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feat: add mamba and dynamic chunking related code and test code

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gameloader
2025-09-04 01:32:13 +00:00
parent 12cb7652cf
commit ef307a57e9
21 changed files with 4550 additions and 86 deletions
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169
layers/SelfAttention_Family.py
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@ -17,25 +17,30 @@ class DSAttention(nn.Module):
self.output_attention = output_attention
self.dropout = nn.Dropout(attention_dropout)
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None, key_padding_mask=None):
"""
key_padding_mask: (B, S) bool, True=valid, False=pad（可选，忽略或由上层应用）
"""
B, L, H, E = queries.shape
_, S, _, D = values.shape
scale = self.scale or 1. / sqrt(E)
tau = 1.0 if tau is None else tau.unsqueeze(
1).unsqueeze(1) # B x 1 x 1 x 1
delta = 0.0 if delta is None else delta.unsqueeze(
1).unsqueeze(1) # B x 1 x 1 x S
tau = 1.0 if tau is None else tau.unsqueeze(1).unsqueeze(1) # B x 1 x 1 x 1
delta = 0.0 if delta is None else delta.unsqueeze(1).unsqueeze(1) # B x 1 x 1 x S
# De-stationary Attention, rescaling pre-softmax score with learned de-stationary factors
scores = torch.einsum("blhe,bshe->bhls", queries, keys) * tau + delta
scores = torch.einsum("blhe,bshe->bhls", queries, keys) * tau + delta # (B,H,L,S)
if self.mask_flag:
if attn_mask is None:
attn_mask = TriangularCausalMask(B, L, device=queries.device)
scores.masked_fill_(attn_mask.mask, -np.inf)
# 可选：基于key_padding_mask的无效键屏蔽（不改变原行为，默认None）
if key_padding_mask is not None:
# key_padding_mask: True 表示有效，False为padding
invalid_k = (~key_padding_mask).unsqueeze(1).unsqueeze(1) # (B,1,1,S)
scores = scores.masked_fill(invalid_k, -np.inf)
A = self.dropout(torch.softmax(scale * scores, dim=-1))
V = torch.einsum("bhls,bshd->blhd", A, values)
@ -46,6 +51,12 @@ class DSAttention(nn.Module):
class FullAttention(nn.Module):
"""
修正点：
- 新增 key_padding_mask 支持，用于屏蔽批内右侧pad的键向量（与DC变长对齐）
- key_padding_mask 约定：shape=(B, S)，True=有效，False=padding
- 其余行为与原实现保持一致
"""
def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
super(FullAttention, self).__init__()
self.scale = scale
@ -53,21 +64,33 @@ class FullAttention(nn.Module):
self.output_attention = output_attention
self.dropout = nn.Dropout(attention_dropout)
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None, key_padding_mask=None):
"""
queries: (B, L, H, E)
keys: (B, S, H, E)
values: (B, S, H, D)
attn_mask: TriangularCausalMask 或 None
key_padding_mask: (B, S) bool，True=有效，False=padding（可选）
"""
B, L, H, E = queries.shape
_, S, _, D = values.shape
scale = self.scale or 1. / sqrt(E)
scores = torch.einsum("blhe,bshe->bhls", queries, keys)
scores = torch.einsum("blhe,bshe->bhls", queries, keys) # (B,H,L,S)
if self.mask_flag:
if attn_mask is None:
attn_mask = TriangularCausalMask(B, L, device=queries.device)
scores.masked_fill_(attn_mask.mask, -np.inf)
A = self.dropout(torch.softmax(scale * scores, dim=-1))
V = torch.einsum("bhls,bshd->blhd", A, values)
# 基于key_padding_mask屏蔽无效键（padding位置不参与注意力）
if key_padding_mask is not None:
# key_padding_mask: True=有效，False=padding
invalid_k = (~key_padding_mask).unsqueeze(1).unsqueeze(1) # (B,1,1,S)
scores = scores.masked_fill(invalid_k, -np.inf)
A = self.dropout(torch.softmax(scale * scores, dim=-1)) # (B,H,L,S)
V = torch.einsum("bhls,bshd->blhd", A, values) # (B,L,H,D)
if self.output_attention:
return V.contiguous(), A
@ -85,100 +108,86 @@ class ProbAttention(nn.Module):
self.dropout = nn.Dropout(attention_dropout)
def _prob_QK(self, Q, K, sample_k, n_top): # n_top: c*ln(L_q)
# Q [B, H, L, D]
# Q [B, H, L_q, D], K [B, H, L_k, D]
B, H, L_K, E = K.shape
_, _, L_Q, _ = Q.shape
# calculate the sampled Q_K
K_expand = K.unsqueeze(-3).expand(B, H, L_Q, L_K, E)
# real U = U_part(factor*ln(L_k))*L_q
index_sample = torch.randint(L_K, (L_Q, sample_k))
K_sample = K_expand[:, :, torch.arange(
L_Q).unsqueeze(1), index_sample, :]
Q_K_sample = torch.matmul(
Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze()
index_sample = torch.randint(L_K, (L_Q, sample_k), device=Q.device)
K_sample = K_expand[:, :, torch.arange(L_Q, device=Q.device).unsqueeze(1), index_sample, :]
Q_K_sample = torch.matmul(Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze(-2) # (B,H,L_Q,sample_k)
# find the Top_k query with sparisty measurement
M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K)
M_top = M.topk(n_top, sorted=False)[1]
M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K) # (B,H,L_Q)
M_top = M.topk(n_top, sorted=False)[1] # indices
# use the reduced Q to calculate Q_K
Q_reduce = Q[torch.arange(B)[:, None, None],
torch.arange(H)[None, :, None],
M_top, :] # factor*ln(L_q)
Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1)) # factor*ln(L_q)*L_k
torch.arange(H)[None, :, None],
M_top, :] # (B,H,n_top,D)
Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1)) # (B,H,n_top,L_K)
return Q_K, M_top
def _get_initial_context(self, V, L_Q):
B, H, L_V, D = V.shape
if not self.mask_flag:
# V_sum = V.sum(dim=-2)
V_sum = V.mean(dim=-2)
contex = V_sum.unsqueeze(-2).expand(B, H,
L_Q, V_sum.shape[-1]).clone()
else: # use mask
# requires that L_Q == L_V, i.e. for self-attention only
assert (L_Q == L_V)
contex = V.cumsum(dim=-2)
return contex
V_mean = V.mean(dim=-2) # (B,H,D)
context = V_mean.unsqueeze(-2).expand(B, H, L_Q, D).clone()
else:
assert L_Q == L_V
context = V.cumsum(dim=-2)
return context
def _update_context(self, context_in, V, scores, index, L_Q, attn_mask):
B, H, L_V, D = V.shape
if self.mask_flag:
attn_mask = ProbMask(B, H, L_Q, index, scores, device=V.device)
scores.masked_fill_(attn_mask.mask, -np.inf)
attn = torch.softmax(scores, dim=-1) # nn.Softmax(dim=-1)(scores)
attn = torch.softmax(scores, dim=-1)
context_in[torch.arange(B)[:, None, None],
torch.arange(H)[None, :, None],
index, :] = torch.matmul(attn, V).type_as(context_in)
torch.arange(H)[None, :, None],
index, :] = torch.matmul(attn, V).type_as(context_in)
if self.output_attention:
attns = (torch.ones([B, H, L_V, L_V]) /
L_V).type_as(attn).to(attn.device)
attns[torch.arange(B)[:, None, None], torch.arange(H)[
None, :, None], index, :] = attn
attns = (torch.ones([B, H, L_V, L_V], device=attn.device, dtype=attn.dtype) / L_V)
attns[torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, :] = attn
return context_in, attns
else:
return context_in, None
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None, key_padding_mask=None):
"""
key_padding_mask 目前未集成到 ProbAttention（如需，可在scores处对无效键置 -inf）
"""
B, L_Q, H, D = queries.shape
_, L_K, _, _ = keys.shape
queries = queries.transpose(2, 1)
keys = keys.transpose(2, 1)
values = values.transpose(2, 1)
queries = queries.transpose(2, 1) # (B,H,L_Q,D)
keys = keys.transpose(2, 1) # (B,H,L_K,D)
values = values.transpose(2, 1) # (B,H,L_K,D)
U_part = self.factor * \
np.ceil(np.log(L_K)).astype('int').item() # c*ln(L_k)
u = self.factor * \
np.ceil(np.log(L_Q)).astype('int').item() # c*ln(L_q)
U_part = self.factor * int(np.ceil(np.log(L_K)))
u = self.factor * int(np.ceil(np.log(L_Q)))
U_part = U_part if U_part < L_K else L_K
u = u if u < L_Q else L_Q
U_part = min(U_part, L_K)
u = min(u, L_Q)
scores_top, index = self._prob_QK(
queries, keys, sample_k=U_part, n_top=u)
scores_top, index = self._prob_QK(queries, keys, sample_k=U_part, n_top=u)
# add scale factor
scale = self.scale or 1. / sqrt(D)
if scale is not None:
scores_top = scores_top * scale
# get the context
scores_top = scores_top * scale
context = self._get_initial_context(values, L_Q)
# update the context with selected top_k queries
context, attn = self._update_context(
context, values, scores_top, index, L_Q, attn_mask)
context, attn = self._update_context(context, values, scores_top, index, L_Q, attn_mask)
return context.contiguous(), attn
class AttentionLayer(nn.Module):
def __init__(self, attention, d_model, n_heads, d_keys=None,
d_values=None):
"""
修正点：
- forward 新增 key_padding_mask 参数，并向 inner_attention 透传
- 保持与旧调用兼容（不传时默认None）
"""
def __init__(self, attention, d_model, n_heads, d_keys=None, d_values=None):
super(AttentionLayer, self).__init__()
d_keys = d_keys or (d_model // n_heads)
@ -191,7 +200,10 @@ class AttentionLayer(nn.Module):
self.out_projection = nn.Linear(d_values * n_heads, d_model)
self.n_heads = n_heads
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None, key_padding_mask=None):
"""
key_padding_mask: (B, S) bool, True=有效，False=padding
"""
B, L, _ = queries.shape
_, S, _ = keys.shape
H = self.n_heads
@ -206,10 +218,10 @@ class AttentionLayer(nn.Module):
values,
attn_mask,
tau=tau,
delta=delta
delta=delta,
key_padding_mask=key_padding_mask,
)
out = out.view(B, L, -1)
return self.out_projection(out), attn
@ -232,12 +244,11 @@ class ReformerLayer(nn.Module):
if N % (self.bucket_size * 2) == 0:
return queries
else:
# fill the time series
fill_len = (self.bucket_size * 2) - (N % (self.bucket_size * 2))
return torch.cat([queries, torch.zeros([B, fill_len, C]).to(queries.device)], dim=1)
def forward(self, queries, keys, values, attn_mask, tau, delta):
# in Reformer: defalut queries=keys
def forward(self, queries, keys, values, attn_mask, tau, delta, key_padding_mask=None):
# queries=keys in Reformer
B, N, C = queries.shape
queries = self.attn(self.fit_length(queries))[:, :N, :]
return queries, None
@ -275,23 +286,23 @@ class TwoStageAttentionLayer(nn.Module):
nn.GELU(),
nn.Linear(d_ff, d_model))
def forward(self, x, attn_mask=None, tau=None, delta=None):
def forward(self, x, attn_mask=None, tau=None, delta=None, key_padding_mask=None):
# Cross Time Stage: Directly apply MSA to each dimension
batch = x.shape[0]
time_in = rearrange(x, 'b ts_d seg_num d_model -> (b ts_d) seg_num d_model')
time_enc, attn = self.time_attention(
time_in, time_in, time_in, attn_mask=None, tau=None, delta=None
time_in, time_in, time_in, attn_mask=None, tau=None, delta=None, key_padding_mask=key_padding_mask
)
dim_in = time_in + self.dropout(time_enc)
dim_in = self.norm1(dim_in)
dim_in = dim_in + self.dropout(self.MLP1(dim_in))
dim_in = self.norm2(dim_in)
# Cross Dimension Stage: use a small set of learnable vectors to aggregate and distribute messages to build the D-to-D connection
# Cross Dimension Stage
dim_send = rearrange(dim_in, '(b ts_d) seg_num d_model -> (b seg_num) ts_d d_model', b=batch)
batch_router = repeat(self.router, 'seg_num factor d_model -> (repeat seg_num) factor d_model', repeat=batch)
dim_buffer, attn = self.dim_sender(batch_router, dim_send, dim_send, attn_mask=None, tau=None, delta=None)
dim_receive, attn = self.dim_receiver(dim_send, dim_buffer, dim_buffer, attn_mask=None, tau=None, delta=None)
dim_buffer, _ = self.dim_sender(batch_router, dim_send, dim_send, attn_mask=None, tau=None, delta=None)
dim_receive, _ = self.dim_receiver(dim_send, dim_buffer, dim_buffer, attn_mask=None, tau=None, delta=None)
dim_enc = dim_send + self.dropout(dim_receive)
dim_enc = self.norm3(dim_enc)
dim_enc = dim_enc + self.dropout(self.MLP2(dim_enc))