【Hackathon 9th No.99】[Feature Enhancement]Add tensorrt backend for compiler (#237)

* add tensorrt for compiler

* fix bug

* fix code

* Update test_compiler.py

Author: fangfangssj

graph_net/torch/test_compiler.py

@@ -13,15 +13,56 @@
 from contextlib import contextmanager
 import time
 
+try:
+    import torch_tensorrt
+except ImportError:
+    torch_tensorrt = None
 
-def load_class_from_file(file_path: str, class_name: str) -> Type[torch.nn.Module]:
+
+class GraphCompilerBackend:
+    def __call__(self, model):
+        raise NotImplementedError()
+
+    def synchronize(self):
+        raise NotImplementedError()
+
+
+class InductorBackend(GraphCompilerBackend):
+    def __call__(self, model):
+        return torch.compile(model, backend="inductor")
+
+    def synchronize(self):
+        torch.cuda.synchronize()
+
+
+class TensorRTBackend(GraphCompilerBackend):
+    def __call__(self, model):
+        return torch.compile(model, backend="tensorrt")
+
+    def synchronize(self):
+        torch.cuda.synchronize()
+
+
+registry_backend = {
+    "inductor": InductorBackend(),
+    "tensorrt": TensorRTBackend(),
+    "default": InductorBackend(),
+}
+
+
+def load_class_from_file(
+    args: argparse.Namespace, class_name: str
+) -> Type[torch.nn.Module]:
+    file_path = f"{args.model_path}/model.py"
     file = Path(file_path).resolve()
     module_name = file.stem
 
     with open(file_path, "r", encoding="utf-8") as f:
         original_code = f.read()
-    import_stmt = "import torch"
-    modified_code = f"{import_stmt}\n{original_code}"
+    if args.device == "cuda":
+        modified_code = original_code.replace("cpu", "cuda")
+    else:
+        modified_code = original_code
     spec = importlib.util.spec_from_loader(module_name, loader=None)
     module = importlib.util.module_from_spec(spec)
     sys.modules[module_name] = module
@@ -32,27 +73,23 @@ def load_class_from_file(file_path: str, class_name: str) -> Type[torch.nn.Modul
     return model_class
 
 
-def get_compiler(args):
-    assert args.compiler == "default"
-    return torch.compile
-
-
-def get_synchronizer_func(args):
-    assert args.compiler == "default"
-    return torch.cuda.synchronize
+def get_compiler_backend(args) -> GraphCompilerBackend:
+    assert args.compiler in registry_backend, f"Unknown compiler: {args.compiler}"
+    return registry_backend[args.compiler]
 
 
 def get_model(args):
-    model_class = load_class_from_file(
-        f"{args.model_path}/model.py", class_name="GraphModule"
-    )
-    return model_class()
+    model_class = load_class_from_file(args, class_name="GraphModule")
+    return model_class().to(torch.device(args.device))
 
 
 def get_input_dict(args):
     inputs_params = utils.load_converted_from_text(f"{args.model_path}")
     params = inputs_params["weight_info"]
-    return {k: utils.replay_tensor(v) for k, v in params.items()}
+    return {
+        k: utils.replay_tensor(v).to(torch.device(args.device))
+        for k, v in params.items()
+    }
 
 
 @dataclass
@@ -71,15 +108,14 @@ def naive_timer(duration_box, get_synchronizer_func):
 
 
 def test_single_model(args):
-    compiler = get_compiler(args)
-    synchronizer_func = get_synchronizer_func(args)
+    compiler = get_compiler_backend(args)
     input_dict = get_input_dict(args)
     model = get_model(args)
     compiled_model = compiler(model)
 
     # eager
     eager_duration_box = DurationBox(-1)
-    with naive_timer(eager_duration_box, synchronizer_func):
+    with naive_timer(eager_duration_box, compiler.synchronize):
         expected_out = model(**input_dict)
 
     # warmup
@@ -88,7 +124,7 @@ def test_single_model(args):
 
     # compiled
     compiled_duration_box = DurationBox(-1)
-    with naive_timer(compiled_duration_box, synchronizer_func):
+    with naive_timer(compiled_duration_box, compiler.synchronize):
         compiled_out = compiled_model(**input_dict)
 
     def print_cmp(key, func, **kwargs):
@@ -157,11 +193,11 @@ def test_multi_models(args):
         cmd = "".join(
             [
                 sys.executable,
-                "-m graph_net.torch.test_compiler",
-                f"--model-path {model_path}",
-                f"--compiler {args.compiler}",
-                f"--warmup {args.warmup}",
-                f"--log-prompt {args.log_prompt}",
+                " -m graph_net.torch.test_compiler",
+                f" --model-path {model_path}",
+                f" --compiler {args.compiler}",
+                f" --warmup {args.warmup}",
+                f" --log-prompt {args.log_prompt}",
             ]
         )
         cmd_ret = os.system(cmd)
@@ -212,6 +248,13 @@ def main(args):
         default="default",
         help="Path to customized compiler python file",
     )
+    parser.add_argument(
+        "--device",
+        type=str,
+        required=False,
+        default="cpu",
+        help="Device for testing the compiler",
+    )
     parser.add_argument(
         "--warmup", type=int, required=False, default=5, help="Number of warmup steps"
     )