diff --git a/colossalai/shardformer/modeling/command.py b/colossalai/shardformer/modeling/command.py index 27021724c..83f4b97ff 100644 --- a/colossalai/shardformer/modeling/command.py +++ b/colossalai/shardformer/modeling/command.py @@ -1,13 +1,15 @@ 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 CrossEntropyLoss from transformers.cache_utils import Cache, DynamicCache from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast -from transformers.models.cohere.modeling_cohere import CohereForCausalLM, CohereModel, StaticCache, repeat_kv +from transformers.models.cohere.modeling_cohere import CohereForCausalLM, CohereModel, StaticCache, apply_rotary_pos_emb, repeat_kv from transformers.utils import logging from colossalai.pipeline.stage_manager import PipelineStageManager @@ -333,121 +335,28 @@ class CommandPipelineForwards: return {"hidden_states": hidden_states} -def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig): - from transformers import CohereForCausalLM - - def forward( - self: CohereForCausalLM, - 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, CohereForCausalLM - - >>> model = CohereForCausalLM.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] - - logits = self.lm_head(hidden_states) - logits = logits * self.logit_scale - logits = logits.float() - - loss = None - if labels is not None: - # Shift so that tokens < n predict n - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - shift_labels = shift_labels.view(-1) - # Enable model parallelism - shift_labels = shift_labels.to(shift_logits.device) - new_vocab_size = logits.shape[-1] - shift_logits = shift_logits.view(-1, new_vocab_size) - loss = cross_entropy_1d( - shift_logits, - shift_labels, - process_group=shard_config.tensor_parallel_process_group, - vocab_size=self.lm_head.out_features, - dtype=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 - - -def get_command_seq_parallel_attention_forward(sp_mode, sp_size, sp_group, use_flash_attention): - from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb - +def get_command_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[Tuple[torch.Tensor]] = None, + past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, - ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + **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"]: @@ -468,29 +377,46 @@ def get_command_seq_parallel_attention_forward(sp_mode, sp_size, sp_group, use_f 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) - past_key_value = getattr(self, "past_key_value", past_key_value) + 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: - # sin and cos are specific to RoPE models; cache_position needed for the static cache 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 use_flash_attention: + + 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) - attn_output = attn_output.transpose(1, 2).contiguous() - attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) else: attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) - if attention_mask is not None: # no matter the length, we just slice it - causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] - attn_weights = attn_weights + causal_mask + 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) @@ -502,25 +428,28 @@ def get_command_seq_parallel_attention_forward(sp_mode, sp_size, sp_group, use_f f" {attn_output.size()}" ) - attn_output = attn_output.transpose(1, 2).contiguous() - attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim) + 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) + attn_output = self.o_proj(attn_output) - if not output_attentions or use_flash_attention: + if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value return forward -def get_command_seq_parallel_model_forward(sp_mode, sp_size, sp_group, use_flash_attention): +def get_command_flash_attention_model_forward(shard_config, sp_mode=None, sp_size=None, sp_group=None): logger = logging.get_logger(__name__) def forward( - self: CohereModel, + self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, @@ -537,18 +466,14 @@ def get_command_seq_parallel_model_forward(sp_mode, sp_size, sp_group, use_flash 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 not None and inputs_embeds is not None: + if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError( - "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time, and must specify either one" + "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" ) - if inputs_embeds is None: - inputs_embeds = self.embed_tokens(input_ids) - if (self.gradient_checkpointing or sp_mode in ["ring", "all_to_all"]) and self.training: if use_cache: logger.warning_once( @@ -556,7 +481,11 @@ def get_command_seq_parallel_model_forward(sp_mode, sp_size, sp_group, use_flash ) 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) @@ -564,18 +493,18 @@ def get_command_seq_parallel_model_forward(sp_mode, sp_size, sp_group, use_flash 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 + inputs_embeds.shape[1], device=inputs_embeds.device - ) + 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) - if use_flash_attention: - hidden_states = inputs_embeds - mask_shape = (hidden_states.shape[0], 1, past_seen_tokens, past_seen_tokens) + + # 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, past_seen_tokens + seq_len, past_seen_tokens + seq_len) attention_mask = ColoAttention.prepare_attn_kwargs( mask_shape, - hidden_states.dtype, - hidden_states.device, + inputs_embeds.dtype, + inputs_embeds.device, q_padding_mask=attention_mask, is_causal=True, ) @@ -586,32 +515,26 @@ def get_command_seq_parallel_model_forward(sp_mode, sp_size, sp_group, use_flash 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 = () if use_cache else None + next_decoder_cache = None - for idx, decoder_layer in enumerate(self.layers): + for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) - - if (self.gradient_checkpointing or sp_mode in ["ring", "all_to_all"]) and self.training: - - def create_custom_forward(module): - def custom_forward(*inputs): - # None for past_key_value - return module(*inputs, past_key_value=past_key_values, output_attentions=output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(decoder_layer), + 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: @@ -628,11 +551,7 @@ def get_command_seq_parallel_model_forward(sp_mode, sp_size, sp_group, use_flash hidden_states = layer_outputs[0] if use_cache: - next_decoder_cache = ( - next_decoder_cache.to_legacy_cache() - if isinstance(next_decoder_cache, Cache) - else next_decoder_cache - ) + next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) @@ -648,7 +567,11 @@ def get_command_seq_parallel_model_forward(sp_mode, sp_size, sp_group, use_flash if output_hidden_states: all_hidden_states += (hidden_states,) - next_cache = next_decoder_cache if use_cache else None + 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) @@ -660,3 +583,104 @@ def get_command_seq_parallel_model_forward(sp_mode, sp_size, sp_group, use_flash ) return forward + +def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig): + from transformers import CohereForCausalLM + + def forward( + self: CohereForCausalLM, + 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, CohereForCausalLM + + >>> model = CohereForCausalLM.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] + + logits = self.lm_head(hidden_states) + logits = logits * self.logit_scale + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + new_vocab_size = logits.shape[-1] + shift_logits = shift_logits.view(-1, new_vocab_size) + loss = cross_entropy_1d( + shift_logits, + shift_labels, + process_group=shard_config.tensor_parallel_process_group, + vocab_size=self.lm_head.out_features, + dtype=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 \ No newline at end of file diff --git a/colossalai/shardformer/policies/command.py b/colossalai/shardformer/policies/command.py index 5284c89f0..77f96e462 100644 --- a/colossalai/shardformer/policies/command.py +++ b/colossalai/shardformer/policies/command.py @@ -19,8 +19,8 @@ from colossalai.shardformer.layer import ( from ..modeling.command import ( CommandPipelineForwards, - get_command_seq_parallel_attention_forward, - get_command_seq_parallel_model_forward, + get_command_flash_attention_forward, + get_command_flash_attention_model_forward, get_lm_forward_with_dist_cross_entropy, ) from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription @@ -80,38 +80,7 @@ class CommandPolicy(Policy): ) sp_partial_derived = sp_mode in ["split_gather", "ring"] - use_flash_attention = self.shard_config.enable_flash_attention - # Currently sp cannot to be used with flashattention - if sp_mode in ["split_gather", "ring", "all_to_all"]: - if use_flash_attention: - warnings.warn( - f"Sequence parallelism mode {sp_mode} need to be used with FlashAttention, will disable FlashAttention automatically." - ) - use_flash_attention = False - - if sp_mode in ["split_gather", "ring"]: - self.append_or_create_method_replacement( - description={ - "forward": get_command_seq_parallel_model_forward( - sp_mode=sp_mode, - sp_size=sp_size, - sp_group=sp_group, - use_flash_attention=use_flash_attention, - ), - }, - policy=policy, - target_key=CohereModel, - ) - self.append_or_create_method_replacement( - description={ - "forward": get_command_seq_parallel_attention_forward( - sp_mode, sp_size, sp_group, use_flash_attention=use_flash_attention - ), - }, - policy=policy, - target_key=attn_cls, - ) - elif sp_mode == "all_to_all": + if sp_mode == "all_to_all": decoder_attribute_replacement = { "num_heads": self.model.config.num_attention_heads // sp_size, } @@ -121,27 +90,28 @@ class CommandPolicy(Policy): policy[attn_cls] = ModulePolicyDescription( attribute_replacement=decoder_attribute_replacement, ) + if self.shard_config.enable_flash_attention or self.shard_config.enable_sequence_parallelism: self.append_or_create_method_replacement( description={ - "forward": get_command_seq_parallel_attention_forward( - sp_mode, sp_size, sp_group, use_flash_attention=use_flash_attention - ), + "forward": get_command_flash_attention_forward(self.shard_config, sp_mode, sp_size, sp_group), }, policy=policy, target_key=attn_cls, ) - self.append_or_create_method_replacement( - description={ - "forward": get_command_seq_parallel_model_forward( - sp_mode=sp_mode, - sp_size=sp_size, - sp_group=sp_group, - use_flash_attention=use_flash_attention, - ), - }, - policy=policy, - target_key=CohereModel, - ) + if self.pipeline_stage_manager is None: + self.append_or_create_method_replacement( + description={ + "forward": get_command_flash_attention_model_forward( + self.shard_config, + sp_mode=sp_mode, + sp_size=sp_size, + sp_group=sp_group, + ), + }, + policy=policy, + target_key=CohereModel, + ) + if self.shard_config.enable_tensor_parallelism: assert ( @@ -236,29 +206,6 @@ class CommandPolicy(Policy): target_key=CohereModel, ) - # use flash attention - if use_flash_attention: - self.append_or_create_method_replacement( - description={ - "forward": get_command_seq_parallel_attention_forward( - sp_mode, sp_group, sp_size, use_flash_attention=use_flash_attention - ), - }, - policy=policy, - target_key=attn_cls, - ) - if self.pipeline_stage_manager is None: - # replace Command model forward method - self.append_or_create_method_replacement( - description={ - "forward": get_command_seq_parallel_model_forward( - sp_mode, sp_size, sp_group, use_flash_attention=use_flash_attention - ), - }, - policy=policy, - target_key=CohereModel, - ) - return policy def postprocess(self):