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