[aoti] Add cpp loader changes

Author: angelayi

README.md

@@ -182,7 +182,7 @@ python3 torchchat.py generate llama3.1 --prompt "write me a story about a boy an
 [skip default]: end
 
 ### Server
-This mode exposes a REST API for interacting with a model. 
+This mode exposes a REST API for interacting with a model.
 The server follows the [OpenAI API specification](https://platform.openai.com/docs/api-reference/chat) for chat completions.
 
 To test out the REST API, **you'll need 2 terminals**: one to host the server, and one to send the request.
@@ -255,14 +255,14 @@ Use the "Max Response Tokens" slider to limit the maximum number of tokens gener
 ## Desktop/Server Execution
 
 ### AOTI (AOT Inductor)
-[AOTI](https://pytorch.org/blog/pytorch2-2/) compiles models before execution for faster inference. The process creates a [DSO](https://en.wikipedia.org/wiki/Shared_library) model (represented by a file with extension `.so`)
+[AOTI](https://pytorch.org/blog/pytorch2-2/) compiles models before execution for faster inference. The process creates a zipped PT2 file containing all the artifacts generated by AOTInductor, and a [.so](https://en.wikipedia.org/wiki/Shared_library) file with the runnable contents
 that is then loaded for inference. This can be done with both Python and C++ enviroments.
 
 The following example exports and executes the Llama3.1 8B Instruct
 model.  The first command compiles and performs the actual export.
 
 ```bash
-python3 torchchat.py export llama3.1 --output-aoti-package-path exportedModels/llama3_1_artifacts
+python3 torchchat.py export llama3.1 --output-aoti-package-path exportedModels/llama3_1_artifacts.pt2
 ```
 
 > [!NOTE]
@@ -274,12 +274,11 @@ case visit our [customization guide](docs/model_customization.md).
 
 ### Run in a Python Enviroment
 
-To run in a python enviroment, use the generate subcommand like before, but include the dso file.
+To run in a python enviroment, use the generate subcommand like before, but include the pt2 file.
 
 ```bash
-python3 torchchat.py generate llama3.1 --dso-path exportedModels/llama3.1.so --prompt "Hello my name is"
+python3 torchchat.py generate llama3.1 --aoti-package-path exportedModels/llama3_1_artifacts.pt2 --prompt "Hello my name is"
 ```
-**Note:** Depending on which accelerator is used to generate the .dso file, the command may need the device specified: `--device (cuda | cpu)`.
 
 
 ### Run using our C++ Runner
@@ -289,17 +288,11 @@ To run in a C++ enviroment, we need to build the runner binary.
 torchchat/utils/scripts/build_native.sh aoti
 ```
 
-To compile the AOTI generated artifacts into a `.so`:
+Then run the compiled executable, with the pt2.
 ```bash
-make -C exportedModels/llama3_1_artifacts
+cmake-out/aoti_run exportedModels/llama3_1_artifacts.pt2 -z `python3 torchchat.py where llama3.1`/tokenizer.model -l 3 -i "Once upon a time"
 ```
 
-Then run the compiled executable, with the compiled DSO.
-```bash
-cmake-out/aoti_run exportedModels/llama3_1_artifacts/llama3_1_artifacts.so -z `python3 torchchat.py where llama3.1`/tokenizer.model -l 3 -i "Once upon a time"
-```
-**Note:** Depending on which accelerator is used to generate the .dso file, the runner may need the device specified: `-d (CUDA | CPU)`.
-
 ## Mobile Execution
 
 [ExecuTorch](https://github.com/pytorch/executorch) enables you to optimize your model for execution on a

install/install_requirements.sh

@@ -47,10 +47,10 @@ fi
 # NOTE: If a newly-fetched version of the executorch repo changes the value of
 # PYTORCH_NIGHTLY_VERSION, you should re-run this script to install the necessary
 # package versions.
-PYTORCH_NIGHTLY_VERSION=dev20240814
+PYTORCH_NIGHTLY_VERSION=dev20240913
 
 # Nightly version for torchvision
-VISION_NIGHTLY_VERSION=dev20240814
+VISION_NIGHTLY_VERSION=dev20240913
 
 # Nightly version for torchao
 AO_NIGHTLY_VERSION=dev20240905
@@ -77,7 +77,7 @@ fi
 
 # pip packages needed by exir.
 REQUIREMENTS_TO_INSTALL=(
-  torch=="2.5.0.${PYTORCH_NIGHTLY_VERSION}"
+  torch=="2.6.0.${PYTORCH_NIGHTLY_VERSION}"
   torchvision=="0.20.0.${VISION_NIGHTLY_VERSION}"
 )
 

runner/run.cpp

@@ -31,10 +31,7 @@ LICENSE file in the root directory of this source tree.
 #endif
 
 #ifdef __AOTI_MODEL__
-#include <torch/csrc/inductor/aoti_runner/model_container_runner_cpu.h>
-#ifdef USE_CUDA
-#include <torch/csrc/inductor/aoti_runner/model_container_runner_cuda.h>
-#endif
+#include <torch/csrc/inductor/aoti_package/model_package_loader.h>
 torch::Device aoti_device(torch::kCPU);
 
 #else // __ET_MODEL__
@@ -93,7 +90,7 @@ typedef struct {
   RunState state; // buffers for the "wave" of activations in the forward pass
 
 #ifdef __AOTI_MODEL__
-  torch::inductor::AOTIModelContainerRunner* runner;
+  torch::inductor::AOTIModelPackageLoader* runner;
 #else // __ET_MODEL__
   Module* runner;
 #endif
@@ -143,16 +140,8 @@ void build_transformer(
   malloc_run_state(&t->state, &t->config);
 
 #ifdef __AOTI_MODEL__
-#ifdef USE_CUDA
-  if (aoti_device.type() == torch::kCUDA) {
-    t->runner = new torch::inductor::AOTIModelContainerRunnerCuda(model_path);
-    aoti_device = torch::Device(torch::kCUDA);
-  } else {
-#else
-  {
-#endif
-    t->runner = new torch::inductor::AOTIModelContainerRunnerCpu(model_path);
-  }
+  t->runner = new torch::inductor::AOTIModelPackageLoader(model_path);
+  aoti_device = t->runner->get_metadata()["AOTI_DEVICE_KEY"] == "cpu" ? torch::Device(torch::kCPU) : torch::Device(torch::kCUDA);
 #else //__ET_MODEL__
   t->runner = new Module(
       /* path to PTE model */ model_path,

torchchat/cli/builder.py

@@ -536,9 +536,12 @@ def _initialize_model(
             # function, e.g. calling model.setup_cache will NOT touch
             # AOTI compiled and maintained model buffers such as kv_cache.
             from torch._inductor.package import load_package
-            model.forward = load_package(
-                str(builder_args.aoti_package_path.absolute()), builder_args.device
+            aoti_compiled_model = load_package(
+                str(builder_args.aoti_package_path.absolute())
             )
+            model.forward = aoti_compiled_model 
+            metadata = aoti_compiled_model.get_metadata()
+            builder_args.device = metadata["AOTI_DEVICE_KEY"]
         except:
             raise RuntimeError(f"Failed to load AOTI compiled {builder_args.aoti_package_path}")
 

torchchat/export.py

@@ -11,6 +11,7 @@
 import torch.nn as nn
 
 from torch.export import Dim
+import torch._inductor
 
 from torchchat.cli.builder import (
     _initialize_model,
@@ -67,16 +68,27 @@ def export_for_server(
         dynamic_shapes = None
 
     with torch.nn.attention.sdpa_kernel([torch.nn.attention.SDPBackend.MATH]):
-        path = torch._export.aot_compile(
+        metadata = {"model_key": model_key}
+        options = {"aot_inductor.package": package, "aot_inductor.metadata": metadata}
+        if not package:
+            options = {"aot_inductor.output_path": output_path}
+
+        ep = torch.export.export(
             model,
-            args=input,
-            options={
-                "aot_inductor.output_path": output_path,
-                "aot_inductor.package": package,
-            },
+            input,
             dynamic_shapes=dynamic_shapes,
         )
-    print(f"The generated DSO model can be found at: {path}")
+        path = torch._inductor.aot_compile(
+            ep.module(),
+            input,
+            options=options,
+        )
+        
+        if package:
+            from torch._inductor.package import package_aoti
+            path = package_aoti(output_path, path)
+    
+    print(f"The generated packaged model can be found at: {path}")
     return path
 
 

torchchat/generate.py

@@ -135,6 +135,8 @@ def validate_build(
             reason = "model compilation"
         if builder_args.aoti_package_path:
             model_type = "PT2"
+        if builder_args.dso_path:
+            model_type = "DSO"
         if builder_args.pte_path:
             model_type = "PTE"
         if model_type and reason:
@@ -148,7 +150,7 @@ def from_args(cls, args):
         pte_path = getattr(args, "pte_path", None)
         aoti_package_path = getattr(args, "aoti_package_path", None)
         sequential_prefill = (
-            args.sequential_prefill or bool(aoti_package_path) or bool(pte_path)
+            args.sequential_prefill or bool(aoti_package_path) or bool(pte_path) or bool(dso_path)
         )
 
         return cls(