# with a caching mechanism for keys and values to avoid a O(N^3) cost
# for auto-regression.
+# This implementation is equipped with RNN layers to replace the MHA
+
import math, warnings
import torch, einops
# 1 for the successive tokens.
#
# Modules able to process brackets may implement a cache that is
-# resetted when the input bracket starts at t=0
+# resetted when init_cache is True
class BracketedSequence:
class DumbRec(nn.Module):
def __init__(
self,
- dim_in,
+ dim_model,
dim_qk,
dim_v,
nb_heads,
self.k_star = randw(nb_lines, dim_qk)
- self.w_qw = randw(nb_heads, dim_qk, dim_in)
- self.w_qr = randw(nb_heads, dim_qk, dim_in)
- # self.w_k = randw(nb_heads, dim_qk, dim_in)
- self.w_v = randw(nb_heads, dim_v, dim_in)
- self.w_o = randw(dim_v * nb_heads, dim_in)
+ self.w_qw = randw(nb_heads, dim_qk, dim_model)
+ self.w_qr = randw(nb_heads, dim_qk, dim_model)
+ # self.w_k = randw(nb_heads, dim_qk, dim_model)
+ self.w_v = randw(nb_heads, dim_v, dim_model)
+ self.w_o = randw(dim_v * nb_heads, dim_model)
def reset_inner_loss(self):
self.acc_attention = 0
class KVRec(nn.Module):
def __init__(
self,
- dim_in,
+ dim_model,
dim_qk,
dim_v,
nb_heads,
self.k_star = randw(nb_lines, dim_qk)
- self.w_qw = randw(nb_heads, dim_qk, dim_in)
- self.w_qr = randw(nb_heads, dim_qk, dim_in)
- self.w_k = randw(nb_heads, dim_qk, dim_in)
- self.w_v = randw(nb_heads, dim_v, dim_in)
- self.w_o = randw(dim_v * nb_heads, dim_in)
+ self.w_qw = randw(nb_heads, dim_qk, dim_model)
+ self.w_qr = randw(nb_heads, dim_qk, dim_model)
+ self.w_k = randw(nb_heads, dim_qk, dim_model)
+ self.w_v = randw(nb_heads, dim_v, dim_model)
+ self.w_o = randw(dim_v * nb_heads, dim_model)
def reset_inner_loss(self):
self.acc_attention = 0
##############################
+# Returns a tensor with an additional index at rank win_dim, that move
+# along the same dimension as dim, on a domain {0...win_size-1}, and
+# dim is restricted on a domain reduced by win_size-1 values.
+
+
def moving_window(x, dim, win_dim, win_size):
size, stride = x.size(), x.stride()
size = size[:dim] + (size[dim] - win_size + 1,) + size[dim + 1 :]
class Caterpillar(nn.Module):
def __init__(
self,
- dim_in,
+ dim_model,
dim_qk,
dim_v,
nb_heads,
self.caterpillar_height = caterpillar_height
self.attention_dropout = attention_dropout
- self.w_G = randw(nb_heads, caterpillar_height, dim_in)
+ self.proba_flashback = 0.0
+ self.proba_gate_dropout = 0.0
+
+ self.w_G = randw(nb_heads, caterpillar_height, dim_model)
self.b_G = nn.Parameter(
torch.full(
(nb_heads, caterpillar_height), -math.log(caterpillar_height - 1)
)
)
- self.w_K = randw(nb_heads, dim_qk, dim_in)
- self.w_V = randw(nb_heads, dim_v, dim_in)
- self.w_Q = randw(nb_heads, dim_qk, dim_in)
- self.w_O = randw(dim_v * nb_heads, dim_in)
+ self.w_K = randw(nb_heads, dim_qk, dim_model)
+ self.w_V = randw(nb_heads, dim_v, dim_model)
+ self.w_Q = randw(nb_heads, dim_qk, dim_model)
+ self.w_O = randw(dim_v * nb_heads, dim_model)
self.init_K_rec = randw(caterpillar_height, caterpillar_length, dim_qk)
self.init_V_rec = randw(caterpillar_height, caterpillar_length, dim_v)
N = bs.x.size(0)
T = bs.x.size(1)
+ H = self.w_V.size(0)
DV = self.w_V.size(1)
DK = self.w_K.size(1)
- Dout = self.w_O.size(1)
+ DM = self.w_O.size(1)
CH = self.caterpillar_height
CL = self.caterpillar_length
t0 >= CL and (t1 - t0) % CL == 0
), f"bs.first should be greater than caterpillar_length, and bs.nb should be a multiple of caterpillar_length"
+ # We cache values to deal efficiently with auto-regression
+
if bs.init_cache:
self.rec_V = X.new_zeros(N, CH, T, DV)
- self.rec_V[:, :, t0 - CL : t0] = self.init_V_rec[None, :, :, :]
self.rec_K = X.new_zeros(N, CH, T, DK)
+ # We start the recurrent sequences with optimizable
+ # initial values. No idea if it helps.
+ self.rec_V[:, :, t0 - CL : t0] = self.init_V_rec[None, :, :, :]
self.rec_K[:, :, t0 - CL : t0] = self.init_K_rec[None, :, :, :]
- self.cache_Y = X.new_zeros(N, T, Dout)
+
+ self.cache_Y = X.new_zeros(N, T, DM)
######################################################################
# Compute the recurrent state
+ # This is the Gating sequence that modulates the storing of
+ # the new key and value in the CH pairs of the current
+ # stack. The CH gating values are independent, which means
+ # that the current K/V could be stored in multiple pairs of the
+ # recurrent state, or not at all.
+
G = (
torch.einsum("ntc,hec->nhet", X, self.w_G) + self.b_G[None, :, :, None]
).sigmoid()
+ if self.training and self.proba_gate_dropout > 0.0:
+ warnings.warn("gate droupout", RuntimeWarning)
+ epsilon = 0.5
+
+ # That was a bad idea
+ # G = F.dropout(G, self.attention_dropout, self.training)
+
V = torch.einsum("ntc,hdc->nhtd", X, self.w_V)
K = torch.einsum("ntc,hdc->nhtd", X, self.w_K)
+ # We prepare the arguments for the parallel scan
+
+ # Clip the gating
+ warnings.warn("gating clipping", RuntimeWarning)
+ G = G / G.sum(1, keepdim=True).clamp(min=1)
+
A = 1 - G.sum(1)
gated_V = torch.einsum("nhet,nhtd->netd", G, V)
gated_K = torch.einsum("nhet,nhtd->netd", G, K)
init_rec_V = self.rec_V[:, :, t0 - CL : t0]
init_rec_K = self.rec_K[:, :, t0 - CL : t0]
+ # Here there is a trick: Since the stack at time t is computed
+ # by updating that at time t-L, the parallel scan operates
+ # with a period of L. To do so we split the time indexing in
+ # two axes, the second of size CL, and run the parallel scan
+ # using the other as the sequence index.
+
A = A.unflatten(2, (-1, CL))
gated_V = gated_V.unflatten(2, (-1, CL))
gated_K = gated_K.unflatten(2, (-1, CL))
next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
+ # Put back the sequence index
+
self.rec_V[:, :, t0:t1] = next_V.flatten(2, 3)
self.rec_K[:, :, t0:t1] = next_K.flatten(2, 3)
+ if self.training and self.proba_flashback > 0.0:
+ warnings.warn("flash back", RuntimeWarning)
+ # This piece of code makes the assumption that there is
+ # nothing informative before t0, otherwise we'd have to
+ # implement a cache for V and K too. This should not be
+ # too much of a problem since this is used only during
+ # train, where full sequence are available
+
+ n = torch.arange(N, device=X.device)[:, None, None, None]
+ t = torch.arange(t0, t1, device=X.device)[None, None, :, None]
+ dv = torch.arange(DV, device=X.device)[None, None, None, :]
+ dk = torch.arange(DK, device=X.device)[None, None, None, :]
+
+ u = (
+ torch.rand(N, CH, t1 - t0, 1, device=X.device).mul(t).long() // CL
+ ) * CL
+
+ src_time = t - u - t0
+ src_head = torch.randint(H, (N, CH, t1 - t0, 1), device=X.device)
+
+ mask = (
+ torch.rand(N, CH, t1 - t0, DV, device=X.device) <= self.proba_flashback
+ ).long()
+
+ self.rec_V[:, :, t0:t1] = (
+ mask * V[n, src_head, src_time, dv]
+ + (1 - mask) * self.rec_V[:, :, t0:t1]
+ )
+
+ self.rec_K[:, :, t0:t1] = (
+ mask * K[n, src_head, src_time, dk]
+ + (1 - mask) * self.rec_K[:, :, t0:t1]
+ )
+
######################################################################
# compute the readout
Q = torch.einsum("ntc,hdc->nhtd", X, self.w_Q)
- uv = moving_window(
+ # We build tensors NxHxTxFxL where N is the sample index, H
+ # the head, T the time, F the row in the caterpillar, and L
+ # the column in the caterpillar
+
+ windowed_V = moving_window(
self.rec_V[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL
)
- uk = moving_window(
+ windowed_K = moving_window(
self.rec_K[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL
)
+ # We have an attention score for each of the CHxCL values
+
ar = torch.einsum(
"nhtd,nftld->nhtfl",
Q,
- uk,
+ windowed_K,
) / math.sqrt(DK)
+ # softmax can operate only on one dimension, hence the
+ # flattening
+
ar = ar.flatten(3).softmax(dim=3).view(ar.size())
ar = F.dropout(ar, self.attention_dropout, self.training)
+ # Compute the output for each head, flatten to concatenate
+
Y = torch.einsum(
"nhtfl,nftld->nthd",
ar,
- uv,
+ windowed_V,
).flatten(2)
+ # Compute the final output
+
self.cache_Y[:, t0:t1] = Y @ self.w_O
return BracketedSequence(self.cache_Y, t0, t1 - t0, bs.init_cache)
class QKVAttention(nn.Module):
def __init__(
self,
- dim_in,
+ dim_model,
dim_qk,
dim_v,
nb_heads=1,
self.attention_dropout = attention_dropout
self.record_attention = False
- self.w_q = randw(nb_heads, dim_qk, dim_in)
- self.w_k = randw(nb_heads, dim_qk, dim_in)
- self.w_v = randw(nb_heads, dim_v, dim_in)
- self.w_o = randw(dim_v * nb_heads, dim_in)
+ self.w_q = randw(nb_heads, dim_qk, dim_model)
+ self.w_k = randw(nb_heads, dim_qk, dim_model)
+ self.w_v = randw(nb_heads, dim_v, dim_model)
+ self.w_o = randw(dim_v * nb_heads, dim_model)
def forward(self, bs):
x_q = bs.x
nb_blocks,
nb_lines=None,
caterpillar_height=None,
- dim_rec_v=-1,
causal=False,
dropout=0.0,
len_max=1e5,
):
super().__init__()
- assert attention_layer in {"mha", "dumbrec", "kvrec", "caterpillar"}
+ assert attention_layer in {
+ "mha",
+ "dumbrec",
+ "kvrec",
+ "caterpillar",
+ }, f"Unknown attention operator {attention_layer}."
if attention_layer == "caterpillar":
assert nb_lines % caterpillar_height == 0
def attlayer():
if attention_layer == "mha":
return QKVAttention(
- dim_in=dim_model,
+ dim_model=dim_model,
dim_qk=dim_keys,
dim_v=dim_model // nb_heads,
nb_heads=nb_heads,
)
elif attention_layer == "dumbrec":
return DumbRec(
- dim_in=dim_model,
+ dim_model=dim_model,
dim_qk=dim_keys,
- dim_v=dim_rec_v,
+ dim_v=dim_model // nb_heads,
nb_heads=nb_heads,
nb_lines=nb_lines,
attention_dropout=dropout,
)
elif attention_layer == "kvrec":
return KVRec(
- dim_in=dim_model,
+ dim_model=dim_model,
dim_qk=dim_keys,
- dim_v=dim_rec_v,
+ dim_v=dim_model // nb_heads,
nb_heads=nb_heads,
nb_lines=nb_lines,
attention_dropout=dropout,
)
elif attention_layer == "caterpillar":
return Caterpillar(
- dim_in=dim_model,
+ dim_model=dim_model,
dim_qk=dim_keys,
- dim_v=dim_rec_v,
+ dim_v=dim_model // nb_heads,
nb_heads=nb_heads,
caterpillar_length=self.caterpillar_length,
caterpillar_height=self.caterpillar_height,
print("Basic check.")
m = Caterpillar(
- dim_in=4,
+ dim_model=4,
dim_qk=3,
dim_v=7,
nb_heads=1,
projects / mygptrnn.git / blobdiff