Replace sglang.srt.layers.quantization.scalar_types with sgl_kernel.scalar_type (#8951)

python/sglang/srt/layers/quantization/awq.py

@@ -29,9 +29,8 @@ from sglang.srt.layers.quantization.marlin_utils import (
     verify_marlin_supported,
     verify_marlin_supports_shape,
 )
-from sglang.srt.layers.quantization.scalar_type import scalar_types
 from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
-from sglang.srt.layers.quantization.utils import replace_parameter
+from sglang.srt.layers.quantization.utils import get_scalar_types, replace_parameter
 
 if TYPE_CHECKING:
     from sglang.srt.layers.moe.topk import TopKOutput
@@ -52,6 +51,7 @@ _is_cuda = is_cuda()
 _is_hip = is_hip()
 if _is_cuda:
     from sgl_kernel import awq_dequantize, fused_marlin_moe
+
 elif _is_hip:
     from sglang.srt.layers.quantization.awq_triton import (
         awq_dequantize_triton as awq_dequantize,
@@ -64,6 +64,9 @@ else:
 logger = logging.getLogger(__name__)
 
 
+ScalarType, scalar_types = get_scalar_types()
+
+
 def is_layer_skipped_awq(prefix: str, modules_to_not_convert: List[str]):
     return any(module_name in prefix for module_name in modules_to_not_convert)
 

python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -16,7 +16,6 @@ from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz, scaled_fp8_qu
 from sglang.srt.layers.quantization.fp8_utils import normalize_e4m3fn_to_e4m3fnuz
 from sglang.srt.layers.quantization.utils import (
     all_close_1d,
-    cpu_has_amx_support,
     per_tensor_dequantize,
     replace_parameter,
 )

python/sglang/srt/layers/quantization/gptq.py

@@ -36,9 +36,9 @@ from sglang.srt.layers.quantization.marlin_utils import (
     marlin_zero_points,
     verify_marlin_supported,
 )
-from sglang.srt.layers.quantization.scalar_type import ScalarType, scalar_types
 from sglang.srt.layers.quantization.utils import (
     get_linear_quant_method,
+    get_scalar_types,
     replace_parameter,
     unpack_cols,
 )
@@ -60,6 +60,7 @@ if _is_cuda:
 
 
 logger = logging.getLogger(__name__)
+ScalarType, scalar_types = get_scalar_types()
 
 
 def check_marlin_format(hf_quant_cfg: Dict[str, Any]) -> bool:

python/sglang/srt/layers/quantization/marlin_utils.py

@@ -19,8 +19,11 @@ from sglang.srt.layers.quantization.base_config import (
     LinearMethodBase,
     QuantizationConfig,
 )
-from sglang.srt.layers.quantization.scalar_type import ScalarType, scalar_types
+from sglang.srt.layers.quantization.utils import (
-from sglang.srt.layers.quantization.utils import pack_cols, unpack_cols
+    get_scalar_types,
+    pack_cols,
+    unpack_cols,
+)
 from sglang.srt.utils import get_device_capability
 
 if TYPE_CHECKING:
@@ -33,6 +36,8 @@ except ImportError:
 
 logger = logging.getLogger(__name__)
 
+ScalarType, scalar_types = get_scalar_types()
+
 GPTQ_MARLIN_TILE = 16
 GPTQ_MARLIN_MIN_THREAD_N = 64
 GPTQ_MARLIN_MIN_THREAD_K = 128

python/sglang/srt/layers/quantization/scalar_type.py

@@ -1,352 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import functools
-import struct
-from dataclasses import dataclass
-from enum import Enum
-from typing import Optional, Union
-
-_SCALAR_TYPES_ID_MAP = {}
-
-
-# Mirrors enum in `core/scalar_type.hpp`
-class NanRepr(Enum):
-    NONE = 0  # nans are not supported
-    IEEE_754 = 1  # nans are: Exp all 1s, mantissa not all 0s
-    EXTD_RANGE_MAX_MIN = 2  # nans are: Exp all 1s, mantissa all 1s
-
-
-# This ScalarType class is a parallel implementation of the C++ ScalarType
-# class found in csrc/core/scalar_type.hpp.  These two classes should be kept
-# in sync until the inductor fully supports custom C++ classes.
-@dataclass(frozen=True)
-class ScalarType:
-    """
-    ScalarType can represent a wide range of floating point and integer
-    types, in particular it can be used to represent sub-byte data types
-    (something that torch.dtype currently does not support). It is also
-    capable of  representing types with a bias, i.e.:
-      `stored_value = value + bias`,
-    this is useful for quantized types (e.g. standard GPTQ 4bit uses a bias
-    of 8). The implementation for this class can be found in
-    csrc/core/scalar_type.hpp, these type signatures should be kept in sync
-    with that file.
-    """
-
-    exponent: int
-    """
-    Number of bits in the exponent if this is a floating point type
-    (zero if this an integer type)
-    """
-
-    mantissa: int
-    """
-    Number of bits in the mantissa if this is a floating point type,
-    or the number bits representing an integer excluding the sign bit if
-    this an integer type.
-    """
-
-    signed: bool
-    "If the type is signed (i.e. has a sign bit)"
-
-    bias: int
-    """
-    bias used to encode the values in this scalar type
-    (value = stored_value - bias, default 0) for example if we store the
-    type as an unsigned integer with a bias of 128 then the value 0 will be
-    stored as 128 and -1 will be stored as 127 and 1 will be stored as 129.
-    """
-
-    _finite_values_only: bool = False
-    """
-    Private: if infs are supported, used `has_infs()` instead.
-    """
-
-    nan_repr: NanRepr = NanRepr.IEEE_754
-    """
-    How NaNs are represent in this scalar type, returns NanRepr value.
-    (not applicable for integer types)
-    """
-
-    def _floating_point_max_int(self) -> int:
-        assert (
-            self.mantissa <= 52 and self.exponent <= 11
-        ), f"Cannot represent max/min as a double for type {self.__str__()}"
-
-        max_mantissa = (1 << self.mantissa) - 1
-        if self.nan_repr == NanRepr.EXTD_RANGE_MAX_MIN:
-            max_mantissa = max_mantissa - 1
-
-        max_exponent = (1 << self.exponent) - 2
-        if self.nan_repr == NanRepr.EXTD_RANGE_MAX_MIN or self.nan_repr == NanRepr.NONE:
-            assert (
-                self.exponent < 11
-            ), f"Cannot represent max/min as a double for type {self.__str__()}"
-            max_exponent = max_exponent + 1
-
-        # adjust the exponent to match that of a double
-        # for now we assume the exponent bias is the standard 2^(e-1) -1, (where
-        # e is the exponent bits), there is some precedent for non-standard
-        # biases, example `float8_e4m3b11fnuz` here:
-        # https://github.com/jax-ml/ml_dtypes but to avoid premature over
-        # complication we are just assuming the standard exponent bias until
-        # there is a need to support non-standard biases
-        exponent_bias = (1 << (self.exponent - 1)) - 1
-        exponent_bias_double = (1 << 10) - 1  # double e = 11
-
-        max_exponent_double = max_exponent - exponent_bias + exponent_bias_double
-
-        # shift the mantissa and exponent into the proper positions for an
-        # IEEE double and bitwise-or them together.
-        return (max_mantissa << (52 - self.mantissa)) | (max_exponent_double << 52)
-
-    def _floating_point_max(self) -> float:
-        double_raw = self._floating_point_max_int()
-        return struct.unpack("!d", struct.pack("!Q", double_raw))[0]
-
-    def _raw_max(self) -> Union[int, float]:
-        if self.is_floating_point():
-            return self._floating_point_max()
-        else:
-            assert (
-                self.size_bits < 64 or self.size_bits == 64 and self.is_signed()
-            ), "Cannot represent max as an int"
-            return (1 << self.mantissa) - 1
-
-    def _raw_min(self) -> Union[int, float]:
-        if self.is_floating_point():
-            assert (
-                self.is_signed()
-            ), "We currently assume all floating point types are signed"
-            sign_bit_double = 1 << 63
-
-            max_raw = self._floating_point_max_int()
-            min_raw = max_raw | sign_bit_double
-            return struct.unpack("!d", struct.pack("!Q", min_raw))[0]
-        else:
-            assert (
-                not self.is_signed() or self.size_bits <= 64
-            ), "Cannot represent min as a int64_t"
-
-            if self.is_signed():
-                return -(1 << (self.size_bits - 1))
-            else:
-                return 0
-
-    @functools.cached_property
-    def id(self) -> int:
-        """
-        Convert the ScalarType to an int which can be passed to pytorch custom
-        ops. This layout of the int must be kept in sync with the C++
-        ScalarType's from_id method.
-        """
-        val = 0
-        offset = 0
-
-        def or_and_advance(member, bit_width):
-            nonlocal val
-            nonlocal offset
-            bit_mask = (1 << bit_width) - 1
-            val = val | (int(member) & bit_mask) << offset
-            offset = offset + bit_width
-
-        or_and_advance(self.exponent, 8)
-        or_and_advance(self.mantissa, 8)
-        or_and_advance(self.signed, 1)
-        or_and_advance(self.bias, 32)
-        or_and_advance(self._finite_values_only, 1)
-        or_and_advance(self.nan_repr.value, 8)
-
-        assert offset <= 64, f"ScalarType fields too big {offset} to fit into an int64"
-
-        _SCALAR_TYPES_ID_MAP[val] = self
-
-        return val
-
-    @property
-    def size_bits(self) -> int:
-        return self.exponent + self.mantissa + int(self.signed)
-
-    def min(self) -> Union[int, float]:
-        """
-        Min representable value for this scalar type.
-        (accounting for bias if there is one)
-        """
-        return self._raw_min() - self.bias
-
-    def max(self) -> Union[int, float]:
-        """
-        Max representable value for this scalar type.
-        (accounting for bias if there is one)
-        """
-        return self._raw_max() - self.bias
-
-    def is_signed(self) -> bool:
-        """
-        If the type is signed (i.e. has a sign bit), same as `signed`
-        added for consistency with:
-        https://pytorch.org/docs/stable/generated/torch.Tensor.is_signed.html
-        """
-        return self.signed
-
-    def is_floating_point(self) -> bool:
-        "If the type is a floating point type"
-        return self.exponent != 0
-
-    def is_integer(self) -> bool:
-        "If the type is an integer type"
-        return self.exponent == 0
-
-    def has_bias(self) -> bool:
-        "If the type has a non-zero bias"
-        return self.bias != 0
-
-    def has_infs(self) -> bool:
-        "If the type is floating point and supports infinity"
-        return not self._finite_values_only
-
-    def has_nans(self) -> bool:
-        return self.nan_repr != NanRepr.NONE.value
-
-    def is_ieee_754(self) -> bool:
-        """
-        If the type is a floating point type that follows IEEE 754
-        conventions
-        """
-        return self.nan_repr == NanRepr.IEEE_754.value and not self._finite_values_only
-
-    def __str__(self) -> str:
-        """
-        naming generally follows: https://github.com/jax-ml/ml_dtypes
-        for floating point types (leading f) the scheme is:
-        `float<size_bits>_e<exponent_bits>m<mantissa_bits>[flags]`
-        flags:
-          - no-flags: means it follows IEEE 754 conventions
-          - f: means finite values only (no infinities)
-          - n: means nans are supported (non-standard encoding)
-        for integer types the scheme is:
-          `[u]int<size_bits>[b<bias>]`
-          - if bias is not present it means its zero
-        """
-        if self.is_floating_point():
-            ret = (
-                "float"
-                + str(self.size_bits)
-                + "_e"
-                + str(self.exponent)
-                + "m"
-                + str(self.mantissa)
-            )
-
-            if not self.is_ieee_754():
-                if self._finite_values_only:
-                    ret = ret + "f"
-                if self.nan_repr != NanRepr.NONE:
-                    ret = ret + "n"
-
-            return ret
-        else:
-            ret = ("int" if self.is_signed() else "uint") + str(self.size_bits)
-            if self.has_bias():
-                ret = ret + "b" + str(self.bias)
-            return ret
-
-    def __repr__(self) -> str:
-        return "ScalarType." + self.__str__()
-
-    # __len__ needs to be defined (and has to throw TypeError) for pytorch's
-    # opcheck to work.
-    def __len__(self) -> int:
-        raise TypeError
-
-    #
-    # Convenience Constructors
-    #
-
-    @classmethod
-    def int_(cls, size_bits: int, bias: Optional[int]) -> "ScalarType":
-        "Create a signed integer scalar type (size_bits includes sign-bit)."
-        ret = cls(0, size_bits - 1, True, bias if bias else 0)
-        ret.id  # noqa B018: make sure the id is cached
-        return ret
-
-    @classmethod
-    def uint(cls, size_bits: int, bias: Optional[int]) -> "ScalarType":
-        """Create a unsigned integer scalar type."""
-        ret = cls(0, size_bits, False, bias if bias else 0)
-        ret.id  # noqa B018: make sure the id is cached
-        return ret
-
-    @classmethod
-    def float_IEEE754(cls, exponent: int, mantissa: int) -> "ScalarType":
-        """
-        Create a standard floating point type
-        (i.e. follows IEEE 754 conventions).
-        """
-        assert mantissa > 0 and exponent > 0
-        ret = cls(exponent, mantissa, True, 0)
-        ret.id  # noqa B018: make sure the id is cached
-        return ret
-
-    @classmethod
-    def float_(
-        cls, exponent: int, mantissa: int, finite_values_only: bool, nan_repr: NanRepr
-    ) -> "ScalarType":
-        """
-        Create a non-standard floating point type
-        (i.e. does not follow IEEE 754 conventions).
-        """
-        assert mantissa > 0 and exponent > 0
-        assert nan_repr != NanRepr.IEEE_754, (
-            "use `float_IEEE754` constructor for floating point types that "
-            "follow IEEE 754 conventions"
-        )
-        ret = cls(exponent, mantissa, True, 0, finite_values_only, nan_repr)
-        ret.id  # noqa B018: make sure the id is cached
-        return ret
-
-    @classmethod
-    def from_id(cls, scalar_type_id: int):
-        if scalar_type_id not in _SCALAR_TYPES_ID_MAP:
-            raise ValueError(f"scalar_type_id {scalar_type_id} doesn't exists.")
-        return _SCALAR_TYPES_ID_MAP[scalar_type_id]
-
-
-# naming generally follows: https://github.com/jax-ml/ml_dtypes
-# for floating point types (leading f) the scheme is:
-#  `float<size_bits>_e<exponent_bits>m<mantissa_bits>[flags]`
-#  flags:
-#  - no-flags: means it follows IEEE 754 conventions
-#  - f: means finite values only (no infinities)
-#  - n: means nans are supported (non-standard encoding)
-# for integer types the scheme is:
-#  `[u]int<size_bits>[b<bias>]`
-#  - if bias is not present it means its zero
-
-
-class scalar_types:
-    int4 = ScalarType.int_(4, None)
-    uint4 = ScalarType.uint(4, None)
-    int8 = ScalarType.int_(8, None)
-    uint8 = ScalarType.uint(8, None)
-    float8_e4m3fn = ScalarType.float_(4, 3, True, NanRepr.EXTD_RANGE_MAX_MIN)
-    float8_e5m2 = ScalarType.float_IEEE754(5, 2)
-    float16_e8m7 = ScalarType.float_IEEE754(8, 7)
-    float16_e5m10 = ScalarType.float_IEEE754(5, 10)
-
-    # fp6, https://github.com/usyd-fsalab/fp6_llm/tree/main
-    float6_e3m2f = ScalarType.float_(3, 2, True, NanRepr.NONE)
-
-    # fp4, https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf
-    float4_e2m1f = ScalarType.float_(2, 1, True, NanRepr.NONE)
-
-    # "gptq" types
-    uint2b2 = ScalarType.uint(2, 2)
-    uint3b4 = ScalarType.uint(3, 4)
-    uint4b8 = ScalarType.uint(4, 8)
-    uint8b128 = ScalarType.uint(8, 128)
-
-    # colloquial names
-    bfloat16 = float16_e8m7
-    float16 = float16_e5m10

python/sglang/srt/layers/quantization/utils.py

@@ -11,13 +11,39 @@ import numpy
 import torch
 
 from sglang.srt.layers.quantization.fp8_kernel import scaled_fp8_quant
-from sglang.srt.layers.quantization.scalar_type import ScalarType, scalar_types
+from sglang.srt.utils import is_cuda
-from sglang.srt.utils import cpu_has_amx_support, is_cpu, is_cuda, is_hip, is_npu
 
 if TYPE_CHECKING:
     from sglang.srt.layers.quantization.base_config import QuantizationConfig
 
 
+def get_scalar_types():
+    """
+    Returns:
+        tuple: (ScalarType, scalar_types)
+    """
+    try:
+        from sgl_kernel.scalar_type import ScalarType, scalar_types
+
+        return ScalarType, scalar_types
+    except ImportError:
+
+        class MockScalarType:
+            pass
+
+        class MockScalarTypes:
+            uint4b8 = "uint4b8"
+            uint8b128 = "uint8b128"
+
+            def __getattr__(self, name):
+                return f"mock_{name}"
+
+        return MockScalarType, MockScalarTypes()
+
+
+ScalarType, scalar_types = get_scalar_types()
+
+
 def is_layer_skipped(
     prefix: str,
     ignored_layers: List[str],

python/sglang/test/test_marlin_moe.py

@@ -4,9 +4,9 @@ from typing import Optional
 import pytest
 import torch
 from sgl_kernel import fused_marlin_moe
+from sgl_kernel.scalar_type import ScalarType, scalar_types
 
 from sglang.srt.layers.activation import SiluAndMul
-from sglang.srt.layers.quantization.scalar_type import ScalarType, scalar_types
 from sglang.test.test_marlin_utils import awq_marlin_quantize, marlin_quantize
 
 

python/sglang/test/test_marlin_utils.py

@@ -10,13 +10,13 @@ from typing import Optional
 
 import numpy as np
 import torch
+from sgl_kernel.scalar_type import ScalarType
 
 from sglang.srt.layers.quantization.marlin_utils import (
     GPTQ_MARLIN_TILE,
     marlin_permute_scales,
     marlin_zero_points,
 )
-from sglang.srt.layers.quantization.scalar_type import ScalarType
 from sglang.srt.layers.quantization.utils import (
     get_pack_factor,
     gptq_quantize_weights,