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@@ -11,6 +11,7 @@ from flash_attn import (
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flash_attn_varlen_func,
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flash_attn_varlen_kvpacked_func,
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flash_attn_varlen_qkvpacked_func,
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+ flash_attn_with_kvcache,
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)
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from test_flash_attn import (
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@@ -18,20 +19,23 @@ from test_flash_attn import (
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convert_flash_attn_S_to_softmax,
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generate_qkv,
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generate_random_padding_mask,
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+ _generate_block_kvcache,
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attention_ref,
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attention_kvpacked_ref,
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attention_qkvpacked_ref,
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)
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+from flash_attn.layers.rotary import apply_rotary_emb
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+
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def is_bwd_hdim_supported(d):
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- return d <= 128 and d % 2 == 0
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+ return d <= 256
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def ck_randval_to_dropout_mask(randval, p):
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# If p = 0.3, randval in 255 * (0.7, 1.0] will be dropout
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# randval in 255 * [0, 0.7] will be kept
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# If return dropout_mask >=0, value will be kept
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- return torch.floor(255.0 * (1 - p) - randval)
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+ return math.floor(255.0 * (1 - p)) - randval.to(torch.float32)
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def pad_rearrange_dropout_mask_hts_to_bhss(S_dmask, cu_seqlens_q, seqlen_q_rounded, seqlen_k_rounded):
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@@ -59,7 +63,7 @@ def pad_rearrange_dropout_mask_hts_to_bhss(S_dmask, cu_seqlens_q, seqlen_q_round
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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-@pytest.mark.parametrize("deterministic", [False])
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+@pytest.mark.parametrize("deterministic", [False, True])
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@pytest.mark.parametrize("alibi", [False, True])
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@pytest.mark.parametrize("local", [False, True])
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@pytest.mark.parametrize("causal", [False, True])
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@@ -152,12 +156,12 @@ def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, local, alibi, determ
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print(f"dV Pytorch max diff: {(dqkv_pt[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}")
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print(f"dQKV Pytorch mean diff: {(dqkv_pt - dqkv_ref).abs().mean().item()}")
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- # TODO - use 10 times to check, wait for ck to change dq type to f32
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+ # TODO - use 10 times to check, wait for ck to fix bwd precision issue
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assert (dqkv - dqkv_ref).abs().max().item() <= 10 * (dqkv_pt - dqkv_ref).abs().max().item()
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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-@pytest.mark.parametrize("deterministic", [False])
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+@pytest.mark.parametrize("deterministic", [False, True])
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@pytest.mark.parametrize("alibi", [False, True])
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@pytest.mark.parametrize("local", [False, True])
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@pytest.mark.parametrize("causal", [False, True])
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@@ -270,14 +274,14 @@ def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, local, alibi,
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print(f"dV Pytorch max diff: {(dqkv_pt[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}")
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print(f"dQKV Pytorch mean diff: {(dqkv_pt - dqkv_ref).abs().mean().item()}")
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- # TODO - use 10 times to check, wait for ck to change dq type to f32
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+ # TODO - use 10 times to check, wait for ck to fix bwd precision issue
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assert (dqkv - dqkv_ref).abs().max().item() <= 10 * (dqkv_pt - dqkv_ref).abs().max().item()
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@pytest.mark.parametrize("kvpacked", [True, False])
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
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-@pytest.mark.parametrize("deterministic", [False])
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+@pytest.mark.parametrize("deterministic", [False, True])
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@pytest.mark.parametrize("alibi", [False, True])
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@pytest.mark.parametrize("local", [False, True])
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@pytest.mark.parametrize("causal", [False, True])
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@@ -484,7 +488,7 @@ def test_flash_attn_output(
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print(f"dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}")
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print(f"dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}")
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- # TODO - use 10 times to check, wait for ck to change dq type to f32
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+ # TODO - use 10 times to check, wait for ck to fix bwd precision issue
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assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item()
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assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item()
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assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item()
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@@ -748,7 +752,869 @@ def test_flash_attn_varlen_output(
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print(f"dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}")
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print(f"dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}")
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- # TODO - use 10 times to check, wait for ck to change dq type to f32
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+ # TODO - use 10 times to check, wait for ck to fix bwd precision issue
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assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item()
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assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item()
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assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item()
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+
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+
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+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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+@pytest.mark.parametrize("local", [False, True])
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+@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
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+@pytest.mark.parametrize("swap_sq_sk", [False, True])
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+@pytest.mark.parametrize(
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+ "seqlen_q,seqlen_k",
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+ [
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+ # (1, 239),
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+ (3, 799),
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+ (127, 512),
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+ (127, 513),
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+ (113, 203),
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+ (128, 217),
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+ (113, 211),
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+ (108, 256),
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+ (256, 512),
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+ (1023, 1024),
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+ ],
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+)
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+def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, local, dtype):
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+ if max(seqlen_q, seqlen_k) >= 2048:
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+ pytest.skip()
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+ if swap_sq_sk:
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+ seqlen_q, seqlen_k = seqlen_k, seqlen_q
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+ device = "cuda"
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+ causal = True
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+ # set seed
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+ torch.random.manual_seed(0)
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+ batch_size = 8
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+ nheads = 9
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+ window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
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+ q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
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+ k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
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+ v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
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+ out = flash_attn_func(q, k, v, 0.0, causal=causal, window_size=window_size)
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+ out_ref, attn_ref = attention_ref(
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+ q, k, v, None, None, None, 0.0, None, causal=causal, window_size=window_size
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+ )
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+ out_pt, attn_pt = attention_ref(
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+ q,
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+ k,
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+ v,
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+ None,
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+ None,
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+ None,
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+ 0.0,
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+ None,
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+ causal=causal,
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+ window_size=window_size,
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+ upcast=False,
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+ reorder_ops=True,
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+ )
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+
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+ print(f"Output max diff: {(out - out_ref).abs().max().item()}")
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+ print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
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+ print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
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+ print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
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+
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+ # Check that FlashAttention's numerical error is at most 4 times the numerical error
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+ # of a Pytorch implementation.
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+ assert (out - out_ref).abs().max().item() <= 4 * (out_pt - out_ref).abs().max().item() + 1e-5
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+
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+ g = torch.randn_like(out)
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+ if is_bwd_hdim_supported(d):
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+ do_o = (g.float() * out.float()).sum(-1)
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+ (
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+ dq,
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+ dk,
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+ dv,
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+ ) = torch.autograd.grad(out, (q, k, v), g)
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+ (
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+ dq_ref,
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+ dk_ref,
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+ dv_ref,
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+ ) = torch.autograd.grad(out_ref, (q, k, v), g)
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+ (
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+ dq_pt,
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+ dk_pt,
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+ dv_pt,
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+ ) = torch.autograd.grad(out_pt, (q, k, v), g)
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+ print(f"dQ max diff: {(dq - dq_ref).abs().max().item()}")
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+ print(f"dK max diff: {(dk - dk_ref).abs().max().item()}")
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+ print(f"dV max diff: {(dv - dv_ref).abs().max().item()}")
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+ print(f"dQ mean diff: {(dq - dq_ref).abs().mean().item()}")
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+ print(f"dK mean diff: {(dk - dk_ref).abs().mean().item()}")
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+ print(f"dV mean diff: {(dv - dv_ref).abs().mean().item()}")
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+ print(f"dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}")
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+ print(f"dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}")
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+ print(f"dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}")
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+ print(f"dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}")
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+ print(f"dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}")
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+ print(f"dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}")
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+
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+ # TODO - use 10 times to check, wait for ck to fix bwd precision issue
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+ assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item() + 1e-4
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+ assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item() + 1e-4
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+ assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item() + 1e-4
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+
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+
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+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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+@pytest.mark.parametrize("local", [False, True])
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+@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
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+@pytest.mark.parametrize("swap_sq_sk", [False, True])
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+@pytest.mark.parametrize(
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+ "seqlen_q,seqlen_k",
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+ [
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+ # (1, 239),
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+ (3, 799),
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+ (127, 512),
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+ (127, 513),
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+ (113, 203),
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+ (128, 217),
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+ (113, 211),
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+ (108, 256),
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+ (256, 512),
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+ (1023, 1024),
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+ ],
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+)
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+# TODO: Support paged_kv_block
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+# @pytest.mark.parametrize("paged_kv_block_size", [None, 256, 512])
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+@pytest.mark.parametrize("paged_kv_block_size", [None])
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+def test_flash_attn_varlen_causal(
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+ seqlen_q, seqlen_k, swap_sq_sk, d, local, paged_kv_block_size, dtype
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+):
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+ if max(seqlen_q, seqlen_k) >= 2048:
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+ pytest.skip()
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+ if swap_sq_sk:
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+ seqlen_q, seqlen_k = seqlen_k, seqlen_q
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+ device = "cuda"
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+ causal = True
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+ # set seed
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+ torch.random.manual_seed(0)
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+ batch_size = 8
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+ nheads = 9
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+ window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
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+ q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
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+
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+ if paged_kv_block_size is None:
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+ k = torch.randn(
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+ batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True
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+ )
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+ v = torch.randn(
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+ batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True
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+ )
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+ block_table = None
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+ else:
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+ k, v, block_table, k_cache_paged, v_cache_paged, num_blocks = _generate_block_kvcache(
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+ seqlen_k, paged_kv_block_size, batch_size, nheads, d, device, dtype
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+ )
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+ query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode="random")
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+ key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode="random")
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+ (
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+ q_unpad,
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+ k_unpad,
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+ v_unpad,
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+ cu_seqlens_q,
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+ cu_seqlens_k,
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+ max_seqlen_q,
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+ max_seqlen_k,
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+ q,
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+ k,
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+ v,
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+ output_pad_fn,
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+ dq_pad_fn,
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+ dk_pad_fn,
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+ ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False)
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+ out_unpad = flash_attn_varlen_func(
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+ q_unpad,
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+ k_unpad if paged_kv_block_size is None else k_cache_paged,
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+ v_unpad if paged_kv_block_size is None else v_cache_paged,
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+ cu_seqlens_q,
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+ cu_seqlens_k,
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+ max_seqlen_q,
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+ max_seqlen_k,
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+ 0.0,
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+ causal=causal,
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+ window_size=window_size,
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+ block_table=block_table,
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+ )
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+ out = output_pad_fn(out_unpad)
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+ out_ref, attn_ref = attention_ref(
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+ q,
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+ k,
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+ v,
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+ query_padding_mask,
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+ key_padding_mask,
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+ None,
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+ 0.0,
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+ None,
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+ causal=causal,
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+ window_size=window_size,
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+ )
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+ out_pt, attn_pt = attention_ref(
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+ q,
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+ k,
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+ v,
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+ query_padding_mask,
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+ key_padding_mask,
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+ None,
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+ 0.0,
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+ None,
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+ causal=causal,
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+ window_size=window_size,
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+ upcast=False,
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+ reorder_ops=True,
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+ )
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+
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+ print(f"Output max diff: {(out - out_ref).abs().max().item()}")
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+ print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
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+ print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
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+ print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
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+
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+ # Check that FlashAttention's numerical error is at most twice the numerical error
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+ # of a Pytorch implementation.
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+ assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item() + 1e-5
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+
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+ g = torch.randn_like(out)
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+ if is_bwd_hdim_supported(d):
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+ do_o = (g.float() * out.float()).sum(-1)
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+ test_backward = block_table is None
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+ if test_backward:
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+ (
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+ dq_unpad,
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+ dk_unpad,
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+ dv_unpad,
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+ ) = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g)
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+ dq = dq_pad_fn(dq_unpad)
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+ dk = dk_pad_fn(dk_unpad)
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+ dv = dk_pad_fn(dv_unpad)
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+ (
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+ dq_ref,
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+ dk_ref,
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|
+ dv_ref,
|
|
|
+ ) = torch.autograd.grad(out_ref, (q, k, v), g)
|
|
|
+ (
|
|
|
+ dq_pt,
|
|
|
+ dk_pt,
|
|
|
+ dv_pt,
|
|
|
+ ) = torch.autograd.grad(out_pt, (q, k, v), g)
|
|
|
+ print(f"dQ max diff: {(dq - dq_ref).abs().max().item()}")
|
|
|
+ print(f"dK max diff: {(dk - dk_ref).abs().max().item()}")
|
|
|
+ print(f"dV max diff: {(dv - dv_ref).abs().max().item()}")
|
|
|
+ print(f"dQ mean diff: {(dq - dq_ref).abs().mean().item()}")
|
|
|
+ print(f"dK mean diff: {(dk - dk_ref).abs().mean().item()}")
|
|
|
+ print(f"dV mean diff: {(dv - dv_ref).abs().mean().item()}")
|
|
|
+ print(f"dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}")
|
|
|
+ print(f"dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}")
|
|
|
+ print(f"dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}")
|
|
|
+ print(f"dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}")
|
|
|
+ print(f"dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}")
|
|
|
+ print(f"dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}")
|
|
|
+
|
|
|
+ if test_backward:
|
|
|
+ # TODO - use 10 times to check, wait for ck to fix bwd precision issue
|
|
|
+ assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item() + 1e-5
|
|
|
+ assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item() + 1e-5
|
|
|
+ assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item() + 1e-5
|
|
|
+
|
|
|
+
|
|
|
+# TODO - support splitkv
|
|
|
+# def test_flash_attn_splitkv
|
|
|
+
|
|
|
+
|
|
|
+# TODO - Support has_leftpad
|
|
|
+@pytest.mark.parametrize("dtype", [torch.float16])
|
|
|
+@pytest.mark.parametrize("num_splits", [1, 0])
|
|
|
+@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
|
|
|
+@pytest.mark.parametrize("new_kv", [False, True])
|
|
|
+@pytest.mark.parametrize("alibi", [False, True])
|
|
|
+@pytest.mark.parametrize("local", [False, True])
|
|
|
+@pytest.mark.parametrize("causal", [False, True])
|
|
|
+@pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True, False])
|
|
|
+@pytest.mark.parametrize("rotary_interleaved", [False, True])
|
|
|
+@pytest.mark.parametrize("rotary_fraction", [0.0, 0.5, 1.0])
|
|
|
+@pytest.mark.parametrize("paged_kv_block_size", [None, 256])
|
|
|
+@pytest.mark.parametrize("has_leftpad", [False])
|
|
|
+@pytest.mark.parametrize("has_batch_idx", [False, True])
|
|
|
+@pytest.mark.parametrize("d", [32, 59, 64, 80, 128, 256])
|
|
|
+@pytest.mark.parametrize(
|
|
|
+ "seqlen_q,seqlen_k",
|
|
|
+ [
|
|
|
+ (1, 128),
|
|
|
+ (1, 339),
|
|
|
+ (3, 1024),
|
|
|
+ (64, 800),
|
|
|
+ (64, 256),
|
|
|
+ (3, 799),
|
|
|
+ (64, 2048),
|
|
|
+ (16, 20000),
|
|
|
+ (1, 128 * 1024),
|
|
|
+ (16, 128 * 1024),
|
|
|
+ (128, 128),
|
|
|
+ ],
|
|
|
+)
|
|
|
+def test_flash_attn_kvcache(
|
|
|
+ seqlen_q,
|
|
|
+ seqlen_k,
|
|
|
+ d,
|
|
|
+ has_batch_idx,
|
|
|
+ has_leftpad,
|
|
|
+ paged_kv_block_size,
|
|
|
+ rotary_fraction,
|
|
|
+ rotary_interleaved,
|
|
|
+ seqlen_new_eq_seqlen_q,
|
|
|
+ causal,
|
|
|
+ local,
|
|
|
+ alibi,
|
|
|
+ new_kv,
|
|
|
+ mha_type,
|
|
|
+ num_splits,
|
|
|
+ dtype,
|
|
|
+):
|
|
|
+ if seqlen_q > seqlen_k and new_kv:
|
|
|
+ pytest.skip()
|
|
|
+ if not new_kv and rotary_fraction > 0.0:
|
|
|
+ pytest.skip()
|
|
|
+ if has_batch_idx and paged_kv_block_size is not None:
|
|
|
+ pytest.skip()
|
|
|
+ if has_leftpad and paged_kv_block_size is not None:
|
|
|
+ pytest.skip()
|
|
|
+ device = "cuda"
|
|
|
+ # set seed
|
|
|
+ torch.random.manual_seed(0)
|
|
|
+ batch_size = 1
|
|
|
+ batch_size_cache = batch_size if not has_batch_idx else batch_size * 2
|
|
|
+ nheads = 6
|
|
|
+ # rotary_dim must be a multiple of 16, and must be <= d
|
|
|
+ rotary_dim = math.floor(int(rotary_fraction * d) / 16) * 16
|
|
|
+ nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
|
|
|
+ assert nheads % nheads_k == 0
|
|
|
+ window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
|
|
|
+ q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype)
|
|
|
+ seqlen_new = seqlen_q if seqlen_new_eq_seqlen_q else torch.randint(1, seqlen_q + 1, (1,)).item()
|
|
|
+ if new_kv:
|
|
|
+ k = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype)
|
|
|
+ v = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype)
|
|
|
+ else:
|
|
|
+ k, v = None, None
|
|
|
+ if paged_kv_block_size is None:
|
|
|
+ k_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype)
|
|
|
+ v_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype)
|
|
|
+ block_table = None
|
|
|
+ else:
|
|
|
+ (
|
|
|
+ k_cache,
|
|
|
+ v_cache,
|
|
|
+ block_table,
|
|
|
+ k_cache_paged,
|
|
|
+ v_cache_paged,
|
|
|
+ num_blocks,
|
|
|
+ ) = _generate_block_kvcache(
|
|
|
+ seqlen_k, paged_kv_block_size, batch_size, nheads_k, d, device, dtype
|
|
|
+ )
|
|
|
+ cache_seqlens = torch.randint(
|
|
|
+ 0 if new_kv else 1,
|
|
|
+ # If we don't use seqlen_q in the case of causal and rotary, cos/sin won't be long enough
|
|
|
+ (
|
|
|
+ (seqlen_k - (seqlen_q if (causal or local) and rotary_dim > 1 else seqlen_new) + 1)
|
|
|
+ if new_kv
|
|
|
+ else (seqlen_k + 1)
|
|
|
+ ),
|
|
|
+ (batch_size,),
|
|
|
+ dtype=torch.int32,
|
|
|
+ device=device,
|
|
|
+ )
|
|
|
+ if has_leftpad:
|
|
|
+ cache_leftpad = torch.cat([torch.randint(0, cache_seqlens[i].item(), (1,), dtype=torch.int32, device=device)
|
|
|
+ if cache_seqlens[i].item() > 0 else torch.zeros(1, dtype=torch.int32, device=device)
|
|
|
+ for i in range(batch_size)])
|
|
|
+ else:
|
|
|
+ cache_leftpad = None
|
|
|
+ arange = rearrange(torch.arange(seqlen_k, device=device), "s -> 1 s")
|
|
|
+ cache_seqlens_expanded = rearrange(cache_seqlens, "b -> b 1")
|
|
|
+ key_padding_mask = arange < cache_seqlens_expanded + (seqlen_new if new_kv else 0)
|
|
|
+ if has_leftpad:
|
|
|
+ key_padding_mask = torch.logical_and(
|
|
|
+ key_padding_mask, arange >= cache_leftpad.unsqueeze(-1).expand(-1, seqlen_k)
|
|
|
+ )
|
|
|
+ if has_batch_idx:
|
|
|
+ cache_batch_idx = torch.randperm(batch_size_cache, dtype=torch.int32, device=device)[
|
|
|
+ :batch_size
|
|
|
+ ]
|
|
|
+ else:
|
|
|
+ cache_batch_idx = None
|
|
|
+ if alibi:
|
|
|
+ alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
|
|
|
+ attn_bias = attn_bias_from_alibi_slopes(
|
|
|
+ alibi_slopes, seqlen_q, seqlen_k, None, key_padding_mask, causal=causal, key_leftpad=cache_leftpad
|
|
|
+ )
|
|
|
+ else:
|
|
|
+ alibi_slopes, attn_bias = None, None
|
|
|
+ # cache_seqlens = torch.tensor([64], dtype=torch.int32, device=device)
|
|
|
+ if rotary_dim > 0:
|
|
|
+ angle = (
|
|
|
+ torch.rand(
|
|
|
+ seqlen_k if paged_kv_block_size is None else num_blocks * paged_kv_block_size,
|
|
|
+ rotary_dim // 2,
|
|
|
+ device=device,
|
|
|
+ )
|
|
|
+ * 2
|
|
|
+ * math.pi
|
|
|
+ )
|
|
|
+ cos = torch.cos(angle).to(dtype=dtype)
|
|
|
+ sin = torch.sin(angle).to(dtype=dtype)
|
|
|
+ if causal or local:
|
|
|
+ q_ro = apply_rotary_emb(
|
|
|
+ q, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved
|
|
|
+ )
|
|
|
+ else:
|
|
|
+ q_ro = rearrange(
|
|
|
+ apply_rotary_emb(
|
|
|
+ rearrange(q, "b s h d -> b 1 (s h) d"),
|
|
|
+ cos,
|
|
|
+ sin,
|
|
|
+ seqlen_offsets=cache_seqlens,
|
|
|
+ interleaved=rotary_interleaved,
|
|
|
+ ),
|
|
|
+ "b 1 (s h) d -> b s h d",
|
|
|
+ s=seqlen_q,
|
|
|
+ )
|
|
|
+ # q_ro = q
|
|
|
+ k_ro = apply_rotary_emb(
|
|
|
+ k, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved
|
|
|
+ )
|
|
|
+ else:
|
|
|
+ cos, sin = None, None
|
|
|
+ q_ro, k_ro = q, k
|
|
|
+ # k_cache[:, 64:] = -1
|
|
|
+ k_cache_ref = (
|
|
|
+ k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)]
|
|
|
+ ).clone()
|
|
|
+ v_cache_ref = (
|
|
|
+ v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)]
|
|
|
+ ).clone()
|
|
|
+ if new_kv:
|
|
|
+ update_mask = torch.logical_and(
|
|
|
+ cache_seqlens_expanded <= arange, arange < cache_seqlens_expanded + seqlen_new
|
|
|
+ )
|
|
|
+ k_cache_ref[update_mask] = rearrange(k_ro, "b s ... -> (b s) ...")
|
|
|
+ v_cache_ref[update_mask] = rearrange(v, "b s ... -> (b s) ...")
|
|
|
+ k_cache_rep = repeat(k_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k)
|
|
|
+ v_cache_rep = repeat(v_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k)
|
|
|
+ out = flash_attn_with_kvcache(
|
|
|
+ q,
|
|
|
+ k_cache if paged_kv_block_size is None else k_cache_paged,
|
|
|
+ v_cache if paged_kv_block_size is None else v_cache_paged,
|
|
|
+ k,
|
|
|
+ v,
|
|
|
+ rotary_cos=cos,
|
|
|
+ rotary_sin=sin,
|
|
|
+ cache_seqlens=cache_seqlens,
|
|
|
+ cache_batch_idx=cache_batch_idx,
|
|
|
+ cache_leftpad=cache_leftpad,
|
|
|
+ block_table=block_table,
|
|
|
+ causal=causal,
|
|
|
+ window_size=window_size,
|
|
|
+ rotary_interleaved=rotary_interleaved,
|
|
|
+ alibi_slopes=alibi_slopes,
|
|
|
+ num_splits=num_splits,
|
|
|
+ )
|
|
|
+ # out = flash_attn_with_kvcache(
|
|
|
+ # q, k_cache, v_cache, cache_seqlens=cache_seqlens, causal=causal, window_size=window_size
|
|
|
+ # )
|
|
|
+ # out = flash_attn_with_kvcache(q, k_cache, v_cache, causal=causal, window_size=window_size)
|
|
|
+ # qk = torch.einsum("bqhd,bkhd->bhqk", q, k_cache_ref)
|
|
|
+ # m = qk.amax(-1, keepdim=True)
|
|
|
+ # s_tmp = torch.exp((qk - m) / math.sqrt(d))
|
|
|
+ # o1 = torch.einsum('bhst,bthd->bshd', s_tmp, v_cache_ref)
|
|
|
+ # lse_ref = torch.logsumexp(qk / math.sqrt(d), -1)
|
|
|
+ # probs = torch.softmax(qk, dim=-1)
|
|
|
+ out_ref, _ = attention_ref(
|
|
|
+ q_ro,
|
|
|
+ k_cache_rep,
|
|
|
+ v_cache_rep,
|
|
|
+ None,
|
|
|
+ key_padding_mask,
|
|
|
+ attn_bias,
|
|
|
+ 0.0,
|
|
|
+ None,
|
|
|
+ causal=causal,
|
|
|
+ window_size=window_size,
|
|
|
+ key_leftpad=cache_leftpad,
|
|
|
+ )
|
|
|
+ out_pt, _ = attention_ref(
|
|
|
+ q_ro,
|
|
|
+ k_cache_rep,
|
|
|
+ v_cache_rep,
|
|
|
+ None,
|
|
|
+ key_padding_mask,
|
|
|
+ attn_bias,
|
|
|
+ 0.0,
|
|
|
+ None,
|
|
|
+ causal=causal,
|
|
|
+ window_size=window_size,
|
|
|
+ upcast=False,
|
|
|
+ reorder_ops=True,
|
|
|
+ key_leftpad=cache_leftpad,
|
|
|
+ )
|
|
|
+ print(f"Output max diff: {(out - out_ref).abs().max().item()}")
|
|
|
+ print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
|
|
|
+ print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
|
|
|
+ print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
|
|
|
+
|
|
|
+ # Check that FlashAttention's numerical error is at most twice the numerical error
|
|
|
+ # of a Pytorch implementation.
|
|
|
+ if new_kv:
|
|
|
+ if paged_kv_block_size is None:
|
|
|
+ k_cache_select = (
|
|
|
+ k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)]
|
|
|
+ )
|
|
|
+ v_cache_select = (
|
|
|
+ v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)]
|
|
|
+ )
|
|
|
+ else:
|
|
|
+ k_cache_select = rearrange(
|
|
|
+ k_cache_paged[block_table.to(dtype=torch.long).flatten()],
|
|
|
+ "(b nblocks) block_size ... -> b (nblocks block_size) ...",
|
|
|
+ b=batch_size,
|
|
|
+ )[:, :seqlen_k]
|
|
|
+ v_cache_select = rearrange(
|
|
|
+ v_cache_paged[block_table.to(dtype=torch.long).flatten()],
|
|
|
+ "(b nblocks) block_size ... -> b (nblocks block_size) ...",
|
|
|
+ b=batch_size,
|
|
|
+ )[:, :seqlen_k]
|
|
|
+ assert torch.allclose(k_cache_select, k_cache_ref, rtol=1e-3, atol=1e-3)
|
|
|
+ assert torch.equal(v_cache_select, v_cache_ref)
|
|
|
+ # mult = 3 if f16, bf16 need 4
|
|
|
+ mult = 4 if not alibi else 5
|
|
|
+ assert (out - out_ref).abs().max().item() <= mult * (out_pt - out_ref).abs().max().item() + 1e-5
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+@pytest.mark.parametrize("dtype", [torch.float16])
|
|
|
+@pytest.mark.parametrize("causal", [False, True])
|
|
|
+@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
|
|
|
+@pytest.mark.parametrize(
|
|
|
+ "seqlen_q,seqlen_k",
|
|
|
+ [
|
|
|
+ (1, 239),
|
|
|
+ (239, 1),
|
|
|
+ (3, 799),
|
|
|
+ (799, 3),
|
|
|
+ (1024, 128),
|
|
|
+ (97, 97),
|
|
|
+ (128, 128),
|
|
|
+ (200, 200),
|
|
|
+ (256, 256),
|
|
|
+ (257, 257),
|
|
|
+ (384, 384),
|
|
|
+ (512, 512),
|
|
|
+ (768, 768),
|
|
|
+ # (1024, 1024),
|
|
|
+ ],
|
|
|
+)
|
|
|
+@pytest.mark.parametrize("dropout_p", [0.0, 0.17])
|
|
|
+def test_flash_attn_race_condition(seqlen_q, seqlen_k, d, dropout_p, causal, dtype):
|
|
|
+ device = "cuda"
|
|
|
+ # set seed
|
|
|
+ torch.random.manual_seed(0)
|
|
|
+ batch_size = 60 # Sometimes we need large batch size for the race conditions to trigger
|
|
|
+ nheads = 4
|
|
|
+ q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
|
|
|
+ k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
|
|
|
+ v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
|
|
|
+ torch.random.manual_seed(42)
|
|
|
+ out0, lse0, _ = flash_attn_func(q, k, v, dropout_p, causal=causal, return_attn_probs=True)
|
|
|
+ g = torch.randn_like(out0)
|
|
|
+ if dropout_p == 0 and is_bwd_hdim_supported(d):
|
|
|
+ (
|
|
|
+ dq0,
|
|
|
+ dk0,
|
|
|
+ dv0,
|
|
|
+ ) = torch.autograd.grad(out0, (q, k, v), g)
|
|
|
+ # Numerical error if we just do any arithmetic on dq
|
|
|
+ dq_atol = 2 * ((dq0 + 0.3 - 0.3) - dq0).abs().max().item()
|
|
|
+
|
|
|
+ for i in range(250):
|
|
|
+ torch.random.manual_seed(42)
|
|
|
+ out, lse, _ = flash_attn_func(q, k, v, dropout_p, causal=causal, return_attn_probs=True)
|
|
|
+ assert torch.equal(out, out0)
|
|
|
+ assert torch.equal(lse, lse0)
|
|
|
+
|
|
|
+ if dropout_p == 0:
|
|
|
+ (
|
|
|
+ dq,
|
|
|
+ dk,
|
|
|
+ dv,
|
|
|
+ ) = torch.autograd.grad(out, (q, k, v), g)
|
|
|
+ dq_equal = torch.allclose(dq, dq0, atol=dq_atol)
|
|
|
+ if not dq_equal:
|
|
|
+ print(f"Iter {i}, {dq_atol = }, dQ max diff: {(dq - dq0).abs().max().item()}")
|
|
|
+
|
|
|
+ assert torch.equal(dv, dv0)
|
|
|
+ assert torch.equal(dk, dk0)
|
|
|
+ assert dq_equal
|
|
|
+
|
|
|
+
|
|
|
+@pytest.mark.parametrize("dtype", [torch.float16])
|
|
|
+@pytest.mark.parametrize("causal", [False, True])
|
|
|
+@pytest.mark.parametrize("d", [16, 32, 64])
|
|
|
+@pytest.mark.parametrize("seqlen", [1, 2, 5, 17, 128])
|
|
|
+def test_flash_attn_bwd_overflow(seqlen, d, causal, dtype):
|
|
|
+ """We previously had a bug where not masking elements beyond seqlen_k caused NaN in dQ,
|
|
|
+ in the case where seqlen % 128 != 0.
|
|
|
+ """
|
|
|
+
|
|
|
+ # TODO - 1 or 2 might fail, need to check
|
|
|
+ if seqlen == 1 or seqlen == 2:
|
|
|
+ pytest.skip()
|
|
|
+
|
|
|
+ device = "cuda"
|
|
|
+ # set seed
|
|
|
+ torch.random.manual_seed(0)
|
|
|
+ batch_size = 2
|
|
|
+ nheads = 5
|
|
|
+ q = torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda") * 5
|
|
|
+ k, v = [
|
|
|
+ torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda") * 3
|
|
|
+ for _ in range(2)
|
|
|
+ ]
|
|
|
+ q.requires_grad_(True)
|
|
|
+ k.requires_grad_(True)
|
|
|
+ v.requires_grad_(True)
|
|
|
+ out = flash_attn_func(q, k, v, causal=causal)
|
|
|
+ g = torch.randn_like(out)
|
|
|
+ out.backward(g)
|
|
|
+ q_pt = q.detach().clone().requires_grad_(True)
|
|
|
+ k_pt = k.detach().clone().requires_grad_(True)
|
|
|
+ v_pt = v.detach().clone().requires_grad_(True)
|
|
|
+ out_pt, _ = attention_ref(q_pt, k_pt, v_pt, causal=causal, upcast=False, reorder_ops=True)
|
|
|
+ out_pt.backward(g)
|
|
|
+ q_ref = q.detach().clone().requires_grad_(True)
|
|
|
+ k_ref = k.detach().clone().requires_grad_(True)
|
|
|
+ v_ref = v.detach().clone().requires_grad_(True)
|
|
|
+ out_ref, attn_ref = attention_ref(q_ref, k_ref, v_ref, causal=causal)
|
|
|
+ out_ref.backward(g)
|
|
|
+ print(f"dQ max diff: {(q.grad - q_ref.grad).abs().max().item()}")
|
|
|
+ print(f"dK max diff: {(k.grad - k_ref.grad).abs().max().item()}")
|
|
|
+ print(f"dV max diff: {(v.grad - v_ref.grad).abs().max().item()}")
|
|
|
+ print(f"dQ Pytorch max diff: {(q_pt.grad - q_ref.grad).abs().max().item()}")
|
|
|
+ print(f"dK Pytorch max diff: {(k_pt.grad - k_ref.grad).abs().max().item()}")
|
|
|
+ print(f"dV Pytorch max diff: {(v_pt.grad - v_ref.grad).abs().max().item()}")
|
|
|
+ assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
|
|
|
+ assert (q.grad - q_ref.grad).abs().max().item() <= 5 * (
|
|
|
+ q_pt.grad - q_ref.grad
|
|
|
+ ).abs().max().item() + 1e-3
|
|
|
+ assert (k.grad - k_ref.grad).abs().max().item() <= 5 * (
|
|
|
+ k_pt.grad - k_ref.grad
|
|
|
+ ).abs().max().item() + 1e-3
|
|
|
+ assert (v.grad - v_ref.grad).abs().max().item() <= 5 * (
|
|
|
+ v_pt.grad - v_ref.grad
|
|
|
+ ).abs().max().item() + 1e-3
|
|
|
+
|
|
|
+
|
|
|
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
|
|
|
+@pytest.mark.parametrize("causal", [False, True])
|
|
|
+@pytest.mark.parametrize("d", [64, 128])
|
|
|
+@pytest.mark.parametrize("seqlen", [97, 128, 200, 256])
|
|
|
+def test_flash_attn_bwd_transpose(seqlen, d, causal, dtype):
|
|
|
+ """We previously had a bug where we were using the wrong strides of dout, which shows up
|
|
|
+ when dout is not contiguous.
|
|
|
+ """
|
|
|
+ device = "cuda"
|
|
|
+ # set seed
|
|
|
+ torch.random.manual_seed(0)
|
|
|
+ batch_size = 5
|
|
|
+ nheads = 2
|
|
|
+ q, k, v = [
|
|
|
+ torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda", requires_grad=True)
|
|
|
+ for _ in range(3)
|
|
|
+ ]
|
|
|
+ out = rearrange(flash_attn_func(q, k, v, causal=causal), "b s ... -> s b ...")
|
|
|
+ # So g is not contiguous
|
|
|
+ g = torch.randn(seqlen, 2 * batch_size, nheads, d, dtype=dtype, device="cuda")[:, ::2]
|
|
|
+ out.backward(g)
|
|
|
+ q_pt = q.detach().clone().requires_grad_(True)
|
|
|
+ k_pt = k.detach().clone().requires_grad_(True)
|
|
|
+ v_pt = v.detach().clone().requires_grad_(True)
|
|
|
+ out_pt, attn_pt = attention_ref(q_pt, k_pt, v_pt, causal=causal, upcast=False, reorder_ops=True)
|
|
|
+ out_pt = rearrange(out_pt, "b s ... -> s b ...")
|
|
|
+ out_pt.backward(g)
|
|
|
+ q_ref = q.detach().clone().requires_grad_(True)
|
|
|
+ k_ref = k.detach().clone().requires_grad_(True)
|
|
|
+ v_ref = v.detach().clone().requires_grad_(True)
|
|
|
+ out_ref, attn_ref = attention_ref(q_ref, k_ref, v_ref, causal=causal)
|
|
|
+ out_ref = rearrange(out_ref, "b s ... -> s b ...")
|
|
|
+ out_ref.backward(g)
|
|
|
+ print(f"dQ max diff: {(q.grad - q_ref.grad).abs().max().item()}")
|
|
|
+ print(f"dK max diff: {(k.grad - k_ref.grad).abs().max().item()}")
|
|
|
+ print(f"dV max diff: {(v.grad - v_ref.grad).abs().max().item()}")
|
|
|
+ print(f"dQ Pytorch max diff: {(q_pt.grad - q_ref.grad).abs().max().item()}")
|
|
|
+ print(f"dK Pytorch max diff: {(k_pt.grad - k_ref.grad).abs().max().item()}")
|
|
|
+ print(f"dV Pytorch max diff: {(v_pt.grad - v_ref.grad).abs().max().item()}")
|
|
|
+ assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
|
|
|
+ assert (q.grad - q_ref.grad).abs().max().item() <= 2 * (
|
|
|
+ q_pt.grad - q_ref.grad
|
|
|
+ ).abs().max().item()
|
|
|
+ assert (k.grad - k_ref.grad).abs().max().item() <= 2 * (
|
|
|
+ k_pt.grad - k_ref.grad
|
|
|
+ ).abs().max().item()
|
|
|
+ assert (v.grad - v_ref.grad).abs().max().item() <= 2 * (
|
|
|
+ v_pt.grad - v_ref.grad
|
|
|
+ ).abs().max().item()
|
|
|
+
|
|
|
+
|
|
|
+@pytest.mark.parametrize("dtype", [torch.float16])
|
|
|
+@pytest.mark.parametrize("causal", [False, True])
|
|
|
+@pytest.mark.parametrize("d", [16, 32, 64])
|
|
|
+def test_flash_attn_bwd_varlen_overflow(d, causal, dtype):
|
|
|
+ """We previously had a bug where not masking elements beyond seqlen_k caused NaN in dQ,
|
|
|
+ in the case where seqlen % 128 != 0 or varlen.
|
|
|
+ """
|
|
|
+ device = "cuda"
|
|
|
+ # set seed
|
|
|
+ torch.random.manual_seed(0)
|
|
|
+ nheads = 5
|
|
|
+ q_cuseqlen = torch.tensor([0, 76, 110, 256], device=device, dtype=torch.int32)
|
|
|
+ k_cuseqlen = torch.tensor([0, 1, 2, 3], device=device, dtype=torch.int32)
|
|
|
+ Mq = 256
|
|
|
+ Mk = 3
|
|
|
+
|
|
|
+ q = torch.randn([Mq, nheads, d], dtype=dtype, device=device) * 3
|
|
|
+ k, v = [torch.randn([Mk, nheads, d], dtype=dtype, device=device) * 3 for _ in range(2)]
|
|
|
+ q.requires_grad_(True)
|
|
|
+ k.requires_grad_(True)
|
|
|
+ v.requires_grad_(True)
|
|
|
+
|
|
|
+ out = flash_attn_varlen_func(q, k, v, q_cuseqlen, k_cuseqlen, Mq, Mk, causal=causal)
|
|
|
+ g = torch.randn_like(out)
|
|
|
+ out.backward(g)
|
|
|
+
|
|
|
+ assert not q.grad.isnan().any()
|
|
|
+ assert not k.grad.isnan().any()
|
|
|
+ assert not v.grad.isnan().any()
|
|
|
+
|
|
|
+
|
|
|
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
|
|
|
+@pytest.mark.parametrize("local", [False, True])
|
|
|
+@pytest.mark.parametrize("causal", [False, True])
|
|
|
+@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
|
|
|
+@pytest.mark.parametrize("swap_sq_sk", [False, True])
|
|
|
+@pytest.mark.parametrize(
|
|
|
+ "seqlen_q,seqlen_k",
|
|
|
+ [
|
|
|
+ (1, 239),
|
|
|
+ (3, 799),
|
|
|
+ (127, 512),
|
|
|
+ (127, 513),
|
|
|
+ (113, 203),
|
|
|
+ (128, 217),
|
|
|
+ (113, 211),
|
|
|
+ (108, 256),
|
|
|
+ (256, 512),
|
|
|
+ (1023, 1024),
|
|
|
+ ],
|
|
|
+)
|
|
|
+def test_flash_attn_deterministic(seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, dtype):
|
|
|
+ if (
|
|
|
+ max(seqlen_q, seqlen_k) >= 2048
|
|
|
+ and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
|
|
|
+ ):
|
|
|
+ pytest.skip() # Reference implementation OOM
|
|
|
+ if swap_sq_sk:
|
|
|
+ seqlen_q, seqlen_k = seqlen_k, seqlen_q
|
|
|
+ device = "cuda"
|
|
|
+ # set seed
|
|
|
+ torch.random.manual_seed(0)
|
|
|
+ batch_size = 4
|
|
|
+ nheads = 9
|
|
|
+ window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
|
|
|
+ q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
|
|
|
+ k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
|
|
|
+ v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
|
|
|
+ out = flash_attn_func(q, k, v, 0.0, causal=causal, window_size=window_size, deterministic=True)
|
|
|
+
|
|
|
+ g = torch.randn_like(out)
|
|
|
+ dq0, dk0, dv0 = torch.autograd.grad(out, (q, k, v), g, retain_graph=True)
|
|
|
+ for _ in range(50):
|
|
|
+ dq, dk, dv = torch.autograd.grad(out, (q, k, v), g, retain_graph=True)
|
|
|
+ assert torch.equal(dv, dv0)
|
|
|
+ assert torch.equal(dk, dk0)
|
|
|
+ assert torch.equal(dq, dq0)
|
|
|
+
|
|
|
+
|
|
|
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
|
|
|
+@pytest.mark.parametrize("local", [False, True])
|
|
|
+@pytest.mark.parametrize("causal", [False, True])
|
|
|
+@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
|
|
|
+@pytest.mark.parametrize("swap_sq_sk", [False, True])
|
|
|
+@pytest.mark.parametrize(
|
|
|
+ "seqlen_q,seqlen_k",
|
|
|
+ [
|
|
|
+ (1, 239),
|
|
|
+ (3, 799),
|
|
|
+ (127, 512),
|
|
|
+ (127, 513),
|
|
|
+ (113, 203),
|
|
|
+ (128, 217),
|
|
|
+ (113, 211),
|
|
|
+ (108, 256),
|
|
|
+ (256, 512),
|
|
|
+ (1023, 1024),
|
|
|
+ ],
|
|
|
+)
|
|
|
+def test_flash_attn_varlen_deterministic(seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, dtype):
|
|
|
+ if (
|
|
|
+ max(seqlen_q, seqlen_k) >= 2048
|
|
|
+ and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
|
|
|
+ ):
|
|
|
+ pytest.skip() # Reference implementation OOM
|
|
|
+ if swap_sq_sk:
|
|
|
+ seqlen_q, seqlen_k = seqlen_k, seqlen_q
|
|
|
+ device = "cuda"
|
|
|
+ # set seed
|
|
|
+ torch.random.manual_seed(0)
|
|
|
+ batch_size = 2
|
|
|
+ nheads = 9
|
|
|
+ window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
|
|
|
+ q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
|
|
|
+ k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
|
|
|
+ v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
|
|
|
+ query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode="random")
|
|
|
+ key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode="random")
|
|
|
+ (
|
|
|
+ q_unpad,
|
|
|
+ k_unpad,
|
|
|
+ v_unpad,
|
|
|
+ cu_seqlens_q,
|
|
|
+ cu_seqlens_k,
|
|
|
+ max_seqlen_q,
|
|
|
+ max_seqlen_k,
|
|
|
+ q,
|
|
|
+ k,
|
|
|
+ v,
|
|
|
+ output_pad_fn,
|
|
|
+ dq_pad_fn,
|
|
|
+ dk_pad_fn,
|
|
|
+ ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False)
|
|
|
+ out = flash_attn_varlen_func(
|
|
|
+ q_unpad,
|
|
|
+ k_unpad,
|
|
|
+ v_unpad,
|
|
|
+ cu_seqlens_q,
|
|
|
+ cu_seqlens_k,
|
|
|
+ max_seqlen_q,
|
|
|
+ max_seqlen_k,
|
|
|
+ 0.0,
|
|
|
+ causal=causal,
|
|
|
+ window_size=window_size,
|
|
|
+ deterministic=True,
|
|
|
+ )
|
|
|
+
|
|
|
+ g = torch.randn_like(out)
|
|
|
+ dq0, dk0, dv0 = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True)
|
|
|
+ for _ in range(50):
|
|
|
+ dq, dk, dv = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True)
|
|
|
+ assert torch.equal(dv, dv0)
|
|
|
+ assert torch.equal(dk, dk0)
|
|
|
+ assert torch.equal(dq, dq0)
|
|
|
+
|
rocking