import math import torch import torch.nn.functional as F import pytest from einops import rearrange, repeat from flash_attn import flash_attn_func, flash_attn_kvpacked_func, flash_attn_qkvpacked_func from flash_attn import flash_attn_varlen_qkvpacked_func, flash_attn_varlen_kvpacked_func from flash_attn import flash_attn_varlen_func from flash_attn.flash_attn_interface import _get_block_size from flash_attn.bert_padding import unpad_input, pad_input, index_first_axis MAX_HEADDIM_SM8x = 192 is_sm75 = torch.cuda.get_device_capability('cuda') == (7, 5) is_sm8x = torch.cuda.get_device_capability('cuda')[0] == 8 is_sm80 = torch.cuda.get_device_capability('cuda') == (8, 0) is_sm90 = torch.cuda.get_device_capability('cuda') == (9, 0) 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, (batch_size, 1), device=device) elif mode == 'third': lengths = torch.randint(max_seqlen // 3, max_seqlen, (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 = 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 = unpad_input(k, key_padding_mask) v_unpad, _, _, _ = 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 attention_ref(q, k, v, query_padding_mask=None, key_padding_mask=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_k, head_dim) v: (batch_size, seqlen_k, nheads_k, head_dim) query_padding_mask: (batch_size, seqlen_q) key_padding_mask: (batch_size, seqlen_k) dropout_p: float dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k) 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: causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=q.device), 1) scores.masked_fill_(causal_mask, float('-inf')) attention = torch.softmax(scores, dim=-1) 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) attention = attention.masked_fill(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), 0.0) return output.to(dtype=dtype_og), attention.to(dtype=dtype_og) def attention_kvpacked_ref(q, kv, query_padding_mask=None, key_padding_mask=None, dropout_p=0.0, dropout_mask=None, causal=False, upcast=True, reorder_ops=False): return attention_ref(q, kv[:, :, 0], kv[:, :, 1], query_padding_mask, key_padding_mask, dropout_p, dropout_mask, upcast=upcast, causal=causal, reorder_ops=reorder_ops) def attention_qkvpacked_ref(qkv, key_padding_mask=None, dropout_p=0.0, dropout_mask=None, causal=False, upcast=True, reorder_ops=False): return attention_ref(qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], key_padding_mask, key_padding_mask, dropout_p, dropout_mask, upcast=upcast, causal=causal, reorder_ops=reorder_ops) def generate_sparsity_mask(seqlen, sparsity=0.3): repeats = seqlen // 16 // 2 # mask = torch.stack([torch.tensor([1, 0] * repeats, dtype=torch.bool, device='cuda'), # torch.tensor([0, 1] * repeats, dtype=torch.bool, device='cuda')], dim=-1) # mask = torch.stack([torch.tensor([1, 1] * repeats, dtype=torch.bool, device='cuda'), # torch.tensor([1, 1] * repeats, dtype=torch.bool, device='cuda')], dim=-1) # mask = torch.stack([torch.tensor([1, 1] * repeats, dtype=torch.bool, device='cuda')], dim=-1) # mask = torch.stack([torch.tensor([1, 0] * repeats, dtype=torch.bool, device='cuda')], dim=-1) nrow, ncol = seqlen // 16, seqlen // 256 mask = torch.rand(nrow, ncol, device='cuda') < sparsity return mask def attention_blocksparse_ref(qkv, blockmask, attn_mask, dropout_p, dropout_mask): """ Arguments: qkv: (batch_size, seqlen, 3, nheads, head_dim) blockmask: (seqlen / 16, seqlen / 256) attn_mask: (batch_size, seqlen) dropout_p: float dropout_mask: (batch_size, nheads, seqlen, seqlen) Output: output: (batch_size, seqlen, nheads, head_dim) attention: softmax after dropout """ q, k, v = qkv.float().unbind(dim=2) d = qkv.shape[-1] seqlen = qkv.shape[1] scores = torch.einsum('bthd,bshd->bhts', q / math.sqrt(d), k) scores.masked_fill_(rearrange(~attn_mask, 'b s -> b 1 1 s'), float('-inf')) blockmask = repeat(blockmask, 's_16 s_256 -> (s_16 16) (s_256 256)') blockmask = blockmask[:seqlen, :seqlen] scores.masked_fill_(rearrange(~blockmask, 't s -> 1 1 t s'), float('-inf')) attention = torch.softmax(scores, dim=-1) attention = attention.masked_fill(rearrange(~attn_mask, 'b s -> b 1 s 1'), 0.0) attention = attention.masked_fill_(rearrange(~blockmask, 't s -> 1 1 t s'), 0.0) attention_drop = attention.masked_fill(~dropout_mask, 0.0) / (1 - dropout_p) output = torch.einsum('bhts,bshd->bthd', attention_drop , v) output.masked_fill_(rearrange(~attn_mask, 'b s -> b s 1 1'), 0) return output.to(dtype=qkv.dtype), attention.to(dtype=qkv.dtype) def convert_flash_attn_S_to_softmax(S, query_padding_mask, key_padding_mask, head_dim, is_dropout, causal=False): """FlashAttention stores the S matrix in a different way. Arguments: S: (batch_size, nheads, seqlen_q_rounded, seqlen_k_rounded) query_padding_mask: (batch_size, seqlen_q) key_padding_mask: (batch_size, seqlen_k) """ seqlen_q, seqlen_k = S.shape[-2:] warps_n = 4 blocksize_m, blocksize_n = _get_block_size(S.device, head_dim, is_dropout, causal) nblocks_n = (seqlen_k + blocksize_n - 1) // blocksize_n nblocks_m = (seqlen_q + blocksize_m - 1) // blocksize_m mmas_n = (blocksize_n + 16 - 1) // 16 S_flat = rearrange(S, 'b h (nblocks_m blocksize_m) (nblocks_n blocksize_n) -> b h nblocks_m nblocks_n (blocksize_m blocksize_n)', blocksize_m=blocksize_m, blocksize_n=blocksize_n) S_converted = rearrange(S_flat, 'b h nblocks_m nblocks_n (mmas_n mmas_m warps_n eight four c2 c1 c0) -> b h (nblocks_m mmas_m warps_n c1 eight) (nblocks_n mmas_n c2 four c0)', mmas_n=mmas_n, warps_n=warps_n, eight=8, c0=2, c1=2, c2=2, four=4) if causal: causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=S.device), 1) S_converted.masked_fill_(causal_mask, 0.0) # Need to zero out things not in attention_mask in case S was initialized with random values # and some of those values aren't overwritten. seqlen_q_og = query_padding_mask.shape[-1] if query_padding_mask is not None else seqlen_q if query_padding_mask is not None: if seqlen_q_og < seqlen_q: query_padding_mask = F.pad(query_padding_mask, (0, seqlen_q - seqlen_q_og)) else: query_padding_mask = query_padding_mask[:, :seqlen_q] S_converted = S_converted.masked_fill(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), 0.0) seqlen_k_og = key_padding_mask.shape[-1] if key_padding_mask is not None else seqlen_k if key_padding_mask is not None: if seqlen_k_og < seqlen_k: key_padding_mask = F.pad(key_padding_mask, (0, seqlen_k - seqlen_k_og)) else: key_padding_mask = key_padding_mask[:, :seqlen_k] S_converted = S_converted.masked_fill(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), 0.0) if seqlen_q_og < seqlen_q: S_converted = S_converted[:, :, :seqlen_q_og, :] else: S_converted = F.pad(S_converted, (0, 0, 0, seqlen_q_og - seqlen_q)) if seqlen_k_og < seqlen_k: S_converted = S_converted[:, :, :, :seqlen_k_og] else: S_converted = F.pad(S_converted, (0, seqlen_k_og - seqlen_k)) return S_converted def normalize_flash_attn_S(attn_unnorm, q, k, v, query_padding_mask=None, key_padding_mask=None, is_dropout=False, causal=False): """ Arguments: q: (batch_size, seqlen_q, nheads, head_dim) k, v: (batch_size, seqlen_k, nheads, head_dim) key_padding_mask: (batch_size, seqlen_q) Output: softmax_lse: (batch_size, nheads, seqlen_q) softmax_max: (batch_size, nheads, seqlen_q) """ q, k, v = q.float(), k.float(), v.float() _, seqlen_q, _, head_dim = q.shape seqlen_k = k.shape[1] scores = torch.einsum('bthd,bshd->bhts', q / math.sqrt(head_dim), k) if key_padding_mask is not None: scores.masked_fill_(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), float('-inf')) if causal: causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=q.device), 1) scores.masked_fill_(causal_mask, float('-inf')) _, block_size_n = _get_block_size(scores.device, head_dim, is_dropout, causal) scores_block = scores.split(block_size_n, dim=-1) lse_block = torch.stack([torch.logsumexp(s, dim=-1) for s in scores_block], dim=-1) lse = torch.logsumexp(lse_block, dim=-1) scores_max_block = torch.stack([torch.amax(s, dim=-1) for s in scores_block], dim=-1) cummax_block = torch.cummax(scores_max_block.flip(-1), dim=-1).values.flip(-1).unbind(dim=-1) attn_unnorm_block = attn_unnorm.split(block_size_n, dim=-1) attn_norm = torch.cat([a / rearrange(torch.exp(lse - m), 'b h s -> b h s 1') for a, m in zip(attn_unnorm_block, cummax_block)], dim=-1) if query_padding_mask is not None: attn_norm.masked_fill_(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), 0.0) return attn_norm.to(dtype=attn_unnorm.dtype) def get_dropout_fraction(dropout_mask, query_padding_mask=None, key_padding_mask=None, causal=False): """ dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k), bool. True means keep, False means drop. query_padding_mask: (batch_size, seqlen_q) key_padding_mask: (batch_size, seqlen_k) """ batch_size, nheads, seqlen_q, seqlen_k = dropout_mask.shape dropped = ~dropout_mask if query_padding_mask is not None: dropped.masked_fill_(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), False) if key_padding_mask is not None: dropped.masked_fill_(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), False) if causal: causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=dropout_mask.device), 1) dropped.masked_fill_(causal_mask, False) dropped_total = dropped.sum() query_lengths = (query_padding_mask.sum(dim=-1) if query_padding_mask is not None else torch.full((batch_size,), seqlen_q, device=dropout_mask.device)) key_lengths = (key_padding_mask.sum(dim=-1) if key_padding_mask is not None else torch.full((batch_size,), seqlen_k, device=dropout_mask.device)) if not causal: numel_per_batch = query_lengths * key_lengths else: numel_per_batch = torch.where( query_lengths <= key_lengths, query_lengths * (query_lengths + 1) / 2, query_lengths * key_lengths - (key_lengths * (key_lengths - 1) / 2) ) return dropped_total / (numel_per_batch.sum() * nheads) @pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16])) # @pytest.mark.parametrize('dtype', [torch.float16]) @pytest.mark.parametrize('causal', [False, True]) # @pytest.mark.parametrize('causal', [True]) @pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]) # @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256]) # @pytest.mark.parametrize('d', [32, 64, 96, 128]) # @pytest.mark.parametrize('d', [64]) # @pytest.mark.parametrize('seqlen', [128, 256, 384, 512, 768, 1024, 2048]) @pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048]) # @pytest.mark.parametrize('seqlen', [97]) @pytest.mark.parametrize('dropout_p', [0.0, 0.17]) # @pytest.mark.parametrize('dropout_p', [0.17]) def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, dtype): if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30: pytest.skip() # Reference implementation OOM device = 'cuda' # set seed torch.random.manual_seed(0) batch_size = 16 nheads = 9 qkv = torch.randn(batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype, requires_grad=True) out, lse, S_dmask = flash_attn_qkvpacked_func( qkv, dropout_p, return_attn_probs=True, causal=causal ) if dropout_p > 0.0: S_dmask_converted = convert_flash_attn_S_to_softmax( S_dmask, None, None, d, dropout_p > 0.0, causal=causal )[:, :, :seqlen, :seqlen] dropout_mask = S_dmask_converted >= 0 attn_unnorm = S_dmask_converted.abs() attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], None, None, dropout_p > 0.0, causal=causal) dropout_fraction = get_dropout_fraction(dropout_mask, None, None, causal=causal).item() print(f'Actual dropout fraction: {dropout_fraction}') else: dropout_mask = None out_ref, attn_ref = attention_qkvpacked_ref(qkv, None, dropout_p, dropout_mask, causal=causal) out_pt, attn_pt = attention_qkvpacked_ref(qkv, None, dropout_p, dropout_mask, causal=causal, upcast=False, reorder_ops=True) # v = qkv[:, :, 2].float() # qk = torch.einsum('bshd,bthd->bhst', qkv[:, :, 0], qkv[:, :, 1]).float() # if causal: # causal_mask = torch.triu(torch.ones(seqlen, seqlen, dtype=torch.bool, device=qkv.device), 1) # qk.masked_fill_(causal_mask, float('-inf')) # m = qk.amax(-1, keepdim=True) # s_tmp = torch.exp((qk - m) / math.sqrt(d)) # p_tmp = torch.softmax(qk / math.sqrt(d), -1) # p_dropped = p_tmp if dropout_mask is None else p_tmp.masked_fill(~dropout_mask, 0) # lse_ref = torch.logsumexp(qk / math.sqrt(d), -1) # qk_max1 = torch.max(qk[:, :, 128:, 192:], -1, keepdim=True).values # qk_max2 = torch.max(qk[:, :, 128:, 128:], -1, keepdim=True).values # qk_max3 = torch.max(qk[:, :, 128:, 64:], -1, keepdim=True).values # qk_max4 = torch.max(qk[:, :, 128:, :], -1, keepdim=True).values # o1 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, 192:] - qk_max1) / math.sqrt(d)), v[:, 192:]) # o2 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, 128:] - qk_max2) / math.sqrt(d)), v[:, 128:]) # o3 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, 64:] - qk_max3) / math.sqrt(d)), v[:, 64:]) # o4 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, :] - qk_max4) / math.sqrt(d)), v[:, :]) 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 dropout_p > 0.0: print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}') print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}') g = torch.randn_like(out) # do_o = (g.float() * out.float()).sum(-1) # dv_tmp = torch.einsum('bhts,bthd->bshd', attn_pt[:, :, :64], g[:, :64]) # dv_tmp1 = torch.einsum('bhts,bthd->bshd', attn_pt[:, :, 64:], g[:, 64:]) if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90): 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()}') # 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 dropout_p > 0.0: assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item() assert abs(dropout_fraction - dropout_p) <= 0.01 if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90): assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item() @pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16])) # @pytest.mark.parametrize('dtype', [torch.float16]) @pytest.mark.parametrize('causal', [False, True]) # @pytest.mark.parametrize('causal', [False]) @pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]) # @pytest.mark.parametrize('d', [64]) @pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048]) # @pytest.mark.parametrize('seqlen', [128]) @pytest.mark.parametrize('dropout_p', [0.0, 0.17]) # @pytest.mark.parametrize('dropout_p', [0.0]) def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype): if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30: pytest.skip() # Reference implementation OOM device = 'cuda' # set seed torch.random.manual_seed(0) batch_size = 5 nheads = 6 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') 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, return_attn_probs=True, causal=causal ) out = output_pad_fn(out_unpad) if dropout_p > 0.0: S_dmask_converted = convert_flash_attn_S_to_softmax( S_dmask, key_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal )[:, :, :seqlen, :seqlen] dropout_mask = S_dmask_converted >= 0 attn_unnorm = S_dmask_converted.abs() attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], key_padding_mask, key_padding_mask, dropout_p > 0.0, causal=causal) dropout_fraction = get_dropout_fraction(dropout_mask, key_padding_mask, key_padding_mask, causal=causal).item() print(f'Actual dropout fraction: {dropout_fraction}') else: dropout_mask = None out_ref, attn_ref = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask, causal=causal) out_pt, attn_pt = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask, causal=causal, upcast=False, reorder_ops=True) print(f'Output max diff: {(out - out_ref).abs().max().item()}') print(f'Output mean diff: {(out - out_ref).abs().mean().item()}') print(f'Pytorch max diff: {(out_pt - out_ref).abs().max().item()}') print(f'Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}') if dropout_p > 0.0: print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}') print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}') g = torch.randn_like(out) if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90): 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()}') # 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 dropout_p > 0.0: assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item() assert abs(dropout_fraction - dropout_p) <= 0.01 if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90): assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item() @pytest.mark.parametrize('kvpacked', [True, False]) # @pytest.mark.parametrize('kvpacked', [False]) @pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16])) # @pytest.mark.parametrize('dtype', [torch.bfloat16]) @pytest.mark.parametrize('mha_type', ["mha", "mqa", "gqa"]) # @pytest.mark.parametrize('mha_type', ["mha"]) @pytest.mark.parametrize('causal', [False, True]) # @pytest.mark.parametrize('causal', [False]) @pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]) # @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256]) # @pytest.mark.parametrize('d', [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]) @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('seqlen_q,seqlen_k', [(128, 128)]) @pytest.mark.parametrize('dropout_p', [0.0, 0.17]) # @pytest.mark.parametrize('dropout_p', [0.0]) def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, dtype, kvpacked): if max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30: pytest.skip() # Reference implementation OOM device = 'cuda' # set seed torch.random.manual_seed(0) batch_size = 16 nheads = 9 nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3) assert nheads % nheads_k == 0 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 kvpacked: out, lse, S_dmask = flash_attn_kvpacked_func( q, kv, dropout_p, return_attn_probs=True, causal=causal ) else: out, lse, S_dmask = flash_attn_func( q, k, v, dropout_p, return_attn_probs=True, causal=causal ) if dropout_p > 0.0: S_dmask_converted = convert_flash_attn_S_to_softmax( S_dmask, None, None, d, dropout_p > 0.0, causal=causal )[:, :, :seqlen_q, :seqlen_k] dropout_mask = S_dmask_converted >= 0 attn_unnorm = S_dmask_converted.abs() 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) attn = normalize_flash_attn_S(attn_unnorm, q, k_rep, v_rep, None, None, dropout_p > 0.0, causal=causal) dropout_fraction = get_dropout_fraction(dropout_mask, None, None, causal=causal).item() print(f'Actual dropout fraction: {dropout_fraction}') else: dropout_mask = None if kvpacked: out_ref, attn_ref = attention_kvpacked_ref(q, kv, None, None, dropout_p, dropout_mask, causal=causal) out_pt, attn_pt = attention_kvpacked_ref(q, kv, None, None, dropout_p, dropout_mask, causal=causal, upcast=False, reorder_ops=True) else: out_ref, attn_ref = attention_ref(q, k, v, None, None, dropout_p, dropout_mask, causal=causal) out_pt, attn_pt = attention_ref(q, k, v, None, None, dropout_p, dropout_mask, causal=causal, upcast=False, reorder_ops=True) print(f'Output max diff: {(out - out_ref).abs().max().item()}') print(f'Output mean diff: {(out - out_ref).abs().mean().item()}') print(f'Pytorch max diff: {(out_pt - out_ref).abs().max().item()}') print(f'Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}') if dropout_p > 0.0: print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}') print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}') g = torch.randn_like(out) do_o = (g.float() * out.float()).sum(-1) if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90): 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()}') # 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 dropout_p > 0.0: assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item() assert abs(dropout_fraction - dropout_p) <= 0.01 if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90): assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item() assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item() assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item() @pytest.mark.parametrize('kvpacked', [True, False]) # @pytest.mark.parametrize('kvpacked', [False]) @pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16])) # @pytest.mark.parametrize('dtype', [torch.float16]) @pytest.mark.parametrize('mha_type', ["mha", "mqa", "gqa"]) # @pytest.mark.parametrize('mha_type', ["mqa"]) @pytest.mark.parametrize('causal', [False, True]) # @pytest.mark.parametrize('causal', [True]) @pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256]) # @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256]) # @pytest.mark.parametrize('d', [64]) @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('seqlen_q,seqlen_k', [(128, 128)]) @pytest.mark.parametrize('dropout_p', [0.0, 0.17]) # @pytest.mark.parametrize('dropout_p', [0.0]) def test_flash_attn_varlen_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, dtype, kvpacked): if max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30: pytest.skip() # Reference implementation OOM device = 'cuda' # set seed torch.random.manual_seed(0) batch_size = 16 nheads = 9 nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3) assert nheads % nheads_k == 0 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 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, return_attn_probs=True, causal=causal ) 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, return_attn_probs=True, causal=causal ) out = output_pad_fn(out_unpad) if dropout_p > 0.0: S_dmask_converted = convert_flash_attn_S_to_softmax( S_dmask, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal )[:, :, :seqlen_q, :seqlen_k] dropout_mask = S_dmask_converted >= 0 attn_unnorm = S_dmask_converted.abs() 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) attn = normalize_flash_attn_S(attn_unnorm, q, k_rep, v_rep, query_padding_mask, key_padding_mask, dropout_p > 0.0, causal=causal) dropout_fraction = get_dropout_fraction(dropout_mask, query_padding_mask, key_padding_mask, causal=causal).item() print(f'Actual dropout fraction: {dropout_fraction}') else: dropout_mask = None if kvpacked: out_ref, attn_ref = attention_kvpacked_ref(q, kv, query_padding_mask, key_padding_mask, dropout_p, dropout_mask, causal=causal) out_pt, attn_pt = attention_kvpacked_ref(q, kv, query_padding_mask, key_padding_mask, dropout_p, dropout_mask, causal=causal, upcast=False, reorder_ops=True) else: out_ref, attn_ref = attention_ref(q, k, v, query_padding_mask, key_padding_mask, dropout_p, dropout_mask, causal=causal) out_pt, attn_pt = attention_ref(q, k, v, query_padding_mask, key_padding_mask, dropout_p, dropout_mask, causal=causal, upcast=False, reorder_ops=True) print(f'Output max diff: {(out - out_ref).abs().max().item()}') print(f'Output mean diff: {(out - out_ref).abs().mean().item()}') print(f'Pytorch max diff: {(out_pt - out_ref).abs().max().item()}') print(f'Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}') if dropout_p > 0.0: print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}') print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}') g = torch.randn_like(out) if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90): 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()}') # 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 dropout_p > 0.0: assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item() assert abs(dropout_fraction - dropout_p) <= 0.01 if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90): assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item() assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item() assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item() @pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16])) # @pytest.mark.parametrize('dtype', [torch.float16]) @pytest.mark.parametrize('causal', [False, True]) # @pytest.mark.parametrize('causal', [False]) # @pytest.mark.parametrize('d', [32, 56, 64, 80, 96, 128]) @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192]) # @pytest.mark.parametrize('d', [128]) # @pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048]) # @pytest.mark.parametrize('seqlen', [128, 256, 384, 512, 768, 1024, 2048]) @pytest.mark.parametrize('seqlen', [128]) # @pytest.mark.parametrize('dropout_p', [0.0, 0.17]) @pytest.mark.parametrize('dropout_p', [0.0]) def test_flash_attn_race_condition(seqlen, 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 qkv = torch.randn(batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype, requires_grad=True) out0, lse0, _ = flash_attn_qkvpacked_func( qkv, dropout_p, return_attn_probs=True, causal=causal ) g = torch.randn_like(out0) if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90): dqkv0, = torch.autograd.grad(out0, qkv, g) # Numerical error if we just do any arithmetic on dq dq_atol = 2 * ((dqkv0[:, :, 0] + 0.3 - 0.3) - dqkv0[:, :, 0]).abs().max().item() for i in range(200): torch.random.manual_seed(0) out, lse, S_dmask = flash_attn_qkvpacked_func( qkv, dropout_p, return_attn_probs=True, causal=causal ) assert torch.equal(out, out0) assert torch.equal(lse, lse0) if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90): dqkv, = torch.autograd.grad(out, qkv, g) dq_equal = torch.allclose(dqkv[:, :, 0], dqkv0[:, :, 0], atol=dq_atol) if not dq_equal: dq0 = dqkv0[:, :, 0] dq = dqkv[:, :, 0] print(f'Iter {i}, {dq_atol = }, dQ max diff: {(dqkv[:, :, 0] - dqkv0[:, :, 0]).abs().max().item()}') assert dq_equal assert torch.equal(dqkv[:, :, 1], dqkv0[:, :, 1]) assert torch.equal(dqkv[:, :, 2], dqkv0[:, :, 2]) @pytest.mark.parametrize('dtype', [torch.float16]) @pytest.mark.parametrize('causal', [False, True]) # @pytest.mark.parametrize('causal', [False]) @pytest.mark.parametrize('d', [16, 32, 64]) # @pytest.mark.parametrize('d', [16]) @pytest.mark.parametrize('seqlen', [1, 2, 5, 17, 128]) # @pytest.mark.parametrize('seqlen', [2]) 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. """ 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] if is_sm75 else [torch.float16, torch.bfloat16])) # @pytest.mark.parametrize('dtype', [torch.bfloat16]) @pytest.mark.parametrize('causal', [False, True]) # @pytest.mark.parametrize('causal', [False]) @pytest.mark.parametrize('d', [64, 128]) # @pytest.mark.parametrize('d', [64]) @pytest.mark.parametrize('seqlen', [97, 128, 200, 256]) # @pytest.mark.parametrize('seqlen', [128]) 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()