python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
import os
from setuptools import find_packages, setup
CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(CURRENT_DIR, "README.md"), encoding="utf-8") as f:
long_description = f.read()
setup(
name="nucleotide_transformer",
version="0.0.1",
packages=find_packages(),
ur... | nucleotide-transformer-main | setup.py |
# Copyright 2022 InstaDeep Ltd
#
# Licensed under the Creative Commons BY-NC-SA 4.0 License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/
#
# Unless required by applicable law or a... | nucleotide-transformer-main | nucleotide_transformer/pretrained.py |
# Copyright 2022 InstaDeep Ltd
#
# Licensed under the Creative Commons BY-NC-SA 4.0 License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/
#
# Unless required by applicable law or a... | nucleotide-transformer-main | nucleotide_transformer/constants.py |
nucleotide-transformer-main | nucleotide_transformer/__init__.py | |
# Copyright 2022 InstaDeep Ltd
#
# Licensed under the Creative Commons BY-NC-SA 4.0 License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/
#
# Unless required by applicable law or a... | nucleotide-transformer-main | nucleotide_transformer/types.py |
# Copyright 2022 InstaDeep Ltd
#
# Licensed under the Creative Commons BY-NC-SA 4.0 License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/
#
# Unless required by applicable law or a... | nucleotide-transformer-main | nucleotide_transformer/model.py |
# Copyright 2022 InstaDeep Ltd
#
# Licensed under the Creative Commons BY-NC-SA 4.0 License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/
#
# Unless required by applicable law or a... | nucleotide-transformer-main | nucleotide_transformer/layers.py |
# Copyright 2022 InstaDeep Ltd
#
# Licensed under the Creative Commons BY-NC-SA 4.0 License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/
#
# Unless required by applicable law or a... | nucleotide-transformer-main | nucleotide_transformer/tokenizers.py |
from setuptools import setup, find_packages
setup(
name = 'Galvatron',
packages = find_packages(exclude=[]),
version = '0.0.3',
license='MIT',
description = 'Swarms - Pytorch',
author = 'Kye Gomez',
author_email = 'kye@apac.ai',
long_description_content_type = 'text/markdown',
url = 'https://github.c... | Galvatron-master | setup.py |
from Galvatron import GalvatronUltra
galvatronMega = GalvatronUltra(use_4bit_quantization=True)
modality_data = {
"Text": "Text data",
"Image": "examples/100-trillion.png",
# "Audio": "/path/to/audio.mp3",
# "Video": "/path/to/video.mp4", # Uncomment if video data is available
# "Point Cloud": "/... | Galvatron-master | examples/test.py |
from Galvatron.model import GalvatronBaseLM, Galvatron, GalvatronMega, GalvatronUltra | Galvatron-master | Galvatron/__init__.py |
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoConfig
class GalvatronBaseLM:
"""
This class is designed to initialize the base language model, in this case MosaicML's mpt-30b-chat,
with the option for 4-bit quantization.
It also integrates additiona... | Galvatron-master | Galvatron/model.py |
#!/usr/bin/env python3
# Portions 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 logging
import math
import torch
import torch.nn as nn
import torchaudio
from PIL ... | Galvatron-master | Galvatron/ImageBind/data.py |
Galvatron-master | Galvatron/ImageBind/models/__init__.py | |
#!/usr/bin/env python3
# Portions 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
from functools import partial
from types import SimpleNamespace
import torch
i... | Galvatron-master | Galvatron/ImageBind/models/imagebind_model.py |
#!/usr/bin/env python3
# Portions 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.
# Code modified from
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/... | Galvatron-master | Galvatron/ImageBind/models/transformer.py |
#!/usr/bin/env python3
# Portions 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 gzip
import html
import io
import math
from functools import lru_cache
from typing ... | Galvatron-master | Galvatron/ImageBind/models/multimodal_preprocessors.py |
#!/usr/bin/env python3
# Portions 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 einops
import numpy as np
import torch
import torch.nn as nn
class Normalize(nn.... | Galvatron-master | Galvatron/ImageBind/models/helpers.py |
from profit.main import ProfitPilot
# Define variables for ProfitPilot
AI_NAME = "Athena"
AI_ROLE = "Sales Representative"
EXTERNAL_TOOLS = None
COMPANY_NAME = "ABC Company"
COMPANY_VALUES = "Quality, Innovation, Customer Satisfaction"
CONVERSATION_TYPE = "Cold Email"
CONVERSATION_PURPOSE = "discuss our new product"... | ProfitPilot-main | example.py |
import streamlit as st
from profit.main import ProfitPilot
from clarifai_utils.modules.css import ClarifaiStreamlitCSS
st.set_page_config(layout="wide")
ClarifaiStreamlitCSS.insert_default_css(st)
st.markdown("Please select a specific page from the sidebar to the left")
# Define variables for ProfitPilot
AI_NAME = ... | ProfitPilot-main | app.py |
import asyncio
import os
# Tools
from contextlib import contextmanager
from typing import Optional
import pandas as pd
from langchain.agents import tool
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain... | ProfitPilot-main | profit/tools.py |
from profit.main import ProfitPilot
# from profit.agent import Agent | ProfitPilot-main | profit/__init__.py |
import logging
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
class LLama:
def __init__(self,
model_id = "georgesung/llama2_7b_chat_uncensored",
device: str = None,
max_length: int = 2000,
quantiz... | ProfitPilot-main | profit/llama.py |
import faiss
from langchain.docstore import InMemoryDocstore
from langchain.embeddings import OpenAIEmbeddings
from langchain.tools.human.tool import HumanInputRun
from langchain.vectorstores import FAISS
from langchain_experimental.autonomous_agents import AutoGPT
from profit.tools import (
ReadFileTool,
Write... | ProfitPilot-main | profit/clarifi_agent.py |
import faiss
from langchain.chat_models import ChatOpenAI
from langchain.docstore import InMemoryDocstore
from langchain.embeddings import OpenAIEmbeddings
from langchain.tools.human.tool import HumanInputRun
from langchain.vectorstores import FAISS
from langchain_experimental.autonomous_agents import AutoGPT
from pro... | ProfitPilot-main | profit/agent.py |
from langchain.llms import Clarifai
# class ClarifiLLM:
# def __init__(
# self,
# clarifai_pat: str = "890cdb0cb5aa4795ba51af9670120a1e",
# user_id="meta",
# app_id="Llama-2",
# model_id="llama2-70b-chat"
# ):
# self.CLARIFAI_PAT = cl... | ProfitPilot-main | profit/clarifi.py |
from profit.clarifi_agent import Agent
class ProfitPilot:
def __init__(
self,
ai_name: str = None,
ai_role: str = None,
external_tools = None,
company_name: str = None,
company_values: str = None,
conversation_type: str = None,
... | ProfitPilot-main | profit/main.py |
from mqa.main import MultiHeadAttention, MultiQueryAttention
from mqa.main import *
from mqa.flash_attn_triton import * | MultiQueryAttention-main | mqa/__init__.py |
"""
Copied from https://github.com/HazyResearch/flash-attention/blob/eff9fe6b8076df59d64d7a3f464696738a3c7c24/flash_attn/flash_attn_triton.py
update imports to use 'triton_pre_mlir'
*Experimental* implementation of FlashAttention in Triton.
Tested with triton==2.0.0.dev20221202.
Triton 2.0 has a new backend (MLIR) but... | MultiQueryAttention-main | mqa/flash_attn_triton.py |
import math
import warnings
from typing import Optional
import torch
import torch.nn as nn
from einops import rearrange
from packaging import version
from typing import Dict, Type
def _cast_if_autocast_enabled(tensor):
if torch.is_autocast_enabled():
if tensor.device.type == 'cuda':
dtype =... | MultiQueryAttention-main | mqa/main.py |
import torch
from kosmosx.model import Kosmos
# Create a sample text token tensor with dtype torch.long
text_tokens = torch.randint(0, 32002, (1, 50), dtype=torch.long)
# Create a sample image tensor
images = torch.randn(1, 3, 224, 224)
images = images.long()
# Instantiate the model
model = Kosmos()
# Pass the samp... | Kosmos-X-master | example.py |
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 model.kosmos import Kosmos, KosmosTokenizer
from accelerat... | Kosmos-X-master | old/training/train_kosmos_stable_3.py |
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 model.kosmos import Kosmos, KosmosTokenizer
from accelerat... | Kosmos-X-master | old/training/train_kosmos_stable_2.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... | Kosmos-X-master | old/training/training_distributed_accelerate.py |
import math
import multiprocessing
import os
from datetime import timedelta
from functools import partial
from itertools import chain
from accelerate import Accelerator
from accelerate.utils import InitProcessGroupKwargs
from datasets import concatenate_datasets, load_dataset
from torch.distributed.algorithms._checkpo... | Kosmos-X-master | old/training/train_kosmos.py |
import math
import multiprocessing
import os
from datetime import timedelta
from functools import partial
from itertools import chain
from accelerate import Accelerator
from accelerate.utils import InitProcessGroupKwargs
from datasets import concatenate_datasets, load_dataset
from torch.distributed.algorithms._checkpo... | Kosmos-X-master | old/training/training.py |
import time
import torch
from accelerate.utils import set_seed
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import default_data_collator, get_linear_schedule_with_warmup
from kosmosx import Kosmos, KosmosTokenizer
from accelerate import Accelerator
from rich.progress im... | Kosmos-X-master | old/training/experiments/train_kosmos_optimized.py |
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 .kosmos import Kosmos, KosmosTokenizer
from accelerate imp... | Kosmos-X-master | old/training/experiments/train_kosmos_original.py |
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 .kosmos import Kosmos, KosmosTokenizer
from accelerate imp... | Kosmos-X-master | old/training/experiments/training_kosmos_apex.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 kosmosx import Kosmos, KosmosTo... | Kosmos-X-master | old/training/experiments/training_kosmos_3.py |
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 .kosmos import Kosmos, KosmosTokenizer
from accelerate imp... | Kosmos-X-master | old/training/experiments/train_kosmos_text.py |
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 .kosmos import Kosmos, KosmosTokenizer
from accelerate imp... | Kosmos-X-master | old/training/experiments/train_kosmos_code.py |
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 .kosmos import Kosmos, KosmosTokenizer
from accelerate imp... | Kosmos-X-master | old/training/experiments/train_kosmos.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel
from transformers import Wav2Vec2Tokenizer
from transformers import Wav2... | Kosmos-X-master | old/training/notebookExperiments/main.py |
from kosmosx.model import KosmosTokenizer, Kosmos
from kosmosx.tokenize import BuildDataset | Kosmos-X-master | kosmosx/__init__.py |
import logging
import torch
import torch.nn as nn
from flamingo_pytorch import PerceiverResampler
from torch.nn import Module
from transformers import AutoTokenizer, CLIPModel, CLIPProcessor
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s - %(levelname)s - %(message)s'
)
# Check if the mod... | Kosmos-X-master | kosmosx/model.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... | Kosmos-X-master | kosmosx/train.py |
import argparse
import hidet
import torch
from einops._torch_specific import allow_ops_in_compiled_graph
from transformers import AutoTokenizer
def Inference():
allow_ops_in_compiled_graph()
torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
parser = argparse.ArgumentParser(description="Gener... | Kosmos-X-master | kosmosx/inference.py |
import argparse
import multiprocessing
from itertools import chain
from datasets import load_dataset
from kosmosx.model import KosmosTokenizer
class BuildDataset:
def __init__(self, seed=42, seq_len=8192, hf_account="YOUR HUGGINGFACE API KEY", dataset_name="uggingFaceM4/VQAv2"):
self.SEED = seed
... | Kosmos-X-master | kosmosx/tokenize.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,
... | Kosmos-X-master | kosmosx/utils/stable_adamw.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel
from flamingo_pytorch import PerceiverResampler
from torch.nn import Mo... | Kosmos-X-master | kosmosx/model/kosmos.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel
from transformers import Data2VecForCTC, Wav2Vec2Processor
from flaming... | Kosmos-X-master | kosmosx/model/video/kosmos_video.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel
from transformers import Data2VecForCTC, Wav2Vec2Processor
from flaming... | Kosmos-X-master | kosmosx/model/video/kosmos_conditional.py |
import torch
import data
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer
# from transformers import Data2VecForCTC, Wav2Vec2Processor
from flamingo_pytorch im... | Kosmos-X-master | kosmosx/model/video/imagebind/kosmos.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import CLIPProcessor, CLIPModel, PreTrainedTokenizerFast
from tokenizers import SentencePieceBPETokenizer
from flami... | Kosmos-X-master | kosmosx/model/experiments/kosmosSP.py |
"""
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright © 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for soft... | Kosmos-X-master | kosmosx/model/allModalities/kosmos3.py |
import os
import requests
import torch
from torch.nn import Module
from torchvision import transforms
from torchvision.models.video import r3d_18
from transformers import (
AutoModel,
AutoTokenizer,
CLIPModel,
CLIPProcessor,
Wav2Vec2ForCTC,
T5Tokenizer,
Wav2Vec2Processor,
)
from torchscale.a... | Kosmos-X-master | kosmosx/model/allModalities/kosmos2.py |
import os
import torch
from torch.nn import Module
from torchvision import transforms
from torchvision.models.video import r3d_18
from transformers import (
AutoModel,
AutoTokenizer,
CLIPModel,
CLIPProcessor,
Data2VecForCTC,
T5Tokenizer,
Wav2Vec2Processor,
list_models
)
# Add additional... | Kosmos-X-master | kosmosx/model/allModalities/kosmos.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel
from transformers import Wav2Vec2Tokenizer
from transformers import Wav2... | Kosmos-X-master | kosmosx/model/allModalities/audio/kosmos_audio.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel
from transformers import Data2VecForCTC, Wav2Vec2Processor
from flaming... | Kosmos-X-master | kosmosx/model/allModalities/audio/kosmos_audio_data2vec.py |
import torch
from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder
from torchscale.component.embedding import PositionalEmbedding
from transformers import T5Tokenizer, CLIPProcessor, CLIPModel
from transformers import Wav2Vec2Tokenizer
from transformers import Wav2... | Kosmos-X-master | kosmosx/model/allModalities/audio/kosmos_conditional.py |
import torch
import time
from torchinfo import summary
from pytorch_memlab import LineProfiler
from kosmosx.torchscale.torchscale.component.multihead_attention import MultiheadAttention
def test_multihead_attention():
batch_size = 64
d_model = 512
num_heads = 8
multihead_attention = MultiheadAttentio... | Kosmos-X-master | testing/attention.py |
import torch
from kosmosx.model import Kosmos
# 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 = Kosmos()
# Pass the sample tensors to the model's forward function
ou... | Kosmos-X-master | testing/model_test.py |
import unittest
import torch
from kosmosx.model import Kosmos, KosmosTokenizer
from kosmosx.utils.stable_adamw import StableAdamWUnfused
class KosmosTest(unittest.TestCase):
def setUp(self):
self.model = Kosmos()
self.tokenizer = KosmosTokenizer()
self.optimizer = StableAdamWUnfused(self.m... | Kosmos-X-master | testing/main.py |
import matplotlib.pyplot as plt
import time
import torch
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import numpy as np
import tracemalloc
from kosmosx.model import Kosmos
from kosmosx.utils.stable_adamw import StableAdamWUnfused
torch.manual_seed(0)
if torch.cuda.is_available... | Kosmos-X-master | testing/benchmarking.py |
# Copyright 2022 MosaicML LLM Foundry authors
# SPDX-License-Identifier: Apache-2.0
"""Run pytest using MCP."""
import argparse
import time
from mcli.sdk import (RunConfig, RunStatus, create_run, follow_run_logs,
stop_run, wait_for_run_status)
if __name__ == '__main__':
parser = argparse.... | Kosmos-X-master | .github/mcp/mcp_pytest.py |
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