Support multiple prompts in the runner (#9817)

Summary:
Pull Request resolved: #9817

As a preparation for the multiturn conversation, we can provide multiple prompts and execute them in sequence. Example command:
```
./qnn_llama3_2_runner --model_path hybrid_llama_qnn.pte    --tokenizer_path tiktokenizer.bin  --eval_mode 1 --prompt "Once upon a time" --prompt "girl named Lily." --prompt "her toys and her favorite toy was a big,"  --kv_updater "ShiftPointer" --logits_scale 0.1 --output_path output.txt --num_iters 1
```
It will be hard to use any char as delimiter, so we use `--prompt` to explicitly mark a prompt and collect them together.

Reviewed By: kirklandsign

Differential Revision: D72276104

Author: cccclai

examples/qualcomm/oss_scripts/llama/qnn_llama_runner.cpp

@@ -14,82 +14,95 @@
  *
  */
 
-#include <executorch/backends/qualcomm/runtime/QnnExecuTorch.h>
-#include <executorch/examples/qualcomm/oss_scripts/llama/runner/runner.h>
-#include <executorch/runtime/platform/log.h>
-#include <gflags/gflags.h>
-#include <fstream>
-#include <vector>
-
-DEFINE_string(
-    model_path,
-    "kv_llama_qnn.pte",
-    "Model serialized in flatbuffer format.");
-DEFINE_string(
-    output_path,
-    "outputs.txt",
-    "Executorch inference data output path.");
-DEFINE_string(
-    performance_output_path,
-    "inference_speed.txt",
-    "Records inference speed. For CI purpose.");
-DEFINE_string(tokenizer_path, "tokenizer.bin", "Tokenizer stuff.");
-DEFINE_string(prompt, "The answer to the ultimate question is", "Prompt.");
-DEFINE_string(
-    system_prompt,
-    "",
-    "Tells the model what kind of assistant it should be. For example, You are a helpful AI assistant for travel tips and recommendations. Default is None");
-DEFINE_double(
-    temperature,
-    0.0f,
-    "Temperature; Default is 0.0f. 0 = greedy argmax sampling (deterministic). Lower temperature = more deterministic");
-DEFINE_int32(
-    seq_len,
-    128,
-    "Total number of tokens to generate (prompt + output).");
-DEFINE_int32(
-    eval_mode,
-    1,
-    "0: TokenGenerator(kv) / 1: HybridMode (prefill+kv)");
-DEFINE_double(logits_scale, 0.0, "Logits scale");
-DEFINE_int32(logits_offset, 0, "Logits offset");
-DEFINE_string(
-    kv_updater,
-    "How to update kv cache. Choose between SmartMask and ShiftPointer",
-    "SmartMask");
-DEFINE_int32(num_iters, 1, "total num of iterations to run.");
-
-int main(int argc, char** argv) {
-  gflags::ParseCommandLineFlags(&argc, &argv, true);
-
-  // create llama runner
-  example::Runner runner(
-      {FLAGS_model_path},
-      FLAGS_tokenizer_path.c_str(),
-      FLAGS_performance_output_path.c_str(),
-      FLAGS_logits_scale,
-      FLAGS_logits_offset,
-      FLAGS_temperature,
-      FLAGS_eval_mode,
-      FLAGS_kv_updater,
-      FLAGS_num_iters);
-  std::vector<char> buf;
-  buf.reserve(5 * FLAGS_seq_len); // assume each token is around 5 char
-  std::ofstream fout(FLAGS_output_path.c_str());
-  auto callback = [&](const std::string& piece) {
-    for (const char c : piece) {
-      buf.push_back(c);
-    }
-  };
-  // generate tokens & store inference output
-  for (int i = 0; i < FLAGS_num_iters; i++) {
-    runner.generate(
-        FLAGS_seq_len,
-        FLAGS_prompt.c_str(),
-        FLAGS_system_prompt.c_str(),
-        callback);
-  }
-  fout.write(buf.data(), buf.size());
-  fout.close();
-  return 0;
-}
+ #include <executorch/backends/qualcomm/runtime/QnnExecuTorch.h>
+ #include <executorch/examples/qualcomm/oss_scripts/llama/runner/runner.h>
+ #include <executorch/runtime/platform/log.h>
+ #include <gflags/gflags.h>
+ #include <fstream>
+ #include <vector>
+ 
+ DEFINE_string(
+     model_path,
+     "kv_llama_qnn.pte",
+     "Model serialized in flatbuffer format.");
+ DEFINE_string(
+     output_path,
+     "outputs.txt",
+     "Executorch inference data output path.");
+ DEFINE_string(
+     performance_output_path,
+     "inference_speed.txt",
+     "Records inference speed. For CI purpose.");
+ DEFINE_string(tokenizer_path, "tokenizer.bin", "Tokenizer stuff.");
+ DEFINE_string(prompt, "The answer to the ultimate question is", "Prompt.");
+ DEFINE_string(
+     system_prompt,
+     "",
+     "Tells the model what kind of assistant it should be. For example, You are a helpful AI assistant for travel tips and recommendations. Default is None");
+ DEFINE_double(
+     temperature,
+     0.0f,
+     "Temperature; Default is 0.0f. 0 = greedy argmax sampling (deterministic). Lower temperature = more deterministic");
+ DEFINE_int32(
+     seq_len,
+     128,
+     "Total number of tokens to generate (prompt + output).");
+ DEFINE_int32(
+     eval_mode,
+     1,
+     "0: TokenGenerator(kv) / 1: HybridMode (prefill+kv)");
+ DEFINE_double(logits_scale, 0.0, "Logits scale");
+ DEFINE_int32(logits_offset, 0, "Logits offset");
+ DEFINE_string(
+     kv_updater,
+     "How to update kv cache. Choose between SmartMask and ShiftPointer",
+     "SmartMask");
+ DEFINE_int32(num_iters, 1, "total num of iterations to run.");
+ 
+ std::vector<std::string> CollectPrompts(int argc, char** argv) {
+   // Collect all prompts from command line, example usage:
+   // --prompt "prompt1" --prompt "prompt2" --prompt "prompt3"
+   std::vector<std::string> prompts;
+   for (int i = 1; i < argc; i++) {
+     if (std::string(argv[i]) == "--prompt" && i + 1 < argc) {
+       prompts.push_back(argv[i + 1]);
+       i++; // Skip the next argument
+     }
+   }
+   return prompts;
+ }
+ 
+ int main(int argc, char** argv) {
+   std::vector<std::string> prompts = CollectPrompts(argc, argv);
+   gflags::ParseCommandLineFlags(&argc, &argv, true);
+   // create llama runner
+   example::Runner runner(
+       {FLAGS_model_path},
+       FLAGS_tokenizer_path.c_str(),
+       FLAGS_performance_output_path.c_str(),
+       FLAGS_logits_scale,
+       FLAGS_logits_offset,
+       FLAGS_temperature,
+       FLAGS_eval_mode,
+       FLAGS_kv_updater,
+       FLAGS_num_iters);
+   std::vector<char> buf;
+   buf.reserve(5 * FLAGS_seq_len); // assume each token is around 5 char
+   std::ofstream fout(FLAGS_output_path.c_str());
+   auto callback = [&](const std::string& piece) {
+     for (const char c : piece) {
+       buf.push_back(c);
+     }
+   };
+   // generate tokens & store inference output
+   for (int i = 0; i < FLAGS_num_iters; i++) {
+     for (const auto& prompt : prompts) {
+       runner.generate(
+           FLAGS_seq_len, prompt.c_str(), FLAGS_system_prompt.c_str(), callback);
+     }
+   }
+   fout.write(buf.data(), buf.size());
+   fout.close();
+   return 0;
+ }
+