import math import pytest import torch import torch.nn.functional as F from einops import rearrange, repeat from flash_attn_interface import flash_attn_func ABS_TOL = 5e-3 REL_TOL = 1e-1 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, upcast=True, reorder_ops=False, ): """ 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 """ dtype_og = q.dtype if upcast: q, k, v = q.float(), k.float(), v.float() 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 key_padding_mask is not None: scores.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf")) if causal: local_mask = construct_local_mask( seqlen_q, seqlen_k, (-1, 0), None, None, 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 causal: 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 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]) @pytest.mark.parametrize("causal", [False, True]) # @pytest.mark.parametrize("causal", [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]) # @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192]) # @pytest.mark.parametrize('d', [56, 80]) @pytest.mark.parametrize("d", [64, 128, 256]) # @pytest.mark.parametrize("d", [128]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (64, 128), (128, 128), (256, 256), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (512, 256), (1024, 1024), (1023, 1024), (1024, 1023), (2048, 2048), ], ) # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)]) def test_flash_attn_output( seqlen_q, seqlen_k, d, causal, dtype ): device = "cuda" # set seed torch.random.manual_seed(0) # batch_size = 40 # nheads = 16 batch_size = 9 nheads = 4 # batch_size = 1 # nheads = 1 q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) k = torch.randn( batch_size, seqlen_k, nheads, 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, lse = flash_attn_func(q, k, v, causal=causal) out_ref, attn_ref = attention_ref( q, k, v, None, None, causal=causal, ) out_pt, attn_pt = attention_ref( q, k, v, None, None, causal=causal, upcast=False, reorder_ops=True, ) # qk = torch.einsum('bshd,bthd->bhst', q, k).float() # m = qk.amax(-1, keepdim=True) # s_tmp = torch.exp((qk - m) / math.sqrt(d)) # exp_sum = s_tmp.sum(-1) qk = torch.einsum('bthd,bshd->bhts', q.float() / math.sqrt(d), k.float()) lse_ref = torch.logsumexp(qk, dim=-1) print(f"Output max diff: {(out - out_ref).abs().max().item()}") print(f"Output mean diff: {(out - out_ref).abs().mean().item()}") print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}") print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}") if not causal: print(f"LSE max diff: {(lse - lse_ref).abs().max().item()}") # breakpoint() # if d <= 128: # g = torch.randn_like(out) # do_o = (g.float() * out.float()).sum(-1) # dq, dk, dv = torch.autograd.grad(out, (q, k, v), g) # dq_ref, dk_ref, dv_ref = torch.autograd.grad(out_ref, (q, k, v), g) # dq_pt, dk_pt, dv_pt = torch.autograd.grad(out_pt, (q, k, v), g) # print(f"dQ max diff: {(dq - dq_ref).abs().max().item()}") # print(f"dK max diff: {(dk - dk_ref).abs().max().item()}") # print(f"dV max diff: {(dv - dv_ref).abs().max().item()}") # print(f"dQ mean diff: {(dq - dq_ref).abs().mean().item()}") # print(f"dK mean diff: {(dk - dk_ref).abs().mean().item()}") # print(f"dV mean diff: {(dv - dv_ref).abs().mean().item()}") # print(f"dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}") # print(f"dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}") # print(f"dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}") # print(f"dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}") # print(f"dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}") # print(f"dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}") # dS = torch.einsum('bthd,bshd->bhts', g.float(), v.float()) # P = torch.softmax(qk, -1) # dP = P * (dS - do_o.unsqueeze(1)) # dQ = torch.einsum('bhts,bshd->bthd', dP, k.float()) # dV = torch.einsum('bhts,bthd->bshd', P, g.float()) # dK = torch.einsum('bhts,bthd->bshd', dP, q.float()) # breakpoint() # Check that FlashAttention's numerical error is at most twice the numerical error # of a Pytorch implementation. assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item() # if d <= 128: # assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item() # assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item() # assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item()