git push origin masterMerge branch 'admu-progvar-master'

run.c

@@ -60,7 +60,7 @@ typedef struct {
     float *q; // query (dim,)
     float *k; // key (dim,)
     float *v; // value (dim,)
-    float *att; // buffer for scores/attention values (seq_len,)
+    float *att; // buffer for scores/attention values (n_heads, seq_len)
     float *logits; // output logits
     // kv cache
     float* key_cache;   // (layer, seq_len, dim)
@@ -77,7 +77,7 @@ void malloc_run_state(RunState* s, Config* p) {
     s->q = calloc(p->dim, sizeof(float));
     s->k = calloc(p->dim, sizeof(float));
     s->v = calloc(p->dim, sizeof(float));
-    s->att = calloc(p->seq_len, sizeof(float));
+    s->att = calloc(p->n_heads * p->seq_len, sizeof(float));
     s->logits = calloc(p->vocab_size, sizeof(float));
     s->key_cache = calloc(p->n_layers * p->seq_len * p->dim, sizeof(float));
     s->value_cache = calloc(p->n_layers * p->seq_len * p->dim, sizeof(float));
@@ -278,9 +278,12 @@ void transformer(int token, int pos, Config* p, RunState* s, TransformerWeights*
         memcpy(value_cache_row, s->v, dim*sizeof(*value_cache_row));
         
         // multihead attention. iterate over all heads
+        #pragma omp parallel for
         for (int h = 0; h < p->n_heads; h++) {
             // get the query vector for this head
             float* q = s->q + h * head_size;
+            // attention scores for this head
+            float* att = s->att + h * p->seq_len;
             // iterate over all timesteps, including the current one
             for (int t = 0; t <= pos; t++) {
                 // get the key vector for this head and at this timestep
@@ -292,17 +295,17 @@ void transformer(int token, int pos, Config* p, RunState* s, TransformerWeights*
                 }
                 score /= sqrtf(head_size);
                 // save the score to the attention buffer
-                s->att[t] = score;
+                att[t] = score;
             }
 
             // softmax the scores to get attention weights, from 0..pos inclusively
-            softmax(s->att, pos + 1);
+            softmax(att, pos + 1);
             
             // weighted sum of the values, store back into xb
             for (int i = 0; i < head_size; i++) {
                 float val = 0.0f;
                 for (int t = 0; t <= pos; t++) {
-                    val += s->att[t] * s->value_cache[loff + t * dim + h * head_size + i]; // note bad locality
+                    val += att[t] * s->value_cache[loff + t * dim + h * head_size + i]; // note bad locality
                 }
                 s->xb[h * head_size + i] = val;
             }