text stringlengths 7 1.24M | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 519 |
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# ResNeSt
A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: \\( V = \text{Concat} \\){\\( V^{1},V^{2}... | pytorch-image-models/hfdocs/source/models/resnest.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/resnest.mdx",
"repo_id": "pytorch-image-models",
"token_count": 5466
} | 200 |
# (Tensorflow) EfficientNet
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scal... | pytorch-image-models/hfdocs/source/models/tf-efficientnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/tf-efficientnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 8002
} | 201 |
""" ONNX export script
Export PyTorch models as ONNX graphs.
This export script originally started as an adaptation of code snippets found at
https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
The default parameters work with PyTorch 1.6 and ONNX 1.7 and produce an optimal ONNX graph
for h... | pytorch-image-models/onnx_export.py/0 | {
"file_path": "pytorch-image-models/onnx_export.py",
"repo_id": "pytorch-image-models",
"token_count": 1811
} | 202 |
import torch
import torch.nn as nn
from timm.layers import create_act_layer, set_layer_config
import importlib
import os
torch_backend = os.environ.get('TORCH_BACKEND')
if torch_backend is not None:
importlib.import_module(torch_backend)
torch_device = os.environ.get('TORCH_DEVICE', 'cpu')
class MLP(nn.Module):... | pytorch-image-models/tests/test_layers.py/0 | {
"file_path": "pytorch-image-models/tests/test_layers.py",
"repo_id": "pytorch-image-models",
"token_count": 871
} | 203 |
import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # m... | pytorch-image-models/timm/data/imagenet_info.py/0 | {
"file_path": "pytorch-image-models/timm/data/imagenet_info.py",
"repo_id": "pytorch-image-models",
"token_count": 1733
} | 204 |
from multiprocessing import Value
class SharedCount:
def __init__(self, epoch: int = 0):
self.shared_epoch = Value('i', epoch)
@property
def value(self):
return self.shared_epoch.value
@value.setter
def value(self, epoch):
self.shared_epoch.value = epoch
| pytorch-image-models/timm/data/readers/shared_count.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/shared_count.py",
"repo_id": "pytorch-image-models",
"token_count": 122
} | 205 |
""" PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import numpy as np
import torch
from torc... | pytorch-image-models/timm/layers/cond_conv2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/cond_conv2d.py",
"repo_id": "pytorch-image-models",
"token_count": 2314
} | 206 |
""" Global Context Attention Block
Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond`
- https://arxiv.org/abs/1904.11492
Official code consulted as reference: https://github.com/xvjiarui/GCNet
Hacked together by / Copyright 2021 Ross Wightman
"""
from torch import nn as nn
import torc... | pytorch-image-models/timm/layers/global_context.py/0 | {
"file_path": "pytorch-image-models/timm/layers/global_context.py",
"repo_id": "pytorch-image-models",
"token_count": 1169
} | 207 |
""" Normalization layers and wrappers
Norm layer definitions that support fast norm and consistent channel arg order (always first arg).
Hacked together by / Copyright 2022 Ross Wightman
"""
import numbers
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .fast_norm im... | pytorch-image-models/timm/layers/norm.py/0 | {
"file_path": "pytorch-image-models/timm/layers/norm.py",
"repo_id": "pytorch-image-models",
"token_count": 2512
} | 208 |
""" Test Time Pooling (Average-Max Pool)
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from torch import nn
import torch.nn.functional as F
from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
_logger = logging.getLogger(__name__)
class TestTimePoolHead(nn.Module):
def __init__(sel... | pytorch-image-models/timm/layers/test_time_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/test_time_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 881
} | 209 |
""" Model creation / weight loading / state_dict helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
from collections import OrderedDict
from typing import Any, Callable, Dict, Optional, Union
import torch
try:
import safetensors.torch
_has_safetensors = True
except ImportEr... | pytorch-image-models/timm/models/_helpers.py/0 | {
"file_path": "pytorch-image-models/timm/models/_helpers.py",
"repo_id": "pytorch-image-models",
"token_count": 2801
} | 210 |
""" ConViT Model
@article{d2021convit,
title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
journal={arXiv preprint arXiv:2103.10697},
year={2021}
}
P... | pytorch-image-models/timm/models/convit.py/0 | {
"file_path": "pytorch-image-models/timm/models/convit.py",
"repo_id": "pytorch-image-models",
"token_count": 7721
} | 211 |
""" EVA
EVA from https://github.com/baaivision/EVA , paper: https://arxiv.org/abs/2211.07636
@article{EVA,
title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},
author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang,
Tiejun and ... | pytorch-image-models/timm/models/eva.py/0 | {
"file_path": "pytorch-image-models/timm/models/eva.py",
"repo_id": "pytorch-image-models",
"token_count": 25759
} | 212 |
""" Pytorch Inception-Resnet-V2 implementation
Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functiona... | pytorch-image-models/timm/models/inception_resnet_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_resnet_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 6034
} | 213 |
"""
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... | pytorch-image-models/timm/models/pnasnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/pnasnet.py",
"repo_id": "pytorch-image-models",
"token_count": 7672
} | 214 |
""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weig... | pytorch-image-models/timm/models/swin_transformer.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer.py",
"repo_id": "pytorch-image-models",
"token_count": 21031
} | 215 |
"""
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Ke... | pytorch-image-models/timm/models/xception.py/0 | {
"file_path": "pytorch-image-models/timm/models/xception.py",
"repo_id": "pytorch-image-models",
"token_count": 3992
} | 216 |
""" NAdamW Optimizer
Based on simplified algorithm in https://github.com/mlcommons/algorithmic-efficiency/tree/main/baselines/nadamw
Added multi-tensor (foreach) path.
"""
import math
from typing import List, Optional
import torch
from torch import Tensor
# Modified from github.com/pytorch/pytorch/blob/v1.12.1/tor... | pytorch-image-models/timm/optim/nadamw.py/0 | {
"file_path": "pytorch-image-models/timm/optim/nadamw.py",
"repo_id": "pytorch-image-models",
"token_count": 5958
} | 217 |
from .agc import adaptive_clip_grad
from .attention_extract import AttentionExtract
from .checkpoint_saver import CheckpointSaver
from .clip_grad import dispatch_clip_grad
from .cuda import ApexScaler, NativeScaler
from .decay_batch import decay_batch_step, check_batch_size_retry
from .distributed import distribute_bn,... | pytorch-image-models/timm/utils/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/utils/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 264
} | 218 |
""" Summary utilities
Hacked together by / Copyright 2020 Ross Wightman
"""
import csv
import os
from collections import OrderedDict
try:
import wandb
except ImportError:
pass
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *paths)
if not os.path.exists(outdir):
os.maked... | pytorch-image-models/timm/utils/summary.py/0 | {
"file_path": "pytorch-image-models/timm/utils/summary.py",
"repo_id": "pytorch-image-models",
"token_count": 633
} | 219 |
{
mkPoetryApplication,
pkg-config,
protobuf,
openssl,
}:
mkPoetryApplication {
# name = "text-generation-server";
projectDir = ./server;
# nativeBuildInputs = [ pkg-config ];
# buildInputs = [ openssl.dev protobuf ];
}
| text-generation-inference/_server.nix/0 | {
"file_path": "text-generation-inference/_server.nix",
"repo_id": "text-generation-inference",
"token_count": 89
} | 220 |
[package]
name = "text-generation-backends-trtllm"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
async-trait = "0.1"
async-stream = "0.3"
clap = { version = "4.5", features = ["derive"] }
cxx = "1.0"
log = { version = "0.4", features = [] }
text-gen... | text-generation-inference/backends/trtllm/Cargo.toml/0 | {
"file_path": "text-generation-inference/backends/trtllm/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 333
} | 221 |
//
// Created by mfuntowicz on 6/30/24.
//
#pragma once
#include <cmath>
#include <exception>
#include <filesystem>
#include <limits>
#include <iterator>
#include <vector>
#include <spdlog/spdlog.h>
#include "backends/trtllm/include/ffi.h"
huggingface::tgi::backends::TensorRtLlmBackendImpl::TensorRtLlmBackendImpl(
... | text-generation-inference/backends/trtllm/src/ffi.cpp/0 | {
"file_path": "text-generation-inference/backends/trtllm/src/ffi.cpp",
"repo_id": "text-generation-inference",
"token_count": 1376
} | 222 |
[package]
name = "text-generation-benchmark"
description = "Text Generation Benchmarking tool"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[lib]
path = "src/lib.rs"
[[bin]]
name = "text-generation-benchmark"
path = "src/main.rs"
[dependencies]
average = "0.14"... | text-generation-inference/benchmark/Cargo.toml/0 | {
"file_path": "text-generation-inference/benchmark/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 368
} | 223 |
from text_generation.errors import (
parse_error,
GenerationError,
IncompleteGenerationError,
OverloadedError,
ValidationError,
BadRequestError,
ShardNotReadyError,
ShardTimeoutError,
NotFoundError,
RateLimitExceededError,
UnknownError,
)
def test_generation_error():
pa... | text-generation-inference/clients/python/tests/test_errors.py/0 | {
"file_path": "text-generation-inference/clients/python/tests/test_errors.py",
"repo_id": "text-generation-inference",
"token_count": 598
} | 224 |
# Non-core Model Serving
TGI supports various LLM architectures (see full list [here](../supported_models)). If you wish to serve a model that is not one of the supported models, TGI will fallback to the `transformers` implementation of that model. This means you will be unable to use some of the features introduced b... | text-generation-inference/docs/source/basic_tutorials/non_core_models.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/non_core_models.md",
"repo_id": "text-generation-inference",
"token_count": 472
} | 225 |
# Text Generation Inference
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5.
![Text Generation Inference](https://hugging... | text-generation-inference/docs/source/index.md/0 | {
"file_path": "text-generation-inference/docs/source/index.md",
"repo_id": "text-generation-inference",
"token_count": 563
} | 226 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 100000,
"logprob": null,
"text": "<｜begin▁of▁sentence｜>"
},
{
"id": 3533,
"logprob": -9.625,
"text": "Test"
},
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_deepseek_v2/test_flash_deepseek_v2.json",
"repo_id": "text-generation-inference",
"token_count": 1052
} | 227 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -13.90625,
"text": "Test"
},
{
"id": 200... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_grammar_llama/test_flash_llama_grammar.json",
"repo_id": "text-generation-inference",
"token_count": 1048
} | 228 |
{
"details": {
"finish_reason": "length",
"generated_tokens": 40,
"prefill": [],
"seed": null,
"tokens": [
{
"id": 13,
"logprob": -0.27416992,
"special": false,
"text": "\n"
},
{
"id": 13,
"logprob": -0.17016602,
"special": ... | text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_with_customer_support_adapter.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_lora_mistral/test_lora_mistral_with_customer_support_adapter.json",
"repo_id": "text-generation-inference",
"token_count": 3128
} | 229 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.8359375,
"text": "Test"
},
{
"id": 20... | text-generation-inference/integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_server_gptq_quantized/test_server_gptq_quantized.json",
"repo_id": "text-generation-inference",
"token_count": 1051
} | 230 |
import pytest
@pytest.fixture(scope="module")
def flash_deepseek_v2_handle(launcher):
with launcher("deepseek-ai/DeepSeek-V2-Lite", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_deepseek_v2(flash_deepseek_v2_handle):
await flash_deepseek_v2_handle.health(300)
... | text-generation-inference/integration-tests/models/test_flash_deepseek_v2.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_deepseek_v2.py",
"repo_id": "text-generation-inference",
"token_count": 710
} | 231 |
import pytest
@pytest.fixture(scope="module")
def flash_neox_sharded_handle(launcher):
with launcher("OpenAssistant/oasst-sft-1-pythia-12b", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_neox_sharded(flash_neox_sharded_handle):
await flash_neox_sharded_handle.h... | text-generation-inference/integration-tests/models/test_flash_neox_sharded.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_neox_sharded.py",
"repo_id": "text-generation-inference",
"token_count": 507
} | 232 |
import pytest
@pytest.fixture(scope="module")
def mt0_base_handle(launcher):
with launcher("bigscience/mt0-base") as handle:
yield handle
@pytest.fixture(scope="module")
async def mt0_base(mt0_base_handle):
await mt0_base_handle.health(300)
return mt0_base_handle.client
@pytest.mark.release
@p... | text-generation-inference/integration-tests/models/test_mt0_base.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_mt0_base.py",
"repo_id": "text-generation-inference",
"token_count": 737
} | 233 |
import json
def main():
with open("./ShareGPT_V3_unfiltered_cleaned_split.json", "r") as f:
data = json.load(f)
# Select only the first 2k conversations that start with a human.
max = 2000
conversations = []
for conversation in data:
conv = conversation.get("conversations")
... | text-generation-inference/load_tests/filter.py/0 | {
"file_path": "text-generation-inference/load_tests/filter.py",
"repo_id": "text-generation-inference",
"token_count": 307
} | 234 |
use crate::infer::Infer;
use crate::{
default_parameters,
server::{generate_internal, ComputeType},
Deserialize, ErrorResponse, GenerateParameters, GenerateRequest, Serialize, ToSchema,
};
use axum::extract::{Extension, Path};
use axum::http::{HeaderMap, StatusCode};
use axum::response::IntoResponse;
use ax... | text-generation-inference/router/src/kserve.rs/0 | {
"file_path": "text-generation-inference/router/src/kserve.rs",
"repo_id": "text-generation-inference",
"token_count": 3505
} | 235 |
flash_att_v2_commit_cuda := v2.6.1
flash_att_v2_commit_rocm := 2554f490101742ccdc56620a938f847f61754be6
build-flash-attention-v2-cuda:
pip install -U packaging wheel
pip install flash-attn==$(flash_att_v2_commit_cuda)
install-flash-attention-v2-cuda: build-flash-attention-v2-cuda
echo "Flash v2 installed"
build-f... | text-generation-inference/server/Makefile-flash-att-v2/0 | {
"file_path": "text-generation-inference/server/Makefile-flash-att-v2",
"repo_id": "text-generation-inference",
"token_count": 396
} | 236 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#include <ATen/cuda/CUDAContext.h>
#include "q4_matrix.cuh"
#include <vector>
#include "../util.cuh"
#include "../matrix.cuh"
using namespace std;
const int UNSHUF_BLOCKSIZE_X = 64;
const int RECONS_THREADS_X = 64; // Block size and thread... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cu/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cu",
"repo_id": "text-generation-inference",
"token_count": 2592
} | 237 |
#include "q_matrix.cuh"
#include "matrix_view.cuh"
#include "util.cuh"
#include "quant/qdq_2.cuh"
#include "quant/qdq_3.cuh"
#include "quant/qdq_4.cuh"
#include "quant/qdq_5.cuh"
#include "quant/qdq_6.cuh"
#include "quant/qdq_8.cuh"
#define BLOCK_KN_SIZE 128
#define THREADS_X 32
#define THREADS_Y 32
// Shuffle quan... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cu/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cu",
"repo_id": "text-generation-inference",
"token_count": 10524
} | 238 |
# Origin: https://github.com/predibase/lorax
# Path: lorax/server/lorax_server/adapters/config.py
# License: Apache License Version 2.0, January 2004
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Dict, Set, Tuple
import torch
from text_generation_server.adapters.weig... | text-generation-inference/server/text_generation_server/adapters/config.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/adapters/config.py",
"repo_id": "text-generation-inference",
"token_count": 275
} | 239 |
from dataclasses import dataclass
import bitsandbytes as bnb
import torch
from bitsandbytes.nn import Int8Params, Params4bit
from text_generation_server.utils.weights import UnquantizedWeight
@dataclass
class BNBWeight(UnquantizedWeight):
weight: torch.Tensor
def get_linear(self, bias: torch.Tensor):
... | text-generation-inference/server/text_generation_server/layers/bnb.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/bnb.py",
"repo_id": "text-generation-inference",
"token_count": 1825
} | 240 |
from typing import Optional
import torch
import torch.nn as nn
from loguru import logger
from text_generation_server.layers.fp8 import fp8_quantize
from text_generation_server.layers.marlin.gptq import _check_valid_shape
from text_generation_server.layers.marlin.util import (
_check_marlin_kernels,
permute_sca... | text-generation-inference/server/text_generation_server/layers/marlin/fp8.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/layers/marlin/fp8.py",
"repo_id": "text-generation-inference",
"token_count": 1787
} | 241 |
# coding=utf-8
# Copyright 2022 HuggingFace Inc. team. 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 requi... | text-generation-inference/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 11877
} | 242 |
# coding=utf-8
# Copyright 2024 the HuggingFace Inc. team. 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 r... | text-generation-inference/server/text_generation_server/models/custom_modeling/idefics2.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics2.py",
"repo_id": "text-generation-inference",
"token_count": 15271
} | 243 |
from contextlib import nullcontext
import math
import os
import time
import torch
import torch.distributed
import numpy as np
from loguru import logger
from dataclasses import dataclass
from opentelemetry import trace
from transformers import (
PreTrainedTokenizerBase,
AutoConfig,
AutoTokenizer,
Gener... | text-generation-inference/server/text_generation_server/models/flash_causal_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/flash_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 39451
} | 244 |
from typing import Iterable
from loguru import logger
from text_generation_server.pb import generate_pb2
def concat_text_chunks(chunks: Iterable[generate_pb2.InputChunk]) -> str:
"""
Concatenate text in text chunks. Non-text chunks are dropped.
"""
text = None
for chunk in chunks:
chunk_... | text-generation-inference/server/text_generation_server/utils/chunks.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/chunks.py",
"repo_id": "text-generation-inference",
"token_count": 332
} | 245 |
import torch
from abc import ABC, abstractmethod
from contextlib import contextmanager
from pathlib import Path
from typing import Dict, List, Optional, Union, Type
from safetensors import safe_open
from dataclasses import dataclass
from text_generation_server.utils.import_utils import SYSTEM
class WeightsLoader(AB... | text-generation-inference/server/text_generation_server/utils/weights.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/weights.py",
"repo_id": "text-generation-inference",
"token_count": 6634
} | 246 |
.PHONY: style check-style test
DATA_DIR = data
dir_guard=@mkdir -p $(@D)
# Format source code automatically
style:
npm run lint
# Check the source code is formatted correctly
check-style:
npm run lint-check
TESTS_RESOURCES = $(DATA_DIR)/small.txt $(DATA_DIR)/roberta.json $(DATA_DIR)/tokenizer-wiki.json $(DATA_DI... | tokenizers/bindings/node/Makefile/0 | {
"file_path": "tokenizers/bindings/node/Makefile",
"repo_id": "tokenizers",
"token_count": 406
} | 247 |
import {
byteLevelPreTokenizer,
metaspacePreTokenizer,
punctuationPreTokenizer,
sequencePreTokenizer,
splitPreTokenizer,
whitespaceSplitPreTokenizer,
} from '../../'
describe('byteLevelPreTokenizer', () => {
it('instantiates correctly', () => {
const processor = byteLevelPreTokenizer()
expect(pro... | tokenizers/bindings/node/lib/bindings/pre-tokenizers.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/pre-tokenizers.test.ts",
"repo_id": "tokenizers",
"token_count": 728
} | 248 |
{
"name": "tokenizers-linux-arm64-gnu",
"version": "0.13.4-rc1",
"os": [
"linux"
],
"cpu": [
"arm64"
],
"main": "tokenizers.linux-arm64-gnu.node",
"files": [
"tokenizers.linux-arm64-gnu.node"
],
"description": "Tokenizers platform specific bindings",
"keywords": [
"napi-rs",
"N... | tokenizers/bindings/node/npm/linux-arm64-gnu/package.json/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm64-gnu/package.json",
"repo_id": "tokenizers",
"token_count": 289
} | 249 |
use crate::arc_rwlock_serde;
use serde::{Deserialize, Serialize};
extern crate tokenizers as tk;
use napi::bindgen_prelude::*;
use napi_derive::napi;
use std::sync::{Arc, RwLock};
use tk::decoders::DecoderWrapper;
/// Decoder
#[derive(Clone, Serialize, Deserialize)]
#[napi]
pub struct Decoder {
#[serde(flatten, wi... | tokenizers/bindings/node/src/decoders.rs/0 | {
"file_path": "tokenizers/bindings/node/src/decoders.rs",
"repo_id": "tokenizers",
"token_count": 2038
} | 250 |
[target.x86_64-apple-darwin]
rustflags = [
"-C", "link-arg=-undefined",
"-C", "link-arg=dynamic_lookup",
"-C", "link-arg=-mmacosx-version-min=10.11",
]
[target.aarch64-apple-darwin]
rustflags = [
"-C", "link-arg=-undefined",
"-C", "link-arg=dynamic_lookup",
"-C", "link-arg=-mmacosx-version-min=10.11",
]
| tokenizers/bindings/python/.cargo/config.toml/0 | {
"file_path": "tokenizers/bindings/python/.cargo/config.toml",
"repo_id": "tokenizers",
"token_count": 146
} | 251 |
# Generated content DO NOT EDIT
class AddedToken:
"""
Represents a token that can be be added to a :class:`~tokenizers.Tokenizer`.
It can have special options that defines the way it should behave.
Args:
content (:obj:`str`): The content of the token
single_word (:obj:`bool`, defaults ... | tokenizers/bindings/python/py_src/tokenizers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 17199
} | 252 |
# Generated content DO NOT EDIT
from .. import processors
PostProcessor = processors.PostProcessor
BertProcessing = processors.BertProcessing
ByteLevel = processors.ByteLevel
RobertaProcessing = processors.RobertaProcessing
Sequence = processors.Sequence
TemplateProcessing = processors.TemplateProcessing
| tokenizers/bindings/python/py_src/tokenizers/processors/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/processors/__init__.py",
"repo_id": "tokenizers",
"token_count": 74
} | 253 |
#![warn(clippy::all)]
#![allow(clippy::upper_case_acronyms)]
// Many false positives with pyo3 it seems &str, and &PyAny get flagged
#![allow(clippy::borrow_deref_ref)]
extern crate tokenizers as tk;
mod decoders;
mod encoding;
mod error;
mod models;
mod normalizers;
mod pre_tokenizers;
mod processors;
mod token;
mod... | tokenizers/bindings/python/src/lib.rs/0 | {
"file_path": "tokenizers/bindings/python/src/lib.rs",
"repo_id": "tokenizers",
"token_count": 1087
} | 254 |
from tokenizers import BertWordPieceTokenizer
from ..utils import bert_files, data_dir, multiprocessing_with_parallelism
class TestBertWordPieceTokenizer:
def test_basic_encode(self, bert_files):
tokenizer = BertWordPieceTokenizer.from_file(bert_files["vocab"])
# Encode with special tokens by de... | tokenizers/bindings/python/tests/implementations/test_bert_wordpiece.py/0 | {
"file_path": "tokenizers/bindings/python/tests/implementations/test_bert_wordpiece.py",
"repo_id": "tokenizers",
"token_count": 914
} | 255 |
# Post-processors
<tokenizerslangcontent>
<python>
## BertProcessing
[[autodoc]] tokenizers.processors.BertProcessing
## ByteLevel
[[autodoc]] tokenizers.processors.ByteLevel
## RobertaProcessing
[[autodoc]] tokenizers.processors.RobertaProcessing
## TemplateProcessing
[[autodoc]] tokenizers.processors.Template... | tokenizers/docs/source-doc-builder/api/post-processors.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/post-processors.mdx",
"repo_id": "tokenizers",
"token_count": 174
} | 256 |
Crates.io
----------------------------------------------------------------------------------------------------
🤗 Tokenizers is available on `crates.io <https://crates.io/crates/tokenizers>`__.
You just need to add it to your :obj:`Cargo.toml`::
tokenizers = "0.10"
| tokenizers/docs/source/installation/rust.inc/0 | {
"file_path": "tokenizers/docs/source/installation/rust.inc",
"repo_id": "tokenizers",
"token_count": 74
} | 257 |
use tokenizers::Tokenizer;
fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
let tokenizer = Tokenizer::from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct", None)?;
let data = std::fs::read_to_string("data/big.txt")?;
let data: Vec<_> = data.lines().collect();
let add_special_tok... | tokenizers/tokenizers/examples/encode_batch.rs/0 | {
"file_path": "tokenizers/tokenizers/examples/encode_batch.rs",
"repo_id": "tokenizers",
"token_count": 165
} | 258 |
import * as wasm from "unstable_wasm";
console.log(wasm.tokenize("ab"));
console.log(wasm.tokenize("abc"));
| tokenizers/tokenizers/examples/unstable_wasm/www/index.js/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/www/index.js",
"repo_id": "tokenizers",
"token_count": 43
} | 259 |
use super::{super::OrderedVocabIter, convert_merges_to_hashmap, BpeBuilder, Pair, BPE};
use serde::{
de::{Error, MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
use std::collections::HashMap;
impl Serialize for BPE {
fn serialize<S>(&self, serializer: S) ... | tokenizers/tokenizers/src/models/bpe/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/bpe/serialization.rs",
"repo_id": "tokenizers",
"token_count": 4848
} | 260 |
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use serde::{Deserialize, Serialize};
use unicode_categories::UnicodeCategories;
/// Checks whether a character is whitespace
fn is_whitespace(c: char) -> bool {
// These are technically control characters but we count them as whitespace
match c {
... | tokenizers/tokenizers/src/normalizers/bert.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/bert.rs",
"repo_id": "tokenizers",
"token_count": 1856
} | 261 |
use crate::pre_tokenizers::PreTokenizerWrapper;
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result};
use crate::utils::macro_rules_attribute;
use serde::{Deserialize, Serialize};
#[derive(Clone, Debug, PartialEq)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct Sequence {
pretokenizers: Vec<PreT... | tokenizers/tokenizers/src/pre_tokenizers/sequence.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/sequence.rs",
"repo_id": "tokenizers",
"token_count": 1011
} | 262 |
use crate::{
normalizer::Range, Encoding, NormalizedString, OffsetReferential, Offsets, Result, Token,
};
use std::collections::HashMap;
/// Various possible types of offsets
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum OffsetType {
Byte,
Char,
None,
}
/// Wrapper for a subpart of a `Normaliz... | tokenizers/tokenizers/src/tokenizer/pre_tokenizer.rs/0 | {
"file_path": "tokenizers/tokenizers/src/tokenizer/pre_tokenizer.rs",
"repo_id": "tokenizers",
"token_count": 5310
} | 263 |
mod common;
use common::*;
use tokenizers::tokenizer::AddedToken;
macro_rules! check_offsets {
($input: expr, $output:expr, $offset:expr, $result:expr) => {
let offsets = $output.get_offsets()[$offset];
assert_eq!(&$input[offsets.0..offsets.1], $result);
};
}
#[test]
fn byte_level_basic() {
... | tokenizers/tokenizers/tests/offsets.rs/0 | {
"file_path": "tokenizers/tokenizers/tests/offsets.rs",
"repo_id": "tokenizers",
"token_count": 2497
} | 264 |
import argparse
import subprocess
def main(config_dir, config_name, args):
subprocess.run(["optimum-benchmark", "--config-dir", f"{config_dir}", "--config-name", f"{config_name}"] + ["hydra/job_logging=disabled", "hydra/hydra_logging=disabled"] + args)
if __name__ == "__main__":
parser = argparse.ArgumentPa... | transformers/benchmark/optimum_benchmark_wrapper.py/0 | {
"file_path": "transformers/benchmark/optimum_benchmark_wrapper.py",
"repo_id": "transformers",
"token_count": 216
} | 265 |
FROM python:3.10
LABEL maintainer="Hugging Face"
RUN apt update
RUN git clone https://github.com/huggingface/transformers
RUN python3 -m pip install --no-cache-dir --upgrade pip && python3 -m pip install --no-cache-dir git+https://github.com/huggingface/doc-builder ./transformers[dev]
RUN apt-get -y update && apt-get... | transformers/docker/transformers-doc-builder/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-doc-builder/Dockerfile",
"repo_id": "transformers",
"token_count": 292
} | 266 |
# docstyle-ignore
INSTALL_CONTENT = """
# Transformers installation
! pip install transformers datasets evaluate accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
notebook_f... | transformers/docs/source/_config.py/0 | {
"file_path": "transformers/docs/source/_config.py",
"repo_id": "transformers",
"token_count": 157
} | 267 |
<!--Copyright 2022 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/de/quicktour.md/0 | {
"file_path": "transformers/docs/source/de/quicktour.md",
"repo_id": "transformers",
"token_count": 7330
} | 268 |
<!--Copyright 2022 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/big_models.md/0 | {
"file_path": "transformers/docs/source/en/big_models.md",
"repo_id": "transformers",
"token_count": 3022
} | 269 |
<!---
Copyright 2022 The HuggingFace Team. 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 applicable law or ... | transformers/docs/source/en/installation.md/0 | {
"file_path": "transformers/docs/source/en/installation.md",
"repo_id": "transformers",
"token_count": 2902
} | 270 |
<!--Copyright 2020 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/main_classes/callback.md/0 | {
"file_path": "transformers/docs/source/en/main_classes/callback.md",
"repo_id": "transformers",
"token_count": 1520
} | 271 |
<!--Copyright 2020 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/main_classes/tokenizer.md/0 | {
"file_path": "transformers/docs/source/en/main_classes/tokenizer.md",
"repo_id": "transformers",
"token_count": 1144
} | 272 |
<!--Copyright 2020 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/model_doc/bertweet.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/bertweet.md",
"repo_id": "transformers",
"token_count": 806
} | 273 |
<!--Copyright 2024 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/model_doc/chameleon.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/chameleon.md",
"repo_id": "transformers",
"token_count": 2563
} | 274 |
<!--Copyright 2022 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/model_doc/cvt.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/cvt.md",
"repo_id": "transformers",
"token_count": 1314
} | 275 |
<!--Copyright 2021 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/model_doc/imagegpt.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/imagegpt.md",
"repo_id": "transformers",
"token_count": 1915
} | 276 |
<!--Copyright 2020 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/model_doc/t5.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/t5.md",
"repo_id": "transformers",
"token_count": 7103
} | 277 |
<!--Copyright 2021 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/model_doc/unispeech.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/unispeech.md",
"repo_id": "transformers",
"token_count": 853
} | 278 |
<!--Copyright 2020 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/model_doc/xlnet.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/xlnet.md",
"repo_id": "transformers",
"token_count": 2042
} | 279 |
<!--Copyright 2022 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/perf_infer_gpu_one.md/0 | {
"file_path": "transformers/docs/source/en/perf_infer_gpu_one.md",
"repo_id": "transformers",
"token_count": 9727
} | 280 |
<!--Copyright 2024 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/quantization/awq.md/0 | {
"file_path": "transformers/docs/source/en/quantization/awq.md",
"repo_id": "transformers",
"token_count": 4388
} | 281 |
<!--Copyright 2023 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/tasks/asr.md/0 | {
"file_path": "transformers/docs/source/en/tasks/asr.md",
"repo_id": "transformers",
"token_count": 4907
} | 282 |
<!--Copyright 2023 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/tasks/prompting.md/0 | {
"file_path": "transformers/docs/source/en/tasks/prompting.md",
"repo_id": "transformers",
"token_count": 5574
} | 283 |
<!--Copyright 2023 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/en/tflite.md/0 | {
"file_path": "transformers/docs/source/en/tflite.md",
"repo_id": "transformers",
"token_count": 878
} | 284 |
<!--Copyright 2022 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/es/create_a_model.md/0 | {
"file_path": "transformers/docs/source/es/create_a_model.md",
"repo_id": "transformers",
"token_count": 6229
} | 285 |
<!---
Copyright 2020 The HuggingFace Team. 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 applicable law or ... | transformers/docs/source/es/pr_checks.md/0 | {
"file_path": "transformers/docs/source/es/pr_checks.md",
"repo_id": "transformers",
"token_count": 2659
} | 286 |
<!--Copyright 2022 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/it/accelerate.md/0 | {
"file_path": "transformers/docs/source/it/accelerate.md",
"repo_id": "transformers",
"token_count": 1891
} | 287 |
<!--Copyright 2022 The HuggingFace Team. 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 applicable law or agreed... | transformers/docs/source/it/perf_infer_cpu.md/0 | {
"file_path": "transformers/docs/source/it/perf_infer_cpu.md",
"repo_id": "transformers",
"token_count": 1497
} | 288 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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