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@@ -206,3 +206,4 @@ SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/lite/expe
 SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/core/kernels/libtfkernel_sobol_op.so filter=lfs diff=lfs merge=lfs -text
 SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/ops/_xla_ops.so filter=lfs diff=lfs merge=lfs -text
 SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/stablehlo/stablehlo_extension.so filter=lfs diff=lfs merge=lfs -text
+SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/pywrap_quantize_model.so filter=lfs diff=lfs merge=lfs -text

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/jit/ops/xla_ops.py

@@ -0,0 +1,352 @@
+"""Python wrappers around TensorFlow ops.
+
+This file is MACHINE GENERATED! Do not edit.
+"""
+
+import collections
+
+from tensorflow.python import pywrap_tfe as pywrap_tfe
+from tensorflow.python.eager import context as _context
+from tensorflow.python.eager import core as _core
+from tensorflow.python.eager import execute as _execute
+from tensorflow.python.framework import dtypes as _dtypes
+from tensorflow.security.fuzzing.py import annotation_types as _atypes
+
+from tensorflow.python.framework import op_def_registry as _op_def_registry
+from tensorflow.python.framework import ops as _ops
+from tensorflow.python.framework import op_def_library as _op_def_library
+from tensorflow.python.util.deprecation import deprecated_endpoints
+from tensorflow.python.util import dispatch as _dispatch
+from tensorflow.python.util.tf_export import tf_export
+
+from typing import TypeVar, List, Any
+from typing_extensions import Annotated
+
+TV_XlaClusterOutput_T = TypeVar("TV_XlaClusterOutput_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_cluster_output')
+def xla_cluster_output(input: Annotated[Any, TV_XlaClusterOutput_T], name=None) -> Annotated[Any, TV_XlaClusterOutput_T]:
+  r"""Operator that connects the output of an XLA computation to other consumer graph nodes.
+
+  Args:
+    input: A `Tensor`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaClusterOutput", name, input)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_cluster_output(
+          (input, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_cluster_output_eager_fallback(
+          input, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_cluster_output, (), dict(input=input, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_cluster_output(
+        (input, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaClusterOutput", input=input, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_cluster_output, (), dict(input=input, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaClusterOutput", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaClusterOutput = tf_export("raw_ops.XlaClusterOutput")(_ops.to_raw_op(xla_cluster_output))
+_dispatcher_for_xla_cluster_output = xla_cluster_output._tf_type_based_dispatcher.Dispatch
+
+
+def xla_cluster_output_eager_fallback(input: Annotated[Any, TV_XlaClusterOutput_T], name, ctx) -> Annotated[Any, TV_XlaClusterOutput_T]:
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  _inputs_flat = [input]
+  _attrs = ("T", _attr_T)
+  _result = _execute.execute(b"XlaClusterOutput", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaClusterOutput", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_launch')
+def xla_launch(constants, args, resources: Annotated[List[Any], _atypes.Resource], Tresults, function, name=None):
+  r"""XLA Launch Op. For use by the XLA JIT only.
+
+  Args:
+    constants: A list of `Tensor` objects.
+    args: A list of `Tensor` objects.
+    resources: A list of `Tensor` objects with type `resource`.
+    Tresults: A list of `tf.DTypes`.
+    function: A function decorated with @Defun.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `Tresults`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaLaunch", name, constants, args, resources, "Tresults",
+        Tresults, "function", function)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_launch(
+          (constants, args, resources, Tresults, function, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_launch_eager_fallback(
+          constants, args, resources, Tresults=Tresults, function=function,
+          name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_launch, (), dict(constants=constants, args=args,
+                                 resources=resources, Tresults=Tresults,
+                                 function=function, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_launch(
+        (constants, args, resources, Tresults, function, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if not isinstance(resources, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'resources' argument to "
+        "'xla_launch' Op, not %r." % resources)
+  _attr_Nresources = len(resources)
+  if not isinstance(Tresults, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tresults' argument to "
+        "'xla_launch' Op, not %r." % Tresults)
+  Tresults = [_execute.make_type(_t, "Tresults") for _t in Tresults]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaLaunch", constants=constants, args=args, resources=resources,
+                     Tresults=Tresults, function=function, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_launch, (), dict(constants=constants, args=args,
+                               resources=resources, Tresults=Tresults,
+                               function=function, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if not _result:
+    return _op
+  if _execute.must_record_gradient():
+    _attrs = ("Tconstants", _op.get_attr("Tconstants"), "Targs",
+              _op.get_attr("Targs"), "Nresources",
+              _op._get_attr_int("Nresources"), "Tresults",
+              _op.get_attr("Tresults"), "function", _op.get_attr("function"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaLaunch", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaLaunch = tf_export("raw_ops.XlaLaunch")(_ops.to_raw_op(xla_launch))
+_dispatcher_for_xla_launch = xla_launch._tf_type_based_dispatcher.Dispatch
+
+
+def xla_launch_eager_fallback(constants, args, resources: Annotated[List[Any], _atypes.Resource], Tresults, function, name, ctx):
+  if not isinstance(resources, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'resources' argument to "
+        "'xla_launch' Op, not %r." % resources)
+  _attr_Nresources = len(resources)
+  if not isinstance(Tresults, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tresults' argument to "
+        "'xla_launch' Op, not %r." % Tresults)
+  Tresults = [_execute.make_type(_t, "Tresults") for _t in Tresults]
+  _attr_Tconstants, constants = _execute.convert_to_mixed_eager_tensors(constants, ctx)
+  _attr_Targs, args = _execute.convert_to_mixed_eager_tensors(args, ctx)
+  resources = _ops.convert_n_to_tensor(resources, _dtypes.resource)
+  _inputs_flat = list(constants) + list(args) + list(resources)
+  _attrs = ("Tconstants", _attr_Tconstants, "Targs", _attr_Targs,
+  "Nresources", _attr_Nresources, "Tresults", Tresults, "function", function)
+  _result = _execute.execute(b"XlaLaunch", len(Tresults), inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaLaunch", _inputs_flat, _attrs, _result)
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_launch_v2')
+def xla_launch_v2(args, Tresults, constants, resources, function, name=None):
+  r"""XLA Launch Op. For use by the XLA JIT only.
+
+  Args:
+    args: A list of `Tensor` objects.
+    Tresults: A list of `tf.DTypes`.
+    constants: A list of `ints`.
+    resources: A list of `ints`.
+    function: A function decorated with @Defun.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `Tresults`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaLaunchV2", name, args, "Tresults", Tresults, "constants",
+        constants, "resources", resources, "function", function)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_launch_v2(
+          (args, Tresults, constants, resources, function, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_launch_v2_eager_fallback(
+          args, Tresults=Tresults, constants=constants, resources=resources,
+          function=function, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_launch_v2, (), dict(args=args, Tresults=Tresults,
+                                    constants=constants, resources=resources,
+                                    function=function, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_launch_v2(
+        (args, Tresults, constants, resources, function, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if not isinstance(Tresults, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tresults' argument to "
+        "'xla_launch_v2' Op, not %r." % Tresults)
+  Tresults = [_execute.make_type(_t, "Tresults") for _t in Tresults]
+  if not isinstance(constants, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'constants' argument to "
+        "'xla_launch_v2' Op, not %r." % constants)
+  constants = [_execute.make_int(_i, "constants") for _i in constants]
+  if not isinstance(resources, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'resources' argument to "
+        "'xla_launch_v2' Op, not %r." % resources)
+  resources = [_execute.make_int(_i, "resources") for _i in resources]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaLaunchV2", args=args, Tresults=Tresults, constants=constants,
+                       resources=resources, function=function, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_launch_v2, (), dict(args=args, Tresults=Tresults,
+                                  constants=constants, resources=resources,
+                                  function=function, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("Targs", _op.get_attr("Targs"), "Tresults",
+              _op.get_attr("Tresults"), "constants",
+              _op.get_attr("constants"), "resources",
+              _op.get_attr("resources"), "function", _op.get_attr("function"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaLaunchV2", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaLaunchV2 = tf_export("raw_ops.XlaLaunchV2")(_ops.to_raw_op(xla_launch_v2))
+_dispatcher_for_xla_launch_v2 = xla_launch_v2._tf_type_based_dispatcher.Dispatch
+
+
+def xla_launch_v2_eager_fallback(args, Tresults, constants, resources, function, name, ctx):
+  if not isinstance(Tresults, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tresults' argument to "
+        "'xla_launch_v2' Op, not %r." % Tresults)
+  Tresults = [_execute.make_type(_t, "Tresults") for _t in Tresults]
+  if not isinstance(constants, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'constants' argument to "
+        "'xla_launch_v2' Op, not %r." % constants)
+  constants = [_execute.make_int(_i, "constants") for _i in constants]
+  if not isinstance(resources, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'resources' argument to "
+        "'xla_launch_v2' Op, not %r." % resources)
+  resources = [_execute.make_int(_i, "resources") for _i in resources]
+  _attr_Targs, args = _execute.convert_to_mixed_eager_tensors(args, ctx)
+  _inputs_flat = list(args)
+  _attrs = ("Targs", _attr_Targs, "Tresults", Tresults, "constants",
+  constants, "resources", resources, "function", function)
+  _result = _execute.execute(b"XlaLaunchV2", len(Tresults),
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaLaunchV2", _inputs_flat, _attrs, _result)
+  return _result
+

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/jit/ops/xla_ops_grad.py

@@ -0,0 +1,25 @@
+"""Gradients for XLA ops."""
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from tensorflow.python.framework import ops
+
+
+@ops.RegisterGradient("XlaClusterOutput")
+def _XlaClusterOutputGrad(_, grad):
+  del grad  # unused
+  raise RuntimeError("Gradient computation of graph in xla.compile() is "
+                     "prohibited because it can cause performance degradation."
+                     "Please move gradient computation inside xla.compile().")

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/py_function_lib.py

@@ -0,0 +1,770 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Defines a wrapper class for overridden python method definitions."""
+
+from collections.abc import Callable, Collection, Mapping, Sequence
+import functools
+import traceback
+from typing import Optional, TypeVar
+
+from absl import logging
+
+from tensorflow.compiler.mlir.quantization.stablehlo import quantization_config_pb2 as stablehlo_quant_config_pb2
+from tensorflow.compiler.mlir.quantization.tensorflow import exported_model_pb2
+from tensorflow.compiler.mlir.quantization.tensorflow import quantization_options_pb2
+from tensorflow.compiler.mlir.quantization.tensorflow.calibrator import calibration_algorithm
+from tensorflow.compiler.mlir.quantization.tensorflow.calibrator import calibration_statistics_pb2
+from tensorflow.compiler.mlir.quantization.tensorflow.python import pywrap_function_lib
+from tensorflow.compiler.mlir.quantization.tensorflow.python import representative_dataset as rd
+from tensorflow.compiler.mlir.quantization.tensorflow.python import save_model
+from tensorflow.core.protobuf import meta_graph_pb2
+from tensorflow.core.protobuf import saver_pb2
+from tensorflow.python.client import session
+from tensorflow.python.eager import context
+from tensorflow.python.eager import wrap_function
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_conversion
+from tensorflow.python.lib.io import file_io
+from tensorflow.python.saved_model import load
+from tensorflow.python.saved_model import loader_impl
+from tensorflow.python.trackable import autotrackable
+from tensorflow.python.types import core
+
+
+# Name of the saved model assets directory.
+_ASSETS_DIR = 'assets'
+_ASSETS_EXTRA_DIR = 'assets.extra'
+
+# Type variable for a type that is not `None`. This represents a return value of
+# methods in `PyFunctionLibrary` that should not be `None`, as `None` represents
+# that the execution was unsucessful, transfored as `std::nullopt_t` from c++.
+NotNoneT = TypeVar('NotNoneT')
+
+
+def _get_saver_def_or_none(
+    exported_model: exported_model_pb2.ExportedModel,
+) -> Optional[saver_pb2.SaverDef]:
+  """Returns the SaverDef from ExportedModel, None otherwise.
+
+  Args:
+    exported_model: ExportedModel to take the SaverDef from.
+
+  Returns:
+    SaverDef instance if the field `saver_def` is set. None otherwise.
+  """
+  if exported_model.HasField('saver_def'):
+    return exported_model.saver_def
+  return None
+
+
+def _copy_assets(src_path: str, dst_path: str) -> None:
+  """Copies the assets directory of the saved model.
+
+  Clones the contents of the assets/ directory from the source saved model
+  directory to the destination saved model directory. Nothing will be copied if
+  there are no assets directory in the source directory.
+
+  Args:
+    src_path: Source saved model directory.
+    dst_path: Destination saved model directory. This directory must exist.
+  """
+  for assets_dir_name in [_ASSETS_DIR, _ASSETS_EXTRA_DIR]:
+    src_assets_path = file_io.join(src_path, assets_dir_name)
+    if not file_io.file_exists_v2(src_assets_path):
+      # Do nothing if the source assets path does not exist.
+      continue
+
+    dst_assets_path = file_io.join(dst_path, assets_dir_name)
+    file_io.create_dir_v2(dst_assets_path)
+
+    for curr_dir, _, files in file_io.walk_v2(src_assets_path):
+      for asset_file_name in files:
+        src_asset_file = file_io.join(curr_dir, asset_file_name)
+
+        # Construct the destination assets file path.
+        curr_dst_dir = curr_dir.replace(src_assets_path, dst_assets_path)
+        dst_asset_file = file_io.join(curr_dst_dir, asset_file_name)
+
+        file_io.copy_v2(src_asset_file, dst_asset_file)
+        logging.info(
+            'Copied asset file: %s -> %s', src_asset_file, dst_asset_file
+        )
+
+
+def _validate_representative_dataset(
+    representative_dataset: rd.RepresentativeDatasetOrMapping,
+    signature_keys: Collection[str],
+) -> None:
+  """Validates the representative dataset, based on the signature keys.
+
+  Representative dataset can be provided in two different forms: a single
+  instance of `RepresentativeDataset` or a map of signature key to the
+  corresponding `RepresentativeDataset`. These have a relationship with
+  `signature_keys`.
+
+  This function validates the following conditions:
+  * If `len(signature_keys) > 1`, then `representative_dataset` should be a
+    mapping where the keys exactly match the elements in `signature_keys`.
+  * If `len(signature_keys) == 1`, then both a mapping and a single instance of
+    `RepresentativeDataset` are allowed.
+  * This function also assumes `len(signature_keys) > 0`.
+
+  Args:
+    representative_dataset: A `RepresentativeDataset` or a map of string to
+      `RepresentativeDataset` to be validated.
+    signature_keys: A collection of strings that contains the signature keys,
+      each identifying a `SignatureDef`.
+
+  Raises:
+    ValueError: Iff `representative_dataset` does not satisfy the conditions
+      above.
+  """
+  if isinstance(representative_dataset, Mapping):
+    if set(signature_keys) != set(representative_dataset.keys()):
+      raise ValueError(
+          'The signature keys and the keys of representative dataset map '
+          f'do not match. Signature keys: {set(signature_keys)}, '
+          f'representative dataset map: {set(representative_dataset.keys())}.'
+      )
+  else:
+    if len(signature_keys) > 1:
+      raise ValueError(
+          'Representative dataset is not a mapping '
+          f'(got: {type(representative_dataset)}), '
+          'but there is more than one signature key provided. '
+          'Please provide a map of {signature_key -> dataset} '
+          'with more than one signature key.'
+      )
+
+
+def _replace_tensors_by_numpy_ndarrays(
+    repr_ds_map: rd.RepresentativeDatasetMapping,
+) -> None:
+  """Replaces tf.Tensors by their evaluated numpy arrays.
+
+  This assumes that tf.Tensors in representative samples are created in the
+  default Graph. It will raise an error if tensors are created in a different
+  graph.
+
+  Args:
+    repr_ds_map: SignatureDef key -> RepresentativeDataset mapping.
+  """
+  with session.Session() as sess:
+    for signature_def_key in repr_ds_map:
+      # Replaces the dataset with a new dataset where tf.Tensors are replaced
+      # by their evaluated values.
+      ds = repr_ds_map[signature_def_key]
+      repr_ds_map[signature_def_key] = rd.replace_tensors_by_numpy_ndarrays(
+          ds, sess
+      )
+
+
+def _create_sample_validator(
+    expected_input_keys: Collection[str],
+) -> Callable[[rd.RepresentativeSample], rd.RepresentativeSample]:
+  """Creates a validator function for a representative sample.
+
+  Args:
+    expected_input_keys: Input keys (keyword argument names) that the function
+      the sample will be used for is expecting to receive.
+
+  Returns:
+    A callable that validates a `RepresentativeSample`.
+  """
+
+  def validator(
+      sample: rd.RepresentativeSample,
+  ) -> rd.RepresentativeSample:
+    """Validates a single instance of representative sample.
+
+    This provides a simple check for `sample` that this is a mapping of
+    {input_key: input_value}.
+
+    Args:
+      sample: A `RepresentativeSample` to validate.
+
+    Returns:
+      `sample` iff it is valid.
+
+    Raises:
+      ValueError: iff the sample isn't an instance of `Mapping`.
+      KeyError: iff the sample does not have the set of input keys that match
+        the input keys of the function.
+    """
+    if not isinstance(sample, Mapping):
+      raise ValueError(
+          'Invalid representative sample type. Provide a mapping '
+          '(usually a dict) of {input_key: input_value}. '
+          f'Got type: {type(sample)} instead.'
+      )
+
+    if set(sample.keys()) != expected_input_keys:
+      raise KeyError(
+          'Invalid input keys for representative sample. The function expects '
+          f'input keys of: {set(expected_input_keys)}. '
+          f'Got: {set(sample.keys())}. Please provide correct input keys for '
+          'representative samples.'
+      )
+
+    return sample
+
+  return validator
+
+
+# TODO(b/249918070): Implement a progress bar.
+def _log_sample_num_for_calibration(
+    representative_dataset: rd.RepresentativeDataset,
+) -> rd.RepresentativeDataset:
+  """Logs the sample number for calibration.
+
+  If in debug logging level, the "sample number / total num samples" is logged
+  for every 5 iterations.
+
+  This is often useful when tracking the progress of the calibration step which
+  is often slow and may look stale if there's no logs being printed.
+
+  Args:
+    representative_dataset: The representative dataset.
+
+  Yields:
+    The representative samples from `representative_dataset` without any
+    modification.
+  """
+  num_samples: Optional[int] = rd.get_num_samples(representative_dataset)
+  if num_samples is None:
+    total_num_samples = '?'
+    logging.info('Representative dataset size unknown.')
+  else:
+    total_num_samples = str(num_samples)
+    logging.info('Using representative dataset of size: %s', total_num_samples)
+
+  sample_num = 0
+  for sample in representative_dataset:
+    sample_num += 1
+
+    # Log the sample number for every 5 iterations.
+    logging.log_every_n(
+        logging.DEBUG,
+        'Running representative sample for calibration: %d / %s',
+        5,
+        sample_num,
+        total_num_samples,
+    )
+    yield sample
+
+  logging.info(
+      'Running representative samples complete: %d / %s',
+      sample_num,
+      total_num_samples,
+  )
+
+
+def _run_function_for_calibration_graph_mode(
+    sess: session.Session,
+    signature_def: meta_graph_pb2.SignatureDef,
+    representative_dataset: rd.RepresentativeDataset,
+) -> None:
+  """Runs the representative dataset through a function for calibration.
+
+  NOTE: This is intended to be run in graph mode (TF1).
+
+  The function is identified by the SignatureDef.
+
+  Args:
+    sess: The Session object to run the function in.
+    signature_def: A SignatureDef that identifies a function by specifying the
+      inputs and outputs.
+    representative_dataset: The representative dataset to run through the
+      function.
+  """
+  output_tensor_names = [
+      output_tensor_info.name
+      for output_tensor_info in signature_def.outputs.values()
+  ]
+
+  sample_validator = _create_sample_validator(
+      expected_input_keys=signature_def.inputs.keys()
+  )
+
+  for sample in map(
+      sample_validator, _log_sample_num_for_calibration(representative_dataset)
+  ):
+    # Create a mapping from input tensor name to the input tensor value.
+    # ex) "Placeholder:0" -> [0, 1, 2]
+    feed_dict = rd.create_feed_dict_from_input_data(sample, signature_def)
+    sess.run(output_tensor_names, feed_dict=feed_dict)
+
+
+def _run_graph_for_calibration_graph_mode(
+    model_dir: str,
+    tags: Collection[str],
+    representative_dataset_map: rd.RepresentativeDatasetMapping,
+) -> None:
+  """Runs the graph for calibration in graph mode.
+
+  This function assumes _graph mode_ (used when legacy TF1 is used or when eager
+  mode is explicitly disabled) when running the graph. This step is used in
+  order to collect the statistics in CustomAggregatorOp for quantization using
+  the representative dataset for the actual data provided for inference.
+
+  Args:
+    model_dir: Path to SavedModel directory.
+    tags: Collection of tags identifying the MetaGraphDef within the SavedModel.
+    representative_dataset_map: A map where signature keys are mapped to
+      corresponding representative datasets.
+
+  Raises:
+    ValueError: When running the function with the representative dataset fails.
+  """
+  # Replace tf.Tensors by numpy ndarrays in order to reuse the samples in a
+  # different graph when running the calibration.
+  _replace_tensors_by_numpy_ndarrays(representative_dataset_map)
+
+  # Run the calibration in a new graph to avoid name collision, which could
+  # happen when the same model is loaded multiple times in the default graph.
+  with ops.Graph().as_default(), session.Session() as sess:
+    meta_graph: meta_graph_pb2.MetaGraphDef = loader_impl.load(
+        sess, tags, export_dir=model_dir
+    )
+
+    for signature_key, repr_ds in representative_dataset_map.items():
+      sig_def = meta_graph.signature_def[signature_key]
+
+      try:
+        _run_function_for_calibration_graph_mode(
+            sess, signature_def=sig_def, representative_dataset=repr_ds
+        )
+      except Exception as ex:
+        raise ValueError(
+            'Failed to run representative dataset through the '
+            f'function with the signature key: {signature_key}.'
+        ) from ex
+
+
+def _convert_values_to_tf_tensors(
+    sample: rd.RepresentativeSample,
+) -> Mapping[str, core.Tensor]:
+  """Converts TensorLike values of `sample` to Tensors.
+
+  Creates a copy of `sample`, where each value is converted to Tensors
+  unless it is already a Tensor.
+  The values are not converted in-place (i.e. `sample` is not mutated).
+
+  Args:
+    sample: A representative sample, which is a map of {name -> tensorlike
+      value}.
+
+  Returns:
+    Converted map of {name -> tensor}.
+  """
+  tensor_mapping = {}
+  for name, tensorlike_value in sample.items():
+    if isinstance(tensorlike_value, core.Tensor):
+      tensor_value = tensorlike_value
+    else:
+      tensor_value = tensor_conversion.convert_to_tensor_v2_with_dispatch(
+          tensorlike_value
+      )
+
+    tensor_mapping[name] = tensor_value
+
+  return tensor_mapping
+
+
+def _run_function_for_calibration_eager_mode(
+    func: wrap_function.WrappedFunction,
+    representative_dataset: rd.RepresentativeDataset,
+) -> None:
+  """Runs the representative dataset through a function for calibration.
+
+  NOTE: This is intended to be run in eager mode (TF2).
+
+  Args:
+    func: The function to run the representative samples through.
+    representative_dataset: Representative dataset used for calibration. The
+      input keys and input values of the representative samples should match the
+      keyword arguments of `func`.
+  """
+  _, keyword_args = func.structured_input_signature
+  sample_validator = _create_sample_validator(
+      expected_input_keys=keyword_args.keys()
+  )
+
+  for sample in map(
+      sample_validator, _log_sample_num_for_calibration(representative_dataset)
+  ):
+    # Convert any non-Tensor values from the sample to Tensors.
+    # This conversion is required because the model saved in `model_dir` is
+    # saved using TF1 SavedModelBuilder, which doesn't save the
+    # SavedObjectGraph.
+    func_kwargs = _convert_values_to_tf_tensors(sample)
+    func(**func_kwargs)
+
+
+def _run_graph_for_calibration_eager_mode(
+    model_dir: str,
+    tags: Collection[str],
+    representative_dataset_map: rd.RepresentativeDatasetMapping,
+) -> None:
+  """Runs the graph for calibration in eager mode.
+
+  This function assumes _eager mode_ (enabled in TF2 by default) when running
+  the graph. This step is used in order to collect the statistics in
+  CustomAggregatorOp for quantization using the representative dataset for the
+  actual data provided for inference.
+
+  Args:
+    model_dir: Path to SavedModel directory.
+    tags: Collection of tags identifying the MetaGraphDef within the SavedModel.
+    representative_dataset_map: A map where signature keys are mapped to
+      corresponding representative datasets.
+
+  Raises:
+    ValueError: When running the function with the representative dataset fails.
+  """
+  root: autotrackable.AutoTrackable = load.load(model_dir, tags)
+  for signature_key, repr_ds in representative_dataset_map.items():
+    try:
+      _run_function_for_calibration_eager_mode(
+          func=root.signatures[signature_key], representative_dataset=repr_ds
+      )
+    except Exception as ex:
+      raise ValueError(
+          'Failed to run representative dataset through the '
+          f'function with the signature key: {signature_key}.'
+      ) from ex
+
+
+def _run_graph_for_calibration(
+    float_model_dir: str,
+    signature_keys: Sequence[str],
+    tags: Collection[str],
+    representative_dataset: rd.RepresentativeDatasetOrMapping,
+    force_graph_mode_calibration: bool,
+) -> None:
+  """Runs the graph for calibration using representative datasets.
+
+  Args:
+    float_model_dir: Path to the model to calibrate.
+    signature_keys: Sequence of keys identifying SignatureDef containing inputs
+      and outputs.
+    tags: Collection of tags identifying the MetaGraphDef within the SavedModel
+      to analyze.
+    representative_dataset: An iterator that returns a dictionary of {input_key:
+      input_value} or a mapping from signature keys to such iterators. When
+      `signature_keys` contains more than one signature key,
+      `representative_datsaet` should be a mapping that maps each signature keys
+      to the corresponding representative dataset.
+    force_graph_mode_calibration: If set to true, it forces calibration in graph
+      model instead of eager mode when the context is in eager mode.
+
+  Raises:
+    ValueError iff:
+      * The representative dataset format is invalid.
+      * It fails to run the functions using the representative datasets.
+  """
+  try:
+    _validate_representative_dataset(representative_dataset, signature_keys)
+  except Exception as ex:
+    raise ValueError('Invalid representative dataset.') from ex
+
+  # If `representative_dataset` is not a mapping, convert to a mapping for the
+  # following functions to handle representative datasets more conveniently.
+  representative_dataset_map = representative_dataset
+  if not isinstance(representative_dataset, Mapping):
+    # `signature_keys` is guaranteed to have only one element after the
+    # validation.
+    representative_dataset_map = {signature_keys[0]: representative_dataset}
+
+  try:
+    if context.executing_eagerly() and not force_graph_mode_calibration:
+      logging.info('Calibration step is executed in eager mode.')
+      _run_graph_for_calibration_eager_mode(
+          float_model_dir, tags, representative_dataset_map
+      )
+    else:
+      logging.info('Calibration step is executed in graph mode.')
+      _run_graph_for_calibration_graph_mode(
+          float_model_dir, tags, representative_dataset_map
+      )
+  except Exception as ex:
+    raise ValueError(
+        'Failed to run graph for post-training quantization calibration.'
+    ) from ex
+
+  logging.info('Calibration step complete.')
+
+
+def _run_calibration(
+    saved_model_path: str,
+    signature_keys: Sequence[str],
+    tags: Collection[str],
+    force_graph_mode_calibration: bool,
+    representative_dataset_file_map: Mapping[
+        str, quantization_options_pb2.RepresentativeDatasetFile
+    ],
+) -> bool:
+  """Runs calibration and adds calibration statistics to exported model.
+
+  Args:
+    saved_model_path: Path to the SavedModel to run calibration.
+    signature_keys: List of signature keys corresponding to SignatureDefs to run
+      calibration on.
+    tags: A set of tags that identify the MetaGraphDef.
+    force_graph_mode_calibration: If True, runs the calibration in graph mode.
+    representative_dataset_file_map: Signature key ->
+      `RepresentativeDatasetFile` mapping for running the calibration step. Each
+      dataset file stores the representative dataset for the function matching
+      the signature key.
+
+  Returns:
+    `True` upon successfully running calibration.
+  """
+  repr_dataset_map = rd.TfRecordRepresentativeDatasetLoader(
+      representative_dataset_file_map
+  ).load()
+
+  # Uses the representative dataset to collect statistics for calibration.
+  # After this operation, min & max values are stored separately in a global
+  # CalibratorSingleton instance.
+  _run_graph_for_calibration(
+      saved_model_path,
+      signature_keys,
+      tags,
+      repr_dataset_map,
+      force_graph_mode_calibration,
+  )
+
+  # Dummy value to indicate successful run, as `None` would indicate error. See
+  # comments in `NotNoneT`.
+  return True
+
+
+def _call_and_return_none_on_error(
+    func: Callable[[], NotNoneT], error_msg: str
+) -> Optional[NotNoneT]:
+  """Calls `func` and returns `None` on error.
+
+  This is used to gracefully return the 'error status' represented as `None`, as
+  raising exceptions from `PyFunctionLibrary` methods crashes the program.
+
+  Args:
+    func: The function to run. The function should be a callable returning a
+      non-None value.
+    error_msg: The error message to log upon error. Used for debugging purposes.
+
+  Returns:
+    `None` if the function raises an exception. The return value of `func`
+    otherwise.
+  """
+  try:
+    return func()
+  except Exception as ex:  # pylint: disable=broad-exception-caught; Required for graceful failing with pybind11.
+    # Prints the exception traceback for debuggability.
+    traceback.print_exception(ex)
+    # Additional error log for debuggability.
+    logging.error(error_msg)
+    return None
+
+
+def _save_model_and_copy_assets(
+    exported_model: exported_model_pb2.ExportedModel,
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    signature_def_map: Mapping[str, meta_graph_pb2.SignatureDef],
+    tags: Collection[str],
+) -> bool:
+  """Saves the model and copies the assets from the source model.
+
+  Args:
+    exported_model: ExportedModel to save.
+    src_saved_model_path: Path to the source SavedModel. This will be used to
+      copy the asset files to `dst_saved_model_path`.
+    dst_saved_model_path: Destination path to save the exported model.
+    signature_def_map: Signature key -> SignatureDef mapping.
+    tags: Tags to attach to the saved MetaGraphDef.
+
+  Returns:
+    `True` upon successfully saving the model.
+  """
+  save_model.save_model_v1(
+      exported_model.graph_def,
+      dst_saved_model_path,
+      signature_def_map,
+      tags,
+      init_op_name=exported_model.init_node_name,
+      saver_def=_get_saver_def_or_none(exported_model),
+      checkpoint_dir=exported_model.checkpoint_dir,
+      function_aliases=exported_model.function_aliases,
+      asset_file_defs=exported_model.asset_file_defs,
+  )
+
+  _copy_assets(src_saved_model_path, dst_saved_model_path)
+
+  # Dummy value to indicate successful run, as `None` would indicate error. See
+  # comments in `NotNoneT`.
+  return True
+
+
+class PyFunctionLibrary(pywrap_function_lib.PyFunctionLibrary):
+  """Wrapper class for overridden python method definitions.
+
+  This class contains python methods that overrides C++ virtual functions
+  declared in `pywrap_function_lib.PyFunctionLibrary`.
+  """
+
+  # LINT.IfChange(save_exported_model)
+  def save_exported_model(
+      self,
+      dst_saved_model_path: str,
+      exported_model_serialized: bytes,
+      src_saved_model_path: str,
+      tags: set[str],
+      serialized_signature_def_map: dict[str, bytes],
+  ) -> Optional[bool]:
+    # LINT.ThenChange(py_function_lib.h:save_exported_model)
+    """Saves `ExportedModel` to `dst_saved_model_path` as a SavedModel.
+
+    Args:
+      dst_saved_model_path: Destination path to save the exported model.
+      exported_model_serialized: Exported model to export as SavedModel.
+      src_saved_model_path: Path to the source SavedModel. This will be used to
+        copy the asset files to `dst_saved_model_path`.
+      tags: Tags to attach to the saved MetaGraphDef.
+      serialized_signature_def_map: Signature key -> serialized SignatureDef.
+
+    Returns:
+      `True` upon successful execution. `None` when an error is raised
+      internally.
+    """
+    exported_model = exported_model_pb2.ExportedModel.FromString(
+        exported_model_serialized
+    )
+
+    # Deserialize values in signature_def_map.
+    signature_def_map = {}
+    for key, serialized_signature_def in serialized_signature_def_map.items():
+      signature_def_map[key] = meta_graph_pb2.SignatureDef.FromString(
+          serialized_signature_def
+      )
+
+    return _call_and_return_none_on_error(
+        func=functools.partial(
+            _save_model_and_copy_assets,
+            exported_model,
+            src_saved_model_path,
+            dst_saved_model_path,
+            signature_def_map,
+            tags,
+        ),
+        error_msg=(
+            f'Failed to save model "{dst_saved_model_path}",'
+            f' signature_def_map: {signature_def_map}, tags: {tags}.'
+        ),
+    )
+
+  # TODO: b/311097139 - Extract calibration related functions into a separate
+  # file.
+  # LINT.IfChange(run_calibration)
+  def run_calibration(
+      self,
+      saved_model_path: str,
+      signature_keys: list[str],
+      tags: set[str],
+      force_graph_mode_calibration: bool,
+      representative_dataset_file_map_serialized: dict[str, bytes],
+  ) -> Optional[bool]:
+    # LINT.ThenChange(py_function_lib.h:run_calibration)
+    """Runs calibration and adds calibration statistics to exported model.
+
+    Args:
+      saved_model_path: Path to the SavedModel to run calibration.
+      signature_keys: List of signature keys corresponding to SignatureDefs to
+        run calibration on.
+      tags: A set of tags that identify the MetaGraphDef.
+      force_graph_mode_calibration: If True, runs the calibration in graph mode.
+      representative_dataset_file_map_serialized: Signature key ->
+        `RepresentativeDatasetFile` mapping for running the calibration step.
+        Each dataset file stores the representative dataset for the function
+        matching the signature key.
+
+    Returns:
+      The error message if the function raises and exception. `None` otherwise.
+    """
+    # Deserialize `RepresentativeDatasetFile` values.
+    dataset_file_map = {}
+    for (
+        signature_key,
+        dataset_file_serialized,
+    ) in representative_dataset_file_map_serialized.items():
+      dataset_file_map[signature_key] = (
+          quantization_options_pb2.RepresentativeDatasetFile.FromString(
+              dataset_file_serialized
+          )
+      )
+
+    return _call_and_return_none_on_error(
+        func=functools.partial(
+            _run_calibration,
+            saved_model_path,
+            signature_keys,
+            tags,
+            force_graph_mode_calibration,
+            dataset_file_map,
+        ),
+        error_msg=(
+            f'Failed to run calibration on model "{saved_model_path}",'
+            f' signature_keys: {signature_keys}, tags: {tags}.'
+        ),
+    )
+
+  # LINT.IfChange(get_calibration_min_max_value)
+  def get_calibration_min_max_value(
+      self,
+      calibration_statistics_serialized: bytes,
+      calibration_options_serialized: bytes,
+  ) -> Optional[tuple[float, float]]:
+    """Calculates min and max values from statistics.
+
+    Args:
+      calibration_statistics_serialized: Serialized `CalibrationStatistics`.
+        This will be the source to calculate min and max values from.
+      calibration_options_serialized: Serialized `CalibrationOptions`. Specifies
+        how the min / max should be calculated.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using calib_opts. `None`
+      upon error.
+    """
+    # LINT.ThenChange(py_function_lib.h:get_calibration_min_max_value)
+
+    # Deserialize values passed from c++.
+    statistics = calibration_statistics_pb2.CalibrationStatistics.FromString(
+        calibration_statistics_serialized
+    )
+    options = stablehlo_quant_config_pb2.CalibrationOptions.FromString(
+        calibration_options_serialized
+    )
+
+    return _call_and_return_none_on_error(
+        functools.partial(
+            calibration_algorithm.get_min_max_value,
+            statistics,
+            options,
+        ),
+        error_msg=(
+            f'Retrieving calibrated min / max failed. Options: {options}.'
+        ),
+    )

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/pywrap_function_lib.pyi

@@ -0,0 +1,48 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+from typing import Any, Optional
+
+class PyFunctionLibrary:
+
+  # LINT.IfChange(save_exported_model)
+  def save_exported_model(
+      self,
+      dst_saved_model_path: str,
+      exported_model_serialized: bytes,
+      src_saved_model_path: str,
+      tags: set[str],
+      serialized_signature_def_map: dict[str, bytes],
+  ) -> Optional[bool]: ...
+  # LINT.ThenChange()
+
+  # LINT.IfChange(run_calibration)
+  def run_calibration(
+      self,
+      saved_model_path: str,
+      signature_keys: list[str],
+      tags: set[str],
+      force_graph_mode_calibration: bool,
+      # Value type: RepresentativeDatasetFile.
+      representative_dataset_file_map_serialized: dict[str, bytes],
+  ) -> Optional[bool]: ...
+  # LINT.ThenChange()
+
+  # LINT.IfChange(get_calibration_min_max_value)
+  def get_calibration_min_max_value(
+      self,
+      calibration_statistics_serialized: bytes,
+      calibration_options_serialized: bytes,
+  ) -> Optional[tuple[float, float]]: ...
+  # LINT.ThenChange()

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/pywrap_quantize_model.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce6434a9bb81081892f212ae21a81fc331353532280da8b44ff7f8640c61b273
+size 2026097

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/tensorflow/gen_mlir_passthrough_op.py

@@ -0,0 +1,127 @@
+"""Python wrappers around TensorFlow ops.
+
+This file is MACHINE GENERATED! Do not edit.
+"""
+
+import collections
+
+from tensorflow.python import pywrap_tfe as pywrap_tfe
+from tensorflow.python.eager import context as _context
+from tensorflow.python.eager import core as _core
+from tensorflow.python.eager import execute as _execute
+from tensorflow.python.framework import dtypes as _dtypes
+from tensorflow.security.fuzzing.py import annotation_types as _atypes
+
+from tensorflow.python.framework import op_def_registry as _op_def_registry
+from tensorflow.python.framework import ops as _ops
+from tensorflow.python.framework import op_def_library as _op_def_library
+from tensorflow.python.util.deprecation import deprecated_endpoints
+from tensorflow.python.util import dispatch as _dispatch
+from tensorflow.python.util.tf_export import tf_export
+
+from typing import TypeVar, List, Any
+from typing_extensions import Annotated
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('mlir_passthrough_op')
+def mlir_passthrough_op(inputs, mlir_module: str, Toutputs, name=None):
+  r"""TODO: add doc.
+
+  Args:
+    inputs: A list of `Tensor` objects.
+    mlir_module: A `string`.
+    Toutputs: A list of `tf.DTypes`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `Toutputs`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "MlirPassthroughOp", name, inputs, "mlir_module", mlir_module,
+        "Toutputs", Toutputs)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_mlir_passthrough_op(
+          (inputs, mlir_module, Toutputs, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return mlir_passthrough_op_eager_fallback(
+          inputs, mlir_module=mlir_module, Toutputs=Toutputs, name=name,
+          ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            mlir_passthrough_op, (), dict(inputs=inputs,
+                                          mlir_module=mlir_module,
+                                          Toutputs=Toutputs, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_mlir_passthrough_op(
+        (inputs, mlir_module, Toutputs, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  mlir_module = _execute.make_str(mlir_module, "mlir_module")
+  if not isinstance(Toutputs, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Toutputs' argument to "
+        "'mlir_passthrough_op' Op, not %r." % Toutputs)
+  Toutputs = [_execute.make_type(_t, "Toutputs") for _t in Toutputs]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "MlirPassthroughOp", inputs=inputs, mlir_module=mlir_module,
+                             Toutputs=Toutputs, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          mlir_passthrough_op, (), dict(inputs=inputs,
+                                        mlir_module=mlir_module,
+                                        Toutputs=Toutputs, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("mlir_module", _op.get_attr("mlir_module"), "Tinputs",
+              _op.get_attr("Tinputs"), "Toutputs", _op.get_attr("Toutputs"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "MlirPassthroughOp", _inputs_flat, _attrs, _result)
+  return _result
+
+MlirPassthroughOp = tf_export("raw_ops.MlirPassthroughOp")(_ops.to_raw_op(mlir_passthrough_op))
+_dispatcher_for_mlir_passthrough_op = mlir_passthrough_op._tf_type_based_dispatcher.Dispatch
+
+
+def mlir_passthrough_op_eager_fallback(inputs, mlir_module: str, Toutputs, name, ctx):
+  mlir_module = _execute.make_str(mlir_module, "mlir_module")
+  if not isinstance(Toutputs, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Toutputs' argument to "
+        "'mlir_passthrough_op' Op, not %r." % Toutputs)
+  Toutputs = [_execute.make_type(_t, "Toutputs") for _t in Toutputs]
+  _attr_Tinputs, inputs = _execute.convert_to_mixed_eager_tensors(inputs, ctx)
+  _inputs_flat = list(inputs)
+  _attrs = ("mlir_module", mlir_module, "Tinputs", _attr_Tinputs, "Toutputs",
+  Toutputs)
+  _result = _execute.execute(b"MlirPassthroughOp", len(Toutputs),
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "MlirPassthroughOp", _inputs_flat, _attrs, _result)
+  return _result
+

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/xla/service/hlo_pb2.py

@@ -0,0 +1,104 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: xla/service/hlo.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from google.protobuf import any_pb2 as google_dot_protobuf_dot_any__pb2
+from tensorflow.compiler.xla import xla_data_pb2 as xla_dot_xla__data__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15xla/service/hlo.proto\x12\x03xla\x1a\x19google/protobuf/any.proto\x1a\x12xla/xla_data.proto\"\xd0\x17\n\x13HloInstructionProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0e\n\x06opcode\x18\x02 \x01(\t\x12\x1e\n\x05shape\x18\x03 \x01(\x0b\x32\x0f.xla.ShapeProto\x12!\n\x08metadata\x18\x07 \x01(\x0b\x32\x0f.xla.OpMetadata\x12\"\n\x07literal\x18\x08 \x01(\x0b\x32\x11.xla.LiteralProto\x12\x18\n\x10parameter_number\x18\t \x01(\x03\x12\x13\n\x0b\x66usion_kind\x18\x0b \x01(\t\x12\x13\n\x0btuple_index\x18\r \x01(\x03\x12\x12\n\ndimensions\x18\x0e \x03(\x03\x12\x1b\n\x06window\x18\x0f \x01(\x0b\x32\x0b.xla.Window\x12G\n\x1d\x63onvolution_dimension_numbers\x18\x10 \x01(\x0b\x32 .xla.ConvolutionDimensionNumbers\x12\x1b\n\x13\x66\x65\x61ture_group_count\x18\x32 \x01(\x03\x12\x19\n\x11\x62\x61tch_group_count\x18: \x01(\x03\x12\x42\n\x10slice_dimensions\x18\x11 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+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'xla.service.hlo_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _HLOINSTRUCTIONPROTO.fields_by_name['replica_groups']._options = None
+  _HLOINSTRUCTIONPROTO.fields_by_name['replica_groups']._serialized_options = b'\030\001'
+  _HLOINSTRUCTIONPROTO.fields_by_name['all_reduce_id']._options = None
+  _HLOINSTRUCTIONPROTO.fields_by_name['all_reduce_id']._serialized_options = b'\030\001'
+  _HLOINSTRUCTIONPROTO.fields_by_name['is_cross_program_prefetch']._options = None
+  _HLOINSTRUCTIONPROTO.fields_by_name['is_cross_program_prefetch']._serialized_options = b'\030\001'
+  _HLOSCHEDULEPROTO_SEQUENCESENTRY._options = None
+  _HLOSCHEDULEPROTO_SEQUENCESENTRY._serialized_options = b'8\001'
+  _LOGICALBUFFERPROTO_LOCATION.fields_by_name['instruction_name']._options = None
+  _LOGICALBUFFERPROTO_LOCATION.fields_by_name['instruction_name']._serialized_options = b'\030\001'
+  _CUSTOMCALLSCHEDULE._serialized_start=7600
+  _CUSTOMCALLSCHEDULE._serialized_end=7683
+  _CUSTOMCALLAPIVERSION._serialized_start=7686
+  _CUSTOMCALLAPIVERSION._serialized_end=7866
+  _KIND._serialized_start=7868
+  _KIND._serialized_end=7926
+  _HLOINSTRUCTIONPROTO._serialized_start=78
+  _HLOINSTRUCTIONPROTO._serialized_end=3102
+  _HLOINSTRUCTIONPROTO_SLICEDIMENSIONS._serialized_start=2739
+  _HLOINSTRUCTIONPROTO_SLICEDIMENSIONS._serialized_end=2802
+  _HLOCOMPUTATIONPROTO._serialized_start=3105
+  _HLOCOMPUTATIONPROTO._serialized_end=3338
+  _HLOSCHEDULEPROTO._serialized_start=3341
+  _HLOSCHEDULEPROTO._serialized_end=3557
+  _HLOSCHEDULEPROTO_INSTRUCTIONSEQUENCE._serialized_start=3418
+  _HLOSCHEDULEPROTO_INSTRUCTIONSEQUENCE._serialized_end=3464
+  _HLOSCHEDULEPROTO_SEQUENCESENTRY._serialized_start=3466
+  _HLOSCHEDULEPROTO_SEQUENCESENTRY._serialized_end=3557
+  _HLOINPUTOUTPUTALIASPROTO._serialized_start=3560
+  _HLOINPUTOUTPUTALIASPROTO._serialized_end=3779
+  _HLOINPUTOUTPUTALIASPROTO_ALIASENTRYPROTO._serialized_start=3652
+  _HLOINPUTOUTPUTALIASPROTO_ALIASENTRYPROTO._serialized_end=3779
+  _HLOBUFFERDONORPROTO._serialized_start=3782
+  _HLOBUFFERDONORPROTO._serialized_end=3950
+  _HLOBUFFERDONORPROTO_BUFFERDONORENTRYPROTO._serialized_start=3870
+  _HLOBUFFERDONORPROTO_BUFFERDONORENTRYPROTO._serialized_end=3950
+  _CROSSPROGRAMPREFETCH._serialized_start=3952
+  _CROSSPROGRAMPREFETCH._serialized_end=4024
+  _STACKFRAMEINDEXPROTO._serialized_start=4027
+  _STACKFRAMEINDEXPROTO._serialized_end=4376
+  _STACKFRAMEINDEXPROTO_FILELOCATION._serialized_start=4219
+  _STACKFRAMEINDEXPROTO_FILELOCATION._serialized_end=4311
+  _STACKFRAMEINDEXPROTO_STACKFRAME._serialized_start=4313
+  _STACKFRAMEINDEXPROTO_STACKFRAME._serialized_end=4376
+  _HLOMODULEPROTO._serialized_start=4379
+  _HLOMODULEPROTO._serialized_end=5494
+  _HLOMODULEPROTO_PROFILEINFO._serialized_start=5168
+  _HLOMODULEPROTO_PROFILEINFO._serialized_end=5377
+  _HLOMODULEPROTO_PROFILETYPE._serialized_start=5379
+  _HLOMODULEPROTO_PROFILETYPE._serialized_end=5461
+  _LOGICALBUFFERPROTO._serialized_start=5497
+  _LOGICALBUFFERPROTO._serialized_end=5705
+  _LOGICALBUFFERPROTO_LOCATION._serialized_start=5614
+  _LOGICALBUFFERPROTO_LOCATION._serialized_end=5705
+  _BUFFERALLOCATIONPROTO._serialized_start=5708
+  _BUFFERALLOCATIONPROTO._serialized_end=6084
+  _BUFFERALLOCATIONPROTO_ASSIGNED._serialized_start=6017
+  _BUFFERALLOCATIONPROTO_ASSIGNED._serialized_end=6084
+  _HEAPSIMULATORTRACE._serialized_start=6087
+  _HEAPSIMULATORTRACE._serialized_end=6429
+  _HEAPSIMULATORTRACE_EVENT._serialized_start=6223
+  _HEAPSIMULATORTRACE_EVENT._serialized_end=6429
+  _HEAPSIMULATORTRACE_EVENT_KIND._serialized_start=6386
+  _HEAPSIMULATORTRACE_EVENT_KIND._serialized_end=6429
+  _HLOMODULEGROUPPROTO._serialized_start=6431
+  _HLOMODULEGROUPPROTO._serialized_end=6508
+  _BUFFERASSIGNMENTPROTO._serialized_start=6511
+  _BUFFERASSIGNMENTPROTO._serialized_end=6853
+  _BUFFERASSIGNMENTPROTO_BUFFERALIAS._serialized_start=6762
+  _BUFFERASSIGNMENTPROTO_BUFFERALIAS._serialized_end=6853
+  _HLOPROTO._serialized_start=6855
+  _HLOPROTO._serialized_end=6981
+  _HLOSNAPSHOT._serialized_start=6984
+  _HLOSNAPSHOT._serialized_end=7126
+  _HLOMODULEMETADATAPROTO._serialized_start=7129
+  _HLOMODULEMETADATAPROTO._serialized_end=7314
+  _HLOPASSMETADATA._serialized_start=7317
+  _HLOPASSMETADATA._serialized_end=7598
+# @@protoc_insertion_point(module_scope)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/xla/tsl/protobuf/bfc_memory_map_pb2.py

@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: xla/tsl/protobuf/bfc_memory_map.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%xla/tsl/protobuf/bfc_memory_map.proto\x12\ntensorflow\"\x92\x01\n\x11MemAllocatorStats\x12\x12\n\nnum_allocs\x18\x01 \x01(\x03\x12\x14\n\x0c\x62ytes_in_use\x18\x02 \x01(\x03\x12\x19\n\x11peak_bytes_in_use\x18\x03 \x01(\x03\x12\x1a\n\x12largest_alloc_size\x18\x04 \x01(\x03\x12\x1c\n\x14\x66ragmentation_metric\x18\x05 \x01(\x02\"\xae\x01\n\x08MemChunk\x12\x0f\n\x07\x61\x64\x64ress\x18\x01 \x01(\x04\x12\x0c\n\x04size\x18\x02 \x01(\x03\x12\x16\n\x0erequested_size\x18\x03 \x01(\x03\x12\x0b\n\x03\x62in\x18\x04 \x01(\x05\x12\x0f\n\x07op_name\x18\x05 \x01(\t\x12\x16\n\x0e\x66reed_at_count\x18\x06 \x01(\x04\x12\x14\n\x0c\x61\x63tion_count\x18\x07 \x01(\x04\x12\x0e\n\x06in_use\x18\x08 \x01(\x08\x12\x0f\n\x07step_id\x18\t \x01(\x04\"\x8b\x01\n\nBinSummary\x12\x0b\n\x03\x62in\x18\x01 \x01(\x05\x12\x1a\n\x12total_bytes_in_use\x18\x02 \x01(\x03\x12\x1a\n\x12total_bytes_in_bin\x18\x03 \x01(\x03\x12\x1b\n\x13total_chunks_in_use\x18\x04 \x01(\x03\x12\x1b\n\x13total_chunks_in_bin\x18\x05 \x01(\x03\".\n\x08SnapShot\x12\x14\n\x0c\x61\x63tion_count\x18\x01 \x01(\x04\x12\x0c\n\x04size\x18\x02 \x01(\x03\"\xcd\x01\n\nMemoryDump\x12\x16\n\x0e\x61llocator_name\x18\x01 \x01(\t\x12+\n\x0b\x62in_summary\x18\x02 \x03(\x0b\x32\x16.tensorflow.BinSummary\x12#\n\x05\x63hunk\x18\x03 \x03(\x0b\x32\x14.tensorflow.MemChunk\x12\'\n\tsnap_shot\x18\x04 \x03(\x0b\x32\x14.tensorflow.SnapShot\x12,\n\x05stats\x18\x05 \x01(\x0b\x32\x1d.tensorflow.MemAllocatorStatsB@Z>github.com/google/tsl/tsl/go/protobuf/for_core_protos_go_protob\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'xla.tsl.protobuf.bfc_memory_map_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'Z>github.com/google/tsl/tsl/go/protobuf/for_core_protos_go_proto'
+  _MEMALLOCATORSTATS._serialized_start=54
+  _MEMALLOCATORSTATS._serialized_end=200
+  _MEMCHUNK._serialized_start=203
+  _MEMCHUNK._serialized_end=377
+  _BINSUMMARY._serialized_start=380
+  _BINSUMMARY._serialized_end=519
+  _SNAPSHOT._serialized_start=521
+  _SNAPSHOT._serialized_end=567
+  _MEMORYDUMP._serialized_start=570
+  _MEMORYDUMP._serialized_end=775
+# @@protoc_insertion_point(module_scope)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/xla/tsl/protobuf/test_log_pb2.py

@@ -0,0 +1,68 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: xla/tsl/protobuf/test_log.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from google.protobuf import any_pb2 as google_dot_protobuf_dot_any__pb2
+from google.protobuf import wrappers_pb2 as google_dot_protobuf_dot_wrappers__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1fxla/tsl/protobuf/test_log.proto\x12\ntensorflow\x1a\x19google/protobuf/any.proto\x1a\x1egoogle/protobuf/wrappers.proto\"D\n\nEntryValue\x12\x16\n\x0c\x64ouble_value\x18\x01 \x01(\x01H\x00\x12\x16\n\x0cstring_value\x18\x02 \x01(\tH\x00\x42\x06\n\x04kind\"\x8c\x01\n\x0bMetricEntry\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x01\x12/\n\tmin_value\x18\x03 \x01(\x0b\x32\x1c.google.protobuf.DoubleValue\x12/\n\tmax_value\x18\x04 \x01(\x0b\x32\x1c.google.protobuf.DoubleValue\"\x8f\x02\n\x0e\x42\x65nchmarkEntry\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05iters\x18\x02 \x01(\x03\x12\x10\n\x08\x63pu_time\x18\x03 \x01(\x01\x12\x11\n\twall_time\x18\x04 \x01(\x01\x12\x12\n\nthroughput\x18\x05 \x01(\x01\x12\x36\n\x06\x65xtras\x18\x06 \x03(\x0b\x32&.tensorflow.BenchmarkEntry.ExtrasEntry\x12(\n\x07metrics\x18\x07 \x03(\x0b\x32\x17.tensorflow.MetricEntry\x1a\x45\n\x0b\x45xtrasEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.tensorflow.EntryValue:\x02\x38\x01\"=\n\x10\x42\x65nchmarkEntries\x12)\n\x05\x65ntry\x18\x01 \x03(\x0b\x32\x1a.tensorflow.BenchmarkEntry\"B\n\x12\x42uildConfiguration\x12\x0c\n\x04mode\x18\x01 \x01(\t\x12\x10\n\x08\x63\x63_flags\x18\x02 \x03(\t\x12\x0c\n\x04opts\x18\x03 \x03(\t\"f\n\x08\x43ommitId\x12\x14\n\nchangelist\x18\x01 \x01(\x03H\x00\x12\x0e\n\x04hash\x18\x02 \x01(\tH\x00\x12\x10\n\x08snapshot\x18\x03 \x01(\t\x12\x1a\n\x12pending_changelist\x18\x04 \x01(\x03\x42\x06\n\x04kind\"\xde\x01\n\x07\x43PUInfo\x12\x11\n\tnum_cores\x18\x01 \x01(\x03\x12\x19\n\x11num_cores_allowed\x18\x02 \x01(\x03\x12\x13\n\x0bmhz_per_cpu\x18\x03 \x01(\x01\x12\x10\n\x08\x63pu_info\x18\x04 \x01(\t\x12\x14\n\x0c\x63pu_governor\x18\x05 \x01(\t\x12\x36\n\ncache_size\x18\x06 \x03(\x0b\x32\".tensorflow.CPUInfo.CacheSizeEntry\x1a\x30\n\x0e\x43\x61\x63heSizeEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x03:\x02\x38\x01\".\n\nMemoryInfo\x12\r\n\x05total\x18\x01 \x01(\x03\x12\x11\n\tavailable\x18\x02 \x01(\x03\"6\n\x07GPUInfo\x12\r\n\x05model\x18\x01 \x01(\t\x12\x0c\n\x04uuid\x18\x02 \x01(\t\x12\x0e\n\x06\x62us_id\x18\x03 \x01(\t\"p\n\x0cPlatformInfo\x12\x0c\n\x04\x62its\x18\x01 \x01(\t\x12\x0f\n\x07linkage\x18\x02 \x01(\t\x12\x0f\n\x07machine\x18\x03 \x01(\t\x12\x0f\n\x07release\x18\x04 \x01(\t\x12\x0e\n\x06system\x18\x05 \x01(\t\x12\x0f\n\x07version\x18\x06 \x01(\t\"e\n\x13\x41vailableDeviceInfo\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0c\n\x04type\x18\x02 \x01(\t\x12\x14\n\x0cmemory_limit\x18\x03 \x01(\x03\x12\x1c\n\x14physical_description\x18\x04 \x01(\t\"\xb3\x02\n\x14MachineConfiguration\x12\x10\n\x08hostname\x18\x01 \x01(\t\x12\x19\n\x11serial_identifier\x18\x07 \x01(\t\x12/\n\rplatform_info\x18\x02 \x01(\x0b\x32\x18.tensorflow.PlatformInfo\x12%\n\x08\x63pu_info\x18\x03 \x01(\x0b\x32\x13.tensorflow.CPUInfo\x12)\n\x0b\x64\x65vice_info\x18\x04 \x03(\x0b\x32\x14.google.protobuf.Any\x12>\n\x15\x61vailable_device_info\x18\x05 \x03(\x0b\x32\x1f.tensorflow.AvailableDeviceInfo\x12+\n\x0bmemory_info\x18\x06 \x01(\x0b\x32\x16.tensorflow.MemoryInfo\"\x91\x01\n\x10RunConfiguration\x12\x10\n\x08\x61rgument\x18\x01 \x03(\t\x12;\n\x08\x65nv_vars\x18\x02 \x03(\x0b\x32).tensorflow.RunConfiguration.EnvVarsEntry\x1a.\n\x0c\x45nvVarsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"\xd0\x04\n\x0bTestResults\x12\x0e\n\x06target\x18\x01 \x01(\t\x12-\n\x07\x65ntries\x18\x02 \x01(\x0b\x32\x1c.tensorflow.BenchmarkEntries\x12;\n\x13\x62uild_configuration\x18\x03 \x01(\x0b\x32\x1e.tensorflow.BuildConfiguration\x12\'\n\tcommit_id\x18\x04 \x01(\x0b\x32\x14.tensorflow.CommitId\x12\x12\n\nstart_time\x18\x05 \x01(\x03\x12\x10\n\x08run_time\x18\x06 \x01(\x01\x12?\n\x15machine_configuration\x18\x07 \x01(\x0b\x32 .tensorflow.MachineConfiguration\x12\x37\n\x11run_configuration\x18\x08 \x01(\x0b\x32\x1c.tensorflow.RunConfiguration\x12\x0c\n\x04name\x18\t \x01(\t\x12=\n\x0e\x62\x65nchmark_type\x18\n \x01(\x0e\x32%.tensorflow.TestResults.BenchmarkType\x12\x10\n\x08run_mode\x18\x0b \x01(\t\x12\x12\n\ntf_version\x18\x0c \x01(\t\"\x88\x01\n\rBenchmarkType\x12\x0b\n\x07UNKNOWN\x10\x00\x12\x16\n\x12\x43PP_MICROBENCHMARK\x10\x01\x12\x14\n\x10PYTHON_BENCHMARK\x10\x02\x12\x15\n\x11\x41NDROID_BENCHMARK\x10\x03\x12\x12\n\x0e\x45\x44GE_BENCHMARK\x10\x04\x12\x11\n\rIOS_BENCHMARK\x10\x05\x42\x31\n\x1borg.tensorflow.util.testlogB\rTestLogProtosP\x01\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'xla.tsl.protobuf.test_log_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\033org.tensorflow.util.testlogB\rTestLogProtosP\001\370\001\001'
+  _BENCHMARKENTRY_EXTRASENTRY._options = None
+  _BENCHMARKENTRY_EXTRASENTRY._serialized_options = b'8\001'
+  _CPUINFO_CACHESIZEENTRY._options = None
+  _CPUINFO_CACHESIZEENTRY._serialized_options = b'8\001'
+  _RUNCONFIGURATION_ENVVARSENTRY._options = None
+  _RUNCONFIGURATION_ENVVARSENTRY._serialized_options = b'8\001'
+  _ENTRYVALUE._serialized_start=106
+  _ENTRYVALUE._serialized_end=174
+  _METRICENTRY._serialized_start=177
+  _METRICENTRY._serialized_end=317
+  _BENCHMARKENTRY._serialized_start=320
+  _BENCHMARKENTRY._serialized_end=591
+  _BENCHMARKENTRY_EXTRASENTRY._serialized_start=522
+  _BENCHMARKENTRY_EXTRASENTRY._serialized_end=591
+  _BENCHMARKENTRIES._serialized_start=593
+  _BENCHMARKENTRIES._serialized_end=654
+  _BUILDCONFIGURATION._serialized_start=656
+  _BUILDCONFIGURATION._serialized_end=722
+  _COMMITID._serialized_start=724
+  _COMMITID._serialized_end=826
+  _CPUINFO._serialized_start=829
+  _CPUINFO._serialized_end=1051
+  _CPUINFO_CACHESIZEENTRY._serialized_start=1003
+  _CPUINFO_CACHESIZEENTRY._serialized_end=1051
+  _MEMORYINFO._serialized_start=1053
+  _MEMORYINFO._serialized_end=1099
+  _GPUINFO._serialized_start=1101
+  _GPUINFO._serialized_end=1155
+  _PLATFORMINFO._serialized_start=1157
+  _PLATFORMINFO._serialized_end=1269
+  _AVAILABLEDEVICEINFO._serialized_start=1271
+  _AVAILABLEDEVICEINFO._serialized_end=1372
+  _MACHINECONFIGURATION._serialized_start=1375
+  _MACHINECONFIGURATION._serialized_end=1682
+  _RUNCONFIGURATION._serialized_start=1685
+  _RUNCONFIGURATION._serialized_end=1830
+  _RUNCONFIGURATION_ENVVARSENTRY._serialized_start=1784
+  _RUNCONFIGURATION_ENVVARSENTRY._serialized_end=1830
+  _TESTRESULTS._serialized_start=1833
+  _TESTRESULTS._serialized_end=2425
+  _TESTRESULTS_BENCHMARKTYPE._serialized_start=2289
+  _TESTRESULTS_BENCHMARKTYPE._serialized_end=2425
+# @@protoc_insertion_point(module_scope)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/xla/xla_data_pb2.py

@@ -0,0 +1,148 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: xla/xla_data.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x12xla/xla_data.proto\x12\x03xla\"\xb7\x01\n\rPaddingConfig\x12=\n\ndimensions\x18\x01 \x03(\x0b\x32).xla.PaddingConfig.PaddingConfigDimension\x1ag\n\x16PaddingConfigDimension\x12\x18\n\x10\x65\x64ge_padding_low\x18\x01 \x01(\x03\x12\x19\n\x11\x65\x64ge_padding_high\x18\x02 \x01(\x03\x12\x18\n\x10interior_padding\x18\x03 \x01(\x03\"\x1f\n\tTileProto\x12\x12\n\ndimensions\x18\x01 \x03(\x03\"<\n\x10SplitConfigProto\x12\x11\n\tdimension\x18\x01 \x01(\x03\x12\x15\n\rsplit_indices\x18\x02 \x03(\x03\"\xba\x04\n\x0bLayoutProto\x12*\n\x0f\x64im_level_types\x18\t \x03(\x0e\x32\x11.xla.DimLevelType\x12\x12\n\ndim_unique\x18\r \x03(\x08\x12\x13\n\x0b\x64im_ordered\x18\x0e \x03(\x08\x12\x16\n\x0eminor_to_major\x18\x01 \x03(\x03\x12\x1d\n\x05tiles\x18\x06 \x03(\x0b\x32\x0e.xla.TileProto\x12*\n\"tail_padding_alignment_in_elements\x18\x10 \x01(\x03\x12\x1c\n\x14\x65lement_size_in_bits\x18\x07 \x01(\x03\x12\x14\n\x0cmemory_space\x18\x08 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+  _PARAMETERREPLICATION._serialized_end=6505
+  _WHILELOOPBACKENDCONFIG._serialized_start=6507
+  _WHILELOOPBACKENDCONFIG._serialized_end=6630
+  _WHILELOOPBACKENDCONFIG_KNOWNTRIPCOUNT._serialized_start=6603
+  _WHILELOOPBACKENDCONFIG_KNOWNTRIPCOUNT._serialized_end=6630
+  _OUTPUTOPERANDALIASING._serialized_start=6632
+  _OUTPUTOPERANDALIASING._serialized_end=6735
+  _ORIGINALARRAYPROTO._serialized_start=6737
+  _ORIGINALARRAYPROTO._serialized_end=6830
+  _ORIGINALVALUEPROTO._serialized_start=6832
+  _ORIGINALVALUEPROTO._serialized_end=6893
+# @@protoc_insertion_point(module_scope)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/dtensor/proto/layout_pb2.py

@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/dtensor/proto/layout.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%tensorflow/dtensor/proto/layout.proto\x12\x12tensorflow.dtensor\"+\n\x0cShardingSpec\x12\x15\n\rsharding_spec\x18\x02 \x01(\tJ\x04\x08\x01\x10\x02\"0\n\x12MeshDimensionProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0c\n\x04size\x18\x02 \x01(\x03\"\xfb\x01\n\x0bLayoutProto\x12\x38\n\x0esharding_specs\x18\x01 \x03(\x0b\x32 .tensorflow.dtensor.ShardingSpec\x12\x32\n\x0bmesh_config\x18\x02 \x01(\x0b\x32\x1d.tensorflow.dtensor.MeshProto\x12\x38\n\x04type\x18\x03 \x01(\x0e\x32*.tensorflow.dtensor.LayoutProto.LayoutType\"D\n\nLayoutType\x12\x0b\n\x07UNKNOWN\x10\x00\x12\n\n\x06STATIC\x10\x01\x12\n\n\x06PARTED\x10\x02\x12\x11\n\rSINGLE_DEVICE\x10\x03\"\xeb\x01\n\tMeshProto\x12?\n\x0fmesh_dimensions\x18\x01 \x03(\x0b\x32&.tensorflow.dtensor.MeshDimensionProto\x12\x19\n\x11global_device_ids\x18\x02 \x03(\x03\x12\x18\n\x10local_device_ids\x18\x04 \x03(\x03\x12\x15\n\rlocal_devices\x18\x05 \x03(\t\x12\x16\n\x0eglobal_devices\x18\x06 \x03(\t\x12\x0c\n\x04name\x18\x03 \x01(\t\x12\x14\n\x0cuse_xla_spmd\x18\x07 \x01(\x08\x12\x15\n\rsingle_device\x18\x08 \x01(\tb\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.dtensor.proto.layout_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _SHARDINGSPEC._serialized_start=61
+  _SHARDINGSPEC._serialized_end=104
+  _MESHDIMENSIONPROTO._serialized_start=106
+  _MESHDIMENSIONPROTO._serialized_end=154
+  _LAYOUTPROTO._serialized_start=157
+  _LAYOUTPROTO._serialized_end=408
+  _LAYOUTPROTO_LAYOUTTYPE._serialized_start=340
+  _LAYOUTPROTO_LAYOUTTYPE._serialized_end=408
+  _MESHPROTO._serialized_start=411
+  _MESHPROTO._serialized_end=646
+# @@protoc_insertion_point(module_scope)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/dtensor/python/__init__.py

@@ -0,0 +1,17 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""DTensor Python API."""
+
+# This file is left empty intentionally.