""" PyTorch ChatGLM model. """ from typing import List, Optional, Tuple import torch import torch.utils.checkpoint from torch.nn import CrossEntropyLoss from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.utils import logging from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.shardformer import ShardConfig from colossalai.shardformer.layer import AttnMaskType, ColoAttention from colossalai.shardformer.layer._operation import ( all_to_all_comm, gather_forward_split_backward, split_forward_gather_backward, ) def get_flash_core_attention_forward(): from .chatglm2_6b.modeling_chatglm import CoreAttention def forward(self: CoreAttention, query_layer, key_layer, value_layer, attention_mask): 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]: attention_mask_type = AttnMaskType.CAUSAL attn_bias = torch.zeros( query_layer.shape[0], 1, query_layer.shape[2], key_layer.shape[2], dtype=query_layer.dtype, device=query_layer.device, ) temp_mask = ( torch.ones( query_layer.shape[2], key_layer.shape[2], dtype=torch.bool, device=query_layer.device, ) .tril(diagonal=0) .expand(query_layer.shape[0], 1, -1, -1) ) attn_bias.masked_fill_(temp_mask.logical_not(), torch.finfo(query_layer.dtype).min) else: attention_mask_type = AttnMaskType.CUSTOM if attention_mask is not None: attn_bias = torch.zeros_like(attention_mask, dtype=query_layer.dtype) attn_bias.masked_fill_(attention_mask, torch.finfo(query_layer.dtype).min) dropout_p = self.attention_dropout.p if self.training else 0.0 context_layer = ColoAttention.attention( query_layer, key_layer, value_layer, attention_mask=attn_bias, attention_mask_type=attention_mask_type, dropout_p=dropout_p, scale=1.0 / self.norm_factor, ) 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) 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, shard_config: ShardConfig = 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] if shard_config and shard_config.enable_sequence_parallelism: if shard_config.sequence_parallelism_mode == "split_gather": hidden_states = split_forward_gather_backward( hidden_states, dim=0, process_group=shard_config.tensor_parallel_process_group, ) elif shard_config.sequence_parallelism_mode == "all_to_all": hidden_states = split_forward_gather_backward( hidden_states, dim=0, process_group=shard_config.sequence_parallel_process_group, grad_scale=1 / shard_config.sequence_parallel_size, ) 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 shard_config and shard_config.enable_sequence_parallelism: if shard_config.sequence_parallelism_mode == "split_gather": hidden_states = gather_forward_split_backward( hidden_states, dim=0, process_group=shard_config.tensor_parallel_process_group, ) elif shard_config.sequence_parallelism_mode == "all_to_all": hidden_states = gather_forward_split_backward( hidden_states, dim=0, process_group=shard_config.sequence_parallel_process_group, grad_scale=shard_config.sequence_parallel_size, ) 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, shard_config: ShardConfig = None, ): 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, shard_config=shard_config, ) 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 def get_chatglm_sequence_parallel_forward_fn(shard_config: ShardConfig, sp_mode, sp_size, sp_group): logger = logging.get_logger(__name__) def forward( self, 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, ): 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 batch_size, seq_length = input_ids.shape if inputs_embeds is None: inputs_embeds = self.embedding(input_ids) 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 sp_mode in ["all_to_all"] and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with sp mode `{sp_mode}`. Setting `use_cache=False`..." ) use_cache = False # Run encoder. # [seq_len, batch_size, hidden_size] -> [seq_len/TP_size, batch_size, hidden_size] if sp_mode in ["split_gather"]: inputs_embeds = split_forward_gather_backward( inputs_embeds, dim=0, process_group=sp_group, ) elif sp_mode == "all_to_all": inputs_embeds = split_forward_gather_backward( inputs_embeds, dim=0, process_group=sp_group, grad_scale=1 / sp_size, ) hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states, ) if sp_mode in ["split_gather"]: hidden_states = gather_forward_split_backward( hidden_states, dim=0, process_group=shard_config.tensor_parallel_process_group, ) elif sp_mode == "all_to_all": hidden_states = gather_forward_split_backward( hidden_states, dim=0, process_group=sp_group, grad_scale=sp_size, ) 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, ) return forward def get_chatglm_sequence_parallel_attention_forward(shard_config: ShardConfig, sp_mode, sp_size, sp_group): from .chatglm2_6b.modeling_chatglm import apply_rotary_pos_emb, split_tensor_along_last_dim def forward( self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, ): if sp_mode is not None: assert sp_mode in ["all_to_all", "split_gather"], "Invalid sp_mode" assert (sp_size is not None) and ( sp_group is not None ), "Must specify sp_size and sp_group for sequence parallel" mixed_x_layer = self.query_key_value(hidden_states) if self.multi_query_attention: (query_layer, key_layer, value_layer) = mixed_x_layer.split( [ self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, ], dim=-1, ) query_layer = query_layer.view( query_layer.size()[:-1] + ( self.num_attention_heads_per_partition, self.hidden_size_per_attention_head, ) ) key_layer = key_layer.view( key_layer.size()[:-1] + ( self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head, ) ) value_layer = value_layer.view( value_layer.size()[:-1] + ( self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head, ) ) else: new_tensor_shape = mixed_x_layer.size()[:-1] + ( self.num_attention_heads_per_partition, 3 * self.hidden_size_per_attention_head, ) mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) # sp: all-to-all comminucation when introducing sequence parallel if sp_mode == "all_to_all": sq, bs, _, _ = value_layer.size() query_layer = query_layer.reshape(sq, bs, -1) key_layer = key_layer.reshape(sq, bs, -1) value_layer = value_layer.reshape(sq, bs, -1) query_layer = all_to_all_comm(query_layer, sp_group, gather_dim=0) key_layer = all_to_all_comm(key_layer, sp_group, gather_dim=0) value_layer = all_to_all_comm(value_layer, sp_group, gather_dim=0) query_layer = query_layer.view( sq * sp_size, bs, self.num_attention_heads_per_partition // sp_size, self.hidden_size_per_attention_head, ).contiguous() key_layer = key_layer.view( sq * sp_size, bs, self.num_attention_heads_per_partition // sp_size, self.hidden_size_per_attention_head, ).contiguous() value_layer = value_layer.view( sq * sp_size, bs, self.num_attention_heads_per_partition // sp_size, self.hidden_size_per_attention_head, ).contiguous() # apply relative positional encoding (rotary embedding) if rotary_pos_emb is not None: query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) # adjust key and value for inference if kv_cache is not None: cache_k, cache_v = kv_cache key_layer = torch.cat((cache_k, key_layer), dim=0) value_layer = torch.cat((cache_v, value_layer), dim=0) if use_cache: kv_cache = (key_layer, value_layer) else: kv_cache = None if self.multi_query_attention: key_layer = key_layer.unsqueeze(-2) key_layer = key_layer.expand( -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1, ) key_layer = key_layer.contiguous().view( key_layer.size()[:2] + ( self.num_attention_heads_per_partition, self.hidden_size_per_attention_head, ) ) value_layer = value_layer.unsqueeze(-2) value_layer = value_layer.expand( -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1, ) value_layer = value_layer.contiguous().view( value_layer.size()[:2] + ( self.num_attention_heads_per_partition // sp_size, self.hidden_size_per_attention_head, ) ) # ================================== # core attention computation # ================================== context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) if sp_mode == "all_to_all": context_layer = all_to_all_comm(context_layer, sp_group, gather_dim=2, scatter_dim=0) # ================= # Output. [sq, b, h] # ================= output = self.dense(context_layer) return output, kv_cache return forward