import math import pytest import torch import torch.nn.functional as F from einops import rearrange, repeat from flash_attn.bert_padding import pad_input, unpad_input from flash_attn_interface import flash_attn_func, flash_attn_varlen_func ABS_TOL = 5e-3 REL_TOL = 1e-1 def generate_random_padding_mask(max_seqlen, batch_size, device, mode="random"): assert mode in ["full", "random", "third"] if mode == "full": lengths = torch.full((batch_size, 1), max_seqlen, device=device, dtype=torch.int32) elif mode == "random": lengths = torch.randint( max(1, max_seqlen - 20), max_seqlen + 1, (batch_size, 1), device=device ) elif mode == "third": lengths = torch.randint(max_seqlen // 3, max_seqlen + 1, (batch_size, 1), device=device) padding_mask = ( repeat(torch.arange(max_seqlen, device=device), "s -> b s", b=batch_size) < lengths ) return padding_mask def generate_qkv( q, k, v, query_padding_mask=None, key_padding_mask=None, kvpacked=False, qkvpacked=False ): """ Arguments: q: (batch_size, seqlen_q, nheads, d) k: (batch_size, seqlen_k, nheads_k, d) v: (batch_size, seqlen_k, nheads_k, d) query_padding_mask: (batch_size, seqlen), bool key_padding_mask: (batch_size, seqlen), bool """ assert not (kvpacked and qkvpacked) batch_size, seqlen_q, nheads, d = q.shape _, seqlen_k, nheads_k, _ = k.shape assert k.shape == (batch_size, seqlen_k, nheads_k, d) assert v.shape == (batch_size, seqlen_k, nheads_k, d) if query_padding_mask is not None: q_unpad, indices_q, cu_seqlens_q, max_seqlen_q, *rest = unpad_input(q, query_padding_mask) output_pad_fn = lambda output_unpad: pad_input( output_unpad, indices_q, batch_size, seqlen_q ) else: q_unpad = rearrange(q, "b s h d -> (b s) h d") cu_seqlens_q = torch.arange( 0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, device=q_unpad.device ) max_seqlen_q = seqlen_q output_pad_fn = lambda output_unpad: rearrange( output_unpad, "(b s) h d -> b s h d", b=batch_size ) if key_padding_mask is not None: k_unpad, indices_k, cu_seqlens_k, max_seqlen_k, *rest = unpad_input(k, key_padding_mask) v_unpad, _, _, _, *rest = unpad_input(v, key_padding_mask) else: k_unpad = rearrange(k, "b s h d -> (b s) h d") v_unpad = rearrange(v, "b s h d -> (b s) h d") cu_seqlens_k = torch.arange( 0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32, device=k_unpad.device ) max_seqlen_k = seqlen_k if qkvpacked: assert (query_padding_mask == key_padding_mask).all() assert nheads == nheads_k qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1) qkv = torch.stack([q, k, v], dim=2) if query_padding_mask is not None: dqkv_pad_fn = lambda dqkv_unpad: pad_input(dqkv_unpad, indices_q, batch_size, seqlen_q) else: dqkv_pad_fn = lambda dqkv_unpad: rearrange( dqkv_unpad, "(b s) t h d -> b s t h d", b=batch_size ) return ( qkv_unpad.detach().requires_grad_(), cu_seqlens_q, max_seqlen_q, qkv.detach().requires_grad_(), output_pad_fn, dqkv_pad_fn, ) elif kvpacked: kv_unpad = torch.stack([k_unpad, v_unpad], dim=1) kv = torch.stack([k, v], dim=2) dq_pad_fn = output_pad_fn if key_padding_mask is not None: dkv_pad_fn = lambda dkv_unpad: pad_input(dkv_unpad, indices_k, batch_size, seqlen_k) else: dkv_pad_fn = lambda dkv_unpad: rearrange( dkv_unpad, "(b s) t h d -> b s t h d", b=batch_size ) return ( q_unpad.detach().requires_grad_(), kv_unpad.detach().requires_grad_(), cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q.detach().requires_grad_(), kv.detach().requires_grad_(), output_pad_fn, dq_pad_fn, dkv_pad_fn, ) else: dq_pad_fn = output_pad_fn if key_padding_mask is not None: dk_pad_fn = lambda dk_unpad: pad_input(dk_unpad, indices_k, batch_size, seqlen_k) else: dk_pad_fn = lambda dk_unpad: rearrange(dk_unpad, "(b s) h d -> b s h d", b=batch_size) return ( q_unpad.detach().requires_grad_(), k_unpad.detach().requires_grad_(), v_unpad.detach().requires_grad_(), cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q.detach().requires_grad_(), k.detach().requires_grad_(), v.detach().requires_grad_(), output_pad_fn, dq_pad_fn, dk_pad_fn, ) def construct_local_mask( seqlen_q, seqlen_k, window_size=(-1, -1), # -1 means infinite window size query_padding_mask=None, key_padding_mask=None, device=None, ): row_idx = rearrange(torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1") col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long) sk = ( seqlen_k if key_padding_mask is None else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1") ) sq = ( seqlen_q if query_padding_mask is None else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1") ) if window_size[0] < 0: return col_idx > row_idx + sk - sq + window_size[1] else: sk = torch.full_like(col_idx, seqlen_k) if key_padding_mask is None else sk return torch.logical_or( col_idx > torch.minimum(row_idx + sk - sq + window_size[1], sk), col_idx < row_idx + sk - sq - window_size[0], ) 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}") def attention_ref( q, k, v, query_padding_mask=None, key_padding_mask=None, attn_bias=None, dropout_p=0.0, dropout_mask=None, causal=False, q_scale=None, k_scale=None, v_scale=None, window_size=(-1, -1), # -1 means infinite window size softcap=0.0, upcast=True, reorder_ops=False, intermediate_dtype=None, ): """ Arguments: q: (batch_size, seqlen_q, nheads, head_dim) k: (batch_size, seqlen_k, nheads, head_dim) v: (batch_size, seqlen_k, nheads, head_dim) query_padding_mask: (batch_size, seqlen_q) key_padding_mask: (batch_size, seqlen_k) attn_bias: broadcastable to (batch_size, nheads, seqlen_q, seqlen_k) dropout_p: float dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k) causal: whether to apply causal masking upcast: whether to cast all inputs to fp32, do all computation in fp32, then cast output back to fp16/bf16. reorder_ops: whether to change the order of operations (scaling k instead of scaling k, etc.) without changing the math. This is to estimate the numerical error from operation reordering. Output: output: (batch_size, seqlen_q, nheads, head_dim) attention: (batch_size, nheads, seqlen_q, seqlen_k), softmax after dropout """ if causal: window_size = (window_size[0], 0) dtype_og = q.dtype if upcast: q, k, v = q.float(), k.float(), v.float() if q_scale is not None: q = (q.float() * q_scale).to(dtype=q.dtype) if k_scale is not None: k = (k.float() * k_scale).to(dtype=k.dtype) if v_scale is not None: v = (v.float() * v_scale).to(dtype=v.dtype) seqlen_q, seqlen_k = q.shape[1], k.shape[1] k = repeat(k, "b s h d -> b s (h g) d", g=q.shape[2] // k.shape[2]) v = repeat(v, "b s h d -> b s (h g) d", g=q.shape[2] // v.shape[2]) d = q.shape[-1] if not reorder_ops: scores = torch.einsum("bthd,bshd->bhts", q / math.sqrt(d), k) else: scores = torch.einsum("bthd,bshd->bhts", q, k / math.sqrt(d)) if softcap > 0: scores = torch.tanh(scores / softcap) * softcap if key_padding_mask is not None: scores.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf")) if window_size[0] >= 0 or window_size[1] >= 0: local_mask = construct_local_mask( seqlen_q, seqlen_k, window_size, query_padding_mask, key_padding_mask, q.device, ) scores.masked_fill_(local_mask, float("-inf")) if attn_bias is not None: scores = scores + attn_bias attention = torch.softmax(scores, dim=-1).to(v.dtype) # We want to mask here so that the attention matrix doesn't have any NaNs # Otherwise we'll get NaN in dV if query_padding_mask is not None: attention = attention.masked_fill(rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0) # Some rows might be completely masked out so we fill them with zero instead of NaN if window_size[0] >= 0 or window_size[1] >= 0: attention = attention.masked_fill(torch.all(local_mask, dim=-1, keepdim=True), 0.0) dropout_scaling = 1.0 / (1 - dropout_p) # attention_drop = attention.masked_fill(~dropout_mask, 0.0) * dropout_scaling # output = torch.einsum('bhts,bshd->bthd', attention_drop , v) if dropout_mask is not None: attention_drop = attention.masked_fill(~dropout_mask, 0.0) else: attention_drop = attention if intermediate_dtype is not None: attention_drop = attention_drop.to(intermediate_dtype).to(attention_drop.dtype) output = torch.einsum("bhts,bshd->bthd", attention_drop, v * dropout_scaling) if query_padding_mask is not None: output.masked_fill_(rearrange(~query_padding_mask, "b s -> b s 1 1"), 0.0) return output.to(dtype=dtype_og), attention.to(dtype=dtype_og) @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float8_e4m3fn]) # @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float8_e4m3fn]) # @pytest.mark.parametrize("dtype", [torch.bfloat16]) # @pytest.mark.parametrize("dtype", [torch.float8_e4m3fn]) @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"]) # @pytest.mark.parametrize("mha_type", ["mha"]) # @pytest.mark.parametrize("deterministic", [False, True]) @pytest.mark.parametrize("deterministic", [False]) @pytest.mark.parametrize("softcap", [0.0, 50.0]) # @pytest.mark.parametrize("softcap", [50.0]) @pytest.mark.parametrize("causal,local", [(False, False), (True, False), (False, True)]) # @pytest.mark.parametrize("causal,local", [(False, False)]) # @pytest.mark.parametrize("causal", [False]) @pytest.mark.parametrize("V_colmajor", [False, True]) # @pytest.mark.parametrize("V_colmajor", [False]) # @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256]) # @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192, 256]) # @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', [32, 40, 64, 80, 96, 128]) # @pytest.mark.parametrize("d", [64, 96, 128, 192]) @pytest.mark.parametrize("d", [64, 96, 128, 192, 256]) # @pytest.mark.parametrize("d", [128]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (64, 128), (128, 192), (256, 256), (113, 203), (113, 128), (128, 217), (113, 211), (108, 256), (256, 512), (384, 256), (640, 128), (512, 256), (1024, 1024), (1023, 1024), (1024, 1023), (2048, 2048), (8192, 8192), ], ) # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)]) def test_flash_attn_output( seqlen_q, seqlen_k, d, causal, local, softcap, V_colmajor, deterministic, mha_type, dtype ): if V_colmajor and (seqlen_k % 16 != 0 or dtype != torch.float8_e4m3fn): pytest.skip("V_colmajor requires seqlen_k to be a multiple of 16 and dtype to be float8_e4m3fn") if softcap > 0.0 and dtype == torch.float8_e4m3fn: pytest.skip("Softcap is not supported for float8_e4m3fn") device = "cuda" # set seed torch.random.manual_seed(0) # batch_size = 40 # nheads = 16 batch_size = 9 if seqlen_k <= 2048 else 2 nheads = 6 # batch_size = 1 # nheads = 1 nheads_kv = nheads if mha_type == "mha" else (2 if mha_type == "gqa" else 1) dtype_ref = torch.bfloat16 if dtype == torch.float8_e4m3fn else dtype q_ref = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype_ref).to(dtype).to(dtype_ref).requires_grad_() if softcap > 0.0: # Ensure the values of qk are at least within softcap range. q_ref = (q_ref * softcap / 2).detach().requires_grad_() k_ref = torch.randn(batch_size, seqlen_k, nheads_kv, d, device=device, dtype=dtype_ref).to(dtype).to(dtype_ref).requires_grad_() v_ref = torch.randn(batch_size, seqlen_k, nheads_kv, d, device=device, dtype=dtype_ref).to(dtype).to(dtype_ref).requires_grad_() # Put window_size after QKV randn so that window_size changes from test to test window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) if dtype == torch.float8_e4m3fn: q_scale, k_scale, v_scale = [torch.rand(1, device=device, dtype=torch.float32) * 2 for _ in range(3)] else: q_scale, k_scale, v_scale = None, None, None q, k, v = [x.detach().to(dtype).requires_grad_() for x in (q_ref, k_ref, v_ref)] if V_colmajor: v = rearrange(rearrange(v.detach(), "b s h d -> b h d s").contiguous(), "b h d s -> b s h d").requires_grad_() out, lse = flash_attn_func( q, k, v, causal=causal, q_scale=q_scale, k_scale=k_scale, v_scale=v_scale, window_size=window_size, softcap=softcap, ) out_ref, attn_ref = attention_ref( q_ref, k_ref, v_ref, None, None, causal=causal, q_scale=q_scale, k_scale=k_scale, v_scale=v_scale, window_size=window_size, softcap=softcap ) out_pt, attn_pt = attention_ref( q_ref, k_ref, v_ref, None, None, causal=causal, q_scale=q_scale, k_scale=k_scale, v_scale=v_scale, window_size=window_size, softcap=softcap, upcast=False, reorder_ops=True, intermediate_dtype=dtype if dtype == torch.float8_e4m3fn else None, ) # qk = torch.einsum('bshd,bthd->bhst', q_ref, k_ref).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_ref.float() / math.sqrt(d), k_ref.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 dtype != torch.float8_e4m3fn and not V_colmajor: g = torch.randn_like(out) do_o = ((g.float() * out.float()).sum(-1)).transpose(1, 2) import flashattn_hopper_cuda dq, dk, dv, softmax_d, dq_accum, dk_accum, dv_accum = flashattn_hopper_cuda.bwd( g, q, k, v, out, lse, None, None, None, d ** (-0.5), causal, window_size[0], window_size[1], softcap, deterministic, ) # print(f"dO_O max diff: {(softmax_d - do_o).abs().max().item()}") # assert (softmax_d - do_o).abs().max().item() <= 1e-5 # assert dq_accum.abs().max().item() == 0.0 # dS = torch.einsum('bthd,bshd->bhts', g.float(), v.float()) # P = torch.softmax(qk, -1) # dP = P * (dS - do_o.transpose(1, 2).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()) # dq, dk, dv = torch.autograd.grad(out, (q, k, v), g) dq_ref, dk_ref, dv_ref = torch.autograd.grad(out_ref, (q_ref, k_ref, v_ref), g) dq_pt, dk_pt, dv_pt = torch.autograd.grad(out_pt, (q_ref, k_ref, v_ref), 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()}") # breakpoint() # Check that FlashAttention's numerical error is at most twice the numerical error # of a Pytorch implementation. # multiple = 2 if dtype != torch.float8_e4m3fn else 3 multiple = 2 assert (out - out_ref).abs().max().item() <= multiple * (out_pt - out_ref).abs().max().item() if dtype != torch.float8_e4m3fn and not V_colmajor: multiple = 2 if softcap == 0.0 else 4 assert (dq - dq_ref).abs().max().item() <= multiple * (dq_pt - dq_ref).abs().max().item() assert (dk - dk_ref).abs().max().item() <= multiple * (dk_pt - dk_ref).abs().max().item() assert (dv - dv_ref).abs().max().item() <= multiple * (dv_pt - dv_ref).abs().max().item() @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float8_e4m3fn]) # @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float8_e4m3fn]) # @pytest.mark.parametrize("dtype", [torch.bfloat16]) # @pytest.mark.parametrize("dtype", [torch.float8_e4m3fn]) @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"]) # @pytest.mark.parametrize("mha_type", ["mha"]) # @pytest.mark.parametrize("deterministic", [False, True]) @pytest.mark.parametrize("deterministic", [False]) @pytest.mark.parametrize("softcap", [0.0, 50.0]) # @pytest.mark.parametrize("softcap", [50.0]) @pytest.mark.parametrize("causal,local", [(False, False), (True, False), (False, True)]) # @pytest.mark.parametrize("causal,local", [(False, False)]) # @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256]) # @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192, 256]) # @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192]) # @pytest.mark.parametrize('d', [56, 80]) # @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128]) # @pytest.mark.parametrize("d", [64, 96, 128]) @pytest.mark.parametrize("d", [64, 96, 128, 192, 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), (8192, 8192), ], ) # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)]) def test_flash_attn_varlen_output( seqlen_q, seqlen_k, d, causal, local, softcap, deterministic, mha_type, dtype ): if softcap > 0.0 and dtype == torch.float8_e4m3fn: pytest.skip("Softcap is not supported for float8_e4m3fn") device = "cuda" # set seed torch.random.manual_seed(seqlen_q + seqlen_k + d + int(causal)) # batch_size = 40 # nheads = 16 batch_size = 9 if seqlen_q <= 2048 else 1 nheads = 6 # batch_size = 2 # nheads = 2 nheads_kv = nheads if mha_type == "mha" else (2 if mha_type == "gqa" else 1) dtype_ref = torch.bfloat16 if dtype == torch.float8_e4m3fn else dtype q_ref = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype_ref).to(dtype).to(dtype_ref).requires_grad_() if softcap > 0.0: # Ensure the values of qk are at least within softcap range. q_ref = (q_ref * softcap / 2).detach().requires_grad_() k_ref = torch.randn(batch_size, seqlen_k, nheads_kv, d, device=device, dtype=dtype_ref).to(dtype).to(dtype_ref).requires_grad_() v_ref = torch.randn(batch_size, seqlen_k, nheads_kv, d, device=device, dtype=dtype_ref).to(dtype).to(dtype_ref).requires_grad_() # Put window_size after QKV randn so that window_size changes from test to test window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) if dtype == torch.float8_e4m3fn: q_scale, k_scale, v_scale = [torch.rand(1, device=device, dtype=torch.float32) * 2 for _ in range(3)] else: q_scale, k_scale, v_scale = None, None, None q, k, v = [x.detach().requires_grad_() for x in (q_ref, k_ref, v_ref)] 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) q_unpad, k_unpad, v_unpad = [x.detach().to(dtype).requires_grad_() for x in (q_unpad, k_unpad, v_unpad)] out_unpad, 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, q_scale=q_scale, k_scale=k_scale, v_scale=v_scale, window_size=window_size, softcap=softcap, ) out = output_pad_fn(out_unpad) out_ref, attn_ref = attention_ref( q_ref, k_ref, v_ref, query_padding_mask, key_padding_mask, causal=causal, q_scale=q_scale, k_scale=k_scale, v_scale=v_scale, window_size=window_size, softcap=softcap ) out_pt, attn_pt = attention_ref( q_ref, k_ref, v_ref, query_padding_mask, key_padding_mask, causal=causal, q_scale=q_scale, k_scale=k_scale, v_scale=v_scale, window_size=window_size, softcap=softcap, upcast=False, reorder_ops=True, intermediate_dtype=dtype if dtype == torch.float8_e4m3fn else None, ) 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 dtype != torch.float8_e4m3fn: g_unpad = torch.randn_like(out_unpad) do_o = ((g_unpad.float() * out_unpad.float()).sum(-1)).transpose(-1, -2) import flashattn_hopper_cuda dq_unpad, dk_unpad, dv_unpad, softmax_d, dq_accum, lse_log2 = flashattn_hopper_cuda.bwd_varlen( g_unpad, q_unpad, k_unpad, v_unpad, out_unpad, lse, None, None, None, cu_seqlens_q, cu_seqlens_k, None, None, max_seqlen_q, max_seqlen_k, d ** (-0.5), causal, window_size[0], window_size[1], softcap, deterministic, ) dq = dq_pad_fn(dq_unpad) dk = dk_pad_fn(dk_unpad) dv = dk_pad_fn(dv_unpad) # print(f"dO_O max diff: {(softmax_d - do_o).abs().max().item()}") # assert (softmax_d - do_o).abs().max().item() <= 1e-5 # assert dq_accum.abs().max().item() == 0.0 g = output_pad_fn(g_unpad) # qk = torch.einsum('bthd,bshd->bhts', q / (d ** 0.5), k).float() # qk = torch.masked_fill(qk, rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf")) # dS = torch.einsum('bthd,bshd->bhts', g.float(), v.float()) # P = torch.softmax(qk, -1) # dP = P * (dS - (g.float() * out.float()).sum(-1).transpose(1, 2).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()) # dq, dk, dv = torch.autograd.grad(out, (q, k, v), g) dq_ref, dk_ref, dv_ref = torch.autograd.grad(out_ref, (q_ref, k_ref, v_ref), g) dq_pt, dk_pt, dv_pt = torch.autograd.grad(out_pt, (q_ref, k_ref, v_ref), 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()}") # breakpoint() # Check that FlashAttention's numerical error is at most twice the numerical error # of a Pytorch implementation. assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item() if dtype != torch.float8_e4m3fn: multiple = 2 if softcap == 0.0 else 4 assert (dq - dq_ref).abs().max().item() <= multiple * (dq_pt - dq_ref).abs().max().item() assert (dk - dk_ref).abs().max().item() <= multiple * (dk_pt - dk_ref).abs().max().item() assert (dv - dv_ref).abs().max().item() <= multiple * (dv_pt - dv_ref).abs().max().item()