2022-03-01 10:17:01 +00:00
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from functools import partial
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2022-03-02 10:28:29 +00:00
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2022-03-01 10:17:01 +00:00
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import torch
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2022-03-02 10:28:29 +00:00
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import torch.distributed as dist
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import torch.nn as nn
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2022-03-01 10:17:01 +00:00
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from colossalai.logging import disable_existing_loggers, get_dist_logger
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2022-03-02 10:28:29 +00:00
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from colossalai.utils import checkpoint
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2022-03-01 10:17:01 +00:00
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LOGGER = get_dist_logger()
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CONFIG = dict(
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fp16=dict(
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mode=None,
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),
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zero=dict(
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level=3,
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verbose=False,
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offload_optimizer_config=dict(
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device='cpu',
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pin_memory=True,
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buffer_count=5,
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fast_init=False
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),
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offload_param_config=dict(
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device='cpu',
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pin_memory=True,
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buffer_count=5,
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buffer_size=1e8,
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max_in_cpu=1e9
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)
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),
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parallel=dict(
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pipeline=dict(size=1),
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tensor=dict(size=1, mode=None)
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)
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)
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2022-03-02 10:28:29 +00:00
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2022-03-01 10:17:01 +00:00
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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)
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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 = [
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self.fc1,
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self.fc2,
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self.fc1,
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self.fc2,
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self.fc3
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]
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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 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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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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assert p.grad.dtype == zero_grad.dtype
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assert allclose(p.grad, zero_grad, loose=loose)
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LOGGER.info(torch.sum(p.grad - zero_grad))
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def check_params(model, zero_model, loose=False):
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for p, zero_p in zip(model.parameters(), zero_model.parameters()):
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zero_p = zero_p.clone().to(p.device)
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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 check_grads_padding(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(dist.get_world_size())
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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_grad.size(0) > grad.size(0):
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zero_grad = zero_grad[:grad.size(0)]
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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_padding(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_p = zero_p.clone().to(p.device)
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chunks = torch.flatten(p).chunk(dist.get_world_size())
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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_p.size(0) > p.size(0):
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zero_p = zero_p[:p.size(0)]
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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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