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177 lines
6.9 KiB
177 lines
6.9 KiB
import pytest
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import torch
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import torch.distributed as dist
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from torch.optim import Adam
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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, TorchDDPPlugin
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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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@clear_cache_before_run()
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@parameterize("shard", [False, True])
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@parameterize("model_name", ["transformers_gpt"])
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def exam_torch_load_from_gemini(shard: bool, model_name: str):
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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(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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optimizer = HybridAdam(model.parameters(), lr=0.001)
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model, optimizer, criterion, _, _ = booster.boost(model, 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)
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booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard)
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dist.barrier()
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new_model = model_fn()
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new_optimizer = Adam(new_model.parameters(), lr=0.001)
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new_plugin = TorchDDPPlugin()
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new_booster = Booster(plugin=new_plugin)
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new_model, new_optimizer, criterion, _, _ = new_booster.boost(new_model, new_optimizer, criterion)
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# Loading HybridAdam states to torch.Adam
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new_booster.load_model(new_model, model_ckpt_path, strict=True)
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# Add prefix to get aligned with pytorch parameter names.
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check_state_dict_equal(
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model.state_dict(only_rank_0=False, prefix="module.module."),
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new_model.state_dict(),
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False,
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ignore_dtype=True,
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)
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new_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(), 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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new_booster.backward(loss, new_optimizer)
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new_optimizer.step()
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new_booster.save_model(new_model, model_ckpt_path, shard=shard)
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new_booster.save_optimizer(new_optimizer, optimizer_ckpt_path, shard=shard)
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@clear_cache_before_run()
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@parameterize("shard", [False, True])
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@parameterize("model_name", ["transformers_gpt"])
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def exam_gemini_load_from_torch(shard: bool, model_name: str):
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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 = TorchDDPPlugin()
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booster = Booster(plugin=plugin)
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model = model_fn()
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optimizer = Adam(model.parameters(), lr=0.001)
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model, optimizer, criterion, _, _ = booster.boost(model, 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)
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booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard)
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dist.barrier()
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new_model = model_fn()
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new_optimizer = HybridAdam(new_model.parameters(), lr=0.001)
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new_plugin = GeminiPlugin()
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new_booster = Booster(plugin=new_plugin)
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new_model, new_optimizer, criterion, _, _ = new_booster.boost(new_model, new_optimizer, criterion)
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# Loading torch.Adam states to HybridAdam
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new_booster.load_model(new_model, model_ckpt_path, strict=True)
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# Add prefix to get aligned with pytorch parameter names.
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check_state_dict_equal(
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new_model.state_dict(only_rank_0=False, prefix="module.module."),
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model.state_dict(),
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False,
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ignore_dtype=True,
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)
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new_booster.load_optimizer(new_optimizer, optimizer_ckpt_path)
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old_state_dict = optimizer.state_dict()
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new_state_dict = new_optimizer.state_dict(only_rank_0=False)
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# Comparison of param_groups needs special care here,
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# since not all hyperparameters in Adam are used by HybridAdam
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hyperparameters_to_examine = ["params", "lr", "betas", "eps", "weight_decay"]
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for old_group, new_group in zip(old_state_dict["param_groups"], new_state_dict["param_groups"]):
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for k in hyperparameters_to_examine:
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assert (
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k in old_group and k in new_group
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), f"Old group's keys: {list(old_group.keys())}, New group's keys: {list(new_group.keys())}"
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assert old_group[k] == new_group[k]
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check_state_dict_equal(old_state_dict["state"], new_state_dict["state"], 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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new_booster.backward(loss, new_optimizer)
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new_optimizer.step()
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new_booster.save_model(new_model, model_ckpt_path, shard=shard)
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new_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_torch_load_from_gemini()
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exam_gemini_load_from_torch()
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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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