[Test] Add simple MPS op benchmarks (#149914)

Lots of benchmark tests has been posted in PRs, but they might get lost over time
So let's create a benchmark and populate it with results (preferably from the run on CI machine)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149914
Approved by: https://github.com/dcci, https://github.com/cyyever

.github/workflows/_mac-test-mps.yml

@@ -160,6 +160,14 @@ jobs:
         run: |
           cat test/**/*_toprint.log || true
 
+      - name: Run OP benchmark
+        run: |
+          if [[ -n "$CONDA_ENV" ]]; then
+            # Use binaries under conda environment
+            export PATH="$CONDA_ENV/bin":$PATH
+          fi
+          ${CONDA_RUN} python3 test/bench_mps_ops.py
+
       - name: Upload test artifacts
         uses: ./.github/actions/upload-test-artifacts
         if: always() && steps.test.conclusion && steps.test.conclusion != 'skipped'

test/bench_mps_ops.py

@@ -0,0 +1,92 @@
+# Owner(s): ["module: mps"]
+# Collection of op level benchmarks for MPS
+# Useful as reference tool when migrating ops from MPS to Metal
+import itertools
+import timeit
+from typing import Optional
+
+import torch
+from torch.utils.benchmark import Compare, Measurement, Timer
+
+
+def bench_unary_op(func, x, label) -> Measurement:
+    sync_cmd = "torch.mps.synchronize()" if "mps" in str(x.device) else ""
+    t = Timer(
+        stmt=f"f(x);{sync_cmd}",
+        globals={"f": func, "x": x},
+        language="python",
+        timer=timeit.default_timer,
+        sub_label=f"{func.__name__} ({str(x.dtype)})",
+        description=label,
+        env=torch.__version__,
+    )
+    return t.blocked_autorange()
+
+
+def bench_binary_op(func, x, y, label) -> Measurement:
+    sync_cmd = "torch.mps.synchronize()" if "mps" in str(x.device) else ""
+    t = Timer(
+        stmt=f"f(x, y);{sync_cmd}",
+        globals={"f": func, "x": x, "y": y},
+        language="python",
+        timer=timeit.default_timer,
+        sub_label=f"{func.__name__} ({str(x.dtype)}, {str(y.dtype)})",
+        description=label,
+        env=torch.__version__,
+    )
+    return t.blocked_autorange()
+
+
+def bench_unary(
+    unary_func, device: str = "mps", dtype: torch.dtype = torch.float32
+) -> list[Measurement]:
+    x = torch.testing.make_tensor(1024, 1024, device=device, dtype=dtype)
+    x_s = torch.testing.make_tensor(1024, 2048, device=device, dtype=dtype)[::, ::2]
+    rc = []
+    rc.append(bench_unary_op(unary_func, x, "dense"))
+    rc.append(bench_unary_op(unary_func, x.t(), "transposed"))
+    rc.append(bench_unary_op(unary_func, x_s, "strided"))
+    rc.append(bench_unary_op(unary_func, x_s.t(), "strided + transposed"))
+    return rc
+
+
+def bench_binary(
+    binary_func,
+    device: str = "mps",
+    dt_a: torch.dtype = torch.float32,
+    dt_b: Optional[torch.dtype] = None,
+) -> list[Measurement]:
+    dt_b = dt_b if dt_b is not None else dt_a
+    x = torch.testing.make_tensor(1024, 1024, device=device, dtype=dt_a)
+    y = torch.testing.make_tensor(1024, 1024, device=device, dtype=dt_b)
+    s = torch.testing.make_tensor((), device=device, dtype=dt_b)
+    rc = []
+    rc.append(bench_binary_op(binary_func, x, y, "dense-dense"))
+    rc.append(bench_binary_op(binary_func, x.t(), y.t(), "transp-transp"))
+    rc.append(bench_binary_op(binary_func, x, y.t(), "dense-transp"))
+    rc.append(bench_binary_op(binary_func, x.t(), y, "transp-dense"))
+    rc.append(bench_binary_op(binary_func, x, s, "dense-scalar"))
+    rc.append(bench_binary_op(binary_func, x, y[0], "dense-bcast"))
+    return rc
+
+
+def main() -> None:
+    dtypes = [torch.float16, torch.float32]
+    # Profile unary ops
+    rc = []
+    for op, dtype in itertools.product([torch.sqrt, torch.sin], dtypes):
+        rc.extend(bench_unary(op, dtype=dtype))
+    Compare(rc).print()
+
+    # Profile binary ops
+    rc = []
+    ops = [torch.fmax, torch.add]
+    for op, dtype in itertools.product(ops, dtypes):
+        rc.extend(bench_binary(op, dt_a=dtype))
+    for op in ops:
+        rc.extend(bench_binary(op, dt_b=torch.float16))
+    Compare(rc).print()
+
+
+if __name__ == "__main__":
+    main()