Repository: hpcaitech/ColossalAI
File: tests/test_utils/test_flash_attention.py (Python)

Code before:
    if dropout == 0.0:
        out_ref = attention_ref(q, k, v, None, causal=True)
        out_pt = attention_ref(q, k, v, None, causal=True, upcast=False, reorder_ops=True)

        dy = torch.rand_like(y)
        grad_q, grad_k, grad_v = torch.autograd.grad(y, (q, k, v), dy)
        grad_ref_q, grad_ref_k, grad_ref_v = torch.autograd.grad(out_ref, (q, k, v), dy)
        grad_pt_q, grad_pt_k, grad_pt_v = torch.autograd.grad(out_pt, (q, k, v), dy)

        if dtype in FLASH_DTYPE:
            assert (y - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
            assert (grad_q - grad_ref_q).abs().max().item() <= 2 * (grad_pt_q - grad_ref_q).abs().max().item()
            assert (grad_k - grad_ref_k).abs().max().item() <= 2 * (grad_pt_k - grad_ref_k).abs().max().item()
            assert (grad_v - grad_ref_v).abs().max().item() <= 2 * (grad_pt_v - grad_ref_v).abs().max().item()
        else:
            torch.allclose(y, out_ref, atol=1e-7), f"{(y - out_ref).abs().max()}"
            torch.allclose(grad_q, grad_ref_q, atol=1e-7), f"{(grad_q - grad_ref_q).abs().max()}"
            torch.allclose(grad_k, grad_ref_k, atol=1e-7), f"{(grad_k - grad_ref_k).abs().max()}"
            torch.allclose(grad_v, grad_ref_v, atol=1e-7), f"{(grad_v - grad_ref_v).abs().max()}"


@pytest.mark.skipif(not HAS_MEM_EFF_ATTN and not HAS_FLASH_ATTN, reason="xformers is not available")
@clear_cache_before_run()
@parameterize('proj_shape', [(6, 8, 4, 16)])
@parameterize('dtype', DTYPE)
def test_padding_process(proj_shape, dtype):
    (B, S, H, D_HEAD) = proj_shape
    D = H * D_HEAD

    x = torch.randn((B, S, D), dtype=dtype, device="cuda", requires_grad=True)

    # attention mask of shape [B, S] with zero padding to max length S
    mask = [torch.ones(S - i, dtype=torch.bool, device="cuda") for i in range(B)]
    mask = torch.nn.utils.rnn.pad_sequence(mask, batch_first=True)

    seq_info = SeqLenInfo.materialize(mask)
    x_unpad = Unpad.apply(x, seq_info.indices)
    x_repad = Repad.apply(x_unpad, seq_info.indices, B, S)
    x_repad = x_repad.reshape((B, S, D))

    g = torch.randn_like(x_repad)
    grad_x = torch.autograd.grad(x_repad, x, g)

    y = repadding_with_zero(x, mask, proj_shape)

    assert torch.equal(y, x_repad)
    assert grad_x[0].shape == (B, S, D)


Review comment (suggestion):
integrate this check into previous checks, like make pad as a parameter and adjust attn mask & attn mask type accordingly

Code after:
import math

import pytest
import torch
from einops import rearrange

from colossalai.kernel.cuda_native.mha.flash_attn_2 import HAS_FLASH_ATTN
from colossalai.kernel.cuda_native.mha.mem_eff_attn import HAS_MEM_EFF_ATTN
from colossalai.testing import clear_cache_before_run, parameterize

if HAS_MEM_EFF_ATTN or HAS_FLASH_ATTN:
    from colossalai.kernel.cuda_native import ColoAttention
    from colossalai.kernel.cuda_native.scaled_softmax import AttnMaskType

DTYPE = [torch.float16, torch.bfloat16, torch.float32]
FLASH_DTYPE = [torch.float16, torch.bfloat16]


def attention_ref(q, k, v, attn_mask=None, causal=False):
    """
    attention output of the control group
    """
    dtype_og = q.dtype
    seqlen_q, seqlen_k = q.shape[1], k.shape[1]
    d = q.shape[-1]
    scale = 1.0 / math.sqrt(d)
    scores = torch.einsum('bthd,bshd->bhts', q * scale, k)

    if attn_mask is not None:
        scores.masked_fill_(rearrange(~attn_mask, 'b s -> b 1 1 s'), float('-inf'))
    if causal:
        causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=q.device), 1)
        scores.masked_fill_(causal_mask, float('-inf'))
    attention = torch.softmax(scores, dim=-1)

    output = torch.einsum('bhts,bshd->bthd', attention, v)
    output = rearrange(output, "b s h d -> b s (h d)")

    # Modify the data at the positions of the mask to 0
    if attn_mask is not None:
        output.masked_fill_(rearrange(~attn_mask, 'b s -> b s 1'), 0.0)

    return output.to(dtype=dtype_og)


@pytest.mark.skipif(not HAS_MEM_EFF_ATTN and not HAS_FLASH_ATTN, reason="xformers is not available")
@clear_cache_before_run()
@parameterize('proj_shape', [(6, 8, 4, 16)])
@parameterize('dtype', DTYPE)
@parameterize('dropout', [0.0])
def test_attention_gpt(proj_shape, dtype, dropout):
    (B, S, H, D_HEAD) = proj_shape
    D = H * D_HEAD

    q = torch.randn((B, S, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
    k = torch.randn((B, S, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
    v = torch.randn((B, S, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)

    mask = [torch.ones(S - i, dtype=torch.bool, device="cuda") for i in range(B)]
    mask = torch.nn.utils.rnn.pad_sequence(mask, batch_first=True)

    attn = ColoAttention(D, H, dropout=dropout)
    y = attn(q, k, v, attn_mask=mask, attn_mask_type=AttnMaskType.paddedcausal)

    assert list(y.shape) == [B, S, D]

    out_ref = attention_ref(q, k, v, mask, causal=True)

    # check gradients
    dy = torch.rand_like(y)
    grad_q, grad_k, grad_v = torch.autograd.grad(y, (q, k, v), dy)
    grad_ref_q, grad_ref_k, grad_ref_v = torch.autograd.grad(out_ref, (q, k, v), dy)

    torch.allclose(y, out_ref, atol=1e-7), f"{(y - out_ref).abs().max()}"
    torch.allclose(grad_q, grad_ref_q, atol=1e-7), f"{(grad_q - grad_ref_q).abs().max()}"
    torch.allclose(grad_k, grad_ref_k, atol=1e-7), f"{(grad_k - grad_ref_k).abs().max()}"
    torch.allclose(grad_v, grad_ref_v, atol=1e-7), f"{(grad_v - grad_ref_v).abs().max()}"


@pytest.mark.skipif(not HAS_MEM_EFF_ATTN and not HAS_FLASH_ATTN, reason="xformers is not available")
@clear_cache_before_run()
@parameterize('proj_shape', [(6, 8, 4, 16)])
@parameterize('dtype', DTYPE)
@parameterize('dropout', [0.0])
def test_attention_bert(proj_shape, dtype, dropout):
    (B, S, H, D_HEAD) = proj_shape
    D = H * D_HEAD

    q = torch.randn((B, S, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
    k = torch.randn((B, S, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
    v = torch.randn((B, S, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)

    # attention mask of shape [B, S] with zero padding to max length S
    mask = torch.randint(0, 2, (B, S), dtype=torch.bool, device="cuda")

    attn = ColoAttention(D, H, dropout=dropout)
    y = attn(q, k, v, attn_mask=mask, attn_mask_type=AttnMaskType.padding)

    assert list(y.shape) == [B, S, D]

    out_ref = attention_ref(q, k, v, mask, causal=False)

    dy = torch.rand_like(y)
    grad_q, grad_k, grad_v = torch.autograd.grad(y, (q, k, v), dy)
    grad_ref_q, grad_ref_k, grad_ref_v = torch.autograd.grad(out_ref, (q, k, v), dy)

    torch.allclose(y, out_ref, atol=1e-7), f"{(y - out_ref).abs().max()}"
    torch.allclose(grad_q, grad_ref_q, atol=1e-7), f"{(grad_q - grad_ref_q).abs().max()}"
    torch.allclose(grad_k, grad_ref_k, atol=1e-7), f"{(grad_k - grad_ref_k).abs().max()}"
    torch.allclose(grad_v, grad_ref_v, atol=1e-7), f"{(grad_v - grad_ref_v).abs().max()}"


@pytest.mark.skipif(not HAS_MEM_EFF_ATTN and not HAS_FLASH_ATTN, reason="xformers is not available")
@clear_cache_before_run()
@parameterize('proj_shape', [(6, 8, 4, 16)])
@parameterize('dtype', DTYPE)
@parameterize('dropout', [0.0])
def test_attention_no_mask(proj_shape, dtype, dropout):
    (B, S, H, D_HEAD) = proj_shape
    D = H * D_HEAD

    q = torch.randn((B, S, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
    k = torch.randn((B, S, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
    v = torch.randn((B, S, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)

    attn = ColoAttention(D, H, dropout=dropout)
    y = attn(q, k, v)

    assert list(y.shape) == [B, S, D]

    out_ref = attention_ref(q, k, v, None, causal=False)

    dy = torch.rand_like(y)
    grad_q, grad_k, grad_v = torch.autograd.grad(y, (q, k, v), dy)
    grad_ref_q, grad_ref_k, grad_ref_v = torch.autograd.grad(out_ref, (q, k, v), dy)

    torch.allclose(y, out_ref, atol=1e-7), f"{(y - out_ref).abs().max()}"
    torch.allclose(grad_q, grad_ref_q, atol=1e-7), f"{(grad_q - grad_ref_q).abs().max()}"
    torch.allclose(grad_k, grad_ref_k, atol=1e-7), f"{(grad_k - grad_ref_k).abs().max()}"
    torch.allclose(grad_v, grad_ref_v, atol=1e-7), f"{(grad_v - grad_ref_v).abs().max()}"


@pytest.mark.skipif(not HAS_MEM_EFF_ATTN and not HAS_FLASH_ATTN, reason="xformers is not available")
@clear_cache_before_run()
@parameterize('proj_shape', [(6, 24, 8, 4, 16)])
@parameterize('dtype', DTYPE)
@parameterize('dropout', [0.0])
def test_cross_attention(proj_shape, dtype, dropout):
    (B, S, T, H, D_HEAD) = proj_shape
    D = H * D_HEAD

    q = torch.randn((B, T, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
    k = torch.randn((B, S, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
    v = torch.randn((B, S, H, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)

    attn = ColoAttention(D, H, dropout=dropout)
    y = attn(q, k, v, attn_mask_type=AttnMaskType.causal)

    assert list(y.shape) == [B, T, D]

    out_ref = attention_ref(q, k, v, None, causal=True)

    dy = torch.rand_like(y)
    grad_q, grad_k, grad_v = torch.autograd.grad(y, (q, k, v), dy)
    grad_ref_q, grad_ref_k, grad_ref_v = torch.autograd.grad(out_ref, (q, k, v), dy)

    torch.allclose(y, out_ref, atol=1e-18), f"{(y - out_ref).abs().max()}"
    torch.allclose(grad_q, grad_ref_q, atol=1e-7), f"{(grad_q - grad_ref_q).abs().max()}"
    torch.allclose(grad_k, grad_ref_k, atol=1e-7), f"{(grad_k - grad_ref_k).abs().max()}"
    torch.allclose(grad_v, grad_ref_v, atol=1e-7), f"{(grad_v - grad_ref_v).abs().max()}"