python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
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# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/version.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/__init__.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/utils.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/experimental/fusion/jaxpr_rewriter.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/experimental/fusion/lowering.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/experimental/fusion/__init__.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/experimental/fusion/fusion.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/registration.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/primitives.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/pallas_call.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/__init__.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/core.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/triton_lowering.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/utils.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/ops/attention.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/ops/__init__.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/ops/softmax.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/ops/rms_norm.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | jax_triton/pallas/ops/layer_norm.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/matmul.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/add.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/block_map.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/fused_attention.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/softmax.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/fusion/nn.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/fusion/benchmark_matmul.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/pallas/blocksparse_matmul.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/pallas/templating.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/pallas/fused_attention.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/pallas/lstm.py |
# Copyright 2023 The jax_triton Authors.
#
# 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 ... | jax-triton-main | examples/pallas/layer_norm.py |
import torch
from lenet5 import LeNet5
input_data = torch.randn(1, 3, 32, 32)#.to(device=device) # 3 channels for color image
model = LeNet5()
result = model(input_data)
print(result)
print(result.shape)
print(result.dtype)
| LeNet5-main | example.py |
from lenet5.model import LeNet5, device
from lenet5.training import train | LeNet5-main | lenet5/__init__.py |
import torch
from torch import nn
import torch.nn.functional as F
class LeNet5(nn.Module):
def __init__(self):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5) #3 = in channels -> 6=out_channels, 5=kernel_size
self.conv2 = nn.Conv2d(6, 16, 5) #6=in_chanels, => 16=out_channels,... | LeNet5-main | lenet5/model.py |
from torch import optim
from torch import nn
from lenet5.model import LeNet5
from lenet5.model import device
model = LeNet5()
loss = nn.CrossEntropyLoss() # init cross entropy
optim = optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # init stochastic gradient descent with the model parameters, don't forget to i... | LeNet5-main | lenet5/training.py |
from setuptools import setup, find_packages
setup(
name = 'orca_transformer',
packages = find_packages(exclude=['examples']),
version = '1.1.4',
license='MIT',
description = 'phi - Pytorch',
author = 'Kye Gomez',
author_email = 'kye@apac.ai',
url = 'https://github.com/kyegomez/Phi',
long_description_... | phi-1-master | setup.py |
import torch
from PHI import phi2
x = torch.randint(0, 256, (1, 1024)).cuda()
phi2(x) # (1, 1024, 20000)
| phi-1-master | example.py |
from old.traingv2 import TrainAndromeda
from old.build_dataset import built_dataset | phi-1-master | PHI/__init__.py |
from optimus_prime import TransformerWrapper, AutoregressiveWrapper, AndromedaEmbedding, Decoder
Phi = TransformerWrapper(
num_tokens=64007,
max_seq_len=8192,
use_abs_pos_emb=False,
# tokenizer=tokenizer,
embedding_provider=AndromedaEmbedding(),
attn_layers = Decoder(
dim=2560, # 2048
... | phi-1-master | PHI/model.py |
import math
import multiprocessing
import os
from datetime import timedelta
from functools import partial
from itertools import chain
import torch
from torch.distributed.fsdp import (
FullyShardedDataParallel,
MixedPrecision,
BackwardPrefetch,
ShardingStrategy,
)
from accelerate import Accelerator
from... | phi-1-master | PHI/train_distributed_accelerate.py |
import torch
from transformers import AutoTokenizer
from einops._torch_specific import allow_ops_in_compiled_graph
import argparse
def main():
allow_ops_in_compiled_graph()
torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
parser = argparse.ArgumentParser(description="Generate text using Phi ... | phi-1-master | PHI/inference.py |
import multiprocessing
import argparse
from itertools import chain
from datasets import load_dataset
from transformers import AutoTokenizer
#falcon tokenizer
"""
Falcon dataset
Data Fields
content: the processed and cleaned text contained in the page;
url: the url of the webpage crawled to produce the sample;
timesta... | phi-1-master | PHI/build_dataset.py |
import math
import multiprocessing
import os
from datetime import timedelta
from functools import partial
from itertools import chain
import torch
from torch.distributed.fsdp import (
FullyShardedDataParallel,
MixedPrecision,
BackwardPrefetch,
ShardingStrategy,
)
from accelerate import Accelerator
from... | phi-1-master | PHI/train_distributed.py |
from math import ceil
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange, pack, unpack
from optimus_prime.autoregressive_wrapper import top_p, top_k, eval_decorator
# helper functions
def exists(val):
return val is not None
def divisible_by(numer, denom):
return ... | phi-1-master | PHI/optimus_prime/xl_autoregressive_wrapper.py |
from math import ceil
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange, pack, unpack
def exists(val):
return val is not None
def eval_decorator(fn):
def inner(self, *args, **kwargs):
was_training = self.training
self.eval()
out = fn(self, ... | phi-1-master | PHI/optimus_prime/autoregressive_wrapper.py |
#add ability to choose your own tokenizer, and embedder, and ask what else can be done for production level training
import math
from random import random
import torch
from torch import nn, einsum, Tensor
import torch.nn.functional as F
from functools import partial, wraps
from inspect import isfunction
from collec... | phi-1-master | PHI/optimus_prime/x_transformers.py |
import torch
from packaging import version
if version.parse(torch.__version__) >= version.parse('2.0.0'):
from einops._torch_specific import allow_ops_in_compiled_graph
allow_ops_in_compiled_graph()
from optimus_prime.x_transformers import XTransformer, Encoder, Decoder, CrossAttender, Attention, TransformerW... | phi-1-master | PHI/optimus_prime/__init__.py |
import torch
from torch import nn
import torch.nn.functional as F
def exists(val):
return val is not None
class ContinuousAutoregressiveWrapper(nn.Module):
def __init__(self, net, ignore_index = -100, pad_value = 0):
super().__init__()
self.net = net
self.max_seq_len = net.max_seq_len
... | phi-1-master | PHI/optimus_prime/continuous_autoregressive_wrapper.py |
from functools import partial
import torch
from torch import nn, einsum, Tensor
import torch.nn.functional as F
from collections import namedtuple
from functools import wraps
from packaging import version
from dataclasses import dataclass
from einops import rearrange
# constants
EfficientAttentionConfig = namedtup... | phi-1-master | PHI/optimus_prime/attend.py |
import math
from random import random
from contextlib import nullcontext
from collections import namedtuple
import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange, repeat, pack, unpack
from optimus_prime.x_transformers import TransformerWrapper
from typing import Optional
# ... | phi-1-master | PHI/optimus_prime/nonautoregressive_wrapper.py |
import math
import multiprocessing
import os
import collections
from datetime import timedelta
from functools import partial
from itertools import chain
import torch
from accelerate import Accelerator
from accelerate.utils import InitProcessGroupKwargs
from datasets import concatenate_datasets, load_dataset
from t... | phi-1-master | PHI/old/training_sophia.py |
import math
import torch
from torch import Tensor
from torch.optim.optimizer import Optimizer
from typing import List, Optional
class SophiaG(Optimizer):
def __init__(self, params, lr=1e-4, betas=(0.965, 0.99), rho = 0.04,
weight_decay=1e-1, *, maximize: bool = False,
capturable: bool = False):
... | phi-1-master | PHI/old/sophia.py |
#quantization + paralleism
import time
import torch
from accelerate.utils import set_seed
from datasets import load_dataset
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader
from transformers import default_data_collator, get_linear_schedule_with_warmup
from accelerate import Accelerator
f... | phi-1-master | PHI/old/training.py |
import torch
# This is the unfused version of StableAdamW. It is slower than the fused version (coming).
class StableAdamWUnfused(torch.optim.Optimizer):
def __init__(
self,
params,
lr=0.002,
weight_decay=0.2,
betas=(0.9, 0.99),
eps=1e-8,
clip_thresh=1.0,
... | phi-1-master | PHI/utils/stable_adamw.py |
import torch
# from palm_rlhf_pytorch.palm import LayerNorm
from torch.nn import LayerNorm
from torch.optim import AdamW
# from palm.utils import print_main
from utils.helpers import print_main
from utils.stable_adamw import StableAdamWUnfused
# optimizers
def decoupled_optimizer(
model: torch.nn.Module,
le... | phi-1-master | PHI/utils/decoupled_optimizer.py |
import math
import torch
from torch import einsum, _nnpack_available
import torch.nn.functional as F
from torch import nn
from einops import rearrange
import copy
from pathlib import PurePath
from tqdm import tqdm_gui
from beartype import beartype
from beartype.typing import Tuple, Optional
from einops import rearra... | phi-1-master | PHI/utils/rf_utils.py |
import torch.distributed as dist # Add this line
def print_num_params(model):
n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
if dist.is_available():
if dist.get_rank() == 0:
print(f"Number of parameters in model: {n_params}")
else:
print(f"Number of p... | phi-1-master | PHI/utils/helpers.py |
import torch
from model import PALME
# Create a sample text token tensor
text_tokens = torch.randint(0, 32002, (1, 50), dtype=torch.long)
# Create a sample image tensor
images = torch.randn(1, 3, 224, 224)
# Instantiate the model
model = PALME()
# Pass the sample tensors to the model's forward function
output = mod... | Minerva-main | Minerva/model_test.py |
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
import torch
import torch.nn as nn
import torch.nn.functional as F
class VisionLanguageEmbedding(nn.Module):
def __init__(self, text_embed, vision_embed):
super().__init__()
self.text_embed = text_embed
... | Minerva-main | Minerva/embedding.py |
import torch
# This is the unfused version of StableAdamW. It is slower than the fused version (coming).
class StableAdamWUnfused(torch.optim.Optimizer):
def __init__(
self,
params,
lr=0.002,
weight_decay=0.2,
betas=(0.9, 0.99),
eps=1e-8,
clip_thresh=1.0,
... | Minerva-main | Minerva/stable_adamw.py |
import torch
import torch.nn as nn
from palm_rlhf_pytorch import PaLM
from transformer import AutoTokenizer
import bitsandbytes as bnb
from Minerva.embedding import PositionalEmbedding
class MinervaTokenizer:
def __init__(self):
try:
self.tokenizer = AutoTokenizer.from_pretrained(
... | Minerva-main | Minerva/model.py |
import torch.distributed as dist # Add this line
def print_num_params(model):
n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
if dist.is_available():
if dist.get_rank() == 0:
print(f"Number of parameters in model: {n_params}")
else:
print(f"Number of p... | Minerva-main | Minerva/utils.py |
import multiprocessing
import argparse
from itertools import chain
from datasets import load_dataset
from model import PALME_Tokenizer
import torch
class CFG:
SEED: int = 42
SEQ_LEN: int = 8192
NUM_CPU: int = multiprocessing.cpu_count()
HF_ACCOUNT_REPO: str = "YOUR HF ACCOUNT"
TOKENIZER: str = "Ele... | Minerva-main | Minerva/build_dataset.py |
import math
import multiprocessing
import os
from datetime import timedelta
from functools import partial
from itertools import chain
import torch
from torch.distributed.fsdp import (
FullyShardedDataParallel,
MixedPrecision,
BackwardPrefetch,
ShardingStrategy,
)
from accelerate.utils import InitProce... | Minerva-main | Minerva/train_distributed.py |
from setuptools import find_packages, setup
setup(
name='gato',
version='0.0.1',
description='Gato: A Generalist Agent',
url='https://github.com/kyegomez/GATO',
author='Kye Gomez',
author_email='kye@apac.ai',
long_description=open('README.md', 'r', encoding='utf-8').read(),
long_descrip... | GATO-master | setup.py |
import torch
from gato.model import Gato, GatoConfig
# Create model instance
config = GatoConfig.small()
gato = Gato(config)
# Fake inputs for Gato
input_dim = config.input_dim
input_ids = torch.cat([
torch.rand((1, 1, input_dim)) for _ in range(20)] + # 20 image patches
[torch.full((1, 1, input_dim), 0.25),... | GATO-master | example.py |
from gato.model import Gato | GATO-master | gato/__init__.py |
import copy
from collections import namedtuple
from dataclasses import dataclass
from functools import wraps
from typing import Any, Dict, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from packaging import version
from torch import Tensor, einsum
# constants
E... | GATO-master | gato/model.py |
from ray.rllib.algorithms.impala import ImpalaConfig
from ray.tune.logger import pretty_print
import datetime
import os
import tempfile
from ray.tune.logger.unified import UnifiedLogger # noqa: E402
def custom_log_creator(custom_path, custom_str):
timestr = datetime.datetime.today().strftime("%Y-%m-%d_%H-%M-%... | GATO-master | datasets/control_env/ALE_Atari/atari_test_impala.py |
import torch
from gpt4.gpt4 import GPT4
x = torch.randint(0, 256, (1, 1024)).cuda()
model = GPT4()
model(x)
| GPT4-main | example_language.py |
import torch
from gpt4.gpt4 import GPT4MultiModal
#usage
img = torch.randn(1, 3, 256, 256)
caption = torch.randint(0, 20000, (1, 1024))
model = GPT4MultiModal()
output = model(img, caption)
print(output.shape) # (1, 1024, 20000)
| GPT4-main | example_multimodal.py |
from gpt4.gpt4 import GPT4
from gpt4.train import train | GPT4-main | gpt4/__init__.py |
import math
from dataclasses import dataclass
from functools import partial, wraps
from inspect import isfunction
# constants
from math import ceil
from random import random
from typing import Callable, List, Optional
import torch
import torch.nn.functional as F
from einops import pack, rearrange, reduce, repeat, unp... | GPT4-main | gpt4/model.py |
from collections import namedtuple
from dataclasses import dataclass
from functools import partial, wraps
from typing import Optional
import torch
import torch.nn.functional as F
from einops import rearrange
from packaging import version
from torch import Tensor, einsum, nn
# constants
EfficientAttentionConfig = nam... | GPT4-main | gpt4/attend.py |
import math
import multiprocessing
import os
from datetime import timedelta
from functools import partial
from itertools import chain
import torch
########### SETUP CONFIG
import torch.distributed as dist
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.state import Acceler... | GPT4-main | gpt4/train.py |
import torch
import torch.nn as nn
from gpt4.model import (
AutoregressiveWrapper,
Decoder,
Encoder,
Transformer,
ViTransformerWrapper,
)
class GPT4(nn.Module):
"""
GPT4 is a transformer-based model architecture. It initializes with
a Transformer and AutoregressiveWrapper with defaul... | GPT4-main | gpt4/gpt4.py |
GPT4-main | gpt4/utils/__init__.py | |
import torch
# This is the unfused version of StableAdamW. It is slower than the fused version (coming).
class StableAdamWUnfused(torch.optim.Optimizer):
def __init__(
self,
params,
lr=0.002,
weight_decay=0.2,
betas=(0.9, 0.99),
eps=1e-8,
clip_thresh=1.0,
... | GPT4-main | gpt4/utils/stable_adam.py |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
from typing import List
from setuptools import find_packages, setup
def get_version() -> str:
# https://packaging.python.org/guides/single-sourcing-package-version/
init = open(os.path.join("stable_alignment", "__init__.py"), "r").read().split()
r... | Stable-Alignment-main | setup.py |
"""Run inference on a trained model.
Make sure you have downloaded the model in the `model_path` directory.
Example:
python stable_alignment/run_inference.py --model_path './models/socially-good-lm' --device 'cuda:0'
"""
import json
import os
from typing import Any, Dict, List, Optional
import torch
import tran... | Stable-Alignment-main | run_inference.py |
"""The script to collect data for social simulations.
Example:
python collect_data.py --model_name 'gpt4' --world_ids "1, 2, 3, 4, 5"
"""
import glob
import math
from typing import Any, Dict, Sequence
import pandas as pd
from absl import app, flags
FLAGS = flags.FLAGS
CACHE_DIR_PREFIX: str = "./data/cache"
fla... | Stable-Alignment-main | collect_data.py |
#! /usr/bin/env python3
# coding=utf-8
# Ruibo Liu @Dartmouth College
#
# 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 ap... | Stable-Alignment-main | test/test_utils.py |
#! /usr/bin/env python3
# coding=utf-8
# Ruibo Liu @Dartmouth College
#
# 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 ap... | Stable-Alignment-main | test/test_agent.py |
#! /usr/bin/env python3
# coding=utf-8
# Ruibo Liu @Dartmouth College
#
# 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 ap... | Stable-Alignment-main | test/__init__.py |
#! /usr/bin/env python3
# coding=utf-8
# Ruibo Liu @Dartmouth College
#
# 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 ap... | Stable-Alignment-main | stable_alignment/simulation.py |
#! /usr/bin/env python3
# coding=utf-8
# Ruibo Liu @Dartmouth College
#
# 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 ap... | Stable-Alignment-main | stable_alignment/alignment.py |
#! /usr/bin/env python3
# coding=utf-8
# Ruibo Liu @Dartmouth College
#
# 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 ap... | Stable-Alignment-main | stable_alignment/__init__.py |
#! /usr/bin/env python3
# coding=utf-8
# Ruibo Liu @Dartmouth College
#
# 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 ap... | Stable-Alignment-main | stable_alignment/sandbox/world.py |
"""Sandbox Package."""
from stable_alignment.sandbox.agent import Agent
from stable_alignment.sandbox.utils import (
call_gpt,
finalize_answer,
get_moral_score_cls,
get_query_questions,
load_initial_data,
sample_init_data,
)
from stable_alignment.sandbox.world import World
__all__ = [
"Age... | Stable-Alignment-main | stable_alignment/sandbox/__init__.py |
#! /usr/bin/env python3
# coding=utf-8
# Ruibo Liu @Dartmouth College
#
# 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 ap... | Stable-Alignment-main | stable_alignment/sandbox/utils.py |
#! /usr/bin/env python3
# coding=utf-8
# Ruibo Liu @Dartmouth College
#
# 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 ap... | Stable-Alignment-main | stable_alignment/sandbox/agent.py |
from fastapi import FastAPI, HTTPException, Depends
from fastapi.security import APIKeyHeader
from pydantic import BaseModel
from typing import Optional
from slowapi import Limiter, _rate_limit_exceeded_handler
from slowapi.util import get_remote_address
from slowapi.errors import RateLimitExceeded
from kosmos_model ... | KosmosX-API-main | api.py |
from fairseq_cli.preprocess import cli_main
if __name__ == "__main__":
cli_main() | KosmosX-API-main | kosmosX/preprocess.py |
from fairseq_cli.generate import cli_main
if __name__ == "__main__":
cli_main() | KosmosX-API-main | kosmosX/generate.py |
#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Train a new model on one or across multiple GPUs.
"""
import argparse
import logging
import math
import os
impor... | KosmosX-API-main | kosmosX/validate.py |
from fairseq_cli.interactive import cli_main
if __name__ == "__main__":
cli_main() | KosmosX-API-main | kosmosX/interactive.py |
from fairseq_cli.train import cli_main
if __name__ == "__main__":
cli_main() | KosmosX-API-main | kosmosX/train.py |
import os
import textwrap
import os
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
pylab.rcParams['figure.figsize'] = 20, 12
import cv2
from decode_string import decode_bbox_from_caption
EOD_SYMBOL = "</doc>"... | KosmosX-API-main | kosmosX/demo/draw_box.py |
#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Translate raw text with a trained model. Batches data on-the-fly.
"""
import sys
sys.path.append( '.' )
import ... | KosmosX-API-main | kosmosX/demo/gradio_app.py |
import re
import numpy as np
def find_patch_index_combinations(s):
# The regular expression pattern for matching the required formats
pattern = r'(?:(<phrase>([^<]+)</phrase>))?<object>((?:<patch_index_\d+><patch_index_\d+></delimiter_of_multi_objects/>)*<patch_index_\d+><patch_index_\d+>)</object>'
... | KosmosX-API-main | kosmosX/demo/decode_string.py |
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