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JittorMirror/python/jittor/attention.py

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# ***************************************************************
# Copyright (c) 2023 Jittor. All Rights Reserved.
# Maintainers:
# Guowei Yang <471184555@qq.com>
# Dun Liang <randonlang@gmail.com>.
#
#
# This file is subject to the terms and conditions defined in
# file 'LICENSE.txt', which is part of this source code package.
# ***************************************************************
from typing import Optional, Tuple, List
import warnings
import math
import jittor as jt
from jittor import Var
from jittor.nn import Module, Linear, softmax, pad, linear, dropout
from jittor.init import xavier_uniform_, xavier_gauss_, constant_
def _canonical_mask(
mask: Optional[Var],
mask_name: str,
other_type,
other_name: str,
target_type,
check_other: bool = True,
) -> Optional[Var]:
if mask is not None:
_mask_dtype = mask.dtype
_mask_is_float = mask.dtype == jt.float16 or mask.dtype == jt.float32 or mask.dtype == jt.float64
if _mask_dtype != jt.bool and not _mask_is_float:
raise AssertionError(
f"only bool and floating types of {mask_name} are supported")
if check_other and other_type is not None:
if _mask_dtype != other_type:
warnings.warn(
f"Support for mismatched {mask_name} and {other_name} "
"is deprecated. Use same type for both instead."
)
if not _mask_is_float:
# WARNING(514flowey): Check Here
new_mask = jt.zeros_like(mask, dtype=target_type)
new_mask[mask] = float("-inf")
mask = new_mask
return mask
def _none_or_dtype(input: Optional[Var]):
if input is None:
return None
elif isinstance(input, jt.Var):
return input.dtype
raise RuntimeError("input to _none_or_dtype() must be None or torch.Tensor")
def baddbmm(input_var:jt.Var, batch1:jt.Var, batch2:jt.Var, beta=1, alpha=1) -> jt.Var:
# WARNING(514flowey): Check here
return beta * input_var + alpha * (batch1 @ batch2)
def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None) -> jt.Var:
# Efficient implementation equivalent to the following:
L, S = query.size(-2), key.size(-2)
scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
attn_bias = jt.zeros(L, S, dtype=query.dtype)
if is_causal:
assert attn_mask is None
temp_mask = jt.ones(L, S, dtype=jt.bool).tril(diagonal=0)
attn_bias[jt.logical_not(temp_mask)] = float("-inf")
# attn_bias.to(query.dtype)
attn_bias = jt.array(attn_bias, query.dtype)
if attn_mask is not None:
if attn_mask.dtype == jt.bool:
attn_bias[jt.logical_not(temp_mask)] = float("-inf")
else:
attn_bias += attn_mask
attn_weight = query @ key.transpose(-2, -1) * scale_factor
attn_weight += attn_bias
attn_weight = softmax(attn_weight, dim=-1)
attn_weight = dropout(attn_weight, dropout_p, is_train=True)
return attn_weight @ value
def _mha_shape_check(query: Var, key: Var, value: Var,
key_padding_mask: Optional[Var], attn_mask: Optional[Var], num_heads: int):
if query.dim() == 3:
is_batched = True
assert key.dim() == 3 and value.dim() == 3, \
("For batched (3-D) `query`, expected `key` and `value` to be 3-D"
f" but found {key.dim()}-D and {value.dim()}-D tensors respectively")
if key_padding_mask is not None:
assert key_padding_mask.dim() == 2, \
("For batched (3-D) `query`, expected `key_padding_mask` to be `None` or 2-D"
f" but found {key_padding_mask.dim()}-D tensor instead")
if attn_mask is not None:
assert attn_mask.dim() in (2, 3), \
("For batched (3-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
f" but found {attn_mask.dim()}-D tensor instead")
elif query.dim() == 2:
is_batched = False
assert key.dim() == 2 and value.dim() == 2, \
("For unbatched (2-D) `query`, expected `key` and `value` to be 2-D"
f" but found {key.dim()}-D and {value.dim()}-D tensors respectively")
if key_padding_mask is not None:
assert key_padding_mask.dim() == 1, \
("For unbatched (2-D) `query`, expected `key_padding_mask` to be `None` or 1-D"
f" but found {key_padding_mask.dim()}-D tensor instead")
if attn_mask is not None:
assert attn_mask.dim() in (2, 3), \
("For unbatched (2-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
f" but found {attn_mask.dim()}-D tensor instead")
if attn_mask.dim() == 3:
expected_shape = (num_heads, query.shape[0], key.shape[0])
assert attn_mask.shape == expected_shape, \
(f"Expected `attn_mask` shape to be {expected_shape} but got {attn_mask.shape}")
else:
raise AssertionError(
f"query should be unbatched 2D or batched 3D tensor but received {query.dim()}-D query tensor")
return is_batched
def _in_projection_packed(
q: Var,
k: Var,
v: Var,
w: Var,
b: Optional[Var] = None,
) -> List[Var]:
E = q.size(-1)
if k is v:
if q is k:
# self-attention
proj = linear(q, w, b)
# reshape to 3, E and not E, 3 is deliberate for better memory coalescing and keeping same order as chunk()
# proj = proj.unflatten(-1, (3, E)).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
nshape = proj.shape[:-1] + (3, E)
proj = proj.reshape(nshape).unsqueeze(0).transpose(0, -2).squeeze(-2)
return proj[0], proj[1], proj[2]
else:
# encoder-decoder attention
w_q, w_kv = w.split([E, E * 2])
if b is None:
b_q = b_kv = None
else:
b_q, b_kv = b.split([E, E * 2])
q_proj = linear(q, w_q, b_q)
kv_proj = linear(k, w_kv, b_kv)
# reshape to 2, E and not E, 2 is deliberate for better memory coalescing and keeping same order as chunk()
# kv_proj = kv_proj.unflatten(-1, (2, E)).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
nshape = kv_proj.shape[:-1] + (2, E)
kv_proj = kv_proj.reshape(nshape).unsqueeze(0).transpose(0, -2).squeeze(-2)
return (q_proj, kv_proj[0], kv_proj[1])
else:
w_q, w_k, w_v = w.chunk(3)
if b is None:
b_q = b_k = b_v = None
else:
b_q, b_k, b_v = b.chunk(3)
return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v)
def _in_projection(
q: Var,
k: Var,
v: Var,
w_q: Var,
w_k: Var,
w_v: Var,
b_q: Optional[Var] = None,
b_k: Optional[Var] = None,
b_v: Optional[Var] = None,
) -> Tuple[Var, Var, Var]:
Eq, Ek, Ev = q.size(-1), k.size(-1), v.size(-1)
assert w_q.shape == (Eq, Eq), f"expecting query weights shape of {(Eq, Eq)}, but got {w_q.shape}"
assert w_k.shape == (Eq, Ek), f"expecting key weights shape of {(Eq, Ek)}, but got {w_k.shape}"
assert w_v.shape == (Eq, Ev), f"expecting value weights shape of {(Eq, Ev)}, but got {w_v.shape}"
assert b_q is None or b_q.shape == (Eq,), f"expecting query bias shape of {(Eq,)}, but got {b_q.shape}"
assert b_k is None or b_k.shape == (Eq,), f"expecting key bias shape of {(Eq,)}, but got {b_k.shape}"
assert b_v is None or b_v.shape == (Eq,), f"expecting value bias shape of {(Eq,)}, but got {b_v.shape}"
return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v)
def multi_head_attention_forward(
query: Var,
key: Var,
value: Var,
embed_dim_to_check: int,
num_heads: int,
in_proj_weight: Optional[Var],
in_proj_bias: Optional[Var],
bias_k: Optional[Var],
bias_v: Optional[Var],
add_zero_attn: bool,
dropout_p: float,
out_proj_weight: Var,
out_proj_bias: Optional[Var],
training: bool = True,
key_padding_mask: Optional[Var] = None,
need_weights: bool = True,
attn_mask: Optional[Var] = None,
use_separate_proj_weight: bool = False,
q_proj_weight: Optional[Var] = None,
k_proj_weight: Optional[Var] = None,
v_proj_weight: Optional[Var] = None,
static_k: Optional[Var] = None,
static_v: Optional[Var] = None,
average_attn_weights: bool = True,
is_causal: bool = False,
) -> Tuple[Var, Optional[Var]]:
is_batched = _mha_shape_check(query, key, value, key_padding_mask, attn_mask, num_heads)
# For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input
# is batched, run the computation and before returning squeeze the
# batch dimension so that the output doesn't carry this temporary batch dimension.
if not is_batched:
# unsqueeze if the input is unbatched
query = query.unsqueeze(1)
key = key.unsqueeze(1)
value = value.unsqueeze(1)
if key_padding_mask is not None:
key_padding_mask = key_padding_mask.unsqueeze(0)
# set up shape vars
tgt_len, bsz, embed_dim = query.shape
src_len, _, _ = key.shape
key_padding_mask = _canonical_mask(
mask=key_padding_mask,
mask_name="key_padding_mask",
other_type=_none_or_dtype(attn_mask),
other_name="attn_mask",
target_type=query.dtype
)
if is_causal and attn_mask is None:
raise RuntimeError(
"Need attn_mask if specifying the is_causal hint. "
"You may use the Transformer module method "
"`generate_square_subsequent_mask` to create this mask."
)
if is_causal and key_padding_mask is None and not need_weights:
# when we have a kpm or need weights, we need attn_mask
# Otherwise, we use the is_causal hint go as is_causal
# indicator to SDPA.
attn_mask = None
else:
attn_mask = _canonical_mask(
mask=attn_mask,
mask_name="attn_mask",
other_type=None,
other_name="",
target_type=query.dtype,
check_other=False,
)
if key_padding_mask is not None:
# We have the attn_mask, and use that to merge kpm into it.
# Turn off use of is_causal hint, as the merged mask is no
# longer causal.
is_causal = False
assert embed_dim == embed_dim_to_check, \
f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
if isinstance(embed_dim, jt.Var):
# embed_dim can be a tensor when JIT tracing
head_dim = embed_dim.div(num_heads, rounding_mode='trunc')
else:
head_dim = embed_dim // num_heads
assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
if use_separate_proj_weight:
# allow MHA to have different embedding dimensions when separate projection weights are used
assert key.shape[:2] == value.shape[:2], \
f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
else:
assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}"
#
# compute in-projection
#
if not use_separate_proj_weight:
assert in_proj_weight is not None, "use_separate_proj_weight is False but in_proj_weight is None"
q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
else:
assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None"
assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None"
assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None"
if in_proj_bias is None:
b_q = b_k = b_v = None
else:
b_q, b_k, b_v = in_proj_bias.chunk(3)
q, k, v = _in_projection(query, key, value, q_proj_weight, k_proj_weight, v_proj_weight, b_q, b_k, b_v)
# prep attention mask
if attn_mask is not None:
# ensure attn_mask's dim is 3
if attn_mask.dim() == 2:
correct_2d_size = (tgt_len, src_len)
if attn_mask.shape != correct_2d_size:
raise RuntimeError(f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}.")
attn_mask = attn_mask.unsqueeze(0)
elif attn_mask.dim() == 3:
correct_3d_size = (bsz * num_heads, tgt_len, src_len)
if attn_mask.shape != correct_3d_size:
raise RuntimeError(f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}.")
else:
raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")
# add bias along batch dimension (currently second)
if bias_k is not None and bias_v is not None:
assert static_k is None, "bias cannot be added to static key."
assert static_v is None, "bias cannot be added to static value."
k = jt.concat([k, bias_k.repeat(1, bsz, 1)])
v = jt.concat([v, bias_v.repeat(1, bsz, 1)])
if attn_mask is not None:
attn_mask = pad(attn_mask, (0, 1))
if key_padding_mask is not None:
key_padding_mask = pad(key_padding_mask, (0, 1))
else:
assert bias_k is None
assert bias_v is None
#
# reshape q, k, v for multihead attention and make em batch first
#
q = q.view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
if static_k is None:
k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
else:
# TODO finish disentangling control flow so we don't do in-projections when statics are passed
assert static_k.size(0) == bsz * num_heads, \
f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
assert static_k.size(2) == head_dim, \
f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
k = static_k
if static_v is None:
v = v.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
else:
# TODO finish disentangling control flow so we don't do in-projections when statics are passed
assert static_v.size(0) == bsz * num_heads, \
f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
assert static_v.size(2) == head_dim, \
f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
v = static_v
# add zero attention along batch dimension (now first)
if add_zero_attn:
zero_attn_shape = (bsz * num_heads, 1, head_dim)
k = jt.concat([k, jt.zeros(zero_attn_shape, dtype=k.dtype)], dim=1)
v = jt.concat([v, jt.zeros(zero_attn_shape, dtype=v.dtype)], dim=1)
if attn_mask is not None:
attn_mask = pad(attn_mask, (0, 1))
if key_padding_mask is not None:
key_padding_mask = pad(key_padding_mask, (0, 1))
# update source sequence length after adjustments
src_len = k.size(1)
# merge key padding and attention masks
if key_padding_mask is not None:
assert key_padding_mask.shape == (bsz, src_len), \
f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
key_padding_mask = key_padding_mask.view(bsz, 1, 1, src_len). \
expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, src_len)
if attn_mask is None:
attn_mask = key_padding_mask
else:
attn_mask = attn_mask + key_padding_mask
# adjust dropout probability
if not training:
dropout_p = 0.0
#
# (deep breath) calculate attention and out projection
#
if need_weights:
B, Nt, E = q.shape
q_scaled = q / math.sqrt(E)
assert not (is_causal and attn_mask is None), "FIXME: is_causal not implemented for need_weights"
if attn_mask is not None:
attn_output_weights = baddbmm(attn_mask, q_scaled, k.transpose(-2, -1))
else:
attn_output_weights = jt.bmm(q_scaled, k.transpose(-2, -1))
attn_output_weights = softmax(attn_output_weights, dim=-1)
if dropout_p > 0.0:
attn_output_weights = dropout(attn_output_weights, p=dropout_p)
attn_output = jt.bmm(attn_output_weights, v)
attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
# optionally average attention weights over heads
attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
if average_attn_weights:
attn_output_weights = attn_output_weights.mean(dim=1)
if not is_batched:
# squeeze the output if input was unbatched
attn_output = attn_output.squeeze(1)
attn_output_weights = attn_output_weights.squeeze(0)
return attn_output, attn_output_weights
else:
# attn_mask can be either (L,S) or (N*num_heads, L, S)
# if attn_mask's shape is (1, L, S) we need to unsqueeze to (1, 1, L, S)
# in order to match the input for SDPA of (N, num_heads, L, S)
if attn_mask is not None:
if attn_mask.size(0) == 1 and attn_mask.dim() == 3:
attn_mask = attn_mask.unsqueeze(0)
else:
attn_mask = attn_mask.view(bsz, num_heads, -1, src_len)
q = q.view(bsz, num_heads, tgt_len, head_dim)
k = k.view(bsz, num_heads, src_len, head_dim)
v = v.view(bsz, num_heads, src_len, head_dim)
attn_output = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
if not is_batched:
# squeeze the output if input was unbatched
attn_output = attn_output.squeeze(1)
return attn_output, None
class MultiheadAttention(Module):
__constants__ = ['batch_first']
bias_k: Optional[jt.Var]
bias_v: Optional[jt.Var]
def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False,
kdim=None, vdim=None, batch_first=False, dtype=jt.float32) -> None:
if embed_dim <= 0 or num_heads <= 0:
raise ValueError(
f"embed_dim and num_heads must be greater than 0,"
f" got embed_dim={embed_dim} and num_heads={num_heads} instead"
)
factory_kwargs = {'dtype': dtype}
super().__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.batch_first = batch_first
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
if not self._qkv_same_embed_dim:
self.q_proj_weight = jt.empty((embed_dim, embed_dim), **factory_kwargs)
self.k_proj_weight = jt.empty((embed_dim, self.kdim), **factory_kwargs)
self.v_proj_weight = jt.empty((embed_dim, self.vdim), **factory_kwargs)
self.in_proj_weight = None
else:
self.q_proj_weight = None
self.k_proj_weight = None
self.v_proj_weight = None
self.in_proj_weight = jt.empty((3 * embed_dim, embed_dim), **factory_kwargs)
if bias:
self.in_proj_bias = jt.empty(3 * embed_dim, **factory_kwargs)
else:
self.in_proj_bias = None
self.out_proj = Linear(embed_dim, embed_dim, bias=bias)
if add_bias_kv:
self.bias_k = jt.empty((1, 1, embed_dim), **factory_kwargs)
self.bias_v = jt.empty((1, 1, embed_dim), **factory_kwargs)
else:
self.bias_k = self.bias_v = None
self.add_zero_attn = add_zero_attn
self._reset_parameters()
def _reset_parameters(self):
if self._qkv_same_embed_dim:
xavier_uniform_(self.in_proj_weight)
else:
xavier_uniform_(self.q_proj_weight)
xavier_uniform_(self.k_proj_weight)
xavier_uniform_(self.v_proj_weight)
if self.in_proj_bias is not None:
constant_(self.in_proj_bias, 0.)
constant_(self.out_proj.bias, 0.)
if self.bias_k is not None:
xavier_gauss_(self.bias_k)
if self.bias_v is not None:
xavier_gauss_(self.bias_v)
def __setstate__(self, state):
# Support loading old MultiheadAttention checkpoints generated by v1.1.0
if '_qkv_same_embed_dim' not in state:
state['_qkv_same_embed_dim'] = True
super().__setstate__(state)
def execute(
self,
query: Var,
key: Var,
value: Var,
key_padding_mask: Optional[Var] = None,
need_weights: bool = True,
attn_mask: Optional[Var] = None,
average_attn_weights: bool = True,
is_causal : bool = False) -> Tuple[Var, Optional[Var]]:
#####
# Fast Path is not Supported.
#####
is_batched = query.dim() == 3
key_padding_mask = _canonical_mask(
mask=key_padding_mask,
mask_name="key_padding_mask",
other_type=_none_or_dtype(attn_mask),
other_name="attn_mask",
target_type=query.dtype
)
attn_mask = _canonical_mask(
mask=attn_mask,
mask_name="attn_mask",
other_type=None,
other_name="",
target_type=query.dtype,
check_other=False,
)
if self.batch_first and is_batched:
# make sure that the transpose op does not affect the "is" property
if key is value:
if query is key:
query = key = value = query.transpose(1, 0)
else:
query, key = (x.transpose(1, 0) for x in (query, key))
value = key
else:
query, key, value = (x.transpose(1, 0) for x in (query, key, value))
if not self._qkv_same_embed_dim:
attn_output, attn_output_weights = multi_head_attention_forward(
query, key, value, self.embed_dim, self.num_heads,
self.in_proj_weight, self.in_proj_bias,
self.bias_k, self.bias_v, self.add_zero_attn,
self.dropout, self.out_proj.weight, self.out_proj.bias,
training=self.is_training(),
key_padding_mask=key_padding_mask, need_weights=need_weights,
attn_mask=attn_mask,
use_separate_proj_weight=True,
q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,
v_proj_weight=self.v_proj_weight,
average_attn_weights=average_attn_weights,
is_causal=is_causal)
else:
attn_output, attn_output_weights = multi_head_attention_forward(
query, key, value, self.embed_dim, self.num_heads,
self.in_proj_weight, self.in_proj_bias,
self.bias_k, self.bias_v, self.add_zero_attn,
self.dropout, self.out_proj.weight, self.out_proj.bias,
training=self.is_training(),
key_padding_mask=key_padding_mask,
need_weights=need_weights,
attn_mask=attn_mask,
average_attn_weights=average_attn_weights,
is_causal=is_causal)
if self.batch_first and is_batched:
return attn_output.transpose(1, 0), attn_output_weights
else:
return attn_output, attn_output_weights
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