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https://github.com/hpcaitech/ColossalAI.git
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[shardformer] llama support DistCrossEntropy (#5176)
* fix aaa fix fix fix * fix * fix * test ci * fix ci fix * llama support dist-cross fix fix fix fix fix fix fix fix * fix * fix * fix fix * test ci * test ci * fix * [Colossal-Llama-2] Add finetuning Colossal-Llama-2 example (#4878) * Add finetuning Colossal-Llama-2 example * Add finetuning Colossal-Llama-2 example 2 * Add finetuning Colossal-Llama-2 example and support NEFTuning * Add inference example and refine neftune * Modify readme file * update the imports --------- Co-authored-by: Xu Yuanchen <yuanchen.xu00@gmail.com> Co-authored-by: Camille Zhong <44392324+Camille7777@users.noreply.github.com> * llama support dist-cross fix fix fix fix fix fix fix fix * fix * fix * fix fix * test ci * test ci * fix * fix ci * fix ci --------- Co-authored-by: Yuanchen <70520919+chengeharrison@users.noreply.github.com> Co-authored-by: Xu Yuanchen <yuanchen.xu00@gmail.com> Co-authored-by: Camille Zhong <44392324+Camille7777@users.noreply.github.com>
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flybird11111
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@@ -2,6 +2,8 @@ import warnings
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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import torch.distributed as dist
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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@@ -12,6 +14,8 @@ from transformers.models.llama.modeling_llama import LlamaForCausalLM, LlamaForS
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from transformers.utils import logging
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from colossalai.pipeline.stage_manager import PipelineStageManager
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from colossalai.shardformer.shard import ShardConfig
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from ..layer import cross_entropy_1d
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try:
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from transformers.models.llama.modeling_llama import _prepare_4d_causal_attention_mask
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@@ -40,6 +44,7 @@ class LlamaPipelineForwards:
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stage_manager: Optional[PipelineStageManager] = None,
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hidden_states: Optional[torch.FloatTensor] = None,
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stage_index: Optional[List[int]] = None,
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shard_config: ShardConfig = None,
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):
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logger = logging.get_logger(__name__)
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@@ -198,6 +203,7 @@ class LlamaPipelineForwards:
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stage_manager: Optional[PipelineStageManager] = None,
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hidden_states: Optional[torch.FloatTensor] = None,
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stage_index: Optional[List[int]] = None,
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shard_config: ShardConfig = None
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):
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r"""
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Args:
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@@ -267,11 +273,17 @@ class LlamaPipelineForwards:
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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loss_fct = CrossEntropyLoss()
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shift_logits = shift_logits.view(-1, self.config.vocab_size)
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shift_labels = shift_labels.view(-1)
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# Enable model parallelism
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shift_labels = shift_labels.to(shift_logits.device)
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loss = loss_fct(shift_logits, shift_labels)
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if shard_config.enable_tensor_parallelism:
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new_vocab_size = logits.shape[-1]
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shift_logits = shift_logits.view(-1, new_vocab_size)
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loss = cross_entropy_1d(shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group)
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else:
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shift_logits = shift_logits.view(-1, self.config.vocab_size)
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loss = loss_fct(shift_logits, shift_labels)
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if not return_dict:
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output = (logits,) + outputs[1:]
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@@ -304,6 +316,7 @@ class LlamaPipelineForwards:
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stage_manager: Optional[PipelineStageManager] = None,
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hidden_states: Optional[torch.FloatTensor] = None,
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stage_index: Optional[List[int]] = None,
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shard_config: ShardConfig = None,
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):
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r"""
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labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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@@ -476,3 +489,106 @@ def get_llama_flash_attention_forward():
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return attn_output, None, past_key_value
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return forward
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def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
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from transformers import LlamaForCausalLM
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def forward(
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self: LlamaForCausalLM,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple, CausalLMOutputWithPast]:
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r"""
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Args:
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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Returns:
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Example:
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```python
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>>> from transformers import AutoTokenizer, LlamaForCausalLM
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>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
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>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
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>>> prompt = "Hey, are you conscious? Can you talk to me?"
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>>> inputs = tokenizer(prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
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>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
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outputs = self.model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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hidden_states = outputs[0]
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if self.config.pretraining_tp > 1:
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lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
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logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
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logits = torch.cat(logits, dim=-1)
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else:
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logits = self.lm_head(hidden_states)
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logits = logits.float()
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loss = None
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if labels is not None:
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# Shift so that tokens < n predict n
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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loss_fct = CrossEntropyLoss()
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shift_labels = shift_labels.view(-1)
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# Enable model parallelism
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shift_labels = shift_labels.to(shift_logits.device)
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if shard_config.enable_tensor_parallelism:
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new_vocab_size = logits.shape[-1]
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shift_logits = shift_logits.view(-1, new_vocab_size)
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loss = cross_entropy_1d(shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group)
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else:
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shift_logits = shift_logits.view(-1, self.config.vocab_size)
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loss = loss_fct(shift_logits, shift_labels)
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if not return_dict:
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output = (logits,) + outputs[1:]
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return (loss,) + output if loss is not None else output
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return CausalLMOutputWithPast(
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loss=loss,
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logits=logits,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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return forward
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