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Grounded-Segment-Anything / transformers_4_35_0 /utils /fx.py

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	# coding=utf-8
	# Copyright 2021 The HuggingFace Team. 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.

	import builtins
	import collections
	import functools
	import inspect
	import math
	import operator
	import os
	import random
	import warnings
	from typing import Any, Callable, Dict, List, Optional, Type, Union

	import torch
	from torch import nn
	from torch.fx import Graph, GraphModule, Proxy, Tracer
	from torch.fx._compatibility import compatibility
	from torch.fx.proxy import ParameterProxy

	from .. import PretrainedConfig, PreTrainedModel, logging
	from ..models.auto import get_values
	from ..models.auto.modeling_auto import (
	MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
	MODEL_FOR_BACKBONE_MAPPING_NAMES,
	MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
	MODEL_FOR_CTC_MAPPING_NAMES,
	MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
	MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES,
	MODEL_FOR_MASKED_LM_MAPPING_NAMES,
	MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
	MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES,
	MODEL_FOR_PRETRAINING_MAPPING_NAMES,
	MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
	MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
	MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
	MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
	MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
	MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
	MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES,
	MODEL_MAPPING_NAMES,
	)
	from ..utils import (
	ENV_VARS_TRUE_VALUES,
	TORCH_FX_REQUIRED_VERSION,
	get_torch_version,
	is_peft_available,
	is_torch_fx_available,
	)


	if is_peft_available():
	from peft import PeftModel


	logger = logging.get_logger(__name__)
	_IS_IN_DEBUG_MODE = os.environ.get("FX_DEBUG_MODE", "").upper() in ENV_VARS_TRUE_VALUES


	def _generate_supported_model_class_names(
	model_name: Type[PretrainedConfig],
	supported_tasks: Optional[Union[str, List[str]]] = None,
	) -> List[str]:
	task_mapping = {
	"default": MODEL_MAPPING_NAMES,
	"pretraining": MODEL_FOR_PRETRAINING_MAPPING_NAMES,
	"next-sentence-prediction": MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES,
	"masked-lm": MODEL_FOR_MASKED_LM_MAPPING_NAMES,
	"causal-lm": MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
	"seq2seq-lm": MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
	"speech-seq2seq": MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,
	"multiple-choice": MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
	"document-question-answering": MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
	"question-answering": MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
	"sequence-classification": MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
	"token-classification": MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
	"masked-image-modeling": MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES,
	"image-classification": MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
	"zero-shot-image-classification": MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES,
	"ctc": MODEL_FOR_CTC_MAPPING_NAMES,
	"audio-classification": MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
	"semantic-segmentation": MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
	"backbone": MODEL_FOR_BACKBONE_MAPPING_NAMES,
	}

	if supported_tasks is None:
	supported_tasks = task_mapping.keys()
	if isinstance(supported_tasks, str):
	supported_tasks = [supported_tasks]

	model_class_names = []
	for task in supported_tasks:
	class_name = task_mapping[task].get(model_name, None)
	if class_name:
	model_class_names.append(class_name)

	return model_class_names


	_REGULAR_SUPPORTED_MODEL_NAMES_AND_TASKS = [
	"altclip",
	"albert",
	"bart",
	"bert",
	"blenderbot",
	"blenderbot-small",
	"bloom",
	"clip",
	"convnext",
	"deberta",
	"deberta-v2",
	"distilbert",
	"donut-swin",
	"electra",
	"gpt2",
	"gpt_neo",
	"gptj",
	"hubert",
	"layoutlm",
	"lxmert",
	"m2m_100",
	"marian",
	"mbart",
	"megatron-bert",
	"mobilebert",
	"mt5",
	"nezha",
	"opt",
	"pegasus",
	"plbart",
	"resnet",
	"roberta",
	"segformer",
	"speech_to_text",
	"speech_to_text_2",
	"swin",
	"t5",
	"trocr",
	"vit",
	"xglm",
	"wav2vec2",
	# "xlnet",
	]

	_REGULAR_SUPPORTED_MODELS = []
	for item in _REGULAR_SUPPORTED_MODEL_NAMES_AND_TASKS:
	if isinstance(item, dict):
	_REGULAR_SUPPORTED_MODELS.extend(_generate_supported_model_class_names(**item))
	else:
	_REGULAR_SUPPORTED_MODELS.extend(_generate_supported_model_class_names(item))

	_SPECIAL_SUPPORTED_MODELS = [
	"CLIPTextModel",
	"CLIPTextModelWithProjection",
	"CLIPVisionModel",
	"CLIPVisionModelWithProjection",
	"AltCLIPTextModel",
	"AltCLIPVisionModel",
	"GitVisionModel",
	"GPT2DoubleHeadsModel",
	"Speech2Text2Decoder",
	"TrOCRDecoder",
	"PeftModelForCausalLM",
	"PeftModelForSeq2SeqLM"
	# TODO: add support for them as it should be quite easy to do so (small blocking issues).
	# XLNetForQuestionAnswering,
	]
	_SUPPORTED_MODELS = tuple(sorted(set(_REGULAR_SUPPORTED_MODELS + _SPECIAL_SUPPORTED_MODELS)))


	def torch_nn_embedding(self, input):
	return torch.empty(*input.shape, self.weight.shape[-1], device="meta", dtype=self.weight.dtype)


	def torch_nn_functional_embedding(
	input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False
	):
	return torch.empty(*input.shape, weight.shape[-1], device="meta", dtype=weight.dtype)


	def torch_nn_layernorm(self, input):
	return input


	def torch_nn_groupnorm(self, input):
	return input


	def torch_nn_linear(self, input):
	return torch.empty(input.shape[:-1] + (self.out_features,), device="meta")


	def torch_relu(x):
	return x


	def torch_nn_relu(self, x):
	return x


	def torch_nn_functional_relu(x, inplace=False):
	if not inplace:
	raise ValueError("Don't support in-place functional.relu for MetaTensor analysis")
	return x


	def torch_where(condition, x, y):
	# torch.where returns the broadcasted tensor of condition, x, and y,
	# so hack it by using addition
	return condition.to(device="meta") + x.to(device="meta") + y.to(device="meta")


	def torch_abs(input, *, out=None):
	if out is not None:
	raise ValueError("Don't support in-place abs for MetaTensor analysis")
	return input


	def torch_arange(args, *kwargs):
	n = len(args)
	step = 1
	if n == 1:
	start = 0
	end = args[0]
	elif n == 2:
	start, end = args
	else:
	start, end, step = args
	if isinstance(start, float):
	start = int(start)
	if isinstance(end, float):
	start = int(end)
	if isinstance(step, float):
	step = int(step)
	step = kwargs.get("step", step)
	dtype = kwargs.get("dtype")
	return torch.empty((end - start) // step, dtype=dtype, device="meta")


	def torch_full(args, *kwargs):
	args = list(args)
	if isinstance(args[1], torch.Tensor) and args[1].device == torch.device("meta"):
	args[1] = 1 # Any value.
	kwargs_without_device = dict(kwargs)
	kwargs_without_device.pop("device", None)
	return torch.full(args, *kwargs_without_device)


	def torch_cat(tensors, dim=None, axis=None, *, out=None):
	if dim is None and axis is None:
	dim = 0
	if dim is None and axis is not None:
	dim = axis
	if dim < 0:
	dim = tensors[0].dim() + dim
	shapes = [t.shape for t in tensors]
	shape = list(shapes[0])
	concatenated_dim = sum(shape[dim] for shape in shapes)
	final_shape = shape[:dim] + [concatenated_dim] + shape[dim + 1 :]
	return torch.empty(final_shape, device="meta")


	def torch_stack(tensors, dim=None, axis=None, *, out=None):
	if dim is None and axis is None:
	dim = 0
	if dim is None and axis is not None:
	dim = axis
	if dim < 0:
	dim = tensors[0].dim() + 1 + dim
	shape = list(tensors[0].shape)
	shape.insert(dim, len(tensors))
	return torch.empty(shape, device="meta")


	def torch_add(input, other, *, alpha=1, out=None):
	if not isinstance(input, torch.Tensor):
	return torch.empty_like(other, device="meta")
	if not isinstance(other, torch.Tensor):
	return torch.empty_like(input, device="meta")
	max_length = max(input.dim(), other.dim())
	input_shape = list(input.shape) + [1] * (max_length - input.dim())
	other_shape = list(other.shape) + [1] * (max_length - other.dim())
	shape = []
	for i in range(max_length):
	shape.append(max(input_shape[i], other_shape[i]))
	return torch.empty(shape, device="meta")


	def torch_mul(input, other, *, out=None):
	return torch_add(input, other, out=out)


	def torch_tensor_mul(self, other):
	return torch_mul(self, other)


	def torch_matmul(input, other, *, out=None):
	d1 = input.dim()
	d2 = other.dim()
	shape = None
	if d1 == 1 and d2 == 1:
	shape = None
	elif d1 == 2 and d2 == 2:
	shape = (input.size(0), other.size(1))
	elif d1 == 1 and d2 == 2:
	shape = (other.size(1),)
	elif d1 == 2 and d1 == 1:
	shape = (input.size(0),)
	else:
	max_length = max(input.dim(), other.dim())
	shape1 = list(input.shape)
	shape2 = list(other.shape)
	if d1 == 1:
	shape1 = [1] + shape1
	if d2 == 1:
	shape2.append(1)
	shape1 = [-1] * (max_length - d1) + list(input.shape)
	shape2 = [-1] * (max_length - d2) + list(other.shape)
	shape = []
	for i in range(max_length):
	shape.append(max(shape1[i], shape2[i]))
	shape[-2] = shape1[-2]
	shape[-1] = shape2[-1]
	if d1 == 1:
	shape.pop(-2)
	if d2 == 1:
	shape.pop(-1)
	if shape is None:
	return torch.tensor(0.0, device="meta")
	return torch.empty(*shape, device="meta")


	def torch_bmm(input, mat2, *, out=None):
	if out is not None:
	raise ValueError("Don't support in-place bmm for MetaTensor analysis")
	batch_size, n, m = input.shape
	_, _, p = mat2.shape
	return torch.empty(batch_size, n, p, device="meta")


	def torch_baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None):
	if out is not None:
	raise ValueError("Don't support in-place baddbmm for MetaTensor analysis")
	return torch_bmm(batch1, batch2)


	def torch_tensor_baddbmm(self, batch1, batch2, *, beta=1, alpha=1, out=None):
	return torch_baddbmm(self, batch1, batch2, beta=beta, alpha=alpha, out=out)


	def torch_einsum(equation, *operands):
	# TODO: infer shape without performing the computation, this might be quite hard.
	concrete_operands = (torch.empty_like(operand, device="cpu") for operand in operands)
	return torch.einsum(equation, *concrete_operands).to("meta")


	def torch_tensor_repeat(self, *sizes):
	shape = list(self.shape)
	for i, x in enumerate(sizes):
	shape[i] *= x
	return torch.empty(shape, device="meta")


	def torch_repeat_interleave(*args, dim=None, output_size=None):
	num_args = len(args)
	if num_args == 1:
	shape = [output_size if output_size is not None else args[0].sum()]
	else:
	shape = list(args[0].shape)
	if dim is None:
	if num_args > 2:
	dim = args[2]
	else:
	shape = [sum(shape)]
	dim = 0
	repeats = args[1]
	if isinstance(repeats, int) or torch.numel(repeats) == 1:
	shape[dim] *= int(repeats)
	else:
	shape[dim] = output_size if output_size is not None else repeats.sum()
	return torch.empty(*shape, device="meta")


	def torch_index_select(input, dim, index, *, out=None):
	shape = list(input.shape)
	shape[dim] = len(index)
	return torch.empty(*shape, device="meta")


	def torch_tensor_index_select(self, dim, index):
	return torch_index_select(self, dim, index)


	def torch_gather(input, dim, index, *, sparse_grad=False, out=None):
	shape = list(input.shape)
	shape[dim] = index.shape[dim]
	return torch.empty(*shape, device="meta")


	def torch_tensor_gather(self, dim, index):
	return torch_gather(self, dim, index)


	def torch_roll(input, shifts, dims=None):
	return input


	def torch_flip(input, dims):
	return input


	def torch_tensor_flip(self, dims):
	return self


	def torch_nn_conv1d(self, input):
	l_in = input.shape[-1]
	shape = None
	padding = self.padding
	if padding == "valid":
	padding = (0, 0)
	if padding == "same":
	shape = list(input.shape)
	if shape is None:
	shape = list(input.shape)
	l_out = math.floor(
	(l_in + 2 * padding[0] - self.dilation[0] * (self.kernel_size[0] - 1) - 1) / self.stride[0] + 1
	)
	shape[-1] = l_out
	shape[-2] = self.out_channels
	return torch.empty(shape, device="meta")


	def torch_nn_conv2d(self, input):
	h_in, w_in = input.shape[-2:]
	shape = None
	padding = self.padding
	if padding == "valid":
	padding = (0, 0)
	if padding == "same":
	shape = list(input.shape)
	if shape is None:
	shape = list(input.shape)
	h_out = math.floor(
	(h_in + 2 * padding[0] - self.dilation[0] * (self.kernel_size[0] - 1) - 1) / self.stride[0] + 1
	)
	w_out = math.floor(
	(w_in + 2 * padding[1] - self.dilation[1] * (self.kernel_size[1] - 1) - 1) / self.stride[1] + 1
	)
	shape[-2:] = [h_out, w_out]
	shape[-3] = self.out_channels
	return torch.empty(shape, device="meta")


	def torch_squeeze(input, dim=None):
	shape = list(input.shape)
	if dim is not None:
	if dim < 0:
	dim = input.dim() + dim
	if shape[dim] == 1:
	shape.pop(dim)
	else:
	new_shape = []
	for dim_value in shape:
	if dim_value == 1:
	continue
	new_shape.append(dim_value)
	shape = new_shape
	return torch.empty(shape, device="meta")


	def torch_tensor_squeeze(self, dim=None):
	return torch_squeeze(self, dim)


	def torch_unsqueeze(input, dim):
	shape = list(input.shape)
	if dim < 0:
	dim = input.dim() + 1 + dim
	shape.insert(dim, 1)
	return torch.empty(shape, device="meta")


	def torch_tensor_unsqueeze(self, dim):
	return torch_unsqueeze(self, dim)


	def torch_unique_consecutive(input, **kwargs):
	output = torch.unique_consecutive(torch.zeros_like(input, device="cpu"), **kwargs)
	if isinstance(output, torch.Tensor):
	return output.to("meta")
	else:
	return tuple(map(output, lambda x: x.to("meta")))


	def torch_nn_functional_one_hot(tensor, num_classes=-1):
	if num_classes < 0:
	raise ValueError("Don't support automatic num_classes inference for MetaTensor analysis")
	shape = list(tensor.shape) + [num_classes]
	return torch.empty(shape, device="meta")


	def torch_nn_mseloss(self, input, target):
	if self.reduction == "none":
	shape = target.shape
	else:
	shape = (1,)
	return torch.empty(shape, device="meta")


	def torch_nn_crossentropyloss(self, input, target):
	if self.reduction == "none":
	shape = target.shape
	else:
	shape = (1,)
	return torch.empty(shape, device="meta")


	def torch_nn_bcewithlogitsloss(self, input, target):
	if self.reduction == "none":
	shape = target.shape
	else:
	shape = (1,)
	return torch.empty(shape, device="meta")


	def operator_getitem(a, b):
	def to_concrete(t):
	if isinstance(t, torch.Tensor):
	concrete = torch.ones_like(t, device="cpu")
	if concrete.dtype in [torch.float16, torch.float32, torch.float64, torch.int32]:
	concrete = concrete.to(torch.int64)
	return concrete
	return t

	if isinstance(a, torch.Tensor):
	# TODO: infer shape without performing the computation.
	if isinstance(b, tuple):
	b = tuple(map(to_concrete, b))
	else:
	b = to_concrete(b)
	return operator.getitem(torch.empty_like(a, device="cpu"), b).to("meta")
	return operator.getitem(a, b)


	_MANUAL_META_OVERRIDES: Dict[Callable, Callable] = {
	torch.nn.Embedding: torch_nn_embedding,
	torch.nn.functional.embedding: torch_nn_functional_embedding,
	torch.nn.LayerNorm: torch_nn_layernorm,
	torch.nn.GroupNorm: torch_nn_groupnorm,
	torch.nn.Linear: torch_nn_linear,
	torch.relu: torch_relu,
	torch.nn.functional.relu: torch_nn_functional_relu,
	torch.nn.ReLU: torch_nn_relu,
	torch.where: torch_where,
	torch.abs: torch_abs,
	torch.arange: torch_arange,
	torch.full: torch_full,
	torch.cat: torch_cat,
	torch.stack: torch_stack,
	torch.add: torch_add,
	torch.mul: torch_mul,
	torch.Tensor.mul: torch_tensor_mul,
	torch.matmul: torch_matmul,
	torch.bmm: torch_bmm,
	torch.baddbmm: torch_baddbmm,
	torch.Tensor.baddbmm: torch_tensor_baddbmm,
	torch.einsum: torch_einsum,
	torch.Tensor.repeat: torch_tensor_repeat,
	torch.repeat_interleave: torch_repeat_interleave,
	torch.roll: torch_roll,
	torch.flip: torch_flip,
	torch.Tensor.flip: torch_tensor_flip,
	torch.index_select: torch_index_select,
	torch.Tensor.index_select: torch_tensor_index_select,
	torch.gather: torch_gather,
	torch.Tensor.gather: torch_tensor_gather,
	torch.nn.Conv1d: torch_nn_conv1d,
	torch.nn.Conv2d: torch_nn_conv2d,
	torch.squeeze: torch_squeeze,
	torch.Tensor.squeeze: torch_tensor_squeeze,
	torch.unsqueeze: torch_unsqueeze,
	torch.Tensor.unsqueeze: torch_tensor_unsqueeze,
	torch.unique_consecutive: torch_unique_consecutive,
	torch.nn.functional.one_hot: torch_nn_functional_one_hot,
	torch.nn.MSELoss: torch_nn_mseloss,
	torch.nn.CrossEntropyLoss: torch_nn_crossentropyloss,
	torch.nn.BCEWithLogitsLoss: torch_nn_bcewithlogitsloss,
	operator.getitem: operator_getitem,
	}


	class HFProxy(Proxy):
	"""
	Proxy that uses metadata to handle data-dependent control-flow.
	"""

	def install_metadata(self, metadata):
	self._metadata = metadata

	@property
	def shape(self):
	return self.tracer.create_proxy("call_method", "size", (self,), {})

	@property
	def device(self):
	# Hack so we can track when devices are used. During meta-tensor propagation,
	# replace these values with a constant 'meta'
	return MetaDeviceAttribute(self, "device")

	def __len__(self):
	if hasattr(self, "_metadata") and self._metadata is not None:
	return len(self._metadata)
	return super().__len__()

	def __bool__(self):
	if hasattr(self, "_metadata") and self._metadata is not None:
	return self._metadata
	return super().__bool__()

	def __getattr__(self, k):
	if k == "_metadata":
	return self.__getattribute__(k)
	# note: not added to the graph yet, if this is a method call
	# we peephole optimize to the method invocation
	return HFAttribute(self, k)

	def __setitem__(self, indices, values):
	return self.tracer.create_proxy("call_function", operator.setitem, (self, indices, values), {})

	def __contains__(self, key):
	if hasattr(self, "_metadata") and self._metadata is not None:
	return key in self._metadata
	return super().__contains__(key)


	class HFAttribute(HFProxy):
	def __init__(self, root, attr: str):
	self.root = root
	self.attr = attr
	self.tracer = root.tracer
	self._node = None

	if hasattr(self.root, "_metadata"):
	self.install_metadata(getattr(self.root._metadata, attr))

	@property
	def node(self):
	# the node for attributes is added lazily, since most will just be method calls
	# which do not rely on the getitem call
	if self._node is None:
	self._node = self.tracer.create_proxy("call_function", builtins.getattr, (self.root, self.attr), {}).node
	return self._node

	def __call__(self, args, *kwargs):
	return self.tracer.create_proxy("call_method", self.attr, (self.root,) + args, kwargs)


	class MetaDeviceAttribute(HFAttribute):
	pass


	def _proxies_to_metas(v):
	"""Returns the underlying metadata for HFProxies, and behaves like the identity for the others."""
	if isinstance(v, MetaDeviceAttribute):
	return "meta"
	if isinstance(v, torch.fx.Proxy):
	if not (isinstance(v, HFProxy) and hasattr(v, "_metadata")):
	raise RuntimeError(f"No metadata was found for {v}")
	return v._metadata
	return v


	def _gen_constructor_wrapper(target):
	@functools.wraps(target)
	def wrapper(args, *kwargs):
	proxy = None

	def check_has_proxy(v):
	if isinstance(v, Proxy):
	nonlocal proxy
	proxy = v

	torch.fx.node.map_aggregate(args, check_has_proxy)
	torch.fx.node.map_aggregate(kwargs, check_has_proxy)

	if proxy is not None:
	return proxy.tracer.create_proxy("call_function", target, args, kwargs)
	else:
	return target(args, *kwargs)

	return wrapper, target


	def _generate_random_int(low: int = 10, high: int = 20, forbidden_values: Optional[List[int]] = None):
	if forbidden_values is None:
	forbidden_values = []
	value = random.randint(low, high)
	while value in forbidden_values:
	value = random.randint(low, high)
	return value


	class HFTracer(Tracer):
	"""
	Tracer that is able to symbolically trace models from the library. To do that, it uses the HFProxy instead of the
	regular PyTorch torch.fx.Proxy.
	"""

	# Feature flag for proxying accesses to buffer values
	proxy_buffer_attributes: bool = True
	allow_insert_stateless_mods: bool = True
	_TORCH_METHODS_TO_PATCH = [
	"arange",
	"zeros",
	"ones",
	"full",
	"full_like",
	"eye",
	"empty",
	"tensor",
	"clamp",
	"finfo",
	]
	supported_archs = (PreTrainedModel,) if not is_peft_available() else (PreTrainedModel, PeftModel)

	def __init__(self, autowrap_modules=(math,), autowrap_functions=()):
	super().__init__(autowrap_modules=autowrap_modules, autowrap_functions=autowrap_functions)

	if not is_torch_fx_available():
	raise ImportError(
	f"Found an incompatible version of torch. Found version {get_torch_version()}, but only version "
	f"{TORCH_FX_REQUIRED_VERSION} is supported."
	)

	def _generate_dummy_input(
	self, model: PreTrainedModel, input_name: str, shape: List[int]
	) -> Dict[str, torch.Tensor]:
	"""Generates dummy input for model inference recording."""
	# Retrieving the model class, either from the "class_for_deserialization" attribute if the model was restored
	# from pickle, or from the "__class__" attribute in the general case.
	model_class_name = getattr(model, "class_for_deserialization", model.__class__).__name__
	device = model.device
	inputs_dict = {}

	if input_name in ["labels", "start_positions", "end_positions"]:
	batch_size = shape[0]
	if model_class_name in [
	*get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES),
	*get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES),
	*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES),
	*get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES),
	*get_values(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES),
	]:
	inputs_dict["labels"] = torch.zeros(batch_size, dtype=torch.long, device=device)
	elif model_class_name in [
	*get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES),
	*get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES),
	"XLNetForQuestionAnswering",
	]:
	inputs_dict["start_positions"] = torch.zeros(batch_size, dtype=torch.long, device=device)
	inputs_dict["end_positions"] = torch.zeros(batch_size, dtype=torch.long, device=device)
	elif model_class_name in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES):
	if not hasattr(model.config, "problem_type") or model.config.problem_type is None:
	raise ValueError(
	"Could not retrieve the problem type for the sequence classification task, please set "
	'model.config.problem_type to one of the following values: "regression", '
	'"single_label_classification", or "multi_label_classification".'
	)

	if model.config.problem_type == "regression":
	labels_shape = (batch_size, model.config.num_labels)
	labels_dtype = torch.float32
	elif model.config.problem_type == "single_label_classification":
	labels_shape = (batch_size,)
	labels_dtype = torch.long
	elif model.config.problem_type == "multi_label_classification":
	labels_shape = (batch_size, model.config.num_labels)
	labels_dtype = torch.float32
	else:
	raise ValueError(
	'Expected model.config.problem_type to be either: "regression", "single_label_classification"'
	f', or "multi_label_classification", but "{model.config.problem_type}" was provided.'
	)
	inputs_dict["labels"] = torch.zeros(*labels_shape, dtype=labels_dtype, device=device)

	elif model_class_name in [
	*get_values(MODEL_FOR_PRETRAINING_MAPPING_NAMES),
	*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES),
	*get_values(MODEL_FOR_CAUSAL_LM_MAPPING_NAMES),
	*get_values(MODEL_FOR_MASKED_LM_MAPPING_NAMES),
	*get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES),
	*get_values(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES),
	"GPT2DoubleHeadsModel",
	"PeftModelForCausalLM",
	"PeftModelForSeq2SeqLM",
	]:
	inputs_dict["labels"] = torch.zeros(shape, dtype=torch.long, device=device)
	elif model_class_name in [*get_values(MODEL_FOR_CTC_MAPPING_NAMES)]:
	inputs_dict["labels"] = torch.zeros(shape, dtype=torch.float32, device=device)
	else:
	raise NotImplementedError(
	f"Generating the dummy input named {input_name} for {model_class_name} is not supported yet."
	)
	elif "pixel_values" in input_name:
	batch_size = shape[0]
	image_size = getattr(model.config, "image_size", None)
	if image_size is None:
	if hasattr(model.config, "vision_config"):
	image_size = model.config.vision_config.image_size
	elif hasattr(model.config, "encoder"):
	image_size = model.config.encoder.image_size
	else:
	image_size = (_generate_random_int(), _generate_random_int())

	# If no num_channels is in the config, use some arbitrary value.
	num_channels = getattr(model.config, "num_channels", 3)
	if not isinstance(image_size, collections.abc.Iterable):
	image_size = (image_size, image_size)
	height, width = image_size
	inputs_dict[input_name] = torch.zeros(
	batch_size, num_channels, height, width, dtype=torch.float32, device=device
	)
	elif "bbox" in input_name:
	inputs_dict[input_name] = torch.zeros(*shape, 4, dtype=torch.float, device=device)
	elif "input_features" in input_name:
	inputs_dict[input_name] = torch.zeros(
	*shape, model.config.input_feat_per_channel, dtype=torch.float, device=device
	)
	elif "visual_feats" in input_name:
	inputs_dict[input_name] = torch.zeros(
	shape
	+ [
	model.config.visual_feat_dim,
	],
	dtype=torch.float,
	device=device,
	)
	elif "visual_pos" in input_name:
	inputs_dict[input_name] = torch.zeros(
	shape
	+ [
	model.config.visual_pos_dim,
	],
	dtype=torch.float,
	device=device,
	)
	elif "inputs" in input_name:
	inputs_dict[input_name] = torch.zeros(*shape, dtype=torch.float, device=device)
	elif "input_values" in input_name:
	batch_size, _ = shape
	# Generating big sequence length for audio inputs.
	seq_length = _generate_random_int(low=10000, high=20000)
	inputs_dict[input_name] = torch.zeros(batch_size, seq_length, dtype=torch.float, device=device)
	elif "mask" in input_name or "ids" in input_name:
	inputs_dict[input_name] = torch.zeros(shape, dtype=torch.long, device=device)
	else:
	shape_with_hidden_size = shape + [model.config.hidden_size]
	inputs_dict[input_name] = torch.zeros(shape_with_hidden_size, dtype=torch.float, device=device)

	return inputs_dict

	def create_proxy(self, kind, target, args, kwargs, name=None, type_expr=None, proxy_factory_fn=None):
	rv = super().create_proxy(kind, target, args, kwargs, name, type_expr, proxy_factory_fn)

	if kind == "placeholder" and target in self.meta_args:
	rv.install_metadata(self.meta_args[target])
	return rv

	if target in self.orig_fns:
	# NOTE: tensor constructors in PyTorch define the `device` argument as
	# kwargs-only. That is why this works. If you add methods to
	# _TORCH_METHODS_TO_PATCH that do not define `device` as kwarg-only,
	# this will break and you will likely see issues where we cannot infer
	# the size of the output.
	if "device" in kwargs:
	kwargs["device"] = "meta"

	try:
	args_metas = torch.fx.node.map_aggregate(args, _proxies_to_metas)
	kwargs_metas = torch.fx.node.map_aggregate(kwargs, _proxies_to_metas)

	if kind == "call_function":
	meta_target = _MANUAL_META_OVERRIDES.get(target, target)
	meta_out = meta_target(args_metas, *kwargs_metas)
	if isinstance(meta_out, torch.Tensor):
	meta_out = meta_out.to(device="meta")
	elif kind == "call_method":
	method = getattr(args_metas[0].__class__, target)
	meta_target = _MANUAL_META_OVERRIDES.get(method, method)
	meta_out = meta_target(args_metas, *kwargs_metas)
	elif kind == "call_module":
	if not hasattr(self, "orig_forward"):
	raise AttributeError(f"{self} does not have an attribute called orig_forward")
	self._disable_module_getattr = True
	try:
	mod = self.root.get_submodule(target)
	mod_type = type(mod)
	if mod_type in _MANUAL_META_OVERRIDES:
	meta_out = _MANUAL_META_OVERRIDES[mod_type](mod, args_metas, *kwargs_metas)
	else:
	meta_out = self.orig_forward(args_metas, *kwargs_metas)
	finally:
	self._disable_module_getattr = False
	elif kind == "get_attr":
	self._disable_module_getattr = True
	try:
	attr_itr = self.root
	atoms = target.split(".")
	for atom in atoms:
	attr_itr = getattr(attr_itr, atom)
	if isinstance(attr_itr, torch.Tensor):
	meta_out = attr_itr.to(device="meta")
	else:
	meta_out = attr_itr
	finally:
	self._disable_module_getattr = False
	else:
	return rv

	if not isinstance(rv, Proxy):
	raise ValueError("Don't support composite output yet")
	rv.install_metadata(meta_out)
	except Exception as e:
	if _IS_IN_DEBUG_MODE:
	warnings.warn(f"Could not compute metadata for {kind} target {target}: {e}")

	return rv

	# Replaced by .getattr from PyTorch 1.13
	def _module_getattr(self, attr, attr_val, parameter_proxy_cache):
	if getattr(self, "_disable_module_getattr", False):
	return attr_val
	else:

	def maybe_get_proxy_for_attr(attr_val, collection_to_search, parameter_proxy_cache):
	for n, p in collection_to_search:
	if attr_val is p:
	if n not in parameter_proxy_cache:
	kwargs = {}
	if "proxy_factory_fn" in inspect.signature(self.create_proxy).parameters:
	kwargs["proxy_factory_fn"] = (
	None
	if not self.param_shapes_constant
	else lambda node: ParameterProxy(self, node, n, attr_val)
	)
	val_proxy = self.create_proxy("get_attr", n, (), {}, **kwargs) # type: ignore[arg-type]
	parameter_proxy_cache[n] = val_proxy
	return parameter_proxy_cache[n]
	return None

	if isinstance(attr_val, torch.nn.Parameter):
	maybe_parameter_proxy = maybe_get_proxy_for_attr(
	attr_val, self.root.named_parameters(), parameter_proxy_cache
	)
	if maybe_parameter_proxy is not None:
	return maybe_parameter_proxy

	if self.proxy_buffer_attributes and isinstance(attr_val, torch.Tensor):
	maybe_buffer_proxy = maybe_get_proxy_for_attr(
	attr_val, self.root.named_buffers(), parameter_proxy_cache
	)
	if maybe_buffer_proxy is not None:
	return maybe_buffer_proxy

	return attr_val

	# Needed for PyTorch 1.13+
	def getattr(self, attr: str, attr_val: Any, parameter_proxy_cache: Dict[str, Any]):
	return self._module_getattr(attr, attr_val, parameter_proxy_cache)

	def call_module(self, m, forward, args, kwargs):
	self.orig_forward = forward
	return super().call_module(m, forward, args, kwargs)

	def proxy(self, node):
	return HFProxy(node, self)

	def trace(
	self,
	root: Union[torch.nn.Module, Callable[..., Any]],
	concrete_args: Optional[Dict[str, Any]] = None,
	dummy_inputs: Optional[Dict[str, Any]] = None,
	complete_concrete_args_with_inputs_not_in_dummy_inputs: bool = True,
	) -> Graph:
	"""
	Traces `root` and returns the corresponding FX `torch.fx.Graph` representation. `root` can either be a
	`torch.nn.Module` instance or a Python callable. Note that after this call, `self.root` may be different from
	the `root` passed in here. For example, when a free function is passed to `trace()`, we will create a
	`torch.nn.Module` instance to use as the root and add embedded constants to.

	Args:
	root (`torch.nn.Module` or `Callable`):
	Either a `torch.nn.Module`` or a function to be traced through. If root is not a
	[`~transformers.PreTrainedModel`], then `dummy_inputs` must be passed, otherwise tracing will fail.
	concrete_args (`Dict[str, Any], optional):
	Concrete arguments that should not be treated as Proxies
	dummy_inputs (`Dict[str, Any]`, optional):
	The dummy inputs needed to handle data-dependent control-flow if `root` is not a
	[`~transformers.PreTrainedModel`]. It can also be used when `root` is a
	[`~transformers.PreTrainedModel`] to specify custom dummy inputs for a subset or all the model inputs.
	complete_concrete_args_with_inputs_not_in_dummy_inputs (`bool`, optional, defaults to `True`):
	If `True`, and `dummy_inputs` is specified, every argument that `root` can take that is not in
	`dummy_inputs` and not in `concrete_args` will be added to `concrete_args`, otherwise does nothing.

	Returns:
	`torch.fx.Graph`:
	A FX `torch.fx.Graph` representing the semantics of the passed-in `root`.

	"""
	sig = inspect.signature(root.forward if isinstance(root, torch.nn.Module) else root)

	if concrete_args is None:
	concrete_args = {}

	if dummy_inputs is not None and complete_concrete_args_with_inputs_not_in_dummy_inputs:
	for param in sig.parameters.values():
	if param.name in dummy_inputs:
	continue
	if param.default is inspect.Parameter.empty:
	raise ValueError(f"You need to specify a default value for the parameter {param.name}.")
	concrete_args.update(
	{
	p.name: p.default
	for p in sig.parameters.values()
	if (p.name not in dummy_inputs and p.name not in concrete_args)
	}
	)

	input_names = sig.parameters.keys() - concrete_args.keys()

	# Creating a random input shape to generate dummy inputs.
	batch_size = _generate_random_int()
	sequence_length = _generate_random_int()
	shape = [batch_size, sequence_length]

	if root.__class__.__name__ in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES):
	num_choices = _generate_random_int(low=2, high=5)
	shape.insert(1, num_choices)

	inputs = dict(dummy_inputs) if dummy_inputs is not None else {}
	for input_name in input_names:
	if input_name in inputs:
	continue
	# We enforce that root must either be a PreTrainedModel or deserialized from a serialized traced model to
	# be able to use HFTracer._generate_dummy_input.
	if isinstance(root, self.supported_archs) or type(root).__qualname__.startswith(
	("_deserialize_graph_module", "_CodeOnlyModule")
	):
	inputs.update(self._generate_dummy_input(root, input_name, shape))
	else:
	raise RuntimeError(
	f"Could not generate input named {input_name} for because root is not a"
	" transformers.PreTrainedModel."
	)

	concrete_metas = {
	input_name: input_.to("meta") if isinstance(input_, torch.Tensor) else input_
	for input_name, input_ in inputs.items()
	}
	for param in sig.parameters.values():
	if param.kind == inspect.Parameter.VAR_KEYWORD and param.name not in input_names:
	concrete_metas[f"**{param.name}"] = {}
	self.meta_args = concrete_metas
	self.patched_torch_methods = {
	target: _gen_constructor_wrapper(getattr(torch, target)) for target in self._TORCH_METHODS_TO_PATCH
	}
	self.orig_fns = set()

	for name, (wrapper, orig) in self.patched_torch_methods.items():
	setattr(torch, name, wrapper)
	self.orig_fns.add(orig)

	try:
	self.graph = super().trace(root, concrete_args=concrete_args)
	finally:
	for name, (_, orig) in self.patched_torch_methods.items():
	setattr(torch, name, orig)

	# This is necessary because concrete args are added as input to the traced module since
	# https://github.com/pytorch/pytorch/pull/55888.
	for node in self.graph.nodes:
	if node.op == "placeholder":
	# Removing default values for inputs as the forward pass will fail with them.
	if node.target in input_names:
	node.args = ()
	# Without this, torch.jit.script fails because the inputs type is Optional[torch.Tensor].
	# It cannot infer on the attributes and methods the input should have, and fails.
	node.type = torch.Tensor
	# It is a concrete arg so it is not used and should be removed.
	else:
	to_visit = [node]
	to_delete = collections.OrderedDict()
	while to_visit:
	n = to_visit.pop(0)
	to_delete[n] = None
	to_visit += list(n.users.keys())

	for user in reversed(to_delete.keys()):
	self.graph.erase_node(user)

	# TODO: solves GraphModule creation.
	# Without this, return type annotation "Tuple" is causing code execution failure.
	if node.op == "output":
	node.type = None

	return self.graph

	def _stateless_mod_instanciation_depends_on_proxies(self, mod: nn.Module) -> bool:
	"""
	Whether the module was instantiated with Proxies. If that is the case, such module cannot be a leaf module
	because its attributes are input-dependent.
	"""
	return any(isinstance(attr, Proxy) for attr in mod.__dict__.values())

	def _insert_module_as_submodule(self, mod: nn.Module) -> str:
	"""
	Helper method which tries to insert a module that was not declared as submodule.
	"""
	# If one of the module attributes is a Proxy, it means that its instantiation is input-dependent.
	# It is not possible to insert such modules, those should be traced through.
	if self._stateless_mod_instanciation_depends_on_proxies(mod):
	return ""
	idx = 0
	mod_name = mod.__class__.__name__.lower()
	path = f"{mod_name}_{idx}"
	already_inserted = False
	while hasattr(self.root, path):
	if getattr(self.root, path) is mod:
	already_inserted = True
	break
	path = f"{mod_name}_{idx}"
	idx += 1

	# No need to add multiple instances of the same module.
	if not already_inserted:
	self.root.add_module(path, mod)
	return path

	def path_of_module(self, mod: nn.Module) -> str:
	"""
	Helper method to find the qualified name of `mod` in the Module hierarchy of `root`. For example, if `root` has
	a submodule named `foo`, which has a submodule named `bar`, passing `bar` into this function will return the
	string "foo.bar".

	Args:
	mod (str): The `Module` to retrieve the qualified name for.
	"""
	try:
	return super().path_of_module(mod)
	except NameError as e:
	if self.allow_insert_stateless_mods and len(list(mod.parameters())) == 0 and len(list(mod.buffers())) == 0:
	path = self._insert_module_as_submodule(mod)
	return path
	raise e

	def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
	return (not self._stateless_mod_instanciation_depends_on_proxies(m)) and super().is_leaf_module(
	m, module_qualified_name
	)

	@compatibility(is_backward_compatible=True)
	def keys(self, obj: "Proxy") -> Any:
	"""Called when a proxy object is has the keys() method called.
	This is what happens when is called on a proxy. This should return an iterator if is supposed to work in
	your custom tracer.
	"""
	attribute = HFAttribute(obj, "keys")()
	if obj.node.target == "**kwargs":
	return attribute._metadata
	return attribute


	def get_concrete_args(model: nn.Module, input_names: List[str]):
	sig = inspect.signature(model.forward)

	if not (set(input_names) <= set(sig.parameters.keys())):
	formatted_input_names = input_names[0] if len(input_names) == 1 else ", ".join(input_names)
	formatted_allowed_input_names = ", ".join(sig.parameters.keys())
	raise ValueError(
	f"The model does not have input(s) named: {formatted_input_names}, expected a subset of the following:"
	f" {formatted_allowed_input_names}"
	)

	return {p.name: p.default for p in sig.parameters.values() if p.name not in input_names}


	def check_if_model_is_supported(model: PreTrainedModel):
	if model.__class__.__name__ not in _SUPPORTED_MODELS:
	supported_model_names = ", ".join(_SUPPORTED_MODELS)
	raise NotImplementedError(
	f"Model {model.__class__.__name__} is not supported yet, supported models: {supported_model_names}"
	)


	def symbolic_trace(
	model: PreTrainedModel,
	input_names: Optional[List[str]] = None,
	disable_check: bool = False,
	tracer_cls: Type[HFTracer] = HFTracer,
	) -> GraphModule:
	"""
	Performs symbolic tracing on the model.

	Args:
	model ([`PretrainedModel`]):
	The model to trace.
	input_names (`List[str]`, optional):
	The names of the inputs of the traced model. If unset, model.dummy_inputs.keys() are used instead.
	disable_check (`bool`, optional, defaults to `False`):
	If `True`, no check is done before trying to trace the model, this is mostly usesul for debugging purposes.
	tracer_cls (`Type[HFTracer]`, optional, defaults to `HFTracer`):
	The tracer class to use for instantiating the tracer. If unset, `HFTracer` is used instead.

	Returns:
	`torch.fx.GraphModule`: A GraphModule constructed by recording operations seen while tracing the model.

	Example:

	```python
	from transformers.utils.fx import symbolic_trace

	traced_model = symbolic_trace(model, input_names=["input_ids", "attention_mask", "token_type_ids"])
	```
	"""
	if input_names is None:
	input_names = model.dummy_inputs.keys()

	input_names = list(input_names)
	concrete_args = get_concrete_args(model, input_names)

	if not disable_check:
	check_if_model_is_supported(model)

	# Tracing.
	tracer = tracer_cls()
	traced_graph = tracer.trace(model, concrete_args=concrete_args)
	traced = torch.fx.GraphModule(model, traced_graph)

	traced.config = model.config
	# The model class must be stored as an attribute to allow model deserialization, which uses trace, and thus
	# _generate_dummy_input, where the model class is needed.
	traced.class_for_deserialization = model.__class__
	traced.device = model.device

	return traced