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import math
import torch
from einops import rearrange, repeat
from flash_attn.bert_padding import pad_input, unpad_input
def generate_random_padding_mask(max_seqlen, batch_size, device, mode="random", zero_lengths=False):
assert mode in ["full", "random", "third"]
if mode == "full":
lengths = torch.full((batch_size, 1), max_seqlen, device=device, dtype=torch.int32)
elif mode == "random":
lengths = torch.randint(
max(0 if zero_lengths else 1, max_seqlen - 20), max_seqlen + 1, (batch_size, 1), device=device
)
elif mode == "third":
lengths = torch.randint(max_seqlen // 3, max_seqlen + 1, (batch_size, 1), device=device)
if zero_lengths:
# Generate zero-lengths every 5 batches and the last batch.
for i in range(batch_size):
if i % 5 == 0:
lengths[i] = 0
lengths[-1] = 0
padding_mask = (
repeat(torch.arange(max_seqlen, device=device), "s -> b s", b=batch_size) < lengths
)
return padding_mask
def generate_qkv(
q, k, v, query_padding_mask=None, key_padding_mask=None,
kvpacked=False, qkvpacked=False, add_unused_qkv=False,
query_unused_mask=None, key_unused_mask=None,
):
"""
Arguments:
q: (batch_size, seqlen_q, nheads, d)
k: (batch_size, seqlen_k, nheads_k, d)
v: (batch_size, seqlen_k, nheads_k, d)
query_padding_mask: (batch_size, seqlen), bool
key_padding_mask: (batch_size, seqlen), bool
"""
assert not (kvpacked and qkvpacked)
batch_size, seqlen_q, nheads, d = q.shape
_, seqlen_k, nheads_k, _ = k.shape
assert k.shape == (batch_size, seqlen_k, nheads_k, d)
assert v.shape == (batch_size, seqlen_k, nheads_k, d)
if query_unused_mask is not None or key_unused_mask is not None:
assert not kvpacked
assert not qkvpacked
if query_padding_mask is not None:
q_unpad, indices_q, cu_seqlens_q, max_seqlen_q, seqused_q = unpad_input(
q, query_padding_mask, query_unused_mask,
)
output_pad_fn = lambda output_unpad: pad_input(
output_unpad, indices_q, batch_size, seqlen_q
)
else:
q_unpad = rearrange(q, "b s h d -> (b s) h d")
cu_seqlens_q = torch.arange(
0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, device=q_unpad.device
)
seqused_q = None
max_seqlen_q = seqlen_q
output_pad_fn = lambda output_unpad: rearrange(
output_unpad, "(b s) h d -> b s h d", b=batch_size
)
if key_padding_mask is not None:
k_unpad, indices_k, cu_seqlens_k, max_seqlen_k, seqused_k = unpad_input(k, key_padding_mask, key_unused_mask)
v_unpad, _, _, _, _ = unpad_input(v, key_padding_mask, key_unused_mask)
else:
k_unpad = rearrange(k, "b s h d -> (b s) h d")
v_unpad = rearrange(v, "b s h d -> (b s) h d")
cu_seqlens_k = torch.arange(
0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32, device=k_unpad.device
)
seqused_k = None
max_seqlen_k = seqlen_k
if qkvpacked:
assert (query_padding_mask == key_padding_mask).all()
assert nheads == nheads_k
qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
qkv = torch.stack([q, k, v], dim=2)
if query_padding_mask is not None:
dqkv_pad_fn = lambda dqkv_unpad: pad_input(dqkv_unpad, indices_q, batch_size, seqlen_q)
else:
dqkv_pad_fn = lambda dqkv_unpad: rearrange(
dqkv_unpad, "(b s) t h d -> b s t h d", b=batch_size
)
return (
qkv_unpad.detach().requires_grad_(),
cu_seqlens_q,
max_seqlen_q,
qkv.detach().requires_grad_(),
output_pad_fn,
dqkv_pad_fn,
)
elif kvpacked:
kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
kv = torch.stack([k, v], dim=2)
dq_pad_fn = output_pad_fn
if key_padding_mask is not None:
dkv_pad_fn = lambda dkv_unpad: pad_input(dkv_unpad, indices_k, batch_size, seqlen_k)
else:
dkv_pad_fn = lambda dkv_unpad: rearrange(
dkv_unpad, "(b s) t h d -> b s t h d", b=batch_size
)
return (
q_unpad.detach().requires_grad_(),
kv_unpad.detach().requires_grad_(),
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
q.detach().requires_grad_(),
kv.detach().requires_grad_(),
output_pad_fn,
dq_pad_fn,
dkv_pad_fn,
)
else:
dq_pad_fn = output_pad_fn
if key_padding_mask is not None:
dk_pad_fn = lambda dk_unpad: pad_input(dk_unpad, indices_k, batch_size, seqlen_k)
else:
dk_pad_fn = lambda dk_unpad: rearrange(dk_unpad, "(b s) h d -> b s h d", b=batch_size)
return (
q_unpad.detach().requires_grad_(),
k_unpad.detach().requires_grad_(),
v_unpad.detach().requires_grad_(),
cu_seqlens_q,
cu_seqlens_k,
seqused_q,
seqused_k,
max_seqlen_q,
max_seqlen_k,
q.detach().requires_grad_(),
k.detach().requires_grad_(),
v.detach().requires_grad_(),
output_pad_fn,
dq_pad_fn,
dk_pad_fn,
)
def construct_local_mask(
seqlen_q,
seqlen_k,
window_size=(-1, -1), # -1 means infinite window size
query_padding_mask=None,
key_padding_mask=None,
device=None,
key_leftpad=None,
):
row_idx = rearrange(torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1")
col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long)
if key_leftpad is not None:
key_leftpad = rearrange(key_leftpad, "b -> b 1 1 1")
col_idx = repeat(col_idx, "s -> b 1 1 s", b=key_leftpad.shape[0])
col_idx = torch.where(col_idx >= key_leftpad, col_idx - key_leftpad, 2**32)
sk = (
seqlen_k
if key_padding_mask is None
else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1")
)
sq = (
seqlen_q
if query_padding_mask is None
else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1")
)
if window_size[0] < 0:
return col_idx > row_idx + sk - sq + window_size[1]
else:
sk = torch.full_like(col_idx, seqlen_k) if key_padding_mask is None else sk
return torch.logical_or(
col_idx > torch.minimum(row_idx + sk - sq + window_size[1], sk),
col_idx < row_idx + sk - sq - window_size[0],
)
def attention_ref(
q,
k,
v,
query_padding_mask=None,
key_padding_mask=None,
attn_bias=None,
dropout_p=0.0,
dropout_mask=None,
causal=False,
window_size=(-1, -1), # -1 means infinite window size
softcap=0.0,
upcast=True,
reorder_ops=False,
key_leftpad=None,
):
"""
Arguments:
q: (batch_size, seqlen_q, nheads, head_dim)
k: (batch_size, seqlen_k, nheads_k, head_dim)
v: (batch_size, seqlen_k, nheads_k, head_dim)
query_padding_mask: (batch_size, seqlen_q)
key_padding_mask: (batch_size, seqlen_k)
attn_bias: broadcastable to (batch_size, nheads, seqlen_q, seqlen_k)
dropout_p: float
dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k)
causal: whether to apply causal masking
window_size: (int, int), left and right window size
upcast: whether to cast all inputs to fp32, do all computation in fp32, then cast
output back to fp16/bf16.
reorder_ops: whether to change the order of operations (scaling k instead of scaling q, etc.)
without changing the math. This is to estimate the numerical error from operation
reordering.
Output:
output: (batch_size, seqlen_q, nheads, head_dim)
attention: (batch_size, nheads, seqlen_q, seqlen_k), softmax after dropout
"""
if causal:
window_size = (window_size[0], 0)
dtype_og = q.dtype
if upcast:
q, k, v = q.float(), k.float(), v.float()
seqlen_q, seqlen_k = q.shape[1], k.shape[1]
k = repeat(k, "b s h d -> b s (h g) d", g=q.shape[2] // k.shape[2])
v = repeat(v, "b s h d -> b s (h g) d", g=q.shape[2] // v.shape[2])
d = q.shape[-1]
if not reorder_ops:
scores = torch.einsum("bthd,bshd->bhts", q / math.sqrt(d), k)
else:
scores = torch.einsum("bthd,bshd->bhts", q, k / math.sqrt(d))
if softcap > 0:
scores /= softcap
scores = scores.tanh()
scores *= softcap
if key_padding_mask is not None:
scores.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf"))
if window_size[0] >= 0 or window_size[1] >= 0:
local_mask = construct_local_mask(
seqlen_q,
seqlen_k,
window_size,
query_padding_mask,
key_padding_mask,
q.device,
key_leftpad=key_leftpad,
)
scores.masked_fill_(local_mask, float("-inf"))
if attn_bias is not None:
scores = scores + attn_bias
attention = torch.softmax(scores, dim=-1).to(v.dtype)
# Some rows might be completely masked out so we fill them with zero instead of NaN
if window_size[0] >= 0 or window_size[1] >= 0:
attention = attention.masked_fill(torch.all(local_mask, dim=-1, keepdim=True), 0.0)
# We want to mask here so that the attention matrix doesn't have any NaNs
# Otherwise we'll get NaN in dV
if query_padding_mask is not None:
attention = attention.masked_fill(rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0)
dropout_scaling = 1.0 / (1 - dropout_p)
# attention_drop = attention.masked_fill(~dropout_mask, 0.0) * dropout_scaling
# output = torch.einsum('bhts,bshd->bthd', attention_drop , v)
if dropout_mask is not None:
attention_drop = attention.masked_fill(~dropout_mask, 0.0)
else:
attention_drop = attention
output = torch.einsum("bhts,bshd->bthd", attention_drop, v * dropout_scaling)
if query_padding_mask is not None:
output.masked_fill_(rearrange(~query_padding_mask, "b s -> b s 1 1"), 0.0)
if key_padding_mask is not None:
output.masked_fill_(rearrange(torch.logical_not(torch.any(key_padding_mask, 1)), "b -> b 1 1 1"), 0.0)
return output.to(dtype=dtype_og), attention.to(dtype=dtype_og)
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