Using new Result interface

Author: Animesh Bohara

.gitmodules

@@ -10,3 +10,6 @@
 [submodule "3rdparty/tvm"]
 	path = 3rdparty/tvm
 	url = https://github.com/mlc-ai/relax.git
+[submodule "3rdparty/stb"]
+	path = 3rdparty/stb
+	url = https://github.com/nothings/stb.git

3rdparty/stb

@@ -88,6 +88,7 @@ target_include_directories(mlc_llm_objs PRIVATE ${MLC_LLM_INCLUDES})
 target_compile_definitions(mlc_llm_objs PRIVATE ${MLC_LLM_COMPILE_DEFS})
 target_include_directories(mlc_llm_objs PRIVATE ${TOKENZIER_CPP_PATH}/include)
 target_compile_definitions(mlc_llm_objs PRIVATE -DMLC_LLM_EXPORTS)
+target_include_directories(mlc_llm_objs PRIVATE 3rdparty/stb)
 
 add_library(mlc_llm SHARED $<TARGET_OBJECTS:mlc_llm_objs>)
 add_library(mlc_llm_static STATIC $<TARGET_OBJECTS:mlc_llm_objs>)

CMakeLists.txt

@@ -3,13 +3,132 @@
 #include <tvm/runtime/registry.h>
 
 #include "../support/json_parser.h"
+#include "image_utils.h"
 
 namespace mlc {
 namespace llm {
 namespace json_ffi {
 
 using namespace mlc::llm;
 
+/****************** Model vision config ******************/
+
+ModelVisionConfig ModelVisionConfig::FromJSON(const picojson::object& json_obj) {
+  ModelVisionConfig config;
+
+  Result<int64_t> hidden_size_res = json::LookupWithResultReturn<int64_t>(json_obj, "hidden_size");
+  if (hidden_size_res.IsOk()) {
+    config.hidden_size = hidden_size_res.Unwrap();
+  }
+
+  Result<int64_t> image_size_res = json::LookupWithResultReturn<int64_t>(json_obj, "image_size");
+  if (image_size_res.IsOk()) {
+    config.image_size = image_size_res.Unwrap();
+  }
+
+  Result<int64_t> intermediate_size_res =
+      json::LookupWithResultReturn<int64_t>(json_obj, "intermediate_size");
+  if (intermediate_size_res.IsOk()) {
+    config.intermediate_size = intermediate_size_res.Unwrap();
+  }
+
+  Result<int64_t> num_attention_heads_res =
+      json::LookupWithResultReturn<int64_t>(json_obj, "num_attention_heads");
+  if (num_attention_heads_res.IsOk()) {
+    config.num_attention_heads = num_attention_heads_res.Unwrap();
+  }
+
+  Result<int64_t> num_hidden_layers_res =
+      json::LookupWithResultReturn<int64_t>(json_obj, "num_hidden_layers");
+  if (num_hidden_layers_res.IsOk()) {
+    config.num_hidden_layers = num_hidden_layers_res.Unwrap();
+  }
+
+  Result<int64_t> patch_size_res = json::LookupWithResultReturn<int64_t>(json_obj, "patch_size");
+  if (patch_size_res.IsOk()) {
+    config.patch_size = patch_size_res.Unwrap();
+  }
+
+  Result<int64_t> projection_dim_res =
+      json::LookupWithResultReturn<int64_t>(json_obj, "projection_dim");
+  if (projection_dim_res.IsOk()) {
+    config.projection_dim = projection_dim_res.Unwrap();
+  }
+
+  Result<int64_t> vocab_size_res = json::LookupWithResultReturn<int64_t>(json_obj, "vocab_size");
+  if (vocab_size_res.IsOk()) {
+    config.vocab_size = vocab_size_res.Unwrap();
+  }
+
+  Result<std::string> dtype_res = json::LookupWithResultReturn<std::string>(json_obj, "dtype");
+  if (dtype_res.IsOk()) {
+    config.dtype = dtype_res.Unwrap();
+  }
+
+  Result<int64_t> num_channels_res =
+      json::LookupWithResultReturn<int64_t>(json_obj, "num_channels");
+  if (num_channels_res.IsOk()) {
+    config.num_channels = num_channels_res.Unwrap();
+  }
+
+  Result<double> layer_norm_eps_res =
+      json::LookupWithResultReturn<double>(json_obj, "layer_norm_eps");
+  if (layer_norm_eps_res.IsOk()) {
+    config.layer_norm_eps = layer_norm_eps_res.Unwrap();
+  }
+
+  return config;
+}
+
+/****************** Model config ******************/
+
+ModelConfig ModelConfig::FromJSON(const picojson::object& json_obj) {
+  ModelConfig config;
+
+  Result<int64_t> vocab_size_res = json::LookupWithResultReturn<int64_t>(json_obj, "vocab_size");
+  if (vocab_size_res.IsOk()) {
+    config.vocab_size = vocab_size_res.Unwrap();
+  }
+
+  Result<int64_t> context_window_size_res =
+      json::LookupWithResultReturn<int64_t>(json_obj, "context_window_size");
+  if (context_window_size_res.IsOk()) {
+    config.context_window_size = context_window_size_res.Unwrap();
+  }
+
+  Result<int64_t> sliding_window_size_res =
+      json::LookupWithResultReturn<int64_t>(json_obj, "sliding_window_size");
+  if (sliding_window_size_res.IsOk()) {
+    config.sliding_window_size = sliding_window_size_res.Unwrap();
+  }
+
+  Result<int64_t> prefill_chunk_size_res =
+      json::LookupWithResultReturn<int64_t>(json_obj, "prefill_chunk_size");
+  if (prefill_chunk_size_res.IsOk()) {
+    config.prefill_chunk_size = prefill_chunk_size_res.Unwrap();
+  }
+
+  Result<int64_t> tensor_parallel_shards_res =
+      json::LookupWithResultReturn<int64_t>(json_obj, "tensor_parallel_shards");
+  if (tensor_parallel_shards_res.IsOk()) {
+    config.tensor_parallel_shards = tensor_parallel_shards_res.Unwrap();
+  }
+
+  Result<int64_t> max_batch_size_res =
+      json::LookupWithResultReturn<int64_t>(json_obj, "max_batch_size");
+  if (max_batch_size_res.IsOk()) {
+    config.max_batch_size = max_batch_size_res.Unwrap();
+  }
+
+  if (json_obj.count("vision_config")) {
+    const picojson::object& vision_config_obj =
+        json_obj.at("vision_config").get<picojson::object>();
+    config.vision_config = ModelVisionConfig::FromJSON(vision_config_obj);
+  }
+
+  return config;
+}
+
 /****************** Conversation template ******************/
 
 std::map<MessagePlaceholders, std::string> PLACEHOLDERS = {
@@ -34,7 +153,7 @@ Conversation::Conversation()
                       {"assistant", PLACEHOLDERS[MessagePlaceholders::ASSISTANT]},
                       {"tool", PLACEHOLDERS[MessagePlaceholders::TOOL]}}) {}
 
-Result<std::vector<Data>> Conversation::AsPrompt() {
+Result<std::vector<Data>> Conversation::AsPrompt(ModelConfig config, DLDevice device) {
   using TResult = Result<std::vector<Data>>;
   // Get the system message
   std::string system_msg = system_template;
@@ -116,6 +235,29 @@ Result<std::vector<Data>> Conversation::AsPrompt() {
           }
         }
         message += role_text;
+      } else if (it_type->second == "image_url") {
+        if (item.find("image_url") == item.end()) {
+          return TResult::Error("Content should have an image_url field");
+        }
+        std::string image_url =
+            item.at("image_url");  // TODO(mlc-team): According to OpenAI API reference this
+                                   // should be a map, with a "url" key containing the URL, but
+                                   // we are just assuming this as the URL for now
+        std::string base64_image = image_url.substr(image_url.find(",") + 1);
+        Result<NDArray> image_data_res = LoadImageFromBase64(base64_image);
+        if (image_data_res.IsErr()) {
+          return TResult::Error(image_data_res.UnwrapErr());
+        }
+        if (!config.vision_config.has_value()) {
+          return TResult::Error("Vision config is required for image input");
+        }
+        int image_size = config.vision_config.value().image_size;
+        int patch_size = config.vision_config.value().patch_size;
+
+        int embed_size = (image_size * image_size) / (patch_size * patch_size);
+
+        auto image_ndarray = ClipPreprocessor(image_data_res.Unwrap(), image_size, device);
+        message_list.push_back(ImageData(image_ndarray, embed_size));
       } else {
         return TResult::Error("Unsupported content type: " + it_type->second);
       }

cpp/json_ffi/conv_template.cc

@@ -19,6 +19,43 @@ namespace mlc {
 namespace llm {
 namespace json_ffi {
 
+/****************** Model vision config ******************/
+
+/*! \brief Defines the Vision config of the model (if present) */
+class ModelVisionConfig {
+ public:
+  int hidden_size;
+  int image_size;
+  int intermediate_size;
+  int num_attention_heads;
+  int num_hidden_layers;
+  int patch_size;
+  int projection_dim;
+  int vocab_size;
+  std::string dtype;
+  int num_channels;
+  double layer_norm_eps;
+
+  static ModelVisionConfig FromJSON(const picojson::object& json_obj);
+};
+
+/****************** Model config ******************/
+
+/*! \brief Defines the config of the model.
+Populated from "model_config" field in mlc-chat-config.json */
+class ModelConfig {
+ public:
+  int vocab_size;
+  int context_window_size;
+  int sliding_window_size;
+  int prefill_chunk_size;
+  int tensor_parallel_shards;
+  int max_batch_size;
+  std::optional<ModelVisionConfig> vision_config = std::nullopt;
+
+  static ModelConfig FromJSON(const picojson::object& json_obj);
+};
+
 /****************** Conversation template ******************/
 
 enum class MessagePlaceholders { SYSTEM, USER, ASSISTANT, TOOL, FUNCTION };
@@ -92,7 +129,7 @@ struct Conversation {
   Conversation();
 
   /*! \brief Create the list of prompts from the messages based on the conversation template. */
-  Result<std::vector<Data>> AsPrompt();
+  Result<std::vector<Data>> AsPrompt(ModelConfig config, DLDevice device);
 
   /*! \brief Create a Conversation instance from the given JSON object. */
   static Result<Conversation> FromJSON(const picojson::object& json);

cpp/json_ffi/conv_template.h

@@ -0,0 +1,156 @@
+#include "image_utils.h"
+
+#include <dmlc/io.h>
+
+#include "../../3rdparty/tvm/src/support/base64.h"
+#define STB_IMAGE_IMPLEMENTATION
+#include "stb_image.h"
+
+namespace mlc {
+namespace llm {
+namespace json_ffi {
+
+using namespace tvm::runtime;
+
+class MemoryBufferStream : public dmlc::Stream {
+ public:
+  MemoryBufferStream(const char* data, size_t size) : data_(data), size_(size), pos_(0) {}
+
+  size_t Read(void* ptr, size_t size) override {
+    size_t remaining = size_ - pos_;
+    if (size > remaining) {
+      size = remaining;
+    }
+    if (size == 0) {
+      return 0;
+    }
+    std::memcpy(ptr, data_ + pos_, size);
+    pos_ += size;
+    return size;
+  }
+
+  void Write(const void* ptr, size_t size) override {
+    LOG(FATAL) << "MemoryBufferStream does not support write";
+  }
+
+ private:
+  const char* data_;
+  size_t size_;
+  size_t pos_;
+};
+
+size_t Base64DecodedSize(const std::string& base64_str) {
+  size_t len = base64_str.size();
+  size_t padding = 0;
+  if (base64_str[len - 1] == '=') {
+    padding++;
+  }
+  if (base64_str[len - 2] == '=') {
+    padding++;
+  }
+  return 3 * len / 4 - padding;
+}
+
+Result<NDArray> LoadImageFromBase64(const std::string& base64_str) {
+  using TResult = Result<NDArray>;
+  MemoryBufferStream stream(base64_str.c_str(), base64_str.size());
+  tvm::support::Base64InStream base64_stream(&stream);
+  size_t decoded_size = Base64DecodedSize(base64_str);
+  std::vector<unsigned char> decoded(decoded_size);
+  base64_stream.InitPosition();
+  base64_stream.Read((void*)decoded.data(), decoded_size);
+  int width, height, num_channels;
+  unsigned char* image_data =
+      stbi_load_from_memory(decoded.data(), decoded_size, &width, &height, &num_channels, 3);
+  if (!image_data) {
+    return TResult::Error(stbi_failure_reason());
+  }
+  auto image_ndarray = NDArray::Empty({height, width, 3}, {kDLUInt, 8, 1}, {kDLCPU, 0});
+  image_ndarray.CopyFromBytes((void*)image_data, width * height * 3);
+  stbi_image_free(image_data);
+  return TResult::Ok(image_ndarray);
+}
+
+NDArray ClipPreprocessor(NDArray image_data, int target_size, DLDevice device) {
+  int height = image_data->shape[0];
+  int width = image_data->shape[1];
+  // Resize
+  const int short_side = width < height ? width : height;
+  const int long_side = width > height ? width : height;
+  const int new_short_side = target_size;
+  const int new_long_side = (int)(new_short_side * (long_side / (float)short_side));
+  const int new_width = width < height ? new_short_side : new_long_side;
+  const int new_height = width > height ? new_short_side : new_long_side;
+
+  std::vector<float> processed_image_data(new_width * new_height * 3);
+
+  // Bilinear Interpolation
+  for (int y = 0; y < new_height; y++) {
+    for (int x = 0; x < new_width; x++) {
+      const float x_ratio = float(width - 1) / new_width;
+      const float y_ratio = float(height - 1) / new_height;
+      const int x1 = int(x_ratio * x);
+      const int y1 = int(y_ratio * y);
+      const int x2 = x1 + 1;
+      const int y2 = y1 + 1;
+      const float x_diff = x_ratio * x - x1;
+      const float y_diff = y_ratio * y - y1;
+      for (int c = 0; c < 3; c++) {
+        const uint8_t top_left = ((uint8_t*)image_data->data)[(y1 * width + x1) * 3 + c];
+        const uint8_t top_right = ((uint8_t*)image_data->data)[(y1 * width + x2) * 3 + c];
+        const uint8_t bottom_left = ((uint8_t*)image_data->data)[(y2 * width + x1) * 3 + c];
+        const uint8_t bottom_right = ((uint8_t*)image_data->data)[(y2 * width + x2) * 3 + c];
+        processed_image_data[(y * new_width + x) * 3 + c] =
+            (float)(int(top_left * (1 - x_diff) * (1 - y_diff) + top_right * x_diff * (1 - y_diff) +
+                        bottom_left * y_diff * (1 - x_diff) + bottom_right * x_diff * y_diff));
+      }
+    }
+  }
+
+  // Center crop
+  const int crop_x = (new_width - target_size) / 2;
+  const int crop_y = (new_height - target_size) / 2;
+  std::vector<float> cropped_image_data(target_size * target_size * 3);
+  for (int y = 0; y < target_size; y++) {
+    for (int x = 0; x < target_size; x++) {
+      for (int c = 0; c < 3; c++) {
+        cropped_image_data[(y * target_size + x) * 3 + c] =
+            processed_image_data[((y + crop_y) * new_width + x + crop_x) * 3 + c];
+      }
+    }
+  }
+
+  // Rescale
+  for (int i = 0; i < target_size * target_size * 3; i++) {
+    cropped_image_data[i] = cropped_image_data[i] / 255.0f;
+  }
+
+  // Normalize
+  const float IMAGE_MEAN[] = {0.48145466f, 0.4578275f, 0.40821073f};
+  const float IMAGE_STD[] = {0.26862954f, 0.26130258f, 0.27577711f};
+  for (int i = 0; i < target_size * target_size * 3; i++) {
+    const int c = i % 3;
+    cropped_image_data[i] = (cropped_image_data[i] - IMAGE_MEAN[c]) / IMAGE_STD[c];
+  }
+
+  std::vector<float> image_data_channel_first(target_size * target_size * 3);
+  for (int y = 0; y < target_size; y++) {
+    for (int x = 0; x < target_size; x++) {
+      for (int c = 0; c < 3; c++) {
+        image_data_channel_first[c * target_size * target_size + y * target_size + x] =
+            cropped_image_data[(y * target_size + x) * 3 + c];
+      }
+    }
+  }
+
+  // Create NDArray
+  auto image_ndarray = NDArray::Empty({1, 3, target_size, target_size}, {kDLFloat, 32, 1}, device);
+  image_ndarray.CopyFromBytes((void*)image_data_channel_first.data(),
+                              target_size * target_size * 3 * sizeof(float));
+
+  return image_ndarray;
+}
+
+}  // namespace json_ffi
+}  // namespace llm
+}  // namespace mlc