[moe training] change api _scaled_grouped_mm -> _quantize_then_scaled_grouped_mm

benchmarks/prototype/moe_training/benchmark_scaled_grouped_mm_dq.py

@@ -19,7 +19,7 @@
     bench_fwd_microseconds,
     profile_fwd_bwd,
 )
-from torchao.prototype.moe_training import _scaled_grouped_mm
+from torchao.prototype.moe_training import _quantize_then_scaled_grouped_mm
 from torchao.prototype.moe_training.conversion_utils import MoEScalingType
 from torchao.prototype.moe_training.utils import generate_jagged_offs
 
@@ -158,7 +158,7 @@ def run_experiment(
 
     # fwd_bwd scaled benchmark + profiling
     scaled_fwd_bwd_us = bench_fwd_bwd_microseconds(
-        _scaled_grouped_mm,
+        _quantize_then_scaled_grouped_mm,
         A,
         B_t,
         offs,
@@ -169,7 +169,7 @@ def run_experiment(
     )
     if args.profile:
         profile_fwd_bwd(
-            _scaled_grouped_mm,
+            _quantize_then_scaled_grouped_mm,
             A,
             B_t,
             offs,
@@ -190,7 +190,7 @@ def run_experiment(
         fullgraph=True,
     )
     scaled_fwd_us = bench_fwd_microseconds(
-        _scaled_grouped_mm,
+        _quantize_then_scaled_grouped_mm,
         A,
         B_t,
         offs,

test/prototype/moe_training/test_scaled_grouped_mm.py

@@ -32,7 +32,7 @@
 from torchao.prototype.moe_training.scaled_grouped_mm import (
     _emulated_mxfp8_scaled_grouped_mm_2d_2d,
     _emulated_mxfp8_scaled_grouped_mm_2d_3d,
-    _scaled_grouped_mm,
+    _quantize_then_scaled_grouped_mm,
 )
 from torchao.prototype.moe_training.utils import (
     _to_mxfp8_per_group_colwise,
@@ -73,7 +73,7 @@ def test_valid_scaled_grouped_mm_2d_3d(m, n, k, n_groups):
     b_t = b.contiguous().transpose(-2, -1).requires_grad_(True)
 
     # Compute output.
-    out = _scaled_grouped_mm(
+    out = _quantize_then_scaled_grouped_mm(
         a,
         b_t,
         offs=offs,
@@ -142,7 +142,7 @@ def test_K_or_N_dim_not_multiple_of_16(m, n, k):
 
     # Compute output.
     with pytest.raises(AssertionError):
-        _scaled_grouped_mm(
+        _quantize_then_scaled_grouped_mm(
             a,
             b_t,
             offs=offs,
@@ -199,7 +199,7 @@ def compute_reference_forward(
         result_list.append(result[start : offs_cpu[i]])
         start = offs_cpu[i]
 
-    # Validate each actual result group from the _scaled_grouped_mm is equal to:
+    # Validate each actual result group from the _quantize_then_scaled_grouped_mm is equal to:
     # 1. A manual _scaled_mm for the group.
     # 2. A matmul_with_hp_or_float8_args for the group (which is differentiable, and thus used to validate gradients).
     outputs = []

torchao/prototype/moe_training/README.md

@@ -27,7 +27,7 @@ This prototype provides:
 import torch
 from torch.nn import functional as F
 from torchao.prototype.moe_training import (
-    _scaled_grouped_mm as torchao_scaled_grouped_mm
+    _quantize_then_scaled_grouped_mm as torchao_scaled_grouped_mm
 )
 from torchao.prototype.moe_training.conversion_utils import MoEScalingType
 from torchao.prototype.moe_training.utils import generate_jagged_offs

torchao/prototype/moe_training/__init__.py

@@ -1,3 +1,5 @@
-from torchao.prototype.moe_training.scaled_grouped_mm import _scaled_grouped_mm
+from torchao.prototype.moe_training.scaled_grouped_mm import (
+    _quantize_then_scaled_grouped_mm,
+)
 
-__all__ = ["_scaled_grouped_mm"]
+__all__ = ["_quantize_then_scaled_grouped_mm"]

torchao/prototype/moe_training/scaled_grouped_mm.py

@@ -5,6 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import logging
+from functools import partial
 from typing import Optional
 
 import torch
@@ -38,15 +39,15 @@
 logger: logging.Logger = logging.getLogger(__name__)
 
 
-def _scaled_grouped_mm(
+def _quantize_then_scaled_grouped_mm(
     A: torch.Tensor,
     B_t: torch.Tensor,
     offs: Optional[torch.Tensor] = None,
     out_dtype: Optional[torch.dtype] = torch.bfloat16,
     scaling_type: MoEScalingType = MoEScalingType.FP8_ROWWISE,
 ) -> torch.Tensor:
     """
-    This function performs dynamic float8 quantization with row-wise scaling
+    This function performs dynamic quantization with the given recipe
     on the input tensors A and B, then performs a scaled grouped GEMM and returns the results.
 
     Args:
@@ -78,6 +79,15 @@ def _scaled_grouped_mm(
         raise ValueError(f"Unsupported scaling type {scaling_type}")
 
 
+# Aliases for convenience/clarity
+_to_mxfp8_then_scaled_grouped_mm = partial(
+    _quantize_then_scaled_grouped_mm, scaling_type=MoEScalingType.MXFP8
+)
+_to_fp8_rowwise_then_scaled_grouped_mm = partial(
+    _quantize_then_scaled_grouped_mm, scaling_type=MoEScalingType.FP8_ROWWISE
+)
+
+
 class _Float8GroupedMM(torch.autograd.Function):
     """Differentiable implementation of grouped GEMM with dynamic float8 quantization."""
 
@@ -89,7 +99,7 @@ def forward(
         offs: Optional[torch.Tensor] = None,
         out_dtype: Optional[torch.dtype] = torch.bfloat16,
     ) -> torch.Tensor:
-        # torchao _scaled_grouped_mm only supports A=2D|3D and B=3D.
+        # torchao _quantize_then_scaled_grouped_mm only supports A=2D|3D and B=3D.
         assert A.ndim == 2 or A.ndim == 3, "A must be 2D or 3D"
         assert B_t.ndim == 3, "B must be 3D"
 
@@ -113,7 +123,7 @@ def forward(
 
         # Assert A and B dims are compatible for a scaled grouped GEMM.
         assert A.size(-1) == B_t.size(-2), (
-            f"shape {A.shape} and {B_t.shape} are not compatible for _scaled_grouped_mm"
+            f"shape {A.shape} and {B_t.shape} are not compatible for _quantize_then_scaled_grouped_mm"
         )
 
         # The left operand in the scaled grouped GEMM must be row-major due to hardware requirements.
@@ -295,7 +305,7 @@ def forward(
         out_dtype: Optional[torch.dtype] = torch.bfloat16,
         emulated: bool = False,
     ) -> torch.Tensor:
-        # torchao _scaled_grouped_mm only supports A=2D and B=3D.
+        # torchao _quantize_then_scaled_grouped_mm only supports A=2D and B=3D.
         assert A.ndim == 2, "A must be 2D"
         assert B_t.ndim == 3, "B must be 3D"
         assert block_size == 32, "Only block_size=32 is supported"

torchao/prototype/moe_training/tensor.py

@@ -15,7 +15,7 @@
 from torch.distributed.device_mesh import DeviceMesh
 from torch.distributed.fsdp import MixedPrecisionPolicy
 
-from torchao.prototype.moe_training import _scaled_grouped_mm
+from torchao.prototype.moe_training import _quantize_then_scaled_grouped_mm
 from torchao.prototype.moe_training.conversion_utils import MoEScalingType
 
 logger: logging.Logger = logging.getLogger(__name__)
@@ -39,7 +39,7 @@ class ScaledGroupedMMTensor(torch.Tensor):
     """
     ScaledGroupedMMTensor is a simple tensor subclass that wraps a regular tensor
     and overrides the torch._grouped_mm op by dispatching to the
-    differentiable _scaled_grouped_mm autograd function.
+    differentiable _quantize_then_scaled_grouped_mm autograd function.
     """
 
     scaling_type: MoEScalingType = MoEScalingType.FP8_ROWWISE
@@ -77,7 +77,7 @@ def __init__(
 
     @classmethod
     def __torch_function__(cls, func, types, args, kwargs={}):
-        # override the grouped mm op to use the differentiable _scaled_grouped_mm
+        # override the grouped mm op to use the differentiable _quantize_then_scaled_grouped_mm
         if func.__name__ == cls.grouped_mm_func_name:
             # Use torchao scaled grouped mm with dynamic quant for
             # "2d x 3d with offsets" case (used for routed experts).
@@ -99,7 +99,7 @@ def __torch_function__(cls, func, types, args, kwargs={}):
             has_offs = kwargs.get(cls.offs_arg_name) is not None
             other_args = args[2:]
             if A_is_2d and B_is_2d_or_3d and has_offs:
-                return _scaled_grouped_mm(
+                return _quantize_then_scaled_grouped_mm(
                     A,
                     B,
                     *other_args,