codellm_1b_rotary / modeling_t5.py

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	# coding=utf-8
	# Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
	#
	# 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.
	""" PyTorch T5 model."""


	import copy
	import math
	import os

	import torch
	from torch import nn
	from torch.nn import CrossEntropyLoss
	from torch.utils.checkpoint import checkpoint
	from transformers import T5Config
	from transformers.activations import ACT2FN
	from transformers.file_utils import (
	DUMMY_INPUTS,
	DUMMY_MASK,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	is_torch_fx_proxy,
	replace_return_docstrings,
	)
	from transformers.modeling_outputs import (
	BaseModelOutput,
	BaseModelOutputWithPastAndCrossAttentions,
	Seq2SeqLMOutput,
	Seq2SeqModelOutput,
	)
	from transformers.modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
	from transformers.utils import logging
	from transformers.utils.model_parallel_utils import assert_device_map, get_device_map

	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "T5Config"
	_TOKENIZER_FOR_DOC = "T5Tokenizer"
	_CHECKPOINT_FOR_DOC = "t5-small"

	####################################################
	# This dict contains ids and associated url
	# for the pretrained weights provided with the models
	####################################################
	T5_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"t5-small",
	"t5-base",
	"t5-large",
	"t5-3b",
	"t5-11b",
	# See all T5 models at https://huggingface.co/models?filter=t5
	]


	####################################################
	# This is a conversion method from TF 1.0 to PyTorch
	# More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28
	####################################################
	def load_tf_weights_in_t5(model, config, tf_checkpoint_path):
	"""Load tf checkpoints in a pytorch model."""
	try:
	import re

	import numpy as np
	import tensorflow as tf
	except ImportError:
	logger.error(
	"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
	"https://www.tensorflow.org/install/ for installation instructions."
	)
	raise
	tf_path = os.path.abspath(tf_checkpoint_path)
	logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
	# Load weights from TF model
	init_vars = tf.train.list_variables(tf_path)
	names = []
	tf_weights = {}
	for name, shape in init_vars:
	logger.info(f"Loading TF weight {name} with shape {shape}")
	array = tf.train.load_variable(tf_path, name)
	names.append(name)
	tf_weights[name] = array

	for txt_name in names:
	name = txt_name.split("/")
	# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
	# which are not required for using pretrained model
	if any(
	n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
	for n in name
	):
	logger.info(f"Skipping {'/'.join(name)}")
	tf_weights.pop(txt_name, None)
	continue
	if "_slot_" in name[-1]:
	logger.info(f"Skipping {'/'.join(name)}")
	tf_weights.pop(txt_name, None)
	continue
	pointer = model
	array = tf_weights[txt_name]

	for m_name in name:
	if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
	scope_names = re.split(r"_(\d+)", m_name)
	else:
	scope_names = [m_name]
	if scope_names[0] in ["kernel", "scale", "embedding"]:
	pointer = getattr(pointer, "weight")
	elif scope_names[0] == "self_attention":
	pointer = getattr(pointer, "layer")
	pointer = pointer[0]
	elif scope_names[0] == "enc_dec_attention":
	pointer = getattr(pointer, "layer")
	pointer = pointer[1]
	elif scope_names[0] == "dense_relu_dense":
	pointer = getattr(pointer, "layer")
	pointer = pointer[2]
	elif scope_names[0] == "rms_norm":
	if hasattr(pointer, "layer_norm"):
	pointer = getattr(pointer, "layer_norm")
	elif hasattr(pointer, "final_layer_norm"):
	pointer = getattr(pointer, "final_layer_norm")
	elif scope_names[0] == "scale":
	pointer = getattr(pointer, "weight")
	elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
	pointer = getattr(pointer, "bias")
	elif scope_names[0] == "squad":
	pointer = getattr(pointer, "classifier")
	elif scope_names[0] == "decoder" and name[1] == "logits":
	continue
	elif scope_names[0] == "logits":
	pointer = getattr(pointer, "lm_head")
	elif scope_names[0] == "wi" and len(scope_names) > 1 and scope_names[1].isdigit():
	pointer = getattr(pointer, f"wi_{scope_names[1]}")
	continue
	else:
	try:
	pointer = getattr(pointer, scope_names[0])
	except AttributeError:
	logger.info(f"Skipping {'/'.join(name)}")
	continue
	if len(scope_names) >= 2:
	num = int(scope_names[1])
	pointer = pointer[num]
	if scope_names[0] not in ["kernel", "scale", "embedding"]:
	pointer = getattr(pointer, "weight")
	if scope_names[0] != "embedding":
	logger.info(f"Transposing numpy weight of shape {array.shape} for {name}")
	array = np.transpose(array)
	try:
	assert (
	pointer.shape == array.shape
	), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
	except AssertionError as e:
	e.args += (pointer.shape, array.shape)
	raise
	logger.info(f"Initialize PyTorch weight {name}")
	pointer.data = torch.from_numpy(array.astype(np.float32))
	tf_weights.pop(txt_name, None)

	logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}.")
	return model


	####################################################
	# PyTorch Models are constructed by sub-classing
	# - torch.nn.Module for the layers and
	# - PreTrainedModel for the models (it-self a sub-class of nn.Module)
	####################################################
	PARALLELIZE_DOCSTRING = r"""
	This is an experimental feature and is a subject to change at a moment's notice.

	Uses a device map to distribute attention modules of the model across several devices. If no device map is given,
	it will evenly distribute blocks across all devices.

	Args:
	device_map (`Dict[int, list]`, optional, defaults to None):
	A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
	automatically mapped to the first device (for esoteric reasons). That means that the first device should
	have fewer attention modules mapped to it than other devices. For reference, the t5 models have the
	following number of attention modules:

	- t5-small: 6
	- t5-base: 12
	- t5-large: 24
	- t5-3b: 24
	- t5-11b: 24

	Example:

	```python
	# Here is an example of a device map on a machine with 4 GPUs using t5-3b, which has a total of 24 attention modules:
	model = T5ForConditionalGeneration.from_pretrained("t5-3b")
	device_map = {
	0: [0, 1, 2],
	1: [3, 4, 5, 6, 7, 8, 9],
	2: [10, 11, 12, 13, 14, 15, 16],
	3: [17, 18, 19, 20, 21, 22, 23],
	}
	model.parallelize(device_map)
	```
	"""
	DEPARALLELIZE_DOCSTRING = r"""
	Moves the model to cpu from a model parallel state.

	Example:

	```python
	# On a 4 GPU machine with t5-3b:
	model = T5ForConditionalGeneration.from_pretrained("t5-3b")
	device_map = {
	0: [0, 1, 2],
	1: [3, 4, 5, 6, 7, 8, 9],
	2: [10, 11, 12, 13, 14, 15, 16],
	3: [17, 18, 19, 20, 21, 22, 23],
	}
	model.parallelize(device_map) # Splits the model across several devices
	model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
	```
	"""


	class T5LayerNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6):
	"""
	Construct a layernorm module in the T5 style No bias and no subtraction of mean.
	"""
	super().__init__()
	self.weight = nn.Parameter(torch.ones(hidden_size))
	self.variance_epsilon = eps

	def forward(self, hidden_states):
	# layer norm should always be calculated in float32
	variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
	hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

	# convert into half-precision if necessary
	if self.weight.dtype in [torch.float16, torch.bfloat16]:
	hidden_states = hidden_states.to(self.weight.dtype)

	return self.weight * hidden_states


	class T5DenseReluDense(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
	self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
	self.dropout = nn.Dropout(config.dropout_rate)

	def forward(self, hidden_states):
	hidden_states = self.wi(hidden_states)
	hidden_states = nn.functional.relu(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.wo(hidden_states)
	return hidden_states


	class T5DenseGatedGeluDense(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
	self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
	self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
	self.dropout = nn.Dropout(config.dropout_rate)
	self.gelu_act = ACT2FN["gelu_new"]

	def forward(self, hidden_states):
	hidden_gelu = self.gelu_act(self.wi_0(hidden_states))
	hidden_linear = self.wi_1(hidden_states)
	hidden_states = hidden_gelu * hidden_linear
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.wo(hidden_states)
	return hidden_states


	class T5LayerFF(nn.Module):
	def __init__(self, config):
	super().__init__()
	if config.feed_forward_proj == "relu":
	self.DenseReluDense = T5DenseReluDense(config)
	elif config.feed_forward_proj == "gated-gelu":
	self.DenseReluDense = T5DenseGatedGeluDense(config)
	else:
	raise ValueError(
	f"{self.config.feed_forward_proj} is not supported. Choose between `relu` and `gated-gelu`"
	)

	self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
	self.dropout = nn.Dropout(config.dropout_rate)

	def forward(self, hidden_states):
	forwarded_states = self.layer_norm(hidden_states)
	forwarded_states = self.DenseReluDense(forwarded_states)
	hidden_states = hidden_states + self.dropout(forwarded_states)
	return hidden_states


	class T5Attention(nn.Module):
	def __init__(self, config: T5Config, has_relative_attention_bias=False):
	super().__init__()
	self.is_decoder = config.is_decoder
	self.has_relative_attention_bias = has_relative_attention_bias

	self.relative_attention_num_buckets = config.relative_attention_num_buckets
	self.d_model = config.d_model
	self.key_value_proj_dim = config.d_kv
	self.n_heads = config.num_heads
	self.dropout = config.dropout_rate
	self.inner_dim = self.n_heads * self.key_value_proj_dim

	# Mesh TensorFlow initialization to avoid scaling before softmax
	self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
	self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
	self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
	self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)

	if self.has_relative_attention_bias:
	self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
	self.pruned_heads = set()
	self.gradient_checkpointing = False

	def prune_heads(self, heads):
	if len(heads) == 0:
	return
	heads, index = find_pruneable_heads_and_indices(
	heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
	)
	# Prune linear layers
	self.q = prune_linear_layer(self.q, index)
	self.k = prune_linear_layer(self.k, index)
	self.v = prune_linear_layer(self.v, index)
	self.o = prune_linear_layer(self.o, index, dim=1)
	# Update hyper params
	self.n_heads = self.n_heads - len(heads)
	self.inner_dim = self.key_value_proj_dim * self.n_heads
	self.pruned_heads = self.pruned_heads.union(heads)

	@staticmethod
	def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
	"""
	Adapted from Mesh Tensorflow:
	https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593

	Translate relative position to a bucket number for relative attention. The relative position is defined as
	memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
	position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
	small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
	positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
	This should allow for more graceful generalization to longer sequences than the model has been trained on

	Args:
	relative_position: an int32 Tensor
	bidirectional: a boolean - whether the attention is bidirectional
	num_buckets: an integer
	max_distance: an integer

	Returns:
	a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
	"""
	relative_buckets = 0
	if bidirectional:
	num_buckets //= 2
	relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
	relative_position = torch.abs(relative_position)
	else:
	relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
	# now relative_position is in the range [0, inf)

	# half of the buckets are for exact increments in positions
	max_exact = num_buckets // 2
	is_small = relative_position < max_exact

	# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
	relative_postion_if_large = max_exact + (
	torch.log(relative_position.float() / max_exact)
	/ math.log(max_distance / max_exact)
	* (num_buckets - max_exact)
	).to(torch.long)
	relative_postion_if_large = torch.min(
	relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1)
	)

	relative_buckets += torch.where(is_small, relative_position, relative_postion_if_large)
	return relative_buckets

	def compute_bias(self, query_length, key_length):
	"""Compute binned relative position bias"""
	context_position = torch.arange(
	query_length, dtype=torch.long, device=self.relative_attention_bias.weight.device
	)[:, None]
	memory_position = torch.arange(
	key_length, dtype=torch.long, device=self.relative_attention_bias.weight.device
	)[None, :]
	relative_position = memory_position - context_position # shape (query_length, key_length)
	relative_position_bucket = self._relative_position_bucket(
	relative_position, # shape (query_length, key_length)
	bidirectional=(not self.is_decoder),
	num_buckets=self.relative_attention_num_buckets,
	)
	values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
	values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
	return values

	def forward(
	self,
	hidden_states,
	mask=None,
	key_value_states=None,
	position_bias=None,
	past_key_value=None,
	layer_head_mask=None,
	query_length=None,
	use_cache=False,
	output_attentions=False,
	):
	"""
	Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
	"""
	# Input is (batch_size, seq_length, dim)
	# Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
	# past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
	batch_size, seq_length = hidden_states.shape[:2]

	real_seq_length = seq_length

	if past_key_value is not None:
	assert (
	len(past_key_value) == 2
	), f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
	real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length

	key_length = real_seq_length if key_value_states is None else key_value_states.shape[1]

	def shape(states):
	"""projection"""
	return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)

	def unshape(states):
	"""reshape"""
	return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)

	def project(hidden_states, proj_layer, key_value_states, past_key_value):
	"""projects hidden states correctly to key/query states"""
	if key_value_states is None:
	# self-attn
	# (batch_size, n_heads, seq_length, dim_per_head)
	hidden_states = shape(proj_layer(hidden_states))
	elif past_key_value is None:
	# cross-attn
	# (batch_size, n_heads, seq_length, dim_per_head)
	hidden_states = shape(proj_layer(key_value_states))

	if past_key_value is not None:
	if key_value_states is None:
	# self-attn
	# (batch_size, n_heads, key_length, dim_per_head)
	hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
	else:
	# cross-attn
	hidden_states = past_key_value
	return hidden_states

	# get query states
	query_states = shape(self.q(hidden_states)) # (batch_size, n_heads, seq_length, dim_per_head)

	# get key/value states
	key_states = project(
	hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None
	)
	value_states = project(
	hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None
	)

	# compute scores
	scores = torch.matmul(
	query_states, key_states.transpose(3, 2)
	) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9

	if position_bias is None:
	if not self.has_relative_attention_bias:
	position_bias = torch.zeros(
	(1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype
	)
	if self.gradient_checkpointing and self.training:
	position_bias.requires_grad = True
	else:
	position_bias = self.compute_bias(real_seq_length, key_length)

	# if key and values are already calculated
	# we want only the last query position bias
	if past_key_value is not None:
	position_bias = position_bias[:, :, -hidden_states.size(1) :, :]

	if mask is not None:
	position_bias = position_bias + mask # (batch_size, n_heads, seq_length, key_length)

	scores += position_bias
	attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
	scores
	) # (batch_size, n_heads, seq_length, key_length)
	attn_weights = nn.functional.dropout(
	attn_weights, p=self.dropout, training=self.training
	) # (batch_size, n_heads, seq_length, key_length)

	# Mask heads if we want to
	if layer_head_mask is not None:
	attn_weights = attn_weights * layer_head_mask

	attn_output = unshape(torch.matmul(attn_weights, value_states)) # (batch_size, seq_length, dim)
	attn_output = self.o(attn_output)

	present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None
	outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)

	if output_attentions:
	outputs = outputs + (attn_weights,)
	return outputs


	class T5LayerSelfAttention(nn.Module):
	def __init__(self, config, has_relative_attention_bias=False):
	super().__init__()
	self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
	self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
	self.dropout = nn.Dropout(config.dropout_rate)

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	position_bias=None,
	layer_head_mask=None,
	past_key_value=None,
	use_cache=False,
	output_attentions=False,
	):
	normed_hidden_states = self.layer_norm(hidden_states)
	attention_output = self.SelfAttention(
	normed_hidden_states,
	mask=attention_mask,
	position_bias=position_bias,
	layer_head_mask=layer_head_mask,
	past_key_value=past_key_value,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	hidden_states = hidden_states + self.dropout(attention_output[0])
	outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
	return outputs


	class T5LayerCrossAttention(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.EncDecAttention = T5Attention(config, has_relative_attention_bias=False)
	self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
	self.dropout = nn.Dropout(config.dropout_rate)

	def forward(
	self,
	hidden_states,
	key_value_states,
	attention_mask=None,
	position_bias=None,
	layer_head_mask=None,
	past_key_value=None,
	use_cache=False,
	query_length=None,
	output_attentions=False,
	):
	normed_hidden_states = self.layer_norm(hidden_states)
	attention_output = self.EncDecAttention(
	normed_hidden_states,
	mask=attention_mask,
	key_value_states=key_value_states,
	position_bias=position_bias,
	layer_head_mask=layer_head_mask,
	past_key_value=past_key_value,
	use_cache=use_cache,
	query_length=query_length,
	output_attentions=output_attentions,
	)
	layer_output = hidden_states + self.dropout(attention_output[0])
	outputs = (layer_output,) + attention_output[1:] # add attentions if we output them
	return outputs


	class T5Block(nn.Module):
	def __init__(self, config, has_relative_attention_bias=False):
	super().__init__()
	self.is_decoder = config.is_decoder
	self.layer = nn.ModuleList()
	self.layer.append(T5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias))
	if self.is_decoder:
	self.layer.append(T5LayerCrossAttention(config))

	self.layer.append(T5LayerFF(config))

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	position_bias=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	encoder_decoder_position_bias=None,
	layer_head_mask=None,
	cross_attn_layer_head_mask=None,
	past_key_value=None,
	use_cache=False,
	output_attentions=False,
	return_dict=True,
	):

	if past_key_value is not None:
	assert self.is_decoder, "Only decoder can use `past_key_values`"
	expected_num_past_key_values = 2 if encoder_hidden_states is None else 4

	if len(past_key_value) != expected_num_past_key_values:
	raise ValueError(
	f"There should be {expected_num_past_key_values} past states. "
	f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
	f"Got {len(past_key_value)} past key / value states"
	)

	self_attn_past_key_value = past_key_value[:2]
	cross_attn_past_key_value = past_key_value[2:]
	else:
	self_attn_past_key_value, cross_attn_past_key_value = None, None

	self_attention_outputs = self.layer[0](
	hidden_states,
	attention_mask=attention_mask,
	position_bias=position_bias,
	layer_head_mask=layer_head_mask,
	past_key_value=self_attn_past_key_value,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	hidden_states, present_key_value_state = self_attention_outputs[:2]
	attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights

	# clamp inf values to enable fp16 training
	if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
	clamp_value = torch.finfo(hidden_states.dtype).max - 1000
	hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

	do_cross_attention = self.is_decoder and encoder_hidden_states is not None
	if do_cross_attention:
	# the actual query length is unknown for cross attention
	# if using past key value states. Need to inject it here
	if present_key_value_state is not None:
	query_length = present_key_value_state[0].shape[2]
	else:
	query_length = None

	cross_attention_outputs = self.layer[1](
	hidden_states,
	key_value_states=encoder_hidden_states,
	attention_mask=encoder_attention_mask,
	position_bias=encoder_decoder_position_bias,
	layer_head_mask=cross_attn_layer_head_mask,
	past_key_value=cross_attn_past_key_value,
	query_length=query_length,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	hidden_states = cross_attention_outputs[0]

	# clamp inf values to enable fp16 training
	if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
	clamp_value = torch.finfo(hidden_states.dtype).max - 1000
	hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

	# Combine self attn and cross attn key value states
	if present_key_value_state is not None:
	present_key_value_state = present_key_value_state + cross_attention_outputs[1]

	# Keep cross-attention outputs and relative position weights
	attention_outputs = attention_outputs + cross_attention_outputs[2:]

	# Apply Feed Forward layer
	hidden_states = self.layer[-1](hidden_states)

	# clamp inf values to enable fp16 training
	if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
	clamp_value = torch.finfo(hidden_states.dtype).max - 1000
	hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

	outputs = (hidden_states,)

	if use_cache:
	outputs = outputs + (present_key_value_state,) + attention_outputs
	else:
	outputs = outputs + attention_outputs

	return outputs # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)


	class T5PreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	config_class = T5Config
	load_tf_weights = load_tf_weights_in_t5
	base_model_prefix = "transformer"
	is_parallelizable = True
	supports_gradient_checkpointing = True

	@property
	def dummy_inputs(self):
	input_ids = torch.tensor(DUMMY_INPUTS)
	input_mask = torch.tensor(DUMMY_MASK)
	dummy_inputs = {
	"decoder_input_ids": input_ids,
	"input_ids": input_ids,
	"decoder_attention_mask": input_mask,
	}
	return dummy_inputs

	def _init_weights(self, module):
	"""Initialize the weights"""
	factor = self.config.initializer_factor # Used for testing weights initialization
	if isinstance(module, T5LayerNorm):
	module.weight.data.fill_(factor * 1.0)
	elif isinstance(module, (T5Model, T5ForConditionalGeneration, T5EncoderModel)):
	# Mesh TensorFlow embeddings initialization
	# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
	module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
	elif isinstance(module, T5DenseReluDense):
	# Mesh TensorFlow FF initialization
	# See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
	# and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
	module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
	if hasattr(module.wi, "bias") and module.wi.bias is not None:
	module.wi.bias.data.zero_()
	module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
	if hasattr(module.wo, "bias") and module.wo.bias is not None:
	module.wo.bias.data.zero_()
	elif isinstance(module, T5DenseGatedGeluDense):
	module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
	if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
	module.wi_0.bias.data.zero_()
	module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
	if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None:
	module.wi_1.bias.data.zero_()
	module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
	if hasattr(module.wo, "bias") and module.wo.bias is not None:
	module.wo.bias.data.zero_()
	elif isinstance(module, T5Attention):
	# Mesh TensorFlow attention initialization to avoid scaling before softmax
	# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
	d_model = self.config.d_model
	key_value_proj_dim = self.config.d_kv
	n_heads = self.config.num_heads
	module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
	module.k.weight.data.normal_(mean=0.0, std=factor * (d_model ** -0.5))
	module.v.weight.data.normal_(mean=0.0, std=factor * (d_model ** -0.5))
	module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
	if module.has_relative_attention_bias:
	module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, (T5Attention, T5Stack)):
	module.gradient_checkpointing = value

	def _shift_right(self, input_ids):
	decoder_start_token_id = self.config.decoder_start_token_id
	pad_token_id = self.config.pad_token_id

	assert (
	decoder_start_token_id is not None
	), "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id. See T5 docs for more information"

	# shift inputs to the right
	if is_torch_fx_proxy(input_ids):
	# Item assignment is not supported natively for proxies.
	shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id)
	shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
	else:
	shifted_input_ids = input_ids.new_zeros(input_ids.shape)
	shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
	shifted_input_ids[..., 0] = decoder_start_token_id

	assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
	# replace possible -100 values in labels by `pad_token_id`
	shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

	assert torch.all(shifted_input_ids >= 0).item(), "Verify that `shifted_input_ids` has only positive values"

	return shifted_input_ids


	class T5Stack(T5PreTrainedModel):
	def __init__(self, config, embed_tokens=None):
	super().__init__(config)

	self.embed_tokens = embed_tokens
	self.is_decoder = config.is_decoder

	self.block = nn.ModuleList(
	[T5Block(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)]
	)
	self.final_layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
	self.dropout = nn.Dropout(config.dropout_rate)

	# Initialize weights and apply final processing
	self.post_init()
	# Model parallel
	self.model_parallel = False
	self.device_map = None
	self.gradient_checkpointing = False

	@add_start_docstrings(PARALLELIZE_DOCSTRING)
	def parallelize(self, device_map=None):
	# Check validity of device_map
	self.device_map = (
	get_device_map(len(self.block), range(torch.cuda.device_count())) if device_map is None else device_map
	)
	assert_device_map(self.device_map, len(self.block))
	self.model_parallel = True
	self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
	self.last_device = "cuda:" + str(max(self.device_map.keys()))
	# Load onto devices
	for k, v in self.device_map.items():
	for layer in v:
	cuda_device = "cuda:" + str(k)
	self.block[layer] = self.block[layer].to(cuda_device)

	# Set embed_tokens to first layer
	self.embed_tokens = self.embed_tokens.to(self.first_device)
	# Set final layer norm to last device
	self.final_layer_norm = self.final_layer_norm.to(self.last_device)

	@add_start_docstrings(PARALLELIZE_DOCSTRING)
	def deparallelize(self):
	self.model_parallel = False
	self.device_map = None
	self.first_device = "cpu"
	self.last_device = "cpu"
	for i in range(len(self.block)):
	self.block[i] = self.block[i].to("cpu")
	self.embed_tokens = self.embed_tokens.to("cpu")
	self.final_layer_norm = self.final_layer_norm.to("cpu")
	torch.cuda.empty_cache()

	def get_input_embeddings(self):
	return self.embed_tokens

	def set_input_embeddings(self, new_embeddings):
	self.embed_tokens = new_embeddings

	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	inputs_embeds=None,
	head_mask=None,
	cross_attn_head_mask=None,
	past_key_values=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	# Model parallel
	if self.model_parallel:
	torch.cuda.set_device(self.first_device)
	self.embed_tokens = self.embed_tokens.to(self.first_device)
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	if input_ids is not None and inputs_embeds is not None:
	err_msg_prefix = "decoder_" if self.is_decoder else ""
	raise ValueError(
	f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
	)
	elif input_ids is not None:
	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	err_msg_prefix = "decoder_" if self.is_decoder else ""
	raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")

	if inputs_embeds is None:
	assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
	inputs_embeds = self.embed_tokens(input_ids)

	batch_size, seq_length = input_shape

	# required mask seq length can be calculated via length of past
	mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length

	if use_cache is True:
	assert self.is_decoder, f"`use_cache` can only be set to `True` if {self} is used as a decoder"

	if attention_mask is None:
	attention_mask = torch.ones(batch_size, mask_seq_length).to(inputs_embeds.device)
	if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
	encoder_seq_length = encoder_hidden_states.shape[1]
	encoder_attention_mask = torch.ones(
	batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
	)

	# initialize past_key_values with `None` if past does not exist
	if past_key_values is None:
	past_key_values = [None] * len(self.block)

	# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
	# ourselves in which case we just need to make it broadcastable to all heads.
	extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, inputs_embeds.device)

	# If a 2D or 3D attention mask is provided for the cross-attention
	# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
	if self.is_decoder and encoder_hidden_states is not None:
	encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
	encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
	if encoder_attention_mask is None:
	encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device)
	encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
	else:
	encoder_extended_attention_mask = None

	# Prepare head mask if needed
	head_mask = self.get_head_mask(head_mask, self.config.num_layers)
	cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
	present_key_value_states = () if use_cache else None
	all_hidden_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None
	all_cross_attentions = () if (output_attentions and self.is_decoder) else None
	position_bias = None
	encoder_decoder_position_bias = None

	hidden_states = self.dropout(inputs_embeds)

	for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
	layer_head_mask = head_mask[i]
	cross_attn_layer_head_mask = cross_attn_head_mask[i]
	# Model parallel
	if self.model_parallel:
	torch.cuda.set_device(hidden_states.device)
	# Ensure that attention_mask is always on the same device as hidden_states
	if attention_mask is not None:
	attention_mask = attention_mask.to(hidden_states.device)
	if position_bias is not None:
	position_bias = position_bias.to(hidden_states.device)
	if encoder_hidden_states is not None:
	encoder_hidden_states = encoder_hidden_states.to(hidden_states.device)
	if encoder_extended_attention_mask is not None:
	encoder_extended_attention_mask = encoder_extended_attention_mask.to(hidden_states.device)
	if encoder_decoder_position_bias is not None:
	encoder_decoder_position_bias = encoder_decoder_position_bias.to(hidden_states.device)
	if layer_head_mask is not None:
	layer_head_mask = layer_head_mask.to(hidden_states.device)
	if cross_attn_layer_head_mask is not None:
	cross_attn_layer_head_mask = cross_attn_layer_head_mask.to(hidden_states.device)
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warn(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return tuple(module(*inputs, use_cache, output_attentions))

	return custom_forward

	layer_outputs = checkpoint(
	create_custom_forward(layer_module),
	hidden_states,
	extended_attention_mask,
	position_bias,
	encoder_hidden_states,
	encoder_extended_attention_mask,
	encoder_decoder_position_bias,
	layer_head_mask,
	cross_attn_layer_head_mask,
	None, # past_key_value is always None with gradient checkpointing
	)
	else:
	layer_outputs = layer_module(
	hidden_states,
	attention_mask=extended_attention_mask,
	position_bias=position_bias,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_extended_attention_mask,
	encoder_decoder_position_bias=encoder_decoder_position_bias,
	layer_head_mask=layer_head_mask,
	cross_attn_layer_head_mask=cross_attn_layer_head_mask,
	past_key_value=past_key_value,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)

	# layer_outputs is a tuple with:
	# hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
	if use_cache is False:
	layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]

	hidden_states, present_key_value_state = layer_outputs[:2]

	# We share the position biases between the layers - the first layer store them
	# layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
	# (cross-attention position bias), (cross-attention weights)
	position_bias = layer_outputs[2]
	if self.is_decoder and encoder_hidden_states is not None:
	encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
	# append next layer key value states
	if use_cache:
	present_key_value_states = present_key_value_states + (present_key_value_state,)

	if output_attentions:
	all_attentions = all_attentions + (layer_outputs[3],)
	if self.is_decoder:
	all_cross_attentions = all_cross_attentions + (layer_outputs[5],)

	# Model Parallel: If it's the last layer for that device, put things on the next device
	if self.model_parallel:
	for k, v in self.device_map.items():
	if i == v[-1] and "cuda:" + str(k) != self.last_device:
	hidden_states = hidden_states.to("cuda:" + str(k + 1))

	hidden_states = self.final_layer_norm(hidden_states)
	hidden_states = self.dropout(hidden_states)

	# Add last layer
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if not return_dict:
	return tuple(
	v
	for v in [
	hidden_states,
	present_key_value_states,
	all_hidden_states,
	all_attentions,
	all_cross_attentions,
	]
	if v is not None
	)
	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=present_key_value_states,
	hidden_states=all_hidden_states,
	attentions=all_attentions,
	cross_attentions=all_cross_attentions,
	)


	T5_START_DOCSTRING = r"""

	The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
	Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
	Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a
	text-to-text denoising generative setting.

	This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
	library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
	etc.)

	This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
	and behavior.

	Parameters:
	config ([`T5Config`]): Model configuration class with all the parameters of the model.
	Initializing with a config file does not load the weights associated with the model, only the
	configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
	"""

	T5_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
	should be able to pad the inputs on both the right and the left.

	Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for detail.

	[What are input IDs?](../glossary#input-ids)

	To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
	attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional):
	Indices of decoder input sequence tokens in the vocabulary.

	Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are decoder input IDs?](../glossary#decoder-input-ids)

	T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
	is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

	To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
	Training](./t5#training).
	decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, optional):
	Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
	be used by default.
	head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional):
	Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0,
	1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional):
	Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
	1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional):
	Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
	`[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	encoder_outputs (`tuple(tuple(torch.FloatTensor)`, optional):
	Tuple consists of (`last_hidden_state`, `optional`: hidden_states, `optional`: attentions)
	`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
	the output of the last layer of the encoder. Used in the cross-attention of the decoder.
	past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
	Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
	don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
	`decoder_input_ids` of shape `(batch_size, sequence_length)`.
	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
	is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
	model's internal embedding lookup matrix.
	decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
	representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
	input (see `past_key_values`). This is useful if you want more control over how to convert
	`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

	If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
	of `inputs_embeds`.

	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).

	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
	"""

	T5_ENCODER_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
	should be able to pad the inputs on both the right and the left.

	Indices can be obtained using [`T5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for detail.

	To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
	attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional):
	Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
	is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
	model's internal embedding lookup matrix.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
	"""

	# Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
	__HEAD_MASK_WARNING_MSG = """
	The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
	`decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
	If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers,
	num_heads)`.
	"""


	@add_start_docstrings(
	"The bare T5 Model transformer outputting raw hidden-states without any specific head on top.",
	T5_START_DOCSTRING,
	)
	class T5Model(T5PreTrainedModel):
	_keys_to_ignore_on_load_missing = [
	r"encoder\.embed_tokens\.weight",
	r"decoder\.embed_tokens\.weight",
	]
	_keys_to_ignore_on_load_unexpected = [
	r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight",
	]

	def __init__(self, config: T5Config):
	super().__init__(config)
	self.shared = nn.Embedding(config.vocab_size, config.d_model)

	encoder_config = copy.deepcopy(config)
	encoder_config.is_decoder = False
	encoder_config.use_cache = False
	encoder_config.is_encoder_decoder = False
	self.encoder = T5Stack(encoder_config, self.shared)

	decoder_config = copy.deepcopy(config)
	decoder_config.is_decoder = True
	decoder_config.is_encoder_decoder = False
	decoder_config.num_layers = config.num_decoder_layers
	self.decoder = T5Stack(decoder_config, self.shared)

	# Initialize weights and apply final processing
	self.post_init()

	# Model parallel
	self.model_parallel = False
	self.device_map = None

	@add_start_docstrings(PARALLELIZE_DOCSTRING)
	def parallelize(self, device_map=None):
	self.device_map = (
	get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
	if device_map is None
	else device_map
	)
	assert_device_map(self.device_map, len(self.encoder.block))
	self.encoder.parallelize(self.device_map)
	self.decoder.parallelize(self.device_map)
	self.model_parallel = True

	@add_start_docstrings(DEPARALLELIZE_DOCSTRING)
	def deparallelize(self):
	self.encoder.deparallelize()
	self.decoder.deparallelize()
	self.encoder = self.encoder.to("cpu")
	self.decoder = self.decoder.to("cpu")
	self.model_parallel = False
	self.device_map = None
	torch.cuda.empty_cache()

	def get_input_embeddings(self):
	return self.shared

	def set_input_embeddings(self, new_embeddings):
	self.shared = new_embeddings
	self.encoder.set_input_embeddings(new_embeddings)
	self.decoder.set_input_embeddings(new_embeddings)

	def get_encoder(self):
	return self.encoder

	def get_decoder(self):
	return self.decoder

	def _prune_heads(self, heads_to_prune):
	"""
	Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
	class PreTrainedModel
	"""
	for layer, heads in heads_to_prune.items():
	self.encoder.layer[layer].attention.prune_heads(heads)

	@add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	decoder_input_ids=None,
	decoder_attention_mask=None,
	head_mask=None,
	decoder_head_mask=None,
	cross_attn_head_mask=None,
	encoder_outputs=None,
	past_key_values=None,
	inputs_embeds=None,
	decoder_inputs_embeds=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	Returns:

	Example:

	```python
	>>> from transformers import T5Tokenizer, T5Model

	>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
	>>> model = T5Model.from_pretrained("t5-small")

	>>> input_ids = tokenizer(
	... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
	>>> ).input_ids # Batch size 1
	>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1

	>>> # forward pass
	>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
	>>> last_hidden_states = outputs.last_hidden_state
	```"""
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
	if head_mask is not None and decoder_head_mask is None:
	if self.config.num_layers == self.config.num_decoder_layers:
	decoder_head_mask = head_mask

	# Encode if needed (training, first prediction pass)
	if encoder_outputs is None:
	encoder_outputs = self.encoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	inputs_embeds=inputs_embeds,
	head_mask=head_mask,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
	encoder_outputs = BaseModelOutput(
	last_hidden_state=encoder_outputs[0],
	hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
	attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
	)

	hidden_states = encoder_outputs[0]
	if self.model_parallel:
	torch.cuda.set_device(self.decoder.first_device)
	# Set device for model parallelism
	if self.model_parallel:
	torch.cuda.set_device(self.decoder.first_device)
	hidden_states = hidden_states.to(self.decoder.first_device)
	if decoder_input_ids is not None:
	decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
	if attention_mask is not None:
	attention_mask = attention_mask.to(self.decoder.first_device)
	if decoder_attention_mask is not None:
	decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)

	# Decode
	decoder_outputs = self.decoder(
	input_ids=decoder_input_ids,
	attention_mask=decoder_attention_mask,
	inputs_embeds=decoder_inputs_embeds,
	past_key_values=past_key_values,
	encoder_hidden_states=hidden_states,
	encoder_attention_mask=attention_mask,
	head_mask=decoder_head_mask,
	cross_attn_head_mask=cross_attn_head_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	if not return_dict:
	return decoder_outputs + encoder_outputs

	return Seq2SeqModelOutput(
	last_hidden_state=decoder_outputs.last_hidden_state,
	past_key_values=decoder_outputs.past_key_values,
	decoder_hidden_states=decoder_outputs.hidden_states,
	decoder_attentions=decoder_outputs.attentions,
	cross_attentions=decoder_outputs.cross_attentions,
	encoder_last_hidden_state=encoder_outputs.last_hidden_state,
	encoder_hidden_states=encoder_outputs.hidden_states,
	encoder_attentions=encoder_outputs.attentions,
	)


	@add_start_docstrings("""T5 Model with a `language modeling` head on top.""", T5_START_DOCSTRING)
	class T5ForConditionalGeneration(T5PreTrainedModel):
	_keys_to_ignore_on_load_missing = [
	r"encoder\.embed_tokens\.weight",
	r"decoder\.embed_tokens\.weight",
	r"lm_head\.weight",
	]
	_keys_to_ignore_on_load_unexpected = [
	r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight",
	]

	def __init__(self, config):
	super().__init__(config)
	self.model_dim = config.d_model

	self.shared = nn.Embedding(config.vocab_size, config.d_model)

	encoder_config = copy.deepcopy(config)
	encoder_config.is_decoder = False
	encoder_config.use_cache = False
	encoder_config.is_encoder_decoder = False
	self.encoder = T5Stack(encoder_config, self.shared)

	decoder_config = copy.deepcopy(config)
	decoder_config.is_decoder = True
	decoder_config.is_encoder_decoder = False
	decoder_config.num_layers = config.num_decoder_layers
	self.decoder = T5Stack(decoder_config, self.shared)

	self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

	# Initialize weights and apply final processing
	self.post_init()

	# Model parallel
	self.model_parallel = False
	self.device_map = None

	@add_start_docstrings(PARALLELIZE_DOCSTRING)
	def parallelize(self, device_map=None):
	self.device_map = (
	get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
	if device_map is None
	else device_map
	)
	assert_device_map(self.device_map, len(self.encoder.block))
	self.encoder.parallelize(self.device_map)
	self.decoder.parallelize(self.device_map)
	self.lm_head = self.lm_head.to(self.decoder.first_device)
	self.model_parallel = True

	@add_start_docstrings(DEPARALLELIZE_DOCSTRING)
	def deparallelize(self):
	self.encoder.deparallelize()
	self.decoder.deparallelize()
	self.encoder = self.encoder.to("cpu")
	self.decoder = self.decoder.to("cpu")
	self.lm_head = self.lm_head.to("cpu")
	self.model_parallel = False
	self.device_map = None
	torch.cuda.empty_cache()

	def get_input_embeddings(self):
	return self.shared

	def set_input_embeddings(self, new_embeddings):
	self.shared = new_embeddings
	self.encoder.set_input_embeddings(new_embeddings)
	self.decoder.set_input_embeddings(new_embeddings)

	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

	def get_output_embeddings(self):
	return self.lm_head

	def get_encoder(self):
	return self.encoder

	def get_decoder(self):
	return self.decoder

	@add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	decoder_input_ids=None,
	decoder_attention_mask=None,
	head_mask=None,
	decoder_head_mask=None,
	cross_attn_head_mask=None,
	encoder_outputs=None,
	past_key_values=None,
	inputs_embeds=None,
	decoder_inputs_embeds=None,
	labels=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
	config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
	labels in `[0, ..., config.vocab_size]`

	Returns:

	Examples:

	```python
	>>> from transformers import T5Tokenizer, T5ForConditionalGeneration

	>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
	>>> model = T5ForConditionalGeneration.from_pretrained("t5-small")

	>>> # training
	>>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
	>>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
	>>> outputs = model(input_ids=input_ids, labels=labels)
	>>> loss = outputs.loss
	>>> logits = outputs.logits

	>>> # inference
	>>> input_ids = tokenizer(
	... "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
	>>> ).input_ids # Batch size 1
	>>> outputs = model.generate(input_ids)
	>>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	>>> # studies have shown that owning a dog is good for you.
	```"""
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
	if head_mask is not None and decoder_head_mask is None:
	if self.config.num_layers == self.config.num_decoder_layers:
	decoder_head_mask = head_mask

	# Encode if needed (training, first prediction pass)
	if encoder_outputs is None:
	# Convert encoder inputs in embeddings if needed
	encoder_outputs = self.encoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	inputs_embeds=inputs_embeds,
	head_mask=head_mask,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
	encoder_outputs = BaseModelOutput(
	last_hidden_state=encoder_outputs[0],
	hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
	attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
	)

	hidden_states = encoder_outputs[0]

	if self.model_parallel:
	torch.cuda.set_device(self.decoder.first_device)

	if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
	# get decoder inputs from shifting lm labels to the right
	decoder_input_ids = self._shift_right(labels)

	# Set device for model parallelism
	if self.model_parallel:
	torch.cuda.set_device(self.decoder.first_device)
	hidden_states = hidden_states.to(self.decoder.first_device)
	if decoder_input_ids is not None:
	decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
	if attention_mask is not None:
	attention_mask = attention_mask.to(self.decoder.first_device)
	if decoder_attention_mask is not None:
	decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)

	# Decode
	decoder_outputs = self.decoder(
	input_ids=decoder_input_ids,
	attention_mask=decoder_attention_mask,
	inputs_embeds=decoder_inputs_embeds,
	past_key_values=past_key_values,
	encoder_hidden_states=hidden_states,
	encoder_attention_mask=attention_mask,
	head_mask=decoder_head_mask,
	cross_attn_head_mask=cross_attn_head_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	sequence_output = decoder_outputs[0]

	# Set device for model parallelism
	if self.model_parallel:
	torch.cuda.set_device(self.encoder.first_device)
	self.lm_head = self.lm_head.to(self.encoder.first_device)
	sequence_output = sequence_output.to(self.lm_head.weight.device)

	if self.config.tie_word_embeddings:
	# Rescale output before projecting on vocab
	# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
	sequence_output = sequence_output * (self.model_dim ** -0.5)

	lm_logits = self.lm_head(sequence_output)

	loss = None
	if labels is not None:
	loss_fct = CrossEntropyLoss(ignore_index=-100)
	loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
	# TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666

	if not return_dict:
	output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
	return ((loss,) + output) if loss is not None else output

	return Seq2SeqLMOutput(
	loss=loss,
	logits=lm_logits,
	past_key_values=decoder_outputs.past_key_values,
	decoder_hidden_states=decoder_outputs.hidden_states,
	decoder_attentions=decoder_outputs.attentions,
	cross_attentions=decoder_outputs.cross_attentions,
	encoder_last_hidden_state=encoder_outputs.last_hidden_state,
	encoder_hidden_states=encoder_outputs.hidden_states,
	encoder_attentions=encoder_outputs.attentions,
	)

	def prepare_inputs_for_generation(
	self,
	input_ids,
	past=None,
	attention_mask=None,
	head_mask=None,
	decoder_head_mask=None,
	cross_attn_head_mask=None,
	use_cache=None,
	encoder_outputs=None,
	**kwargs
	):

	# cut decoder_input_ids if past is used
	if past is not None:
	input_ids = input_ids[:, -1:]

	return {
	"decoder_input_ids": input_ids,
	"past_key_values": past,
	"encoder_outputs": encoder_outputs,
	"attention_mask": attention_mask,
	"head_mask": head_mask,
	"decoder_head_mask": decoder_head_mask,
	"cross_attn_head_mask": cross_attn_head_mask,
	"use_cache": use_cache,
	}

	def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
	return self._shift_right(labels)

	def _reorder_cache(self, past, beam_idx):
	# if decoder past is not included in output
	# speedy decoding is disabled and no need to reorder
	if past is None:
	logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
	return past

	reordered_decoder_past = ()
	for layer_past_states in past:
	# get the correct batch idx from layer past batch dim
	# batch dim of `past` is at 2nd position
	reordered_layer_past_states = ()
	for layer_past_state in layer_past_states:
	# need to set correct `past` for each of the four key / value states
	reordered_layer_past_states = reordered_layer_past_states + (
	layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),
	)

	assert reordered_layer_past_states[0].shape == layer_past_states[0].shape
	assert len(reordered_layer_past_states) == len(layer_past_states)

	reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
	return reordered_decoder_past


	@add_start_docstrings(
	"The bare T5 Model transformer outputting encoder's raw hidden-states without any specific head on top.",
	T5_START_DOCSTRING,
	)
	class T5EncoderModel(T5PreTrainedModel):
	authorized_missing_keys = [
	r"encoder\.embed_tokens\.weight",
	]

	def __init__(self, config: T5Config):
	super().__init__(config)
	self.shared = nn.Embedding(config.vocab_size, config.d_model)

	encoder_config = copy.deepcopy(config)
	encoder_config.use_cache = False
	encoder_config.is_encoder_decoder = False
	self.encoder = T5Stack(encoder_config, self.shared)

	# Initialize weights and apply final processing
	self.post_init()

	# Model parallel
	self.model_parallel = False
	self.device_map = None

	@add_start_docstrings(PARALLELIZE_DOCSTRING)
	def parallelize(self, device_map=None):
	self.device_map = (
	get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
	if device_map is None
	else device_map
	)
	assert_device_map(self.device_map, len(self.encoder.block))
	self.encoder.parallelize(self.device_map)
	self.model_parallel = True

	@add_start_docstrings(DEPARALLELIZE_DOCSTRING)
	def deparallelize(self):
	self.encoder.deparallelize()
	self.encoder = self.encoder.to("cpu")
	self.model_parallel = False
	self.device_map = None
	torch.cuda.empty_cache()

	def get_input_embeddings(self):
	return self.shared

	def set_input_embeddings(self, new_embeddings):
	self.shared = new_embeddings
	self.encoder.set_input_embeddings(new_embeddings)

	def get_encoder(self):
	return self.encoder

	def _prune_heads(self, heads_to_prune):
	"""
	Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
	class PreTrainedModel
	"""
	for layer, heads in heads_to_prune.items():
	self.encoder.layer[layer].attention.prune_heads(heads)

	@add_start_docstrings_to_model_forward(T5_ENCODER_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	head_mask=None,
	inputs_embeds=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	Returns:

	Example:

	```python
	>>> from transformers import T5Tokenizer, T5EncoderModel

	>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
	>>> model = T5EncoderModel.from_pretrained("t5-small")
	>>> input_ids = tokenizer(
	... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
	>>> ).input_ids # Batch size 1
	>>> outputs = model(input_ids=input_ids)
	>>> last_hidden_states = outputs.last_hidden_state
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	encoder_outputs = self.encoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	inputs_embeds=inputs_embeds,
	head_mask=head_mask,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	return encoder_outputs