X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=d8fd227f63c39a70dded3c55f3c230c3a9d58862;hb=3e4af6d54fb3d7bd6794035cb79e30ecdcadeb6f;hp=21ae73901d2525f9865ed48694f6a48dccb461a2;hpb=aad820c7d81e962b5f6459093fe558126198f1ed;p=mygptrnn.git

diff --git a/mygpt.py b/mygpt.py
index 21ae739..d8fd227 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -10,6 +10,8 @@
 # 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
@@ -37,7 +39,7 @@ import ffutils
 # 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:
@@ -457,77 +459,6 @@ def moving_window(x, dim, win_dim, win_size):
 
 ##############################
 
-# This is one order of magnitude more complicated than I expected, not
-# elegant, slow, hopefully not buggy
-
-
-def flash_back_time_src(N, H, t0, t1, CL, CH, proba, device):
-    # starting flash backs
-    fb_start = (torch.rand(N, CH, t1 - t0, device=device) <= proba).long()
-    fb_start[:, :, -CL:] = 0
-    fb_start[:, :, :CL] = 0
-
-    # Remove series longer than CL
-    fb_body = fb_start.clone()
-    fb_body[:, :, CL + 1 :] -= fb_start[:, :, : -(CL + 1)]
-    fb_body = fb_body.cumsum(dim=2)
-    fb_start = fb_start * (fb_body == 1)
-
-    # Set a origin source time (starting time of the chunck to copy
-    # here) We set it as the current time minus a multiple of CL to be
-    # consistent with the "rolling" caterpillar
-    t = torch.arange(fb_start.size(2), device=fb_start.device)[None, None, :]
-    src_time = fb_start * (
-        t
-        - CL
-        * (
-            1
-            + (
-                torch.rand(fb_start.size(), device=fb_start.device) * (t // CL - 1)
-            ).long()
-        )
-    )
-    src_time[:, :, CL:] -= src_time.clone()[:, :, :-CL]
-    src_time = src_time.cumsum(dim=2)
-
-    src_head = fb_start * torch.randint(H, fb_start.size(), device=fb_start.device)
-    src_head[:, :, CL:] -= src_head.clone()[:, :, :-CL]
-    src_head = src_head.cumsum(dim=2)
-
-    # combine
-    src_delta = fb_start.clone()
-    src_delta[:, :, CL:] -= fb_start[:, :, :-CL]
-    src_delta = src_delta.cumsum(dim=2)
-    src_delta[:, :, CL:] -= CL * fb_start[:, :, :-CL]
-    src_time += src_delta.cumsum(dim=2) - 1
-
-    return src_time, src_head
-
-
-def insert_flash_back(rec_V, V, rec_K, K, t0, t1, CL, proba):
-    N, H, CH = V.size(0), V.size(1), rec_V.size(1)
-
-    fbt, fbh = flash_back_time_src(N, H, t0, t1, CL, CH, proba, rec_V.device)
-
-    fbt_V = fbt[:, :, :, None]
-    fbh_V = fbh[:, :, :, None]
-    t = fbt_V.clamp(min=0)
-    n = torch.arange(V.size(0), device=V.device)[:, None, None, None]
-    d = torch.arange(V.size(3), device=V.device)[None, None, None, :]
-    q = V[:, :, t0:t1][n, fbh_V, t, d]
-    rec_V[:, :, t0:t1] = q * (fbt_V >= 0) + rec_V[:, :, t0:t1] * (fbt_V < 0)
-
-    fbt_K = fbt[:, :, :, None]
-    fbh_K = fbh[:, :, :, None]
-    t = fbt_K.clamp(min=0)
-    n = torch.arange(K.size(0), device=K.device)[:, None, None, None]
-    d = torch.arange(K.size(3), device=K.device)[None, None, None, :]
-    q = K[:, :, t0:t1][n, fbh_K, t, d]
-    rec_K[:, :, t0:t1] = q * (fbt_K >= 0) + rec_K[:, :, t0:t1] * (fbt_K < 0)
-
-
-######################################################################
-
 
 class Caterpillar(nn.Module):
     def __init__(
@@ -552,8 +483,7 @@ class Caterpillar(nn.Module):
         self.caterpillar_height = caterpillar_height
         self.attention_dropout = attention_dropout
 
-        warnings.warn("flash back", RuntimeWarning)
-        self.proba_flashback = 0.1
+        self.proba_gate_dropout = 0.0
 
         self.w_G = randw(nb_heads, caterpillar_height, dim_model)
         self.b_G = nn.Parameter(
@@ -609,24 +539,26 @@ class Caterpillar(nn.Module):
 
             self.cache_Y = X.new_zeros(N, T, DM)
 
+        V = torch.einsum("ntc,hdc->nhtd", X, self.w_V)
+        K = torch.einsum("ntc,hdc->nhtd", X, self.w_K)
+
         ######################################################################
         # 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
+        # stack. There are CH independent gating values, which means
+        # that the current K/V may 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()
 
-        # That bas a bad idea
-        # G = F.dropout(G, self.attention_dropout, self.training)
+        # Clip the gating to avoid values greater than 1 when several
+        # heads hit the same row
 
-        V = torch.einsum("ntc,hdc->nhtd", X, self.w_V)
-        K = torch.einsum("ntc,hdc->nhtd", X, self.w_K)
+        G = G / G.sum(1, keepdim=True).clamp(min=1)
 
         # We prepare the arguments for the parallel scan
 
@@ -634,14 +566,25 @@ class Caterpillar(nn.Module):
         gated_V = torch.einsum("nhet,nhtd->netd", G, V)
         gated_K = torch.einsum("nhet,nhtd->netd", G, K)
 
+        # We start from cached values, which matters in inference
+
         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.
+        ######################################################################
+
+        if self.training and self.proba_gate_dropout > 0.0:
+            warnings.warn("gate dropout", RuntimeWarning)
+            epsilon = 0.5
+
+        #################################################################
+        # Associative scan
+
+        # Here there is a trick: Since the stack at position t is
+        # computed by updating that at position t-CL, the parallel
+        # scan operates with a period of CL. To do so we split the
+        # sequence indexing in two axes, the second of size CL, and
+        # run the parallel scan using the first as the sequence index.
 
         A = A.unflatten(2, (-1, CL))
         gated_V = gated_V.unflatten(2, (-1, CL))
@@ -650,43 +593,9 @@ class Caterpillar(nn.Module):
         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:
-            insert_flash_back(
-                self.rec_V,
-                V,
-                self.rec_K,
-                K,
-                t0,
-                t1,
-                CL,
-                proba=self.proba_flashback / CL,
-            )
-
-            # 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)[None, None, None, :]
-            # dk = torch.arange(DK)[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)
-
-            # mk = (
-            # torch.rand(self.rec_V[:, :, t0:t1].size()) <= self.proba_flashback
-            # ).long()
-            # self.rec_V[:, :, t0:t1] = V[n, src_head, src_time, dv]
-            # self.rec_K[:, :, t0:t1] = K[n, src_head, src_time, dk]
-
-        exit(0)
-
         ######################################################################
         # compute the readout
 
@@ -833,7 +742,6 @@ class MyGPT(nn.Module):
         nb_blocks,
         nb_lines=None,
         caterpillar_height=None,
-        dim_rec_v=-1,
         causal=False,
         dropout=0.0,
         len_max=1e5,
@@ -841,7 +749,12 @@ class MyGPT(nn.Module):
     ):
         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
@@ -874,7 +787,7 @@ class MyGPT(nn.Module):
                 return DumbRec(
                     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,
@@ -883,7 +796,7 @@ class MyGPT(nn.Module):
                 return KVRec(
                     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,
@@ -892,7 +805,7 @@ class MyGPT(nn.Module):
                 return Caterpillar(
                     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,