num_channels_per_target", "use_rpc_for_inprocess_master", b"use_rpc_for_inprocess_master"]) -> None: ...
def ClearField(self, field_name: typing.Literal["cache_rpc_response", b"cache_rpc_response", "compression_algorithm", b"compression_algorithm", "compression_level", b"compression_level", "disable_session_connection_sharing", b"disable_session_connection_sharing", "num_channels_per_target", b"num_channels_per_target", "use_rpc_for_inprocess_master", b"use_rpc_for_inprocess_master"]) -> None: ...
global___RPCOptions = RPCOptions
stubs/tensorflow/tensorflow/tsl/protobuf/test_log_pb2.pyi
+75 -57
View File
@@ -2,6 +2,7 @@
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
Protocol messages for describing the results of benchmarks and unit tests."""
import builtins
import collections.abc
import sys
@@ -21,7 +22,7 @@ else:
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
@typing_extensions.final
@typing.final
class EntryValue(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -35,13 +36,13 @@ class EntryValue(google.protobuf.message.Message):
double_value: builtins.float | None = ...,
string_value: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["double_value", b"double_value", "kind", b"kind", "string_value", b"string_value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["double_value", b"double_value", "kind", b"kind", "string_value", b"string_value"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["kind", b"kind"]) -> typing_extensions.Literal["double_value", "string_value"] | None: ...
def HasField(self, field_name: typing.Literal["double_value", b"double_value", "kind", b"kind", "string_value", b"string_value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["double_value", b"double_value", "kind", b"kind", "string_value", b"string_value"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["kind", b"kind"]) -> typing.Literal["double_value", "string_value"] | None: ...
global___EntryValue = EntryValue
@typing_extensions.final
@typing.final
class MetricEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -56,9 +57,11 @@ class MetricEntry(google.protobuf.message.Message):
@property
def min_value(self) -> google.protobuf.wrappers_pb2.DoubleValue:
"""The minimum acceptable value for the metric if specified"""
@property
def max_value(self) -> google.protobuf.wrappers_pb2.DoubleValue:
"""The maximum acceptable value for the metric if specified"""
def __init__(
self,
*,
@@ -67,12 +70,12 @@ class MetricEntry(google.protobuf.message.Message):
min_value: google.protobuf.wrappers_pb2.DoubleValue | None = ...,
max_value: google.protobuf.wrappers_pb2.DoubleValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["max_value", b"max_value", "min_value", b"min_value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["max_value", b"max_value", "min_value", b"min_value", "name", b"name", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["max_value", b"max_value", "min_value", b"min_value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["max_value", b"max_value", "min_value", b"min_value", "name", b"name", "value", b"value"]) -> None: ...
global___MetricEntry = MetricEntry
@typing_extensions.final
@typing.final
class BenchmarkEntry(google.protobuf.message.Message):
"""Each unit test or benchmark in a test or benchmark run provides
some set of information. Here we provide some reasonable keys
@@ -85,7 +88,7 @@ class BenchmarkEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class ExtrasEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -100,8 +103,8 @@ class BenchmarkEntry(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: global___EntryValue | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def HasField(self, field_name: typing.Literal["value", b"value"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
NAME_FIELD_NUMBER: builtins.int
ITERS_FIELD_NUMBER: builtins.int
@@ -125,11 +128,13 @@ class BenchmarkEntry(google.protobuf.message.Message):
@property
def extras(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___EntryValue]:
"""Generic map from result key to value."""
@property
def metrics(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___MetricEntry]:
"""Metric name, value and expected range. This can include accuracy metrics
typically used to determine whether the accuracy test has passed
"""
def __init__(
self,
*,
@@ -141,11 +146,11 @@ class BenchmarkEntry(google.protobuf.message.Message):
extras: collections.abc.Mapping[builtins.str, global___EntryValue] | None = ...,
metrics: collections.abc.Iterable[global___MetricEntry] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["cpu_time", b"cpu_time", "extras", b"extras", "iters", b"iters", "metrics", b"metrics", "name", b"name", "throughput", b"throughput", "wall_time", b"wall_time"]) -> None: ...
def ClearField(self, field_name: typing.Literal["cpu_time", b"cpu_time", "extras", b"extras", "iters", b"iters", "metrics", b"metrics", "name", b"name", "throughput", b"throughput", "wall_time", b"wall_time"]) -> None: ...
global___BenchmarkEntry = BenchmarkEntry
@typing_extensions.final
@typing.final
class BenchmarkEntries(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -157,11 +162,11 @@ class BenchmarkEntries(google.protobuf.message.Message):
*,
entry: collections.abc.Iterable[global___BenchmarkEntry] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["entry", b"entry"]) -> None: ...
def ClearField(self, field_name: typing.Literal["entry", b"entry"]) -> None: ...
global___BenchmarkEntries = BenchmarkEntries
@typing_extensions.final
@typing.final
class BuildConfiguration(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -173,9 +178,11 @@ class BuildConfiguration(google.protobuf.message.Message):
@property
def cc_flags(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""CC compiler flags, if known"""
@property
def opts(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Bazel compilation options, if known"""
def __init__(
self,
*,
@@ -183,11 +190,11 @@ class BuildConfiguration(google.protobuf.message.Message):
cc_flags: collections.abc.Iterable[builtins.str] | None = ...,
opts: collections.abc.Iterable[builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["cc_flags", b"cc_flags", "mode", b"mode", "opts", b"opts"]) -> None: ...
def ClearField(self, field_name: typing.Literal["cc_flags", b"cc_flags", "mode", b"mode", "opts", b"opts"]) -> None: ...
global___BuildConfiguration = BuildConfiguration
@typing_extensions.final
@typing.final
class CommitId(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -212,17 +219,17 @@ class CommitId(google.protobuf.message.Message):
snapshot: builtins.str | None = ...,
pending_changelist: builtins.int | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["changelist", b"changelist", "hash", b"hash", "kind", b"kind"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["changelist", b"changelist", "hash", b"hash", "kind", b"kind", "pending_changelist", b"pending_changelist", "snapshot", b"snapshot"]) -> None: ...
def WhichOneof(self, oneof_group: typing_extensions.Literal["kind", b"kind"]) -> typing_extensions.Literal["changelist", "hash"] | None: ...
def HasField(self, field_name: typing.Literal["changelist", b"changelist", "hash", b"hash", "kind", b"kind"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["changelist", b"changelist", "hash", b"hash", "kind", b"kind", "pending_changelist", b"pending_changelist", "snapshot", b"snapshot"]) -> None: ...
def WhichOneof(self, oneof_group: typing.Literal["kind", b"kind"]) -> typing.Literal["changelist", "hash"] | None: ...
global___CommitId = CommitId
@typing_extensions.final
@typing.final
class CPUInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class CacheSizeEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -236,7 +243,7 @@ class CPUInfo(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
NUM_CORES_FIELD_NUMBER: builtins.int
NUM_CORES_ALLOWED_FIELD_NUMBER: builtins.int
@@ -259,6 +266,7 @@ class CPUInfo(google.protobuf.message.Message):
@property
def cache_size(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.int]:
"""Cache sizes (in bytes), e.g. "L2": 262144 (for 256KB)"""
def __init__(
self,
*,
@@ -269,11 +277,11 @@ class CPUInfo(google.protobuf.message.Message):
cpu_governor: builtins.str | None = ...,
cache_size: collections.abc.Mapping[builtins.str, builtins.int] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["cache_size", b"cache_size", "cpu_governor", b"cpu_governor", "cpu_info", b"cpu_info", "mhz_per_cpu", b"mhz_per_cpu", "num_cores", b"num_cores", "num_cores_allowed", b"num_cores_allowed"]) -> None: ...
def ClearField(self, field_name: typing.Literal["cache_size", b"cache_size", "cpu_governor", b"cpu_governor", "cpu_info", b"cpu_info", "mhz_per_cpu", b"mhz_per_cpu", "num_cores", b"num_cores", "num_cores_allowed", b"num_cores_allowed"]) -> None: ...
global___CPUInfo = CPUInfo
@typing_extensions.final
@typing.final
class MemoryInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -289,11 +297,11 @@ class MemoryInfo(google.protobuf.message.Message):
total: builtins.int | None = ...,
available: builtins.int | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["available", b"available", "total", b"total"]) -> None: ...
def ClearField(self, field_name: typing.Literal["available", b"available", "total", b"total"]) -> None: ...
global___MemoryInfo = MemoryInfo
@typing_extensions.final
@typing.final
class GPUInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -313,11 +321,11 @@ class GPUInfo(google.protobuf.message.Message):
uuid: builtins.str | None = ...,
bus_id: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["bus_id", b"bus_id", "model", b"model", "uuid", b"uuid"]) -> None: ...
def ClearField(self, field_name: typing.Literal["bus_id", b"bus_id", "model", b"model", "uuid", b"uuid"]) -> None: ...
global___GPUInfo = GPUInfo
@typing_extensions.final
@typing.final
class PlatformInfo(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -349,11 +357,11 @@ class PlatformInfo(google.protobuf.message.Message):
system: builtins.str | None = ...,
version: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["bits", b"bits", "linkage", b"linkage", "machine", b"machine", "release", b"release", "system", b"system", "version", b"version"]) -> None: ...
def ClearField(self, field_name: typing.Literal["bits", b"bits", "linkage", b"linkage", "machine", b"machine", "release", b"release", "system", b"system", "version", b"version"]) -> None: ...
global___PlatformInfo = PlatformInfo
@typing_extensions.final
@typing.final
class AvailableDeviceInfo(google.protobuf.message.Message):
"""Matches DeviceAttributes"""
@@ -379,11 +387,11 @@ class AvailableDeviceInfo(google.protobuf.message.Message):
memory_limit: builtins.int | None = ...,
physical_description: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["memory_limit", b"memory_limit", "name", b"name", "physical_description", b"physical_description", "type", b"type"]) -> None: ...
def ClearField(self, field_name: typing.Literal["memory_limit", b"memory_limit", "name", b"name", "physical_description", b"physical_description", "type", b"type"]) -> None: ...
global___AvailableDeviceInfo = AvailableDeviceInfo
@typing_extensions.final
@typing.final
class MachineConfiguration(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -401,15 +409,19 @@ class MachineConfiguration(google.protobuf.message.Message):
@property
def platform_info(self) -> global___PlatformInfo:
"""Additional platform information."""
@property
def cpu_info(self) -> global___CPUInfo:
"""CPU Information."""
@property
def device_info(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[google.protobuf.any_pb2.Any]:
"""Other devices that are attached and relevant (e.g. GPUInfo)."""
@property
def available_device_info(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___AvailableDeviceInfo]:
"""Devices accessible to the test (e.g. as given by list_local_devices)."""
@property
def memory_info(self) -> global___MemoryInfo: ...
def __init__(
@@ -423,18 +435,18 @@ class MachineConfiguration(google.protobuf.message.Message):
available_device_info: collections.abc.Iterable[global___AvailableDeviceInfo] | None = ...,
memory_info: global___MemoryInfo | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["cpu_info", b"cpu_info", "memory_info", b"memory_info", "platform_info", b"platform_info"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["available_device_info", b"available_device_info", "cpu_info", b"cpu_info", "device_info", b"device_info", "hostname", b"hostname", "memory_info", b"memory_info", "platform_info", b"platform_info", "serial_identifier", b"serial_identifier"]) -> None: ...
def HasField(self, field_name: typing.Literal["cpu_info", b"cpu_info", "memory_info", b"memory_info", "platform_info", b"platform_info"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["available_device_info", b"available_device_info", "cpu_info", b"cpu_info", "device_info", b"device_info", "hostname", b"hostname", "memory_info", b"memory_info", "platform_info", b"platform_info", "serial_identifier", b"serial_identifier"]) -> None: ...
global___MachineConfiguration = MachineConfiguration
@typing_extensions.final
@typing.final
class RunConfiguration(google.protobuf.message.Message):
"""Run-specific items such as arguments to the test / benchmark."""
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@typing_extensions.final
@typing.final
class EnvVarsEntry(google.protobuf.message.Message):
DESCRIPTOR: google.protobuf.descriptor.Descriptor
@@ -448,7 +460,7 @@ class RunConfiguration(google.protobuf.message.Message):
key: builtins.str | None = ...,
value: builtins.str | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ...
ARGUMENT_FIELD_NUMBER: builtins.int
ENV_VARS_FIELD_NUMBER: builtins.int
@@ -457,17 +469,18 @@ class RunConfiguration(google.protobuf.message.Message):
@property
def env_vars(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]:
"""Environment variables used to run the test/benchmark."""
def __init__(
self,
*,
argument: collections.abc.Iterable[builtins.str] | None = ...,
env_vars: collections.abc.Mapping[builtins.str, builtins.str] | None = ...,
) -> None: ...
def ClearField(self, field_name: typing_extensions.Literal["argument", b"argument", "env_vars", b"env_vars"]) -> None: ...
def ClearField(self, field_name: typing.Literal["argument", b"argument", "env_vars", b"env_vars"]) -> None: ...
global___RunConfiguration = RunConfiguration
@typing_extensions.final
@typing.final
class TestResults(google.protobuf.message.Message):
"""The output of one benchmark / test run. Each run contains a list of
tests or benchmarks, stored as BenchmarkEntry messages.
@@ -521,25 +534,10 @@ class TestResults(google.protobuf.message.Message):
"""The target of the run, e.g.:
//tensorflow/core:kernels_adjust_contrast_op_benchmark_test
"""
@property
def entries(self) -> global___BenchmarkEntries:
"""The list of tests or benchmarks in this run."""
@property
def build_configuration(self) -> global___BuildConfiguration:
"""The configuration of the build (compiled opt? with cuda? any copts?)"""
@property
def commit_id(self) -> global___CommitId:
"""The commit id (git hash or changelist)"""
start_time: builtins.int
"""The time the run started (in seconds of UTC time since Unix epoch)"""
run_time: builtins.float
"""The amount of time the total run took (wall time in seconds)"""
@property
def machine_configuration(self) -> global___MachineConfiguration:
"""Machine-specific parameters (Platform and CPU info)"""
@property
def run_configuration(self) -> global___RunConfiguration:
"""Run-specific parameters (arguments, etc)"""
name: builtins.str
"""Benchmark target identifier."""
benchmark_type: global___TestResults.BenchmarkType.ValueType
@@ -554,6 +552,26 @@ class TestResults(google.protobuf.message.Message):
"""TensorFlow version this benchmark runs against.
This can be either set to full version or just the major version.
"""
@property
def entries(self) -> global___BenchmarkEntries:
"""The list of tests or benchmarks in this run."""
@property
def build_configuration(self) -> global___BuildConfiguration:
"""The configuration of the build (compiled opt? with cuda? any copts?)"""
@property
def commit_id(self) -> global___CommitId:
"""The commit id (git hash or changelist)"""
@property
def machine_configuration(self) -> global___MachineConfiguration:
"""Machine-specific parameters (Platform and CPU info)"""
@property
def run_configuration(self) -> global___RunConfiguration:
"""Run-specific parameters (arguments, etc)"""
def __init__(
self,
*,
@@ -570,7 +588,7 @@ class TestResults(google.protobuf.message.Message):
run_mode: builtins.str | None = ...,
tf_version: builtins.str | None = ...,
) -> None: ...
def HasField(self, field_name: typing_extensions.Literal["build_configuration", b"build_configuration", "commit_id", b"commit_id", "entries", b"entries", "machine_configuration", b"machine_configuration", "run_configuration", b"run_configuration"]) -> builtins.bool: ...
def ClearField(self, field_name: typing_extensions.Literal["benchmark_type", b"benchmark_type", "build_configuration", b"build_configuration", "commit_id", b"commit_id", "entries", b"entries", "machine_configuration", b"machine_configuration", "name", b"name", "run_configuration", b"run_configuration", "run_mode", b"run_mode", "run_time", b"run_time", "start_time", b"start_time", "target", b"target", "tf_version", b"tf_version"]) -> None: ...
def HasField(self, field_name: typing.Literal["build_configuration", b"build_configuration", "commit_id", b"commit_id", "entries", b"entries", "machine_configuration", b"machine_configuration", "run_configuration", b"run_configuration"]) -> builtins.bool: ...
def ClearField(self, field_name: typing.Literal["benchmark_type", b"benchmark_type", "build_configuration", b"build_configuration", "commit_id", b"commit_id", "entries", b"entries", "machine_configuration", b"machine_configuration", "name", b"name", "run_configuration", b"run_configuration", "run_mode", b"run_mode", "run_time", b"run_time", "start_time", b"start_time", "target", b"target", "tf_version", b"tf_version"]) -> None: ...
global___TestResults = TestResults