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# Copyright 2024 ByteDance and/or its 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.
import os
import time
import torch
class Checker:
@staticmethod
def is_permutation(x: torch.Tensor):
"""
Checks if the input tensor `x` is a permutation of integers from 0 to N-1.
Args:
x (torch.Tensor): A 1D tensor of size [N].
"""
assert x.dim() == 1
N = x.size(0)
assert torch.equal(torch.sort(x)[0], torch.arange(N, device=x.device))
@staticmethod
def are_permutations(x: torch.Tensor, dim=-1):
"""
Checks if slices along the specified dimension in `x` are permutations of integers from 0 to N-1.
Args:
x (torch.Tensor): A tensor with any number of dimensions, containing slices of size N along `dim`.
dim (int, optional): The dimension along which to check for permutations. Defaults to -1.
"""
assert x.dim() > 0
N = x.size(dim)
# Create a view of x that moves the specified dimension to -1
x = x.transpose(dim, -1).contiguous()
x = x.reshape(-1, N)
expected = torch.arange(N, device=x.device)
for i in range(x.size(0)):
Checker.is_permutation(x[i])
@staticmethod
def contains_identity(x: torch.Tensor, dim=-1):
"""
Check if x contains the identity permutation
Args:
x (torch.Tensor): A tensor with any number of dimensions, containing slices of size N along `dim`.
dim (int, optional): The dimension along which to check for permutations. Defaults to -1.
"""
assert x.dim() > 0
N = x.size(dim)
# Create a view of x that moves the specified dimension to -1
x = x.transpose(dim, -1).contiguous()
x = x.reshape(-1, N)
expected = torch.arange(N, device=x.device).unsqueeze(dim=0)
assert (x == expected).all(dim=-1).any()
@staticmethod
def not_contain_identity(x: torch.Tensor, dim=-1):
"""
Check if x does not contain the identity permutation
Args:
x (torch.Tensor): A tensor with any number of dimensions, containing slices of size N along `dim`.
dim (int, optional): The dimension along which to check for permutations. Defaults to -1.
"""
assert x.dim() > 0
N = x.size(dim)
# Create a view of x that moves the specified dimension to -1
x = x.transpose(dim, -1).contiguous()
x = x.reshape(-1, N)
expected = torch.arange(N, device=x.device).unsqueeze(dim=0)
assert not (x == expected).all(dim=-1).any()
@staticmethod
def batch_permute(perm: torch.Tensor, x: torch.Tensor, x_permuted: torch.Tensor):
"""
Args:
perm (torch.Tensor):
[..., N]
x (torch.Tensor):
[N, batch_dims_x]
x_permuted (torch.Tensor):
[..., N, batch_dims_x]
"""
batch_shape = perm.shape[:-1]
N = perm.size(-1)
assert x.size(0) == N
perm = perm.view(-1, N)
permuted_x = [x[perm[i]] for i in range(len(perm))]
permuted_x = torch.stack(permuted_x, dim=0) # [-1, N, batch_dims_x]
target_shape = batch_shape + (N,) + x.shape[1:]
assert torch.allclose(permuted_x.reshape(target_shape), x_permuted)
def save_permutation_error(data, error_dir: str = None, max_cases: int = 50):
"""
Saves the permutation error data to a specified directory.
Args:
data: The data to be saved.
error_dir (str): The directory where the error data should be saved.
max_cases (int): The maximum number of error cases to save.
Raises:
Exception: If an error occurs while saving the data, the exception is caught and printed.
"""
if error_dir is None:
return
# error_dir = os.path.join(self.error_dir, dir_name)
os.makedirs(error_dir, exist_ok=True)
if len(os.listdir(error_dir)) >= max_cases:
# Only record the first {max_cases} error cases for debug
return
filename = "T_" + time.strftime("%Y%m%d_%H%M%S") + ".pt"
fpath = os.path.join(error_dir, filename)
if not os.path.exists(fpath):
try:
torch.save(data, fpath)
except Exception as e:
print(f"Exception occurrs in save_permutation_error: {e}")
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