import math import pytest import torch import torch.nn.functional as F from einops import rearrange, repeat from flash_attn import ( flash_attn_func, flash_attn_kvpacked_func, flash_attn_qkvpacked_func, flash_attn_varlen_func, flash_attn_varlen_kvpacked_func, flash_attn_varlen_qkvpacked_func, flash_attn_with_kvcache, ) from test_flash_attn import ( attn_bias_from_alibi_slopes, convert_flash_attn_S_to_softmax, generate_qkv, generate_random_padding_mask, _generate_block_kvcache, attention_ref, attention_kvpacked_ref, attention_qkvpacked_ref, ) from flash_attn.layers.rotary import apply_rotary_emb def is_bwd_hdim_supported(d): return d <= 256 def ck_randval_to_dropout_mask(randval, p): # If p = 0.3, randval in 255 * (0.7, 1.0] will be dropout # randval in 255 * [0, 0.7] will be kept # If return dropout_mask >=0, value will be kept return math.floor(255.0 * (1 - p)) - randval.to(torch.float32) def pad_rearrange_dropout_mask_hts_to_bhss(S_dmask, cu_seqlens_q, seqlen_q_rounded, seqlen_k_rounded): """ pad + rearrange [nheads, total_q, max_seqlen_k] into [b, nheads, seqlen_q_rounded, seqlen_k_rounded] Arguments: S_dmask: (nheads, total_q, max_seqlen_k) cu_seqlens_q: (b + 1) Output: S_dmask: (b, nheads, seqlen_q_rounded, seqlen_k_rounded) """ batch_size = cu_seqlens_q.numel() - 1 seqlens_q = torch.roll(cu_seqlens_q, shifts = -1) - cu_seqlens_q seqlens_q = seqlens_q[0:batch_size].tolist() S_dmask = torch.split(S_dmask, seqlens_q, dim=1) # [(nheads, seqlen_q0, max_seqlen_k), (nheads, seqlen_q1, max_seqlen_k), ..., (nheads, seqlen_qb, max_seqlen_k)] masks = () for mask in S_dmask: # (nheads, seqlen_qi, max_seqlen_k) -> (nheads, seqlen_q_rounded, seqlen_k_rounded) mask = F.pad(mask, (0, seqlen_k_rounded - mask.shape[2], 0, seqlen_q_rounded - mask.shape[1], 0, 0)).unsqueeze(1) masks = masks + (mask, ) S_dmask = torch.cat(masks, dim=1) S_dmask = S_dmask.transpose(0, 1) return S_dmask @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) @pytest.mark.parametrize("deterministic", [False, True]) @pytest.mark.parametrize("alibi", [False, True]) @pytest.mark.parametrize("local", [False, True]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]) @pytest.mark.parametrize("seqlen", [97, 128, 200, 384, 768, 1024, 1025, 2048]) @pytest.mark.parametrize("dropout_p", [0.0, 0.17]) def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, local, alibi, deterministic, dtype): if d > 256: pytest.skip() device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 4 nheads = 9 window_size = (-1, -1) if not local else torch.randint(0, seqlen, (2,)) qkv = torch.randn( batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype, requires_grad=True ) if alibi: alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3 attn_bias = attn_bias_from_alibi_slopes(alibi_slopes, seqlen, seqlen, causal=causal) else: alibi_slopes, attn_bias = None, None out, lse, S_dmask = flash_attn_qkvpacked_func( qkv, dropout_p, causal=causal, window_size=window_size, alibi_slopes=alibi_slopes, deterministic=deterministic, return_attn_probs=True, ) if dropout_p > 0.0: # TODO - move to c++ mha_varlen_fwd() S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p) S_dmask_converted = convert_flash_attn_S_to_softmax( S_dmask, seqlen, seqlen, None, None, d, dropout_p > 0.0, causal=causal, window_size=window_size, ) dropout_mask = S_dmask_converted >= 0 # CK does not return P. Hence, we don't test the attn here. else: dropout_mask = None out_ref, attn_ref = attention_qkvpacked_ref( qkv, None, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size ) out_pt, attn_pt = attention_qkvpacked_ref( qkv, None, attn_bias, dropout_p, dropout_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()}") # 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() g = torch.randn_like(out) if is_bwd_hdim_supported(d): (dqkv,) = torch.autograd.grad(out, qkv, g) (dqkv_ref,) = torch.autograd.grad(out_ref, qkv, g) (dqkv_pt,) = torch.autograd.grad(out_pt, qkv, g) print(f"dQ max diff: {(dqkv[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}") print(f"dK max diff: {(dqkv[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}") print(f"dV max diff: {(dqkv[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}") print(f"dQKV mean diff: {(dqkv - dqkv_ref).abs().mean().item()}") print(f"dQ Pytorch max diff: {(dqkv_pt[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}") print(f"dK Pytorch max diff: {(dqkv_pt[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}") print(f"dV Pytorch max diff: {(dqkv_pt[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}") print(f"dQKV Pytorch mean diff: {(dqkv_pt - dqkv_ref).abs().mean().item()}") # TODO - use 10 times to check, wait for ck to fix bwd precision issue assert (dqkv - dqkv_ref).abs().max().item() <= 10 * (dqkv_pt - dqkv_ref).abs().max().item() @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) @pytest.mark.parametrize("deterministic", [False, True]) @pytest.mark.parametrize("alibi", [False, True]) @pytest.mark.parametrize("local", [False, True]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 128, 160, 192, 224, 256]) @pytest.mark.parametrize("seqlen", [97, 128, 200, 257, 384, 512, 768, 1025, 2048]) @pytest.mark.parametrize("dropout_p", [0, 0.17]) def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, local, alibi, deterministic, dtype): if d > 256: pytest.skip() device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 5 nheads = 6 window_size = (-1, -1) if not local else torch.randint(0, seqlen, (2,)) qkv = torch.randn( batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype, requires_grad=True ) key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode="random") # key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='full') if alibi: alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3 attn_bias = attn_bias_from_alibi_slopes( alibi_slopes, seqlen, seqlen, key_padding_mask, key_padding_mask, causal=causal ) else: alibi_slopes, attn_bias = None, None qkv_unpad, cu_seqlens, max_seqlen, qkv, output_pad_fn, dqkv_pad_fn = generate_qkv( *qkv.unbind(dim=2), key_padding_mask, key_padding_mask, qkvpacked=True ) out_unpad, sm_lse, S_dmask = flash_attn_varlen_qkvpacked_func( qkv_unpad, cu_seqlens, max_seqlen, dropout_p, causal=causal, window_size=window_size, alibi_slopes=alibi_slopes, deterministic=deterministic, return_attn_probs=True, ) out = output_pad_fn(out_unpad) if dropout_p > 0.0: # TODO - move to c++ mha_varlen_fwd() S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p) S_dmask = pad_rearrange_dropout_mask_hts_to_bhss(S_dmask, cu_seqlens, seqlen, seqlen) S_dmask_converted = convert_flash_attn_S_to_softmax( S_dmask, seqlen, seqlen, key_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal, window_size=window_size, ) dropout_mask = S_dmask_converted >= 0 # CK does not return P. Hence, we don't test the attn here. else: dropout_mask = None out_ref, attn_ref = attention_qkvpacked_ref( qkv, key_padding_mask, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size, ) out_pt, attn_pt = attention_qkvpacked_ref( qkv, key_padding_mask, attn_bias, dropout_p, dropout_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()}") # 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() g = torch.randn_like(out) if is_bwd_hdim_supported(d): (dqkv_unpad,) = torch.autograd.grad(out, qkv_unpad, g) dqkv = dqkv_pad_fn(dqkv_unpad) (dqkv_ref,) = torch.autograd.grad(out_ref, qkv, g) (dqkv_pt,) = torch.autograd.grad(out_pt, qkv, g) print(f"dQ max diff: {(dqkv[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}") print(f"dK max diff: {(dqkv[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}") print(f"dV max diff: {(dqkv[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}") print(f"dQKV mean diff: {(dqkv - dqkv_ref).abs().mean().item()}") print(f"dQ Pytorch max diff: {(dqkv_pt[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}") print(f"dK Pytorch max diff: {(dqkv_pt[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}") print(f"dV Pytorch max diff: {(dqkv_pt[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}") print(f"dQKV Pytorch mean diff: {(dqkv_pt - dqkv_ref).abs().mean().item()}") # TODO - use 10 times to check, wait for ck to fix bwd precision issue assert (dqkv - dqkv_ref).abs().max().item() <= 10 * (dqkv_pt - dqkv_ref).abs().max().item() @pytest.mark.parametrize("kvpacked", [True, False]) @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"]) @pytest.mark.parametrize("deterministic", [False, True]) @pytest.mark.parametrize("alibi", [False, True]) @pytest.mark.parametrize("local", [False, True]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("d", [32, 40, 59, 64, 96, 111, 128, 160, 192, 224, 256]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (512, 256), (1024, 1024), (1023, 1024), (1024, 1023), (2048, 2048), ], ) @pytest.mark.parametrize("dropout_p", [0.0, 0.17]) def test_flash_attn_output( seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked ): device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 4 nheads = 9 nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3) assert nheads % nheads_k == 0 window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) if kvpacked: kv = torch.randn( batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype, requires_grad=True ) else: k = torch.randn( batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True ) v = torch.randn( batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True ) if alibi: alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3 attn_bias = attn_bias_from_alibi_slopes(alibi_slopes, seqlen_q, seqlen_k, causal=causal) else: alibi_slopes, attn_bias = None, None if kvpacked: out, lse, S_dmask = flash_attn_kvpacked_func( q, kv, dropout_p, causal=causal, window_size=window_size, alibi_slopes=alibi_slopes, deterministic=deterministic, return_attn_probs=True, ) else: out, lse, S_dmask = flash_attn_func( q, k, v, dropout_p, causal=causal, window_size=window_size, alibi_slopes=alibi_slopes, deterministic=deterministic, return_attn_probs=True, ) if dropout_p > 0.0: # TODO - move to c++ mha_varlen_fwd() S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p) S_dmask_converted = convert_flash_attn_S_to_softmax( S_dmask, seqlen_q, seqlen_k, None, None, d, dropout_p > 0.0, causal=causal, window_size=window_size, ) dropout_mask = S_dmask_converted >= 0 if kvpacked: kv_rep = repeat(kv, "b s two h d -> b s two (h g) d", g=nheads // nheads_k) k_rep, v_rep = kv_rep.unbind(dim=2) else: k_rep = repeat(k, "b s h d -> b s (h g) d", g=nheads // nheads_k) v_rep = repeat(v, "b s h d -> b s (h g) d", g=nheads // nheads_k) # CK does not return P. Hence, we don't test the attn here. else: dropout_mask = None if kvpacked: out_ref, attn_ref = attention_kvpacked_ref( q, kv, None, None, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size, ) out_pt, attn_pt = attention_kvpacked_ref( q, kv, None, None, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size, upcast=False, reorder_ops=True, ) else: out_ref, attn_ref = attention_ref( q, k, v, None, None, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size, ) out_pt, attn_pt = attention_ref( q, k, v, None, None, attn_bias, dropout_p, dropout_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()}") # 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() g = torch.randn_like(out) if is_bwd_hdim_supported(d): if kvpacked: ( dq, dkv, ) = torch.autograd.grad(out, (q, kv), g) dk, dv = dkv.unbind(2) ( dq_ref, dkv_ref, ) = torch.autograd.grad(out_ref, (q, kv), g) dk_ref, dv_ref = dkv_ref.unbind(2) ( dq_pt, dkv_pt, ) = torch.autograd.grad(out_pt, (q, kv), g) dk_pt, dv_pt = dkv_pt.unbind(2) else: ( 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()}") # TODO - use 10 times to check, wait for ck to fix bwd precision issue assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item() assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item() assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item() @pytest.mark.parametrize("kvpacked", [True, False]) @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"]) @pytest.mark.parametrize("deterministic", [False, True]) @pytest.mark.parametrize("alibi", [False, True]) @pytest.mark.parametrize("local", [False, True]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (1, 147), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (512, 256), (1024, 1024), (1023, 1024), (1024, 1023), (2048, 2048), ], ) @pytest.mark.parametrize("dropout_p", [0.0, 0.17]) def test_flash_attn_varlen_output( seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked ): device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 4 nheads = 9 nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3) assert nheads % nheads_k == 0 window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) if kvpacked: kv = torch.randn( batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype, requires_grad=True ) else: k = torch.randn( batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True ) v = torch.randn( batch_size, seqlen_k, nheads_k, 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') if alibi: alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3 attn_bias = attn_bias_from_alibi_slopes( alibi_slopes, seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, causal=causal ) else: alibi_slopes, attn_bias = None, None if kvpacked: ( q_unpad, kv_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q, kv, output_pad_fn, dq_pad_fn, dkv_pad_fn, ) = generate_qkv(q, *kv.unbind(dim=2), query_padding_mask, key_padding_mask, kvpacked=True) out_unpad, sm_lse, S_dmask = flash_attn_varlen_kvpacked_func( q_unpad, kv_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, causal=causal, window_size=window_size, alibi_slopes=alibi_slopes, deterministic=deterministic, return_attn_probs=True, ) else: ( q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q, k, v, output_pad_fn, dq_pad_fn, dk_pad_fn, ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False) out_unpad, sm_lse, S_dmask = flash_attn_varlen_func( q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, causal=causal, window_size=window_size, alibi_slopes=alibi_slopes, deterministic=deterministic, return_attn_probs=True, ) out = output_pad_fn(out_unpad) if dropout_p > 0.0: # TODO - move to c++ mha_varlen_fwd() S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p) S_dmask = pad_rearrange_dropout_mask_hts_to_bhss(S_dmask, cu_seqlens_q, seqlen_q, seqlen_k) S_dmask_converted = convert_flash_attn_S_to_softmax( S_dmask, seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal, window_size=window_size, ) dropout_mask = S_dmask_converted >= 0 if kvpacked: kv_rep = repeat(kv, "b s two h d -> b s two (h g) d", g=nheads // nheads_k) k_rep, v_rep = kv_rep.unbind(dim=2) else: k_rep = repeat(k, "b s h d -> b s (h g) d", g=nheads // nheads_k) v_rep = repeat(v, "b s h d -> b s (h g) d", g=nheads // nheads_k) # CK does not return P. Hence, we don't test the attn here. else: dropout_mask = None if kvpacked: out_ref, attn_ref = attention_kvpacked_ref( q, kv, query_padding_mask, key_padding_mask, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size, ) out_pt, attn_pt = attention_kvpacked_ref( q, kv, query_padding_mask, key_padding_mask, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size, upcast=False, reorder_ops=True, ) else: out_ref, attn_ref = attention_ref( q, k, v, query_padding_mask, key_padding_mask, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size, ) out_pt, attn_pt = attention_ref( q, k, v, query_padding_mask, key_padding_mask, attn_bias, dropout_p, dropout_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()}") # Check that FlashAttention's numerical error is at most 4 times the numerical error # of a Pytorch implementation. assert (out - out_ref).abs().max().item() <= 4 * (out_pt - out_ref).abs().max().item() g = torch.randn_like(out) if is_bwd_hdim_supported(d): if kvpacked: ( dq_unpad, dkv_unpad, ) = torch.autograd.grad(out, (q_unpad, kv_unpad), g) dk, dv = dkv_pad_fn(dkv_unpad).unbind(2) ( dq_ref, dkv_ref, ) = torch.autograd.grad(out_ref, (q, kv), g) dk_ref, dv_ref = dkv_ref.unbind(2) ( dq_pt, dkv_pt, ) = torch.autograd.grad(out_pt, (q, kv), g) dk_pt, dv_pt = dkv_pt.unbind(2) else: ( 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) ( 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) dq = dq_pad_fn(dq_unpad) 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()}") # TODO - use 10 times to check, wait for ck to fix bwd precision issue assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item() assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item() assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item() @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) @pytest.mark.parametrize("local", [False, True]) @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]) @pytest.mark.parametrize("swap_sq_sk", [False, True]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ # (1, 239), (3, 799), (127, 512), (127, 513), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (1023, 1024), ], ) def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, local, dtype): if max(seqlen_q, seqlen_k) >= 2048: pytest.skip() 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) k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) out = flash_attn_func(q, k, v, 0.0, causal=causal, window_size=window_size) out_ref, attn_ref = attention_ref( q, k, v, None, None, None, 0.0, None, causal=causal, window_size=window_size ) out_pt, attn_pt = attention_ref( q, k, v, None, None, 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()}") # Check that FlashAttention's numerical error is at most 4 times the numerical error # of a Pytorch implementation. assert (out - out_ref).abs().max().item() <= 4 * (out_pt - out_ref).abs().max().item() + 1e-5 g = torch.randn_like(out) if is_bwd_hdim_supported(d): 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()}") # TODO - use 10 times to check, wait for ck to fix bwd precision issue assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item() + 1e-4 assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item() + 1e-4 assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item() + 1e-4 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) @pytest.mark.parametrize("local", [False, True]) @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]) @pytest.mark.parametrize("swap_sq_sk", [False, True]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ # (1, 239), (3, 799), (127, 512), (127, 513), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (1023, 1024), ], ) @pytest.mark.parametrize("paged_kv_block_size", [None, 256, 512]) def test_flash_attn_varlen_causal( seqlen_q, seqlen_k, swap_sq_sk, d, local, paged_kv_block_size, dtype ): if max(seqlen_q, seqlen_k) >= 2048: pytest.skip() 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") ( q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q, k, v, output_pad_fn, dq_pad_fn, dk_pad_fn, ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False) out_unpad = 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, 0.0, causal=causal, window_size=window_size, 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()}") # 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 g = torch.randn_like(out) if is_bwd_hdim_supported(d): 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()}") if test_backward: # TODO - use 10 times to check, wait for ck to fix bwd precision issue assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item() + 1e-5 assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item() + 1e-5 assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item() + 1e-5 # TODO - support splitkv # def test_flash_attn_splitkv # TODO - Support has_leftpad @pytest.mark.parametrize("dtype", [torch.float16]) @pytest.mark.parametrize("num_splits", [1, 0]) @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"]) @pytest.mark.parametrize("new_kv", [False, True]) @pytest.mark.parametrize("alibi", [False, True]) @pytest.mark.parametrize("local", [False, True]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True, False]) @pytest.mark.parametrize("rotary_interleaved", [False, True]) @pytest.mark.parametrize("rotary_fraction", [0.0, 0.5, 1.0]) @pytest.mark.parametrize("paged_kv_block_size", [None, 256]) @pytest.mark.parametrize("has_leftpad", [False]) @pytest.mark.parametrize("has_batch_idx", [False, True]) @pytest.mark.parametrize("d", [32, 59, 64, 80, 128, 256]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (1, 128), (1, 339), (3, 1024), (64, 800), (64, 256), (3, 799), (64, 2048), (16, 20000), (1, 128 * 1024), (16, 128 * 1024), (128, 128), ], ) def test_flash_attn_kvcache( seqlen_q, seqlen_k, d, has_batch_idx, has_leftpad, paged_kv_block_size, rotary_fraction, rotary_interleaved, seqlen_new_eq_seqlen_q, causal, local, alibi, new_kv, mha_type, num_splits, dtype, ): if seqlen_q > seqlen_k and new_kv: pytest.skip() if not new_kv and rotary_fraction > 0.0: pytest.skip() if has_batch_idx and paged_kv_block_size is not None: pytest.skip() if has_leftpad and paged_kv_block_size is not None: pytest.skip() device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 1 batch_size_cache = batch_size if not has_batch_idx else batch_size * 2 nheads = 6 # rotary_dim must be a multiple of 16, and must be <= d rotary_dim = math.floor(int(rotary_fraction * d) / 16) * 16 nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3) assert nheads % nheads_k == 0 window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype) seqlen_new = seqlen_q if seqlen_new_eq_seqlen_q else torch.randint(1, seqlen_q + 1, (1,)).item() if new_kv: k = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype) v = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype) else: k, v = None, None if paged_kv_block_size is None: k_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype) v_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype) block_table = None else: ( k_cache, v_cache, block_table, k_cache_paged, v_cache_paged, num_blocks, ) = _generate_block_kvcache( seqlen_k, paged_kv_block_size, batch_size, nheads_k, d, device, dtype ) cache_seqlens = torch.randint( 0 if new_kv else 1, # If we don't use seqlen_q in the case of causal and rotary, cos/sin won't be long enough ( (seqlen_k - (seqlen_q if (causal or local) and rotary_dim > 1 else seqlen_new) + 1) if new_kv else (seqlen_k + 1) ), (batch_size,), dtype=torch.int32, device=device, ) if has_leftpad: cache_leftpad = torch.cat([torch.randint(0, cache_seqlens[i].item(), (1,), dtype=torch.int32, device=device) if cache_seqlens[i].item() > 0 else torch.zeros(1, dtype=torch.int32, device=device) for i in range(batch_size)]) else: cache_leftpad = None arange = rearrange(torch.arange(seqlen_k, device=device), "s -> 1 s") cache_seqlens_expanded = rearrange(cache_seqlens, "b -> b 1") key_padding_mask = arange < cache_seqlens_expanded + (seqlen_new if new_kv else 0) if has_leftpad: key_padding_mask = torch.logical_and( key_padding_mask, arange >= cache_leftpad.unsqueeze(-1).expand(-1, seqlen_k) ) if has_batch_idx: cache_batch_idx = torch.randperm(batch_size_cache, dtype=torch.int32, device=device)[ :batch_size ] else: cache_batch_idx = None if alibi: alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3 attn_bias = attn_bias_from_alibi_slopes( alibi_slopes, seqlen_q, seqlen_k, None, key_padding_mask, causal=causal, key_leftpad=cache_leftpad ) else: alibi_slopes, attn_bias = None, None # cache_seqlens = torch.tensor([64], dtype=torch.int32, device=device) if rotary_dim > 0: angle = ( torch.rand( seqlen_k if paged_kv_block_size is None else num_blocks * paged_kv_block_size, rotary_dim // 2, device=device, ) * 2 * math.pi ) cos = torch.cos(angle).to(dtype=dtype) sin = torch.sin(angle).to(dtype=dtype) if causal or local: q_ro = apply_rotary_emb( q, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved ) else: q_ro = rearrange( apply_rotary_emb( rearrange(q, "b s h d -> b 1 (s h) d"), cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved, ), "b 1 (s h) d -> b s h d", s=seqlen_q, ) # q_ro = q k_ro = apply_rotary_emb( k, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved ) else: cos, sin = None, None q_ro, k_ro = q, k # k_cache[:, 64:] = -1 k_cache_ref = ( k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)] ).clone() v_cache_ref = ( v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)] ).clone() if new_kv: update_mask = torch.logical_and( cache_seqlens_expanded <= arange, arange < cache_seqlens_expanded + seqlen_new ) k_cache_ref[update_mask] = rearrange(k_ro, "b s ... -> (b s) ...") v_cache_ref[update_mask] = rearrange(v, "b s ... -> (b s) ...") k_cache_rep = repeat(k_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k) v_cache_rep = repeat(v_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k) out = flash_attn_with_kvcache( q, k_cache if paged_kv_block_size is None else k_cache_paged, v_cache if paged_kv_block_size is None else v_cache_paged, k, v, rotary_cos=cos, rotary_sin=sin, cache_seqlens=cache_seqlens, cache_batch_idx=cache_batch_idx, cache_leftpad=cache_leftpad, block_table=block_table, causal=causal, window_size=window_size, rotary_interleaved=rotary_interleaved, alibi_slopes=alibi_slopes, num_splits=num_splits, ) # out = flash_attn_with_kvcache( # q, k_cache, v_cache, cache_seqlens=cache_seqlens, causal=causal, window_size=window_size # ) # out = flash_attn_with_kvcache(q, k_cache, v_cache, causal=causal, window_size=window_size) # qk = torch.einsum("bqhd,bkhd->bhqk", q, k_cache_ref) # m = qk.amax(-1, keepdim=True) # s_tmp = torch.exp((qk - m) / math.sqrt(d)) # o1 = torch.einsum('bhst,bthd->bshd', s_tmp, v_cache_ref) # lse_ref = torch.logsumexp(qk / math.sqrt(d), -1) # probs = torch.softmax(qk, dim=-1) out_ref, _ = attention_ref( q_ro, k_cache_rep, v_cache_rep, None, key_padding_mask, attn_bias, 0.0, None, causal=causal, window_size=window_size, key_leftpad=cache_leftpad, ) out_pt, _ = attention_ref( q_ro, k_cache_rep, v_cache_rep, None, key_padding_mask, attn_bias, 0.0, None, causal=causal, window_size=window_size, upcast=False, reorder_ops=True, key_leftpad=cache_leftpad, ) print(f"Output max diff: {(out - out_ref).abs().max().item()}") print(f"Output mean diff: {(out - out_ref).abs().mean().item()}") print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}") print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}") # Check that FlashAttention's numerical error is at most twice the numerical error # of a Pytorch implementation. if new_kv: if paged_kv_block_size is None: k_cache_select = ( k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)] ) v_cache_select = ( v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)] ) else: k_cache_select = rearrange( k_cache_paged[block_table.to(dtype=torch.long).flatten()], "(b nblocks) block_size ... -> b (nblocks block_size) ...", b=batch_size, )[:, :seqlen_k] v_cache_select = rearrange( v_cache_paged[block_table.to(dtype=torch.long).flatten()], "(b nblocks) block_size ... -> b (nblocks block_size) ...", b=batch_size, )[:, :seqlen_k] assert torch.allclose(k_cache_select, k_cache_ref, rtol=1e-3, atol=1e-3) assert torch.equal(v_cache_select, v_cache_ref) # mult = 3 if f16, bf16 need 4 mult = 4 if not alibi else 5 assert (out - out_ref).abs().max().item() <= mult * (out_pt - out_ref).abs().max().item() + 1e-5 @pytest.mark.parametrize("dtype", [torch.float16]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (1, 239), (239, 1), (3, 799), (799, 3), (1024, 128), (97, 97), (128, 128), (200, 200), (256, 256), (257, 257), (384, 384), (512, 512), (768, 768), # (1024, 1024), ], ) @pytest.mark.parametrize("dropout_p", [0.0, 0.17]) def test_flash_attn_race_condition(seqlen_q, seqlen_k, d, dropout_p, causal, dtype): device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 60 # Sometimes we need large batch size for the race conditions to trigger nheads = 4 q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) torch.random.manual_seed(42) out0, lse0, _ = flash_attn_func(q, k, v, dropout_p, causal=causal, return_attn_probs=True) g = torch.randn_like(out0) if dropout_p == 0 and is_bwd_hdim_supported(d): ( dq0, dk0, dv0, ) = torch.autograd.grad(out0, (q, k, v), g) # Numerical error if we just do any arithmetic on dq dq_atol = 2 * ((dq0 + 0.3 - 0.3) - dq0).abs().max().item() for i in range(250): torch.random.manual_seed(42) out, lse, _ = flash_attn_func(q, k, v, dropout_p, causal=causal, return_attn_probs=True) assert torch.equal(out, out0) assert torch.equal(lse, lse0) if dropout_p == 0: ( dq, dk, dv, ) = torch.autograd.grad(out, (q, k, v), g) dq_equal = torch.allclose(dq, dq0, atol=dq_atol) if not dq_equal: print(f"Iter {i}, {dq_atol = }, dQ max diff: {(dq - dq0).abs().max().item()}") assert torch.equal(dv, dv0) assert torch.equal(dk, dk0) assert dq_equal @pytest.mark.parametrize("dtype", [torch.float16]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("d", [16, 32, 64]) @pytest.mark.parametrize("seqlen", [1, 2, 5, 17, 128]) def test_flash_attn_bwd_overflow(seqlen, d, causal, dtype): """We previously had a bug where not masking elements beyond seqlen_k caused NaN in dQ, in the case where seqlen % 128 != 0. """ # TODO - 1 or 2 might fail, need to check if seqlen == 1 or seqlen == 2: pytest.skip() device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 2 nheads = 5 q = torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda") * 5 k, v = [ torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda") * 3 for _ in range(2) ] q.requires_grad_(True) k.requires_grad_(True) v.requires_grad_(True) out = flash_attn_func(q, k, v, causal=causal) g = torch.randn_like(out) out.backward(g) q_pt = q.detach().clone().requires_grad_(True) k_pt = k.detach().clone().requires_grad_(True) v_pt = v.detach().clone().requires_grad_(True) out_pt, _ = attention_ref(q_pt, k_pt, v_pt, causal=causal, upcast=False, reorder_ops=True) out_pt.backward(g) q_ref = q.detach().clone().requires_grad_(True) k_ref = k.detach().clone().requires_grad_(True) v_ref = v.detach().clone().requires_grad_(True) out_ref, attn_ref = attention_ref(q_ref, k_ref, v_ref, causal=causal) out_ref.backward(g) print(f"dQ max diff: {(q.grad - q_ref.grad).abs().max().item()}") print(f"dK max diff: {(k.grad - k_ref.grad).abs().max().item()}") print(f"dV max diff: {(v.grad - v_ref.grad).abs().max().item()}") print(f"dQ Pytorch max diff: {(q_pt.grad - q_ref.grad).abs().max().item()}") print(f"dK Pytorch max diff: {(k_pt.grad - k_ref.grad).abs().max().item()}") print(f"dV Pytorch max diff: {(v_pt.grad - v_ref.grad).abs().max().item()}") assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item() assert (q.grad - q_ref.grad).abs().max().item() <= 5 * ( q_pt.grad - q_ref.grad ).abs().max().item() + 1e-3 assert (k.grad - k_ref.grad).abs().max().item() <= 5 * ( k_pt.grad - k_ref.grad ).abs().max().item() + 1e-3 assert (v.grad - v_ref.grad).abs().max().item() <= 5 * ( v_pt.grad - v_ref.grad ).abs().max().item() + 1e-3 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("d", [64, 128]) @pytest.mark.parametrize("seqlen", [97, 128, 200, 256]) def test_flash_attn_bwd_transpose(seqlen, d, causal, dtype): """We previously had a bug where we were using the wrong strides of dout, which shows up when dout is not contiguous. """ device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 5 nheads = 2 q, k, v = [ torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda", requires_grad=True) for _ in range(3) ] out = rearrange(flash_attn_func(q, k, v, causal=causal), "b s ... -> s b ...") # So g is not contiguous g = torch.randn(seqlen, 2 * batch_size, nheads, d, dtype=dtype, device="cuda")[:, ::2] out.backward(g) q_pt = q.detach().clone().requires_grad_(True) k_pt = k.detach().clone().requires_grad_(True) v_pt = v.detach().clone().requires_grad_(True) out_pt, attn_pt = attention_ref(q_pt, k_pt, v_pt, causal=causal, upcast=False, reorder_ops=True) out_pt = rearrange(out_pt, "b s ... -> s b ...") out_pt.backward(g) q_ref = q.detach().clone().requires_grad_(True) k_ref = k.detach().clone().requires_grad_(True) v_ref = v.detach().clone().requires_grad_(True) out_ref, attn_ref = attention_ref(q_ref, k_ref, v_ref, causal=causal) out_ref = rearrange(out_ref, "b s ... -> s b ...") out_ref.backward(g) print(f"dQ max diff: {(q.grad - q_ref.grad).abs().max().item()}") print(f"dK max diff: {(k.grad - k_ref.grad).abs().max().item()}") print(f"dV max diff: {(v.grad - v_ref.grad).abs().max().item()}") print(f"dQ Pytorch max diff: {(q_pt.grad - q_ref.grad).abs().max().item()}") print(f"dK Pytorch max diff: {(k_pt.grad - k_ref.grad).abs().max().item()}") print(f"dV Pytorch max diff: {(v_pt.grad - v_ref.grad).abs().max().item()}") assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item() assert (q.grad - q_ref.grad).abs().max().item() <= 2 * ( q_pt.grad - q_ref.grad ).abs().max().item() assert (k.grad - k_ref.grad).abs().max().item() <= 2 * ( k_pt.grad - k_ref.grad ).abs().max().item() assert (v.grad - v_ref.grad).abs().max().item() <= 2 * ( v_pt.grad - v_ref.grad ).abs().max().item() @pytest.mark.parametrize("dtype", [torch.float16]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("d", [16, 32, 64]) def test_flash_attn_bwd_varlen_overflow(d, causal, dtype): """We previously had a bug where not masking elements beyond seqlen_k caused NaN in dQ, in the case where seqlen % 128 != 0 or varlen. """ device = "cuda" # set seed torch.random.manual_seed(0) nheads = 5 q_cuseqlen = torch.tensor([0, 76, 110, 256], device=device, dtype=torch.int32) k_cuseqlen = torch.tensor([0, 1, 2, 3], device=device, dtype=torch.int32) Mq = 256 Mk = 3 q = torch.randn([Mq, nheads, d], dtype=dtype, device=device) * 3 k, v = [torch.randn([Mk, nheads, d], dtype=dtype, device=device) * 3 for _ in range(2)] q.requires_grad_(True) k.requires_grad_(True) v.requires_grad_(True) out = flash_attn_varlen_func(q, k, v, q_cuseqlen, k_cuseqlen, Mq, Mk, causal=causal) g = torch.randn_like(out) out.backward(g) assert not q.grad.isnan().any() assert not k.grad.isnan().any() assert not v.grad.isnan().any() @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) @pytest.mark.parametrize("local", [False, True]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]) @pytest.mark.parametrize("swap_sq_sk", [False, True]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (1, 239), (3, 799), (127, 512), (127, 513), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (1023, 1024), ], ) def test_flash_attn_deterministic(seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, dtype): if ( max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30 ): pytest.skip() # Reference implementation OOM if swap_sq_sk: seqlen_q, seqlen_k = seqlen_k, seqlen_q device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 4 nheads = 9 window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) out = flash_attn_func(q, k, v, 0.0, causal=causal, window_size=window_size, deterministic=True) g = torch.randn_like(out) dq0, dk0, dv0 = torch.autograd.grad(out, (q, k, v), g, retain_graph=True) for _ in range(50): dq, dk, dv = torch.autograd.grad(out, (q, k, v), g, retain_graph=True) assert torch.equal(dv, dv0) assert torch.equal(dk, dk0) assert torch.equal(dq, dq0) @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) @pytest.mark.parametrize("local", [False, True]) @pytest.mark.parametrize("causal", [False, True]) @pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]) @pytest.mark.parametrize("swap_sq_sk", [False, True]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (1, 239), (3, 799), (127, 512), (127, 513), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (1023, 1024), ], ) def test_flash_attn_varlen_deterministic(seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, dtype): if ( max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30 ): pytest.skip() # Reference implementation OOM if swap_sq_sk: seqlen_q, seqlen_k = seqlen_k, seqlen_q device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 2 nheads = 9 window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode="random") key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode="random") ( q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q, k, v, output_pad_fn, dq_pad_fn, dk_pad_fn, ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False) out = flash_attn_varlen_func( q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, 0.0, causal=causal, window_size=window_size, deterministic=True, ) g = torch.randn_like(out) dq0, dk0, dv0 = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True) for _ in range(50): dq, dk, dv = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True) assert torch.equal(dv, dv0) assert torch.equal(dk, dk0) assert torch.equal(dq, dq0)