mirror of https://github.com/hpcaitech/ColossalAI
116 lines
3.9 KiB
Python
116 lines
3.9 KiB
Python
import copy
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import os
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import random
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import pytest
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import torch
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from transformers import LlamaConfig, LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizerFast
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import colossalai
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from colossalai.logging import disable_existing_loggers
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from colossalai.shardformer import ShardConfig, ShardFormer
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from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn
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os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
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tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
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def build_model(world_size, model_fn):
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# create new model
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config = LlamaConfig(num_hidden_layers=4,
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hidden_size=128,
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intermediate_size=256,
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num_attention_heads=4,
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max_position_embeddings=128)
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org_model = model_fn(config).cuda()
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# shard model
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shard_config = ShardConfig(tensor_parallel_size=world_size)
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model_copy = copy.deepcopy(org_model)
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shard_former = ShardFormer(shard_config=shard_config)
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shard_former.init_distributed()
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sharded_model = shard_former.shard_model(model_copy)
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return org_model, sharded_model
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def check_forward_backward(org_model, sharded_model):
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# prepare input
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input = 'Hello, my dog is cute'
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tokenized_input = tokenizer(input, return_tensors='pt').to('cuda')
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del tokenized_input["token_type_ids"]
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del tokenized_input["attention_mask"]
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# switch to train mode
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org_model.train()
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sharded_model.train()
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if isinstance(org_model, (LlamaModel, LlamaForSequenceClassification)):
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org_output = org_model(**tokenized_input)
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org_loss = org_output.last_hidden_state.mean()
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shard_output = sharded_model(**tokenized_input)
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shard_loss = shard_output.last_hidden_state.mean()
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elif isinstance(org_model, LlamaForCausalLM):
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labels = tokenized_input['input_ids'].clone()
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labels[labels == tokenizer.pad_token_id] = -100
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tokenized_input['labels'] = labels
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org_output = org_model(**tokenized_input)
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org_loss = org_output.loss
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shard_output = sharded_model(**tokenized_input)
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shard_loss = shard_output.loss
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assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-4)
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# run backward
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org_loss.backward()
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shard_loss.backward()
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# check grad
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if isinstance(org_model, LlamaModel):
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llama_model = org_model
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shard_llama_model = sharded_model
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else:
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llama_model = org_model.model
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shard_llama_model = sharded_model.model
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org_grad = llama_model.layers[0].self_attn.q_proj.weight.grad
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shard_grad = shard_llama_model.layers[0].self_attn.q_proj.weight.grad
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shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
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shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
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all_shard_grad = torch.cat(shard_grad_list, dim=0)
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assert torch.allclose(org_loss, shard_loss,
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atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
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assert torch.allclose(org_grad, all_shard_grad,
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atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
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def check_llama(rank, world_size, port):
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disable_existing_loggers()
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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model_list = [
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LlamaModel,
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# LlamaForCausalLM,
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# TODO: do not work yet
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# LlamaForSequenceClassification
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]
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for model_fn in model_list:
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org_model, sharded_model = build_model(world_size, model_fn)
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check_forward_backward(org_model, sharded_model)
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torch.cuda.empty_cache()
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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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@clear_cache_before_run()
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def test_llama():
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spawn(check_llama, 4)
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if __name__ == "__main__":
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test_llama()
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