Update.
[mygptrnn.git] / mygpt.py
index 90102bf..7c8e9f4 100755 (executable)
--- a/mygpt.py
+++ b/mygpt.py
 # 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
 
 from torch import nn
 from torch.nn import functional as F
-from functorch.dim import dims
 
 import ffutils
 
@@ -38,7 +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:
@@ -182,7 +183,7 @@ def nsum_shape(X, Y_init):
 class DumbRec(nn.Module):
     def __init__(
         self,
-        dim_in,
+        dim_model,
         dim_qk,
         dim_v,
         nb_heads,
@@ -200,11 +201,11 @@ class DumbRec(nn.Module):
 
         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
@@ -311,7 +312,7 @@ class DumbRec(nn.Module):
 class KVRec(nn.Module):
     def __init__(
         self,
-        dim_in,
+        dim_model,
         dim_qk,
         dim_v,
         nb_heads,
@@ -329,11 +330,11 @@ class KVRec(nn.Module):
 
         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
@@ -352,8 +353,6 @@ class KVRec(nn.Module):
     def forward(self, bs):
         x_q, t0, t1 = bs.x, bs.first, bs.first + bs.nb
 
-        # n,h,l,t,d = dims(5)
-
         if bs.init_cache:
             self.rec_v = x_q.new_zeros(
                 x_q.size(0), self.nb_lines, x_q.size(1), self.w_v.size(1)
@@ -444,6 +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 :]
@@ -459,7 +463,7 @@ def moving_window(x, dim, win_dim, win_size):
 class Caterpillar(nn.Module):
     def __init__(
         self,
-        dim_in,
+        dim_model,
         dim_qk,
         dim_v,
         nb_heads,
@@ -472,27 +476,35 @@ 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.w_G = randw(nb_heads, caterpillar_height, dim_in)
+        self.proba_gate_dropout = 0.0
+
+        self.w_G = randw(nb_heads, caterpillar_height, dim_model, amplitude=1e-5)
         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)
+        self.init_K_rec = randw(
+            caterpillar_height, caterpillar_length, dim_qk, amplitude=1e-5
+        )
+        self.init_V_rec = randw(
+            caterpillar_height, caterpillar_length, dim_v, amplitude=1e-5
+        )
 
     def reset_inner_loss(self):
         self.acc_attention = 0
@@ -510,9 +522,10 @@ 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)
+        DM = self.w_O.size(1)
         CH = self.caterpillar_height
         CL = self.caterpillar_length
 
@@ -520,30 +533,99 @@ class Caterpillar(nn.Module):
             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)
+
+        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. 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]
+            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)
+        ######################################################################
+        # 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_start = (
+                (
+                    torch.rand(G.size(), device=G.device)
+                    .flatten(2, 3)
+                    .sort(dim=2)
+                    .indices
+                    == 0
+                )
+                .unflatten(2, (CH, t1 - t0))
+                .float()
+            )
+
+            dropout_tail = dropout_start.cumsum(dim=3) - dropout_start
+
+            dropout_active = (
+                torch.rand(N, 1, 1, 1, device=G.device) < self.proba_gate_dropout
+            ).long()
+
+            dropout_start *= dropout_active
+            dropout_tail *= dropout_active
+
+            G = (
+                G
+                + dropout_start * (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)
+        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 - CL : t0]
         init_rec_K = self.rec_K[:, :, t0 - CL : t0]
 
+        #################################################################
+        # 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))
         gated_K = gated_K.unflatten(2, (-1, CL))
@@ -559,30 +641,43 @@ class Caterpillar(nn.Module):
 
         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)
@@ -594,7 +689,7 @@ class Caterpillar(nn.Module):
 class QKVAttention(nn.Module):
     def __init__(
         self,
-        dim_in,
+        dim_model,
         dim_qk,
         dim_v,
         nb_heads=1,
@@ -610,10 +705,10 @@ class QKVAttention(nn.Module):
         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
@@ -687,7 +782,6 @@ class MyGPT(nn.Module):
         nb_blocks,
         nb_lines=None,
         caterpillar_height=None,
-        dim_rec_v=-1,
         causal=False,
         dropout=0.0,
         len_max=1e5,
@@ -695,7 +789,12 @@ 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
@@ -717,7 +816,7 @@ class MyGPT(nn.Module):
         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,
@@ -726,27 +825,27 @@ class MyGPT(nn.Module):
                 )
             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,
@@ -884,7 +983,7 @@ if __name__ == "__main__":
     print("Basic check.")
 
     m = Caterpillar(
-        dim_in=4,
+        dim_model=4,
         dim_qk=3,
         dim_v=7,
         nb_heads=1,