Making large AI models cheaper, faster and more accessible
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3 years ago
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch
import torch.nn.functional as F
from colossalai.legacy.context.parallel_mode import ParallelMode
from colossalai.legacy.context.random import add_seed, reset_seeds, seed, set_mode
from colossalai.legacy.utils.activation_checkpoint import checkpoint
from colossalai.testing import clear_cache_before_run, parameterize
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def forward(x, weight):
out = torch.matmul(x, weight)
with seed(ParallelMode.DATA):
out_ = F.dropout(out, p=0.4, training=True)
return out_
def forward_inplace_ckpt(x, weight, cpu_offload=False):
out = torch.matmul(x, weight)
bn = torch.nn.BatchNorm1d(4, affine=False)
bn = bn.to(device="cuda")
out = bn(out)
def ckpt0(x):
return F.relu(x, inplace=True)
out = checkpoint(ckpt0, cpu_offload, out, use_reentrant=False)
return out
def forward_inplace(x, weight):
out = torch.matmul(x, weight)
bn = torch.nn.BatchNorm1d(4, affine=False)
bn = bn.to(device="cuda")
out = bn(out)
out = F.relu(out, inplace=True)
return out
@clear_cache_before_run()
@parameterize("use_reentrant", [True, False])
@parameterize("cpu_offload", [True, False])
def test_activation_checkpointing(cpu_offload, use_reentrant):
# as seed manager is singleton
# if we don't reset seeds here,
# other tests might affect this test
reset_seeds()
# We put initialization 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)
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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)
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loss = out.sum()
loss.backward()
# Recover cuda rng states
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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_, use_reentrant=use_reentrant)
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loss = out.sum()
loss.backward()
assert torch.all(inputs.grad == inputs_.grad), "Gradient of the input does not match"
torch.cuda.empty_cache()
# Extra test for use_reentrant=False
if use_reentrant == False:
# 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 = forward_inplace(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 = forward_inplace_ckpt(inputs_, weight_, cpu_offload=cpu_offload)
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()
if __name__ == "__main__":
test_activation_checkpointing(False, False)