Merge remote-tracking branch 'upstream/master'

Author: anthonix

train_gpt2.cu

@@ -330,9 +330,12 @@ void* malloc_and_point_activations(TensorSpec (&tensors)[NUM_ACTIVATION_TENSORS]
 
     void* acts_memory;
     cudaCheck(cudaMalloc((void**)&acts_memory, bytes));
-#ifdef BUILD_AMD
-    cudaCheck(cudaMemset(acts_memory, 0, bytes)); // TODO: fix this properly :p
-#endif
+
+    // cudaMalloc does not guarantee initial memory values so we memset the allocation here
+    // this matters because e.g. non-cuDNN attention assumes the attention buffer is zeroed
+    // todo - up to ~100ms on slow GPUs, could theoretically be more selective, but this is safer
+    cudaCheck(cudaMemset(acts_memory, 0, bytes));
+
     char* acts_memory_iterator = (char*)acts_memory;
     for (size_t i = 0; i < NUM_ACTIVATION_TENSORS; i++) {
         // extra protection so we don't accidentally use an empty buffer
@@ -626,9 +629,9 @@ void gpt2_forward(GPT2 *model, const int* inputs, size_t B, size_t T) {
         cudaCheck(cudaMalloc(((void**)&model->accumulated_mean_loss), sizeof(float)));
         cudaCheck(cudaMallocHost((void**)&model->cpu_losses, B * T * sizeof(float)));
     } else {
-        // validate B,T is consistent with how we've allocated the memory before
-        // in principle we could get more clever here in the future, for now this is safest
-        if (B != model->batch_size || T != model->seq_len) {
+        // validate B,T are not larger than the values used at initialisation
+        // (smaller B,T are okay for inference only)
+        if (B > model->batch_size || T > model->seq_len) {
             printf("Model: B=%d T=%d, Desired: B=%d T=%d\n", model->batch_size, model->seq_len, (int)B, (int)T);
             exit(EXIT_FAILURE);
         }
@@ -692,6 +695,9 @@ void gpt2_forward(GPT2 *model, const int* inputs, size_t B, size_t T) {
         attention_forward_cudnn(l_atty, (float*)l_att, l_qkvr, B, T, NH, C, main_stream);
         #else
         floatX* l_att = acts.att + l * B * NH * T * T;
+        if (T != model->seq_len) { // unused parts of attention buffer must be zeroed (T-dependent)
+            cudaCheck(cudaMemset(l_att, 0, B * NH * T * T * sizeof(floatX)));
+        }
         // these are only needed as scratchpads for the forward pass, but
         // need not be stored for backward
         matmul_forward_cublaslt(scratch, l_ln1, l_qkvw, l_qkvb, B, T, C, 3*C, main_stream);
@@ -1756,14 +1762,14 @@ int main(int argc, char *argv[]) {
             printf("generating:\n---\n");
             for (int t = 1; t < genT; t++) {
                 NvtxRange generation_range("Generation step", t);
-                // note that inference is very wasteful here because for each token
-                // we re-calculate the forward pass for all of (B,T) positions from scratch
-                // but the inference here is just for sanity checking anyway
-                // and we can maybe optimize a bit more later, with careful tests
-                gpt2_forward(&model, gen_tokens, B, T);
-                // furthermore, below we're only using b=0 (i.e. the first row) of all B rows
-                // we're in principle running B "inference streams" in parallel here
-                // only using position 0 because it's a bit faster (copy less probs from GPU -> CPU)
+                // we try not to be too wasteful for inference by not calculating all of B,T
+                // Using a smaller B is always bit-for-bit identical, but T is more tricky
+                // for non-CUDNN, we need to make sure the attention buffer is memset to 0
+                // for cuDNN, it might suddenly decide to use a slightly different algorithm...
+                // on cuDNN 9.2.1 with cuDNN FrontEnd 1.5.2, T >= 256 seems bit-for-bit identical
+                // (but even if it wasn't fully identical that's probably not the end of the world)
+                // note this is still somewhat wasteful because we don't have a KV cache!
+                gpt2_forward(&model, gen_tokens, 1, CEIL_DIV(t, min(T,256)) * min(T,256));
                 // get the V-dimensional vector probs[0, t-1, :]
                 floatX* logits = model.acts.output + (t - 1) * model.config.padded_vocab_size;
                 // move probs back to CPU and sample (note we only move the first vocab_size logits, ignoring the padding)