X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=633ad642c19a3045064ef858c0ee494a7c733425;hb=6e87fe0cb8bd8a0042bbf7b2ede9d8ed0372fb6b;hp=d1acf22b1a6359cf2ffc31fe2d0885306674d6e4;hpb=ca56d3dfa53f3486da1d651f31f1e34ea0dc4652;p=mygptrnn.git

diff --git a/mygpt.py b/mygpt.py
index d1acf22..633ad64 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:
@@ -441,6 +443,11 @@ class KVRec(nn.Module):
 ##############################
 
 
+# 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 :]
@@ -469,13 +476,17 @@ class Caterpillar(nn.Module):
 
         warnings.warn("Caterpillar", RuntimeWarning)
 
-        def randw(*d):
-            return nn.Parameter(torch.randn(*d) / math.sqrt(d[-1]))
+        def randw(*d, amplitude=None):
+            if amplitude is None:
+                amplitude = 1 / math.sqrt(d[-1])
+            return nn.Parameter(amplitude * torch.randn(*d))
 
         self.caterpillar_length = caterpillar_length
         self.caterpillar_height = caterpillar_height
         self.attention_dropout = attention_dropout
 
+        self.proba_gate_dropout = 0.0
+
         self.w_G = randw(nb_heads, caterpillar_height, dim_model)
         self.b_G = nn.Parameter(
             torch.full(
@@ -488,8 +499,16 @@ class Caterpillar(nn.Module):
         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)
+        self.init_K_rec = randw(
+            caterpillar_height,
+            caterpillar_length,
+            dim_qk,
+        )
+        self.init_V_rec = randw(
+            caterpillar_height,
+            caterpillar_length,
+            dim_v,
+        )
 
     def reset_inner_loss(self):
         self.acc_attention = 0
@@ -507,65 +526,136 @@ class Caterpillar(nn.Module):
 
         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)
-        CH = self.caterpillar_height
-        CL = self.caterpillar_length
+        DM = self.w_O.size(1)
+        R = self.caterpillar_height
+        L = self.caterpillar_length
 
         assert (
-            t0 >= CL and (t1 - t0) % CL == 0
+            t0 >= L and (t1 - t0) % L == 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_K = X.new_zeros(N, CH, T, DK)
+            self.rec_V = X.new_zeros(N, R, T, DV)
+            self.rec_K = X.new_zeros(N, R, 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.rec_V[:, :, t0 - L : t0] = self.init_V_rec[None, :, :, :]
+            self.rec_K[:, :, t0 - L : t0] = self.init_K_rec[None, :, :, :]
 
-            self.cache_Y = X.new_zeros(N, T, Dout)
+            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 if they key and
-        # values should be stored in one of the CH pairs of the
-        # current stack. The CH gating values are independent, which
-        # means that the same thing could be stored up to CH times or
-        # not at all
+        # This is the Gating sequence that modulates the storing of
+        # the new key and value in the R pairs of the current
+        # stack. There are R 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]
+            torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None]
         ).sigmoid()
 
-        V = torch.einsum("ntc,hdc->nhtd", X, self.w_V)
-        K = torch.einsum("ntc,hdc->nhtd", X, self.w_K)
+        ######################################################################
+        # Roll the gating indexes
+
+        warnings.warn("rotating barrel", RuntimeWarning)
+
+        # print(f"SANITY2 {N=} {H=} {R=} {t0=} {t1=} {G.size()=}")
+
+        n_barrel = torch.arange(N, device=G.device)[:, None, None, None]
+        h_barrel = torch.arange(H, device=G.device)[None, :, None, None]
+        r_barrel = torch.arange(R, device=G.device)[None, None, :, None]
+        t_barrel = torch.arange(t1 - t0, device=G.device)[None, None, None, :]
+        r_barrel = (r_barrel + (t_barrel + t0) // L) % R
+
+        # GG = G.gather(dim=2,index=r_barrel)
+        G = G[n_barrel, h_barrel, r_barrel, t_barrel]
+
+        # print("SANITY", (GG-G).abs())
+        # exit(0)
+
+        ######################################################################
+        # The "flashbacks"
+
+        if self.training and self.proba_gate_dropout > 0.0:
+            # This is a better implementation of "flashbacks".
+
+            # G is NxHxExT where e is the caterpillar's row.
+
+            warnings.warn("gate dropout", RuntimeWarning)
+            epsilon = 0.5
+
+            dropout_head = (
+                (torch.rand(N, H, 1, t1 - t0, device=G.device).sort(dim=3).indices == 0)
+                .expand_as(G)
+                .float()
+            )
+
+            dropout_tail = dropout_head.cumsum(dim=3) - dropout_head
+
+            dropout_active = (
+                torch.rand(N, 1, 1, 1, device=G.device) < self.proba_gate_dropout
+            ).long()
+
+            dropout_head *= dropout_active
+            dropout_tail *= dropout_active
+
+            G = (
+                G
+                + dropout_head * (1 - epsilon - G.detach())
+                - dropout_tail * G.detach()
+            )
+
+        ######################################################################
 
         # We prepare the arguments for the parallel scan
 
+        # Clip the gating to avoid values greater than 1 when several
+        # heads hit the same row
+
+        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]
+        # warnings.warn("harmonic recurrence", RuntimeWarning)
+        # har = torch.arange(t0, t1, device = G.device).float() + 1
+        # A = har / (har + 1)
+        # G = G / har
+
+        gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V)
+        gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K)
+
+        # We start from cached values, which matters in inference
+
+        init_rec_V = self.rec_V[:, :, t0 - L : t0]
+        init_rec_K = self.rec_K[:, :, t0 - L : t0]
 
-        # Here there is a trick: The parallel scan operates with a
-        # period of L, so we split the sequence indexing in two axes,
-        # the second of size CL, and run the parallel scan using the
-        # other alone as the sequence index.
+        #################################################################
+        # Associative scan
 
-        A = A.unflatten(2, (-1, CL))
-        gated_V = gated_V.unflatten(2, (-1, CL))
-        gated_K = gated_K.unflatten(2, (-1, CL))
+        # Here there is a trick: Since the stack at position t is
+        # computed by updating that at position t-L, the parallel
+        # scan operates with a period of L. To do so we split the
+        # sequence indexing in two axes, the second of size L, and
+        # run the parallel scan using the first as the sequence index.
+
+        A = A.unflatten(2, (-1, L))
+        gated_V = gated_V.unflatten(2, (-1, L))
+        gated_K = gated_K.unflatten(2, (-1, L))
 
         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)
 
@@ -579,14 +669,14 @@ class Caterpillar(nn.Module):
         # the column in the caterpillar
 
         windowed_V = moving_window(
-            self.rec_V[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL
+            self.rec_V[:, :, t0 - L + 1 : t1], dim=2, win_dim=3, win_size=L
         )
 
         windowed_K = moving_window(
-            self.rec_K[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL
+            self.rec_K[:, :, t0 - L + 1 : t1], dim=2, win_dim=3, win_size=L
         )
 
-        # We have an attention score for each of the CHxCL values
+        # We have an attention score for each of the RxL values
 
         ar = torch.einsum(
             "nhtd,nftld->nhtfl",
@@ -715,7 +805,6 @@ class MyGPT(nn.Module):
         nb_blocks,
         nb_lines=None,
         caterpillar_height=None,
-        dim_rec_v=-1,
         causal=False,
         dropout=0.0,
         len_max=1e5,
@@ -723,7 +812,12 @@ 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
@@ -756,7 +850,7 @@ 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,
@@ -765,7 +859,7 @@ 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,
@@ -774,7 +868,7 @@ 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,