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chore: import upstream snapshot with attribution
2026-07-13 12:32:31 +08:00

70 lines
2.4 KiB
Python

# Copyright (c) 2026 LightSeek Foundation
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import tokenspeed_kernel
import torch
from torch.nn.parameter import Parameter
from tokenspeed.runtime.layers.quantization.base_config import LinearMethodBase
from tokenspeed.runtime.utils import set_weight_attrs
class UnquantizedLinearMethod(LinearMethodBase):
"""Linear method without quantization."""
def create_weights(
self,
layer: torch.nn.Module,
input_size_per_partition: int,
output_partition_sizes: list[int],
input_size: int,
output_size: int,
params_dtype: torch.dtype,
**extra_weight_attrs,
):
weight = Parameter(
torch.empty(
sum(output_partition_sizes),
input_size_per_partition,
dtype=params_dtype,
),
requires_grad=False,
)
set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
layer.register_parameter("weight", weight)
set_weight_attrs(weight, extra_weight_attrs)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
return
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: torch.Tensor | None = None,
) -> torch.Tensor:
return tokenspeed_kernel.mm(
x,
layer.weight,
bias=bias,
)