[Hotfix] solve fp8 w8a8 ci test fail (#4531)
This commit is contained in:
Xiaoyu Zhang
@@ -799,9 +799,18 @@ class Fp8MoEMethod:
|
||||
layer.w13_weight[expert_id][start : start + shard_size, :],
|
||||
layer.w13_weight_scale[expert_id][shard_id],
|
||||
)
|
||||
layer.w13_weight[expert_id][start : start + shard_size, :], _ = (
|
||||
ops.scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
|
||||
)
|
||||
if _is_cuda:
|
||||
(
|
||||
layer.w13_weight[expert_id][start : start + shard_size, :],
|
||||
_,
|
||||
) = sgl_scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
|
||||
else:
|
||||
(
|
||||
layer.w13_weight[expert_id][start : start + shard_size, :],
|
||||
_,
|
||||
) = vllm_ops.scaled_fp8_quant(
|
||||
dq_weight, max_w13_scales[expert_id]
|
||||
)
|
||||
start += shard_size
|
||||
|
||||
layer.w13_weight_scale = torch.nn.Parameter(
|
||||
|
||||
@@ -15,6 +15,13 @@ from sglang.srt.utils import (
|
||||
is_hip,
|
||||
)
|
||||
|
||||
try:
|
||||
import vllm
|
||||
|
||||
VLLM_AVAILABLE = True
|
||||
except ImportError:
|
||||
VLLM_AVAILABLE = False
|
||||
|
||||
use_vllm_cutlass_w8a8_fp8_kernel = get_bool_env_var("USE_VLLM_CUTLASS_W8A8_FP8_KERNEL")
|
||||
|
||||
_is_hip = is_hip()
|
||||
@@ -27,13 +34,8 @@ if _is_cuda:
|
||||
|
||||
from sglang.srt.layers.quantization.fp8_kernel import sglang_per_token_quant_fp8
|
||||
|
||||
if use_vllm_cutlass_w8a8_fp8_kernel:
|
||||
try:
|
||||
from vllm import _custom_ops as ops
|
||||
|
||||
VLLM_AVAILABLE = True
|
||||
except ImportError:
|
||||
VLLM_AVAILABLE = False
|
||||
if use_vllm_cutlass_w8a8_fp8_kernel and VLLM_AVAILABLE:
|
||||
from vllm import _custom_ops as ops
|
||||
else:
|
||||
from sgl_kernel import fp8_scaled_mm
|
||||
|
||||
@@ -253,68 +255,69 @@ def apply_fp8_linear(
|
||||
|
||||
# torch.scaled_mm supports per tensor weights + activations only
|
||||
# so fallback to naive if per channel or per token
|
||||
per_tensor_weights = weight_scale.numel() == 1
|
||||
per_tensor_activations = x_scale.numel() == 1
|
||||
|
||||
if per_tensor_weights and per_tensor_activations:
|
||||
# Fused GEMM_DQ
|
||||
output = torch._scaled_mm(
|
||||
qinput,
|
||||
weight,
|
||||
out_dtype=input.dtype,
|
||||
scale_a=x_scale,
|
||||
scale_b=weight_scale,
|
||||
bias=bias,
|
||||
)
|
||||
# A fix for discrepancy in scaled_mm which returns tuple
|
||||
# for torch < 2.5 and a single value in torch >= 2.5
|
||||
if type(output) is tuple and len(output) == 2:
|
||||
output = output[0]
|
||||
|
||||
return torch.narrow(output, 0, 0, input_2d.shape[0]).view(*output_shape)
|
||||
|
||||
else:
|
||||
# Fallback for channelwise case, where we use unfused DQ
|
||||
# due to limitations with scaled_mm
|
||||
per_tensor_weights = weight_scale.numel() == 1
|
||||
per_tensor_activations = x_scale.numel() == 1
|
||||
|
||||
# Symmetric quantized GEMM by definition computes the following:
|
||||
# C = (s_x * X) (s_w * W) + bias
|
||||
# This is equivalent to dequantizing the weights and activations
|
||||
# before applying a GEMM.
|
||||
#
|
||||
# In order to compute quantized operands, a quantized kernel
|
||||
# will rewrite the above like so:
|
||||
# C = s_w * s_x * (X * W) + bias
|
||||
#
|
||||
# For the scaled_mm fallback case, we break this down, since it
|
||||
# does not support s_w being a vector.
|
||||
if per_tensor_weights and per_tensor_activations:
|
||||
# Fused GEMM_DQ
|
||||
output = torch._scaled_mm(
|
||||
qinput,
|
||||
weight,
|
||||
out_dtype=input.dtype,
|
||||
scale_a=x_scale,
|
||||
scale_b=weight_scale,
|
||||
bias=bias,
|
||||
)
|
||||
# A fix for discrepancy in scaled_mm which returns tuple
|
||||
# for torch < 2.5 and a single value in torch >= 2.5
|
||||
if type(output) is tuple and len(output) == 2:
|
||||
output = output[0]
|
||||
|
||||
# Making sure the dummy tensor is on the same device as the weight
|
||||
global TORCH_DEVICE_IDENTITY
|
||||
if TORCH_DEVICE_IDENTITY.device != weight.device:
|
||||
TORCH_DEVICE_IDENTITY = TORCH_DEVICE_IDENTITY.to(weight.device)
|
||||
return torch.narrow(output, 0, 0, input_2d.shape[0]).view(*output_shape)
|
||||
|
||||
# GEMM
|
||||
# This computes C = (X * W).
|
||||
# Output in fp32 to allow subsequent ops to happen in-place
|
||||
output = torch._scaled_mm(
|
||||
qinput,
|
||||
weight,
|
||||
scale_a=TORCH_DEVICE_IDENTITY,
|
||||
scale_b=TORCH_DEVICE_IDENTITY,
|
||||
out_dtype=torch.float32,
|
||||
)
|
||||
# A fix for discrepancy in scaled_mm which returns tuple
|
||||
# for torch < 2.5 and a single value in torch >= 2.5
|
||||
if type(output) is tuple and len(output) == 2:
|
||||
output = output[0]
|
||||
# Unpad (undo num_token_padding)
|
||||
output = torch.narrow(output, 0, 0, input_2d.shape[0])
|
||||
x_scale = torch.narrow(x_scale, 0, 0, input_2d.shape[0])
|
||||
else:
|
||||
# Fallback for channelwise case, where we use unfused DQ
|
||||
# due to limitations with scaled_mm
|
||||
|
||||
# DQ
|
||||
# C = sw * sx * (X * W) + bias
|
||||
output = output * x_scale * weight_scale.t()
|
||||
if bias is not None:
|
||||
output = output + bias
|
||||
return output.to(dtype=input.dtype).view(*output_shape)
|
||||
# Symmetric quantized GEMM by definition computes the following:
|
||||
# C = (s_x * X) (s_w * W) + bias
|
||||
# This is equivalent to dequantizing the weights and activations
|
||||
# before applying a GEMM.
|
||||
#
|
||||
# In order to compute quantized operands, a quantized kernel
|
||||
# will rewrite the above like so:
|
||||
# C = s_w * s_x * (X * W) + bias
|
||||
#
|
||||
# For the scaled_mm fallback case, we break this down, since it
|
||||
# does not support s_w being a vector.
|
||||
|
||||
# Making sure the dummy tensor is on the same device as the weight
|
||||
global TORCH_DEVICE_IDENTITY
|
||||
if TORCH_DEVICE_IDENTITY.device != weight.device:
|
||||
TORCH_DEVICE_IDENTITY = TORCH_DEVICE_IDENTITY.to(weight.device)
|
||||
|
||||
# GEMM
|
||||
# This computes C = (X * W).
|
||||
# Output in fp32 to allow subsequent ops to happen in-place
|
||||
output = torch._scaled_mm(
|
||||
qinput,
|
||||
weight,
|
||||
scale_a=TORCH_DEVICE_IDENTITY,
|
||||
scale_b=TORCH_DEVICE_IDENTITY,
|
||||
out_dtype=torch.float32,
|
||||
)
|
||||
# A fix for discrepancy in scaled_mm which returns tuple
|
||||
# for torch < 2.5 and a single value in torch >= 2.5
|
||||
if type(output) is tuple and len(output) == 2:
|
||||
output = output[0]
|
||||
# Unpad (undo num_token_padding)
|
||||
output = torch.narrow(output, 0, 0, input_2d.shape[0])
|
||||
x_scale = torch.narrow(x_scale, 0, 0, input_2d.shape[0])
|
||||
|
||||
# DQ
|
||||
# C = sw * sx * (X * W) + bias
|
||||
output = output * x_scale * weight_scale.t()
|
||||
if bias is not None:
|
||||
output = output + bias
|
||||
return output.to(dtype=input.dtype).view(*output_shape)
|
||||
|
||||
@@ -6,7 +6,6 @@ import torch
|
||||
|
||||
from sglang.srt.layers.linear import LinearBase
|
||||
from sglang.srt.layers.quantization.base_config import QuantizationConfig
|
||||
from sglang.srt.layers.quantization.utils import scalar_types
|
||||
from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
|
||||
from sglang.srt.utils import is_cuda
|
||||
|
||||
@@ -133,11 +132,16 @@ class GPTQConfig(QuantizationConfig):
|
||||
class GPTQMarlinConfig(QuantizationConfig):
|
||||
"""Config class for GPTQ Marlin"""
|
||||
|
||||
# (num_bits, is_sym) -> quant_type
|
||||
TYPE_MAP = {
|
||||
(4, True): scalar_types.uint4b8,
|
||||
(8, True): scalar_types.uint8b128,
|
||||
}
|
||||
if VLLM_AVAILABLE:
|
||||
from vllm.scalar_type import scalar_types
|
||||
|
||||
# (num_bits, is_sym) -> quant_type
|
||||
TYPE_MAP = {
|
||||
(4, True): scalar_types.uint4b8,
|
||||
(8, True): scalar_types.uint8b128,
|
||||
}
|
||||
else:
|
||||
raise ImportError("vllm is not installed")
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
|
||||
@@ -1,15 +1,19 @@
|
||||
# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/utils/quant_utils.py
|
||||
# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/scalar_type.py
|
||||
|
||||
import functools
|
||||
import struct
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
from types import MappingProxyType
|
||||
from typing import List, Mapping, Optional, Tuple, Union
|
||||
from typing import List, Mapping, Tuple, Union
|
||||
|
||||
import torch
|
||||
|
||||
from sglang.srt.utils import is_cuda
|
||||
|
||||
_is_cuda = is_cuda()
|
||||
|
||||
if _is_cuda:
|
||||
from sglang.srt.custom_op import scaled_fp8_quant as sgl_scaled_fp8_quant
|
||||
else:
|
||||
from vllm import _custom_ops as vllm_ops
|
||||
|
||||
|
||||
def is_layer_skipped(
|
||||
prefix: str,
|
||||
@@ -102,341 +106,12 @@ def requantize_with_max_scale(
|
||||
for idx, logical_width in enumerate(logical_widths):
|
||||
end = start + logical_width
|
||||
weight_dq = per_tensor_dequantize(weight[start:end, :], weight_scale[idx])
|
||||
weight[start:end, :], _ = ops.scaled_fp8_quant(weight_dq, max_w_scale)
|
||||
if _is_cuda:
|
||||
weight[start:end, :], _ = sgl_scaled_fp8_quant(weight_dq, max_w_scale)
|
||||
else:
|
||||
weight[start:end, :], _ = vllm_ops.scaled_fp8_quant(
|
||||
weight_dq, max_w_scale
|
||||
)
|
||||
start = end
|
||||
|
||||
return max_w_scale, weight
|
||||
|
||||
|
||||
# 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"
|
||||
|
||||
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
|
||||
|
||||
|
||||
# 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_e2m1fn = 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
|
||||
|
||||
Reference in New Issue
Block a user