mirror of https://github.com/hpcaitech/ColossalAI
300 lines
14 KiB
Python
300 lines
14 KiB
Python
""" PyTorch ChatGLM model. """
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from typing import Any, Dict, 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.utils.checkpoint
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from torch.nn import CrossEntropyLoss, LayerNorm
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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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.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig
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from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import (
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ChatGLMForConditionalGeneration,
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ChatGLMModel,
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GLMBlock,
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)
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def get_flash_core_attention_forward():
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from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention
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from .chatglm2_6b.modeling_chatglm import CoreAttention
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def forward(self: CoreAttention, query_layer, key_layer, value_layer, attention_mask):
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pytorch_major_version = int(torch.__version__.split(".")[0])
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if pytorch_major_version >= 2:
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query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]]
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if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
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context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer,
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key_layer,
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value_layer,
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is_causal=True)
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else:
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if attention_mask is not None:
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attention_mask = ~attention_mask
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context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
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attention_mask)
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context_layer = context_layer.permute(2, 0, 1, 3)
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new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
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context_layer = context_layer.reshape(*new_context_layer_shape)
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else:
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# Raw attention scores
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query_layer = query_layer.permute(1, 0, 2, 3).contiguous()
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key_layer = key_layer.permute(1, 0, 2, 3).contiguous()
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value_layer = value_layer.permute(1, 0, 2, 3).contiguous()
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scale = 1.0 / self.norm_factor
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if self.coeff is not None:
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scale = scale * self.coeff
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flash_attention_mask = None
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attn_mask_type = None
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if attention_mask is None:
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attn_mask_type = AttnMaskType.causal
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else:
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flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool)).contiguous()
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attn_mask_type = AttnMaskType.paddedcausal
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attention = ColoAttention(embed_dim=self.hidden_size_per_partition,
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num_heads=self.num_attention_heads_per_partition,
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dropout=self.attention_dropout.p,
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scale=scale)
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context_layer = attention(query_layer,
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key_layer,
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value_layer,
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attn_mask=flash_attention_mask,
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attn_mask_type=attn_mask_type)
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context_layer = context_layer.permute(1, 0, -1).contiguous()
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return context_layer
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return forward
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def get_jit_fused_glm_block_forward():
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from .chatglm2_6b.modeling_chatglm import GLMBlock
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def forward(
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self: GLMBlock,
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hidden_states,
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attention_mask,
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rotary_pos_emb,
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kv_cache=None,
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use_cache=True,
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):
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# hidden_states: [s, b, h]
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# Layer norm at the beginning of the transformer layer.
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layernorm_output = self.input_layernorm(hidden_states)
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# Self attention.
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attention_output, kv_cache = self.self_attention(
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layernorm_output,
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attention_mask,
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rotary_pos_emb,
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kv_cache=kv_cache,
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use_cache=use_cache,
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)
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# Residual connection.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = hidden_states
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layernorm_input = self.dropout_add(attention_output, residual, self.hidden_dropout, self.training)
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# Layer norm post the self attention.
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layernorm_output = self.post_attention_layernorm(layernorm_input)
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# MLP.
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mlp_output = self.mlp(layernorm_output)
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# Second residual connection.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = layernorm_input
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output = self.dropout_add(mlp_output, residual, self.hidden_dropout, self.training)
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return output, kv_cache
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return forward
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class ChatGLMPipelineForwards:
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'''
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This class serves as a micro library for ChatGLM model forwards under pipeline parallelism.
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'''
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@staticmethod
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def chatglm_model_forward(
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self: ChatGLMModel,
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input_ids,
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position_ids: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.BoolTensor] = None,
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full_attention_mask: Optional[torch.BoolTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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use_cache: 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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output_hidden_states = (output_hidden_states
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if output_hidden_states is not None else self.config.output_hidden_states)
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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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# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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if past_key_values:
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logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.')
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past_key_values = None
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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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if stage_manager.is_first_stage():
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batch_size, seq_length = input_ids.shape
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if inputs_embeds is None:
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inputs_embeds = self.embedding(input_ids)
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hidden_states = inputs_embeds
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else:
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seq_length, batch_size = hidden_states.shape[:2]
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if self.pre_seq_len is not None:
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if past_key_values is None:
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past_key_values = self.get_prompt(batch_size=batch_size,
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device=input_ids.device,
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dtype=inputs_embeds.dtype)
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if attention_mask is not None:
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attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), attention_mask],
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dim=-1)
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if full_attention_mask is None:
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if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1):
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full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask)
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# Rotary positional embeddings
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rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
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if position_ids is not None:
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rotary_pos_emb = rotary_pos_emb[position_ids]
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else:
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rotary_pos_emb = rotary_pos_emb[None, :seq_length]
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rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
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if not past_key_values:
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past_key_values = [None for _ in range(self.num_layers)]
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presents = () if use_cache else None
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if self.encoder.gradient_checkpointing and self.encoder.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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all_self_attentions = None
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all_hidden_states = () if output_hidden_states else None
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start_idx, end_idx = stage_index[0], stage_index[1]
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for idx in range(start_idx, end_idx):
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layer = self.encoder._get_layer(idx)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if self.encoder.gradient_checkpointing and self.encoder.training:
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layer_ret = torch.utils.checkpoint.checkpoint(layer, hidden_states, attention_mask, rotary_pos_emb,
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past_key_values[idx], use_cache)
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else:
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layer_ret = layer(hidden_states,
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full_attention_mask,
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rotary_pos_emb,
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kv_cache=past_key_values[idx],
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use_cache=use_cache)
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hidden_states, kv_cache = layer_ret
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if use_cache:
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presents = presents + (kv_cache,)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if stage_manager.is_last_stage():
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# final layer_norm
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if self.encoder.post_layer_norm:
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hidden_states = self.encoder.final_layernorm(hidden_states)
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if not return_dict:
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return tuple(
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v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] 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=presents,
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hidden_states=all_hidden_states,
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attentions=all_self_attentions,
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)
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else:
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return {'hidden_states': hidden_states}
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@staticmethod
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def chatglm_for_conditional_generation_forward(
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self: ChatGLMForConditionalGeneration,
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input_ids: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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labels: Optional[torch.Tensor] = 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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return_last_logit: Optional[bool] = False,
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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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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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transformer_outputs = ChatGLMPipelineForwards.chatglm_model_forward(
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self.transformer,
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input_ids=input_ids,
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position_ids=position_ids,
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attention_mask=attention_mask,
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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_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 = transformer_outputs[0]
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if return_last_logit:
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hidden_states = hidden_states[-1:]
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lm_logits = self.transformer.output_layer(hidden_states)
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lm_logits = lm_logits.transpose(0, 1).contiguous()
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loss = None
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if labels is not None:
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lm_logits = lm_logits.to(torch.float32)
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# Shift so that tokens < n predict n
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shift_logits = lm_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(ignore_index=-100)
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loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
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lm_logits = lm_logits.to(hidden_states.dtype)
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loss = loss.to(hidden_states.dtype)
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if not return_dict:
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output = (lm_logits,) + transformer_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=lm_logits,
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past_key_values=transformer_outputs.past_key_values,
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hidden_states=transformer_outputs.hidden_states,
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attentions=transformer_outputs.attentions,
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)
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else:
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return transformer_outputs
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