Move scaling logic to input generation

Summary:
Move scaling logic for FP8 benchmarks to `get_input_iter()`.

This diff aligns our fp8_gemm benchmarking suite with real-world practices: input tensors are of high precision types (`bfloat16`, `float16`), scales are computed on the high-precision input tensors, and input tensors are then casted to a lower precision (`float8_e4m3fn`).

This diff also circumvents performing unsupported operations, like `torch.max` and `torch.abs`, on low-precision data types.

Note: this diff is a copy of a diff I made a few days ago but couldn't land due to diff train sync issues.

Differential Revision: D80716975

Author: jananisriram

tritonbench/operators/fp8_gemm/fp8_gemm.py

@@ -56,14 +56,22 @@ def __init__(
 
     def get_input_iter(self):
         def args(m, n, k):
-            a = torch.randn(m, k, device=self.device).to(torch.float8_e4m3fn)
-            b = (
-                torch.randn(k, n, device=self.device)
-                .to(torch.float8_e4m3fn)
-                .T.contiguous()
-                .T
-            )
-            return (a, b)
+            a = torch.randn(m, k, device=self.device).to(torch.float16)
+            b = torch.randn(k, n, device=self.device).to(torch.float16).T.contiguous().T
+
+            if self.extra_args.scaling_rowwise:
+                M, N = a.shape[0], b.shape[1]
+                scale_a = torch.ones((M, 1), dtype=torch.float32, device=a.device)
+                scale_b = torch.ones((1, N), dtype=torch.float32, device=b.device)
+            else:
+                scale_a = torch.tensor(1.0, device=a.device)
+                scale_b = torch.tensor(1.0, device=a.device)
+
+            # Kernels expect dtype=float8_e4m3fn
+            a = a.to(torch.float8_e4m3fn)
+            b = b.to(torch.float8_e4m3fn)
+
+            return (a, b, scale_a, scale_b)
 
         if (
             hasattr(self, "external_shapes") and self.external_shapes
@@ -90,62 +98,54 @@ def args(m, n, k):
                     yield args(m, n, k)
 
     def get_x_val(self, example_inputs) -> float:
-        a, b = example_inputs
+        a, b, _, _ = example_inputs
         m, k = a.size()
         _, n = b.size()
         return (m, n, k)
 
-    @register_benchmark(baseline=True)
-    def torch_fp8_gemm(self, a, b):
+    def _get_out_dtype(self):
         if self.extra_args.scaling_rowwise:
-            M, N = a.shape[0], b.shape[1]
-            scale_a = torch.ones((M, 1), dtype=torch.float32, device=a.device)
-            scale_b = torch.ones((1, N), dtype=torch.float32, device=b.device)
-            out_dtype = torch.bfloat16
+            return torch.bfloat16
         else:
-            scale_a = torch.tensor(1.0, device=a.device)
-            scale_b = torch.tensor(1.0, device=a.device)
-            out_dtype = torch.float16
+            return torch.float16
 
+    @register_benchmark(baseline=True)
+    def torch_fp8_gemm(self, a, b, scale_a, scale_b):
         return lambda: torch._scaled_mm(
-            a, b, scale_a, scale_b, use_fast_accum=True, out_dtype=out_dtype
+            a, b, scale_a, scale_b, use_fast_accum=True, out_dtype=self._get_out_dtype()
         )
 
     @register_benchmark()
-    def pt2_fp8_gemm(self, a, b) -> Callable:
+    def pt2_fp8_gemm(self, a, b, scale_a, scale_b) -> Callable:
         torch._dynamo.reset()
         with inductor_config.patch(
             max_autotune=True,
             max_autotune_gemm_backends="TRITON",
             autotune_fallback_to_aten=False,
         ):
-            if self.extra_args.scaling_rowwise:
-                M, N = a.shape[0], b.shape[1]
-                scale_a = torch.ones((M, 1), dtype=torch.float32, device=a.device)
-                scale_b = torch.ones((1, N), dtype=torch.float32, device=b.device)
-                out_dtype = torch.bfloat16
-            else:
-                scale_a = torch.tensor(1.0, device=a.device)
-                scale_b = torch.tensor(1.0, device=b.device)
-                out_dtype = torch.float16
             f = lambda a, b: torch._scaled_mm(
-                a, b, scale_a, scale_b, use_fast_accum=True, out_dtype=out_dtype
+                a,
+                b,
+                scale_a,
+                scale_b,
+                use_fast_accum=True,
+                out_dtype=self._get_out_dtype(),
             )
             compiled = torch.compile(f, dynamic=False)
             compiled(a, b)
 
         return lambda: compiled(a, b)
 
     @register_benchmark()
-    def triton_fp8_gemm(self, a, b):
+    def triton_fp8_gemm(self, a, b, scale_a, scale_b):
         return lambda: tutorial_matmul(a, b)
 
     @register_benchmark(enabled=HAS_TMA)
-    def triton_persistent_fp8_gemm(self, a, b):
+    def triton_persistent_fp8_gemm(self, a, b, scale_a, scale_b):
         return lambda: matmul_persistent(a, b)
 
     @register_benchmark(enabled=HAS_TMA)
-    def triton_tma_persistent_fp8_gemm(self, a, b):
+    def triton_tma_persistent_fp8_gemm(self, a, b, scale_a, scale_b):
         b = b.T.contiguous()
         c, desc_a, desc_b, desc_c = allocate_matmul_tma(a, b)
         return lambda: matmul_tma_persistent(a, b, c, desc_a, desc_b, desc_c)
@@ -155,7 +155,7 @@ def gbps(self, fn, example_inputs: Any, metrics: BenchmarkOperatorMetrics) -> fl
         def nbytes(t):
             return t.numel() * t.element_size()
 
-        a, b = example_inputs
+        a, b, _, _ = example_inputs
         c = fn()
         c = c[0] if isinstance(c, tuple) else c
 
@@ -168,7 +168,7 @@ def nbytes(t):
     def flops(
         self, fn_name: str, example_inputs: Any, metrics: BenchmarkOperatorMetrics
     ) -> float:
-        a, b = example_inputs
+        a, b, _, _ = example_inputs
         m, k = a.size()
         _, n = b.size()
         flops = 2 * m * n * k