[RELAND] Add __torch_function__ benchmarks (pytorch#36138)

Summary:
Re-land of pytorch#35530 and pytorch#34645
Pull Request resolved: pytorch#36138

Differential Revision: D20893770

Pulled By: ezyang

fbshipit-source-id: 75ab688a086f5fb87412a853df5246c0c39704ca

.jenkins/pytorch/test.sh

@@ -218,6 +218,18 @@ test_custom_script_ops() {
   fi
 }
 
+test_torch_function_benchmark() {
+  echo "Testing __torch_function__ benchmarks"
+  pushd benchmarks/overrides_benchmark
+  python bench.py -n 1 -m 2
+  python pyspybench.py Tensor -n 1
+  python pyspybench.py SubTensor -n 1
+  python pyspybench.py WithTorchFunction -n 1
+  python pyspybench.py SubWithTorchFunction -n 1
+  popd
+  assert_git_not_dirty
+}
+
 test_xla() {
   export XLA_USE_XRT=1 XRT_DEVICE_MAP="CPU:0;/job:localservice/replica:0/task:0/device:XLA_CPU:0"
   # Issue #30717: randomize the port of XLA/gRPC workers is listening on to reduce flaky tests.
@@ -286,6 +298,7 @@ elif [[ "${BUILD_ENVIRONMENT}" == *-test2 || "${JOB_BASE_NAME}" == *-test2 ]]; t
   test_aten
   test_libtorch
   test_custom_script_ops
+  test_torch_function_benchmark
 elif [[ "${BUILD_ENVIRONMENT}" == *-bazel-* ]]; then
   test_bazel
 else
@@ -295,4 +308,5 @@ else
   test_aten
   test_libtorch
   test_custom_script_ops
+  test_torch_function_benchmark
 fi

benchmarks/overrides_benchmark/README.md

@@ -0,0 +1,40 @@
+# `__torch_function__` micro-benchmarks
+
+This benchmark suite provides a systemic way to measure the performance of `__torch_function__` overhead.
+
+## Getting started
+### Initial Setup
+Install `py-spy` by doing:
+
+```bash
+pip install py-spy
+```
+
+Note that more extensive documentation on using `py-spy` is available in `CONTRIBUTING.md`.
+
+### Running the benchmark
+Run one of the following commands in the terminal, with the working directory being `${PYTORCH_CLONE_DIR}/benchmarks/overrides_benchmark`:
+
+```bash
+# Benchmark all the cases
+python bench.py
+
+# Flame graph pertaining to each case.
+py-spy record -o tensor.svg --native -- python pyspybench.py Tensor
+py-spy record -o subtensor.svg --native -- python pyspybench.py SubTensor
+py-spy record -o overridden.svg --native -- python pyspybench.py WithTorchFunction
+py-spy record -o suboverridden.svg --native -- python pyspybench.py SubWithTorchFunction
+```
+
+Here is a brief overview of what the results should look like, if run correctly:
+
+* Overhead for `torch` functions when run on `torch.Tensor` objects is on the order of 2 μs.
+* `__torch_function__` should add zero overhead for `torch.Tensor` inputs, a small overhead for subclasses of `torch.Tensor`, and a couple of microseconds for `Tensor`-likes with `__torch_function__`.
+* Changing the dispatching mechanism may result in changes that are on the order of 100 ns, which are hard to detect due to noise, but important.
+
+## Reporting benchmark results
+When modifying any of the machinery around `__torch_function__`, run the benchmark for both the feature branch and the point it diverges from `master`. For each of these:
+
+* Run `bench.py`, and include the output in your result.
+* For each case where `bench.py` shows a regression, run the commands described above, prefixing the output SVG filename (the input to the `-o` switch) with `base-` or `branch-` depending on the commit you are running the benchmark on.
+* For each SVG, open it in the browser, take a screenshot and include it in your result. Also include a ZIP file with all SVGs thus produced included.

benchmarks/overrides_benchmark/bench.py

@@ -0,0 +1,67 @@
+import torch
+import time
+import argparse
+
+from common import SubTensor, WithTorchFunction, SubWithTorchFunction
+
+NUM_REPEATS = 1000
+NUM_REPEAT_OF_REPEATS = 1000
+
+
+def bench(t1, t2):
+    bench_times = []
+    for _ in range(NUM_REPEAT_OF_REPEATS):
+        time_start = time.time()
+        for _ in range(NUM_REPEATS):
+            torch.add(t1, t2)
+        bench_times.append(time.time() - time_start)
+
+    bench_time = float(torch.min(torch.Tensor(bench_times))) / 1000
+    bench_std = float(torch.std(torch.Tensor(bench_times))) / 1000
+
+    return bench_time, bench_std
+
+
+def main():
+    global NUM_REPEATS
+    global NUM_REPEAT_OF_REPEATS
+
+    parser = argparse.ArgumentParser(
+        description="Run the __torch_function__ benchmarks."
+    )
+    parser.add_argument(
+        "--nreps",
+        "-n",
+        type=int,
+        default=NUM_REPEATS,
+        help="The number of repeats for one measurement.",
+    )
+    parser.add_argument(
+        "--nrepreps",
+        "-m",
+        type=int,
+        default=NUM_REPEAT_OF_REPEATS,
+        help="The number of measurements.",
+    )
+    args = parser.parse_args()
+
+    NUM_REPEATS = args.nreps
+    NUM_REPEAT_OF_REPEATS = args.nrepreps
+
+    types = torch.Tensor, SubTensor, WithTorchFunction, SubWithTorchFunction
+
+    for t in types:
+        tensor_1 = t(1)
+        tensor_2 = t(2)
+
+        bench_min, bench_std = bench(tensor_1, tensor_2)
+        print(
+            "Type {0} had a minimum time of {1} us"
+            " and a standard deviation of {2} us.".format(
+                t.__name__, (10 ** 6 * bench_min), (10 ** 6) * bench_std
+            )
+        )
+
+
+if __name__ == "__main__":
+    main()

benchmarks/overrides_benchmark/common.py

@@ -0,0 +1,31 @@
+import torch
+
+NUM_REPEATS = 1000
+NUM_REPEAT_OF_REPEATS = 1000
+
+
+class SubTensor(torch.Tensor):
+    pass
+
+
+class WithTorchFunction:
+    def __init__(self, data, requires_grad=False):
+        if isinstance(data, torch.Tensor):
+            self._tensor = data
+            return
+
+        self._tensor = torch.Tensor(data, requires_grad)
+
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+
+        return WithTorchFunction(args[0]._tensor + args[1]._tensor)
+
+
+class SubWithTorchFunction(torch.Tensor):
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+
+        return args[0] + args[1]

benchmarks/overrides_benchmark/pyspybench.py

@@ -0,0 +1,28 @@
+import torch
+import argparse
+from common import SubTensor, WithTorchFunction, SubWithTorchFunction  # noqa: F401
+
+Tensor = torch.Tensor
+
+NUM_REPEATS = 1000000
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Run the torch.add for a given class a given number of times."
+    )
+    parser.add_argument(
+        "tensor_class", metavar="TensorClass", type=str, help="The class to benchmark."
+    )
+    parser.add_argument(
+        "--nreps", "-n", type=int, default=NUM_REPEATS, help="The number of repeats."
+    )
+    args = parser.parse_args()
+
+    TensorClass = globals()[args.tensor_class]
+    NUM_REPEATS = args.nreps
+
+    t1 = TensorClass(1)
+    t2 = TensorClass(2)
+
+    for _ in range(NUM_REPEATS):
+        torch.add(t1, t2)

torch/_overrides.py

@@ -13,6 +13,9 @@
 https://www.numpy.org/neps/nep-0018-array-function-protocol.html
 )
 
+If changing this file in a way that can affect ``__torch_function__`` overhead,
+please report the benchmarks in ``benchmarks/overrides_benchmark``. See the
+instructions in the ``README.md`` in that directory.
 """
 
 import __future__

torch/csrc/utils/python_arg_parser.cpp

@@ -215,6 +215,10 @@ auto handle_torch_function(PythonArgs &r, PyObject* args, PyObject* kwargs, PyOb
  *  precedence.
  *
  *  'obj' is an object to check for a __torch_function__ implementation
+ * 
+ * If changing this file in a way that can affect the __torch_function__
+ * overhead, please report the benchmarks in 'benchmarks/overrides_benchmark'.
+ * See the instructions in the 'README.md' in that directory.
  *
  */