# Copyright (c) Alibaba, Inc. and its affiliates. # 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 warnings from typing import Optional, Tuple, Union import torch from torch import nn from torch.utils.checkpoint import checkpoint from transformers.activations import ACT2FN from transformers.modeling_outputs import \ BaseModelOutputWithPastAndCrossAttentions from transformers.modeling_utils import (PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer) from transformers.utils import (DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, replace_return_docstrings) from transformers.utils.model_parallel_utils import (assert_device_map, get_device_map) from modelscope.metainfo import Models from modelscope.models.base import Model, Tensor, TorchModel from modelscope.models.builder import MODELS from modelscope.outputs import AttentionBackboneModelOutput, Seq2SeqModelOutput from modelscope.utils.constant import Tasks from modelscope.utils.logger import get_logger from .configuration import T5Config logger = get_logger() ################################################### # 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 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): # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for # half-precision inputs is done in fp32 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: T5Config): 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: T5Config): 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: T5Config): 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.relative_attention_max_distance = config.relative_attention_max_distance 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 relateive_pos_log = torch.log(relative_position.float() / max_exact) max_dis_log = math.log(max_distance / max_exact) origin_relative_position = relateive_pos_log / max_dis_log * ( num_buckets - max_exact) relative_postion_if_large = max_exact + origin_relative_position.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, max_distance=self.relative_attention_max_distance, ) 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: if not self.is_decoder: logger.warning( '`past_key_values` is passed to the encoder. Please make sure this is intended.' ) 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 # hidden-states, present_key_value_states, (self-attention position # bias), (self-attention weights), (cross-attention position bias), # (cross-attention weights) return outputs class T5PreTrainedModel(TorchModel, 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 def __init__(self, config, **kwargs): super().__init__(config.name_or_path, **kwargs) super(Model, self).__init__(config) @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): # 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.' # 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 @classmethod def _instantiate(cls, **kwargs): """Instantiate the model. Args: kwargs: Input args. model_dir: The model dir used to load the checkpoint and the label information. num_labels: An optional arg to tell the model how many classes to initialize. Method will call utils.parse_label_mapping if num_labels not supplied. If num_labels is not found, the model will use the default setting (2 classes). Returns: The loaded model, which is initialized by transformers.PreTrainedModel.from_pretrained """ model_dir = kwargs.get('model_dir', None) if model_dir is None: config = T5Config(**kwargs) model = cls(config) else: model_kwargs = {} model = super(Model, cls).from_pretrained( pretrained_model_name_or_path=model_dir, **model_kwargs) model.model_dir = model_dir return model 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 def parallelize(self, device_map=None): 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: >>> # 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) >>> # all of the parallelize methods in this file are the same """ # 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) def deparallelize(self): r""" Moves the model to cpu from a model parallel state. Example: >>> # 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() >>> # all of the deparallelize methods in this file are the same """ 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.warning( '`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, ) # 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)`. """ @MODELS.register_module(group_key=Tasks.backbone, module_name=Models.T5) class T5Model(T5PreTrainedModel): """The bare T5 Model transformer outputting raw hidden-states without any specific head on top. 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. """ _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 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 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) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.Tensor] = None, decoder_inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]: 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 [`~utils.ModelOutput`] instead of a plain tuple. Returns: Example: >>> 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: warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning) 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, AttentionBackboneModelOutput): encoder_outputs = AttentionBackboneModelOutput( 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, )