198 lines
8.6 KiB
Python
198 lines
8.6 KiB
Python
# Run test with:
|
|
# torchrun --no_python --nproc_per_node=8 pytest -q -s tests/ops/test_fused_dense_parallel.py
|
|
|
|
import math
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import pytest
|
|
|
|
from apex.transformer import parallel_state
|
|
from apex.transformer import tensor_parallel
|
|
|
|
from flash_attn.ops.fused_dense import FusedDense, FusedDenseGeluDense
|
|
from flash_attn.ops.fused_dense import ColumnParallelLinear, ParallelFusedDenseGeluDense
|
|
|
|
is_sm8x = torch.cuda.get_device_capability('cuda')[0] >= 8
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', [torch.float16] + ([torch.bfloat16] if is_sm8x else []))
|
|
# @pytest.mark.parametrize('dtype', [torch.bfloat16])
|
|
@pytest.mark.parametrize('world_size', [1, 2, 4, 8])
|
|
# @pytest.mark.parametrize('world_size', [2])
|
|
@pytest.mark.parametrize('sequence_parallel', [True, False])
|
|
# @pytest.mark.parametrize('sequence_parallel', [False])
|
|
@pytest.mark.parametrize('has_bias', [True, False])
|
|
# @pytest.mark.parametrize('has_bias', [False])
|
|
@pytest.mark.parametrize('out_features', [1024])
|
|
@pytest.mark.parametrize('in_features', [4096])
|
|
def test_fused_linear_bias(in_features, out_features, has_bias, sequence_parallel,
|
|
world_size, dtype):
|
|
assert out_features % world_size == 0
|
|
rtol, atol = (3e-3, 3e-2) if dtype == torch.bfloat16 else (3e-3, 3e-3)
|
|
if not torch.distributed.is_initialized():
|
|
torch.distributed.init_process_group(backend='nccl', init_method='env://')
|
|
device = f'cuda:{torch.distributed.get_rank()}'
|
|
assert world_size <= torch.distributed.get_world_size()
|
|
parallel_state.initialize_model_parallel(tensor_model_parallel_size_=world_size)
|
|
rank = parallel_state.get_tensor_model_parallel_rank()
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 2
|
|
seqlen = 512
|
|
assert batch_size * seqlen % world_size == 0
|
|
x_pt = torch.randn(batch_size * seqlen, in_features, device=device, dtype=dtype,
|
|
requires_grad=True)
|
|
if sequence_parallel:
|
|
x = tensor_parallel.scatter_to_sequence_parallel_region(x_pt).detach().clone().requires_grad_()
|
|
else:
|
|
x = x_pt.detach().clone().requires_grad_()
|
|
|
|
model_pt = torch.nn.Linear(in_features, out_features, bias=has_bias, device=device, dtype=dtype)
|
|
partition_out_features = out_features // world_size
|
|
model = ColumnParallelLinear(in_features, out_features,
|
|
parallel_state.get_tensor_model_parallel_group(), bias=has_bias,
|
|
sequence_parallel=sequence_parallel, device=device, dtype=dtype)
|
|
with torch.no_grad():
|
|
model.weight.copy_(
|
|
model_pt.weight[rank * partition_out_features:(rank + 1) * partition_out_features]
|
|
)
|
|
if has_bias:
|
|
model.bias.copy_(
|
|
model_pt.bias[rank * partition_out_features:(rank + 1) * partition_out_features]
|
|
)
|
|
|
|
out = model(x)
|
|
out_pt = model_pt(x_pt)
|
|
assert torch.allclose(
|
|
out, out_pt[:, rank * partition_out_features:(rank + 1) * partition_out_features],
|
|
rtol=rtol, atol=atol
|
|
)
|
|
|
|
# If we don't divide by batch_size, the gradient gets a bit too large.
|
|
g = torch.randn_like(out_pt) / 32
|
|
out_pt.backward(g)
|
|
out.backward(g[:, rank * partition_out_features:(rank + 1) * partition_out_features])
|
|
parallel_state.destroy_model_parallel()
|
|
|
|
partition_batch_dim = batch_size * seqlen // world_size
|
|
assert torch.allclose(
|
|
x.grad,
|
|
x_pt.grad[rank * partition_batch_dim:(rank + 1) * partition_batch_dim]
|
|
if sequence_parallel else x_pt.grad,
|
|
rtol=rtol, atol=atol
|
|
)
|
|
# The error for d_weight and d_bias is quite a bit higher
|
|
assert torch.allclose(
|
|
model.weight.grad,
|
|
model_pt.weight.grad[rank * partition_out_features:(rank + 1) * partition_out_features],
|
|
rtol=rtol, atol=atol * 10
|
|
)
|
|
if has_bias:
|
|
assert torch.allclose(
|
|
model.bias.grad,
|
|
model_pt.bias.grad[rank * partition_out_features:(rank + 1) * partition_out_features],
|
|
rtol=rtol, atol=atol * 5
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', [torch.float16] + ([torch.bfloat16] if is_sm8x else []))
|
|
# @pytest.mark.parametrize('dtype', [torch.bfloat16])
|
|
@pytest.mark.parametrize('world_size', [1, 2, 4, 8])
|
|
# @pytest.mark.parametrize('world_size', [2])
|
|
@pytest.mark.parametrize('sequence_parallel', [True, False])
|
|
# @pytest.mark.parametrize('sequence_parallel', [False])
|
|
@pytest.mark.parametrize('has_bias2', [True, False])
|
|
# @pytest.mark.parametrize('has_bias2', [True])
|
|
@pytest.mark.parametrize('out_features', [4096])
|
|
@pytest.mark.parametrize('in_features', [1024])
|
|
def test_fused_dense_gelu_dense(in_features, out_features, has_bias2, sequence_parallel,
|
|
world_size, dtype):
|
|
assert out_features % world_size == 0
|
|
rtol, atol = (3e-3, 3e-2) if dtype == torch.bfloat16 else (3e-3, 3e-3)
|
|
if not torch.distributed.is_initialized():
|
|
torch.distributed.init_process_group(backend='nccl', init_method='env://')
|
|
device = f'cuda:{torch.distributed.get_rank()}'
|
|
assert world_size <= torch.distributed.get_world_size()
|
|
parallel_state.initialize_model_parallel(tensor_model_parallel_size_=world_size)
|
|
rank = parallel_state.get_tensor_model_parallel_rank()
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 2
|
|
seqlen = 512
|
|
assert batch_size * seqlen % world_size == 0
|
|
x_pt = torch.randn(batch_size * seqlen, in_features, device=device, dtype=dtype,
|
|
requires_grad=True)
|
|
# We need to generate g here so that all processes get the same gradient,
|
|
# as rank 0 will have an extra bias that changes the RNG.
|
|
# If we don't divide by batch_size, the gradient gets a bit too large.
|
|
g = torch.randn_like(x_pt) / 32
|
|
if sequence_parallel:
|
|
x = tensor_parallel.scatter_to_sequence_parallel_region(x_pt).detach().clone().requires_grad_()
|
|
else:
|
|
x = x_pt.detach().clone().requires_grad_()
|
|
|
|
model_pt_fc1 = torch.nn.Linear(in_features, out_features, device=device, dtype=dtype)
|
|
model_pt_fc2 = torch.nn.Linear(out_features, in_features, bias=has_bias2, device=device,
|
|
dtype=dtype)
|
|
partition_out_features = out_features // world_size
|
|
partition_in_features = in_features // world_size
|
|
model = ParallelFusedDenseGeluDense(in_features, out_features, in_features,
|
|
process_group=parallel_state.get_tensor_model_parallel_group(),
|
|
bias2=has_bias2 and rank == 0,
|
|
sequence_parallel=sequence_parallel,
|
|
device=device, dtype=dtype)
|
|
|
|
with torch.no_grad():
|
|
model.fc1.weight.copy_(
|
|
model_pt_fc1.weight[rank * partition_out_features:(rank + 1) * partition_out_features]
|
|
)
|
|
model.fc1.bias.copy_(
|
|
model_pt_fc1.bias[rank * partition_out_features:(rank + 1) * partition_out_features]
|
|
)
|
|
model.fc2.weight.copy_(
|
|
model_pt_fc2.weight[:, rank * partition_out_features:(rank + 1) * partition_out_features]
|
|
)
|
|
if has_bias2 and rank == 0:
|
|
model.fc2.bias.copy_(model_pt_fc2.bias)
|
|
|
|
out = model(x)
|
|
out_pt = model_pt_fc2(F.gelu(model_pt_fc1(x_pt), approximate='tanh'))
|
|
partition_batch_dim = batch_size * seqlen // world_size
|
|
assert torch.allclose(
|
|
out,
|
|
out_pt[rank * partition_batch_dim:(rank + 1) * partition_batch_dim]
|
|
if sequence_parallel else out_pt,
|
|
rtol=rtol, atol=atol
|
|
)
|
|
|
|
out_pt.backward(g)
|
|
out.backward(g[rank * partition_batch_dim:(rank + 1) * partition_batch_dim]
|
|
if sequence_parallel else g)
|
|
parallel_state.destroy_model_parallel()
|
|
|
|
assert torch.allclose(
|
|
x.grad,
|
|
x_pt.grad[rank * partition_batch_dim:(rank + 1) * partition_batch_dim]
|
|
if sequence_parallel else x_pt.grad,
|
|
rtol=rtol, atol=atol
|
|
)
|
|
# The error for d_weight and d_bias is quite a bit higher
|
|
assert torch.allclose(
|
|
model.fc1.weight.grad,
|
|
model_pt_fc1.weight.grad[rank * partition_out_features:(rank + 1) * partition_out_features],
|
|
rtol=rtol, atol=atol * 10
|
|
)
|
|
assert torch.allclose(
|
|
model.fc1.bias.grad,
|
|
model_pt_fc1.bias.grad[rank * partition_out_features:(rank + 1) * partition_out_features],
|
|
rtol=rtol, atol=atol * 5
|
|
)
|
|
assert torch.allclose(
|
|
model.fc2.weight.grad,
|
|
model_pt_fc2.weight.grad[:, rank * partition_out_features:(rank + 1) * partition_out_features],
|
|
rtol=rtol, atol=atol * 10
|
|
)
|
|
if has_bias2 and rank == 0:
|
|
assert torch.allclose(model.fc2.bias.grad, model_pt_fc2.bias.grad, rtol=rtol, atol=atol * 5)
|