Make IOManager use Module instead of Method. (pytorch#13542)

Summary:

Let's not expose Method from Module so that it's not getting misused beyond its owner.

Differential Revision: D80595261

Author: shoumikhin

examples/models/llava/runner/llava_runner.h

@@ -42,7 +42,7 @@ class ET_EXPERIMENTAL LlavaRunner {
       const float temperature = 0.8f)
       : temperature_(temperature),
         module_(std::make_unique<Module>(model_path, Module::LoadMode::File)),
-        io_manager_(std::make_unique<IOManager>()),
+        io_manager_(std::make_unique<IOManager>(*module_)),
         tokenizer_path_(tokenizer_path) {
     ET_LOG(
         Info,

extension/llm/runner/io_manager/io_manager.h

@@ -8,12 +8,8 @@
 
 #pragma once
 
-#include <vector>
-
+#include <executorch/extension/module/module.h>
 #include <executorch/extension/tensor/tensor.h>
-#include <executorch/runtime/core/exec_aten/exec_aten.h>
-#include <executorch/runtime/executor/method.h>
-#include <executorch/runtime/executor/method_meta.h>
 
 namespace executorch {
 namespace extension {
@@ -29,6 +25,8 @@ namespace llm {
  */
 class ET_EXPERIMENTAL IOManager {
  public:
+  explicit IOManager(ET_MODULE_NAMESPACE::Module& module) : module_(module) {}
+
   /**
    * @brief Virtual destructor to allow proper cleanup in derived classes.
    */
@@ -38,88 +36,111 @@ class ET_EXPERIMENTAL IOManager {
    * @brief Load the IO manager with method metadata for prefill and
    * decode operations.
    *
-   * @param program The program prefill and decode methods are loaded from.
    * @param prefill_method The prefill method to initialize with.
    * @param decode_method The decode method to initialize with.
    */
   ET_NODISCARD virtual runtime::Error load(
-      const executorch::ET_RUNTIME_NAMESPACE::Program& program,
-      executorch::ET_RUNTIME_NAMESPACE::Method& prefill_method,
-      executorch::ET_RUNTIME_NAMESPACE::Method& decode_method) {
-    (void)program;
+      const std::string& prefill_method,
+      const std::string& decode_method) {
     (void)prefill_method;
     (void)decode_method;
     return runtime::Error::Ok;
   }
 
+  ET_NODISCARD runtime::Error load() {
+    return load("forward", "forward");
+  }
+
   /**
    * @brief Reset the IO manager state.
    *
    * @param prefill_method The prefill method to reset with.
    * @param decode_method The decode method to reset with.
    */
   ET_NODISCARD virtual runtime::Error reset(
-      executorch::ET_RUNTIME_NAMESPACE::Method& prefill_method,
-      executorch::ET_RUNTIME_NAMESPACE::Method& decode_method) {
+      const std::string& prefill_method,
+      const std::string& decode_method) {
     (void)prefill_method;
     (void)decode_method;
     return runtime::Error::Ok;
   }
 
+  ET_NODISCARD runtime::Error reset() {
+    return reset("forward", "forward");
+  }
+
   /**
    * @brief Prepare inputs for the prefill phase of LLM inference.
    *
    * @param input The input tensor containing token IDs.
    * @param start_pos The tensor containing the starting position of the current
    * input within the context.
    * @param prefill_method The prefill method to prepare inputs for.
-   * @return std::vector<executorch::runtime::EValue> Vector of prepared inputs
+   * @return std::vector<runtime::EValue> Vector of prepared inputs
    * for the prefill method.
    */
-  virtual runtime::Result<std::vector<executorch::runtime::EValue>>
-  prepare_prefill(
-      const executorch::extension::TensorPtr& input,
-      const executorch::extension::TensorPtr& start_pos,
-      executorch::ET_RUNTIME_NAMESPACE::Method& prefill_method) {
-    if (prefill_method.inputs_size() != 2) {
+  virtual runtime::Result<std::vector<runtime::EValue>> prepare_prefill(
+      const TensorPtr& input,
+      const TensorPtr& start_pos,
+      const std::string& prefill_method) {
+    auto method_meta = module_.method_meta(prefill_method);
+    if (!method_meta.ok()) {
+      return method_meta.error();
+    }
+    if (method_meta->num_inputs() != 2) {
       ET_LOG(
           Error,
           "Expected 2 inputs for prefill method, got %zu. Likely the model takes the caches or mask as an argument which this IOManager does not support.",
-          prefill_method.inputs_size());
+          method_meta->num_inputs());
       return runtime::Error::InvalidState;
     }
     // Cpu IO Manager supports dynamic shapes for prefill, so no work to be done
     // here.
     return std::vector<runtime::EValue>{input, start_pos};
   }
 
+  runtime::Result<std::vector<runtime::EValue>> prepare_prefill(
+      const TensorPtr& input,
+      const TensorPtr& start_pos) {
+    return prepare_prefill(input, start_pos, "forward");
+  }
+
   /**
    * @brief Prepare inputs for the decode phase of LLM inference.
    *
    * @param input The input tensor containing token IDs.
    * @param start_pos The tensor containing the starting position of the current
    * input within the context.
    * @param decode_method The decode method to prepare inputs for.
-   * @return std::vector<executorch::runtime::EValue> Vector of prepared inputs
+   * @return std::vector<runtime::EValue> Vector of prepared inputs
    * for the decode method.
    */
-  virtual runtime::Result<std::vector<executorch::runtime::EValue>>
-  prepare_decode(
-      const executorch::extension::TensorPtr& input,
-      const executorch::extension::TensorPtr& start_pos,
-      executorch::ET_RUNTIME_NAMESPACE::Method& decode_method) {
-    if (decode_method.inputs_size() != 2) {
+  virtual runtime::Result<std::vector<runtime::EValue>> prepare_decode(
+      const TensorPtr& input,
+      const TensorPtr& start_pos,
+      const std::string& decode_method) {
+    auto method_meta = module_.method_meta(decode_method);
+    if (!method_meta.ok()) {
+      return method_meta.error();
+    }
+    if (method_meta->num_inputs() != 2) {
       ET_LOG(
           Error,
           "Expected 2 inputs for decode method, got %zu. Likely the model takes the caches or mask as an argument which this IOManager does not support.",
-          decode_method.inputs_size());
+          method_meta->num_inputs());
       return runtime::Error::InvalidState;
     }
     // Cpu IO Manager supports dynamic shapes for prefill, so no work to be done
     // here.
     return std::vector<runtime::EValue>{input, start_pos};
   }
 
+  runtime::Result<std::vector<runtime::EValue>> prepare_decode(
+      const TensorPtr& input,
+      const TensorPtr& start_pos) {
+    return prepare_decode(input, start_pos, "forward");
+  }
+
   /**
    * @brief Process and update internal state with outputs from the prefill
    * phase.
@@ -128,14 +149,19 @@ class ET_EXPERIMENTAL IOManager {
    * @param model_outputs Vector of outputs from the prefill method execution.
    */
   ET_NODISCARD virtual runtime::Error update_prefill(
-      executorch::ET_RUNTIME_NAMESPACE::Method& prefill_method,
-      const std::vector<executorch::runtime::EValue>& model_outputs) {
-    (void)prefill_method;
+      const std::vector<runtime::EValue>& model_outputs,
+      const std::string& prefill_method) {
     (void)model_outputs;
+    (void)prefill_method;
     // No post inference work to do.
     return runtime::Error::Ok;
   }
 
+  ET_NODISCARD runtime::Error update_prefill(
+      const std::vector<runtime::EValue>& model_outputs) {
+    return update_prefill(model_outputs, "forward");
+  }
+
   /**
    * @brief Process and update internal state with outputs from the decode
    * phase.
@@ -144,13 +170,21 @@ class ET_EXPERIMENTAL IOManager {
    * @param model_outputs Vector of outputs from the decode method execution.
    */
   ET_NODISCARD virtual runtime::Error update_decode(
-      const executorch::ET_RUNTIME_NAMESPACE::Method& decode_method,
-      const std::vector<executorch::runtime::EValue>& model_outputs) {
-    (void)decode_method;
+      const std::vector<runtime::EValue>& model_outputs,
+      const std::string& decode_method) {
     (void)model_outputs;
+    (void)decode_method;
     // No post inference work to do.
     return runtime::Error::Ok;
   }
+
+  ET_NODISCARD runtime::Error update_decode(
+      const std::vector<runtime::EValue>& model_outputs) {
+    return update_decode(model_outputs, "forward");
+  }
+
+ private:
+  ET_MODULE_NAMESPACE::Module& module_;
 };
 
 } // namespace llm

extension/llm/runner/io_manager/targets.bzl

@@ -11,10 +11,9 @@ def define_common_targets():
             exported_headers = [
                 "io_manager.h",
             ],
-            deps = [
+            exported_deps = [
                 "//executorch/extension/tensor:tensor" + aten_suffix,
-                "//executorch/runtime/core/exec_aten:lib" + aten_suffix,
-                "//executorch/runtime/executor:program_no_prim_ops" + aten_suffix,
+                "//executorch/extension/module:module" + aten_suffix,
             ],
             visibility = [
                 "@EXECUTORCH_CLIENTS",

extension/llm/runner/io_manager/test/TARGETS

@@ -10,14 +10,12 @@ define_common_targets()
 
 runtime.cxx_test(
     name = "test_io_manager",
-    srcs = ["test_io_manager.cpp"],
+    srcs = [
+        "test_io_manager.cpp",
+    ],
     deps = [
         "//executorch/extension/llm/runner/io_manager:io_manager",
-        "//executorch/extension/llm/runner/io_manager:io_manager",
-        "//executorch/extension/module:module",
-        "//executorch/extension/tensor:tensor",
-        "//executorch/runtime/executor:program",
-         "//executorch/kernels/portable:generated_lib",
+        "//executorch/kernels/portable:generated_lib",
     ],
     env = {
         "KVCACHE_CACHE_POS": "$(location fbcode//executorch/test/models:exported_programs[ModuleKVCacheCachePos.pte])",