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f618189 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | # Copyright 2024-2025 ModelCloud.ai
# Copyright 2024-2025 qubitium@modelcloud.ai
# Contact: qubitium@modelcloud.ai, x.com/qubitium
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import numpy as np
import torch
# helper method to get accurate parameter count for a gptq model
def tensor_parameters(
tensor_name: str, # name of tensor weight in model
tensor_shape: torch.Size, # shape of tensor
bits: int, # gptq bits
) -> int:
# only .qweight is relevant for `parameters` in gptq model
if tensor_name.endswith(".qweight"):
real_infeatures = math.ceil(tensor_shape[0] / bits * 32)
real_tensor_shape = (real_infeatures,) + tensor_shape[1:]
return np.prod(real_tensor_shape)
# .scales and .qzeros are not model parameters but aux data for .qweight
elif tensor_name.endswith((".scales", ".qzeros", ".g_idx")):
return 0
else:
return np.prod(tensor_shape)
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