Processing all the batch inputs

Signed-off-by: Shrinath Suresh <shrinath@ideas2it.com>

cpp/src/examples/babyllama/baby_llama_handler.cc

@@ -160,67 +160,74 @@ torch::Tensor LlmHandler::Inference(
     std::shared_ptr<torch::Device>& device,
     std::pair<std::string&, std::map<uint8_t, std::string>&>& idx_to_req_id,
     std::shared_ptr<torchserve::InferenceResponseBatch>& response_batch) {
-  std::vector<torch::Tensor> tensor_vector;
-  tensor_vector.reserve(steps);
-  torch::Tensor tokens_list_tensor = inputs[0].toTensor();
+  std::vector<torch::Tensor> batch_output_vector;
+  for (const torch::jit::IValue& input : inputs) {
+    std::vector<torch::Tensor> tensor_vector;
+    tensor_vector.reserve(steps);
+    torch::Tensor tokens_list_tensor = input.toTensor();
 
-  int64_t num_elements = tokens_list_tensor.numel();
+    int64_t num_elements = tokens_list_tensor.numel();
 
-  int64_t* data_ptr = tokens_list_tensor.data_ptr<int64_t>();
+    int64_t* data_ptr = tokens_list_tensor.data_ptr<int64_t>();
 
-  std::unique_ptr<int[]> prompt_tokens(new int[num_elements]);
+    std::unique_ptr<int[]> prompt_tokens(new int[num_elements]);
 
-  for (int64_t i = 0; i < num_elements; ++i) {
-    prompt_tokens[i] = data_ptr[i];
-  }
-
-  // start the main loop
-  long start =
-      0;     // used to time our code, only initialized after first iteration
-  int next;  // will store the next token in the sequence
-  int token = prompt_tokens[0];  // kick off with the first token in the prompt
-  int pos = 0;                   // position in the sequence
-  while (pos < steps) {
-    // forward the transformer to get logits for the next token
-    float* logits = forward(&transformer, token, pos);
-
-    // advance the state state machine
-    if (pos < num_elements - 1) {
-      // if we are still processing the input prompt, force the next prompt
-      // token
-      next = prompt_tokens[pos + 1];
-    } else {
-      // otherwise sample the next token from the logits
-      next = sample(&sampler, logits);
+    for (int64_t i = 0; i < num_elements; ++i) {
+      prompt_tokens[i] = data_ptr[i];
     }
-    pos++;
 
-    torch::Tensor tensor = torch::tensor(next, torch::kLong);
-    tensor_vector.push_back(tensor);
+    // start the main loop
+    long start =
+        0;     // used to time our code, only initialized after first iteration
+    int next;  // will store the next token in the sequence
+    int token =
+        prompt_tokens[0];  // kick off with the first token in the prompt
+    int pos = 0;           // position in the sequence
+    while (pos < steps) {
+      // forward the transformer to get logits for the next token
+      float* logits = forward(&transformer, token, pos);
+
+      // advance the state state machine
+      if (pos < num_elements - 1) {
+        // if we are still processing the input prompt, force the next prompt
+        // token
+        next = prompt_tokens[pos + 1];
+      } else {
+        // otherwise sample the next token from the logits
+        next = sample(&sampler, logits);
+      }
+      pos++;
+
+      torch::Tensor tensor = torch::tensor(next, torch::kLong);
+      tensor_vector.push_back(tensor);
 
-    // data-dependent terminating condition: the BOS (=1) token delimits
-    // sequences
-    if (next == 1) {
-      break;
+      // data-dependent terminating condition: the BOS (=1) token delimits
+      // sequences
+      if (next == 1) {
+        break;
+      }
+      token = next;
+
+      // init the timer here because the first iteration can be slower
+      if (start == 0) {
+        start = time_in_ms();
+      }
     }
-    token = next;
 
-    // init the timer here because the first iteration can be slower
-    if (start == 0) {
-      start = time_in_ms();
+    // report achieved tok/s (pos-1 because the timer starts after first
+    // iteration)
+    if (pos > 1) {
+      long end = time_in_ms();
+      double token_per_sec = (pos - 1) / (double)(end - start) * 1000;
+      std::cout << "Achieved tok per sec: " << token_per_sec << std::endl;
     }
-  }
 
-  // report achieved tok/s (pos-1 because the timer starts after first
-  // iteration)
-  if (pos > 1) {
-    long end = time_in_ms();
-    double token_per_sec = (pos - 1) / (double)(end - start) * 1000;
-    std::cout << "Achieved tok per sec: " << token_per_sec << std::endl;
+    torch::Tensor stacked_tensor = torch::stack(tensor_vector);
+
+    batch_output_vector.push_back(stacked_tensor);
   }
 
-  torch::Tensor stacked_tensor = torch::stack(tensor_vector);
-  return stacked_tensor;
+  return torch::stack(batch_output_vector);
 }
 
 void LlmHandler::Postprocess(