[PYTHON] CUTLASS wrapper for fair benchmarks (#75)

Before this commit, the benchmarking infrastructure used heterogeneous protocols between library (e.g., CUTLASS uses a C++ binary that reports mean TFLOPS; torch and triton use python call and report 10th, 50th and 90th quantiles). For the sake of uniformity and fair benchmark practices, this PR adds a python wrapper for auto-tuned CUTLASS matrix multiplication. Benchmarks have been rewritten to use this wrapper with `triton.testing.do_bench` rather than system calls to CUTLASS profiler. Importantly, this also ensures that all the matmuls are done on the *same* input data which should stabilize clock across providers.

python/bench/bench_matmul.py

@@ -48,7 +48,7 @@ transformer_confs = [
 ]
 
 
-@triton.testing.perf_report(transformer_confs)
+@triton.testing.perf_report(square_confs)
 def bench_op(M, N, K, AT, BT, dtype, provider, warmup=10, rep=50):
     a = torch.rand((K, M) if AT else (M, K), device="cuda", dtype=dtype)
     b = torch.rand((N, K) if BT else (K, N), device="cuda", dtype=dtype)
@@ -62,37 +62,11 @@ def bench_op(M, N, K, AT, BT, dtype, provider, warmup=10, rep=50):
     if provider == "triton":
         ms, min_ms, max_ms = triton.testing.do_bench(lambda: triton.ops.matmul(a, b), warmup=warmup, rep=rep)
         return tflops(ms), tflops(max_ms), tflops(min_ms)
-    if provider == "cutlass" and "CUTLASS_PROFILER" in os.environ:
-        import subprocess
-        import tempfile
-        import pandas as pd
-        # run program specified by CUTLASS_PROFILER env variable
-        layout_a = "column" if AT else "row"
-        layout_b = "column" if BT else "row"
-        # create temporary file name
-        fd, fname = tempfile.mkstemp()
-        # run program and gets its output
-        cmd = [
-            os.environ["CUTLASS_PROFILER"],
-            f"--m={M}",
-            f"--n={N}",
-            f"--k={K}",
-            f"--A=f16:{layout_a}",
-            f"--B=f16:{layout_b}",
-            "--C=f16:column",
-            "--accum=f32",
-            "--operation=gemm",
-            "--verification-enabled=false",
-            f"--warmup-iterations={warmup}",
-            f"--profiling-iterations={rep}",
-            f"--output={fname}",
-            "--dist=uniform,min:0,max:1,scale:-1",
-            "--verbose=false",
-        ]
-        # run cmd
-        subprocess.run(cmd, stdout=subprocess.PIPE)
-        # read CSV output
-        df_c = pd.read_csv(f"{fname}.gemm.csv")
-        tflops = max(df_c["GFLOPs"]) / 1e3
-        return tflops
+    if provider == "cutlass":
+        cutlass_matmul = triton.testing.cutlass_matmul
+        try:
+            ms, min_ms, max_ms = triton.testing.do_bench(lambda: cutlass_matmul(a, b), warmup=warmup, rep=rep)
+            return tflops(ms), tflops(max_ms), tflops(min_ms)
+        except:
+            return None
     return None