# 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:
self.caterpillar_height = caterpillar_height
self.attention_dropout = attention_dropout
- warnings.warn("flash back", RuntimeWarning)
- self.proba_flashback = 0.1
+ 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.einsum("ntc,hec->nhet", X, self.w_G) + self.b_G[None, :, :, None]
).sigmoid()
- # That bas a bad idea
+ 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)
# 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)
self.rec_K[:, :, t0:t1] = next_K.flatten(2, 3)
if self.training and self.proba_flashback > 0.0:
- # insert_flash_back(self.rec_V,V,self.rec_K,K,t0,t1,CL,proba=self.proba_flashback / CL,)
-
+ 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
src_time = t - u - t0
src_head = torch.randint(H, (N, CH, t1 - t0, 1), device=X.device)
- mask_V = (
+ mask = (
torch.rand(N, CH, t1 - t0, DV, device=X.device) <= self.proba_flashback
).long()
+
self.rec_V[:, :, t0:t1] = (
- mask_V * V[n, src_head, src_time, dv]
- + (1 - mask_V) * self.rec_V[:, :, t0:t1]
+ mask * V[n, src_head, src_time, dv]
+ + (1 - mask) * self.rec_V[:, :, t0:t1]
)
- mask_K = (
- torch.rand(N, CH, t1 - t0, DK, device=X.device) <= self.proba_flashback
- ).long()
self.rec_K[:, :, t0:t1] = (
- mask_K * K[n, src_head, src_time, dk]
- + (1 - mask_K) * self.rec_K[:, :, t0:t1]
+ mask * K[n, src_head, src_time, dk]
+ + (1 - mask) * self.rec_K[:, :, t0:t1]
)
######################################################################
):
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