[mxfp8 moe training] test floor vs rceil in 3d quantization benchmarks

benchmarks/prototype/moe_training/mxfp8/bench_quantize_3d.py

@@ -5,6 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 # this benchmarking script is a modified version of the original script from: https://github.com/drisspg/transformer_nuggets/blob/main/transformer_nuggets/utils/benchmark.py
 
+import itertools
 from dataclasses import dataclass
 from typing import List
 
@@ -17,6 +18,7 @@
 from torchao.prototype.moe_training.scaled_grouped_mm import (
     _to_mxfp8_dim1_3d,
 )
+from torchao.prototype.mx_formats.config import ScaleCalculationMode
 from torchao.prototype.mx_formats.mx_tensor import to_mx
 
 device = torch.device("cuda")
@@ -28,6 +30,7 @@
 @dataclass(frozen=True)
 class ExperimentConfig:
     input_shape: tuple[int]
+    scaling_mode: ScaleCalculationMode
 
 
 @dataclass(frozen=True)
@@ -58,11 +61,13 @@ def get_configs() -> List[ExperimentConfig]:
         (16, 8192, 5120),
         (64, 8192, 5120),
     ]
+    round_modes = [ScaleCalculationMode.FLOOR, ScaleCalculationMode.RCEIL]
     configs = []
-    for shape in input_shapes:
+    for shape, scaling_mode in itertools.product(input_shapes, round_modes):
         configs.append(
             ExperimentConfig(
                 input_shape=shape,
+                scaling_mode=scaling_mode,
             )
         )
     return configs
@@ -76,6 +81,7 @@ def run_experiment(config: ExperimentConfig) -> ExperimentResult:
         dtype=torch.bfloat16,
         device=device,
     )
+    scaling_mode = config.scaling_mode
 
     def using_to_mx(x: torch.Tensor) -> torch.Tensor:
         # Reference implementation
@@ -85,6 +91,7 @@ def using_to_mx(x: torch.Tensor) -> torch.Tensor:
             x.transpose(-2, -1).contiguous(),
             elem_dtype=torch.float8_e4m3fn,
             block_size=block_size,
+            scaling_mode=scaling_mode,
         )
 
         # Transpose tensors and scales back so we have effectively
@@ -107,13 +114,17 @@ def using_to_mx(x: torch.Tensor) -> torch.Tensor:
     time_cuda_2d_us = benchmark_cuda_function_in_microseconds(
         using_cuda_2d_c,
         input_tensor,
+        block_size=block_size,
+        scaling_mode=scaling_mode,
     )
 
     # bench 3d cuda kernel
     data_cuda_3d, scales_cuda_3d = mxfp8_quantize_cuda_3d(input_tensor)
     time_cuda_3d_us = benchmark_cuda_function_in_microseconds(
         mxfp8_quantize_cuda_3d,
         input_tensor,
+        block_size=block_size,
+        scaling_mode=str(scaling_mode.value),
     )
 
     # mem bw calculations
@@ -146,6 +157,7 @@ def using_to_mx(x: torch.Tensor) -> torch.Tensor:
 def print_results(experiments: List[Experiment]):
     headers = [
         "input_shape",
+        "scaling_mode",
         "to_mx_us",
         "cuda_2d_us",
         "cuda_3d_us",
@@ -158,6 +170,7 @@ def print_results(experiments: List[Experiment]):
         rows.append(
             [
                 str(experiment.config.input_shape),
+                str(experiment.config.scaling_mode),
                 experiment.result.to_mx_us,
                 experiment.result.cuda_2d_us,
                 experiment.result.cuda_3d_us,