import math import pytest import torch import torch.nn.functional as F from einops import rearrange, repeat from flash_attn_interface import flash_attn_func, flash_attn_varlen_func from tests.test_util import generate_random_padding_mask, generate_qkv, construct_local_mask, attention_ref ABS_TOL = 5e-3 REL_TOL = 1e-1 def print_diffs(out, out_ref): out_1d = out.flatten() out_ref_1d = out_ref.flatten() for idx, (e_o, e_o_ref) in enumerate(zip(out_1d, out_ref_1d)): diff = e_o - e_o_ref abs_diff = abs(diff) abs_ref = abs(e_o_ref + 1e-5) relative_diff = abs_diff / abs_ref if abs_diff > ABS_TOL or relative_diff > REL_TOL: print(f"==== diff ==== {idx}, test: {e_o}, ref: {e_o_ref}") @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) # @pytest.mark.parametrize("dtype", [torch.bfloat16]) @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"]) # @pytest.mark.parametrize("mha_type", ["gqa"]) @pytest.mark.parametrize("causal", [False, True]) # @pytest.mark.parametrize("causal", [True]) # @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, 128, 256]) # @pytest.mark.parametrize("d", [128]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (257, 1), (64, 128), (128, 128), (256, 256), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (384, 256), (640, 128), (512, 256), (1024, 1024), (1023, 1024), (1024, 1023), (2048, 2048), ], ) # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)]) def test_flash_attn_output( seqlen_q, seqlen_k, d, causal, mha_type, dtype ): device = "cuda" # set seed torch.random.manual_seed(0) # batch_size = 40 # nheads = 16 batch_size = 9 nheads = 6 nheads_kv = 6 if mha_type == "mha" else (2 if mha_type == "gqa" else 1) # nheads_kv = 2 # batch_size = 9 # nheads = 6 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) out, lse = flash_attn_func(q, k, v, causal=causal) out_ref, attn_ref = attention_ref( q, k, v, None, None, causal=causal, ) out_pt, attn_pt = attention_ref( q, k, v, None, None, causal=causal, upcast=False, reorder_ops=True, ) # qk = torch.einsum('bshd,bthd->bhst', q, k).float() # m = qk.amax(-1, keepdim=True) # s_tmp = torch.exp((qk - m) / math.sqrt(d)) # exp_sum = s_tmp.sum(-1) # qk = torch.einsum('bthd,bshd->bhts', q.float() / math.sqrt(d), k.float()) # lse_ref = torch.logsumexp(qk, dim=-1) 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()}") # if not causal: # print(f"LSE max diff: {(lse - lse_ref).abs().max().item()}") # breakpoint() # if d <= 128: # g = torch.randn_like(out) # do_o = (g.float() * out.float()).sum(-1) # dq, dk, dv = torch.autograd.grad(out, (q, k, v), g) # 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()}") # dS = torch.einsum('bthd,bshd->bhts', g.float(), v.float()) # P = torch.softmax(qk, -1) # dP = P * (dS - do_o.unsqueeze(1)) # dQ = torch.einsum('bhts,bshd->bthd', dP, k.float()) # dV = torch.einsum('bhts,bthd->bshd', P, g.float()) # dK = torch.einsum('bhts,bthd->bshd', dP, q.float()) # breakpoint() # Check that FlashAttention's numerical error is at most twice the numerical error # of a Pytorch implementation. # breakpoint() 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() <= 2 * (dq_pt - dq_ref).abs().max().item() # assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item() # assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item() @pytest.mark.parametrize("dtype", [torch.float16]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"]) # @pytest.mark.parametrize('causal', [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', [128]) @pytest.mark.parametrize("d", [64, 128, 256]) @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, 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 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) 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") 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') ( 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) # 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, ) out = output_pad_fn(out_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"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()}") @pytest.mark.parametrize("dtype", [torch.float8_e4m3fn]) # @pytest.mark.parametrize("dtype", [torch.bfloat16]) @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"]) # @pytest.mark.parametrize("mha_type", ["gqa"]) @pytest.mark.parametrize("causal", [False, True]) # @pytest.mark.parametrize("causal", [True]) # @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, 128, 256]) #@pytest.mark.parametrize("d", [128]) # @pytest.mark.parametrize("d", [256]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (64, 128), (128, 128), (256, 256), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (384, 256), (640, 128), (512, 256), (1024, 1024), (1023, 1024), (1024, 1023), (2048, 2048), ], ) def test_flash_attn_output_fp8( seqlen_q, seqlen_k, d, causal, mha_type, dtype ): device = "cuda" # set seed torch.random.manual_seed(0) # batch_size = 40 # nheads = 16 batch_size = 9 nheads = 6 nheads_kv = 6 if mha_type == "mha" else (2 if mha_type == "gqa" else 1) # batch_size = 1 # nheads = 1 q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=torch.float16, requires_grad=True) k = torch.randn(batch_size, seqlen_k, nheads_kv, d, device=device, dtype=torch.float16, requires_grad=True) v = torch.randn(batch_size, seqlen_k, nheads_kv, d, device=device, dtype=torch.float16, requires_grad=True) out, lse = flash_attn_func(q.to(dtype), k.to(dtype), v.to(dtype).transpose(1,3).contiguous().clone(), causal=causal) q = q.to(dtype).to(torch.float16) k = k.to(dtype).to(torch.float16) v = v.to(dtype).to(torch.float16) out_ref, attn_ref = attention_ref( q, k, v, None, None, causal=causal, ) out_pt, attn_pt = attention_ref( q, k, v, None, None, causal=causal, 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()}") assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()