5018ac6ac5
* base version * restructure pipelines, add special fp8 epilogue * add variants * add fp8 causal and modify dynamic tile scheduler * better causal schedule * maintain two schedules for non causal and causal * removing macros * fix regression * clean up unneeded methods and variants * fix mistake with NumProducerThreads * base version * restructure pipelines, add special fp8 epilogue * add variants * add fp8 causal and modify dynamic tile scheduler * better causal schedule * maintain two schedules for non causal and causal * removing macros * fix regression * clean up unneeded methods and variants * fix mistake with NumProducerThreads * use seqlen traits * add fp8 .cu files and benchmark script * fix merge issue * fix merge issue * fix merge issue * remove duplicate code * fix regression with varseqlen * move varseqlen init in constexpr * fix test script * more constexpr on varseqlen and add max offset * add back test cases
331 lines
12 KiB
Python
331 lines
12 KiB
Python
import math
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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 flash_attn_func, flash_attn_varlen_func
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from tests.test_util import generate_random_padding_mask, generate_qkv, construct_local_mask, attention_ref
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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.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", [True])
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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", [128])
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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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(257, 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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],
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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, seqlen_k, d, causal, mha_type, dtype,
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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.float16
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else:
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dtype_init = dtype
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print(dtype)
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# set seed
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torch.random.manual_seed(0)
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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 = 2
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# batch_size = 9
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# nheads = 6
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q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype_init, requires_grad=True)
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k = torch.randn(batch_size, seqlen_k, nheads_kv, d, device=device, dtype=dtype_init, requires_grad=True)
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v = torch.randn(batch_size, seqlen_k, nheads_kv, d, device=device, dtype=dtype_init, requires_grad=True)
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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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out, lse = flash_attn_func(q, k, v, causal=causal)
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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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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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)
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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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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:
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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 * (out_pt - out_ref).abs().max().item()
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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() <= 40 * (out_pt - out_ref).abs().max().item()
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# if d <= 128:
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# assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item()
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# assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item()
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# assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item()
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@pytest.mark.parametrize("dtype", [torch.float16])
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@pytest.mark.parametrize("causal", [False, True])
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@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
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# @pytest.mark.parametrize('causal', [True])
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# @pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
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# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
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# @pytest.mark.parametrize('d', [128])
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@pytest.mark.parametrize("d", [64, 128, 256])
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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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(1, 3),
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(2, 1),
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(511, 1),
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(3, 513),
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(64, 128),
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(113, 203),
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(128, 128),
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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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(512, 256),
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(640, 128),
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(1024, 1024),
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(1023, 1024),
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(1024, 1023),
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(2048, 2048),
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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_varlen_output(
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seqlen_q, seqlen_k, d, causal, mha_type, dtype
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):
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if (
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max(seqlen_q, seqlen_k) >= 2048
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and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
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):
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pytest.skip() # Reference implementation OOM
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device = "cuda"
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# set seed
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torch.random.manual_seed(0)
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# batch_size = 1
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# nheads = 1
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batch_size = 9
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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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q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
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k = torch.randn(
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batch_size, seqlen_k, nheads_kv, 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_kv, d, device=device, dtype=dtype, requires_grad=True
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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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# key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode='full')
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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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# print("cu_seqlens_q: ", cu_seqlens_q)
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# print("cu_seqlens_k: ", cu_seqlens_k)
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# print("q_unpad, shape: ", q_unpad.shape)
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# print("k_unpad, shape: ", k_unpad.shape)
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# print("v_unpad, shape: ", v_unpad.shape)
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out_unpad, sm_lse = flash_attn_varlen_func(
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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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causal=causal,
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)
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out = output_pad_fn(out_unpad)
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dropout_mask = None
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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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causal=causal,
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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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causal=causal,
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upcast=False,
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reorder_ops=True,
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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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# g = torch.randn_like(out)
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# if d <= 128:
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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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# 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,
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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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# dq = dq_pad_fn(dq_unpad)
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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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# 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()
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# if d <= 128:
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# assert (dq - dq_ref).abs().max().item() < 1e-4 or (dq - dq_ref).abs().max().item() <= 3 * (dq_pt - dq_ref).abs().max().item()
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# assert (dk - dk_ref).abs().max().item() < 1e-4 or (dk - dk_ref).abs().max().item() <= 3 * (dk_pt - dk_ref).abs().max().item()
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# assert (dk - dk_ref).abs().max().item() < 1e-4 or (dv - dv_ref).abs().max().item() <= 3 * (dv_pt - dv_ref).abs().max().item()
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