Enable group size 64 for Machete (#20290)

Signed-off-by: czhu-cohere <conway.zhu@cohere.com>

tests/kernels/quantization/test_machete_mm.py

@@ -14,6 +14,8 @@ import torch
 
 from tests.kernels.utils import opcheck
 from vllm import _custom_ops as ops
+from vllm.model_executor.layers.quantization.utils.machete_utils import (
+    query_machete_supported_group_sizes)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     pack_rows, quantize_weights)
 from vllm.platforms import current_platform
@@ -46,8 +48,6 @@ MNK_SHAPES = [
     (1024, 8192, 4096),
 ]
 
-GROUP_SIZES_TO_TEST: list[Optional[int]] = [128, -1]
-
 
 @dataclass
 class TypeConfig:
@@ -270,7 +270,7 @@ def test_machete_all_schedules(shape, types: TypeConfig):
     if types.group_scale_type is None:
         group_sizes = [None]
     else:
-        group_sizes = GROUP_SIZES_TO_TEST
+        group_sizes = query_machete_supported_group_sizes(types.act_type)
 
     for group_size in group_sizes:
         if not group_size_valid(shape, group_size):
@@ -299,7 +299,7 @@ def test_machete_heuristic(shape, types: TypeConfig):
     if types.group_scale_type is None:
         group_sizes = [None]
     else:
-        group_sizes = GROUP_SIZES_TO_TEST
+        group_sizes = query_machete_supported_group_sizes(types.act_type)
 
     for group_size in group_sizes:
         if not group_size_valid(shape, group_size):

vllm/model_executor/layers/quantization/kernels/mixed_precision/machete.py

@@ -8,7 +8,7 @@ import torch
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.machete_utils import (
-    MACHETE_SUPPORTED_GROUP_SIZES, check_machete_supports_shape,
+    check_machete_supports_shape, query_machete_supported_group_sizes,
     query_machete_supported_quant_types)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     pack_quantized_values_into_int32, unpack_quantized_values_into_int32)
@@ -40,10 +40,10 @@ class MacheteLinearKernel(MPLinearKernel):
                            "Machete, supported types are: "\
                            f"{query_machete_supported_quant_types(c.zero_points)}"
 
-        if c.group_size not in MACHETE_SUPPORTED_GROUP_SIZES:
+        if c.group_size not in query_machete_supported_group_sizes(c.act_type):
             return False, f"Group size ({c.group_size}) not supported by "\
                             "Machete, supported group sizes are: "\
-                            f"{MACHETE_SUPPORTED_GROUP_SIZES}"
+                            f"{query_machete_supported_group_sizes(c.act_type)}"
 
         return check_machete_supports_shape(c.partition_weight_shape[0],
                                             c.partition_weight_shape[1])

vllm/model_executor/layers/quantization/utils/machete_utils.py

@@ -7,7 +7,6 @@ import torch
 
 from vllm.scalar_type import ScalarType, scalar_types
 
-MACHETE_SUPPORTED_GROUP_SIZES = [-1, 128]
 MACHETE_PREPACKED_BLOCK_SHAPE = [64, 128]
 
 
@@ -22,6 +21,24 @@ def query_machete_supported_act_types(zero_points: bool) -> list[ScalarType]:
     return [torch.float16, torch.bfloat16]
 
 
+def query_machete_supported_group_sizes(act_type: torch.dtype) -> list[int]:
+    """
+    Queries the supported group sizes for Machete based on the activation type.
+
+    Args:
+        act_type: The activation data type (torch.float16, torch.bfloat16).
+
+    Returns:
+        A list of supported group sizes. The group size must
+        be divisible by `TileShapeK = 128 * 8 // num_bits(act_type)`.
+        -1 indicates per-channel quantization.
+    """
+    if act_type in [torch.float16, torch.bfloat16]:
+        return [-1, 64, 128]
+    else:
+        return [-1, 128]
+
+
 def check_machete_supports_shape(in_features: int, out_featrues: int) \
     -> tuple[bool, Optional[str]]:
     if in_features % MACHETE_PREPACKED_BLOCK_SHAPE[0] != 0: