import warnings from typing import Dict, List, Optional, Tuple, Union import torch from torch.nn import CrossEntropyLoss from torch.utils.checkpoint import checkpoint from transformers.modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, ) from transformers.models.t5.modeling_t5 import T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Stack from transformers.utils import logging from colossalai.pipeline.stage_manager import PipelineStageManager class T5PipelineForwards: ''' This class serves as a micro library for forward function substitution of T5 models under pipeline setting. ''' @staticmethod def t5_stack_forward( self: T5Stack, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = None, stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, position_bias: Optional[torch.Tensor] = None, encoder_decoder_position_bias: Optional[torch.Tensor] = None, stage_index: Optional[List[int]] = None, decoder_starting_stage: Optional[int] = None, ) -> Union[Dict, Tuple, BaseModelOutputWithPastAndCrossAttentions]: # This function is modified on the basis of transformers.models.t5.modeling_t5.T5Stack.forward. # Please refer to original code of transformers for more details. logger = logging.get_logger(__name__) # TODO(baizhou): left the recording kv-value tensors as () or None type, this feature may be added in the future. if past_key_values: logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') past_key_values = None if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if use_cache: logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') use_cache = False if use_cache is True: if not in_decoder: raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") use_cache = False stage = stage_manager.stage in_decoder = self.is_decoder if in_decoder != (stage >= decoder_starting_stage): raise ValueError("Config in T5Stack is not aligned with pipeline setting.") # at_first_stage: current stage is the first stage of encoder/decoder, taking input_ids/input_embedds # at_last_stage: current stage is the last stage of encoder/decoder, making outputs the same form as huggingface at_first_stage = (stage == 0) or (stage == decoder_starting_stage) at_last_stage = (stage == decoder_starting_stage - 1) or (stage == stage_manager.num_stages - 1) # Process inputs if at the first stage of encoder/decoder. if at_first_stage: if input_ids is not None and inputs_embeds is not None: err_msg_prefix = "decoder_" if in_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 in_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: if self.embed_tokens is None: raise ValueError("You have to initialize the model with valid token embeddings") inputs_embeds = self.embed_tokens(input_ids) batch_size, seq_length = input_shape device = inputs_embeds.device hidden_states = self.dropout(inputs_embeds) else: if hidden_states is None: raise ValueError( "hidden_states shouldn't be None for stages other than the first stage of encoder/decoder.") input_shape = hidden_states.size()[:-1] batch_size, seq_length = input_shape[0], input_shape[1] device = hidden_states.device # 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 attention_mask is None: attention_mask = torch.ones(batch_size, mask_seq_length, device=device) if in_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=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) # 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 # Going through held blocks. start_idx, end_idx = stage_index[0], stage_index[1] for i in range(start_idx, end_idx): past_key_value = past_key_values[i] layer_module = self.block[i] layer_head_mask = head_mask[i] cross_attn_layer_head_mask = cross_attn_head_mask[i] torch.cuda.set_device(hidden_states.device) if self.gradient_checkpointing and self.training: 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 or use_cache is None: 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 in_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,) # last layer if at_last_stage: hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.dropout(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, ) else: return { 'hidden_states': hidden_states, 'position_bias': position_bias, 'encoder_decoder_position_bias': encoder_decoder_position_bias, 'backward_tensor_keys': ['hidden_states'] } @staticmethod def t5_model_forward( self: T5Model, 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, stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, position_bias: Optional[torch.Tensor] = None, encoder_decoder_position_bias: Optional[torch.Tensor] = None, backward_tensor_keys: Optional[List[str]] = None, stage_index: Optional[List[int]] = None, decoder_starting_stage: Optional[int] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]: # This function is modified on the basis of transformers.models.t5.modeling_t5.T5Model.forward. # Please refer to original code of transformers for more details. __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)`. """ 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 logger = logging.get_logger(__name__) # TODO(baizhou): left the recording kv-value tensors as () or None type, this feature may be added in the future. if past_key_values: logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') past_key_values = None if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if use_cache: logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') use_cache = False # 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 in_decoder = stage_manager.stage >= decoder_starting_stage # Stage is in encoder, directly return the output of t5_stack_forward if not in_decoder: encoder_outputs = T5PipelineForwards.t5_stack_forward( 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, stage_manager=stage_manager, hidden_states=hidden_states, position_bias=position_bias, encoder_decoder_position_bias=encoder_decoder_position_bias, stage_index=stage_index, decoder_starting_stage=decoder_starting_stage) if stage_manager.stage == decoder_starting_stage - 1: # last stage of encoder return {'encoder_hidden_states': encoder_outputs[0]} else: return encoder_outputs at_last_decoder_stage = stage_manager.is_last_stage() at_first_decoder_stage = stage_manager.stage == decoder_starting_stage if encoder_outputs is not None: encoder_hidden_states = encoder_outputs[0] elif encoder_hidden_states is None: raise ValueError("Non-empty encoder_hidden_states should be passed in at decoder stages.") if not at_first_decoder_stage and hidden_states is None: raise ValueError("If not at the first layer of decoder, non-empty hidden_states must be provided.") # Decode decoder_outputs = T5PipelineForwards.t5_stack_forward( 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=encoder_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, stage_manager=stage_manager, hidden_states=hidden_states, position_bias=position_bias, encoder_decoder_position_bias=encoder_decoder_position_bias, stage_index=stage_index, decoder_starting_stage=decoder_starting_stage) # Directly return outputs of overloaded T5Stack forward if not at last stage. if not at_last_decoder_stage: # encoder_hidden_states should be passed to the next stage decoder_outputs['encoder_hidden_states'] = encoder_hidden_states return decoder_outputs if not return_dict: return decoder_outputs + encoder_hidden_states else: 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_hidden_states) @staticmethod def t5_for_conditional_generation_forward( self: T5ForConditionalGeneration, 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.Tensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, position_bias: Optional[torch.Tensor] = None, encoder_decoder_position_bias: Optional[torch.Tensor] = None, backward_tensor_keys: Optional[List[str]] = None, stage_index: Optional[List[int]] = None, decoder_starting_stage: Optional[int] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: # This function is modified on the basis of transformers.models.t5.modeling_t5.T5ForConditionalGeneration.forward. # Please refer to original code of transformers for more details. __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)`. """ 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 logger = logging.get_logger(__name__) # TODO(baizhou): left the recording kv-value tensors as () or None type, this feature may be added in the future. if past_key_values: logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.') past_key_values = None if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if use_cache: logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') use_cache = False # 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 in_decoder = stage_manager.stage >= decoder_starting_stage # Stage is in encoder, directly return the output of t5_stack_forward if not in_decoder: encoder_outputs = T5PipelineForwards.t5_stack_forward( 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, stage_manager=stage_manager, hidden_states=hidden_states, position_bias=position_bias, encoder_decoder_position_bias=encoder_decoder_position_bias, stage_index=stage_index, decoder_starting_stage=decoder_starting_stage) if stage_manager.stage == decoder_starting_stage - 1: # last stage of encoder return {'encoder_hidden_states': encoder_outputs[0]} else: return encoder_outputs at_last_decoder_stage = stage_manager.is_last_stage() at_first_decoder_stage = stage_manager.stage == decoder_starting_stage if encoder_outputs is not None: encoder_hidden_states = encoder_outputs[0] elif encoder_hidden_states is None: raise ValueError("Non-empty encoder_hidden_states should be passed in at decoder stages.") if not at_first_decoder_stage and hidden_states is None: raise ValueError("If not at the first layer of decoder, non-empty hidden_states must be provided.") 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) # Decode decoder_outputs = T5PipelineForwards.t5_stack_forward( 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=encoder_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, stage_manager=stage_manager, hidden_states=hidden_states, position_bias=position_bias, encoder_decoder_position_bias=encoder_decoder_position_bias, stage_index=stage_index, decoder_starting_stage=decoder_starting_stage) # Directly return outputs of overloaded T5Stack forward if not at last stage. if not at_last_decoder_stage: # encoder_hidden_states should be passed to the next stage decoder_outputs['encoder_hidden_states'] = encoder_hidden_states return decoder_outputs sequence_output = decoder_outputs[0] 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) # move labels to correct device to enable PP labels = labels.to(lm_logits.device) loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) if not return_dict: output = (lm_logits,) + decoder_outputs[1:] + encoder_hidden_states 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_hidden_states) @staticmethod def t5_encoder_model_forward( self: T5EncoderModel, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, position_bias: Optional[torch.Tensor] = None, encoder_decoder_position_bias: Optional[torch.Tensor] = None, backward_tensor_keys: Optional[List[str]] = None, stage_index: Optional[List[int]] = None, decoder_starting_stage: Optional[int] = None, ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]: r""" This function is modified on the basis of transformers.models.t5.modeling_gpt2.T5EncoderModel.forward. Please refer to original code of transformers for more details. ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = T5PipelineForwards.t5_stack_forward(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, stage_manager=stage_manager, hidden_states=hidden_states, position_bias=position_bias, encoder_decoder_position_bias=encoder_decoder_position_bias, stage_index=stage_index, decoder_starting_stage=decoder_starting_stage) return outputs def get_t5_flash_attention_forward(): try: from xformers.ops import memory_efficient_attention as me_attention except: raise ImportError("Error: xformers module is not installed. Please install it to use flash attention.") from transformers.models.t5.modeling_t5 import T5Attention def forward( self: T5Attention, hidden_states: torch.Tensor, mask: Optional[torch.Tensor] = None, key_value_states: Optional[torch.Tensor] = None, position_bias: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, layer_head_mask: Optional[torch.Tensor] = None, query_length: Optional[int] = None, use_cache: bool = False, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: """ 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: if len(past_key_value) != 2: raise ValueError( 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) def unshape(states): """reshape""" return states.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=1) elif past_key_value.shape[1] != key_value_states.shape[1]: # checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning # cross-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(key_value_states)) 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) 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=query_states.device, dtype=query_states.dtype) if self.gradient_checkpointing and self.training: position_bias.requires_grad = True else: position_bias = self.compute_bias(real_seq_length, key_length, device=query_states.device) # 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) if self.pruned_heads: mask = torch.ones(position_bias.shape[1]) mask[list(self.pruned_heads)] = 0 position_bias_masked = position_bias[:, mask.bool()] else: position_bias_masked = position_bias position_bias_masked = position_bias_masked.contiguous() attn_output = me_attention(query_states, key_states, value_states, attn_bias=position_bias_masked, p=self.dropout, scale=1.0) attn_output = unshape(attn_output) 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,) return outputs return forward def get_jit_fused_T5_layer_ff_forward(): from transformers.models.t5.modeling_t5 import T5LayerFF def forward(self: T5LayerFF, hidden_states: torch.Tensor) -> torch.Tensor: forwarded_states = self.layer_norm(hidden_states) forwarded_states = self.DenseReluDense(forwarded_states) hidden_states = self.dropout_add(forwarded_states, hidden_states, self.dropout.p, self.dropout.training) return hidden_states return forward def get_T5_layer_self_attention_forward(): from transformers.models.t5.modeling_t5 import T5LayerSelfAttention def forward( self: T5LayerSelfAttention, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_bias: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, use_cache: bool = False, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: 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 = self.dropout_add(attention_output[0], hidden_states, self.dropout.p, self.dropout.training) outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them return outputs return forward def get_T5_layer_cross_attention_forward(): from transformers.models.t5.modeling_t5 import T5LayerCrossAttention def forward( self: T5LayerCrossAttention, hidden_states: torch.Tensor, key_value_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_bias: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, use_cache: bool = False, query_length: Optional[int] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: 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 = self.dropout_add(attention_output[0], hidden_states, self.dropout.p, self.dropout.training) outputs = (layer_output,) + attention_output[1:] # add attentions if we output them return outputs return forward