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# Run test with:
# torchrun --no_python --nproc_per_node=8 pytest -q -s tests/modules/test_mha_parallel.py
import math
import pytest
import torch
import torch.nn.functional as F
from apex.transformer import parallel_state, tensor_parallel
from einops import rearrange
from flash_attn.modules.mha import MHA, ParallelMHA
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.float16])
@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("head_dim", [64, 128])
# @pytest.mark.parametrize('head_dim', [64])
@pytest.mark.parametrize("embed_dim", [1024, 4096])
# @pytest.mark.parametrize('embed_dim', [1024])
def test_mha_parallel(embed_dim, head_dim, sequence_parallel, world_size, dtype):
assert embed_dim % head_dim == 0
num_heads = embed_dim // head_dim
assert num_heads % world_size == 0
rtol, atol = (3e-3, 1e-2) if dtype == torch.bfloat16 else (3e-3, 1e-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 = 1024
assert (batch_size * seqlen) % world_size == 0
x_pt = torch.randn(
batch_size * seqlen, embed_dim, 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 = MHA(
embed_dim,
num_heads,
rotary_emb_dim=int(head_dim // 2),
use_flash_attn=True,
device=device,
dtype=dtype,
)
partition_dim = embed_dim // world_size
model = ParallelMHA(
embed_dim,
num_heads,
parallel_state.get_tensor_model_parallel_group(),
rotary_emb_dim=int(head_dim // 2),
use_flash_attn=True,
sequence_parallel=sequence_parallel,
device=device,
dtype=dtype,
)
with torch.no_grad():
model.Wqkv.weight.copy_(
rearrange(
rearrange(model_pt.Wqkv.weight, "(three o) i -> three o i", three=3)[
:, rank * partition_dim : (rank + 1) * partition_dim
],
"three o i -> (three o) i",
)
)
model.Wqkv.bias.copy_(
rearrange(
rearrange(model_pt.Wqkv.bias, "(three o) -> three o", three=3)[
:, rank * partition_dim : (rank + 1) * partition_dim
],
"three o -> (three o)",
)
)
model.out_proj.weight.copy_(
model_pt.out_proj.weight[:, rank * partition_dim : (rank + 1) * partition_dim]
)
if rank == 0:
model.out_proj.bias.copy_(model_pt.out_proj.bias)
out = model(x, seqlen=seqlen)
out_pt = rearrange(model_pt(rearrange(x_pt, "(b s) d -> b s d", s=seqlen)), "b s d -> (b s) d")
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 / 100, # magnitude of x.grad is quite small
)
# The error for d_weight and d_bias is quite a bit higher
assert torch.allclose(
model.Wqkv.weight.grad,
rearrange(
rearrange(model_pt.Wqkv.weight.grad, "(three o) i -> three o i", three=3)[
:, rank * partition_dim : (rank + 1) * partition_dim
],
"three o i -> (three o) i",
),
rtol=rtol,
atol=atol * 10,
)
assert torch.allclose(
model.Wqkv.bias.grad,
rearrange(
rearrange(model_pt.Wqkv.bias.grad, "(three o) -> three o", three=3)[
:, rank * partition_dim : (rank + 1) * partition_dim
],
"three o -> (three o)",
),
rtol=rtol,
atol=atol * 5,
)
assert torch.allclose(
model.out_proj.weight.grad,
model_pt.out_proj.weight.grad[:, rank * partition_dim : (rank + 1) * partition_dim],
rtol=rtol,
atol=atol * 10,
)
if rank == 0:
assert torch.allclose(
model.out_proj.bias.grad, model_pt.out_proj.bias.grad, rtol=rtol, atol=atol * 5
)
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