1188 lines
36 KiB
Python
1188 lines
36 KiB
Python
import math
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import einops
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import pytest
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import torch
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from flash_attn_interface import (
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_flash_attn_forward,
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flash_attn_func,
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flash_attn_varlen_func,
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)
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from tests.test_util import (
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attention_ref,
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construct_local_mask,
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generate_qkv,
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generate_random_padding_mask,
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)
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ABS_TOL = 5e-3
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REL_TOL = 1e-1
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def print_diffs(out, out_ref):
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out_1d = out.flatten()
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out_ref_1d = out_ref.flatten()
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for idx, (e_o, e_o_ref) in enumerate(zip(out_1d, out_ref_1d)):
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diff = e_o - e_o_ref
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abs_diff = abs(diff)
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abs_ref = abs(e_o_ref + 1e-5)
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relative_diff = abs_diff / abs_ref
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if abs_diff > ABS_TOL or relative_diff > REL_TOL:
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print(f"==== diff ==== {idx}, test: {e_o}, ref: {e_o_ref}")
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@pytest.mark.parametrize("dtype", [torch.float8_e4m3fn])
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@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
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@pytest.mark.parametrize("causal", [False, True])
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@pytest.mark.parametrize("local", [False, True])
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@pytest.mark.parametrize("deterministic", [True])
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@pytest.mark.parametrize("gqa_parallel", [False, True])
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@pytest.mark.parametrize("d", [64, 128, 256])
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# @pytest.mark.parametrize("descale", [1.0])
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@pytest.mark.parametrize("descale", [1.0, 2.0, 3.0])
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@pytest.mark.parametrize(
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"seqlen_q,seqlen_k",
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[
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(1, 1),
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(64, 128),
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(128, 128),
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(256, 256),
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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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(384, 256),
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(640, 128),
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(512, 256),
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(1024, 1024),
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(1023, 1024),
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(1024, 1023),
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(4096, 4096),
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(4224, 4224),
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],
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)
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def test_flash_attn_output_fp8(
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seqlen_q,
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seqlen_k,
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d,
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causal,
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local,
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deterministic,
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mha_type,
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dtype,
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descale,
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gqa_parallel,
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):
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device = "cuda"
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dtype_init = torch.bfloat16
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print(dtype)
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print("causal", causal)
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print("local", local)
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print("gqa_parallel", gqa_parallel)
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# set seed
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torch.random.manual_seed(42)
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# batch_size = 40
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# nheads = 16
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batch_size = 4
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nheads = 6
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nheads_kv = 6 if mha_type == "mha" else (2 if mha_type == "gqa" else 1)
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# nheads_kv = 1
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# batch_size = 9
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# nheads = 6
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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(
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batch_size,
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seqlen_q,
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nheads,
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d,
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device=device,
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dtype=dtype_init,
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requires_grad=True,
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)
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k = torch.randn(
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batch_size,
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seqlen_k,
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nheads_kv,
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d,
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device=device,
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dtype=dtype_init,
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requires_grad=True,
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)
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v = torch.randn(
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batch_size,
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seqlen_k,
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nheads_kv,
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d,
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device=device,
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dtype=dtype_init,
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requires_grad=True,
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)
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q = q.to(dtype)
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k = k.to(dtype)
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v = v.to(dtype)
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softmax_scale = q.shape[-1] ** (-0.5)
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descale_q = torch.tensor([descale], dtype=torch.float32, device="cuda")
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descale_k = torch.tensor([descale], dtype=torch.float32, device="cuda")
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descale_v = torch.tensor([descale], dtype=torch.float32, device="cuda")
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out, lse = flash_attn_func(
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q,
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k,
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v,
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causal=causal,
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window_size=window_size,
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deterministic=deterministic,
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gqa_parallel=gqa_parallel,
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descale_q=descale_q,
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descale_k=descale_k,
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descale_v=descale_v,
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)
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q = q.to(dtype_init)
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k = k.to(dtype_init)
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v = v.to(dtype_init)
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descale_q = descale_q.to(dtype_init)
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descale_k = descale_k.to(dtype_init)
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descale_v = descale_v.to(dtype_init)
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q = q * descale_q
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k = k * descale_k
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v = v * descale_v
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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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None,
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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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None,
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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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# qk = torch.einsum('bshd,bthd->bhst', q, k).float()
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# m = qk.amax(-1, keepdim=True)
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# s_tmp = torch.exp((qk - m) / math.sqrt(d))
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# exp_sum = s_tmp.sum(-1)
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# qk = torch.einsum('bthd,bshd->bhts', q.float() / math.sqrt(d), k.float())
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# lse_ref = torch.logsumexp(qk, dim=-1)
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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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# if not causal:
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# print(f"LSE max diff: {(lse - lse_ref).abs().max().item()}")
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# breakpoint()
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# dS = torch.einsum('bthd,bshd->bhts', g.float(), v.float())
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# P = torch.softmax(qk, -1)
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# dP = P * (dS - do_o.unsqueeze(1))
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# dQ = torch.einsum('bhts,bshd->bthd', dP, k.float())
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# dV = torch.einsum('bhts,bthd->bshd', P, g.float())
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# dK = torch.einsum('bhts,bthd->bshd', dP, q.float())
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# breakpoint()
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# assert (out - out_ref).abs().max().item() <= 4 * (out_pt - out_ref).abs().max().item() + 1e-2
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atol = 4 * (out_pt - out_ref).abs().max().item() + 1e-2
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torch.testing.assert_close(out, out_ref, rtol=1e-2, atol=atol, check_dtype=False)
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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# @pytest.mark.parametrize("dtype", [torch.float8_e4m3fn])
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@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
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# @pytest.mark.parametrize("mha_type", ["mha"])
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@pytest.mark.parametrize("causal", [False, True])
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# @pytest.mark.parametrize("causal", [False])
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@pytest.mark.parametrize("local", [False, True])
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# @pytest.mark.parametrize("local", [True])
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@pytest.mark.parametrize("deterministic", [False, True])
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# @pytest.mark.parametrize("deterministic", [True])
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@pytest.mark.parametrize("gqa_parallel", [False, True])
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# @pytest.mark.parametrize("gqa_parallel", [False])
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# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
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# @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192])
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# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192])
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# @pytest.mark.parametrize('d', [56, 80])
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# @pytest.mark.parametrize("d", [64, 128, 256])
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# @pytest.mark.parametrize("d", [64, 96, 128])
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# @pytest.mark.parametrize("d", [64])
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@pytest.mark.parametrize("d", [64, 128, 256])
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@pytest.mark.parametrize("descale", [1.0])
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# @pytest.mark.parametrize("descale", [1.0, 2.0, 3.0, 4.0])
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@pytest.mark.parametrize(
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"seqlen_q,seqlen_k",
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[
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(1, 1),
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(64, 128),
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(128, 128),
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(256, 256),
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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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(384, 256),
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(640, 128),
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(512, 256),
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(1024, 1024),
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(1023, 1024),
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(1024, 1023),
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(4096, 4096),
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(4224, 4224),
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],
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)
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# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)])
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def test_flash_attn_output(
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seqlen_q,
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seqlen_k,
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d,
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causal,
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local,
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deterministic,
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mha_type,
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dtype,
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descale,
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gqa_parallel,
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):
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device = "cuda"
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if dtype == torch.float8_e4m3fn:
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dtype_init = torch.bfloat16
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else:
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dtype_init = dtype
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print(dtype)
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print("causal", causal)
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print("local", local)
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print("gqa_parallel", gqa_parallel)
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# set seed
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torch.random.manual_seed(42)
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# batch_size = 40
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# nheads = 16
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batch_size = 4
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nheads = 6
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nheads_kv = 6 if mha_type == "mha" else (2 if mha_type == "gqa" else 1)
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# nheads_kv = 1
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# batch_size = 9
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# nheads = 6
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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(
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batch_size,
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seqlen_q,
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nheads,
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d,
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device=device,
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dtype=dtype_init,
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requires_grad=True,
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)
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k = torch.randn(
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batch_size,
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seqlen_k,
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nheads_kv,
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d,
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device=device,
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dtype=dtype_init,
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requires_grad=True,
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)
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v = torch.randn(
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batch_size,
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seqlen_k,
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nheads_kv,
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d,
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device=device,
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dtype=dtype_init,
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requires_grad=True,
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)
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q = q.to(dtype)
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k = k.to(dtype)
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v = v.to(dtype)
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softmax_scale = q.shape[-1] ** (-0.5)
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descale_q = torch.tensor([descale], dtype=torch.float32, device="cuda")
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descale_k = torch.tensor([descale], dtype=torch.float32, device="cuda")
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descale_v = torch.tensor([descale], dtype=torch.float32, device="cuda")
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if dtype != torch.float8_e4m3fn:
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out, lse = flash_attn_func(
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q,
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k,
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v,
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causal=causal,
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window_size=window_size,
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deterministic=deterministic,
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gqa_parallel=gqa_parallel,
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)
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else:
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out, lse = flash_attn_func(
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q,
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k,
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v,
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causal=causal,
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window_size=window_size,
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deterministic=deterministic,
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gqa_parallel=gqa_parallel,
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descale_q=descale_q,
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descale_k=descale_k,
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descale_v=descale_v,
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)
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q = q.to(dtype_init)
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k = k.to(dtype_init)
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v = v.to(dtype_init)
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if dtype == torch.float8_e4m3fn:
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descale_q = descale_q.to(dtype_init)
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descale_k = descale_k.to(dtype_init)
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descale_v = descale_v.to(dtype_init)
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q = q * descale_q
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k = k * descale_k
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v = v * descale_v
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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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None,
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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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None,
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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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# qk = torch.einsum('bshd,bthd->bhst', q, k).float()
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# m = qk.amax(-1, keepdim=True)
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# s_tmp = torch.exp((qk - m) / math.sqrt(d))
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# exp_sum = s_tmp.sum(-1)
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# qk = torch.einsum('bthd,bshd->bhts', q.float() / math.sqrt(d), k.float())
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# lse_ref = torch.logsumexp(qk, dim=-1)
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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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# if not causal:
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# print(f"LSE max diff: {(lse - lse_ref).abs().max().item()}")
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# breakpoint()
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if d <= 128 and dtype != torch.float8_e4m3fn:
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g = torch.randn_like(out)
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do_o = (g.float() * out.float()).sum(-1)
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dq, dk, dv = torch.autograd.grad(out, (q, k, v), g)
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dq_ref, dk_ref, dv_ref = torch.autograd.grad(out_ref, (q, k, v), g)
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dq_pt, dk_pt, dv_pt = 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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# dS = torch.einsum('bthd,bshd->bhts', g.float(), v.float())
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# P = torch.softmax(qk, -1)
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# dP = P * (dS - do_o.unsqueeze(1))
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# dQ = torch.einsum('bhts,bshd->bthd', dP, k.float())
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# dV = torch.einsum('bhts,bthd->bshd', P, g.float())
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# dK = torch.einsum('bhts,bthd->bshd', dP, q.float())
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# breakpoint()
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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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# breakpoint()
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if dtype != torch.float8_e4m3fn:
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assert (out - out_ref).abs().max().item() <= 2 * (
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out_pt - out_ref
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).abs().max().item() + 3e-5
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else:
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# just test correctness of fp8 kernel w/o further quantization techniques
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assert (out - out_ref).abs().max().item() <= 4 * (
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out_pt - out_ref
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).abs().max().item() + 2e-2
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if d <= 128 and dtype != torch.float8_e4m3fn:
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assert (dq - dq_ref).abs().max().item() <= 2 * (
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dq_pt - dq_ref
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).abs().max().item() + 3e-5
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assert (dk - dk_ref).abs().max().item() <= 2 * (
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dk_pt - dk_ref
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).abs().max().item() + 3e-5
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assert (dv - dv_ref).abs().max().item() <= 2 * (
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dv_pt - dv_ref
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).abs().max().item() + 3e-5
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|
|
|
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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|
# @pytest.mark.parametrize("dtype", [torch.float16])
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@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
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# @pytest.mark.parametrize("mha_type", ["mha"])
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|
@pytest.mark.parametrize("causal", [False, True])
|
|
# @pytest.mark.parametrize("causal", [True])
|
|
@pytest.mark.parametrize("local", [False, True])
|
|
# @pytest.mark.parametrize("local", [False])
|
|
@pytest.mark.parametrize("deterministic", [False, True])
|
|
# @pytest.mark.parametrize("deterministic", [False])
|
|
@pytest.mark.parametrize("add_unused_qkv", [False, True])
|
|
# @pytest.mark.parametrize("add_unused_qkv", [True])
|
|
# @pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize('d', [256])
|
|
# @pytest.mark.parametrize("d", [64, 128, 256])
|
|
@pytest.mark.parametrize("d", [64, 128])
|
|
# @pytest.mark.parametrize("d", [128])
|
|
@pytest.mark.parametrize(
|
|
"seqlen_q,seqlen_k",
|
|
[
|
|
(1, 1),
|
|
(1, 3),
|
|
(2, 1),
|
|
(511, 1),
|
|
(3, 513),
|
|
(64, 128),
|
|
(113, 203),
|
|
(128, 128),
|
|
(128, 217),
|
|
(113, 211),
|
|
(108, 256),
|
|
(256, 512),
|
|
(384, 256),
|
|
(512, 256),
|
|
(640, 128),
|
|
(1024, 1024),
|
|
(1023, 1024),
|
|
(1024, 1023),
|
|
(2048, 2048),
|
|
],
|
|
)
|
|
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)])
|
|
def test_flash_attn_varlen_output(
|
|
seqlen_q, seqlen_k, d, causal, local, deterministic, add_unused_qkv, mha_type, 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
|
|
device = "cuda"
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
# batch_size = 1
|
|
# nheads = 1
|
|
# nheads_kv = 1
|
|
batch_size = 9
|
|
nheads = 6
|
|
nheads_kv = 6 if mha_type == "mha" else (2 if mha_type == "gqa" else 1)
|
|
|
|
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_kv,
|
|
d,
|
|
device=device,
|
|
dtype=dtype,
|
|
requires_grad=True,
|
|
)
|
|
v = torch.randn(
|
|
batch_size,
|
|
seqlen_k,
|
|
nheads_kv,
|
|
d,
|
|
device=device,
|
|
dtype=dtype,
|
|
requires_grad=True,
|
|
)
|
|
|
|
query_padding_mask = generate_random_padding_mask(
|
|
seqlen_q, batch_size, device, mode="random", zero_lengths=False
|
|
)
|
|
key_padding_mask = generate_random_padding_mask(
|
|
seqlen_k, batch_size, device, mode="random", zero_lengths=True
|
|
)
|
|
# key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode='full')
|
|
|
|
def _gen_unused_masks(padding_mask, add_unused, max_seq_len, bs, device):
|
|
if add_unused:
|
|
another_mask = generate_random_padding_mask(max_seq_len, bs, device)
|
|
attn_mask = torch.logical_and(padding_mask, another_mask)
|
|
unused_mask = torch.logical_xor(
|
|
torch.logical_or(padding_mask, another_mask), attn_mask
|
|
)
|
|
else:
|
|
attn_mask = padding_mask
|
|
unused_mask = None
|
|
return attn_mask, unused_mask
|
|
|
|
query_padding_mask, query_unused_mask = _gen_unused_masks(
|
|
query_padding_mask, add_unused_qkv, seqlen_q, batch_size, q.device
|
|
)
|
|
key_padding_mask, key_unused_mask = _gen_unused_masks(
|
|
key_padding_mask, add_unused_qkv, seqlen_k, batch_size, k.device
|
|
)
|
|
|
|
(
|
|
q_unpad,
|
|
k_unpad,
|
|
v_unpad,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
seqused_q,
|
|
seqused_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,
|
|
query_unused_mask=query_unused_mask,
|
|
key_unused_mask=key_unused_mask,
|
|
)
|
|
# print("cu_seqlens_q: ", cu_seqlens_q)
|
|
# print("cu_seqlens_k: ", cu_seqlens_k)
|
|
# print("q_unpad, shape: ", q_unpad.shape)
|
|
# print("k_unpad, shape: ", k_unpad.shape)
|
|
# print("v_unpad, shape: ", v_unpad.shape)
|
|
out_unpad, sm_lse = flash_attn_varlen_func(
|
|
q_unpad,
|
|
k_unpad,
|
|
v_unpad,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
max_seqlen_q,
|
|
max_seqlen_k,
|
|
causal=causal,
|
|
deterministic=deterministic,
|
|
seqused_q=seqused_q,
|
|
seqused_k=seqused_k,
|
|
window_size=window_size,
|
|
)
|
|
out = output_pad_fn(out_unpad)
|
|
if query_unused_mask is not None:
|
|
q_zero_masking = rearrange(query_unused_mask, "b s -> b s 1 1")
|
|
out.masked_fill_(q_zero_masking, 0.0)
|
|
dropout_mask = None
|
|
|
|
out_ref, attn_ref = attention_ref(
|
|
q,
|
|
k,
|
|
v,
|
|
query_padding_mask,
|
|
key_padding_mask,
|
|
causal=causal,
|
|
window_size=window_size,
|
|
)
|
|
out_pt, attn_pt = attention_ref(
|
|
q,
|
|
k,
|
|
v,
|
|
query_padding_mask,
|
|
key_padding_mask,
|
|
causal=causal,
|
|
window_size=window_size,
|
|
upcast=False,
|
|
reorder_ops=True,
|
|
)
|
|
|
|
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()}")
|
|
|
|
g = torch.randn_like(out)
|
|
if d <= 128:
|
|
(
|
|
dq_unpad,
|
|
dk_unpad,
|
|
dv_unpad,
|
|
) = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g)
|
|
dk = dk_pad_fn(dk_unpad)
|
|
dv = dk_pad_fn(dv_unpad)
|
|
if key_unused_mask is not None:
|
|
k_zero_masking = rearrange(key_unused_mask, "b s -> b s 1 1")
|
|
dk.masked_fill_(k_zero_masking, 0.0)
|
|
dv.masked_fill_(k_zero_masking, 0.0)
|
|
(
|
|
dq_ref,
|
|
dk_ref,
|
|
dv_ref,
|
|
) = torch.autograd.grad(out_ref, (q, k, v), g)
|
|
zero_masking = rearrange(
|
|
torch.logical_not(torch.any(key_padding_mask, 1)), "b -> b 1 1 1"
|
|
)
|
|
dk_ref.masked_fill_(zero_masking, 0.0)
|
|
dv_ref.masked_fill_(zero_masking, 0.0)
|
|
(
|
|
dq_pt,
|
|
dk_pt,
|
|
dv_pt,
|
|
) = torch.autograd.grad(out_pt, (q, k, v), g)
|
|
dk_pt.masked_fill_(zero_masking, 0.0)
|
|
dv_pt.masked_fill_(zero_masking, 0.0)
|
|
dq = dq_pad_fn(dq_unpad)
|
|
if query_unused_mask is not None:
|
|
dq.masked_fill_(q_zero_masking, 0.0)
|
|
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()}")
|
|
|
|
# Check that FlashAttention's numerical error is at most twice the numerical error
|
|
# of a Pytorch implementation.
|
|
assert (out - out_ref).abs().max().item() <= 2 * (
|
|
out_pt - out_ref
|
|
).abs().max().item()
|
|
|
|
if d <= 128:
|
|
assert (dq - dq_ref).abs().max().item() < 1e-4 or (
|
|
dq - dq_ref
|
|
).abs().max().item() <= 3 * (dq_pt - dq_ref).abs().max().item()
|
|
assert (dk - dk_ref).abs().max().item() < 1e-4 or (
|
|
dk - dk_ref
|
|
).abs().max().item() <= 3 * (dk_pt - dk_ref).abs().max().item()
|
|
assert (dv - dv_ref).abs().max().item() < 1e-4 or (
|
|
dv - dv_ref
|
|
).abs().max().item() <= 3 * (dv_pt - dv_ref).abs().max().item()
|
|
|
|
|
|
@pytest.mark.parametrize("dtype", [torch.bfloat16])
|
|
# @pytest.mark.parametrize("dtype", [torch.float16])
|
|
@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
|
|
@pytest.mark.parametrize("causal", [False, True])
|
|
# @pytest.mark.parametrize("causal", [False])
|
|
@pytest.mark.parametrize("deterministic", [True, False])
|
|
# @pytest.mark.parametrize("deterministic", [False])
|
|
# @pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize('d', [128])
|
|
# @pytest.mark.parametrize("d", [64, 128, 256])
|
|
@pytest.mark.parametrize("d", [128, 64])
|
|
# @pytest.mark.parametrize("d", [128])
|
|
@pytest.mark.parametrize(
|
|
"seqlen_q,seqlen_k",
|
|
[
|
|
# (1, 1),
|
|
# (1, 3),
|
|
# (2, 1),
|
|
# (511, 1),
|
|
# (3, 513),
|
|
# (64, 128),
|
|
# (113, 203),
|
|
# (128, 128),
|
|
# (128, 217),
|
|
# (113, 211),
|
|
# (108, 256),
|
|
(256, 512),
|
|
# (384, 256),
|
|
(768, 512),
|
|
# (512, 256),
|
|
# (640, 128),
|
|
(1024, 1024),
|
|
# (1023, 1024),
|
|
# (1024, 1023),
|
|
# (2048, 2048),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("add_unused_qkv", [False])
|
|
@pytest.mark.parametrize("shuffle_pages", [True, False])
|
|
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)])
|
|
def test_flash_attn_paged1(
|
|
seqlen_q,
|
|
seqlen_k,
|
|
d,
|
|
causal,
|
|
deterministic,
|
|
add_unused_qkv,
|
|
mha_type,
|
|
dtype,
|
|
shuffle_pages,
|
|
):
|
|
run_conf = locals()
|
|
if (
|
|
max(seqlen_q, seqlen_k) >= 2048
|
|
and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
|
|
):
|
|
pytest.skip() # Reference implementation OOM
|
|
device = "cuda"
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
# batch_size = 1
|
|
# nheads = 1
|
|
batch_size = 9
|
|
nheads = 6
|
|
nheads_kv = 6 if mha_type == "mha" else (2 if mha_type == "gqa" else 1)
|
|
|
|
q = torch.randn(
|
|
batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True
|
|
)
|
|
|
|
page_size = 256
|
|
num_pages = batch_size * seqlen_k // page_size
|
|
assert seqlen_k % page_size == 0, "Max seqlen must be divisible by page size"
|
|
block_table = torch.reshape(
|
|
torch.arange(num_pages, dtype=torch.int32, device=device), (batch_size, -1)
|
|
)
|
|
|
|
k_paged = torch.randn(
|
|
num_pages,
|
|
page_size,
|
|
nheads_kv,
|
|
d,
|
|
device=device,
|
|
dtype=dtype,
|
|
requires_grad=True,
|
|
)
|
|
v_paged = torch.randn(
|
|
num_pages,
|
|
page_size,
|
|
nheads_kv,
|
|
d,
|
|
device=device,
|
|
dtype=dtype,
|
|
requires_grad=True,
|
|
)
|
|
|
|
if shuffle_pages:
|
|
block_table = torch.randperm(num_pages, dtype=torch.int32, device=device).view(
|
|
batch_size, -1
|
|
)
|
|
k = torch.index_select(k_paged, 0, block_table.view(-1)).view(
|
|
batch_size, seqlen_k, nheads_kv, d
|
|
)
|
|
v = torch.index_select(v_paged, 0, block_table.view(-1)).view(
|
|
batch_size, seqlen_k, nheads_kv, d
|
|
)
|
|
else:
|
|
k = torch.reshape(k_paged, (batch_size, seqlen_k, nheads_kv, d))
|
|
v = torch.reshape(v_paged, (batch_size, seqlen_k, nheads_kv, d))
|
|
|
|
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"
|
|
)
|
|
# key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode='full')
|
|
|
|
def _gen_unused_masks(padding_mask, add_unused, max_seq_len, bs, device):
|
|
if add_unused:
|
|
another_mask = generate_random_padding_mask(max_seq_len, bs, device)
|
|
attn_mask = torch.logical_and(padding_mask, another_mask)
|
|
unused_mask = torch.logical_xor(
|
|
torch.logical_or(padding_mask, another_mask), attn_mask
|
|
)
|
|
else:
|
|
attn_mask = padding_mask
|
|
unused_mask = None
|
|
return attn_mask, unused_mask
|
|
|
|
query_padding_mask, query_unused_mask = _gen_unused_masks(
|
|
query_padding_mask, add_unused_qkv, seqlen_q, batch_size, q.device
|
|
)
|
|
key_padding_mask, key_unused_mask = _gen_unused_masks(
|
|
key_padding_mask, add_unused_qkv, seqlen_k, batch_size, k.device
|
|
)
|
|
|
|
(
|
|
q_unpad,
|
|
k_unpad,
|
|
v_unpad,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
seqused_q,
|
|
seqused_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,
|
|
query_unused_mask=query_unused_mask,
|
|
key_unused_mask=key_unused_mask,
|
|
)
|
|
# print("cu_seqlens_q: ", cu_seqlens_q)
|
|
# print("cu_seqlens_k: ", cu_seqlens_k)
|
|
# print("q_unpad, shape: ", q_unpad.shape)
|
|
# print("k_unpad, shape: ", k_unpad.shape)
|
|
# print("v_unpad, shape: ", v_unpad.shape)
|
|
|
|
out_unpad, sm_lse = flash_attn_varlen_func(
|
|
q_unpad,
|
|
k_paged,
|
|
v_paged,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
max_seqlen_q,
|
|
max_seqlen_k,
|
|
causal=causal,
|
|
deterministic=deterministic,
|
|
block_table=block_table,
|
|
)
|
|
out = output_pad_fn(out_unpad)
|
|
|
|
out_unpaged_unpad, sm_unpaged_lse = flash_attn_varlen_func(
|
|
q_unpad,
|
|
k_unpad,
|
|
v_unpad,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
max_seqlen_q,
|
|
max_seqlen_k,
|
|
causal=causal,
|
|
deterministic=deterministic,
|
|
)
|
|
out_unpaged = output_pad_fn(out_unpaged_unpad)
|
|
|
|
dropout_mask = None
|
|
|
|
out_ref, attn_ref = attention_ref(
|
|
q,
|
|
k,
|
|
v,
|
|
query_padding_mask,
|
|
key_padding_mask,
|
|
causal=causal,
|
|
)
|
|
out_pt, attn_pt = attention_ref(
|
|
q,
|
|
k,
|
|
v,
|
|
query_padding_mask,
|
|
key_padding_mask,
|
|
causal=causal,
|
|
upcast=False,
|
|
reorder_ops=True,
|
|
)
|
|
# print(f"{k.stride()=}, {v.stride()=}, {k_paged.stride()=}, {v_paged.stride()=}, {block_table.stride()=}")
|
|
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()}")
|
|
|
|
print(f"Output max diff paged vs varlen: {(out - out_unpaged).abs().max().item()}")
|
|
print(
|
|
f"Output mean diff paged vs varlen: {(out - out_unpaged).abs().mean().item()}"
|
|
)
|
|
|
|
# Check that FlashAttention's numerical error is at most twice the numerical error
|
|
# of a Pytorch implementation.
|
|
# import fbvscode; fbvscode.set_trace()
|
|
assert (out - out_ref).abs().max().item() <= 2 * (
|
|
out_pt - out_ref
|
|
).abs().max().item()
|
|
|
|
|
|
@pytest.mark.parametrize("dtype", ([torch.bfloat16]))
|
|
# @pytest.mark.parametrize("dtype", [torch.bfloat16])
|
|
@pytest.mark.parametrize("local", [False])
|
|
# @pytest.mark.parametrize("local", [True])
|
|
@pytest.mark.parametrize(
|
|
"d", [128, 64]
|
|
) # [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192])
|
|
# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192])
|
|
# @pytest.mark.parametrize('d', [56, 80])
|
|
# @pytest.mark.parametrize("d", [64])
|
|
@pytest.mark.parametrize("swap_sq_sk", [False, True])
|
|
# @pytest.mark.parametrize("swap_sq_sk", [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),
|
|
],
|
|
)
|
|
# TODO: add smaller page sizes when https://github.com/Dao-AILab/flash-attention/pull/824 is merged
|
|
@pytest.mark.parametrize("paged_kv_block_size", [256, 512])
|
|
# @pytest.mark.parametrize("seqlen_q,seqlen_k", [(256, 128)])
|
|
def test_flash_attn_varlen_paged2(
|
|
seqlen_q, seqlen_k, swap_sq_sk, d, local, paged_kv_block_size, dtype
|
|
):
|
|
# Test ported from FlashAttention V2 test test_flash_attn_varlen_causal
|
|
|
|
def _generate_block_kvcache(
|
|
seqlen_k, paged_kv_block_size, batch_size, nheads_k, d, device, dtype
|
|
):
|
|
num_blocks = math.ceil(seqlen_k / paged_kv_block_size) * batch_size * 3
|
|
k_cache_paged = torch.randn(
|
|
num_blocks, paged_kv_block_size, nheads_k, d, device=device, dtype=dtype
|
|
)
|
|
v_cache_paged = torch.randn(
|
|
num_blocks, paged_kv_block_size, nheads_k, d, device=device, dtype=dtype
|
|
)
|
|
block_table = rearrange(
|
|
torch.randperm(num_blocks, dtype=torch.int32, device=device),
|
|
"(b nblocks) -> b nblocks",
|
|
b=batch_size,
|
|
)
|
|
k_cache = rearrange(
|
|
# pytorch 1.12 doesn't have indexing with int32
|
|
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 = rearrange(
|
|
v_cache_paged[block_table.to(dtype=torch.long).flatten()],
|
|
"(b nblocks) block_size ... -> b (nblocks block_size) ...",
|
|
b=batch_size,
|
|
)[:, :seqlen_k]
|
|
return k_cache, v_cache, block_table, k_cache_paged, v_cache_paged, num_blocks
|
|
|
|
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"
|
|
causal = True
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 8
|
|
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
|
|
)
|
|
|
|
if paged_kv_block_size is None:
|
|
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,
|
|
)
|
|
block_table = None
|
|
else:
|
|
k, v, block_table, k_cache_paged, v_cache_paged, num_blocks = (
|
|
_generate_block_kvcache(
|
|
seqlen_k, paged_kv_block_size, batch_size, nheads, d, device, dtype
|
|
)
|
|
)
|
|
|
|
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"
|
|
)
|
|
|
|
def _gen_unused_masks(padding_mask, add_unused, max_seq_len, bs, device):
|
|
if add_unused:
|
|
another_mask = generate_random_padding_mask(max_seq_len, bs, device)
|
|
attn_mask = torch.logical_and(padding_mask, another_mask)
|
|
unused_mask = torch.logical_xor(
|
|
torch.logical_or(padding_mask, another_mask), attn_mask
|
|
)
|
|
else:
|
|
attn_mask = padding_mask
|
|
unused_mask = None
|
|
return attn_mask, unused_mask
|
|
|
|
query_padding_mask, query_unused_mask = _gen_unused_masks(
|
|
query_padding_mask, False, seqlen_q, batch_size, q.device
|
|
)
|
|
key_padding_mask, key_unused_mask = _gen_unused_masks(
|
|
key_padding_mask, False, seqlen_k, batch_size, k.device
|
|
)
|
|
(
|
|
q_unpad,
|
|
k_unpad,
|
|
v_unpad,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
seqused_q,
|
|
seqused_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_unpad, sm_lse = flash_attn_varlen_func(
|
|
q_unpad,
|
|
k_unpad if paged_kv_block_size is None else k_cache_paged,
|
|
v_unpad if paged_kv_block_size is None else v_cache_paged,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
max_seqlen_q,
|
|
max_seqlen_k,
|
|
causal=causal,
|
|
block_table=block_table,
|
|
)
|
|
out = output_pad_fn(out_unpad)
|
|
out_ref, attn_ref = attention_ref(
|
|
q,
|
|
k,
|
|
v,
|
|
query_padding_mask,
|
|
key_padding_mask,
|
|
None,
|
|
0.0,
|
|
None,
|
|
causal=causal,
|
|
window_size=window_size,
|
|
)
|
|
out_pt, attn_pt = attention_ref(
|
|
q,
|
|
k,
|
|
v,
|
|
query_padding_mask,
|
|
key_padding_mask,
|
|
None,
|
|
0.0,
|
|
None,
|
|
causal=causal,
|
|
window_size=window_size,
|
|
upcast=False,
|
|
reorder_ops=True,
|
|
)
|
|
|
|
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()}")
|
|
|
|
g = torch.randn_like(out)
|
|
do_o = (g.float() * out.float()).sum(-1)
|
|
test_backward = block_table is None
|
|
if test_backward:
|
|
(
|
|
dq_unpad,
|
|
dk_unpad,
|
|
dv_unpad,
|
|
) = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g)
|
|
dq = dq_pad_fn(dq_unpad)
|
|
dk = dk_pad_fn(dk_unpad)
|
|
dv = dk_pad_fn(dv_unpad)
|
|
(
|
|
dq_ref,
|
|
dk_ref,
|
|
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()}")
|
|
|
|
# Check that FlashAttention's numerical error is at most twice the numerical error
|
|
# of a Pytorch implementation.
|
|
assert (out - out_ref).abs().max().item() <= 2 * (
|
|
out_pt - out_ref
|
|
).abs().max().item() + 1e-5
|
|
|
|
if test_backward:
|
|
assert (dq - dq_ref).abs().max().item() <= 2 * (
|
|
dq_pt - dq_ref
|
|
).abs().max().item() + 1e-5
|
|
assert (dk - dk_ref).abs().max().item() <= 2 * (
|
|
dk_pt - dk_ref
|
|
).abs().max().item() + 1e-5
|
|
assert (dv - dv_ref).abs().max().item() <= 2 * (
|
|
dv_pt - dv_ref
|
|
).abs().max().item() + 1e-5
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_flash_attn_varlen_causal(512, 768, False, 128, False, 256, torch.bfloat16)
|