Fixes and updates to bench_per_token_quant_fp8 (#25591)

Signed-off-by: Michael Goin <mgoin64@gmail.com>

Author: mgoin

benchmarks/kernels/bench_per_token_quant_fp8.py

@@ -51,31 +51,33 @@ def calculate_diff(
 ):
     """Calculate the difference between Inductor and CUDA implementations."""
     device = torch.device("cuda")
-    x = torch.rand((batch_size * hidden_size, 4096), dtype=dtype, device=device)
+    x = torch.randn((batch_size, hidden_size), dtype=dtype, device=device)
 
     quant_fp8 = QuantFP8(False, group_shape, column_major_scales=False)
 
     torch_out, torch_scale = bench_compile(quant_fp8.forward_native)(x)
     torch_eager_out, torch_eager_scale = quant_fp8.forward_native(x)
     cuda_out, cuda_scale = quant_fp8.forward_cuda(x)
 
-    out_allclose = lambda o1, o2: torch.allclose(
-        o1.to(torch.float32),
-        o2.to(torch.float32),
-        rtol=1e-3,
-        atol=1e-5,
-    )
-    scale_allclose = lambda s1, s2: torch.allclose(s1, s2, rtol=1e-3, atol=1e-5)
-
-    if (
-        out_allclose(cuda_out, torch_out)
-        and scale_allclose(cuda_scale, torch_scale)
-        and out_allclose(cuda_out, torch_eager_out)
-        and scale_allclose(cuda_scale, torch_eager_scale)
-    ):
+    try:
+        torch.testing.assert_close(
+            cuda_out.to(torch.float32),
+            torch_out.to(torch.float32),
+            rtol=1e-3,
+            atol=1e-5,
+        )
+        torch.testing.assert_close(cuda_scale, torch_scale, rtol=1e-3, atol=1e-5)
+        torch.testing.assert_close(
+            cuda_out.to(torch.float32),
+            torch_eager_out.to(torch.float32),
+            rtol=1e-3,
+            atol=1e-5,
+        )
+        torch.testing.assert_close(cuda_scale, torch_eager_scale, rtol=1e-3, atol=1e-5)
         print("✅ All implementations match")
-    else:
+    except AssertionError as e:
         print("❌ Implementations differ")
+        print(e)
 
 
 configs = []
@@ -91,7 +93,7 @@ def benchmark_quantization(
 ):
     device = torch.device("cuda")
 
-    x = torch.randn(batch_size * hidden_size, 4096, device=device, dtype=dtype)
+    x = torch.randn(batch_size, hidden_size, device=device, dtype=dtype)
 
     quantiles = [0.5, 0.2, 0.8]
     quant_fp8 = QuantFP8(False, group_shape, column_major_scales=col_major)
@@ -157,21 +159,21 @@ def geo_speedup(group: pd.DataFrame) -> pd.Series:
     )
     parser.add_argument("-c", "--check", action="store_true")
     parser.add_argument(
-        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="half"
+        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="bfloat16"
     )
     parser.add_argument(
         "--hidden-sizes",
         type=int,
         nargs="+",
-        default=None,
-        help="Hidden sizes to benchmark (default: 1,16,64,128,256,512,1024,2048,4096)",
+        default=[896, 1024, 2048, 4096, 7168],
+        help="Hidden sizes to benchmark",
     )
     parser.add_argument(
         "--batch-sizes",
         type=int,
         nargs="+",
-        default=None,
-        help="Batch sizes to benchmark (default: 1,16,32,64,128)",
+        default=[1, 16, 128, 512, 1024],
+        help="Batch sizes to benchmark",
     )
     parser.add_argument(
         "--group-sizes",
@@ -192,8 +194,8 @@ def geo_speedup(group: pd.DataFrame) -> pd.Series:
 
     dtype = STR_DTYPE_TO_TORCH_DTYPE[args.dtype]
 
-    hidden_sizes = args.hidden_sizes or [1, 16, 64, 128, 256, 512, 1024, 2048, 4096]
-    batch_sizes = args.batch_sizes or [1, 16, 32, 64, 128]
+    hidden_sizes = args.hidden_sizes
+    batch_sizes = args.batch_sizes
 
     if args.group_sizes is not None:
         group_shapes = []