927 lines
49 KiB
Python
927 lines
49 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 import flash_attn_func, flash_attn_kvpacked_func, flash_attn_qkvpacked_func
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from flash_attn import flash_attn_varlen_qkvpacked_func, flash_attn_varlen_kvpacked_func
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from flash_attn import flash_attn_varlen_func
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from flash_attn.flash_attn_interface import _get_block_size
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from flash_attn.bert_padding import unpad_input, pad_input, index_first_axis
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MAX_HEADDIM_SM8x = 192
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is_sm75 = torch.cuda.get_device_capability('cuda') == (7, 5)
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is_sm8x = torch.cuda.get_device_capability('cuda')[0] == 8
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is_sm80 = torch.cuda.get_device_capability('cuda') == (8, 0)
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is_sm90 = torch.cuda.get_device_capability('cuda') == (9, 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']
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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, (batch_size, 1), device=device)
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elif mode == 'third':
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lengths = torch.randint(max_seqlen // 3, max_seqlen, (batch_size, 1), device=device)
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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(q, k, v, 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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q: (batch_size, seqlen_q, nheads, d)
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k: (batch_size, seqlen_k, nheads_k, d)
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v: (batch_size, seqlen_k, nheads_k, 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_q, nheads, d = q.shape
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_, seqlen_k, nheads_k, _ = k.shape
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assert k.shape == (batch_size, seqlen_k, nheads_k, d)
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assert v.shape == (batch_size, seqlen_k, nheads_k, d)
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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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output_pad_fn = lambda output_unpad: pad_input(output_unpad, indices_q, batch_size, seqlen_q)
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else:
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q_unpad = rearrange(q, 'b s h d -> (b s) h d')
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cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32,
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device=q_unpad.device)
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max_seqlen_q = seqlen_q
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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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v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
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else:
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k_unpad = rearrange(k, 'b s h d -> (b s) h d')
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v_unpad = rearrange(v, 'b s h d -> (b s) h d')
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cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
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device=k_unpad.device)
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max_seqlen_k = seqlen_k
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if qkvpacked:
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assert (query_padding_mask == key_padding_mask).all()
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assert nheads == nheads_k
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qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
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qkv = torch.stack([q, k, v], dim=2)
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if query_padding_mask is not None:
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dqkv_pad_fn = lambda dqkv_unpad: pad_input(dqkv_unpad, indices_q, batch_size, seqlen_q)
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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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kv = torch.stack([k, v], dim=2)
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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: pad_input(dkv_unpad, indices_k, batch_size, seqlen_k)
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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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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: pad_input(dk_unpad, indices_k, batch_size, seqlen_k)
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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.detach().requires_grad_(), k.detach().requires_grad_(),
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v.detach().requires_grad_(),
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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_k, head_dim)
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v: (batch_size, seqlen_k, nheads_k, 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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k = repeat(k, "b s h d -> b s (h g) d", g=q.shape[2] // k.shape[2])
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v = repeat(v, "b s h d -> b s (h g) d", g=q.shape[2] // v.shape[2])
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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_rounded, seqlen_k_rounded)
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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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seqlen_q, seqlen_k = S.shape[-2:]
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warps_n = 4
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blocksize_m, blocksize_n = _get_block_size(S.device, head_dim, is_dropout, causal)
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nblocks_n = (seqlen_k + blocksize_n - 1) // blocksize_n
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nblocks_m = (seqlen_q + blocksize_m - 1) // blocksize_m
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mmas_n = (blocksize_n + 16 - 1) // 16
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S_flat = rearrange(S, 'b h (nblocks_m blocksize_m) (nblocks_n blocksize_n) -> b h nblocks_m nblocks_n (blocksize_m blocksize_n)',
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blocksize_m=blocksize_m, blocksize_n=blocksize_n)
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S_converted = rearrange(S_flat, 'b h nblocks_m nblocks_n (mmas_n mmas_m warps_n eight four c2 c1 c0) -> b h (nblocks_m mmas_m warps_n c1 eight) (nblocks_n mmas_n c2 four c0)',
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mmas_n=mmas_n, warps_n=warps_n, eight=8, c0=2, c1=2, c2=2, four=4)
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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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# 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] if query_padding_mask is not None else seqlen_q
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if query_padding_mask is not None:
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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] if key_padding_mask is not None else seqlen_k
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if key_padding_mask is not None:
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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 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_n = _get_block_size(scores.device, head_dim, is_dropout, causal)
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scores_block = scores.split(block_size_n, 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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lse = torch.logsumexp(lse_block, dim=-1)
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scores_max_block = torch.stack([torch.amax(s, dim=-1) for s in scores_block], dim=-1)
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cummax_block = torch.cummax(scores_max_block.flip(-1), dim=-1).values.flip(-1).unbind(dim=-1)
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attn_unnorm_block = attn_unnorm.split(block_size_n, dim=-1)
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attn_norm = torch.cat([a / rearrange(torch.exp(lse - m), 'b h s -> b h s 1')
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for a, m in zip(attn_unnorm_block, cummax_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
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else torch.full((batch_size,), seqlen_q, device=dropout_mask.device))
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key_lengths = (key_padding_mask.sum(dim=-1) if key_padding_mask is not None
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else torch.full((batch_size,), seqlen_k, device=dropout_mask.device))
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if not causal:
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numel_per_batch = query_lengths * key_lengths
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else:
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numel_per_batch = torch.where(
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query_lengths <= key_lengths,
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query_lengths * (query_lengths + 1) / 2,
|
|
query_lengths * key_lengths - (key_lengths * (key_lengths - 1) / 2)
|
|
)
|
|
return dropped_total / (numel_per_batch.sum() * nheads)
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
|
|
# @pytest.mark.parametrize('dtype', [torch.float16])
|
|
@pytest.mark.parametrize('causal', [False, True])
|
|
# @pytest.mark.parametrize('causal', [True])
|
|
@pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize('d', [32, 64, 96, 128])
|
|
# @pytest.mark.parametrize('d', [64])
|
|
# @pytest.mark.parametrize('seqlen', [128, 256, 384, 512, 768, 1024, 2048])
|
|
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
|
|
# @pytest.mark.parametrize('seqlen', [97])
|
|
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
|
|
# @pytest.mark.parametrize('dropout_p', [0.17])
|
|
def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, dtype):
|
|
if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
|
|
pytest.skip() # Reference implementation OOM
|
|
device = 'cuda'
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 16
|
|
nheads = 9
|
|
qkv = torch.randn(batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype,
|
|
requires_grad=True)
|
|
out, lse, S_dmask = flash_attn_qkvpacked_func(
|
|
qkv, dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
if dropout_p > 0.0:
|
|
S_dmask_converted = convert_flash_attn_S_to_softmax(
|
|
S_dmask, None, None, d, dropout_p > 0.0, causal=causal
|
|
)[:, :, :seqlen, :seqlen]
|
|
dropout_mask = S_dmask_converted >= 0
|
|
attn_unnorm = S_dmask_converted.abs()
|
|
attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2],
|
|
None, None, dropout_p > 0.0, causal=causal)
|
|
dropout_fraction = get_dropout_fraction(dropout_mask, None, None, causal=causal).item()
|
|
print(f'Actual dropout fraction: {dropout_fraction}')
|
|
else:
|
|
dropout_mask = None
|
|
|
|
out_ref, attn_ref = attention_qkvpacked_ref(qkv, None, dropout_p, dropout_mask, causal=causal)
|
|
out_pt, attn_pt = attention_qkvpacked_ref(qkv, None, dropout_p, dropout_mask, causal=causal,
|
|
upcast=False, reorder_ops=True)
|
|
# v = qkv[:, :, 2].float()
|
|
# qk = torch.einsum('bshd,bthd->bhst', qkv[:, :, 0], qkv[:, :, 1]).float()
|
|
# if causal:
|
|
# causal_mask = torch.triu(torch.ones(seqlen, seqlen, dtype=torch.bool, device=qkv.device), 1)
|
|
# qk.masked_fill_(causal_mask, float('-inf'))
|
|
# m = qk.amax(-1, keepdim=True)
|
|
# s_tmp = torch.exp((qk - m) / math.sqrt(d))
|
|
# p_tmp = torch.softmax(qk / math.sqrt(d), -1)
|
|
# p_dropped = p_tmp if dropout_mask is None else p_tmp.masked_fill(~dropout_mask, 0)
|
|
# lse_ref = torch.logsumexp(qk / math.sqrt(d), -1)
|
|
# qk_max1 = torch.max(qk[:, :, 128:, 192:], -1, keepdim=True).values
|
|
# qk_max2 = torch.max(qk[:, :, 128:, 128:], -1, keepdim=True).values
|
|
# qk_max3 = torch.max(qk[:, :, 128:, 64:], -1, keepdim=True).values
|
|
# qk_max4 = torch.max(qk[:, :, 128:, :], -1, keepdim=True).values
|
|
# o1 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, 192:] - qk_max1) / math.sqrt(d)), v[:, 192:])
|
|
# o2 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, 128:] - qk_max2) / math.sqrt(d)), v[:, 128:])
|
|
# o3 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, 64:] - qk_max3) / math.sqrt(d)), v[:, 64:])
|
|
# o4 = torch.einsum('bhst,bthd->bshd', torch.exp((qk[:, :, 128:, :] - qk_max4) / math.sqrt(d)), v[:, :])
|
|
print(f'Output max diff: {(out - out_ref).abs().max().item()}')
|
|
print(f'Output mean diff: {(out - out_ref).abs().mean().item()}')
|
|
print(f'Pytorch max diff: {(out_pt - out_ref).abs().max().item()}')
|
|
print(f'Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}')
|
|
if dropout_p > 0.0:
|
|
print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
|
|
print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')
|
|
|
|
g = torch.randn_like(out)
|
|
# do_o = (g.float() * out.float()).sum(-1)
|
|
# dv_tmp = torch.einsum('bhts,bthd->bshd', attn_pt[:, :, :64], g[:, :64])
|
|
# dv_tmp1 = torch.einsum('bhts,bthd->bshd', attn_pt[:, :, 64:], g[:, 64:])
|
|
if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
|
|
dqkv, = torch.autograd.grad(out, qkv, g)
|
|
dqkv_ref, = torch.autograd.grad(out_ref, qkv, g)
|
|
dqkv_pt, = torch.autograd.grad(out_pt, qkv, g)
|
|
print(f'dQ max diff: {(dqkv[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}')
|
|
print(f'dK max diff: {(dqkv[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}')
|
|
print(f'dV max diff: {(dqkv[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}')
|
|
print(f'dQKV mean diff: {(dqkv - dqkv_ref).abs().mean().item()}')
|
|
print(f'dQ Pytorch max diff: {(dqkv_pt[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}')
|
|
print(f'dK Pytorch max diff: {(dqkv_pt[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}')
|
|
print(f'dV Pytorch max diff: {(dqkv_pt[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}')
|
|
print(f'dQKV Pytorch mean diff: {(dqkv_pt - dqkv_ref).abs().mean().item()}')
|
|
|
|
# Check that FlashAttention's numerical error is at most twice the numerical error
|
|
# of a Pytorch implementation.
|
|
assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
|
|
|
|
if dropout_p > 0.0:
|
|
assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
|
|
assert abs(dropout_fraction - dropout_p) <= 0.01
|
|
|
|
if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
|
|
assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item()
|
|
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
|
|
# @pytest.mark.parametrize('dtype', [torch.float16])
|
|
@pytest.mark.parametrize('causal', [False, True])
|
|
# @pytest.mark.parametrize('causal', [False])
|
|
@pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize('d', [64])
|
|
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
|
|
# @pytest.mark.parametrize('seqlen', [128])
|
|
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
|
|
# @pytest.mark.parametrize('dropout_p', [0.0])
|
|
def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, dtype):
|
|
if seqlen >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
|
|
pytest.skip() # Reference implementation OOM
|
|
device = 'cuda'
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 5
|
|
nheads = 6
|
|
qkv = torch.randn(batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype,
|
|
requires_grad=True)
|
|
|
|
key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='random')
|
|
# key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='full')
|
|
|
|
qkv_unpad, cu_seqlens, max_seqlen, qkv, output_pad_fn, dqkv_pad_fn = generate_qkv(
|
|
*qkv.unbind(dim=2), key_padding_mask, key_padding_mask, qkvpacked=True
|
|
)
|
|
|
|
out_unpad, sm_lse, S_dmask = flash_attn_varlen_qkvpacked_func(
|
|
qkv_unpad, cu_seqlens, max_seqlen, dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
out = output_pad_fn(out_unpad)
|
|
if dropout_p > 0.0:
|
|
S_dmask_converted = convert_flash_attn_S_to_softmax(
|
|
S_dmask, key_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
|
|
)[:, :, :seqlen, :seqlen]
|
|
dropout_mask = S_dmask_converted >= 0
|
|
attn_unnorm = S_dmask_converted.abs()
|
|
attn = normalize_flash_attn_S(attn_unnorm, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2],
|
|
key_padding_mask, key_padding_mask, dropout_p > 0.0,
|
|
causal=causal)
|
|
dropout_fraction = get_dropout_fraction(dropout_mask, key_padding_mask, key_padding_mask,
|
|
causal=causal).item()
|
|
print(f'Actual dropout fraction: {dropout_fraction}')
|
|
else:
|
|
dropout_mask = None
|
|
|
|
out_ref, attn_ref = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
|
|
causal=causal)
|
|
out_pt, attn_pt = attention_qkvpacked_ref(qkv, key_padding_mask, dropout_p, dropout_mask,
|
|
causal=causal, upcast=False, reorder_ops=True)
|
|
print(f'Output max diff: {(out - out_ref).abs().max().item()}')
|
|
print(f'Output mean diff: {(out - out_ref).abs().mean().item()}')
|
|
print(f'Pytorch max diff: {(out_pt - out_ref).abs().max().item()}')
|
|
print(f'Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}')
|
|
if dropout_p > 0.0:
|
|
print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
|
|
print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')
|
|
|
|
g = torch.randn_like(out)
|
|
if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
|
|
dqkv_unpad, = torch.autograd.grad(out, qkv_unpad, g)
|
|
dqkv = dqkv_pad_fn(dqkv_unpad)
|
|
dqkv_ref, = torch.autograd.grad(out_ref, qkv, g)
|
|
dqkv_pt, = torch.autograd.grad(out_pt, qkv, g)
|
|
print(f'dQ max diff: {(dqkv[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}')
|
|
print(f'dK max diff: {(dqkv[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}')
|
|
print(f'dV max diff: {(dqkv[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}')
|
|
print(f'dQKV mean diff: {(dqkv - dqkv_ref).abs().mean().item()}')
|
|
print(f'dQ Pytorch max diff: {(dqkv_pt[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}')
|
|
print(f'dK Pytorch max diff: {(dqkv_pt[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}')
|
|
print(f'dV Pytorch max diff: {(dqkv_pt[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}')
|
|
print(f'dQKV Pytorch mean diff: {(dqkv_pt - dqkv_ref).abs().mean().item()}')
|
|
|
|
# Check that FlashAttention's numerical error is at most twice the numerical error
|
|
# of a Pytorch implementation.
|
|
assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
|
|
|
|
if dropout_p > 0.0:
|
|
assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
|
|
assert abs(dropout_fraction - dropout_p) <= 0.01
|
|
|
|
if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
|
|
assert (dqkv - dqkv_ref).abs().max().item() <= 2 * (dqkv_pt - dqkv_ref).abs().max().item()
|
|
|
|
|
|
@pytest.mark.parametrize('kvpacked', [True, False])
|
|
# @pytest.mark.parametrize('kvpacked', [False])
|
|
@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
|
|
# @pytest.mark.parametrize('dtype', [torch.bfloat16])
|
|
@pytest.mark.parametrize('mha_type', ["mha", "mqa", "gqa"])
|
|
# @pytest.mark.parametrize('mha_type', ["mha"])
|
|
@pytest.mark.parametrize('causal', [False, True])
|
|
# @pytest.mark.parametrize('causal', [False])
|
|
@pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192])
|
|
# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192])
|
|
# @pytest.mark.parametrize('d', [56, 80])
|
|
# @pytest.mark.parametrize('d', [64])
|
|
@pytest.mark.parametrize('seqlen_q,seqlen_k', [(113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (512, 256), (1024, 1024), (1023, 1024), (1024, 1023), (2048, 2048)])
|
|
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)])
|
|
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
|
|
# @pytest.mark.parametrize('dropout_p', [0.0])
|
|
def test_flash_attn_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, dtype, kvpacked):
|
|
if max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
|
|
pytest.skip() # Reference implementation OOM
|
|
device = 'cuda'
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 16
|
|
nheads = 9
|
|
nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
|
|
assert nheads % nheads_k == 0
|
|
q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
|
|
if kvpacked:
|
|
kv = torch.randn(batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype,
|
|
requires_grad=True)
|
|
else:
|
|
k = torch.randn(batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype,
|
|
requires_grad=True)
|
|
v = torch.randn(batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype,
|
|
requires_grad=True)
|
|
|
|
if kvpacked:
|
|
out, lse, S_dmask = flash_attn_kvpacked_func(
|
|
q, kv, dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
else:
|
|
out, lse, S_dmask = flash_attn_func(
|
|
q, k, v, dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
if dropout_p > 0.0:
|
|
S_dmask_converted = convert_flash_attn_S_to_softmax(
|
|
S_dmask, None, None, d, dropout_p > 0.0, causal=causal
|
|
)[:, :, :seqlen_q, :seqlen_k]
|
|
dropout_mask = S_dmask_converted >= 0
|
|
attn_unnorm = S_dmask_converted.abs()
|
|
if kvpacked:
|
|
kv_rep = repeat(kv, "b s two h d -> b s two (h g) d", g=nheads // nheads_k)
|
|
k_rep, v_rep = kv_rep.unbind(dim=2)
|
|
else:
|
|
k_rep = repeat(k, "b s h d -> b s (h g) d", g=nheads // nheads_k)
|
|
v_rep = repeat(v, "b s h d -> b s (h g) d", g=nheads // nheads_k)
|
|
attn = normalize_flash_attn_S(attn_unnorm, q, k_rep, v_rep,
|
|
None, None, dropout_p > 0.0, causal=causal)
|
|
dropout_fraction = get_dropout_fraction(dropout_mask, None, None, causal=causal).item()
|
|
print(f'Actual dropout fraction: {dropout_fraction}')
|
|
else:
|
|
dropout_mask = None
|
|
|
|
if kvpacked:
|
|
out_ref, attn_ref = attention_kvpacked_ref(q, kv, None, None, dropout_p, dropout_mask,
|
|
causal=causal)
|
|
out_pt, attn_pt = attention_kvpacked_ref(q, kv, None, None, dropout_p, dropout_mask,
|
|
causal=causal, upcast=False, reorder_ops=True)
|
|
else:
|
|
out_ref, attn_ref = attention_ref(q, k, v, None, None, dropout_p, dropout_mask,
|
|
causal=causal)
|
|
out_pt, attn_pt = attention_ref(q, k, v, None, None, dropout_p, dropout_mask,
|
|
causal=causal, upcast=False, reorder_ops=True)
|
|
|
|
print(f'Output max diff: {(out - out_ref).abs().max().item()}')
|
|
print(f'Output mean diff: {(out - out_ref).abs().mean().item()}')
|
|
print(f'Pytorch max diff: {(out_pt - out_ref).abs().max().item()}')
|
|
print(f'Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}')
|
|
if dropout_p > 0.0:
|
|
print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
|
|
print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')
|
|
|
|
g = torch.randn_like(out)
|
|
do_o = (g.float() * out.float()).sum(-1)
|
|
if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
|
|
if kvpacked:
|
|
dq, dkv, = torch.autograd.grad(out, (q, kv), g)
|
|
dk, dv = dkv.unbind(2)
|
|
dq_ref, dkv_ref, = torch.autograd.grad(out_ref, (q, kv), g)
|
|
dk_ref, dv_ref = dkv_ref.unbind(2)
|
|
dq_pt, dkv_pt, = torch.autograd.grad(out_pt, (q, kv), g)
|
|
dk_pt, dv_pt = dkv_pt.unbind(2)
|
|
else:
|
|
dq, dk, dv, = torch.autograd.grad(out, (q, k, v), g)
|
|
dq_ref, dk_ref, dv_ref, = torch.autograd.grad(out_ref, (q, k, v), g)
|
|
dq_pt, dk_pt, dv_pt, = torch.autograd.grad(out_pt, (q, k, v), g)
|
|
print(f'dQ max diff: {(dq - dq_ref).abs().max().item()}')
|
|
print(f'dK max diff: {(dk - dk_ref).abs().max().item()}')
|
|
print(f'dV max diff: {(dv - dv_ref).abs().max().item()}')
|
|
print(f'dQ mean diff: {(dq - dq_ref).abs().mean().item()}')
|
|
print(f'dK mean diff: {(dk - dk_ref).abs().mean().item()}')
|
|
print(f'dV mean diff: {(dv - dv_ref).abs().mean().item()}')
|
|
print(f'dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}')
|
|
print(f'dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}')
|
|
print(f'dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}')
|
|
print(f'dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}')
|
|
print(f'dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}')
|
|
print(f'dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}')
|
|
|
|
# Check that FlashAttention's numerical error is at most twice the numerical error
|
|
# of a Pytorch implementation.
|
|
assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
|
|
|
|
if dropout_p > 0.0:
|
|
assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
|
|
assert abs(dropout_fraction - dropout_p) <= 0.01
|
|
|
|
if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
|
|
assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item()
|
|
assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item()
|
|
assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item()
|
|
|
|
|
|
@pytest.mark.parametrize('kvpacked', [True, False])
|
|
# @pytest.mark.parametrize('kvpacked', [False])
|
|
@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
|
|
# @pytest.mark.parametrize('dtype', [torch.float16])
|
|
@pytest.mark.parametrize('mha_type', ["mha", "mqa", "gqa"])
|
|
# @pytest.mark.parametrize('mha_type', ["mqa"])
|
|
@pytest.mark.parametrize('causal', [False, True])
|
|
# @pytest.mark.parametrize('causal', [True])
|
|
@pytest.mark.parametrize('d', [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192, 224, 256])
|
|
# @pytest.mark.parametrize('d', [64])
|
|
@pytest.mark.parametrize('seqlen_q,seqlen_k', [(113, 203), (128, 217), (113, 211), (108, 256), (256, 512), (512, 256), (1024, 1024), (1023, 1024), (1024, 1023), (2048, 2048)])
|
|
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(128, 128)])
|
|
@pytest.mark.parametrize('dropout_p', [0.0, 0.17])
|
|
# @pytest.mark.parametrize('dropout_p', [0.0])
|
|
def test_flash_attn_varlen_output(seqlen_q, seqlen_k, d, dropout_p, causal, mha_type, dtype,
|
|
kvpacked):
|
|
if max(seqlen_q, seqlen_k) >= 2048 and torch.cuda.get_device_properties('cuda').total_memory <= 16 * 2**30:
|
|
pytest.skip() # Reference implementation OOM
|
|
device = 'cuda'
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 16
|
|
nheads = 9
|
|
nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
|
|
assert nheads % nheads_k == 0
|
|
q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
|
|
if kvpacked:
|
|
kv = torch.randn(batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype,
|
|
requires_grad=True)
|
|
else:
|
|
k = torch.randn(batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype,
|
|
requires_grad=True)
|
|
v = torch.randn(batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype,
|
|
requires_grad=True)
|
|
|
|
query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode='random')
|
|
key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode='random')
|
|
# key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode='full')
|
|
|
|
if kvpacked:
|
|
(q_unpad, kv_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q, kv,
|
|
output_pad_fn, dq_pad_fn, dkv_pad_fn) = generate_qkv(
|
|
q, *kv.unbind(dim=2), query_padding_mask, key_padding_mask, kvpacked=True
|
|
)
|
|
out_unpad, sm_lse, S_dmask = flash_attn_varlen_kvpacked_func(
|
|
q_unpad, kv_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
|
|
dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
else:
|
|
(q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, q, k, v,
|
|
output_pad_fn, dq_pad_fn, dk_pad_fn) = generate_qkv(
|
|
q, k, v, query_padding_mask, key_padding_mask, kvpacked=False
|
|
)
|
|
out_unpad, sm_lse, S_dmask = flash_attn_varlen_func(
|
|
q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
|
|
dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
out = output_pad_fn(out_unpad)
|
|
if dropout_p > 0.0:
|
|
S_dmask_converted = convert_flash_attn_S_to_softmax(
|
|
S_dmask, query_padding_mask, key_padding_mask, d, dropout_p > 0.0, causal=causal
|
|
)[:, :, :seqlen_q, :seqlen_k]
|
|
dropout_mask = S_dmask_converted >= 0
|
|
attn_unnorm = S_dmask_converted.abs()
|
|
if kvpacked:
|
|
kv_rep = repeat(kv, "b s two h d -> b s two (h g) d", g=nheads // nheads_k)
|
|
k_rep, v_rep = kv_rep.unbind(dim=2)
|
|
else:
|
|
k_rep = repeat(k, "b s h d -> b s (h g) d", g=nheads // nheads_k)
|
|
v_rep = repeat(v, "b s h d -> b s (h g) d", g=nheads // nheads_k)
|
|
attn = normalize_flash_attn_S(attn_unnorm, q, k_rep, v_rep,
|
|
query_padding_mask, key_padding_mask,
|
|
dropout_p > 0.0, causal=causal)
|
|
dropout_fraction = get_dropout_fraction(dropout_mask, query_padding_mask,
|
|
key_padding_mask, causal=causal).item()
|
|
print(f'Actual dropout fraction: {dropout_fraction}')
|
|
else:
|
|
dropout_mask = None
|
|
|
|
if kvpacked:
|
|
out_ref, attn_ref = attention_kvpacked_ref(q, kv, query_padding_mask, key_padding_mask,
|
|
dropout_p, dropout_mask, causal=causal)
|
|
out_pt, attn_pt = attention_kvpacked_ref(q, kv, query_padding_mask, key_padding_mask,
|
|
dropout_p, dropout_mask,
|
|
causal=causal, upcast=False, reorder_ops=True)
|
|
else:
|
|
out_ref, attn_ref = attention_ref(q, k, v, query_padding_mask, key_padding_mask,
|
|
dropout_p, dropout_mask, causal=causal)
|
|
out_pt, attn_pt = attention_ref(q, k, v, query_padding_mask, key_padding_mask,
|
|
dropout_p, dropout_mask,
|
|
causal=causal, upcast=False, reorder_ops=True)
|
|
|
|
print(f'Output max diff: {(out - out_ref).abs().max().item()}')
|
|
print(f'Output mean diff: {(out - out_ref).abs().mean().item()}')
|
|
print(f'Pytorch max diff: {(out_pt - out_ref).abs().max().item()}')
|
|
print(f'Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}')
|
|
if dropout_p > 0.0:
|
|
print(f'Attention max diff: {(attn - attn_ref).abs().max().item()}')
|
|
print(f'Attention Pytorch max diff: {(attn_pt - attn_ref).abs().max().item()}')
|
|
|
|
g = torch.randn_like(out)
|
|
if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
|
|
if kvpacked:
|
|
dq_unpad, dkv_unpad, = torch.autograd.grad(out, (q_unpad, kv_unpad), g)
|
|
dk, dv = dkv_pad_fn(dkv_unpad).unbind(2)
|
|
dq_ref, dkv_ref, = torch.autograd.grad(out_ref, (q, kv), g)
|
|
dk_ref, dv_ref = dkv_ref.unbind(2)
|
|
dq_pt, dkv_pt, = torch.autograd.grad(out_pt, (q, kv), g)
|
|
dk_pt, dv_pt = dkv_pt.unbind(2)
|
|
else:
|
|
dq_unpad, dk_unpad, dv_unpad, = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g)
|
|
dk = dk_pad_fn(dk_unpad)
|
|
dv = dk_pad_fn(dv_unpad)
|
|
dq_ref, dk_ref, dv_ref, = torch.autograd.grad(out_ref, (q, k, v), g)
|
|
dq_pt, dk_pt, dv_pt, = torch.autograd.grad(out_pt, (q, k, v), g)
|
|
dq = dq_pad_fn(dq_unpad)
|
|
print(f'dQ max diff: {(dq - dq_ref).abs().max().item()}')
|
|
print(f'dK max diff: {(dk - dk_ref).abs().max().item()}')
|
|
print(f'dV max diff: {(dv - dv_ref).abs().max().item()}')
|
|
print(f'dQ mean diff: {(dq - dq_ref).abs().mean().item()}')
|
|
print(f'dK mean diff: {(dk - dk_ref).abs().mean().item()}')
|
|
print(f'dV mean diff: {(dv - dv_ref).abs().mean().item()}')
|
|
print(f'dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}')
|
|
print(f'dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}')
|
|
print(f'dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}')
|
|
print(f'dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}')
|
|
print(f'dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}')
|
|
print(f'dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}')
|
|
|
|
# Check that FlashAttention's numerical error is at most twice the numerical error
|
|
# of a Pytorch implementation.
|
|
assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
|
|
|
|
if dropout_p > 0.0:
|
|
assert (attn - attn_ref).abs().max().item() <= 2 * (attn_pt - attn_ref).abs().max().item()
|
|
assert abs(dropout_fraction - dropout_p) <= 0.01
|
|
|
|
if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
|
|
assert (dq - dq_ref).abs().max().item() <= 2 * (dq_pt - dq_ref).abs().max().item()
|
|
assert (dk - dk_ref).abs().max().item() <= 2 * (dk_pt - dk_ref).abs().max().item()
|
|
assert (dv - dv_ref).abs().max().item() <= 2 * (dv_pt - dv_ref).abs().max().item()
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
|
|
# @pytest.mark.parametrize('dtype', [torch.float16])
|
|
@pytest.mark.parametrize('causal', [False, True])
|
|
# @pytest.mark.parametrize('causal', [False])
|
|
# @pytest.mark.parametrize('d', [32, 56, 64, 80, 96, 128])
|
|
@pytest.mark.parametrize('d', [32, 64, 96, 128, 160, 192])
|
|
# @pytest.mark.parametrize('d', [128])
|
|
# @pytest.mark.parametrize('seqlen', [97, 128, 200, 256, 257, 384, 512, 768, 1024, 1025, 2048])
|
|
# @pytest.mark.parametrize('seqlen', [128, 256, 384, 512, 768, 1024, 2048])
|
|
@pytest.mark.parametrize('seqlen', [128])
|
|
# @pytest.mark.parametrize('dropout_p', [0.0, 0.17])
|
|
@pytest.mark.parametrize('dropout_p', [0.0])
|
|
def test_flash_attn_race_condition(seqlen, d, dropout_p, causal, dtype):
|
|
device = 'cuda'
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 60 # Sometimes we need large batch size for the race conditions to trigger
|
|
nheads = 4
|
|
qkv = torch.randn(batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype,
|
|
requires_grad=True)
|
|
out0, lse0, _ = flash_attn_qkvpacked_func(
|
|
qkv, dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
g = torch.randn_like(out0)
|
|
if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
|
|
dqkv0, = torch.autograd.grad(out0, qkv, g)
|
|
# Numerical error if we just do any arithmetic on dq
|
|
dq_atol = 2 * ((dqkv0[:, :, 0] + 0.3 - 0.3) - dqkv0[:, :, 0]).abs().max().item()
|
|
|
|
for i in range(200):
|
|
torch.random.manual_seed(0)
|
|
out, lse, S_dmask = flash_attn_qkvpacked_func(
|
|
qkv, dropout_p, return_attn_probs=True, causal=causal
|
|
)
|
|
assert torch.equal(out, out0)
|
|
assert torch.equal(lse, lse0)
|
|
|
|
if d <= MAX_HEADDIM_SM8x or (is_sm80 or is_sm90):
|
|
dqkv, = torch.autograd.grad(out, qkv, g)
|
|
dq_equal = torch.allclose(dqkv[:, :, 0], dqkv0[:, :, 0], atol=dq_atol)
|
|
if not dq_equal:
|
|
dq0 = dqkv0[:, :, 0]
|
|
dq = dqkv[:, :, 0]
|
|
print(f'Iter {i}, {dq_atol = }, dQ max diff: {(dqkv[:, :, 0] - dqkv0[:, :, 0]).abs().max().item()}')
|
|
assert dq_equal
|
|
assert torch.equal(dqkv[:, :, 1], dqkv0[:, :, 1])
|
|
assert torch.equal(dqkv[:, :, 2], dqkv0[:, :, 2])
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', [torch.float16])
|
|
@pytest.mark.parametrize('causal', [False, True])
|
|
# @pytest.mark.parametrize('causal', [False])
|
|
@pytest.mark.parametrize('d', [16, 32, 64])
|
|
# @pytest.mark.parametrize('d', [16])
|
|
@pytest.mark.parametrize('seqlen', [1, 2, 5, 17, 128])
|
|
# @pytest.mark.parametrize('seqlen', [2])
|
|
def test_flash_attn_bwd_overflow(seqlen, d, causal, dtype):
|
|
""" We previously had a bug where not masking elements beyond seqlen_k caused NaN in dQ,
|
|
in the case where seqlen % 128 != 0.
|
|
"""
|
|
device = 'cuda'
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 2
|
|
nheads = 5
|
|
q = torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda") * 5
|
|
k, v = [torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda") * 3 for _ in range(2)]
|
|
q.requires_grad_(True)
|
|
k.requires_grad_(True)
|
|
v.requires_grad_(True)
|
|
out = flash_attn_func(q, k, v, causal=causal)
|
|
g = torch.randn_like(out)
|
|
out.backward(g)
|
|
q_pt = q.detach().clone().requires_grad_(True)
|
|
k_pt = k.detach().clone().requires_grad_(True)
|
|
v_pt = v.detach().clone().requires_grad_(True)
|
|
out_pt, _ = attention_ref(q_pt, k_pt, v_pt, causal=causal, upcast=False, reorder_ops=True)
|
|
out_pt.backward(g)
|
|
q_ref = q.detach().clone().requires_grad_(True)
|
|
k_ref = k.detach().clone().requires_grad_(True)
|
|
v_ref = v.detach().clone().requires_grad_(True)
|
|
out_ref, attn_ref = attention_ref(q_ref, k_ref, v_ref, causal=causal)
|
|
out_ref.backward(g)
|
|
print(f'dQ max diff: {(q.grad - q_ref.grad).abs().max().item()}')
|
|
print(f'dK max diff: {(k.grad - k_ref.grad).abs().max().item()}')
|
|
print(f'dV max diff: {(v.grad - v_ref.grad).abs().max().item()}')
|
|
print(f'dQ Pytorch max diff: {(q_pt.grad - q_ref.grad).abs().max().item()}')
|
|
print(f'dK Pytorch max diff: {(k_pt.grad - k_ref.grad).abs().max().item()}')
|
|
print(f'dV Pytorch max diff: {(v_pt.grad - v_ref.grad).abs().max().item()}')
|
|
assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
|
|
assert (q.grad - q_ref.grad).abs().max().item() <= 5 * (q_pt.grad - q_ref.grad).abs().max().item() + 1e-3
|
|
assert (k.grad - k_ref.grad).abs().max().item() <= 5 * (k_pt.grad - k_ref.grad).abs().max().item() + 1e-3
|
|
assert (v.grad - v_ref.grad).abs().max().item() <= 5 * (v_pt.grad - v_ref.grad).abs().max().item() + 1e-3
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', ([torch.float16] if is_sm75 else [torch.float16, torch.bfloat16]))
|
|
# @pytest.mark.parametrize('dtype', [torch.bfloat16])
|
|
@pytest.mark.parametrize('causal', [False, True])
|
|
# @pytest.mark.parametrize('causal', [False])
|
|
@pytest.mark.parametrize('d', [64, 128])
|
|
# @pytest.mark.parametrize('d', [64])
|
|
@pytest.mark.parametrize('seqlen', [97, 128, 200, 256])
|
|
# @pytest.mark.parametrize('seqlen', [128])
|
|
def test_flash_attn_bwd_transpose(seqlen, d, causal, dtype):
|
|
""" We previously had a bug where we were using the wrong strides of dout, which shows up
|
|
when dout is not contiguous.
|
|
"""
|
|
device = 'cuda'
|
|
# set seed
|
|
torch.random.manual_seed(0)
|
|
batch_size = 5
|
|
nheads = 2
|
|
q, k, v = [torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda",
|
|
requires_grad=True)
|
|
for _ in range(3)]
|
|
out = rearrange(flash_attn_func(q, k, v, causal=causal), "b s ... -> s b ...")
|
|
# So g is not contiguous
|
|
g = torch.randn(seqlen, 2 * batch_size, nheads, d, dtype=dtype, device="cuda")[:, ::2]
|
|
out.backward(g)
|
|
q_pt = q.detach().clone().requires_grad_(True)
|
|
k_pt = k.detach().clone().requires_grad_(True)
|
|
v_pt = v.detach().clone().requires_grad_(True)
|
|
out_pt, attn_pt = attention_ref(q_pt, k_pt, v_pt, causal=causal, upcast=False, reorder_ops=True)
|
|
out_pt = rearrange(out_pt, "b s ... -> s b ...")
|
|
out_pt.backward(g)
|
|
q_ref = q.detach().clone().requires_grad_(True)
|
|
k_ref = k.detach().clone().requires_grad_(True)
|
|
v_ref = v.detach().clone().requires_grad_(True)
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out_ref, attn_ref = attention_ref(q_ref, k_ref, v_ref, causal=causal)
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out_ref = rearrange(out_ref, "b s ... -> s b ...")
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out_ref.backward(g)
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print(f'dQ max diff: {(q.grad - q_ref.grad).abs().max().item()}')
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print(f'dK max diff: {(k.grad - k_ref.grad).abs().max().item()}')
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print(f'dV max diff: {(v.grad - v_ref.grad).abs().max().item()}')
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print(f'dQ Pytorch max diff: {(q_pt.grad - q_ref.grad).abs().max().item()}')
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print(f'dK Pytorch max diff: {(k_pt.grad - k_ref.grad).abs().max().item()}')
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print(f'dV Pytorch max diff: {(v_pt.grad - v_ref.grad).abs().max().item()}')
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assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
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assert (q.grad - q_ref.grad).abs().max().item() <= 2 * (q_pt.grad - q_ref.grad).abs().max().item()
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assert (k.grad - k_ref.grad).abs().max().item() <= 2 * (k_pt.grad - k_ref.grad).abs().max().item()
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assert (v.grad - v_ref.grad).abs().max().item() <= 2 * (v_pt.grad - v_ref.grad).abs().max().item()
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