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, ) from test_flash_attn import ( attn_bias_from_alibi_slopes, convert_flash_attn_S_to_softmax, generate_qkv, generate_random_padding_mask, attention_ref, attention_kvpacked_ref, attention_qkvpacked_ref, ) def is_bwd_hdim_supported(d): return d <= 128 and d % 2 == 0 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 torch.floor(255.0 * (1 - p) - randval) 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]) @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 change dq type to f32 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]) @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 change dq type to f32 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]) @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 change dq type to f32 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 change dq type to f32 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()