Update torchdynamo backend for torchbench

Summary:
Previously not added to TARGETS build file, so trying to run internally would fail when `--dynamo` is added to torchbench command

Also removed aot_autograd_speedup_strategy to reflect changes in pytorch/torchdynamo#201 (i.e. use `--torchdynamo aot_nvfuser` instead)

Differential Revision: D38445665

fbshipit-source-id: 4385902594ff79a5fe2cf2f3366961fcd552befc

Author: davidberard98

configs/torchdynamo/nvfuser-aot-speedup.yaml

@@ -7,4 +7,4 @@ args:
   # empty argument means the default pytorch eager mode
   - ""
   - "--torchdynamo nvfuser"
-  - "--torchdynamo aot_autograd_speedup_strategy"
+  - "--torchdynamo aot_nvfuser"

torchbenchmark/util/backends/torchdynamo.py

@@ -5,16 +5,10 @@
 import functools
 from typing import List
 import torchdynamo
-from torchdynamo.optimizations.training import aot_autograd_speedup_strategy
-
-EXTRA_BACKENDS = {
-    "aot_autograd_speedup_strategy": aot_autograd_speedup_strategy,
-}
 
 def parse_torchdynamo_args(model: 'torchbenchmark.util.model.BenchmarkModel', dyamo_args: List[str]) -> argparse.Namespace:
     parser = argparse.ArgumentParser()
     available_backends = torchdynamo.list_backends()
-    available_backends.extend(EXTRA_BACKENDS.keys())
     parser.add_argument(
         "--torchdynamo", choices=available_backends, help="Specify torchdynamo backends"
     )
@@ -23,9 +17,7 @@ def parse_torchdynamo_args(model: 'torchbenchmark.util.model.BenchmarkModel', dy
 
 
 def apply_torchdynamo_args(model: 'torchbenchmark.util.model.BenchmarkModel', args: argparse.Namespace, precision: str):
-    if args.torchdynamo in EXTRA_BACKENDS:
-        model.add_context(functools.partial(torchdynamo.optimize, EXTRA_BACKENDS[args.torchdynamo]))
-    elif args.torchdynamo == "fx2trt" and precision == "fp16":
+    if args.torchdynamo == "fx2trt" and precision == "fp16":
         model.add_context(functools.partial(torchdynamo.optimize, torchdynamo.optimizations.backends.fx2trt_compiler_fp16))
     else:
         model.add_context(functools.partial(torchdynamo.optimize, args.torchdynamo))