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| 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 | |
| ): | |
| 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) | |
| 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, | |
| ): | |
| row_idx = rearrange(torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1") | |
| col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long) | |
| sk = ( | |
| seqlen_k | |
| if key_padding_mask is None | |
| else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1") | |
| ) | |
| sq = ( | |
| seqlen_q | |
| if query_padding_mask is None | |
| else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1") | |
| ) | |
| if window_size[0] < 0: | |
| return col_idx > row_idx + sk - sq + window_size[1] | |
| else: | |
| sk = torch.full_like(col_idx, seqlen_k) if key_padding_mask is None else sk | |
| return torch.logical_or( | |
| col_idx > torch.minimum(row_idx + sk - sq + window_size[1], sk), | |
| col_idx < row_idx + sk - sq - window_size[0], | |
| ) | |
| def 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 | |
| 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) | |
| 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 k, etc.) | |
| without changing the math. This is to estimate the numerical error from operation | |
| reordering. | |
| Output: | |
| output: (batch_size, seqlen_q, nheads, head_dim) | |
| attention: (batch_size, nheads, seqlen_q, seqlen_k), softmax after dropout | |
| """ | |
| if causal: | |
| window_size = (window_size[0], 0) | |
| dtype_og = q.dtype | |
| if upcast: | |
| q, k, v = q.float(), k.float(), v.float() | |
| 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 window_size[0] >= 0 or window_size[1] >= 0: | |
| local_mask = construct_local_mask( | |
| seqlen_q, | |
| seqlen_k, | |
| window_size, | |
| query_padding_mask, | |
| key_padding_mask, | |
| q.device, | |
| ) | |
| scores.masked_fill_(local_mask, float("-inf")) | |
| if attn_bias is not None: | |
| scores = scores + attn_bias | |
| attention = torch.softmax(scores, dim=-1).to(v.dtype) | |
| # 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 | |
| upcast=True, | |
| reorder_ops=False, | |
| ): | |
| 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, | |
| reorder_ops=reorder_ops, | |
| ) | |
| 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 | |
| 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, | |
| 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]) | |
| # @pytest.mark.parametrize("deterministic", [False]) | |
| # @pytest.mark.parametrize("alibi", [False]) | |
| # @pytest.mark.parametrize("local", [False]) | |
| # @pytest.mark.parametrize("causal", [False]) | |
| # @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", [512]) | |
| # @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 (d > 224 and 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 (d > 224 and 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]) | |
| # @pytest.mark.parametrize("deterministic", [True]) | |
| # @pytest.mark.parametrize("alibi", [True]) | |
| # @pytest.mark.parametrize("local", [True]) | |
| # @pytest.mark.parametrize('causal', [False]) | |
| # @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256]) | |
| # @pytest.mark.parametrize('d', [64]) | |
| # @pytest.mark.parametrize('seqlen', [128]) | |
| # @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 (d > 224 and 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 (d > 224 and 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", [False]) | |
| # @pytest.mark.parametrize("dtype", [torch.bfloat16]) | |
| # @pytest.mark.parametrize("mha_type", ["mha"]) | |
| # @pytest.mark.parametrize("deterministic", [True]) | |
| # @pytest.mark.parametrize("alibi", [True]) | |
| # @pytest.mark.parametrize("local", [True]) | |
| # @pytest.mark.parametrize("causal", [True]) | |
| # @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', [(256, 128)]) | |
| # @pytest.mark.parametrize("dropout_p", [0.17]) | |
| def test_flash_attn_output( | |
| seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, 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 = 4 | |
| nheads = 9 | |
| nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3) | |
| assert nheads % nheads_k == 0 | |
| window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) | |
| q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) | |
| if kvpacked: | |
| kv = torch.randn( | |
| batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype, requires_grad=True | |
| ) | |
| else: | |
| k = torch.randn( | |
| batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True | |
| ) | |
| v = torch.randn( | |
| batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True | |
| ) | |
| if alibi: | |
| alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3 | |
| attn_bias = attn_bias_from_alibi_slopes(alibi_slopes, seqlen_q, seqlen_k, causal=causal) | |
| else: | |
| alibi_slopes, attn_bias = None, None | |
| if kvpacked: | |
| out, lse, S_dmask = flash_attn_kvpacked_func( | |
| q, | |
| kv, | |
| dropout_p, | |
| causal=causal, | |
| window_size=window_size, | |
| alibi_slopes=alibi_slopes, | |
| deterministic=deterministic, | |
| return_attn_probs=True, | |
| ) | |
| else: | |
| out, lse, S_dmask = flash_attn_func( | |
| q, | |
| k, | |
| v, | |
| dropout_p, | |
| causal=causal, | |
| window_size=window_size, | |
| alibi_slopes=alibi_slopes, | |
| deterministic=deterministic, | |
| return_attn_probs=True, | |
| ) | |
| if dropout_p > 0.0: | |
| 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, | |
| ) | |
| out_pt, attn_pt = attention_kvpacked_ref( | |
| q, | |
| kv, | |
| None, | |
| None, | |
| attn_bias, | |
| dropout_p, | |
| dropout_mask, | |
| causal=causal, | |
| window_size=window_size, | |
| upcast=False, | |
| reorder_ops=True, | |
| ) | |
| else: | |
| out_ref, attn_ref = attention_ref( | |
| q, | |
| k, | |
| v, | |
| None, | |
| None, | |
| attn_bias, | |
| dropout_p, | |
| dropout_mask, | |
| causal=causal, | |
| window_size=window_size, | |
| ) | |
| out_pt, attn_pt = attention_ref( | |
| q, | |
| k, | |
| v, | |
| None, | |
| None, | |
| attn_bias, | |
| dropout_p, | |
| dropout_mask, | |
| causal=causal, | |
| window_size=window_size, | |
| upcast=False, | |
| reorder_ops=True, | |
| ) | |
| print(f"Output max diff: {(out - out_ref).abs().max().item()}") | |
| print(f"Output mean diff: {(out - out_ref).abs().mean().item()}") | |
| print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}") | |
| print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}") | |
| 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 (d > 224 and 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 (d > 224 and dropout_p == 0)) 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', [False]) | |
| # @pytest.mark.parametrize('dtype', [torch.float16]) | |
| # @pytest.mark.parametrize('mha_type', ["mqa"]) | |
| # @pytest.mark.parametrize("deterministic", [True]) | |
| # @pytest.mark.parametrize("alibi", [True]) | |
| # @pytest.mark.parametrize("local", [True]) | |
| # @pytest.mark.parametrize('causal', [True]) | |
| # @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256]) | |
| # @pytest.mark.parametrize('d', [64]) | |
| # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)]) | |
| # @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 | |
| ): | |
| 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 = 4 | |
| nheads = 9 | |
| nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3) | |
| assert nheads % nheads_k == 0 | |
| window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,)) | |
| q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True) | |
| if kvpacked: | |
| kv = torch.randn( | |
| batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype, requires_grad=True | |
| ) | |
| else: | |
| k = torch.randn( | |
| batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True | |
| ) | |
| v = torch.randn( | |
| batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True | |
| ) | |
| query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode="random") | |
| key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode="random") | |
| # key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode='full') | |
| if alibi: | |
| alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3 | |
| attn_bias = attn_bias_from_alibi_slopes( | |
| alibi_slopes, seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, causal=causal | |
| ) | |
| else: | |
| alibi_slopes, attn_bias = None, None | |
| if kvpacked: | |
| ( | |
| q_unpad, | |
| kv_unpad, | |
| cu_seqlens_q, | |
| cu_seqlens_k, | |
| max_seqlen_q, | |
| max_seqlen_k, | |
| q, | |
| kv, | |
| output_pad_fn, | |
| dq_pad_fn, | |
| dkv_pad_fn, | |
| ) = generate_qkv(q, *kv.unbind(dim=2), query_padding_mask, key_padding_mask, kvpacked=True) | |
| out_unpad, sm_lse, S_dmask = flash_attn_varlen_kvpacked_func( | |
| q_unpad, | |
| kv_unpad, | |
| cu_seqlens_q, | |
| cu_seqlens_k, | |
| max_seqlen_q, | |
| max_seqlen_k, | |
| dropout_p, | |
| causal=causal, | |
| window_size=window_size, | |
| alibi_slopes=alibi_slopes, | |
| deterministic=deterministic, | |
| return_attn_probs=True, | |
| ) | |
| else: | |
| ( | |
| q_unpad, | |
| k_unpad, | |
| v_unpad, | |
| cu_seqlens_q, | |
| cu_seqlens_k, | |
| max_seqlen_q, | |
| max_seqlen_k, | |
| q, | |
| k, | |
| v, | |
| output_pad_fn, | |
| dq_pad_fn, | |
| dk_pad_fn, | |
| ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False) | |
| out_unpad, sm_lse, S_dmask = flash_attn_varlen_func( | |
| q_unpad, | |
| k_unpad, | |
| v_unpad, | |
| cu_seqlens_q, | |
| cu_seqlens_k, | |
| max_seqlen_q, | |
| max_seqlen_k, | |
| dropout_p, | |
| causal=causal, | |
| window_size=window_size, | |
| alibi_slopes=alibi_slopes, | |
| deterministic=deterministic, | |
| return_attn_probs=True, | |
| ) | |
| out = output_pad_fn(out_unpad) | |
| if dropout_p > 0.0: | |
| 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, | |
| ) | |
| out_pt, attn_pt = attention_kvpacked_ref( | |
| q, | |
| kv, | |
| query_padding_mask, | |
| key_padding_mask, | |
| attn_bias, | |
| dropout_p, | |
| dropout_mask, | |
| causal=causal, | |
| window_size=window_size, | |
| upcast=False, | |
| reorder_ops=True, | |
| ) | |
| else: | |
| out_ref, attn_ref = attention_ref( | |
| q, | |
| k, | |
| v, | |
| query_padding_mask, | |
| key_padding_mask, | |
| attn_bias, | |
| dropout_p, | |
| dropout_mask, | |
| causal=causal, | |
| window_size=window_size, | |
| ) | |
| out_pt, attn_pt = attention_ref( | |
| q, | |
| k, | |
| v, | |
| query_padding_mask, | |
| key_padding_mask, | |
| attn_bias, | |
| dropout_p, | |
| dropout_mask, | |
| causal=causal, | |
| window_size=window_size, | |
| upcast=False, | |
| reorder_ops=True, | |
| ) | |
| print(f"Output max diff: {(out - out_ref).abs().max().item()}") | |
| print(f"Output mean diff: {(out - out_ref).abs().mean().item()}") | |
| print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}") | |
| print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}") | |
| 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 (d > 224 and 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 0.025) | |
| if (d <= MAX_HEADDIM_SM8x or (d > 224 and 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.bfloat16]) | |
| # @pytest.mark.parametrize("local", [True]) | |
| # @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", [True]) | |
| # @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) | |
| if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90): | |
| ( | |
| 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 | |
| if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90): | |
| 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.bfloat16]) | |
| # @pytest.mark.parametrize("local", [True]) | |
| # @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", [True]) | |
| # TODO: add smaller page sizes when https://github.com/Dao-AILab/flash-attention/pull/824 is merged | |
| # @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 = (d <= MAX_HEADDIM_SM8x or d > 224 or is_sm80 or is_sm90) and 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]) | |
| # @pytest.mark.parametrize("deterministic", [True]) | |
| # @pytest.mark.parametrize("alibi", [True]) | |
| # @pytest.mark.parametrize("local", [False]) | |
| # @pytest.mark.parametrize("causal", [True]) | |
| # @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]) | |
| # @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) | |
| if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90): | |
| ( | |
| 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 | |
| if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90): | |
| 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("num_splits", [1]) | |
| # @pytest.mark.parametrize("mha_type", ["mha"]) | |
| # @pytest.mark.parametrize("new_kv", [False]) | |
| # @pytest.mark.parametrize("alibi", [False]) | |
| # @pytest.mark.parametrize("local", [False]) | |
| # @pytest.mark.parametrize("causal", [False]) | |
| # @pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True]) | |
| # @pytest.mark.parametrize("rotary_interleaved", [False]) | |
| # @pytest.mark.parametrize("rotary_fraction", [0.0]) | |
| # @pytest.mark.parametrize("paged_kv_block_size", [256, 512]) | |
| # @pytest.mark.parametrize("paged_kv_block_size", [256]) | |
| # @pytest.mark.parametrize("has_batch_idx", [False]) | |
| # @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256]) | |
| # @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192]) | |
| # @pytest.mark.parametrize('d', [56, 80]) | |
| # @pytest.mark.parametrize("d", [128]) | |
| # @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)]) | |
| def test_flash_attn_kvcache( | |
| seqlen_q, | |
| seqlen_k, | |
| d, | |
| has_batch_idx, | |
| 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() | |
| 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, | |
| ) | |
| 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_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 | |
| ) | |
| 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, | |
| 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, | |
| ) | |
| 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, | |
| ) | |
| 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('causal', [True]) | |
| # @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("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 (d > 224 and 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 (d > 224 and 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('causal', [False]) | |
| # @pytest.mark.parametrize('d', [16]) | |
| # @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.bfloat16]) | |
| # @pytest.mark.parametrize('causal', [False]) | |
| # @pytest.mark.parametrize('d', [64]) | |
| # @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('causal', [False]) | |
| # @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.bfloat16]) | |
| # @pytest.mark.parametrize("local", [True]) | |
| # @pytest.mark.parametrize("causal", [True]) | |
| # @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]) | |
| # @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) | |
| if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90): | |
| 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.bfloat16]) | |
| # @pytest.mark.parametrize("local", [True]) | |
| # @pytest.mark.parametrize("causal", [True]) | |
| # @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", [True]) | |
| # @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) | |
| if (d <= MAX_HEADDIM_SM8x or d > 224) or (is_sm80 or is_sm90): | |
| dq, dk, dv = 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, dv) | |
| assert torch.equal(dk, dk) | |
| assert torch.equal(dq, dq) | |