mirror of https://github.com/hpcaitech/ColossalAI
156 lines
5.6 KiB
Python
156 lines
5.6 KiB
Python
import os
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import pytest
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import torch
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import torch.distributed as dist
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from utils import shared_tempdir
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import colossalai
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from colossalai.booster import Booster
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from colossalai.booster.plugin import GeminiPlugin
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.testing import (
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check_state_dict_equal,
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clear_cache_before_run,
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parameterize,
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rerun_if_address_is_in_use,
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spawn,
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)
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from tests.kit.model_zoo import model_zoo
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MODEL_PLACEMENT_CONFIGS = [
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{
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'placement_policy': 'static',
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'shard_param_frac': 0.0
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}, # zero2
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{
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'placement_policy': 'static',
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'shard_param_frac': 1.0
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}, # zero3
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{
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'placement_policy': 'static',
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'shard_param_frac': 0.5
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}, # zero3-half
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]
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OPTIM_PLACEMENT_CONFIGS = [
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{
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'placement_policy': 'static',
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'shard_param_frac': 0.0,
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'offload_optim_frac': 0.0
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}, # zero2
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{
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'placement_policy': 'static',
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'shard_param_frac': 0.0,
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'offload_optim_frac': 1.0
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}, # zero2-offload
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{
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'placement_policy': 'static',
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'shard_param_frac': 0.0,
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'offload_optim_frac': 0.5
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}, # zero2-offload-half
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]
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@clear_cache_before_run()
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@parameterize('placement_config', MODEL_PLACEMENT_CONFIGS)
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@parameterize('model_name', ['transformers_bert_for_sequence_classification'])
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@parameterize('use_safetensors', [False, True])
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def exam_state_dict_with_origin(placement_config, model_name, use_safetensors: bool):
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from transformers import BertForSequenceClassification
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(model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values()))
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bert_model = model_fn()
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with shared_tempdir() as tempdir:
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pretrained_path = os.path.join(tempdir, 'pretrained')
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bert_model.config.save_pretrained(save_directory=pretrained_path)
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plugin = GeminiPlugin(**placement_config)
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booster = Booster(plugin=plugin)
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bert_model, _, _, _, _ = booster.boost(bert_model)
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model_size = sum(p.numel() * p.element_size() for p in bert_model.parameters()) / 1024**2
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booster.save_model(bert_model,
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pretrained_path,
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True,
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True,
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'', (model_size / 3),
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use_safetensors=use_safetensors)
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dist.barrier()
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new_bert_model = BertForSequenceClassification.from_pretrained(pretrained_path)
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check_state_dict_equal(bert_model.state_dict(only_rank_0=False, dtype=torch.float32),
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new_bert_model.state_dict(), False)
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@clear_cache_before_run()
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@parameterize('placement_config', OPTIM_PLACEMENT_CONFIGS)
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@parameterize('shard', [False, True])
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@parameterize('model_name', ['transformers_gpt'])
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@parameterize('size_per_shard', [32])
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def exam_state_dict(placement_config, shard: bool, model_name: str, size_per_shard: int):
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(model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values()))
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criterion = lambda x: x.mean()
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plugin = GeminiPlugin(**placement_config, precision="fp16", initial_scale=(2**14))
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booster = Booster(plugin=plugin)
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model = model_fn()
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new_model = model_fn()
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optimizer = HybridAdam(model.parameters(), lr=0.001)
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model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)
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new_optimizer = HybridAdam(new_model.parameters(), lr=0.001)
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new_model, new_optimizer, criterion, _, _ = booster.boost(new_model, new_optimizer, criterion)
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data = data_gen_fn()
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data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()}
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output = model(**data)
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output = output_transform_fn(output)
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output_key = list(output.keys())[0]
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loss = criterion(output[output_key])
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booster.backward(loss, optimizer)
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optimizer.step()
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with shared_tempdir() as tempdir:
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model_ckpt_path = f"{tempdir}/model"
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optimizer_ckpt_path = f"{tempdir}/optimizer"
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booster.save_model(model, model_ckpt_path, shard=shard, size_per_shard=size_per_shard)
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booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard, size_per_shard=size_per_shard)
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dist.barrier()
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booster.load_model(new_model, model_ckpt_path)
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check_state_dict_equal(model.state_dict(only_rank_0=False), new_model.state_dict(only_rank_0=False), False)
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booster.load_optimizer(new_optimizer, optimizer_ckpt_path)
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check_state_dict_equal(optimizer.state_dict(only_rank_0=False), new_optimizer.state_dict(only_rank_0=False),
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False)
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# Check the new model/optimizer can successfully run.
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data = data_gen_fn()
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data = {
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k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()
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}
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output = new_model(**data)
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output = output_transform_fn(output)
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output_key = list(output.keys())[0]
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loss = criterion(output[output_key])
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booster.backward(loss, new_optimizer)
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new_optimizer.step()
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booster.save_model(new_model, model_ckpt_path, shard=shard)
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booster.save_optimizer(new_optimizer, optimizer_ckpt_path, shard=shard)
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def run_dist(rank, world_size, port):
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config = {}
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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exam_state_dict()
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exam_state_dict_with_origin()
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [2])
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@rerun_if_address_is_in_use()
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def test_gemini_ckpIO(world_size):
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spawn(run_dist, world_size)
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