956 lines
51 KiB
Python
956 lines
51 KiB
Python
import math
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import torch
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import torch.nn.functional as F
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import pytest
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from einops import rearrange, repeat
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from flash_attn.flash_attn_interface import flash_attn_func, flash_attn_unpadded_qkvpacked_func, _get_block_size, flash_attn_unpadded_kvpacked_func, flash_attn_unpadded_func
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from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_split_func
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from flash_attn.bert_padding import unpad_input, pad_input, index_first_axis
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is_sm75 = torch.cuda.get_device_capability('cuda') == (7, 5)
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is_sm80 = torch.cuda.get_device_capability('cuda') == (8, 0)
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def generate_random_padding_mask(max_seqlen, batch_size, device, mode='random'):
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assert mode in ['full', 'random', 'third', 'split']
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if mode == 'full':
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lengths = torch.full((batch_size, 1), max_seqlen, device=device, dtype=torch.int32)
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elif mode == 'random':
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lengths = torch.randint(max(1, max_seqlen - 20), max_seqlen + 1, (batch_size, 1), device=device)
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elif mode == 'third':
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lengths = torch.randint(max_seqlen // 3, max_seqlen + 1, (batch_size, 1), device=device)
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elif mode == 'split':
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lengths0 = torch.randint(min(128, max_seqlen), max_seqlen + 1,
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(batch_size // 4 * 3, 1), device=device)
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lengths1 = torch.randint(min(max(1, max_seqlen - 20), 128), min(max_seqlen, 128) + 1,
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(batch_size - batch_size // 4 * 3, 1), device=device)
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lengths = torch.cat([lengths0, lengths1], dim=0)
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padding_mask = repeat(torch.arange(max_seqlen, device=device), 's -> b s', b=batch_size) < lengths
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return padding_mask
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def generate_qkv(x, Wqkv, nheads, query_padding_mask=None, key_padding_mask=None,
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kvpacked=False, qkvpacked=False):
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"""
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Arguments:
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x: (batch_size, seqlen, nheads * d)
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Wqkv: nn.Linear(nheads * d, 3 * nheads * d)
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query_padding_mask: (batch_size, seqlen), bool
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key_padding_mask: (batch_size, seqlen), bool
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"""
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assert not (kvpacked and qkvpacked)
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batch_size, seqlen, dim = x.shape
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q, k, v = Wqkv(x).chunk(3, dim=-1)
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if query_padding_mask is not None:
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q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, query_padding_mask)
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q_unpad = rearrange(q_unpad, 'nnz (h d) -> nnz h d', h=nheads)
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output_pad_fn = lambda output_unpad: rearrange(
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pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen),
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'b s (h d) -> b s h d', h=nheads
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)
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else:
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q_unpad = rearrange(q, 'b s (h d) -> (b s) h d', h=nheads)
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cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
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device=q_unpad.device)
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max_seqlen_q = seqlen
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output_pad_fn = lambda output_unpad: rearrange(output_unpad, '(b s) h d -> b s h d', b=batch_size)
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if key_padding_mask is not None:
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k_unpad, indices_k, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask)
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k_unpad = rearrange(k_unpad, 'nnz (h d) -> nnz h d', h=nheads)
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v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
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v_unpad = rearrange(v_unpad, 'nnz (h d) -> nnz h d', h=nheads)
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else:
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k_unpad = rearrange(k, 'b s (h d) -> (b s) h d', h=nheads)
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v_unpad = rearrange(v, 'b s (h d) -> (b s) h d', h=nheads)
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cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
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device=q_unpad.device)
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max_seqlen_k = seqlen
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if qkvpacked:
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assert (query_padding_mask == key_padding_mask).all()
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qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
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qkv = rearrange(torch.stack([q, k, v], dim=2), 'b s t (h d) -> b s t h d', h=nheads)
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if query_padding_mask is not None:
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dqkv_pad_fn = lambda dqkv_unpad: rearrange(
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pad_input(rearrange(dqkv_unpad, 'nnz t h d -> nnz (t h d)'), indices_q, batch_size, seqlen),
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'b s (t h d) -> b s t h d', t=3, h=nheads
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)
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else:
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dqkv_pad_fn = lambda dqkv_unpad: rearrange(dqkv_unpad, '(b s) t h d -> b s t h d', b=batch_size)
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return (qkv_unpad.detach().requires_grad_(), cu_seqlens_q, max_seqlen_q,
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qkv.detach().requires_grad_(), output_pad_fn, dqkv_pad_fn)
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elif kvpacked:
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kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
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q = rearrange(q, 'b s (h d) -> b s h d', h=nheads)
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kv = rearrange(torch.stack([k, v], dim=2), 'b s t (h d) -> b s t h d', h=nheads)
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dq_pad_fn = output_pad_fn
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if key_padding_mask is not None:
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dkv_pad_fn = lambda dkv_unpad: rearrange(
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pad_input(rearrange(dkv_unpad, 'nnz t h d -> nnz (t h d)'), indices_k, batch_size, seqlen),
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'b s (t h d) -> b s t h d', t=2, h=nheads
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)
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else:
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dkv_pad_fn = lambda dkv_unpad: rearrange(dkv_unpad, '(b s) t h d -> b s t h d', b=batch_size)
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return (q_unpad.detach().requires_grad_(), kv_unpad.detach().requires_grad_(),
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cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
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q.detach().requires_grad_(), kv.detach().requires_grad_(),
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output_pad_fn, dq_pad_fn, dkv_pad_fn)
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else:
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q, k, v = [rearrange(z, 'b s (h d) -> b s h d', h=nheads).detach().requires_grad_()
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for z in [q, k, v]]
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dq_pad_fn = output_pad_fn
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if key_padding_mask is not None:
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dk_pad_fn = lambda dk_unpad: rearrange(
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pad_input(rearrange(dk_unpad, 'nnz h d -> nnz (h d)'), indices_k, batch_size, seqlen),
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'b s (h d) -> b s h d', h=nheads
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)
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else:
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dk_pad_fn = lambda dk_unpad: rearrange(dk_unpad, '(b s) h d -> b s h d', b=batch_size)
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return (q_unpad.detach().requires_grad_(), k_unpad.detach().requires_grad_(),
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v_unpad.detach().requires_grad_(),
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cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
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q, k, v,
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output_pad_fn, dq_pad_fn, dk_pad_fn)
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def attention_ref(q, k, v, query_padding_mask=None, key_padding_mask=None, dropout_p=0.0,
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dropout_mask=None, causal=False, upcast=True, reorder_ops=False):
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"""
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Arguments:
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q: (batch_size, seqlen_q, nheads, head_dim)
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k: (batch_size, seqlen_k, nheads, head_dim)
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v: (batch_size, seqlen_k, nheads, head_dim)
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query_padding_mask: (batch_size, seqlen_q)
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key_padding_mask: (batch_size, seqlen_k)
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dropout_p: float
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dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k)
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upcast: whether to cast all inputs to fp32, do all computation in fp32, then cast
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output back to fp16/bf16.
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reorder_ops: whether to change the order of operations (scaling k instead of scaling k, etc.)
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without changing the math. This is to estimate the numerical error from operation
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reordering.
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Output:
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output: (batch_size, seqlen_q, nheads, head_dim)
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attention: (batch_size, nheads, seqlen_q, seqlen_k), softmax after dropout
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"""
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dtype_og = q.dtype
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if upcast:
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q, k, v = q.float(), k.float(), v.float()
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seqlen_q, seqlen_k = q.shape[1], k.shape[1]
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d = q.shape[-1]
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if not reorder_ops:
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scores = torch.einsum('bthd,bshd->bhts', q / math.sqrt(d), k)
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else:
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scores = torch.einsum('bthd,bshd->bhts', q, k / math.sqrt(d))
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if key_padding_mask is not None:
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scores.masked_fill_(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), float('-inf'))
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if causal:
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causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=q.device), 1)
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scores.masked_fill_(causal_mask, float('-inf'))
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attention = torch.softmax(scores, dim=-1)
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dropout_scaling = 1.0 / (1 - dropout_p)
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# attention_drop = attention.masked_fill(~dropout_mask, 0.0) * dropout_scaling
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# output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
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if dropout_mask is not None:
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attention_drop = attention.masked_fill(~dropout_mask, 0.0)
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else:
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attention_drop = attention
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output = torch.einsum('bhts,bshd->bthd', attention_drop, v * dropout_scaling)
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if query_padding_mask is not None:
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output.masked_fill_(rearrange(~query_padding_mask, 'b s -> b s 1 1'), 0.0)
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attention = attention.masked_fill(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), 0.0)
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return output.to(dtype=dtype_og), attention.to(dtype=dtype_og)
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def attention_kvpacked_ref(q, kv, query_padding_mask=None, key_padding_mask=None, dropout_p=0.0,
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dropout_mask=None, causal=False, upcast=True, reorder_ops=False):
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return attention_ref(q, kv[:, :, 0], kv[:, :, 1], query_padding_mask,
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key_padding_mask, dropout_p, dropout_mask, upcast=upcast, causal=causal,
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reorder_ops=reorder_ops)
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def attention_qkvpacked_ref(qkv, key_padding_mask=None, dropout_p=0.0,
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dropout_mask=None, causal=False, upcast=True, reorder_ops=False):
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return attention_ref(qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], key_padding_mask,
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key_padding_mask, dropout_p, dropout_mask, upcast=upcast, causal=causal,
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reorder_ops=reorder_ops)
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def generate_sparsity_mask(seqlen, sparsity=0.3):
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repeats = seqlen // 16 // 2
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# mask = torch.stack([torch.tensor([1, 0] * repeats, dtype=torch.bool, device='cuda'),
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# torch.tensor([0, 1] * repeats, dtype=torch.bool, device='cuda')], dim=-1)
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# mask = torch.stack([torch.tensor([1, 1] * repeats, dtype=torch.bool, device='cuda'),
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# torch.tensor([1, 1] * repeats, dtype=torch.bool, device='cuda')], dim=-1)
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# mask = torch.stack([torch.tensor([1, 1] * repeats, dtype=torch.bool, device='cuda')], dim=-1)
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# mask = torch.stack([torch.tensor([1, 0] * repeats, dtype=torch.bool, device='cuda')], dim=-1)
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nrow, ncol = seqlen // 16, seqlen // 256
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mask = torch.rand(nrow, ncol, device='cuda') < sparsity
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return mask
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def attention_blocksparse_ref(qkv, blockmask, attn_mask, dropout_p, dropout_mask):
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"""
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Arguments:
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qkv: (batch_size, seqlen, 3, nheads, head_dim)
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blockmask: (seqlen / 16, seqlen / 256)
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attn_mask: (batch_size, seqlen)
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dropout_p: float
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dropout_mask: (batch_size, nheads, seqlen, seqlen)
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Output:
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output: (batch_size, seqlen, nheads, head_dim)
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attention: softmax after dropout
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"""
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q, k, v = qkv.float().unbind(dim=2)
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d = qkv.shape[-1]
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seqlen = qkv.shape[1]
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scores = torch.einsum('bthd,bshd->bhts', q / math.sqrt(d), k)
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scores.masked_fill_(rearrange(~attn_mask, 'b s -> b 1 1 s'), float('-inf'))
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blockmask = repeat(blockmask, 's_16 s_256 -> (s_16 16) (s_256 256)')
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blockmask = blockmask[:seqlen, :seqlen]
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scores.masked_fill_(rearrange(~blockmask, 't s -> 1 1 t s'), float('-inf'))
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attention = torch.softmax(scores, dim=-1)
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attention = attention.masked_fill(rearrange(~attn_mask, 'b s -> b 1 s 1'), 0.0)
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attention = attention.masked_fill_(rearrange(~blockmask, 't s -> 1 1 t s'), 0.0)
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attention_drop = attention.masked_fill(~dropout_mask, 0.0) / (1 - dropout_p)
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output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
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output.masked_fill_(rearrange(~attn_mask, 'b s -> b s 1 1'), 0)
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return output.to(dtype=qkv.dtype), attention.to(dtype=qkv.dtype)
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def convert_flash_attn_S_to_softmax(S, query_padding_mask, key_padding_mask, head_dim, is_dropout,
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causal=False):
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"""FlashAttention stores the S matrix in a different way.
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Arguments:
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S: (batch_size, nheads, seqlen_q, seqlen_k)
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query_padding_mask: (batch_size, seqlen_q)
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key_padding_mask: (batch_size, seqlen_k)
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"""
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S_flat = rearrange(S, 'b h t s -> b h (t s)')
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seqlen_q, seqlen_k = S.shape[-2:]
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block_size = _get_block_size(S.device, head_dim, is_dropout)
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loop_steps = (seqlen_k + block_size - 1) // block_size
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warps_n = 4
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mmas_n = (seqlen_k // warps_n // 16) if seqlen_k <= block_size else (block_size // warps_n // 16)
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S_converted = rearrange(S_flat, 'b h (loop nsteps mmas_n warps_n eight t r c0 c1) -> b h (nsteps r eight) (loop mmas_n warps_n c0 t c1)',
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loop=loop_steps, nsteps=seqlen_q // 16, mmas_n=mmas_n, warps_n=warps_n, eight=8, t=4,
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r=2, c0=2, c1=2)
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# Need to zero out things not in attention_mask in case S was initialized with random values
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# and some of those values aren't overwritten.
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seqlen_q_og = query_padding_mask.shape[-1]
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if seqlen_q_og < seqlen_q:
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query_padding_mask = F.pad(query_padding_mask, (0, seqlen_q - seqlen_q_og))
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else:
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query_padding_mask = query_padding_mask[:, :seqlen_q]
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S_converted = S_converted.masked_fill(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), 0.0)
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seqlen_k_og = key_padding_mask.shape[-1]
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if seqlen_k_og < seqlen_k:
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key_padding_mask = F.pad(key_padding_mask, (0, seqlen_k - seqlen_k_og))
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else:
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key_padding_mask = key_padding_mask[:, :seqlen_k]
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S_converted = S_converted.masked_fill(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), 0.0)
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if causal:
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causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=S.device), 1)
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S_converted.masked_fill_(causal_mask, 0.0)
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if seqlen_q_og < seqlen_q:
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S_converted = S_converted[:, :, :seqlen_q_og, :]
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else:
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S_converted = F.pad(S_converted, (0, 0, 0, seqlen_q_og - seqlen_q))
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if seqlen_k_og < seqlen_k:
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S_converted = S_converted[:, :, :, :seqlen_k_og]
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else:
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S_converted = F.pad(S_converted, (0, seqlen_k_og - seqlen_k))
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return S_converted
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def normalize_flash_attn_S(attn_unnorm, q, k, v, query_padding_mask=None, key_padding_mask=None,
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is_dropout=False, causal=False):
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"""
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Arguments:
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q: (batch_size, seqlen_q, nheads, head_dim)
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k, v: (batch_size, seqlen_k, nheads, head_dim)
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key_padding_mask: (batch_size, seqlen_q)
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Output:
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softmax_lse: (batch_size, nheads, seqlen_q)
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softmax_max: (batch_size, nheads, seqlen_q)
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"""
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q, k, v = q.float(), k.float(), v.float()
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_, seqlen_q, _, head_dim = q.shape
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seqlen_k = k.shape[1]
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scores = torch.einsum('bthd,bshd->bhts', q / math.sqrt(head_dim), k)
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if key_padding_mask is not None:
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scores.masked_fill_(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), float('-inf'))
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if causal:
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causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool, device=q.device), 1)
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scores.masked_fill_(causal_mask, float('-inf'))
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block_size = _get_block_size(scores.device, head_dim, is_dropout)
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scores_block = scores.split(block_size, dim=-1)
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lse_block = torch.stack([torch.logsumexp(s, dim=-1) for s in scores_block], dim=-1)
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lcse_block = torch.logcumsumexp(lse_block, dim=-1).unbind(dim=-1)
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scores_max_block = ([torch.amax(scores_block[0], dim=-1)]
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+ [torch.maximum(torch.amax(s, dim=-1), lcse)
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for s, lcse in zip(scores_block[1:], lcse_block[:-1])])
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attn_unnorm_block = attn_unnorm.split(block_size, dim=-1)
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attn_norm = torch.cat([a / rearrange(torch.exp(lcse_block[-1] - m), 'b h s -> b h s 1')
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for a, m in zip(attn_unnorm_block, scores_max_block)], dim=-1)
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if query_padding_mask is not None:
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attn_norm.masked_fill_(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), 0.0)
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return attn_norm.to(dtype=attn_unnorm.dtype)
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def get_dropout_fraction(dropout_mask, query_padding_mask=None, key_padding_mask=None, causal=False):
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"""
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dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k), bool. True means keep, False means drop.
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query_padding_mask: (batch_size, seqlen_q)
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key_padding_mask: (batch_size, seqlen_k)
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"""
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batch_size, nheads, seqlen_q, seqlen_k = dropout_mask.shape
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dropped = ~dropout_mask
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if query_padding_mask is not None:
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dropped.masked_fill_(rearrange(~query_padding_mask, 'b s -> b 1 s 1'), False)
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if key_padding_mask is not None:
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dropped.masked_fill_(rearrange(~key_padding_mask, 'b s -> b 1 1 s'), False)
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if causal:
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causal_mask = torch.triu(torch.ones(seqlen_q, seqlen_k, dtype=torch.bool,
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device=dropout_mask.device), 1)
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dropped.masked_fill_(causal_mask, False)
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dropped_total = dropped.sum()
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|
query_lengths = (query_padding_mask.sum(dim=-1) if query_padding_mask is not None
|
|
else torch.full((batch_size,), seqlen_q, device=dropout_mask.device))
|
|
key_lengths = (key_padding_mask.sum(dim=-1) if key_padding_mask is not None
|
|
else torch.full((batch_size,), seqlen_k, device=dropout_mask.device))
|
|
if not causal:
|
|
numel_per_batch = query_lengths * key_lengths
|
|
else:
|
|
numel_per_batch = torch.where(
|
|
query_lengths <= key_lengths,
|
|
query_lengths * (query_lengths + 1) / 2,
|
|
query_lengths * key_lengths - (key_lengths * (key_lengths - 1) / 2)
|
|
)
|
|
return dropped_total / (numel_per_batch.sum() * nheads)
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
|
|
# @pytest.mark.parametrize('dtype', [torch.float16])
|
|
@pytest.mark.parametrize('causal', [False, True])
|
|
# @pytest.mark.parametrize('causal', [False])
|
|
@pytest.mark.parametrize('d', [128, 64, 80, 40, 32, 16])
|
|
# @pytest.mark.parametrize('d', [64])
|
|
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
|
|
# @pytest.mark.parametrize('seqlen', [128])
|
|
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
|
|
# @pytest.mark.parametrize('dropout_p', [0.0])
|
|
def test_flash_attn_unpadded_qkvpacked(seqlen, d, dropout_p, causal, dtype):
|
|
if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
|
|
pytest.skip() # Reference implementation OOM
|
|
device = 'cuda'
|
|
# if dtype == torch.float16:
|
|
# rtol, atol = (1e-3, 3e-4) if not causal else (1e-3, 1e-3)
|
|
# else: # torch.bfloat16
|
|
# rtol, atol = (3e-3, 3e-3) if not causal else (1e-3, 1e-3)
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
# Set smaller batch size so it would trigger num_splits > 1
|
|
batch_size = 8
|
|
nheads = 4
|
|
x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
|
|
Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)
|
|
|
|
# key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='random')
|
|
key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='full')
|
|
|
|
qkv_unpad, cu_seqlens, max_seqlen, qkv, output_pad_fn, dqkv_pad_fn = generate_qkv(
|
|
x, Wqkv, nheads, key_padding_mask, key_padding_mask, qkvpacked=True
|
|
)
|
|
|
|
output_unpad, sm_lse, S_dmask = flash_attn_unpadded_qkvpacked_func(
|
|
qkv_unpad, cu_seqlens, max_seqlen, dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
output = output_pad_fn(output_unpad)
|
|
S_dmask_converted = convert_flash_attn_S_to_softmax(
|
|
S_dmask, key_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
|
|
)
|
|
dropout_mask = S_dmask_converted >= 0
|
|
attn_unnorm = S_dmask_converted.abs()
|
|
attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2],
|
|
key_padding_mask, key_padding_mask, dropout_p > 0.0, causal=causal)
|
|
dropout_fraction = get_dropout_fraction(dropout_mask, key_padding_mask, key_padding_mask,
|
|
causal=causal).item()
|
|
|
|
output_ref, attn_ref = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
|
|
causal=causal)
|
|
output_pt, attn_pt = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
|
|
causal=causal, upcast=False, reorder_ops=True)
|
|
print(f'Actual dropout fraction: {dropout_fraction}')
|
|
print(f'Output max diff: {(output - output_ref).abs().max().item()}')
|
|
print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
|
|
print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
|
|
print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')
|
|
print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
|
|
print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')
|
|
|
|
if is_sm80 or d <= 64: # Only run backward for d=128 on A100
|
|
g = torch.randn_like(output)
|
|
dqkv_unpad, = torch.autograd.grad(output, qkv_unpad, g)
|
|
dqkv = dqkv_pad_fn(dqkv_unpad)
|
|
dqkv_ref, = torch.autograd.grad(output_ref, qkv, g)
|
|
dqkv_pt, = torch.autograd.grad(output_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 (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
|
|
# assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)
|
|
assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
|
|
# assert torch.allclose(attn, attn_ref, rtol=rtol, atol=atol)
|
|
if dropout_p == 0.0:
|
|
assert dropout_mask.all()
|
|
else:
|
|
assert 0.98 <= dropout_fraction / dropout_p <= 1.02
|
|
|
|
if is_sm80 or d <= 64: # Only run backward for d=128 on A100
|
|
# Error for dK and dV could be a bit higher if we're splitting along seqlen_q dimension
|
|
assert (dqkv - dqkv_ref).abs().max().item() <= 4 * (dqkv_pt - dqkv_ref).abs().max().item()
|
|
# assert torch.allclose(dqkv, dqkv_ref, rtol=rtol, atol=atol)
|
|
|
|
|
|
@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('d', [128, 64, 80, 40, 32, 16])
|
|
# @pytest.mark.parametrize('d', [64])
|
|
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
|
|
# @pytest.mark.parametrize('seqlen', [128])
|
|
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
|
|
# @pytest.mark.parametrize('dropout_p', [0.0])
|
|
def test_flash_attn_unpadded_kvpacked(seqlen, d, dropout_p, causal, dtype):
|
|
if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
|
|
pytest.skip() # Reference implementation OOM
|
|
device = 'cuda'
|
|
# if dtype == torch.float16:
|
|
# rtol, atol = (1e-3, 3e-4) if not causal else (1e-3, 1e-3)
|
|
# else: # torch.bfloat16
|
|
# rtol, atol = (3e-3, 3e-3) if not causal else (1e-3, 1e-3)
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 32
|
|
nheads = 4
|
|
x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
|
|
Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)
|
|
|
|
query_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='random')
|
|
key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='random')
|
|
|
|
(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(
|
|
x, Wqkv, nheads, query_padding_mask, key_padding_mask, kvpacked=True
|
|
)
|
|
|
|
output_unpad, sm_lse, S_dmask = flash_attn_unpadded_kvpacked_func(
|
|
q_unpad, kv_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
|
|
dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
output = output_pad_fn(output_unpad)
|
|
S_dmask_converted = convert_flash_attn_S_to_softmax(
|
|
S_dmask, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
|
|
)
|
|
dropout_mask = S_dmask_converted >= 0
|
|
attn_unnorm = S_dmask_converted.abs()
|
|
attn = normalize_flash_attn_S(attn_unnorm, q, kv[:, :, 0], kv[:, :, 1],
|
|
query_padding_mask, key_padding_mask, dropout_p > 0.0, causal=causal)
|
|
dropout_fraction = get_dropout_fraction(dropout_mask, query_padding_mask, key_padding_mask,
|
|
causal=causal)
|
|
|
|
output_ref, attn_ref = attention_kvpacked_ref(q, kv, query_padding_mask, key_padding_mask,
|
|
dropout_p, dropout_mask, causal=causal)
|
|
output_pt, attn_pt = attention_kvpacked_ref(q, kv, query_padding_mask, key_padding_mask,
|
|
dropout_p, dropout_mask, causal=causal,
|
|
upcast=False, reorder_ops=True)
|
|
print(f'Actual dropout fraction: {dropout_fraction}')
|
|
print(f'Output max diff: {(output - output_ref).abs().max().item()}')
|
|
print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
|
|
print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
|
|
print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')
|
|
print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
|
|
print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')
|
|
|
|
if is_sm80 or d <= 64: # Only run backward for d=128 on A100
|
|
g = torch.randn_like(output)
|
|
dq_unpad, dkv_unpad, = torch.autograd.grad(output, (q_unpad, kv_unpad), g)
|
|
dq = dq_pad_fn(dq_unpad)
|
|
dkv = dkv_pad_fn(dkv_unpad)
|
|
dq_ref, dkv_ref, = torch.autograd.grad(output_ref, (q, kv), g)
|
|
dq_pt, dkv_pt = torch.autograd.grad(output_pt, (q, kv), g)
|
|
print(f'dQ max diff: {(dq - dq_ref).abs().max().item()}')
|
|
print(f'dK max diff: {(dkv[:, :, 0] - dkv_ref[:, :, 0]).abs().max().item()}')
|
|
print(f'dV max diff: {(dkv[:, :, 1] - dkv_ref[:, :, 1]).abs().max().item()}')
|
|
print(f'dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}')
|
|
print(f'dK Pytorch max diff: {(dkv_pt[:, :, 0] - dkv_ref[:, :, 0]).abs().max().item()}')
|
|
print(f'dV Pytorch max diff: {(dkv_pt[:, :, 1] - dkv_ref[:, :, 1]).abs().max().item()}')
|
|
|
|
# Check that FlashAttention's numerical error is at most twice the numerical error
|
|
# of a Pytorch implementation.
|
|
assert (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
|
|
# assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)
|
|
assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
|
|
# assert torch.allclose(attn, attn_ref, rtol=rtol, atol=atol)
|
|
if dropout_p == 0.0:
|
|
assert dropout_mask.all()
|
|
else:
|
|
assert 0.99 <= dropout_fraction / dropout_p <= 1.01
|
|
|
|
if is_sm80 or d <= 64: # Only run backward for d=128 on A100
|
|
assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item()
|
|
assert (dkv - dkv_ref).abs().max().item() <= 2 * (dkv_pt - dkv_ref).abs().max().item()
|
|
# assert torch.allclose(dq, dq_ref, rtol=rtol, atol=atol)
|
|
# assert torch.allclose(dkv, dkv_ref, rtol=rtol, atol=atol)
|
|
|
|
|
|
@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('d', [128, 64, 80, 40, 32, 16])
|
|
# @pytest.mark.parametrize('d', [64])
|
|
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
|
|
# @pytest.mark.parametrize('seqlen', [128])
|
|
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
|
|
# @pytest.mark.parametrize('dropout_p', [0.0])
|
|
def test_flash_attn_unpadded(seqlen, d, dropout_p, causal, dtype):
|
|
if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
|
|
pytest.skip() # Reference implementation OOM
|
|
device = 'cuda'
|
|
# if dtype == torch.float16:
|
|
# rtol, atol = (1e-3, 3e-4) if not causal else (1e-3, 1e-3)
|
|
# else: # torch.bfloat16
|
|
# rtol, atol = (3e-3, 3e-3) if not causal else (1e-3, 1e-3)
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 32
|
|
nheads = 4
|
|
x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
|
|
Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)
|
|
|
|
query_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='random')
|
|
key_padding_mask = generate_random_padding_mask(seqlen, 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(
|
|
x, Wqkv, nheads, query_padding_mask, key_padding_mask
|
|
)
|
|
|
|
output_unpad, sm_lse, S_dmask = flash_attn_unpadded_func(
|
|
q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
|
|
dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
output = output_pad_fn(output_unpad)
|
|
S_dmask_converted = convert_flash_attn_S_to_softmax(
|
|
S_dmask, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
|
|
)
|
|
dropout_mask = S_dmask_converted >= 0
|
|
attn_unnorm = S_dmask_converted.abs()
|
|
attn = normalize_flash_attn_S(attn_unnorm, q, k, v, query_padding_mask, key_padding_mask,
|
|
dropout_p > 0.0, causal=causal)
|
|
dropout_fraction = get_dropout_fraction(dropout_mask, query_padding_mask, key_padding_mask,
|
|
causal=causal)
|
|
|
|
output_ref, attn_ref = attention_ref(q, k, v, query_padding_mask, key_padding_mask,
|
|
dropout_p, dropout_mask, causal=causal)
|
|
output_pt, attn_pt = attention_ref(q, k, v, query_padding_mask, key_padding_mask,
|
|
dropout_p, dropout_mask, causal=causal,
|
|
upcast=False, reorder_ops=True)
|
|
print(f'Actual dropout fraction: {dropout_fraction}')
|
|
print(f'Output max diff: {(output - output_ref).abs().max().item()}')
|
|
print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
|
|
print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
|
|
print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')
|
|
print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
|
|
print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')
|
|
|
|
if is_sm80 or d <= 64: # Only run backward for d=128 on A100
|
|
g = torch.randn_like(output)
|
|
dq_unpad, dk_unpad, dv_unpad, = torch.autograd.grad(output, (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(output_ref, (q, k, v), g)
|
|
dq_pt, dk_pt, dv_pt, = torch.autograd.grad(output_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 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()}')
|
|
|
|
# Check that FlashAttention's numerical error is at most twice the numerical error
|
|
# of a Pytorch implementation.
|
|
assert (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
|
|
# assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)
|
|
assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
|
|
# assert torch.allclose(attn, attn_ref, rtol=rtol, atol=atol)
|
|
if dropout_p == 0.0:
|
|
assert dropout_mask.all()
|
|
else:
|
|
assert 0.99 <= dropout_fraction / dropout_p <= 1.01
|
|
|
|
if is_sm80 or d <= 64: # Only run backward for d=128 on A100
|
|
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()
|
|
# assert torch.allclose(dq, dq_ref, rtol=rtol, atol=atol)
|
|
# assert torch.allclose(dk, dk_ref, rtol=rtol, atol=atol)
|
|
# assert torch.allclose(dv, dv_ref, rtol=rtol, atol=atol)
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
|
|
# @pytest.mark.parametrize('dtype', [torch.float16])
|
|
@pytest.mark.parametrize('causal', [False, True])
|
|
# @pytest.mark.parametrize('causal', [False])
|
|
@pytest.mark.parametrize('d', [128, 64, 80, 40, 32, 16])
|
|
# @pytest.mark.parametrize('d', [64])
|
|
@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_split(seqlen, d, dropout_p, causal, dtype):
|
|
if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
|
|
pytest.skip() # Reference implementation OOM
|
|
device = 'cuda'
|
|
# if dtype == torch.float16:
|
|
# rtol, atol = (1e-3, 3e-4) if not causal else (1e-3, 1e-3)
|
|
# else: # torch.bfloat16
|
|
# rtol, atol = (3e-3, 3e-3) if not causal else (1e-3, 1e-3)
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 32
|
|
nheads = 4
|
|
x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
|
|
Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)
|
|
|
|
key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='split')
|
|
batch_size0 = batch_size // 4 * 3 # this must match what's in generate_random_padding_mask
|
|
# key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='full')
|
|
|
|
qkv_unpad, cu_seqlens, max_seqlen0, qkv, output_pad_fn, dqkv_pad_fn = generate_qkv(
|
|
x, Wqkv, nheads, key_padding_mask, key_padding_mask, qkvpacked=True
|
|
)
|
|
max_seqlen1 = 128
|
|
|
|
output_unpad, sm_lse, S_dmask0, S_dmask1 = flash_attn_unpadded_qkvpacked_split_func(
|
|
qkv_unpad, cu_seqlens, max_seqlen0, max_seqlen1, batch_size0, dropout_p,
|
|
return_attn_probs=True, causal=causal
|
|
)
|
|
output = output_pad_fn(output_unpad)
|
|
S_dmask0_converted = convert_flash_attn_S_to_softmax(
|
|
S_dmask0, key_padding_mask[:batch_size0], key_padding_mask[:batch_size0], d, dropout_p > 0.0, causal=causal
|
|
)
|
|
S_dmask1_converted = convert_flash_attn_S_to_softmax(
|
|
S_dmask1, key_padding_mask[batch_size0:, :max_seqlen1], key_padding_mask[batch_size0:, :max_seqlen1], d, dropout_p > 0.0, causal=causal
|
|
)
|
|
padding = (S_dmask0_converted.shape[-1] - S_dmask1_converted.shape[-1],
|
|
S_dmask0_converted.shape[-2] - S_dmask1_converted.shape[-2])
|
|
S_dmask_converted = torch.cat([S_dmask0_converted,
|
|
F.pad(S_dmask1_converted, (0, padding[0], 0, padding[1]))], dim=0)
|
|
dropout_mask = S_dmask_converted >= 0
|
|
attn_unnorm = S_dmask_converted.abs()
|
|
attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2],
|
|
key_padding_mask, key_padding_mask, dropout_p > 0.0, causal=causal)
|
|
dropout_fraction = get_dropout_fraction(dropout_mask, key_padding_mask, key_padding_mask,
|
|
causal=causal).item()
|
|
|
|
output_ref, attn_ref = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
|
|
causal=causal)
|
|
output_pt, attn_pt = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
|
|
causal=causal, upcast=False, reorder_ops=True)
|
|
print(f'Actual dropout fraction: {dropout_fraction}')
|
|
print(f'Output max diff: {(output - output_ref).abs().max().item()}')
|
|
print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
|
|
print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
|
|
print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')
|
|
print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
|
|
print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')
|
|
|
|
if is_sm80 or d <= 64: # Only run backward for d=128 on A100
|
|
g = torch.randn_like(output)
|
|
dqkv_unpad, = torch.autograd.grad(output, qkv_unpad, g)
|
|
dqkv = dqkv_pad_fn(dqkv_unpad)
|
|
dqkv_ref, = torch.autograd.grad(output_ref, qkv, g)
|
|
dqkv_pt, = torch.autograd.grad(output_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 (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
|
|
# assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)
|
|
assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
|
|
# assert torch.allclose(attn, attn_ref, rtol=rtol, atol=atol)
|
|
if dropout_p == 0.0:
|
|
assert dropout_mask.all()
|
|
else:
|
|
assert 0.99 <= dropout_fraction / dropout_p <= 1.01
|
|
|
|
if is_sm80 or d <= 64: # Only run backward for d=128 on A100
|
|
assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item()
|
|
# assert torch.allclose(dqkv, dqkv_ref, rtol=rtol, atol=atol)
|
|
|
|
|
|
@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('d', [128, 64, 80, 40, 32, 16])
|
|
# @pytest.mark.parametrize('d', [64])
|
|
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
|
|
# @pytest.mark.parametrize('seqlen', [128])
|
|
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
|
|
# @pytest.mark.parametrize('dropout_p', [0.0])
|
|
def test_flash_attn_race_condition(seqlen, d, dropout_p, causal, dtype):
|
|
if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
|
|
pytest.skip() # Reference implementation OOM
|
|
device = 'cuda'
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 32
|
|
nheads = 4
|
|
x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
|
|
Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)
|
|
|
|
query_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='random')
|
|
key_padding_mask = generate_random_padding_mask(seqlen, 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(
|
|
x, Wqkv, nheads, query_padding_mask, key_padding_mask
|
|
)
|
|
|
|
torch.random.manual_seed(0)
|
|
output_unpad_0, sm_lse_0, S_dmask_0 = flash_attn_unpadded_func(
|
|
q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
|
|
dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
S_dmask_converted_0 = convert_flash_attn_S_to_softmax(
|
|
S_dmask_0, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
|
|
)
|
|
|
|
if is_sm80 or d <= 64: # Only run backward for d=128 on A100
|
|
g = torch.randn_like(output_unpad_0)
|
|
dq_unpad_0, dk_unpad_0, dv_unpad_0, = torch.autograd.grad(output_unpad_0,
|
|
(q_unpad, k_unpad, v_unpad), g)
|
|
|
|
for _ in range(10):
|
|
torch.random.manual_seed(0)
|
|
output_unpad, sm_lse, S_dmask = flash_attn_unpadded_func(
|
|
q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
|
|
dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
S_dmask_converted = convert_flash_attn_S_to_softmax(
|
|
S_dmask, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
|
|
)
|
|
assert torch.equal(output_unpad, output_unpad_0)
|
|
# sm_lse has some parts that are uninitialized from torch.empty
|
|
# assert torch.equal(sm_lse, sm_lse_0)
|
|
assert torch.equal(S_dmask_converted, S_dmask_converted_0)
|
|
|
|
if is_sm80 or d <= 64: # Only run backward for d=128 on A100
|
|
dq_unpad, dk_unpad, dv_unpad, = torch.autograd.grad(output_unpad,
|
|
(q_unpad, k_unpad, v_unpad), g)
|
|
assert torch.equal(dq_unpad, dq_unpad_0)
|
|
assert torch.equal(dk_unpad, dk_unpad_0)
|
|
assert torch.equal(dv_unpad, dv_unpad_0)
|
|
|
|
|
|
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason='requires multiple GPUs')
|
|
def test_flash_attn_multigpu():
|
|
seqlen = 256
|
|
d = 64
|
|
dropout_p = 0.0
|
|
causal = False
|
|
dtype = torch.float16
|
|
device = 'cuda:1'
|
|
torch.random.manual_seed(0)
|
|
batch_size = 32
|
|
nheads = 4
|
|
x = torch.randn(batch_size, seqlen, nheads * d, device=device, dtype=dtype, requires_grad=True)
|
|
Wqkv = torch.nn.Linear(nheads * d, 3 * nheads * d, device=device, dtype=dtype)
|
|
|
|
key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='random')
|
|
# key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='full')
|
|
|
|
qkv_unpad, cu_seqlens, max_seqlen, qkv, output_pad_fn, dqkv_pad_fn = generate_qkv(
|
|
x, Wqkv, nheads, key_padding_mask, key_padding_mask, qkvpacked=True
|
|
)
|
|
|
|
output_unpad, sm_lse, S_dmask = flash_attn_unpadded_qkvpacked_func(
|
|
qkv_unpad, cu_seqlens, max_seqlen, dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
output = output_pad_fn(output_unpad)
|
|
S_dmask_converted = convert_flash_attn_S_to_softmax(
|
|
S_dmask, key_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
|
|
)
|
|
dropout_mask = S_dmask_converted >= 0
|
|
attn_unnorm = S_dmask_converted.abs()
|
|
attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2],
|
|
key_padding_mask, key_padding_mask, dropout_p > 0.0, causal=causal)
|
|
dropout_fraction = get_dropout_fraction(dropout_mask, key_padding_mask, key_padding_mask,
|
|
causal=causal).item()
|
|
|
|
output_ref, attn_ref = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
|
|
causal=causal)
|
|
output_pt, attn_pt = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
|
|
causal=causal, upcast=False, reorder_ops=True)
|
|
print(f'Actual dropout fraction: {dropout_fraction}')
|
|
print(f'Output max diff: {(output - output_ref).abs().max().item()}')
|
|
print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
|
|
print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
|
|
print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')
|
|
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(output)
|
|
dqkv_unpad, = torch.autograd.grad(output, qkv_unpad, g)
|
|
dqkv = dqkv_pad_fn(dqkv_unpad)
|
|
dqkv_ref, = torch.autograd.grad(output_ref, qkv, g)
|
|
dqkv_pt, = torch.autograd.grad(output_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 (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
|
|
# assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)
|
|
assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
|
|
# assert torch.allclose(attn, attn_ref, rtol=rtol, atol=atol)
|
|
if dropout_p == 0.0:
|
|
assert dropout_mask.all()
|
|
else:
|
|
assert 0.99 <= dropout_fraction / dropout_p <= 1.01
|
|
|
|
assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item()
|
|
|
|
|
|
from flash_attn.flash_attn_triton import flash_attn_func
|
|
|
|
|
|
@pytest.mark.skipif(not is_sm80, reason='Triton version is only tested on A100')
|
|
@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
|
|
# @pytest.mark.parametrize('dtype', [torch.float16])
|
|
@pytest.mark.parametrize('causal', [False, True])
|
|
# @pytest.mark.parametrize('causal', [False])
|
|
@pytest.mark.parametrize('d', [40, 48, 64, 128, 80, 88, 96])
|
|
# @pytest.mark.parametrize('d', [40])
|
|
# @pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
|
|
@pytest.mark.parametrize('seqlen_q,seqlen_k', [(113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (512, 256), (1024, 1024), (2048, 2048)])
|
|
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(1024, 1024)])
|
|
def test_flash_attn_triton(seqlen_q, seqlen_k, d, causal, dtype):
|
|
if seqlen_q >= 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 = 32
|
|
nheads = 4
|
|
q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype)
|
|
k, v = torch.randn(batch_size, seqlen_k, 2, nheads, d, device=device, dtype=dtype).unbind(dim=2)
|
|
|
|
q, k, v = [x.detach().requires_grad_() for x in [q, k, v]]
|
|
output = flash_attn_func(q, k, v, causal)
|
|
|
|
output_ref, attn_ref = attention_ref(q, k, v, causal=causal)
|
|
output_pt, attn_pt = attention_ref(q, k, v, causal=causal, upcast=False, reorder_ops=True)
|
|
print(f'Output max diff: {(output - output_ref).abs().max().item()}')
|
|
print(f'Output mean diff: {(output - output_ref).abs().mean().item()}')
|
|
print(f'Pytorch max diff: {(output_pt - output_ref).abs().max().item()}')
|
|
print(f'Pytorch mean diff: {(output_pt - output_ref).abs().mean().item()}')
|
|
|
|
g = torch.randn_like(output)
|
|
dq, dk, dv = torch.autograd.grad(output, (q, k, v), g)
|
|
dq_ref, dk_ref, dv_ref, = torch.autograd.grad(output_ref, (q, k, v), g)
|
|
dq_pt, dk_pt, dv_pt, = torch.autograd.grad(output_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 (output - output_ref).abs().max().item() <= 2 * (output_pt - output_ref).abs().max().item()
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# assert torch.allclose(output, output_ref, rtol=rtol, atol=atol)
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assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item()
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assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item()
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assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item()
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@pytest.mark.skipif(not is_sm80, reason='Triton version is only tested on A100')
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@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
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# @pytest.mark.parametrize('dtype', [torch.float16])
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@pytest.mark.parametrize('causal', [False, True])
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# @pytest.mark.parametrize('causal', [True])
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@pytest.mark.parametrize('d', [40, 48, 64, 128, 80, 88, 96])
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# @pytest.mark.parametrize('d', [64])
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# @pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
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@pytest.mark.parametrize('seqlen_q,seqlen_k', [(113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (512, 256), (1024, 1024), (1023, 1024), (2048, 2048)])
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# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(1023, 1024)])
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def test_flash_attn_triton_race_condition(seqlen_q, seqlen_k, d, causal, dtype):
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if seqlen_q >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
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pytest.skip() # Reference implementation OOM
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device = 'cuda'
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# set seed
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torch.random.manual_seed(0)
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batch_size = 32
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nheads = 4
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q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype)
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k, v = torch.randn(batch_size, seqlen_k, 2, nheads, d, device=device, dtype=dtype).unbind(dim=2)
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q, k, v = [x.detach().requires_grad_() for x in [q, k, v]]
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output_0 = flash_attn_func(q, k, v, causal)
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g = torch.randn_like(output_0)
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dq_0, dk_0, dv_0 = torch.autograd.grad(output_0, (q, k, v), g)
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# Disable the SEQUENCE_PARALLEL option for the bwd to make sure it's deterministic
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for i in range(10000):
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output = flash_attn_func(q, k, v, causal)
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# print(f'Output max diff: {(output - output_0).abs().max().item()}')
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# dq, dk, dv = torch.autograd.grad(output, (q, k, v), g)
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# print(f'dQ max diff: {(dq - dq_0).abs().max().item()}')
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# print(f'dK max diff: {(dk - dk_0).abs().max().item()}')
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# print(f'dV max diff: {(dv - dv_0).abs().max().item()}')
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assert torch.equal(output, output_0)
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# assert torch.equal(dq, dq_0)
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# assert torch.equal(dk, dk_0)
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# assert torch.equal(dv, dv_0)
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