mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2026-04-27 02:03:13 +00:00
26b7aac0be
* [zero] refactor low-level zero folder structure * [zero] fix legacy zero import path * [zero] fix legacy zero import path * [zero] remove useless import * [zero] refactor gemini folder structure * [zero] refactor gemini folder structure * [zero] refactor legacy zero import path * [zero] refactor gemini folder structure * [zero] refactor gemini folder structure * [zero] refactor gemini folder structure * [zero] refactor legacy zero import path * [zero] fix test import path * [zero] fix test * [zero] fix circular import * [zero] update import
115 lines
3.5 KiB
Python
115 lines
3.5 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import copy
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from functools import partial
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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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import torch.multiprocessing as mp
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import torch.nn as nn
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.nn.utils import clip_grad_norm_
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import colossalai
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from colossalai.logging import disable_existing_loggers
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from colossalai.testing import parameterize, rerun_if_address_is_in_use
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from colossalai.utils import checkpoint, clip_grad_norm_fp32, free_port
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from colossalai.zero.legacy.shard_utils.tensor_shard_strategy import TensorShardStrategy
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from colossalai.zero.legacy.sharded_model.sharded_model_v2 import ShardedModelV2
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def checkpoint_wrapper(module, enable=True):
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if enable:
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module.forward = partial(checkpoint, module.forward, False)
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return module
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class Net(nn.Module):
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def __init__(self, checkpoint=False) -> None:
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super().__init__()
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self.fc1 = nn.Linear(5, 5)
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self.fc2 = nn.Linear(5, 5)
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self.fc3 = nn.Linear(5, 1)
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if checkpoint:
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self.fc1 = checkpoint_wrapper(self.fc1)
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self.layers = [self.fc1, self.fc2, self.fc1, self.fc2, self.fc3]
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def forward(self, x):
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for layer in self.layers:
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x = layer(x)
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return x
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def run_step(model, optimizer, x, enable_autocast=False, norm_type=2.0):
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model.train()
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optimizer.zero_grad()
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with torch.cuda.amp.autocast(enabled=enable_autocast):
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y = model(x)
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loss = y.sum()
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loss = loss.float()
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loss.backward()
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clip_grad(model, norm_type)
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optimizer.step()
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def clip_grad(model, norm_type):
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if isinstance(model, DDP):
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clip_grad_norm_(model.parameters(), max_norm=1.0, norm_type=norm_type)
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else:
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clip_grad_norm_fp32(model.parameters(), max_norm=1.0, norm_type=norm_type)
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def allclose(tensor_a: torch.Tensor, tensor_b: torch.Tensor, loose=False) -> bool:
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if loose:
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return torch.allclose(tensor_a, tensor_b, atol=1e-3, rtol=1e-3)
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return torch.allclose(tensor_a, tensor_b)
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def check_grads(model, zero_model, loose=False):
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rank = dist.get_rank()
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for p, zero_p in zip(model.parameters(), zero_model.parameters()):
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zero_grad = zero_p.grad.clone().to(p.device)
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chunks = torch.flatten(p.grad).chunk(4)
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if rank >= len(chunks):
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continue
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grad = chunks[rank]
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if zero_p.zero_shard_padding > 0:
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zero_grad = zero_grad[:-zero_p.zero_shard_padding]
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assert grad.dtype == zero_grad.dtype
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assert allclose(grad, zero_grad, loose=loose)
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def check_params(model, zero_model, loose=False):
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rank = dist.get_rank()
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for p, zero_p in zip(model.parameters(), zero_model.parameters()):
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zero_shard_padding = zero_p.zero_shard_padding
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zero_p = zero_p.clone().to(p.device)
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chunks = torch.flatten(p).chunk(4)
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if rank >= len(chunks):
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continue
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p = chunks[rank]
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if zero_shard_padding > 0:
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zero_p = zero_p[:-zero_shard_padding]
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assert p.dtype == zero_p.dtype
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assert allclose(p, zero_p, loose=loose)
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def run_dist(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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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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def test_zero_clip_grad():
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world_size = 4
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_zero_clip_grad()
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