259 lines
11 KiB
Python
259 lines
11 KiB
Python
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"""Python wrappers around TensorFlow ops.
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This file is MACHINE GENERATED! Do not edit.
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"""
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import collections
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from tensorflow.python import pywrap_tfe as pywrap_tfe
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from tensorflow.python.eager import context as _context
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from tensorflow.python.eager import core as _core
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from tensorflow.python.eager import execute as _execute
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from tensorflow.python.framework import dtypes as _dtypes
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from tensorflow.security.fuzzing.py import annotation_types as _atypes
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from tensorflow.python.framework import op_def_registry as _op_def_registry
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from tensorflow.python.framework import ops as _ops
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from tensorflow.python.framework import op_def_library as _op_def_library
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from tensorflow.python.util.deprecation import deprecated_endpoints
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from tensorflow.python.util import dispatch as _dispatch
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from tensorflow.python.util.tf_export import tf_export
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from typing import TypeVar, List, Any
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from typing_extensions import Annotated
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TV_NcclAllReduce_T = TypeVar("TV_NcclAllReduce_T", _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int32, _atypes.Int64)
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def nccl_all_reduce(input: Annotated[Any, TV_NcclAllReduce_T], reduction: str, num_devices: int, shared_name: str, name=None) -> Annotated[Any, TV_NcclAllReduce_T]:
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r"""Outputs a tensor containing the reduction across all input tensors.
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Outputs a tensor containing the reduction across all input tensors passed to ops
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within the same `shared_name.
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The graph should be constructed so if one op runs with shared_name value `c`,
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then `num_devices` ops will run with shared_name value `c`. Failure to do so
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will cause the graph execution to fail to complete.
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input: the input to the reduction
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data: the value of the reduction across all `num_devices` devices.
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reduction: the reduction operation to perform.
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num_devices: The number of devices participating in this reduction.
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shared_name: Identifier that shared between ops of the same reduction.
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Args:
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input: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`.
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reduction: A `string` from: `"min", "max", "prod", "sum"`.
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num_devices: An `int`.
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shared_name: A `string`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `input`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "NcclAllReduce", name, input, "reduction", reduction,
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"num_devices", num_devices, "shared_name", shared_name)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return nccl_all_reduce_eager_fallback(
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input, reduction=reduction, num_devices=num_devices,
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shared_name=shared_name, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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reduction = _execute.make_str(reduction, "reduction")
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num_devices = _execute.make_int(num_devices, "num_devices")
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shared_name = _execute.make_str(shared_name, "shared_name")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"NcclAllReduce", input=input, reduction=reduction,
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num_devices=num_devices, shared_name=shared_name,
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name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("reduction", _op.get_attr("reduction"), "T",
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_op._get_attr_type("T"), "num_devices",
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_op._get_attr_int("num_devices"), "shared_name",
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_op.get_attr("shared_name"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"NcclAllReduce", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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NcclAllReduce = tf_export("raw_ops.NcclAllReduce")(_ops.to_raw_op(nccl_all_reduce))
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def nccl_all_reduce_eager_fallback(input: Annotated[Any, TV_NcclAllReduce_T], reduction: str, num_devices: int, shared_name: str, name, ctx) -> Annotated[Any, TV_NcclAllReduce_T]:
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reduction = _execute.make_str(reduction, "reduction")
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num_devices = _execute.make_int(num_devices, "num_devices")
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shared_name = _execute.make_str(shared_name, "shared_name")
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_attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [_dtypes.half, _dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.int64, ])
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_inputs_flat = [input]
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_attrs = ("reduction", reduction, "T", _attr_T, "num_devices", num_devices,
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"shared_name", shared_name)
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_result = _execute.execute(b"NcclAllReduce", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"NcclAllReduce", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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TV_NcclBroadcast_T = TypeVar("TV_NcclBroadcast_T", _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int32, _atypes.Int64)
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def nccl_broadcast(input: Annotated[Any, TV_NcclBroadcast_T], shape, name=None) -> Annotated[Any, TV_NcclBroadcast_T]:
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r"""Sends `input` to all devices that are connected to the output.
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Sends `input` to all devices that are connected to the output.
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The graph should be constructed so that all ops connected to the output have a
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valid device assignment, and the op itself is assigned one of these devices.
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input: The input to the broadcast.
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output: The same as input.
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shape: The shape of the input tensor.
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Args:
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input: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`.
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shape: A `tf.TensorShape` or list of `ints`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `input`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "NcclBroadcast", name, input, "shape", shape)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return nccl_broadcast_eager_fallback(
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input, shape=shape, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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shape = _execute.make_shape(shape, "shape")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"NcclBroadcast", input=input, shape=shape, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("T", _op._get_attr_type("T"), "shape", _op.get_attr("shape"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"NcclBroadcast", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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NcclBroadcast = tf_export("raw_ops.NcclBroadcast")(_ops.to_raw_op(nccl_broadcast))
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def nccl_broadcast_eager_fallback(input: Annotated[Any, TV_NcclBroadcast_T], shape, name, ctx) -> Annotated[Any, TV_NcclBroadcast_T]:
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shape = _execute.make_shape(shape, "shape")
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_attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [_dtypes.half, _dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.int64, ])
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_inputs_flat = [input]
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_attrs = ("T", _attr_T, "shape", shape)
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_result = _execute.execute(b"NcclBroadcast", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"NcclBroadcast", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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TV_NcclReduce_T = TypeVar("TV_NcclReduce_T", _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int32, _atypes.Int64)
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def nccl_reduce(input: Annotated[List[Any], TV_NcclReduce_T], reduction: str, name=None) -> Annotated[Any, TV_NcclReduce_T]:
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r"""Reduces `input` from `num_devices` using `reduction` to a single device.
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Reduces `input` from `num_devices` using `reduction` to a single device.
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The graph should be constructed so that all inputs have a valid device
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assignment, and the op itself is assigned one of these devices.
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input: The input to the reduction.
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data: the value of the reduction across all `num_devices` devices.
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reduction: the reduction operation to perform.
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Args:
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input: A list of at least 1 `Tensor` objects with the same type in: `half`, `float32`, `float64`, `int32`, `int64`.
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reduction: A `string` from: `"min", "max", "prod", "sum"`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `input`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "NcclReduce", name, input, "reduction", reduction)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return nccl_reduce_eager_fallback(
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input, reduction=reduction, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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if not isinstance(input, (list, tuple)):
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raise TypeError(
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"Expected list for 'input' argument to "
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"'nccl_reduce' Op, not %r." % input)
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_attr_num_devices = len(input)
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reduction = _execute.make_str(reduction, "reduction")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"NcclReduce", input=input, reduction=reduction, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("reduction", _op.get_attr("reduction"), "T",
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_op._get_attr_type("T"), "num_devices",
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_op._get_attr_int("num_devices"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"NcclReduce", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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NcclReduce = tf_export("raw_ops.NcclReduce")(_ops.to_raw_op(nccl_reduce))
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def nccl_reduce_eager_fallback(input: Annotated[List[Any], TV_NcclReduce_T], reduction: str, name, ctx) -> Annotated[Any, TV_NcclReduce_T]:
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if not isinstance(input, (list, tuple)):
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raise TypeError(
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"Expected list for 'input' argument to "
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"'nccl_reduce' Op, not %r." % input)
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_attr_num_devices = len(input)
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reduction = _execute.make_str(reduction, "reduction")
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_attr_T, input = _execute.args_to_matching_eager(list(input), ctx, [_dtypes.half, _dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.int64, ])
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_inputs_flat = list(input)
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_attrs = ("reduction", reduction, "T", _attr_T, "num_devices",
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_attr_num_devices)
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_result = _execute.execute(b"NcclReduce", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"NcclReduce", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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