mirror of https://github.com/hpcaitech/ColossalAI
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import pytest
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import torch
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import torch.nn.functional as F
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.context.random import add_seed, seed, set_mode, reset_seeds
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from colossalai.utils import checkpoint
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def forward(x, weight):
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out = torch.matmul(x, weight)
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with seed(ParallelMode.DATA):
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out_ = F.dropout(out, p=0.4, training=True)
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return out_
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@pytest.mark.gpu
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@pytest.mark.parametrize("cpu_offload", [True, False])
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def test_activation_checkpointing(cpu_offload):
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# We put initilization here to avoid change cuda rng state below
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inputs = torch.rand(2, 2, requires_grad=True, device='cuda')
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weight = torch.rand(2, 4, requires_grad=True, device='cuda')
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# Get a copy of input tensors
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inputs_ = torch.empty(2, 2, requires_grad=True, device='cuda')
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inputs_.data.copy_(inputs.data)
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weight_ = torch.empty(2, 4, requires_grad=True, device='cuda')
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weight_.data.copy_(weight.data)
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add_seed(ParallelMode.GLOBAL, 1024)
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add_seed(ParallelMode.DATA, 1026)
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set_mode(ParallelMode.GLOBAL)
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global_cuda_rng_state = torch.cuda.get_rng_state()
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set_mode(ParallelMode.DATA)
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data_parallel_cuda_rng_state = torch.cuda.get_rng_state()
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set_mode(ParallelMode.GLOBAL)
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out = forward(inputs, weight)
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loss = out.sum()
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loss.backward()
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# Recover cuda rng states
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set_mode(ParallelMode.GLOBAL)
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torch.cuda.set_rng_state(global_cuda_rng_state)
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set_mode(ParallelMode.DATA)
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torch.cuda.set_rng_state(data_parallel_cuda_rng_state)
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set_mode(ParallelMode.GLOBAL)
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out = checkpoint(forward, cpu_offload, inputs_, weight_)
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loss = out.sum()
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loss.backward()
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assert torch.all(inputs.grad == inputs_.grad), 'Gradient of the input does not match'
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torch.cuda.empty_cache()
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# as seed manager is singleton
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# if we don't reset seeds here,
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# other tests will fail if running together with this test
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# as other tests can't overwrite the seed set by this test
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reset_seeds()
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