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