python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import torch
# from PIL import Image
import imageio
import numpy as np
from cotracker.datasets.utils import CoT... | co-tracker-main | cotracker/datasets/fast_capture_dataset.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| co-tracker-main | cotracker/datasets/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import dataclasses
import torch.nn.functional as F
from dataclasses import dataclass
from typing import Any,... | co-tracker-main | cotracker/datasets/utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| co-tracker-main | cotracker/utils/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import cv2
import torch
import flow_vis
from matplotlib import cm
import torch.nn.functional... | co-tracker-main | cotracker/utils/visualizer.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| co-tracker-main | cotracker/models/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
from typing import Tuple
from cotracker.models.core.cotracker.cotracker impo... | co-tracker-main | cotracker/models/evaluation_predictor.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from cotracker.models.core.cotracker.cotracker import CoTracker
def build_cotracker(
checkpoint: str,
... | co-tracker-main | cotracker/models/build_cotracker.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
EPS = 1e-6
def smart_cat(tensor1, tensor2, dim):
if tensor1 is None:
return tensor2
return... | co-tracker-main | cotracker/models/core/model_utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| co-tracker-main | cotracker/models/core/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
... | co-tracker-main | cotracker/models/core/embeddings.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| co-tracker-main | cotracker/models/core/cotracker/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from einops import rearrange
from cotracker.models.core.cotracker.blocks import (
... | co-tracker-main | cotracker/models/core/cotracker/cotracker.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
from cotracker.models.core.model_utils import reduce_masked_mean
EPS = 1e-6
... | co-tracker-main | cotracker/models/core/cotracker/losses.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from timm.models.vision_t... | co-tracker-main | cotracker/models/core/cotracker/blocks.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| co-tracker-main | cotracker/evaluation/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
from dataclasses import dataclass, field
import hydra
import numpy as np
import torch
from omegaco... | co-tracker-main | cotracker/evaluation/evaluate.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
| co-tracker-main | cotracker/evaluation/core/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from typing import Iterable, Mapping, Tuple, Union
def compute_tapvid_metrics(
query_points: np.... | co-tracker-main | cotracker/evaluation/core/eval_utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from collections import defaultdict
import os
from typing import Optional
import torch
from tqdm import tqdm
import numpy ... | co-tracker-main | cotracker/evaluation/core/evaluator.py |
import os
import torch
import timm
import einops
import tqdm
import cv2
import gradio as gr
from cotracker.utils.visualizer import Visualizer, read_video_from_path
def cotracker_demo(
input_video,
grid_size: int = 10,
grid_query_frame: int = 0,
backward_tracking: bool = False,
tracks_leave_tra... | co-tracker-main | gradio_demo/app.py |
'''
Standalone Long Conv class.
The `LongConvModel` class defined in this file provides a simple backbone to train models.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from opt_einsum import contract
class OurModule(nn.Module):
""" Interface for Module that... | safari-main | standalone_long_convs.py |
'''
Train a long conv model on sequential CIFAR10 / sequential MNIST with PyTorch for demonstration purposes.
This code borrows heavily from https://github.com/kuangliu/pytorch-cifar and is based on https://github.com/HazyResearch/state-spaces.
* Train standard sequential CIFAR:
python -m standalone_cifar
* Train ... | safari-main | standalone_cifar.py |
"""
Simplified standalone version of Hyena: https://arxiv.org/abs/2302.10866, designed for quick experimentation.
A complete version is available under `src.models.sequence.hyena`.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
def fftconv(u, k, D):
... | safari-main | standalone_hyena.py |
import copy
import os
import random
import time
from functools import partial, wraps
from typing import Callable, List, Sequence
import hydra
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn as nn
import wandb
from hydra.utils import get_original_cwd
from omegaconf import DictConfig, Omeg... | safari-main | train.py |
import torch
import torch.nn.functional as F
from einops import rearrange
from fftconv import fftconv_fwd, fftconv_bwd
def fftconv_ref(u, k, D, dropout_mask):
seqlen = u.shape[-1]
fft_size = 2 * seqlen
k_f = torch.fft.rfft(k, n=fft_size) / fft_size
u_f = torch.fft.rfft(u.to(dtype=k.dtype), n=fft_siz... | safari-main | csrc/fftconv/launch_fftconv.py |
# Adapted from https://github.com/NVIDIA/apex/blob/master/setup.py
import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME
from setuptools import setup, find_packages
import subprocess
import sys
import warnings
import os
# ninja build does not work unless include_dir... | safari-main | csrc/fftconv/setup.py |
import math
import re
import numpy as np
# N = 8192
N = 16384
# The case of 0 / N is special, we want to simplify it to 0 / 2 instead of 0 / 1
numerator = np.arange(1, N // 8 + 1)
gcd = np.gcd(numerator, N)
num = numerator // gcd
denom = N // gcd
lut_vals = ['T_2_0'] + [f'T_{d}_{n}' for n, d in zip(num, denom)]
lut_... | safari-main | csrc/fftconv/lut_code_gen.py |
import torch
import argparse
import os
import sys
import yaml
from tqdm import tqdm
import json
sys.path.append(os.environ.get("SAFARI_PATH", "."))
from src.models.sequence.long_conv_lm import ConvLMHeadModel
from transformers import AutoTokenizer, GPT2LMHeadModel
from spacy.lang.en.stop_words import STOP_WORDS
... | safari-main | evals/lambada.py |
import sys
from pathlib import Path
import torch
import torch.utils.benchmark as benchmark
from src.models.sequence.hyena import HyenaOperator
from flash_attn.flash_attention import FlashMHA
def benchmark_forward(fn, *inputs, repeats = 10, desc='', verbose=True, **kwinputs):
if verbose:
print(desc, '- F... | safari-main | benchmarks/runtime_hyena_flashmha.py |
import math
import torch
import torch.nn.functional as F
from sklearn.metrics import f1_score, roc_auc_score
from functools import partial
import torchmetrics.functional as tm_f
def _student_t_map(mu, sigma, nu):
sigma = F.softplus(sigma)
nu = 2.0 + F.softplus(nu)
return mu.squeeze(axis=-1), sigma.squeeze(... | safari-main | src/tasks/metrics.py |
# Inspired by https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/metrics/perplexity.py
# But we compute the perplexity correctly: exp(average(nll)), not average(exp(nll))
# Also adapted from https://github.com/Lightning-AI/metrics/blob/master/src/torchmetrics/text/perplexity.py
# But we pass in the loss t... | safari-main | src/tasks/torchmetrics.py |
from typing import Optional, List, Tuple
import math
import functools
import collections
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from omegaconf import ListConfig
from src.models.nn.components import ReversibleInstanceNorm1dInput, ReversibleInstanceNorm1dOutput, \
... | safari-main | src/tasks/tasks.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, reduce
import src.models.nn.utils as U
import src.utils as utils
import src.utils.config
import src.utils.train
log = src.utils.train.get_logger(__name__)
class Decoder(nn.Module):
"""This class doesn't do much but ... | safari-main | src/tasks/decoders.py |
import datetime
import math
from typing import ForwardRef
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange, repeat
import src.models.nn.utils as U
import src.utils as utils
import src.utils.config
from src.models.sequence.block import SequenceResidualBlock
from src.models... | safari-main | src/tasks/encoders.py |
from typing import Any
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_only
from pytorch_lightning.utilities.parsing import AttributeDict
class ParamsLog(pl.Callback):
""" Log the number of parameters of the model """
def __init__(
self,
total: bool = True,
... | safari-main | src/callbacks/params.py |
### https://github.com/HazyResearch/transformers/blob/master/src/callbacks/wandb_callbacks.py
import glob
import os
from typing import List
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
import torch
import wandb
from pytorch_lightning import Callback, Trainer
from pytorch_lightning.loggers ... | safari-main | src/callbacks/wandb.py |
### https://github.com/HazyResearch/transformers/blob/master/src/callbacks/speed_monitor.py
# Adapted from https://pytorch-lightning.readthedocs.io/en/latest/_modules/pytorch_lightning/callbacks/gpu_stats_monitor.html#GPUStatsMonitor
# We only need the speed monitoring, not the GPU monitoring
import time
from typing i... | safari-main | src/callbacks/timer.py |
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_only
from pytorch_lightning.utilities.parsing import AttributeDict
from omegaconf import OmegaConf
class TrackNorms(pl.Callback):
# TODO do callbacks happen before or after the method in the main LightningModule?
# @rank_zero_onl... | safari-main | src/callbacks/norms.py |
import numpy as np
from pytorch_lightning.callbacks import Callback
import src.utils as utils
from src.utils import registry
class ProgressiveResizing(Callback):
def __init__(self, stage_params: list):
"""
stage_params is a list of dicts
e.g. stage_params = [
{'resolution': 4... | safari-main | src/callbacks/progressive_resizing.py |
"""Long Range Arena datasets"""
import io
import logging
import os
import pickle
from pathlib import Path
import torch
from torch import nn
import torch.nn.functional as F
import torchtext
import torchvision
from einops.layers.torch import Rearrange, Reduce
from PIL import Image # Only used for Pathfinder
from datase... | safari-main | src/dataloaders/lra.py |
"""Miscellaneous vision datasets."""
import os
import torch
from torch import nn
from torch.nn import functional as F
import torchvision
from src.dataloaders.base import default_data_path, SequenceDataset
class ImageNet(SequenceDataset):
"""
.. figure:: https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-s... | safari-main | src/dataloaders/vision.py |
'''Synthetic datasets to test in-context learning ability.'''
import os
import torch
from torch.utils.data import TensorDataset, Dataset, DataLoader
from typing import Dict
import numpy as np
from tqdm import tqdm
from collections import Counter
from src.dataloaders.base import SequenceDataset
class Vocab:
"""Cu... | safari-main | src/dataloaders/synthetics.py |
"""
ET Dataset from Informer Paper.
Dataset: https://github.com/zhouhaoyi/ETDataset
Dataloader: https://github.com/zhouhaoyi/Informer2020
"""
from typing import List
import os
import numpy as np
import pandas as pd
from pandas.tseries import offsets
from pandas.tseries.frequencies import to_offset
import torch
from to... | safari-main | src/dataloaders/et.py |
# Adapted from https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_clm.py
# Adapted from https://github.com/HazyResearch/flash-attention/blob/main/training/src/datamodules/language_modeling_hf.py
from itertools import chain
from pathlib import Path
import pickle
from typing im... | safari-main | src/dataloaders/language_modeling_hf.py |
from . import basic, et, lra, language_modeling_hf, synthetics, vision
from .base import SequenceDataset
| safari-main | src/dataloaders/__init__.py |
# Adapted from https://github.com/Lightning-AI/lightning/blob/2845e7565dbe6b765ae32870e7d2bc456529c30a/tests/tests_pytorch/utilities/test_auto_restart.py#L1397
from typing import Iterator
import math
import torch
from torch.utils.data import RandomSampler, DistributedSampler
class RandomFaultTolerantSampler(RandomSa... | safari-main | src/dataloaders/fault_tolerant_sampler.py |
"""Implementation of basic benchmark datasets used in S4 experiments: MNIST, CIFAR10 and Speech Commands."""
import numpy as np
import torch
import torchvision
from einops.layers.torch import Rearrange
from src.utils import permutations
from src.dataloaders.base import default_data_path, ImageResolutionSequenceDataset... | safari-main | src/dataloaders/basic.py |
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# 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... | safari-main | src/dataloaders/lm.py |
""" Datasets for core experimental results """
import os
import pickle
from functools import partial
from pathlib import Path
import numpy as np
import torch
import torchvision
from einops import rearrange
from einops.layers.torch import Rearrange
from src.utils import is_list, permutations
from torch.nn import funct... | safari-main | src/dataloaders/base.py |
# Copied from https://github.com/stanford-crfm/mistral/blob/main/src/corpora/detokenization.py
# Which was originally from https://github.com/NVIDIA/Megatron-LM/blob/aed2f75e209e525c842aec7c044af7acae2a4614/tasks/zeroshot_gpt/detokenizer.py
"""
Handle detokenization for different dataset for zero-shot LM evaluation.
"... | safari-main | src/dataloaders/datasets/detokenizer.py |
# Inspired by https://github.com/NVIDIA/Megatron-LM/blob/main/tasks/zeroshot_gpt/datasets.py
# Except we don't pad the last block and don't use overlapping eval
# And we return both the input and the target
import math
import numpy as np
import torch
class LMDataset(torch.utils.data.Dataset):
def __init__(self,... | safari-main | src/dataloaders/datasets/lm_dataset.py |
"""
Borrowed from https://github.com/hysts/pytorch_image_classification/tree/9ff4248905850c68aa9c09c17914307eb81769e7/pytorch_image_classification/transforms
"""
import torch
import numpy as np
import PIL
import PIL.Image
from PIL.Image import Image
class NpNormalize:
def __init__(self, mean: np.ndarray, std: np.... | safari-main | src/dataloaders/utils/cifar_augmentations.py |
import torch
from timm.data import Mixup
from timm.data.mixup import mixup_target
class TimmMixup(Mixup):
""" Wrap timm.data.Mixup that avoids the assert that batch size must be even.
"""
def __call__(self, x, target, *args):
if self.mode == 'elem':
lam = self._mix_elem(x)
eli... | safari-main | src/dataloaders/utils/timm_mixup.py |
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# 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... | safari-main | src/dataloaders/utils/vocabulary.py |
"""Utilities for special optimizer hyperparameters.
group_parameters_for_optimizer is a modification of timm's optimizer logic, which is currently unused
add_optimizer_hooks is an improved version that uses this codebase's _optim dictionary
"""
import inspect
import torch.nn as nn
import hydra
def add_optimizer_h... | safari-main | src/utils/optim_groups.py |
""" Utilities for dealing with collection objects (lists, dicts) and configs """
from typing import Sequence, Mapping, Optional, Callable
import functools
import hydra
from omegaconf import ListConfig, DictConfig
# TODO this is usually used in a pattern where it's turned into a list, so can just do that here
def is_li... | safari-main | src/utils/config.py |
optimizer = {
"adam": "torch.optim.Adam",
"adamw": "torch.optim.AdamW",
"rmsprop": "torch.optim.RMSprop",
"sgd": "torch.optim.SGD",
"lamb": "src.utils.optim.lamb.JITLamb",
}
scheduler = {
"constant": "transformers.get_constant_schedule",
"plateau": "torch.optim.lr_scheduler.ReduceLROnPlatea... | safari-main | src/utils/registry.py |
from .config import is_list, is_dict, to_list, to_dict, get_class, instantiate
| safari-main | src/utils/__init__.py |
import math
import numpy as np
import torch
### Bit reversal permutation
def bitreversal_po2(n):
m = int(math.log(n)/math.log(2))
perm = np.arange(n).reshape(n,1)
for i in range(m):
n1 = perm.shape[0]//2
perm = np.hstack((perm[:n1],perm[n1:]))
return perm.squeeze(0)
def bitreversal_p... | safari-main | src/utils/permutations.py |
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# 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... | safari-main | src/utils/distributed.py |
""" Utils for the training loop. Copied from https://github.com/HazyResearch/transformers/blob/master/src/utils/utils.py """
import logging
import os
import warnings
from typing import List, Sequence
import torch.nn as nn
import pytorch_lightning as pl
import rich.syntax
import rich.tree
from omegaconf import DictConf... | safari-main | src/utils/train.py |
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# 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... | safari-main | src/utils/optim/lamb.py |
"""Custom learning rate schedulers"""
import math
import warnings
import torch
from timm.scheduler import CosineLRScheduler
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html
class CosineWarmup(torch.optim.lr_scheduler.CosineAnnealingLR):
def __init__(self, optimizer, T_max, eta_min=0, wa... | safari-main | src/utils/optim/schedulers.py |
""" Implementations of different types of residual functions. """
import torch
from torch import nn
class Residual(nn.Module):
""" Residual connection with constant affine weights. Can simulate standard residual, no residual, and "constant gates". """
def __init__(self, i_layer, d_input, d_model, alpha=1.0, ... | safari-main | src/models/nn/residual.py |
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# 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... | safari-main | src/models/nn/adaptive_softmax.py |
from .components import LinearActivation, Activation, Normalization, DropoutNd
| safari-main | src/models/nn/__init__.py |
""" Utility wrappers around modules to let them handle Args and extra arguments """
import inspect
from functools import wraps
import torch
from torch import nn
def wrap_kwargs(f):
"""
Given a callable f that can consume some named arguments,
wrap it with a kwargs that passes back any unused args
EXA... | safari-main | src/models/nn/utils.py |
""" Defines flexible gating mechanisms based on ideas from LSSL paper and UR-LSTM paper https://arxiv.org/abs/1910.09890 """
import torch
import torch.nn as nn
class Gate(nn.Module):
""" Implements gating mechanisms. TODO update this with more detailed description with reference to LSSL paper when it's on arxiv
... | safari-main | src/models/nn/gate.py |
"""Implementations of several types of Discrete Sin/Cosine Transforms with various reductions to FFT.
Currently not used by S4
"""
import torch
import torch.nn as nn
import numpy as np
import scipy.fft
from einops import rearrange, repeat
class DCT(nn.Module):
""" Reductions adapted from https://dsp.stackexchang... | safari-main | src/models/nn/dxt.py |
""" Utility nn components, in particular handling activations, initializations, and normalization layers """
from functools import partial
import math
from typing import ForwardRef
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from opt_einsum import contract
def stoc... | safari-main | src/models/nn/components.py |
# Copyright (c) 2023, Tri Dao, Dan Fu.
# Simplified, mostly standalone version of LongConvLM for synthetics.
import math
from functools import partial
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.ops import StochasticDepth
from einops import ... | safari-main | src/models/sequence/simple_lm.py |
""" Implementation of FFN block in the style of Transformers """
from functools import partial
from torch import nn
from src.models.sequence.base import SequenceModule
from src.models.nn import LinearActivation, DropoutNd
class FF(SequenceModule):
def __init__(self, d_input, expand=2, d_output=None, transposed=Fa... | safari-main | src/models/sequence/ff.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from src.models.sequence.ssm.ss_kernel import SSKernel
try:
from src.ops.fftconv import fftconv_func
except ImportError:
fftconv_func = None
@torch.jit.script
def mul_sum(q, y):
return (q * y).sum(dim=1)
c... | safari-main | src/models/sequence/h3.py |
'''PyTorch version of the block FFT convolution as described in the H3 paper.'''
import torch
from einops import rearrange
import math
from torch import nn
from src.models.nn import Activation
from src.utils.train import OptimModule
def ref_dft_matrix(N, H=1):
"""Compute the DFT matrix of size N x N.
Thi... | safari-main | src/models/sequence/block_fft.py |
from .base import SequenceModule, TransposedModule
from .model import SequenceModel
from .ff import FF
| safari-main | src/models/sequence/__init__.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
import opt_einsum as oe
optimized = True
if optimized:
contract = oe.contract
else:
contract = torch.einsum
from src.models.nn import LinearActivation, Activation, DropoutNd
from src.models.sequence.block_fft impo... | safari-main | src/models/sequence/long_conv.py |
# Copyright (c) 2023, Tri Dao, Dan Fu.
import copy
import math
import re
from functools import partial
from collections import namedtuple, OrderedDict
from collections.abc import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.models.gpt2.configuration_gpt2 import GPT2C... | safari-main | src/models/sequence/long_conv_lm.py |
""" Isotropic deep sequence model backbone, in the style of ResNets / Transformers.
The SequenceModel class implements a generic (batch, length, d_input) -> (batch, length, d_output) transformation
"""
from functools import partial
import torch
import torch.nn as nn
from einops import rearrange
from src.utils.confi... | safari-main | src/models/sequence/model.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import repeat
from src.utils.train import OptimModule
class LongConvKernel(OptimModule):
def __init__(
self,
H,
L,
channels=1,
learning_rate=None,
lam=0.1,
causal=True,
... | safari-main | src/models/sequence/long_conv_kernel.py |
import math
from re import U
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from einops import rearrange, repeat
try:
from src.ops.fftconv import fftconv_ref, fftconv_func
except ImportError:
fftconv_func = None
try:
from flash_attn.ops.fused_dense impo... | safari-main | src/models/sequence/hyena.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from src.models.sequence.long_conv_kernel import LongConvKernel
try:
from src.ops.fftconv import fftconv_func
except ImportError:
fftconv_func = None
@torch.jit.script
def mul_sum(q, y):
return (q * y).sum(d... | safari-main | src/models/sequence/h3_conv.py |
""" Implements a full residual block around a black box layer
Configurable options include:
normalization position: prenorm or postnorm
normalization type: batchnorm, layernorm etc.
subsampling/pooling
residual options: feedforward, residual, affine scalars, depth-dependent scaling, etc.
"""
from torch import nn
fro... | safari-main | src/models/sequence/block.py |
"""Implements downsampling and upsampling on sequences."""
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange, repeat, reduce
from src.models.sequence import SequenceModule
from src.models.nn import LinearActivation
""" Simple pooling functions that just downsample or repe... | safari-main | src/models/sequence/pool.py |
from torch import nn
import functools
class SequenceModule(nn.Module):
"""Abstract sequence model class. All models must adhere to this interface
A SequenceModule is generally a model that transforms an input of shape
(n_batch, l_sequence, d_model) to (n_batch, l_sequence, d_output)
REQUIRED methods ... | safari-main | src/models/sequence/base.py |
""" Wrapper around nn.MultiheadAttention to adhere to SequenceModule interface. """
import torch
import torch.nn.functional as F
from torch import nn
import hydra
from src.models.sequence.base import SequenceModule, TransposedModule
import src.models.nn.utils as U
from einops import rearrange
@TransposedModule
class ... | safari-main | src/models/sequence/mha.py |
""" Standalone version of Structured (Sequence) State Space (S4) model. """
import logging
from functools import partial
import math
import numpy as np
from scipy import special as ss
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_lightning.utilities import rank_zero_only
from einops ... | safari-main | src/models/sequence/ssm/s4d.py |
# TD: [2023-01-05]: Extracted the SSKernel class from
# https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/sequence/ss/kernel.py
# We add option to use the shift kernel, and remove the option of SSKernelNPLR
"""SSM convolution kernels.
SSKernel wraps different kernels... | safari-main | src/models/sequence/ssm/ss_kernel.py |
# Copied from https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/hippo/hippo.py
""" Definitions of A and B matrices for various HiPPO operators. """
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from scipy import special as ss
... | safari-main | src/models/sequence/ssm/hippo.py |
# TD: [2023-01-05]: Extracted the SSKernelDiag class from
# https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/sequence/ss/kernel.py
# We make a small change to use the log_vandermonde CUDA code.
"""SSKernelDiag is the S4D kernel, a simpler algorithm for computing the... | safari-main | src/models/sequence/ssm/ss_kernel_shift.py |
import torch
import torch.nn as nn
from src.models.nn import LinearActivation, Activation, DropoutNd
from einops import rearrange, repeat
import opt_einsum as oe
import math
class OurModule(nn.Module):
def __init__(self): super().__init__()
def register(self, name, tensor, trainable=False, lr=None, wd=None):
... | safari-main | src/models/sequence/ssm/s4_simple.py |
# TD: [2023-01-05]: Extracted the SSKernelDiag class from
# https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/sequence/ss/kernel.py
# We make a small change to use the log_vandermonde CUDA code.
"""SSKernelDiag is the S4D kernel, a simpler algorithm for computing the... | safari-main | src/models/sequence/ssm/ss_kernel_diag.py |
# Copied from https://github.com/HazyResearch/state-spaces/blob/06dbbdfd0876501a7f12bf3262121badbc7658af/src/models/sequence/ss/dplr.py
"""Initializations of structured state space models"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from src.mo... | safari-main | src/models/sequence/ssm/dplr.py |
"""
The original Vision Transformer (ViT) from timm, copyright belongs to / Copyright 2020 Ross Wightman
"""
import math
import logging
from functools import partial
from collections import OrderedDict
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.helpe... | safari-main | src/models/baselines/vit_all.py |
import math
import torch
import torch.nn.functional as F
from einops import rearrange
from fftconv import fftconv_fwd, fftconv_bwd
@torch.jit.script
def _mul_sum(y, q):
return (y * q).sum(dim=1)
# reference convolution with residual connection
def fftconv_ref(u, k, D, dropout_mask, gelu=True, k_rev=None):
... | safari-main | src/ops/fftconv.py |
"""pykeops implementations of the Vandermonde matrix multiplication kernel used in the S4D kernel."""
import math
import torch
from einops import rearrange, repeat
from opt_einsum import contract
import os
try:
import pykeops
from pykeops.torch import LazyTensor, Genred
except:
pass
try:
from cauchy... | safari-main | src/ops/vandermonde.py |
""" Old utilities for parallel scan implementation of Linear RNNs. """
# TODO this file could use much cleanup
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from src.models.functional.toeplitz import triangular_toeplitz_multiply, triangular_toeplitz_multiply_padded
... | safari-main | src/ops/unroll.py |
""" Compute a Krylov function efficiently. (S4 renames the Krylov function to a "state space kernel")
A : (N, N)
b : (N,)
c : (N,)
Return: [c^T A^i b for i in [L]]
"""
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from src.ops.toeplitz import causal_convolution
def krylov_sequent... | safari-main | src/ops/krylov.py |
""" Utilities for computing convolutions.
There are 3 equivalent views:
1. causal convolution
2. multiplication of (lower) triangular Toeplitz matrices
3. polynomial multiplication (mod x^N)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def construct_toeplitz(v, f=0.0):
"""E... | safari-main | src/ops/toeplitz.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import math
import torch
import torch.nn.functional as F
from torch.autograd import grad
def gPenalty(inputs, loss, la... | AdversarialAndDimensionality-master | penalties.py |
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