ggml : revert change to ggml_cpy, add ggml_cont_Nd instead (#3275)

ggml-ci

ggml.c

@@ -6343,7 +6343,7 @@ static struct ggml_tensor * ggml_cpy_impl(
     }
 
     // make a view of the destination
-    struct ggml_tensor * result = b->op == GGML_OP_NONE ? b : ggml_view_tensor(ctx, b);
+    struct ggml_tensor * result = ggml_view_tensor(ctx, b);
     if (strlen(b->name) > 0) {
         ggml_format_name(result, "%s (copy of %s)", b->name, a->name);
     } else {
@@ -6406,6 +6406,54 @@ struct ggml_tensor * ggml_cont_inplace(
     return ggml_cont_impl(ctx, a, true);
 }
 
+
+// make contiguous, with new shape
+GGML_API struct ggml_tensor * ggml_cont_1d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0) {
+    return ggml_cont_4d(ctx, a, ne0, 1, 1, 1);
+}
+
+GGML_API struct ggml_tensor * ggml_cont_2d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1) {
+    return ggml_cont_4d(ctx, a, ne0, ne1, 1, 1);
+}
+
+GGML_API struct ggml_tensor * ggml_cont_3d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1,
+        int64_t               ne2) {
+    return ggml_cont_4d(ctx, a, ne0, ne1, ne2, 1);
+}
+
+struct ggml_tensor * ggml_cont_4d(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int64_t               ne0,
+        int64_t               ne1,
+        int64_t               ne2,
+        int64_t               ne3) {
+    GGML_ASSERT(ggml_nelements(a) == (ne0*ne1*ne2*ne3));
+
+    bool is_node = false;
+
+    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
+    ggml_format_name(result, "%s (cont)", a->name);
+
+    result->op   = GGML_OP_CONT;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+
+    return result;
+}
+
+
 // ggml_reshape
 
 struct ggml_tensor * ggml_reshape(

ggml.h

@@ -1049,7 +1049,6 @@ extern "C" {
             size_t                nb1,
             size_t                offset);
 
-
     // a -> b, return view(b)
     GGML_API struct ggml_tensor * ggml_cpy(
             struct ggml_context * ctx,
@@ -1072,6 +1071,33 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
+    // make contiguous, with new shape
+    GGML_API struct ggml_tensor * ggml_cont_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0);
+
+    GGML_API struct ggml_tensor * ggml_cont_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1);
+
+    GGML_API struct ggml_tensor * ggml_cont_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2);
+
+    GGML_API struct ggml_tensor * ggml_cont_4d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            int64_t               ne3);
+
     // return view(a), b specifies the new shape
     // TODO: when we start computing gradient, make a copy instead of view
     GGML_API struct ggml_tensor * ggml_reshape(

llama.cpp

@@ -2893,9 +2893,7 @@ static struct ggml_cgraph * llm_build_llama(
             ggml_set_name(KQV_merged, "KQV_merged");
 
             // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
-            cur = ggml_cpy(ctx0,
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-                    KQV_merged,
-                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens));
             offload_func_v(cur);
             ggml_set_name(cur, "KQV_merged_contiguous");
 
@@ -3302,9 +3300,7 @@ static struct ggml_cgraph * llm_build_baichaun(
             ggml_set_name(KQV_merged, "KQV_merged");
 
             // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
-            cur = ggml_cpy(ctx0,
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-                    KQV_merged,
-                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens));
             offload_func_v(cur);
             ggml_set_name(cur, "KQV_merged_contiguous");
 
@@ -3710,7 +3706,7 @@ static struct ggml_cgraph * llm_build_falcon(
             offload_func_v(KQV_merged);
             ggml_set_name(KQV_merged, "KQV_merged");
 
-            cur = ggml_cpy(ctx0, KQV_merged, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens));
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
             offload_func_v(cur);
             ggml_set_name(cur, "KQV_merged_contiguous");
 
@@ -3964,9 +3960,7 @@ static struct ggml_cgraph * llm_build_starcoder(
             ggml_set_name(KQV_merged, "KQV_merged");
 
             // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
-            cur = ggml_cpy(ctx0,
+            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-                    KQV_merged,
-                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens));
             ggml_set_name(cur, "KQV_merged_contiguous");
         }