ggml: fix gradient allocation logic

examples/mnist/mnist-common.cpp

@@ -536,7 +536,7 @@ void mnist_model_train(mnist_model & model, const float * images, const float *
 
     // gb_grad == graph backward gradients, forward pass, then backward pass to calculate gradients.
     struct ggml_cgraph * gb_grad = ggml_graph_dup(model.ctx_compute, gf);
-    ggml_build_backward_expand(model.ctx_compute, gf, gb_grad, /*accumulate =*/ true, false);
+    ggml_build_backward_expand(model.ctx_compute, gf, gb_grad, /*accumulate =*/ true);
 
     // gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step.
     struct ggml_cgraph * gb_opt = ggml_graph_dup(model.ctx_compute, gb_grad);

include/ggml.h

@@ -574,10 +574,10 @@ extern "C" {
 
     // this tensor...
     enum ggml_tensor_flag {
-        GGML_TENSOR_FLAG_INPUT    = 1, // ...is an input for the GGML compute graph
-        GGML_TENSOR_FLAG_OUTPUT   = 2, // ...is an output for the GGML compute graph
-        GGML_TENSOR_FLAG_PARAM    = 4, // ...contains trainable parameters
-        GGML_TENSOR_FLAG_LOSS     = 8, // ...defines loss for numerical optimization (multiple loss tensors add up)
+        GGML_TENSOR_FLAG_INPUT  =  1, // ...is an input for the GGML compute graph
+        GGML_TENSOR_FLAG_OUTPUT =  2, // ...is an output for the GGML compute graph
+        GGML_TENSOR_FLAG_PARAM  =  4, // ...contains trainable parameters
+        GGML_TENSOR_FLAG_LOSS   =  8, // ...defines loss for numerical optimization (multiple loss tensors add up)
     };
 
     // n-dimensional tensor
@@ -1407,14 +1407,14 @@ extern "C" {
     // supports 3D: a->ne[2] == b->ne[1]
     GGML_API struct ggml_tensor * ggml_get_rows(
             struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * a,  // data
+            struct ggml_tensor  * b); // row indices
 
     GGML_API struct ggml_tensor * ggml_get_rows_back(
             struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            struct ggml_tensor  * b,
-            struct ggml_tensor  * c);
+            struct ggml_tensor  * a,  // gradients of ggml_get_rows result
+            struct ggml_tensor  * b,  // row indices
+            struct ggml_tensor  * c); // data for ggml_get_rows, only used for its shape
 
     GGML_API struct ggml_tensor * ggml_diag(
         struct ggml_context     * ctx,
@@ -1565,9 +1565,9 @@ extern "C" {
     // a - dy
     GGML_API struct ggml_tensor * ggml_rope_back(
             struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            struct ggml_tensor  * b,
-            struct ggml_tensor  * c,
+            struct ggml_tensor  * a, // gradients of ggml_rope result
+            struct ggml_tensor  * b, // positions
+            struct ggml_tensor  * c, // freq factors
             int                   n_dims,
             int                   mode,
             int                   n_ctx_orig,
@@ -2033,15 +2033,15 @@ extern "C" {
     // loss function
 
     GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
-            struct ggml_context         * ctx,
-            struct ggml_tensor          * a,
-            struct ggml_tensor          * b);
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // logits
+            struct ggml_tensor  * b); // labels
 
     GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back(
-            struct ggml_context         * ctx,
-            struct ggml_tensor          * a,
-            struct ggml_tensor          * b,
-            struct ggml_tensor          * c);
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // logits
+            struct ggml_tensor  * b,  // labels
+            struct ggml_tensor  * c); // gradients of cross_entropy_loss result
 
     // AdamW optimizer step
     // Paper: https://arxiv.org/pdf/1711.05101v3.pdf
@@ -2063,7 +2063,7 @@ extern "C" {
     GGML_API void ggml_set_loss(struct ggml_tensor * tensor);
 
     GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
-    GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate, bool keep);
+    GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate);
 
     GGML_API void ggml_build_opt_adamw(
             struct ggml_context * ctx,