mirror of https://github.com/hpcaitech/ColossalAI
feat: support qwen2 model
Wenhao Chen
アマデウス
parent
61545fcfee
commit
5c2a47a667
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@ -0,0 +1,604 @@
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from typing import List, Optional, Tuple, Union
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import torch
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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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CausalLMOutputWithPast,
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SequenceClassifierOutputWithPast,
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)
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from transformers.models.qwen2.modeling_qwen2 import (
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Qwen2ForCausalLM,
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Qwen2ForSequenceClassification,
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Qwen2Model,
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_prepare_4d_causal_attention_mask,
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_prepare_4d_causal_attention_mask_for_sdpa,
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)
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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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class Qwen2PipelineForwards:
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"""
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This class serves as a micro library for forward function substitution of Qwen2 models
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under pipeline setting.
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"""
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@staticmethod
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def qwen2_model_forward(
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self: Qwen2Model,
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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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shard_config: ShardConfig = None,
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):
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logger = logging.get_logger(__name__)
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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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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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# TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
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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 use_cache:
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logger.warning_once("use_cache=True is not supported for pipeline models at the moment.")
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use_cache = False
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assert past_key_values is None, "past_key_values is not supported for Qwen2 models at the moment."
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past_key_values_length = 0
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if position_ids is None:
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position_ids = torch.arange(
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past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
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)
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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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if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
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is_padding_right = attention_mask[:, -1].sum().item() != batch_size
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if is_padding_right:
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raise ValueError(
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"You are attempting to perform batched generation with padding_side='right'"
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" this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to "
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" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
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)
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if self._attn_implementation == "flash_attention_2":
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# 2d mask is passed through the layers
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attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
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elif self._attn_implementation == "sdpa" and not output_attentions:
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# output_attentions=True can not be supported when using SDPA, and we fall back on
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# the manual implementation that requires a 4D causal mask in all cases.
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attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
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attention_mask,
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(batch_size, seq_length),
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inputs_embeds,
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past_key_values_length,
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)
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else:
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# 4d mask is passed through the layers
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attention_mask = _prepare_4d_causal_attention_mask(
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attention_mask,
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(batch_size, seq_length),
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inputs_embeds,
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past_key_values_length,
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sliding_window=self.config.sliding_window,
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)
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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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)
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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 = None
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start_idx, end_idx = stage_index[0], stage_index[1]
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for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_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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if stage_manager.is_last_stage():
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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# always return dict for imediate stage
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return {"hidden_states": hidden_states}
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@staticmethod
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def qwen2_for_causal_lm_forward(
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self: Qwen2ForCausalLM,
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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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shard_config: ShardConfig = 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, Qwen2ForCausalLM
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>>> model = Qwen2ForCausalLM.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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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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# TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
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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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# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
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outputs = Qwen2PipelineForwards.qwen2_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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if stage_manager.is_last_stage():
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hidden_states = outputs[0]
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logits = self.lm_head(hidden_states)
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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(
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shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group
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)
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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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else:
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hidden_states = outputs.get("hidden_states")
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return {"hidden_states": hidden_states}
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@staticmethod
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def qwen2_for_sequence_classification_forward(
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self: Qwen2ForSequenceClassification,
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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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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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Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
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config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
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`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
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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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# TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
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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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transformer_outputs = Qwen2PipelineForwards.qwen2_model_forward(
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self.model,
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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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if input_ids is not None:
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batch_size = input_ids.shape[0]
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elif inputs_embeds is not None:
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batch_size = inputs_embeds.shape[0]
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else:
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batch_size = hidden_states.shape[0]
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if stage_manager.is_last_stage():
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hidden_states = transformer_outputs[0]
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logits = self.score(hidden_states)
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if self.config.pad_token_id is None and batch_size != 1:
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print(self.config.pad_token_id)
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raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
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if self.config.pad_token_id is None:
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sequence_lengths = -1
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else:
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if input_ids is not None:
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sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
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else:
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sequence_lengths = -1
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pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
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loss = None
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if labels is not None:
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labels = labels.to(logits.device)
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if self.config.problem_type is None:
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if self.num_labels == 1:
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self.config.problem_type = "regression"
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elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
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self.config.problem_type = "single_label_classification"
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else:
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self.config.problem_type = "multi_label_classification"
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if self.config.problem_type == "regression":
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loss_fct = MSELoss()
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if self.num_labels == 1:
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loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
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else:
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loss = loss_fct(pooled_logits, labels)
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elif self.config.problem_type == "single_label_classification":
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
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elif self.config.problem_type == "multi_label_classification":
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loss_fct = BCEWithLogitsLoss()
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loss = loss_fct(pooled_logits, labels)
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if not return_dict:
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output = (pooled_logits,) + transformer_outputs[1:]
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return ((loss,) + output) if loss is not None else output
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return SequenceClassifierOutputWithPast(
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loss=loss,
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logits=pooled_logits,
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past_key_values=transformer_outputs.past_key_values,
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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_qwen2_flash_attention_forward(shard_config: ShardConfig):
|
||||
from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention, apply_rotary_pos_emb, repeat_kv
|
||||
|
||||
from colossalai.nn.layer.colo_attention import AttnMaskType, ColoAttention
|
||||
|
||||
def forward(
|
||||
self: Qwen2Attention,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
**kwargs,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self.k_proj(hidden_states)
|
||||
value_states = self.v_proj(hidden_states)
|
||||
|
||||
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:
|
||||
kv_seq_len += past_key_value[0].shape[-2]
|
||||
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||||
|
||||
assert past_key_value is None, "past_key_value is not supported for Qwen2 models at the moment."
|
||||
|
||||
past_key_value = (key_states, value_states) if use_cache else None
|
||||
|
||||
# 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)
|
||||
|
||||
me_input_shape = (bsz, q_len, self.num_heads, self.head_dim)
|
||||
query_states = query_states.transpose(1, 2).contiguous().view(*me_input_shape)
|
||||
key_states = key_states.transpose(1, 2).contiguous().view(*me_input_shape)
|
||||
value_states = value_states.transpose(1, 2).contiguous().view(*me_input_shape)
|
||||
|
||||
flash_attention_mask = None
|
||||
attn_mask_type = AttnMaskType.causal
|
||||
if not getattr(shard_config, "causal_lm", False) and attention_mask != 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()}"
|
||||
)
|
||||
flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool)).contiguous()
|
||||
attn_mask_type = AttnMaskType.paddedcausal
|
||||
|
||||
attention = ColoAttention(embed_dim=self.hidden_size, num_heads=self.num_heads)
|
||||
attn_output = attention(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attn_mask=flash_attention_mask,
|
||||
attn_mask_type=attn_mask_type,
|
||||
origin_attn_mask=attention_mask,
|
||||
)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
return attn_output, None, past_key_value
|
||||
|
||||
return forward
|
||||
|
||||
|
||||
def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
|
||||
from transformers import Qwen2ForCausalLM
|
||||
|
||||
def forward(
|
||||
self: Qwen2ForCausalLM,
|
||||
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,
|
||||
) -> 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, Qwen2ForCausalLM
|
||||
|
||||
>>> model = Qwen2ForCausalLM.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,
|
||||
)
|
||||
|
||||
hidden_states = outputs[0]
|
||||
logits = self.lm_head(hidden_states)
|
||||
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()
|
||||
# Flatten the tokens
|
||||
loss_fct = CrossEntropyLoss()
|
||||
shift_labels = shift_labels.view(-1)
|
||||
# Enable model parallelism
|
||||
shift_labels = shift_labels.to(shift_logits.device)
|
||||
if shard_config.enable_tensor_parallelism:
|
||||
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
|
||||
)
|
||||
else:
|
||||
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
||||
loss = loss_fct(shift_logits, shift_labels)
|
||||
|
||||
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
|
||||
|
|
@ -0,0 +1,336 @@
|
|||
import warnings
|
||||
from functools import partial
|
||||
from typing import Callable, Dict, List, Union
|
||||
|
||||
import torch.nn as nn
|
||||
from torch import Tensor
|
||||
from torch.nn import Module
|
||||
|
||||
from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, RMSNorm, VocabParallelEmbedding1D
|
||||
|
||||
from ..modeling.qwen2 import (
|
||||
Qwen2PipelineForwards,
|
||||
get_lm_forward_with_dist_cross_entropy,
|
||||
get_qwen2_flash_attention_forward,
|
||||
)
|
||||
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
|
||||
|
||||
__all__ = ["Qwen2Policy", "Qwen2ForCausalLMPolicy", "Qwen2ForSequenceClassificationPolicy"]
|
||||
|
||||
|
||||
class Qwen2Policy(Policy):
|
||||
def config_sanity_check(self):
|
||||
pass
|
||||
|
||||
def preprocess(self):
|
||||
if self.shard_config.enable_tensor_parallelism:
|
||||
# Resize embedding
|
||||
vocab_size = self.model.config.vocab_size
|
||||
world_size = self.shard_config.tensor_parallel_size
|
||||
|
||||
if vocab_size % world_size != 0:
|
||||
new_vocab_size = vocab_size + world_size - vocab_size % world_size
|
||||
self.model.resize_token_embeddings(new_vocab_size)
|
||||
|
||||
return self.model
|
||||
|
||||
def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
|
||||
from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention, Qwen2DecoderLayer, Qwen2Model
|
||||
|
||||
policy = {}
|
||||
|
||||
norm_cls = FusedRMSNorm if self.shard_config.enable_fused_normalization else RMSNorm
|
||||
|
||||
if self.shard_config.enable_sequence_parallelism:
|
||||
self.shard_config.enable_sequence_parallelism = False
|
||||
warnings.warn("Qwen2 doesn't support sequence parallelism now, will ignore the sequence parallelism flag.")
|
||||
|
||||
if self.shard_config.enable_tensor_parallelism:
|
||||
decoder_attribute_replacement = {
|
||||
"self_attn.hidden_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
|
||||
"self_attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
|
||||
}
|
||||
if getattr(self.model.config, "num_key_value_heads", False):
|
||||
decoder_attribute_replacement["self_attn.num_key_value_heads"] = (
|
||||
self.model.config.num_key_value_heads // self.shard_config.tensor_parallel_size
|
||||
)
|
||||
|
||||
policy[Qwen2DecoderLayer] = ModulePolicyDescription(
|
||||
attribute_replacement=decoder_attribute_replacement,
|
||||
sub_module_replacement=[
|
||||
SubModuleReplacementDescription(
|
||||
suffix="self_attn.q_proj",
|
||||
target_module=Linear1D_Col,
|
||||
),
|
||||
SubModuleReplacementDescription(
|
||||
suffix="self_attn.k_proj",
|
||||
target_module=Linear1D_Col,
|
||||
),
|
||||
SubModuleReplacementDescription(
|
||||
suffix="self_attn.v_proj",
|
||||
target_module=Linear1D_Col,
|
||||
),
|
||||
SubModuleReplacementDescription(
|
||||
suffix="self_attn.o_proj",
|
||||
target_module=Linear1D_Row,
|
||||
),
|
||||
SubModuleReplacementDescription(
|
||||
suffix="mlp.gate_proj",
|
||||
target_module=Linear1D_Col,
|
||||
),
|
||||
SubModuleReplacementDescription(
|
||||
suffix="mlp.up_proj",
|
||||
target_module=Linear1D_Col,
|
||||
),
|
||||
SubModuleReplacementDescription(
|
||||
suffix="mlp.down_proj",
|
||||
target_module=Linear1D_Row,
|
||||
),
|
||||
],
|
||||
)
|
||||
|
||||
self.append_or_create_submodule_replacement(
|
||||
description=SubModuleReplacementDescription(
|
||||
suffix="embed_tokens",
|
||||
target_module=VocabParallelEmbedding1D,
|
||||
),
|
||||
policy=policy,
|
||||
target_key=Qwen2Model,
|
||||
)
|
||||
|
||||
# optimization configuration
|
||||
self.append_or_create_submodule_replacement(
|
||||
description=[
|
||||
SubModuleReplacementDescription(
|
||||
suffix="input_layernorm",
|
||||
target_module=norm_cls,
|
||||
),
|
||||
SubModuleReplacementDescription(
|
||||
suffix="post_attention_layernorm",
|
||||
target_module=norm_cls,
|
||||
),
|
||||
],
|
||||
policy=policy,
|
||||
target_key=Qwen2DecoderLayer,
|
||||
)
|
||||
|
||||
self.append_or_create_submodule_replacement(
|
||||
description=SubModuleReplacementDescription(
|
||||
suffix="norm",
|
||||
target_module=norm_cls,
|
||||
),
|
||||
policy=policy,
|
||||
target_key=Qwen2Model,
|
||||
)
|
||||
|
||||
# use flash attention
|
||||
if self.shard_config.enable_flash_attention:
|
||||
self.append_or_create_method_replacement(
|
||||
description={
|
||||
"forward": get_qwen2_flash_attention_forward(self.shard_config),
|
||||
},
|
||||
policy=policy,
|
||||
target_key=Qwen2Attention,
|
||||
)
|
||||
|
||||
return policy
|
||||
|
||||
def postprocess(self):
|
||||
return self.model
|
||||
|
||||
def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
|
||||
"""If under pipeline parallel setting, replacing the original forward method of huggingface
|
||||
to customized forward method, and add this changing to policy."""
|
||||
if self.pipeline_stage_manager is None:
|
||||
return
|
||||
|
||||
stage_manager = self.pipeline_stage_manager
|
||||
if self.model.__class__.__name__ == "Qwen2Model":
|
||||
module = self.model
|
||||
else:
|
||||
module = self.model.model
|
||||
|
||||
if stage_manager.is_interleave:
|
||||
layers_per_stage = self.distribute_layers(
|
||||
len(module.layers), stage_manager.num_stages * stage_manager.num_model_chunks
|
||||
)
|
||||
stage_manager.stage_indices = Policy.get_stage_index(
|
||||
layers_per_stage,
|
||||
stage_manager.stage,
|
||||
num_model_chunks=stage_manager.num_model_chunks,
|
||||
num_stages=stage_manager.num_stages,
|
||||
)
|
||||
method_replacement = {
|
||||
"forward": partial(new_forward, stage_manager=stage_manager, shard_config=self.shard_config)
|
||||
}
|
||||
|
||||
else:
|
||||
layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages)
|
||||
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
|
||||
method_replacement = {
|
||||
"forward": partial(
|
||||
new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config
|
||||
)
|
||||
}
|
||||
self.append_or_create_method_replacement(
|
||||
description=method_replacement, policy=policy, target_key=model_cls
|
||||
)
|
||||
|
||||
self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
|
||||
|
||||
def get_held_layers(self) -> List[Module]:
|
||||
"""Get pipeline layers for current stage."""
|
||||
assert self.pipeline_stage_manager is not None
|
||||
|
||||
if self.model.__class__.__name__ == "Qwen2Model":
|
||||
module = self.model
|
||||
else:
|
||||
module = self.model.model
|
||||
|
||||
stage_manager = self.pipeline_stage_manager
|
||||
|
||||
held_layers = []
|
||||
if stage_manager.is_interleave:
|
||||
assert stage_manager.num_model_chunks is not None
|
||||
layers_per_stage = self.distribute_layers(
|
||||
len(module.layers), stage_manager.num_stages * stage_manager.num_model_chunks
|
||||
)
|
||||
stage_indices = Policy.get_stage_index(
|
||||
layers_per_stage,
|
||||
stage_manager.stage,
|
||||
num_model_chunks=stage_manager.num_model_chunks,
|
||||
num_stages=stage_manager.num_stages,
|
||||
)
|
||||
if stage_manager.is_first_stage(ignore_chunk=True):
|
||||
held_layers.append(module.embed_tokens)
|
||||
for start_idx, end_idx in stage_indices:
|
||||
held_layers.extend(module.layers[start_idx:end_idx])
|
||||
if stage_manager.is_last_stage(ignore_chunk=True):
|
||||
held_layers.append(module.norm)
|
||||
|
||||
else:
|
||||
layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
|
||||
if stage_manager.is_first_stage():
|
||||
held_layers.append(module.embed_tokens)
|
||||
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
|
||||
held_layers.extend(module.layers[start_idx:end_idx])
|
||||
if stage_manager.is_last_stage():
|
||||
held_layers.append(module.norm)
|
||||
|
||||
return held_layers
|
||||
|
||||
|
||||
class Qwen2ModelPolicy(Qwen2Policy):
|
||||
def module_policy(self):
|
||||
policy = super().module_policy()
|
||||
from transformers.models.qwen2.modeling_qwen2 import Qwen2Model
|
||||
|
||||
if self.pipeline_stage_manager:
|
||||
# set None as default
|
||||
self.set_pipeline_forward(
|
||||
model_cls=Qwen2Model, new_forward=Qwen2PipelineForwards.qwen2_model_forward, policy=policy
|
||||
)
|
||||
return policy
|
||||
|
||||
def get_held_layers(self) -> List[Module]:
|
||||
"""Get pipeline layers for current stage."""
|
||||
held_layers = super().get_held_layers()
|
||||
return held_layers
|
||||
|
||||
def get_shared_params(self) -> List[Dict[int, Tensor]]:
|
||||
"""No shared params in Qwen2 model"""
|
||||
return []
|
||||
|
||||
|
||||
class Qwen2ForCausalLMPolicy(Qwen2Policy):
|
||||
def module_policy(self):
|
||||
from transformers import Qwen2ForCausalLM
|
||||
|
||||
policy = super().module_policy()
|
||||
|
||||
setattr(self.shard_config, "causal_lm", True)
|
||||
|
||||
if self.shard_config.enable_tensor_parallelism:
|
||||
# add a new item for casual lm
|
||||
new_item = {
|
||||
Qwen2ForCausalLM: ModulePolicyDescription(
|
||||
sub_module_replacement=[
|
||||
SubModuleReplacementDescription(suffix="lm_head", target_module=Linear1D_Col)
|
||||
],
|
||||
method_replacement={"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)},
|
||||
)
|
||||
}
|
||||
policy.update(new_item)
|
||||
|
||||
if self.pipeline_stage_manager:
|
||||
# set None as default
|
||||
self.set_pipeline_forward(
|
||||
model_cls=Qwen2ForCausalLM, new_forward=Qwen2PipelineForwards.qwen2_for_causal_lm_forward, policy=policy
|
||||
)
|
||||
|
||||
return policy
|
||||
|
||||
def get_held_layers(self) -> List[Module]:
|
||||
"""Get pipeline layers for current stage."""
|
||||
stage_manager = self.pipeline_stage_manager
|
||||
held_layers = super().get_held_layers()
|
||||
if stage_manager.is_last_stage(ignore_chunk=True):
|
||||
held_layers.append(self.model.lm_head)
|
||||
return held_layers
|
||||
|
||||
def get_shared_params(self) -> List[Dict[int, Tensor]]:
|
||||
qwen2_model = self.model.model
|
||||
if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
|
||||
if (
|
||||
id(qwen2_model.embed_tokens.weight) == id(self.model.lm_head.weight)
|
||||
and self.pipeline_stage_manager.num_stages > 1
|
||||
):
|
||||
# tie weights
|
||||
return [
|
||||
{
|
||||
0: qwen2_model.embed_tokens.weight,
|
||||
self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight,
|
||||
}
|
||||
]
|
||||
return []
|
||||
|
||||
|
||||
class Qwen2ForSequenceClassificationPolicy(Qwen2Policy):
|
||||
def module_policy(self):
|
||||
from transformers import Qwen2ForSequenceClassification
|
||||
|
||||
policy = super().module_policy()
|
||||
|
||||
if self.shard_config.enable_tensor_parallelism:
|
||||
# add a new item for sequence classification
|
||||
new_item = {
|
||||
Qwen2ForSequenceClassification: ModulePolicyDescription(
|
||||
sub_module_replacement=[
|
||||
SubModuleReplacementDescription(
|
||||
suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True)
|
||||
)
|
||||
]
|
||||
)
|
||||
}
|
||||
policy.update(new_item)
|
||||
# to be confirmed
|
||||
if self.pipeline_stage_manager:
|
||||
# set None as default
|
||||
self.set_pipeline_forward(
|
||||
model_cls=Qwen2ForSequenceClassification,
|
||||
new_forward=Qwen2PipelineForwards.qwen2_for_sequence_classification_forward,
|
||||
policy=policy,
|
||||
)
|
||||
return policy
|
||||
|
||||
def get_held_layers(self) -> List[Module]:
|
||||
"""Get pipeline layers for current stage."""
|
||||
stage_manager = self.pipeline_stage_manager
|
||||
held_layers = super().get_held_layers()
|
||||
if stage_manager.is_last_stage(ignore_chunk=True):
|
||||
held_layers.append(self.model.score)
|
||||
return held_layers
|
||||
|
||||
def get_shared_params(self) -> List[Dict[int, Tensor]]:
|
||||
"""No shared params in Qwen2 for sequence classification model"""
|
||||
return []
|
||||
Loading…
Reference in New Issue