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@@ -204,3 +204,5 @@ SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/lite/pyth
 SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/lite/python/analyzer_wrapper/_pywrap_analyzer_wrapper.so filter=lfs diff=lfs merge=lfs -text
 SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/lite/experimental/microfrontend/python/ops/_audio_microfrontend_op.so filter=lfs diff=lfs merge=lfs -text
 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/lite/converter_flags_pb2.py

@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/lite/converter_flags.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 tensorflow.compiler.mlir.lite.debug import debug_options_pb2 as tensorflow_dot_compiler_dot_mlir_dot_lite_dot_debug_dot_debug__options__pb2
+from tensorflow.compiler.mlir.lite import types_pb2 as tensorflow_dot_compiler_dot_mlir_dot_lite_dot_types__pb2
+from tensorflow.compiler.mlir.quantization.stablehlo import quantization_config_pb2 as tensorflow_dot_compiler_dot_mlir_dot_quantization_dot_stablehlo_dot_quantization__config__pb2
+from tensorflow.compiler.mlir.quantization.stablehlo import quantization_options_pb2 as tensorflow_dot_compiler_dot_mlir_dot_quantization_dot_stablehlo_dot_quantization__options__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n3tensorflow/compiler/mlir/lite/converter_flags.proto\x12\x06tflite\x1a\x37tensorflow/compiler/mlir/lite/debug/debug_options.proto\x1a)tensorflow/compiler/mlir/lite/types.proto\x1aItensorflow/compiler/mlir/quantization/stablehlo/quantization_config.proto\x1aJtensorflow/compiler/mlir/quantization/stablehlo/quantization_options.proto\"\xd3\x14\n\x0e\x43onverterFlags\x12(\n\x0cinput_format\x18\x01 \x01(\x0e\x32\x12.tflite.FileFormat\x12)\n\routput_format\x18\x02 \x01(\x0e\x32\x12.tflite.FileFormat\x12\x30\n\x14inference_input_type\x18\x0b \x01(\x0e\x32\x12.tflite.IODataType\x12*\n\x0einference_type\x18\x04 \x01(\x0e\x32\x12.tflite.IODataType\x12\x1a\n\x12\x64\x65\x66\x61ult_ranges_min\x18\x05 \x01(\x02\x12\x1a\n\x12\x64\x65\x66\x61ult_ranges_max\x18\x06 \x01(\x02\x12 \n\x18\x64\x65\x66\x61ult_int16_ranges_min\x18\x0f \x01(\x02\x12 \n\x18\x64\x65\x66\x61ult_int16_ranges_max\x18\x10 \x01(\x02\x12\x17\n\x0f\x64rop_fake_quant\x18\x07 \x01(\x08\x12!\n\x19reorder_across_fake_quant\x18\x08 \x01(\x08\x12\x18\n\x10\x61llow_custom_ops\x18\n \x01(\x08\x12\x1f\n\x17\x64rop_control_dependency\x18\x0c \x01(\x08\x12+\n#debug_disable_recurrent_cell_fusion\x18\r \x01(\x08\x12%\n\x1dpropagate_fake_quant_num_bits\x18\x0e \x01(\x08\x12\x35\n-allow_nudging_weights_to_use_fast_gemm_kernel\x18\x11 \x01(\x08\x12\'\n\x1b\x64\x65\x64upe_array_min_size_bytes\x18\x12 \x01(\x03:\x02\x36\x34\x12&\n\x18split_tflite_lstm_inputs\x18\x13 \x01(\x08:\x04true\x12\x1f\n\x10quantize_weights\x18\x14 \x01(\x08:\x05\x66\x61lse\x12\x19\n\x11\x64ump_graphviz_dir\x18\x18 \x01(\t\x12#\n\x1b\x64ump_graphviz_include_video\x18\x19 \x01(\x08\x12%\n\x16post_training_quantize\x18\x1a \x01(\x08:\x05\x66\x61lse\x12#\n\x14\x65nable_select_tf_ops\x18\x1b \x01(\x08:\x05\x66\x61lse\x12\"\n\x13\x66orce_select_tf_ops\x18\x1c \x01(\x08:\x05\x66\x61lse\x12\"\n\x13quantize_to_float16\x18\x1d \x01(\x08:\x05\x66\x61lse\x12#\n\x15\x61llow_dynamic_tensors\x18\x1e \x01(\x08:\x04true\x12\x1e\n\x16\x63onversion_summary_dir\x18\x1f \x01(\t\x12\x19\n\rcustom_opdefs\x18  \x03(\tB\x02\x18\x01\x12\x1a\n\x12select_user_tf_ops\x18! \x03(\t\x12.\n enable_tflite_resource_variables\x18\" \x01(\x08:\x04true\x12!\n\x12unfold_batchmatmul\x18# \x01(\x08:\x05\x66\x61lse\x12#\n\x15lower_tensor_list_ops\x18$ \x01(\x08:\x04true\x12-\n\x11\x61\x63\x63umulation_type\x18% \x01(\x0e\x32\x12.tflite.IODataType\x12\x1d\n\x0e\x61llow_bfloat16\x18& \x01(\x08:\x05\x66\x61lse\x12\x1f\n\x17\x61llow_all_select_tf_ops\x18\' \x01(\x08\x12*\n\x1bunfold_large_splat_constant\x18( \x01(\x08:\x05\x66\x61lse\x12\x1a\n\x12supported_backends\x18) \x03(\t\x12\x39\n*default_to_single_batch_in_tensor_list_ops\x18* \x01(\x08:\x05\x66\x61lse\x12/\n disable_per_channel_quantization\x18+ \x01(\x08:\x05\x66\x61lse\x12\x32\n#enable_mlir_dynamic_range_quantizer\x18, \x01(\x08:\x05\x66\x61lse\x12\x1c\n\x14tf_quantization_mode\x18- \x01(\t\x12)\n\x1a\x64isable_infer_tensor_range\x18. \x01(\x08:\x05\x66\x61lse\x12&\n\x17use_fake_quant_num_bits\x18/ \x01(\x08:\x05\x66\x61lse\x12*\n\x1b\x65nable_dynamic_update_slice\x18\x30 \x01(\x08:\x05\x66\x61lse\x12!\n\x12preserve_assert_op\x18\x31 \x01(\x08:\x05\x66\x61lse\x12*\n\x1bguarantee_all_funcs_one_use\x18\x32 \x01(\x08:\x05\x66\x61lse\x12#\n\x14\x63onvert_to_stablehlo\x18\x33 \x01(\x08:\x05\x66\x61lse\x12\x30\n!enable_mlir_variable_quantization\x18\x34 \x01(\x08:\x05\x66\x61lse\x12&\n\x17\x64isable_fuse_mul_and_fc\x18\x35 \x01(\x08:\x05\x66\x61lse\x12M\n\x14quantization_options\x18\x36 \x01(\x0b\x32+.stablehlo.quantization.QuantizationOptionsB\x02\x18\x01\x12.\n\x1b\x65nable_hlo_to_tf_conversion\x18\x37 \x01(\x08:\x05\x66\x61lseB\x02\x18\x01\x12\x39\n\rdebug_options\x18\x38 \x01(\x0b\x32\".tensorflow.converter.DebugOptions\x12 \n\x11use_buffer_offset\x18\x39 \x01(\x08:\x05\x66\x61lse\x12.\n\x1flegalize_custom_tensor_list_ops\x18: \x01(\x08:\x05\x66\x61lse\x12$\n\x15reduce_type_precision\x18; \x01(\x08:\x05\x66\x61lse\x12!\n\x13qdq_conversion_mode\x18< \x01(\t:\x04NONE\x12G\n\x13quantization_config\x18= \x01(\x0b\x32*.stablehlo.quantization.QuantizationConfig\x12@\n1disable_per_channel_quantization_for_dense_layers\x18> \x01(\x08:\x05\x66\x61lse\x12/\n enable_composite_direct_lowering\x18? \x01(\x08:\x05\x66\x61lse\x12R\n\x16model_origin_framework\x18@ \x01(\x0e\x32+.tflite.ConverterFlags.ModelOriginFramework:\x05UNSET\x12\x32\n#canonicalizing_inf_as_min_max_float\x18\x41 \x01(\x08:\x05\x66\x61lse\x12\'\n\x18serialize_debug_metadata\x18\x42 \x01(\x08:\x05\x66\x61lse\"R\n\x14ModelOriginFramework\x12\t\n\x05UNSET\x10\x00\x12\x0e\n\nTENSORFLOW\x10\x01\x12\t\n\x05KERAS\x10\x02\x12\x07\n\x03JAX\x10\x03\x12\x0b\n\x07PYTORCH\x10\x04*\\\n\nFileFormat\x12\x17\n\x13\x46ILE_FORMAT_UNKNOWN\x10\x00\x12\x17\n\x13TENSORFLOW_GRAPHDEF\x10\x01\x12\n\n\x06TFLITE\x10\x02\x12\x10\n\x0cGRAPHVIZ_DOT\x10\x03')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.lite.converter_flags_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _CONVERTERFLAGS.fields_by_name['custom_opdefs']._options = None
+  _CONVERTERFLAGS.fields_by_name['custom_opdefs']._serialized_options = b'\030\001'
+  _CONVERTERFLAGS.fields_by_name['quantization_options']._options = None
+  _CONVERTERFLAGS.fields_by_name['quantization_options']._serialized_options = b'\030\001'
+  _CONVERTERFLAGS.fields_by_name['enable_hlo_to_tf_conversion']._options = None
+  _CONVERTERFLAGS.fields_by_name['enable_hlo_to_tf_conversion']._serialized_options = b'\030\001'
+  _FILEFORMAT._serialized_start=2960
+  _FILEFORMAT._serialized_end=3052
+  _CONVERTERFLAGS._serialized_start=315
+  _CONVERTERFLAGS._serialized_end=2958
+  _CONVERTERFLAGS_MODELORIGINFRAMEWORK._serialized_start=2876
+  _CONVERTERFLAGS_MODELORIGINFRAMEWORK._serialized_end=2958
+# @@protoc_insertion_point(module_scope)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/lite/debug/debug_options_pb2.py

@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/lite/debug/debug_options.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'\n7tensorflow/compiler/mlir/lite/debug/debug_options.proto\x12\x14tensorflow.converter\"\x84\x02\n\x0c\x44\x65\x62ugOptions\x12\x15\n\x0bir_dump_dir\x18\x01 \x01(\t:\x00\x12\x1e\n\x12ir_dump_pass_regex\x18\x02 \x01(\t:\x02.*\x12\x1e\n\x12ir_dump_func_regex\x18\x03 \x01(\t:\x02.*\x12\x1c\n\renable_timing\x18\x04 \x01(\x08:\x05\x66\x61lse\x12\x19\n\x0fprint_ir_before\x18\x05 \x01(\t:\x00\x12\x18\n\x0eprint_ir_after\x18\x06 \x01(\t:\x00\x12#\n\x15print_ir_module_scope\x18\x07 \x01(\x08:\x04true\x12%\n\x1d\x65lide_elementsattrs_if_larger\x18\x08 \x01(\x03')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.lite.debug.debug_options_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _DEBUGOPTIONS._serialized_start=82
+  _DEBUGOPTIONS._serialized_end=342
+# @@protoc_insertion_point(module_scope)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/lite/metrics/converter_error_data_pb2.py

@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/lite/metrics/converter_error_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@tensorflow/compiler/mlir/lite/metrics/converter_error_data.proto\x12\x0etflite.metrics\"\xdc\x06\n\x12\x43onverterErrorData\x12\x11\n\tcomponent\x18\x01 \x01(\t\x12\x14\n\x0csubcomponent\x18\x02 \x01(\t\x12@\n\nerror_code\x18\x03 \x01(\x0e\x32,.tflite.metrics.ConverterErrorData.ErrorCode\x12\x15\n\rerror_message\x18\x04 \x01(\t\x12=\n\x08operator\x18\x05 \x01(\x0b\x32+.tflite.metrics.ConverterErrorData.Operator\x12=\n\x08location\x18\x06 \x01(\x0b\x32+.tflite.metrics.ConverterErrorData.Location\x1a\x18\n\x08Operator\x12\x0c\n\x04name\x18\x01 \x01(\t\x1a\x39\n\x07\x46ileLoc\x12\x10\n\x08\x66ilename\x18\x01 \x01(\t\x12\x0c\n\x04line\x18\x02 \x01(\r\x12\x0e\n\x06\x63olumn\x18\x03 \x01(\r\x1aU\n\tSourceLoc\x12\x0c\n\x04name\x18\x01 \x01(\t\x12:\n\x06source\x18\x02 \x01(\x0b\x32*.tflite.metrics.ConverterErrorData.FileLoc\x1a\x85\x01\n\x08Location\x12=\n\x04type\x18\x01 \x01(\x0e\x32/.tflite.metrics.ConverterErrorData.LocationType\x12:\n\x04\x63\x61ll\x18\x02 \x03(\x0b\x32,.tflite.metrics.ConverterErrorData.SourceLoc\"\xc5\x01\n\tErrorCode\x12\x0b\n\x07UNKNOWN\x10\x00\x12\x18\n\x14\x45RROR_NEEDS_FLEX_OPS\x10\x01\x12\x1a\n\x16\x45RROR_NEEDS_CUSTOM_OPS\x10\x02\x12%\n!ERROR_UNSUPPORTED_CONTROL_FLOW_V1\x10\x03\x12/\n+ERROR_STATEFUL_PARTITIONED_CALL_IN_FINAL_IR\x10\x04\x12\x1d\n\x18\x45RROR_GPU_NOT_COMPATIBLE\x10\xc8\x01\"J\n\x0cLocationType\x12\x0e\n\nUNKNOWNLOC\x10\x00\x12\x0b\n\x07NAMELOC\x10\x01\x12\x0f\n\x0b\x43\x41LLSITELOC\x10\x02\x12\x0c\n\x08\x46USEDLOC\x10\x03')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.lite.metrics.converter_error_data_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _CONVERTERERRORDATA._serialized_start=85
+  _CONVERTERERRORDATA._serialized_end=945
+  _CONVERTERERRORDATA_OPERATOR._serialized_start=363
+  _CONVERTERERRORDATA_OPERATOR._serialized_end=387
+  _CONVERTERERRORDATA_FILELOC._serialized_start=389
+  _CONVERTERERRORDATA_FILELOC._serialized_end=446
+  _CONVERTERERRORDATA_SOURCELOC._serialized_start=448
+  _CONVERTERERRORDATA_SOURCELOC._serialized_end=533
+  _CONVERTERERRORDATA_LOCATION._serialized_start=536
+  _CONVERTERERRORDATA_LOCATION._serialized_end=669
+  _CONVERTERERRORDATA_ERRORCODE._serialized_start=672
+  _CONVERTERERRORDATA_ERRORCODE._serialized_end=869
+  _CONVERTERERRORDATA_LOCATIONTYPE._serialized_start=871
+  _CONVERTERERRORDATA_LOCATIONTYPE._serialized_end=945
+# @@protoc_insertion_point(module_scope)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/lite/model_flags_pb2.py

@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/lite/model_flags.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 tensorflow.compiler.mlir.lite import types_pb2 as tensorflow_dot_compiler_dot_mlir_dot_lite_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n/tensorflow/compiler/mlir/lite/model_flags.proto\x12\x06tflite\x1a)tensorflow/compiler/mlir/lite/types.proto\"5\n\x0fInputArrayShape\x12\x0c\n\x04\x64ims\x18\x02 \x03(\x05\x12\x14\n\x0cunknown_rank\x18\x03 \x01(\x08\"\x93\x01\n\nInputArray\x12\x0c\n\x04name\x18\x01 \x01(\t\x12&\n\x05shape\x18\x06 \x01(\x0b\x32\x17.tflite.InputArrayShape\x12\x12\n\nmean_value\x18\x03 \x01(\x02\x12\x14\n\tstd_value\x18\x04 \x01(\x02:\x01\x31\x12%\n\tdata_type\x18\x05 \x01(\x0e\x32\x12.tflite.IODataType\"t\n\x08RnnState\x12\x13\n\x0bstate_array\x18\x01 \x01(\t\x12\x1e\n\x16\x62\x61\x63k_edge_source_array\x18\x02 \x01(\t\x12\x13\n\x0b\x64iscardable\x18\x05 \x01(\x08\x12\x0c\n\x04size\x18\x03 \x01(\x05\x12\x10\n\x08num_dims\x18\x04 \x01(\x05\"\xf5\x01\n\x0f\x41rraysExtraInfo\x12.\n\x07\x65ntries\x18\x01 \x03(\x0b\x32\x1d.tflite.ArraysExtraInfo.Entry\x1a\xb1\x01\n\x05\x45ntry\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x13\n\x0bname_regexp\x18\x07 \x01(\t\x12\x0b\n\x03min\x18\x02 \x01(\x01\x12\x0b\n\x03max\x18\x03 \x01(\x01\x12%\n\tdata_type\x18\x04 \x01(\x0e\x32\x12.tflite.IODataType\x12&\n\x05shape\x18\x05 \x01(\x0b\x32\x17.tflite.InputArrayShape\x12\x1c\n\x14\x63onstant_float_value\x18\x06 \x01(\x02\"\xd0\x05\n\nModelFlags\x12(\n\x0cinput_arrays\x18\x01 \x03(\x0b\x32\x12.tflite.InputArray\x12\x15\n\routput_arrays\x18\x02 \x03(\t\x12\x1d\n\x15\x63ontrol_output_arrays\x18\x18 \x03(\t\x12\x16\n\x0evariable_batch\x18\n \x01(\x08\x12$\n\nrnn_states\x18\x0c \x03(\x0b\x32\x10.tflite.RnnState\x12\x33\n\x0cmodel_checks\x18\x0e \x03(\x0b\x32\x1d.tflite.ModelFlags.ModelCheck\x12 \n\x18\x61llow_nonexistent_arrays\x18\x10 \x01(\x08\x12\x1d\n\x15\x61llow_nonascii_arrays\x18\x11 \x01(\x08\x12\x32\n\x11\x61rrays_extra_info\x18\x12 \x01(\x0b\x32\x17.tflite.ArraysExtraInfo\x12(\n\x1a\x63hange_concat_input_ranges\x18\x13 \x01(\x08:\x04true\x12\x17\n\x0fsaved_model_dir\x18\x14 \x01(\t\x12\x1b\n\x13saved_model_version\x18\x15 \x01(\x05\x12\x18\n\x10saved_model_tags\x18\x16 \x03(\t\x12\"\n\x1asaved_model_exported_names\x18\x17 \x03(\t\x12\x16\n\x0euse_hlo_import\x18\x19 \x01(\x08\x12\x35\n\rhlo_file_type\x18\x1a \x01(\x0e\x32\x1e.tflite.ModelFlags.HloFileType\x1aT\n\nModelCheck\x12\x18\n\ncount_type\x18\x01 \x01(\t:\x04None\x12\x15\n\tcount_min\x18\x02 \x01(\x05:\x02-1\x12\x15\n\tcount_max\x18\x03 \x01(\x05:\x02-1\"7\n\x0bHloFileType\x12\x0b\n\x07UNKNOWN\x10\x00\x12\x0c\n\x08HLO_TEXT\x10\x01\x12\r\n\tHLO_PROTO\x10\x02')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.lite.model_flags_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _INPUTARRAYSHAPE._serialized_start=102
+  _INPUTARRAYSHAPE._serialized_end=155
+  _INPUTARRAY._serialized_start=158
+  _INPUTARRAY._serialized_end=305
+  _RNNSTATE._serialized_start=307
+  _RNNSTATE._serialized_end=423
+  _ARRAYSEXTRAINFO._serialized_start=426
+  _ARRAYSEXTRAINFO._serialized_end=671
+  _ARRAYSEXTRAINFO_ENTRY._serialized_start=494
+  _ARRAYSEXTRAINFO_ENTRY._serialized_end=671
+  _MODELFLAGS._serialized_start=674
+  _MODELFLAGS._serialized_end=1394
+  _MODELFLAGS_MODELCHECK._serialized_start=1253
+  _MODELFLAGS_MODELCHECK._serialized_end=1337
+  _MODELFLAGS_HLOFILETYPE._serialized_start=1339
+  _MODELFLAGS_HLOFILETYPE._serialized_end=1394
+# @@protoc_insertion_point(module_scope)

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

@@ -0,0 +1,21 @@
+# 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.
+# ==============================================================================
+
+def Convert(model_flags_proto_txt_raw: object, toco_flags_proto_txt_raw: object, input_contents_txt_raw: object, extended_return: bool = ..., debug_info_txt_raw: object = ..., enable_mlir_converter: bool = ..., quantization_py_function_library = ...) -> object: ...
+def ExperimentalMlirQuantizeModel(input_contents_txt_raw: object, disable_per_channel: bool = ..., fully_quantize: bool = ..., inference_type: int = ..., input_data_type: int = ..., output_data_type: int = ..., enable_numeric_verify: bool = ..., enable_whole_model_verify: bool = ..., op_blocklist: object = ..., node_blocklist: object = ..., enable_variable_quantization: bool = ..., disable_per_channel_for_dense_layers: bool = ..., debug_options_proto_txt_raw: object = ...) -> object: ...
+def ExperimentalMlirSparsifyModel(input_contents_txt_raw: object) -> object: ...
+def FlatBufferToMlir(arg0: str, arg1: bool) -> str: ...
+def RegisterCustomOpdefs(custom_opdefs_txt_raw: object) -> object: ...
+def RetrieveCollectedErrors() -> list: ...

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

@@ -0,0 +1,92 @@
+# Copyright 2019 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.
+# ==============================================================================
+"""Wraps TFLite Converter interface with python lazy loader."""
+# We need to import pywrap_tensorflow prior to the converter wrapper.
+# pylint: disable=invalid-import-order,g-bad-import-order
+from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
+from tensorflow.compiler.mlir.lite.python import _pywrap_converter_api
+from tensorflow.compiler.mlir.quantization.tensorflow.python import py_function_lib
+
+
+def wrapped_convert(
+    model_flags_str,
+    toco_flags_str,
+    input_data_str,
+    debug_info_str,
+    enable_mlir_converter,
+):
+  """Wraps TocoConvert with lazy loader."""
+  return _pywrap_converter_api.Convert(
+      model_flags_str,
+      toco_flags_str,
+      input_data_str,
+      False,  # extended_return
+      debug_info_str,
+      enable_mlir_converter,
+      py_function_lib.PyFunctionLibrary(),
+  )
+
+
+def wrapped_experimental_mlir_quantize(
+    input_data_str,
+    disable_per_channel,
+    fully_quantize,
+    inference_type,
+    input_data_type,
+    output_data_type,
+    enable_numeric_verify,
+    enable_whole_model_verify,
+    denylisted_ops,
+    denylisted_nodes,
+    enable_variable_quantization,
+    disable_per_channel_for_dense_layers,
+    debug_options_str,
+):
+  """Wraps experimental mlir quantize model."""
+  return _pywrap_converter_api.ExperimentalMlirQuantizeModel(
+      input_data_str,
+      disable_per_channel,
+      fully_quantize,
+      inference_type,
+      input_data_type,
+      output_data_type,
+      enable_numeric_verify,
+      enable_whole_model_verify,
+      denylisted_ops,
+      denylisted_nodes,
+      enable_variable_quantization,
+      disable_per_channel_for_dense_layers,
+      debug_options_str,
+  )
+
+
+def wrapped_experimental_mlir_sparsify(input_data_str):
+  """Wraps experimental mlir sparsify model."""
+  return _pywrap_converter_api.ExperimentalMlirSparsifyModel(input_data_str)
+
+
+def wrapped_register_custom_opdefs(custom_opdefs_list):
+  """Wraps RegisterCustomOpdefs with lazy loader."""
+  return _pywrap_converter_api.RegisterCustomOpdefs(custom_opdefs_list)
+
+
+def wrapped_retrieve_collected_errors():
+  """Wraps RetrieveCollectedErrors with lazy loader."""
+  return _pywrap_converter_api.RetrieveCollectedErrors()
+
+
+def wrapped_flat_buffer_file_to_mlir(model, input_is_filepath):
+  """Wraps FlatBufferFileToMlir with lazy loader."""
+  return _pywrap_converter_api.FlatBufferToMlir(model, input_is_filepath)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/lite/types_pb2.py

@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/lite/types.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/compiler/mlir/lite/types.proto\x12\x06tflite*\xb3\x02\n\nIODataType\x12\x18\n\x14IO_DATA_TYPE_UNKNOWN\x10\x00\x12\t\n\x05\x46LOAT\x10\x01\x12\x13\n\x0fQUANTIZED_UINT8\x10\x02\x12\t\n\x05INT32\x10\x03\x12\t\n\x05INT64\x10\x04\x12\n\n\x06STRING\x10\x05\x12\x13\n\x0fQUANTIZED_INT16\x10\x06\x12\x08\n\x04\x42OOL\x10\x07\x12\r\n\tCOMPLEX64\x10\x08\x12\x12\n\x0eQUANTIZED_INT8\x10\t\x12\x0b\n\x07\x46LOAT16\x10\n\x12\x0b\n\x07\x46LOAT64\x10\x0b\x12\x0e\n\nCOMPLEX128\x10\x0c\x12\n\n\x06UINT64\x10\r\x12\x0c\n\x08RESOURCE\x10\x0e\x12\x0b\n\x07VARIANT\x10\x0f\x12\n\n\x06UINT32\x10\x10\x12\t\n\x05UINT8\x10\x11\x12\x08\n\x04INT8\x10\x12\x12\t\n\x05INT16\x10\x13\x12\n\n\x06UINT16\x10\x14')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.lite.types_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _IODATATYPE._serialized_start=54
+  _IODATATYPE._serialized_end=361
+# @@protoc_insertion_point(module_scope)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/stablehlo/quantization_config_pb2.py

@@ -0,0 +1,86 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/quantization/stablehlo/quantization_config.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'\nItensorflow/compiler/mlir/quantization/stablehlo/quantization_config.proto\x12\x16stablehlo.quantization\"\x1c\n\x0cTfRecordFile\x12\x0c\n\x04path\x18\x01 \x01(\t\"\x8e\x01\n\x1bRepresentativeDatasetConfig\x12\x39\n\ttf_record\x18\x01 \x01(\x0b\x32$.stablehlo.quantization.TfRecordFileH\x00\x12\x1a\n\rsignature_key\x18\x02 \x01(\tH\x01\x88\x01\x01\x42\x06\n\x04\x66ileB\x10\n\x0e_signature_key\"\xc3\x01\n\x14StaticRangePtqPreset\x12T\n\x17representative_datasets\x18\x01 \x03(\x0b\x32\x33.stablehlo.quantization.RepresentativeDatasetConfig\x12/\n#enable_per_channel_quantized_weight\x18\x02 \x01(\x08\x42\x02\x18\x01\x12$\n\x1c\x65nable_full_int_quantization\x18\x03 \x01(\x08\"\x15\n\x13WeightOnlyPtqPreset\"\"\n\x12TfSavedModelConfig\x12\x0c\n\x04tags\x18\x01 \x03(\t\"v\n\x0ePipelineConfig\x12#\n\x16unpack_quantized_types\x18\x01 \x01(\x08H\x00\x88\x01\x01\x12$\n\x1cmerge_fusion_with_dequantize\x18\x02 \x01(\x08\x42\x19\n\x17_unpack_quantized_types\"\x1f\n\x0fQuantizableUnit\x12\x0c\n\x04name\x18\x01 \x01(\t\"\x87\x01\n\x12QuantizationResult\x12\x41\n\x10quantizable_unit\x18\x01 \x01(\x0b\x32\'.stablehlo.quantization.QuantizableUnit\x12.\n\x06method\x18\x02 \x01(\x0b\x32\x1e.stablehlo.quantization.Method\"R\n\x13QuantizationResults\x12;\n\x07results\x18\x01 \x03(\x0b\x32*.stablehlo.quantization.QuantizationResult\":\n\x12QuantizedDimension\x12\x16\n\tdimension\x18\x01 \x01(\x05H\x00\x88\x01\x01\x42\x0c\n\n_dimension\"\x0b\n\tPerTensor\"\x97\x01\n\rQuantizedType\x12\x45\n\x0f\x64imension_specs\x18\x01 \x01(\x0b\x32*.stablehlo.quantization.QuantizedDimensionH\x00\x12\x37\n\nper_tensor\x18\x02 \x01(\x0b\x32!.stablehlo.quantization.PerTensorH\x00\x42\x06\n\x04type\"\x10\n\x0eNoQuantization\"\xd3\x01\n\x0eStaticRangePtq\x12^\n\x15input_quantized_types\x18\x01 \x03(\x0b\x32?.stablehlo.quantization.StaticRangePtq.InputQuantizedTypesEntry\x1a\x61\n\x18InputQuantizedTypesEntry\x12\x0b\n\x03key\x18\x01 \x01(\x05\x12\x34\n\x05value\x18\x02 \x01(\x0b\x32%.stablehlo.quantization.QuantizedType:\x02\x38\x01\"\xd1\x01\n\rWeightOnlyPtq\x12]\n\x15input_quantized_types\x18\x01 \x03(\x0b\x32>.stablehlo.quantization.WeightOnlyPtq.InputQuantizedTypesEntry\x1a\x61\n\x18InputQuantizedTypesEntry\x12\x0b\n\x03key\x18\x01 \x01(\x05\x12\x34\n\x05value\x18\x02 \x01(\x0b\x32%.stablehlo.quantization.QuantizedType:\x02\x38\x01\"(\n\x17\x46unctionNameMatcherSpec\x12\r\n\x05regex\x18\x01 \x01(\t\"U\n\x0bMatcherSpec\x12\x46\n\rfunction_name\x18\x01 \x01(\x0b\x32/.stablehlo.quantization.FunctionNameMatcherSpec\"\xdb\x01\n\x06Method\x12\x41\n\x0fno_quantization\x18\x01 \x01(\x0b\x32&.stablehlo.quantization.NoQuantizationH\x00\x12\x42\n\x10static_range_ptq\x18\x02 \x01(\x0b\x32&.stablehlo.quantization.StaticRangePtqH\x00\x12@\n\x0fweight_only_ptq\x18\x03 \x01(\x0b\x32%.stablehlo.quantization.WeightOnlyPtqH\x00\x42\x08\n\x06method\"x\n\x10QuantizationSpec\x12\x34\n\x07matcher\x18\x01 \x01(\x0b\x32#.stablehlo.quantization.MatcherSpec\x12.\n\x06method\x18\x02 \x01(\x0b\x32\x1e.stablehlo.quantization.Method\"L\n\x11QuantizationSpecs\x12\x37\n\x05specs\x18\x01 \x03(\x0b\x32(.stablehlo.quantization.QuantizationSpec\"\xaa\x02\n\x0e\x44\x65\x62uggerConfig\x12J\n\rdebugger_type\x18\x01 \x01(\x0e\x32\x33.stablehlo.quantization.DebuggerConfig.DebuggerType\x12#\n\x1bunquantized_dump_model_path\x18\x02 \x01(\t\x12\x14\n\x0clog_dir_path\x18\x03 \x01(\t\"\x90\x01\n\x0c\x44\x65\x62uggerType\x12\x1d\n\x19\x44\x45\x42UGGER_TYPE_UNSPECIFIED\x10\x00\x12\x1d\n\x19\x44\x45\x42UGGER_TYPE_WHOLE_MODEL\x10\x01\x12\x1f\n\x1b\x44\x45\x42UGGER_TYPE_INT_PER_LAYER\x10\x02\x12!\n\x1d\x44\x45\x42UGGER_TYPE_FLOAT_PER_LAYER\x10\x03\"\x8e\x06\n\x12\x43\x61librationOptions\x12X\n\x12\x63\x61libration_method\x18\x01 \x01(\x0e\x32<.stablehlo.quantization.CalibrationOptions.CalibrationMethod\x12`\n\x16\x63\x61libration_parameters\x18\x02 \x01(\x0b\x32@.stablehlo.quantization.CalibrationOptions.CalibrationParameters\x12T\n\x17representative_datasets\x18\x03 \x03(\x0b\x32\x33.stablehlo.quantization.RepresentativeDatasetConfig\x12\x1c\n\x14\x63\x61libration_data_dir\x18\x04 \x01(\t\x12)\n!force_regenerate_calibration_data\x18\x05 \x01(\x08\x1aY\n\x15\x43\x61librationParameters\x12\x10\n\x08num_bins\x18\x01 \x01(\x05\x12\x16\n\x0emin_percentile\x18\x02 \x01(\x02\x12\x16\n\x0emax_percentile\x18\x03 \x01(\x02\"\xc1\x02\n\x11\x43\x61librationMethod\x12\"\n\x1e\x43\x41LIBRATION_METHOD_UNSPECIFIED\x10\x00\x12\x1e\n\x1a\x43\x41LIBRATION_METHOD_MIN_MAX\x10\x01\x12&\n\"CALIBRATION_METHOD_AVERAGE_MIN_MAX\x10\x02\x12+\n\'CALIBRATION_METHOD_HISTOGRAM_PERCENTILE\x10\x03\x12/\n+CALIBRATION_METHOD_HISTOGRAM_MSE_BRUTEFORCE\x10\x04\x12\x32\n.CALIBRATION_METHOD_HISTOGRAM_MSE_MAX_FREQUENCY\x10\x05\x12.\n*CALIBRATION_METHOD_HISTOGRAM_MSE_SYMMETRIC\x10\x06\"\xbb\x04\n\x12QuantizationConfig\x12O\n\x17static_range_ptq_preset\x18\x01 \x01(\x0b\x32,.stablehlo.quantization.StaticRangePtqPresetH\x00\x12M\n\x16weight_only_ptq_preset\x18\x07 \x01(\x0b\x32+.stablehlo.quantization.WeightOnlyPtqPresetH\x00\x12\x42\n\x0etf_saved_model\x18\x02 \x01(\x0b\x32*.stablehlo.quantization.TfSavedModelConfig\x12?\n\x0fpipeline_config\x18\x03 \x01(\x0b\x32&.stablehlo.quantization.PipelineConfig\x12\x38\n\x05specs\x18\x04 \x01(\x0b\x32).stablehlo.quantization.QuantizationSpecs\x12?\n\x0f\x64\x65\x62ugger_config\x18\x05 \x01(\x0b\x32&.stablehlo.quantization.DebuggerConfig\x12G\n\x13\x63\x61libration_options\x18\x06 \x01(\x0b\x32*.stablehlo.quantization.CalibrationOptions\x12\x1d\n\x10report_file_path\x18\x08 \x01(\tH\x01\x88\x01\x01\x42\x08\n\x06presetB\x13\n\x11_report_file_pathB\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.quantization.stablehlo.quantization_config_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _STATICRANGEPTQPRESET.fields_by_name['enable_per_channel_quantized_weight']._options = None
+  _STATICRANGEPTQPRESET.fields_by_name['enable_per_channel_quantized_weight']._serialized_options = b'\030\001'
+  _STATICRANGEPTQ_INPUTQUANTIZEDTYPESENTRY._options = None
+  _STATICRANGEPTQ_INPUTQUANTIZEDTYPESENTRY._serialized_options = b'8\001'
+  _WEIGHTONLYPTQ_INPUTQUANTIZEDTYPESENTRY._options = None
+  _WEIGHTONLYPTQ_INPUTQUANTIZEDTYPESENTRY._serialized_options = b'8\001'
+  _TFRECORDFILE._serialized_start=101
+  _TFRECORDFILE._serialized_end=129
+  _REPRESENTATIVEDATASETCONFIG._serialized_start=132
+  _REPRESENTATIVEDATASETCONFIG._serialized_end=274
+  _STATICRANGEPTQPRESET._serialized_start=277
+  _STATICRANGEPTQPRESET._serialized_end=472
+  _WEIGHTONLYPTQPRESET._serialized_start=474
+  _WEIGHTONLYPTQPRESET._serialized_end=495
+  _TFSAVEDMODELCONFIG._serialized_start=497
+  _TFSAVEDMODELCONFIG._serialized_end=531
+  _PIPELINECONFIG._serialized_start=533
+  _PIPELINECONFIG._serialized_end=651
+  _QUANTIZABLEUNIT._serialized_start=653
+  _QUANTIZABLEUNIT._serialized_end=684
+  _QUANTIZATIONRESULT._serialized_start=687
+  _QUANTIZATIONRESULT._serialized_end=822
+  _QUANTIZATIONRESULTS._serialized_start=824
+  _QUANTIZATIONRESULTS._serialized_end=906
+  _QUANTIZEDDIMENSION._serialized_start=908
+  _QUANTIZEDDIMENSION._serialized_end=966
+  _PERTENSOR._serialized_start=968
+  _PERTENSOR._serialized_end=979
+  _QUANTIZEDTYPE._serialized_start=982
+  _QUANTIZEDTYPE._serialized_end=1133
+  _NOQUANTIZATION._serialized_start=1135
+  _NOQUANTIZATION._serialized_end=1151
+  _STATICRANGEPTQ._serialized_start=1154
+  _STATICRANGEPTQ._serialized_end=1365
+  _STATICRANGEPTQ_INPUTQUANTIZEDTYPESENTRY._serialized_start=1268
+  _STATICRANGEPTQ_INPUTQUANTIZEDTYPESENTRY._serialized_end=1365
+  _WEIGHTONLYPTQ._serialized_start=1368
+  _WEIGHTONLYPTQ._serialized_end=1577
+  _WEIGHTONLYPTQ_INPUTQUANTIZEDTYPESENTRY._serialized_start=1268
+  _WEIGHTONLYPTQ_INPUTQUANTIZEDTYPESENTRY._serialized_end=1365
+  _FUNCTIONNAMEMATCHERSPEC._serialized_start=1579
+  _FUNCTIONNAMEMATCHERSPEC._serialized_end=1619
+  _MATCHERSPEC._serialized_start=1621
+  _MATCHERSPEC._serialized_end=1706
+  _METHOD._serialized_start=1709
+  _METHOD._serialized_end=1928
+  _QUANTIZATIONSPEC._serialized_start=1930
+  _QUANTIZATIONSPEC._serialized_end=2050
+  _QUANTIZATIONSPECS._serialized_start=2052
+  _QUANTIZATIONSPECS._serialized_end=2128
+  _DEBUGGERCONFIG._serialized_start=2131
+  _DEBUGGERCONFIG._serialized_end=2429
+  _DEBUGGERCONFIG_DEBUGGERTYPE._serialized_start=2285
+  _DEBUGGERCONFIG_DEBUGGERTYPE._serialized_end=2429
+  _CALIBRATIONOPTIONS._serialized_start=2432
+  _CALIBRATIONOPTIONS._serialized_end=3214
+  _CALIBRATIONOPTIONS_CALIBRATIONPARAMETERS._serialized_start=2801
+  _CALIBRATIONOPTIONS_CALIBRATIONPARAMETERS._serialized_end=2890
+  _CALIBRATIONOPTIONS_CALIBRATIONMETHOD._serialized_start=2893
+  _CALIBRATIONOPTIONS_CALIBRATIONMETHOD._serialized_end=3214
+  _QUANTIZATIONCONFIG._serialized_start=3217
+  _QUANTIZATIONCONFIG._serialized_end=3788
+# @@protoc_insertion_point(module_scope)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/stablehlo/quantization_options_pb2.py

@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/quantization/stablehlo/quantization_options.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'\nJtensorflow/compiler/mlir/quantization/stablehlo/quantization_options.proto\x12\x16stablehlo.quantization\"^\n\x13QuantizationOptions\x12G\n\x13quantization_method\x18\x01 \x01(\x0b\x32*.stablehlo.quantization.QuantizationMethod\"\xdb\x01\n\x12QuantizationMethod\x12V\n\x1apreset_quantization_method\x18\x01 \x01(\x0b\x32\x30.stablehlo.quantization.PresetQuantizationMethodH\x00\x12V\n\x1a\x63ustom_quantization_method\x18\x02 \x01(\x0b\x32\x30.stablehlo.quantization.CustomQuantizationMethodH\x00\x42\x15\n\x13quantization_method\"\x92\x02\n\x18PresetQuantizationMethod\x12T\n\rpreset_method\x18\x01 \x01(\x0e\x32=.stablehlo.quantization.PresetQuantizationMethod.PresetMethod\"\x9f\x01\n\x0cPresetMethod\x12\x16\n\x12METHOD_UNSPECIFIED\x10\x00\x12\x0f\n\x0bWEIGHT_ONLY\x10\x01\x12,\n(POST_TRAINING_QUANTIZATION_DYNAMIC_RANGE\x10\x02\x12\x0b\n\x07\x46LOAT16\x10\x03\x12+\n\'POST_TRAINING_QUANTIZATION_STATIC_RANGE\x10\x04\"r\n\x18\x43ustomQuantizationMethod\x12V\n\x1bquantization_component_spec\x18\x01 \x03(\x0b\x32\x31.stablehlo.quantization.QuantizationComponentSpec\"\xc5\x05\n\x19QuantizationComponentSpec\x12g\n\x16quantization_component\x18\x01 \x01(\x0e\x32G.stablehlo.quantization.QuantizationComponentSpec.QuantizationComponent\x12M\n\tbit_width\x18\x02 \x01(\x0e\x32:.stablehlo.quantization.QuantizationComponentSpec.BitWidth\x12K\n\x08\x62it_type\x18\x03 \x01(\x0e\x32\x39.stablehlo.quantization.QuantizationComponentSpec.BitType\x12\x1b\n\x13\x65nable_narrow_range\x18\x04 \x01(\x08\x12\'\n\x1f\x65nable_per_channel_quantization\x18\x05 \x01(\x08\x12\x18\n\x10\x65nable_symmetric\x18\x06 \x01(\x08\"v\n\x15QuantizationComponent\x12\x19\n\x15\x43OMPONENT_UNSPECIFIED\x10\x00\x12\x18\n\x14\x43OMPONENT_ACTIVATION\x10\x01\x12\x14\n\x10\x43OMPONENT_WEIGHT\x10\x02\x12\x12\n\x0e\x43OMPONENT_BIAS\x10\x03\"k\n\x08\x42itWidth\x12\x19\n\x15\x42IT_WIDTH_UNSPECIFIED\x10\x00\x12\x0f\n\x0b\x42IT_WIDTH_4\x10\x01\x12\x0f\n\x0b\x42IT_WIDTH_8\x10\x02\x12\x10\n\x0c\x42IT_WIDTH_16\x10\x03\x12\x10\n\x0c\x42IT_WIDTH_32\x10\x04\"^\n\x07\x42itType\x12\x18\n\x14\x42IT_TYPE_UNSPECIFIED\x10\x00\x12\x10\n\x0c\x42IT_TYPE_INT\x10\x01\x12\x12\n\x0e\x42IT_TYPE_FLOAT\x10\x02\x12\x13\n\x0f\x42IT_TYPE_BFLOAT\x10\x03\x42\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.quantization.stablehlo.quantization_options_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _QUANTIZATIONOPTIONS._serialized_start=102
+  _QUANTIZATIONOPTIONS._serialized_end=196
+  _QUANTIZATIONMETHOD._serialized_start=199
+  _QUANTIZATIONMETHOD._serialized_end=418
+  _PRESETQUANTIZATIONMETHOD._serialized_start=421
+  _PRESETQUANTIZATIONMETHOD._serialized_end=695
+  _PRESETQUANTIZATIONMETHOD_PRESETMETHOD._serialized_start=536
+  _PRESETQUANTIZATIONMETHOD_PRESETMETHOD._serialized_end=695
+  _CUSTOMQUANTIZATIONMETHOD._serialized_start=697
+  _CUSTOMQUANTIZATIONMETHOD._serialized_end=811
+  _QUANTIZATIONCOMPONENTSPEC._serialized_start=814
+  _QUANTIZATIONCOMPONENTSPEC._serialized_end=1523
+  _QUANTIZATIONCOMPONENTSPEC_QUANTIZATIONCOMPONENT._serialized_start=1200
+  _QUANTIZATIONCOMPONENTSPEC_QUANTIZATIONCOMPONENT._serialized_end=1318
+  _QUANTIZATIONCOMPONENTSPEC_BITWIDTH._serialized_start=1320
+  _QUANTIZATIONCOMPONENTSPEC_BITWIDTH._serialized_end=1427
+  _QUANTIZATIONCOMPONENTSPEC_BITTYPE._serialized_start=1429
+  _QUANTIZATIONCOMPONENTSPEC_BITTYPE._serialized_end=1523
+# @@protoc_insertion_point(module_scope)

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

@@ -0,0 +1,395 @@
+# 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 CalibrationAlgorithm for calculating min and max values calculated by calibration method."""
+import abc
+import itertools
+import logging
+
+import numpy as np
+
+from tensorflow.compiler.mlir.quantization.stablehlo import quantization_config_pb2 as stablehlo_quant_config_pb2
+from tensorflow.compiler.mlir.quantization.tensorflow.calibrator import calibration_statistics_pb2 as calib_stats_pb2
+
+
+_CalibrationMethod = (
+    stablehlo_quant_config_pb2.CalibrationOptions.CalibrationMethod
+)
+_REGISTRY = {}
+
+
+def _implements(calib_method: _CalibrationMethod):
+  def decorator(cls):
+    assert calib_method not in _REGISTRY
+    _REGISTRY[calib_method] = cls
+    return cls
+
+  return decorator
+
+
+class _CalibrationAlgorithmBase(abc.ABC):
+  """Abstract base class for calibration algorithm."""
+
+  def __init__(
+      self,
+      statistics: calib_stats_pb2.CalibrationStatistics,
+      calib_opts: stablehlo_quant_config_pb2.CalibrationOptions,
+  ):
+    self._statistics = statistics
+    self._calib_opts = calib_opts
+
+  @abc.abstractmethod
+  def get_min_max_value(self) -> tuple[float, float]:
+    pass
+
+
+class _HistogramCalibrationAlgorithmBase(_CalibrationAlgorithmBase):
+  """Base class for histogram calibrators."""
+
+  def __init__(
+      self,
+      statistics: calib_stats_pb2.CalibrationStatistics,
+      calib_opts: stablehlo_quant_config_pb2.CalibrationOptions,
+  ):
+    """Builds histogram using statistics.histogram_statistics.
+
+    lower_bound                                    hist_mid
+         v                                            v
+         |=========|=========|=========|=========|=========|
+                    bin width
+
+    Args:
+      statistics: Collected calibration statistics.
+      calib_opts: Calibration options used for calculating min and max.
+    """
+    super().__init__(statistics, calib_opts)
+    hist_stats = statistics.histogram_statistics
+    self._bin_width = hist_stats.bin_width
+    self._lower_bound = hist_stats.lower_bound
+    self._hist_freq = np.array(hist_stats.hist_freq)
+    self._num_bins = len(self._hist_freq)
+    self._num_bits = 8
+    # i-th bin has a range [bins[i], bins[i + 1]).
+    # bins[i] = lower_bound + i * bin_width
+    # bins[i + 1] = lower_bound + (i + 1) * bin_width
+    # So hist_mids[i] = (lower_bound + bin_width / 2) + bin_width * i
+    first_mid = self._lower_bound + self._bin_width / 2
+    last_mid = first_mid + (self._num_bins - 1) * self._bin_width
+    self._hist_mids = np.linspace(first_mid, last_mid, self._num_bins)
+
+  def _get_dequantized_hist_mids_after_quantize(
+      self, quant_min: float, quant_max: float
+  ) -> np.ndarray:
+    """Quantizes and dequantizes hist_mids using quant_min and quant_max.
+
+    Quantization converts the range of numbers from [quant_min, quant_max] to
+    [0, 2^num_bits - 1]. Values less than quant_min are converted to 0, and
+    values greater than quant_max are converted to 2^num_bits - 1.
+
+    The histogram represents the distribution of the data, and our goal is to
+    find the quant_min and quant_max that best describe this distribution. To do
+    this, we quantize hist_mids using quant_min and quant_max and dequantize
+    them again. Then the difference between hist_mids and dequantized hist_mids
+    equates to quantization error when using quant_min and quant_max.
+
+
+    Args:
+      quant_min: The minimum real value that can be represented by a quantized
+        value.
+      quant_max: The maximum real value that can be represented by a quantized
+        value.
+
+    Returns:
+      dequantized hist_mids after quantizing by quant_min and quant_max
+    """
+    maxbound = 2**self._num_bits - 1
+    minbound = 0
+    scale = (quant_max - quant_min) / maxbound
+    zero_point = -quant_min / scale
+
+    # Limit the range of zero_point and scale in case (quant_max - quant_min)
+    # is unusually small.
+    if abs(zero_point) > 9e9:
+      zero_point = 9e9
+    if abs(scale) < 1e-9:
+      scale = 1e-9
+
+    zero_point = round(zero_point)
+    quantized_hist_mids = np.clip(
+        np.round(self._hist_mids / scale) + zero_point, minbound, maxbound
+    )
+    dequantized_hist_mids = scale * (quantized_hist_mids - zero_point)
+    return dequantized_hist_mids
+
+  def _get_weighted_mean_squared_error(
+      self, quant_min, quant_max
+  ) -> tuple[float, float, float]:
+    """Gets mean squared error between hist_mids and dequantized hist_mids.
+
+    Quantization converts the range of numbers from [quant_min, quant_max] to
+    [0, 2^num_bits - 1]. Values less than quant_min are converted to 0, and
+    values greater than quant_max are converted to 2^num_bits - 1.
+
+    Args:
+      quant_min: The minimum real value that can be represented by a quantized
+        value.
+      quant_max: The maximum real value that can be represented by a quantized
+        value.
+
+    Returns:
+      (error, quant_min, quant_max): Tuple of weighted mean squared error.
+      error = (hist_mids - dequantized_hist_mids)**2 * hist_freq
+    """
+    dequantized_hist_mids = self._get_dequantized_hist_mids_after_quantize(
+        quant_min, quant_max
+    )
+    squared_error = (self._hist_mids - dequantized_hist_mids) ** 2
+    weighted_error = np.sum(squared_error * self._hist_freq)
+    return (weighted_error, quant_min, quant_max)
+
+  def _get_min_max_value_by_expanding_range(
+      self, start_idx: int
+  ) -> tuple[float, float]:
+    """Starting from start_idx, expand left and right alternately to find the min value of mse loss.
+
+    Args:
+      start_idx: Index to start quantization.
+
+    Returns:
+      (min_value, max_value): Min and max calculated.
+    """
+    # Tuple of (mse_error, quant_min, quant_max).
+    mse_min = (float('inf'), float('inf'), float('inf'))
+    left, right = start_idx, start_idx
+
+    # If this value is true, it moves left, otherwise it moves right.
+    move_left = True
+    while not (left == 0 and right == self._num_bins - 1):
+      # Decrease left if right can't be moved or move_left is true.
+      if (move_left and left > 0) or (right == self._num_bins - 1):
+        left = max(left - 1, 0)
+      # Else increase right.
+      else:
+        right = min(right + 1, self._num_bins - 1)
+      # Toogle the move_left.
+      move_left = not move_left
+      quant_min, quant_max = self._hist_mids[left], self._hist_mids[right]
+      mse_tuple = self._get_weighted_mean_squared_error(quant_min, quant_max)
+      mse_min = min(mse_tuple, mse_min)
+    # Extract (quant_min, quant_max) from (mse_error, quant_min, quant_max).
+    min_value, max_value = mse_min[1], mse_min[2]
+    return min_value, max_value
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_MIN_MAX)
+class _MinMax(_CalibrationAlgorithmBase):
+  """MinMaxCalibrationAlgorithm for calculating min and max values of calibration result.
+
+  MinMax calibration calculates the global min and global max values.
+
+  global min = min of given sample inputs
+  global max = max of given sample inputs
+  """
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Calculates the global min and max values.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using MinMax
+    """
+    return (
+        self._statistics.min_max_statistics.global_min,
+        self._statistics.min_max_statistics.global_max,
+    )
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_AVERAGE_MIN_MAX)
+class _AverageMinMax(_CalibrationAlgorithmBase):
+  """AverageMinMaxCalibrationAlgorithm for calculating min and max values of calibration result.
+
+  AverageMinMax calibration calculates the average of min and max values.
+  average of min = sum of min values / number of samples
+  average of max = sum of max values / number of samples
+  """
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Calculates the average of min and max values.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using AverageMinMax
+
+    Raises:
+      ValueError: num_samples is 0.
+    """
+    average_min_max_statistics = self._statistics.average_min_max_statistics
+    # num_samples is guaranteed to be larger than 0 because
+    # get_statistics_from_calibrator throws an exception if num_samples == 0.
+    num_samples = average_min_max_statistics.num_samples
+    if num_samples == 0:
+      raise ValueError(
+          'num_samples must not be 0 when calibration method is'
+          f' AverageMinMax: {self._calib_opts}'
+      )
+    min_value, max_value = (
+        average_min_max_statistics.min_sum / num_samples,
+        average_min_max_statistics.max_sum / num_samples,
+    )
+
+    return min_value, max_value
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_PERCENTILE)
+class _HistogramPercentile(_HistogramCalibrationAlgorithmBase):
+  """HistogramPercentile for calculating min and max values of calibration result."""
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Calculates min and max from statistics using calibration options.
+
+    A "percentile" is a statistical concept that represents the value below
+    which a given percentage of data falls in a dataset. It involves sorting the
+    data from smallest to largest and then finding the value at a specified
+    percentage position. For example, the 0.01 percentile represents the value
+    in a given data set that corresponds to the lowest 0.01% of the data.
+
+    HistogramPercentile calibration uses min_percentile and max_percentile to
+    find min and max.
+
+    min_percentile and max_percentile must be in range [0, 100].
+    min_percentile is 0.001 by default.
+    max_percentile is 99.999 by default.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using HistogramPercentile
+    """
+    total_freq = sum(self._hist_freq)
+    # hist_freq_cumsum is dividing cumulative sum of hist_freq by total_freq
+    # hist_freq_cumsum's value is in range [0, 1] by its definition
+    hist_freq_cumsum = np.cumsum(self._hist_freq) / total_freq
+
+    # min_percentile and max_percentile are converted from [0, 100] to [0, 1].
+    min_quantile, max_quantile = (
+        self._calib_opts.calibration_parameters.min_percentile / 100.0,
+        self._calib_opts.calibration_parameters.max_percentile / 100.0,
+    )
+
+    # Get index of min/max quantile.
+    min_quantile_idx, max_quantile_idx = (
+        np.searchsorted(hist_freq_cumsum, min_quantile, side='right'),
+        np.searchsorted(hist_freq_cumsum, max_quantile, side='left'),
+    )
+
+    # Get value of min/max quantile index.
+    min_value, max_value = (
+        self._hist_mids[min_quantile_idx],
+        self._hist_mids[max_quantile_idx],
+    )
+
+    return min_value, max_value
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_BRUTEFORCE)
+class _HistogramMseBruteforce(_HistogramCalibrationAlgorithmBase):
+  """HistogramMseBruteforce for calculating min and max values of calibration result."""
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Finds the optimal quant_min and quant_max by testing all possible cases.
+
+    It guarantees optimal quant_min and quant_max for the representative
+    dataset, but not for the test dataset.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using
+      HistogramMseBruteforce.
+    """
+    if self._num_bins > 512:
+      logging.warning(
+          'num_bins=%d is too large. The HISTOGRAM_MSE_BRUTEFORCE method tests'
+          ' all histogram mid value pairs, so it may take a long time.',
+          self._num_bins,
+      )
+    # Tuple of (mse_error, quant_min, quant_max).
+    mse_min = (float('inf'), float('inf'), float('inf'))
+
+    # Calculate the error for all hist_mid pairs.
+    for left, right in itertools.combinations(range(self._num_bins), 2):
+      quant_min, quant_max = self._hist_mids[left], self._hist_mids[right]
+      mse_tuple = self._get_weighted_mean_squared_error(quant_min, quant_max)
+      mse_min = min(mse_tuple, mse_min)
+    min_value, max_value = mse_min[1], mse_min[2]
+
+    return min_value, max_value
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_MAX_FREQUENCY)
+class _HistogramMseMaxFrequency(_HistogramCalibrationAlgorithmBase):
+  """HistogramMseMaxFrequency for calculating min and max values of calibration result."""
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Finds min and max starting from the index of the max frequency.
+
+     The HistogramMseMaxFrequency method starts from the bin with the highest
+     frequency and expands the range to both sides. This performs well when data
+     is well spread on both sides of the max frequency.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using method to expand the
+      range based on max frequency.
+    """
+    # Find the index of max frequency.
+    freq_max_idx = np.argmax(self._hist_freq)
+    return self._get_min_max_value_by_expanding_range(freq_max_idx)
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_SYMMETRIC)
+class _HistogramMseSymmetric(_HistogramCalibrationAlgorithmBase):
+  """HistogramMseSymmetric for calculating min and max values of calibration result."""
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Finds min and max starting from the center index.
+
+    The HistogramMseSymmetric method starts from the center bin and expands the
+    range to both sides. This works better when the data is well-centered.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using the method starting
+      from center and expanding.
+    """
+
+    # This function is currently only called in this method, but will be used in
+    # other methods in the future.
+    return self._get_min_max_value_by_expanding_range(self._num_bins // 2)
+
+
+def get_min_max_value(
+    statistics: calib_stats_pb2.CalibrationStatistics,
+    calib_opts: stablehlo_quant_config_pb2.CalibrationOptions,
+) -> tuple[float, float]:
+  """Calculates min and max from statistics using calibration options.
+
+  Args:
+    statistics: Collected calibration statistics.
+    calib_opts: Calibration options used for calculating min and max.
+
+  Returns:
+    (min_value, max_value): Min and max calculated using calib_opts.
+
+  Raises:
+    ValueError: Unsupported calibration method is given.
+  """
+  calib_method = calib_opts.calibration_method
+  if calib_method not in _REGISTRY:
+    raise ValueError(f'Unsupported calibration method: {calib_method}')
+
+  calibration_algorithm = _REGISTRY[calib_method](statistics, calib_opts)
+  return calibration_algorithm.get_min_max_value()

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics_pb2.py

@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics.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'\nXtensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics.proto\x12\x15tensorflow.calibrator\"\x9c\x04\n\x15\x43\x61librationStatistics\x12Y\n\x12min_max_statistics\x18\x01 \x01(\x0b\x32=.tensorflow.calibrator.CalibrationStatistics.MinMaxStatistics\x12h\n\x1a\x61verage_min_max_statistics\x18\x02 \x01(\x0b\x32\x44.tensorflow.calibrator.CalibrationStatistics.AverageMinMaxStatistics\x12^\n\x14histogram_statistics\x18\x03 \x01(\x0b\x32@.tensorflow.calibrator.CalibrationStatistics.HistogramStatistics\x1a:\n\x10MinMaxStatistics\x12\x12\n\nglobal_min\x18\x01 \x01(\x02\x12\x12\n\nglobal_max\x18\x02 \x01(\x02\x1aP\n\x17\x41verageMinMaxStatistics\x12\x0f\n\x07min_sum\x18\x01 \x01(\x02\x12\x0f\n\x07max_sum\x18\x02 \x01(\x02\x12\x13\n\x0bnum_samples\x18\x03 \x01(\x05\x1aP\n\x13HistogramStatistics\x12\x11\n\tbin_width\x18\x01 \x01(\x02\x12\x13\n\x0blower_bound\x18\x02 \x01(\x02\x12\x11\n\thist_freq\x18\x03 \x03(\x02\"\xd0\x01\n\x18\x43\x61librationStatisticsMap\x12S\n\nstatistics\x18\x01 \x03(\x0b\x32?.tensorflow.calibrator.CalibrationStatisticsMap.StatisticsEntry\x1a_\n\x0fStatisticsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12;\n\x05value\x18\x02 \x01(\x0b\x32,.tensorflow.calibrator.CalibrationStatistics:\x02\x38\x01\x42\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.quantization.tensorflow.calibrator.calibration_statistics_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _CALIBRATIONSTATISTICSMAP_STATISTICSENTRY._options = None
+  _CALIBRATIONSTATISTICSMAP_STATISTICSENTRY._serialized_options = b'8\001'
+  _CALIBRATIONSTATISTICS._serialized_start=116
+  _CALIBRATIONSTATISTICS._serialized_end=656
+  _CALIBRATIONSTATISTICS_MINMAXSTATISTICS._serialized_start=434
+  _CALIBRATIONSTATISTICS_MINMAXSTATISTICS._serialized_end=492
+  _CALIBRATIONSTATISTICS_AVERAGEMINMAXSTATISTICS._serialized_start=494
+  _CALIBRATIONSTATISTICS_AVERAGEMINMAXSTATISTICS._serialized_end=574
+  _CALIBRATIONSTATISTICS_HISTOGRAMSTATISTICS._serialized_start=576
+  _CALIBRATIONSTATISTICS_HISTOGRAMSTATISTICS._serialized_end=656
+  _CALIBRATIONSTATISTICSMAP._serialized_start=659
+  _CALIBRATIONSTATISTICSMAP._serialized_end=867
+  _CALIBRATIONSTATISTICSMAP_STATISTICSENTRY._serialized_start=772
+  _CALIBRATIONSTATISTICSMAP_STATISTICSENTRY._serialized_end=867
+# @@protoc_insertion_point(module_scope)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/exported_model_pb2.py

@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/quantization/tensorflow/exported_model.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 tensorflow.core.framework import graph_pb2 as tensorflow_dot_core_dot_framework_dot_graph__pb2
+from tensorflow.core.protobuf import meta_graph_pb2 as tensorflow_dot_core_dot_protobuf_dot_meta__graph__pb2
+from tensorflow.core.protobuf import saver_pb2 as tensorflow_dot_core_dot_protobuf_dot_saver__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\nEtensorflow/compiler/mlir/quantization/tensorflow/exported_model.proto\x12\x17tensorflow.quantization\x1a%tensorflow/core/framework/graph.proto\x1a)tensorflow/core/protobuf/meta_graph.proto\x1a$tensorflow/core/protobuf/saver.proto\"\xbe\x03\n\rExportedModel\x12\'\n\tgraph_def\x18\x01 \x01(\x0b\x32\x14.tensorflow.GraphDef\x12\x16\n\x0einit_node_name\x18\x02 \x01(\t\x12\x16\n\x0e\x63heckpoint_dir\x18\x05 \x01(\t\x12U\n\x10\x66unction_aliases\x18\x06 \x03(\x0b\x32;.tensorflow.quantization.ExportedModel.FunctionAliasesEntry\x12\x31\n\x0f\x61sset_file_defs\x18\x08 \x03(\x0b\x32\x18.tensorflow.AssetFileDef\x12\'\n\tsaver_def\x18\n \x01(\x0b\x32\x14.tensorflow.SaverDef\x1a\x36\n\x14\x46unctionAliasesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01J\x04\x08\x03\x10\x04J\x04\x08\x04\x10\x05J\x04\x08\x07\x10\x08J\x04\x08\t\x10\nR\x15variable_shared_namesR\x11restore_node_nameR\x0esave_node_nameR\x17\x66ile_prefix_tensor_nameB\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.quantization.tensorflow.exported_model_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _EXPORTEDMODEL_FUNCTIONALIASESENTRY._options = None
+  _EXPORTEDMODEL_FUNCTIONALIASESENTRY._serialized_options = b'8\001'
+  _EXPORTEDMODEL._serialized_start=219
+  _EXPORTEDMODEL._serialized_end=665
+  _EXPORTEDMODEL_FUNCTIONALIASESENTRY._serialized_start=504
+  _EXPORTEDMODEL_FUNCTIONALIASESENTRY._serialized_end=558
+# @@protoc_insertion_point(module_scope)

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

@@ -0,0 +1,72 @@
+# 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
+
+from tensorflow.compiler.mlir.quantization.tensorflow.calibrator import calibration_statistics_pb2
+from tensorflow.compiler.mlir.quantization.tensorflow.python import py_function_lib
+from tensorflow.compiler.mlir.quantization.tensorflow.python import representative_dataset as rd
+
+# LINT.IfChange(quantize_qat_model)
+def quantize_qat_model(
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    quantization_options_serialized: bytes,
+    *,
+    signature_keys: list[str],
+    signature_def_map_serialized: dict[str, bytes],
+    py_function_library: py_function_lib.PyFunctionLibrary,
+) -> Any: ...  # Status
+
+# LINT.ThenChange()
+
+# LINT.IfChange(quantize_ptq_dynamic_range)
+def quantize_ptq_dynamic_range(
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    quantization_options_serialized: bytes,
+    *,
+    signature_keys: list[str],
+    signature_def_map_serialized: dict[str, bytes],
+    py_function_library: py_function_lib.PyFunctionLibrary,
+) -> Any: ...  # Status
+
+# LINT.ThenChange()
+
+# LINT.IfChange(quantize_weight_only)
+def quantize_weight_only(
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    quantization_options_serialized: bytes,
+    *,
+    signature_def_map_serialized: dict[str, bytes],
+    py_function_library: py_function_lib.PyFunctionLibrary,
+) -> Any: ...  # Status
+
+# LINT.ThenChange()
+
+# LINT.IfChange(quantize_ptq_static_range)
+def quantize_ptq_static_range(
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    quantization_options_serialized: bytes,
+    *,
+    signature_keys: list[str],
+    signature_def_map_serialized: dict[str, bytes],
+    py_function_library: py_function_lib.PyFunctionLibrary,
+    # Value type: RepresentativeDatasetFile.
+    representative_dataset_file_map_serialized: dict[str, bytes],
+) -> Any: ...  # Status
+
+# LINT.ThenChange()

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

@@ -0,0 +1,926 @@
+# 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.
+# ==============================================================================
+"""Defines TF Quantization API from SavedModel to SavedModel."""
+
+import tempfile
+from typing import Mapping, Optional
+
+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 quantization_options_pb2 as quant_opts_pb2
+from tensorflow.compiler.mlir.quantization.tensorflow.python import py_function_lib
+from tensorflow.compiler.mlir.quantization.tensorflow.python import pywrap_quantize_model
+from tensorflow.compiler.mlir.quantization.tensorflow.python import representative_dataset as repr_dataset
+from tensorflow.compiler.mlir.quantization.tensorflow.python import save_model
+from tensorflow.core.protobuf import meta_graph_pb2
+from tensorflow.python.lib.io import file_io
+from tensorflow.python.saved_model import load as saved_model_load
+from tensorflow.python.saved_model import loader_impl as saved_model_loader
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.saved_model import tag_constants
+from tensorflow.python.trackable import autotrackable
+from tensorflow.python.util import tf_export
+
+# Type aliases for quant_opts_pb2 messages.
+_QuantizationOptions = tf_export.tf_export(
+    'quantization.experimental.QuantizationOptions'
+)(quant_opts_pb2.QuantizationOptions)
+
+_QuantizationMethod = tf_export.tf_export(
+    'quantization.experimental.QuantizationMethod'
+)(quant_opts_pb2.QuantizationMethod)
+
+_QuantizationComponentSpec = tf_export.tf_export(
+    'quantization.experimental.QuantizationComponentSpec'
+)(quant_opts_pb2.QuantizationComponentSpec)
+
+_UnitWiseQuantizationSpec = tf_export.tf_export(
+    'quantization.experimental.UnitWiseQuantizationSpec'
+)(quant_opts_pb2.UnitWiseQuantizationSpec)
+
+_PresetMethod = _QuantizationMethod.PresetMethod
+_CalibrationMethod = (
+    stablehlo_quant_config_pb2.CalibrationOptions.CalibrationMethod
+)
+
+_QuantizationComponent = _QuantizationComponentSpec.QuantizationComponent
+_TensorType = _QuantizationComponentSpec.TensorType
+
+_RepresentativeDatasetFile = quant_opts_pb2.RepresentativeDatasetFile
+
+# Mapping of signature def key -> SignatureDef.
+_SignatureDefMap = Mapping[str, meta_graph_pb2.SignatureDef]
+
+# Default minimum number of elements in the weights for them to be quantized
+# during dynamic range quantization (DRQ) and weight-only quantization.
+_DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS = 1024
+
+
+def _is_qat_saved_model(saved_model_path: str):
+  """Checks if the SavedModel is QAT-enabled by looking for 'FakeQuant' ops."""
+  saved_model_proto = saved_model_loader.parse_saved_model(saved_model_path)
+  for meta_graph in saved_model_proto.meta_graphs:
+    if any(
+        node.op.startswith('FakeQuant') for node in meta_graph.graph_def.node
+    ):
+      return True
+    for function in meta_graph.graph_def.library.function:
+      if any(node.op.startswith('FakeQuant') for node in function.node_def):
+        return True
+  return False
+
+
+def _serialize_signature_def_map(
+    signature_def_map: _SignatureDefMap,
+) -> dict[str, bytes]:
+  """Serializes SignatureDef values in `signature_def_map`.
+
+  Args:
+    signature_def_map: Signature key -> SignatureDef mapping.
+
+  Returns:
+    Signature def map where the values (`SignatureDef`) are serialized.
+  """
+  signature_def_map_serialized = {}
+  for key, signature_def in signature_def_map.items():
+    signature_def_map_serialized[key] = signature_def.SerializeToString()
+
+  return signature_def_map_serialized
+
+
+def _save_representative_dataset(
+    representative_dataset: repr_dataset.RepresentativeDatasetOrMapping,
+    signature_def_map: _SignatureDefMap,
+) -> Mapping[str, _RepresentativeDatasetFile]:
+  """Saves the representative dataset to temporary TFRecord files.
+
+  Args:
+    representative_dataset: Representative dataset used for the calibration
+      step. Representative datasets should exist for each signature def key in
+      `signature_def_keys`.
+    signature_def_map: Signature def key -> SignatureDef mapping.
+
+  Returns:
+    A map from signature key to the saved representative dataset file.
+  """
+  if isinstance(representative_dataset, Mapping):
+    if set(signature_def_map.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_def_map.keys())}, '
+          f'representative dataset map: {set(representative_dataset.keys())}.'
+      )
+    representative_dataset_map = representative_dataset
+  elif len(signature_def_map.keys()) > 1:
+    raise ValueError(
+        'Representative dataset is not a mapping (got: '
+        f'{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.'
+    )
+  else:
+    representative_dataset_map = {
+        list(signature_def_map.keys())[0]: representative_dataset,
+    }
+
+  # Save the representative dataset to temporary TFRecord files.
+  path_map = {}
+  expected_input_key_map = {}
+  for signature_key, signature_def in signature_def_map.items():
+    # Filepath is the second return value of mkstemp.
+    _, path_map[signature_key] = tempfile.mkstemp(
+        suffix='.tfrecord', prefix=signature_key
+    )
+    expected_input_key_map[signature_key] = signature_def.inputs.keys()
+
+  return repr_dataset.TfRecordRepresentativeDatasetSaver(
+      path_map=path_map,
+      expected_input_key_map=expected_input_key_map,
+  ).save(representative_dataset_map)
+
+
+def _run_static_range_qat(
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    quant_opts: _QuantizationOptions,
+    signature_def_map: _SignatureDefMap,
+) -> None:
+  """Runs static-range quantization for a Quantization-Aware Trained model.
+
+  Runs the quantization for a model trained using QAT.
+
+  Args:
+    src_saved_model_path: Path to the source SavedModel directory.
+    dst_saved_model_path: Path to the destination SavedModel directory.
+    quant_opts: Quantization options.
+    signature_def_map: Signature def key -> SignatureDef mapping.
+  """
+  logging.info('Running static-range quantization for QAT model.')
+
+  pywrap_quantize_model.quantize_qat_model(
+      src_saved_model_path,
+      dst_saved_model_path,
+      quantization_options_serialized=quant_opts.SerializeToString(),
+      signature_keys=list(quant_opts.signature_keys),
+      signature_def_map_serialized=_serialize_signature_def_map(
+          signature_def_map
+      ),
+      py_function_library=py_function_lib.PyFunctionLibrary(),
+  )
+
+
+def _run_static_range_ptq(
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    quant_opts: _QuantizationOptions,
+    representative_dataset: Mapping[str, _RepresentativeDatasetFile],
+    signature_def_map: _SignatureDefMap,
+) -> None:
+  """Runs static-range Post-Training Quantization.
+
+  Runs static-range PTQ for the model. Runs the calibration step with
+  `representative_dataset` to collect statistics required for quantization. This
+  produces the quantized GraphDef along with the SignatureDefs which might have
+  been modified according to the changes in the graph.
+
+  Args:
+    src_saved_model_path: Path to the source SavedModel directory.
+    dst_saved_model_path: Path to the destination SavedModel directory.
+    quant_opts: Quantization options.
+    representative_dataset: A map from signature key to the saved representative
+      dataset file.
+    signature_def_map: Signature def key -> SignatureDef mapping.
+
+  Raises:
+    ValueError if the graph doesn't contain a valid signature.
+  """
+  logging.info('Running static-range post-training quantization.')
+
+  signature_def_map_serialized = _serialize_signature_def_map(signature_def_map)
+
+  # `quantize_ptq_static_range` requires `RepresentativeDatasetFile`s to be
+  # serialized. Serialize the values to match the type.
+  dataset_file_map_serialized = {
+      signature_key: dataset_file.SerializeToString()
+      for signature_key, dataset_file in representative_dataset.items()
+  }
+  pywrap_quantize_model.quantize_ptq_static_range(
+      src_saved_model_path,
+      dst_saved_model_path,
+      quantization_options_serialized=quant_opts.SerializeToString(),
+      signature_keys=list(quant_opts.signature_keys),
+      signature_def_map_serialized=signature_def_map_serialized,
+      py_function_library=py_function_lib.PyFunctionLibrary(),
+      representative_dataset_file_map_serialized=dataset_file_map_serialized,
+  )
+
+
+def _static_range_quantize(
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    quantization_options: _QuantizationOptions,
+    representative_dataset: Optional[
+        repr_dataset.RepresentativeDatasetOrMapping
+    ] = None,
+) -> autotrackable.AutoTrackable:
+  """Quantizes the given SavedModel via static range quantization.
+
+  If the model is not trained with Quantization-Aware Training (QAT) technique,
+  it requires `representative_dataset` to collect statistics required for
+  quantization. If non-None `representative_dataset` is provided with a QAT
+  model input, `representative_dataset` will be ignored.
+
+  Args:
+    src_saved_model_path: Path to the saved model. When representative_dataset
+      is not provided, this should be a model trained with QAT.
+    dst_saved_model_path: The path to save the output SavedModel. The directory
+      will be overwritten if not empty.
+    quantization_options: QuantizationOptions proto describing quantization
+      related config.
+    representative_dataset: a generator that returns a dictionary in {input_key:
+      input_value} format or a tuple with signature key and a dictionary in
+      {input_key: input_value} format that feeds calibration data for quantizing
+      model. This should be provided when the model is not a QAT model.
+
+  Returns:
+    A SavedModel object with TF quantization applied.
+
+  Raises:
+    ValueError: when representative_dataset is not provided for non-QAT model.
+    RuntimeError: When a MetaGraphDef could not be found associated with `tags`
+      in the SavedModel.
+  """
+  logging.info(
+      'Running static range quantization on model: %s', src_saved_model_path
+  )
+  logging.info('QuantizationOptions: \n%s', quantization_options)
+
+  is_qat_saved_model_or_method_no_quantize = _is_qat_saved_model(
+      src_saved_model_path
+  ) or (
+      quantization_options.quantization_method.preset_method
+      == _QuantizationMethod.METHOD_NO_QUANTIZE
+  )
+  signature_def_map = save_model.get_signatures_from_saved_model(
+      src_saved_model_path,
+      quantization_options.signature_keys,
+      set(quantization_options.tags),
+  )
+
+  if (
+      representative_dataset is not None
+      and quantization_options.representative_datasets
+  ):
+    raise ValueError(
+        'Do not specify both the `representative_dataset` argument and'
+        ' the `representative_datasets` field in `QuantizationOptions`.'
+    )
+
+  saved_representative_dataset = quantization_options.representative_datasets
+  if representative_dataset is not None:
+    saved_representative_dataset = _save_representative_dataset(
+        representative_dataset, signature_def_map
+    )
+
+  # Checks if the model is from QAT or method is METHOD_NO_QUANTIZE.
+  if (
+      not saved_representative_dataset
+      and not is_qat_saved_model_or_method_no_quantize
+  ):
+    raise ValueError(
+        'When `representative_dataset` is not provided, the model should be '
+        'trained with quantization-aware training (QAT).'
+    )
+  if quantization_options.min_num_elements_for_weights > 0:
+    logging.warn(
+        'min_num_elements_for_weights is set but is not supported for the '
+        'Post-training static range quantization. '
+        'The flag is ignored.'
+    )
+
+  if is_qat_saved_model_or_method_no_quantize:
+    _run_static_range_qat(
+        src_saved_model_path,
+        dst_saved_model_path,
+        quantization_options,
+        signature_def_map,
+    )
+  else:
+    _run_static_range_ptq(
+        src_saved_model_path,
+        dst_saved_model_path,
+        quantization_options,
+        saved_representative_dataset,
+        signature_def_map,
+    )
+
+  return saved_model_load.load(dst_saved_model_path)
+
+
+def _dynamic_range_quantize(
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    quantization_options: _QuantizationOptions,
+) -> autotrackable.AutoTrackable:
+  """Quantizes the given SavedModel via post-training dynamic range quantization.
+
+  Args:
+    src_saved_model_path: Path to the saved model.
+    dst_saved_model_path: The path to save the output SavedModel. The directory
+      will be overwritten if not empty.
+    quantization_options: QuantizationOptions proto describing quantization
+      related config.
+
+  Returns:
+    A SavedModel object with TF quantization applied.
+
+  Raises:
+    ValueError: when the model is QAT model.
+  """
+  mode_str = 'dynamic-range quantization'
+  if _is_qat_saved_model(src_saved_model_path):
+    raise ValueError(
+        'The models trained with quantization-aware training (QAT) is not '
+        'supported for %s.' % mode_str
+    )
+
+  logging.info(
+      'Running post-training %s on model: %s', mode_str, src_saved_model_path
+  )
+  logging.info('QuantizationOptions: \n%s', quantization_options)
+
+  signature_def_map = save_model.get_signatures_from_saved_model(
+      src_saved_model_path,
+      quantization_options.signature_keys,
+      quantization_options.tags,
+  )
+
+  # Apply post-training dynamic range quantization to the model.
+  pywrap_quantize_model.quantize_ptq_dynamic_range(
+      src_saved_model_path,
+      dst_saved_model_path,
+      quantization_options_serialized=quantization_options.SerializeToString(),
+      signature_keys=list(quantization_options.signature_keys),
+      signature_def_map_serialized=_serialize_signature_def_map(
+          signature_def_map
+      ),
+      py_function_library=py_function_lib.PyFunctionLibrary(),
+  )
+
+  return saved_model_load.load(dst_saved_model_path)
+
+
+def _weight_only_quantize(
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    quantization_options: quant_opts_pb2.QuantizationOptions,
+) -> autotrackable.AutoTrackable:
+  """Quantizes the given SavedModel via weight-only quantization.
+
+  Args:
+    src_saved_model_path: Path to the saved model.
+    dst_saved_model_path: The path to save the output SavedModel. The directory
+      will be overwritten if not empty.
+    quantization_options: QuantizationOptions proto describing quantization
+      related config.
+
+  Returns:
+    A SavedModel object with TF quantization applied.
+
+  Raises:
+    ValueError: when the model is QAT model.
+  """
+  mode_str = 'weight-only quantization'
+
+  # QAT weight-only is not supported yet.
+  if _is_qat_saved_model(src_saved_model_path):
+    raise ValueError(
+        'The models trained with quantization-aware training (QAT) is not '
+        'supported for %s.' % mode_str
+    )
+
+  logging.info(
+      'Running post-training %s on model: %s', mode_str, src_saved_model_path
+  )
+  logging.info('QuantizationOptions: \n%s', quantization_options)
+
+  signature_def_map = save_model.get_signatures_from_saved_model(
+      src_saved_model_path,
+      list(quantization_options.signature_keys),
+      set(quantization_options.tags),
+  )
+
+  pywrap_quantize_model.quantize_weight_only(
+      src_saved_model_path,
+      dst_saved_model_path,
+      quantization_options_serialized=quantization_options.SerializeToString(),
+      signature_def_map_serialized=_serialize_signature_def_map(
+          signature_def_map
+      ),
+      py_function_library=py_function_lib.PyFunctionLibrary(),
+  )
+
+  return saved_model_load.load(dst_saved_model_path)
+
+
+def _verify_output_dir(output_dir: Optional[str], overwrite: bool) -> None:
+  """Verifies the output directory.
+
+  Raises an error if `output_dir` is not suitable for writing the output saved
+  model.
+
+  Args:
+    output_dir: Output directory.
+    overwrite: An option allowing to overwrite the existing output directory if
+      set to true. Does not actually create or modify the `output_dir` in this
+      function.
+
+  Raises:
+    FileExistsError: Iff `output_dir` is not empty and `overwrite` is false.
+  """
+  dir_not_empty = (
+      output_dir is not None
+      and file_io.file_exists_v2(output_dir)
+      and file_io.list_directory_v2(output_dir)
+  )
+
+  if dir_not_empty and not overwrite:
+    raise FileExistsError(
+        f'Output directory already exists: {output_dir} . '
+        'Please set overwrite_output_directory to true to '
+        'overwrite the existing directory.'
+    )
+
+
+def _populate_quantization_component_spec(
+    quant_method: _QuantizationMethod,
+) -> None:
+  """Populates default values for QuantizationComponentSpec.
+
+  Args:
+    quant_method: The quantization method to be updated.
+  """
+  # Make sure creating one spec per component.
+  updated_component_spec = dict()
+
+  # Populate default configuration.
+  if (
+      quant_method.preset_method == _PresetMethod.METHOD_STATIC_RANGE_INT8
+      or quant_method.preset_method == _PresetMethod.METHOD_DYNAMIC_RANGE_INT8
+  ):
+    updated_component_spec[_QuantizationComponent.COMPONENT_ACTIVATION] = (
+        _QuantizationComponentSpec(
+            quantization_component=_QuantizationComponent.COMPONENT_ACTIVATION,
+            tensor_type=_TensorType.TENSORTYPE_INT_8,
+        )
+    )
+    updated_component_spec[_QuantizationComponent.COMPONENT_WEIGHT] = (
+        _QuantizationComponentSpec(
+            quantization_component=_QuantizationComponent.COMPONENT_WEIGHT,
+            tensor_type=_TensorType.TENSORTYPE_INT_8,
+        )
+    )
+    updated_component_spec[_QuantizationComponent.COMPONENT_BIAS] = (
+        _QuantizationComponentSpec(
+            quantization_component=_QuantizationComponent.COMPONENT_BIAS,
+            tensor_type=_TensorType.TENSORTYPE_INT_32,
+        )
+    )
+  elif (
+      quant_method.preset_method
+      == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+  ):
+    updated_component_spec[_QuantizationComponent.COMPONENT_WEIGHT] = (
+        _QuantizationComponentSpec(
+            quantization_component=_QuantizationComponent.COMPONENT_WEIGHT,
+            tensor_type=_TensorType.TENSORTYPE_INT_8,
+        )
+    )
+
+  # Override if quantization_component_spec is specified.
+  if quant_method.quantization_component_specs:
+    # Check if the component spec is supported configuration in TF-Quant.
+    for component_spec in quant_method.quantization_component_specs:
+      if component_spec.quantization_component in [
+          _QuantizationComponent.COMPONENT_WEIGHT,
+          _QuantizationComponent.COMPONENT_ACTIVATION,
+      ]:
+        if component_spec.tensor_type != _TensorType.TENSORTYPE_INT_8:
+          raise ValueError(
+              'Only int8 precision is supported for input operands.'
+          )
+      else:
+        if component_spec.tensor_type != _TensorType.TENSORTYPE_INT_32:
+          raise ValueError('Only int32 precision is supported for bias.')
+      # Update with the custom spec.
+      updated_component_spec[component_spec.quantization_component] = (
+          component_spec
+      )
+
+  # Update the componet spec
+  del quant_method.quantization_component_specs[:]
+  quant_method.quantization_component_specs.extend(
+      updated_component_spec.values()
+  )
+
+  if (
+      quant_method.preset_method == _PresetMethod.METHOD_STATIC_RANGE_INT8
+      or quant_method.preset_method == _PresetMethod.METHOD_DYNAMIC_RANGE_INT8
+  ) and (len(quant_method.quantization_component_specs) != 3):
+    raise ValueError('Only 3 components are needed for', quant_method)
+  elif (
+      quant_method.preset_method
+      == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+  ) and len(quant_method.quantization_component_specs) != 1:
+    raise ValueError('At least one component spec needs to be specified.')
+
+
+def _populate_unitwise_quantization_specs(
+    quantization_options: _QuantizationOptions,
+) -> None:
+  """Verifies and pupulates unitwise quantization specs."""
+  if not quantization_options.unit_wise_quantization_specs:
+    return
+
+  sorted_top_level_component_specs = sorted(
+      quantization_options.quantization_method.quantization_component_specs,
+      key=lambda x: x.quantization_component,
+  )
+
+  for unitwise_spec in quantization_options.unit_wise_quantization_specs:
+    if not unitwise_spec.unit:
+      raise ValueError(
+          'UnitWiseQuantizationSpec must contain at least one unit.'
+      )
+
+    for unit in unitwise_spec.unit:
+      if not unit.op_type and not unit.node_name:
+        raise ValueError('Either `op_type` or `node_name` must be specified.')
+
+    _populate_quantization_component_spec(unitwise_spec.quantization_method)
+
+    component_specs = (
+        unitwise_spec.quantization_method.quantization_component_specs
+    )
+    if component_specs and (
+        sorted_top_level_component_specs
+        != sorted(component_specs, key=lambda x: x.quantization_component)
+    ):
+      raise ValueError(
+          'Currently unit-wise quantization spec only supports NO_QUANTIZE and'
+          ' same quantization method as the top-level `quantization_method`'
+      )
+
+
+def _populate_calibration_options(
+    quantization_options: quant_opts_pb2.QuantizationOptions,
+):
+  """Populates default values for CalibrationOptions.
+
+  Args:
+    quantization_options: An instance of QuantizationOptions with a field
+      specifying CalibrationOptions
+  """
+
+  calib_opts = quantization_options.calibration_options
+  if (
+      calib_opts.calibration_method
+      == _CalibrationMethod.CALIBRATION_METHOD_UNSPECIFIED
+  ):
+    calib_opts.calibration_method = (
+        _CalibrationMethod.CALIBRATION_METHOD_MIN_MAX
+    )
+  elif (
+      calib_opts.calibration_method
+      == _CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_PERCENTILE
+  ):
+    if not calib_opts.calibration_parameters.num_bins:
+      calib_opts.calibration_parameters.num_bins = 512
+    if not calib_opts.calibration_parameters.min_percentile:
+      calib_opts.calibration_parameters.min_percentile = 0.001
+    if not calib_opts.calibration_parameters.max_percentile:
+      calib_opts.calibration_parameters.max_percentile = 99.999
+  # Check the activation_tensor_type of HISTOGRAM_MSE methods.
+  elif calib_opts.calibration_method in [
+      _CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_BRUTEFORCE,
+      _CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_MAX_FREQUENCY,
+      _CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_SYMMETRIC,
+  ]:
+    activation_tensor_type = (
+        quantization_options.quantization_method.quantization_component_specs[
+            _QuantizationComponent.COMPONENT_ACTIVATION
+        ].tensor_type
+    )
+    # Unlike the HISTOGRAM_PERCENTILE method, the HISTOGRAM_MSE method uses
+    # num_bits because it actually quantizes and dequantizes values.
+    if activation_tensor_type != _TensorType.TENSORTYPE_INT_8:
+      raise ValueError(
+          'Only TENSORTYPE_INT_8 is supported for HISTOGRAM_MSE calibration'
+          f' methods. calibration_method={calib_opts.calibration_method}'
+      )
+
+    if not calib_opts.calibration_parameters.num_bins:
+      calib_opts.calibration_parameters.num_bins = 512
+
+  if calib_opts.calibration_data_dir:
+    save_model.create_empty_output_dir(
+        calib_opts.calibration_data_dir,
+        overwrite=calib_opts.force_regenerate_calibration_data,
+    )
+
+
+def _populate_quantization_options_default_values(
+    quantization_options: _QuantizationOptions,
+) -> None:
+  """Populates default values for QuantizationOptions.
+
+  Populates unspecified or unset fields of QuantizationOptions with the default
+  values.
+
+  * If `op_set` is unspecified, it defaults to `OpSet.XLA`.
+  * If `freeze_all_variables` is not set, it defaults to `True`.
+  * Check if configurations are set correctly:
+    - Per-channel quantization is supported for Uniform Quantized opset only.
+
+  Args:
+    quantization_options: An instance of QuantizationOptions.
+  """
+  if quantization_options.op_set == quant_opts_pb2.OpSet.OP_SET_UNSPECIFIED:
+    quantization_options.op_set = quant_opts_pb2.OpSet.XLA
+
+  if not quantization_options.tags:
+    quantization_options.tags.append(tag_constants.SERVING)
+
+  if not quantization_options.signature_keys:
+    quantization_options.signature_keys.append(
+        signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
+    )
+
+  if not quantization_options.HasField('freeze_all_variables'):
+    quantization_options.freeze_all_variables = True
+
+  if quantization_options.enable_legacy_weight_only:
+    raise ValueError(
+        'Legacy weight-only is deprecated. Use weight-only quantization method.'
+    )
+
+  # Converter assumes options are specified. So set SRQ explicitly.
+  if (
+      quantization_options.quantization_method.preset_method
+      == _PresetMethod.METHOD_UNSPECIFIED
+  ):
+    logging.debug(
+        '"preset_method" for QuantizationMethod is not specified.'
+        'Static range quantization is used by default.'
+    )
+    quantization_options.quantization_method.preset_method = (
+        _PresetMethod.METHOD_STATIC_RANGE_INT8
+    )
+
+  # Check default quantization option values for weight-only quantization.
+  # TODO(b/242805842): Find good minimum_elements_for_weights number for server.
+  # please also update default value in tflite converter:
+  # tensorflow/compiler/mlir/lite/tf_to_tfl_flatbuffer.cc;l=201
+  if quantization_options.min_num_elements_for_weights == 0:
+    quantization_options.min_num_elements_for_weights = (
+        _DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS
+    )
+    logging.warning(
+        (
+            'QuantizationOptions.min_num_elements_for_weights is not set (0).'
+            ' Setting to the default value: %d.'
+        ),
+        _DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS,
+    )
+
+  if not quantization_options.HasField('enable_per_channel_quantization'):
+    quantization_options.enable_per_channel_quantization = False
+
+  if quantization_options.enable_per_channel_quantization and not (
+      (
+          quantization_options.op_set == quant_opts_pb2.OpSet.UNIFORM_QUANTIZED
+          or quantization_options.quantization_method.preset_method
+          == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+      )
+      or (
+          quantization_options.op_set
+          in (quant_opts_pb2.OpSet.XLA, quant_opts_pb2.OpSet.STABLEHLO)
+          and quantization_options.quantization_method.preset_method
+          == _PresetMethod.METHOD_STATIC_RANGE_INT8
+      )
+  ):
+    raise ValueError(
+        'Currently, per-channel quantization is supported for Uniform Quantized'
+        ' opset, weight only quantization, or XLA/StableHLO opset with static'
+        ' range quantization.'
+    )
+
+  if (
+      quantization_options.quantization_method.preset_method
+      == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+      and (
+          quantization_options.op_set == quant_opts_pb2.OpSet.UNIFORM_QUANTIZED
+          or quantization_options.op_set == quant_opts_pb2.OpSet.TF
+      )
+  ):
+    raise ValueError('TF/Uniform quantized opset does not support weight-only.')
+
+  if (quantization_options.op_set == quant_opts_pb2.OpSet.STABLEHLO) and (
+      quantization_options.quantization_method.preset_method
+      != _PresetMethod.METHOD_STATIC_RANGE_INT8
+      and quantization_options.quantization_method.preset_method
+      != _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+  ):
+    raise ValueError(
+        'StableHLO quantized opset currently only supports static range'
+        ' quantization and weight-only quantizationvia TF Quantizer.'
+    )
+
+  # Set `force_graph_mode_calibration` to True to avoid skipping op execution,
+  # which are not connected to return ops, during calibration execution.
+  # TODO: b/335031954 - Bring back support to run calibration in Eager mode.
+  logging.debug(
+      'Setting `force_graph_mode_calibration = True` to ensure the calibration'
+      ' mode is executed properly.'
+  )
+  quantization_options.force_graph_mode_calibration = True
+
+  if quantization_options.HasField('debugger_config'):
+    if not quantization_options.debugger_config.log_dir_path:
+      quantization_options.debugger_config.log_dir_path = '/tmp/dumps'
+
+    if (
+        quantization_options.debugger_config.debugger_type
+        == stablehlo_quant_config_pb2.DebuggerConfig.DebuggerType.DEBUGGER_TYPE_UNSPECIFIED
+    ):
+      raise ValueError(
+          'Debugger is enabled but debugger type was not specified.'
+      )
+
+    if (
+        quantization_options.debugger_config.debugger_type
+        == stablehlo_quant_config_pb2.DebuggerConfig.DebuggerType.DEBUGGER_TYPE_WHOLE_MODEL
+        and not quantization_options.debugger_config.unquantized_dump_model_path
+    ):
+      raise ValueError(
+          'Debugger type whole model verify was used but'
+          ' unquantized_dump_model_path was not specified.'
+      )
+
+  # Check and populate quantization component spec.
+  _populate_quantization_component_spec(
+      quantization_options.quantization_method
+  )
+  # Verify and populate unit-wise quantization specs.
+  _populate_unitwise_quantization_specs(quantization_options)
+
+  if (
+      quantization_options.quantization_method.preset_method
+      == _PresetMethod.METHOD_STATIC_RANGE_INT8
+  ):
+    # Check and populate calibration options.
+    _populate_calibration_options(quantization_options)
+
+
+@tf_export.tf_export('quantization.experimental.quantize_saved_model')
+def quantize(
+    saved_model_path: str,
+    output_directory: Optional[str] = None,
+    quantization_options: Optional[_QuantizationOptions] = None,
+    representative_dataset: Optional[
+        repr_dataset.RepresentativeDatasetOrMapping
+    ] = None,
+    *,
+    overwrite_output_directory: bool = False,
+) -> autotrackable.AutoTrackable:
+  """Quantizes the SavedModel with the given quantization options.
+
+  Example usage:
+  ```python
+  # Quantizing a model trained with QAT.
+  quantization_options = tf.quantization.experimental.QuantizationOptions(
+      signature_keys=['your_signature_key'],
+  )
+  tf.quantization.experimental.quantize_saved_model(
+      '/tmp/input_model',
+      '/tmp/output_model',
+      quantization_options=quantization_options,
+  )
+
+  # When quantizing a model trained without QAT (Post-Training Quantization),
+  # a representative dataset is required.
+  representative_dataset = [{"input": tf.random.uniform(shape=(3, 3))}
+                        for _ in range(256)]
+  tf.quantization.experimental.quantize_saved_model(
+      '/tmp/input_model',
+      '/tmp/output_model',
+      quantization_options=quantization_options,
+      representative_dataset={'your_signature_key': representative_dataset},
+    )
+
+  # In addition to preset quantization methods, fine-grained control of
+  # quantization for each component is also supported.
+  _QuantizationComponentSpec = (
+      tf.quantization.experimental.QuantizationComponentSpec
+  )
+  quantization_options = tf.quantization.experimental.QuantizationOptions(
+      signature_keys=['your_signature_key'],
+      quantization_method=tf.quantization.experimental.QuantizationMethod(
+          quantization_component_specs=[
+              _QuantizationComponentSpec(
+                  quantization_component=(
+                      _QuantizationComponentSpec.COMPONENT_ACTIVATION
+                  ),
+                  tensor_type=_QuantizationComponentSpec.TENSORTYPE_INT_8,
+              )
+          ]
+      )
+  )
+  tf.quantization.experimental.quantize_saved_model(
+      '/tmp/input_model',
+      '/tmp/output_model',
+      quantization_options=quantization_options,
+  )
+  ```
+
+  Args:
+    saved_model_path: Path to the saved model. When representative_dataset is
+      not provided, this should be a model trained with QAT.
+    output_directory: The path to save the output SavedModel. Set
+      `overwrite_output_directory` to `True` to overwrite any existing contents
+      in the directory if not empty.
+    quantization_options: A set of options for quantization. If None, it uses
+      post-training static range quantization with XLA opset by default.
+    representative_dataset: an iterator that returns a dictionary of {input_key:
+      input_value} or a map from signature key to a dictionary of {input_key:
+      input_value} that feeds calibration data for quantizing model. The
+      representative should be provided when the model is a PTQ model. It can be
+      provided either via this parameter or via the `representative_datasets`
+      field in `QuantizationOptions`.
+    overwrite_output_directory: If set to true, overwrites the output directory
+      iff it isn't empty. The default value is false.
+
+  Returns:
+    A SavedModel object with TF quantization applied, or None if no quantization
+    is performed.
+
+  Raises:
+    ValueError: When 1) representative_dataset is not provided for non QAT model
+      for enabling static range quantization, 2) invalid value is provided as
+      a quantization method, or 3) provide representative dataset via both
+      argument and QuantizationOptions.
+    ValueError: When the specified quantization method is not yet supported.
+  """
+  _verify_output_dir(output_directory, overwrite_output_directory)
+
+  # Set default values for None arguments.
+  if output_directory is None:
+    output_directory = tempfile.mkdtemp()
+
+  if quantization_options is None:
+    quantization_options = _QuantizationOptions()
+
+  _populate_quantization_options_default_values(quantization_options)
+
+  method: _QuantizationMethod = quantization_options.quantization_method
+  if (
+      method.preset_method == _PresetMethod.METHOD_STATIC_RANGE_INT8
+      or method.preset_method == _PresetMethod.METHOD_NO_QUANTIZE
+  ):
+    return _static_range_quantize(
+        saved_model_path,
+        output_directory,
+        quantization_options,
+        representative_dataset,
+    )
+  elif method.preset_method == _PresetMethod.METHOD_DYNAMIC_RANGE_INT8:
+    return _dynamic_range_quantize(
+        saved_model_path,
+        output_directory,
+        quantization_options,
+    )
+  elif (
+      method.preset_method == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+  ):
+    return _weight_only_quantize(
+        saved_model_path,
+        output_directory,
+        quantization_options,
+    )
+  else:
+    raise ValueError(
+        'Quantization method {method.preset_method} is not supported.'
+    )

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

@@ -0,0 +1,402 @@
+# 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.
+# ==============================================================================
+"""Defines types required for representative datasets for quantization."""
+
+from collections.abc import Collection, Sized
+import os
+from typing import Iterable, Mapping, Optional, Union
+
+import numpy as np
+
+from tensorflow.compiler.mlir.quantization.tensorflow import quantization_options_pb2
+from tensorflow.core.protobuf import meta_graph_pb2
+from tensorflow.python.client import session
+from tensorflow.python.data.ops import readers
+from tensorflow.python.eager import context
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.lib.io import python_io
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.types import core
+from tensorflow.python.util import tf_export
+
+# A representative sample is a map of: input_key -> input_value.
+# Ex.: {'dense_input': tf.constant([1, 2, 3])}
+# Ex.: {'x1': np.ndarray([4, 5, 6]}
+RepresentativeSample = Mapping[str, core.TensorLike]
+
+# A representative dataset is an iterable of representative samples.
+RepresentativeDataset = Iterable[RepresentativeSample]
+
+# A type representing a map from: signature key -> representative dataset.
+# Ex.: {'serving_default': [tf.constant([1, 2, 3]), tf.constant([4, 5, 6])],
+#       'other_signature_key': [tf.constant([[2, 2], [9, 9]])]}
+RepresentativeDatasetMapping = Mapping[str, RepresentativeDataset]
+
+# A type alias expressing that it can be either a RepresentativeDataset or
+# a mapping of signature key to RepresentativeDataset.
+RepresentativeDatasetOrMapping = Union[
+    RepresentativeDataset, RepresentativeDatasetMapping
+]
+
+# Type aliases for quantization_options_pb2 messages.
+_RepresentativeDataSample = quantization_options_pb2.RepresentativeDataSample
+_RepresentativeDatasetFile = quantization_options_pb2.RepresentativeDatasetFile
+
+
+class RepresentativeDatasetSaver:
+  """Representative dataset saver.
+
+  Exposes a single method `save` that saves the provided representative dataset
+  into files.
+
+  This is useful when you would like to keep a snapshot of your representative
+  dataset at a file system or when you need to pass the representative dataset
+  as files.
+  """
+
+  def save(
+      self, representative_dataset: RepresentativeDatasetMapping
+  ) -> Mapping[str, _RepresentativeDatasetFile]:
+    """Saves the representative dataset.
+
+    Args:
+      representative_dataset: RepresentativeDatasetMapping which is a
+        signature_def_key -> representative dataset mapping.
+    """
+    raise NotImplementedError('Method "save" is not implemented.')
+
+
+@tf_export.tf_export(
+    'quantization.experimental.TfRecordRepresentativeDatasetSaver'
+)
+class TfRecordRepresentativeDatasetSaver(RepresentativeDatasetSaver):
+  """Representative dataset saver in TFRecord format.
+
+  Saves representative datasets for quantization calibration in TFRecord format.
+  The samples are serialized as `RepresentativeDataSample`.
+
+  The `save` method return a signature key to `RepresentativeDatasetFile` map,
+  which can be used for QuantizationOptions.
+
+  Example usage:
+
+  ```python
+  # Creating the representative dataset.
+  representative_dataset = [{"input": tf.random.uniform(shape=(3, 3))}
+                        for _ in range(256)]
+
+  # Saving to a TFRecord file.
+  dataset_file_map = (
+    tf.quantization.experimental.TfRecordRepresentativeDatasetSaver(
+          path_map={'serving_default': '/tmp/representative_dataset_path'}
+      ).save({'serving_default': representative_dataset})
+  )
+
+  # Using in QuantizationOptions.
+  quantization_options = tf.quantization.experimental.QuantizationOptions(
+      signature_keys=['serving_default'],
+      representative_datasets=dataset_file_map,
+  )
+  tf.quantization.experimental.quantize_saved_model(
+      '/tmp/input_model',
+      '/tmp/output_model',
+      quantization_options=quantization_options,
+  )
+  ```
+  """
+
+  def __init__(
+      self,
+      path_map: Mapping[str, os.PathLike[str]],
+      expected_input_key_map: Optional[Mapping[str, Collection[str]]] = None,
+  ):
+    """Initializes TFRecord represenatative dataset saver.
+
+    Args:
+      path_map: Signature def key -> path mapping. Each path is a TFRecord file
+        to which a `RepresentativeDataset` is saved. The signature def keys
+        should be a subset of the `SignatureDef` keys of the
+        `representative_dataset` argument of the `save()` call.
+      expected_input_key_map: Signature def key -> expected input keys. If set,
+        validate that the sample has same set of input keys before saving.
+
+    Raises:
+      KeyError: If path_map and expected_input_key_map have different keys.
+    """
+    self.path_map: Mapping[str, os.PathLike[str]] = path_map
+    self.expected_input_key_map: Mapping[str, Collection[str]] = {}
+    if expected_input_key_map is not None:
+      if set(path_map.keys()) != set(expected_input_key_map.keys()):
+        raise KeyError(
+            'The `path_map` and `expected_input_key_map` should have the same'
+            ' set of keys.'
+        )
+
+      self.expected_input_key_map = expected_input_key_map
+
+  def _save_tf_record_dataset(
+      self,
+      repr_ds: RepresentativeDataset,
+      signature_def_key: str,
+  ) -> _RepresentativeDatasetFile:
+    """Saves `repr_ds` to a TFRecord file.
+
+    Each sample in `repr_ds` is serialized as `RepresentativeDataSample`.
+
+    Args:
+      repr_ds: `RepresentativeDataset` to save.
+      signature_def_key: The signature def key associated with `repr_ds`.
+
+    Returns:
+      a RepresentativeDatasetFile instance contains the path to the saved file.
+
+    Raises:
+      KeyError: If the set of input keys in the dataset samples doesn't match
+      the set of expected input keys.
+    """
+    # When running in graph mode (TF1), tf.Tensor types should be converted to
+    # numpy ndarray types to be compatible with `make_tensor_proto`.
+    if not context.executing_eagerly():
+      with session.Session() as sess:
+        repr_ds = replace_tensors_by_numpy_ndarrays(repr_ds, sess)
+
+    expected_input_keys = self.expected_input_key_map.get(
+        signature_def_key, None
+    )
+    tfrecord_file_path = self.path_map[signature_def_key]
+    with python_io.TFRecordWriter(tfrecord_file_path) as writer:
+      for repr_sample in repr_ds:
+        if (
+            expected_input_keys is not None
+            and set(repr_sample.keys()) != expected_input_keys
+        ):
+          raise KeyError(
+              'Invalid input keys for representative sample. The function'
+              f' expects input keys of: {set(expected_input_keys)}. Got:'
+              f' {set(repr_sample.keys())}. Please provide correct input keys'
+              ' for representative samples.'
+          )
+
+        sample = _RepresentativeDataSample()
+        for input_name, input_value in repr_sample.items():
+          sample.tensor_proto_inputs[input_name].CopyFrom(
+              tensor_util.make_tensor_proto(input_value)
+          )
+
+        writer.write(sample.SerializeToString())
+
+    logging.info(
+        'Saved representative dataset for signature def: %s to: %s',
+        signature_def_key,
+        tfrecord_file_path,
+    )
+    return _RepresentativeDatasetFile(
+        tfrecord_file_path=str(tfrecord_file_path)
+    )
+
+  def save(
+      self, representative_dataset: RepresentativeDatasetMapping
+  ) -> Mapping[str, _RepresentativeDatasetFile]:
+    """Saves the representative dataset.
+
+    Args:
+      representative_dataset: Signature def key -> representative dataset
+        mapping. Each dataset is saved in a separate TFRecord file whose path
+        matches the signature def key of `path_map`.
+
+    Raises:
+      ValueError: When the signature def key in `representative_dataset` is not
+      present in the `path_map`.
+
+    Returns:
+      A map from signature key to the RepresentativeDatasetFile instance
+      contains the path to the saved file.
+    """
+    dataset_file_map = {}
+    for signature_def_key, repr_ds in representative_dataset.items():
+      if signature_def_key not in self.path_map:
+        raise ValueError(
+            'SignatureDef key does not exist in the provided path_map:'
+            f' {signature_def_key}'
+        )
+
+      dataset_file_map[signature_def_key] = self._save_tf_record_dataset(
+          repr_ds, signature_def_key
+      )
+    return dataset_file_map
+
+
+class RepresentativeDatasetLoader:
+  """Representative dataset loader.
+
+  Exposes the `load` method that loads the representative dataset from files.
+  """
+
+  def load(self) -> RepresentativeDatasetMapping:
+    """Loads the representative datasets.
+
+    Returns:
+      representative dataset mapping: A loaded signature def key ->
+      representative mapping.
+    """
+    raise NotImplementedError('Method "load" is not implemented.')
+
+
+class TfRecordRepresentativeDatasetLoader(RepresentativeDatasetLoader):
+  """TFRecord representative dataset loader.
+
+  Loads representative dataset stored in TFRecord files.
+  """
+
+  def __init__(
+      self,
+      dataset_file_map: Mapping[str, _RepresentativeDatasetFile],
+  ) -> None:
+    """Initializes TFRecord represenatative dataset loader.
+
+    Args:
+      dataset_file_map: Signature key -> `RepresentativeDatasetFile` mapping.
+
+    Raises:
+      DecodeError: If the sample is not RepresentativeDataSample.
+    """
+    self.dataset_file_map = dataset_file_map
+
+  def _load_tf_record(self, tf_record_path: str) -> RepresentativeDataset:
+    """Loads TFRecord containing samples of type`RepresentativeDataSample`."""
+    samples = []
+    with context.eager_mode():
+      for sample_bytes in readers.TFRecordDatasetV2(filenames=[tf_record_path]):
+        sample_proto = _RepresentativeDataSample.FromString(
+            sample_bytes.numpy()
+        )
+        sample = {}
+        for input_key, tensor_proto in sample_proto.tensor_proto_inputs.items():
+          sample[input_key] = tensor_util.MakeNdarray(tensor_proto)
+        samples.append(sample)
+    return samples
+
+  def load(self) -> RepresentativeDatasetMapping:
+    """Loads the representative datasets.
+
+    Returns:
+      representative dataset mapping: A signature def key -> representative
+      mapping. The loader loads `RepresentativeDataset` for each path in
+      `self.dataset_file_map` and associates the loaded dataset to the
+      corresponding signature def key.
+    """
+    repr_dataset_map = {}
+    for signature_def_key, dataset_file in self.dataset_file_map.items():
+      if dataset_file.HasField('tfrecord_file_path'):
+        repr_dataset_map[signature_def_key] = self._load_tf_record(
+            dataset_file.tfrecord_file_path
+        )
+      else:
+        raise ValueError('Unsupported Representative Dataset filetype')
+
+    return repr_dataset_map
+
+
+def replace_tensors_by_numpy_ndarrays(
+    repr_ds: RepresentativeDataset, sess: session.Session
+) -> RepresentativeDataset:
+  """Replaces tf.Tensors in samples by their evaluated numpy arrays.
+
+  Note: This should be run in graph mode (default in TF1) only.
+
+  Args:
+    repr_ds: Representative dataset to replace the tf.Tensors with their
+      evaluated values. `repr_ds` is iterated through, so it may not be reusable
+      (e.g. if it is a generator object).
+    sess: Session instance used to evaluate tf.Tensors.
+
+  Returns:
+    The new representative dataset where each tf.Tensor is replaced by its
+    evaluated numpy ndarrays.
+  """
+  new_repr_ds = []
+  for sample in repr_ds:
+    new_sample = {}
+    for input_key, input_data in sample.items():
+      # Evaluate the Tensor to get the actual value.
+      if isinstance(input_data, core.Tensor):
+        input_data = input_data.eval(session=sess)
+
+      new_sample[input_key] = input_data
+
+    new_repr_ds.append(new_sample)
+  return new_repr_ds
+
+
+def get_num_samples(repr_ds: RepresentativeDataset) -> Optional[int]:
+  """Returns the number of samples if known.
+
+  Args:
+    repr_ds: Representative dataset.
+
+  Returns:
+    Returns the total number of samples in `repr_ds` if it can be determined
+    without iterating the entier dataset. Returns None iff otherwise. When it
+    returns None it does not mean the representative dataset is infinite or it
+    is malformed; it simply means the size cannot be determined without
+    iterating the whole dataset.
+  """
+  if isinstance(repr_ds, Sized):
+    try:
+      return len(repr_ds)
+    except Exception as ex:  # pylint: disable=broad-except
+      # There are some cases where calling __len__() raises an exception.
+      # Handle this as if the size is unknown.
+      logging.info('Cannot determine the size of the dataset (%s).', ex)
+      return None
+  else:
+    return None
+
+
+def create_feed_dict_from_input_data(
+    input_data: RepresentativeSample,
+    signature_def: meta_graph_pb2.SignatureDef,
+) -> Mapping[str, np.ndarray]:
+  """Constructs a feed_dict from input data.
+
+  Note: This function should only be used in graph mode.
+
+  This is a helper function that converts an 'input key -> input value' mapping
+  to a feed dict. A feed dict is an 'input tensor name -> input value' mapping
+  and can be directly passed to the `feed_dict` argument of `sess.run()`.
+
+  Args:
+    input_data: Input key -> input value mapping. The input keys should match
+      the input keys of `signature_def`.
+    signature_def: A SignatureDef representing the function that `input_data` is
+      an input to.
+
+  Returns:
+    Feed dict, which is intended to be used as input for `sess.run`. It is
+    essentially a mapping: input tensor name -> input value. Note that the input
+    value in the feed dict is not a `Tensor`.
+  """
+  feed_dict = {}
+  for input_key, input_value in input_data.items():
+    input_tensor_name = signature_def.inputs[input_key].name
+
+    value = input_value
+    if isinstance(input_value, core.Tensor):
+      # Take the data out of the tensor.
+      value = input_value.eval()
+
+    feed_dict[input_tensor_name] = value
+
+  return feed_dict

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

@@ -0,0 +1,346 @@
+# 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.
+# ==============================================================================
+"""Defines utilities involving SavedModel."""
+from typing import Collection, Dict, Mapping, Optional, Sequence
+
+from absl import logging
+
+# pylint: disable=g-importing-member
+from google.protobuf.any_pb2 import Any
+# pylint: enable=g-importing-member
+from tensorflow.core.framework import graph_pb2
+from tensorflow.core.protobuf import meta_graph_pb2
+from tensorflow.core.protobuf import saver_pb2
+from tensorflow.python.client import session
+from tensorflow.python.framework import importer
+from tensorflow.python.framework import ops
+from tensorflow.python.lib.io import file_io
+from tensorflow.python.saved_model import builder
+from tensorflow.python.saved_model import constants as saved_model_constants
+from tensorflow.python.saved_model import loader_impl as saved_model_loader
+from tensorflow.python.saved_model import tag_constants
+from tensorflow.python.training import saver
+
+# Mapping of signature def key -> SignatureDef.
+_SignatureDefMap = Mapping[str, meta_graph_pb2.SignatureDef]
+
+
+def get_signatures_from_saved_model(
+    saved_model_path: str,
+    signature_keys: Optional[Sequence[str]] = None,
+    tags: Optional[Collection[str]] = None,
+) -> Dict[str, meta_graph_pb2.SignatureDef]:
+  """Gets a map from signature keys to their SignatureDef.
+
+  Args:
+    saved_model_path: Path to the saved model.
+    signature_keys: List of keys identifying SignatureDef to retrieve. If None,
+      retrieve all except the init signature.
+    tags: Set of tags identifying the MetaGraphDef within the SavedModel.
+
+  Returns:
+    A map from signature_key to its SignatureDef.
+  """
+  if tags is None:
+    tags = {tag_constants.SERVING}
+
+  loader = saved_model_loader.SavedModelLoader(saved_model_path)
+  meta_graphdef = loader.get_meta_graph_def_from_tags(tags)
+  signatures = {}
+  for key, signature_def in meta_graphdef.signature_def.items():
+    if key == saved_model_constants.INIT_OP_SIGNATURE_KEY:
+      continue
+    if signature_keys is not None and key not in signature_keys:
+      continue
+    signatures[key] = signature_def
+
+  return signatures
+
+
+def _restore_output_tensor_names(
+    graph_def: graph_pb2.GraphDef,
+) -> graph_pb2.GraphDef:
+  """Restores the output tensor names of the converted model.
+
+  During the conversion, the output tensor names of the original model are
+  embedded in the `tf_saved_model.index_path` attribute of the RetVal nodes and
+  might become the name of Retval nodes as well (with an index suffix if there
+  are multiple output tensors from one node). Since Retval nodes are not used in
+  SavedModel, this function removes them and restore the names to the actual
+  output tensors.
+
+  Args:
+    graph_def: the converted GraphDef.
+
+  Returns:
+    The GraphDef with Retval nodes removed and output tensor names restored.
+  """
+  output_renaming_map = {}
+  with session.Session(graph=ops.Graph()):
+    importer.import_graph_def(graph_def, name='')
+    graph = ops.get_default_graph()
+    for op in graph.get_operations():
+      if op.type == '_Retval':
+        expected_node_name = op.name
+        if op.get_attr('tf_saved_model.index_path') is not None:
+          index_path_name = op.get_attr('tf_saved_model.index_path')[0]
+          index_path_name = index_path_name.decode('utf-8').split(':')[0]
+          try:
+            # Only use the index_path name if it points to a Retval node.
+            index_path_node = graph.get_operation_by_name(index_path_name)
+            if index_path_node.type == '_Retval':
+              expected_node_name = index_path_name
+          except KeyError:
+            pass
+        retval_input_node_name = op.inputs[0].op.name
+        output_renaming_map[retval_input_node_name] = expected_node_name
+
+  for node in reversed(graph_def.node):
+    if node.name in output_renaming_map:
+      node.name = output_renaming_map[node.name]
+    elif node.op == '_Retval':
+      graph_def.node.remove(node)
+    else:
+      # Update the inputs referring to the pre-renaming node.
+      for idx, input_name in enumerate(node.input):
+        if input_name in output_renaming_map:
+          node.input[idx] = output_renaming_map[input_name]
+      # Update the control inputs referring to the pre-renaming node.
+      updating_inputs = []
+      for input_name in reversed(node.input):
+        if input_name.startswith('^') and input_name[1:] in output_renaming_map:
+          updating_inputs.append(input_name[1:])
+          node.input.remove(input_name)
+      for updating_input in updating_inputs:
+        node.input.append('^' + output_renaming_map[updating_input])
+  return graph_def
+
+
+def create_empty_output_dir(
+    output_directory: str, overwrite: bool = True
+) -> None:
+  """Creates the `output_directory`.
+
+  If `output_directory` already exists, it recursively deletes all contents
+  inside the directory.
+
+  Also creates the parent & intermediate directories.
+
+  Args:
+    output_directory: Output directory.
+    overwrite: Where to clean the output directory if exists.
+  """
+  if overwrite and file_io.file_exists_v2(output_directory):
+    logging.info(
+        'Deleting existing output directory: %s .',
+        output_directory,
+    )
+    file_io.delete_recursively_v2(output_directory)
+
+  file_io.recursive_create_dir_v2(output_directory)
+
+
+def _validate_signatures(
+    signature_def_map: _SignatureDefMap, exported_graph: ops.Graph
+) -> _SignatureDefMap:
+  """Validates if the tensor names in signatures are consistent with the graph.
+
+  This function checks if the input and output tensor names in the signatures
+  exist if the graph. The output tensor names might change during conversion,
+  we try to fix that with `_restore_output_tensor_names`. Besides, if there
+  are duplicated tensor names, they we will be prefixed with the signature name.
+  However, if that doesn't work the signatures can't be used with the converted
+  graph.
+
+  Args:
+    signature_def_map: the signatures to validate.
+    exported_graph: The PTQ-exported GraphDef.
+
+  Returns:
+    The signatures with tensor names prefixed with signature name if necessary.
+
+  Raises:
+    ValueError: Iff the signatures are not consistent with the graph.
+  """
+  for signature_key, signature_def in signature_def_map.items():
+    for tensor_info in signature_def.inputs.values():
+      try:
+        exported_graph.get_tensor_by_name(tensor_info.name)
+      except KeyError as exc:
+        try:
+          prefixed_name = signature_key + '_' + tensor_info.name
+          exported_graph.get_tensor_by_name(prefixed_name)
+          tensor_info.name = prefixed_name
+        except KeyError:
+          raise ValueError(
+              'Cannot find the input tensor with name %s in the graph.'
+              % tensor_info.name
+          ) from exc
+
+    for tensor_info in signature_def.outputs.values():
+      try:
+        exported_graph.get_tensor_by_name(tensor_info.name)
+      except KeyError as exc:
+        try:
+          prefixed_name = signature_key + '_' + tensor_info.name
+          exported_graph.get_tensor_by_name(prefixed_name)
+          tensor_info.name = prefixed_name
+        except KeyError:
+          raise ValueError(
+              'Cannot find the output tensor with name %s in the graph.'
+              % tensor_info.name
+          ) from exc
+
+  return signature_def_map
+
+
+def _find_op(
+    graph: ops.Graph, op_name: Optional[str]
+) -> Optional[ops.Operation]:
+  """Finds the operation with `op_name`.
+
+  Args:
+    graph: The graph to find from.
+    op_name: Name of the node.
+
+  Returns:
+    The operation that corresponds to `op_name`. Returns None iff op_name is an
+    empty string or None.
+
+  Raises:
+    ValueError: `op_name` is malformed.
+  """
+  if not op_name:
+    return None
+
+  init_op = graph.get_operation_by_name(op_name)
+  logging.debug('Op found in the graph: %s', op_name)
+
+  return init_op
+
+
+def _save_function_alias(
+    saved_model_dir: str,
+    tags: Collection[str],
+    function_aliases: Mapping[str, str],
+) -> None:
+  """Saves the function alias to the SavedModel.
+
+  SavedModelBuilder (TF1 saved model saver) does not support saving function
+  aliases, so this function loads the SavedModel proto and adds the
+  `function_aliases` field.
+
+  Args:
+    saved_model_dir: Path to the saved model directory.
+    tags: A collection of tags to specify the meta graph.
+    function_aliases: Function name -> function alias mapping.
+  """
+  loader = saved_model_loader.SavedModelLoader(saved_model_dir)
+  meta_graph_def = loader.get_meta_graph_def_from_tags(tags)
+
+  for function_name, function_alias in function_aliases.items():
+    meta_graph_def.meta_info_def.function_aliases[function_name] = (
+        function_alias
+    )
+
+  saved_model_proto_serialized = loader.saved_model.SerializeToString()
+
+  # TODO(b/266015731): Also update and set the SavedModel fingerprint.
+  path = file_io.join(
+      saved_model_dir, saved_model_constants.SAVED_MODEL_FILENAME_PB
+  )
+  file_io.atomic_write_string_to_file(path, saved_model_proto_serialized)
+
+
+def save_model_v1(
+    graph_def: graph_pb2.GraphDef,
+    output_dir: str,
+    signature_def_map: _SignatureDefMap,
+    tags: Collection[str],
+    init_op_name: Optional[str] = None,
+    saver_def: Optional[saver_pb2.SaverDef] = None,
+    checkpoint_dir: Optional[str] = None,
+    function_aliases: Optional[Mapping[str, str]] = None,
+    asset_file_defs: Sequence[meta_graph_pb2.AssetFileDef] = (),
+) -> None:
+  """Saves the model.
+
+  Saves the provided graph def as SavedModel.
+  Uses TF1 SavedModel semantics (i.e. no object graph).
+
+  Args:
+    graph_def: Graph to save.
+    output_dir: Output directory for the SavedModel.
+    signature_def_map: Mapping of signature def key -> SignatureDef.
+    tags: Tags for the meta graph def.
+    init_op_name: Name of the node for initialization.
+    saver_def: `saver_pb2.SaverDef` to create a `saver.Saver` from. The created
+      saver will be used to save and load variables. This may be `None` if no
+      variables exist in the graph.
+    checkpoint_dir: Path to checkpoint file where variable values are saved.
+    function_aliases: Function name -> function alias mapping.
+    asset_file_defs: `AssetFileDef`s that associates the asset files and the
+      name of the tensors to which the asset file names should be fed. The
+      caller should make sure the asset files exist in the output saved model
+      directory.
+
+  Raises:
+    ValueError iff the graph does not contain a valid signature or the file
+    prefix tensor is not found in the graph.
+  """
+  create_empty_output_dir(output_dir)
+  v1_builder = builder.SavedModelBuilder(output_dir)
+
+  graph_def = _restore_output_tensor_names(graph_def)
+  with session.Session(graph=ops.Graph()) as sess:
+    importer.import_graph_def(graph_def, name='')
+
+    signature_def_map = _validate_signatures(
+        signature_def_map, ops.get_default_graph()
+    )
+
+    # Add `AssetFileDef`s to the collection so that correct values are fed to
+    # the tensors that accept asset file paths.
+    for asset_file_def in asset_file_defs:
+      asset_any_proto = Any()
+      asset_any_proto.Pack(asset_file_def)
+      ops.add_to_collection(
+          saved_model_constants.ASSETS_KEY,
+          asset_any_proto,
+      )
+
+    model_saver = None
+    # If `saver_def` is not None, it means there are variables in the graph.
+    if saver_def:
+      model_saver = saver.Saver(saver_def=saver_def)
+      logging.info('Saver created with SaverDef: %s', saver_def)
+
+      # Variables should be restored once before exporting as saved model
+      # because the variables are not initialized when the GraphDef was
+      # imported.
+      model_saver.restore(sess, checkpoint_dir)
+
+    v1_builder.add_meta_graph_and_variables(
+        sess,
+        tags,
+        signature_def_map=signature_def_map,
+        main_op=_find_op(sess.graph, op_name=init_op_name),
+        saver=model_saver,
+    )
+
+  v1_builder.save()
+
+  if function_aliases:
+    _save_function_alias(output_dir, tags, function_aliases)

SwarmUI/dlbackend/ComfyUI/venv/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/quantization_options_pb2.py

@@ -0,0 +1,56 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.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 tensorflow.compiler.mlir.quantization.stablehlo import quantization_config_pb2 as tensorflow_dot_compiler_dot_mlir_dot_quantization_dot_stablehlo_dot_quantization__config__pb2
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\nKtensorflow/compiler/mlir/quantization/tensorflow/quantization_options.proto\x12\x17tensorflow.quantization\x1aItensorflow/compiler/mlir/quantization/stablehlo/quantization_config.proto\x1a&tensorflow/core/framework/tensor.proto\"\xed\x02\n\x12QuantizationMethod\x12O\n\rpreset_method\x18\x04 \x01(\x0e\x32\x38.tensorflow.quantization.QuantizationMethod.PresetMethod\x12X\n\x1cquantization_component_specs\x18\x03 \x03(\x0b\x32\x32.tensorflow.quantization.QuantizationComponentSpec\"\xa5\x01\n\x0cPresetMethod\x12\x16\n\x12METHOD_UNSPECIFIED\x10\x00\x12\x16\n\x12METHOD_NO_QUANTIZE\x10\x01\x12\x1c\n\x18METHOD_STATIC_RANGE_INT8\x10\x02\x12\x1d\n\x19METHOD_DYNAMIC_RANGE_INT8\x10\x03\x12(\n$METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8\x10\x04J\x04\x08\x01\x10\x03\"\xbe\x03\n\x19QuantizationComponentSpec\x12h\n\x16quantization_component\x18\x01 \x01(\x0e\x32H.tensorflow.quantization.QuantizationComponentSpec.QuantizationComponent\x12R\n\x0btensor_type\x18\x02 \x01(\x0e\x32=.tensorflow.quantization.QuantizationComponentSpec.TensorType\"v\n\x15QuantizationComponent\x12\x19\n\x15\x43OMPONENT_UNSPECIFIED\x10\x00\x12\x18\n\x14\x43OMPONENT_ACTIVATION\x10\x01\x12\x14\n\x10\x43OMPONENT_WEIGHT\x10\x02\x12\x12\n\x0e\x43OMPONENT_BIAS\x10\x03\"k\n\nTensorType\x12\x1a\n\x16TENSORTYPE_UNSPECIFIED\x10\x00\x12\x14\n\x10TENSORTYPE_INT_4\x10\x01\x12\x14\n\x10TENSORTYPE_INT_8\x10\x02\x12\x15\n\x11TENSORTYPE_INT_32\x10\x03\"\x87\x02\n\x18UnitWiseQuantizationSpec\x12P\n\x04unit\x18\x05 \x03(\x0b\x32\x42.tensorflow.quantization.UnitWiseQuantizationSpec.QuantizationUnit\x12H\n\x13quantization_method\x18\x06 \x01(\x0b\x32+.tensorflow.quantization.QuantizationMethod\x1aI\n\x10QuantizationUnit\x12\x0f\n\x07op_type\x18\x01 \x01(\t\x12\x11\n\tnode_name\x18\x02 \x01(\t\x12\x11\n\tfunc_name\x18\x03 \x01(\tJ\x04\x08\x01\x10\x05\"\xd4\x01\n\x18RepresentativeDataSample\x12\x65\n\x13tensor_proto_inputs\x18\x02 \x03(\x0b\x32H.tensorflow.quantization.RepresentativeDataSample.TensorProtoInputsEntry\x1aQ\n\x16TensorProtoInputsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12&\n\x05value\x18\x02 \x01(\x0b\x32\x17.tensorflow.TensorProto:\x02\x38\x01\"I\n\x19RepresentativeDatasetFile\x12\x1c\n\x12tfrecord_file_path\x18\x01 \x01(\tH\x00\x42\x0e\n\x0c\x64\x61taset_file\"\xca\x07\n\x13QuantizationOptions\x12H\n\x13quantization_method\x18\x01 \x01(\x0b\x32+.tensorflow.quantization.QuantizationMethod\x12.\n\x06op_set\x18\x02 \x01(\x0e\x32\x1e.tensorflow.quantization.OpSet\x12W\n\x1cunit_wise_quantization_specs\x18\x11 \x03(\x0b\x32\x31.tensorflow.quantization.UnitWiseQuantizationSpec\x12\x0c\n\x04tags\x18\x05 \x03(\t\x12\x16\n\x0esignature_keys\x18\x06 \x03(\t\x12i\n\x17representative_datasets\x18\x07 \x03(\x0b\x32H.tensorflow.quantization.QuantizationOptions.RepresentativeDatasetsEntry\x12$\n\x1cmin_num_elements_for_weights\x18\x08 \x01(\x03\x12!\n\x14\x66reeze_all_variables\x18\t \x01(\x08H\x00\x88\x01\x01\x12,\n\x1f\x65nable_per_channel_quantization\x18\n \x01(\x08H\x01\x88\x01\x01\x12 \n\x18\x65nable_two_input_tensors\x18\x0b \x01(\x08\x12-\n%experimental_enable_tpu_model_support\x18\x0c \x01(\x08\x12!\n\x19\x65nable_legacy_weight_only\x18\r \x01(\x08\x12$\n\x1c\x66orce_graph_mode_calibration\x18\x0e \x01(\x08\x12G\n\x13\x63\x61libration_options\x18\x0f \x01(\x0b\x32*.stablehlo.quantization.CalibrationOptions\x12?\n\x0f\x64\x65\x62ugger_config\x18\x10 \x01(\x0b\x32&.stablehlo.quantization.DebuggerConfig\x1aq\n\x1bRepresentativeDatasetsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x41\n\x05value\x18\x02 \x01(\x0b\x32\x32.tensorflow.quantization.RepresentativeDatasetFile:\x02\x38\x01\x42\x17\n\x15_freeze_all_variablesB\"\n _enable_per_channel_quantizationJ\x04\x08\x03\x10\x04*V\n\x05OpSet\x12\x16\n\x12OP_SET_UNSPECIFIED\x10\x00\x12\x06\n\x02TF\x10\x01\x12\x07\n\x03XLA\x10\x02\x12\x15\n\x11UNIFORM_QUANTIZED\x10\x03\x12\r\n\tSTABLEHLO\x10\x04\x42\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.quantization.tensorflow.quantization_options_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _REPRESENTATIVEDATASAMPLE_TENSORPROTOINPUTSENTRY._options = None
+  _REPRESENTATIVEDATASAMPLE_TENSORPROTOINPUTSENTRY._serialized_options = b'8\001'
+  _QUANTIZATIONOPTIONS_REPRESENTATIVEDATASETSENTRY._options = None
+  _QUANTIZATIONOPTIONS_REPRESENTATIVEDATASETSENTRY._serialized_options = b'8\001'
+  _OPSET._serialized_start=2565
+  _OPSET._serialized_end=2651
+  _QUANTIZATIONMETHOD._serialized_start=220
+  _QUANTIZATIONMETHOD._serialized_end=585
+  _QUANTIZATIONMETHOD_PRESETMETHOD._serialized_start=414
+  _QUANTIZATIONMETHOD_PRESETMETHOD._serialized_end=579
+  _QUANTIZATIONCOMPONENTSPEC._serialized_start=588
+  _QUANTIZATIONCOMPONENTSPEC._serialized_end=1034
+  _QUANTIZATIONCOMPONENTSPEC_QUANTIZATIONCOMPONENT._serialized_start=807
+  _QUANTIZATIONCOMPONENTSPEC_QUANTIZATIONCOMPONENT._serialized_end=925
+  _QUANTIZATIONCOMPONENTSPEC_TENSORTYPE._serialized_start=927
+  _QUANTIZATIONCOMPONENTSPEC_TENSORTYPE._serialized_end=1034
+  _UNITWISEQUANTIZATIONSPEC._serialized_start=1037
+  _UNITWISEQUANTIZATIONSPEC._serialized_end=1300
+  _UNITWISEQUANTIZATIONSPEC_QUANTIZATIONUNIT._serialized_start=1221
+  _UNITWISEQUANTIZATIONSPEC_QUANTIZATIONUNIT._serialized_end=1294
+  _REPRESENTATIVEDATASAMPLE._serialized_start=1303
+  _REPRESENTATIVEDATASAMPLE._serialized_end=1515
+  _REPRESENTATIVEDATASAMPLE_TENSORPROTOINPUTSENTRY._serialized_start=1434
+  _REPRESENTATIVEDATASAMPLE_TENSORPROTOINPUTSENTRY._serialized_end=1515
+  _REPRESENTATIVEDATASETFILE._serialized_start=1517
+  _REPRESENTATIVEDATASETFILE._serialized_end=1590
+  _QUANTIZATIONOPTIONS._serialized_start=1593
+  _QUANTIZATIONOPTIONS._serialized_end=2563
+  _QUANTIZATIONOPTIONS_REPRESENTATIVEDATASETSENTRY._serialized_start=2383
+  _QUANTIZATIONOPTIONS_REPRESENTATIVEDATASETSENTRY._serialized_end=2496
+# @@protoc_insertion_point(module_scope)