import math import warnings from typing import List, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from transformers.cache_utils import Cache, DynamicCache from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast, ) from transformers.models.llama.modeling_llama import ( LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, StaticCache, apply_rotary_pos_emb, repeat_kv, ) from transformers.utils import logging from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer.layer._operation import ( all_to_all_comm, gather_forward_split_backward, split_forward_gather_backward, ) from colossalai.shardformer.shard import ShardConfig from ..layer import ColoAttention, dist_cross_entropy class LlamaPipelineForwards: """ This class serves as a micro library for forward function substitution of Llama models under pipeline setting. """ @staticmethod def llama_model_forward( self: LlamaModel, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, stage_index: Optional[List[int]] = None, shard_config: ShardConfig = None, ): logger = logging.get_logger(__name__) 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 ) use_cache = use_cache if use_cache is not None else self.config.use_cache if use_cache: logger.warning_once( "`use_cache=True` is incompatible with pipeline parallelism. Setting `use_cache=False`..." ) use_cache = False return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if stage_manager.is_first_stage(): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: batch_size, seq_length = input_ids.shape[:2] elif inputs_embeds is not None: batch_size, seq_length, _ = inputs_embeds.shape[:2] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) hidden_states = inputs_embeds else: input_shape = hidden_states.shape[:-1] batch_size, seq_length = input_shape device = hidden_states.device # Support SP + PP sp_mode = shard_config.sequence_parallelism_mode sp_group = shard_config.sequence_parallel_process_group sp_size = shard_config.sequence_parallel_size if sp_mode == "all_to_all" and not stage_manager.is_first_stage(): # For correct positions ids. The states will be gather along the seq dim in the attention layer later. seq_length *= sp_size past_seen_tokens = 0 if use_cache: # kept for BC (cache positions) if not isinstance(past_key_values, StaticCache): past_key_values = DynamicCache.from_legacy_cache(past_key_values) past_seen_tokens = past_key_values.get_seq_length() if cache_position is None: if isinstance(past_key_values, StaticCache): raise ValueError("cache_position is a required argument when using StaticCache.") cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_length, device=device) seq_length_with_past = seq_length + past_seen_tokens # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. 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 position_ids is None: position_ids = cache_position.unsqueeze(0) # embed positions, for the first stage, hidden_states is the input embeddings, # for the other stages, hidden_states is the output of the previous stage if shard_config.enable_flash_attention: # in this case, attention_mask is a dict rather than a tensor mask_shape = (batch_size, 1, seq_length_with_past, seq_length_with_past) attention_mask = ColoAttention.prepare_attn_kwargs( mask_shape, hidden_states.dtype, hidden_states.device, q_padding_mask=attention_mask, is_causal=True, ) else: attention_mask = self._update_causal_mask(attention_mask, hidden_states, cache_position) # Support SP + PP if stage_manager.is_first_stage(): if sp_mode in ["ring", "split_gather"]: hidden_states = split_forward_gather_backward(hidden_states, 1, sp_group) elif sp_mode == "all_to_all": hidden_states = split_forward_gather_backward(hidden_states, 1, sp_group, 1 / sp_size) if self.gradient_checkpointing and self.training and use_cache: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None start_idx, end_idx = stage_index[0], stage_index[1] num_ckpt_layers = 0 if self.gradient_checkpointing and self.training: num_ckpt_layers = end_idx - start_idx # TODO: We can replace `gradient_checkpointing_enable` fn and initialize a gradient_checkpointing (List[bool]) for each layer if shard_config.gradient_checkpoint_config is not None: num_ckpt_layers = shard_config.gradient_checkpoint_config.get_num_ckpt_layers( stage=stage_manager.stage, num_stages=stage_manager.num_stages, num_layers=end_idx - start_idx, model_chunk_id=(stage_manager.model_chunk_id if stage_manager.is_interleave else 0), num_model_chunks=stage_manager.num_model_chunks, ) assert num_ckpt_layers <= end_idx - start_idx for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_idx): if output_hidden_states: all_hidden_states += (hidden_states,) if idx - start_idx < num_ckpt_layers: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) if stage_manager.is_last_stage(): hidden_states = self.norm(hidden_states) if sp_mode == "ring" or sp_mode == "split_gather": hidden_states = gather_forward_split_backward(hidden_states, 1, sp_group) elif sp_mode == "all_to_all": hidden_states = gather_forward_split_backward(hidden_states, 1, sp_group, grad_scale=sp_size) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if stage_manager.is_last_stage(): if not return_dict: return tuple( v for v in [ hidden_states, next_cache, all_hidden_states, all_self_attns, ] if v is not None ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) # always return dict for imediate stage return {"hidden_states": hidden_states} @staticmethod def llama_for_causal_lm_forward( self: LlamaForCausalLM, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, 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, cache_position: Optional[torch.LongTensor] = None, stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, stage_index: Optional[List[int]] = None, shard_config: ShardConfig = None, ): r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, LlamaForCausalLM >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```""" logger = logging.get_logger(__name__) 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 # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. 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 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = LlamaPipelineForwards.llama_model_forward( self.model, input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, stage_manager=stage_manager, hidden_states=hidden_states, stage_index=stage_index, shard_config=shard_config, ) past_key_values = None if stage_manager.is_last_stage(): hidden_states = outputs[0] logits = self.lm_head(hidden_states) loss = dist_cross_entropy( labels, logits, shard_config, self.lm_head.out_features, self.config.vocab_size, self.model.dtype ) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) else: hidden_states = outputs.get("hidden_states") return {"hidden_states": hidden_states} @staticmethod def llama_for_sequence_classification_forward( self: LlamaForSequenceClassification, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, 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, cache_position: Optional[torch.LongTensor] = None, stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, stage_index: Optional[List[int]] = None, shard_config: ShardConfig = None, ): r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ logger = logging.get_logger(__name__) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future. 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 transformer_outputs = LlamaPipelineForwards.llama_model_forward( self.model, input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, stage_manager=stage_manager, hidden_states=hidden_states, stage_index=stage_index, shard_config=shard_config, ) if input_ids is not None: batch_size = input_ids.shape[0] elif inputs_embeds is not None: batch_size = inputs_embeds.shape[0] else: batch_size = hidden_states.shape[0] if stage_manager.is_last_stage(): hidden_states = transformer_outputs[0] logits = self.score(hidden_states) if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) else: sequence_lengths = -1 pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] loss = None if labels is not None: labels = labels.to(logits.device) if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) else: hidden_states = transformer_outputs.get("hidden_states") return {"hidden_states": hidden_states} def get_llama_flash_attention_forward(shard_config, sp_mode=None, sp_size=None, sp_group=None): def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]: if sp_mode is not None: assert sp_mode in ["all_to_all", "split_gather", "ring"], "Invalid sp_mode" assert (sp_size is not None) and ( sp_group is not None ), "Must specify sp_size and sp_group for sequence parallel" if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" ) bsz, q_len, _ = hidden_states.size() # sp: modify sp_len when sequence parallel mode is ring if sp_mode in ["split_gather", "ring"]: q_len *= sp_size if self.config.pretraining_tp > 1: key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp query_slices = self.q_proj.weight.split( (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0 ) key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)] query_states = torch.cat(query_states, dim=-1) key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)] key_states = torch.cat(key_states, dim=-1) value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)] value_states = torch.cat(value_states, dim=-1) else: query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) # sp: all-to-all comminucation when introducing sequence parallel if sp_mode == "all_to_all": query_states = all_to_all_comm(query_states, sp_group) key_states = all_to_all_comm(key_states, sp_group) value_states = all_to_all_comm(value_states, sp_group) bsz, q_len, _ = query_states.size() query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) kv_seq_len = key_states.shape[-2] if past_key_value is not None: if self.layer_idx is None: raise ValueError( f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " "with a layer index." ) kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) cos, sin = self.rotary_emb(value_states, position_ids) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) if shard_config.enable_flash_attention: assert isinstance(attention_mask, dict), "Flash Attention Error: attention_mask should be a dict." attn_output = ColoAttention.attention(query_states, key_states, value_states, **attention_mask) else: attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): raise ValueError( f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() # sp: all-to-all comminucation when introducing sequence parallel if sp_mode == "all_to_all": attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim) attn_output = all_to_all_comm(attn_output, sp_group, scatter_dim=1, gather_dim=2) else: attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) if self.config.pretraining_tp > 1: attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2) o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1) attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)]) else: attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value return forward def get_llama_flash_attention_model_forward(shard_config, sp_mode=None, sp_size=None, sp_group=None): logger = logging.get_logger(__name__) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: 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 ) 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 # retrieve input_ids and inputs_embeds if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError( "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" ) if (self.gradient_checkpointing or sp_mode in ["ring", "all_to_all"]) and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) past_seen_tokens = 0 seq_len = inputs_embeds.shape[1] if use_cache: # kept for BC (cache positions) if not isinstance(past_key_values, StaticCache): past_key_values = DynamicCache.from_legacy_cache(past_key_values) past_seen_tokens = past_key_values.get_seq_length() if cache_position is None: if isinstance(past_key_values, StaticCache): raise ValueError("cache_position is a required argument when using StaticCache.") cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_len, device=inputs_embeds.device) if position_ids is None: position_ids = cache_position.unsqueeze(0) # in this case, attention_mask is a dict rather than a tensor if shard_config.enable_flash_attention: mask_shape = (inputs_embeds.shape[0], 1, seq_len, past_seen_tokens + seq_len) attention_mask = ColoAttention.prepare_attn_kwargs( mask_shape, inputs_embeds.dtype, inputs_embeds.device, q_padding_mask=attention_mask, is_causal=True, ) else: attention_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position) if sp_mode in ["ring", "split_gather"]: inputs_embeds = split_forward_gather_backward(inputs_embeds, 1, sp_group) elif sp_mode == "all_to_all": inputs_embeds = split_forward_gather_backward(inputs_embeds, 1, sp_group, 1 / sp_size) hidden_states = inputs_embeds # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) if sp_mode == "ring" or sp_mode == "split_gather": hidden_states = gather_forward_split_backward(hidden_states, 1, sp_group) elif sp_mode == "all_to_all": hidden_states = gather_forward_split_backward(hidden_states, 1, sp_group, grad_scale=sp_size) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = None if use_cache: next_cache = ( next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache ) if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) return forward def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig): from transformers import LlamaForCausalLM def forward( self: LlamaForCausalLM, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, 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, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, LlamaForCausalLM >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```""" 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 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, ) hidden_states = outputs[0] if self.config.pretraining_tp > 1: lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0) logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)] logits = torch.cat(logits, dim=-1) else: logits = self.lm_head(hidden_states) logits = logits.float() loss = dist_cross_entropy( labels, logits, shard_config, self.lm_head.out_features, self.config.vocab_size, self.model.dtype ) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) return forward