finetuning

Author: SoufianeNoubir

gbmi/exp_indhead/finetunebound.py

@@ -36,7 +36,7 @@
 
 
 # %%
-def loss_bound(model, s, w):
+def loss_bound(model, s):
 
     W_pos = model.W_pos
     W_E = model.W_E
@@ -53,7 +53,7 @@ def loss_bound(model, s, w):
 
     e_p = W_E.unsqueeze(dim=0) + W_pos.unsqueeze(dim=1)
 
-    everything = (
+    term_0 = (
         einops.einsum(
             e_p,
             W_Q_0,
@@ -70,16 +70,16 @@ def loss_bound(model, s, w):
     for p in range(2, n_ctx):  #
         tmp = torch.zeros((p, d_voc))
         for t_q in range(d_voc):
-            tmp[-1, :] = everything[p - 1, t_q, p - 1, t_q]
+            tmp[-1, :] = term_0[p - 1, t_q, p - 1, t_q]
 
             for t_k in range(d_voc):
-                tmp[-2, :] = everything[p - 1, t_q, p - 2, t_k]
-                tmp[:-2, :] = everything[p - 1, t_q, : p - 2, :]
+                tmp[-2, :] = term_0[p - 1, t_q, p - 2, t_k]
+                tmp[:-2, :] = term_0[p - 1, t_q, : p - 2, :]
                 tmp_sm = tmp.softmax(dim=0)
                 table[t_q, t_k, p - 2, :] = tmp_sm[-2, :]
     # Table represents post softmax attention paid to t_k, if the final entry is spammed everywhere, and t_q is used as the first entry, at pth poisition
 
-    # everything looks like EQKE, table looks like you're indexing by query, key, position (of key?), and other token in the sequence.
+    # term_0 looks like EQKE, table looks like you're indexing by query, key, position (of key?), and other token in the sequence.
     # They you're computing softmax of d_voc - 2 copies of the other token, one copy of t_k in p-2, and the query in p-1.
     # Then you store the post-softmax attention paid to t_k.
     #
@@ -177,6 +177,9 @@ def loss_bound(model, s, w):
         "q_pos q_val k, k l, l m, m n, n p, p q -> q_pos q_val q",
     )
 
+    if s == -1:
+        return (term_0, term_1, term_2, term_3, term_4, term_5, term_6, term_7, term_8)
+
     if s == 0:
         reduced_3 = einops.einsum(
             term_3, "q_pos q_val k_pos k_val -> q_pos q_val k_pos"
@@ -421,17 +424,27 @@ def least_attention(a, i_1, i_2, j, dic):
     if s == 2:
         return (attn_1, bound, bound_2)
 
-    def loss_diff_1(b, i_1, i_2, dic):
+    def loss_diff_1(b, i_1, i_2, dic, n=None):
+
+        if n == b:
+            return 0
 
-        n = torch.arange(d_voc)[torch.arange(d_voc) != b]
+        if n is None:
+
+            n = torch.arange(d_voc)[torch.arange(d_voc) != b]
 
         return (
-            term_5[i_2, dic[i_2]][..., n] - term_5[i_2, :, b].unsqueeze(dim=-1)
+            term_5[i_2, dic[i_2]][..., n] - term_5[i_2, dic[i_2], b].unsqueeze(dim=-1)
         ).max()
 
-    def loss_diff_2(b, i_1, i_2, dic):
+    def loss_diff_2(b, i_1, i_2, dic, n=None):
+
+        if n == b:
+            return 0
+
+        if n is None:
 
-        n = torch.arange(d_voc)[torch.arange(d_voc) != b]
+            n = torch.arange(d_voc)[torch.arange(d_voc) != b]
 
         c = (term_6[0, dic[0]][..., n] - term_6[0, dic[0], b].unsqueeze(dim=-1)).max()
 
@@ -460,8 +473,12 @@ def loss_diff_2(b, i_1, i_2, dic):
         )
         return ld_2
 
-    def loss_diff_3(b, i_1, i_2, dic):
-        n = torch.arange(d_voc)[torch.arange(d_voc) != b]
+    def loss_diff_3(b, i_1, i_2, dic, n=None):
+        if n == b:
+            return 0
+
+        if n is None:
+            n = torch.arange(d_voc)[torch.arange(d_voc) != b]
         c = (term_7[0, dic[0]][..., n] - term_7[0, dic[0], b].unsqueeze(dim=-1)).max()
         for i in range(i_1):
             c = torch.max(
@@ -488,9 +505,14 @@ def loss_diff_3(b, i_1, i_2, dic):
         )
         return ld_3
 
-    def loss_diff_4(b, i_1, i_2, dic):
+    def loss_diff_4(b, i_1, i_2, dic, n=None):
 
-        n = torch.arange(d_voc)[torch.arange(d_voc) != b]
+        if n == b:
+            return 0
+
+        if n is None:
+
+            n = torch.arange(d_voc)[torch.arange(d_voc) != b]
 
         for k in range(i_2 + 1):
             if k != 0 and k != 1:
@@ -546,32 +568,57 @@ def loss_diff_4(b, i_1, i_2, dic):
         )
         return ld_4
 
-    def total_bound(b, i_1, i_2, dic):
+    def total_bound(b, i_1, i_2, dic, n=None):
         return (
-            loss_diff_1(b, i_1, i_2, dic)
-            + loss_diff_2(b, i_1, i_2, dic)
-            + loss_diff_3(b, i_1, i_2, dic)
-            + loss_diff_4(b, i_1, i_2, dic)
+            loss_diff_1(b, i_1, i_2, dic, n)
+            + loss_diff_2(b, i_1, i_2, dic, n)
+            + loss_diff_3(b, i_1, i_2, dic, n)
+            + loss_diff_4(b, i_1, i_2, dic, n)
         )
 
-    out = torch.zeros((d_voc, n_ctx, n_ctx)) + torch.inf
+    if s == 3:
+
+        out = torch.zeros((d_voc, n_ctx, n_ctx)) + torch.inf
+        # b i_2 i_1
+
+        for b in range(e_p.shape[1]):
+
+            for i_2 in range(e_p.shape[0] - 1):
+                for i_1 in range(1, i_2):
+
+                    if (i_1 < i_2) & (i_1 > 0):
+                        dic = {i_1: b}
+                        for i in range(8):
+                            dic.setdefault(i, torch.arange(26))
+
+                        out[b, i_2, i_1] = total_bound(b, i_1, i_2, dic)
+
+        out_2 = 1 / (1 + ((d_voc - 1) * torch.exp(out)))
+
+        return (attn_1, bound, bound_2, out, out_2)
+
+    out = torch.zeros((d_voc, n_ctx, n_ctx, d_voc)) + torch.inf
     # b i_2 i_1
 
     for b in range(e_p.shape[1]):
+        for n in range(e_p.shape[1]):
+            for i_2 in range(e_p.shape[0] - 1):
+                for i_1 in range(1, i_2):
 
-        for i_2 in range(e_p.shape[0] - 1):
-            for i_1 in range(1, i_2):
+                    if (i_1 < i_2) & (i_1 > 0):
+                        dic = {i_1: b}
+                        for i in range(8):
+                            dic.setdefault(i, torch.arange(26))
 
-                if (i_1 < i_2) & (i_1 > 0):
-                    dic = {i_1: b}
-                    for i in range(8):
-                        dic.setdefault(i, torch.arange(26))
+                        out[b, i_2, i_1, n] = total_bound(b, i_1, i_2, dic, n)
 
-                    out[b, i_2, i_1] = total_bound(b, i_1, i_2, dic)
+    out_2 = einops.einsum(out.softmax(dim=-1), "b i_2 i_1 b -> b i_2 i_1")
 
-    out_2 = 1 / (1 + ((d_voc - 1) * torch.exp(out)))
+    out_3 = einops.einsum(
+        out - out.max(dim=-1).values.unsqueeze(dim=-1), "b i_2 i_1 b -> b i_2 i_1"
+    )
 
-    return (attn_1, bound, bound_2, out, out_2)
+    return (attn_1, bound, bound_2, out, out_2, out_3)
 
 
 # %%
@@ -647,22 +694,40 @@ def total_bound(b, i_1, i_2, dic):
     counter += 1
     print(counter)
 
-
+# %%
+valid = (
+    ein.array(
+        lambda i, j, k: where(k > 0, where(j > k, where(j < 7, 1, 0), 0), 0),
+        sizes=[d_voc, n_ctx, n_ctx],
+    )
+    .bool()
+    .to(device)
+)
 optimiser = torch.optim.AdamW(
-    model_1.parameters(), lr=5e-3, betas=(0.9, 0.999), weight_decay=1.0
+    model_1.parameters(), lr=0.5, betas=(0.9, 0.999), weight_decay=0
 )
+# %%
+a = loss_bound(model_1, 3)[4]
+loss = 1 - a[valid].min()
+print(a[valid].min())
+print(a[valid].mean())
+print(a[valid].max())
+for i in range(1):
+    print(i + 1)
 
-a = loss_bound(model_1, 3, 8)[4]
-loss = 1 - a[a != 0].mean()
-for i in range(30):
-    print(a[a != 0].mean())
     loss.backward()
     optimiser.step()
     optimiser.zero_grad()
-    a = loss_bound(model_1, 3, 8)[4][5]
-    loss = 1 - a[a != 0].mean()
-    counter += 1
-    print(counter)
+    a = loss_bound(model_1, 3)[4]
+    loss = 1 - a[valid].min()
+    print(a[valid].min())
+    print(a[valid].mean())
+    print(a[valid].max())
+    if i % 10 == 1:
+        r = loss_bound(model_1, 4)[5]
+        print(r[valid].min())
+        print(r[valid].mean())
+        print(r[valid].max())
 
 # %%
 '''