[backends] Add functionality to TRT backend

- Add argument parsing for backend arguments to pass to TRT
- Add capability to specify IR via command line CLI
- Add functionality to compilation path and clean up code

torchbenchmark/util/backends/trt.py

@@ -1,9 +1,24 @@
 from typing import List
 import torch
+import argparse
 
 from torchbenchmark.util.backends import create_backend 
 from torchbenchmark.util.env_check import is_hf_model
 
+def parse_torch_trt_args(backend_args: List[str]):
+    """Parses CLI-provided backend arguments to extract Torch-TRT keywords
+
+    Returns kwargs dictionary and remainder arguments which were unrecognized
+    """
+    arg_parser = argparse.ArgumentParser()
+    arg_parser.add_argument("--truncate_long_and_double", default=False, action="store_true")
+    arg_parser.add_argument("--workspace_size", type=int)
+    arg_parser.add_argument("--min_block_size", type=int)
+    arg_parser.add_argument("--ir", type=str, default="ts")
+    args, unknown = arg_parser.parse_known_args(backend_args)
+
+    return vars(args), unknown
+
 @create_backend
 def fx2trt(model: 'torchbenchmark.util.model.BenchmarkModel', backend_args: List[str]):
     FP16 = True if model.dargs.precision == "fp16" else False
@@ -40,18 +55,37 @@ def _fx2trt():
 
 @create_backend
 def torch_trt(model: 'torchbenchmark.util.model.BenchmarkModel', backend_args: List[str]):
+    """Backend for Torch-TRT
+
+    Can be directly invoked from the command line, for example via:
+    python run.py resnet18 -d cuda -t eval --backend torch_trt --precision fp32 --truncate_long_and_double
+
+    Options include:
+        --truncate_long_and_double: Whether to automatically truncate long and double operations
+        --min_block_size: Minimum number of operations in an accelerated TRT block
+        --workspace_size: Size of workspace allotted to TensorRT
+        --ir: Which internal representation to use: {"ts", "dynamo_compile", "fx_ts_compat", ...}
+    """
     FP16 = True if model.dargs.precision == "fp16" else False
-    assert model.device == "cuda" and model.test == "eval", f"fx2trt only works on CUDA inference tests."
+    assert model.device == "cuda" and model.test == "eval", f"Torch-TRT only works on CUDA inference tests."
+
+    # Extract relevant Torch-TRT arguments from the provided CLI arguments
+    torch_trt_kwargs, backend_args = parse_torch_trt_args(backend_args)
+
     def _torch_trt():
+        """Helper function for invoking Torch-TRT
+        """
         import torch_tensorrt
         module, example_inputs = model.get_module()
-        if FP16:
-            torchtrt_dtype = torch_tensorrt.dtype.half
-            torch_dtype = torch.half
-        else:
-            torchtrt_dtype = torch_tensorrt.dtype.float
-            torch_dtype = torch.float32
-        trt_input = [torch_tensorrt.Input(shape=example_inputs[0].shape, dtype=torch_dtype)]
-        trt_module = torch_tensorrt.compile(module, inputs=trt_input, enabled_precisions=torchtrt_dtype)
+        torch_dtype_precision = torch.half if FP16 else torch.float32
+
+        trt_input = [torch_tensorrt.Input(shape=input_.shape, dtype=input_.dtype)
+                     for input_ in example_inputs]
+
+        trt_module = torch_tensorrt.compile(module,
+                                            inputs=trt_input,
+                                            enabled_precisions={torch_dtype_precision},
+                                            **torch_trt_kwargs)
         model.set_module(trt_module)
+
     return _torch_trt, backend_args