Update AOTI package

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.
@@ -255,13 +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.
-```
-python3 torchchat.py export llama3.1 --output-dso-path exportedModels/llama3.1.so
+
+```bash
+python3 torchchat.py export llama3.1 --output-aoti-package-path exportedModels/llama3_1_artifacts.pt2
 ```
 
 > [!NOTE]
@@ -273,12 +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 --aoti-package-path exportedModels/llama3_1_artifacts.pt2 --prompt "Hello my name is"
 ```
-python3 torchchat.py generate llama3.1 --dso-path exportedModels/llama3.1.so --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
@@ -288,11 +288,10 @@ To run in a C++ enviroment, we need to build the runner binary.
 torchchat/utils/scripts/build_native.sh aoti
 ```
 
-Then run the compiled executable, with the exported DSO from earlier.
+Then run the compiled executable, with the pt2.
 ```bash
-cmake-out/aoti_run exportedModels/llama3.1.so -z `python3 torchchat.py where llama3.1`/tokenizer.model -l 3 -i "Once upon a time"
+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"
 ```
-**Note:** Depending on which accelerator is used to generate the .dso file, the runner may need the device specified: `-d (CUDA | CPU)`.
 
 ## Mobile Execution
 

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 torchtune
 TUNE_NIGHTLY_VERSION=dev20240916
@@ -74,7 +74,7 @@ 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.${VISION_NIGHTLY_VERSION}"
   torchtune=="0.3.0.${TUNE_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

@@ -57,6 +57,7 @@ class BuilderArgs:
     gguf_path: Optional[Union[Path, str]] = None
     gguf_kwargs: Optional[Dict[str, Any]] = None
     dso_path: Optional[Union[Path, str]] = None
+    aoti_package_path: Optional[Union[Path, str]] = None
     pte_path: Optional[Union[Path, str]] = None
     device: Optional[str] = None
     precision: torch.dtype = torch.float32
@@ -76,28 +77,29 @@ def __post_init__(self):
             or (self.checkpoint_dir and self.checkpoint_dir.is_dir())
             or (self.gguf_path and self.gguf_path.is_file())
             or (self.dso_path and Path(self.dso_path).is_file())
+            or (self.aoti_package_path and Path(self.aoti_package_path).is_file())
             or (self.pte_path and Path(self.pte_path).is_file())
         ):
             raise RuntimeError(
                 "need to specified a valid checkpoint path, checkpoint dir, gguf path, DSO path, or PTE path"
             )
 
-        if self.dso_path and self.pte_path:
-            raise RuntimeError("specify either DSO path or PTE path, but not both")
+        if self.aoti_package_path and self.pte_path:
+            raise RuntimeError("specify either AOTI Package path or PTE path, but not more than one")
 
-        if self.checkpoint_path and (self.dso_path or self.pte_path):
+        if self.checkpoint_path and (self.aoti_package_path or self.pte_path):
             print(
-                "Warning: checkpoint path ignored because an exported DSO or PTE path specified"
+                "Warning: checkpoint path ignored because an exported AOTI or PTE path specified"
             )
-        if self.checkpoint_dir and (self.dso_path or self.pte_path):
+        if self.checkpoint_dir and (self.aoti_package_path or self.pte_path):
             print(
-                "Warning: checkpoint dir ignored because an exported DSO or PTE path specified"
+                "Warning: checkpoint dir ignored because an exported AOTI or PTE path specified"
             )
-        if self.gguf_path and (self.dso_path or self.pte_path):
+        if self.gguf_path and (self.aoti_package_path or self.pte_path):
             print(
-                "Warning: GGUF path ignored because an exported DSO or PTE path specified"
+                "Warning: GGUF path ignored because an exported AOTI or PTE path specified"
             )
-        if not (self.dso_path) and not (self.pte_path):
+        if not (self.aoti_package_path) and not (self.pte_path):
             self.prefill_possible = True
 
     @classmethod
@@ -127,6 +129,7 @@ def from_args(cls, args):  # -> BuilderArgs:
 
         dso_path = getattr(args, "dso_path", None)
         pte_path = getattr(args, "pte_path", None)
+        aoti_package_path = getattr(args, "aoti_package_path", None)
 
         is_chat_model = False
         if args.is_chat_model:
@@ -137,6 +140,7 @@ def from_args(cls, args):  # -> BuilderArgs:
                 checkpoint_dir,
                 dso_path,
                 pte_path,
+                aoti_package_path,
                 args.gguf_path,
             ]:
                 if path is not None:
@@ -151,6 +155,7 @@ def from_args(cls, args):  # -> BuilderArgs:
                         is_chat_model = True
 
         output_pte_path = getattr(args, "output_pte_path", None)
+        output_aoti_package_path = getattr(args, "output_aoti_package_path", None)
         output_dso_path = getattr(args, "output_dso_path", None)
         if output_pte_path and args.dtype.startswith("fast"):
             if args.dtype == "fast":
@@ -172,10 +177,11 @@ def from_args(cls, args):  # -> BuilderArgs:
             gguf_path=args.gguf_path,
             gguf_kwargs=None,
             dso_path=dso_path,
+            aoti_package_path=aoti_package_path,
             pte_path=pte_path,
             device=args.device,
             precision=dtype,
-            setup_caches=(output_dso_path or output_pte_path),
+            setup_caches=(output_dso_path or output_pte_path or output_aoti_package_path),
             use_distributed=args.distributed,
             is_chat_model=is_chat_model,
             dynamic_shapes=getattr(args, "dynamic_shapes", False),
@@ -190,6 +196,7 @@ def from_speculative_args(cls, args):  # -> BuilderArgs:
         speculative_builder_args.checkpoint_path = args.draft_checkpoint_path
         speculative_builder_args.gguf_path = None
         speculative_builder_args.dso_path = None
+        speculative_builder_args.aoti_package_path = None
         speculative_builder_args.pte_path = None
         return speculative_builder_args
 
@@ -482,11 +489,12 @@ def _initialize_model(
 ):
     print("Loading model...")
 
-    if builder_args.gguf_path and (builder_args.dso_path or builder_args.pte_path):
+    if builder_args.gguf_path and (builder_args.dso_path or builder_args.aoti_package_path or builder_args.pte_path):
         print("Setting gguf_kwargs for generate.")
         is_dso = builder_args.dso_path is not None
+        is_aoti_package = builder_args.aoti_package_path is not None
         is_pte = builder_args.pte_path is not None
-        assert not (is_dso and is_pte)
+        assert not (is_dso and is_aoti_package and is_pte)
         assert builder_args.gguf_kwargs is None
         # TODO: make GGUF load independent of backend
         # currently not working because AVX int_mm broken
@@ -520,6 +528,39 @@ def _initialize_model(
             )
         except:
             raise RuntimeError(f"Failed to load AOTI compiled {builder_args.dso_path}")
+    
+    elif builder_args.aoti_package_path:
+        if not is_cuda_or_cpu_device(builder_args.device):
+            print(
+                f"Cannot load specified PT2 to {builder_args.device}. Attempting to load model to CPU instead"
+            )
+            builder_args.device = "cpu"
+
+        # assert (
+        #     quantize is None or quantize == "{ }"
+        # ), "quantize not valid for exported PT2 model. Specify quantization during export."
+
+        with measure_time("Time to load model: {time:.02f} seconds"):
+            model = _load_model(builder_args, only_config=True)
+            device_sync(device=builder_args.device)
+
+        try:
+            # Replace model forward with the AOT-compiled forward
+            # This is a hacky way to quickly demo AOTI's capability.
+            # model is still a Python object, and any mutation to its
+            # attributes will NOT be seen on by AOTI-compiled forward
+            # 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
+            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}")
+
     elif builder_args.pte_path:
         if not is_cpu_device(builder_args.device):
             print(

torchchat/cli/cli.py

@@ -191,6 +191,12 @@ def _add_export_output_path_args(parser) -> None:
         default=None,
         help="Output to the specified AOT Inductor .dso model file",
     )
+    exclusive_parser.add_argument(
+        "--output-aoti-package-path",
+        type=str,
+        default=None,
+        help="Output directory for AOTInductor compiled artifacts",
+    )
 
 
 def _add_export_args(parser) -> None:
@@ -220,6 +226,12 @@ def _add_exported_input_path_args(parser) -> None:
         default=None,
         help="Use the specified AOT Inductor .dso model file",
     )
+    exclusive_parser.add_argument(
+        "--aoti-package-path",
+        type=Path,
+        default=None,
+        help="Use the specified directory containing AOT Inductor compiled files",
+    )
     exclusive_parser.add_argument(
         "--pte-path",
         type=Path,