import math import pytest import torch import torch.nn.functional as F from einops import rearrange, repeat from flash_attn import ( flash_attn_func, flash_attn_kvpacked_func, flash_attn_qkvpacked_func, flash_attn_varlen_func, flash_attn_varlen_kvpacked_func, flash_attn_varlen_qkvpacked_func, flash_attn_with_kvcache, ) from flash_attn.bert_padding import pad_input, unpad_input from flash_attn.flash_attn_interface import _get_block_size_n from flash_attn.layers.rotary import apply_rotary_emb 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 attn_bias_from_alibi_slopes( slopes, seqlen_q, seqlen_k, query_padding_mask=None, key_padding_mask=None, causal=False, key_leftpad=None ): batch, nheads = slopes.shape device = slopes.device slopes = rearrange(slopes, "b h -> b h 1 1") if causal: return torch.arange(-seqlen_k + 1, 1, device=device, dtype=torch.float32) * slopes else: 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) if key_leftpad is not None: key_leftpad = rearrange(key_leftpad, "b -> b 1 1 1") col_idx = repeat(col_idx, "s -> b 1 1 s", b=key_leftpad.shape[0]) col_idx = torch.where(col_idx >= key_leftpad, col_idx - key_leftpad, 2**32) 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") ) relative_pos = torch.abs(row_idx + sk - sq - col_idx) return -slopes * relative_pos.to(dtype=slopes.dtype) 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, _ = 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 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, key_leftpad=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) if key_leftpad is not None: key_leftpad = rearrange(key_leftpad, "b -> b 1 1 1") col_idx = repeat(col_idx, "s -> b 1 1 s", b=key_leftpad.shape[0]) col_idx = torch.where(col_idx >= key_leftpad, col_idx - key_leftpad, 2**32) 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 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, window_size=(-1, -1), # -1 means infinite window size softcap=0.0, upcast=True, reorder_ops=False, key_leftpad=None, ): """ 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) 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 window_size: (int, int), left and right window size 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 q, 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() 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 = scores / softcap scores = scores.tanh() scores = scores * 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, key_leftpad=key_leftpad, ) 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) # 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) # 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) 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) def attention_kvpacked_ref( q, kv, query_padding_mask=None, key_padding_mask=None, attn_bias=None, dropout_p=0.0, dropout_mask=None, causal=False, window_size=(-1, -1), # -1 means infinite window size softcap=0.0, upcast=True, reorder_ops=False, key_leftpad=None, ): return attention_ref( q, kv[:, :, 0], kv[:, :, 1], query_padding_mask, key_padding_mask, attn_bias, dropout_p, dropout_mask, upcast=upcast, causal=causal, window_size=window_size, softcap=softcap, reorder_ops=reorder_ops, key_leftpad=key_leftpad, ) def attention_qkvpacked_ref( qkv, key_padding_mask=None, attn_bias=None, dropout_p=0.0, dropout_mask=None, causal=False, window_size=(-1, -1), # -1 means infinite window size softcap=0.0, upcast=True, reorder_ops=False, ): return attention_ref( qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], key_padding_mask, key_padding_mask, attn_bias, dropout_p, dropout_mask, upcast=upcast, causal=causal, window_size=window_size, softcap=softcap, 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, seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, head_dim, is_dropout, causal=False, window_size=(-1, -1), # -1 means infinite window size ): """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_rounded) key_padding_mask: (batch_size, seqlen_k_rounded) """ if causal: window_size = (window_size[0], 0) seqlen_q_rounded, seqlen_k_rounded = S.shape[-2:] S_converted = S 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, S.device, ) local_mask = F.pad( local_mask, (0, seqlen_k_rounded - seqlen_k, 0, seqlen_q_rounded - seqlen_q), value=True, ) S_converted = S_converted.masked_fill(local_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_rounded ) if query_padding_mask is not None: query_padding_mask = F.pad(query_padding_mask, (0, seqlen_q_rounded - seqlen_q_og)) 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: key_padding_mask = F.pad(key_padding_mask, (0, seqlen_k_rounded - seqlen_k_og)) S_converted = S_converted.masked_fill(rearrange(~key_padding_mask, "b s -> b 1 1 s"), 0.0) S_converted = F.pad(S_converted, (0, 0, 0, seqlen_q_og - seqlen_q_rounded)) S_converted = F.pad(S_converted, (0, seqlen_k_og - seqlen_k_rounded)) return S_converted[:, :, :seqlen_q, :seqlen_k] def normalize_flash_attn_S( attn_unnorm, q, k, v, query_padding_mask=None, key_padding_mask=None, attn_bias=None, is_dropout=False, causal=False, window_size=(-1, -1), # -1 means infinite window size ): """ 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) attn_bias: broadcastable to (batch_size, nheads, seqlen_q, seqlen_k) Output: softmax_lse: (batch_size, nheads, seqlen_q) softmax_max: (batch_size, nheads, seqlen_q) """ if causal: window_size = (window_size[0], 0) 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 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.to(dtype=scores.dtype) block_size_n = _get_block_size_n(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) # lse could be -inf (i.e. all values in scores are -inf), and we want to set those to inf # so that when we do torch.exp(m - lse), we get 0.0 instead of NaN. lse[lse == float("-inf")] = float("inf") 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(m - lse), "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, window_size=(-1, -1), # -1 means infinite window size ): """ 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) """ if causal: window_size = (window_size[0], 0) batch_size, nheads, seqlen_q, seqlen_k = dropout_mask.shape dropped = ~dropout_mask valid = torch.ones_like(dropout_mask) if query_padding_mask is not None: dropped.masked_fill_(rearrange(~query_padding_mask, "b s -> b 1 s 1"), False) valid.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) valid.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), False) 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, dropout_mask.device, ) dropped.masked_fill_(local_mask, False) valid.masked_fill_(local_mask, False) dropped_total = dropped.sum() return dropped.sum() / valid.sum() @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16])) # @pytest.mark.parametrize("dtype", [torch.float16]) @pytest.mark.parametrize("deterministic", [False, True]) # @pytest.mark.parametrize("deterministic", [False]) @pytest.mark.parametrize("alibi", [False, True]) # @pytest.mark.parametrize("alibi", [False]) @pytest.mark.parametrize("local", [False, True]) # @pytest.mark.parametrize("local", [False]) @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, 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, 384, 768, 1024, 1025, 2048]) # @pytest.mark.parametrize("seqlen", [512]) @pytest.mark.parametrize("dropout_p", [0.0, 0.17]) # @pytest.mark.parametrize("dropout_p", [0.0]) def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, local, alibi, deterministic, 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 = 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: 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 attn_unnorm = S_dmask_converted.abs() attn = normalize_flash_attn_S( attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], None, None, attn_bias, dropout_p > 0.0, causal=causal, window_size=window_size, ) dropout_fraction = get_dropout_fraction( dropout_mask, None, None, causal=causal, window_size=window_size ).item() print(f"Actual dropout fraction: {dropout_fraction}") 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, ) # 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 dropout_p == 0) 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() # With alibi, many of the prob values are 0.0 & -0.0 so dropout_fraction isn't accurate if not alibi: assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025) if (d <= MAX_HEADDIM_SM8x or dropout_p == 0) 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("deterministic", [False, True]) # @pytest.mark.parametrize("deterministic", [True]) @pytest.mark.parametrize("alibi", [False, True]) # @pytest.mark.parametrize("alibi", [True]) @pytest.mark.parametrize("local", [False, True]) # @pytest.mark.parametrize("local", [True]) @pytest.mark.parametrize("causal", [False, True]) # @pytest.mark.parametrize('causal', [False]) @pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 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", [97, 128, 200, 257, 384, 512, 768, 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, local, alibi, deterministic, 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 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: 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 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, attn_bias, dropout_p > 0.0, causal=causal, window_size=window_size, ) dropout_fraction = get_dropout_fraction( dropout_mask, key_padding_mask, key_padding_mask, causal=causal, window_size=window_size ).item() print(f"Actual dropout fraction: {dropout_fraction}") 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()}") 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 dropout_p == 0) 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() # With alibi, many of the prob values are 0.0 & -0.0 so dropout_fraction isn't accurate if not alibi: assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025) if (d <= MAX_HEADDIM_SM8x or dropout_p == 0) 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("deterministic", [False, True]) # @pytest.mark.parametrize("deterministic", [True]) @pytest.mark.parametrize("alibi", [False, True]) # @pytest.mark.parametrize("alibi", [False]) @pytest.mark.parametrize("local", [False, True]) # @pytest.mark.parametrize("local", [False]) @pytest.mark.parametrize("causal", [False, True]) # @pytest.mark.parametrize("causal", [True]) @pytest.mark.parametrize("d", [32, 40, 59, 64, 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', [(256, 128)]) @pytest.mark.parametrize("dropout_p", [0.0, 0.17]) # @pytest.mark.parametrize("dropout_p", [0.0]) @pytest.mark.parametrize("softcap", [0.0, 50.0]) def test_flash_attn_output( seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked, softcap ): if ( max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30 ): pytest.skip() # Reference implementation OOM if softcap > 0.0 and dropout_p > 0.0: pytest.skip("Softcap and dropout not supported together") device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 4 nheads = 6 if softcap == 0.0 else 4 # softcap reference impl takes more memory nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 2) 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 softcap > 0: # Ensure the values of qk are at least within softcap range. q = q * softcap 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, softcap=softcap, 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, softcap=softcap, alibi_slopes=alibi_slopes, deterministic=deterministic, return_attn_probs=True, ) if dropout_p > 0.0: 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 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, attn_bias, dropout_p > 0.0, causal=causal, window_size=window_size, ) dropout_fraction = get_dropout_fraction( dropout_mask, None, None, causal=causal, window_size=window_size ).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, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size, softcap=softcap, ) out_pt, attn_pt = attention_kvpacked_ref( q, kv, None, None, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size, softcap=softcap, 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, softcap=softcap, ) out_pt, attn_pt = attention_ref( q, k, v, None, None, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size, softcap=softcap, 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 dropout_p == 0) 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() # With alibi, many of the prob values are 0.0 & -0.0 so dropout_fraction isn't accurate if not alibi: assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.025) if (d <= MAX_HEADDIM_SM8x or dropout_p == 0) or (is_sm80 or is_sm90): assert (dq - dq_ref).abs().max().item() <= 3 * (dq_pt - dq_ref).abs().max().item() assert (dk - dk_ref).abs().max().item() <= 3 * (dk_pt - dk_ref).abs().max().item() assert (dv - dv_ref).abs().max().item() <= 3 * (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("deterministic", [False, True]) # @pytest.mark.parametrize("deterministic", [True]) @pytest.mark.parametrize("alibi", [False, True]) # @pytest.mark.parametrize("alibi", [True]) @pytest.mark.parametrize("local", [False, True]) # @pytest.mark.parametrize("local", [True]) @pytest.mark.parametrize("causal", [False, True]) # @pytest.mark.parametrize('causal', [True]) @pytest.mark.parametrize("d", [32, 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", [ (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('seqlen_q,seqlen_k', [(128, 128)]) @pytest.mark.parametrize("dropout_p", [0.0, 0.17]) @pytest.mark.parametrize("softcap", [0.0, 50.0]) # @pytest.mark.parametrize('dropout_p', [0.0]) def test_flash_attn_varlen_output( seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked, softcap ): if ( max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30 ): pytest.skip() # Reference implementation OOM if softcap > 0.0 and dropout_p > 0.0: pytest.skip("Softcap and dropout not supported together") device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 4 nheads = 6 if softcap == 0.0 else 4 # softcap reference impl takes more memory nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 2) 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 softcap > 0: # Ensure the values of qk are at least within softcap range. q = q * softcap 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, softcap=softcap, 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, softcap=softcap, alibi_slopes=alibi_slopes, deterministic=deterministic, return_attn_probs=True, ) out = output_pad_fn(out_unpad) if dropout_p > 0.0: 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 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, attn_bias, dropout_p > 0.0, causal=causal, window_size=window_size, ) dropout_fraction = get_dropout_fraction( dropout_mask, query_padding_mask, key_padding_mask, causal=causal, window_size=window_size, ).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, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size, softcap=softcap, ) 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, softcap=softcap, 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, softcap=softcap, ) 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, softcap=softcap, 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 dropout_p == 0) 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() # With alibi, many of the prob values are 0.0 & -0.0 so dropout_fraction isn't accurate if not alibi: assert abs(dropout_fraction - dropout_p) <= (0.01 if not local else 0.04) if (d <= MAX_HEADDIM_SM8x or dropout_p == 0) or (is_sm80 or is_sm90): assert (dq - dq_ref).abs().max().item() <= 3 * (dq_pt - dq_ref).abs().max().item() assert (dk - dk_ref).abs().max().item() <= 3 * (dk_pt - dk_ref).abs().max().item() assert (dv - dv_ref).abs().max().item() <= 3 * (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.bfloat16]) @pytest.mark.parametrize("local", [False, True]) # @pytest.mark.parametrize("local", [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, 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]) @pytest.mark.parametrize("swap_sq_sk", [False, True]) # @pytest.mark.parametrize("swap_sq_sk", [True]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (1, 239), (3, 799), (127, 512), (127, 513), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (1023, 1024), ], ) # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)]) def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, local, dtype): if ( max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30 ): pytest.skip() # Reference implementation OOM if swap_sq_sk: seqlen_q, seqlen_k = seqlen_k, seqlen_q device = "cuda" causal = True # set seed torch.random.manual_seed(0) batch_size = 8 nheads = 9 window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) out = flash_attn_func(q, k, v, 0.0, causal=causal, window_size=window_size) out_ref, attn_ref = attention_ref( q, k, v, None, None, None, 0.0, None, causal=causal, window_size=window_size ) out_pt, attn_pt = attention_ref( q, k, v, None, None, None, 0.0, None, causal=causal, window_size=window_size, upcast=False, reorder_ops=True, ) print(f"Output max diff: {(out - out_ref).abs().max().item()}") print(f"Output mean diff: {(out - out_ref).abs().mean().item()}") print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}") print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}") 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()}") # Check that FlashAttention's numerical error is at most twice the numerical error # of a Pytorch implementation. assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item() + 1e-5 assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item() + 1e-5 assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item() + 1e-5 assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item() + 1e-5 @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16])) # @pytest.mark.parametrize("dtype", [torch.bfloat16]) @pytest.mark.parametrize("local", [False, True]) # @pytest.mark.parametrize("local", [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, 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("swap_sq_sk", [False, True]) # @pytest.mark.parametrize("swap_sq_sk", [True]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (1, 239), (3, 799), (127, 512), (127, 513), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (1023, 1024), ], ) # TODO: add smaller page sizes when https://github.com/Dao-AILab/flash-attention/pull/824 is merged @pytest.mark.parametrize("paged_kv_block_size", [None, 16, 256, 512]) # @pytest.mark.parametrize("seqlen_q,seqlen_k", [(256, 128)]) def test_flash_attn_varlen_causal( seqlen_q, seqlen_k, swap_sq_sk, d, local, paged_kv_block_size, dtype ): if ( max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30 ): pytest.skip() # Reference implementation OOM if swap_sq_sk: seqlen_q, seqlen_k = seqlen_k, seqlen_q device = "cuda" causal = True # set seed torch.random.manual_seed(0) batch_size = 8 nheads = 9 window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) if paged_kv_block_size is None: k = torch.randn( batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True ) v = torch.randn( batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True ) block_table = None else: k, v, block_table, k_cache_paged, v_cache_paged, num_blocks = _generate_block_kvcache( seqlen_k, paged_kv_block_size, batch_size, nheads, d, device, dtype ) query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode="random") key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode="random") ( q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q, k, v, output_pad_fn, dq_pad_fn, dk_pad_fn, ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False) out_unpad = flash_attn_varlen_func( q_unpad, k_unpad if paged_kv_block_size is None else k_cache_paged, v_unpad if paged_kv_block_size is None else v_cache_paged, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, 0.0, causal=causal, window_size=window_size, block_table=block_table, ) out = output_pad_fn(out_unpad) out_ref, attn_ref = attention_ref( q, k, v, query_padding_mask, key_padding_mask, None, 0.0, None, causal=causal, window_size=window_size, ) out_pt, attn_pt = attention_ref( q, k, v, query_padding_mask, key_padding_mask, None, 0.0, None, causal=causal, window_size=window_size, upcast=False, reorder_ops=True, ) print(f"Output max diff: {(out - out_ref).abs().max().item()}") print(f"Output mean diff: {(out - out_ref).abs().mean().item()}") print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}") print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}") g = torch.randn_like(out) do_o = (g.float() * out.float()).sum(-1) test_backward = block_table is None if test_backward: ( dq_unpad, dk_unpad, dv_unpad, ) = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g) dq = dq_pad_fn(dq_unpad) dk = dk_pad_fn(dk_unpad) dv = dk_pad_fn(dv_unpad) ( dq_ref, dk_ref, dv_ref, ) = torch.autograd.grad(out_ref, (q, k, v), g) ( dq_pt, dk_pt, dv_pt, ) = torch.autograd.grad(out_pt, (q, k, v), g) print(f"dQ max diff: {(dq - dq_ref).abs().max().item()}") print(f"dK max diff: {(dk - dk_ref).abs().max().item()}") print(f"dV max diff: {(dv - dv_ref).abs().max().item()}") print(f"dQ mean diff: {(dq - dq_ref).abs().mean().item()}") print(f"dK mean diff: {(dk - dk_ref).abs().mean().item()}") print(f"dV mean diff: {(dv - dv_ref).abs().mean().item()}") print(f"dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}") print(f"dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}") print(f"dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}") print(f"dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}") print(f"dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}") print(f"dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}") # Check that FlashAttention's numerical error is at most twice the numerical error # of a Pytorch implementation. assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item() + 1e-5 if test_backward: assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item() + 1e-5 assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item() + 1e-5 assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item() + 1e-5 @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16])) # @pytest.mark.parametrize("dtype", [torch.float16]) @pytest.mark.parametrize("deterministic", [False, True]) # @pytest.mark.parametrize("deterministic", [True]) @pytest.mark.parametrize("alibi", [False, True]) # @pytest.mark.parametrize("alibi", [True]) @pytest.mark.parametrize("local", [False, True]) # @pytest.mark.parametrize("local", [False]) @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, 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("swap_sq_sk", [False, True]) # @pytest.mark.parametrize("swap_sq_sk", [False]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (3, 1024), (1, 339), (64, 800), (3, 799), (64, 2048), (16, 20000), (16, 100000), (128, 128), (256, 256), ], ) # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)]) def test_flash_attn_splitkv( seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, alibi, deterministic, dtype ): if swap_sq_sk: seqlen_q, seqlen_k = seqlen_k, seqlen_q device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 1 nheads = 12 window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) 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 out, lse, _ = flash_attn_func( q, k, v, 0.0, causal=causal, window_size=window_size, alibi_slopes=alibi_slopes, deterministic=deterministic, return_attn_probs=True, ) out_ref, attn_ref = attention_ref( q, k, v, None, None, attn_bias, 0.0, None, causal=causal, window_size=window_size ) out_pt, attn_pt = attention_ref( q, k, v, None, None, attn_bias, 0.0, None, causal=causal, window_size=window_size, upcast=False, reorder_ops=True, ) print(f"Output max diff: {(out - out_ref).abs().max().item()}") print(f"Output mean diff: {(out - out_ref).abs().mean().item()}") print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}") print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}") 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()}") # Check that FlashAttention's numerical error is at most twice the numerical error # of a Pytorch implementation. assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item() + 1e-5 mult = 2 if not alibi else 8 assert (dq - dq_ref).abs().max().item() <= mult * (dq_pt - dq_ref).abs().max().item() + 2e-4 assert (dk - dk_ref).abs().max().item() <= mult * (dk_pt - dk_ref).abs().max().item() + 2e-4 assert (dv - dv_ref).abs().max().item() <= mult * (dv_pt - dv_ref).abs().max().item() + 2e-4 # @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16])) @pytest.mark.parametrize("dtype", [torch.float16]) @pytest.mark.parametrize("num_splits", [1, 0]) # @pytest.mark.parametrize("num_splits", [1]) @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"]) # @pytest.mark.parametrize("mha_type", ["mha"]) @pytest.mark.parametrize("new_kv", [False, True]) # @pytest.mark.parametrize("new_kv", [False]) @pytest.mark.parametrize("alibi", [False, True]) # @pytest.mark.parametrize("alibi", [False]) @pytest.mark.parametrize("local", [False, True]) # @pytest.mark.parametrize("local", [False]) @pytest.mark.parametrize("causal", [False, True]) # @pytest.mark.parametrize("causal", [False]) @pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True, False]) # @pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True]) @pytest.mark.parametrize("rotary_interleaved", [False, True]) # @pytest.mark.parametrize("rotary_interleaved", [False]) @pytest.mark.parametrize("rotary_fraction", [0.0, 0.5, 1.0]) # @pytest.mark.parametrize("rotary_fraction", [0.0]) @pytest.mark.parametrize("paged_kv_block_size", [None, 16, 256, 512]) # @pytest.mark.parametrize("paged_kv_block_size", [256, 512]) # @pytest.mark.parametrize("paged_kv_block_size", [None]) @pytest.mark.parametrize("has_leftpad", [False, True]) # @pytest.mark.parametrize("has_leftpad", [True]) # @pytest.mark.parametrize("has_batch_idx", [False, True]) @pytest.mark.parametrize("has_batch_idx", [False]) @pytest.mark.parametrize("d", [32, 59, 64, 80, 128, 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', [56, 80]) # @pytest.mark.parametrize("d", [128]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (1, 128), (1, 339), (3, 1024), (64, 800), (64, 256), (3, 799), (64, 2048), (16, 20000), (1, 128 * 1024), (16, 128 * 1024), (128, 128), ], ) # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)]) def test_flash_attn_kvcache( seqlen_q, seqlen_k, d, has_batch_idx, has_leftpad, paged_kv_block_size, rotary_fraction, rotary_interleaved, seqlen_new_eq_seqlen_q, causal, local, alibi, new_kv, mha_type, num_splits, dtype, ): if seqlen_q > seqlen_k and new_kv: pytest.skip() if not new_kv and rotary_fraction > 0.0: pytest.skip() if has_batch_idx and paged_kv_block_size is not None: pytest.skip() if has_leftpad and paged_kv_block_size is not None: pytest.skip() device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 2 batch_size_cache = batch_size if not has_batch_idx else batch_size * 2 nheads = 6 # rotary_dim must be a multiple of 16, and must be <= d rotary_dim = math.floor(int(rotary_fraction * d) / 16) * 16 nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3) assert nheads % nheads_k == 0 window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype) seqlen_new = seqlen_q if seqlen_new_eq_seqlen_q else torch.randint(1, seqlen_q + 1, (1,)).item() if new_kv: k = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype) v = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype) else: k, v = None, None if paged_kv_block_size is None: k_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype) v_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype) block_table = None else: ( k_cache, v_cache, block_table, k_cache_paged, v_cache_paged, num_blocks, ) = _generate_block_kvcache( seqlen_k, paged_kv_block_size, batch_size, nheads_k, d, device, dtype ) cache_seqlens = torch.randint( 0 if new_kv else 1, # If we don't use seqlen_q in the case of causal and rotary, cos/sin won't be long enough ( (seqlen_k - (seqlen_q if (causal or local) and rotary_dim > 1 else seqlen_new) + 1) if new_kv else (seqlen_k + 1) ), (batch_size,), dtype=torch.int32, device=device, ) if has_leftpad: cache_leftpad = torch.cat([torch.randint(0, cache_seqlens[i].item(), (1,), dtype=torch.int32, device=device) if cache_seqlens[i].item() > 0 else torch.zeros(1, dtype=torch.int32, device=device) for i in range(batch_size)]) else: cache_leftpad = None arange = rearrange(torch.arange(seqlen_k, device=device), "s -> 1 s") cache_seqlens_expanded = rearrange(cache_seqlens, "b -> b 1") key_padding_mask = arange < cache_seqlens_expanded + (seqlen_new if new_kv else 0) if has_leftpad: key_padding_mask = torch.logical_and( key_padding_mask, arange >= cache_leftpad.unsqueeze(-1).expand(-1, seqlen_k) ) if has_batch_idx: cache_batch_idx = torch.randperm(batch_size_cache, dtype=torch.int32, device=device)[ :batch_size ] else: cache_batch_idx = None if alibi: alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3 attn_bias = attn_bias_from_alibi_slopes( alibi_slopes, seqlen_q, seqlen_k, None, key_padding_mask, causal=causal, key_leftpad=cache_leftpad ) else: alibi_slopes, attn_bias = None, None # cache_seqlens = torch.tensor([64], dtype=torch.int32, device=device) if rotary_dim > 0: angle = ( torch.rand( seqlen_k if paged_kv_block_size is None else num_blocks * paged_kv_block_size, rotary_dim // 2, device=device, ) * 2 * math.pi ) cos = torch.cos(angle).to(dtype=dtype) sin = torch.sin(angle).to(dtype=dtype) if causal or local: q_ro = apply_rotary_emb( q, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved ) else: q_ro = rearrange( apply_rotary_emb( rearrange(q, "b s h d -> b 1 (s h) d"), cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved, ), "b 1 (s h) d -> b s h d", s=seqlen_q, ) # q_ro = q k_ro = apply_rotary_emb( k, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved ) else: cos, sin = None, None q_ro, k_ro = q, k # k_cache[:, 64:] = -1 k_cache_ref = ( k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)] ).clone() v_cache_ref = ( v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)] ).clone() if new_kv: update_mask = torch.logical_and( cache_seqlens_expanded <= arange, arange < cache_seqlens_expanded + seqlen_new ) k_cache_ref[update_mask] = rearrange(k_ro, "b s ... -> (b s) ...") v_cache_ref[update_mask] = rearrange(v, "b s ... -> (b s) ...") k_cache_rep = repeat(k_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k) v_cache_rep = repeat(v_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k) out = flash_attn_with_kvcache( q, k_cache if paged_kv_block_size is None else k_cache_paged, v_cache if paged_kv_block_size is None else v_cache_paged, k, v, rotary_cos=cos, rotary_sin=sin, cache_seqlens=cache_seqlens, cache_batch_idx=cache_batch_idx, cache_leftpad=cache_leftpad, block_table=block_table, causal=causal, window_size=window_size, rotary_interleaved=rotary_interleaved, alibi_slopes=alibi_slopes, num_splits=num_splits, ) # out = flash_attn_with_kvcache( # q, k_cache, v_cache, cache_seqlens=cache_seqlens, causal=causal, window_size=window_size # ) # out = flash_attn_with_kvcache(q, k_cache, v_cache, causal=causal, window_size=window_size) # qk = torch.einsum("bqhd,bkhd->bhqk", q, k_cache_ref) # m = qk.amax(-1, keepdim=True) # s_tmp = torch.exp((qk - m) / math.sqrt(d)) # o1 = torch.einsum('bhst,bthd->bshd', s_tmp, v_cache_ref) # lse_ref = torch.logsumexp(qk / math.sqrt(d), -1) # probs = torch.softmax(qk, dim=-1) out_ref, _ = attention_ref( q_ro, k_cache_rep, v_cache_rep, None, key_padding_mask, attn_bias, 0.0, None, causal=causal, window_size=window_size, key_leftpad=cache_leftpad, ) out_pt, _ = attention_ref( q_ro, k_cache_rep, v_cache_rep, None, key_padding_mask, attn_bias, 0.0, None, causal=causal, window_size=window_size, upcast=False, reorder_ops=True, key_leftpad=cache_leftpad, ) print(f"Output max diff: {(out - out_ref).abs().max().item()}") print(f"Output mean diff: {(out - out_ref).abs().mean().item()}") print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}") print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}") # Check that FlashAttention's numerical error is at most twice the numerical error # of a Pytorch implementation. if new_kv: if paged_kv_block_size is None: k_cache_select = ( k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)] ) v_cache_select = ( v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)] ) else: k_cache_select = rearrange( k_cache_paged[block_table.to(dtype=torch.long).flatten()], "(b nblocks) block_size ... -> b (nblocks block_size) ...", b=batch_size, )[:, :seqlen_k] v_cache_select = rearrange( v_cache_paged[block_table.to(dtype=torch.long).flatten()], "(b nblocks) block_size ... -> b (nblocks block_size) ...", b=batch_size, )[:, :seqlen_k] assert torch.allclose(k_cache_select, k_cache_ref, rtol=1e-3, atol=1e-3) assert torch.equal(v_cache_select, v_cache_ref) mult = 3 if not alibi else 5 assert (out - out_ref).abs().max().item() <= mult * (out_pt - out_ref).abs().max().item() + 1e-5 def _generate_block_kvcache(seqlen_k, paged_kv_block_size, batch_size, nheads_k, d, device, dtype): num_blocks = math.ceil(seqlen_k / paged_kv_block_size) * batch_size * 3 k_cache_paged = torch.randn( num_blocks, paged_kv_block_size, nheads_k, d, device=device, dtype=dtype ) v_cache_paged = torch.randn( num_blocks, paged_kv_block_size, nheads_k, d, device=device, dtype=dtype ) block_table = rearrange( torch.randperm(num_blocks, dtype=torch.int32, device=device), "(b nblocks) -> b nblocks", b=batch_size, ) k_cache = rearrange( # pytorch 1.12 doesn't have indexing with int32 k_cache_paged[block_table.to(dtype=torch.long).flatten()], "(b nblocks) block_size ... -> b (nblocks block_size) ...", b=batch_size, )[:, :seqlen_k] v_cache = rearrange( v_cache_paged[block_table.to(dtype=torch.long).flatten()], "(b nblocks) block_size ... -> b (nblocks block_size) ...", b=batch_size, )[:, :seqlen_k] return k_cache, v_cache, block_table, k_cache_paged, v_cache_paged, num_blocks # @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, 56, 64, 80, 96, 128]) # @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192]) # @pytest.mark.parametrize('d', [128]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (1, 239), (239, 1), (3, 799), (799, 3), (1024, 128), (97, 97), (128, 128), (200, 200), (256, 256), (257, 257), (384, 384), (512, 512), (768, 768), (1024, 1024), ], ) @pytest.mark.parametrize("dropout_p", [0.0, 0.17]) # @pytest.mark.parametrize("dropout_p", [0.0]) def test_flash_attn_race_condition(seqlen_q, seqlen_k, d, dropout_p, causal, dtype): device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 60 # Sometimes we need large batch size for the race conditions to trigger nheads = 4 q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) torch.random.manual_seed(42) out0, lse0, _ = flash_attn_func(q, k, v, dropout_p, causal=causal, return_attn_probs=True) g = torch.randn_like(out0) if (d <= MAX_HEADDIM_SM8x or dropout_p == 0) or (is_sm80 or is_sm90): ( dq0, dk0, dv0, ) = torch.autograd.grad(out0, (q, k, v), g) # Numerical error if we just do any arithmetic on dq dq_atol = 2 * ((dq0 + 0.3 - 0.3) - dq0).abs().max().item() for i in range(250): torch.random.manual_seed(42) out, lse, _ = flash_attn_func(q, k, v, dropout_p, causal=causal, return_attn_probs=True) assert torch.equal(out, out0) assert torch.equal(lse, lse0) if (d <= MAX_HEADDIM_SM8x or dropout_p == 0) or (is_sm80 or is_sm90): ( dq, dk, dv, ) = torch.autograd.grad(out, (q, k, v), g) dq_equal = torch.allclose(dq, dq0, atol=dq_atol) if not dq_equal: print(f"Iter {i}, {dq_atol = }, dQ max diff: {(dq - dq0).abs().max().item()}") assert torch.equal(dv, dv0) assert torch.equal(dk, dk0) assert dq_equal @pytest.mark.parametrize("dtype", [torch.float16]) @pytest.mark.parametrize("causal", [False, True]) # @pytest.mark.parametrize('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() @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]) def test_flash_attn_bwd_varlen_overflow(d, causal, dtype): """We previously had a bug where not masking elements beyond seqlen_k caused NaN in dQ, in the case where seqlen % 128 != 0 or varlen. """ device = "cuda" # set seed torch.random.manual_seed(0) nheads = 5 q_cuseqlen = torch.tensor([0, 76, 110, 256], device=device, dtype=torch.int32) k_cuseqlen = torch.tensor([0, 1, 2, 3], device=device, dtype=torch.int32) Mq = 256 Mk = 3 q = torch.randn([Mq, nheads, d], dtype=dtype, device=device) * 3 k, v = [torch.randn([Mk, nheads, d], dtype=dtype, device=device) * 3 for _ in range(2)] q.requires_grad_(True) k.requires_grad_(True) v.requires_grad_(True) out = flash_attn_varlen_func(q, k, v, q_cuseqlen, k_cuseqlen, Mq, Mk, causal=causal) g = torch.randn_like(out) out.backward(g) assert not q.grad.isnan().any() assert not k.grad.isnan().any() assert not v.grad.isnan().any() @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16])) # @pytest.mark.parametrize("dtype", [torch.bfloat16]) @pytest.mark.parametrize("local", [False, True]) # @pytest.mark.parametrize("local", [True]) @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, 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("swap_sq_sk", [False, True]) # @pytest.mark.parametrize("swap_sq_sk", [False]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (1, 239), (3, 799), (127, 512), (127, 513), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (1023, 1024), ], ) # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)]) def test_flash_attn_deterministic(seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, dtype): if ( max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30 ): pytest.skip() # Reference implementation OOM if swap_sq_sk: seqlen_q, seqlen_k = seqlen_k, seqlen_q device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 4 nheads = 9 window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) out = flash_attn_func(q, k, v, 0.0, causal=causal, window_size=window_size, deterministic=True) g = torch.randn_like(out) dq0, dk0, dv0 = torch.autograd.grad(out, (q, k, v), g, retain_graph=True) for _ in range(50): dq, dk, dv = torch.autograd.grad(out, (q, k, v), g, retain_graph=True) assert torch.equal(dv, dv0) assert torch.equal(dk, dk0) assert torch.equal(dq, dq0) @pytest.mark.parametrize("dtype", ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16])) # @pytest.mark.parametrize("dtype", [torch.bfloat16]) @pytest.mark.parametrize("local", [False, True]) # @pytest.mark.parametrize("local", [True]) @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, 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("swap_sq_sk", [False, True]) # @pytest.mark.parametrize("swap_sq_sk", [True]) @pytest.mark.parametrize( "seqlen_q,seqlen_k", [ (1, 239), (3, 799), (127, 512), (127, 513), (113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (1023, 1024), ], ) # @pytest.mark.parametrize("seqlen_q,seqlen_k", [(256, 128)]) def test_flash_attn_varlen_deterministic(seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, dtype): if ( max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30 ): pytest.skip() # Reference implementation OOM if swap_sq_sk: seqlen_q, seqlen_k = seqlen_k, seqlen_q device = "cuda" # set seed torch.random.manual_seed(0) batch_size = 2 nheads = 9 window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True) query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode="random") key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode="random") ( q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q, k, v, output_pad_fn, dq_pad_fn, dk_pad_fn, ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False) out = flash_attn_varlen_func( q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, 0.0, causal=causal, window_size=window_size, deterministic=True, ) g = torch.randn_like(out) dq0, dk0, dv0 = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True) for _ in range(50): dq, dk, dv = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True) assert torch.equal(dv, dv0) assert torch.equal(dk, dk0) assert torch.equal(dq, dq0) @pytest.mark.parametrize("dtype", [torch.float16]) @pytest.mark.parametrize("causal", [False, True]) # @pytest.mark.parametrize("causal", [False]) @pytest.mark.parametrize("paged_kv_block_size", [16]) # @pytest.mark.parametrize("has_batch_idx", [False]) @pytest.mark.parametrize("d", [128]) @pytest.mark.parametrize("nheads", [32]) @pytest.mark.parametrize("b", [4]) @pytest.mark.parametrize("n", [10]) @pytest.mark.parametrize("seqlen_q,seqlen_k", [(170, 170)]) def test_flash_attn_paged_kvcache_overflow( seqlen_q, seqlen_k, d, nheads, b, n, paged_kv_block_size, causal, dtype, ): device = "cuda" num_blocks = 1000*16//paged_kv_block_size key_cache = torch.rand([num_blocks, paged_kv_block_size, nheads, d], dtype=dtype, device=device) value_cache = torch.rand([num_blocks, paged_kv_block_size, nheads, d], dtype=dtype, device=device) cache_seqlens = torch.zeros(b, dtype=torch.int32, device=device) for _ in range(n): query = torch.rand([b, seqlen_q, nheads, d], dtype=dtype, device=device) key = torch.rand([b, seqlen_k, nheads, d], dtype=dtype, device=device) value = torch.rand([b, seqlen_k, nheads, d], dtype=dtype, device=device) block_tables = torch.randint(0, num_blocks, size=(b, (seqlen_k + paged_kv_block_size - 1) // paged_kv_block_size), dtype=torch.int32, device=device) output = flash_attn_with_kvcache( query, key_cache, value_cache, k=key, v=value, cache_seqlens=cache_seqlens, block_table=block_tables, causal=causal, )