repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/inference/benchmark_ops/benchmark_flash_decoding_attention.py | examples/inference/benchmark_ops/benchmark_flash_decoding_attention.py | import torch
from colossalai.kernel.kernel_loader import InferenceOpsLoader
from colossalai.kernel.triton import flash_decoding_attention
from colossalai.utils import get_current_device
from tests.test_infer.test_kernels.triton.kernel_utils import (
generate_caches_and_block_tables_v2,
generate_caches_and_bloc... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/inference/benchmark_ops/benchmark_rotary_embedding.py | examples/inference/benchmark_ops/benchmark_rotary_embedding.py | import torch
import triton
from vllm._C import ops
from colossalai.kernel.kernel_loader import InferenceOpsLoader
from colossalai.kernel.triton import rotary_embedding
inference_ops = InferenceOpsLoader().load()
BATCH = 16
configs = [
triton.testing.Benchmark(
x_names=["num_tokens"],
x_vals=[2**i... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/inference/benchmark_ops/benchmark_xine_copy.py | examples/inference/benchmark_ops/benchmark_xine_copy.py | import torch
from colossalai.kernel.triton import get_xine_cache
from tests.test_infer.test_kernels.triton.test_xine_copy import get_cos_sin
try:
import triton # noqa
except ImportError:
print("please install triton from https://github.com/openai/triton")
configs = [
triton.testing.Benchmark(
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/inference/stable_diffusion/benchmark_sd3.py | examples/inference/stable_diffusion/benchmark_sd3.py | import argparse
import json
import time
from contextlib import nullcontext
import torch
import torch.distributed as dist
from diffusers import DiffusionPipeline
import colossalai
from colossalai.inference.config import DiffusionGenerationConfig, InferenceConfig
from colossalai.inference.core.engine import InferenceEn... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/inference/stable_diffusion/compute_metric.py | examples/inference/stable_diffusion/compute_metric.py | # Code from https://github.com/mit-han-lab/distrifuser/blob/main/scripts/compute_metrics.py
import argparse
import os
import numpy as np
import torch
from cleanfid import fid
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchmetrics.image import LearnedPerceptualImagePatchSimilarity, Pea... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/inference/stable_diffusion/sd3_generation.py | examples/inference/stable_diffusion/sd3_generation.py | import argparse
from diffusers import DiffusionPipeline
from torch import bfloat16
from torch import distributed as dist
from torch import float16, float32
import colossalai
from colossalai.cluster import DistCoordinator
from colossalai.inference.config import DiffusionGenerationConfig, InferenceConfig
from colossala... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/inference/client/locustfile.py | examples/inference/client/locustfile.py | from locust import HttpUser, between, tag, task
class QuickstartUser(HttpUser):
wait_time = between(1, 5)
@tag("online-generation")
@task(5)
def completion(self):
self.client.post("/completion", json={"prompt": "hello, who are you? ", "stream": "False"})
@tag("online-generation")
@ta... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/inference/llama/benchmark_llama.py | examples/inference/llama/benchmark_llama.py | import argparse
import time
from contextlib import nullcontext
import torch
import torch.distributed as dist
import transformers
from transformers import AutoTokenizer, GenerationConfig
from vllm import LLM, SamplingParams
import colossalai
from colossalai.accelerator import get_accelerator
from colossalai.inference.... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/inference/llama/llama_generation.py | examples/inference/llama/llama_generation.py | import argparse
from torch import bfloat16, float16, float32
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import colossalai
from colossalai.cluster import DistCoordinator
from colossalai.inference.config import InferenceConfig
from colossalai.inference.core.engine import InferenceEng... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/inference/llama/benchmark_llama3.py | examples/inference/llama/benchmark_llama3.py | import argparse
import time
from contextlib import nullcontext
import torch
import transformers
from transformers import AutoTokenizer, GenerationConfig
import colossalai
from colossalai.accelerator import get_accelerator
from colossalai.cluster import DistCoordinator
from colossalai.inference.config import Inference... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/vit/args.py | examples/images/vit/args.py | import argparse
def parse_demo_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_name_or_path",
type=str,
default="google/vit-base-patch16-224",
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/vit/vit_train_demo.py | examples/images/vit/vit_train_demo.py | from typing import Any, Callable, Iterator
import torch
import torch.distributed as dist
import torch.nn as nn
import transformers
from args import parse_demo_args
from data import BeansDataset, beans_collator
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
from torch... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/vit/vit_benchmark.py | examples/images/vit/vit_benchmark.py | import time
import torch
import transformers
from args import parse_benchmark_args
from tqdm import tqdm
from transformers import ViTConfig, ViTForImageClassification
import colossalai
from colossalai.booster import Booster
from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, LowLevelZeroPlugin, ... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/vit/data.py | examples/images/vit/data.py | import torch
from datasets import load_dataset
from torch.utils.data import Dataset
class BeansDataset(Dataset):
def __init__(self, image_processor, tp_size=1, split="train"):
super().__init__()
self.image_processor = image_processor
self.ds = load_dataset("beans")[split]
self.labe... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/setup.py | examples/images/diffusion/setup.py | from setuptools import find_packages, setup
setup(
name="latent-diffusion",
version="0.0.1",
description="",
packages=find_packages(),
install_requires=[
"torch",
"numpy",
"tqdm",
],
)
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/main.py | examples/images/diffusion/main.py | import argparse
import datetime
import glob
import os
import sys
import time
from functools import partial
import lightning.pytorch as pl
import numpy as np
import torch
import torchvision
from ldm.models.diffusion.ddpm import LatentDiffusion
from lightning.pytorch import seed_everything
from lightning.pytorch.callbac... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | true |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/scripts/train_searcher.py | examples/images/diffusion/scripts/train_searcher.py | import argparse
import glob
import os
import sys
from multiprocessing import cpu_count
import numpy as np
import scann
from ldm.util import parallel_data_prefetch
from tqdm import tqdm
def search_bruteforce(searcher):
return searcher.score_brute_force().build()
def search_partioned_ah(
searcher, dims_per_b... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/scripts/img2img.py | examples/images/diffusion/scripts/img2img.py | """make variations of input image"""
import argparse
import os
from contextlib import nullcontext
from itertools import islice
import numpy as np
import PIL
import torch
from einops import rearrange, repeat
from omegaconf import OmegaConf
from PIL import Image
from torch import autocast
from torchvision.utils import ... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/scripts/sample_diffusion.py | examples/images/diffusion/scripts/sample_diffusion.py | import argparse
import datetime
import glob
import os
import sys
import time
import numpy as np
import torch
import yaml
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
rescale = lambda x: (x +... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/scripts/utils.py | examples/images/diffusion/scripts/utils.py | import bitsandbytes as bnb
import torch
import torch.nn as nn
class Linear8bit(nn.Linear):
def __init__(
self,
input_features,
output_features,
bias=True,
has_fp16_weights=False,
memory_efficient_backward=False,
threshold=6.0,
weight_data=None,
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/scripts/inpaint.py | examples/images/diffusion/scripts/inpaint.py | import argparse
import glob
import os
import numpy as np
import torch
from ldm.models.diffusion.ddim import DDIMSampler
from main import instantiate_from_config
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm
def make_batch(image, mask, device):
image = np.array(Image.open(image).conv... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/scripts/txt2img.py | examples/images/diffusion/scripts/txt2img.py | import argparse
import os
from itertools import islice
import cv2
import numpy as np
import torch
from einops import rearrange
from omegaconf import OmegaConf
from PIL import Image
from torchvision.utils import make_grid
from tqdm import tqdm, trange
try:
from lightning.pytorch import seed_everything
except:
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/scripts/knn2img.py | examples/images/diffusion/scripts/knn2img.py | import argparse
import glob
import os
import time
from itertools import islice
from multiprocessing import cpu_count
import numpy as np
import scann
import torch
from einops import rearrange
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.modules.encoders.mo... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/scripts/tests/test_watermark.py | examples/images/diffusion/scripts/tests/test_watermark.py | import cv2
import fire
from imwatermark import WatermarkDecoder
def testit(img_path):
bgr = cv2.imread(img_path)
decoder = WatermarkDecoder("bytes", 136)
watermark = decoder.decode(bgr, "dwtDct")
try:
dec = watermark.decode("utf-8")
except:
dec = "null"
print(dec)
if __name__... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/scripts/tests/test_checkpoint.py | examples/images/diffusion/scripts/tests/test_checkpoint.py | import torch
import yaml
from diffusers import StableDiffusionPipeline
from ldm.modules.diffusionmodules.openaimodel import UNetModel
if __name__ == "__main__":
with torch.no_grad():
yaml_path = "../../train_colossalai.yaml"
with open(yaml_path, "r", encoding="utf-8") as f:
config = f.r... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/util.py | examples/images/diffusion/ldm/util.py | import importlib
from inspect import isfunction
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
from torch import optim
def log_txt_as_img(wh, xc, size=10):
# wh a tuple of (width, height)
# xc a list of captions to plot
b = len(xc)
txts = list()
for bi in range(b):
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/lr_scheduler.py | examples/images/diffusion/ldm/lr_scheduler.py | import numpy as np
class LambdaWarmUpCosineScheduler:
"""
note: use with a base_lr of 1.0
"""
def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
self.lr_warm_up_steps = warm_up_steps
self.lr_start = lr_start
self.lr_min = lr_min... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/models/autoencoder.py | examples/images/diffusion/ldm/models/autoencoder.py | from contextlib import contextmanager
import lightning.pytorch as pl
import torch
from ldm.modules.diffusionmodules.model import Decoder, Encoder
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from ldm.modules.ema import LitEma
from torch.nn import Identity
from torch.nn import functi... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/models/diffusion/ddim.py | examples/images/diffusion/ldm/models/diffusion/ddim.py | """SAMPLING ONLY."""
import numpy as np
import torch
from ldm.modules.diffusionmodules.util import (
extract_into_tensor,
make_ddim_sampling_parameters,
make_ddim_timesteps,
noise_like,
)
from tqdm import tqdm
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/models/diffusion/ddpm.py | examples/images/diffusion/ldm/models/diffusion/ddpm.py | """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | true |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/models/diffusion/__init__.py | examples/images/diffusion/ldm/models/diffusion/__init__.py | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false | |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/models/diffusion/sampling_util.py | examples/images/diffusion/ldm/models/diffusion/sampling_util.py | def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions.
From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f"input has {x.ndim} ... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/models/diffusion/classifier.py | examples/images/diffusion/ldm/models/diffusion/classifier.py | import os
from copy import deepcopy
from glob import glob
import lightning.pytorch as pl
import torch
from einops import rearrange
from ldm.lr_scheduler import LambdaLinearScheduler
from ldm.models.diffusion.ddpm import LatentDiffusion
from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
fr... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/models/diffusion/plms.py | examples/images/diffusion/ldm/models/diffusion/plms.py | """SAMPLING ONLY."""
import numpy as np
import torch
from ldm.models.diffusion.sampling_util import norm_thresholding
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
from tqdm import tqdm
class PLMSSampler(object):
def __init__(self, model, schedule="l... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py | examples/images/diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py | import math
import torch
from tqdm import tqdm
class NoiseScheduleVP:
def __init__(
self,
schedule="discrete",
betas=None,
alphas_cumprod=None,
continuous_beta_0=0.1,
continuous_beta_1=20.0,
):
"""Create a wrapper class for the forward SDE (VP type).
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | true |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/models/diffusion/dpm_solver/sampler.py | examples/images/diffusion/ldm/models/diffusion/dpm_solver/sampler.py | """SAMPLING ONLY."""
import torch
from .dpm_solver import DPM_Solver, NoiseScheduleVP, model_wrapper
MODEL_TYPES = {"eps": "noise", "v": "v"}
class DPMSolverSampler(object):
def __init__(self, model, **kwargs):
super().__init__()
self.model = model
to_torch = lambda x: x.clone().detach(... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/models/diffusion/dpm_solver/__init__.py | examples/images/diffusion/ldm/models/diffusion/dpm_solver/__init__.py | from .sampler import DPMSolverSampler
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/ema.py | examples/images/diffusion/ldm/modules/ema.py | import torch
from torch import nn
class LitEma(nn.Module):
def __init__(self, model, decay=0.9999, use_num_upates=True):
super().__init__()
if decay < 0.0 or decay > 1.0:
raise ValueError("Decay must be between 0 and 1")
self.m_name2s_name = {}
self.register_buffer("de... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/attention.py | examples/images/diffusion/ldm/modules/attention.py | import math
from inspect import isfunction
from typing import Any, Optional
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
from torch import einsum, nn
try:
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/image_degradation/bsrgan.py | examples/images/diffusion/ldm/modules/image_degradation/bsrgan.py | # -*- coding: utf-8 -*-
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""
import random
from functools import part... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/image_degradation/bsrgan_light.py | examples/images/diffusion/ldm/modules/image_degradation/bsrgan_light.py | # -*- coding: utf-8 -*-
import random
from functools import partial
import albumentations
import cv2
import ldm.modules.image_degradation.utils_image as util
import numpy as np
import scipy
import scipy.stats as ss
import torch
from scipy import ndimage
from scipy.interpolate import interp2d
from scipy.linalg import o... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/image_degradation/__init__.py | examples/images/diffusion/ldm/modules/image_degradation/__init__.py | from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/image_degradation/utils_image.py | examples/images/diffusion/ldm/modules/image_degradation/utils_image.py | import math
import os
import random
from datetime import datetime
import cv2
import numpy as np
import torch
from torchvision.utils import make_grid
# import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
"""
# -----------------... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/distributions/distributions.py | examples/images/diffusion/ldm/modules/distributions/distributions.py | import numpy as np
import torch
class AbstractDistribution:
def sample(self):
raise NotImplementedError()
def mode(self):
raise NotImplementedError()
class DiracDistribution(AbstractDistribution):
def __init__(self, value):
self.value = value
def sample(self):
retur... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/distributions/__init__.py | examples/images/diffusion/ldm/modules/distributions/__init__.py | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false | |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/encoders/modules.py | examples/images/diffusion/ldm/modules/encoders/modules.py | import open_clip
import torch
import torch.nn as nn
from ldm.util import count_params
from torch.utils.checkpoint import checkpoint
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def encode(se... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/encoders/__init__.py | examples/images/diffusion/ldm/modules/encoders/__init__.py | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false | |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/midas/api.py | examples/images/diffusion/ldm/modules/midas/api.py | # based on https://github.com/isl-org/MiDaS
import cv2
import torch
import torch.nn as nn
from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
from ldm.modules.midas.midas.midas_net import MidasNet
from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
from ldm.modules.midas.midas.transforms import... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/midas/utils.py | examples/images/diffusion/ldm/modules/midas/utils.py | """Utils for monoDepth."""
import re
import sys
import cv2
import numpy as np
import torch
def read_pfm(path):
"""Read pfm file.
Args:
path (str): path to file
Returns:
tuple: (data, scale)
"""
with open(path, "rb") as file:
color = None
width = None
hei... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/midas/__init__.py | examples/images/diffusion/ldm/modules/midas/__init__.py | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false | |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/midas/midas/midas_net_custom.py | examples/images/diffusion/ldm/modules/midas/midas/midas_net_custom.py | """MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/midas/midas/base_model.py | examples/images/diffusion/ldm/modules/midas/midas/base_model.py | import torch
class BaseModel(torch.nn.Module):
def load(self, path):
"""Load model from file.
Args:
path (str): file path
"""
parameters = torch.load(path, map_location=torch.device("cpu"))
if "optimizer" in parameters:
parameters = parameters["mod... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/midas/midas/dpt_depth.py | examples/images/diffusion/ldm/modules/midas/midas/dpt_depth.py | import torch
import torch.nn as nn
from .base_model import BaseModel
from .blocks import FeatureFusionBlock_custom, Interpolate, _make_encoder
from .vit import forward_vit
def _make_fusion_block(features, use_bn):
return FeatureFusionBlock_custom(
features,
nn.ReLU(False),
deconv=False,
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/midas/midas/blocks.py | examples/images/diffusion/ldm/modules/midas/midas/blocks.py | import torch
import torch.nn as nn
from .vit import _make_pretrained_vitb16_384, _make_pretrained_vitb_rn50_384, _make_pretrained_vitl16_384
def _make_encoder(
backbone,
features,
use_pretrained,
groups=1,
expand=False,
exportable=True,
hooks=None,
use_vit_only=False,
use_readout=... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/midas/midas/midas_net.py | examples/images/diffusion/ldm/modules/midas/midas/midas_net.py | """MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/midas/midas/vit.py | examples/images/diffusion/ldm/modules/midas/midas/vit.py | import math
import types
import timm
import torch
import torch.nn as nn
import torch.nn.functional as F
class Slice(nn.Module):
def __init__(self, start_index=1):
super(Slice, self).__init__()
self.start_index = start_index
def forward(self, x):
return x[:, self.start_index :]
clas... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/midas/midas/__init__.py | examples/images/diffusion/ldm/modules/midas/midas/__init__.py | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false | |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/midas/midas/transforms.py | examples/images/diffusion/ldm/modules/midas/midas/transforms.py | import math
import cv2
import numpy as np
def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
"""Rezise the sample to ensure the given size. Keeps aspect ratio.
Args:
sample (dict): sample
size (tuple): image size
Returns:
tuple: new size
"""
sha... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/diffusionmodules/util.py | examples/images/diffusion/ldm/modules/diffusionmodules/util.py | # adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/gu... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/diffusionmodules/model.py | examples/images/diffusion/ldm/modules/diffusionmodules/model.py | # pytorch_diffusion + derived encoder decoder
import math
from typing import Any, Optional
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
try:
from lightning.pytorch.utilities import rank_zero_info
except:
from pytorch_lightning.utilities import rank_zero_info
from ldm.mod... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/diffusionmodules/openaimodel.py | examples/images/diffusion/ldm/modules/diffusionmodules/openaimodel.py | import math
from abc import abstractmethod
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.attention import SpatialTransformer
from ldm.modules.diffusionmodules.util import (
avg_pool_nd,
checkpoint,
conv_nd,
linear,
normalization,
ti... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/diffusionmodules/upscaling.py | examples/images/diffusion/ldm/modules/diffusionmodules/upscaling.py | from functools import partial
import numpy as np
import torch
import torch.nn as nn
from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
from ldm.util import default
class AbstractLowScaleModel(nn.Module):
# for concatenating a downsampled image to the latent representation
d... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/modules/diffusionmodules/__init__.py | examples/images/diffusion/ldm/modules/diffusionmodules/__init__.py | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false | |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/data/teyvat.py | examples/images/diffusion/ldm/data/teyvat.py | import json
from pathlib import Path
from typing import Dict
import torch
from datasets import load_dataset
from einops import rearrange
from ldm.util import instantiate_from_config
from omegaconf import DictConfig, ListConfig
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transform... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/data/__init__.py | examples/images/diffusion/ldm/data/__init__.py | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false | |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/data/lsun.py | examples/images/diffusion/ldm/data/lsun.py | import os
import numpy as np
import PIL
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
# This class is used to create a dataset of images from LSUN dataset for training
class LSUNBase(Dataset):
def __init__(
self,
txt_file, # path to the text file c... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/data/cifar10.py | examples/images/diffusion/ldm/data/cifar10.py | import json
from pathlib import Path
from typing import Dict
import torch
from datasets import load_dataset
from einops import rearrange
from ldm.util import instantiate_from_config
from omegaconf import DictConfig, ListConfig
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transform... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/data/imagenet.py | examples/images/diffusion/ldm/data/imagenet.py | import glob
import os
import pickle
import shutil
import tarfile
from functools import partial
import albumentations
import cv2
import numpy as np
import PIL
import taming.data.utils as tdu
import torchvision.transforms.functional as TF
import yaml
from ldm.modules.image_degradation import degradation_fn_bsr, degradat... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/diffusion/ldm/data/base.py | examples/images/diffusion/ldm/data/base.py | import os
import cv2
import numpy as np
import torch
from torch.utils.data import IterableDataset
class Txt2ImgIterableBaseDataset(IterableDataset):
"""
Define an interface to make the IterableDatasets for text2img data chainable
"""
def __init__(self, file_path: str, rank, world_size):
supe... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/resnet/train.py | examples/images/resnet/train.py | import argparse
import os
from pathlib import Path
import torch
import torch.distributed as dist
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from torch.optim import Optimizer
from torch.optim.lr_scheduler import MultiStepLR
from torch.utils.data import DataLoader
from tqdm impo... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/resnet/eval.py | examples/images/resnet/eval.py | import argparse
import torch
import torchvision
import torchvision.transforms as transforms
# ==============================
# Parse Arguments
# ==============================
parser = argparse.ArgumentParser()
parser.add_argument("-e", "--epoch", type=int, default=80, help="resume from the epoch's checkpoint")
parse... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/dreambooth/debug.py | examples/images/dreambooth/debug.py | """
torchrun --standalone --nproc_per_node=1 debug.py
"""
from diffusers import AutoencoderKL
import colossalai
from colossalai.zero import ColoInitContext
path = "/data/scratch/diffuser/stable-diffusion-v1-4"
colossalai.launch_from_torch()
with ColoInitContext(device="cpu"):
vae = AutoencoderKL.from_pretrained... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/dreambooth/inference.py | examples/images/dreambooth/inference.py | import torch
from diffusers import DiffusionPipeline
model_id = "<Your Model Path>"
print(f"Loading model... from{model_id}")
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
prompt = "A photo of an apple."
image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).image... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/dreambooth/train_dreambooth_colossalai.py | examples/images/dreambooth/train_dreambooth_colossalai.py | import argparse
import hashlib
import math
import os
import shutil
from pathlib import Path
from typing import Optional
import torch
import torch.distributed as dist
import torch.nn.functional as F
import torch.utils.checkpoint
from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/dreambooth/train_dreambooth_inpaint.py | examples/images/dreambooth/train_dreambooth_inpaint.py | import argparse
import hashlib
import itertools
import math
import os
import random
from pathlib import Path
from typing import Optional
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from accelerate import Accelerator
from accelerate.logging import get_logger
from accele... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/dreambooth/train_dreambooth_colossalai_lora.py | examples/images/dreambooth/train_dreambooth_colossalai_lora.py | import argparse
import hashlib
import math
import os
import shutil
from pathlib import Path
from typing import Optional
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
from diffusers.loaders import At... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/images/dreambooth/train_dreambooth.py | examples/images/dreambooth/train_dreambooth.py | import argparse
import hashlib
import itertools
import math
import os
from pathlib import Path
from typing import Optional
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from d... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/data_utils.py | examples/language/data_utils.py | import json
import random
from typing import Iterator, Optional
import numpy as np
import torch
from torch.distributed import ProcessGroup
from torch.distributed.distributed_c10d import _get_default_group
from torch.utils.data import DataLoader, Dataset, DistributedSampler
from colossalai.accelerator import get_accel... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/model_utils.py | examples/language/model_utils.py | from contextlib import contextmanager
import torch
import torch.nn as nn
@contextmanager
def low_precision_init(target_dtype: torch.dtype = torch.float16):
dtype = torch.get_default_dtype()
try:
torch.set_default_dtype(target_dtype)
yield
finally:
torch.set_default_dtype(dtype)
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/__init__.py | examples/language/__init__.py | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false | |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/performance_evaluator.py | examples/language/performance_evaluator.py | from time import time
from typing import Optional
import torch
import torch.distributed as dist
from torch import Tensor
from torch.profiler import ProfilerActivity, profile, schedule, tensorboard_trace_handler
from colossalai.cluster import DistCoordinator
from colossalai.utils import get_current_device
def divide... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/commons/utils.py | examples/language/commons/utils.py | import torch
# Randomly Generated Data
def get_data(batch_size, seq_len, vocab_size):
input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device())
attention_mask = torch.ones_like(input_ids)
return input_ids, attention_mask
def get_tflops(model_numel, batch_size, s... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/palm/train.py | examples/language/palm/train.py | import argparse
import gzip
from contextlib import nullcontext
from functools import partial
from time import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import tqdm
from palm_pytorch import PaLM
from palm_pytorch.autoregressive_wrapper import AutoregressiveWrapper
from torch... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/palm/palm_pytorch/palm_pytorch.py | examples/language/palm/palm_pytorch/palm_pytorch.py | import torch
import torch.nn.functional as F
from einops import rearrange
from torch import matmul, nn
# normalization
# they use layernorm without bias, something that pytorch does not offer
class LayerNorm(nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
self.eps = eps
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/palm/palm_pytorch/autoregressive_wrapper.py | examples/language/palm/palm_pytorch/autoregressive_wrapper.py | import torch
import torch.nn.functional as F
from einops import rearrange
from torch import nn
# helper function
def exists(val):
return val is not None
def eval_decorator(fn):
def inner(model, *args, **kwargs):
was_training = model.training
model.eval()
out = fn(model, *args, **kwa... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/palm/palm_pytorch/__init__.py | examples/language/palm/palm_pytorch/__init__.py | from palm_pytorch.palm_pytorch import PaLM
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/bert/finetune.py | examples/language/bert/finetune.py | import argparse
from typing import Callable, List, Union
import evaluate
import torch
import torch.distributed as dist
import torch.nn as nn
from data import GLUEDataBuilder
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
from torch.utils.data import DataLoader
from t... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/bert/benchmark.py | examples/language/bert/benchmark.py | import argparse
import torch
from benchmark_utils import benchmark
from torch.utils.data import DataLoader, Dataset
from transformers import (
AlbertConfig,
AlbertForSequenceClassification,
BertConfig,
BertForSequenceClassification,
get_linear_schedule_with_warmup,
)
import colossalai
from colossa... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/bert/benchmark_utils.py | examples/language/bert/benchmark_utils.py | import inspect
from logging import getLogger
from time import time
from typing import Callable
import torch
import yaml
from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
from torch.utils.data import DataLoader
from tqdm import tqdm
from colossalai.accelerator import get_accelerator
from colossalai.boos... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/bert/data.py | examples/language/bert/data.py | import datasets
from transformers import AutoTokenizer, PreTrainedTokenizer
from colossalai.booster.plugin.dp_plugin_base import DPPluginBase
class GLUEDataBuilder:
task_text_field_map = {
"cola": ["sentence"],
"sst2": ["sentence"],
"mrpc": ["sentence1", "sentence2"],
"qqp": ["que... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/opt/args.py | examples/language/opt/args.py | import argparse
def parse_demo_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_name_or_path",
type=str,
default="facebook/opt-350m",
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/opt/opt_train_demo.py | examples/language/opt/opt_train_demo.py | from contextlib import nullcontext
import datasets
import torch
import transformers
from args import parse_demo_args
from data import NetflixDataset, netflix_collator
from tqdm import tqdm
from transformers import AutoConfig, AutoTokenizer, OPTForCausalLM, get_linear_schedule_with_warmup
from transformers.utils.versio... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/opt/opt_benchmark.py | examples/language/opt/opt_benchmark.py | import time
from contextlib import nullcontext
import torch
import tqdm
import transformers
from args import parse_benchmark_args
from transformers import AutoConfig, OPTForCausalLM
from transformers.utils.versions import require_version
import colossalai
from colossalai.accelerator import get_accelerator
from coloss... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/opt/data.py | examples/language/opt/data.py | import torch
from datasets import load_dataset
from torch.utils.data import Dataset
class NetflixDataset(Dataset):
def __init__(self, tokenizer):
super().__init__()
self.tokenizer = tokenizer
self.input_ids = []
self.attn_masks = []
self.labels = []
self.txt_list =... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/grok-1/inference_tp.py | examples/language/grok-1/inference_tp.py | import time
import torch
from grok1_policy import Grok1ForCausalLMPolicy
from transformers import AutoModelForCausalLM, AutoTokenizer
from utils import get_default_parser, inference, print_output
import colossalai
from colossalai.booster import Booster
from colossalai.booster.plugin import HybridParallelPlugin
from c... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/grok-1/inference.py | examples/language/grok-1/inference.py | import time
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from utils import get_default_parser, inference, print_output
if __name__ == "__main__":
parser = get_default_parser()
args = parser.parse_args()
start = time.time()
torch.set_default_dtype(torch.bfloat16)
token... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/grok-1/utils.py | examples/language/grok-1/utils.py | import argparse
import torch
class Bcolors:
HEADER = "\033[95m"
OKBLUE = "\033[94m"
OKCYAN = "\033[96m"
OKGREEN = "\033[92m"
WARNING = "\033[93m"
FAIL = "\033[91m"
ENDC = "\033[0m"
BOLD = "\033[1m"
UNDERLINE = "\033[4m"
def print_output(text, output):
print(f"-----\n{Bcolors... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/grok-1/grok1_policy.py | examples/language/grok-1/grok1_policy.py | from typing import Dict, Union
import torch.nn as nn
from colossalai.shardformer.layer import Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D
from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
class Grok1Policy(Policy):
def config_sanity... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/mixtral/data_utils.py | examples/language/mixtral/data_utils.py | import json
import random
from typing import Iterator, Optional
import numpy as np
import torch
from torch.distributed import ProcessGroup
from torch.distributed.distributed_c10d import _get_default_group
from torch.utils.data import DataLoader, Dataset, DistributedSampler
from colossalai.accelerator import get_accel... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/examples/language/mixtral/model_utils.py | examples/language/mixtral/model_utils.py | from contextlib import contextmanager
import torch
import torch.nn as nn
@contextmanager
def low_precision_init(target_dtype: torch.dtype = torch.float16):
dtype = torch.get_default_dtype()
try:
torch.set_default_dtype(target_dtype)
yield
finally:
torch.set_default_dtype(dtype)
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
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