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# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from typing import Union
import numpy as np
import torch
def gen_suffix(rank, use_smooth_quant, quant_per_channel):
suffix = f"{rank}.bin"
if use_smooth_quant:
sq_prefix = "int8."
if quant_per_channel:
sq_prefix += "col."
suffix = sq_prefix + suffix
return suffix
def extract_layer_idx(name):
ss = name.split('.')
for s in ss:
if s.isdigit():
return s
return None
def split(v: Union[np.ndarray, torch.Tensor],
tp_size: int,
tp_rank: int,
dim=0):
if tp_size == 1:
return v
assert len(v.shape) > 1 or dim == 0
if isinstance(v, np.ndarray):
return np.ascontiguousarray(
np.split(v, tp_size, axis=dim)[tp_rank].copy())
else:
assert v.shape[dim] % tp_size == 0, \
'Unable to split: shape={v.shape} (dim={dim}) tp_size={tp_size}.'
split_size = v.shape[dim] // tp_size
return v.split(split_size, dim=dim)[tp_rank].clone().detach()
def dup_kv_weight(v, num_head, tp_size):
assert tp_size % num_head == 0
reps = tp_size // num_head
head_size = v.shape[0] // num_head
v = v.reshape(num_head, head_size,
-1)[:, None, :, :].expand(num_head, reps, head_size,
v.shape[1])
return v.reshape(num_head * reps * head_size, -1).clone().detach()
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