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/colossalai/shardformer/modeling/__init__.py | colossalai/shardformer/modeling/__init__.py | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false | |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/modeling/mixtral.py | colossalai/shardformer/modeling/mixtral.py | import inspect
import warnings
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch.distributed import ProcessGroup
from torch.nn import CrossEntropyLoss
from transformers.cache_utils import Cache, DynamicCache
from transform... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | true |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/modeling/mistral.py | colossalai/shardformer/modeling/mistral.py | import warnings
from typing import List, Optional, Tuple, Union
import torch
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
SequenceClassi... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/modeling/chatglm2_6b/modeling_chatglm.py | colossalai/shardformer/modeling/chatglm2_6b/modeling_chatglm.py | """
The ChatGLM2-6B License
1. Definitions
“Licensor” means the ChatGLM2-6B Model Team that distributes its Software.
“Software” means the ChatGLM2-6B model parameters made available under this license.
2. License Grant
Subject to the terms and conditions of this License, the Licensor hereby grants to you a non-ex... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | true |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/modeling/chatglm2_6b/configuration_chatglm.py | colossalai/shardformer/modeling/chatglm2_6b/configuration_chatglm.py | from transformers import PretrainedConfig
class ChatGLMConfig(PretrainedConfig):
model_type = "chatglm"
def __init__(
self,
num_layers=28,
padded_vocab_size=65024,
hidden_size=4096,
ffn_hidden_size=13696,
kv_channels=128,
num_attention_heads=32,
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/modeling/chatglm2_6b/__init__.py | colossalai/shardformer/modeling/chatglm2_6b/__init__.py | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false | |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/examples/convergence_benchmark.py | colossalai/shardformer/examples/convergence_benchmark.py | import argparse
import math
from typing import Any, List, Union
import evaluate
import torch
import torch.distributed as dist
from data import GLUEDataBuilder
from torch import nn
from torch.optim import Adam, Optimizer
from torch.utils._pytree import tree_map
from torch.utils.data import DataLoader
from tqdm import t... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/examples/performance_benchmark.py | colossalai/shardformer/examples/performance_benchmark.py | """
Shardformer Benchmark
"""
import torch
import torch.distributed as dist
import transformers
import triton
import colossalai
from colossalai.shardformer import ShardConfig, ShardFormer
def data_gen(batch_size, seq_length):
input_ids = torch.randint(0, seq_length, (batch_size, seq_length), dtype=torch.long)
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/examples/data.py | colossalai/shardformer/examples/data.py | import datasets
from torch.utils.data import DataLoader
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": ["senten... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/shard/grad_ckpt_config.py | colossalai/shardformer/shard/grad_ckpt_config.py | from dataclasses import dataclass
from typing import List, Optional
@dataclass
class GradientCheckpointConfig:
gradient_checkpointing_ratio: float = 0.0
def get_num_ckpt_layers(self, num_layers: int) -> int:
return int(self.gradient_checkpointing_ratio * num_layers)
@dataclass
class PipelineGradien... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/shard/sharder.py | colossalai/shardformer/shard/sharder.py | from types import MethodType
from typing import Any, Callable, Dict, List, Optional, Set, Union
import torch.nn as nn
from torch import Tensor
from colossalai.lazy import LazyInitContext
from .._utils import getattr_, setattr_
from ..policies.auto_policy import get_autopolicy
from ..policies.base_policy import Polic... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/shard/utils.py | colossalai/shardformer/shard/utils.py | from typing import Set
import torch.nn as nn
def set_tensors_to_none(model: nn.Module, exclude: Set[nn.Module] = set()) -> None:
"""Set all parameters and buffers of model to None
Args:
model (nn.Module): The model to set
"""
if model in exclude:
return
for child in model.childre... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/shard/__init__.py | colossalai/shardformer/shard/__init__.py | from .grad_ckpt_config import GradientCheckpointConfig, PipelineGradientCheckpointConfig
from .shard_config import ShardConfig
from .sharder import ModelSharder
from .shardformer import ShardFormer
__all__ = ["ShardConfig", "ModelSharder", "ShardFormer", "PipelineGradientCheckpointConfig", "GradientCheckpointConfig"]
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/shard/shard_config.py | colossalai/shardformer/shard/shard_config.py | import warnings
from dataclasses import dataclass, field
from typing import Any, Dict, Optional
import torch.distributed as dist
from torch.distributed import ProcessGroup
from colossalai.pipeline.stage_manager import PipelineStageManager
from .grad_ckpt_config import GradientCheckpointConfig
__all__ = ["ShardConfi... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/shardformer/shard/shardformer.py | colossalai/shardformer/shard/shardformer.py | from typing import Dict, List, Tuple
import torch.distributed as dist
import torch.nn as nn
from torch import Tensor
from colossalai.cluster import DistCoordinator
from ..policies.base_policy import Policy
from .shard_config import ShardConfig
from .sharder import ModelSharder
class ShardFormer:
"""
Parall... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/context/singleton_meta.py | colossalai/context/singleton_meta.py | import threading
class SingletonMeta(type):
"""
Thread-safe Singleton Meta with double-checked locking.
Reference: https://en.wikipedia.org/wiki/Double-checked_locking
"""
_instances = {}
_lock = threading.Lock()
def __call__(cls, *args, **kwargs):
# First check (without locking)... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/context/config.py | colossalai/context/config.py | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
import inspect
import sys
from importlib.machinery import SourceFileLoader
from pathlib import Path
from colossalai.logging import get_dist_logger
class Config(dict):
"""This is a wrapper class for dict objects so that values of which can be
accessed as attrib... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/context/__init__.py | colossalai/context/__init__.py | from .config import Config, ConfigException
__all__ = [
"Config",
"ConfigException",
]
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/_compatibility.py | colossalai/fx/_compatibility.py | from typing import Callable
import torch
TORCH_MAJOR = int(torch.__version__.split(".")[0])
TORCH_MINOR = int(torch.__version__.split(".")[1])
if TORCH_MAJOR == 1 and TORCH_MINOR < 12:
META_COMPATIBILITY = False
elif TORCH_MAJOR == 1 and TORCH_MINOR == 12:
META_COMPATIBILITY = True
elif TORCH_MAJOR == 1 and ... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/_meta_regist_12.py | colossalai/fx/_meta_regist_12.py | # meta patch from https://github.com/pytorch/pytorch/blob/master/torch/_meta_registrations.py
# should be activated for PyTorch version 1.12.0 and below
# refer to https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml
# for more meta_registrations
from typing import List, Optional, ... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/_meta_regist_13.py | colossalai/fx/_meta_regist_13.py | import torch
from torch._meta_registrations import register_meta
from torch._prims_common import check
aten = torch.ops.aten
# since we fix the torch version to 1.13.1, we have to add unimplemented meta ops
# all these functions are from here https://github.com/pytorch/pytorch/blob/master/torch/_meta_registrations.p... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/__init__.py | colossalai/fx/__init__.py | from ._compatibility import compatibility, is_compatible_with_meta
from .graph_module import ColoGraphModule
from .passes import MetaInfoProp, metainfo_trace
from .tracer import ColoTracer, meta_trace, symbolic_trace
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/graph_module.py | colossalai/fx/graph_module.py | import os
import warnings
from pathlib import Path
from typing import Any, Dict, Optional, Union
import torch
import torch.nn as nn
from torch.nn.modules.module import _addindent
try:
from torch.fx.graph import Graph, PythonCode, _PyTreeCodeGen
from torch.fx.graph_module import GraphModule, _exec_with_source,... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/proxy.py | colossalai/fx/proxy.py | from typing import Any
import torch
from torch.fx.proxy import Proxy
from colossalai.fx.tracer.meta_patch import meta_patched_function
__all__ = ["ColoProxy"]
class ColoProxy(Proxy):
"""
ColoProxy is a proxy class which uses meta tensor to handle data-dependent control flow. The original torch.fx proxy
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/memory_utils.py | colossalai/fx/profiler/memory_utils.py | from typing import Dict, List, Tuple, Union
import torch
from torch.fx import Node
from .._compatibility import compatibility, is_compatible_with_meta
__all__ = ["activation_size", "parameter_size", "is_inplace"]
@compatibility(is_backward_compatible=True)
def activation_size(out: Union[torch.Tensor, Dict, List, T... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/constants.py | colossalai/fx/profiler/constants.py | import torch
__all__ = ["ALIAS_ATEN", "INPLACE_NEW", "INPLACE_MATH_ATEN", "CLONE_ATEN", "RELU_LIKE_OPS", "RELU_LIKE_MOD"]
aten = torch.ops.aten
ALIAS_ATEN = [
aten.detach.default,
aten.t.default,
aten.transpose.int,
aten.view.default,
aten._unsafe_view.default,
aten._reshape_alias.default,
]
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/profiler.py | colossalai/fx/profiler/profiler.py | import time
from functools import partial
from typing import Any, Callable, Dict, Tuple
import torch
from torch.fx import Graph, Node
from torch.fx.node import Argument, Target
from torch.nn.parameter import Parameter
from torch.utils._pytree import tree_map
from .._compatibility import compatibility
from .constants ... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/opcount.py | colossalai/fx/profiler/opcount.py | # adopted from https://github.com/facebookresearch/fvcore/blob/main/fvcore/nn/jit_handles.py
# ideas from https://pastebin.com/AkvAyJBw
import operator
from functools import partial, reduce
from numbers import Number
from typing import Any, Callable, List
import torch
from packaging import version
aten = torch.ops.a... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/dataflow.py | colossalai/fx/profiler/dataflow.py | from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List
from torch.fx import Graph, Node
from .._compatibility import compatibility
from .memory_utils import activation_size, is_inplace
class Phase(Enum):
FORWARD = 0
BACKWARD = 1
PLACEHOLDER = 2
@compatibility(is_b... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/__init__.py | colossalai/fx/profiler/__init__.py | from .._compatibility import is_compatible_with_meta
if is_compatible_with_meta():
from .opcount import flop_mapping
from .profiler import profile_function, profile_method, profile_module
from .shard_utils import (
calculate_bwd_time,
calculate_fwd_in,
calculate_fwd_out,
cal... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/tensor.py | colossalai/fx/profiler/tensor.py | import uuid
import torch
from torch.types import _device
from torch.utils._pytree import tree_map
from .._compatibility import compatibility
from .constants import ALIAS_ATEN
__all__ = ["MetaTensor"]
def set_data_ptr(x):
if isinstance(x, torch.Tensor):
if not x.data_ptr():
data_ptr = uuid.u... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/shard_utils.py | colossalai/fx/profiler/shard_utils.py | import torch
from torch.fx import Node
from .._compatibility import compatibility, is_compatible_with_meta
from .memory_utils import activation_size
if is_compatible_with_meta():
from .constants import OUTPUT_SAVED_MOD, OUTPUT_SAVED_OPS
__all__ = ["calculate_fwd_in", "calculate_fwd_tmp", "calculate_fwd_out"]
@... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/registry.py | colossalai/fx/profiler/experimental/registry.py | class ProfilerRegistry:
def __init__(self, name):
self.name = name
self.store = {}
def register(self, source):
def wrapper(func):
self.store[source] = func
return func
return wrapper
def get(self, source):
assert source in self.store
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/constants.py | colossalai/fx/profiler/experimental/constants.py | from operator import add, floordiv, getitem, mul, neg, pos, setitem, sub
import torch
__all__ = ["INPLACE_OPS", "INPLACE_METHOD", "NON_INPLACE_METHOD"]
# TODO fill out the inplace ops
INPLACE_OPS = [
add,
sub,
mul,
floordiv,
neg,
pos,
getitem,
setitem,
getattr,
torch.Tensor.cp... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler.py | colossalai/fx/profiler/experimental/profiler.py | from dataclasses import dataclass
from typing import Any, Callable, Dict, Tuple
import torch
from torch.fx.node import Argument, Target
from ..._compatibility import compatibility
from ..memory_utils import activation_size
from .constants import INPLACE_METHOD, INPLACE_OPS, NON_INPLACE_METHOD
from .registry import me... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/__init__.py | colossalai/fx/profiler/experimental/__init__.py | from .profiler import profile_function, profile_method, profile_module
from .profiler_function import *
from .profiler_module import *
from .registry import meta_profiler_function, meta_profiler_module
from .shard_utils import calculate_fwd_in, calculate_fwd_out, calculate_fwd_tmp
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/shard_utils.py | colossalai/fx/profiler/experimental/shard_utils.py | # for PyTorch 1.11 compatibility uses
from torch.fx import Node
from ..._compatibility import compatibility
__all__ = ["calculate_fwd_in", "calculate_fwd_tmp", "calculate_fwd_out"]
@compatibility(is_backward_compatible=True)
def calculate_fwd_in(n: Node) -> bool:
"""A helper function to calculate `fwd_in`
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_function/torch_ops.py | colossalai/fx/profiler/experimental/profiler_function/torch_ops.py | import operator
from functools import reduce
from typing import Any, Optional, Tuple
import torch
from ..registry import meta_profiler_function
@meta_profiler_function.register(torch.arange)
@meta_profiler_function.register(torch.finfo)
@meta_profiler_function.register(torch.permute)
@meta_profiler_function.registe... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_function/arithmetic.py | colossalai/fx/profiler/experimental/profiler_function/arithmetic.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import operator
from functools import reduce
from typing import Any, Optional, Tuple, Union
import torch
from ..registry import meta_profiler_function
def _elementwise_flops_compute(input, other):
# copied from https://github.com/microsof... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_function/pooling.py | colossalai/fx/profiler/experimental/profiler_function/pooling.py | from typing import Tuple
import torch
from ..registry import meta_profiler_function
@meta_profiler_function.register(torch.nn.functional.avg_pool1d)
@meta_profiler_function.register(torch.nn.functional.avg_pool2d)
@meta_profiler_function.register(torch.nn.functional.avg_pool3d)
@meta_profiler_function.register(torc... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_function/activation_function.py | colossalai/fx/profiler/experimental/profiler_function/activation_function.py | from typing import Tuple
import torch
from ..registry import meta_profiler_function
# TODO: different activation has different FLOPs count, currently unused.
_multiplier = {
torch.nn.functional.relu: 1,
torch.nn.functional.prelu: 4,
torch.nn.functional.sigmoid: 4,
torch.nn.functional.tanh: 5,
tor... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_function/python_ops.py | colossalai/fx/profiler/experimental/profiler_function/python_ops.py | import operator
from typing import Any, Tuple
from ..registry import meta_profiler_function
@meta_profiler_function.register(operator.getitem)
def operator_getitem(a: Any, b: Any) -> Tuple[int, int]:
flops = 0
macs = 0
return flops, macs
@meta_profiler_function.register(getattr)
def python_getattr(a: A... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_function/__init__.py | colossalai/fx/profiler/experimental/profiler_function/__init__.py | from .activation_function import *
from .arithmetic import *
from .embedding import *
from .linear import *
from .normalization import *
from .pooling import *
from .python_ops import *
from .torch_ops import *
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_function/embedding.py | colossalai/fx/profiler/experimental/profiler_function/embedding.py | from typing import Optional
import torch
from ..registry import meta_profiler_function
@meta_profiler_function.register(torch.nn.functional.embedding)
def torch_nn_functional_embedding(
input: torch.Tensor,
weight: torch.Tensor,
padding_idx: Optional[int] = None,
max_norm: Optional[float] = None,
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_function/normalization.py | colossalai/fx/profiler/experimental/profiler_function/normalization.py | from typing import List, Optional, Tuple
import torch
from ..registry import meta_profiler_function
@meta_profiler_function.register(torch.nn.functional.instance_norm)
def torch_nn_func_instancenorm(
input: torch.Tensor,
running_mean: Optional[torch.Tensor] = None,
running_var: Optional[torch.Tensor] = ... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_function/linear.py | colossalai/fx/profiler/experimental/profiler_function/linear.py | from typing import Tuple
import torch
from ..registry import meta_profiler_function
@meta_profiler_function.register(torch.nn.functional.linear)
def torch_nn_linear(input: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor = None) -> Tuple[int, int]:
out_features = weight.shape[0]
macs = torch.numel(inp... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_module/convolution.py | colossalai/fx/profiler/experimental/profiler_module/convolution.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import math
import operator
from functools import reduce
from typing import Tuple
import torch
from ..registry import meta_profiler_module
@meta_profiler_module.register(torch.nn.Conv1d)
def torch_nn_conv1d(self: torch.nn.Conv1d, input: torch... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_module/pooling.py | colossalai/fx/profiler/experimental/profiler_module/pooling.py | from typing import Tuple
import torch
from ..registry import meta_profiler_module
@meta_profiler_module.register(torch.nn.AvgPool1d)
@meta_profiler_module.register(torch.nn.AvgPool2d)
@meta_profiler_module.register(torch.nn.AvgPool3d)
@meta_profiler_module.register(torch.nn.MaxPool1d)
@meta_profiler_module.register... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_module/torch_op.py | colossalai/fx/profiler/experimental/profiler_module/torch_op.py | from typing import Tuple
import torch
from ..registry import meta_profiler_module
@meta_profiler_module.register(torch.nn.Flatten)
def torch_nn_flatten(self: torch.nn.Flatten, input: torch.Tensor) -> Tuple[int, int]:
flops = 0
macs = 0
return flops, macs
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_module/rnn.py | colossalai/fx/profiler/experimental/profiler_module/rnn.py | import operator
from functools import reduce
from typing import Optional, Tuple
import torch
from ..registry import meta_profiler_module
def _rnn_flops(
flops: int, macs: int, module: torch.nn.RNNBase, w_ih: torch.Tensor, w_hh: torch.Tensor
) -> Tuple[int, int]:
# copied from https://github.com/sovrasov/flo... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_module/dropout.py | colossalai/fx/profiler/experimental/profiler_module/dropout.py | from typing import Tuple
import torch
from ..registry import meta_profiler_module
@meta_profiler_module.register(torch.nn.Dropout)
def torch_nn_dropout(self: torch.nn.Module, input: torch.Tensor) -> Tuple[int, int]:
# nn.Embedding is a dictionary lookup, so technically it has 0 FLOPs. (https://discuss.pytorch.o... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_module/activation_function.py | colossalai/fx/profiler/experimental/profiler_module/activation_function.py | from typing import Tuple
import torch
from ..registry import meta_profiler_module
# TODO: different activation has different FLOPs count, currently unused.
_multiplier = {
torch.nn.ReLU: 1,
torch.nn.PReLU: 4,
torch.nn.Sigmoid: 4,
torch.nn.Tanh: 5,
torch.nn.LeakyReLU: 3,
torch.nn.ELU: 4,
t... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_module/__init__.py | colossalai/fx/profiler/experimental/profiler_module/__init__.py | from .activation_function import *
from .attention import *
from .convolution import *
from .dropout import *
from .embedding import *
from .linear import *
from .normalization import *
from .pooling import *
from .rnn import *
from .torch_op import *
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_module/attention.py | colossalai/fx/profiler/experimental/profiler_module/attention.py | from typing import Optional, Tuple
import torch
from ..registry import meta_profiler_module
# TODO: This is hard to compute memory cost
@meta_profiler_module.register(torch.nn.MultiheadAttention)
def torch_nn_msa(
self: torch.nn.MultiheadAttention,
query: torch.Tensor,
key: torch.Tensor,
value: torc... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_module/embedding.py | colossalai/fx/profiler/experimental/profiler_module/embedding.py | from typing import Tuple
import torch
from ..registry import meta_profiler_module
@meta_profiler_module.register(torch.nn.Embedding)
def torch_nn_embedding(self: torch.nn.Embedding, input: torch.Tensor) -> Tuple[int, int]:
# nn.Embedding is a dictionary lookup, so technically it has 0 FLOPs. (https://discuss.py... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_module/normalization.py | colossalai/fx/profiler/experimental/profiler_module/normalization.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from typing import Tuple, Union
import torch
from ..registry import meta_profiler_module
@meta_profiler_module.register(torch.nn.InstanceNorm1d)
@meta_profiler_module.register(torch.nn.InstanceNorm2d)
@meta_profiler_module.register(torch.nn.I... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/profiler/experimental/profiler_module/linear.py | colossalai/fx/profiler/experimental/profiler_module/linear.py | from typing import Tuple
import torch
from ..registry import meta_profiler_module
@meta_profiler_module.register(torch.nn.Linear)
@meta_profiler_module.register(torch.nn.modules.linear.NonDynamicallyQuantizableLinear)
def torch_nn_linear(self: torch.nn.Linear, input: torch.Tensor) -> Tuple[int, int]:
out_featur... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/_symbolic_trace.py | colossalai/fx/tracer/_symbolic_trace.py | from typing import Any, Callable, Dict, Optional, Union
import torch
from colossalai.fx import ColoGraphModule
from colossalai.fx._compatibility import compatibility
from .tracer import ColoTracer
@compatibility(is_backward_compatible=True)
def symbolic_trace(
root: Union[torch.nn.Module, Callable[..., Any]],
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/registry.py | colossalai/fx/tracer/registry.py | class PatchRegistry:
def __init__(self, name):
self.name = name
self.store = {}
def register(self, source):
def wrapper(func):
self.store[source] = func
return func
return wrapper
def get(self, source):
assert source in self.store
ta... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/tracer.py | colossalai/fx/tracer/tracer.py | #!/usr/bin/env python
"""
tracer.py:
Implemented a tracer which supports control flow and user-defined meta arguments.
The implementation is partly inspired HuggingFace's fx tracer
"""
import enum
import functools
import inspect
import operator
from contextlib import contextmanager
from typing import Any, Dict,... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/_meta_trace.py | colossalai/fx/tracer/_meta_trace.py | import torch
from torch.fx import Graph, Node
from torch.utils._pytree import tree_map
def normalize_tuple(x):
if not isinstance(x, tuple):
return (x,)
return x
def is_autogradable(x):
return isinstance(x, torch.Tensor) and x.is_floating_point()
def meta_trace(module: torch.nn.Module, fake_dev... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/_tracer_utils.py | colossalai/fx/tracer/_tracer_utils.py | from typing import Any, List, Union
import torch
from ..proxy import ColoProxy
from .meta_patch import meta_patched_function
__all__ = ["is_element_in_list", "extract_meta"]
def is_element_in_list(elements: Union[List[Any], Any], list_: List[Any]):
if isinstance(elements, (tuple, list, set)):
for ele i... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/__init__.py | colossalai/fx/tracer/__init__.py | from colossalai.fx.tracer.meta_patch.patched_function.python_ops import operator_getitem
from ._meta_trace import meta_trace
from ._symbolic_trace import symbolic_trace
from .tracer import ColoTracer
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/experimental.py | colossalai/fx/tracer/experimental.py | import functools
import inspect
import operator
from contextlib import contextmanager
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
import torch
from torch.fx import Graph, Node, Proxy, Tracer
from torch.utils._pytree import tree_map
from colossalai.fx import ColoGraphModule, compatibility... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/__init__.py | colossalai/fx/tracer/meta_patch/__init__.py | from .patched_function import *
from .patched_module import *
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_function/convolution.py | colossalai/fx/tracer/meta_patch/patched_function/convolution.py | import collections
import math
from itertools import repeat
import torch
from ...registry import meta_patched_function
def _ntuple(n, name="parse"):
def parse(x):
if isinstance(x, collections.abc.Iterable):
return tuple(x)
return tuple(repeat(x, n))
parse.__name__ = name
ret... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_function/torch_ops.py | colossalai/fx/tracer/meta_patch/patched_function/torch_ops.py | import torch
from ...registry import meta_patched_function
@meta_patched_function.register(torch.arange)
def torch_arange(*args, **kwargs):
n = len(args)
step = 1
if n == 1:
start = 0
end = args[0]
elif n == 2:
start, end = args
else:
start, end, step = args
if... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_function/arithmetic.py | colossalai/fx/tracer/meta_patch/patched_function/arithmetic.py | import torch
from ...registry import meta_patched_function
@meta_patched_function.register(torch.matmul)
@meta_patched_function.register("matmul") # for built-in op @
def torch_matmul(input, other, *, out=None):
# copied from huggingface.utils.fx
d1 = input.dim()
d2 = other.dim()
shape = None
if... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_function/activation_function.py | colossalai/fx/tracer/meta_patch/patched_function/activation_function.py | import torch
from ...registry import meta_patched_function
@meta_patched_function.register(torch.nn.functional.relu)
def torch_nn_func_relu(input, inplace=False):
return torch.empty(input.shape, device="meta")
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_function/python_ops.py | colossalai/fx/tracer/meta_patch/patched_function/python_ops.py | import operator
import torch
from colossalai.fx.proxy import ColoProxy
from ...registry import meta_patched_function
@meta_patched_function.register(operator.getitem)
def operator_getitem(a, b):
# copied from huggingface.utils.fx
def to_concrete(t):
if isinstance(t, torch.Tensor):
concr... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_function/__init__.py | colossalai/fx/tracer/meta_patch/patched_function/__init__.py | from .activation_function import *
from .arithmetic import *
from .convolution import *
from .embedding import *
from .normalization import *
from .torch_ops import *
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_function/embedding.py | colossalai/fx/tracer/meta_patch/patched_function/embedding.py | import torch
from ...registry import meta_patched_function
@meta_patched_function.register(torch.nn.functional.embedding)
def torch_nn_functional_embedding(
input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False
):
return torch.empty(*input.shape, weight.shape[-... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_function/normalization.py | colossalai/fx/tracer/meta_patch/patched_function/normalization.py | import torch
from ...registry import meta_patched_function
@meta_patched_function.register(torch.nn.functional.layer_norm)
def torch_nn_func_layernorm(input, normalized_shape, weight=None, bias=None, eps=1e-05):
return torch.empty(input.shape, device="meta")
@meta_patched_function.register(torch.nn.functional.... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_module/convolution.py | colossalai/fx/tracer/meta_patch/patched_module/convolution.py | import math
import torch
from ...registry import meta_patched_module
@meta_patched_module.register(torch.nn.Conv1d)
def torch_nn_conv1d(self, input):
# the output shape is calculated using the formula stated
# at https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html#torch.nn.Conv1d
l_in = input... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_module/pooling.py | colossalai/fx/tracer/meta_patch/patched_module/pooling.py | import math
import torch
from ...registry import meta_patched_module
@meta_patched_module.register(torch.nn.AvgPool1d)
def torch_nn_avgpool1d(self, input):
num_dim = input.dim()
assert num_dim in [2, 3], f"expected the input to have 2 or 3 dimensions, but got {num_dim} dimensions"
l_in = input.shape[-1... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_module/rnn.py | colossalai/fx/tracer/meta_patch/patched_module/rnn.py | import torch
from ...registry import meta_patched_module
@meta_patched_module.register(torch.nn.GRU)
@meta_patched_module.register(torch.nn.RNN)
def torch_nn_rnn(self, input, hx):
assert (
input.shape[-1] == self.input_size
), f"Expected input to have input size {self.input_size} but got {input.shape... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_module/activation_function.py | colossalai/fx/tracer/meta_patch/patched_module/activation_function.py | import torch
from ...registry import meta_patched_module
@meta_patched_module.register(torch.nn.ReLU)
@meta_patched_module.register(torch.nn.Sigmoid)
@meta_patched_module.register(torch.nn.GELU)
@meta_patched_module.register(torch.nn.Tanh)
@meta_patched_module.register(torch.nn.ReLU6)
@meta_patched_module.register(t... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_module/__init__.py | colossalai/fx/tracer/meta_patch/patched_module/__init__.py | from .activation_function import *
from .convolution import *
from .embedding import *
from .linear import *
from .normalization import *
from .pooling import *
from .rnn import *
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_module/embedding.py | colossalai/fx/tracer/meta_patch/patched_module/embedding.py | import torch
from ...registry import meta_patched_module
@meta_patched_module.register(torch.nn.Embedding)
def torch_nn_embedding(self, input):
result_shape = input.shape + (self.embedding_dim,)
return torch.empty(result_shape, device="meta")
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_module/normalization.py | colossalai/fx/tracer/meta_patch/patched_module/normalization.py | import torch
from ...registry import meta_patched_module
@meta_patched_module.register(torch.nn.LayerNorm)
@meta_patched_module.register(torch.nn.GroupNorm)
@meta_patched_module.register(torch.nn.BatchNorm1d)
@meta_patched_module.register(torch.nn.BatchNorm2d)
@meta_patched_module.register(torch.nn.BatchNorm3d)
def ... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/meta_patch/patched_module/linear.py | colossalai/fx/tracer/meta_patch/patched_module/linear.py | import torch
from ...registry import meta_patched_module
@meta_patched_module.register(torch.nn.Linear)
def torch_nn_linear(self, input):
last_dim = input.shape[-1]
assert (
last_dim == self.in_features
), f"Expected hidden size {self.in_features} but got {last_dim} for the torch.nn.Linear patch"... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/bias_addition_patch/__init__.py | colossalai/fx/tracer/bias_addition_patch/__init__.py | from .patched_bias_addition_function import *
from .patched_bias_addition_module import *
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/conv.py | colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/conv.py | import torch
from torch.nn.modules.utils import _pair, _single, _triple
from ...registry import bias_addition_module
from .bias_addition_module import BiasAdditionModule
@bias_addition_module.register(torch.nn.Conv1d)
@bias_addition_module.register(torch.nn.Conv2d)
@bias_addition_module.register(torch.nn.Conv3d)
cla... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/bias_addition_module.py | colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/bias_addition_module.py | import operator
from abc import ABC, abstractmethod
import torch
import torch.nn.functional as F
class BiasAdditionModule(ABC):
"""
This class is used to construct the restructure computation graph for
call_module node with bias addition inside.
"""
def __init__(self, tracer, target, args, kwarg... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/__init__.py | colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/__init__.py | from .bias_addition_module import *
from .conv import *
from .linear import *
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/linear.py | colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/linear.py | import torch
from ...registry import bias_addition_module
from .bias_addition_module import BiasAdditionModule
@bias_addition_module.register(torch.nn.Linear)
class BiasAdditionLinear(BiasAdditionModule):
def extract_kwargs_from_mod(self):
return {}
def generate(self):
non_bias_linear_func_p... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/addbmm.py | colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/addbmm.py | import torch
from ...registry import bias_addition_function, bias_addition_method
from .bias_addition_function import LinearBasedBiasFunc
@bias_addition_method.register(torch.Tensor.addbmm)
@bias_addition_function.register(torch.addbmm)
class Addbmm(LinearBasedBiasFunc):
def extract_kwargs_from_origin_func(self)... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/addmm.py | colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/addmm.py | import torch
from ...registry import bias_addition_function, bias_addition_method
from .bias_addition_function import LinearBasedBiasFunc
@bias_addition_method.register(torch.Tensor.addmm)
@bias_addition_function.register(torch.addmm)
class Addmm(LinearBasedBiasFunc):
def extract_kwargs_from_origin_func(self):
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/bias_addition_function.py | colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/bias_addition_function.py | import operator
from abc import ABC, abstractmethod
import torch
import torch.nn.functional as F
class BiasAdditionFunc(ABC):
"""
This class is used to construct the restructure computation graph for
call_func node with bias addition inside.
"""
def __init__(self, tracer, target, args, kwargs, s... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/__init__.py | colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/__init__.py | from .addbmm import Addbmm
from .addmm import Addmm
from .bias_addition_function import BiasAdditionFunc, LinearBasedBiasFunc, func_to_func_dict, method_to_func_dict
from .linear import Linear
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/linear.py | colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/linear.py | import torch.nn.functional as F
from ...registry import bias_addition_function
from .bias_addition_function import LinearBasedBiasFunc
@bias_addition_function.register(F.linear)
class Linear(LinearBasedBiasFunc):
def extract_kwargs_from_origin_func(self):
assert "bias" in self.kwargs
kwargs = {}
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/codegen/activation_checkpoint_codegen.py | colossalai/fx/codegen/activation_checkpoint_codegen.py | from typing import Any, Dict, Iterable, List, Tuple
import torch
import colossalai
try:
from torch.fx.graph import (
CodeGen,
PythonCode,
_custom_builtins,
_CustomBuiltin,
_format_target,
_is_from_torch,
_Namespace,
_origin_type_map,
inplace... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | true |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/codegen/__init__.py | colossalai/fx/codegen/__init__.py | from .activation_checkpoint_codegen import *
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/passes/shard_1d_pass.py | colossalai/fx/passes/shard_1d_pass.py | import operator
import torch
import torch.nn as nn
from colossalai.legacy.tensor import ProcessGroup
from colossalai.legacy.tensor.compute_spec import ComputePattern, ComputeSpec
from colossalai.legacy.tensor.distspec import ShardSpec
ELEMENTWISE_MODULE_OP = [torch.nn.Dropout, torch.nn.ReLU]
ELEMENTWISE_FUNC_OP = [
... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/passes/adding_split_node_pass.py | colossalai/fx/passes/adding_split_node_pass.py | import numpy as np
import torch
import tqdm
from colossalai.fx.passes.split_module import split_module
def pipe_split():
pass
def block_split():
pass
# Construct blocks with the condition that (block_flops / total_flops) >= limit.
def construct_blocks(gm: torch.fx.GraphModule, limit=0.01):
total_fwd_... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/passes/utils.py | colossalai/fx/passes/utils.py | from typing import Dict
import torch
from torch.fx.graph import Graph
from torch.fx.node import Node, map_arg
def get_comm_size(prev_partition, next_partition):
"""
Given two partitions (parent and child),
calculate the communication size between the two.
"""
# Keep tracking the communication siz... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/passes/meta_info_prop.py | colossalai/fx/passes/meta_info_prop.py | from dataclasses import asdict
from typing import Any, Dict, List, NamedTuple, Tuple
import torch
import torch.fx
from torch.fx.node import Argument, Node, Target
from torch.utils._pytree import tree_map
from colossalai.fx._compatibility import compatibility, is_compatible_with_meta
from colossalai.fx.profiler import... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/passes/concrete_info_prop.py | colossalai/fx/passes/concrete_info_prop.py | from dataclasses import asdict
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.fx
from torch.fx.node import Argument, Node, Target
from torch.utils._pytree import tree_flatten
from colossalai.fx._compatibility import compatibility
from colossalai.fx.profiler import GraphInfo, profile_fun... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/passes/__init__.py | colossalai/fx/passes/__init__.py | from .adding_split_node_pass import balanced_split_pass, split_with_split_nodes_pass
from .concrete_info_prop import ConcreteInfoProp
from .meta_info_prop import MetaInfoProp, metainfo_trace
from .shard_1d_pass import column_shard_linear_pass, row_shard_linear_pass
| python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
hpcaitech/ColossalAI | https://github.com/hpcaitech/ColossalAI/blob/b1915d2889543949eb5b610241f1515c73df5059/colossalai/fx/passes/split_module.py | colossalai/fx/passes/split_module.py | import inspect
from typing import Any, Callable, Dict, List, Optional
import torch
from packaging import version
from torch.fx._compatibility import compatibility
from torch.fx.graph_module import GraphModule
@compatibility(is_backward_compatible=True)
class Partition:
"""
Adapted from https://github.com/pyt... | python | Apache-2.0 | b1915d2889543949eb5b610241f1515c73df5059 | 2026-01-04T14:40:19.002665Z | false |
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