X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=492a9bb96872e93f99ea9d9609ba64fe557c57fa;hb=e3d5af800ccd197580265709c4499bf281beecb8;hp=daaec016ee94326147c4bf00d5bfd6157ef2920d;hpb=b22210b3eb0940c9cb5f9f29af6ede69204d78cf;p=mygptrnn.git

diff --git a/mygpt.py b/mygpt.py
index daaec01..492a9bb 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:
@@ -124,7 +126,6 @@ class AddPositionalEncoding(nn.Module):
 
 import pscan
 
-
 # X is /.../xTxD   A is /.../xT   Y_init is /.../xD
 
 
@@ -145,6 +146,18 @@ def pscan_dim(A, X, Y_init, dim=-2):
     return Y
 
 
+def pscan_rgrad(grad_Y, A, X, Y_init, dim=-2, eps=1e-2):
+    with torch.no_grad():
+        s_A, s_X = 0, 0
+        for t in range(X.size(dim) - 1, 0, -1):
+            delta = (grad_Y[t] - s_A) / A[t].grad
+            s_A += A[t].grad * delta
+            A[t].grad = delta
+            delta = (grad_Y[t] - s_X) / X[t].grad
+            s_X += X[t].grad * delta
+            X[t].grad = delta
+
+
 def pscan_shape(A, X, Y_init):
     s = X.size()
     A = A.reshape(-1, s[-2])
@@ -181,13 +194,15 @@ def nsum_shape(X, Y_init):
 class DumbRec(nn.Module):
     def __init__(
         self,
-        dim_in,
+        dim_model,
         dim_qk,
         dim_v,
         nb_heads,
         nb_lines,
         attention_dropout=0.0,
         len_max=1e5,
+        logger=print,
+        **kwargs,
     ):
         super().__init__()
 
@@ -199,11 +214,11 @@ 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
@@ -310,13 +325,15 @@ class DumbRec(nn.Module):
 class KVRec(nn.Module):
     def __init__(
         self,
-        dim_in,
+        dim_model,
         dim_qk,
         dim_v,
         nb_heads,
         nb_lines,
         attention_dropout=0.0,
         len_max=1e5,
+        logger=print,
+        **kwargs,
     ):
         super().__init__()
 
@@ -328,11 +345,11 @@ 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
@@ -441,6 +458,11 @@ 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 :]
@@ -456,7 +478,7 @@ 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,
@@ -464,32 +486,61 @@ class Caterpillar(nn.Module):
         caterpillar_height,
         attention_dropout=0.0,
         len_max=1e5,
+        logger=print,
+        **kwargs,
     ):
         super().__init__()
 
         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.b_G = nn.Parameter(
-            torch.full(
-                (nb_heads, caterpillar_height), -math.log(caterpillar_height - 1)
-            )
-        )
+        ######################################################################
+        # sup_args
+
+        x = kwargs.get("gate_dropout")
+        if x is None:
+            self.proba_gate_dropout = 0.0
+        else:
+            self.proba_gate_dropout = float(x)
 
-        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)
+        logger(f"self.proba_gate_dropout {self.proba_gate_dropout}")
 
-        self.init_K_rec = randw(caterpillar_height, caterpillar_length, dim_qk)
-        self.init_V_rec = randw(caterpillar_height, caterpillar_length, dim_v)
+        x = kwargs.get("default_bg")
+        if x is None:
+            default_bg = -math.log(caterpillar_height - 1)
+        else:
+            default_bg = float(x)
+
+        logger(f"default_bg {default_bg}")
+
+        ######################################################################
+
+        self.w_G = randw(nb_heads, caterpillar_height, dim_model)
+        self.b_G = nn.Parameter(torch.full((nb_heads, caterpillar_height), default_bg))
+
+        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,
+        )
 
     def reset_inner_loss(self):
         self.acc_attention = 0
@@ -507,67 +558,111 @@ 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 multiple 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)
+        # 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)
+
+        ######################################################################
+
+        def recurrence(G, V, K):
+            # We prepare the arguments for the parallel scan
+
+            A = 1 - G.sum(1)
+
+            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]
 
-        # We prepare the arguments for the parallel scan
+            # Associative scan
 
-        A = 1 - G.sum(1)
-        gated_V = torch.einsum("nhet,nhtd->netd", G, V)
-        gated_K = torch.einsum("nhet,nhtd->netd", G, K)
+            # 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.
 
-        init_rec_V = self.rec_V[:, :, t0 - CL : t0]
-        init_rec_K = self.rec_K[:, :, t0 - CL : t0]
+            A = A.unflatten(2, (-1, L))
+            gated_V = gated_V.unflatten(2, (-1, L))
+            gated_K = gated_K.unflatten(2, (-1, L))
 
-        # 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.
+            next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
+            next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
 
-        A = A.unflatten(2, (-1, CL))
-        gated_V = gated_V.unflatten(2, (-1, CL))
-        gated_K = gated_K.unflatten(2, (-1, CL))
+            next_V = next_V.flatten(2, 3)
+            next_K = next_K.flatten(2, 3)
 
-        next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
-        next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
+            return next_V, next_K
 
-        # 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)
+        next_V, next_K = recurrence(G, V, K)
+
+        if self.training and self.proba_gate_dropout > 0.0:
+            # G is NxHxRxT where r is the caterpillar's row.
+
+            warnings.warn("gate dropout", RuntimeWarning)
+
+            kill = (
+                torch.rand(G.size(), device=G.device) <= self.proba_gate_dropout
+            ).float()
+
+            mask = 1 - kill
+
+            masked_next_V, masked_next_K = recurrence(G * mask, V, K)
+
+            next_V = next_V.detach() + (masked_next_V - masked_next_V.detach()) / (
+                1 - self.proba_gate_dropout
+            )
+            next_K = next_K.detach() + (masked_next_K - masked_next_K.detach()) / (
+                1 - self.proba_gate_dropout
+            )
+
+        self.rec_V[:, :, t0:t1] = next_V
+        self.rec_K[:, :, t0:t1] = next_K
 
         ######################################################################
         # compute the readout
@@ -579,14 +674,14 @@ 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 value
+        # We have an attention score for each of the RxL values
 
         ar = torch.einsum(
             "nhtd,nftld->nhtfl",
@@ -622,12 +717,14 @@ class Caterpillar(nn.Module):
 class QKVAttention(nn.Module):
     def __init__(
         self,
-        dim_in,
+        dim_model,
         dim_qk,
         dim_v,
         nb_heads=1,
         causal=False,
         attention_dropout=0.0,
+        logger=print,
+        **kwargs,
     ):
         super().__init__()
 
@@ -638,10 +735,10 @@ 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
@@ -715,15 +812,21 @@ class MyGPT(nn.Module):
         nb_blocks,
         nb_lines=None,
         caterpillar_height=None,
-        dim_rec_v=-1,
         causal=False,
         dropout=0.0,
         len_max=1e5,
         attention_layer="kvrec",
+        logger=print,
+        **kwargs,
     ):
         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
@@ -745,40 +848,48 @@ 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,
                     causal=causal,
                     attention_dropout=dropout,
+                    logger=logger,
+                    **kwargs,
                 )
             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,
+                    logger=logger,
+                    **kwargs,
                 )
             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,
+                    logger=logger,
+                    **kwargs,
                 )
             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,
                     attention_dropout=dropout,
+                    logger=logger,
+                    **kwargs,
                 )
             else:
                 raise ValueError(f"Unknown attention type {attention_layer}.")
@@ -912,7 +1023,7 @@ if __name__ == "__main__":
     print("Basic check.")
 
     m = Caterpillar(
-        dim_in=4,
+        dim_model=4,
         dim_qk=3,
         dim_v=7,
         nb_heads=1,