mirror of
https://github.com/hpcaitech/ColossalAI.git
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[pipeline] add chatglm (#4363)
* add pipeline policy and bert forward to be done * add bertmodel pipeline forward and make tests * add Bert_Policy and test for policy * update formatting * update formatting * update the code * fix bugs * fix name confilt * add bloom model and policy ,revise the base class of policy * revise * revision * add bert_for_pretraining * add bert_for_pretraining forward and policy * fix typos * cancel warning * change the imediate output to default dict * change the default output of get_shared_params * add chatglm * add * chatglm * chatglm * finish chatglm * deletes * fix rmsnorm * chatglm * fix chatglm shard * init
This commit is contained in:
Jianghai
Hongxin Liu
parent
b1feeced8e
commit
a88e92251d
189
colossalai/shardformer/modeling/chatglm.py
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189
colossalai/shardformer/modeling/chatglm.py
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@ -0,0 +1,189 @@
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""" 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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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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@ -0,0 +1,58 @@
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from transformers import PretrainedConfig
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class ChatGLMConfig(PretrainedConfig):
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model_type = "chatglm"
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def __init__(self,
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num_layers=28,
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padded_vocab_size=65024,
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hidden_size=4096,
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ffn_hidden_size=13696,
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kv_channels=128,
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num_attention_heads=32,
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seq_length=2048,
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hidden_dropout=0.0,
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attention_dropout=0.0,
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layernorm_epsilon=1e-5,
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rmsnorm=True,
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apply_residual_connection_post_layernorm=False,
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post_layer_norm=True,
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add_bias_linear=False,
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add_qkv_bias=False,
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bias_dropout_fusion=True,
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multi_query_attention=False,
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multi_query_group_num=1,
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apply_query_key_layer_scaling=True,
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attention_softmax_in_fp32=True,
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fp32_residual_connection=False,
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quantization_bit=0,
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pre_seq_len=None,
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prefix_projection=False,
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**kwargs):
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self.num_layers = num_layers
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self.vocab_size = padded_vocab_size
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self.padded_vocab_size = padded_vocab_size
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self.hidden_size = hidden_size
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self.ffn_hidden_size = ffn_hidden_size
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self.kv_channels = kv_channels
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self.num_attention_heads = num_attention_heads
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self.seq_length = seq_length
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.layernorm_epsilon = layernorm_epsilon
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self.rmsnorm = rmsnorm
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self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
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self.post_layer_norm = post_layer_norm
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self.add_bias_linear = add_bias_linear
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self.add_qkv_bias = add_qkv_bias
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self.bias_dropout_fusion = bias_dropout_fusion
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self.multi_query_attention = multi_query_attention
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self.multi_query_group_num = multi_query_group_num
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self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
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self.attention_softmax_in_fp32 = attention_softmax_in_fp32
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self.fp32_residual_connection = fp32_residual_connection
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self.quantization_bit = quantization_bit
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self.pre_seq_len = pre_seq_len
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self.prefix_projection = prefix_projection
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super().__init__(**kwargs)
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1373
colossalai/shardformer/modeling/chatglm2_6b/modeling_chatglm.py
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1373
colossalai/shardformer/modeling/chatglm2_6b/modeling_chatglm.py
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File diff suppressed because it is too large
Load Diff
@ -1,21 +1,33 @@
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from typing import Dict, Union
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from functools import partial
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from typing import Callable, Dict, List, Optional, Tuple, Union
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import torch.nn as nn
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from torch import Tensor
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from transformers.modeling_outputs import BaseModelOutputWithPast
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import colossalai.shardformer.layer as col_nn
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from colossalai.pipeline.stage_manager import PipelineStageManager
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from colossalai.shardformer.modeling.chatglm import ChatGLMPipelineForwards
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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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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = ['ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy']
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__all__ = ['ChatGLMPolicy', 'ChatGLMModelPolicy', 'ChatGLMForConditionalGenerationPolicy']
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class ChatGLMModelPolicy(Policy):
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class ChatGLMPolicy(Policy):
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def config_sanity_check(self):
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pass
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def preprocess(self):
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# Resize embedding
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if self.shard_config.enable_tensor_parallelism:
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vocab_size = self.model.config.padded_vocab_size
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world_size = self.shard_config.tensor_parallel_size
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@ -23,10 +35,12 @@ class ChatGLMModelPolicy(Policy):
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new_vocab_size = vocab_size + world_size - vocab_size % world_size
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self.model.resize_token_embeddings(new_vocab_size)
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return self.model
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def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
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from tests.kit.model_zoo.transformers.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock
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from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMModel, GLMBlock
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policy = {}
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@ -112,9 +126,91 @@ class ChatGLMModelPolicy(Policy):
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def postprocess(self):
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return self.model
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def get_held_layers(self) -> List[nn.Module]:
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"""Get pipeline layers for current stage."""
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assert self.pipeline_stage_manager is not None
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if self.model.__class__.__name__ == 'ChatGLMModel':
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module = self.model
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else:
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module = self.model.transformer
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stage_manager = self.pipeline_stage_manager
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held_layers = []
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layers_per_stage = self.distribute_layers(module.num_layers, stage_manager.num_stages)
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if stage_manager.is_first_stage():
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held_layers.append(module.embedding)
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start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
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held_layers.extend(module.encoder.layers[start_idx:end_idx])
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if stage_manager.is_last_stage():
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if module.encoder.post_layer_norm:
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held_layers.append(module.encoder.final_layernorm)
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# rotary_pos_emb is needed for all stages
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held_layers.append(module.rotary_pos_emb)
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return held_layers
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def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
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"""If under pipeline parallel setting, replacing the original forward method of huggingface
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to customized forward method, and add this changing to policy."""
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if not self.pipeline_stage_manager:
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raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.")
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stage_manager = self.pipeline_stage_manager
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if self.model.__class__.__name__ == 'ChatGLMModel':
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module = self.model
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else:
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module = self.model.transformer
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layers_per_stage = Policy.distribute_layers(module.num_layers, stage_manager.num_stages)
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stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
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method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
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self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
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class ChatGLMModelPolicy(ChatGLMPolicy):
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def __init__(self) -> None:
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super().__init__()
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def module_policy(self):
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from transformers.models.gpt2.modeling_gpt2 import GPT2Model
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policy = super().module_policy()
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if self.pipeline_stage_manager is not None:
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self.set_pipeline_forward(model_cls=ChatGLMModel,
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new_forward=ChatGLMPipelineForwards.chatglm_model_forward,
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policy=policy)
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return policy
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def get_held_layers(self) -> List[nn.Module]:
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return super().get_held_layers()
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in ChatGLMModel."""
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return []
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class ChatGLMForConditionalGenerationPolicy(ChatGLMModelPolicy):
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def module_policy(self):
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policy = super().module_policy()
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if self.pipeline_stage_manager is not None:
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self.set_pipeline_forward(model_cls=ChatGLMForConditionalGeneration,
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new_forward=ChatGLMPipelineForwards.chatglm_for_conditional_generation_forward,
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policy=policy)
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return policy
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def get_held_layers(self) -> List[nn.Module]:
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held_layers = super().get_held_layers()
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if self.pipeline_stage_manager.is_last_stage():
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held_layers.append(self.model.transformer.output_layer)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in ChatGLMForConditionalGenerationModel."""
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return []
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@ -1,9 +1,11 @@
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import torch
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import transformers
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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 ChatGLMForConditionalGeneration, ChatGLMModel
|
||||
|
||||
from ..registry import ModelAttribute, model_zoo
|
||||
from .chatglm2_6b.configuration_chatglm import ChatGLMConfig
|
||||
from .chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel
|
||||
|
||||
|
||||
# ================================
|
||||
# Register single-sentence ChatGLM
|
||||
@ -20,15 +22,18 @@ def data_gen():
|
||||
output_transform_fn = lambda x: x
|
||||
|
||||
# define loss function
|
||||
loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.mean()
|
||||
loss_fn = lambda x: x.logits.mean()
|
||||
loss_fn_for_chatglm_model = lambda x: x.last_hidden_state.sum()
|
||||
loss_fn = lambda x: x.logits.sum()
|
||||
|
||||
config = ChatGLMConfig(num_layers=1,
|
||||
padded_vocab_size=65024,
|
||||
hidden_size=64,
|
||||
num_attention_heads=8,
|
||||
rmsnorm=False,
|
||||
rmsnorm=True,
|
||||
original_rope=True,
|
||||
use_cache=True)
|
||||
use_cache=True,
|
||||
torch_dtype=torch.float32)
|
||||
|
||||
|
||||
model_zoo.register(name='transformers_chatglm',
|
||||
model_fn=lambda: ChatGLMModel(config, empty_init=False),
|
||||
|
||||
@ -1,39 +0,0 @@
|
||||
from colossalai.shardformer.policies.t5 import T5BasePolicy
|
||||
|
||||
|
||||
def test_t5_pipeline_distribution():
|
||||
num_test_cases = 8
|
||||
test_dict = {
|
||||
'num_encoder_layers': [2, 1, 3, 2, 3, 2, 10, 5],
|
||||
'num_decoder_layers': [2, 8, 0, 2, 1, 5, 6, 22],
|
||||
'num_stages': [2, 2, 2, 4, 4, 4, 8, 8],
|
||||
'decoder_starting_stage': [1, 1, 2, 2, 3, 1, 5, 2]
|
||||
}
|
||||
|
||||
for i in range(num_test_cases):
|
||||
_, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(test_dict['num_encoder_layers'][i],
|
||||
test_dict['num_decoder_layers'][i],
|
||||
test_dict['num_stages'][i])
|
||||
assert test_dict['decoder_starting_stage'][i] == decoder_starting_stage
|
||||
|
||||
|
||||
def test_t5_pipeline_layers():
|
||||
num_test_cases = 4
|
||||
test_dict = {
|
||||
'num_encoder_layers': [2, 3, 2, 4],
|
||||
'num_decoder_layers': [2, 0, 2, 8],
|
||||
'num_stages': [2, 2, 4, 4],
|
||||
'layers_per_stage': [[[0, 2], [0, 2]], [[0, 1], [1, 3]], [[0, 1], [1, 2], [0, 1], [1, 2]],
|
||||
[[0, 4], [0, 3], [3, 6], [6, 8]]]
|
||||
}
|
||||
|
||||
for i in range(num_test_cases):
|
||||
layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
|
||||
test_dict['num_encoder_layers'][i], test_dict['num_decoder_layers'][i], test_dict['num_stages'][i])
|
||||
|
||||
for stage in range(test_dict['num_stages'][i]):
|
||||
start_idx, end_idx = test_dict['layers_per_stage'][i][stage]
|
||||
predicted_start, predicted_end = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage,
|
||||
decoder_starting_stage)
|
||||
assert start_idx == predicted_start
|
||||
assert end_idx == predicted_end
|
||||
@ -1,5 +1,6 @@
|
||||
import copy
|
||||
from contextlib import nullcontext
|
||||
from typing import Optional
|
||||
from typing import Any, Callable, Dict, List, Optional
|
||||
|
||||
import torch
|
||||
@ -15,6 +16,7 @@ from colossalai.booster.plugin import HybridParallelPlugin
|
||||
from colossalai.lazy import LazyInitContext
|
||||
from colossalai.pipeline.stage_manager import PipelineStageManager
|
||||
from colossalai.shardformer import ShardConfig, ShardFormer
|
||||
from colossalai.shardformer.policies.auto_policy import Policy
|
||||
from colossalai.shardformer._utils import getattr_
|
||||
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
|
||||
|
||||
@ -39,7 +41,8 @@ def build_pipeline_model(model_fn,
|
||||
stage_manager=None,
|
||||
enable_fused_normalization=False,
|
||||
enable_tensor_parallelism=False,
|
||||
use_lazy_init: bool = False):
|
||||
use_lazy_init: bool = False,
|
||||
policy: Optional[Policy] = None):
|
||||
ctx = LazyInitContext() if use_lazy_init else nullcontext()
|
||||
with ctx:
|
||||
# create new model
|
||||
@ -54,7 +57,7 @@ def build_pipeline_model(model_fn,
|
||||
pipeline_stage_manager=stage_manager)
|
||||
|
||||
shard_former = ShardFormer(shard_config=shard_config)
|
||||
sharded_model, shared_params = shard_former.optimize(model_copy)
|
||||
sharded_model, shared_params = shard_former.optimize(model_copy, policy=policy)
|
||||
return org_model.cuda(), sharded_model.cuda()
|
||||
|
||||
|
||||
|
||||
@ -60,7 +60,7 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
|
||||
shard_weight = shard_chatglm_model.embedding.word_embeddings.weight
|
||||
|
||||
if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
|
||||
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
|
||||
shard_grad_list = [torch.zeros_like(shard_grad) for _ in range(2)]
|
||||
torch.distributed.all_gather(shard_grad_list, shard_grad)
|
||||
all_shard_grad = torch.cat(shard_grad_list, dim=0)
|
||||
else:
|
||||
|
||||
@ -0,0 +1,86 @@
|
||||
import copy
|
||||
import os
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
import colossalai
|
||||
from colossalai.cluster import ProcessGroupMesh
|
||||
from colossalai.logging import disable_existing_loggers
|
||||
from colossalai.pipeline.stage_manager import PipelineStageManager
|
||||
from colossalai.shardformer.policies.chatglm import ChatGLMForConditionalGenerationPolicy, ChatGLMModelPolicy
|
||||
from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
|
||||
from colossalai.testing import (
|
||||
assert_hf_output_close,
|
||||
clear_cache_before_run,
|
||||
parameterize,
|
||||
rerun_if_address_is_in_use,
|
||||
spawn,
|
||||
)
|
||||
from tests.kit.model_zoo import model_zoo
|
||||
from tests.test_shardformer.test_model._utils import build_model, build_pipeline_model, run_forward
|
||||
|
||||
|
||||
@parameterize('enable_fused_normalization', [False])
|
||||
@parameterize('enable_tensor_parallelism', [False])
|
||||
@parameterize('use_lazy_init', [False])
|
||||
def run_chatglm_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
|
||||
DP_DIM, PP_DIM = 0, 1
|
||||
DP_SIZE, PP_SIZE = 2, 2
|
||||
pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
|
||||
stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
|
||||
sub_model_zoo = model_zoo.get_sub_registry('transformers_chatglm')
|
||||
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
|
||||
# create new model for test
|
||||
inputs = data_gen_fn()
|
||||
inputs = {k: v.cuda() for k, v in inputs.items()}
|
||||
input_ids = inputs['input_ids']
|
||||
hidden_size = 64
|
||||
batch_size, seq_len = input_ids.shape
|
||||
hidden_state_shape = (seq_len, batch_size, hidden_size)
|
||||
if name == "transformers_chatglm":
|
||||
_, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization,
|
||||
enable_tensor_parallelism, use_lazy_init, ChatGLMModelPolicy())
|
||||
if stage_manager.is_last_stage():
|
||||
hidden_states = torch.randn(*hidden_state_shape).cuda()
|
||||
inputs['input_ids'] = None
|
||||
inputs['hidden_states'] = hidden_states
|
||||
outputs = sharded_model(**inputs)
|
||||
if stage_manager.is_last_stage():
|
||||
assert outputs[0].shape == hidden_state_shape
|
||||
|
||||
else:
|
||||
assert outputs['hidden_states'].shape == hidden_state_shape
|
||||
|
||||
if name == "transformers_chatglm_for_conditional_generation":
|
||||
_, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization,
|
||||
enable_tensor_parallelism, use_lazy_init,
|
||||
ChatGLMForConditionalGenerationPolicy())
|
||||
if stage_manager.is_last_stage():
|
||||
hidden_states = torch.randn(*hidden_state_shape).cuda()
|
||||
inputs['input_ids'] = None
|
||||
inputs['hidden_states'] = hidden_states
|
||||
outputs = sharded_model(**inputs)
|
||||
if stage_manager.is_last_stage():
|
||||
assert outputs[0].shape == (batch_size, seq_len, 65024)
|
||||
else:
|
||||
assert outputs['hidden_states'].shape == hidden_state_shape
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
|
||||
def check_chatglm(rank, world_size, port):
|
||||
disable_existing_loggers()
|
||||
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
||||
run_chatglm_test()
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
@rerun_if_address_is_in_use()
|
||||
@clear_cache_before_run()
|
||||
def test_chatglm():
|
||||
spawn(check_chatglm, 4)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_chatglm()
|
||||
Loading…
Reference in New Issue
Block a user