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# Copyright (c) 2025 NVIDIA CORPORATION.
# Licensed under the MIT license.
# Adapted from https://github.com/NVlabs/VILA/tree/main under the Apache 2.0 license.
# LICENSE is in incl_licenses directory.
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
def list_has_common_element(list1, list2):
set1 = set(list1)
set2 = set(list2)
return len(set1.intersection(set2)) > 0
def calculate_scale_num(input, row_block, col_block):
if len(input.shape) > 2:
input = input.reshape(-1, input.shape[2])
elif len(input.shape) == 2:
pass
else:
raise ValueError(f"input shape {input.shape} does not match for block cut, {input}")
M, N = input.shape[0], input.shape[1]
if row_block == -1:
row_block = M
if col_block == -1:
col_block = N
return input.numel() / (row_block * col_block)
def quant_get_local_rank() -> int:
return int(os.environ.get("LOCAL_RANK") or 0)
def format_string_with_condition(
input_string,
condition_config,
symm,
bits,
blocksize_config,
input_pad=20,
):
padded_string = input_string.ljust(input_pad)
output_string = padded_string
for k, v in condition_config.items():
if v:
output_string = output_string + k.ljust(10) + "True".ljust(6) + "".ljust(6)
else:
output_string = output_string + k.ljust(10) + "".ljust(6) + "False".ljust(6)
output_string = output_string + f"Symm {symm}".ljust(10)
for k, v in bits.items():
output_string = output_string + f"{k} bit".ljust(10) + v.ljust(10)
for k, v in blocksize_config.items():
output_string += f"{k}: {v}".ljust(15)
return output_string
def print_warning(sentence):
print("*" * (len(sentence) + 4))
print(f"* {sentence} *")
print("*" * (len(sentence) + 4))
def check_nan_inf(tensor, check_nan, check_inf):
if check_nan:
contain_nan = torch.isnan(tensor).any()
else:
contain_nan = False
if check_inf:
contain_inf = torch.isinf(tensor).any()
else:
contain_inf = False
return contain_nan, contain_inf
def move_torch_to_numpy(tensor):
if tensor is None:
return None
if tensor.is_cuda:
tensor = tensor.cpu()
return tensor.detach().float().numpy()
def flatten_to_1d(tensor):
if tensor is None:
return None
return tensor.reshape(-1)
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