mirror of https://github.com/hpcaitech/ColossalAI
232 lines
10 KiB
Python
232 lines
10 KiB
Python
from typing import List, Optional, Tuple
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import torch
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from torch.nn import CrossEntropyLoss, MSELoss
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from transformers.models.llama.modeling_llama import LlamaForCausalLM, LlamaModel
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from transformers.utils import logging
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from colossalai.pipeline.stage_manager import PipelineStageManager
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class LlamaPipelineForwards:
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'''
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This class serves as a micro library for forward function substitution of Llama models
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under pipeline setting.
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'''
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def llama_model_forward(
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self: LlamaModel,
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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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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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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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):
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logger = logging.get_logger(__name__)
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# Preprocess passed in arguments
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if output_attentions:
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logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
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output_attentions = False
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if output_hidden_states:
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logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
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output_hidden_states = False
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# retrieve input_ids and inputs_embeds
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if stage_manager.is_first_stage():
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
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elif input_ids is not None:
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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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else:
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raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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hidden_states = inputs_embeds
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else:
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input_shape = hidden_states.shape[:-1]
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batch_size, seq_length = input_shape
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device = hidden_states.device
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seq_length_with_past = seq_length
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past_key_values_length = 0
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if past_key_values is not None:
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past_key_values_length = past_key_values[0][0].shape[2]
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seq_length_with_past = seq_length_with_past + past_key_values_length
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if position_ids is None:
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position_ids = torch.arange(past_key_values_length,
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seq_length + past_key_values_length,
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dtype=torch.long,
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device=device)
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position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
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else:
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position_ids = position_ids.view(-1, seq_length).long()
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# embed positions, for the first stage, hidden_states is the input embeddings,
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# for the other stages, hidden_states is the output of the previous stage
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if attention_mask is None:
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attention_mask = torch.ones((batch_size, seq_length_with_past),
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dtype=torch.bool,
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device=hidden_states.device)
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attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), hidden_states,
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past_key_values_length)
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
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use_cache = False
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = () if use_cache else None
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start_idx, end_idx = stage_index[0], stage_index[1]
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if past_key_values is None:
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past_key_values = tuple([None] * (end_idx - start_idx + 1))
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for idx, past_key_value in zip(range(start_idx, end_idx), past_key_values):
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decoder_layer = self.layers[idx]
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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# past_key_value = past_key_values[idx] if past_key_values is not None else None
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if self.gradient_checkpointing and self.training:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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# None for past_key_value
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return module(*inputs, output_attentions, None)
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return custom_forward
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layer_outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(decoder_layer),
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hidden_states,
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attention_mask,
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position_ids,
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None,
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)
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else:
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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if stage_manager.is_last_stage():
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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# always return dict for imediate stage
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return {'hidden_states': hidden_states, 'past_key_values': next_cache}
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def llama_for_causal_lm_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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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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):
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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 consciours? 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 consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
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```"""
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logger = logging.get_logger(__name__)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if output_attentions:
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logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
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output_attentions = False
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if output_hidden_states:
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logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
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output_hidden_states = False
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# If is first stage and after warmup, go throught lm_head first
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if stage_manager.is_first_stage() and hidden_states is not None:
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lm_logits = self.lm_head(hidden_states)
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return {'logits': lm_logits}
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# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
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outputs = LlamaPipelineForwards.llama_model_forward(
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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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stage_manager=stage_manager,
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hidden_states=hidden_states,
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stage_index=stage_index,
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)
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return outputs
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