mnist : minor fixes and adjustments

AI-Jie01 · Apr 13, 2023 · c0ca5a3 · c0ca5a3
1 parent 7399915
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diff --git a/examples/mnist/README.md b/examples/mnist/README.md
@@ -12,7 +12,7 @@ use this to save a pytorch model to be converted to ggml format.
 
 ## GGML Format Conversion
 
-GGML "format" is whatever you choose for efficient loading. In our case, we just save the hyperparameters used 
+GGML "format" is whatever you choose for efficient loading. In our case, we just save the hyperparameters used
 plus the model weights and biases. Run convert-h5-to-ggml.py to convert your pytorch model. The output format is:
 
 - magic constant (int32)
@@ -45,3 +45,43 @@ make -j4 mnist
 ./bin/mnist ../examples/mnist/models/mnist/ggml-model-f32.bin ../examples/mnist/models/mnist/t10k-images.idx3-ubyte
 
 For more information, checkout the corresponding programs in the [examples](examples) folder.
+
+# Sample output
+
+
+```
+$  ./bin/mnist ./models/mnist/ggml-model-f32.bin ../examples/mnist/models/mnist/t10k-images.idx3-ubyte
+mnist_model_load: loading model from './models/mnist/ggml-model-f32.bin'
+mnist_model_load: ggml ctx size =   1.52 MB
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+
+ggml_graph_dump_dot: dot -Tpng mnist.dot -o mnist.dot.png && open mnist.dot.png
+Predicted digit is 9
+```
diff --git a/examples/mnist/main.cpp b/examples/mnist/main.cpp
@@ -14,7 +14,7 @@
 #include <unistd.h>
 #include <time.h>
 
-// default hparams 
+// default hparams
 struct mnist_hparams {
     int32_t n_input = 784;
     int32_t n_hidden = 500;
@@ -84,14 +84,15 @@ bool mnist_model_load(const std::string & fname, mnist_model & model) {
         struct ggml_init_params params = {
             .mem_size   = ctx_size + 1024*1024,
             .mem_buffer = NULL,
+            .no_alloc   = false,
         };
 
         model.ctx = ggml_init(params);
         if (!model.ctx) {
             fprintf(stderr, "%s: ggml_init() failed\n", __func__);
             return false;
         }
-    }  
+    }
 
     // Read FC1 layer 1
     {
@@ -141,8 +142,8 @@ bool mnist_model_load(const std::string & fname, mnist_model & model) {
         for (int i = 0; i < n_dims; ++i) {
             fin.read(reinterpret_cast<char *>(&ne_bias[i]), sizeof(ne_bias[i]));
         }
-        model.fc2_bias     = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_classes);  
-        fin.read(reinterpret_cast<char *>(model.fc2_bias->data), ggml_nbytes(model.fc2_bias));   
+        model.fc2_bias     = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_classes);
+        fin.read(reinterpret_cast<char *>(model.fc2_bias->data), ggml_nbytes(model.fc2_bias));
     }
     fin.close();
 
@@ -162,9 +163,9 @@ int mnist_eval(
         ) {
 
     const auto & hparams = model.hparams;
- 
+
     static size_t buf_size = hparams.n_input * sizeof(float) * 4;
-    static void * buf = malloc(buf_size);  
+    static void * buf = malloc(buf_size);
 
     struct ggml_init_params params = {
         .mem_size   = buf_size,
@@ -180,18 +181,18 @@ int mnist_eval(
     // fc1 MLP = Ax + b
     ggml_tensor * fc1 = ggml_add(ctx0, ggml_mul_mat(ctx0, model.fc1_weight, input), model.fc1_bias);
     ggml_tensor * fc2 = ggml_add(ctx0, ggml_mul_mat(ctx0, model.fc2_weight, ggml_relu(ctx0, fc1)), model.fc2_bias);
-    
+
     // soft max
     ggml_tensor * final = ggml_soft_max(ctx0, fc2);
-   
+
     // run the computation
     ggml_build_forward_expand(&gf, final);
     ggml_graph_compute       (ctx0, &gf);
 
-    ggml_graph_print   (&gf);
+    //ggml_graph_print   (&gf);
     ggml_graph_dump_dot(&gf, NULL, "mnist.dot");
     float* finalData = ggml_get_data_f32(final);
-  
+
     int prediction = std::max_element(finalData, finalData + 10) - finalData;
     ggml_free(ctx0);
     return prediction;
@@ -223,19 +224,19 @@ int main(int argc, char ** argv) {
             fprintf(stderr, "%s: failed to open '%s'\n", __func__, argv[2]);
             return 1;
         }
-    
+
         unsigned char buf[784];
         srand(time(NULL));
         // Seek to a random digit: 16-byte header + 28*28 * (random 0 - 10000)
         fin.seekg(16 + 784 * (rand() % 10000));
         fin.read((char *) &buf, sizeof(buf));
         digit.resize(sizeof(buf));
-        
+
         // render the digit in ASCII
         for(int row = 0; row < 28; row++) {
             for (int col = 0; col < 28; col++) {
-               fprintf(stderr, "%c ", (float)buf[row*28 + col] > 230 ? '*' : '_');
-                            digit[row*28+col]=((float)buf[row*28+col]);
+                fprintf(stderr, "%c ", (float)buf[row*28 + col] > 230 ? '*' : '_');
+                digit[row*28+col]=((float)buf[row*28+col]);
 
             }
             fprintf(stderr, "\n");
@@ -245,7 +246,7 @@ int main(int argc, char ** argv) {
         t_load_us = ggml_time_us() - t_start_us;
     }
 
-    
+
     fprintf(stdout, "Predicted digit is %d\n", mnist_eval(model, 1, digit));
     ggml_free(model.ctx);
 

diff --git a/src/ggml.c b/src/ggml.c
@@ -3054,9 +3054,11 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
         return NULL;
     }
 
+    const size_t mem_size = (params.mem_size + GGML_MEM_ALIGN - 1) & ~(GGML_MEM_ALIGN - 1);
+
     *ctx = (struct ggml_context) {
-        /*.mem_size           =*/ params.mem_size,
-        /*.mem_buffer         =*/ params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(params.mem_size),
+        /*.mem_size           =*/ mem_size,
+        /*.mem_buffer         =*/ params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(mem_size),
         /*.mem_buffer_owned   =*/ params.mem_buffer ? false : true,
         /*.no_alloc           =*/ params.no_alloc,
         /*.n_objects          =*/ 0,
@@ -3066,7 +3068,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
         /*.scratch_save       =*/ { 0, 0, NULL, },
     };
 
-    GGML_ASSERT(ctx->mem_buffer != NULL); // check for allocation failure
+    GGML_ASSERT(ctx->mem_buffer != NULL);
 
     ggml_assert_aligned(ctx->mem_buffer);