VTimeLLM/flash-attention/tests/modules/test_mha_parallel.py

# 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
        )