Add resnet50 benchmark (#443) (#451)

thunder/benchmarks/__init__.py

@@ -2825,6 +2825,39 @@ def foo(a, m, v, w, b, training):
         return foo
 
 
+class ResNet50Benchmark(Benchmark, metaclass=UserFacingBenchmarkMeta):
+    def __init__(
+        self,
+        batch_size: int,
+        input_shape: tuple[int, int, int],
+        device: str = "cuda",
+        dtype: dtypes.dtype = thunder.float32,
+        requires_grad: bool = False,
+    ) -> None:
+        super().__init__()
+
+        # the typical input image size of ResNet50 is (3, 224, 224)
+        self.shape: tuple[int, int, int, int] = (batch_size,) + input_shape
+        self.device: str = device
+        self.dtype: dtypes.dtype = dtype
+        self.tdtype: torch.dtype = ltorch.to_torch_dtype(dtype)
+        self.requires_grad: bool = requires_grad
+
+        self.devices: list[str] = [device]
+
+    def make_batch(self) -> tuple[list, dict]:
+        make = partial(make_tensor, device=self.device, dtype=self.tdtype, requires_grad=self.requires_grad)
+        a = make(self.shape)
+        return (a,), {}
+
+    def fn(self) -> Callable:
+        from torchvision.models import resnet50
+
+        model = resnet50()
+        model = model.to(device=self.device, dtype=self.tdtype).requires_grad_(self.requires_grad)
+        return model
+
+
 # TODO Add descriptions to the executors when listed, and list them alphabetically
 # TODO Allow querying benchmark for details
 # TODO Allow specifying benchmark arguments

thunder/benchmarks/targets.py

@@ -23,6 +23,7 @@
     NanoGPTCrossEntropyBenchmark,
     LitGPTGeluBenchmark,
     NanoGPTLayerNormBenchmark,
+    ResNet50Benchmark,
     thunder_apex_executor,
     thunder_apex_nvfuser_executor,
     thunder_cudnn_executor,
@@ -721,3 +722,27 @@ def test_interpreter_nanogpt_gpt2_fwd(benchmark, executor: Callable):
     fn = executor(bench.fn())
 
     benchmark(fn, *args, **kwargs)
+
+
+#
+# vision benchmarks
+#
+
+
+# Sample command to run this benchmark:
+# pytest thunder/benchmarks/targets.py -k "test_resnet50" --benchmark-group-by='param:compute_type'
+@pytest.mark.parametrize(
+    "executor,",
+    executors,
+    ids=executors_ids,
+)
+@parametrize_compute_type
+def test_resnet50(benchmark, executor: Callable, compute_type: ComputeType):
+    b = ResNet50Benchmark(
+        64, (3, 224, 224), device="cuda:0", dtype=torch.bfloat16, requires_grad=is_requires_grad(compute_type)
+    )
+
+    args, kwargs = b.make_batch()
+    fn = executor(b.fn())
+
+    benchmark_for_compute_type(compute_type, benchmark, fn, args, kwargs)