Merge remote-tracking branch 'origin/feature/fp8_comm' into feature/fp8_comm

# Conflicts:
#	colossalai/quantization/fp8.py

.pre-commit-config.yaml

@@ -1,34 +1,34 @@
 repos:
 
   - repo: https://github.com/PyCQA/autoflake
-    rev: v2.2.1
+    rev: v2.3.1
     hooks:
       - id: autoflake
         name: autoflake (python)
         args: ['--in-place', '--remove-unused-variables', '--remove-all-unused-imports', '--ignore-init-module-imports']
 
   - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
+    rev: 5.13.2
     hooks:
       - id: isort
         name: sort all imports (python)
 
   - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.9.1
+    rev: 24.4.2
     hooks:
     - id: black
       name: black formatter
       args: ['--line-length=120', '--target-version=py37', '--target-version=py38', '--target-version=py39','--target-version=py310']
 
   - repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v13.0.1
+    rev: v18.1.8
     hooks:
     - id: clang-format
       name: clang formatter
       types_or: [c++, c]
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v4.6.0
     hooks:
       - id: check-yaml
       - id: check-merge-conflict

applications/ColossalChat/coati/dataset/loader.py

@@ -83,15 +83,19 @@ class DataCollatorForSupervisedDataset(object):
 
         # `List[torch.Tensor]`
         batch_input_ids = [
-            torch.LongTensor(instance["input_ids"][: self.max_length])
-            if len(instance["input_ids"]) > self.max_length
-            else torch.LongTensor(instance["input_ids"])
+            (
+                torch.LongTensor(instance["input_ids"][: self.max_length])
+                if len(instance["input_ids"]) > self.max_length
+                else torch.LongTensor(instance["input_ids"])
+            )
             for instance in instances
         ]
         batch_labels = [
-            torch.LongTensor(instance["labels"][: self.max_length])
-            if len(instance["labels"]) > self.max_length
-            else torch.LongTensor(instance["labels"])
+            (
+                torch.LongTensor(instance["labels"][: self.max_length])
+                if len(instance["labels"]) > self.max_length
+                else torch.LongTensor(instance["labels"])
+            )
             for instance in instances
         ]
         if self.tokenizer.padding_side == "right":

applications/ColossalChat/coati/models/loss.py

@@ -1,6 +1,7 @@
 """
 loss functions
 """
+
 from typing import Optional, Tuple
 
 import torch

applications/ColossalChat/coati/models/reward_model.py

@@ -1,6 +1,7 @@
 """
 reward model
 """
+
 from typing import Optional
 
 import torch

applications/ColossalChat/coati/trainer/utils.py

@@ -1,6 +1,7 @@
 """
 Training utilities for Coati.
 """
+
 from typing import Any
 
 import torch

applications/ColossalEval/colossal_eval/dataset/agieval.py

@@ -78,7 +78,9 @@ def get_prompt(line: Dict, dataset_name: str, logger: DistributedLogger) -> Dict
             option_string = "ABCDEFG"
             count = len(line["options"])
 
-            input = "问题：" + line["question"] + " " + "从以下选项中选择：" + " ".join(line["options"]) + "\n" + "答案："
+            input = (
+                "问题：" + line["question"] + " " + "从以下选项中选择：" + " ".join(line["options"]) + "\n" + "答案："
+            )
 
             all_classes = list(option_string[0:count])
 
@@ -150,7 +152,15 @@ def combine_prompt(prompt_path, dataset_name, load_explanation=True, chat_mode=F
             )
         elif dataset_name in chinese_qa_datasets:
             question_input = (
-                "问题：" + passage + " " + question + "\n" + "从以下选项中选择：" + " ".join(options) + "\n" + "答案：{}".format(label)
+                "问题："
+                + passage
+                + " "
+                + question
+                + "\n"
+                + "从以下选项中选择："
+                + " ".join(options)
+                + "\n"
+                + "答案：{}".format(label)
             )
         elif dataset_name in english_cloze_datasets:
             question_input = "Question: ".format(idx + 1) + question + "\n" + "Answer: {}".format(answer)

applications/ColossalEval/colossal_eval/dataset/ceval.py

@@ -57,7 +57,11 @@ ceval_subject_mapping = {
     "urban_and_rural_planner": ["Urban and Rural Planner", "注册城乡规划师", "Other"],
     "accountant": ["Accountant", "注册会计师", "Other"],
     "fire_engineer": ["Fire Engineer", "注册消防工程师", "Other"],
-    "environmental_impact_assessment_engineer": ["Environmental Impact Assessment Engineer", "环境影响评价工程师", "Other"],
+    "environmental_impact_assessment_engineer": [
+        "Environmental Impact Assessment Engineer",
+        "环境影响评价工程师",
+        "Other",
+    ],
     "tax_accountant": ["Tax Accountant", "税务师", "Other"],
     "physician": ["Physician", "医师资格", "Other"],
 }

applications/ColossalEval/colossal_eval/dataset/mtbench.py

@@ -56,9 +56,11 @@ class MTBenchDataset(BaseDataset):
                     "instruction": question["turns"],
                     "input": "",
                     "output": [],
-                    "target": [""] * turn_number
-                    if question["question_id"] not in reference
-                    else reference[question["question_id"]],
+                    "target": (
+                        [""] * turn_number
+                        if question["question_id"] not in reference
+                        else reference[question["question_id"]]
+                    ),
                 }
 
                 if category in dataset["test"]:

applications/ColossalEval/colossal_eval/models/huggingface.py

@@ -77,7 +77,9 @@ class HuggingFaceModel(BaseModel):
             self.indices_for_choices[0].append(
                 self.tokenizer(f"Answer: {choice}", add_special_tokens=False).input_ids[-1]
             )
-            self.indices_for_choices[1].append(self.tokenizer(f"答案：{choice}", add_special_tokens=False).input_ids[-1])
+            self.indices_for_choices[1].append(
+                self.tokenizer(f"答案：{choice}", add_special_tokens=False).input_ids[-1]
+            )
 
     def _load_tokenizer(self, path: str, tokenizer_path: Optional[str], tokenizer_kwargs: dict):
         """

applications/ColossalQA/colossalqa/chain/retrieval_qa/base.py

@@ -7,6 +7,7 @@ This code is based on LangChain Ai's langchain, which can be found at
 https://github.com/langchain-ai/langchain
 The original code is licensed under the MIT license.
 """
+
 from __future__ import annotations
 
 import copy

applications/ColossalQA/colossalqa/chain/retrieval_qa/load_chain.py

@@ -8,6 +8,7 @@ This code is based on LangChain Ai's langchain, which can be found at
 https://github.com/langchain-ai/langchain
 The original code is licensed under the MIT license.
 """
+
 import copy
 from typing import Any, Mapping, Optional, Protocol
 

applications/ColossalQA/colossalqa/chain/retrieval_qa/stuff.py

@@ -7,6 +7,7 @@ This code is based on LangChain Ai's langchain, which can be found at
 https://github.com/langchain-ai/langchain
 The original code is licensed under the MIT license.
 """
+
 import copy
 from typing import Any, List
 

applications/ColossalQA/colossalqa/data_loader/table_dataloader.py

@@ -2,7 +2,6 @@
 Class for loading table type data. please refer to Pandas-Input/Output for file format details.
 """
 
-
 import glob
 import os
 

applications/ColossalQA/colossalqa/local/llm.py

@@ -12,6 +12,7 @@ TEST_PROMPT_CHATGLM="续写文章：惊蛰一过，春寒加剧。先是料料
 logger.info(llm(TEST_PROMPT_CHATGLM, max_new_tokens=100), verbose=True)
 
 """
+
 from typing import Any, List, Mapping, Optional
 
 import torch

applications/ColossalQA/colossalqa/local/utils.py

@@ -1,6 +1,7 @@
 """
 Generation utilities
 """
+
 import json
 from typing import List
 

applications/ColossalQA/colossalqa/memory.py

@@ -2,6 +2,7 @@
 Implement a memory class for storing conversation history
 Support long term and short term memory
 """
+
 from typing import Any, Dict, List
 
 from colossalqa.chain.memory.summary import ConversationSummaryMemory

applications/ColossalQA/colossalqa/mylogging.py

@@ -1,6 +1,7 @@
 """
 Class for logging with extra control for debugging
 """
+
 import logging
 
 

applications/ColossalQA/colossalqa/retrieval_conversation_en.py

@@ -1,6 +1,7 @@
 """
 Script for Chinese retrieval based conversation system backed by ChatGLM
 """
+
 from typing import Tuple
 
 from colossalqa.chain.retrieval_qa.base import RetrievalQA

applications/ColossalQA/colossalqa/retrieval_conversation_universal.py

@@ -1,6 +1,7 @@
 """
 Multilingual retrieval based conversation system
 """
+
 from typing import List
 
 from colossalqa.data_loader.document_loader import DocumentLoader

applications/ColossalQA/colossalqa/retrieval_conversation_zh.py

@@ -1,6 +1,7 @@
 """
 Script for Chinese retrieval based conversation system backed by ChatGLM
 """
+
 from typing import Tuple
 
 from colossalqa.chain.retrieval_qa.base import RetrievalQA

applications/ColossalQA/colossalqa/retriever.py

@@ -1,6 +1,7 @@
 """
 Code for custom retriver with incremental update
 """
+
 import copy
 import hashlib
 import os

applications/ColossalQA/colossalqa/text_splitter/chinese_text_splitter.py

@@ -1,6 +1,7 @@
 """
 Code for Chinese text splitter
 """
+
 from typing import Any, List, Optional
 
 from colossalqa.text_splitter.utils import get_cleaned_paragraph

applications/ColossalQA/examples/retrieval_conversation_en.py

@@ -1,6 +1,7 @@
 """
 Script for English retrieval based conversation system backed by LLaMa2
 """
+
 import argparse
 import os
 

applications/ColossalQA/examples/retrieval_conversation_en_customer_service.py

@@ -1,6 +1,7 @@
 """
 Script for English retrieval based conversation system backed by LLaMa2
 """
+
 import argparse
 import json
 import os

applications/ColossalQA/examples/retrieval_conversation_zh.py

@@ -1,6 +1,7 @@
 """
 Script for Chinese retrieval based conversation system backed by ChatGLM
 """
+
 import argparse
 import os
 

applications/ColossalQA/examples/retrieval_intent_classification_zh_customer_service.py

@@ -1,6 +1,7 @@
 """
 Script for English retrieval based conversation system backed by LLaMa2
 """
+
 import argparse
 import os
 

colossalai/auto_parallel/meta_profiler/meta_registry/conv.py

@@ -107,20 +107,22 @@ def convnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L
     # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
     fwd_memory_cost = MemoryCost(
         activation=compute_size_in_bytes([input_tensor, output_tensor]),
-        parameter=compute_size_in_bytes([weight_tensor, bias_tensor])
-        if has_bias
-        else compute_size_in_bytes(weight_tensor),
+        parameter=(
+            compute_size_in_bytes([weight_tensor, bias_tensor]) if has_bias else compute_size_in_bytes(weight_tensor)
+        ),
         temp=0,
         buffer=0,
     )
 
     bwd_memory_cost = MemoryCost(
-        activation=compute_size_in_bytes([input_tensor, weight_tensor, bias_tensor])
-        if has_bias
-        else compute_size_in_bytes([input_tensor, weight_tensor]),
-        parameter=compute_size_in_bytes([weight_tensor, bias_tensor])
-        if has_bias
-        else compute_size_in_bytes(weight_tensor),
+        activation=(
+            compute_size_in_bytes([input_tensor, weight_tensor, bias_tensor])
+            if has_bias
+            else compute_size_in_bytes([input_tensor, weight_tensor])
+        ),
+        parameter=(
+            compute_size_in_bytes([weight_tensor, bias_tensor]) if has_bias else compute_size_in_bytes(weight_tensor)
+        ),
         temp=0,
         buffer=0,
     )

colossalai/booster/plugin/__init__.py

@@ -1,10 +1,18 @@
 from .gemini_plugin import GeminiPlugin
 from .hybrid_parallel_plugin import HybridParallelPlugin
 from .low_level_zero_plugin import LowLevelZeroPlugin
+from .moe_hybrid_parallel_plugin import MoeHybridParallelPlugin
 from .plugin_base import Plugin
 from .torch_ddp_plugin import TorchDDPPlugin
 
-__all__ = ["Plugin", "TorchDDPPlugin", "GeminiPlugin", "LowLevelZeroPlugin", "HybridParallelPlugin"]
+__all__ = [
+    "Plugin",
+    "TorchDDPPlugin",
+    "GeminiPlugin",
+    "LowLevelZeroPlugin",
+    "HybridParallelPlugin",
+    "MoeHybridParallelPlugin",
+]
 
 import torch
 from packaging import version

colossalai/inference/batch_bucket.py

@@ -247,16 +247,16 @@ class BatchBucket:
                 self._sequences_dict[seq.request_id] = seq
                 self._sequences_indexes[seq.request_id] = self._current_batch_size + i
             # TODO external (rename): modify Sequence.sentence_len to seq_len
-            self._sequence_lengths[
-                self._current_batch_size : self._current_batch_size + num_seqs_to_add
-            ] = torch.tensor([seq.sentence_len for seq in seqs[:num_seqs_to_add]], dtype=torch.int32)
+            self._sequence_lengths[self._current_batch_size : self._current_batch_size + num_seqs_to_add] = (
+                torch.tensor([seq.sentence_len for seq in seqs[:num_seqs_to_add]], dtype=torch.int32)
+            )
             # NOTE block tables to be updated by kvcache manager
             block_tables = self._block_tables[self._current_batch_size : self._current_batch_size + num_seqs_to_add]
             if alloc_block_tables is not None:
                 # copy block ids from provided block tables
-                self._block_tables[
-                    self._current_batch_size : self._current_batch_size + num_seqs_to_add
-                ] = alloc_block_tables
+                self._block_tables[self._current_batch_size : self._current_batch_size + num_seqs_to_add] = (
+                    alloc_block_tables
+                )
             elif alloc_block_tables_fn:
                 alloc_block_tables_fn(
                     block_tables,

colossalai/inference/config.py

@@ -1,6 +1,7 @@
 """
 Our config contains various options for inference optimization, it is a unified API that wraps all the configurations for inference.
 """
+
 import logging
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, fields
@@ -82,9 +83,9 @@ class InputMetaData(RPC_PARAM):
     dtype: torch.dtype = torch.float32
     use_spec_dec: bool = False
     num_tokens_to_verify: int = 0
-    batch_token_ids: Optional[
-        List[List[int]]
-    ] = None  # for `repetition_penalty`, `no_repeat_ngram_size` in sampler process
+    batch_token_ids: Optional[List[List[int]]] = (
+        None  # for `repetition_penalty`, `no_repeat_ngram_size` in sampler process
+    )
 
     def to_rpc_param(self) -> Dict[str, any]:
         return {
@@ -202,9 +203,9 @@ class InferenceConfig(RPC_PARAM):
     prompt_template: Optional[str] = None
     do_sample: bool = False
     beam_width: int = 1  # TODO: beam search is not support for now
-    prefill_ratio: Optional[
-        float
-    ] = 1.2  # the ratio of prefill sequences to decoding sequences, we do prefill step once the actual value exceeds ratio
+    prefill_ratio: Optional[float] = (
+        1.2  # the ratio of prefill sequences to decoding sequences, we do prefill step once the actual value exceeds ratio
+    )
     pad_input: bool = False
     early_stopping: Optional[bool] = False
     top_k: Optional[int] = 50
@@ -234,7 +235,9 @@ class InferenceConfig(RPC_PARAM):
     high_precision: Optional[bool] = False
 
     # cuda_graph
-    use_cuda_graph: bool = False  # NOTE only when we have the graph for specific decoding batch size can we use the cuda graph for inference
+    use_cuda_graph: bool = (
+        False  # NOTE only when we have the graph for specific decoding batch size can we use the cuda graph for inference
+    )
     max_context_len_to_capture: int = 512
 
     # StreamingLLM (sliding window attention with attention sinks)

colossalai/inference/core/engine.py

@@ -47,7 +47,6 @@ _BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [8 * i for i in range(1, 33)]
 
 
 class InferenceEngine:
-
     """
     InferenceEngine which manages the inference process..
 

colossalai/inference/core/rpc_engine.py

@@ -34,7 +34,6 @@ def run_server(host, port, event: mp.Event = None):
 
 
 class RPCInferenceEngine(InferenceEngine):
-
     """
     InferenceEngine which manages the inference process..
 

colossalai/inference/executor/rpc_worker.py

@@ -42,7 +42,6 @@ logger = get_dist_logger(__name__)
 
 
 class rpcWorkerService(rpyc.Service):
-
     """
     Execute the computation tasks and manage its own kv cache
 

colossalai/inference/kv_cache/kvcache_manager.py

@@ -279,9 +279,11 @@ class KVCacheManager:
             block.add_ref()
             self._allocate_on_block(
                 block,
-                block.block_size
-                if context_lengths[i] % block.block_size == 0
-                else context_lengths[i].item() % block.block_size,
+                (
+                    block.block_size
+                    if context_lengths[i] % block.block_size == 0
+                    else context_lengths[i].item() % block.block_size
+                ),
             )
         for block_id in alloc_block_ids:
             if block_id in alloc_block_ids[last_block_locs]:

colossalai/inference/utils.py

@@ -1,6 +1,7 @@
 """
 Utils for model inference
 """
+
 import math
 import os
 import re

colossalai/legacy/context/process_group_initializer/initializer_2d.py

@@ -138,9 +138,7 @@ class Initializer_2D(ProcessGroupInitializer):
         self.num_group = self.world_size // self.tensor_parallel_size
         self.summa_dim = int(math.sqrt(self.tensor_parallel_size))
 
-        assert (
-            self.tensor_parallel_size == self.summa_dim**2
-        ), "2D summa dim should equal to tensor parallel size ^ 0.5"
+        assert self.tensor_parallel_size == self.summa_dim**2, "2D summa dim should equal to tensor parallel size ^ 0.5"
         _check_summa_env_var(self.summa_dim)
 
         self.col_initializer = Initializer_2D_Col(self.num_group, self.summa_dim, *args, **kwargs)

colossalai/legacy/inference/async_engine.py

@@ -54,7 +54,6 @@ class RequestTracker:
 
 
 class Async_Engine:
-
     """
     Use an engine to launch RAY Driver --> RAY Worker --> Async_Manager
     Background loop: inference reqs in waiting list (Listen)

colossalai/legacy/inference/dynamic_batching/io_struct.py

@@ -118,16 +118,16 @@ class Batch:
 
 class BatchTokenIdOut:
     def __init__(self):
-        self.reqs_infs: List[
-            Tuple[str, int, Dict, bool, bool]
-        ] = []  # [req_id, new_token_id, gen_metadata, finished_state, abort_state]
+        self.reqs_infs: List[Tuple[str, int, Dict, bool, bool]] = (
+            []
+        )  # [req_id, new_token_id, gen_metadata, finished_state, abort_state]
 
 
 class BatchStrOut:
     def __init__(self):
-        self.reqs_infs: List[
-            Tuple[str, str, Dict, bool, bool]
-        ] = []  # [req_id, token_str, gen_metadata, finished_state, abort_state]
+        self.reqs_infs: List[Tuple[str, str, Dict, bool, bool]] = (
+            []
+        )  # [req_id, token_str, gen_metadata, finished_state, abort_state]
 
 
 class AbortReq:

colossalai/legacy/inference/hybridengine/modeling/_utils.py

@@ -1,6 +1,7 @@
 """
 Utils for model inference
 """
+
 import os
 
 import torch

colossalai/legacy/inference/tensor_parallel/batch_infer_state.py

@@ -14,6 +14,7 @@ class BatchInferState:
     Information to be passed and used for a batch of inputs during
     a single model forward
     """
+
     batch_size: int
     max_len_in_batch: int
 

colossalai/legacy/inference/tensor_parallel/kvcache_manager.py

@@ -4,6 +4,7 @@ of the ModelTC/lightllm GitHub repository
 https://github.com/ModelTC/lightllm/blob/050af3ce65edca617e2f30ec2479397d5bb248c9/lightllm/common/mem_manager.py
 we slightly changed it to make it suitable for our colossal-ai shardformer TP-engine design.
 """
+
 import torch
 from transformers.utils import logging
 

colossalai/legacy/inference/tensor_parallel/modeling/_utils.py

@@ -1,6 +1,7 @@
 """
 Utils for model inference
 """
+
 import os
 
 import torch

colossalai/quantization/bnb.py

@@ -1,17 +1,25 @@
 # adapted from Hugging Face accelerate/utils/bnb.py accelerate/utils/modeling.py
 
+import importlib.metadata
 import logging
 
 import torch
 import torch.nn as nn
+from packaging.version import Version
 
 from .bnb_config import BnbQuantizationConfig
 
 try:
     import bitsandbytes as bnb
 
-    IS_4BIT_BNB_AVAILABLE = bnb.__version__ >= "0.39.0"
-    IS_8BIT_BNB_AVAILABLE = bnb.__version__ >= "0.37.2"
+    try:
+        # in case lower version of bitsandbytes does not have __version__ attribute
+        BNB_VERSION = Version(bnb.__version__)
+    except AttributeError:
+        BNB_VERSION = Version(importlib.metadata.version("bitsandbytes"))
+
+    IS_4BIT_BNB_AVAILABLE = BNB_VERSION >= Version("0.39.0")
+    IS_8BIT_BNB_AVAILABLE = BNB_VERSION >= Version("0.37.2")
 except ImportError:
     pass
 

colossalai/shardformer/modeling/chatglm2_6b/modeling_chatglm.py

@@ -33,6 +33,7 @@ This license shall be governed and construed in accordance with the laws of Peop
 
 Note that the license is subject to update to a more comprehensive version.  For any questions related to the license and copyright, please contact us at glm-130b@googlegroups.com.
 """
+
 """ PyTorch ChatGLM model. """
 
 import copy

colossalai/shardformer/modeling/opt.py

@@ -221,7 +221,7 @@ class OPTPipelineForwards:
             past_key_value = past_key_values[idx] if past_key_values is not None else None
 
             if decoder.gradient_checkpointing and decoder.training:
-                layer_outputs = self._gradient_checkpointing_func(
+                layer_outputs = self.decoder._gradient_checkpointing_func(
                     decoder_layer.__call__,
                     hidden_states,
                     causal_attention_mask,

colossalai/shardformer/modeling/qwen2.py

@@ -168,13 +168,27 @@ class Qwen2PipelineForwards:
         next_decoder_cache = None
 
         start_idx, end_idx = stage_index[0], stage_index[1]
+        num_ckpt_layers = 0
+        if self.gradient_checkpointing and self.training:
+            num_ckpt_layers = end_idx - start_idx
+            # TODO: We can replace `gradient_checkpointing_enable` fn and initialize a gradient_checkpointing (List[bool]) for each layer
+            if shard_config.gradient_checkpoint_config is not None:
+                num_ckpt_layers = shard_config.gradient_checkpoint_config.get_num_ckpt_layers(
+                    stage=stage_manager.stage,
+                    num_stages=stage_manager.num_stages,
+                    num_layers=end_idx - start_idx,
+                    model_chunk_id=(stage_manager.model_chunk_id if stage_manager.is_interleave else 0),
+                    num_model_chunks=stage_manager.num_model_chunks,
+                )
+            assert num_ckpt_layers <= end_idx - start_idx
+
         for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_idx):
             if output_hidden_states:
                 all_hidden_states += (hidden_states,)
 
             past_key_value = past_key_values[idx] if past_key_values is not None else None
 
-            if self.gradient_checkpointing and self.training:
+            if idx - start_idx < num_ckpt_layers:
                 layer_outputs = self._gradient_checkpointing_func(
                     decoder_layer.__call__,
                     hidden_states,
@@ -198,7 +212,6 @@ class Qwen2PipelineForwards:
 
             if use_cache:
                 next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
-
             if output_attentions:
                 all_self_attns += (layer_outputs[1],)
 

colossalai/shardformer/policies/mixtral.py

@@ -40,21 +40,19 @@ class MixtralPolicy(Policy):
 
         if self.shard_config.enable_tensor_parallelism:
             raise NotImplementedError("Tensor parallelism is not supported for Mixtral model now.")
-        if getattr(self.shard_config, "ep_group", None) is None:
-            raise ValueError("You must pass in ep_group via shard_config for expert parallel!")
-
-        # expert parallel
-        self.append_or_create_submodule_replacement(
-            description=[
-                SubModuleReplacementDescription(
-                    suffix="block_sparse_moe",
-                    target_module=EPMixtralSparseMoeBlock,
-                    kwargs={"ep_group": self.shard_config.ep_group},
-                )
-            ],
-            policy=policy,
-            target_key=MixtralDecoderLayer,
-        )
+        if getattr(self.shard_config, "ep_group", None) is not None:
+            # expert parallel
+            self.append_or_create_submodule_replacement(
+                description=[
+                    SubModuleReplacementDescription(
+                        suffix="block_sparse_moe",
+                        target_module=EPMixtralSparseMoeBlock,
+                        kwargs={"ep_group": self.shard_config.ep_group},
+                    )
+                ],
+                policy=policy,
+                target_key=MixtralDecoderLayer,
+            )
 
         # optimization configuration
         if self.shard_config.enable_fused_normalization:

colossalai/zero/low_level/low_level_optim.py

@@ -549,6 +549,13 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
                 working_param = real_working_params[group_id][idx]
                 param_to_gather = master_param.to(device).to(self._dtype)
                 pg = self.param_to_pg[working_param]
+                if param_to_gather.numel() > self.pg_to_tensor_bucket[pg].max_size:
+                    buffer_tensor = torch.empty_like(
+                        torch.cat([param_to_gather for _ in range(dist.get_world_size(pg))])
+                    )
+                    dist.all_gather_into_tensor(buffer_tensor, param_to_gather, pg)
+                    working_param.data.copy_(buffer_tensor[: working_param.numel()].reshape_as(working_param))
+                    continue
                 try:
                     self.pg_to_tensor_bucket[pg].add_to_bucket(param_to_gather, write_back_tensor=working_param)
                 except RuntimeError:

docs/source/en/features/distributed_optimizers.md

@@ -87,44 +87,42 @@ optim = DistGaloreAwamW(
 ## Plugin compatibility
 <table>
   <tr>
-    <th nowrap="nowrap">Model/Feature</th>
-    <th nowrap="nowrap" align="center" title="Lamb">Lamb</th>
-    <th nowrap="nowrap" align="center" title="GaLore">GaLore</th>
-    <th nowrap="nowrap" align="center" title="Adafactor">Adafactor</th>
-    <th nowrap="nowrap" align="center" title="CAME">CAME</th>
+    <th nowrap="nowrap">Optimizer/Plugin</th>
+    <th nowrap="nowrap" align="center">Hybrid Parallel Plugin</th>
+    <th nowrap="nowrap" align="center">Low Level Zero Plugin</th>
+    <th nowrap="nowrap" align="center">Torch DDP Plugin</th>
+    <th nowrap="nowrap" align="center">Gemini Plugin</th>
+    <th nowrap="nowrap" align="center">Moe Hybrid Plugin</th>
   </tr>
   <tr>
-    <td nowrap="nowrap">Hybrid Parallel<br />Plugin</td>
-    <td nowrap="nowrap" align="center">✔️</td>
+    <td nowrap="nowrap" align="center" title="Lamb">Lamb</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
+    <td nowrap="nowrap" align="center">❌</td>
+    <td nowrap="nowrap" align="center">❌</td>
   </tr>
   <tr>
-    <td nowrap="nowrap">Low Level Zero<br />Plugin</td>
+    <td nowrap="nowrap" align="center" title="GaLore">GaLore</td>
     <td nowrap="nowrap" align="center">✔️</td>
-    <td nowrap="nowrap" align="center">❌</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
+    <td nowrap="nowrap" align="center">❌</td>
+    <td nowrap="nowrap" align="center">❌</td>
   </tr>
   <tr>
-    <td nowrap="nowrap">Torch DDP<br />Plugin</td>
+    <td nowrap="nowrap" align="center" title="Adafactor">Adafactor</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
-    <td nowrap="nowrap" align="center">✔️</td>
-  </tr>
-  <tr>
-    <td nowrap="nowrap">Gemini<br />Plugin</td>
-    <td nowrap="nowrap" align="center">❌</td>
-    <td nowrap="nowrap" align="center">❌</td>
     <td nowrap="nowrap" align="center">❌</td>
     <td nowrap="nowrap" align="center">❌</td>
   </tr>
   <tr>
-    <td nowrap="nowrap">Moe Hybrid<br />Plugin</td>
-    <td nowrap="nowrap" align="center">❌</td>
-    <td nowrap="nowrap" align="center">❌</td>
+    <td nowrap="nowrap" align="center" title="CAME">CAME</td>
+    <td nowrap="nowrap" align="center">✔️</td>
+    <td nowrap="nowrap" align="center">✔️</td>
+    <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">❌</td>
     <td nowrap="nowrap" align="center">❌</td>
   </tr>

docs/source/en/features/shardformer.md

@@ -55,7 +55,7 @@ Model/Feature Compatibility Matrix:
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
-    <td nowrap="nowrap" align="center">❌</td>
+    <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">❌</td>
   </tr>
   <tr>

docs/source/zh-Hans/features/distributed_optimizers.md

@@ -84,44 +84,42 @@ optim = DistGaloreAwamW(
 ## 兼容性
 <table>
   <tr>
-    <th nowrap="nowrap">Model/Feature</th>
-    <th nowrap="nowrap" align="center" title="Lamb">Lamb</th>
-    <th nowrap="nowrap" align="center" title="GaLore">GaLore</th>
-    <th nowrap="nowrap" align="center" title="Adafactor">Adafactor</th>
-    <th nowrap="nowrap" align="center" title="CAME">CAME</th>
+    <th nowrap="nowrap">Optimizer/Plugin</th>
+    <th nowrap="nowrap" align="center">Hybrid Parallel Plugin</th>
+    <th nowrap="nowrap" align="center">Low Level Zero Plugin</th>
+    <th nowrap="nowrap" align="center">Torch DDP Plugin</th>
+    <th nowrap="nowrap" align="center">Gemini Plugin</th>
+    <th nowrap="nowrap" align="center">Moe Hybrid Plugin</th>
   </tr>
   <tr>
-    <td nowrap="nowrap">Hybrid Parallel<br />Plugin</td>
-    <td nowrap="nowrap" align="center">✔️</td>
+    <td nowrap="nowrap" align="center" title="Lamb">Lamb</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
+    <td nowrap="nowrap" align="center">❌</td>
+    <td nowrap="nowrap" align="center">❌</td>
   </tr>
   <tr>
-    <td nowrap="nowrap">Low Level Zero<br />Plugin</td>
+    <td nowrap="nowrap" align="center" title="GaLore">GaLore</td>
     <td nowrap="nowrap" align="center">✔️</td>
-    <td nowrap="nowrap" align="center">❌</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
+    <td nowrap="nowrap" align="center">❌</td>
+    <td nowrap="nowrap" align="center">❌</td>
   </tr>
   <tr>
-    <td nowrap="nowrap">Torch DDP<br />Plugin</td>
-    <td nowrap="nowrap" align="center">✔️</td>
+    <td nowrap="nowrap" align="center" title="Adafactor">Adafactor</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
-  </tr>
-  <tr>
-    <td nowrap="nowrap">Gemini<br />Plugin</td>
-    <td nowrap="nowrap" align="center">❌</td>
-    <td nowrap="nowrap" align="center">❌</td>
     <td nowrap="nowrap" align="center">❌</td>
     <td nowrap="nowrap" align="center">❌</td>
   </tr>
   <tr>
-    <td nowrap="nowrap">Moe Hybrid<br />Plugin</td>
-    <td nowrap="nowrap" align="center">❌</td>
-    <td nowrap="nowrap" align="center">❌</td>
+    <td nowrap="nowrap" align="center" title="CAME">CAME</td>
+    <td nowrap="nowrap" align="center">✔️</td>
+    <td nowrap="nowrap" align="center">✔️</td>
+    <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">❌</td>
     <td nowrap="nowrap" align="center">❌</td>
   </tr>
@@ -130,6 +128,7 @@ optim = DistGaloreAwamW(
   </tr>
 </table>
 
+
 <!-- doc-test-command: colossalai run --nproc_per_node 4 distributed_optimizers.py  -->
 
 ## API 参考

docs/source/zh-Hans/features/shardformer.md

@@ -51,7 +51,7 @@ Author: [Baizhou Zhang](https://github.com/Fridge003), [Bin Jia](https://github.
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">✔️</td>
-    <td nowrap="nowrap" align="center">❌</td>
+    <td nowrap="nowrap" align="center">✔️</td>
     <td nowrap="nowrap" align="center">❌</td>
   </tr>
   <tr>

examples/community/roberta/pretraining/nvidia_bert_dataset_provider.py

@@ -52,9 +52,11 @@ class pretraining_dataset(Dataset):
 
     def __getitem__(self, index):
         [input_ids, input_mask, segment_ids, masked_lm_labels] = [
-            torch.from_numpy(input[index].astype(np.int64))
-            if indice < 5
-            else torch.from_numpy(np.asarray(input[index].astype(np.int64)))
+            (
+                torch.from_numpy(input[index].astype(np.int64))
+                if indice < 5
+                else torch.from_numpy(np.asarray(input[index].astype(np.int64)))
+            )
             for indice, input in enumerate(self.inputs)
         ]
 

examples/images/diffusion/ldm/models/diffusion/ddpm.py

@@ -229,9 +229,7 @@ class DDPM(pl.LightningModule):
         )
 
         if self.parameterization == "eps":
-            lvlb_weights = self.betas**2 / (
-                2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)
-            )
+            lvlb_weights = self.betas**2 / (2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
         elif self.parameterization == "x0":
             lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2.0 * 1 - torch.Tensor(alphas_cumprod))
         elif self.parameterization == "v":
@@ -1186,9 +1184,11 @@ class LatentDiffusion(DDPM):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {
-                    key: cond[key][:batch_size]
-                    if not isinstance(cond[key], list)
-                    else list(map(lambda x: x[:batch_size], cond[key]))
+                    key: (
+                        cond[key][:batch_size]
+                        if not isinstance(cond[key], list)
+                        else list(map(lambda x: x[:batch_size], cond[key]))
+                    )
                     for key in cond
                 }
             else:
@@ -1321,9 +1321,11 @@ class LatentDiffusion(DDPM):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {
-                    key: cond[key][:batch_size]
-                    if not isinstance(cond[key], list)
-                    else list(map(lambda x: x[:batch_size], cond[key]))
+                    key: (
+                        cond[key][:batch_size]
+                        if not isinstance(cond[key], list)
+                        else list(map(lambda x: x[:batch_size], cond[key]))
+                    )
                     for key in cond
                 }
             else:

examples/images/diffusion/ldm/models/diffusion/dpm_solver/sampler.py

@@ -1,4 +1,5 @@
 """SAMPLING ONLY."""
+
 import torch
 
 from .dpm_solver import DPM_Solver, NoiseScheduleVP, model_wrapper

examples/images/diffusion/ldm/modules/diffusionmodules/openaimodel.py

@@ -640,23 +640,25 @@ class UNetModel(nn.Module):
                 use_checkpoint=use_checkpoint,
                 use_scale_shift_norm=use_scale_shift_norm,
             ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=dim_head,
-                use_new_attention_order=use_new_attention_order,
-            )
-            if not use_spatial_transformer
-            else SpatialTransformer(  # always uses a self-attn
-                ch,
-                num_heads,
-                dim_head,
-                depth=transformer_depth,
-                context_dim=context_dim,
-                disable_self_attn=disable_middle_self_attn,
-                use_linear=use_linear_in_transformer,
-                use_checkpoint=use_checkpoint,
+            (
+                AttentionBlock(
+                    ch,
+                    use_checkpoint=use_checkpoint,
+                    num_heads=num_heads,
+                    num_head_channels=dim_head,
+                    use_new_attention_order=use_new_attention_order,
+                )
+                if not use_spatial_transformer
+                else SpatialTransformer(  # always uses a self-attn
+                    ch,
+                    num_heads,
+                    dim_head,
+                    depth=transformer_depth,
+                    context_dim=context_dim,
+                    disable_self_attn=disable_middle_self_attn,
+                    use_linear=use_linear_in_transformer,
+                    use_checkpoint=use_checkpoint,
+                )
             ),
             ResBlock(
                 ch,

examples/images/diffusion/ldm/modules/midas/midas/midas_net.py

@@ -2,6 +2,7 @@
 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
 

examples/images/diffusion/ldm/modules/midas/midas/midas_net_custom.py

@@ -2,6 +2,7 @@
 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
 

examples/images/diffusion/ldm/modules/midas/utils.py

@@ -1,4 +1,5 @@
 """Utils for monoDepth."""
+
 import re
 import sys
 

examples/tutorial/sequence_parallel/data/datasets/helpers.cpp

@@ -369,9 +369,9 @@ py::array build_mapping_impl(const py::array_t<int64_t>& docs_,
             }
 
           }  // for (auto sent_index=sent_index_first; ...
-        }    // if (num_remain_sent > 1) {
-      }      // for (int doc=0; doc < num_docs; ++doc) {
-    }        // for (int epoch=0; epoch < num_epochs; ++epoch) {
+        }  // if (num_remain_sent > 1) {
+      }  // for (int doc=0; doc < num_docs; ++doc) {
+    }  // for (int epoch=0; epoch < num_epochs; ++epoch) {
 
     if (!second) {
       if (verbose) {
@@ -606,9 +606,9 @@ py::array build_blocks_mapping_impl(
               num_sent = 0;
             }
           }  // for (auto sent_index=sent_index_first; ...
-        }    // if (num_remain_sent > 1) {
-      }      // for (int doc=0; doc < num_docs; ++doc) {
-    }        // for (int epoch=0; epoch < num_epochs; ++epoch) {
+        }  // if (num_remain_sent > 1) {
+      }  // for (int doc=0; doc < num_docs; ++doc) {
+    }  // for (int epoch=0; epoch < num_epochs; ++epoch) {
 
     if (!second) {
       if (verbose) {

extensions/csrc/kernel/arm/cpu_adam_arm.h

@@ -4,7 +4,7 @@
 
 #include <cmath>
 
-#define ROUND_DOWN(size, step) ((size) & ~((step)-1))
+#define ROUND_DOWN(size, step) ((size) & ~((step) - 1))
 #define TILE (128 * 1024 * 1024)
 
 #if defined(__aarch64__)

extensions/csrc/kernel/x86/cpu_adam.h

@@ -32,7 +32,7 @@ SOFTWARE
 #include <x86intrin.h>
 #endif
 
-#define ROUND_DOWN(size, step) ((size) & ~((step)-1))
+#define ROUND_DOWN(size, step) ((size) & ~((step) - 1))
 #define TILE (128 * 1024 * 1024)
 
 #if defined(__AVX512__) or defined(__AVX256__) or defined(__AVX2__)

tests/kit/model_zoo/torchvision/torchvision.py

@@ -34,14 +34,14 @@ def swin_s():
 
 
 # special output transform fn
-google_net_output_transform_fn = (
-    lambda x: dict(output=sum(x)) if isinstance(x, torchvision.models.GoogLeNetOutputs) else dict(output=x)
+google_net_output_transform_fn = lambda x: (
+    dict(output=sum(x)) if isinstance(x, torchvision.models.GoogLeNetOutputs) else dict(output=x)
 )
-swin_s_output_output_transform_fn = (
-    lambda x: {f"output{idx}": val for idx, val in enumerate(x)} if isinstance(x, tuple) else dict(output=x)
+swin_s_output_output_transform_fn = lambda x: (
+    {f"output{idx}": val for idx, val in enumerate(x)} if isinstance(x, tuple) else dict(output=x)
 )
-inception_v3_output_transform_fn = (
-    lambda x: dict(output=sum(x)) if isinstance(x, torchvision.models.InceptionOutputs) else dict(output=x)
+inception_v3_output_transform_fn = lambda x: (
+    dict(output=sum(x)) if isinstance(x, torchvision.models.InceptionOutputs) else dict(output=x)
 )
 
 model_zoo.register(