From 2d2c55194cc95c3ed82f5fd8323640a5745051b9 Mon Sep 17 00:00:00 2001
From: "chris.ccy" <chris.ccy@antgroup.com>
Date: Mon, 18 Nov 2024 17:48:11 +0800
Subject: [PATCH 1/5] refactor 0.6
---
mftcoder_accelerate/README.md | 57 ++-
mftcoder_accelerate/README_cn.md | 46 +--
.../{src => }/accelerate_ds_config.yaml | 0
.../{src => }/accelerate_fsdp_config.yaml | 0
mftcoder_accelerate/{src => }/data/Makefile | 0
.../{src => }/data/__init__.py | 0
.../{src => }/data/blendable_dataset.py | 2 -
.../{src => }/data/data_utils.py | 2 +-
.../{src => }/data/gpt2_dataset.py | 2 +-
.../{src => }/data/helpers.cpp | 0
.../{src => }/data/indexed_dataset.py | 2 +-
.../{src => }/data/lm_dataformat.py | 64 +---
.../{src => }/data/multi_task_dataset.py | 4 +-
.../{src => }/data/preprocess_data.py | 2 +-
.../{src => }/ds_multinode_launch.sh | 4 +-
.../{src => }/ds_single_launch.sh | 4 +-
.../{src => }/ds_zero3_single_launch.sh | 4 +-
.../{src => }/fsdp_single_launch.sh | 4 +-
.../{src/model => inference}/__init__.py | 0
.../{hf_inference.py => demo_inference.py} | 6 -
mftcoder_accelerate/inference/gen.py | 126 ++++++
mftcoder_accelerate/inference/infer_base.py | 175 +++++++++
mftcoder_accelerate/inference/infer_utils.py | 76 ++++
mftcoder_accelerate/inference/ppl.py | 122 ++++++
mftcoder_accelerate/inference/reward.py | 131 +++++++
mftcoder_accelerate/model/__init__.py | 0
mftcoder_accelerate/model/aquila2/__init__.py | 5 +
.../model/aquila2/configuration_aquila.py | 0
.../model/aquila2/modeling_aquila.py | 0
.../model/baichuan2/__init__.py | 5 +
.../model/baichuan2/configuration_baichuan.py | 0
.../model/baichuan2/generation_utils.py | 0
.../model/baichuan2/modeling_baichuan.py | 0
.../{src => }/model/baichuan2/quantizer.py | 0
.../model/baichuan2/tokenization_baichuan.py | 0
.../model/chatglm2/__init__.py | 5 +
.../{src => }/model/chatglm2/config.json | 0
.../model/chatglm2/configuration_chatglm.py | 0
.../model/chatglm2/modeling_chatglm.py | 0
.../{src => }/model/chatglm2/quantization.py | 0
.../model/chatglm2/tokenization_chatglm.py | 0
.../model/chatglm2/tokenizer_config.json | 0
.../model/chatglm3/__init__.py | 5 +
.../{src => }/model/chatglm3/config.json | 0
.../model/chatglm3/configuration_chatglm.py | 0
.../model/chatglm3/modeling_chatglm.py | 0
.../{src => }/model/chatglm3/quantization.py | 0
.../model/chatglm3/tokenization_chatglm.py | 0
.../{src => }/model/code_llama/__init__.py | 0
.../model/code_llama/configuration_llama.py | 0
.../code_llama/convert_llama_weights_to_hf.py | 0
.../model/code_llama/modeling_llama.py | 0
.../code_llama/tokenization_code_llama.py | 0
.../tokenization_code_llama_fast.py | 0
.../model/code_llama/tokenization_llama.py | 0
.../code_llama/tokenization_llama_fast.py | 0
.../model/deepseek_v2/__init__.py | 5 +
.../deepseek_v2/configuration_deepseek.py | 0
.../model/deepseek_v2/modeling_deepseek.py | 0
.../deepseek_v2/tokenization_deepseek_fast.py | 0
.../{src => }/model/gpt_bigcode/__init__.py | 0
.../gpt_bigcode/configuration_gpt_bigcode.py | 0
.../model/gpt_bigcode/modeling_gpt_bigcode.py | 0
.../{src => }/model/gpt_neox/__init__.py | 0
.../{src => }/model/gpt_neox/config.json | 0
.../model/gpt_neox/configuration_gpt_neox.py | 0
.../model/gpt_neox/generation_config.json | 0
.../model/gpt_neox/modeling_gpt_neox.py | 0
.../gpt_neox/tokenization_gpt_neox_fast.py | 0
mftcoder_accelerate/model/phi/__init__.py | 5 +
.../phi/configuration_mixformer_sequential.py | 0
.../phi/modeling_mixformer_sequential.py | 0
mftcoder_accelerate/model/qwen/__init__.py | 5 +
.../model/qwen/cache_autogptq_cuda_256.cpp | 0
.../qwen/cache_autogptq_cuda_kernel_256.cu | 0
.../model/qwen/configuration_qwen.py | 0
.../{src => }/model/qwen/cpp_kernels.py | 0
.../{src => }/model/qwen/modeling_qwen.py | 0
.../model/qwen/qwen_generation_utils.py | 0
.../{src => }/model/qwen/tokenization_qwen.py | 0
.../model/qwen/tokenizer_config.json | 0
.../{src => }/run_offline_tokenization.sh | 2 +-
.../src/offline_tokenization/lm_fmt.py | 360 ------------------
.../src/offline_tokenization/pack_encoder.py | 335 ----------------
mftcoder_accelerate/src/utils/agd.py | 138 -------
.../tokenization/bin_encoder.py | 99 +++++
.../concat_sst_bin_tokenization.py | 119 +++---
.../writer.py | 24 +-
.../{src => }/tokenizer/__init__.py | 0
.../{src => }/tokenizer/chat_template.py | 6 +-
.../{src => }/tokenizer/tokenizer.py | 6 +-
.../configs/coba_train_config.json | 0
.../configs/dpo_train_config.json | 0
.../configs/full_train_config.json | 0
.../configs/lora_train_config.json | 0
.../configs/qlora_train_config.json | 0
.../merge_base_and_lora_to_hf.py | 2 +-
.../{src/pefts => training}/mft_accelerate.py | 10 +-
mftcoder_accelerate/training/mpt/__init__.py | 4 +
.../{src => training}/mpt/mpt_arguments.py | 0
.../{src => training}/mpt/mpt_trainer.py | 4 +-
.../{src/mpt => training}/mpt_accelerate.py | 10 +-
.../training/pefts/__init__.py | 4 +
.../{src => training}/pefts/mft_arguments.py | 0
.../{src => training}/pefts/mft_trainer.py | 4 +-
.../train_utils}/__init__.py | 0
.../train_utils}/common_utils.py | 0
.../train_utils}/loss_utils.py | 2 +-
.../train_utils}/model_mapping.py | 1 -
mftcoder_accelerate/training/xxpo/__init__.py | 4 +
.../xxpo/custom_callbacks.py | 0
.../{src => training}/xxpo/xxpo_arguments.py | 0
.../{src/xxpo => training}/xxpo_accelerate.py | 10 +-
requirements.txt | 5 +-
114 files changed, 959 insertions(+), 1058 deletions(-)
rename mftcoder_accelerate/{src => }/accelerate_ds_config.yaml (100%)
rename mftcoder_accelerate/{src => }/accelerate_fsdp_config.yaml (100%)
rename mftcoder_accelerate/{src => }/data/Makefile (100%)
rename mftcoder_accelerate/{src => }/data/__init__.py (100%)
rename mftcoder_accelerate/{src => }/data/blendable_dataset.py (98%)
rename mftcoder_accelerate/{src => }/data/data_utils.py (99%)
rename mftcoder_accelerate/{src => }/data/gpt2_dataset.py (99%)
rename mftcoder_accelerate/{src => }/data/helpers.cpp (100%)
rename mftcoder_accelerate/{src => }/data/indexed_dataset.py (99%)
rename mftcoder_accelerate/{src => }/data/lm_dataformat.py (86%)
rename mftcoder_accelerate/{src => }/data/multi_task_dataset.py (99%)
rename mftcoder_accelerate/{src => }/data/preprocess_data.py (99%)
rename mftcoder_accelerate/{src => }/ds_multinode_launch.sh (89%)
rename mftcoder_accelerate/{src => }/ds_single_launch.sh (90%)
rename mftcoder_accelerate/{src => }/ds_zero3_single_launch.sh (90%)
rename mftcoder_accelerate/{src => }/fsdp_single_launch.sh (91%)
rename mftcoder_accelerate/{src/model => inference}/__init__.py (100%)
mode change 100755 => 100644
rename mftcoder_accelerate/inference/{hf_inference.py => demo_inference.py} (98%)
create mode 100644 mftcoder_accelerate/inference/gen.py
create mode 100644 mftcoder_accelerate/inference/infer_base.py
create mode 100644 mftcoder_accelerate/inference/infer_utils.py
create mode 100644 mftcoder_accelerate/inference/ppl.py
create mode 100644 mftcoder_accelerate/inference/reward.py
create mode 100755 mftcoder_accelerate/model/__init__.py
create mode 100644 mftcoder_accelerate/model/aquila2/__init__.py
rename mftcoder_accelerate/{src => }/model/aquila2/configuration_aquila.py (100%)
rename mftcoder_accelerate/{src => }/model/aquila2/modeling_aquila.py (100%)
create mode 100644 mftcoder_accelerate/model/baichuan2/__init__.py
rename mftcoder_accelerate/{src => }/model/baichuan2/configuration_baichuan.py (100%)
rename mftcoder_accelerate/{src => }/model/baichuan2/generation_utils.py (100%)
rename mftcoder_accelerate/{src => }/model/baichuan2/modeling_baichuan.py (100%)
rename mftcoder_accelerate/{src => }/model/baichuan2/quantizer.py (100%)
rename mftcoder_accelerate/{src => }/model/baichuan2/tokenization_baichuan.py (100%)
create mode 100644 mftcoder_accelerate/model/chatglm2/__init__.py
rename mftcoder_accelerate/{src => }/model/chatglm2/config.json (100%)
rename mftcoder_accelerate/{src => }/model/chatglm2/configuration_chatglm.py (100%)
rename mftcoder_accelerate/{src => }/model/chatglm2/modeling_chatglm.py (100%)
rename mftcoder_accelerate/{src => }/model/chatglm2/quantization.py (100%)
rename mftcoder_accelerate/{src => }/model/chatglm2/tokenization_chatglm.py (100%)
rename mftcoder_accelerate/{src => }/model/chatglm2/tokenizer_config.json (100%)
create mode 100644 mftcoder_accelerate/model/chatglm3/__init__.py
rename mftcoder_accelerate/{src => }/model/chatglm3/config.json (100%)
rename mftcoder_accelerate/{src => }/model/chatglm3/configuration_chatglm.py (100%)
rename mftcoder_accelerate/{src => }/model/chatglm3/modeling_chatglm.py (100%)
rename mftcoder_accelerate/{src => }/model/chatglm3/quantization.py (100%)
rename mftcoder_accelerate/{src => }/model/chatglm3/tokenization_chatglm.py (100%)
rename mftcoder_accelerate/{src => }/model/code_llama/__init__.py (100%)
rename mftcoder_accelerate/{src => }/model/code_llama/configuration_llama.py (100%)
rename mftcoder_accelerate/{src => }/model/code_llama/convert_llama_weights_to_hf.py (100%)
rename mftcoder_accelerate/{src => }/model/code_llama/modeling_llama.py (100%)
rename mftcoder_accelerate/{src => }/model/code_llama/tokenization_code_llama.py (100%)
rename mftcoder_accelerate/{src => }/model/code_llama/tokenization_code_llama_fast.py (100%)
rename mftcoder_accelerate/{src => }/model/code_llama/tokenization_llama.py (100%)
rename mftcoder_accelerate/{src => }/model/code_llama/tokenization_llama_fast.py (100%)
create mode 100644 mftcoder_accelerate/model/deepseek_v2/__init__.py
rename mftcoder_accelerate/{src => }/model/deepseek_v2/configuration_deepseek.py (100%)
rename mftcoder_accelerate/{src => }/model/deepseek_v2/modeling_deepseek.py (100%)
rename mftcoder_accelerate/{src => }/model/deepseek_v2/tokenization_deepseek_fast.py (100%)
rename mftcoder_accelerate/{src => }/model/gpt_bigcode/__init__.py (100%)
rename mftcoder_accelerate/{src => }/model/gpt_bigcode/configuration_gpt_bigcode.py (100%)
rename mftcoder_accelerate/{src => }/model/gpt_bigcode/modeling_gpt_bigcode.py (100%)
rename mftcoder_accelerate/{src => }/model/gpt_neox/__init__.py (100%)
rename mftcoder_accelerate/{src => }/model/gpt_neox/config.json (100%)
rename mftcoder_accelerate/{src => }/model/gpt_neox/configuration_gpt_neox.py (100%)
rename mftcoder_accelerate/{src => }/model/gpt_neox/generation_config.json (100%)
rename mftcoder_accelerate/{src => }/model/gpt_neox/modeling_gpt_neox.py (100%)
rename mftcoder_accelerate/{src => }/model/gpt_neox/tokenization_gpt_neox_fast.py (100%)
create mode 100644 mftcoder_accelerate/model/phi/__init__.py
rename mftcoder_accelerate/{src => }/model/phi/configuration_mixformer_sequential.py (100%)
rename mftcoder_accelerate/{src => }/model/phi/modeling_mixformer_sequential.py (100%)
create mode 100644 mftcoder_accelerate/model/qwen/__init__.py
rename mftcoder_accelerate/{src => }/model/qwen/cache_autogptq_cuda_256.cpp (100%)
rename mftcoder_accelerate/{src => }/model/qwen/cache_autogptq_cuda_kernel_256.cu (100%)
rename mftcoder_accelerate/{src => }/model/qwen/configuration_qwen.py (100%)
rename mftcoder_accelerate/{src => }/model/qwen/cpp_kernels.py (100%)
rename mftcoder_accelerate/{src => }/model/qwen/modeling_qwen.py (100%)
rename mftcoder_accelerate/{src => }/model/qwen/qwen_generation_utils.py (100%)
rename mftcoder_accelerate/{src => }/model/qwen/tokenization_qwen.py (100%)
rename mftcoder_accelerate/{src => }/model/qwen/tokenizer_config.json (100%)
rename mftcoder_accelerate/{src => }/run_offline_tokenization.sh (78%)
delete mode 100644 mftcoder_accelerate/src/offline_tokenization/lm_fmt.py
delete mode 100644 mftcoder_accelerate/src/offline_tokenization/pack_encoder.py
delete mode 100644 mftcoder_accelerate/src/utils/agd.py
create mode 100644 mftcoder_accelerate/tokenization/bin_encoder.py
rename mftcoder_accelerate/{src/offline_tokenization => tokenization}/concat_sst_bin_tokenization.py (64%)
rename mftcoder_accelerate/{src/offline_tokenization => tokenization}/writer.py (67%)
rename mftcoder_accelerate/{src => }/tokenizer/__init__.py (100%)
rename mftcoder_accelerate/{src => }/tokenizer/chat_template.py (97%)
rename mftcoder_accelerate/{src => }/tokenizer/tokenizer.py (96%)
rename mftcoder_accelerate/{src => training}/configs/coba_train_config.json (100%)
rename mftcoder_accelerate/{src => training}/configs/dpo_train_config.json (100%)
rename mftcoder_accelerate/{src => training}/configs/full_train_config.json (100%)
rename mftcoder_accelerate/{src => training}/configs/lora_train_config.json (100%)
rename mftcoder_accelerate/{src => training}/configs/qlora_train_config.json (100%)
rename mftcoder_accelerate/{src/pefts => training}/merge_base_and_lora_to_hf.py (98%)
rename mftcoder_accelerate/{src/pefts => training}/mft_accelerate.py (98%)
create mode 100644 mftcoder_accelerate/training/mpt/__init__.py
rename mftcoder_accelerate/{src => training}/mpt/mpt_arguments.py (100%)
rename mftcoder_accelerate/{src => training}/mpt/mpt_trainer.py (99%)
rename mftcoder_accelerate/{src/mpt => training}/mpt_accelerate.py (98%)
create mode 100644 mftcoder_accelerate/training/pefts/__init__.py
rename mftcoder_accelerate/{src => training}/pefts/mft_arguments.py (100%)
rename mftcoder_accelerate/{src => training}/pefts/mft_trainer.py (99%)
rename mftcoder_accelerate/{src/utils => training/train_utils}/__init__.py (100%)
rename mftcoder_accelerate/{src/utils => training/train_utils}/common_utils.py (100%)
rename mftcoder_accelerate/{src/utils => training/train_utils}/loss_utils.py (99%)
rename mftcoder_accelerate/{src/utils => training/train_utils}/model_mapping.py (95%)
create mode 100644 mftcoder_accelerate/training/xxpo/__init__.py
rename mftcoder_accelerate/{src => training}/xxpo/custom_callbacks.py (100%)
rename mftcoder_accelerate/{src => training}/xxpo/xxpo_arguments.py (100%)
rename mftcoder_accelerate/{src/xxpo => training}/xxpo_accelerate.py (98%)
diff --git a/mftcoder_accelerate/README.md b/mftcoder_accelerate/README.md
index 87b4b63..d720e66 100644
--- a/mftcoder_accelerate/README.md
+++ b/mftcoder_accelerate/README.md
@@ -161,12 +161,19 @@ Here are some excellent pre-trained models weights available on Huggingface that
```
mftcoder_accelerate
|
- src
- configs
- |
- data
+ data
+ |
+ inference
+ |
+ model
+ |
+ tokenization
+ |
+ tokenizer
+ |
+ training
|
- model
+ configs
|
*pefts*
|
@@ -174,41 +181,22 @@ mftcoder_accelerate
|
*mpt*
|
- *offline_tokenization*
- |
- tokenizer
- |
- utils
- |
- evals
-```
-我们将训练中使用的各种组件抽取出来，以便后续的扩展和优化， 详见```src```目录下的实现。
-
-MFT训练入口文件是```mftcoder_accelerate/src/pefts/mft_accelerate.py```
+ train_utils
-DPO/ORPO训练入口文件是```mftcoder_accelerate/src/xxpo/xxpo_accelerate.py```
-
-MPT(全量加训)训练入口文件是```mftcoder_accelerate/src/mpt/mpt_accelerate.py```
-
-参数配置存储在```mftcoder_accelerate/src/configs```目录下，方便统一管理和更改。
-
-**_所以，在你开启训练之前，请进入src目录_**
-```
-cd mftcoder_accelerate/src
```
-You can find the implementations in the ```mftcoder_accelerate/src``` directory
-The entry file for MFT training is ```mftcoder_accelerate/src/pefts/mft_accelerate.py```.
+You can find the implementations in the ```mftcoder_accelerate``` directory
+The entry file for MFT training is ```mftcoder_accelerate/training/mft_accelerate.py```.
-The entry file for DPO/ORPO training is ```mftcoder_accelerate/src/xxpo/xxpo_accelerate.py```.
+The entry file for DPO/ORPO training is ```mftcoder_accelerate/training/xxpo_accelerate.py```.
-The entry file for MPT(Continue Training) is ```mftcoder_accelerate/src/mpt/mpt_accelerate.py```. You need finish offline tokenization of your data via ```mftcoder_accelerate/src/run_offline_tokenization.sh```, which is different from the online tokenizaion used in MFT/DPO.
+The entry file for MPT(Continue Training) is ```mftcoder_accelerate/training/mpt_accelerate.py```. You need finish offline tokenization of your data via ```mftcoder_accelerate/run_offline_tokenization.sh```, which is different from the online tokenizaion used in MFT/DPO.
-Configurations are stored in the ```mftcoder_accelerate/src/configs``` directory for easy management and modification.
+Configurations are stored in the ```mftcoder_accelerate/training/configs``` directory for easy management and modification.
**_As a result, before you start training, you should first change your dir by_**
```
-cd mftcoder_accelerate/src
+cd mftcoder_accelerate
```
### 3.1 MFT Tokenization
@@ -217,7 +205,6 @@ During training, we concatenate multi-turn dialogues into the following format (
In default format, ```<s>human\n``` starts the user's input (i.e., prompt),```<s>bot\n``` starts the assistant's output (i.e., response)
```{EOS_TOKEN}``` represents the proper eos_token.
-We have different eos_tokens in ```src/pefts/model_mapping.py``` which fits different base models.
Here is a visionable example of the training data after formatting:
```
@@ -244,7 +231,7 @@ To perform LoRA/QLoRA fine-tuning, you can execute the following command:
#### Launch via Deepspeed
DeepSpeed config in accelerate_ds_config.yaml.
```bash
-accelerate launch --config_file accelerate_ds_config.yaml pefts/mft_accelerate.py --train_config configs/xxx_train_config.json --distributed_type "DeepSpeed"
+accelerate launch --config_file accelerate_ds_config.yaml training/mft_accelerate.py --train_config training/configs/xxx_train_config.json --distributed_type "DeepSpeed"
```
or
DeepSpeed Zero2 config in command line arguments
@@ -259,7 +246,7 @@ sh ds_zero3_single_launch.sh
#### Launch via FSDP
FSDP config in accelerate_fsdp_config.yaml.
```bash
-accelerate launch --config_file accelerate_fsdp_config.yaml pefts/mft_accelerate.py --train_config configs/xxx_train_config.json --distributed_type "FSDP"
+accelerate launch --config_file accelerate_fsdp_config.yaml training/mft_accelerate.py --train_config training/configs/xxx_train_config.json --distributed_type "FSDP"
```
or
FSDP config in command line arguments
@@ -276,7 +263,7 @@ sh ds_multinode_launch.sh
#### Traing Arguments
All arguments allowed in ***_train_config.josn are defined in ```arguments.py```.
-Frequently used arguments are provided in ```configs/***_train_config``` and explained as follows. You can modify these parameters according to your needs:
+Frequently used arguments are provided in ```training/configs/***_train_config``` and explained as follows. You can modify these parameters according to your needs:
- **load_raw_dataset**: Need to be true at present. Only JSONL format is supported.
diff --git a/mftcoder_accelerate/README_cn.md b/mftcoder_accelerate/README_cn.md
index 39631c5..62d729b 100644
--- a/mftcoder_accelerate/README_cn.md
+++ b/mftcoder_accelerate/README_cn.md
@@ -152,12 +152,19 @@
```
mftcoder_accelerate
|
- src
- configs
- |
- data
+ data
+ |
+ inference
+ |
+ model
+ |
+ tokenization
+ |
+ tokenizer
+ |
+ training
|
- model
+ configs
|
*pefts*
|
@@ -165,27 +172,22 @@ mftcoder_accelerate
|
*mpt*
|
- *offline_tokenization*
- |
- tokenizer
- |
- utils
- |
- evals
+ train_utils
+
```
-我们将训练中使用的各种组件抽取出来，以便后续的扩展和优化， 详见```src```目录下的实现。
+我们将训练中使用的各种组件抽取出来，以便后续的扩展和优化， 详见```mftcoder_accelerate```目录下的实现。
-MFT训练入口文件是```mftcoder_accelerate/src/pefts/mft_accelerate.py```
+MFT训练入口文件是```mftcoder_accelerate/training/mft_accelerate.py```
-DPO/ORPO训练入口文件是```mftcoder_accelerate/src/xxpo/xxpo_accelerate.py```
+DPO/ORPO训练入口文件是```mftcoder_accelerate/training/xxpo_accelerate.py```
-MPT(全量加训)训练入口文件是```mftcoder_accelerate/src/mpt/mpt_accelerate.py```. MPT加训需要提前做好数据的tokenziation，通过```mftcoder_accelerate/src/run_offline_tokenization.sh```，你可以将数据通过cpu进行离线的tokenization。这和MFT/DPO中使用的在线tokenziation不同。
+MPT(全量加训)训练入口文件是```mftcoder_accelerate/training/mpt_accelerate.py```. MPT加训需要提前做好数据的tokenziation，通过```mftcoder_accelerate/run_offline_tokenization.sh```，你可以将数据通过cpu进行离线的tokenization。这和MFT/DPO中使用的在线tokenziation不同。
-参数配置存储在```mftcoder_accelerate/src/configs```目录下，方便统一管理和更改。
+参数配置存储在```mftcoder_accelerate/training/configs```目录下，方便统一管理和更改。
-**_所以，在你开启训练之前，请进入src目录_**
+**你开启训练之前**
```
-cd mftcoder_accelerate/src
+cd mftcoder_accelerate
```
@@ -216,7 +218,7 @@ QLoRA论文指出，该方法可以在一张V100上对33B的模型进行微调
#### Deepspeed 单机启动
DeepSpeed配置在accelerate_ds_config.yaml中。
```bash
-accelerate launch --config_file accelerate_ds_config.yaml pefts/mft_accelerate.py --train_config configs/xxx_train_config.json --distributed_type "DeepSpeed"
+accelerate launch --config_file accelerate_ds_config.yaml training/mft_accelerate.py --train_config training/configs/xxx_train_config.json --distributed_type "DeepSpeed"
```
或者
@@ -233,7 +235,7 @@ sh ds_zero3_single_launch.sh
#### FSDP 单机启动
FSDP配置在accelerate_fsdp_config.yaml中。
```bash
-accelerate launch --config_file accelerate_fsdp_config.yaml pefts/mft_accelerate.py --train_config configs/xxx_train_config.json --distributed_type "FSDP"
+accelerate launch --config_file accelerate_fsdp_config.yaml training/mft_accelerate.py --train_config training/configs/xxx_train_config.json --distributed_type "FSDP"
```
或者
@@ -249,7 +251,7 @@ sh ds_multinode_launch.sh
```
#### 训练参数
-_**训练需要的参数配置在```configs/*_train_config```中，主要参数说明如下：**_
+_**训练需要的参数配置在```training/configs/*_train_config```中，主要参数说明如下：**_
- **load_raw_dataset**: 需要保持true，后续会支持其它模式数据，当前仅支持jsonl输入
- **data_paths**: "[path1,path2,path3]" 输入数据地址，字符串，开头结尾用[]，中间用```,```间隔不同path，每个path是一个目录，目录的最后一级名字作为任务名称，下面包含1到多个jsonl数据
diff --git a/mftcoder_accelerate/src/accelerate_ds_config.yaml b/mftcoder_accelerate/accelerate_ds_config.yaml
similarity index 100%
rename from mftcoder_accelerate/src/accelerate_ds_config.yaml
rename to mftcoder_accelerate/accelerate_ds_config.yaml
diff --git a/mftcoder_accelerate/src/accelerate_fsdp_config.yaml b/mftcoder_accelerate/accelerate_fsdp_config.yaml
similarity index 100%
rename from mftcoder_accelerate/src/accelerate_fsdp_config.yaml
rename to mftcoder_accelerate/accelerate_fsdp_config.yaml
diff --git a/mftcoder_accelerate/src/data/Makefile b/mftcoder_accelerate/data/Makefile
similarity index 100%
rename from mftcoder_accelerate/src/data/Makefile
rename to mftcoder_accelerate/data/Makefile
diff --git a/mftcoder_accelerate/src/data/__init__.py b/mftcoder_accelerate/data/__init__.py
similarity index 100%
rename from mftcoder_accelerate/src/data/__init__.py
rename to mftcoder_accelerate/data/__init__.py
diff --git a/mftcoder_accelerate/src/data/blendable_dataset.py b/mftcoder_accelerate/data/blendable_dataset.py
similarity index 98%
rename from mftcoder_accelerate/src/data/blendable_dataset.py
rename to mftcoder_accelerate/data/blendable_dataset.py
index 84b9756..d810da8 100644
--- a/mftcoder_accelerate/src/data/blendable_dataset.py
+++ b/mftcoder_accelerate/data/blendable_dataset.py
@@ -22,8 +22,6 @@
import numpy as np
import torch
-from utils.common_utils import print_rank_0
-
class BlendableDataset(torch.utils.data.Dataset):
def __init__(self, datasets, weights):
diff --git a/mftcoder_accelerate/src/data/data_utils.py b/mftcoder_accelerate/data/data_utils.py
similarity index 99%
rename from mftcoder_accelerate/src/data/data_utils.py
rename to mftcoder_accelerate/data/data_utils.py
index 8d168bd..1f8c30d 100644
--- a/mftcoder_accelerate/src/data/data_utils.py
+++ b/mftcoder_accelerate/data/data_utils.py
@@ -20,7 +20,7 @@
from typing import List, Tuple
from functools import partial
-from utils.common_utils import print_rank_0, TASK2ID, ID2TASK
+from training.train_utils.common_utils import print_rank_0, TASK2ID, ID2TASK
from data.indexed_dataset import make_dataset as make_indexed_dataset
from data.blendable_dataset import BlendableDataset
from data.gpt2_dataset import GPT2Dataset, GPT2PromptDataset
diff --git a/mftcoder_accelerate/src/data/gpt2_dataset.py b/mftcoder_accelerate/data/gpt2_dataset.py
similarity index 99%
rename from mftcoder_accelerate/src/data/gpt2_dataset.py
rename to mftcoder_accelerate/data/gpt2_dataset.py
index 12eeb87..6b780c2 100644
--- a/mftcoder_accelerate/src/data/gpt2_dataset.py
+++ b/mftcoder_accelerate/data/gpt2_dataset.py
@@ -23,7 +23,7 @@
import numpy as np
import torch
-from utils.common_utils import print_rank_0, TASK2ID, ID2TASK
+from training.train_utils.common_utils import print_rank_0, TASK2ID, ID2TASK
class GPT2PromptDataset(torch.utils.data.Dataset):
diff --git a/mftcoder_accelerate/src/data/helpers.cpp b/mftcoder_accelerate/data/helpers.cpp
similarity index 100%
rename from mftcoder_accelerate/src/data/helpers.cpp
rename to mftcoder_accelerate/data/helpers.cpp
diff --git a/mftcoder_accelerate/src/data/indexed_dataset.py b/mftcoder_accelerate/data/indexed_dataset.py
similarity index 99%
rename from mftcoder_accelerate/src/data/indexed_dataset.py
rename to mftcoder_accelerate/data/indexed_dataset.py
index 12ea9c2..40389f3 100644
--- a/mftcoder_accelerate/src/data/indexed_dataset.py
+++ b/mftcoder_accelerate/data/indexed_dataset.py
@@ -22,7 +22,7 @@
import numpy as np
import torch
-from utils.common_utils import print_rank_0
+from training.train_utils.common_utils import print_rank_0
def __best_fitting_dtype(vocab_size=None):
diff --git a/mftcoder_accelerate/src/data/lm_dataformat.py b/mftcoder_accelerate/data/lm_dataformat.py
similarity index 86%
rename from mftcoder_accelerate/src/data/lm_dataformat.py
rename to mftcoder_accelerate/data/lm_dataformat.py
index 340377b..ed42d08 100644
--- a/mftcoder_accelerate/src/data/lm_dataformat.py
+++ b/mftcoder_accelerate/data/lm_dataformat.py
@@ -126,58 +126,32 @@ def handle_jsonl(jsonl_reader, get_meta, autojoin_paragraphs, para_joiner, key="
yield ob
continue
- if isinstance(key, str):
- text = ob.get(key)
- if autojoin_paragraphs and isinstance(text, list):
- text = para_joiner.join(text)
+ if key is None:
+ yield ob
+ continue
- if get_meta:
- yield text, (ob["meta"] if "meta" in ob else {})
- else:
- yield text
- elif isinstance(key, list):
-
- task = ob.get(key[0], "")
- src_language = ob.get(key[1], "")
- src_code = ob.get(key[2], "")
- tgt_language = ob.get(key[3], "")
- tgt_code = ob.get(key[4], "")
- sql = ob.get(key[5], "")
- prompt_in = ob.get(key[6], "")
- content_in = ob.get(key[7], "")
- bad_content_in = ob.get(key[8], "")
-
- if task:
- task = "task: " + task
- if src_language:
- src_language = "language: " + src_language
- if sql:
- sql = sql.strip() + "\n"
- task = "task: text to sql\n"
- src_language = "language: text\n"
- tgt_language = "language: sql\n"
- prompt_in = prompt_in.strip() + "\n"
- elif tgt_language:
- tgt_language = "language: " + tgt_language
-
- prompt = task + src_language + src_code + prompt_in + tgt_language
- content = tgt_code + sql + content_in
- bad_content = bad_content_in
-
- yield (prompt, content, bad_content)
+ text = ob[key]
+
+ if autojoin_paragraphs and isinstance(text, list):
+ text = para_joiner.join(text)
+
+ if get_meta:
+ yield text, (ob["meta"] if "meta" in ob else {})
+ else:
+ yield text
class Reader:
def __init__(self, in_path):
self.in_path = in_path
- def stream_data(self, get_meta=False, threaded=False, key=["prompt", "content", "bad_content"]):
+ def stream_data(self, get_meta=False, threaded=False, key=None):
if not threaded:
- yield from self._stream_data(get_meta, jsonl_key=key)
+ yield from self._stream_data(get_meta, key=key)
return
q = mp.Queue(1000)
- p = mp.Process(target=self._stream_data_threaded, args=(q, get_meta))
+ p = mp.Process(target=self._stream_data_threaded, args=(q, get_meta), kwargs={"key": key})
p.start()
while p.is_alive():
res = q.get()
@@ -190,7 +164,7 @@ def _stream_data_threaded(self, q, get_meta=False):
q.put(data)
q.put(None)
- def _stream_data(self, get_meta=False, jsonl_key="text"):
+ def _stream_data(self, get_meta=False, key="text"):
self.f_name = ""
files = listdir_or_file(self.in_path)
if not files:
@@ -210,11 +184,11 @@ def _stream_data(self, get_meta=False, jsonl_key="text"):
yield from self.read_dat(f)
elif f.endswith(".jsonl"):
- yield from self.read_jsonl(f, get_meta, key=jsonl_key)
+ yield from self.read_jsonl(f, get_meta, key=key)
elif f.endswith(".jsonl.zst"):
- yield from self.read_jsonl_zst(f, get_meta, key=jsonl_key)
+ yield from self.read_jsonl_zst(f, get_meta, key=key)
elif f.endswith(".jsonl.zst.tar"):
- yield from self.read_jsonl_tar(f, get_meta, jsonl_key=key)
+ yield from self.read_jsonl_tar(f, get_meta, key=key)
elif f.endswith(".json.zst"):
assert not get_meta
diff --git a/mftcoder_accelerate/src/data/multi_task_dataset.py b/mftcoder_accelerate/data/multi_task_dataset.py
similarity index 99%
rename from mftcoder_accelerate/src/data/multi_task_dataset.py
rename to mftcoder_accelerate/data/multi_task_dataset.py
index 63c4b27..6db0056 100644
--- a/mftcoder_accelerate/src/data/multi_task_dataset.py
+++ b/mftcoder_accelerate/data/multi_task_dataset.py
@@ -14,7 +14,7 @@
import torch
from functools import partial
from data.preprocess_data import UniformEncoder
-from utils.common_utils import TASK2ID
+from training.train_utils.common_utils import TASK2ID
class GPT2FromRawDataset(torch.utils.data.Dataset):
@@ -193,7 +193,7 @@ def load_dataset_from_jsonl(args, shard_data=False, world_size=1, global_rank=0,
local_train_num = 0
local_valid_num = 0
- # 不同数据集在不同文件夹下
+ # each path as a task
for dataset_index in range(len(data_prefixes)):
# files = os.listdir(data_prefixes[dataset_index])
# get all jsonl files and corresponding reading handler
diff --git a/mftcoder_accelerate/src/data/preprocess_data.py b/mftcoder_accelerate/data/preprocess_data.py
similarity index 99%
rename from mftcoder_accelerate/src/data/preprocess_data.py
rename to mftcoder_accelerate/data/preprocess_data.py
index f7226bd..dfd1ceb 100644
--- a/mftcoder_accelerate/src/data/preprocess_data.py
+++ b/mftcoder_accelerate/data/preprocess_data.py
@@ -9,7 +9,7 @@
import ftfy
import glob
-# print("In preprocess_data_new.py, sys path:", sys.path)
+# print("In preprocess_data.py, sys path:", sys.path)
from tokenizer import build_tokenizer
diff --git a/mftcoder_accelerate/src/ds_multinode_launch.sh b/mftcoder_accelerate/ds_multinode_launch.sh
similarity index 89%
rename from mftcoder_accelerate/src/ds_multinode_launch.sh
rename to mftcoder_accelerate/ds_multinode_launch.sh
index dca0670..88acd50 100755
--- a/mftcoder_accelerate/src/ds_multinode_launch.sh
+++ b/mftcoder_accelerate/ds_multinode_launch.sh
@@ -12,7 +12,7 @@ N_GPU_PER_NODE=8
# You need to export $MACHINE_RANK, $MASTER_ADDR, $MASTER_PORT automatically for each Node.
# config path
-CONFIG="configs/xxx_train_config.json"
+CONFIG="training/configs/xxx_train_config.json"
# envs used inside training
export OMP_NUM_THREADS=4
@@ -41,4 +41,4 @@ accelerate launch \
--main_process_ip $MASTER_ADDR \
--main_process_port $MASTER_PORT \
--rdzv_backend 'static' \
- pefts/mft_accelerate.py --train_config "$CONFIG" --distributed_type "deepspeed"
\ No newline at end of file
+ training/mft_accelerate.py --train_config "$CONFIG" --distributed_type "deepspeed"
\ No newline at end of file
diff --git a/mftcoder_accelerate/src/ds_single_launch.sh b/mftcoder_accelerate/ds_single_launch.sh
similarity index 90%
rename from mftcoder_accelerate/src/ds_single_launch.sh
rename to mftcoder_accelerate/ds_single_launch.sh
index d6c84bb..0f5edee 100755
--- a/mftcoder_accelerate/src/ds_single_launch.sh
+++ b/mftcoder_accelerate/ds_single_launch.sh
@@ -7,7 +7,7 @@
N_GPU_PER_NODE=8
# config path
-CONFIG="configs/xxx_train_config.json"
+CONFIG="training/configs/xxx_train_config.json"
# envs used inside training
export OMP_NUM_THREADS=4
@@ -33,6 +33,6 @@ accelerate launch \
--same_network \
--machine_rank 0 \
--rdzv_backend 'static' \
- pefts/mft_accelerate.py --train_config "$CONFIG" \
+ training/mft_accelerate.py --train_config "$CONFIG" \
--distributed_type "deepspeed" \
> MFTCoder-training-"$TODAY".log 2>&1 &
diff --git a/mftcoder_accelerate/src/ds_zero3_single_launch.sh b/mftcoder_accelerate/ds_zero3_single_launch.sh
similarity index 90%
rename from mftcoder_accelerate/src/ds_zero3_single_launch.sh
rename to mftcoder_accelerate/ds_zero3_single_launch.sh
index 5f581c9..838ca44 100755
--- a/mftcoder_accelerate/src/ds_zero3_single_launch.sh
+++ b/mftcoder_accelerate/ds_zero3_single_launch.sh
@@ -7,7 +7,7 @@
N_GPU_PER_NODE=8
# config path
-CONFIG="configs/xxx_train_config.json"
+CONFIG="training/configs/xxx_train_config.json"
# envs used inside training
export OMP_NUM_THREADS=4
@@ -33,6 +33,6 @@ accelerate launch \
--same_network \
--machine_rank 0 \
--rdzv_backend 'static' \
- pefts/mft_accelerate.py --train_config "$CONFIG" \
+ training/mft_accelerate.py --train_config "$CONFIG" \
--distributed_type "deepspeed" \
> MFTCoder-training-"$TODAY".log 2>&1 &
diff --git a/mftcoder_accelerate/src/fsdp_single_launch.sh b/mftcoder_accelerate/fsdp_single_launch.sh
similarity index 91%
rename from mftcoder_accelerate/src/fsdp_single_launch.sh
rename to mftcoder_accelerate/fsdp_single_launch.sh
index 2dc8f89..ce073e8 100755
--- a/mftcoder_accelerate/src/fsdp_single_launch.sh
+++ b/mftcoder_accelerate/fsdp_single_launch.sh
@@ -7,7 +7,7 @@
N_GPU_PER_NODE=8
# config path
-CONFIG="configs/xxx_train_config.json"
+CONFIG="training/configs/xxx_train_config.json"
# fsdp_transformer_layer_cls_to_wrap, choose the DecoderLayer
WRAP_MODULE="LlamaDecoderLayer"
@@ -37,7 +37,7 @@ accelerate launch \
--same_network \
--machine_rank=0 \
--rdzv_backend=static \
- pefts/mft_accelerate.py --train_config "$CONFIG" \
+ training/mft_accelerate.py --train_config "$CONFIG" \
--distributed_type "fsdp" \
> MFTCoder-training-"$TODAY".log 2>&1 &
diff --git a/mftcoder_accelerate/src/model/__init__.py b/mftcoder_accelerate/inference/__init__.py
old mode 100755
new mode 100644
similarity index 100%
rename from mftcoder_accelerate/src/model/__init__.py
rename to mftcoder_accelerate/inference/__init__.py
diff --git a/mftcoder_accelerate/inference/hf_inference.py b/mftcoder_accelerate/inference/demo_inference.py
similarity index 98%
rename from mftcoder_accelerate/inference/hf_inference.py
rename to mftcoder_accelerate/inference/demo_inference.py
index 67f9ba0..7af3a6a 100644
--- a/mftcoder_accelerate/inference/hf_inference.py
+++ b/mftcoder_accelerate/inference/demo_inference.py
@@ -2,15 +2,9 @@
# @author Chaoyu Chen
# @date 2024/1/4
# @module hf_inference.py
-"""
-# @author qumu
-# @date 2023/9/19
-# @module hf_inference.py
-"""
import os
import sys
import torch
-import textwrap
from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList
from peft import PeftModel
diff --git a/mftcoder_accelerate/inference/gen.py b/mftcoder_accelerate/inference/gen.py
new file mode 100644
index 0000000..ae8184d
--- /dev/null
+++ b/mftcoder_accelerate/inference/gen.py
@@ -0,0 +1,126 @@
+# @author Chaoyu Chen
+# @date 2024/11/13
+# @module gen.py
+"""Gen demo"""
+
+from typing import Iterable, Dict, List
+import gzip
+import json
+import os
+import argparse
+import time
+import sys
+from tqdm import tqdm
+import torch
+
+from transformers import (
+ AutoConfig,
+ AutoTokenizer,
+ AutoModelForCausalLM,
+)
+from peft import PeftModel
+from infer_base import HFInferenceBase
+from tokenizer import MFTCoder_template
+
+
+class HFGenerationInfer(HFInferenceBase):
+ def __init__(self):
+ super().__init__()
+
+ def load_model_tokenizer(self, args):
+ """
+ load generation model and tokenizer using self._load_model_tokenizer
+ """
+ self._load_model_tokenizer(AutoModelForCausalLM, args.model_path, peft_path=args.peft_path)
+
+ def handler(self, dataloader, args):
+ for batch in dataloader:
+ prompts = [
+ self.tokenizer.apply_chat_template([{"role": "user", "content": sample["prompt"]}], tokenize=False)
+ for sample in batch
+ ]
+ inputs = self.tokenizer(prompts, return_tensors="pt", padding=True, add_special_tokens=False).to("cuda")
+
+ # print(inputs)
+ print("=================Prompts and Generations===================")
+
+ outputs = self.model.generate(
+ inputs=inputs["input_ids"],
+ attention_mask=inputs["attention_mask"],
+ max_new_tokens=args.max_new_tokens,
+ do_sample=args.do_sample,
+ top_p=args.top_p,
+ temperature=args.temperature,
+ num_beams=args.num_beams,
+ num_return_sequences=args.num_return_sequences,
+ eos_token_id=self.tokenizer.eos_token_id,
+ pad_token_id=self.tokenizer.pad_token_id,
+ )
+
+ gen_text = self.tokenizer.batch_decode(
+ outputs[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+ )
+ num_return_sequences = len(gen_text) // len(prompts)
+ for i in range(len(prompts)):
+ print("=========" * 10)
+ print(f"Prompt:\n{prompts[i]}")
+ batch[i]["generations"] = gen_text[
+ i * num_return_sequences : i * num_return_sequences + num_return_sequences
+ ]
+ for j in range(num_return_sequences):
+ print(f"Generation {j+1}/{num_return_sequences}:\n{gen_text[i * num_return_sequences + j]}")
+ # print(f"Outputs ids:\n{outputs[i]}")
+ sys.stdout.flush()
+ yield batch
+
+ def prepare_args(self, args):
+ name = args.data_file.split("/")[-1].replace(".jsonl", "") + "-GEN"
+ args.output_path = os.path.join(args.output_dir, f"{name}.jsonl")
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description="Generation args.")
+ parser.add_argument(
+ "--model_path",
+ type=str,
+ help="huggingface model path",
+ )
+ parser.add_argument(
+ "--data_file",
+ type=str,
+ help="data file path",
+ )
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ help="output directory",
+ )
+ parser.add_argument("--batch_size", type=int, default=1)
+ parser.add_argument("--num_return_sequences", type=int, default=20, help="pass1:20,pass10:20,pass100:100")
+ parser.add_argument("--num_beams", type=int, default=1, help="beam1, beam3, beam5, beam7")
+ parser.add_argument("--do_sample", action="store_true", default=False)
+ parser.add_argument("--temperature", type=float, default=0.2)
+ parser.add_argument("--top_p", type=float, default=0.95)
+
+ parser.add_argument("--peft_path", type=str, default="", help="peft path：None")
+ parser.add_argument("--eos_token", type=str, default=None, help="eos token")
+ args = parser.parse_args()
+ return args
+
+
+def main(args):
+ st = time.time()
+ runner = HFGenerationInfer()
+ runner.run(args)
+
+ print("{} finish in: {:.4f} Minutes {}".format("+++" * 10, (time.time() - st) / 60, "+++" * 10))
+
+
+if __name__ == "__main__":
+ print("-----------" * 10)
+ print("-----------" * 10)
+ print("-----------" * 10)
+ main(get_args())
+ print("-----------" * 10)
+ print("-----------" * 10)
+ print("-----------" * 10)
diff --git a/mftcoder_accelerate/inference/infer_base.py b/mftcoder_accelerate/inference/infer_base.py
new file mode 100644
index 0000000..cb46fc0
--- /dev/null
+++ b/mftcoder_accelerate/inference/infer_base.py
@@ -0,0 +1,175 @@
+# @author Chaoyu Chen
+# @date 2024/11/12
+# @module infer_base.py
+"""HF InferenceBase"""
+
+from typing import Iterable, Dict, List
+import gzip
+import json
+import os
+import argparse
+import time
+from tqdm import tqdm
+import torch
+from transformers import (
+ AutoConfig,
+ AutoTokenizer,
+ AutoModel,
+ AutoModelForCausalLM,
+ AutoModelForSequenceClassification,
+)
+from peft import PeftModel
+
+from infer_utils import (
+ print_args,
+ get_line_count,
+ stream_jsonl,
+ batch_stream_jsonl,
+ flatten_batch_stream,
+ write_jsonl,
+)
+
+
+class HFInferenceBase:
+ def __init__(self):
+ # load model and tokenizer
+ self.tokenizer = None
+ self.model = None
+
+ def _load_model_tokenizer(
+ self,
+ model_cls,
+ model_path,
+ torch_dtype=torch.bfloat16,
+ peft_path=None,
+ quantization=None,
+ eos_token=None,
+ **kwargs,
+ ):
+ """
+ load model and tokenizer by transfromers
+ """
+ # pass if ckpt already loaded
+ if self.model and self.tokenizer:
+ print("CKPT: {} already loaded".format(model_path))
+ return
+
+ # load tokenizer first
+ print("LOAD CKPT and Tokenizer: {}".format(model_path))
+ tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+ tokenizer.padding_side = "left"
+
+ config, unused_kwargs = AutoConfig.from_pretrained(
+ model_path, trust_remote_code=True, return_unused_kwargs=True
+ )
+ print("unused_kwargs:", unused_kwargs)
+ print("config input:\n", config)
+
+ # eos token parsing
+ if eos_token:
+ eos_token = eos_token
+ eos_token_id = tokenizer.convert_tokens_to_ids(eos_token)
+ print(f"eos_token {eos_token} from user input")
+ elif hasattr(tokenizer, "eos_token_id") and tokenizer.eos_token_id:
+ print(f"Initial eos_token_id {tokenizer.eos_token_id} from tokenizer")
+ eos_token_id = tokenizer.eos_token_id
+ eos_token = tokenizer.convert_ids_to_tokens(eos_token_id)
+ elif hasattr(tokenizer, "eos_token") and tokenizer.eos_token:
+ print(f"Initial eos_token {tokenizer.eos_token} from tokenizer")
+ eos_token = tokenizer.eos_token
+ eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
+ elif hasattr(config, "eos_token_id") and config.eos_token_id:
+ print(f"Initial eos_token_id {config.eos_token_id} from config.json")
+ eos_token_id = config.eos_token_id
+ eos_token = tokenizer.convert_ids_to_tokens(config.eos_token_id)
+ elif hasattr(config, "eos_token") and config.eos_token:
+ print(f"Initial eos_token {config.eos_token} from config.json")
+ eos_token = config.eos_token
+ eos_token_id = tokenizer.convert_tokens_to_ids(config.eos_token)
+ else:
+ raise ValueError("No available eos_token or eos_token_id.")
+
+ try:
+ tokenizer.eos_token = eos_token
+ tokenizer.eos_token_id = eos_token_id
+ # set pad_token to be same as eos_token, it is ok because is will be masked out.
+ tokenizer.pad_token = eos_token
+ tokenizer.pad_token_id = eos_token_id
+ except:
+ print(f"[WARNING]Cannot set tokenizer.eos_token")
+
+ print(f"tokenizer's eos_token: {tokenizer.eos_token}, pad_token: {tokenizer.pad_token}")
+ print(f"tokenizer's eos_token_id: {tokenizer.eos_token_id}, pad_token_id: {tokenizer.pad_token_id}")
+ print(type(tokenizer))
+
+ base_model = model_cls.from_pretrained(
+ model_path,
+ config=config,
+ load_in_8bit=(quantization == "8bit"),
+ load_in_4bit=(quantization == "4bit"),
+ device_map="auto",
+ torch_dtype=torch_dtype,
+ attn_implementation="flash_attention_2",
+ trust_remote_code=True,
+ low_cpu_mem_usage=True,
+ **kwargs,
+ )
+
+ if peft_path:
+ print("Loading PEFT MODEL...")
+ model = PeftModel.from_pretrained(base_model, peft_path)
+ else:
+ print("Loading Original MODEL...")
+ model = base_model
+
+ model.eval()
+
+ print("===============================MODEL Configs=============================")
+ print(model.config)
+ print("=========================================================================")
+ print("==============================MODEL Archetecture=========================")
+ print(model)
+ print("=========================================================================")
+
+ self.model = model
+ self.tokenizer = tokenizer
+
+ def load_model_tokenizer(self, args):
+ raise NotImplementedError
+
+ def handler(self, dataloader, args):
+ """consume batch dataloader, yield result batch"""
+ raise NotImplementedError
+
+ @staticmethod
+ def check_args(args):
+ if not os.path.exists(args.model_path):
+ raise FileNotFoundError("Model path {} not exists!".format(args.model_path))
+
+ if not os.path.exists(args.data_file):
+ raise FileNotFoundError("Data file {} not exists!".format(args.data_file))
+
+ os.makedirs(args.output_dir, exist_ok=True)
+
+ def prepare_args(self, args):
+ raise NotImplementedError("You need assign args.output_path for result writing")
+
+ def run(self, args):
+ self.check_args(args)
+ self.prepare_args(args)
+ print_args(args)
+
+ # load model if not yet loaded
+ self.load_model_tokenizer(args)
+
+ # get dataloader
+ total_num = get_line_count(args.data_file)
+ stream = stream_jsonl(args.data_file)
+ dataloader = batch_stream_jsonl(stream, args.batch_size)
+
+ # handel batch dataloader
+ batch_res_stream = self.handler(dataloader, args)
+ res_stream = flatten_batch_stream(batch_res_stream)
+
+ # write results into output_path streamingly
+ write_jsonl(args.output_path, res_stream, total=total_num)
diff --git a/mftcoder_accelerate/inference/infer_utils.py b/mftcoder_accelerate/inference/infer_utils.py
new file mode 100644
index 0000000..2fe3be2
--- /dev/null
+++ b/mftcoder_accelerate/inference/infer_utils.py
@@ -0,0 +1,76 @@
+# @author Chaoyu Chen
+# @date 2024/11/12
+"""Some inference utils"""
+import torch
+import json
+import os
+import gzip
+from tqdm import tqdm
+from typing import Iterable, Dict, List
+
+
+def print_args(args):
+ message = "\n".join([f"{k:<20}: {v}" for k, v in vars(args).items()])
+ print("====" * 30)
+ print(message)
+ print("====" * 30)
+ print("GPU: {}".format(torch.cuda.current_device()))
+
+
+def get_line_count(file_path):
+ with open(file_path, "r", encoding="utf-8") as file:
+ return sum(1 for _ in file)
+
+
+def stream_jsonl(filename: str) -> Iterable[Dict]:
+ """
+ Parses each jsonl line and yields it as a dictionary
+ """
+ if filename.endswith(".gz"):
+ with open(filename, "rb") as gzfp:
+ with gzip.open(gzfp, "rt") as fp:
+ for line in fp:
+ if any(not x.isspace() for x in line):
+ yield json.loads(line)
+ else:
+ with open(filename, "r") as fp:
+ for line in fp:
+ if any(not x.isspace() for x in line):
+ yield json.loads(line)
+
+
+def batch_stream_jsonl(stream: Iterable[Dict], batch_size) -> Iterable[List]:
+ batch = list()
+ for item in stream:
+ batch.append(item)
+ if len(batch) == batch_size:
+ yield batch
+ batch = []
+ if batch:
+ yield batch
+
+
+def flatten_batch_stream(batch_stream):
+ for batch in batch_stream:
+ for item in batch:
+ yield item
+
+
+def write_jsonl(filename: str, data: Iterable[Dict], total, append: bool = False):
+ """
+ Writes an iterable of dictionaries to jsonl
+ """
+ if append:
+ mode = "ab"
+ else:
+ mode = "wb"
+ filename = os.path.expanduser(filename)
+ if filename.endswith(".gz"):
+ with open(filename, mode) as fp:
+ with gzip.GzipFile(fileobj=fp, mode="wb") as gzfp:
+ for x in data:
+ gzfp.write((json.dumps(x) + "\n").encode("utf-8"))
+ else:
+ with open(filename, mode) as fp:
+ for x in tqdm(data, total=total):
+ fp.write((json.dumps(x) + "\n").encode("utf-8"))
diff --git a/mftcoder_accelerate/inference/ppl.py b/mftcoder_accelerate/inference/ppl.py
new file mode 100644
index 0000000..8690000
--- /dev/null
+++ b/mftcoder_accelerate/inference/ppl.py
@@ -0,0 +1,122 @@
+# coding=utf-8
+# @author Chaoyu Chen
+# @date 2024/11/12
+# @module ppl.py
+"""ppl demo"""
+from typing import Iterable, Dict, List
+import gzip
+import json
+import os
+import argparse
+import time
+from tqdm import tqdm
+import torch
+
+from transformers import (
+ AutoConfig,
+ AutoTokenizer,
+ AutoModelForCausalLM,
+)
+from peft import PeftModel
+from infer_base import HFInferenceBase
+from tokenizer import MFTCoder_template
+
+
+class HFPerplexityPairInfer(HFInferenceBase):
+ def __init__(self):
+ super().__init__()
+
+ def load_model_tokenizer(self, args):
+ """
+ load ppl model and tokenizer using self._load_model_tokenizer
+ """
+ self._load_model_tokenizer(AutoModelForCausalLM, args.model_path)
+
+ def handler(self, dataloader, args):
+ for batch in dataloader:
+ for key in ["chosen", "rejected"]:
+ # apply chat template on chatml messages
+ input_text = [self.tokenizer.apply_chat_template(sample[key], tokenize=False) for sample in batch]
+
+ # tokenization
+ inputs = self.tokenizer(
+ input_text,
+ return_tensors="pt",
+ padding=True,
+ truncation=True,
+ max_length=4096,
+ ).to("cuda")
+ # print(inputs)
+
+ # ppl computing
+ input_ids = inputs["input_ids"]
+ attention_mask = inputs["attention_mask"]
+ labels = inputs["input_ids"]
+ print(input_ids.shape)
+ # forward outputs
+ with torch.no_grad():
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ labels=input_ids,
+ )
+ logits = outputs.logits
+
+ # loss fuction
+ loss_fct = torch.nn.CrossEntropyLoss(ignore_index=self.tokenizer.pad_token_id, reduction="none")
+ # shift logits and labels to make them aligned
+ shift_logits = logits[:, :-1, :].contiguous()
+ shift_labels = labels[:, 1:].contiguous()
+
+ loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+ # get mean loss of all tokens of each sample
+ losses = loss.view(len(input_text), -1).mean(dim=1)
+ # save loss and ppl
+ for i, individual_loss in enumerate(losses):
+ batch[i][f"{key}_loss"] = individual_loss.item()
+ batch[i][f"{key}_ppl"] = torch.exp(individual_loss).item()
+ yield batch
+
+ def prepare_args(self, args):
+ name = args.data_file.split("/")[-1].replace(".jsonl", "") + "-PPL"
+ args.output_path = os.path.join(args.output_dir, f"{name}.jsonl")
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description="PPL args.")
+ parser.add_argument(
+ "--model_path",
+ type=str,
+ help="huggingface model path",
+ )
+ parser.add_argument(
+ "--data_file",
+ type=str,
+ help="data file path",
+ )
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ help="output directory",
+ )
+ parser.add_argument("--batch_size", type=int, default=1)
+ args = parser.parse_args()
+ return args
+
+
+def main(args):
+ st = time.time()
+ runner = HFPerplexityPairInfer()
+ runner.run(args)
+
+ print("{} finish in: {:.4f} Minutes {}".format("+++" * 10, (time.time() - st) / 60, "+++" * 10))
+
+
+if __name__ == "__main__":
+ print("-----------" * 10)
+ print("-----------" * 10)
+ print("-----------" * 10)
+ main(get_args())
+ print("-----------" * 10)
+ print("-----------" * 10)
+ print("-----------" * 10)
diff --git a/mftcoder_accelerate/inference/reward.py b/mftcoder_accelerate/inference/reward.py
new file mode 100644
index 0000000..fc35b06
--- /dev/null
+++ b/mftcoder_accelerate/inference/reward.py
@@ -0,0 +1,131 @@
+# @author Chaoyu Chen
+# @date 2024/11/12
+# @module reward.py
+"""Reward demo"""
+
+from typing import Iterable, Dict, List
+import gzip
+import json
+import os
+import argparse
+import time
+from tqdm import tqdm
+import torch
+from transformers import (
+ AutoConfig,
+ AutoTokenizer,
+ AutoModelForCausalLM,
+ AutoModelForSequenceClassification,
+)
+
+from infer_base import HFInferenceBase
+from tokenizer import MFTCoder_template
+
+
+class HFRewardPairInfer(HFInferenceBase):
+ def __init__(self):
+ super().__init__()
+
+ def load_model_tokenizer(self, args):
+ """
+ load ppl model and tokenizer using self._load_model_tokenizer
+ """
+ self._load_model_tokenizer(AutoModelForCausalLM, args.model_path)
+
+ def handler(self, dataloader, args):
+ correct_num = 0
+ total_num = 0
+ for batch in dataloader:
+ try:
+ for key in ["chosen", "rejected"]:
+
+ input_text = [
+ self.tokenizer.apply_chat_template(sample[key], tokenize=False, add_generation_prompt=False)
+ for sample in batch
+ ]
+
+ # tokenization
+ inputs = self.tokenizer(
+ input_text,
+ return_tensors="pt",
+ padding=True,
+ truncation=True,
+ max_length=4096,
+ add_special_tokens=False,
+ ).to("cuda")
+
+ # prepare input_ids and attention_mask
+ input_ids = inputs["input_ids"]
+ attention_mask = inputs["attention_mask"]
+
+ # generate score
+ response_token_ids = self.model.generate(
+ input_ids,
+ attention_mask=attention_mask,
+ max_new_tokens=1,
+ return_dict_in_generate=True,
+ output_scores=True,
+ )
+
+ # print(response_token_ids["scores"], response_token_ids["scores"][0].shape)
+ rewards = response_token_ids["scores"][0][:, 0].reshape(-1)
+ for i, reward in enumerate(rewards):
+ batch[i][f"{key}_reward"] = reward.item()
+ # print(reward.item())
+ for sample in batch:
+ total_num += 1
+ # print(sample["chosen_reward"], sample["rejected_reward"])
+ if sample["chosen_reward"] > sample["rejected_reward"]:
+ correct_num += 1
+ print(f"correct {correct_num} of total {total_num}")
+ torch.cuda.empty_cache()
+ yield batch
+ except Exception as e:
+ print(f"[ERROR] {e}")
+ continue
+ # break
+ print(f"correct {correct_num} of total {total_num}")
+
+ def prepare_args(self, args):
+ name = args.data_file.split("/")[-1].replace(".jsonl", "") + "-REWARD"
+ args.output_path = os.path.join(args.output_dir, f"{name}.jsonl")
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description="REWARD args.")
+ parser.add_argument(
+ "--model_path",
+ type=str,
+ help="huggingface model path",
+ )
+ parser.add_argument(
+ "--data_file",
+ type=str,
+ help="data file path",
+ )
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ help="output directory",
+ )
+ parser.add_argument("--batch_size", type=int, default=1)
+
+ return parser.parse_args()
+
+
+def main(args):
+ st = time.time()
+ runner = HFRewardPairInfer()
+ runner.run(args)
+
+ print("{} finish in: {:.4f} Minutes {}".format("+++" * 10, (time.time() - st) / 60, "+++" * 10))
+
+
+if __name__ == "__main__":
+ print("-----------" * 10)
+ print("-----------" * 10)
+ print("-----------" * 10)
+ main(get_args())
+ print("-----------" * 10)
+ print("-----------" * 10)
+ print("-----------" * 10)
diff --git a/mftcoder_accelerate/model/__init__.py b/mftcoder_accelerate/model/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/mftcoder_accelerate/model/aquila2/__init__.py b/mftcoder_accelerate/model/aquila2/__init__.py
new file mode 100644
index 0000000..ab80b6d
--- /dev/null
+++ b/mftcoder_accelerate/model/aquila2/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+# @author Chaoyu Chen
+# @date 2024/11/18
+# @module __init__.py
+"""Introduce the module"""
diff --git a/mftcoder_accelerate/src/model/aquila2/configuration_aquila.py b/mftcoder_accelerate/model/aquila2/configuration_aquila.py
similarity index 100%
rename from mftcoder_accelerate/src/model/aquila2/configuration_aquila.py
rename to mftcoder_accelerate/model/aquila2/configuration_aquila.py
diff --git a/mftcoder_accelerate/src/model/aquila2/modeling_aquila.py b/mftcoder_accelerate/model/aquila2/modeling_aquila.py
similarity index 100%
rename from mftcoder_accelerate/src/model/aquila2/modeling_aquila.py
rename to mftcoder_accelerate/model/aquila2/modeling_aquila.py
diff --git a/mftcoder_accelerate/model/baichuan2/__init__.py b/mftcoder_accelerate/model/baichuan2/__init__.py
new file mode 100644
index 0000000..ab80b6d
--- /dev/null
+++ b/mftcoder_accelerate/model/baichuan2/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+# @author Chaoyu Chen
+# @date 2024/11/18
+# @module __init__.py
+"""Introduce the module"""
diff --git a/mftcoder_accelerate/src/model/baichuan2/configuration_baichuan.py b/mftcoder_accelerate/model/baichuan2/configuration_baichuan.py
similarity index 100%
rename from mftcoder_accelerate/src/model/baichuan2/configuration_baichuan.py
rename to mftcoder_accelerate/model/baichuan2/configuration_baichuan.py
diff --git a/mftcoder_accelerate/src/model/baichuan2/generation_utils.py b/mftcoder_accelerate/model/baichuan2/generation_utils.py
similarity index 100%
rename from mftcoder_accelerate/src/model/baichuan2/generation_utils.py
rename to mftcoder_accelerate/model/baichuan2/generation_utils.py
diff --git a/mftcoder_accelerate/src/model/baichuan2/modeling_baichuan.py b/mftcoder_accelerate/model/baichuan2/modeling_baichuan.py
similarity index 100%
rename from mftcoder_accelerate/src/model/baichuan2/modeling_baichuan.py
rename to mftcoder_accelerate/model/baichuan2/modeling_baichuan.py
diff --git a/mftcoder_accelerate/src/model/baichuan2/quantizer.py b/mftcoder_accelerate/model/baichuan2/quantizer.py
similarity index 100%
rename from mftcoder_accelerate/src/model/baichuan2/quantizer.py
rename to mftcoder_accelerate/model/baichuan2/quantizer.py
diff --git a/mftcoder_accelerate/src/model/baichuan2/tokenization_baichuan.py b/mftcoder_accelerate/model/baichuan2/tokenization_baichuan.py
similarity index 100%
rename from mftcoder_accelerate/src/model/baichuan2/tokenization_baichuan.py
rename to mftcoder_accelerate/model/baichuan2/tokenization_baichuan.py
diff --git a/mftcoder_accelerate/model/chatglm2/__init__.py b/mftcoder_accelerate/model/chatglm2/__init__.py
new file mode 100644
index 0000000..ab80b6d
--- /dev/null
+++ b/mftcoder_accelerate/model/chatglm2/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+# @author Chaoyu Chen
+# @date 2024/11/18
+# @module __init__.py
+"""Introduce the module"""
diff --git a/mftcoder_accelerate/src/model/chatglm2/config.json b/mftcoder_accelerate/model/chatglm2/config.json
similarity index 100%
rename from mftcoder_accelerate/src/model/chatglm2/config.json
rename to mftcoder_accelerate/model/chatglm2/config.json
diff --git a/mftcoder_accelerate/src/model/chatglm2/configuration_chatglm.py b/mftcoder_accelerate/model/chatglm2/configuration_chatglm.py
similarity index 100%
rename from mftcoder_accelerate/src/model/chatglm2/configuration_chatglm.py
rename to mftcoder_accelerate/model/chatglm2/configuration_chatglm.py
diff --git a/mftcoder_accelerate/src/model/chatglm2/modeling_chatglm.py b/mftcoder_accelerate/model/chatglm2/modeling_chatglm.py
similarity index 100%
rename from mftcoder_accelerate/src/model/chatglm2/modeling_chatglm.py
rename to mftcoder_accelerate/model/chatglm2/modeling_chatglm.py
diff --git a/mftcoder_accelerate/src/model/chatglm2/quantization.py b/mftcoder_accelerate/model/chatglm2/quantization.py
similarity index 100%
rename from mftcoder_accelerate/src/model/chatglm2/quantization.py
rename to mftcoder_accelerate/model/chatglm2/quantization.py
diff --git a/mftcoder_accelerate/src/model/chatglm2/tokenization_chatglm.py b/mftcoder_accelerate/model/chatglm2/tokenization_chatglm.py
similarity index 100%
rename from mftcoder_accelerate/src/model/chatglm2/tokenization_chatglm.py
rename to mftcoder_accelerate/model/chatglm2/tokenization_chatglm.py
diff --git a/mftcoder_accelerate/src/model/chatglm2/tokenizer_config.json b/mftcoder_accelerate/model/chatglm2/tokenizer_config.json
similarity index 100%
rename from mftcoder_accelerate/src/model/chatglm2/tokenizer_config.json
rename to mftcoder_accelerate/model/chatglm2/tokenizer_config.json
diff --git a/mftcoder_accelerate/model/chatglm3/__init__.py b/mftcoder_accelerate/model/chatglm3/__init__.py
new file mode 100644
index 0000000..ab80b6d
--- /dev/null
+++ b/mftcoder_accelerate/model/chatglm3/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+# @author Chaoyu Chen
+# @date 2024/11/18
+# @module __init__.py
+"""Introduce the module"""
diff --git a/mftcoder_accelerate/src/model/chatglm3/config.json b/mftcoder_accelerate/model/chatglm3/config.json
similarity index 100%
rename from mftcoder_accelerate/src/model/chatglm3/config.json
rename to mftcoder_accelerate/model/chatglm3/config.json
diff --git a/mftcoder_accelerate/src/model/chatglm3/configuration_chatglm.py b/mftcoder_accelerate/model/chatglm3/configuration_chatglm.py
similarity index 100%
rename from mftcoder_accelerate/src/model/chatglm3/configuration_chatglm.py
rename to mftcoder_accelerate/model/chatglm3/configuration_chatglm.py
diff --git a/mftcoder_accelerate/src/model/chatglm3/modeling_chatglm.py b/mftcoder_accelerate/model/chatglm3/modeling_chatglm.py
similarity index 100%
rename from mftcoder_accelerate/src/model/chatglm3/modeling_chatglm.py
rename to mftcoder_accelerate/model/chatglm3/modeling_chatglm.py
diff --git a/mftcoder_accelerate/src/model/chatglm3/quantization.py b/mftcoder_accelerate/model/chatglm3/quantization.py
similarity index 100%
rename from mftcoder_accelerate/src/model/chatglm3/quantization.py
rename to mftcoder_accelerate/model/chatglm3/quantization.py
diff --git a/mftcoder_accelerate/src/model/chatglm3/tokenization_chatglm.py b/mftcoder_accelerate/model/chatglm3/tokenization_chatglm.py
similarity index 100%
rename from mftcoder_accelerate/src/model/chatglm3/tokenization_chatglm.py
rename to mftcoder_accelerate/model/chatglm3/tokenization_chatglm.py
diff --git a/mftcoder_accelerate/src/model/code_llama/__init__.py b/mftcoder_accelerate/model/code_llama/__init__.py
similarity index 100%
rename from mftcoder_accelerate/src/model/code_llama/__init__.py
rename to mftcoder_accelerate/model/code_llama/__init__.py
diff --git a/mftcoder_accelerate/src/model/code_llama/configuration_llama.py b/mftcoder_accelerate/model/code_llama/configuration_llama.py
similarity index 100%
rename from mftcoder_accelerate/src/model/code_llama/configuration_llama.py
rename to mftcoder_accelerate/model/code_llama/configuration_llama.py
diff --git a/mftcoder_accelerate/src/model/code_llama/convert_llama_weights_to_hf.py b/mftcoder_accelerate/model/code_llama/convert_llama_weights_to_hf.py
similarity index 100%
rename from mftcoder_accelerate/src/model/code_llama/convert_llama_weights_to_hf.py
rename to mftcoder_accelerate/model/code_llama/convert_llama_weights_to_hf.py
diff --git a/mftcoder_accelerate/src/model/code_llama/modeling_llama.py b/mftcoder_accelerate/model/code_llama/modeling_llama.py
similarity index 100%
rename from mftcoder_accelerate/src/model/code_llama/modeling_llama.py
rename to mftcoder_accelerate/model/code_llama/modeling_llama.py
diff --git a/mftcoder_accelerate/src/model/code_llama/tokenization_code_llama.py b/mftcoder_accelerate/model/code_llama/tokenization_code_llama.py
similarity index 100%
rename from mftcoder_accelerate/src/model/code_llama/tokenization_code_llama.py
rename to mftcoder_accelerate/model/code_llama/tokenization_code_llama.py
diff --git a/mftcoder_accelerate/src/model/code_llama/tokenization_code_llama_fast.py b/mftcoder_accelerate/model/code_llama/tokenization_code_llama_fast.py
similarity index 100%
rename from mftcoder_accelerate/src/model/code_llama/tokenization_code_llama_fast.py
rename to mftcoder_accelerate/model/code_llama/tokenization_code_llama_fast.py
diff --git a/mftcoder_accelerate/src/model/code_llama/tokenization_llama.py b/mftcoder_accelerate/model/code_llama/tokenization_llama.py
similarity index 100%
rename from mftcoder_accelerate/src/model/code_llama/tokenization_llama.py
rename to mftcoder_accelerate/model/code_llama/tokenization_llama.py
diff --git a/mftcoder_accelerate/src/model/code_llama/tokenization_llama_fast.py b/mftcoder_accelerate/model/code_llama/tokenization_llama_fast.py
similarity index 100%
rename from mftcoder_accelerate/src/model/code_llama/tokenization_llama_fast.py
rename to mftcoder_accelerate/model/code_llama/tokenization_llama_fast.py
diff --git a/mftcoder_accelerate/model/deepseek_v2/__init__.py b/mftcoder_accelerate/model/deepseek_v2/__init__.py
new file mode 100644
index 0000000..ab80b6d
--- /dev/null
+++ b/mftcoder_accelerate/model/deepseek_v2/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+# @author Chaoyu Chen
+# @date 2024/11/18
+# @module __init__.py
+"""Introduce the module"""
diff --git a/mftcoder_accelerate/src/model/deepseek_v2/configuration_deepseek.py b/mftcoder_accelerate/model/deepseek_v2/configuration_deepseek.py
similarity index 100%
rename from mftcoder_accelerate/src/model/deepseek_v2/configuration_deepseek.py
rename to mftcoder_accelerate/model/deepseek_v2/configuration_deepseek.py
diff --git a/mftcoder_accelerate/src/model/deepseek_v2/modeling_deepseek.py b/mftcoder_accelerate/model/deepseek_v2/modeling_deepseek.py
similarity index 100%
rename from mftcoder_accelerate/src/model/deepseek_v2/modeling_deepseek.py
rename to mftcoder_accelerate/model/deepseek_v2/modeling_deepseek.py
diff --git a/mftcoder_accelerate/src/model/deepseek_v2/tokenization_deepseek_fast.py b/mftcoder_accelerate/model/deepseek_v2/tokenization_deepseek_fast.py
similarity index 100%
rename from mftcoder_accelerate/src/model/deepseek_v2/tokenization_deepseek_fast.py
rename to mftcoder_accelerate/model/deepseek_v2/tokenization_deepseek_fast.py
diff --git a/mftcoder_accelerate/src/model/gpt_bigcode/__init__.py b/mftcoder_accelerate/model/gpt_bigcode/__init__.py
similarity index 100%
rename from mftcoder_accelerate/src/model/gpt_bigcode/__init__.py
rename to mftcoder_accelerate/model/gpt_bigcode/__init__.py
diff --git a/mftcoder_accelerate/src/model/gpt_bigcode/configuration_gpt_bigcode.py b/mftcoder_accelerate/model/gpt_bigcode/configuration_gpt_bigcode.py
similarity index 100%
rename from mftcoder_accelerate/src/model/gpt_bigcode/configuration_gpt_bigcode.py
rename to mftcoder_accelerate/model/gpt_bigcode/configuration_gpt_bigcode.py
diff --git a/mftcoder_accelerate/src/model/gpt_bigcode/modeling_gpt_bigcode.py b/mftcoder_accelerate/model/gpt_bigcode/modeling_gpt_bigcode.py
similarity index 100%
rename from mftcoder_accelerate/src/model/gpt_bigcode/modeling_gpt_bigcode.py
rename to mftcoder_accelerate/model/gpt_bigcode/modeling_gpt_bigcode.py
diff --git a/mftcoder_accelerate/src/model/gpt_neox/__init__.py b/mftcoder_accelerate/model/gpt_neox/__init__.py
similarity index 100%
rename from mftcoder_accelerate/src/model/gpt_neox/__init__.py
rename to mftcoder_accelerate/model/gpt_neox/__init__.py
diff --git a/mftcoder_accelerate/src/model/gpt_neox/config.json b/mftcoder_accelerate/model/gpt_neox/config.json
similarity index 100%
rename from mftcoder_accelerate/src/model/gpt_neox/config.json
rename to mftcoder_accelerate/model/gpt_neox/config.json
diff --git a/mftcoder_accelerate/src/model/gpt_neox/configuration_gpt_neox.py b/mftcoder_accelerate/model/gpt_neox/configuration_gpt_neox.py
similarity index 100%
rename from mftcoder_accelerate/src/model/gpt_neox/configuration_gpt_neox.py
rename to mftcoder_accelerate/model/gpt_neox/configuration_gpt_neox.py
diff --git a/mftcoder_accelerate/src/model/gpt_neox/generation_config.json b/mftcoder_accelerate/model/gpt_neox/generation_config.json
similarity index 100%
rename from mftcoder_accelerate/src/model/gpt_neox/generation_config.json
rename to mftcoder_accelerate/model/gpt_neox/generation_config.json
diff --git a/mftcoder_accelerate/src/model/gpt_neox/modeling_gpt_neox.py b/mftcoder_accelerate/model/gpt_neox/modeling_gpt_neox.py
similarity index 100%
rename from mftcoder_accelerate/src/model/gpt_neox/modeling_gpt_neox.py
rename to mftcoder_accelerate/model/gpt_neox/modeling_gpt_neox.py
diff --git a/mftcoder_accelerate/src/model/gpt_neox/tokenization_gpt_neox_fast.py b/mftcoder_accelerate/model/gpt_neox/tokenization_gpt_neox_fast.py
similarity index 100%
rename from mftcoder_accelerate/src/model/gpt_neox/tokenization_gpt_neox_fast.py
rename to mftcoder_accelerate/model/gpt_neox/tokenization_gpt_neox_fast.py
diff --git a/mftcoder_accelerate/model/phi/__init__.py b/mftcoder_accelerate/model/phi/__init__.py
new file mode 100644
index 0000000..ab80b6d
--- /dev/null
+++ b/mftcoder_accelerate/model/phi/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+# @author Chaoyu Chen
+# @date 2024/11/18
+# @module __init__.py
+"""Introduce the module"""
diff --git a/mftcoder_accelerate/src/model/phi/configuration_mixformer_sequential.py b/mftcoder_accelerate/model/phi/configuration_mixformer_sequential.py
similarity index 100%
rename from mftcoder_accelerate/src/model/phi/configuration_mixformer_sequential.py
rename to mftcoder_accelerate/model/phi/configuration_mixformer_sequential.py
diff --git a/mftcoder_accelerate/src/model/phi/modeling_mixformer_sequential.py b/mftcoder_accelerate/model/phi/modeling_mixformer_sequential.py
similarity index 100%
rename from mftcoder_accelerate/src/model/phi/modeling_mixformer_sequential.py
rename to mftcoder_accelerate/model/phi/modeling_mixformer_sequential.py
diff --git a/mftcoder_accelerate/model/qwen/__init__.py b/mftcoder_accelerate/model/qwen/__init__.py
new file mode 100644
index 0000000..ab80b6d
--- /dev/null
+++ b/mftcoder_accelerate/model/qwen/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+# @author Chaoyu Chen
+# @date 2024/11/18
+# @module __init__.py
+"""Introduce the module"""
diff --git a/mftcoder_accelerate/src/model/qwen/cache_autogptq_cuda_256.cpp b/mftcoder_accelerate/model/qwen/cache_autogptq_cuda_256.cpp
similarity index 100%
rename from mftcoder_accelerate/src/model/qwen/cache_autogptq_cuda_256.cpp
rename to mftcoder_accelerate/model/qwen/cache_autogptq_cuda_256.cpp
diff --git a/mftcoder_accelerate/src/model/qwen/cache_autogptq_cuda_kernel_256.cu b/mftcoder_accelerate/model/qwen/cache_autogptq_cuda_kernel_256.cu
similarity index 100%
rename from mftcoder_accelerate/src/model/qwen/cache_autogptq_cuda_kernel_256.cu
rename to mftcoder_accelerate/model/qwen/cache_autogptq_cuda_kernel_256.cu
diff --git a/mftcoder_accelerate/src/model/qwen/configuration_qwen.py b/mftcoder_accelerate/model/qwen/configuration_qwen.py
similarity index 100%
rename from mftcoder_accelerate/src/model/qwen/configuration_qwen.py
rename to mftcoder_accelerate/model/qwen/configuration_qwen.py
diff --git a/mftcoder_accelerate/src/model/qwen/cpp_kernels.py b/mftcoder_accelerate/model/qwen/cpp_kernels.py
similarity index 100%
rename from mftcoder_accelerate/src/model/qwen/cpp_kernels.py
rename to mftcoder_accelerate/model/qwen/cpp_kernels.py
diff --git a/mftcoder_accelerate/src/model/qwen/modeling_qwen.py b/mftcoder_accelerate/model/qwen/modeling_qwen.py
similarity index 100%
rename from mftcoder_accelerate/src/model/qwen/modeling_qwen.py
rename to mftcoder_accelerate/model/qwen/modeling_qwen.py
diff --git a/mftcoder_accelerate/src/model/qwen/qwen_generation_utils.py b/mftcoder_accelerate/model/qwen/qwen_generation_utils.py
similarity index 100%
rename from mftcoder_accelerate/src/model/qwen/qwen_generation_utils.py
rename to mftcoder_accelerate/model/qwen/qwen_generation_utils.py
diff --git a/mftcoder_accelerate/src/model/qwen/tokenization_qwen.py b/mftcoder_accelerate/model/qwen/tokenization_qwen.py
similarity index 100%
rename from mftcoder_accelerate/src/model/qwen/tokenization_qwen.py
rename to mftcoder_accelerate/model/qwen/tokenization_qwen.py
diff --git a/mftcoder_accelerate/src/model/qwen/tokenizer_config.json b/mftcoder_accelerate/model/qwen/tokenizer_config.json
similarity index 100%
rename from mftcoder_accelerate/src/model/qwen/tokenizer_config.json
rename to mftcoder_accelerate/model/qwen/tokenizer_config.json
diff --git a/mftcoder_accelerate/src/run_offline_tokenization.sh b/mftcoder_accelerate/run_offline_tokenization.sh
similarity index 78%
rename from mftcoder_accelerate/src/run_offline_tokenization.sh
rename to mftcoder_accelerate/run_offline_tokenization.sh
index ed916da..e1fb273 100644
--- a/mftcoder_accelerate/src/run_offline_tokenization.sh
+++ b/mftcoder_accelerate/run_offline_tokenization.sh
@@ -3,7 +3,7 @@ DATA_PATH=
DATASET_NAME=
OUTPUT_PATH=
-python offline_tokenization/concat_sst_bin_tokenization.py \
+python tokenization/concat_sst_bin_tokenization.py \
--model-path ${MODEL_PATH} \
--data-path ${DATA_PATH} \
--dataset-name ${DATASET_NAME} \
diff --git a/mftcoder_accelerate/src/offline_tokenization/lm_fmt.py b/mftcoder_accelerate/src/offline_tokenization/lm_fmt.py
deleted file mode 100644
index c922859..0000000
--- a/mftcoder_accelerate/src/offline_tokenization/lm_fmt.py
+++ /dev/null
@@ -1,360 +0,0 @@
-import os
-import zstandard
-import ujson as json
-import time
-import tarfile
-import codecs
-from functools import reduce
-import jsonlines
-import io
-from zipfile import ZipFile
-import gzip
-from math import ceil
-import mmap
-import multiprocessing as mp
-from pathlib import Path
-
-VALID_EXTENSIONS = ['openwebtext.tar.xz', '_data.xz', '.dat.zst', '.jsonl', '.jsonl.zst', '.jsonl.zst.tar', '.json.zst', '.txt', '.zip', '.tar.gz', '.json.gz', '.gz']
-
-def has_valid_extension(file):
- return any([file.endswith(ext) for ext in VALID_EXTENSIONS])
-
-def _listdir_or_file(x):
- if isinstance(x, list):
- return reduce(lambda x, y: x + y, map(listdir_or_file, sorted(x)))
- if os.path.isfile(x):
- return [x]
- elif os.path.isdir(x):
- return [str(Path(x) / fn) for fn in sorted(os.listdir(x))]
- else:
- raise FileNotFoundError(f"{x} not found")
-
-def listdir_or_file(x):
- return list(filter(has_valid_extension, _listdir_or_file(x)))
-
-def tarfile_reader(file, streaming=False):
- # we need our own tarfile parser because `tarfile` doesn't work well for
- # big tarfiles; it seems to be reading the entire file to get a list of
- # where all the files are - but we don't need that because we just need
- # to see each file once. surprisingly, `tarfile` doesn't expose any
- # facilities for this. the only options are 1. load the entire tarfile
- # and then query by filename or 2. extract to disk - and neither of
- # these is what we want.
-
- offset = 0
- paxfilesize = None
- while True:
- hdr = file.read(512)
- offset += 512
-
- # https://www.gnu.org/software/tar/manual/html_node/Standard.html
- # end at 135 not 136 because of \0 terminator
- if hdr[124:135] == b'\0'*11:
- # end of record
- break
-
- fname = hdr[:100].split(b'\0')[0]
-
- # if the file is too big to fit in the size field, tarfiles will actually
- # include a PaxHeader with the size in it, applicable to the immediate next file.
- if paxfilesize is not None:
- size = paxfilesize
- paxfilesize = None
- else:
- size = int(hdr[124:135], 8)
-
- padded_size = ceil(size / 512) * 512
-
- # for handling PaxHeader files (which contain extra metadata about file size) and directories
- # https://pubs.opengroup.org/onlinepubs/9699919799/utilities/pax.html#tag_20_92_13_03
- type = chr(hdr[156])
-
- if type == 'x':
- meta = file.read(padded_size)[:size]
- def kv(x):
- return x.decode('utf-8').split(' ')[1].split('=')
- paxfileattrs = {
- kv(x)[0]: kv(x)[1]
- for x in meta.split(b'\n') if x
- }
- paxfilesize = int(paxfileattrs['size'])
-
- offset += padded_size
- continue
- elif type != '0' and type != '\0':
- if streaming:
- file.seek(padded_size, os.SEEK_CUR)
- else:
- file.read(padded_size)
- offset += padded_size
- continue
-
- if streaming:
- # skip directory entries
- if size != 0:
- mmo = mmap.mmap(file.fileno(), length=offset + size, access=mmap.ACCESS_READ)
- mmo.seek(offset)
- yield mmo
-
- file.seek(padded_size, os.SEEK_CUR)
- else:
- yield file.read(padded_size)[:size]
- offset += padded_size
-
-def handle_jsonl(jsonl_reader, get_meta, autojoin_paragraphs, para_joiner, key='text'):
- for ob in jsonl_reader:
- # naive jsonl where each object is just the string itself, with no meta. For legacy compatibility.
- if isinstance(ob, str):
- assert not get_meta
- yield ob
- continue
-
- if key is None:
- yield ob
- continue
-
- text = ob[key]
-
- if autojoin_paragraphs and isinstance(text, list):
- text = para_joiner.join(text)
-
- if get_meta:
- yield text, (ob['meta'] if 'meta' in ob else {})
- else:
- yield text
-
-
-class Reader:
- def __init__(self, in_path):
- self.in_path = in_path
-
- def stream_data(self, get_meta=False, threaded=False, key=None):
- if not threaded:
- yield from self._stream_data(get_meta, key=key)
- return
-
- q = mp.Queue(1000)
- p = mp.Process(target=self._stream_data_threaded, args=(q, get_meta), kwargs={"key": key})
- p.start()
- while p.is_alive():
- res = q.get()
- if res is None: break
- yield res
-
- def _stream_data_threaded(self, q, get_meta=False):
- for data in self._stream_data(get_meta):
- q.put(data)
- q.put(None)
-
- def _stream_data(self, get_meta=False, key="text"):
- self.f_name = ""
- files = listdir_or_file(self.in_path)
- if not files:
- raise FileNotFoundError(f"No valid file(s) found in {self.in_path}")
- for f in files:
- self.f_name = f
- if f == 'openwebtext.tar.xz':
- assert not get_meta
-
- yield from self.read_owt(f)
- elif 'urlsf_subset' in f and f.endswith('_data.xz'):
- assert not get_meta
-
- yield from self.read_owt_subset(f)
- elif f.endswith('.dat.zst'):
- assert not get_meta
-
- yield from self.read_dat(f)
- elif f.endswith('.jsonl'):
- yield from self.read_jsonl(f, get_meta, key=key)
- elif f.endswith('.jsonl.zst'):
- yield from self.read_jsonl_zst(f, get_meta, key=key)
- elif f.endswith('.jsonl.zst.tar'):
- yield from self.read_jsonl_tar(f, get_meta, key=key)
- elif f.endswith('.json.zst'):
- assert not get_meta
-
- yield from self.read_json(f)
- elif f.endswith('.txt'):
- assert not get_meta
-
- yield from self.read_txt(f)
- elif f.endswith('.zip'):
- assert not get_meta
-
- yield from self.read_zip(f)
- elif f.endswith('.tar.gz'):
- assert not get_meta
-
- yield from self.read_tgz(f)
- elif f.endswith('.json.gz'):
- assert not get_meta
-
- yield from self.read_jsongz(f)
- elif f.endswith('.gz'):
- assert not get_meta
-
- yield from self.read_gz(f)
- else:
- # shouldn't be reached
- print(f'Skipping {f} as streaming for that filetype is not implemented')
-
- def read_txt(self, file):
- with open(file, 'r') as fh:
- yield fh.read()
-
- def read_zip(self, file):
- archive = ZipFile(file, 'r')
- for f in archive.namelist():
- yield archive.read(f).decode('UTF-8')
-
- def read_tgz(self, file):
- gz = gzip.open(file)
- yield from (x.decode('utf-8') for x in tarfile_reader(gz, streaming=False))
-
- def read_gz(self, file):
- with gzip.open(file, 'rb') as f:
- for line in f:
- yield line.decode('utf-8')
-
- def read_jsongz(self, file):
- for line in self.read_gz(file):
- yield json.loads(line)
-
- def read_json(self, file):
- with open(file, 'rb') as fh:
- cctx = zstandard.ZstdDecompressor()
- reader = cctx.stream_reader(fh)
- ob = json.load(reader)
- yield from ob
-
- def read_dat(self, file):
- with open(file, 'rb') as fh:
- cctx = zstandard.ZstdDecompressor()
- reader = cctx.stream_reader(fh)
- while True:
- ln = reader.read(16).decode('UTF-8')
- if not ln:
- break
-
- ln = int(ln)
-
- yield reader.read(ln).decode('UTF-8')
-
- def read_jsonl(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'):
- with jsonlines.open(file) as rdr:
- yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key)
-
- def read_jsonl_zst(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'):
- with open(file, 'rb') as fh:
- cctx = zstandard.ZstdDecompressor()
- reader = io.BufferedReader(cctx.stream_reader(fh))
- rdr = jsonlines.Reader(reader)
- yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key)
-
- def read_jsonl_tar(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'):
- with open(file, 'rb') as fh:
- for f in tarfile_reader(fh, streaming=True):
- cctx = zstandard.ZstdDecompressor()
- reader = io.BufferedReader(cctx.stream_reader(f))
- rdr = jsonlines.Reader(reader)
- yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key)
- f.close()
-
- def read_owt(self, file):
- tar = tarfile.open(file, encoding='utf-8')
- utf8reader = codecs.getreader('utf-8')
-
- for name in tar.getmembers():
- fp = tar.extractfile(name)
- inner_tar = tarfile.open(fileobj=fp, encoding='utf-8')
- for inner_name in inner_tar.getmembers():
- inner_fp = utf8reader(inner_tar.extractfile(inner_name))
- contents = inner_fp.read()
- yield contents
-
- def read_owt_subset(self, file):
- utf8reader = codecs.getreader('utf-8')
- tar = tarfile.open(file, encoding='utf-8')
- for name in tar.getmembers():
- fp = utf8reader(tar.extractfile(name))
- contents = fp.read()
- yield contents
-
-
-class Archive:
- def __init__(self, out_dir, compression_level=3, threads=8):
- self.out_dir = out_dir
- os.makedirs(out_dir, exist_ok=True)
- self.i = 0
-
- self.fh = open(self.out_dir + '/current_chunk_incomplete', 'wb')
- self.cctx = zstandard.ZstdCompressor(level=compression_level, threads=threads)
- self.compressor = self.cctx.stream_writer(self.fh)
-
-
- def add_data(self, data, meta={}):
- self.compressor.write(json.dumps({'text': data, 'meta': meta}).encode('UTF-8') + b'\n')
-
- def commit(self, archive_name='default'):
- fname = self.out_dir + '/data_' + str(self.i) + '_time' + str(int(time.time())) + '_' + archive_name + '.jsonl.zst'
- self.compressor.flush(zstandard.FLUSH_FRAME)
-
- self.fh.flush()
- self.fh.close()
- os.rename(self.out_dir + '/current_chunk_incomplete', fname)
- self.fh = open(self.out_dir + '/current_chunk_incomplete', 'wb')
- self.compressor = self.cctx.stream_writer(self.fh)
-
- self.i += 1
-
-
-class DatArchive:
- def __init__(self, out_dir):
- self.out_dir = out_dir
- os.makedirs(out_dir, exist_ok=True)
- self.data = []
- self.i = 0
- if os.path.exists(out_dir) and len(os.listdir(out_dir)) > 0:
- self.i = max(map(lambda x: int(x.split('_')[1].split('.')[0]), os.listdir(out_dir))) + 1
-
- def add_data(self, data):
- self.data.append(data)
-
- def commit(self, archive_name=None):
- # TODO: streaming
- cctx = zstandard.ZstdCompressor(level=3)
-
- if archive_name is None:
- archive_name = str(int(time.time()))
-
- res = b''.join(map(lambda x: ("%016d" % len(x)).encode('UTF-8') + x, map(lambda x: x.encode('UTF-8'), self.data)))
- cdata = cctx.compress(res)
-
- with open(self.out_dir + '/data_' + str(self.i) + '_' + archive_name + '.dat.zst', 'wb') as fh:
- fh.write(cdata)
-
- self.i += 1
- self.data = []
-
-class JSONArchive:
- def __init__(self, out_dir):
- self.out_dir = out_dir
- os.makedirs(out_dir, exist_ok=True)
- self.data = []
- self.i = 0
- if os.path.exists(out_dir) and len(os.listdir(out_dir)) > 0:
- self.i = max(map(lambda x: int(x.split('_')[1].split('.')[0]), os.listdir(out_dir))) + 1
-
- def add_data(self, data):
- self.data.append(data)
-
- def commit(self):
- cctx = zstandard.ZstdCompressor(level=3)
-
- cdata = cctx.compress(json.dumps(self.data).encode('UTF-8'))
- with open(self.out_dir + '/data_' + str(self.i) + '_' + str(int(time.time())) + '.json.zst', 'wb') as fh:
- fh.write(cdata)
-
- self.i += 1
- self.data = []
diff --git a/mftcoder_accelerate/src/offline_tokenization/pack_encoder.py b/mftcoder_accelerate/src/offline_tokenization/pack_encoder.py
deleted file mode 100644
index 0678e27..0000000
--- a/mftcoder_accelerate/src/offline_tokenization/pack_encoder.py
+++ /dev/null
@@ -1,335 +0,0 @@
-from transformers import AutoTokenizer
-from tokenizer import init_tokenizer
-
-
-def load_tokenizer(model_path, tokenizer_type=None):
- """
- Load tokenizer from the given <model_path>
- """
-
- def load_tokenizer_manual(model_path, tokenizer_type):
- """
- Load tokenizer by the concrete Tokenizer class instead of AutoTokenizer
- """
- try:
- if tokenizer_type.lower() == "LlamaTokenizer".lower():
- return LlamaTokenizer.from_pretrained(model_path)
-
- raise Exception(f"Unsupported tokenizer type {tokenizer_type}")
- except:
- raise Exception(f"Unable to load tokenizer {tokenizer_type} from the given path: {model_path}")
-
- def load_tokenizer_auto(model_path):
- """
- Load tokenizer from the given path by HuggingFace AutoTokenizer
- """
- try:
- # tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, trust_remote_code=True) # support CodeLlama
- tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
- return tokenizer
- except:
- raise Exception(
- f'Unable to load tokenizer from the given path: {model_path} using auto mode.\nPlease specify the tokenizer type with the command argument "--tokenizer-type" and retry.'
- )
-
- # First, try to load tokenizer by huggingface AutoTokenizer, If fail, try another manual way
- try:
- return load_tokenizer_auto(model_path)
- except Exception as e:
- print(str(e))
- if tokenizer_type is not None:
- try:
- tokenizer = load_tokenizer_manual(model_path, tokenizer_type)
- return tokenizer
- except Exception as ee:
- raise ee
-
-
-class PackPFTEncoder:
- """
- A sample of this format will be:
- <|role_start|>system<|role_end|> content of system_1
- <|role_start|>human<|role_end|> content of human_1
- <|role_start|>bot<|role_end|> content of bot_1
- <|endoftext|>
- <|role_start|>system<|role_end|> content of system_2
- <|role_start|>human<|role_end|> content of human_2
- <|role_start|>bot<|role_end|> content of bot_2
- <|endoftext|>
- <|role_start|>human<|role_end|> content of human_3
- <|role_start|>bot<|role_end|> content of bot_3
- <|endoftext|>
- ....
- <|role_start|>human<|role_end|> content of human_n
- <|role_start|>bot<|role_end|> content of bot_n
- <|endoftext|>
- <endoftext>
- <|pad|><|pad|>...<|pad|>
-
- system part is optional, i.e. '<|role_start|>system<|role_end|> content of system_i'
- """
-
- def __init__(self, seq_length, eod_token_id, pad_token_id, role_start_tag, role_end_tag, mode="pft"):
- self.mode = mode
- self.seq_length = seq_length
- self.eod_token_id = eod_token_id
- self.pad_token_id = pad_token_id
- self.role_start_tag = role_start_tag
- self.role_end_tag = role_end_tag
-
- def initializer(self, model_path, tokenizer_type=None):
- # Use Encoder class as a container for global data
- assert model_path is not None
- self.tokenizer = load_tokenizer(model_path, tokenizer_type)
-
- def encode(self, item):
- encode_res = {
- "input_ids": [],
- }
-
- item_len = sum([len(x["content"]) for x in item["chat_rounds"]])
- for token_res in self.tokenize_chat_prompt(item):
- for k, v in token_res.items():
- encode_res[k].append(v)
- return encode_res, item_len
-
- def tokenize_chat_prompt(self, item):
- # role_start_marker = self.tokenizer.encode(self.role_start_tag, add_special_tokens=False)
- # role_end_marker = self.tokenizer.encode(self.role_end_tag, add_special_tokens=False)
- end_marker = [self.eod_token_id]
-
- input_ids = []
- raw_input = ""
- # loss_mask = []
- for chat_round in item["chat_rounds"]:
- role = chat_round["role"].strip()
- # skip system prompt
- # if role == 'system':
- # continue
-
- content = chat_round["content"]
- content = content if content.endswith("\n") else f"{content}\n"
- text = f"{self.role_start_tag}{role}{self.role_end_tag}{content}"
- chat_input_ids = self.tokenizer.encode(text, add_special_tokens=False)
-
- if role != "bot":
- chat_input_ids = chat_input_ids
- else:
- chat_input_ids = chat_input_ids + end_marker
-
- input_ids += chat_input_ids
-
- # if this sample's length is more than the specified max length, drop it
- # here, we don't add padding tokens for a single sample, however, we will append padding tokens for a combinated samaple
- if len(input_ids) > self.seq_length:
- yield {}
- else:
- yield {"input_ids": input_ids}
-
- def padding(self, key, data):
- assert len(data) <= self.seq_length, f"padding sequence: {len(data)} > {self.seq_length}"
- if key == "input_ids":
- return data + [self.pad_token_id] * (self.seq_length - len(data))
-
- if key == "loss_mask":
- return data + [0] * (self.seq_length - len(data))
-
- raise Exception("Should not reach here. There must be something wrong.")
-
-
-class PackSFTEncoder:
- """
- A sample of this format will be:
- <|role_start|>system<|role_end|> content of system_1
- <|role_start|>human<|role_end|> content of human_1
- <|role_start|>bot<|role_end|> content of bot_1
- <|endoftext|>
- <|role_start|>system<|role_end|> content of system_2
- <|role_start|>human<|role_end|> content of human_2
- <|role_start|>bot<|role_end|> content of bot_2
- <|endoftext|>
- <|role_start|>human<|role_end|> content of human_3
- <|role_start|>bot<|role_end|> content of bot_3
- <|endoftext|>
- ....
- <|role_start|>human<|role_end|> content of human_n
- <|role_start|>bot<|role_end|> content of bot_n
- <|endoftext|>
- <endoftext>
- <|pad|><|pad|>...<|pad|>
-
- system part is optional, i.e. '<|role_start|>system<|role_end|> content of system_i'
- """
-
- def __init__(self, seq_length, eod_token, role_start_tag, role_end_tag, mode="sft"):
- self.mode = mode
- self.seq_length = seq_length
- self.eod_token = eod_token
- self.role_start_tag = role_start_tag
- self.role_end_tag = role_end_tag
-
- def initializer(self, model_path, tokenizer_type=None):
- # Use Encoder class as a container for global data
- assert model_path is not None
- self.tokenizer = load_tokenizer(
- model_path, tokenizer_type
- ) # AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-
- def encode(self, item):
- encode_res = {"input_ids": [], "raw_input": []}
-
- item_len = sum([len(x["content"]) for x in item["chat_rounds"]])
- for token_res in self.tokenize_chat_prompt(item):
- for k, v in token_res.items():
- encode_res[k].append(v)
- return encode_res, item_len
-
- def tokenize_chat_prompt(self, item):
- role_start_marker = self.tokenizer.encode(self.role_start_tag, add_special_tokens=False)
- role_end_marker = self.tokenizer.encode(self.role_end_tag, add_special_tokens=False)
- end_marker = [self.tokenizer.convert_tokens_to_ids(self.eod_token)]
-
- input_ids = []
- raw_input = ""
- # loss_mask = []
- for chat_round in item["chat_rounds"]:
- role = chat_round["role"]
- content = chat_round["content"]
- content = content if content.endswith("\n") else f"{content}\n"
- chat_input_ids = self.tokenizer.encode(content, add_special_tokens=False)
- role_input_ids = self.tokenizer.encode(role, add_special_tokens=False)
- role_raw_input = ""
-
- if role != "bot":
- # chat_loss_mask = [0] * len(role_start_marker) + [0] * len(role_input_ids) + [0] * len(role_end_marker) + [0] * len(chat_input_ids)
- chat_input_ids = role_start_marker + role_input_ids + role_end_marker + chat_input_ids
- role_raw_input = ROLE_START_MARKER + role + ROLE_END_MARKER + content
- elif role == "human":
- # chat_loss_mask = [0] * len(role_start_marker) + [0] * len(role_input_ids) + [0] * len(role_end_marker) + [1] * len(chat_input_ids) + [1] * len(end_marker)
- chat_input_ids = role_start_marker + role_input_ids + role_end_marker + chat_input_ids + end_marker
- role_raw_input = ROLE_START_MARKER + role + ROLE_END_MARKER + content + self.eod_token
-
- input_ids += chat_input_ids
- raw_input += role_raw_input
- # loss_mask += chat_loss_mask
-
- # assert len(input_ids) == len(loss_mask)
-
- # if this sample's length is more than the specified max length, drop it
- # here, we don't add padding tokens for a single sample, however, we will append padding tokens for a combinated samaple
- if len(input_ids) > self.seq_length:
- yield {}
- else:
- yield {
- "input_ids": input_ids,
- "raw_input": raw_input,
- # "loss_mask": loss_mask
- }
-
- def padding(self, key, data, pad_token_id):
- assert len(data) <= self.seq_length, f"padding sequence: {len(data)} > {self.seq_length}"
- if key == "input_ids":
- return data + [pad_token_id] * (self.seq_length - len(data))
-
- if key == "loss_mask":
- return data + [0] * (self.seq_length - len(data))
-
- raise Exception("Should not reach here. There must be something wrong.")
-
-
-class PackSSTBinEncoder:
- """
- A sample of this format will be:
- content of sample_1<eod>
- content of sample_2<eod>
- ...
- content of sample_n<eod>
- <|pad|><|pad|>...<|pad|>
- """
-
- def __init__(self, seq_length, model_path):
- self.seq_length = seq_length
- self.model_path = model_path
-
- def initializer(self):
- # Use Encoder class as a container for global data
- assert self.model_path is not None
- # self.tokenizer = load_tokenizer(model_path, tokenizer_type) #AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
- # PackSSTBinEncoder.tokenizer = load_tokenizer(self.model_path, self.tokenizer_type)
- PackSSTBinEncoder.tokenizer = init_tokenizer(self.model_path)
-
- def _encode_content(self, item, encode_res):
- if "content" in item:
- content = item["content"]
- else:
- content = item["text"]
-
- item_len = len(content)
-
- input_ids = self.tokenize_string(content)
- encode_res["input_ids"].append(input_ids)
-
- return encode_res, item_len
-
- def _encode_chatml(self, item, encode_res):
- input_ids = []
- item_len = 0
- one_round_content = ""
- for i in range(len(item["chat_rounds"])):
- chat_round = item["chat_rounds"][i]
- role = chat_round["role"]
- content = chat_round["content"]
- content = content if content.endswith("\n") else f"{content}\n"
- if role.lower() == "system":
- continue
- if role.lower() == "human":
- one_round_content = content
- else:
- one_round_content += content
- input_ids += self.tokenize_string(one_round_content)
- item_len += len(one_round_content)
-
- encode_res["input_ids"].append(input_ids)
-
- return encode_res, item_len
-
- def encode(self, item):
- encode_res = {
- "input_ids": [],
- }
-
- try:
- if item is None:
- encode_res["input_ids"].append([])
- return encode_res, 0
-
- if "content" in item or "text" in item:
- return self._encode_content(item, encode_res)
-
- if "chat_rounds" in item:
- return self._encode_chatml(item, encode_res)
- except Exception as e:
- print("####JSON Exception", e, str(item))
- encode_res["input_ids"].append([])
- return encode_res, 0
-
- raise Exception("Unsupported Format!")
-
- def tokenize_string(self, text):
- end_marker = [PackSSTBinEncoder.tokenizer.eos_token_id]
-
- input_ids = []
- try:
- input_ids = PackSSTBinEncoder.tokenizer.encode(text, add_special_tokens=False)
- input_ids = input_ids + end_marker
- return input_ids
- except Exception as e:
- print("####Tokenization Exception:", e, text)
- return []
- except BaseException as e:
- print("####Tokenization BaseException:", e, "Length of text", len(text))
- return []
-
- def padding(self, data, pad_token_id):
- assert len(data) <= self.seq_length, f"padding sequence: {len(data)} > {self.seq_length}"
- return data + [pad_token_id] * (self.seq_length - len(data))
diff --git a/mftcoder_accelerate/src/utils/agd.py b/mftcoder_accelerate/src/utils/agd.py
deleted file mode 100644
index 11929e3..0000000
--- a/mftcoder_accelerate/src/utils/agd.py
+++ /dev/null
@@ -1,138 +0,0 @@
-from typing import Any, Callable, Dict, Iterable, Optional, Tuple, Union
-
-import numpy as np
-import torch
-from torch import Tensor
-
-Params = Union[Iterable[Tensor], Iterable[Dict[str, Any]]]
-
-LossClosure = Callable[[], float]
-OptLossClosure = Optional[LossClosure]
-Betas2 = Tuple[float, float]
-State = Dict[str, Any]
-OptFloat = Optional[float]
-Nus2 = Tuple[float, float]
-
-__all__ = ("AGD",)
-
-
-class AGD(torch.optim.Optimizer):
- r"""AGD: an Auto-switchable Optimizer using Stepwise Gradient Difference as Preconditioning Matrix.
- Arguments:
- params (Params): Collection of parameters to be optimized, or an iterable of dictionaries specifying separate groups.
- lr (float, optional): The learning rate. Default is 1e-3.
- betas (tuple of 2 floats, optional): Coefficients used for computing running averages of gradient and its square. Default is (0.9, 0.999).
- delta (float, optional): Small constant for numerical stability to prevent division by zero. Default is 1e-5.
- weight_decay (float, optional): Weight decay coefficient. Default is 0.0.
- amsgrad (bool, optional): If set to True, applies the AMSGrad variant of the optimizer. Default is False.
- win (bool, optional): If set to True, applies the Win variant of the optimizer. Default is False.
- clip (bool, optional): Total update clip to prevent abnormal updates. Default is None.
- """
-
- def __init__(
- self,
- params: Params,
- lr: float = 1e-3,
- betas: Betas2 = (0.9, 0.999),
- delta: float = 1e-5,
- weight_decay: float = 0.0,
- amsgrad: bool = False,
- win: bool = False,
- clip: float = None,
- ) -> None:
- if lr <= 0.0:
- raise ValueError("Invalid learning rate: {}".format(lr))
- if delta < 0.0:
- raise ValueError("Invalid delta value: {}".format(delta))
- if not 0.0 <= betas[0] < 1.0:
- raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
- if not 0.0 <= betas[1] < 1.0:
- raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
- if weight_decay < 0.0:
- raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
-
- defaults = dict(
- lr=lr,
- betas=betas,
- delta=delta,
- weight_decay=weight_decay,
- amsgrad=amsgrad,
- win=win,
- clip=clip,
- )
- super(AGD, self).__init__(params, defaults)
-
- def step(self, closure: OptLossClosure = None) -> OptFloat:
- loss = None
- if closure is not None:
- loss = closure()
-
- for group in self.param_groups:
- beta1, beta2 = group["betas"]
-
- for p in group["params"]:
- if p.grad is None:
- continue
- grad = p.grad.data
- if grad.is_sparse:
- msg = "AGD does not support sparse gradients."
- raise RuntimeError(msg)
-
- state = self.state[p]
- # Lazy state initialization
- if len(state) == 0:
- state["step"] = 0
- # Exponential moving average of gradient values
- state["exp_avg"] = torch.zeros_like(p, memory_format=torch.preserve_format)
- # Exponential moving average of squared gradient values
- state["exp_avg_sq"] = torch.zeros_like(p, memory_format=torch.preserve_format)
- if group["amsgrad"]:
- # Maintains max of all exp. moving avg. of sq. grad. values
- state["max_exp_avg_sq"] = torch.zeros_like(p, memory_format=torch.preserve_format)
- if group["win"]:
- state["z"] = torch.zeros_like(p, memory_format=torch.preserve_format)
-
- exp_avg, exp_avg_sq = (
- state["exp_avg"],
- state["exp_avg_sq"],
- )
-
- state["step"] += 1
- exp_avg_old = exp_avg.detach().clone()
- exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
- bias_correction1_old = 1 - beta1 ** (state["step"] - 1)
- bias_correction1, bias_correction2 = (
- 1 - beta1 ** state["step"],
- 1 - beta2 ** state["step"],
- )
- update = (
- exp_avg * (1 / bias_correction1)
- if state["step"] == 1
- else exp_avg * (1 / bias_correction1) - exp_avg_old * (1 / bias_correction1_old)
- )
- exp_avg_sq.mul_(beta2).addcmul_(update, update, value=1 - beta2)
-
- if group["amsgrad"]:
- max_exp_avg_sq = state["max_exp_avg_sq"]
- torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
- update = max_exp_avg_sq.sqrt()
- else:
- update = exp_avg_sq.sqrt()
-
- delta_adjust = group["delta"] * np.sqrt(bias_correction2)
- update.clamp_(min=delta_adjust)
-
- lr_adjust = group["lr"] * np.sqrt(bias_correction2) / bias_correction1
- update = exp_avg / update
- if group["clip"] is not None:
- update.clamp_(min=-group["clip"], max=group["clip"])
- weight_decay = group["weight_decay"]
- if not group["win"]:
- p.data.mul_(1 - group["lr"] * weight_decay).add_(update, alpha=-lr_adjust)
- else:
- z = state["z"]
- z.data.add_(update, alpha=-lr_adjust).mul_(1.0 / (1.0 + weight_decay * lr_adjust))
- lr_adjust2 = 2 * lr_adjust
- tao = 1.0 / (3.0 + lr_adjust2 * weight_decay)
- p.data.mul_(tao).add_(update, alpha=-tao * lr_adjust2).add_(z, alpha=2 * tao)
- return loss
diff --git a/mftcoder_accelerate/tokenization/bin_encoder.py b/mftcoder_accelerate/tokenization/bin_encoder.py
new file mode 100644
index 0000000..45d39bf
--- /dev/null
+++ b/mftcoder_accelerate/tokenization/bin_encoder.py
@@ -0,0 +1,99 @@
+from transformers import AutoTokenizer
+from tokenizer import init_tokenizer
+
+
+class SSTBinEncoder:
+ """
+ A sample of this format will be:
+ content of sample_1<eod>
+ content of sample_2<eod>
+ ...
+ content of sample_n<eod>
+ <|pad|><|pad|>...<|pad|>
+ """
+ tokenizer = None
+
+ def __init__(self, seq_length, model_path):
+ self.seq_length = seq_length
+ self.model_path = model_path
+
+ def initializer(self):
+ # Use Encoder class as a container for global data
+ assert self.model_path is not None
+ SSTBinEncoder.tokenizer = init_tokenizer(self.model_path)
+
+ def _encode_content(self, item, encode_res):
+ if "content" in item:
+ content = item["content"]
+ else:
+ content = item["text"]
+
+ item_len = len(content)
+
+ input_ids = self.tokenize_string(content)
+ encode_res["input_ids"].append(input_ids)
+
+ return encode_res, item_len
+
+ def _encode_chatml(self, item, encode_res):
+ input_ids = []
+ item_len = 0
+ one_round_content = ""
+ for i in range(len(item["chat_rounds"])):
+ chat_round = item["chat_rounds"][i]
+ role = chat_round["role"]
+ content = chat_round["content"]
+ content = content if content.endswith("\n") else f"{content}\n"
+ if role.lower() == "system":
+ continue
+ if role.lower() == "human":
+ one_round_content = content
+ else:
+ one_round_content += content
+ input_ids += self.tokenize_string(one_round_content)
+ item_len += len(one_round_content)
+
+ encode_res["input_ids"].append(input_ids)
+
+ return encode_res, item_len
+
+ def encode(self, item):
+ encode_res = {
+ "input_ids": [],
+ }
+
+ try:
+ if item is None:
+ encode_res["input_ids"].append([])
+ return encode_res, 0
+
+ if "content" in item or "text" in item:
+ return self._encode_content(item, encode_res)
+
+ if "chat_rounds" in item:
+ return self._encode_chatml(item, encode_res)
+ except Exception as e:
+ print("####JSON Exception", e, str(item))
+ encode_res["input_ids"].append([])
+ return encode_res, 0
+
+ raise Exception("Unsupported Format!")
+
+ def tokenize_string(self, text):
+ end_marker = [SSTBinEncoder.tokenizer.eos_token_id]
+
+ input_ids = []
+ try:
+ input_ids = SSTBinEncoder.tokenizer.encode(text, add_special_tokens=False)
+ input_ids = input_ids + end_marker
+ return input_ids
+ except Exception as e:
+ print("####Tokenization Exception:", e, text)
+ return []
+ except BaseException as e:
+ print("####Tokenization BaseException:", e, "Length of text", len(text))
+ return []
+
+ def padding(self, data, pad_token_id):
+ assert len(data) <= self.seq_length, f"padding sequence: {len(data)} > {self.seq_length}"
+ return data + [pad_token_id] * (self.seq_length - len(data))
diff --git a/mftcoder_accelerate/src/offline_tokenization/concat_sst_bin_tokenization.py b/mftcoder_accelerate/tokenization/concat_sst_bin_tokenization.py
similarity index 64%
rename from mftcoder_accelerate/src/offline_tokenization/concat_sst_bin_tokenization.py
rename to mftcoder_accelerate/tokenization/concat_sst_bin_tokenization.py
index ca4347e..03ae2b0 100644
--- a/mftcoder_accelerate/src/offline_tokenization/concat_sst_bin_tokenization.py
+++ b/mftcoder_accelerate/tokenization/concat_sst_bin_tokenization.py
@@ -1,4 +1,8 @@
-# -*- coding: utf-8 -*-
+"""
+# @author Chaoyu Chen
+# @date 2023/11/05
+Do tokenization for pretraining data and write binary files.
+"""
import argparse
import multiprocessing
@@ -10,31 +14,29 @@
import glob
import json
import numpy as np
+from threading import Semaphore
+from colorama import Fore
-# 将父目录的父目录加入path
+# add root to path
current_path = os.path.abspath(__file__)
parent_dir = os.path.dirname(os.path.dirname(current_path))
-grandparent_dir = os.path.dirname(parent_dir)
-sys.path.append(grandparent_dir)
+sys.path.append(parent_dir)
from tokenizer import init_tokenizer
-from pack_encoder import PackSSTBinEncoder, load_tokenizer
from data import indexed_dataset
+import data.lm_dataformat as lmd
-from threading import Semaphore
-from colorama import Fore
-import lm_fmt as lmd
+from bin_encoder import SSTBinEncoder
def yield_from_files(files: list, semaphore):
"""
- Iterator over input documents
-
- :param fnames: list of filenames
+ Iterator over input documents
"""
+
def yielder(fname, semaphore):
- with open(fname, 'r') as f:
+ with open(fname, "r") as f:
for line in f:
semaphore.acquire()
yield json.loads(line)
@@ -43,29 +45,29 @@ def yielder(fname, semaphore):
semaphore.acquire()
yield from yielder(fname, semaphore)
+
def yield_from_files2(fnames: list, semaphore, sample_percent):
"""
Iterator over input documents using lm_dataformat. Should be able to handle jsons / texts /
other compressed formats. Also filters out empty documents.
-
- :param fnames: list of filenames
"""
+
def yielder(fname, semaphore):
try:
- sample_interval = int(1/sample_percent)
+ sample_interval = int(1 / sample_percent)
for f in filter(lambda x: x, lmd.Reader(fname).stream_data(key=None)):
- rand_value = random.randint(1, sample_interval*100)
+ rand_value = random.randint(1, sample_interval * 100)
if rand_value % sample_interval != 0:
continue
semaphore.acquire()
-
- #rand_value = random.randint(1, sample_interval*100)
- #if rand_value % sample_interval != 0:
+
+ # rand_value = random.randint(1, sample_interval*100)
+ # if rand_value % sample_interval != 0:
# yield None
yield f
except Exception as e:
- print('####Exception:', e.args)
+ print("####Exception:", e.args)
yield None
for fname in fnames:
@@ -75,17 +77,17 @@ def yielder(fname, semaphore):
def print_example_doc(input_ids, tokenizer):
- print(Fore.YELLOW + f'INPUT IDS len: {len(input_ids)}')
- print(Fore.BLUE + f'INPUT IDS:\n {input_ids}\n\n')
+ print(Fore.YELLOW + f"INPUT IDS len: {len(input_ids)}")
+ print(Fore.BLUE + f"INPUT IDS:\n {input_ids}\n\n")
- print(Fore.RED + f'DETOKENIZED INPUT:\n{tokenizer.decode(input_ids)}')
+ print(Fore.RED + f"DETOKENIZED INPUT:\n{tokenizer.decode(input_ids)}")
def core_process(encoded_docs, semaphore, seq_length, tokenizer, encoder, builder, output_idx_file):
"""
core of Data Pack SFT processing
"""
- input_ids_key = 'input_ids'
+ input_ids_key = "input_ids"
proc_start = time.time()
total_bytes_processed = 0
@@ -95,7 +97,7 @@ def core_process(encoded_docs, semaphore, seq_length, tokenizer, encoder, builde
print("PRINT BEFORE STREAM PROCESS DATA")
- print_example_count = 0
+ print_example_count = 0
for i, (doc, bytes_processed) in enumerate(encoded_docs, start=1):
total_bytes_processed += bytes_processed
@@ -111,8 +113,8 @@ def core_process(encoded_docs, semaphore, seq_length, tokenizer, encoder, builde
builder.add_item(np.array(input_ids_sentence, dtype=builder.dtype))
builder.end_document()
- #builder.finalize_without_close(output_idx_file)
- #builder.add_item_and_end_document_and_finalize(np.array(input_ids_sentence, dtype=builder.dtype), output_idx_file)
+ # builder.finalize_without_close(output_idx_file)
+ # builder.add_item_and_end_document_and_finalize(np.array(input_ids_sentence, dtype=builder.dtype), output_idx_file)
# print the first packed sample as example
if print_example_count < 1:
@@ -124,9 +126,7 @@ def core_process(encoded_docs, semaphore, seq_length, tokenizer, encoder, builde
current = time.time()
elapsed = current - proc_start
mbs = total_bytes_processed / elapsed / 1024 / 1024
- pbar.set_description(
- f"Processed {i} documents ({i / elapsed} docs/s, {mbs} MB/s)."
- )
+ pbar.set_description(f"Processed {i} documents ({i / elapsed} docs/s, {mbs} MB/s).")
if i != 0:
pbar.update(100)
@@ -142,35 +142,31 @@ def process_dataset(dataset_path, output_path, model_path, parallel_num, seq_len
"""
# get all jsonl files and corresponding reading handler
- files = glob.glob(os.path.join(dataset_path, '**/*.jsonl'), recursive=True)
+ files = glob.glob(os.path.join(dataset_path, "**/*.jsonl"), recursive=True)
- # build a semaphore object to stop `yield_from_files` from getting ahead
+ # build a semaphore object to stop `yield_from_files` from getting ahead
# of encoder.encode and hence building up memory
semaphore = Semaphore(1000 + parallel_num)
# build sample iterator
- sample_iterator = yield_from_files2(files, semaphore, sample_percent)
+ sample_iterator = yield_from_files2(files, semaphore, sample_percent)
# load tokenizer
# tokenizer = load_tokenizer(model_path, tokenizer_type)
tokenizer = init_tokenizer(model_path)
- print('TOKEN of id=2:', tokenizer.convert_ids_to_tokens(2))
- print('ID of </s>:', tokenizer.convert_tokens_to_ids('</s>'))
- print('TOKEN of id=0:', tokenizer.convert_ids_to_tokens(0))
- print('ID of </unk>:', tokenizer.convert_tokens_to_ids('</unk>'))
+ print("TOKEN of id=2:", tokenizer.convert_ids_to_tokens(2))
+ print("ID of </s>:", tokenizer.convert_tokens_to_ids("</s>"))
+ print("TOKEN of id=0:", tokenizer.convert_ids_to_tokens(0))
+ print("ID of </unk>:", tokenizer.convert_tokens_to_ids("</unk>"))
# init encoder
- encoder = PackSSTBinEncoder(seq_length, model_path)
+ encoder = SSTBinEncoder(seq_length, model_path)
# create writer builder
key = "input_ids"
output_prefix = os.path.join(output_path, dataset_name)
- output_bin_file = "{}_{}.bin".format(
- output_prefix, key
- )
- output_idx_file = "{}_{}.idx".format(
- output_prefix, key
- )
+ output_bin_file = "{}_{}.bin".format(output_prefix, key)
+ output_idx_file = "{}_{}.idx".format(output_prefix, key)
builder = indexed_dataset.make_builder(
output_bin_file,
impl="mmap",
@@ -201,20 +197,37 @@ def main(data_path, output_path, model_path, parallel_num, seq_length, dataset_n
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate a packed jsonl file in the Data Pack SFT way.")
- parser.add_argument('--model-path', type=str, help='Path of a pretrained model which contains tokenizer-related files.')
- parser.add_argument('--parallel', type=int, default=1, help='The num of parallel processing.')
- parser.add_argument('--output-path', type=str, help='Path to store the genered result file.')
- parser.add_argument('--data-path', type=str, default=None, help='Path of files to be processed')
- parser.add_argument('--seq-length', type=int, default=4096, help='The max input length (i.e. the max number of tokens in a sample)')
+ parser.add_argument(
+ "--model-path", type=str, help="Path of a pretrained model which contains tokenizer-related files."
+ )
+ parser.add_argument("--parallel", type=int, default=1, help="The num of parallel processing.")
+ parser.add_argument("--output-path", type=str, help="Path to store the genered result file.")
+ parser.add_argument("--data-path", type=str, default=None, help="Path of files to be processed")
+ parser.add_argument(
+ "--seq-length", type=int, default=4096, help="The max input length (i.e. the max number of tokens in a sample)"
+ )
# parser.add_argument('--eod-token-id', type=int, default=2, help='EOD token id')
# parser.add_argument('--pad-token-id', type=int, default=0, help='PAD token id')
# parser.add_argument('--tokenizer-type', type=str, choices=["LLAMATokenizer", None], default=None, help="What type of tokenizer to use. Default is None.")
- parser.add_argument('--dataset-name', type=str, default=None, help='The generated result dataset name. The folder name will be token by default.')
- parser.add_argument('--sample-percent', type=float, default=1.0, help='Sample percentage')
+ parser.add_argument(
+ "--dataset-name",
+ type=str,
+ default=None,
+ help="The generated result dataset name. The folder name will be token by default.",
+ )
+ parser.add_argument("--sample-percent", type=float, default=1.0, help="Sample percentage")
args = parser.parse_args()
- print('ARGS\n', '\n'.join([str(key) + ':' + str(value) for key,value in vars(args).items()]))
+ print("ARGS\n", "\n".join([str(key) + ":" + str(value) for key, value in vars(args).items()]))
random.seed(9999)
- main(args.data_path, args.output_path, args.model_path, args.parallel, args.seq_length, args.dataset_name, args.sample_percent)
+ main(
+ args.data_path,
+ args.output_path,
+ args.model_path,
+ args.parallel,
+ args.seq_length,
+ args.dataset_name,
+ args.sample_percent,
+ )
diff --git a/mftcoder_accelerate/src/offline_tokenization/writer.py b/mftcoder_accelerate/tokenization/writer.py
similarity index 67%
rename from mftcoder_accelerate/src/offline_tokenization/writer.py
rename to mftcoder_accelerate/tokenization/writer.py
index ab526a7..96b823c 100644
--- a/mftcoder_accelerate/src/offline_tokenization/writer.py
+++ b/mftcoder_accelerate/tokenization/writer.py
@@ -1,42 +1,36 @@
-
-import threading
import fcntl
import json
-class JSONLWriter():
+
+class JSONLWriter:
"""
A writer used to save jsonl lines into a file.
"""
+
def __init__(self, output_path, dataset_name):
self.output_path = output_path
- self.out_file = open(output_path, 'w')
+ self.out_file = open(output_path, "w")
self.cache = []
self.cache_size = 4096
self.dataset_name = dataset_name
self.index = 0
def pack_into_jsonl(self, line_text):
- new_item = {
- "data_name": self.dataset_name,
- "id": self.index,
- "content": line_text
- }
+ new_item = {"data_name": self.dataset_name, "id": self.index, "content": line_text}
return new_item
-
def add_item(self, line_text):
if len(self.cache) >= self.cache_size:
self.flush()
-
+
item = self.pack_into_jsonl(line_text)
self.cache.append(json.dumps(item))
self.index += 1
-
def flush(self):
- content = '\n'.join(self.cache)
+ content = "\n".join(self.cache)
fcntl.flock(self.out_file, fcntl.LOCK_EX)
- self.out_file.write(f'{content}\n')
+ self.out_file.write(f"{content}\n")
fcntl.flock(self.out_file, fcntl.LOCK_UN)
- self.cache = []
+ self.cache = []
diff --git a/mftcoder_accelerate/src/tokenizer/__init__.py b/mftcoder_accelerate/tokenizer/__init__.py
similarity index 100%
rename from mftcoder_accelerate/src/tokenizer/__init__.py
rename to mftcoder_accelerate/tokenizer/__init__.py
diff --git a/mftcoder_accelerate/src/tokenizer/chat_template.py b/mftcoder_accelerate/tokenizer/chat_template.py
similarity index 97%
rename from mftcoder_accelerate/src/tokenizer/chat_template.py
rename to mftcoder_accelerate/tokenizer/chat_template.py
index 3d2ad03..e1af06e 100644
--- a/mftcoder_accelerate/src/tokenizer/chat_template.py
+++ b/mftcoder_accelerate/tokenizer/chat_template.py
@@ -11,8 +11,7 @@
{"role": "system", "content": "Be smart"},
{"role": "human", "content": "Hello, how are you?"},
{"role": "bot", "content": "I'm doing great. How can I help you today?"},
- {"role": "human", "content": "I'd like to show off how chat templating works!"},
-]
+ {"role": "human", "content": "I'd like to show off how chat templating works!"}]
prompts = tokenizer.apply_chat_template(message, chat_template=MFTCoder_template, tokenize=False, add_generation_prompt=True)
"""
@@ -45,6 +44,3 @@
"{{ '<s>bot\n' }}"
"{% endif %}"
)
-
-if __name__ == "__main__":
- pass
diff --git a/mftcoder_accelerate/src/tokenizer/tokenizer.py b/mftcoder_accelerate/tokenizer/tokenizer.py
similarity index 96%
rename from mftcoder_accelerate/src/tokenizer/tokenizer.py
rename to mftcoder_accelerate/tokenizer/tokenizer.py
index bc3ab56..9f67e2e 100644
--- a/mftcoder_accelerate/src/tokenizer/tokenizer.py
+++ b/mftcoder_accelerate/tokenizer/tokenizer.py
@@ -3,11 +3,9 @@
# @date 2023/6/19
"""
-import numpy as np
-from typing import List, Union
-from utils.common_utils import print_rank_0
+from training.train_utils.common_utils import print_rank_0
from transformers import AutoTokenizer, AutoConfig
-from tokenizer.chat_template import MFTCoder_template
+from .chat_template import MFTCoder_template
def init_tokenizer(path):
diff --git a/mftcoder_accelerate/src/configs/coba_train_config.json b/mftcoder_accelerate/training/configs/coba_train_config.json
similarity index 100%
rename from mftcoder_accelerate/src/configs/coba_train_config.json
rename to mftcoder_accelerate/training/configs/coba_train_config.json
diff --git a/mftcoder_accelerate/src/configs/dpo_train_config.json b/mftcoder_accelerate/training/configs/dpo_train_config.json
similarity index 100%
rename from mftcoder_accelerate/src/configs/dpo_train_config.json
rename to mftcoder_accelerate/training/configs/dpo_train_config.json
diff --git a/mftcoder_accelerate/src/configs/full_train_config.json b/mftcoder_accelerate/training/configs/full_train_config.json
similarity index 100%
rename from mftcoder_accelerate/src/configs/full_train_config.json
rename to mftcoder_accelerate/training/configs/full_train_config.json
diff --git a/mftcoder_accelerate/src/configs/lora_train_config.json b/mftcoder_accelerate/training/configs/lora_train_config.json
similarity index 100%
rename from mftcoder_accelerate/src/configs/lora_train_config.json
rename to mftcoder_accelerate/training/configs/lora_train_config.json
diff --git a/mftcoder_accelerate/src/configs/qlora_train_config.json b/mftcoder_accelerate/training/configs/qlora_train_config.json
similarity index 100%
rename from mftcoder_accelerate/src/configs/qlora_train_config.json
rename to mftcoder_accelerate/training/configs/qlora_train_config.json
diff --git a/mftcoder_accelerate/src/pefts/merge_base_and_lora_to_hf.py b/mftcoder_accelerate/training/merge_base_and_lora_to_hf.py
similarity index 98%
rename from mftcoder_accelerate/src/pefts/merge_base_and_lora_to_hf.py
rename to mftcoder_accelerate/training/merge_base_and_lora_to_hf.py
index 26f8ec1..9cf7351 100644
--- a/mftcoder_accelerate/src/pefts/merge_base_and_lora_to_hf.py
+++ b/mftcoder_accelerate/training/merge_base_and_lora_to_hf.py
@@ -17,7 +17,7 @@
from peft import LoraConfig, get_peft_model
from peft import PeftModel
-# insert src as import path
+# insert project root as import path
current_path = os.path.abspath(__file__)
parent_dir = os.path.dirname(os.path.dirname(current_path))
sys.path.insert(0, parent_dir)
diff --git a/mftcoder_accelerate/src/pefts/mft_accelerate.py b/mftcoder_accelerate/training/mft_accelerate.py
similarity index 98%
rename from mftcoder_accelerate/src/pefts/mft_accelerate.py
rename to mftcoder_accelerate/training/mft_accelerate.py
index 0a0d42a..4cdf7ea 100644
--- a/mftcoder_accelerate/src/pefts/mft_accelerate.py
+++ b/mftcoder_accelerate/training/mft_accelerate.py
@@ -1,6 +1,6 @@
"""
# @author Chaoyu Chen
-# @date 2024/10/24
+# @date 2023/11/05
# @module mft_accelerate.py
Accelerate + DeepSpeed/FSDP + QLoRA/LoRA/Full + Multi-task Finetuning
@@ -56,11 +56,11 @@
from tokenizer import build_tokenizer
from data.multi_task_dataset import load_dataset_from_jsonl, compile_helper
from data.data_utils import load_dataset_from_bin
-from utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK
+from training.train_utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK
+from training.train_utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS
-from pefts.mft_trainer import MftTrainer
-from pefts.mft_arguments import MftTrainArgs
-from utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS
+from training.pefts import MftTrainer
+from training.pefts import MftTrainArgs
logger = get_logger(__name__)
diff --git a/mftcoder_accelerate/training/mpt/__init__.py b/mftcoder_accelerate/training/mpt/__init__.py
new file mode 100644
index 0000000..35cbc21
--- /dev/null
+++ b/mftcoder_accelerate/training/mpt/__init__.py
@@ -0,0 +1,4 @@
+# @author Chaoyu Chen
+# @date 2024/11/18
+from .mpt_arguments import MptTrainArgs
+from .mpt_trainer import MptTrainer
diff --git a/mftcoder_accelerate/src/mpt/mpt_arguments.py b/mftcoder_accelerate/training/mpt/mpt_arguments.py
similarity index 100%
rename from mftcoder_accelerate/src/mpt/mpt_arguments.py
rename to mftcoder_accelerate/training/mpt/mpt_arguments.py
diff --git a/mftcoder_accelerate/src/mpt/mpt_trainer.py b/mftcoder_accelerate/training/mpt/mpt_trainer.py
similarity index 99%
rename from mftcoder_accelerate/src/mpt/mpt_trainer.py
rename to mftcoder_accelerate/training/mpt/mpt_trainer.py
index b5e2da8..48f0079 100644
--- a/mftcoder_accelerate/src/mpt/mpt_trainer.py
+++ b/mftcoder_accelerate/training/mpt/mpt_trainer.py
@@ -29,8 +29,8 @@
from transformers import set_seed
# sys.path.append("..")
-from utils.common_utils import generate_task_id, TASK2ID, ID2TASK
-from utils.loss_utils import loss_func_mft, CoBaStatus, load_balancing_loss_func
+from training.train_utils.common_utils import generate_task_id, TASK2ID, ID2TASK
+from training.train_utils.loss_utils import loss_func_mft, CoBaStatus, load_balancing_loss_func
logger = get_logger(__name__)
diff --git a/mftcoder_accelerate/src/mpt/mpt_accelerate.py b/mftcoder_accelerate/training/mpt_accelerate.py
similarity index 98%
rename from mftcoder_accelerate/src/mpt/mpt_accelerate.py
rename to mftcoder_accelerate/training/mpt_accelerate.py
index 5d187c9..1bc4c6c 100644
--- a/mftcoder_accelerate/src/mpt/mpt_accelerate.py
+++ b/mftcoder_accelerate/training/mpt_accelerate.py
@@ -1,6 +1,6 @@
"""
# @author Chaoyu Chen
-# @date 2024/6/1
+# @date 2023/11/05
# @module mpt_accelerate.py
Accelerate + DeepSpeed + Full-parameter + Multi-task + Pre-training/Continue Training/Finetuning
@@ -49,10 +49,10 @@
from tokenizer import build_tokenizer
from data.multi_task_dataset import load_dataset_from_jsonl, compile_helper
from data.data_utils import load_dataset_from_bin
-from utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK
-from mpt.mpt_trainer import MptTrainer
-from mpt.mpt_arguments import MptTrainArgs
-from utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS
+from training.train_utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK
+from training.train_utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS
+from training.mpt import MptTrainer
+from training.mpt import MptTrainArgs
logger = get_logger(__name__)
diff --git a/mftcoder_accelerate/training/pefts/__init__.py b/mftcoder_accelerate/training/pefts/__init__.py
new file mode 100644
index 0000000..163341d
--- /dev/null
+++ b/mftcoder_accelerate/training/pefts/__init__.py
@@ -0,0 +1,4 @@
+# @author Chaoyu Chen
+# @date 2024/11/18
+from .mft_arguments import MftTrainArgs
+from .mft_trainer import MftTrainer
\ No newline at end of file
diff --git a/mftcoder_accelerate/src/pefts/mft_arguments.py b/mftcoder_accelerate/training/pefts/mft_arguments.py
similarity index 100%
rename from mftcoder_accelerate/src/pefts/mft_arguments.py
rename to mftcoder_accelerate/training/pefts/mft_arguments.py
diff --git a/mftcoder_accelerate/src/pefts/mft_trainer.py b/mftcoder_accelerate/training/pefts/mft_trainer.py
similarity index 99%
rename from mftcoder_accelerate/src/pefts/mft_trainer.py
rename to mftcoder_accelerate/training/pefts/mft_trainer.py
index a2b00fb..033396b 100644
--- a/mftcoder_accelerate/src/pefts/mft_trainer.py
+++ b/mftcoder_accelerate/training/pefts/mft_trainer.py
@@ -32,8 +32,8 @@
from transformers import set_seed
# sys.path.append("..")
-from utils.common_utils import generate_task_id, TASK2ID, ID2TASK
-from utils.loss_utils import loss_func_mft, CoBaStatus, load_balancing_loss_func
+from training.train_utils.common_utils import generate_task_id, TASK2ID, ID2TASK
+from training.train_utils.loss_utils import loss_func_mft, CoBaStatus, load_balancing_loss_func
logger = get_logger(__name__)
diff --git a/mftcoder_accelerate/src/utils/__init__.py b/mftcoder_accelerate/training/train_utils/__init__.py
similarity index 100%
rename from mftcoder_accelerate/src/utils/__init__.py
rename to mftcoder_accelerate/training/train_utils/__init__.py
diff --git a/mftcoder_accelerate/src/utils/common_utils.py b/mftcoder_accelerate/training/train_utils/common_utils.py
similarity index 100%
rename from mftcoder_accelerate/src/utils/common_utils.py
rename to mftcoder_accelerate/training/train_utils/common_utils.py
diff --git a/mftcoder_accelerate/src/utils/loss_utils.py b/mftcoder_accelerate/training/train_utils/loss_utils.py
similarity index 99%
rename from mftcoder_accelerate/src/utils/loss_utils.py
rename to mftcoder_accelerate/training/train_utils/loss_utils.py
index 5ca7c73..89333f4 100644
--- a/mftcoder_accelerate/src/utils/loss_utils.py
+++ b/mftcoder_accelerate/training/train_utils/loss_utils.py
@@ -1,6 +1,6 @@
import sys
import torch
-from utils.common_utils import print_rank_0, TASK2ID, ID2TASK
+from training.train_utils.common_utils import print_rank_0, TASK2ID, ID2TASK
from torch.nn import CrossEntropyLoss
import torch.nn.functional as F
from dataclasses import dataclass
diff --git a/mftcoder_accelerate/src/utils/model_mapping.py b/mftcoder_accelerate/training/train_utils/model_mapping.py
similarity index 95%
rename from mftcoder_accelerate/src/utils/model_mapping.py
rename to mftcoder_accelerate/training/train_utils/model_mapping.py
index 8592e86..4dfd80b 100644
--- a/mftcoder_accelerate/src/utils/model_mapping.py
+++ b/mftcoder_accelerate/training/train_utils/model_mapping.py
@@ -27,7 +27,6 @@
from model.chatglm2.modeling_chatglm import ChatGLMForConditionalGeneration as ChatGLMForConditionalGeneration2
from model.chatglm3.modeling_chatglm import ChatGLMForConditionalGeneration as ChatGLMForConditionalGeneration3
-# from model.phi.modeling_mixformer_sequential import MixFormerSequentialForCausalLM
MODEL_TYPES = {
"aquila2": AquilaForCausalLM,
diff --git a/mftcoder_accelerate/training/xxpo/__init__.py b/mftcoder_accelerate/training/xxpo/__init__.py
new file mode 100644
index 0000000..5301cfc
--- /dev/null
+++ b/mftcoder_accelerate/training/xxpo/__init__.py
@@ -0,0 +1,4 @@
+# @author Chaoyu Chen
+# @date 2024/11/18
+from .custom_callbacks import CustomProgressCallback, LogCallback
+from .xxpo_arguments import XXPOTrainArgs
diff --git a/mftcoder_accelerate/src/xxpo/custom_callbacks.py b/mftcoder_accelerate/training/xxpo/custom_callbacks.py
similarity index 100%
rename from mftcoder_accelerate/src/xxpo/custom_callbacks.py
rename to mftcoder_accelerate/training/xxpo/custom_callbacks.py
diff --git a/mftcoder_accelerate/src/xxpo/xxpo_arguments.py b/mftcoder_accelerate/training/xxpo/xxpo_arguments.py
similarity index 100%
rename from mftcoder_accelerate/src/xxpo/xxpo_arguments.py
rename to mftcoder_accelerate/training/xxpo/xxpo_arguments.py
diff --git a/mftcoder_accelerate/src/xxpo/xxpo_accelerate.py b/mftcoder_accelerate/training/xxpo_accelerate.py
similarity index 98%
rename from mftcoder_accelerate/src/xxpo/xxpo_accelerate.py
rename to mftcoder_accelerate/training/xxpo_accelerate.py
index 4c93520..dcfeacf 100644
--- a/mftcoder_accelerate/src/xxpo/xxpo_accelerate.py
+++ b/mftcoder_accelerate/training/xxpo_accelerate.py
@@ -1,7 +1,7 @@
"""
-# @author qumu
-# @date 2023/12/11
-# @module mft_accelerate.py
+# @author Chaoyu Chen
+# @date 2023/11/05
+# @module xxpo_accelerate.py
Accelerate + DeepSpeed/FSDP + QLoRA/LoRA/Full + DPO/RPO/ORPO
@@ -54,8 +54,8 @@
from tokenizer import build_tokenizer
-from utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK
-from utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS
+from training.train_utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK
+from training.train_utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS
logger = get_logger(__name__)
diff --git a/requirements.txt b/requirements.txt
index 189518b..c93eeff 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -16,4 +16,7 @@ sentencepiece
ujson
jsonlines
tiktoken
-transformers_stream_generator
\ No newline at end of file
+transformers_stream_generator
+tqdm
+jinja2
+colorama
\ No newline at end of file
From 1aea144e18ad23e4352b1ec756d6892939c25eb1 Mon Sep 17 00:00:00 2001
From: "chris.ccy" <chris.ccy@antgroup.com>
Date: Thu, 28 Nov 2024 18:36:43 +0800
Subject: [PATCH 2/5] accelerate infer
---
.../inference/infer_accelerate.py | 170 ++++++++++++++++++
1 file changed, 170 insertions(+)
create mode 100644 mftcoder_accelerate/inference/infer_accelerate.py
diff --git a/mftcoder_accelerate/inference/infer_accelerate.py b/mftcoder_accelerate/inference/infer_accelerate.py
new file mode 100644
index 0000000..ff5f602
--- /dev/null
+++ b/mftcoder_accelerate/inference/infer_accelerate.py
@@ -0,0 +1,170 @@
+# @author Chaoyu Chen
+# @date 2024/11/19
+"""Distributed Inference by accelerate"""
+import os
+import json
+import argparse
+import queue
+from concurrent.futures import ThreadPoolExecutor
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from accelerate import PartialState
+from accelerate.utils import gather_object
+
+from infer_utils import (
+ print_args,
+ get_line_count,
+ stream_jsonl,
+ batch_stream_jsonl,
+ flatten_batch_stream,
+ write_jsonl,
+)
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description="Generation args.")
+ parser.add_argument(
+ "--model_path",
+ type=str,
+ help="huggingface model path",
+ )
+ parser.add_argument(
+ "--data_file",
+ type=str,
+ help="data file path",
+ )
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ help="output directory",
+ )
+ parser.add_argument("--batch_size", type=int, default=1)
+ parser.add_argument("--num_return_sequences", type=int, default=20, help="pass1:20,pass10:20,pass100:100")
+ parser.add_argument("--num_beams", type=int, default=1, help="beam1, beam3, beam5, beam7")
+ parser.add_argument("--do_sample", action="store_true", default=False)
+ parser.add_argument("--temperature", type=float, default=0.2)
+ parser.add_argument("--top_p", type=float, default=0.95)
+
+ parser.add_argument("--peft_path", type=str, default="", help="peft path：None")
+ parser.add_argument("--eos_token", type=str, default=None, help="eos token")
+ args = parser.parse_args()
+
+ name = args.data_file.split("/")[-1].replace(".jsonl", "") + "-GEN"
+ args.output_path = os.path.join(args.output_dir, f"{name}.jsonl")
+
+ return args
+
+
+def main():
+ args = get_args()
+ # Start up the distributed environment without needing the Accelerator.
+ distributed_state = PartialState()
+
+ # You can change the model to any LLM
+ model = AutoModelForCausalLM.from_pretrained(
+ args.model_path, device_map=distributed_state.device, torch_dtype=torch.bfloat16
+ )
+
+ tokenizer = AutoTokenizer.from_pretrained(args.model_path)
+ # Need to set the padding token to the eos token for generation
+ tokenizer.pad_token = tokenizer.eos_token
+
+ # get dataloader
+ total_num = get_line_count(args.data_file)
+ # You can change the batch size depending on your GPU RAM
+ global_batch_size = args.batch_size * distributed_state.num_processes
+ stream = stream_jsonl(args.data_file)
+ dataloader = batch_stream_jsonl(stream, global_batch_size)
+
+ # We set it to 8 since it is better for some hardware. More information here https://github.com/huggingface/tokenizers/issues/991
+ pad_to_multiple_of = 8
+
+ def handler():
+ # streaming process
+ for samples in dataloader:
+ prompts = [sample['prompt'] for sample in samples]
+ distributed_state.print(f"length of global batch: {len(prompts)}")
+
+ # Split a global batch into batches with automatic padding so that the GPUs will have 1 batch with same size, and you can then gather the results.
+ if len(prompts) == global_batch_size:
+ formatted_prompts = [prompts[i: i + args.batch_size] for i in range(0, len(prompts), args.batch_size)]
+ else:
+ padded_prompts = prompts + [prompts[-1]] * (global_batch_size - len(prompts))
+ formatted_prompts = [padded_prompts[i: i + args.batch_size] for i in
+ range(0, len(padded_prompts), args.batch_size)]
+ # distributed_state.print(f"formatted prompts {formatted_prompts}")
+
+ # Apply padding on the left since we are doing generation
+ padding_side_default = tokenizer.padding_side
+ tokenizer.padding_side = "left"
+ # Tokenize each batch
+ tokenized_prompts = [
+ tokenizer(formatted_prompt, padding=True, pad_to_multiple_of=pad_to_multiple_of, return_tensors="pt")
+ for formatted_prompt in formatted_prompts
+ ]
+ # Put back the original padding behavior
+ tokenizer.padding_side = padding_side_default
+
+ completions_per_process = []
+ with distributed_state.split_between_processes(tokenized_prompts) as batched_prompts:
+
+ for batch in batched_prompts:
+ # Move the batch to the device
+ batch = batch.to(distributed_state.device)
+
+ # YOU WILL FILL YOUR CODE HERE!
+ # We generate the text, decode it and add it to the list completions_per_process
+ outputs = model.generate(
+ inputs=batch["input_ids"],
+ attention_mask=batch["attention_mask"],
+ max_new_tokens=128
+ )
+
+ # generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+ generated_text = tokenizer.batch_decode(outputs[:, batch["input_ids"].shape[1]:],
+ skip_special_tokens=True)
+ # print(f"index: {distributed_state.process_index}, {generated_text}, {len(generated_text)}")
+ completions_per_process.extend(generated_text)
+
+ # We are gathering string, so we need to use gather_object.
+ # If you need to gather tensors, you can use gather from accelerate.utils
+ completions_gather = gather_object(completions_per_process)
+ # Drop duplicates produced by apply_padding in split_between_processes
+ completions = completions_gather[: len(prompts)]
+ distributed_state.print(completions, len(completions))
+ for sample, completion in zip(samples, completions):
+ sample['generation'] = completion
+
+ yield samples
+
+ def save_results(output_queue: queue.Queue, output_path):
+ with open(output_path, "w") as fp:
+ while True:
+ try:
+ item = output_queue.get(timeout=5)
+ if item is None:
+ break
+ fp.write(json.dumps(item, ensure_ascii=False) + "\n")
+ fp.flush()
+ except queue.Empty:
+ continue
+
+ # handel batch dataloader
+ batch_res_stream = handler()
+ res_stream = flatten_batch_stream(batch_res_stream)
+
+ if distributed_state.is_main_process:
+ output_queue = queue.Queue()
+ save_thread = ThreadPoolExecutor(max_workers=16)
+ save_future = save_thread.submit(save_results, output_queue, args.output_path)
+ for res in res_stream:
+ output_queue.put(res)
+ output_queue.put(None)
+ save_thread.shutdown(wait=True)
+ else:
+ list(res_stream)
+
+
+if __name__ == "__main__":
+ main()
From bab58b6721418385dc6a519df303caf0eaf96335 Mon Sep 17 00:00:00 2001
From: "chris.ccy" <chris.ccy@antgroup.com>
Date: Wed, 11 Dec 2024 14:22:01 +0800
Subject: [PATCH 3/5] refactor and readme
---
README.md | 11 +-
README_cn.md | 6 +-
mftcoder_accelerate/README_cn.md | 2 +-
mftcoder_atorch/.gitignore | 7 -
mftcoder_atorch/README.md | 231 -
mftcoder_atorch/README_cn.md | 218 -
mftcoder_atorch/arguments/get_arguments.py | 557 -
mftcoder_atorch/data/Makefile | 9 -
mftcoder_atorch/data/__init__.py | 1 -
.../data/gpt2_multi_task_dataset.py | 349 -
mftcoder_atorch/data/helpers.cpp | 758 -
.../helpers.cpython-38-x86_64-linux-gnu.so | Bin 180656 -> 0 bytes
mftcoder_atorch/data/preprocess_data.py | 313 -
.../data/tokenization/lm_dataformat.py | 386 -
.../data/tokenization/preprocess_data.py | 327 -
mftcoder_atorch/model/__init__.py | 20 -
mftcoder_atorch/model/build_model.py | 153 -
mftcoder_atorch/model/gpt_neox/__init__.py | 76 -
mftcoder_atorch/model/gpt_neox/config.json | 24 -
.../model/gpt_neox/configuration_gpt_neox.py | 122 -
.../model/gpt_neox/generation_config.json | 6 -
.../model/gpt_neox/modeling_gpt_neox.py | 753 -
.../gpt_neox/tokenization_gpt_neox_fast.py | 142 -
mftcoder_atorch/model/peft/__init__.py | 16 -
mftcoder_atorch/model/peft/modeling_peft.py | 653 -
mftcoder_atorch/model/peft/tuner/__init__.py | 29 -
mftcoder_atorch/model/peft/tuner/adalora.py | 721 -
mftcoder_atorch/model/peft/tuner/bitfit.py | 154 -
.../model/peft/tuner/pe_base_model.py | 19 -
mftcoder_atorch/model/peft/tuner/roem.py | 168 -
mftcoder_atorch/model/peft/tuner/routelora.py | 622 -
mftcoder_atorch/model/peft/tuner/unipelt.py | 1021 -
mftcoder_atorch/model/peft/utils/__init__.py | 14 -
mftcoder_atorch/model/peft/utils/config.py | 29 -
mftcoder_atorch/model/peft/utils/mapping.py | 141 -
mftcoder_atorch/model/peft/utils/others.py | 318 -
mftcoder_atorch/tokenizer/__init__.py | 16 -
.../tokenizer/gpt2_tokenization.py | 368 -
mftcoder_atorch/tokenizer/tokenizer.py | 409 -
mftcoder_atorch/tokenizer/train_tokenizer.py | 126 -
mftcoder_atorch/train/__init__.py | 1 -
mftcoder_atorch/train/run_gpt_mft.sh | 96 -
mftcoder_atorch/train/run_gpt_mft_peft.sh | 97 -
mftcoder_atorch/train/run_train.py | 136 -
.../train/trainer/atorch_trainer.py | 1041 -
mftcoder_atorch/utils/__init__.py | 2 -
.../utils/auto_accelerate_utils.py | 226 -
mftcoder_atorch/utils/common_utils.py | 371 -
mftcoder_atorch/utils/learning_rates.py | 148 -
.../utils/merge_base_and_lora_to_hf.py | 38 -
mftcoder_atorch/utils/vocab.json | 205457 ---------------
51 files changed, 5 insertions(+), 216903 deletions(-)
delete mode 100644 mftcoder_atorch/.gitignore
delete mode 100644 mftcoder_atorch/README.md
delete mode 100644 mftcoder_atorch/README_cn.md
delete mode 100644 mftcoder_atorch/arguments/get_arguments.py
delete mode 100644 mftcoder_atorch/data/Makefile
delete mode 100644 mftcoder_atorch/data/__init__.py
delete mode 100644 mftcoder_atorch/data/gpt2_multi_task_dataset.py
delete mode 100644 mftcoder_atorch/data/helpers.cpp
delete mode 100755 mftcoder_atorch/data/helpers.cpython-38-x86_64-linux-gnu.so
delete mode 100644 mftcoder_atorch/data/preprocess_data.py
delete mode 100644 mftcoder_atorch/data/tokenization/lm_dataformat.py
delete mode 100644 mftcoder_atorch/data/tokenization/preprocess_data.py
delete mode 100755 mftcoder_atorch/model/__init__.py
delete mode 100644 mftcoder_atorch/model/build_model.py
delete mode 100644 mftcoder_atorch/model/gpt_neox/__init__.py
delete mode 100644 mftcoder_atorch/model/gpt_neox/config.json
delete mode 100644 mftcoder_atorch/model/gpt_neox/configuration_gpt_neox.py
delete mode 100644 mftcoder_atorch/model/gpt_neox/generation_config.json
delete mode 100644 mftcoder_atorch/model/gpt_neox/modeling_gpt_neox.py
delete mode 100644 mftcoder_atorch/model/gpt_neox/tokenization_gpt_neox_fast.py
delete mode 100644 mftcoder_atorch/model/peft/__init__.py
delete mode 100644 mftcoder_atorch/model/peft/modeling_peft.py
delete mode 100644 mftcoder_atorch/model/peft/tuner/__init__.py
delete mode 100644 mftcoder_atorch/model/peft/tuner/adalora.py
delete mode 100644 mftcoder_atorch/model/peft/tuner/bitfit.py
delete mode 100644 mftcoder_atorch/model/peft/tuner/pe_base_model.py
delete mode 100644 mftcoder_atorch/model/peft/tuner/roem.py
delete mode 100644 mftcoder_atorch/model/peft/tuner/routelora.py
delete mode 100644 mftcoder_atorch/model/peft/tuner/unipelt.py
delete mode 100644 mftcoder_atorch/model/peft/utils/__init__.py
delete mode 100644 mftcoder_atorch/model/peft/utils/config.py
delete mode 100644 mftcoder_atorch/model/peft/utils/mapping.py
delete mode 100644 mftcoder_atorch/model/peft/utils/others.py
delete mode 100644 mftcoder_atorch/tokenizer/__init__.py
delete mode 100644 mftcoder_atorch/tokenizer/gpt2_tokenization.py
delete mode 100644 mftcoder_atorch/tokenizer/tokenizer.py
delete mode 100644 mftcoder_atorch/tokenizer/train_tokenizer.py
delete mode 100644 mftcoder_atorch/train/__init__.py
delete mode 100644 mftcoder_atorch/train/run_gpt_mft.sh
delete mode 100644 mftcoder_atorch/train/run_gpt_mft_peft.sh
delete mode 100644 mftcoder_atorch/train/run_train.py
delete mode 100644 mftcoder_atorch/train/trainer/atorch_trainer.py
delete mode 100644 mftcoder_atorch/utils/__init__.py
delete mode 100644 mftcoder_atorch/utils/auto_accelerate_utils.py
delete mode 100644 mftcoder_atorch/utils/common_utils.py
delete mode 100644 mftcoder_atorch/utils/learning_rates.py
delete mode 100644 mftcoder_atorch/utils/merge_base_and_lora_to_hf.py
delete mode 100644 mftcoder_atorch/utils/vocab.json
diff --git a/README.md b/README.md
index 468cc88..53b5687 100644
--- a/README.md
+++ b/README.md
@@ -107,9 +107,8 @@
**MFTCoder** is an open-source project of CodeFuse for accurate and efficient Multi-task Fine-tuning(MFT) on Large Language Models(LLMs), especially on Code-LLMs(large language model for code tasks).
Moreover, we open source Code LLM models and code-related datasets along with the MFTCoder framework.
-In MFTCoder, we released two codebases for finetuning Large Language Models:
+In MFTCoder, we maintained an open-source codebase for finetuning Large Language Models:
- **```MFTCoder-accelerate```** is a framework with accelerate and DeepSpeed/FSDP. All tech-stacks are open-source and vibrant. We highly recommend you try this framework and make your fintuning accurate and efficient.
-- ```MFTCoder-atorch``` is based on the [ATorch frameworks](https://github.com/intelligent-machine-learning/dlrover), which is a fast distributed training framework of LLM.
The aim of this project is to foster collaboration and share advancements in large language models, particularly within the domain of code development.
@@ -121,7 +120,7 @@ The aim of this project is to foster collaboration and share advancements in lar
:white_check_mark: **Multi-model**: It integrates state-of-the-art open-source models such as gpt-neox, llama, llama-2, baichuan, Qwen, chatglm2, and more. (These finetuned models will be released in the near future.)
-:white_check_mark: **Multi-framework**: It provides support for both Accelerate (with Deepspeed and FSDP) and ATorch
+:white_check_mark: **Multi-framework**: It provides support for both Accelerate with Deepspeed and FSDP.
:white_check_mark: **Efficient fine-tuning**: It supports LoRA, QLoRA as well as Full-parameters training, enabling fine-tuning of large models with minimal resources. The training speed meets the demands of almost all fine-tuning scenarios.
@@ -145,17 +144,13 @@ We highly recommend training with flash attention(version >= 2.3.0), please refe
## Training
-As mentioned above, we open source two training frameworks. You could refer to their own READMEs for more details as followed.
+You could refer to their own READMEs for more details as followed.
If you are familiar with open source ```transformers```, ```DeepSpeed``` or ```FSDP```, we highly recommend you try:
🚀🚀 [**MFTCoder-accelerate: Accelerate + Deepspeed/FSDP Codebase for MFT(Multi-task Finetuning)**](mftcoder_accelerate/README.md)
-If you want to explore some new framework like atorch, you could check:
-
-🚀 [MFTCoder-atorch: Atorch Codebase for MFT(Multi-task Finetuning)](mftcoder_atorch/README.md)
-
## Models
diff --git a/README_cn.md b/README_cn.md
index 3102d9f..8bf01cf 100644
--- a/README_cn.md
+++ b/README_cn.md
@@ -113,7 +113,7 @@
:white_check_mark: **多模型**：支持最新的多个开源模型，包括gpt-neox，llama，llama-2，baichuan，Qwen，chatglm2等；
-:white_check_mark: **多框架**：既支持主流开源的Accelerate+DeepSpeed/FSDP，也支持新开源的[ATorch 框架](https://github.com/intelligent-machine-learning/dlrover)；
+:white_check_mark: **多框架**：既支持主流开源的Accelerate+DeepSpeed/FSDP；
:white_check_mark: **高效微调**：支持LoRA和QLoRA，可以用很少的资源去微调很大的模型，且训练速度能满足几乎所有微调场景；
@@ -142,10 +142,6 @@ sh init_env.sh
🚀🚀 [MFTCoder-accelerate: Accelerate + DeepSpeed/FSDP Codebase for MFT(Multi-task Finetuning)](mftcoder_accelerate/README.md)
-如果你想探索一些新兴的训练框架，可以尝试：
-
-🚀 [MFTCoder-atorch: Atorch Codebase for MFT(Multi-task Finetuning)](mftcoder_atorch/README.md)
-
## 模型
diff --git a/mftcoder_accelerate/README_cn.md b/mftcoder_accelerate/README_cn.md
index 62d729b..4cdcade 100644
--- a/mftcoder_accelerate/README_cn.md
+++ b/mftcoder_accelerate/README_cn.md
@@ -297,7 +297,7 @@ _**训练需要的参数配置在```training/configs/*_train_config```中，主
- **xxpo**: 偏好对齐方法, "dpo" 或者 "orpo"。
- **beta**: DPO beta, beta 越小，允许对齐后的dpo模型与ref模型的距离越远。
- **rpo_alpha**: 加到dop损失的```chosen``` NLL损失的系数，0的话就是原始DPO。
--
+
## 4. 模型使用
### 4.1 权重合并
diff --git a/mftcoder_atorch/.gitignore b/mftcoder_atorch/.gitignore
deleted file mode 100644
index ecb01a4..0000000
--- a/mftcoder_atorch/.gitignore
+++ /dev/null
@@ -1,7 +0,0 @@
-*.log
-*/__pycache__/
-*.pyc
-*.ipynb
-.DS_Store
-.idea/
-evals/
\ No newline at end of file
diff --git a/mftcoder_atorch/README.md b/mftcoder_atorch/README.md
deleted file mode 100644
index 8ba6844..0000000
--- a/mftcoder_atorch/README.md
+++ /dev/null
@@ -1,231 +0,0 @@
-# MFTCoder Training: Atorch Framework
-[](https://huggingface.co/codefuse-ai)
-<a href="https://github.com/codefuse-ai/MFTCoder/blob/main/LICENSE">
- <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
-</a>
-
-[[中文]](README_cn.md) [**English**]
-
-## 1. Updates
-
-🔥 MFTCoder supports fine-tuning of the GPTNeoX model under the Atorch framework.
-
-🔥 MFTCoder supports both fully supervised fine-tuning.
-
-🔥 MFTCoder supports LoRA using the Atorch Framework.
-
-## 2. Data Format
-### 2.1 Training Data Format
-The training data is in a uniformed JSONL format, in which each line of data has the following JSON format. The "chat_rounds" field is required, and other fields can be added or removed based on the specific need.
-
-```json
-{
- "id":0,
- "data_name":"code-helper",
- "chat_rounds":[
- {
- "role": "system",
- "content": "You are a expert in coding and help answer code questions",
- "chat_round_id": 0
- },
- {
- "role": "human",
- "content": "Write a python function of quick sort",
- "chat_round_id": 1
- },
- {
- "role": "bot",
- "content": "Below is the function of quick sort: ...",
- "chat_round_id": 1
- },
- {
- "role": "human",
- "content": "Explain the code",
- "chat_round_id": 2
- },
- {
- "role": "bot",
- "content": "OK, this code ...",
- "chat_round_id": 2
- }
- ]
-}
-```
-
-### 2.2 Inference Data Format
-The inference data contains strings concatenated by conversation data(system, human and bot contents) in the training data format.
-It is used as the data "seen"(before tokenization) by the model in training process.
-It is used as input during the inference process as well.
-Here is an example format of the concatenated string:
-
-```python
-"""
-<|role_start|>system<|role_end|>System instruction
-<|role_start|>human<|role_end|>Human 1st round input
-<|role_start|>bot<|role_end|>Bot 1st round output</s>
-<|role_start|>human<|role_end|>Human 2nd round input
-<|role_start|>bot<|role_end|>Bot 2nd round output</s>
-...
-...
-...
-<|role_start|>human<|role_end|>Human nth round input
-<|role_start|>bot<|role_end|>{Bot output to be genreated}</s>
-"""
-```
-When applying inference, you always make your input string end with "<|role_start|>bot<|role_end|>" to request the model generating answers.
-
-## 3. Model Training
-Currently, the "MFTCoder/mft_atorch" code repository supports fully instruction fine-tuning, and LoRA instruction fine-tuning. Only the training of the GPTNeoX model is supported. In theory, the pretrained weights of the GPTNeoX model available on HuggingFace can be used for training within this project.
-
-We have extracted various components used in training to facilitate future extension and optimization. Please refer to the implementation in the main directory for more details. The entry directory for fine-tuning training is ```train/```, and the entry file for training is ```train/run_train.py```. The parameter configurations are stored in the launch scripts such as ```train/run_gpt_*.sh```, making it easier to manage and modify them uniformly.
-
-### 3.1 Tokenization
-During training, we concatenate multi-turn dialogues into the following format (also known as the inference data format mentioned earlier) and then tokenize it. In this format, <|role_start|>human<|role_end|> represents the human input (i.e., prompt), <|role_start|>bot<|role_end|> represents the bot output, and </s> represents the eos_token.
-You can modify and replace the eos_token based on different models' requirements.
-
-Here is an example of the concatenated format with prompts:
-```
-"<|role_start|>human<|role_end|>input1</s>target1</s>input2</s>target2</s>...
-```
-During the calculation of loss, we use a ```loss mask``` to ensure that the loss from the input part does not contribute to the parameter updates. Only the loss from the ```target</s>``` part is used for updating parameters.
-This approach takes full advantage of the benefits of model parallelism, making training more efficient. It also leverages the characteristic of decoder-only models with left-to-right attention.
-By including all target parts from multiple turns in a single training iteration, the training process becomes more efficient.
-
-### 3.2 Fully Supervised Fine-Tuning (SFT)
-To perform fully SFT, you can execute the following command:
-```bash
-sh run_gpt_mft.sh 10 1 8 5
-```
-Please note that the four parameters after the launch script have the following meanings:
-- The first parameter is the per GPU batch size.
-- The second parameter is the number of tensor parallelism (currently only supports 1).
-- The third parameter is the number of data parallelism, which should match the number of GPUs used.
-- The fourth parameter is the number of training epochs.
-
-For other training modes, the same four parameters need to be configured in the launch script.
-
-### 3.3 LoRA Supervised Fine-Tuning
-To perform LoRA SFT, you can execute the following command:
-```bash
-sh run_gpt_mft_peft.sh 10 1 8 5
-```
-
-### 3.4 Parameter Explanations
-The main parameter explanations for the ```train/run_gpt_*.sh``` are as follows. You can modify these parameters according to your needs:
-
-- **tokenize_mode**: Need to be 'sft' at present.
-
-- **train_mode**: Need to be 'sft' at present.
-
-- **load_raw_dataset**: Need to be 'True' at present. Only JSONL format is supported.
-
-- **data_paths**: "[path1,path2,path3]" Input data addresses, a string enclosed in [], with different paths separated by commas (,). Each path is a directory where the last level of the directory name is considered as the task name. Each task directory contains 1 to multiple jsonl data files.
-
-- **output_dir**: Training output directory to store checkpoints, lora_adaptor checkpoints, etc.
-
-- **tensorboard_dir**: Can be temporarily ignored, as the actual tensorboard is stored in the runs directory under output_dir.
-
-- **model_type**: Currently only supports gpt_neox.
-
-- **peft_type**: Currently only supports lora.
-
-- **pretrained_model_path**: Local directory of the pre-trained model.
-
-- **total_train_batch_size**: The total batch size for training across all GPUs, calculated automatically based on per gpu batch size entered in the script.
-
-- **per_device_valid_batch_size**: The batch size for evaluation on each GPU, calculated automatically based on per gpu batch size entered in the script.
-
-- **gradient_accumulation_steps**: Number of gradient accumulation steps. Global batch size = num_gpus * per_device_train_batch_size * gradient_accumulation_steps.
-
-- **checkpoint_activations**: Enable if running out of GPU memory. Trades time for space by not caching activation states, resulting in two forward passes to save memory.
-
-- **learning_rate**: Learning rate. When fine-tuning the entire model, it is recommended to use a smaller value, such as 1e-5 or 5e-6. For lora, a larger learning rate is generally used, such as 1e-4 or 2e-4.
-
-- **min_lr**: Minimum learning rate, usually one-tenth of the learning_rate.
-
-- **seq_length**: Maximum length during training. Set according to your device, longer lengths require more memory.
-
-- **log_interval**: Frequency of logging training loss.
-
-- **checkpointing_steps**: Frequency of saving a model checkpoint.
-
-- **evalation_steps**: Frequency of evaluating on the validation set.
-
-- **early_stopping_patience**: Number of consecutive eval points without further convergence to stop training.
-
-- **lr_scheduler_type**: Learning rate changing strategy.
-
-- **num_warmup_steps**: Number of warm-up steps for the learning rate to increase to the specified value.
-
-- **seed**: Random seed used for reproducibility of experimental results.
-
-- **train_iters**: Can be temporarily set to a small value, such as 10, which does not affect the actual number of training steps, kept for future expansion to support reading datasets in other formats.
-
-- **valid_iters**: Can be temporarily set to a small value, such as 10, which does not affect the actual number of training steps, kept for future expansion to support reading datasets in other formats.
-
-- **evaluation_strategy**: Evaluation strategy during training. "steps" means to evaluate every "valid_interval" steps, "epoch" means to evaluate every epoch. Both can be enabled simultaneously.
-
-- **save_strategy**: Strategy for saving model weights during training. "steps" means to save every "checkpointing_steps" steps.
-- **extra_save_by_epoch**: Whether to save an epoch-level checkpoint every epoch.
-
-- **save_total_limit**: Maximum number of model checkpoints to keep. Generally set to 2, retaining the checkpoint with the lowest valid loss and the latest checkpoint. Note that epoch-level checkpoints will always be retained and are not subject to this limit.
-
-- **weighted_loss_mode**: Loss weighting method for multi-task training.
-
-## 4. Model Usage
-
-### 4.1 Merge Adaptor weights
-Using LoRA or QLoRA for training, this project only saves the weights and configuration files of the adapters.
-To merge the adapter weights with the base model, see ```src/pefts/merge_base_and_lora_to_hf.py```
-
-### 4.2 Inference demo
-Here is the script for inference on our trained models, which is compatible with most Hugging Face models:
-```python
-from transformers import (
- AutoTokenizer,
- AutoModelForCausalLM,
-)
-tokenizer = AutoTokenizer.from_pretrained(mode_name_or_path, trust_remote_code=True, use_fast=False, legacy=False)
-tokenizer.padding_side = "left"
-tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids("<unk>")
-tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("</s>")
-model = AutoModelForCausalLM.from_pretrained(mode_name_or_path, trust_remote_code=True)
-
-HUMAN_ROLE_START_TAG = "<|role_start|>human<|role_end|>"
-BOT_ROLE_START_TAG = "<|role_start|>bot<|role_end|>"
-texts = ["write a python function of quick sort."]
-texts = [f"{HUMAN_ROLE_START_TAG}{text}{BOT_ROLE_START_TAG}" for text in texts]
-
-inputs = tokenizer(texts, return_tensors='pt', padding=True, add_special_tokens=False).to("cuda")
-outputs = model.generate(
- inputs=inputs["input_ids"],
- attention_mask=inputs["attention_mask"],
- max_new_tokens=512,
- top_p=0.95,
- temperature=0.1,
- do_sample=True,
- eos_token_id=tokenizer.eos_token_id,
- pad_token_id=tokenizer.pad_token_id
- )
-gen_text = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
-print(gen_text)
-```
-
-Indeed, the parameters top_p, temperature, repetition_penalty, do_sample, etc., have a significant impact on the model's generation output.
-You can modify these parameters based on your specific use case.
-
-In code generation scenarios, if you are using the sampling mode (do_sample=True), the following parameter settings can yield good results for the Pass@1 metric:
-
-top_p: Set a higher value, such as 0.95, to retain highly probable generated words. This helps ensure more accurate and fluent generation results.
-
-temperature: Set a lower value, such as 0.1, to reduce randomness. Lower temperature values make the generation output more deterministic.
-
-These parameter combinations can control the diversity of the generated outputs while maintaining naturalness. Additionally, you can adjust other related parameters, such as repetition_penalty, to reduce repetition in the generated results.
-
-If you choose the non-sampling mode (do_sample=False), you can consider the following parameter settings:
-
-beam_num: Set a smaller value such as 1 or 3. ```beam_num=1``` represents greedy decoding, which selects the most probable single generated word. ```beam_num=3``` represents beam search mode, which considers multiple potential generation paths and chooses the best path among them.
-
-## 5. FAQ
-### Q1：What should I do when cuda OOM happens？
-If OOM (Out of Memory) occurs, you can mitigate it by reducing parameters such as per GPU batch size (the first argument when starting the training script) and seq_length. You can also set gradient_checkpointing=true, which significantly reduces memory usage but may slow down the training speed.
diff --git a/mftcoder_atorch/README_cn.md b/mftcoder_atorch/README_cn.md
deleted file mode 100644
index 12a4cde..0000000
--- a/mftcoder_atorch/README_cn.md
+++ /dev/null
@@ -1,218 +0,0 @@
-# MFTCoder训练: Atorch框架篇
-[](https://huggingface.co/codefuse-ai)
-<a href="https://github.com/codefuse-ai/MFTCoder/blob/main/LICENSE">
- <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
-</a>
-
-[**中文**] [[English]](README.md)
-
-## 1. 更新
-
-🔥 MFTCoder在Atorch框架下支持GPTNeoX模型的微调；
-
-🔥 MFTCoder支持全量的有监督微调；
-
-🔥 MFTCoder支持LoRA微调；
-
-## 2. 数据格式
-
-### 2.1 训练数据格式
-训练数据为jsonl格式，每一行的数据格式如下，其中chat_rounds字段是必需的，可以根据实际需求添加或删除其他字段。
-可以参考项目中的xxx.jsonl文件。
-```json
-{
- "id":0,
- "data_name":"code-helper",
- "chat_rounds":[
- {
- "role": "system",
- "content": "你是一个智能代码助手，可以回复用户与代码相关的问题",
- "chat_round_id": 0
- },
- {
- "role": "human",
- "content": "写一个快速排序",
- "chat_round_id": 1
- },
- {
- "role": "bot",
- "content": "以下是一个快速排序算法xxxxxx",
- "chat_round_id": 1
- },
- {
- "role": "human",
- "content": "解释一下这段代码",
- "chat_round_id": 2
- },
- {
- "role": "bot",
- "content": "好的，这段代码xxx",
- "chat_round_id": 2
- }
- ]
-}
-```
-
-### 2.2 推理数据格式
-推理数据格式为模型在训练数据格式下拼接的字符串形式，它也是推理时输入prompt拼接的方式：
-```python
-"""
-<|role_start|>system<|role_end|>这是System指令
-<|role_start|>human<|role_end|>这是第1轮用户输入的问题
-<|role_start|>bot<|role_end|>这是第1轮模型生成的内容</s>
-<|role_start|>human<|role_end|>这是第2轮用户输入的问题
-<|role_start|>bot<|role_end|>这是第2轮模型生成的内容</s>
-...
-...
-...
-<|role_start|>human<|role_end|>这是第n轮用户输入的问题
-<|role_start|>bot<|role_end|>{模型现在要生成的内容}</s>
-"""
-```
-
-
-## 3. 模型训练
-目前 "MFTCoder/mft_atorch" 代码库支持全量参数指令微调和LoRA指令微调。
-目前仅支持GPTNeoX模型的训练，理论上，HuggingFace上开源的GPTNeoX模型权重，均可使用本项目进行训练。
-
-我们将训练中使用的各种组件抽取出来，以便后续的扩展和优化，详见主目录下的实现。微调训练的入口目录是```train/```, 训练入口文件是```train/run_train.py```, 参数配置存储在启动脚本```train/run_gpt_*.sh```等文件中，方便统一管理和更改。
-
-### 3.1 数据格式
-训练时，我们将多轮对话拼接成如下格式，然后进行tokenize。其中<|role_start|>human<|role_end|>表示human输入提示符，<|role_start|>bot<|role_end|>表示bot输出提示符，`````</s>````` 表示eos_token。
-```
-"<|role_start|>human<|role_end|>input1</s>target1</s>input2</s>target2</s>...
-```
-在计算loss时，我们通过mask的方式，input部分的loss不参与参数更新，只有“target</s>”部分的loss参与参数更新。
-这种方式充分利用了模型并行计算的优势，训练更加高效，且多轮对话中的每个target部分都参与了训练，训练更充分。
-否则，就需要把一个n轮对话，拆分成n条数据，且只计算最后一个target的loss，大大降低了训练效率。
-
-### 3.2 全量SFT
-
-执行如下命令即可进行全量SFT：
-```bash
-sh run_gpt_mft.sh 10 1 8 5
-```
-
-需注意，启动脚本后的四个参数，分别是：
-- 第一个参数是总的per gpu batch size
-- 第二个参数是tensor parallel数（暂时只支持1）
-- 第三个参数是data parallel数，与所用GPU数保持一致
-- 第四个参数是训练epoch数
-
-后面其他的训练方式启动脚本，也同样需要配置这四个参数
-
-### 3.3 LoRA微调
-
-执行如下命令即可进行Lora微调：
-```bash
-sh run_gpt_mft_peft.sh 10 1 8 5
-```
-
-### 3.4 启动脚本中主要参数说明
-```train/run_gpt_*.sh```中的主要参数说明如下，以下参数可以根据需求进行修改，其他参数建议不做修改：
-- tokenize_mode: 目前仅支持"sft"。
-
-- train_mode: 目前仅支持"sft"。
-
-- load_raw_dataset: 需要保持"True"，后续会支持其它模式数据，当前仅支持jsonl输入
-
-- data_paths: "[path1,path2,path3]" 输入数据地址，字符串，开头结尾用[]，中间用```,```间隔不同path，每个path是一个目录，目录的最后一级名字作为任务名称，下面包含1到多个jsonl数据。
-
-- output_dir: 训练输出目录，存储checkpoint、lora_adaptor checkpoint等。
-
-- tensorboard_dir: 可以暂时忽略，实际tensorboard存储在output_dir的runs目录下。
-
-- model_type: 目前仅支持 gpt_neox。
-
-- peft_type: 目前仅支持 lora。
-
-- pretrained_model_path: 预训练模型的本地目录。
-
-- total_train_batch_size: 所有显卡train的batch size的总和，会根据启动脚本时输入的per gpu batch size自动计算。
-
-- per_device_valid_batch_size: 每张显卡eval的batch size，会根据启动脚本时输入的per gpu batch size自动计算。
-
-- gradient_accumulation_steps: 梯度累计步数。global batch=num_gpus * per_device_train_batch_size * gradient_accumulation_steps。
-
-- checkpoint_activations: 如果显存捉襟见肘，可以开启。以时间换空间，模型不缓存激活状态，会进行两次forward计算，以节省显存。
-
-- learning_rate: 学习率。全量参数微调的时候，建议小一些，1e-5或5e-6。qlora中的学习率设置更大一些，一般为1e-4、2e-4。
-
-- min_lr: 最低学习率， 一般是learning_rate的十分之一。
-
-- seq_length: 训练时的最大长度。按照自己的设备进行设置，越长需要占用越多显存。
-
-- log_interval: 每隔多少步统计一次train loss。
-
-- checkpointing_steps: 每隔多少步保存一个模型。
-
-- evalation_steps: 每隔多少步在验证集上evaluate一次。
-
-- early_stopping_patience: 多少个eval point不继续收敛，则停止训练。
-
-- lr_scheduler_type: 学习率变化策略。
-
-- num_warmup_steps: warm up步数，学习率经过多少步，增长到指定的数值。
-
-- seed: 随机种子，用于复现实验结果。
-
-- train_iters: 可以暂时设为比较小的数，如10，实际上不会影响训练步数，留作后面拓展读取其他形式数据集的功能。
-
-- valid_iters: 可以暂时设为比较小的数，如10，实际上不会影响训练步数，留作后面拓展读取其他形式数据集的功能。
-
-- evaluation_strategy: 训练期间evaluate的策略，"steps"表示每隔"valid_interval"步做一次evaluate，"epoch"表示每隔一个epoch做一次evaluate，支持同时开启。
-
-- save_strategy: 训练期间保存模型权重的策略，"steps"表示每隔"checkpointing_steps"步保存一次。
-
-- extra_save_by_epoch: 每过一个epoch是否要保存一个epoch级别的checkpoint。
-
-- save_total_limit: 最多保留的模型checkpoint个数，一般设置为2，会保留valid loss最低，以及最新的checkpoint，注意epoch级别的checkpoint会一直保留，且不受限制。
-
-- weighted_loss_mode: 多任务训练的loss加权方式。
-
-
-## 4. 模型使用
-
-### 4.1 权重合并
-如果使用LoRA进行训练，本项目仅保存adapter的权重和配置文件，需要将adapter权重与base model进行合并。脚本见```utils/merge_base_and_lora_to_hf.py```
-
-### 4.2 模型推理
-我们提供了单轮对话和多轮对话的如下脚本，该脚本可同时兼容大部分huggingface格式的模型。
-```python
-from transformers import (
- AutoTokenizer,
- AutoModelForCausalLM,
-)
-tokenizer = AutoTokenizer.from_pretrained(mode_name_or_path, trust_remote_code=True, use_fast=False, legacy=False)
-tokenizer.padding_side = "left"
-tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids("<unk>")
-tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("</s>")
-model = AutoModelForCausalLM.from_pretrained(mode_name_or_path, trust_remote_code=True)
-
-HUMAN_ROLE_START_TAG = "<|role_start|>human<|role_end|>"
-BOT_ROLE_START_TAG = "<|role_start|>bot<|role_end|>"
-texts = ["write a python function of quick sort."]
-texts = [f"{HUMAN_ROLE_START_TAG}{text}{BOT_ROLE_START_TAG}" for text in texts]
-
-inputs = tokenizer(texts, return_tensors='pt', padding=True, add_special_tokens=False).to("cuda")
-outputs = model.generate(
- inputs=inputs["input_ids"],
- attention_mask=inputs["attention_mask"],
- max_new_tokens=512,
- top_p=0.95,
- temperature=0.1,
- do_sample=True,
- eos_token_id=tokenizer.eos_token_id,
- pad_token_id=tokenizer.pad_token_id
- )
-gen_text = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
-print(gen_text)
-```
-
-生成脚本中的top_p、temperature、repetition_penalty、do_sample等参数对模型的生成效果影响较大，可按照自己的使用场景进行调试修改。
-实践中，在代码生成场景中，如果采样模式，do_sample=True, top_p=0.95, temperature=0.1是pass@1指标的不错选择；
-如果非采样模式， do_sample=False, beam_num=1或者3是不错的选择，其中beam_num=1即为greedy decoding。
-
-## 5. FAQ
-#### 问题1：OOM如何解决？
-如果发生OOM，可以缩小per GPU batch size (启动训练脚本时的第一个参数)、seq_length等参数来缓解。也可以设gradient_checkpointing=true，可以大幅降低显存占用，但训练速度会变慢一些。
diff --git a/mftcoder_atorch/arguments/get_arguments.py b/mftcoder_atorch/arguments/get_arguments.py
deleted file mode 100644
index 52e52e4..0000000
--- a/mftcoder_atorch/arguments/get_arguments.py
+++ /dev/null
@@ -1,557 +0,0 @@
-import os
-import argparse
-from transformers import MODEL_MAPPING, SchedulerType
-
-MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description="Finetune a transformers model on a causal language modeling task")
- parser.add_argument(
- "--load_raw_dataset",
- action="store_true",
- help="If passed, will load raw dataset.",
- )
- parser.add_argument(
- "--data_paths",
- type=str,
- default=None,
- help="Data path list.",
- )
- parser.add_argument(
- "--data_weights",
- type=str,
- default=None,
- help="Data weights.",
- )
- parser.add_argument(
- "--data_split",
- type=str,
- default=None,
- help="Data split.",
- )
- parser.add_argument(
- "--padding",
- action="store_true",
- help="use padding in preprocess.",
- )
- parser.add_argument(
- "--tokenize_mode",
- type=str,
- default='sft',
- choices=['pretrain', 'sft', 'coh'],
- help="training mode"
- )
- parser.add_argument(
- "--train_mode",
- type=str,
- default='sft',
- choices=['sst', 'sft'],
- help="training mode"
- )
- parser.add_argument(
- "--weighted_loss_mode",
- type=str,
- default=None,
- help="weighted loss mode.",
- )
- parser.add_argument(
- "--padding_mode",
- type=str,
- default='padding',
- choices=['padding', 'concat', 'pack'],
- help="padding mode"
- )
- parser.add_argument(
- "--shuffle_before_split",
- action="store_true",
- help="shuffle before split.",
- )
- parser.add_argument(
- "--use_random_sampler",
- action="store_true",
- help="use random sampler.",
- )
- parser.add_argument(
- "--use_weighted_loss",
- action="store_true",
- help="use weighted loss.",
- )
- parser.add_argument(
- "--early_stopping_patience",
- type=int,
- default=-1,
- help="patience of early stopping.",
- )
- parser.add_argument(
- "--weight_by_num_documents",
- action="store_true",
- help="weight by num documents.",
- )
- parser.add_argument(
- "--make_vocab_size_divisible_by",
- type=int,
- default=128,
- help="Pad the vocab size to be divisible by this value. This is added for computational efficiency reasons.",
- )
- parser.add_argument(
- "--validation_split_percentage",
- default=5,
- help="The percentage of the train set used as validation set in case there's no validation split",
- )
- parser.add_argument(
- "--pretrained_model_path",
- type=str,
- help="Path to pretrained model or model identifier from huggingface.co/models.",
- required=False,
- )
- parser.add_argument(
- "--config_path",
- type=str,
- default=None,
- help="Pretrained config name or path if not the same as model_name",
- )
- parser.add_argument(
- "--model_type",
- type=str,
- default='gpt_neox',
- help="model type",
- )
- parser.add_argument(
- "--peft_type",
- type=str,
- default=None,
- help="peft type",
- )
- parser.add_argument(
- "--bits",
- type=int,
- default=4,
- help="How many bits to use when using qlora. Should be 4 or 8.",
- )
- parser.add_argument(
- "--use_xformers",
- action="store_true",
- help="use xformers in llama",
- )
- parser.add_argument(
- "--use_dynamic_padding",
- action="store_true",
- help="use xformers in llama",
- )
- parser.add_argument(
- "--tokenizer_path",
- type=str,
- default=None,
- help="Pretrained tokenizer name or path if not the same as model_name",
- )
- parser.add_argument(
- "--vocab_file",
- type=str,
- default=None,
- help="Vocab path",
- )
- parser.add_argument(
- "--tokenizer_type",
- type=str,
- default=None,
- help="Pretrained tokenizer type",
- )
- parser.add_argument(
- "--use_slow_tokenizer",
- action="store_true",
- help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
- )
- parser.add_argument(
- "--trust_remote_code",
- action="store_true",
- help="If passed, will set trust_remote_code=True when calling from_pretrained.",
- )
- parser.add_argument(
- "--ignore_mismatched_sizes",
- action="store_true",
- help="If passed, will set ignore_mismatched_sizes=True when calling from_pretrained.",
- )
- parser.add_argument(
- "--per_device_train_batch_size",
- type=int,
- default=8,
- help="Batch size (per device) for the training dataloader.",
- )
- parser.add_argument(
- "--per_device_valid_batch_size",
- type=int,
- default=8,
- help="Batch size (per device) for the evaluation dataloader.",
- )
- parser.add_argument(
- "--total_train_batch_size",
- type=int,
- default=8,
- help="All batch size for the training dataloader. Equals to per_device_train_batch_size * world_size",
- )
- parser.add_argument(
- "--world_size",
- type=int,
- default=8,
- help="Total world size (i.e number of gpus in cluster). Configured post-launch using distributed launcher",
- )
- parser.add_argument(
- "--local_rank",
- type=int,
- default=8,
- help="local rank passed from distributed launcher",
- )
- parser.add_argument(
- "--learning_rate",
- type=float,
- default=1e-5,
- help="Initial learning rate (after the potential warmup period) to use.",
- )
- parser.add_argument(
- "--min_lr",
- type=float,
- default=1e-7,
- help="Initial learning rate (after the potential warmup period) to use.",
- )
- parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
- parser.add_argument("--adam_beta1", type=float, default=0.9)
- parser.add_argument("--adam_beta2", type=float, default=0.999)
- parser.add_argument("--adam_epsilon", type=float, default=1e-8)
- parser.add_argument(
- "--num_train_epochs",
- type=int,
- default=3,
- help="Total number of training epochs to perform.",
- )
- parser.add_argument(
- "--max_steps",
- type=int,
- default=-1,
- help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
- )
- parser.add_argument(
- "--gradient_accumulation_steps",
- type=int,
- default=1,
- help="Number of updates steps to accumulate before performing a backward/update pass.",
- )
- parser.add_argument(
- "--lr_scheduler_type",
- type=SchedulerType,
- default="linear",
- help="The scheduler type to use.",
- choices=[
- "linear",
- "cosine",
- "cosine_with_restarts",
- "polynomial",
- "constant",
- "constant_with_warmup",
- ],
- )
- parser.add_argument(
- "--custom_lr_scheduler_type",
- type=str,
- default=None,
- help="The scheduler type to use.",
- )
- parser.add_argument(
- "--num_warmup_steps",
- type=int,
- default=100,
- help="Number of steps for the warmup in the lr scheduler.",
- )
- parser.add_argument(
- "--num_update_steps_per_epoch",
- type=int,
- default=500,
- help="Number of update steps per epoch.",
- )
- parser.add_argument(
- "--resume_step",
- type=int,
- default=0,
- help="resume step in checkpoint.",
- )
- parser.add_argument(
- "--fp16_lm_cross_entropy",
- action="store_true",
- help="Move the cross entropy unreduced loss calculation for lm head to fp16.",
- )
- parser.add_argument(
- "--bf16",
- action="store_true",
- help="use bf16.",
- )
- parser.add_argument(
- "--fp16",
- action="store_true",
- help="use fp16.",
- )
- parser.add_argument(
- "--output_dir",
- type=str,
- default=None,
- help="Where to store the final model.")
- parser.add_argument(
- "--tensorboard_dir",
- type=str,
- default=None,
- help="Where to store the tensorboard.")
- parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
- parser.add_argument(
- "--model_types",
- type=str,
- default=None,
- help="Model type to use if training from scratch.",
- choices=MODEL_TYPES,
- )
- parser.add_argument(
- "--seq_length",
- type=int,
- default=None,
- help=(
- "Optional input sequence length after tokenization. The training dataset will be truncated in block of"
- " this size for training. Default to the model max input length for single sentence inputs (take into"
- " account special tokens)."
- ),
- )
- parser.add_argument(
- "--hidden_size",
- type=int,
- default=None,
- help=(
- "hidden states size"
- ),
- )
- parser.add_argument(
- "--num_hidden_layers",
- type=int,
- default=None,
- help=(
- "hidden states layers number"
- ),
- )
- parser.add_argument(
- "--vocab_size",
- type=int,
- default=None,
- help=(
- "vocab size"
- ),
- )
- parser.add_argument(
- "--total_model_param",
- type=int,
- default=None,
- help=(
- "total model parameters"
- ),
- )
- parser.add_argument(
- "--preprocessing_num_workers",
- type=int,
- default=None,
- help="The number of processes to use for the preprocessing.",
- )
- parser.add_argument(
- "--num_workers",
- type=int,
- default=0,
- help="The number of sub-processes to use for the dataloader.",
- )
- parser.add_argument(
- "--iteration",
- type=int,
- default=0,
- help="Iteration to load checkpoint from in evaluate.py / generate.py. If None is provided, uses the latest iteration.",
- )
- parser.add_argument(
- "--overwrite_cache",
- action="store_true",
- help="Overwrite the cached training and evaluation sets",
- )
- parser.add_argument(
- "--no_keep_linebreaks",
- action="store_true",
- help="Do not keep line breaks when using TXT files.",
- )
- parser.add_argument(
- "--push_to_hub",
- action="store_true",
- help="Whether or not to push the model to the Hub.",
- )
- parser.add_argument(
- "--hub_model_id",
- type=str,
- help="The name of the repository to keep in sync with the local `output_dir`.",
- )
- parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
- parser.add_argument(
- "--checkpointing_steps",
- type=int,
- default=None,
- help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
- )
- parser.add_argument(
- "--resume_from_checkpoint",
- type=str,
- default=None,
- help="If the training should continue from a checkpoint folder, including model checkpoint, optimizer and lr scheduler. path",
- )
- parser.add_argument(
- "--flash_attention",
- action="store_true",
- )
- parser.add_argument(
- "--with_tracking",
- action="store_true",
- help="Whether to enable experiment trackers for logging.",
- )
- parser.add_argument(
- "--skip_atorch_autoacc_dryrun",
- action="store_true",
- )
- parser.add_argument(
- "--zero_opt_level",
- type=str,
- default="zero2",
- help="Model type to use if training from scratch.",
- choices=["zero2", "zero3", "fsdp"],
- )
- parser.add_argument(
- "--report_to",
- type=str,
- default="all",
- help=(
- 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
- ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.'
- "Only applicable when `--with_tracking` is passed."
- ),
- )
- parser.add_argument(
- "--real_dataloader",
- action="store_true",
- help="Whether to use real full dataset.",
- )
- parser.add_argument(
- "--split_before_read",
- action="store_true",
- help="Whether to use real full dataset.",
- )
- parser.add_argument(
- "--tp",
- type=int,
- help="tp size",
- required=True,
- )
- parser.add_argument(
- "--dp",
- type=int,
- help="dp size",
- required=True,
- )
- parser.add_argument(
- "--pipe_parallel_size",
- type=int,
- default=0,
- help="Number of pipeline parallel stages. Disable with 0.",
- )
- parser.add_argument(
- "--model_parallel_size",
- type=int,
- default=1,
- help="model parallelism. size",
- )
- parser.add_argument(
- "--train_iters",
- type=int,
- default=20000,
- help="Number of iterations to run for training.",
- )
- parser.add_argument(
- "--valid_iters",
- type=int,
- default=100,
- help="Number of iterations to run for evaluation validation/test for.",
- )
- parser.add_argument(
- "--valid_interval",
- type=int,
- default=1000,
- help="Interval between running evaluation on validation set.",
- )
- parser.add_argument(
- '--is_pipe_parallel',
- action='store_true',
- help="")
- parser.add_argument(
- "--log_interval",
- type=int,
- help="interval of logger",
- required=True,
- )
- parser.add_argument(
- '--checkpoint_activations',
- action='store_true',
- help="whether to use gradient checkpointing")
- parser.add_argument(
- "--max_grad_norm",
- type=int,
- help="max_grad_norm",
- required=True,
- )
- parser.add_argument(
- "--evaluation_strategy",
- type=str,
- default="steps",
- help="evaluation strategy",
- choices=["steps", "epoch", "steps,epoch"],
- )
- parser.add_argument(
- "--save_strategy",
- type=str,
- default="steps",
- help="save strategy",
- choices=["steps", "epoch", "steps,epoch"],
- )
- parser.add_argument(
- '--extra_save_by_epoch',
- action='store_true',
- help="whether to save extra checkpoint for per epoch"
- )
- parser.add_argument(
- "--save_total_limit",
- type=int,
- help="num limitation of step strategy checkpoint file",
- required=True,
- )
- parser.add_argument(
- "--metric_for_best_model",
- type=str,
- default="loss",
- help="metric for best model checkpoint",
- )
- parser.add_argument(
- "--greater_is_better",
- type=str,
- default="false",
- help="whether the metric greater is better",
- )
- args = parser.parse_args()
-
- if os.getenv("OMPI_COMM_WORLD_LOCAL_RANK"):
- args.local_rank = int(os.getenv("OMPI_COMM_WORLD_LOCAL_RANK"))
- print(args.local_rank)
- # Sanity checks
- if (
- args.data_paths is None
- ):
- raise ValueError("Need either a dataset name or a training/validation file.")
-
- if args.push_to_hub:
- assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
-
- return args
\ No newline at end of file
diff --git a/mftcoder_atorch/data/Makefile b/mftcoder_atorch/data/Makefile
deleted file mode 100644
index 8f9db76..0000000
--- a/mftcoder_atorch/data/Makefile
+++ /dev/null
@@ -1,9 +0,0 @@
-CXXFLAGS += -O3 -Wall -shared -std=c++11 -fPIC -fdiagnostics-color
-CPPFLAGS += $(shell python3 -m pybind11 --includes)
-LIBNAME = helpers
-LIBEXT = $(shell python3-config --extension-suffix)
-
-default: $(LIBNAME)$(LIBEXT)
-
-%$(LIBEXT): %.cpp
- $(CXX) $(CXXFLAGS) $(CPPFLAGS) $< -o $@
diff --git a/mftcoder_atorch/data/__init__.py b/mftcoder_atorch/data/__init__.py
deleted file mode 100644
index b974282..0000000
--- a/mftcoder_atorch/data/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from . import *
\ No newline at end of file
diff --git a/mftcoder_atorch/data/gpt2_multi_task_dataset.py b/mftcoder_atorch/data/gpt2_multi_task_dataset.py
deleted file mode 100644
index e97a6e9..0000000
--- a/mftcoder_atorch/data/gpt2_multi_task_dataset.py
+++ /dev/null
@@ -1,349 +0,0 @@
-import os
-import json
-import math
-import time
-import numpy as np
-import torch
-from functools import partial
-from data.tokenization.preprocess_data import UniformEncoder
-from utils.common_utils import get_local_rank, print_rank_0, TASK2ID, ID2TASK
-
-
-class GPT2FromRawDataset(torch.utils.data.Dataset):
- def __init__(
- self,
- name,
- data_prefix,
- input_dataset,
- seq_length,
- weighted_loss_mode=None,
- ds_weight=1.0,
- ):
-
- self.name = name
- self.input_dataset = input_dataset
- self.num_samples = len(self.input_dataset['input_ids'])
- self.seq_length = seq_length
-
- self.weighted_loss_mode = weighted_loss_mode
- self.ds_weight = ds_weight
- self.task_name = data_prefix.split('/')[-1]
- self.task_id = TASK2ID[self.task_name]
-
- def update_ds_weight(self, weight):
- self.ds_weight = weight
-
- def __len__(self):
- return self.num_samples
-
- def __getitem__(self, idx):
- try:
- # Get the shuffled index.
- idx = idx % self.num_samples
- idx_data = {key: self.input_dataset[key][idx]
- for key in self.input_dataset}
-
- if self.weighted_loss_mode:
- idx_data["weight"] = np.array([self.ds_weight], dtype=np.float32)
- idx_data["task_id"] = np.array([self.task_id], dtype=np.int)
- return idx_data
- else:
- idx_data["task_id"] = np.array([self.task_id], dtype=np.int)
- return idx_data
- except IndexError:
- new_idx = idx % len(self)
- print(
- f"WARNING in GPT2FromRawDataset: Got index out of bounds error with index {idx} - taking modulo of index instead ({new_idx})"
- )
- return self[new_idx]
-
-
-
-def ds_weights_by_num_docs_sft(l, alpha=0.3):
- # ignore alpha
- weights = [1 / i for i in l]
- weights_sum = sum(weights)
- weights = [weight / weights_sum for weight in weights]
- return weights
-
-
-class GPT2BlendableDataset(torch.utils.data.Dataset):
- def __init__(self, datasets, weights, global_num_samples, local_num_samples):
- self.datasets = datasets
- num_datasets = len(datasets)
- assert num_datasets == len(weights)
-
- self.size = 0
- for dataset in self.datasets:
- self.size += len(dataset)
-
- assert local_num_samples == self.size
- # Normalize weights.
- weights = np.array(weights, dtype=np.float64)
- sum_weights = np.sum(weights)
- assert sum_weights > 0.0
- weights /= sum_weights
-
- # recompute weights
- weights = self.calc_weights()
-
- # Build indices.
- start_time = time.time()
- assert num_datasets < 255
- self.dataset_index = np.zeros(self.size, dtype=np.uint8)
- self.dataset_sample_index = np.zeros(self.size, dtype=np.int64)
-
- self.global_num_samples = global_num_samples
- self.local_num_samples = local_num_samples
-
- from data import helpers
-
- helpers.build_blending_indices(
- self.dataset_index,
- self.dataset_sample_index,
- weights,
- num_datasets,
- self.size,
- torch.distributed.get_rank() == 0,
- )
-
- print(
- "> RANK {} elapsed time for building blendable dataset indices: "
- "{:.2f} (sec)".format(
- torch.distributed.get_rank(), time.time() - start_time
- )
- )
-
- def calc_weights(self):
- dataset_sample_cnt = [len(ds) for ds in self.datasets]
- total_cnt = sum(dataset_sample_cnt)
- weights = np.array([(cnt + 0.0) / total_cnt for cnt in dataset_sample_cnt], dtype=np.float64)
- return weights
-
- def __len__(self):
- return self.global_num_samples
-
- def __getitem__(self, idx):
- try:
- idx = idx % self.local_num_samples
- dataset_idx = self.dataset_index[idx]
- sample_idx = self.dataset_sample_index[idx]
- return self.datasets[dataset_idx][sample_idx]
- except IndexError:
- # new_idx = idx % len(self)
- new_idx = idx % self.local_num_samples
- print(self.local_num_samples)
- print(
- f"WARNING in GPT2MultiTaskDataset: Got index out of bounds error with index {idx} - taking modulo of index instead ({new_idx})"
- )
- return self[new_idx]
-
-
-def shuffle_arrays(arrays, set_seed=-1):
- """Shuffles arrays in-place, in the same order, along axis=0
-
- Parameters:
- -----------
- arrays : List of NumPy arrays.
- set_seed : Seed value if int >= 0, else seed is random.
- """
- assert all(len(arr) == len(arrays[0]) for arr in arrays)
- seed = np.random.randint(0, 2**(32 - 1) - 1) if set_seed < 0 else set_seed
-
- for arr in arrays:
- rstate = np.random.RandomState(seed)
- rstate.shuffle(arr)
-
-
-def load_dataset_from_jsonl(args, tokenizer=None, shard_data=False, world_size=1, global_rank=0):
-
- # tokenization encoder
- encoder = UniformEncoder(args, args.tokenize_mode, tokenizer)
- encoder.initializer()
-
- data_prefixes = list(args.data_paths[1:-1].split(','))
-
- splits = []
- splits_string = args.data_split
- if splits_string.find(",") != -1:
- splits = [float(s) for s in splits_string.split(",")]
- elif splits_string.find("/") != -1:
- splits = [float(s) for s in splits_string.split("/")]
- else:
- splits = [float(splits_string)]
- while len(splits) < 3:
- splits.append(0.0)
- splits = splits[:3]
- print(f'data splits: {splits}')
-
- all_train_datasets = []
- all_valid_datasets = []
- all_train_datasets_length = []
- all_valid_datasets_length = []
- # valid token count in every dataset
- num_tokens = []
- effective_token_rate = []
- total_sample_cnt = []
-
- local_train_num = 0
- local_valid_num = 0
-
- # multiple dataset paths
- for dataset_index in range(len(data_prefixes)):
- files = os.listdir(data_prefixes[dataset_index])
- cur_dataset_input_ids = []
- cur_dataset_loss_mask = []
- cur_dataset_global_num_samples = 0
- cur_dataset_num_tokens = 0
- # multiple jsonl files
- for file in files:
- file_name = data_prefixes[dataset_index] + '/' + file
- if os.path.isdir(file_name):
- continue
- fin = open(file_name, 'r')
- print(f'[Global Rank {global_rank}] open file {file_name}')
-
- if args.padding_mode == 'padding' or args.padding_mode == 'pack':
- for i, line in enumerate(fin):
- # pre-sharding
- if shard_data and i % world_size != global_rank:
- continue
- data = json.loads(line.rstrip('\n\r'))
- features, length = encoder.encode(data)
- # multiple samples per document
- for idx in range(len(features['input_ids'])):
- cur_dataset_input_ids.append(features['input_ids'][idx])
- cur_dataset_loss_mask.append(features['loss_mask'][idx])
-
- fin.close()
- else:
- i = 0
- for line in fin:
- data = json.loads(line.rstrip('\n\r'))
- features, length = encoder.encode(data)
-
- for idx in range(len(features['input_ids'])):
- # post-sharding
- if shard_data and i % world_size != global_rank:
- i += 1
- continue
- i += 1
- cur_dataset_input_ids.append(features['input_ids'][idx])
- cur_dataset_loss_mask.append(features['loss_mask'][idx])
-
- fin.close()
-
- cur_dataset_input_ids = np.array(cur_dataset_input_ids, dtype=np.float32)
- cur_dataset_loss_mask = np.array(cur_dataset_loss_mask, dtype=np.float32)
- cur_dataset_num_tokens = np.sum(cur_dataset_loss_mask, dtype=np.int32)
- cur_dataset_sample_num = len(cur_dataset_input_ids)
- num_tokens.append(cur_dataset_num_tokens)
- total_sample_cnt.append(cur_dataset_sample_num)
- effective_token_rate.append(cur_dataset_num_tokens / (cur_dataset_sample_num * args.seq_length))
-
- # shuffle before split
- shuffle_arrays([cur_dataset_input_ids, cur_dataset_loss_mask], args.seed)
- train_ratio = splits[0] / 100.0
- train_num = int(math.ceil(train_ratio * cur_dataset_sample_num))
- # split train/valid
- cur_train_input_ids, cur_valid_input_ids = cur_dataset_input_ids[: train_num], cur_dataset_input_ids[train_num: ]
- cur_train_loss_mask, cur_valid_loss_mask = cur_dataset_loss_mask[: train_num], cur_dataset_loss_mask[train_num: ]
- local_train_num += train_num
- local_valid_num += (cur_dataset_sample_num - train_num)
-
- cur_train_dataset = {'input_ids': cur_train_input_ids,
- 'loss_mask': cur_train_loss_mask
- }
- cur_valid_dataset = {'input_ids': cur_valid_input_ids,
- 'loss_mask': cur_valid_loss_mask
- }
- print(f"[Global Rank {global_rank}]shape of cur train dataset: {cur_train_dataset['input_ids'].shape}")
- print(f"[Global Rank {global_rank}]shape of cur valid dataset: {cur_valid_dataset['input_ids'].shape}")
-
- cur_train_ds = GPT2FromRawDataset(
- 'train',
- data_prefixes[dataset_index],
- cur_train_dataset,
- args.seq_length,
- weighted_loss_mode=args.weighted_loss_mode,
- ds_weight=splits[0]
- )
- cur_valid_ds = GPT2FromRawDataset(
- 'valid',
- data_prefixes[dataset_index],
- cur_valid_dataset,
- args.seq_length,
- weighted_loss_mode=args.weighted_loss_mode,
- ds_weight=splits[1]
- )
-
- all_train_datasets.append(cur_train_ds)
- all_valid_datasets.append(cur_valid_ds)
- all_train_datasets_length.append(len(cur_train_ds))
- all_valid_datasets_length.append(len(cur_valid_ds))
-
- print(f'[Global Rank {global_rank}]num tokens: {num_tokens}')
- print(f'[Global Rank {global_rank}]effective token rate: {effective_token_rate}')
-
- num_tokens = []
- ds_fn = partial(ds_weights_by_num_docs_sft)
- train_loss_weights, valid_loss_weights = (
- ds_fn(all_train_datasets_length),
- ds_fn(all_valid_datasets_length),
- )
-
- print(f"> train loss weights in rank {global_rank}: {train_loss_weights}")
- print(f"> valid loss weights in rank {global_rank}: {valid_loss_weights}")
-
- factor = 1
- # calcualte common factor based on token cnt and total sample cnt
- if num_tokens:
- factor = sum(num_tokens) / (sum(total_sample_cnt) * args.seq_length)
- factor /= sum([1.0 / w for w in train_loss_weights]) / len(train_loss_weights)
- print(f"> common denomination factor for CE loss in rank {global_rank}: {factor}")
-
- train_sample_weights = [x / sum(all_train_datasets_length) for x in all_train_datasets_length]
- valid_sample_weights = [x / sum(all_valid_datasets_length) for x in all_valid_datasets_length]
- print(f"> train sample weights in rank {global_rank}: {train_sample_weights}")
- print(f"> valid sample weights in rank {global_rank}: {valid_sample_weights}")
-
- # re-compute global_train_num and global_valid_num
-
- torch.distributed.barrier()
- device = f"cuda:{get_local_rank()}"
-
- global_train_num_samples_tensor = torch.tensor(local_train_num, dtype=torch.int32)
- global_train_num_samples_tensor = global_train_num_samples_tensor.to(device)
- torch.distributed.all_reduce(global_train_num_samples_tensor, op=torch.distributed.ReduceOp.SUM)
- global_train_num = global_train_num_samples_tensor.item()
-
- global_valid_num_samples_tensor = torch.tensor(local_valid_num, dtype=torch.int32)
- global_valid_num_samples_tensor = global_valid_num_samples_tensor.to(device)
- torch.distributed.all_reduce(global_valid_num_samples_tensor, op=torch.distributed.ReduceOp.SUM)
- global_valid_num = global_valid_num_samples_tensor.item()
- print(f"> global train num in rank {global_rank}: {global_train_num}")
- print(f"> global valid num in rank {global_rank}: {global_valid_num}")
-
- torch.distributed.barrier()
-
- for i in range(len(all_train_datasets)):
- print(f'loss weight of train dataset {i} before update in rank {global_rank}: {all_train_datasets[i].ds_weight}')
- blending_train_dataset = None
- if all_train_datasets:
- args.do_train = True
- for i in range(len(all_train_datasets)):
- all_train_datasets[i].update_ds_weight(train_loss_weights[i] / factor)
- print(f'loss weight of train dataset {i} after update in rank {global_rank}: {all_train_datasets[i].ds_weight}')
- blending_train_dataset = GPT2BlendableDataset(all_train_datasets, train_sample_weights, global_train_num, local_train_num)
-
- for i in range(len(all_train_datasets)):
- print(f'loss weight of valid dataset {i} before update in rank {global_rank}: {all_train_datasets[i].ds_weight}')
- blending_valid_dataset = None
- if all_valid_datasets:
- args.do_valid = True
- for i in range(len(all_valid_datasets)):
- all_valid_datasets[i].update_ds_weight(valid_loss_weights[i] / factor)
- print(f'loss weight of valid dataset {i} after update in rank {global_rank}: {all_train_datasets[i].ds_weight}')
- blending_valid_dataset = GPT2BlendableDataset(all_valid_datasets, valid_sample_weights, global_valid_num, local_valid_num)
-
- return blending_train_dataset, blending_valid_dataset
diff --git a/mftcoder_atorch/data/helpers.cpp b/mftcoder_atorch/data/helpers.cpp
deleted file mode 100644
index a688d54..0000000
--- a/mftcoder_atorch/data/helpers.cpp
+++ /dev/null
@@ -1,758 +0,0 @@
-/*
- coding=utf-8
- Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
- */
-
-/* Helper methods for fast index mapping builds */
-
-#include <math.h>
-#include <pybind11/numpy.h>
-#include <pybind11/pybind11.h>
-#include <algorithm>
-#include <iostream>
-#include <limits>
-#include <random>
-#include <stdexcept>
-
-namespace py = pybind11;
-using namespace std;
-
-const int32_t LONG_SENTENCE_LEN = 512;
-
-void build_blending_indices(py::array_t<uint8_t>& dataset_index,
- py::array_t<int64_t>& dataset_sample_index,
- const py::array_t<double>& weights,
- const int32_t num_datasets,
- const int64_t size,
- const bool verbose)
-{
- /* Given multiple datasets and a weighting array, build samples
- such that it follows those weights.*/
-
- if (verbose) { std::cout << "> building indices for blendable datasets ..." << std::endl; }
-
- // Get the pointer access without the checks.
- auto dataset_index_ptr = dataset_index.mutable_unchecked<1>();
- auto dataset_sample_index_ptr = dataset_sample_index.mutable_unchecked<1>();
- auto weights_ptr = weights.unchecked<1>();
-
- // Initialize buffer for number of samples used for each dataset.
- int64_t current_samples[num_datasets];
- for (int64_t i = 0; i < num_datasets; ++i) { current_samples[i] = 0; }
-
- // For each sample:
- for (int64_t sample_idx = 0; sample_idx < size; ++sample_idx) {
- // Determine where the max error in sampling is happening.
- double sample_idx_double = std::max(static_cast<double>(sample_idx), 1.0);
- int64_t max_error_index = 0;
- double max_error =
- weights_ptr[0] * sample_idx_double - static_cast<double>(current_samples[0]);
- for (int64_t dataset_idx = 1; dataset_idx < num_datasets; ++dataset_idx) {
- double error = weights_ptr[dataset_idx] * sample_idx_double -
- static_cast<double>(current_samples[dataset_idx]);
- if (error > max_error) {
- max_error = error;
- max_error_index = dataset_idx;
- }
- }
-
- // Populate the indices.
- dataset_index_ptr[sample_idx] = static_cast<uint8_t>(max_error_index);
- dataset_sample_index_ptr[sample_idx] = current_samples[max_error_index];
-
- // Update the total samples.
- current_samples[max_error_index] += 1;
- }
-
- // print info
- if (verbose) {
- std::cout << " > sample ratios:" << std::endl;
- for (int64_t dataset_idx = 0; dataset_idx < num_datasets; ++dataset_idx) {
- auto ratio =
- static_cast<double>(current_samples[dataset_idx]) / static_cast<double>(size);
- std::cout << " dataset " << dataset_idx << ", input: " << weights_ptr[dataset_idx]
- << ", achieved: " << ratio << std::endl;
- }
- }
-}
-
-py::array build_sample_idx_int32(const py::array_t<int32_t>& sizes_,
- const py::array_t<int32_t>& doc_idx_,
- const int32_t seq_length,
- const int32_t num_epochs,
- const int64_t tokens_per_epoch)
-{
- /* Sample index (sample_idx) is used for gpt2 like dataset for which
- the documents are flattened and the samples are built based on this
- 1-D flatten array. It is a 2D array with sizes [number-of-samples + 1, 2]
- where [..., 0] contains the index into `doc_idx` and [..., 1] is the
- starting offset in that document.*/
-
- // Consistency checks.
- assert(seq_length > 1);
- assert(num_epochs > 0);
- assert(tokens_per_epoch > 1);
-
- // Remove bound checks.
- auto sizes = sizes_.unchecked<1>();
- auto doc_idx = doc_idx_.unchecked<1>();
-
- // Mapping and it's length (1D).
- // 这里的num_samples并不是实际的num_samples，因为计算Epoch时会向上取整
- int64_t num_samples = (num_epochs * tokens_per_epoch - 1) / seq_length;
- int32_t* sample_idx = new int32_t[2 * (num_samples + 1)];
-
- cout << " using:" << endl << std::flush;
- cout << " number of documents: " << doc_idx_.shape(0) / num_epochs << endl
- << std::flush;
- cout << " number of epochs: " << num_epochs << endl << std::flush;
- cout << " sequence length: " << seq_length << endl << std::flush;
- cout << " total number of samples: " << num_samples << endl << std::flush;
-
- // Index into sample_idx.
- int64_t sample_index = 0;
- // Index into doc_idx.
- int64_t doc_idx_index = 0;
- // Beginning offset for each document.
- int32_t doc_offset = 0;
- // Start with first document and no offset.
- sample_idx[2 * sample_index] = doc_idx_index;
- sample_idx[2 * sample_index + 1] = doc_offset;
- ++sample_index;
-
- while (sample_index <= num_samples) {
- // Start with a fresh sequence.
- int32_t remaining_seq_length = seq_length + 1;
- while (remaining_seq_length != 0) {
- // Get the document length.
- auto doc_id = doc_idx[doc_idx_index];
- auto doc_length = sizes[doc_id] - doc_offset;
- // And add it to the current sequence.
- remaining_seq_length -= doc_length;
- // If we have more than a full sequence, adjust offset and set
- // remaining length to zero so we return from the while loop.
- // Note that -1 here is for the same reason we have -1 in
- // `_num_epochs` calculations.
- if (remaining_seq_length <= 0) {
- doc_offset += (remaining_seq_length + doc_length - 1);
- remaining_seq_length = 0;
- } else {
- // Otherwise, start from the beginning of the next document.
- ++doc_idx_index;
- doc_offset = 0;
- }
- }
- // Record the sequence.
- sample_idx[2 * sample_index] = doc_idx_index;
- sample_idx[2 * sample_index + 1] = doc_offset;
- ++sample_index;
- }
-
- // Method to deallocate memory.
- py::capsule free_when_done(sample_idx, [](void* mem_) {
- int32_t* mem = reinterpret_cast<int32_t*>(mem_);
- delete[] mem;
- });
-
- // Return the numpy array.
- const auto byte_size = sizeof(int32_t);
- return py::array(std::vector<int64_t>{num_samples + 1, 2}, // shape
- {2 * byte_size, byte_size}, // C-style contiguous strides
- sample_idx, // the data pointer
- free_when_done); // numpy array references
-}
-
-
-py::array build_sample_idx_int64(const py::array_t<int32_t>& sizes_,
- const py::array_t<int32_t>& doc_idx_,
- const int32_t seq_length,
- const int32_t num_epochs,
- const int64_t tokens_per_epoch)
-{
- /* Sample index (sample_idx) is used for gpt2 like dataset for which
- the documents are flattened and the samples are built based on this
- 1-D flatten array. It is a 2D array with sizes [number-of-samples + 1, 2]
- where [..., 0] contains the index into `doc_idx` and [..., 1] is the
- starting offset in that document.*/
-
- // Consistency checks.
- assert(seq_length > 1);
- assert(num_epochs > 0);
- assert(tokens_per_epoch > 1);
-
- // Remove bound checks.
- auto sizes = sizes_.unchecked<1>();
- auto doc_idx = doc_idx_.unchecked<1>();
-
- // Mapping and it's length (1D).
- int64_t num_samples = (num_epochs * tokens_per_epoch - 1) / seq_length;
- int64_t* sample_idx = new int64_t[2 * (num_samples + 1)];
-
- cout << " using:" << endl << std::flush;
- cout << " number of documents: " << doc_idx_.shape(0) / num_epochs << endl
- << std::flush;
- cout << " number of epochs: " << num_epochs << endl << std::flush;
- cout << " sequence length: " << seq_length << endl << std::flush;
- cout << " total number of samples: " << num_samples << endl << std::flush;
-
- // Index into sample_idx.
- int64_t sample_index = 0;
- // Index into doc_idx.
- int64_t doc_idx_index = 0;
- // Beginning offset for each document.
- int32_t doc_offset = 0;
- // Start with first document and no offset.
- sample_idx[2 * sample_index] = doc_idx_index;
- sample_idx[2 * sample_index + 1] = doc_offset;
- ++sample_index;
-
- while (sample_index <= num_samples) {
- // Start with a fresh sequence.
- int32_t remaining_seq_length = seq_length + 1;
- while (remaining_seq_length != 0) {
- // Get the document length.
- auto doc_id = doc_idx[doc_idx_index];
- auto doc_length = sizes[doc_id] - doc_offset;
- // And add it to the current sequence.
- remaining_seq_length -= doc_length;
- // If we have more than a full sequence, adjust offset and set
- // remaining length to zero so we return from the while loop.
- // Note that -1 here is for the same reason we have -1 in
- // `_num_epochs` calculations.
- if (remaining_seq_length <= 0) {
- doc_offset += (remaining_seq_length + doc_length - 1);
- remaining_seq_length = 0;
- } else {
- // Otherwise, start from the beginning of the next document.
- ++doc_idx_index;
- doc_offset = 0;
- }
- }
- // Record the sequence.
- sample_idx[2 * sample_index] = doc_idx_index;
- sample_idx[2 * sample_index + 1] = doc_offset;
- ++sample_index;
- }
-
- // Method to deallocate memory.
- py::capsule free_when_done(sample_idx, [](void* mem_) {
- int64_t* mem = reinterpret_cast<int64_t*>(mem_);
- delete[] mem;
- });
-
- // Return the numpy array.
- const auto byte_size = sizeof(int64_t);
- return py::array(std::vector<int64_t>{num_samples + 1, 2}, // shape
- {2 * byte_size, byte_size}, // C-style contiguous strides
- sample_idx, // the data pointer
- free_when_done); // numpy array references
-}
-
-inline int32_t get_target_sample_len(const int32_t short_seq_ratio,
- const int32_t max_length,
- std::mt19937& rand32_gen)
-{
- /* Training sample length. */
- const auto random_number = rand32_gen();
- if ((random_number % short_seq_ratio) == 0) { return 2 + random_number % (max_length - 1); }
- return max_length;
-}
-
-template <typename DocIdx>
-py::array build_mapping_impl(const py::array_t<int64_t>& docs_,
- const py::array_t<int32_t>& sizes_,
- const int32_t num_epochs,
- const uint64_t max_num_samples,
- const int32_t max_seq_length,
- const double short_seq_prob,
- const int32_t seed,
- const bool verbose)
-{
- /* Build a mapping of (start-index, end-index, sequence-length) where
- start and end index are the indices of the sentences in the sample
- and sequence-length is the target sequence length.
- */
-
- // Consistency checks.
- assert(num_epochs > 0);
- assert(max_seq_length > 1);
- assert(short_seq_prob > 0.0);
- assert(short_seq_prob <= 1.0);
- assert(seed > 0);
-
- // Remove bound checks.
- auto docs = docs_.unchecked<1>();
- auto sizes = sizes_.unchecked<1>();
-
- // For efficiency, convert probability to ratio. Note: rand() generates int.
- const auto short_seq_ratio = static_cast<int32_t>(round(1.0 / short_seq_prob));
-
- if (verbose) {
- const auto sent_start_index = docs[0];
- const auto sent_end_index = docs[docs_.shape(0) - 1];
- const auto num_sentences = sent_end_index - sent_start_index;
- cout << " using:" << endl << std::flush;
- cout << " number of documents: " << docs_.shape(0) - 1 << endl << std::flush;
- cout << " sentences range: [" << sent_start_index << ", "
- << sent_end_index << ")" << endl
- << std::flush;
- cout << " total number of sentences: " << num_sentences << endl << std::flush;
- cout << " number of epochs: " << num_epochs << endl << std::flush;
- cout << " maximum number of samples: " << max_num_samples << endl << std::flush;
- cout << " maximum sequence length: " << max_seq_length << endl << std::flush;
- cout << " short sequence probability: " << short_seq_prob << endl << std::flush;
- cout << " short sequence ration (1/prob): " << short_seq_ratio << endl << std::flush;
- cout << " seed: " << seed << endl << std::flush;
- }
-
- // Mapping and it's length (1D).
- int64_t num_samples = -1;
- DocIdx* maps = NULL;
-
- // Perform two iterations, in the first iteration get the size
- // and allocate memory and in the second iteration populate the map.
- bool second = false;
- for (int32_t iteration = 0; iteration < 2; ++iteration) {
- // Set the seed so both iterations produce the same results.
- std::mt19937 rand32_gen(seed);
-
- // Set the flag on second iteration.
- second = (iteration == 1);
-
- // Counters:
- uint64_t empty_docs = 0;
- uint64_t one_sent_docs = 0;
- uint64_t long_sent_docs = 0;
-
- // Current map index.
- uint64_t map_index = 0;
-
- // For each epoch:
- for (int32_t epoch = 0; epoch < num_epochs; ++epoch) {
- if (map_index >= max_num_samples) {
- if (verbose && (!second)) {
- cout << " reached " << max_num_samples << " samples after " << epoch
- << " epochs ..." << endl
- << std::flush;
- }
- break;
- }
- // For each document:
- for (int32_t doc = 0; doc < (docs.shape(0) - 1); ++doc) {
- // Document sentences are in [sent_index_first, sent_index_last)
- const auto sent_index_first = docs[doc];
- const auto sent_index_last = docs[doc + 1];
-
- // At the beginning of the document previous index is the
- // start index.
- auto prev_start_index = sent_index_first;
-
- // Remaining documents.
- auto num_remain_sent = sent_index_last - sent_index_first;
-
- // Some bookkeeping
- if ((epoch == 0) && (!second)) {
- if (num_remain_sent == 0) { ++empty_docs; }
- if (num_remain_sent == 1) { ++one_sent_docs; }
- }
-
- // Detect documents with long sentences.
- bool contains_long_sentence = false;
- if (num_remain_sent > 1) {
- for (auto sent_index = sent_index_first; sent_index < sent_index_last;
- ++sent_index) {
- if (sizes[sent_index] > LONG_SENTENCE_LEN) {
- if ((epoch == 0) && (!second)) { ++long_sent_docs; }
- contains_long_sentence = true;
- break;
- }
- }
- }
-
- // If we have more than two sentences.
- if ((num_remain_sent > 1) && (!contains_long_sentence)) {
- // Set values.
- auto seq_len = int32_t{0};
- auto num_sent = int32_t{0};
- auto target_seq_len =
- get_target_sample_len(short_seq_ratio, max_seq_length, rand32_gen);
-
- // Loop through sentences.
- for (auto sent_index = sent_index_first; sent_index < sent_index_last;
- ++sent_index) {
- // Add the size and number of sentences.
- seq_len += sizes[sent_index];
- ++num_sent;
- --num_remain_sent;
-
- // If we have reached the target length.
- // and if not only one sentence is left in the document.
- // and if we have at least two sentneces.
- // and if we have reached end of the document.
- if (((seq_len >= target_seq_len) && (num_remain_sent > 1) &&
- (num_sent > 1)) ||
- (num_remain_sent == 0)) {
- // Check for overflow.
- if ((3 * map_index + 2) > std::numeric_limits<int64_t>::max()) {
- cout << "number of samples exceeded maximum "
- << "allowed by type int64: "
- << std::numeric_limits<int64_t>::max() << endl;
- throw std::overflow_error("Number of samples");
- }
-
- // Populate the map.
- if (second) {
- const auto map_index_0 = 3 * map_index;
- maps[map_index_0] = static_cast<DocIdx>(prev_start_index);
- maps[map_index_0 + 1] = static_cast<DocIdx>(sent_index + 1);
- maps[map_index_0 + 2] = static_cast<DocIdx>(target_seq_len);
- }
-
- // Update indices / counters.
- ++map_index;
- prev_start_index = sent_index + 1;
- target_seq_len =
- get_target_sample_len(short_seq_ratio, max_seq_length, rand32_gen);
- seq_len = 0;
- 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 (!second) {
- if (verbose) {
- cout << " number of empty documents: " << empty_docs << endl << std::flush;
- cout << " number of documents with one sentence: " << one_sent_docs << endl
- << std::flush;
- cout << " number of documents with long sentences: " << long_sent_docs << endl
- << std::flush;
- cout << " will create mapping for " << map_index << " samples" << endl
- << std::flush;
- }
- assert(maps == NULL);
- assert(num_samples < 0);
- maps = new DocIdx[3 * map_index];
- num_samples = static_cast<int64_t>(map_index);
- }
-
- } // for (int iteration=0; iteration < 2; ++iteration) {
-
- // Shuffle.
- // We need a 64 bit random number generator as we might have more
- // than 2 billion samples.
- std::mt19937_64 rand64_gen(seed + 1);
- for (auto i = (num_samples - 1); i > 0; --i) {
- const auto j = static_cast<int64_t>(rand64_gen() % (i + 1));
- const auto i0 = 3 * i;
- const auto j0 = 3 * j;
- // Swap values.
- swap(maps[i0], maps[j0]);
- swap(maps[i0 + 1], maps[j0 + 1]);
- swap(maps[i0 + 2], maps[j0 + 2]);
- }
-
- // Method to deallocate memory.
- py::capsule free_when_done(maps, [](void* mem_) {
- DocIdx* mem = reinterpret_cast<DocIdx*>(mem_);
- delete[] mem;
- });
-
- // Return the numpy array.
- const auto byte_size = sizeof(DocIdx);
- return py::array(std::vector<int64_t>{num_samples, 3}, // shape
- {3 * byte_size, byte_size}, // C-style contiguous strides
- maps, // the data pointer
- free_when_done); // numpy array references
-}
-
-py::array build_mapping(const py::array_t<int64_t>& docs_,
- const py::array_t<int>& sizes_,
- const int num_epochs,
- const uint64_t max_num_samples,
- const int max_seq_length,
- const double short_seq_prob,
- const int seed,
- const bool verbose)
-{
- if (sizes_.size() > std::numeric_limits<uint32_t>::max()) {
- if (verbose) { cout << " using uint64 for data mapping..." << endl << std::flush; }
- return build_mapping_impl<uint64_t>(docs_,
- sizes_,
- num_epochs,
- max_num_samples,
- max_seq_length,
- short_seq_prob,
- seed,
- verbose);
- } else {
- if (verbose) { cout << " using uint32 for data mapping..." << endl << std::flush; }
- return build_mapping_impl<uint32_t>(docs_,
- sizes_,
- num_epochs,
- max_num_samples,
- max_seq_length,
- short_seq_prob,
- seed,
- verbose);
- }
-}
-
-template <typename DocIdx>
-py::array build_blocks_mapping_impl(const py::array_t<int64_t>& docs_,
- const py::array_t<int32_t>& sizes_,
- const py::array_t<int32_t>& titles_sizes_,
- const int32_t num_epochs,
- const uint64_t max_num_samples,
- const int32_t max_seq_length,
- const int32_t seed,
- const bool verbose,
- const bool use_one_sent_blocks)
-{
- /* Build a mapping of (start-index, end-index, sequence-length) where
- start and end index are the indices of the sentences in the sample
- and sequence-length is the target sequence length.
- */
-
- // Consistency checks.
- assert(num_epochs > 0);
- assert(max_seq_length > 1);
- assert(seed > 0);
-
- // Remove bound checks.
- auto docs = docs_.unchecked<1>();
- auto sizes = sizes_.unchecked<1>();
- auto titles_sizes = titles_sizes_.unchecked<1>();
-
- if (verbose) {
- const auto sent_start_index = docs[0];
- const auto sent_end_index = docs[docs_.shape(0) - 1];
- const auto num_sentences = sent_end_index - sent_start_index;
- cout << " using:" << endl << std::flush;
- cout << " number of documents: " << docs_.shape(0) - 1 << endl << std::flush;
- cout << " sentences range: [" << sent_start_index << ", "
- << sent_end_index << ")" << endl
- << std::flush;
- cout << " total number of sentences: " << num_sentences << endl << std::flush;
- cout << " number of epochs: " << num_epochs << endl << std::flush;
- cout << " maximum number of samples: " << max_num_samples << endl << std::flush;
- cout << " maximum sequence length: " << max_seq_length << endl << std::flush;
- cout << " seed: " << seed << endl << std::flush;
- }
-
- // Mapping and its length (1D).
- int64_t num_samples = -1;
- DocIdx* maps = NULL;
-
- // Acceptable number of sentences per block.
- int min_num_sent = 2;
- if (use_one_sent_blocks) { min_num_sent = 1; }
-
- // Perform two iterations, in the first iteration get the size
- // and allocate memory and in the second iteration populate the map.
- bool second = false;
- for (int32_t iteration = 0; iteration < 2; ++iteration) {
- // Set the flag on second iteration.
- second = (iteration == 1);
-
- // Current map index.
- uint64_t map_index = 0;
-
- uint64_t empty_docs = 0;
- uint64_t one_sent_docs = 0;
- uint64_t long_sent_docs = 0;
- // For each epoch:
- for (int32_t epoch = 0; epoch < num_epochs; ++epoch) {
- // assign every block a unique id
- int32_t block_id = 0;
-
- if (map_index >= max_num_samples) {
- if (verbose && (!second)) {
- cout << " reached " << max_num_samples << " samples after " << epoch
- << " epochs ..." << endl
- << std::flush;
- }
- break;
- }
- // For each document:
- for (int32_t doc = 0; doc < (docs.shape(0) - 1); ++doc) {
- // Document sentences are in [sent_index_first, sent_index_last)
- const auto sent_index_first = docs[doc];
- const auto sent_index_last = docs[doc + 1];
- const auto target_seq_len = max_seq_length - titles_sizes[doc];
-
- // At the beginning of the document previous index is the
- // start index.
- auto prev_start_index = sent_index_first;
-
- // Remaining documents.
- auto num_remain_sent = sent_index_last - sent_index_first;
-
- // Some bookkeeping
- if ((epoch == 0) && (!second)) {
- if (num_remain_sent == 0) { ++empty_docs; }
- if (num_remain_sent == 1) { ++one_sent_docs; }
- }
- // Detect documents with long sentences.
- bool contains_long_sentence = false;
- if (num_remain_sent >= min_num_sent) {
- for (auto sent_index = sent_index_first; sent_index < sent_index_last;
- ++sent_index) {
- if (sizes[sent_index] > LONG_SENTENCE_LEN) {
- if ((epoch == 0) && (!second)) { ++long_sent_docs; }
- contains_long_sentence = true;
- break;
- }
- }
- }
- // If we have enough sentences and no long sentences.
- if ((num_remain_sent >= min_num_sent) && (!contains_long_sentence)) {
- // Set values.
- auto seq_len = int32_t{0};
- auto num_sent = int32_t{0};
-
- // Loop through sentences.
- for (auto sent_index = sent_index_first; sent_index < sent_index_last;
- ++sent_index) {
- // Add the size and number of sentences.
- seq_len += sizes[sent_index];
- ++num_sent;
- --num_remain_sent;
-
- // If we have reached the target length.
- // and there are an acceptable number of sentences left
- // and if we have at least the minimum number of sentences.
- // or if we have reached end of the document.
- if (((seq_len >= target_seq_len) && (num_remain_sent >= min_num_sent) &&
- (num_sent >= min_num_sent)) ||
- (num_remain_sent == 0)) {
- // Populate the map.
- if (second) {
- const auto map_index_0 = 4 * map_index;
- // Each sample has 4 items: the starting sentence index, ending
- // sentence index, the index of the document from which the block
- // comes (used for fetching titles) and the unique id of the block
- // (used for creating block indexes)
-
- maps[map_index_0] = static_cast<DocIdx>(prev_start_index);
- maps[map_index_0 + 1] = static_cast<DocIdx>(sent_index + 1);
- maps[map_index_0 + 2] = static_cast<DocIdx>(doc);
- maps[map_index_0 + 3] = static_cast<DocIdx>(block_id);
- }
-
- // Update indices / counters.
- ++map_index;
- ++block_id;
- prev_start_index = sent_index + 1;
- seq_len = 0;
- 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 (!second) {
- if (verbose) {
- cout << " number of empty documents: " << empty_docs << endl << std::flush;
- cout << " number of documents with one sentence: " << one_sent_docs << endl
- << std::flush;
- cout << " number of documents with long sentences: " << long_sent_docs << endl
- << std::flush;
- cout << " will create mapping for " << map_index << " samples" << endl
- << std::flush;
- }
- assert(maps == NULL);
- assert(num_samples < 0);
- maps = new DocIdx[4 * map_index];
- num_samples = static_cast<int64_t>(map_index);
- }
-
- } // for (int iteration=0; iteration < 2; ++iteration) {
-
- // Shuffle.
- // We need a 64 bit random number generator as we might have more
- // than 2 billion samples.
- std::mt19937_64 rand64_gen(seed + 1);
- for (auto i = (num_samples - 1); i > 0; --i) {
- const auto j = static_cast<int64_t>(rand64_gen() % (i + 1));
- const auto i0 = 4 * i;
- const auto j0 = 4 * j;
- // Swap values.
- swap(maps[i0], maps[j0]);
- swap(maps[i0 + 1], maps[j0 + 1]);
- swap(maps[i0 + 2], maps[j0 + 2]);
- swap(maps[i0 + 3], maps[j0 + 3]);
- }
-
- // Method to deallocate memory.
- py::capsule free_when_done(maps, [](void* mem_) {
- DocIdx* mem = reinterpret_cast<DocIdx*>(mem_);
- delete[] mem;
- });
-
- // Return the numpy array.
- const auto byte_size = sizeof(DocIdx);
- return py::array(std::vector<int64_t>{num_samples, 4}, // shape
- {4 * byte_size, byte_size}, // C-style contiguous strides
- maps, // the data pointer
- free_when_done); // numpy array references
-}
-
-py::array build_blocks_mapping(const py::array_t<int64_t>& docs_,
- const py::array_t<int>& sizes_,
- const py::array_t<int>& titles_sizes_,
- const int num_epochs,
- const uint64_t max_num_samples,
- const int max_seq_length,
- const int seed,
- const bool verbose,
- const bool use_one_sent_blocks)
-{
- if (sizes_.size() > std::numeric_limits<uint32_t>::max()) {
- if (verbose) { cout << " using uint64 for data mapping..." << endl << std::flush; }
- return build_blocks_mapping_impl<uint64_t>(docs_,
- sizes_,
- titles_sizes_,
- num_epochs,
- max_num_samples,
- max_seq_length,
- seed,
- verbose,
- use_one_sent_blocks);
- } else {
- if (verbose) { cout << " using uint32 for data mapping..." << endl << std::flush; }
- return build_blocks_mapping_impl<uint32_t>(docs_,
- sizes_,
- titles_sizes_,
- num_epochs,
- max_num_samples,
- max_seq_length,
- seed,
- verbose,
- use_one_sent_blocks);
- }
-}
-
-PYBIND11_MODULE(helpers, m)
-{
- m.def("build_mapping", &build_mapping);
- m.def("build_blocks_mapping", &build_blocks_mapping);
- m.def("build_sample_idx_int32", &build_sample_idx_int32);
- m.def("build_sample_idx_int64", &build_sample_idx_int64);
- m.def("build_blending_indices", &build_blending_indices);
-}
diff --git a/mftcoder_atorch/data/helpers.cpython-38-x86_64-linux-gnu.so b/mftcoder_atorch/data/helpers.cpython-38-x86_64-linux-gnu.so
deleted file mode 100755
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NOyqy5l%e#q@qgv{;LHF3
diff --git a/mftcoder_atorch/data/preprocess_data.py b/mftcoder_atorch/data/preprocess_data.py
deleted file mode 100644
index 4ccdec2..0000000
--- a/mftcoder_atorch/data/preprocess_data.py
+++ /dev/null
@@ -1,313 +0,0 @@
-"""Processing data for pretraining."""
-
-import argparse
-import multiprocessing
-import os
-import sys
-import numpy as np
-import random
-
-# add src root path
-current_path = os.path.abspath(__file__)
-parent_dir = os.path.dirname(os.path.dirname(current_path))
-grandparent_dir = os.path.dirname(parent_dir)
-sys.path.append(grandparent_dir)
-# print(grandparent_dir)
-
-import data.tokenization.lm_dataformat as lmd
-
-import time
-import tqdm
-import torch
-import ftfy
-import glob
-
-from tokenizer import build_tokenizer
-from threading import Semaphore
-
-
-table = {ord(f):ord(t) for f,t in zip(
- u'，。！？：【】（）％＃＠＆１２３４５６７８９０',
- u',.!?:[]()%#@&1234567890')}
-
-
-def punctuation_format(text: str):
- # Replace non-breaking space with space
- # text = text.strip() + '\n'
- text = text.replace('\u202f', ' ').replace('\xa0', ' ')
- # change chinese punctuation to english ones
- text = text.translate(table)
- return text
-
-def is_prompt_answer_format(data):
-
- if "prompt" in data and "answer" in data:
- return True
- else:
- return False
-
-
-def is_chatml_format(data):
- if "chat_rounds" in data and len(data["chat_rounds"]) > 0:
- return True
- else:
- return False
-
-
-def is_text_format(data):
- if "text" in data:
- return True
- else:
- return False
-
-class Encoder(object):
- def __init__(self, args, tokenizer=None):
- self.args = args
- self.tokenizer = tokenizer
-
- def initializer(self):
- # Use Encoder class as a container for global data
- if self.tokenizer is None:
- self.tokenizer = build_tokenizer(self.args)
- else:
- self.tokenizer = self.tokenizer
-
- def encode(self, text):
- if self.args.ftfy:
- text = ftfy.fix_text(text)
- ids = {}
- for key in self.args.jsonl_keys:
- doc_ids = []
- text_ids = self.tokenizer.encode(text, add_special_tokens=False)
- if len(text_ids) > 0:
- doc_ids.append(text_ids)
- if self.args.append_eod:
- doc_ids[-1].append(self.tokenizer.eod_id)
- ids[key] = doc_ids
- return ids, len(text)
-
-
-class UniformEncoder(Encoder):
- def __init__(self, args, mode='sft', tokenizer=None):
- super().__init__(args, tokenizer=tokenizer)
- self.mode = mode
- # seq_length + 1 for shifting
- if args.load_raw_dataset:
- self.seq_length = args.seq_length + 1
- self.stride = args.seq_length
- else:
- self.seq_length = args.seq_length
-
- self.remain_input_ids = []
- self.remain_loss_mask = []
-
- def encode(self, data):
-
- encode_res = {
- "input_ids":[],
- "loss_mask":[]
- }
-
- if is_prompt_answer_format(data):
- data_type = 'prompt_answer'
- elif is_chatml_format(data):
- data_type = 'chatML'
- elif is_text_format(data):
- data_type = 'text'
- else:
- raise ValueError("data format not supported, please use prompt/answer, or chatML or pretrain text")
-
- for token_res in self._tokenize_fields(data, data_type=data_type):
- for k, v in token_res.items():
- encode_res[k].append(v)
-
- length = 0
- if data_type == 'prompt_answer':
- length = len(data['prompt']) + len(data['answer'])
- elif data_type == 'chatML':
- for chat in data['chat_rounds']:
- length += len(chat['content'])
- elif data_type == 'text':
- length += len(data['text'])
-
- return encode_res, length
-
-
- def _tokenize_fields(self, data, data_type):
-
- CHAT_COL = 'chat_rounds'
- ROLE_COL = 'role'
- CONTENT_COL = 'content'
-
- PROMPT_COL = 'prompt'
- ANSWER_COL = 'answer'
- SYSTEM_COL = 'system'
-
- TEXT_COL = 'text'
-
- if self.mode == 'sft':
- HUMAN = 'human'
- BOT = 'bot'
- SYSTEM = 'system'
- ROLE_START_MARKER = '<|role_start|>'
- ROLE_END_MARKER = '<|role_end|>'
- elif self.mode == 'pretrain' or data_type == 'text':
- HUMAN = ''
- BOT = ''
- SYSTEM = ''
- ROLE_START_MARKER = ''
- ROLE_END_MARKER = ''
- else:
- raise ValueError(f"tokenize_mode does not support {self.mode}, please use sft or pretrain")
-
-
- human_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{HUMAN}{ROLE_END_MARKER}", add_special_tokens=False)
- bot_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{BOT}{ROLE_END_MARKER}", add_special_tokens=False)
- system_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{SYSTEM}{ROLE_END_MARKER}", add_special_tokens=False)
- sft_end_marker_ids = [self.tokenizer.eod_id]
-
- # uniform SST,SFT,MFT
-
- input_ids = []
- loss_mask = []
-
- if data_type == "prompt_answer":
- system = data.get(SYSTEM_COL, '')
- prompt = data[PROMPT_COL]
- answer = data[ANSWER_COL]
- system = punctuation_format(system)
- prompt = punctuation_format(prompt)
- answer = punctuation_format(answer)
- system_ids = system_marker_ids + self.tokenizer.encode(system, add_special_tokens=False) if system else []
- prompt_ids = self.tokenizer.encode(prompt, add_special_tokens=False)
- answer_ids = self.tokenizer.encode(answer, add_special_tokens=False) + sft_end_marker_ids
- input_ids += system_ids + human_marker_ids + prompt_ids + bot_marker_ids + answer_ids
- loss_mask += [0] * len(system_ids) + [0] * len(human_marker_ids) + [0] * len(prompt_ids) + \
- [0] * len(bot_marker_ids) + [1] * len(answer_ids)
- elif data_type == 'chatML':
- chat = data[CHAT_COL]
- for r in chat:
- role = r[ROLE_COL]
- content = r[CONTENT_COL]
- content = punctuation_format(content)
- if role == HUMAN:
- role_marker_ids = human_marker_ids
- content_ids = self.tokenizer.encode(content, add_special_tokens=False)
- elif role == BOT:
- # compute loss for eos token after bot's content
- role_marker_ids = bot_marker_ids
- content_ids = self.tokenizer.encode(content, add_special_tokens=False) + sft_end_marker_ids
- elif role == SYSTEM:
- role_marker_ids = system_marker_ids
- content_ids = self.tokenizer.encode(content, add_special_tokens=False)
- else:
- raise ValueError(f"Role {role} not supported.")
-
- input_ids += role_marker_ids + content_ids
- masklet = [1] if role == BOT else [0]
- loss_mask += [0] * len(role_marker_ids) + masklet * len(content_ids)
- elif data_type == "text":
- text = data[TEXT_COL]
- text = punctuation_format(text)
- text_ids = self.tokenizer.encode(text, add_special_tokens=False) + sft_end_marker_ids
- input_ids += text_ids
- loss_mask += [1] * len(text_ids)
- else:
- raise ValueError(
- f"data_type does not support {self.args.data_type}, please use chatML or prompt_answer or text(for pretrain)")
-
- # print(self.mode)
- if self.mode == 'pretrain':
- # change loss mask to all 1s
- input_ids = input_ids
- loss_mask = [1] * len(loss_mask)
- elif self.mode == 'sft':
- # do nothing
- input_ids = input_ids
- loss_mask = loss_mask
-
- assert len(input_ids) == len(loss_mask)
- if self.args.padding_mode == 'padding':
- if len(input_ids) <= self.seq_length:
- yield self.padding(input_ids, loss_mask)
-
- # drop if too long
- else:
- yield {}
- elif self.args.padding_mode == 'concat':
- input_ids = self.remain_input_ids + input_ids
- loss_mask = self.remain_loss_mask + loss_mask
- if len(input_ids) < self.seq_length:
- self.remain_input_ids = input_ids
- self.remain_loss_mask = loss_mask
- assert len(self.remain_input_ids) == len(self.remain_loss_mask)
- yield {}
- else:
- cursor = 0
- while cursor + self.seq_length <= len(input_ids):
- yield {
- "input_ids": input_ids[cursor: cursor + self.seq_length],
- "loss_mask": loss_mask[cursor: cursor + self.seq_length]
- }
- cursor = cursor + self.stride
- self.remain_input_ids = input_ids[cursor:]
- self.remain_loss_mask = loss_mask[cursor:]
- assert len(self.remain_input_ids) == len(self.remain_loss_mask)
- yield {}
- elif self.args.padding_mode == 'pack':
- if len(input_ids) > self.seq_length:
- yield {}
- elif len(self.remain_input_ids) + len(input_ids) > self.seq_length:
- input_ids, self.remain_input_ids = self.remain_input_ids, input_ids
- loss_mask, self.remain_loss_mask = self.remain_loss_mask, loss_mask
- assert len(input_ids) == len(loss_mask)
- yield self.padding(input_ids, loss_mask)
- else:
- self.remain_input_ids = self.remain_input_ids + input_ids
- self.remain_loss_mask = self.remain_loss_mask + loss_mask
- assert len(self.remain_input_ids) == len(self.remain_loss_mask)
- yield {}
-
-
- def padding(self, input_ids, loss_mask):
- pad_id = self.tokenizer.pad_id
- assert len(input_ids) <= self.seq_length, f"padding sequence: {len(input_ids)} > {self.seq_length}"
- input_ids += [pad_id] * (self.seq_length - len(input_ids))
- loss_mask += [0] * (self.seq_length - len(loss_mask))
- return {
- "input_ids": input_ids,
- "loss_mask": loss_mask
- }
-
-def find_jsonl_fnames(inputs):
- fnames = []
- for p in inputs.split(","):
- if not os.path.isdir(p):
- if p.endswith(".jsonl"):
- print(f"loading from {p}")
- fnames.append(p)
- else:
- p_list = glob.glob(p + "/*")
- for p_ in p_list:
- if p_.endswith(".jsonl"):
- print(f"loading from {p_}")
- fnames.append(p_)
- return fnames
-
-def yield_from_files(fnames: list, semaphore):
- """
- Iterator over input documents using lm_dataformat. Should be able to handle jsons / texts /
- other compressed formats. Also filters out empty documents.
-
- :param fnames: list of filenames
- """
-
- def yielder(fname, semaphore):
- for f in filter(lambda x: x, lmd.Reader(fname).stream_data(key=['task', 'src_language', 'src_code', 'tgt_language', 'tgt_code', 'sql', 'prompt', 'answer', 'bad_answer'])):
- semaphore.acquire()
- yield f
-
- for fname in fnames:
- semaphore.acquire()
-
- yield from yielder(fname, semaphore)
\ No newline at end of file
diff --git a/mftcoder_atorch/data/tokenization/lm_dataformat.py b/mftcoder_atorch/data/tokenization/lm_dataformat.py
deleted file mode 100644
index f0e121b..0000000
--- a/mftcoder_atorch/data/tokenization/lm_dataformat.py
+++ /dev/null
@@ -1,386 +0,0 @@
-import os
-import zstandard
-import ujson as json
-import time
-import tarfile
-import codecs
-from functools import reduce
-import jsonlines
-import io
-from zipfile import ZipFile
-import gzip
-from math import ceil
-import mmap
-import multiprocessing as mp
-from pathlib import Path
-
-VALID_EXTENSIONS = ['openwebtext.tar.xz', '_data.xz', '.dat.zst', '.jsonl', '.jsonl.zst', '.jsonl.zst.tar', '.json.zst', '.txt', '.zip', '.tar.gz', '.json.gz', '.gz']
-
-def has_valid_extension(file):
- return any([file.endswith(ext) for ext in VALID_EXTENSIONS])
-
-def _listdir_or_file(x):
- if isinstance(x, list):
- return reduce(lambda x, y: x + y, map(listdir_or_file, sorted(x)))
- if os.path.isfile(x):
- return [x]
- elif os.path.isdir(x):
- return [str(Path(x) / fn) for fn in sorted(os.listdir(x))]
- else:
- raise FileNotFoundError(f"{x} not found")
-
-def listdir_or_file(x):
- return list(filter(has_valid_extension, _listdir_or_file(x)))
-
-def tarfile_reader(file, streaming=False):
- # we need our own tarfile parser because `tarfile` doesn't work well for
- # big tarfiles; it seems to be reading the entire file to get a list of
- # where all the files are - but we don't need that because we just need
- # to see each file once. surprisingly, `tarfile` doesn't expose any
- # facilities for this. the only options are 1. load the entire tarfile
- # and then query by filename or 2. extract to disk - and neither of
- # these is what we want.
-
- offset = 0
- paxfilesize = None
- while True:
- hdr = file.read(512)
- offset += 512
-
- # https://www.gnu.org/software/tar/manual/html_node/Standard.html
- # end at 135 not 136 because of \0 terminator
- if hdr[124:135] == b'\0'*11:
- # end of record
- break
-
- fname = hdr[:100].split(b'\0')[0]
-
- # if the file is too big to fit in the size field, tarfiles will actually
- # include a PaxHeader with the size in it, applicable to the immediate next file.
- if paxfilesize is not None:
- size = paxfilesize
- paxfilesize = None
- else:
- size = int(hdr[124:135], 8)
-
- padded_size = ceil(size / 512) * 512
-
- # for handling PaxHeader files (which contain extra metadata about file size) and directories
- # https://pubs.opengroup.org/onlinepubs/9699919799/utilities/pax.html#tag_20_92_13_03
- type = chr(hdr[156])
-
- if type == 'x':
- meta = file.read(padded_size)[:size]
- def kv(x):
- return x.decode('utf-8').split(' ')[1].split('=')
- paxfileattrs = {
- kv(x)[0]: kv(x)[1]
- for x in meta.split(b'\n') if x
- }
- paxfilesize = int(paxfileattrs['size'])
-
- offset += padded_size
- continue
- elif type != '0' and type != '\0':
- if streaming:
- file.seek(padded_size, os.SEEK_CUR)
- else:
- file.read(padded_size)
- offset += padded_size
- continue
-
- if streaming:
- # skip directory entries
- if size != 0:
- mmo = mmap.mmap(file.fileno(), length=offset + size, access=mmap.ACCESS_READ)
- mmo.seek(offset)
- yield mmo
-
- file.seek(padded_size, os.SEEK_CUR)
- else:
- yield file.read(padded_size)[:size]
- offset += padded_size
-
-def handle_jsonl(jsonl_reader, get_meta, autojoin_paragraphs, para_joiner, key='text'):
- for ob in jsonl_reader:
- # naive jsonl where each object is just the string itself, with no meta. For legacy compatibility.
- if isinstance(ob, str):
- assert not get_meta
- yield ob
- continue
-
- if isinstance(key, str):
- text = ob.get(key)
- if autojoin_paragraphs and isinstance(text, list):
- text = para_joiner.join(text)
-
- if get_meta:
- yield text, (ob['meta'] if 'meta' in ob else {})
- else:
- yield text
- elif isinstance(key, list):
-
- task = ob.get(key[0], '')
- src_language = ob.get(key[1], '')
- src_code = ob.get(key[2], '')
- tgt_language = ob.get(key[3], '')
- tgt_code = ob.get(key[4], '')
- sql = ob.get(key[5], '')
- prompt_in = ob.get(key[6], '')
- content_in = ob.get(key[7], '')
- bad_content_in = ob.get(key[8], '')
-
- if task:
- task = "task: " + task
- if src_language:
- src_language = "language: " + src_language
- if sql:
- sql = sql.strip() + '\n'
- task = "task: text to sql\n"
- src_language = 'language: text\n'
- tgt_language = "language: sql\n"
- prompt_in = prompt_in.strip() + '\n'
- elif tgt_language:
- tgt_language = "language: " + tgt_language
-
- prompt = task + src_language + src_code + prompt_in + tgt_language
- content = tgt_code + sql + content_in
- bad_content = bad_content_in
-
- yield (prompt, content, bad_content)
-
-
-class Reader:
- def __init__(self, in_path):
- self.in_path = in_path
-
- def stream_data(self, get_meta=False, threaded=False, key=['prompt', 'content', 'bad_content']):
- if not threaded:
- yield from self._stream_data(get_meta, jsonl_key=key)
- return
-
- q = mp.Queue(1000)
- p = mp.Process(target=self._stream_data_threaded, args=(q, get_meta))
- p.start()
- while p.is_alive():
- res = q.get()
- if res is None: break
- yield res
-
- def _stream_data_threaded(self, q, get_meta=False):
- for data in self._stream_data(get_meta):
- q.put(data)
- q.put(None)
-
- def _stream_data(self, get_meta=False, jsonl_key="text"):
- self.f_name = ""
- files = listdir_or_file(self.in_path)
- if not files:
- raise FileNotFoundError(f"No valid file(s) found in {self.in_path}")
- for f in files:
- self.f_name = f
- if f == 'openwebtext.tar.xz':
- assert not get_meta
-
- yield from self.read_owt(f)
- elif 'urlsf_subset' in f and f.endswith('_data.xz'):
- assert not get_meta
-
- yield from self.read_owt_subset(f)
- elif f.endswith('.dat.zst'):
- assert not get_meta
-
- yield from self.read_dat(f)
- elif f.endswith('.jsonl'):
- yield from self.read_jsonl(f, get_meta, key=jsonl_key)
- elif f.endswith('.jsonl.zst'):
- yield from self.read_jsonl_zst(f, get_meta, key=jsonl_key)
- elif f.endswith('.jsonl.zst.tar'):
- yield from self.read_jsonl_tar(f, get_meta, jsonl_key=key)
- elif f.endswith('.json.zst'):
- assert not get_meta
-
- yield from self.read_json(f)
- elif f.endswith('.txt'):
- assert not get_meta
-
- yield from self.read_txt(f)
- elif f.endswith('.zip'):
- assert not get_meta
-
- yield from self.read_zip(f)
- elif f.endswith('.tar.gz'):
- assert not get_meta
-
- yield from self.read_tgz(f)
- elif f.endswith('.json.gz'):
- assert not get_meta
-
- yield from self.read_jsongz(f)
- elif f.endswith('.gz'):
- assert not get_meta
-
- yield from self.read_gz(f)
- else:
- # shouldn't be reached
- print(f'Skipping {f} as streaming for that filetype is not implemented')
-
- def read_txt(self, file):
- with open(file, 'r') as fh:
- yield fh.read()
-
- def read_zip(self, file):
- archive = ZipFile(file, 'r')
- for f in archive.namelist():
- yield archive.read(f).decode('UTF-8')
-
- def read_tgz(self, file):
- gz = gzip.open(file)
- yield from (x.decode('utf-8') for x in tarfile_reader(gz, streaming=False))
-
- def read_gz(self, file):
- with gzip.open(file, 'rb') as f:
- for line in f:
- yield line.decode('utf-8')
-
- def read_jsongz(self, file):
- for line in self.read_gz(file):
- yield json.loads(line)
-
- def read_json(self, file):
- with open(file, 'rb') as fh:
- cctx = zstandard.ZstdDecompressor()
- reader = cctx.stream_reader(fh)
- ob = json.load(reader)
- yield from ob
-
- def read_dat(self, file):
- with open(file, 'rb') as fh:
- cctx = zstandard.ZstdDecompressor()
- reader = cctx.stream_reader(fh)
- while True:
- ln = reader.read(16).decode('UTF-8')
- if not ln:
- break
-
- ln = int(ln)
-
- yield reader.read(ln).decode('UTF-8')
-
- def read_jsonl(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'):
- with jsonlines.open(file) as rdr:
- yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key)
-
- def read_jsonl_zst(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'):
- with open(file, 'rb') as fh:
- cctx = zstandard.ZstdDecompressor()
- reader = io.BufferedReader(cctx.stream_reader(fh))
- rdr = jsonlines.Reader(reader)
- yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key)
-
- def read_jsonl_tar(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'):
- with open(file, 'rb') as fh:
- for f in tarfile_reader(fh, streaming=True):
- cctx = zstandard.ZstdDecompressor()
- reader = io.BufferedReader(cctx.stream_reader(f))
- rdr = jsonlines.Reader(reader)
- yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key)
- f.close()
-
- def read_owt(self, file):
- tar = tarfile.open(file, encoding='utf-8')
- utf8reader = codecs.getreader('utf-8')
-
- for name in tar.getmembers():
- fp = tar.extractfile(name)
- inner_tar = tarfile.open(fileobj=fp, encoding='utf-8')
- for inner_name in inner_tar.getmembers():
- inner_fp = utf8reader(inner_tar.extractfile(inner_name))
- contents = inner_fp.read()
- yield contents
-
- def read_owt_subset(self, file):
- utf8reader = codecs.getreader('utf-8')
- tar = tarfile.open(file, encoding='utf-8')
- for name in tar.getmembers():
- fp = utf8reader(tar.extractfile(name))
- contents = fp.read()
- yield contents
-
-
-class Archive:
- def __init__(self, out_dir, compression_level=3, threads=8):
- self.out_dir = out_dir
- os.makedirs(out_dir, exist_ok=True)
- self.i = 0
-
- self.fh = open(self.out_dir + '/current_chunk_incomplete', 'wb')
- self.cctx = zstandard.ZstdCompressor(level=compression_level, threads=threads)
- self.compressor = self.cctx.stream_writer(self.fh)
-
-
- def add_data(self, data, meta={}):
- self.compressor.write(json.dumps({'text': data, 'meta': meta}).encode('UTF-8') + b'\n')
-
- def commit(self, archive_name='default'):
- fname = self.out_dir + '/data_' + str(self.i) + '_time' + str(int(time.time())) + '_' + archive_name + '.jsonl.zst'
- self.compressor.flush(zstandard.FLUSH_FRAME)
-
- self.fh.flush()
- self.fh.close()
- os.rename(self.out_dir + '/current_chunk_incomplete', fname)
- self.fh = open(self.out_dir + '/current_chunk_incomplete', 'wb')
- self.compressor = self.cctx.stream_writer(self.fh)
-
- self.i += 1
-
-
-class DatArchive:
- def __init__(self, out_dir):
- self.out_dir = out_dir
- os.makedirs(out_dir, exist_ok=True)
- self.data = []
- self.i = 0
- if os.path.exists(out_dir) and len(os.listdir(out_dir)) > 0:
- self.i = max(map(lambda x: int(x.split('_')[1].split('.')[0]), os.listdir(out_dir))) + 1
-
- def add_data(self, data):
- self.data.append(data)
-
- def commit(self, archive_name=None):
- # TODO: streaming
- cctx = zstandard.ZstdCompressor(level=3)
-
- if archive_name is None:
- archive_name = str(int(time.time()))
-
- res = b''.join(map(lambda x: ("%016d" % len(x)).encode('UTF-8') + x, map(lambda x: x.encode('UTF-8'), self.data)))
- cdata = cctx.compress(res)
-
- with open(self.out_dir + '/data_' + str(self.i) + '_' + archive_name + '.dat.zst', 'wb') as fh:
- fh.write(cdata)
-
- self.i += 1
- self.data = []
-
-class JSONArchive:
- def __init__(self, out_dir):
- self.out_dir = out_dir
- os.makedirs(out_dir, exist_ok=True)
- self.data = []
- self.i = 0
- if os.path.exists(out_dir) and len(os.listdir(out_dir)) > 0:
- self.i = max(map(lambda x: int(x.split('_')[1].split('.')[0]), os.listdir(out_dir))) + 1
-
- def add_data(self, data):
- self.data.append(data)
-
- def commit(self):
- cctx = zstandard.ZstdCompressor(level=3)
-
- cdata = cctx.compress(json.dumps(self.data).encode('UTF-8'))
- with open(self.out_dir + '/data_' + str(self.i) + '_' + str(int(time.time())) + '.json.zst', 'wb') as fh:
- fh.write(cdata)
-
- self.i += 1
- self.data = []
\ No newline at end of file
diff --git a/mftcoder_atorch/data/tokenization/preprocess_data.py b/mftcoder_atorch/data/tokenization/preprocess_data.py
deleted file mode 100644
index 79a9dd7..0000000
--- a/mftcoder_atorch/data/tokenization/preprocess_data.py
+++ /dev/null
@@ -1,327 +0,0 @@
-"""Processing data for pretraining."""
-
-import argparse
-import multiprocessing
-import os
-import sys
-import numpy as np
-import random
-# sys.path.append(
-# os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))
-# )
-
-# 将父目录的父目录加入path
-current_path = os.path.abspath(__file__)
-parent_dir = os.path.dirname(os.path.dirname(current_path))
-grandparent_dir = os.path.dirname(parent_dir)
-sys.path.append(grandparent_dir)
-# print(grandparent_dir)
-
-import data.tokenization.lm_dataformat as lmd
-
-import time
-import tqdm
-import torch
-import ftfy
-import glob
-
-from tokenizer import build_tokenizer
-from threading import Semaphore
-
-
-
-
-table = {ord(f):ord(t) for f,t in zip(
- u'，。！？：【】（）％＃＠＆１２３４５６７８９０',
- u',.!?:[]()%#@&1234567890')}
-
-
-def punctuation_format(text: str):
- # Replace non-breaking space with space
- # text = text.strip() + '\n'
- text = text.replace('\u202f', ' ').replace('\xa0', ' ')
- # change chinese punctuation to english ones
- text = text.translate(table)
- return text
-
-def is_prompt_answer_format(data):
-
- if "prompt" in data and "answer" in data:
- return True
- else:
- return False
-
-
-def is_chatml_format(data):
- if "chat_rounds" in data and len(data["chat_rounds"]) > 0:
- return True
- else:
- return False
-
-
-def is_text_format(data):
- if "text" in data:
- return True
- else:
- return False
-
-class Encoder(object):
- def __init__(self, args, tokenizer=None):
- self.args = args
- self.tokenizer = tokenizer
-
- def initializer(self):
- # Use Encoder class as a container for global data
- if self.tokenizer is None:
- self.tokenizer = build_tokenizer(self.args)
- else:
- self.tokenizer = self.tokenizer
-
- def encode(self, text):
- if self.args.ftfy:
- text = ftfy.fix_text(text)
- ids = {}
- for key in self.args.jsonl_keys:
- doc_ids = []
- text_ids = self.tokenizer.encode(text, add_special_tokens=False)
- if len(text_ids) > 0:
- doc_ids.append(text_ids)
- if self.args.append_eod:
- doc_ids[-1].append(self.tokenizer.eod_id)
- ids[key] = doc_ids
- return ids, len(text)
-
-
-class UniformEncoder(Encoder):
- def __init__(self, args, mode='sft', tokenizer=None):
- super().__init__(args, tokenizer=tokenizer)
- self.mode = mode
- # 实际计算时会Shift一位，因此这里seq_length + 1
- if args.load_raw_dataset:
- self.seq_length = args.seq_length + 1
- self.stride = args.seq_length
- else:
- self.seq_length = args.seq_length
-
- self.remain_input_ids = []
- self.remain_loss_mask = []
-
- def encode(self, data):
-
- encode_res = {
- "input_ids":[],
- "loss_mask":[]
- }
-
- if is_prompt_answer_format(data):
- data_type = 'prompt_answer'
- elif is_chatml_format(data):
- data_type = 'chatML'
- elif is_text_format(data):
- data_type = 'text'
- else:
- raise ValueError("data format not supported, please use prompt/answer, or chatML or pretrain text")
-
- for token_res in self._tokenize_fields(data, data_type=data_type):
- for k, v in token_res.items():
- encode_res[k].append(v)
-
- length = 0
- if data_type == 'prompt_answer':
- length = len(data['prompt']) + len(data['answer'])
- elif data_type == 'chatML':
- for chat in data['chat_rounds']:
- length += len(chat['content'])
- elif data_type == 'text':
- length += len(data['text'])
-
- return encode_res, length
-
-
- def _tokenize_fields(self, data, data_type):
-
- CHAT_COL = 'chat_rounds'
- ROLE_COL = 'role'
- CONTENT_COL = 'content'
-
- PROMPT_COL = 'prompt'
- ANSWER_COL = 'answer'
- SYSTEM_COL = 'system'
-
- TEXT_COL = 'text'
-
- if self.mode == 'sft':
- HUMAN = 'human'
- BOT = 'bot'
- SYSTEM = 'system'
- ROLE_START_MARKER = '<|role_start|>'
- ROLE_END_MARKER = '<|role_end|>'
- elif self.mode == 'pretrain' or data_type == 'text':
- HUMAN = ''
- BOT = ''
- SYSTEM = ''
- ROLE_START_MARKER = ''
- ROLE_END_MARKER = ''
- else:
- raise ValueError(f"tokenize_mode does not support {self.mode}, please use sft or pretrain")
-
-
- human_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{HUMAN}{ROLE_END_MARKER}", add_special_tokens=False)
- bot_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{BOT}{ROLE_END_MARKER}", add_special_tokens=False)
- system_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{SYSTEM}{ROLE_END_MARKER}", add_special_tokens=False)
- sft_end_marker_ids = [self.tokenizer.eod_id]
-
- # 处理逻辑：
- # 统一处理SST,单轮、多轮sft的需求
-
- input_ids = []
- loss_mask = []
-
- if data_type == "prompt_answer":
- system = data.get(SYSTEM_COL, '')
- prompt = data[PROMPT_COL]
- answer = data[ANSWER_COL]
- system = punctuation_format(system)
- prompt = punctuation_format(prompt)
- answer = punctuation_format(answer)
- system_ids = system_marker_ids + self.tokenizer.encode(system, add_special_tokens=False) if system else []
- prompt_ids = self.tokenizer.encode(prompt, add_special_tokens=False)
- answer_ids = self.tokenizer.encode(answer, add_special_tokens=False) + sft_end_marker_ids
- input_ids += system_ids + human_marker_ids + prompt_ids + bot_marker_ids + answer_ids
- loss_mask += [0] * len(system_ids) + [0] * len(human_marker_ids) + [0] * len(prompt_ids) + \
- [0] * len(bot_marker_ids) + [1] * len(answer_ids)
- elif data_type == 'chatML':
- chat = data[CHAT_COL]
- for r in chat:
- role = r[ROLE_COL]
- content = r[CONTENT_COL]
- content = punctuation_format(content)
- # if not content.endswith('\n'): # chatML格式
- # content = content + '\n'
- if role == HUMAN:
- role_marker_ids = human_marker_ids
- content_ids = self.tokenizer.encode(content, add_special_tokens=False)
- elif role == BOT:
- # 每一个bot输出结尾的eod,计算loss, 学会在哪里停， human和system的eod不需要计算loss
- role_marker_ids = bot_marker_ids
- content_ids = self.tokenizer.encode(content, add_special_tokens=False) + sft_end_marker_ids
- elif role == SYSTEM:
- role_marker_ids = system_marker_ids
- content_ids = self.tokenizer.encode(content, add_special_tokens=False)
- else:
- raise ValueError(f"Role {role} not supported.")
-
- input_ids += role_marker_ids + content_ids
- masklet = [1] if role == BOT else [0]
- loss_mask += [0] * len(role_marker_ids) + masklet * len(content_ids)
- elif data_type == "text":
- text = data[TEXT_COL]
- text = punctuation_format(text)
- text_ids = self.tokenizer.encode(text, add_special_tokens=False) + sft_end_marker_ids
- input_ids += text_ids
- loss_mask += [1] * len(text_ids)
- else:
- raise ValueError(
- f"data_type does not support {self.args.data_type}, please use chatML or prompt_answer or text(for pretrain)")
-
- # print(self.mode)
- if self.mode == 'pretrain':
- # change loss mask to all 1s
- input_ids = input_ids
- loss_mask = [1] * len(loss_mask)
- elif self.mode == 'sft':
- # do nothing
- input_ids = input_ids
- loss_mask = loss_mask
-
- assert len(input_ids) == len(loss_mask)
- if self.args.padding_mode == 'padding':
- if len(input_ids) <= self.seq_length:
- yield self.padding(input_ids, loss_mask)
-
- # 如果超长，直接抛弃 or 使用seq_length窗口滑动采样
- else:
- # cursor = 0
- # while cursor < len(input_ids):
- # end_idx = min(cursor + self.seq_length, len(input_ids))
- # yield self.padding(input_ids[cursor: end_idx], loss_mask[cursor: end_idx])
- # cursor = end_idx
- yield {}
- elif self.args.padding_mode == 'concat':
- input_ids = self.remain_input_ids + input_ids
- loss_mask = self.remain_loss_mask + loss_mask
- if len(input_ids) < self.seq_length:
- self.remain_input_ids = input_ids
- self.remain_loss_mask = loss_mask
- assert len(self.remain_input_ids) == len(self.remain_loss_mask)
- yield {}
- else:
- cursor = 0
- while cursor + self.seq_length <= len(input_ids):
- yield {
- "input_ids": input_ids[cursor: cursor + self.seq_length],
- "loss_mask": loss_mask[cursor: cursor + self.seq_length]
- }
- cursor = cursor + self.stride
- self.remain_input_ids = input_ids[cursor:]
- self.remain_loss_mask = loss_mask[cursor:]
- assert len(self.remain_input_ids) == len(self.remain_loss_mask)
- yield {}
- elif self.args.padding_mode == 'pack':
- if len(input_ids) > self.seq_length:
- yield {}
- elif len(self.remain_input_ids) + len(input_ids) > self.seq_length:
- input_ids, self.remain_input_ids = self.remain_input_ids, input_ids
- loss_mask, self.remain_loss_mask = self.remain_loss_mask, loss_mask
- assert len(input_ids) == len(loss_mask)
- yield self.padding(input_ids, loss_mask)
- else:
- self.remain_input_ids = self.remain_input_ids + input_ids
- self.remain_loss_mask = self.remain_loss_mask + loss_mask
- assert len(self.remain_input_ids) == len(self.remain_loss_mask)
- yield {}
-
-
- def padding(self, input_ids, loss_mask):
- pad_id = self.tokenizer.pad_id
- assert len(input_ids) <= self.seq_length, f"padding sequence: {len(input_ids)} > {self.seq_length}"
- input_ids += [pad_id] * (self.seq_length - len(input_ids))
- loss_mask += [0] * (self.seq_length - len(loss_mask))
- return {
- "input_ids": input_ids,
- "loss_mask": loss_mask
- }
-
-# 输入为args.input, 使用","进行分割，每一个itme可以是jsonl或文件夹
-def find_jsonl_fnames(inputs):
- fnames = []
- for p in inputs.split(","):
- if not os.path.isdir(p):
- if p.endswith(".jsonl"):
- print(f"loading from {p}")
- fnames.append(p)
- else:
- p_list = glob.glob(p + "/*")
- for p_ in p_list:
- if p_.endswith(".jsonl"):
- print(f"loading from {p_}")
- fnames.append(p_)
- return fnames
-
-def yield_from_files(fnames: list, semaphore):
- """
- Iterator over input documents using lm_dataformat. Should be able to handle jsons / texts /
- other compressed formats. Also filters out empty documents.
-
- :param fnames: list of filenames
- """
-
- def yielder(fname, semaphore):
- for f in filter(lambda x: x, lmd.Reader(fname).stream_data(key=['task', 'src_language', 'src_code', 'tgt_language', 'tgt_code', 'sql', 'prompt', 'answer', 'bad_answer'])):
- semaphore.acquire()
- yield f
-
- for fname in fnames:
- semaphore.acquire()
-
- yield from yielder(fname, semaphore)
\ No newline at end of file
diff --git a/mftcoder_atorch/model/__init__.py b/mftcoder_atorch/model/__init__.py
deleted file mode 100755
index acaafbd..0000000
--- a/mftcoder_atorch/model/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-#
-# Copyright 2021 Biderman et al. This file is based on code by the authors denoted below and has been modified from its original version.
-#
-# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# from .gpt2_model import GPT2ModelPipe
-# from .utils import get_params_for_weight_decay_optimization
-# from .word_embeddings import SoftEmbedding
diff --git a/mftcoder_atorch/model/build_model.py b/mftcoder_atorch/model/build_model.py
deleted file mode 100644
index 067d306..0000000
--- a/mftcoder_atorch/model/build_model.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os
-import torch
-import sys
-sys.path.append("..")
-from utils.common_utils import get_model_params_num
-from transformers import ( # noqa: E402
- CONFIG_MAPPING,
- AutoConfig,
- AutoModelForCausalLM,
- AutoTokenizer,
- PreTrainedTokenizerFast
-)
-from .gpt_neox.configuration_gpt_neox import GPTNeoXConfig
-from .gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM
-from .gpt_neox.tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
-
-from torch.distributed.fsdp import (
- FullyShardedDataParallel as FSDP,
- StateDictType,
-)
-from utils.common_utils import print_rank_0, is_old_version
-from tokenizer import build_tokenizer
-from tokenizer.tokenizer import HFTokenizer
-
-import peft
-from peft.tuners.lora import LoraLayer
-from model.peft.utils import prepare_model_for_kbit_training
-from peft import ( # noqa
- LoraConfig,
- PrefixTuningConfig,
- PromptEncoderConfig,
- PromptEncoderReparameterizationType,
- PromptTuningConfig,
- PromptTuningInit,
- TaskType,
- get_peft_model
-)
-import model.peft.modeling_peft # noqa
-from model.peft.tuner import AdaLoraConfig
-
-try:
- from transformers import BitsAndBytesConfig
-except ImportError:
- BitsAndBytesConfig = None
-try:
- import bitsandbytes as bnb # noqa
-except ImportError:
- bnb = None
-from packaging import version
-
-
-def find_all_linear_names(args, model):
- cls = bnb.nn.Linear4bit if args.bits == 4 else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear)
- lora_module_names = set()
- for name, module in model.named_modules():
- if isinstance(module, cls):
- names = name.split('.')
- lora_module_names.add(names[0] if len(names) == 1 else names[-1])
- if 'lm_head' in lora_module_names: # needed for 16-bit
- lora_module_names.remove('lm_head')
- return list(lora_module_names)
-
-
-def setup_model(args, logger, use_cache=False):
- # Load pretrained model and tokenizer
-
- if args.pretrained_model_path:
- if args.model_type == 'gpt_neox':
- tokenizer = GPTNeoXTokenizerFast.from_pretrained(args.pretrained_model_path)
- tokenizer.eod_token = "<|endoftext|>"
- tokenizer.pad_token = "<|pad|>"
- tokenizer.sop_token = "<|endoftext|>"
- tokenizer.eop_token = "<|endoftext|>"
- tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)
- tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
-
- print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')
- print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')
- else:
- raise ValueError(
- "You are instantiating a new tokenizer from scratch. This is not supported by this script."
- "You can do it from another script, save it, and load it from here, using --tokenizer_path."
- )
-
- if args.model_type == 'gpt_neox':
- auto_config = GPTNeoXConfig
- auto_model_class = GPTNeoXForCausalLM
- else:
- auto_config = AutoConfig
- auto_model_class = AutoModelForCausalLM
-
- # with init_empty_weights_with_disk_offload(ignore_tie_weights=False):
- if args.pretrained_model_path:
- logger.info("Training model from checkpoint")
- config = auto_config.from_pretrained(args.pretrained_model_path)
- if args.peft_type != "qlora":
- model = auto_model_class.from_pretrained(args.pretrained_model_path, trust_remote_code=True).cuda()
- # TODO: qlora
- else:
- logger.info("Training model from scratch")
- if args.model_type == 'gpt_neox':
- config = GPTNeoXConfig.from_json_file(args.config_path + '/config.json')
- model = GPTNeoXForCausalLM._from_config(config)
- else:
- config = AutoConfig.from_json_file(args.config_path + '/config.json')
- model = AutoModelForCausalLM.from_config(config, trust_remote_code=args.trust_remote_code)
-
- # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
- # on a small vocab and want a smaller embedding size, remove this test.
- embedding_size = model.get_input_embeddings().weight.shape[0]
- print_rank_0('embedding size: ' + str(embedding_size))
- print_rank_0('vocab size: ' + str(tokenizer.vocab_size))
- if tokenizer.vocab_size > embedding_size:
- model.resize_token_embeddings(tokenizer.vocab_size)
- print_rank_0('resize embedding size: ' + str(model.get_input_embeddings().weight.shape[0]))
-
- print_rank_0(config)
- num_params = get_model_params_num(model)
- print_rank_0("num_params of this model:", num_params)
- args.total_model_param = num_params
- args.hidden_size = config.hidden_size
- args.num_hidden_layers = config.num_hidden_layers
- args.vocab_size = tokenizer.vocab_size
- print_rank_0(f'hidden size: {args.hidden_size}')
- print_rank_0(f'num hidden layers: {args.num_hidden_layers}')
- print_rank_0(f'vocab size: {args.vocab_size}')
-
- if args.peft_type:
- if args.peft_type in ['lora', 'qlora']:
- target_modules = None
- # TODO: qlora
- target_modules = ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"]
- print_rank_0(f'target modules: {target_modules}')
- peft_config = LoraConfig(
- task_type=TaskType.ANT_CAUSAL_LM,
- inference_mode=False,
- r=96,
- lora_alpha=32,
- lora_dropout=0.05,
- target_modules=target_modules,
- )
- logger.info(
- f"Load Peft {args.peft_type} model ......")
- if args.checkpoint_activations and args.peft_type in ["lora", "qlora"]:
- # Make Lora and gradient checkpointing compatible
- # https://github.com/huggingface/peft/issues/137
- model.enable_input_require_grads()
- model = get_peft_model(model, peft_config)
- logger.info(
- f"Reduce trainalbe params:\n")
- model.print_trainable_parameters()
-
- return model, config, tokenizer
diff --git a/mftcoder_atorch/model/gpt_neox/__init__.py b/mftcoder_atorch/model/gpt_neox/__init__.py
deleted file mode 100644
index 12bf883..0000000
--- a/mftcoder_atorch/model/gpt_neox/__init__.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import TYPE_CHECKING
-
-from transformers.file_utils import _LazyModule, is_tokenizers_available, is_torch_available
-from transformers.utils import OptionalDependencyNotAvailable
-# from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
-# from ...utils import OptionalDependencyNotAvailable
-
-
-_import_structure = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
-
-try:
- if not is_tokenizers_available():
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- pass
-else:
- _import_structure["tokenization_gpt_neox_fast"] = ["GPTNeoXTokenizerFast"]
-
-try:
- if not is_torch_available():
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- pass
-else:
- _import_structure["modeling_gpt_neox"] = [
- "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
- "GPTNeoXForCausalLM",
- "GPTNeoXLayer",
- "GPTNeoXModel",
- "GPTNeoXPreTrainedModel",
- ]
-
-
-if TYPE_CHECKING:
- from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
-
- try:
- if not is_tokenizers_available():
- raise OptionalDependencyNotAvailable()
- except OptionalDependencyNotAvailable:
- pass
- else:
- from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
-
- try:
- if not is_torch_available():
- raise OptionalDependencyNotAvailable()
- except OptionalDependencyNotAvailable:
- pass
- else:
- from .modeling_gpt_neox import (
- GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
- GPTNeoXForCausalLM,
- GPTNeoXLayer,
- GPTNeoXModel,
- GPTNeoXPreTrainedModel,
- )
-
-
-else:
- import sys
-
- sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
\ No newline at end of file
diff --git a/mftcoder_atorch/model/gpt_neox/config.json b/mftcoder_atorch/model/gpt_neox/config.json
deleted file mode 100644
index b691b2c..0000000
--- a/mftcoder_atorch/model/gpt_neox/config.json
+++ /dev/null
@@ -1,24 +0,0 @@
-{
- "architectures": [
- "GPTNeoXForCausalLM"
- ],
- "bos_token_id": 100256,
- "eos_token_id": 100256,
- "hidden_act": "gelu",
- "hidden_size": 768,
- "initializer_range": 0.02,
- "intermediate_size": 3072,
- "layer_norm_eps": 1e-05,
- "max_position_embeddings": 4096,
- "model_type": "gpt_neox",
- "num_attention_heads": 12,
- "num_hidden_layers": 12,
- "rotary_emb_base": 10000,
- "rotary_pct": 1.0,
- "tie_word_embeddings": false,
- "torch_dtype": "float16",
- "transformers_version": "4.26.1",
- "use_cache": true,
- "use_parallel_residual": true,
- "vocab_size": 100864
-}
diff --git a/mftcoder_atorch/model/gpt_neox/configuration_gpt_neox.py b/mftcoder_atorch/model/gpt_neox/configuration_gpt_neox.py
deleted file mode 100644
index 60f8525..0000000
--- a/mftcoder_atorch/model/gpt_neox/configuration_gpt_neox.py
+++ /dev/null
@@ -1,122 +0,0 @@
-# coding=utf-8
-# Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" GPTNeoX model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP = {
- "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
- # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
-}
-
-
-class GPTNeoXConfig(PretrainedConfig):
- r"""
- This is the configuration class to store the configuration of a [`GPTNeoXModel`]. It is used to instantiate an
- GPTNeoX model according to the specified arguments, defining the model architecture. Instantiating a configuration
- with the defaults will yield a similar configuration to that of the GPTNeoX
- [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) architecture.
-
- Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
- documentation from [`PretrainedConfig`] for more information.
-
-
- Args:
- vocab_size (`int`, *optional*, defaults to 50432):
- Vocabulary size of the GPTNeoX model. Defines the number of different tokens that can be represented by the
- `inputs_ids` passed when calling [`GPTNeoXModel`].
- hidden_size (`int`, *optional*, defaults to 6144):
- Dimension of the encoder layers and the pooler layer.
- num_hidden_layers (`int`, *optional*, defaults to 44):
- Number of hidden layers in the Transformer encoder.
- num_attention_heads (`int`, *optional*, defaults to 64):
- Number of attention heads for each attention layer in the Transformer encoder.
- intermediate_size (`int`, *optional*, defaults to 24576):
- Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
- hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
- The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
- `"relu"`, `"selu"` and `"gelu_new"` are supported.
- rotary_pct (`float`, *optional*, defaults to 0.25):
- percentage of hidden dimensions to allocate to rotary embeddings
- rotary_emb_base (`int`, *optional*, defaults to 10000)
- base for computing rotary embeddings frequency
- max_position_embeddings (`int`, *optional*, defaults to 2048):
- The maximum sequence length that this model might ever be used with. Typically set this to something large
- just in case (e.g., 512 or 1024 or 2048).
- initializer_range (`float`, *optional*, defaults to 1e-5):
- The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- layer_norm_eps (`float`, *optional*, defaults to 1e-12):
- The epsilon used by the layer normalization layers.
- use_cache (`bool`, *optional*, defaults to `True`):
- Whether or not the model should return the last key/values attentions (not used by all models). Only
- relevant if `config.is_decoder=True`.
- use_parallel_residual (`bool`, *optional*, defaults to `True`):
- Whether to use a "parallel" formulation in each Transformer layer, which can provide a slight training
- speedup at large scales (e.g. 20B).
- Example:
-
- ```python
- >>> from transformers import GPTNeoXConfig, GPTNeoXModel
-
- >>> # Initializing a GPTNeoX gpt-neox-20b style configuration
- >>> configuration = GPTNeoXConfig()
-
- >>> # Initializing a model (with random weights) from the gpt-neox-20b style configuration
- >>> model = GPTNeoXModel(configuration) # doctest: +SKIP
-
- >>> # Accessing the model configuration
- >>> configuration = model.config # doctest: +SKIP
- ```"""
- model_type = "gpt_neox"
-
- def __init__(
- self,
- vocab_size=50432,
- hidden_size=6144,
- num_hidden_layers=44,
- num_attention_heads=64,
- intermediate_size=24576,
- hidden_act="gelu",
- rotary_pct=0.25,
- rotary_emb_base=10000,
- max_position_embeddings=2048,
- initializer_range=0.02,
- layer_norm_eps=1e-5,
- use_cache=True,
- bos_token_id=0,
- eos_token_id=2,
- tie_word_embeddings=False,
- use_parallel_residual=True,
- **kwargs,
- ):
- super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
- self.vocab_size = vocab_size
- self.max_position_embeddings = max_position_embeddings
- self.hidden_size = hidden_size
- self.num_hidden_layers = num_hidden_layers
- self.num_attention_heads = num_attention_heads
- self.intermediate_size = intermediate_size
- self.hidden_act = hidden_act
- self.rotary_pct = rotary_pct
- self.rotary_emb_base = rotary_emb_base
- self.initializer_range = initializer_range
- self.layer_norm_eps = layer_norm_eps
- self.use_cache = use_cache
- self.tie_word_embeddings = tie_word_embeddings
- self.use_parallel_residual = use_parallel_residual
\ No newline at end of file
diff --git a/mftcoder_atorch/model/gpt_neox/generation_config.json b/mftcoder_atorch/model/gpt_neox/generation_config.json
deleted file mode 100644
index 3dc481e..0000000
--- a/mftcoder_atorch/model/gpt_neox/generation_config.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "bos_token_id": 50256,
- "eos_token_id": 50256,
- "transformers_version": "4.26.0.dev0",
- "_from_model_config": true
-}
diff --git a/mftcoder_atorch/model/gpt_neox/modeling_gpt_neox.py b/mftcoder_atorch/model/gpt_neox/modeling_gpt_neox.py
deleted file mode 100644
index 78ca1d7..0000000
--- a/mftcoder_atorch/model/gpt_neox/modeling_gpt_neox.py
+++ /dev/null
@@ -1,753 +0,0 @@
-# coding=utf-8
-# Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch GPTNeoX model."""
-
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-import torch.nn.functional as F
-
-from transformers.activations import ACT2FN
-from transformers.file_utils import (
- add_code_sample_docstrings,
- add_start_docstrings,
- add_start_docstrings_to_model_forward,
- replace_return_docstrings,
-)
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import logging
-from .configuration_gpt_neox import GPTNeoXConfig
-
-from utils.common_utils import print_rank_0
-
-# import xformers.ops
-
-def check_fa2():
- import flash_attn
- return hasattr(flash_attn, '__version__')
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "trl-internal-testing/tiny-random-GPTNeoXForCausalLM"
-_REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-neox-20b"
-_CONFIG_FOR_DOC = "GPTNeoXConfig"
-
-GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST = [
- "EleutherAI/gpt-neox-20b",
- # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
-]
-
-
-class GPTNeoXPreTrainedModel(PreTrainedModel):
- """
- An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
- models.
- """
-
- config_class = GPTNeoXConfig
- base_model_prefix = "gpt_neox"
- supports_gradient_checkpointing = True
- _no_split_modules = ["GPTNeoXLayer"]
-
- def _init_weights(self, module):
- """Initialize the weights"""
- if isinstance(module, nn.Linear):
- module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
- if module.bias is not None:
- module.bias.data.zero_()
- elif isinstance(module, nn.Embedding):
- module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
- if module.padding_idx is not None:
- module.weight.data[module.padding_idx].zero_()
- elif isinstance(module, nn.LayerNorm):
- module.bias.data.zero_()
- module.weight.data.fill_(1.0)
-
- def _set_gradient_checkpointing(self, module, value=False):
- if isinstance(module, GPTNeoXModel):
- module.gradient_checkpointing = value
-
-
-class GPTNeoXAttention(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.num_attention_heads = config.num_attention_heads
- self.hidden_size = config.hidden_size
- self.head_size = self.hidden_size // self.num_attention_heads
- self.rotary_ndims = int(self.head_size * config.rotary_pct)
- max_positions = config.max_position_embeddings
- self.register_buffer(
- "bias",
- torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
- 1, 1, max_positions, max_positions
- ),
- )
- self.register_buffer("masked_bias", torch.tensor(-1e9))
- self.rotary_emb = RotaryEmbedding(
- self.rotary_ndims, config.max_position_embeddings, base=config.rotary_emb_base
- )
- self.norm_factor = torch.sqrt(torch.tensor(self.head_size, dtype=torch.float32)).to(torch.get_default_dtype())
- self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size)
- self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-
-
- # new flash attention
- def forward(
- self,
- hidden_states,
- attention_mask,
- position_ids=None,
- head_mask=None,
- layer_past=None,
- use_cache=False,
- output_attentions=False,
- ):
- has_layer_past = layer_past is not None
-
- # Compute QKV
- # Attention heads [batch, seq_len, hidden_size]
- # --> [batch, seq_len, (np * 3 * head_size)]
- qkv = self.query_key_value(hidden_states)
-
- # [batch, seq_len, (num_heads * 3 * head_size)]
- # --> [batch, seq_len, num_heads, 3 * head_size]
- new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size)
- qkv = qkv.view(*new_qkv_shape)
-
- # [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size]
- query = qkv[..., : self.head_size].permute(0, 2, 1, 3)
- key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3)
- value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3)
-
- # Compute rotary embeddings on rotary_ndims
- query_rot = query[..., : self.rotary_ndims]
- query_pass = query[..., self.rotary_ndims :]
- key_rot = key[..., : self.rotary_ndims]
- key_pass = key[..., self.rotary_ndims :]
-
- # Compute token offset for rotary embeddings (when decoding)
- seq_len = key.shape[-2]
- offset = 0
- if has_layer_past:
- offset = layer_past[0].shape[-2]
- seq_len += offset
- cos, sin = self.rotary_emb(value, seq_len=seq_len)
- query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, offset=offset)
- query = torch.cat((query, query_pass), dim=-1)
- key = torch.cat((key, key_pass), dim=-1)
-
- # Cache QKV values
- if has_layer_past:
- past_key = layer_past[0]
- past_value = layer_past[1]
- key = torch.cat((past_key, key), dim=-2)
- value = torch.cat((past_value, value), dim=-2)
- present = (key, value) if use_cache else None
- if query.dtype != value.dtype:
- query = query.to(value.dtype)
- key = key.to(value.dtype)
-
- query = query.permute(0, 2, 1, 3)
- key = key.permute(0, 2, 1, 3)
- value = value.permute(0, 2, 1, 3)
- dropout_p = 0.0
- softmax_scale = 1.0 / (value.size(-1) ** 0.5)
- if check_fa2():
- from flash_attn import flash_attn_func, flash_attn_qkvpacked_func
- attn_output = flash_attn_func(query, key, value, dropout_p=dropout_p, softmax_scale=softmax_scale, causal=True)
- # qkv = torch.cat([query.unsqueeze(2), key.unsqueeze(2), value.unsqueeze(2)], 2)
- # attn_output = flash_attn_qkvpacked_func(qkv, dropout_p=dropout_p, softmax_scale=softmax_scale, causal=True)
-
- else:
- from atorch.modules.transformer.layers import flash_attn_with_mask_bias
- attn_output = flash_attn_with_mask_bias(
- query,
- key,
- value,
- mask=None,
- dropout_p=dropout_p,
- softmax_scale=softmax_scale,
- causal=True,
- )
-
- # attn_output = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=xformers.ops.LowerTriangularMask(),
- # op=xformers.ops.MemoryEfficientAttentionFlashAttentionOp)
-
- attn_output = attn_output.contiguous().view(
- attn_output.size(0), attn_output.size(1), self.num_attention_heads * self.head_size
- )
-
- # Compute attention
- #attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
-
- # Reshape outputs
- #attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_size)
- attn_output = self.dense(attn_output)
-
- outputs = (attn_output, present)
- if output_attentions:
- outputs += (attn_weights,)
-
- return outputs
-
- @classmethod
- def _split_heads(cls, tensor, num_attention_heads, attn_head_size):
- """
- Splits hidden dim into attn_head_size and num_attention_heads
- """
- # tensor: [bs, seq_len, hidden_size]
- new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
- # -> [bs, seq_len, num_attention_heads, attn_head_size]
- tensor = tensor.view(new_shape)
- # -> [bs, num_attention_heads, seq_len, attn_head_size]
- tensor = tensor.permute(0, 2, 1, 3)
- return tensor
-
- @classmethod
- def _merge_heads(cls, tensor, num_attention_heads, attn_head_size):
- """
- Merges attn_head_size dim and num_attn_heads dim into hidden dim
- """
- # tensor [bs, num_attention_heads, seq_len, attn_head_size]
- tensor = tensor.permute(0, 2, 1, 3).contiguous()
- # -> [bs, seq_len, num_attention_heads, attn_head_size]
- tensor = tensor.view(tensor.size(0), tensor.size(1), num_attention_heads * attn_head_size)
- # -> [bs, seq_len, hidden_size]
- return tensor
-
- def _attn(self, query, key, value, attention_mask=None, head_mask=None):
- # q, k, v: [bs, num_attention_heads, seq_len, attn_head_size]
- # compute causal mask from causal mask buffer
- batch_size, num_attention_heads, query_length, attn_head_size = query.size()
- key_length = key.size(-2)
-
- causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
-
- query = query.view(batch_size * num_attention_heads, query_length, attn_head_size)
- key = key.view(batch_size * num_attention_heads, key_length, attn_head_size)
- attn_scores = torch.zeros(
- batch_size * num_attention_heads,
- query_length,
- key_length,
- dtype=query.dtype,
- device=key.device,
- )
- attn_scores = torch.baddbmm(
- attn_scores,
- query,
- key.transpose(1, 2),
- beta=1.0,
- alpha=(torch.tensor(1.0, dtype=self.norm_factor.dtype, device=self.norm_factor.device) / self.norm_factor),
- )
- attn_scores = attn_scores.view(batch_size, num_attention_heads, query_length, key_length)
-
- mask_value = torch.finfo(attn_scores.dtype).min
- # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
- # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
- mask_value = torch.tensor(mask_value, dtype=attn_scores.dtype).to(attn_scores.device)
- attn_scores = torch.where(causal_mask, attn_scores, mask_value)
-
- if attention_mask is not None:
- # Apply the attention mask
- attn_scores = attn_scores + attention_mask
-
- attn_weights = nn.functional.softmax(attn_scores, dim=-1)
- attn_weights = attn_weights.to(value.dtype)
-
- # Mask heads if we want to
- if head_mask is not None:
- attn_weights = attn_weights * head_mask
-
- attn_output = torch.matmul(attn_weights, value)
- return attn_output, attn_weights
-
-
-def attention_mask_func(attention_scores, ltor_mask):
- attention_scores.masked_fill_(~ltor_mask, torch.finfo(attention_scores.dtype).min)
- return attention_scores
-
-
-class RotaryEmbedding(torch.nn.Module):
- def __init__(self, dim, max_position_embeddings, base=10000, device=None):
- super().__init__()
- inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
- self.register_buffer("inv_freq", inv_freq)
-
- # Build here to make `torch.jit.trace` work.
- self.max_seq_len_cached = max_position_embeddings
- t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
- freqs = torch.einsum("i,j->ij", t, self.inv_freq)
- # Different from paper, but it uses a different permutation in order to obtain the same calculation
- emb = torch.cat((freqs, freqs), dim=-1)
- self.cos_cached = emb.cos()[None, None, :, :]
- self.sin_cached = emb.sin()[None, None, :, :]
-
- def forward(self, x, seq_len=None):
- # x: [bs, num_attention_heads, seq_len, head_size]
- # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
- if seq_len > self.max_seq_len_cached:
- self.max_seq_len_cached = seq_len
- t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
- freqs = torch.einsum("i,j->ij", t, self.inv_freq)
- # Different from paper, but it uses a different permutation in order to obtain the same calculation
- emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
- self.cos_cached = emb.cos()[None, None, :, :]
- self.sin_cached = emb.sin()[None, None, :, :]
- return self.cos_cached[:seq_len, ...].to(x.device), self.sin_cached[:seq_len, ...].to(x.device)
-
-
-def rotate_half(x):
- """Rotates half the hidden dims of the input."""
- x1 = x[..., : x.shape[-1] // 2]
- x2 = x[..., x.shape[-1] // 2 :]
- return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0):
- cos = cos[..., offset : q.shape[-2] + offset, :]
- sin = sin[..., offset : q.shape[-2] + offset, :]
- q_embed = (q * cos) + (rotate_half(q) * sin)
- k_embed = (k * cos) + (rotate_half(k) * sin)
- return q_embed, k_embed
-
-
-class GPTNeoXMLP(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size)
- self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size)
- self.act = ACT2FN[config.hidden_act]
-
- def forward(self, hidden_states):
- hidden_states = self.dense_h_to_4h(hidden_states)
- hidden_states = self.act(hidden_states)
- hidden_states = self.dense_4h_to_h(hidden_states)
- return hidden_states
-
-
-class GPTNeoXLayer(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.use_parallel_residual = config.use_parallel_residual
- self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
- self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
- self.attention = GPTNeoXAttention(config)
- self.mlp = GPTNeoXMLP(config)
-
- def forward(
- self,
- hidden_states,
- attention_mask=None,
- position_ids=None,
- head_mask=None,
- use_cache=False,
- layer_past=None,
- output_attentions=False,
- ):
- attention_layer_outputs = self.attention(
- self.input_layernorm(hidden_states),
- attention_mask=attention_mask,
- position_ids=position_ids,
- layer_past=layer_past,
- head_mask=head_mask,
- use_cache=use_cache,
- output_attentions=output_attentions,
- )
- attn_output = attention_layer_outputs[0] # output_attn: attn_output, present, (attn_weights)
- outputs = attention_layer_outputs[1:]
-
- if self.use_parallel_residual:
- # pseudocode:
- # x = x + attn(ln1(x)) + mlp(ln2(x))
- mlp_output = self.mlp(self.post_attention_layernorm(hidden_states))
- hidden_states = mlp_output + attn_output + hidden_states
- else:
- # pseudocode:
- # x = x + attn(ln1(x))
- # x = x + mlp(ln2(x))
- attn_output = attn_output + hidden_states
- mlp_output = self.mlp(self.post_attention_layernorm(attn_output))
- hidden_states = mlp_output + attn_output
-
- if use_cache:
- outputs = (hidden_states,) + outputs # hidden_states, present, (attn_weights)
- else:
- outputs = (hidden_states,) + outputs[1:] # hidden_states, (attn_weights)
-
- return outputs
-
-
-GPT_NEOX_START_DOCSTRING = r"""
- This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
- it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
- behavior.
-
- Parameters:
- config ([`~GPTNeoXConfig`]): Model configuration class with all the parameters of the model.
- Initializing with a config file does not load the weights associated with the model, only the
- configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-GPT_NEOX_INPUTS_DOCSTRING = r"""
- Args:
- input_ids (`torch.LongTensor` of shape `({0})`):
- Indices of input sequence tokens in the vocabulary.
-
- Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
- [`PreTrainedTokenizer.__call__`] for details.
-
- [What are input IDs?](../glossary#input-ids)
- attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
- - 1 for tokens that are **not masked**,
- - 0 for tokens that are **masked**.
-
- [What are attention masks?](../glossary#attention-mask)
- head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
- Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
-
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
-
- inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
- Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
- is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
- model's internal embedding lookup matrix.
- output_attentions (`bool`, *optional*):
- Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
- tensors for more detail.
- output_hidden_states (`bool`, *optional*):
- Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
- more detail.
- return_dict (`bool`, *optional*):
- Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
- "The bare GPTNeoX Model transformer outputting raw hidden-states without any specific head on top.",
- GPT_NEOX_START_DOCSTRING,
-)
-class GPTNeoXModel(GPTNeoXPreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
- self.config = config
-
- self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
- self.layers = nn.ModuleList([GPTNeoXLayer(config) for _ in range(config.num_hidden_layers)])
- self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
- self.gradient_checkpointing = False
-
- # Initialize weights and apply final processing
- self.post_init()
-
- def get_input_embeddings(self):
- return self.embed_in
-
- def set_input_embeddings(self, value):
- self.embed_in = value
-
- @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @add_code_sample_docstrings(
- checkpoint=_CHECKPOINT_FOR_DOC,
- real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
- output_type=BaseModelOutputWithPast,
- config_class=_CONFIG_FOR_DOC,
- )
- def forward(
- self,
- input_ids: Optional[torch.LongTensor] = None,
- attention_mask: Optional[torch.FloatTensor] = None,
- head_mask: Optional[torch.FloatTensor] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
- use_cache: Optional[bool] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- position_ids=None
- ) -> Union[Tuple, BaseModelOutputWithPast]:
- r"""
- past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
- Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
- If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
- don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
- `decoder_input_ids` of shape `(batch_size, sequence_length)`.
- use_cache (`bool`, *optional*):
- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
- `past_key_values`).
- """
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
- use_cache = use_cache if use_cache is not None else self.config.use_cache
-
- if input_ids is not None and inputs_embeds is not None:
- raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
- elif input_ids is not None:
- input_shape = input_ids.size()
- elif inputs_embeds is not None:
- input_shape = inputs_embeds.size()[:-1]
- else:
- raise ValueError("You have to specify either input_ids or inputs_embeds")
-
- batch_size, seq_length = input_shape
-
- if past_key_values is None:
- past_key_values = tuple([None] * self.config.num_hidden_layers)
-
- # Attention mask.
- if attention_mask is not None:
- assert batch_size > 0, "batch_size has to be defined and > 0"
- attention_mask = attention_mask.view(batch_size, -1)
- # We create a 3D attention mask from a 2D tensor mask.
- # Sizes are [batch_size, 1, 1, to_seq_length]
- # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
- # this attention mask is more simple than the triangular masking of causal attention
- # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
- attention_mask = attention_mask[:, None, None, :]
-
- # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
- # masked positions, this operation will create a tensor which is 0.0 for
- # positions we want to attend and the dtype's smallest value for masked positions.
- # Since we are adding it to the raw scores before the softmax, this is
- # effectively the same as removing these entirely.
- attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
- attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
-
- # Prepare head mask if needed
- # 1.0 in head_mask indicate we keep the head
- # attention_probs has shape bsz x n_heads x N x N
- # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
- # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
- head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
- if inputs_embeds is None:
- inputs_embeds = self.embed_in(input_ids)
-
- hidden_states = inputs_embeds
-
- if self.gradient_checkpointing and self.training:
- if use_cache:
- logger.warning(
- "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
- )
- use_cache = False
-
- presents = () if use_cache else None
- all_attentions = () if output_attentions else None
- all_hidden_states = () if output_hidden_states else None
- for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)):
- if output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- if self.gradient_checkpointing and self.training:
-
- def create_custom_forward(module):
- def custom_forward(*inputs):
- # None for layer_past
- return module(*inputs, use_cache, None, output_attentions)
-
- return custom_forward
-
- outputs = torch.utils.checkpoint.checkpoint(
- create_custom_forward(layer),
- hidden_states,
- attention_mask,
- position_ids,
- head_mask[i],
- )
- else:
- outputs = layer(
- hidden_states,
- attention_mask=attention_mask,
- position_ids=position_ids,
- head_mask=head_mask[i],
- layer_past=layer_past,
- use_cache=use_cache,
- output_attentions=output_attentions,
- )
- hidden_states = outputs[0]
- if use_cache is True:
- presents = presents + (outputs[1],)
- if output_attentions:
- all_attentions = all_attentions + (outputs[2 if use_cache else 1],)
-
- hidden_states = self.final_layer_norm(hidden_states)
- # Add last hidden state
- if output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- if not return_dict:
- return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None)
-
- return BaseModelOutputWithPast(
- last_hidden_state=hidden_states,
- past_key_values=presents,
- hidden_states=all_hidden_states,
- attentions=all_attentions,
- )
-
-
-@add_start_docstrings(
- """GPTNeoX Model with a `language modeling` head on top for CLM fine-tuning.""", GPT_NEOX_START_DOCSTRING
-)
-class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel):
- # _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
- _keys_to_ignore_on_load_missing = [r"predictions.decoder.bias"]
-
- def __init__(self, config):
- super().__init__(config)
-
- self.gpt_neox = GPTNeoXModel(config)
- self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- def get_output_embeddings(self):
- return self.embed_out
-
- def set_output_embeddings(self, new_embeddings):
- self.embed_out = new_embeddings
-
- @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
- def forward(
- self,
- input_ids: Optional[torch.LongTensor] = None,
- attention_mask: Optional[torch.FloatTensor] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- head_mask: Optional[torch.FloatTensor] = None,
- past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
- labels: Optional[torch.LongTensor] = None,
- use_cache: Optional[bool] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- position_ids=None,
- ) -> Union[Tuple, CausalLMOutputWithPast]:
- r"""
- past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
- `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
- `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are
- only required when the model is used as a decoder in a Sequence to Sequence model.
-
- Contains pre-computed hidden-states (key and values in the self-attention blocks that can be used (see
- `past_key_values` input) to speed up sequential decoding.
-
- If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
- don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
- `decoder_input_ids` of shape `(batch_size, sequence_length)`.
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
- Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
- `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
- ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.
- use_cache (`bool`, *optional*):
- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
- `past_key_values`).
-
- Returns:
-
- Example:
-
- ```python
- >>> from transformers import AutoTokenizer, GPTNeoXForCausalLM, GPTNeoXConfig
- >>> import torch
-
- >>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
- >>> config = GPTNeoXConfig.from_pretrained("EleutherAI/gpt-neox-20b")
- >>> config.is_decoder = True
- >>> model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", config=config)
-
- >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
- >>> outputs = model(**inputs)
-
- >>> prediction_logits = outputs.logits
- ```"""
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.gpt_neox(
- input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- past_key_values=past_key_values,
- use_cache=use_cache,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- hidden_states = outputs[0]
- lm_logits = self.embed_out(hidden_states)
-
- lm_loss = None
- if labels is not None:
- # move labels to correct device to enable model parallelism
- labels = labels.to(lm_logits.device)
- # we are doing next-token prediction; shift prediction scores and input ids by one
- shift_logits = lm_logits[:, :-1, :].contiguous()
- labels = labels[:, 1:].contiguous()
- loss_fct = CrossEntropyLoss()
- lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1))
-
- if not return_dict:
- output = (lm_logits,) + outputs[1:]
- return ((lm_loss) + output) if lm_loss is not None else output
-
- return CausalLMOutputWithPast(
- loss=lm_loss,
- logits=lm_logits,
- past_key_values=outputs.past_key_values,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
- def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
- input_shape = input_ids.shape
-
- # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
- if attention_mask is None:
- attention_mask = input_ids.new_ones(input_shape)
-
- # cut decoder_input_ids if past is used
- if past_key_values and past_key_values[0] is not None:
- input_ids = input_ids[:, -1:]
-
- return {
- "input_ids": input_ids,
- "attention_mask": attention_mask,
- "past_key_values": past_key_values,
- }
-
- def _reorder_cache(self, past_key_values, beam_idx):
- reordered_past = ()
- for layer_past in past_key_values:
- reordered_past += (
- tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
- )
- return reordered_past
diff --git a/mftcoder_atorch/model/gpt_neox/tokenization_gpt_neox_fast.py b/mftcoder_atorch/model/gpt_neox/tokenization_gpt_neox_fast.py
deleted file mode 100644
index c5b533a..0000000
--- a/mftcoder_atorch/model/gpt_neox/tokenization_gpt_neox_fast.py
+++ /dev/null
@@ -1,142 +0,0 @@
-# coding=utf-8
-# Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tokenization classes for GPTNeoX."""
-import json
-from typing import TYPE_CHECKING, List, Optional, Tuple
-
-from tokenizers import pre_tokenizers
-
-from transformers import PreTrainedTokenizerFast
-from transformers.utils import logging
-
-
-if TYPE_CHECKING:
- from transformers.pipelines.conversational import Conversation
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
- "tokenizer_file": {
- "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
- },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
- "gpt-neox-20b": 2048,
-}
-
-
-class GPTNeoXTokenizerFast(PreTrainedTokenizerFast):
- """
- Construct a "fast" GPT-NeoX-20B tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
- Byte-Pair-Encoding.
-
- This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
- be encoded differently whether it is at the beginning of the sentence (without space) or not:
-
- ```
- >>> from transformers import GPTNeoXTokenizerFast
- >>> tokenizer = GPTNeoXTokenizerFast.from_pretrained("gpt2")
- >>> tokenizer("Hello world")['input_ids']
- [15496, 995]
- >>> tokenizer(" Hello world")['input_ids']
- [18435, 995]
- ```
-
- You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since
- the model was not pretrained this way, it might yield a decrease in performance.
-
- <Tip>
-
- When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
-
- </Tip>
-
- This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
- refer to this superclass for more information regarding those methods.
-
- Args:
- vocab_file (`str`):
- Path to the vocabulary file.
- merges_file (`str`):
- Path to the merges file.
- errors (`str`, *optional*, defaults to `"replace"`):
- Paradigm to follow when decoding bytes to UTF-8. See
- [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
- unk_token (`str`, *optional*, defaults to `<|endoftext|>`):
- The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
- token instead.
- bos_token (`str`, *optional*, defaults to `<|endoftext|>`):
- The beginning of sequence token.
- eos_token (`str`, *optional*, defaults to `<|endoftext|>`):
- The end of sequence token.
- add_prefix_space (`bool`, *optional*, defaults to `False`):
- Whether or not to add an initial space to the input. This allows to treat the leading word just as any
- other word. (GPTNeoX tokenizer detect beginning of words by the preceding space).
- trim_offsets (`bool`, *optional*, defaults to `True`):
- Whether or not the post-processing step should trim offsets to avoid including whitespaces.
- """
-
- vocab_files_names = VOCAB_FILES_NAMES
- pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
- max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
- model_input_names = ["input_ids", "attention_mask"]
-
- def __init__(
- self,
- vocab_file=None,
- merges_file=None,
- tokenizer_file=None,
- unk_token="<|endoftext|>",
- bos_token="<|endoftext|>",
- eos_token="<|endoftext|>",
- add_prefix_space=False,
- **kwargs,
- ):
- super().__init__(
- vocab_file,
- merges_file,
- tokenizer_file=tokenizer_file,
- unk_token=unk_token,
- bos_token=bos_token,
- eos_token=eos_token,
- add_prefix_space=add_prefix_space,
- **kwargs,
- )
-
- pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
- if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
- pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
- pre_tok_state["add_prefix_space"] = add_prefix_space
- self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)
-
- self.add_prefix_space = add_prefix_space
-
- def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
- files = self._tokenizer.model.save(save_directory, name=filename_prefix)
- return tuple(files)
-
- def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]:
- """This corresponds to DialoGPT variants of models."""
- input_ids = []
- for is_user, text in conversation.iter_texts():
- input_ids.extend(self.encode(text, add_special_tokens=False) + [self.eos_token_id])
-
- if len(input_ids) > self.model_max_length:
- input_ids = input_ids[-self.model_max_length :]
- return input_ids
\ No newline at end of file
diff --git a/mftcoder_atorch/model/peft/__init__.py b/mftcoder_atorch/model/peft/__init__.py
deleted file mode 100644
index daf63ee..0000000
--- a/mftcoder_atorch/model/peft/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""peft models interface."""
-
-from . import utils, tuner
-from peft.mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING
-from peft.utils import TaskType
-from .modeling_peft import AntPeftForCausalLM, AntPeftForEmbedding
-
-
-SUPPORTED_PEFT_TYPES = ["prefix", "lora", "adalora", "bitfit", "roem", "unipelt", "prompt", "ptuning"]
-
-# Register the Ant Causal Language Model
-MODEL_TYPE_TO_PEFT_MODEL_MAPPING["ANT_CAUSAL_LM"] = AntPeftForCausalLM
-TaskType.ANT_CAUSAL_LM = "ANT_CAUSAL_LM"
-
-MODEL_TYPE_TO_PEFT_MODEL_MAPPING["ANT_EMBEDDING"] = AntPeftForEmbedding
-TaskType.ANT_EMBEDDING = "ANT_EMBEDDING"
diff --git a/mftcoder_atorch/model/peft/modeling_peft.py b/mftcoder_atorch/model/peft/modeling_peft.py
deleted file mode 100644
index df707a6..0000000
--- a/mftcoder_atorch/model/peft/modeling_peft.py
+++ /dev/null
@@ -1,653 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-
-import sys
-sys.path.append("..")
-import os
-import torch
-import inspect
-import warnings
-from accelerate.utils import get_balanced_memory
-from accelerate import dispatch_model, infer_auto_device_map
-from accelerate.hooks import AlignDevicesHook, add_hook_to_module, remove_hook_from_submodules
-
-from peft.peft_model import (
- PeftModel,
- PeftModelForCausalLM,
-)
-from peft.utils import (
- PeftConfig,
- PromptLearningConfig,
- _set_trainable,
-)
-from model.peft.utils import (
- WEIGHTS_NAME,
- get_peft_model_state_dict,
- set_peft_model_state_dict
-)
-from model.peft.tuner import ( # noqa
- AdaLoraModel,
- RouteLoraModel,
- PeftType,
- PEFT_TYPE_TO_MODEL_MAPPING
-)
-
-
-class AntPeftForCausalLM(PeftModelForCausalLM):
- def __init__(self, model, peft_config: PeftConfig, adapter_name: str = "default"):
- super(PeftModel, self).__init__()
- self.base_model = model
- self.config = self.base_model.config
- self.modules_to_save = None
- self.peft_config = {}
- self.active_adapter = adapter_name
- self.peft_type = peft_config.peft_type
- # compatible with peft==0.4.0
- try:
- self.base_model_torch_dtype = getattr(model, "dtype", None)
- except AttributeError:
- pass
- if not isinstance(peft_config, PromptLearningConfig):
- self.peft_config[adapter_name] = peft_config
- self.base_model = PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type](
- self.base_model, self.peft_config, adapter_name
- )
- self.set_additional_trainable_modules(peft_config, adapter_name)
- else:
- self.add_adapter(adapter_name, peft_config)
-
- if getattr(self.peft_config[adapter_name], "modules_to_save", None) is not None:
- self.modules_to_save = self.peft_config[adapter_name].modules_to_save
- _set_trainable(self, adapter_name)
- self.device = torch.device(
- "cuda" if torch.cuda.is_available() else "cpu")
-
- self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
- self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
-
- def set_route_id(self, route_id: int):
- peft_config = self.active_peft_config
- if peft_config.peft_type == PeftType.ROUTELORA:
- self.base_model.activate_route_lora(route_id)
- else:
- warnings.warn("The route setting only support for Route Lora method,"
- f"but the current method is {peft_config.peft_type}")
-
- def expand_external_router(self, path: str):
- peft_config = self.active_peft_config
- if peft_config.peft_type == PeftType.ROUTELORA:
- self.base_model.expand_external_router(path)
- else:
- warnings.warn("The route setting only support for Route Lora method,"
- f"but the current method is {peft_config.peft_type}")
-
- def forward(
- self,
- input_ids=None,
- position_ids=None,
- attention_mask=None,
- inputs_embeds=None,
- labels=None,
- route_id: int = 0,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- **kwargs,
- ):
- peft_config = self.active_peft_config
- if not isinstance(peft_config, PromptLearningConfig):
- if peft_config.peft_type == PeftType.ROUTELORA:
- self.base_model.activate_route_lora(route_id)
-
- return self.base_model(
- input_ids=input_ids,
- position_ids=position_ids,
- attention_mask=attention_mask,
- inputs_embeds=inputs_embeds,
- labels=labels,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- **kwargs,
- )
-
- batch_size = input_ids.shape[0]
- if attention_mask is not None:
- # concat prompt attention mask
- if len(attention_mask.size()) == 2:
- prefix_attention_mask = torch.ones(
- batch_size, peft_config.num_virtual_tokens).to(self.device)
- attention_mask = torch.cat(
- (prefix_attention_mask, attention_mask), dim=1)
- elif len(attention_mask.size()) == 1:
- for i in range(batch_size):
- attention_mask[i] += peft_config.num_virtual_tokens
- attention_mask = attention_mask.contiguous()
- else:
- assert ValueError(
- f"The size of attention mask must in 1 or 2 dim, but get {len(attention_mask.size())}")
-
- # if kwargs.get("position_ids", None) is not None:
- # warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
- # kwargs["position_ids"] = None
- if kwargs.get("token_type_ids", None) is not None:
- warnings.warn(
- "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
- kwargs["token_type_ids"] = None
- kwargs.update(
- {
- "attention_mask": attention_mask,
- "labels": labels,
- "position_ids": position_ids,
- "output_attentions": output_attentions,
- "output_hidden_states": output_hidden_states,
- "return_dict": return_dict,
- }
- )
-
- model_config = self.base_model.config.to_dict()
- if peft_config.peft_type == PeftType.PREFIX_TUNING:
- past_key_values = self.get_prompt(batch_size)
- if model_config["model_type"] == "glm":
- batch_size = past_key_values[0].size(1)
- past_key_values = [feat[0].permute(
- 0, 2, 1, 3).contiguous() for feat in past_key_values]
- past_key_values = [
- feat.view(
- batch_size, peft_config.num_virtual_tokens, -1)
- for feat in past_key_values
- ]
- if model_config["block_position_encoding"] is True:
- position_ids[:, 0] += peft_config.num_virtual_tokens
- else:
- position_ids += peft_config.num_virtual_tokens
- kwargs["position_ids"] = position_ids.contiguous()
- return self.base_model(input_ids=input_ids, mems=past_key_values, **kwargs)
-
- return self.base_model(input_ids=input_ids, past_key_values=past_key_values, **kwargs)
- else:
- # TODO: support p-tuning and prompt tuning for GLM
- if inputs_embeds is None:
- inputs_embeds = self.word_embeddings(input_ids)
- # concat prompt labels
- if labels is not None:
- prefix_labels = torch.full(
- (batch_size, peft_config.num_virtual_tokens), -100).to(self.device)
- kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1)
- prompts = self.get_prompt(batch_size=batch_size)
- prompts = prompts.to(inputs_embeds.dtype)
-
- attention_mask = attention_mask + peft_config.num_virtual_tokens
-
- virtual_token_position_ids = torch.arange(
- peft_config.num_virtual_tokens, device=self.device, dtype=torch.long).unsqueeze(0)
- virtual_token_position_ids = virtual_token_position_ids.expand(batch_size, -1)
- if model_config["block_position_encoding"] is True:
- position_ids[:, 0] += peft_config.num_virtual_tokens
- virtual_token_position_ids = virtual_token_position_ids.unsqueeze(1)
- block_virtual_token_position_ids = position_ids.new_zeros((
- batch_size, 1, peft_config.num_virtual_tokens))
- position_ids = torch.cat([
- torch.cat([virtual_token_position_ids, block_virtual_token_position_ids], dim=1),
- position_ids], dim=-1
- )
-
- else:
- position_ids += peft_config.num_virtual_tokens
- position_ids = torch.cat([virtual_token_position_ids, position_ids], dim=-1)
-
- kwargs["attention_mask"] = attention_mask.contiguous()
- kwargs["position_ids"] = position_ids.contiguous()
-
- inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
- return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
-
- def save_pretrained(self, save_directory, **kwargs):
- r"""
- This function saves the adapter model and the adapter configuration files to a directory, so that it can be
- reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`]
- method.
-
- Args:
- save_directory (`str`):
- Directory where the adapter model and configuration files will be saved (will be created if it does not
- exist).
- kwargs (additional keyword arguments, *optional*):
- Additional keyword arguments passed along to the `push_to_hub` method.
- """
- if os.path.isfile(save_directory):
- raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
- os.makedirs(save_directory, exist_ok=True)
-
- for adapter_name, peft_config in self.peft_config.items():
- # save only the trainable weights
- output_state_dict = get_peft_model_state_dict(
- self, state_dict=kwargs.get("state_dict", None), adapter_name=adapter_name
- )
- output_dir = os.path.join(save_directory, adapter_name) if adapter_name != "default" else save_directory
- os.makedirs(output_dir, exist_ok=True)
- torch.save(output_state_dict, os.path.join(output_dir, WEIGHTS_NAME))
-
- # save the config and change the inference mode to `True`
- if peft_config.base_model_name_or_path is None:
- peft_config.base_model_name_or_path = (
- self.base_model.__dict__.get("name_or_path", None)
- if isinstance(peft_config, PromptLearningConfig)
- else self.base_model.model.__dict__.get("name_or_path", None)
- )
- inference_mode = peft_config.inference_mode
- peft_config.inference_mode = True
- peft_config.save_pretrained(output_dir)
- peft_config.inference_mode = inference_mode
-
- def load_adapter(self, model_id, adapter_name, is_trainable=False, **kwargs):
-
- from .tuner import PEFT_TYPE_TO_CONFIG_MAPPING
- from huggingface_hub import hf_hub_download
-
- if adapter_name not in self.peft_config:
- # load the config
- peft_config = PEFT_TYPE_TO_CONFIG_MAPPING[
- PeftConfig.from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)).peft_type
- ].from_pretrained(model_id, subfolder=kwargs.get("subfolder", None))
- if isinstance(peft_config, PromptLearningConfig) and is_trainable:
- raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
- else:
- peft_config.inference_mode = not is_trainable
- self.add_adapter(adapter_name, peft_config)
-
- # load weights if any
- path = os.path.join(model_id, kwargs["subfolder"]) if kwargs.get("subfolder", None) is not None else model_id
-
- if os.path.exists(os.path.join(path, WEIGHTS_NAME)):
- filename = os.path.join(path, WEIGHTS_NAME)
- else:
- try:
- filename = hf_hub_download(model_id, WEIGHTS_NAME, subfolder=kwargs.get("subfolder", None))
- except: # noqa
- raise ValueError(
- f"Can't find weights for {model_id} in {model_id} or in the Hugging Face Hub. "
- f"Please check that the file {WEIGHTS_NAME} is present at {model_id}."
- )
-
- adapters_weights = torch.load(
- filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu")
- )
- # load the weights into the model
- set_peft_model_state_dict(self, adapters_weights, adapter_name=adapter_name)
- if (
- (getattr(self, "hf_device_map", None) is not None)
- and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0)
- and len(self.peft_config) == 1
- ):
- device_map = kwargs.get("device_map", "auto")
- max_memory = kwargs.get("max_memory", None)
- offload_dir = kwargs.get("offload_folder", None)
- offload_index = kwargs.get("offload_index", None)
-
- dispatch_model_kwargs = {}
- # Safety checker for previous `accelerate` versions
- # `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/
- if "offload_index" in inspect.signature(dispatch_model).parameters:
- dispatch_model_kwargs["offload_index"] = offload_index
-
- no_split_module_classes = self._no_split_modules
-
- if device_map != "sequential":
- max_memory = get_balanced_memory(
- self,
- max_memory=max_memory,
- no_split_module_classes=no_split_module_classes,
- low_zero=(device_map == "balanced_low_0"),
- )
- if isinstance(device_map, str):
- device_map = infer_auto_device_map(
- self, max_memory=max_memory, no_split_module_classes=no_split_module_classes
- )
- dispatch_model(
- self,
- device_map=device_map,
- offload_dir=offload_dir,
- **dispatch_model_kwargs,
- )
- hook = AlignDevicesHook(io_same_device=True)
- if isinstance(self.peft_config[adapter_name], PromptLearningConfig):
- remove_hook_from_submodules(self.prompt_encoder)
- add_hook_to_module(self.get_base_model(), hook)
-
- # Set model in evaluation mode to deactivate Dropout modules by default
- self.eval()
-
- @classmethod
- def from_pretrained(
- cls,
- model,
- model_id: str,
- adapter_name: str = "default",
- is_trainable: bool = False,
- resume_from_checkpoint: bool = False,
- **kwargs
- ):
- r"""
- Instantiate a [`LoraModel`] from a pretrained Lora configuration and weights.
-
- Args:
- model ([`~transformers.PreTrainedModel`]):
- The model to be adapted. The model should be initialized with the
- [`~transformers.PreTrainedModel.from_pretrained`] method from the 🤗 Transformers library.
- model_id (`str` or `os.PathLike`):
- The name of the Lora configuration to use. Can be either:
- - A string, the `model id` of a Lora configuration hosted inside a model repo on the Hugging Face
- Hub.
- - A path to a directory containing a Lora configuration file saved using the `save_pretrained`
- method (`./my_lora_config_directory/`).
- """
- from .tuner import PEFT_TYPE_TO_CONFIG_MAPPING
- from peft import MODEL_TYPE_TO_PEFT_MODEL_MAPPING
- from transformers.trainer_utils import get_last_checkpoint
-
- if resume_from_checkpoint is True:
- model_id = get_last_checkpoint(model_id)
- print(f"The last checkpoint path is: {model_id}")
-
- # load the config
- config = PEFT_TYPE_TO_CONFIG_MAPPING[
- PeftConfig.from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)).peft_type
- ].from_pretrained(model_id, subfolder=kwargs.get("subfolder", None))
-
- if (getattr(model, "hf_device_map", None) is not None) and len(
- set(model.hf_device_map.values()).intersection({"cpu", "disk"})
- ) > 0:
- remove_hook_from_submodules(model)
-
- if isinstance(config, PromptLearningConfig) and is_trainable:
- raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
- else:
- config.inference_mode = not is_trainable
-
- if config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys():
- model = cls(model, config, adapter_name)
- else:
- model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config, adapter_name)
- model.load_adapter(model_id, adapter_name, **kwargs)
- return model
-
-
-class AntPeftForEmbedding(PeftModel):
- def __init__(self, model, peft_config: PeftConfig, adapter_name: str = "default"):
- super(PeftModel, self).__init__()
- self.base_model = model
- self.config = self.base_model.config
- self.modules_to_save = None
- self.peft_config = {}
- self.active_adapter = adapter_name
- self.peft_type = peft_config.peft_type
- self.base_model_torch_dtype = getattr(model, "dtype", None)
- if not isinstance(peft_config, PromptLearningConfig):
- self.peft_config[adapter_name] = peft_config
- self.base_model = PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type](
- self.base_model, self.peft_config, adapter_name
- )
- self.set_additional_trainable_modules(peft_config, adapter_name)
- else:
- self.add_adapter(adapter_name, peft_config)
-
- if getattr(self.peft_config[adapter_name], "modules_to_save", None) is not None:
- self.modules_to_save = self.peft_config[adapter_name].modules_to_save
- _set_trainable(self, adapter_name)
- self.device = torch.device(
- "cuda" if torch.cuda.is_available() else "cpu")
-
- self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
- self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
-
- def set_route_id(self, route_id: int):
- peft_config = self.active_peft_config
- if peft_config.peft_type == PeftType.ROUTELORA:
- self.base_model.activate_route_lora(route_id)
- else:
- warnings.warn("The route setting only support for Route Lora method,"
- f"but the current method is {peft_config.peft_type}")
-
- def expand_external_router(self, path: str):
- peft_config = self.active_peft_config
- if peft_config.peft_type == PeftType.ROUTELORA:
- self.base_model.expand_external_router(path)
- else:
- warnings.warn("The route setting only support for Route Lora method,"
- f"but the current method is {peft_config.peft_type}")
-
- def forward(
- self,
- query_ids: torch.Tensor,
- query_position_ids: torch.Tensor = None,
- query_attention_mask: torch.Tensor = None,
- query_mask: torch.Tensor = None,
- passage_ids: torch.Tensor = None,
- passage_position_ids: torch.Tensor = None,
- passage_attention_mask: torch.Tensor = None,
- passage_mask: torch.Tensor = None,
- route_id: int = 0,
- inputs_embeds=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- **kwargs,
- ):
- peft_config = self.active_peft_config
- if not isinstance(peft_config, PromptLearningConfig):
- if peft_config.peft_type == PeftType.ROUTELORA:
- self.base_model.activate_route_lora(route_id)
-
- return self.base_model(
- query_ids=query_ids,
- query_position_ids=query_position_ids,
- query_attention_mask=query_attention_mask,
- query_mask=query_mask,
- passage_ids=passage_ids,
- passage_position_ids=passage_position_ids,
- passage_attention_mask=passage_attention_mask,
- passage_mask=passage_mask,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- **kwargs,
- )
-
- batch_size = query_ids.shape[0]
- if query_attention_mask is not None and passage_attention_mask is not None:
- # concat prompt attention mask
- if len(query_attention_mask.size()) == 2:
- prefix_attention_mask = torch.ones(
- batch_size, peft_config.num_virtual_tokens).to(self.device)
- query_attention_mask = torch.cat(
- (prefix_attention_mask, query_attention_mask), dim=1)
- passage_attention_mask = torch.cat(
- (prefix_attention_mask, passage_attention_mask), dim=1)
- elif len(query_attention_mask.size()) == 1:
- for i in range(batch_size):
- query_attention_mask[i] += peft_config.num_virtual_tokens
- passage_attention_mask[i] += passage_attention_mask.num_virtual_tokens
- query_attention_mask = query_attention_mask.contiguous()
- passage_attention_mask = passage_attention_mask.contiguous()
- else:
- assert ValueError(
- f"The size of attention mask must in 1 or 2 dim, "
- f"but get {len(query_attention_mask.size())} and {len(passage_attention_mask.size())}"
- )
-
- # if kwargs.get("position_ids", None) is not None:
- # warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
- # kwargs["position_ids"] = None
- if kwargs.get("token_type_ids", None) is not None:
- warnings.warn(
- "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
- kwargs["token_type_ids"] = None
- kwargs.update(
- {
- "query_position_ids": query_position_ids,
- "query_attention_mask": query_attention_mask,
- "query_mask": query_mask,
- "passage_ids": passage_ids,
- "passage_position_ids": passage_position_ids,
- "passage_attention_mask": passage_attention_mask,
- "passage_mask": passage_mask,
- "output_attentions": output_attentions,
- "output_hidden_states": output_hidden_states,
- "return_dict": return_dict,
- }
- )
-
- if peft_config.peft_type == PeftType.PREFIX_TUNING:
- past_key_values = self.get_prompt(batch_size)
- model_config = self.base_model.config.to_dict()
- if model_config["model_type"] == "glm":
- batch_size = past_key_values[0].size(1)
- past_key_values = [feat[0].permute(
- 0, 2, 1, 3).contiguous() for feat in past_key_values]
- past_key_values = [
- feat.view(
- batch_size, peft_config.num_virtual_tokens, -1)
- for feat in past_key_values
- ]
- if model_config["block_position_encoding"] is True:
- query_position_ids[:, 0] += peft_config.num_virtual_tokens
- passage_position_ids[:, 0] += peft_config.num_virtual_tokens
- else:
- query_position_ids += peft_config.num_virtual_tokens
- passage_position_ids += peft_config.num_virtual_tokens
- kwargs["query_position_ids"] = query_position_ids.contiguous()
- kwargs["passage_position_ids"] = passage_position_ids.contiguous()
- return self.base_model(
- query_ids=query_ids, query_mems=past_key_values, passage_mems=past_key_values, **kwargs)
-
- return self.base_model(query_ids=query_ids, past_key_values=past_key_values, **kwargs)
- else:
- # TODO: support p-tuning and prompt tuning for GLM
- if inputs_embeds is None:
- inputs_embeds = self.word_embeddings(query_ids)
- # concat prompt labels
- # if labels is not None:
- # prefix_labels = torch.full(
- # (batch_size, peft_config.num_virtual_tokens), -100).to(self.device)
- # kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1)
- prompts = self.get_prompt(batch_size=batch_size)
- prompts = prompts.to(inputs_embeds.dtype)
- inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
- return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
-
- def save_pretrained(self, save_directory, **kwargs):
- r"""
- This function saves the adapter model and the adapter configuration files to a directory, so that it can be
- reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`]
- method.
-
- Args:
- save_directory (`str`):
- Directory where the adapter model and configuration files will be saved (will be created if it does not
- exist).
- kwargs (additional keyword arguments, *optional*):
- Additional keyword arguments passed along to the `push_to_hub` method.
- """
- if os.path.isfile(save_directory):
- raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
- os.makedirs(save_directory, exist_ok=True)
-
- for adapter_name, peft_config in self.peft_config.items():
- # save only the trainable weights
- output_state_dict = get_peft_model_state_dict(
- self, state_dict=kwargs.get("state_dict", None), adapter_name=adapter_name
- )
- output_dir = os.path.join(save_directory, adapter_name) if adapter_name != "default" else save_directory
- os.makedirs(output_dir, exist_ok=True)
- torch.save(output_state_dict, os.path.join(output_dir, WEIGHTS_NAME))
-
- # save the config and change the inference mode to `True`
- if peft_config.base_model_name_or_path is None:
- peft_config.base_model_name_or_path = (
- self.base_model.__dict__.get("name_or_path", None)
- if isinstance(peft_config, PromptLearningConfig)
- else self.base_model.model.__dict__.get("name_or_path", None)
- )
- inference_mode = peft_config.inference_mode
- peft_config.inference_mode = True
- peft_config.save_pretrained(output_dir)
- peft_config.inference_mode = inference_mode
-
- def load_adapter(self, model_id, adapter_name, is_trainable=False, **kwargs):
-
- from .tuner import PEFT_TYPE_TO_CONFIG_MAPPING
- from huggingface_hub import hf_hub_download
-
- if adapter_name not in self.peft_config:
- # load the config
- peft_config = PEFT_TYPE_TO_CONFIG_MAPPING[
- PeftConfig.from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)).peft_type
- ].from_pretrained(model_id, subfolder=kwargs.get("subfolder", None))
- if isinstance(peft_config, PromptLearningConfig) and is_trainable:
- raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
- else:
- peft_config.inference_mode = not is_trainable
- self.add_adapter(adapter_name, peft_config)
-
- # load weights if any
- path = os.path.join(model_id, kwargs["subfolder"]) if kwargs.get("subfolder", None) is not None else model_id
-
- if os.path.exists(os.path.join(path, WEIGHTS_NAME)):
- filename = os.path.join(path, WEIGHTS_NAME)
- else:
- try:
- filename = hf_hub_download(model_id, WEIGHTS_NAME, subfolder=kwargs.get("subfolder", None))
- except: # noqa
- raise ValueError(
- f"Can't find weights for {model_id} in {model_id} or in the Hugging Face Hub. "
- f"Please check that the file {WEIGHTS_NAME} is present at {model_id}."
- )
-
- adapters_weights = torch.load(
- filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu")
- )
- # load the weights into the model
- set_peft_model_state_dict(self, adapters_weights, adapter_name=adapter_name)
- if (
- (getattr(self, "hf_device_map", None) is not None)
- and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0)
- and len(self.peft_config) == 1
- ):
- device_map = kwargs.get("device_map", "auto")
- max_memory = kwargs.get("max_memory", None)
- offload_dir = kwargs.get("offload_folder", None)
- offload_index = kwargs.get("offload_index", None)
-
- dispatch_model_kwargs = {}
- # Safety checker for previous `accelerate` versions
- # `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/
- if "offload_index" in inspect.signature(dispatch_model).parameters:
- dispatch_model_kwargs["offload_index"] = offload_index
-
- no_split_module_classes = self._no_split_modules
-
- if device_map != "sequential":
- max_memory = get_balanced_memory(
- self,
- max_memory=max_memory,
- no_split_module_classes=no_split_module_classes,
- low_zero=(device_map == "balanced_low_0"),
- )
- if isinstance(device_map, str):
- device_map = infer_auto_device_map(
- self, max_memory=max_memory, no_split_module_classes=no_split_module_classes
- )
- dispatch_model(
- self,
- device_map=device_map,
- offload_dir=offload_dir,
- **dispatch_model_kwargs,
- )
- hook = AlignDevicesHook(io_same_device=True)
- if isinstance(self.peft_config[adapter_name], PromptLearningConfig):
- remove_hook_from_submodules(self.prompt_encoder)
- add_hook_to_module(self.get_base_model(), hook)
-
- # Set model in evaluation mode to deactivate Dropout modules by default
- self.eval()
diff --git a/mftcoder_atorch/model/peft/tuner/__init__.py b/mftcoder_atorch/model/peft/tuner/__init__.py
deleted file mode 100644
index 4d65d05..0000000
--- a/mftcoder_atorch/model/peft/tuner/__init__.py
+++ /dev/null
@@ -1,29 +0,0 @@
-"""peft tuner methods interface."""
-
-from peft.utils import PeftType
-from peft.peft_model import PEFT_TYPE_TO_MODEL_MAPPING
-from peft.mapping import PEFT_TYPE_TO_CONFIG_MAPPING
-
-from .adalora import AdaLoraConfig, AdaLoraModel
-from .routelora import RouteLoraConfig, RouteLoraModel
-from .unipelt import UniPELTConfig, UniPELTModel, PEUniPELTModel
-from .pe_base_model import PEBaseModel
-from .bitfit import PeftBitfitConfig, PEBitfitModel, PeftBitfitModel
-from .roem import PeftROEMConfig, PEROEMModel, PeftROEMModel
-
-# Register new ant peft methods
-PeftType.ROUTELORA = "ROUTELORA"
-PEFT_TYPE_TO_MODEL_MAPPING[PeftType.ROUTELORA] = RouteLoraModel
-PEFT_TYPE_TO_CONFIG_MAPPING[PeftType.ROUTELORA] = RouteLoraConfig
-
-PeftType.UNIPELT = "UNIPELT"
-PEFT_TYPE_TO_MODEL_MAPPING[PeftType.UNIPELT] = UniPELTModel
-PEFT_TYPE_TO_CONFIG_MAPPING[PeftType.UNIPELT] = UniPELTConfig
-
-PeftType.ROEM = "ROEM"
-PEFT_TYPE_TO_MODEL_MAPPING[PeftType.ROEM] = PeftROEMModel
-PEFT_TYPE_TO_CONFIG_MAPPING[PeftType.ROEM] = PeftROEMConfig
-
-PeftType.BITFIT = "BITFIT"
-PEFT_TYPE_TO_MODEL_MAPPING[PeftType.BITFIT] = PeftBitfitModel
-PEFT_TYPE_TO_CONFIG_MAPPING[PeftType.BITFIT] = PeftBitfitConfig
\ No newline at end of file
diff --git a/mftcoder_atorch/model/peft/tuner/adalora.py b/mftcoder_atorch/model/peft/tuner/adalora.py
deleted file mode 100644
index a61916f..0000000
--- a/mftcoder_atorch/model/peft/tuner/adalora.py
+++ /dev/null
@@ -1,721 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-
-import sys
-sys.path.append("..")
-sys.path.append("../..")
-import importlib
-import re
-import warnings
-from dataclasses import dataclass, field
-from typing import Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from peft.utils import transpose
-from transformers.pytorch_utils import Conv1D
-from model.peft.tuner import PeftType
-
-from peft.tuners.lora import (
- LoraConfig,
- LoraLayer,
- LoraModel,
- mark_only_lora_as_trainable,
-)
-
-
-def is_bnb_available():
- return importlib.util.find_spec("bitsandbytes") is not None
-
-
-if is_bnb_available():
- import bitsandbytes as bnb
-
-
-TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING = {
- "t5": ["q", "k", "v", "o", "wi", "wo"],
- "mt5": ["q", "k", "v", "o", "wi_0", "wi_1", "wo"],
- "bart": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
- "opt": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
- "roberta": ["query", "key", "value", "dense"],
- "deberta-v2": ["query_proj", "key_proj", "value_proj", "dense"],
- "glm": ["query_key_value", "dense"]
-}
-
-
-def _get_submodules(model, key):
- parent = model.get_submodule(".".join(key.split(".")[:-1]))
- target_name = key.split(".")[-1]
- target = model.get_submodule(key)
- return parent, target, target_name
-
-
-def _freeze_adapter(model, adapter_name):
- for n, p in model.named_parameters():
- if adapter_name in n:
- p.requires_grad = False
-
-
-@dataclass
-class AdaLoraConfig(LoraConfig):
- """
- This is the configuration class to store the configuration of a [`~peft.AdaLora`].
-
- Args:
- target_r (`int`): The target average rank of incremental matrix.
- init_r (`int`): The initial rank for each incremental matrix.
- tinit (`int`): The steps of initial fine-tuning warmup.
- tfinal (`int`): The step of final fine-tuning.
- deltaT (`int`): The time internval between two budget allocations.
- beta1 (`float`): The hyperparameter of EMA for sensitivity smoothing.
- beta2 (`float`): The hyperparameter of EMA for undertainty quantification.
- orth_reg_weight (`float`): The coefficient of orthogonal regularization.
- total_step (`int`): The total training steps that should be specified before training.
- rank_pattern (`list`): The allocated rank for each weight matrix by RankAllocator.
- """
-
- target_r: int = field(default=8, metadata={"help": "Target Lora matrix dimension."})
- init_r: int = field(default=12, metadata={"help": "Intial Lora matrix dimension."})
- tinit: int = field(default=0, metadata={"help": "The steps of initial warmup."})
- tfinal: int = field(default=0, metadata={"help": "The steps of final warmup."})
- deltaT: int = field(default=1, metadata={"help": "Step interval of rank allocation."})
- beta1: float = field(default=0.85, metadata={"help": "Hyperparameter of EMA."})
- beta2: float = field(default=0.85, metadata={"help": "Hyperparameter of EMA."})
- orth_reg_weight: float = field(default=0.5, metadata={"help": "The orthogonal regularization coefficient."})
- total_step: Optional[int] = field(default=None, metadata={"help": "The total training steps."})
- rank_pattern: Optional[dict] = field(default=None, metadata={"help": "The saved rank pattern."})
- init_lora_weights: bool = field(
- default=True,
- metadata={"help": "Whether to initialize the weights of the Lora layers."},
- )
-
- def __post_init__(self):
- self.peft_type = PeftType.ADALORA
-
-
-class AdaLoraModel(LoraModel):
- """
- Creates AdaLoRA (Adaptive LoRA) model from a pretrained transformers model. Paper:
- https://openreview.net/pdf?id=lq62uWRJjiY
-
- Args:
- model ([`transformers.PreTrainedModel`]): The model to be adapted.
- config ([`AdaLoraConfig`]): The configuration of the AdaLora model.
-
- Returns:
- `torch.nn.Module`: The AdaLora model.
-
- Example::
-
- >>> from transformers import AutoModelForSeq2SeqLM, LoraConfig >>> from peft import AdaLoraModel, AdaLoraConfig
- >>> config = AdaLoraConfig(
- peft_type="ADALORA", task_type="SEQ_2_SEQ_LM", r=8, lora_alpha=32, target_modules=["q", "v"],
- lora_dropout=0.01,
- )
- >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> model = AdaLoraModel(config, model)
-
- **Attributes**:
- - **model** ([`transformers.PreTrainedModel`]) -- The model to be adapted.
- - **peft_config** ([`AdaLoraConfig`]): The configuration of the AdaLora model.
- """
-
- def __init__(self, model, config, adapter_name):
- nn.Module.__init__(self)
- self.model = model
- self.peft_config = config
- self.add_adapter(adapter_name, self.peft_config[adapter_name])
-
- def add_adapter(self, adapter_name, config=None):
- if config is not None:
- model_config = self.model.config.to_dict() if hasattr(self.model.config, "to_dict") else self.model.config
- config = self._prepare_adalora_config(config, model_config)
- self.peft_config[adapter_name] = config
- self._find_and_replace(adapter_name)
- if len(self.peft_config) > 1 and self.peft_config[adapter_name].bias != "none":
- raise ValueError(
- "AdaLoraModel supports only 1 adapter with bias."
- "When using multiple adapters, set bias to 'none' for all adapters."
- )
- traininable_mode_counter = 0
- for config in self.peft_config.values():
- if not config.inference_mode:
- traininable_mode_counter += 1
-
- if traininable_mode_counter > 1:
- raise ValueError(
- "AdaLoraModel supports only 1 trainable adapter. "
- "When using multiple adapters, set inference_mode to"
- "True for all adapters except the one you want to train."
- )
-
- mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias)
- if self.peft_config[adapter_name].inference_mode:
- _freeze_adapter(self.model, adapter_name)
- else:
- self.trainable_adapter_name = adapter_name
- self.rankallocator = RankAllocator(self.model, self.peft_config[adapter_name], self.trainable_adapter_name)
-
- def _find_and_replace(self, adapter_name):
- lora_config = self.peft_config[adapter_name]
- loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
- if loaded_in_8bit and not is_bnb_available():
- raise ImportError(
- "To use Lora with 8-bit quantization, please install the `bitsandbytes` package. "
- "You can install it with `pip install bitsandbytes`."
- )
- is_target_modules_in_base_model = False
- kwargs = {
- "r": lora_config.init_r,
- "lora_alpha": lora_config.lora_alpha,
- "lora_dropout": lora_config.lora_dropout,
- "fan_in_fan_out": lora_config.fan_in_fan_out,
- "init_lora_weights": lora_config.init_lora_weights,
- "merge_weights": lora_config.merge_weights or lora_config.inference_mode,
- }
- key_list = [key for key, _ in self.model.named_modules()]
- for key in key_list:
- if isinstance(lora_config.target_modules, str):
- target_module_found = re.fullmatch(lora_config.target_modules, key)
- else:
- target_module_found = any(key.endswith(target_key) for target_key in lora_config.target_modules)
- if target_module_found:
- if not is_target_modules_in_base_model:
- is_target_modules_in_base_model = True
- parent, target, target_name = _get_submodules(self.model, key)
- bias = target.bias is not None
- if isinstance(target, LoraLayer):
- target.update_layer(
- adapter_name,
- lora_config.init_r,
- lora_config.lora_alpha,
- lora_config.lora_dropout,
- lora_config.init_lora_weights,
- )
- else:
- if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt):
- kwargs.update(
- {
- "has_fp16_weights": target.state.has_fp16_weights,
- "memory_efficient_backward": target.state.memory_efficient_backward,
- "threshold": target.state.threshold,
- "index": target.index,
- }
- )
- new_module = SVDLinear8bitLt(
- adapter_name, target.in_features, target.out_features, bias=bias, **kwargs
- )
- else:
- if isinstance(target, torch.nn.Linear):
- in_features, out_features = target.in_features, target.out_features
- if kwargs["fan_in_fan_out"]:
- warnings.warn(
- "fan_in_fan_out is set to True but the target module is `torch.nn.Linear`. "
- "Setting fan_in_fan_out to False."
- )
- kwargs["fan_in_fan_out"] = lora_config.fan_in_fan_out = False
- elif isinstance(target, Conv1D):
- in_features, out_features = (
- target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape
- )
- if not kwargs["fan_in_fan_out"]:
- warnings.warn(
- "fan_in_fan_out is set to False but the target module is `Conv1D`. "
- "Setting fan_in_fan_out to True."
- )
- kwargs["fan_in_fan_out"] = lora_config.fan_in_fan_out = True
- else:
- raise ValueError(
- f"Target module {target} is not supported. "
- f"Currently, only `torch.nn.Linear` and `Conv1D` are supported."
- )
- new_module = SVDLinear(adapter_name, in_features, out_features, bias=bias, **kwargs)
-
- self._replace_module(parent, target_name, new_module, target)
- if not is_target_modules_in_base_model:
- raise ValueError(
- f"Target modules {lora_config.target_modules} not found in the base model. "
- f"Please check the target modules and try again."
- )
-
- def __getattr__(self, name: str):
- """Forward missing attributes to the wrapped module."""
- try:
- return super().__getattr__(name) # defer to nn.Module's logic
- except AttributeError:
- return getattr(self.model, name)
-
- def forward(self, *args, **kwargs):
- outputs = self.model.forward(*args, **kwargs)
-
- # Calculate the orthogonal regularization
- orth_reg_weight = self.peft_config[self.trainable_adapter_name].orth_reg_weight
- assert orth_reg_weight > 0
-
- if hasattr(outputs, "loss"):
- regu_loss = 0
- num_param = 0
- for n, p in self.model.named_parameters():
- if ("lora_A" in n or "lora_B" in n) and self.trainable_adapter_name in n:
- para_cov = p @ p.T if "lora_A" in n else p.T @ p
- EYE = torch.eye(*para_cov.size(), out=torch.empty_like(para_cov))
- EYE.requires_grad = False
- num_param += 1
- regu_loss += torch.norm(para_cov - EYE, p="fro")
- regu_loss = regu_loss / num_param
- outputs.loss += orth_reg_weight * regu_loss
- return outputs
-
- def resize_modules_by_rank_pattern(self, rank_pattern, adapter_name):
- lora_config = self.peft_config[adapter_name]
- for name, rank_idx in rank_pattern.items():
- if isinstance(rank_idx, list):
- rank = sum(rank_idx)
- elif isinstance(rank_idx, torch.Tensor):
- rank_idx = rank_idx.view(-1)
- rank = rank_idx.sum().item()
- else:
- raise ValueError("Unexcepted type of rank_idx")
- key = ".".join(name.split(".")[0:-2]) if adapter_name in name else ".".join(name.split(".")[0:-1])
- _, target, _ = _get_submodules(self.model, key)
- lora_E_weights = target.lora_E[adapter_name][rank_idx]
- lora_A_weights = target.lora_A[adapter_name][rank_idx]
- lora_B_weights = target.lora_B[adapter_name][:, rank_idx]
- ranknum = target.ranknum[adapter_name]
- target.update_layer(
- adapter_name,
- rank,
- lora_config.lora_alpha,
- lora_config.lora_dropout,
- lora_config.init_lora_weights,
- )
- with torch.no_grad():
- if rank > 0:
- target.lora_E[adapter_name].copy_(lora_E_weights)
- target.lora_A[adapter_name].copy_(lora_A_weights)
- target.lora_B[adapter_name].copy_(lora_B_weights)
- # The scaling is exactly as the previous
- target.ranknum[adapter_name].copy_(ranknum)
-
- def resize_state_dict_by_rank_pattern(self, rank_pattern, state_dict, adapter_name):
- for name, rank_idx in rank_pattern.items():
- rank = sum(rank_idx)
- prefix = ".".join(name.split(".")[0:-2]) if adapter_name in name else ".".join(name.split(".")[0:-1])
- for layer in ["lora_E", "lora_A", "lora_B"]:
- key = f"base_model.model.{prefix}.{layer}.{adapter_name}"
- if layer != "lora_B":
- state_dict[key] = (
- state_dict[key][rank_idx] if rank != state_dict[key].shape[0] else state_dict[key]
- )
- else:
- state_dict[key] = (
- state_dict[key][:, rank_idx] if rank != state_dict[key].shape[1] else state_dict[key]
- )
- return state_dict
-
- def update_and_allocate(self, global_step):
- lora_config = self.peft_config[self.trainable_adapter_name]
- # Update the importance score and allocate the budget
- if global_step < lora_config.total_step - lora_config.tfinal:
- _, rank_pattern = self.rankallocator.update_and_allocate(self.model, global_step)
- if rank_pattern:
- lora_config.rank_pattern = rank_pattern
- # Finalize the budget allocation
- elif global_step == lora_config.total_step - lora_config.tfinal:
- _, rank_pattern = self.rankallocator.update_and_allocate(self.model, global_step, force_mask=True)
- # for some reason, this freezes the trainable parameters and nothing gets updates
- # self.resize_modules_by_rank_pattern(rank_pattern, self.trainable_adapter_name)
- lora_config.rank_pattern = rank_pattern
- self.rankallocator.reset_ipt()
- # Currently using inefficient way to mask the unimportant weights using the rank pattern
- # due to problem mentioned above
- elif global_step > lora_config.total_step - lora_config.tfinal:
- self.rankallocator.mask_using_rank_pattern(self.model, lora_config.rank_pattern)
- # Pass the function and do forward propagation
- else:
- return None
-
- @staticmethod
- def _prepare_adalora_config(peft_config, model_config):
- if peft_config.target_modules is None:
- if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING:
- raise ValueError("Please specify `target_modules` in `peft_config`")
- peft_config.target_modules = TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING[
- model_config["model_type"]
- ]
- if peft_config.inference_mode:
- peft_config.merge_weights = True
- return peft_config
-
-
-class AdaLoraLayer(LoraLayer):
- def __init__(
- self,
- in_features: int,
- out_features: int,
- lora_dropout: float,
- merge_weights: bool = False,
- ):
- super().__init__(in_features, out_features, lora_dropout, merge_weights)
- self.lora_E = nn.ParameterDict({})
- self.lora_A = nn.ParameterDict({})
- self.lora_B = nn.ParameterDict({})
- self.ranknum = nn.ParameterDict({})
- self.r = {}
- self.lora_alpha = {}
- del self.lora_dropout
- self.lora_dropout = None
- self.lora_dropout = {}
- self.scaling = {}
-
- def update_layer(self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights):
- self.r[adapter_name] = r
- self.lora_alpha[adapter_name] = lora_alpha
- if lora_dropout > 0.0:
- lora_dropout_layer = nn.Dropout(p=lora_dropout)
- else:
-
- def lora_dropout_layer(x):
- return x
-
- self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer}))
- # Actual trainable parameters
- # Right singular vectors
- self.lora_A.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(r, self.in_features))}))
- # Singular values
- self.lora_E.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(r, 1))}))
- # Left singular vectors
- self.lora_B.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(self.out_features, r))}))
- # The current rank
- self.ranknum.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(1), requires_grad=False)}))
- self.ranknum[adapter_name].data.fill_(float(r))
- self.ranknum[adapter_name].requires_grad = False
- self.scaling[adapter_name] = lora_alpha if lora_alpha > 0 else float(r)
- if init_lora_weights:
- self.reset_lora_parameters(adapter_name)
- self.to(self.weight.device)
-
- def reset_lora_parameters(self, adapter_name):
- if adapter_name in self.lora_A.keys():
- nn.init.zeros_(self.lora_E[adapter_name])
- nn.init.normal_(self.lora_A[adapter_name], mean=0.0, std=0.02)
- nn.init.normal_(self.lora_B[adapter_name], mean=0.0, std=0.02)
-
-
-class SVDLinear(nn.Linear, AdaLoraLayer):
- # SVD-based adaptation by a dense layer
- def __init__(
- self,
- adapter_name: str,
- in_features: int,
- out_features: int,
- r: int = 0,
- lora_alpha: int = 1,
- lora_dropout: float = 0.0,
- fan_in_fan_out: bool = False,
- merge_weights: bool = False,
- **kwargs,
- ):
- init_lora_weights = kwargs.pop("init_lora_weights", True)
- nn.Linear.__init__(self, in_features, out_features, **kwargs)
- AdaLoraLayer.__init__(
- self,
- in_features=in_features,
- out_features=out_features,
- lora_dropout=lora_dropout,
- merge_weights=merge_weights
- )
- # Freezing the pre-trained weight matrix
- self.weight.requires_grad = False
-
- self.fan_in_fan_out = fan_in_fan_out
- if fan_in_fan_out:
- self.weight.data = self.weight.data.T
-
- nn.Linear.reset_parameters(self)
- self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
- self.active_adapter = adapter_name
-
- def merge(self):
- if self.active_adapter not in self.lora_A.keys():
- return
- if self.merged:
- warnings.warn("Already merged. Nothing to do.")
- return
- if self.r[self.active_adapter] > 0:
- self.weight.data += (
- transpose(
- self.lora_B[self.active_adapter]
- @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter])
- )
- * self.scaling[self.active_adapter]
- / (self.ranknum[self.active_adapter] + 1e-5)
- )
- self.merged = True
-
- def unmerge(self):
- if self.active_adapter not in self.lora_A.keys():
- return
- if not self.merged:
- warnings.warn("Already unmerged. Nothing to do.")
- return
- if self.r[self.active_adapter] > 0:
- self.weight.data -= (
- transpose(
- self.lora_B[self.active_adapter]
- @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter])
- )
- * self.scaling[self.active_adapter]
- / (self.ranknum[self.active_adapter] + 1e-5)
- )
- self.merged = False
-
- def forward(self, x: torch.Tensor):
- if self.active_adapter not in self.lora_A.keys():
- return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
- if self.disable_adapters:
- if self.r[self.active_adapter] > 0 and self.merged:
- self.unmerge()
- result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
- elif self.r[self.active_adapter] > 0 and not self.merged:
- result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
- result += (
- (
- self.lora_dropout[self.active_adapter](x)
- @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]).T
- @ self.lora_B[self.active_adapter].T
- )
- * self.scaling[self.active_adapter]
- / (self.ranknum[self.active_adapter] + 1e-5)
- )
- else:
- result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
- return result
-
-
-if is_bnb_available():
-
- class SVDLinear8bitLt(bnb.nn.Linear8bitLt, AdaLoraLayer):
- # Low-rank matrix for SVD-based adaptation
- def __init__(
- self,
- adapter_name,
- in_features,
- out_features,
- r: int = 0,
- lora_alpha: int = 1,
- lora_dropout: float = 0.0,
- **kwargs,
- ):
- bnb.nn.Linear8bitLt.__init__(
- self,
- in_features,
- out_features,
- bias=kwargs.get("bias", True),
- has_fp16_weights=kwargs.get("has_fp16_weights", True),
- memory_efficient_backward=kwargs.get("memory_efficient_backward", False),
- threshold=kwargs.get("threshold", 0.0),
- index=kwargs.get("index", None),
- )
- AdaLoraLayer.__init__(self, in_features=in_features, out_features=out_features)
- # Freezing the pre-trained weight matrix
- self.weight.requires_grad = False
-
- init_lora_weights = kwargs.pop("init_lora_weights", True)
- self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
- self.active_adapter = adapter_name
-
- def forward(self, x: torch.Tensor):
- result = super().forward(x)
-
- if self.disable_adapters or self.active_adapter not in self.lora_A.keys():
- return result
- elif self.r[self.active_adapter] > 0:
- if not torch.is_autocast_enabled():
- expected_dtype = result.dtype
-
- if x.dtype != torch.float32:
- x = x.float()
- output = (
- (
- self.lora_dropout[self.active_adapter](x)
- @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]).T
- @ self.lora_B[self.active_adapter].T
- ).to(expected_dtype)
- * self.scaling[self.active_adapter]
- / (self.ranknum[self.active_adapter] + 1e-5)
- )
- else:
- output = (
- (
- self.lora_dropout[self.active_adapter](x)
- @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]).T
- @ self.lora_B[self.active_adapter].T
- )
- * self.scaling[self.active_adapter]
- / (self.ranknum[self.active_adapter] + 1e-5)
- )
- result += output
- return result
-
-
-class RankAllocator(object):
- """
- The RankAllocator for AdaLoraModel. Paper: https://openreview.net/pdf?id=lq62uWRJjiY
-
- Args:
- config ([`AdaLoraConfig`]): The configuration of the AdaLora model.
- model: the model that we apply AdaLoRA to.
-
- """
-
- def __init__(self, model, peft_config, adapter_name):
- self.peft_config = peft_config
- self.adapter_name = adapter_name
- self.beta1 = peft_config.beta1
- self.beta2 = peft_config.beta2
- assert self.beta1 > 0 and self.beta1 < 1
- assert self.beta2 > 0 and self.beta2 < 1
-
- self.reset_ipt()
- self._set_budget_scheduler(model)
-
- def set_total_step(self, total_step):
- self.peft_config.total_step = total_step
-
- def reset_ipt(self):
- self.ipt = {}
- self.exp_avg_ipt = {}
- self.exp_avg_unc = {}
-
- def _set_budget_scheduler(self, model):
- self.init_bgt = 0
- self.name_set = set()
- for n, p in model.named_parameters():
- if f"lora_A.{self.adapter_name}" in n:
- self.init_bgt += p.size(0)
- self.name_set.add(n.replace("lora_A", "%s"))
- self.name_set = sorted(self.name_set)
- # The total final rank budget
- self.target_bgt = self.peft_config.target_r * len(self.name_set)
-
- def budget_schedule(self, step: int):
- tinit = self.peft_config.tinit
- tfinal = self.peft_config.tfinal
- total_step = self.peft_config.total_step
- # Initial warmup
- if step <= tinit:
- budget = self.init_bgt
- mask_ind = False
- # Final fine-tuning
- elif step > total_step - tfinal:
- budget = self.target_bgt
- mask_ind = True
- else:
- # Budget decreasing with a cubic scheduler
- mul_coeff = 1 - (step - tinit) / (total_step - tfinal - tinit)
- budget = int((self.init_bgt - self.target_bgt) * (mul_coeff**3) + self.target_bgt)
- mask_ind = True if step % self.peft_config.deltaT == 0 else False
- return budget, mask_ind
-
- def update_ipt(self, model):
- # Update the sensitivity and uncertainty for every weight
- for n, p in model.named_parameters():
- if "lora_" in n and self.adapter_name in n:
- if n not in self.ipt:
- self.ipt[n] = torch.zeros_like(p)
- self.exp_avg_ipt[n] = torch.zeros_like(p)
- self.exp_avg_unc[n] = torch.zeros_like(p)
- with torch.no_grad():
- self.ipt[n] = (p * p.grad).abs().detach()
- # Sensitivity smoothing
- self.exp_avg_ipt[n] = self.beta1 * self.exp_avg_ipt[n] + (1 - self.beta1) * self.ipt[n]
- # Uncertainty quantification
- self.exp_avg_unc[n] = (
- self.beta2 * self.exp_avg_unc[n] + (1 - self.beta2) * (self.ipt[n] - self.exp_avg_ipt[n]).abs()
- )
-
- def _element_score(self, n):
- return self.exp_avg_ipt[n] * self.exp_avg_unc[n]
-
- def _combine_ipt(self, ipt_E, ipt_AB):
- ipt_AB = ipt_AB.sum(dim=1, keepdim=False)
- sum_ipt = ipt_E.view(-1) + ipt_AB.view(-1)
- return sum_ipt
-
- def mask_to_budget(self, model, budget):
- value_ipt = {}
- vector_ipt = {}
- triplet_ipt = {}
- # Get the importance score for A, E, B
- for n, p in model.named_parameters():
- if f"lora_A.{self.adapter_name}" in n:
- entry_ipt = self._element_score(n)
- comb_ipt = torch.mean(entry_ipt, dim=1, keepdim=True)
- name_m = n.replace("lora_A", "%s")
- if name_m not in vector_ipt:
- vector_ipt[name_m] = [comb_ipt]
- else:
- vector_ipt[name_m].append(comb_ipt)
- if f"lora_B.{self.adapter_name}" in n:
- entry_ipt = self._element_score(n)
- comb_ipt = torch.mean(entry_ipt, dim=0, keepdim=False).view(-1, 1)
- name_m = n.replace("lora_B", "%s")
- if name_m not in vector_ipt:
- vector_ipt[name_m] = [comb_ipt]
- else:
- vector_ipt[name_m].append(comb_ipt)
- if f"lora_E.{self.adapter_name}" in n:
- entry_ipt = self._element_score(n)
- name_m = n.replace("lora_E", "%s")
- value_ipt[name_m] = entry_ipt
-
- all_score = []
- # Calculate the score for each triplet
- for name_m in vector_ipt:
- ipt_E = value_ipt[name_m]
- ipt_AB = torch.cat(vector_ipt[name_m], dim=1)
- sum_ipt = self._combine_ipt(ipt_E, ipt_AB)
- name_E = name_m % "lora_E"
- triplet_ipt[name_E] = sum_ipt.view(-1, 1)
- all_score.append(sum_ipt.view(-1))
-
- # Get the threshold by ranking ipt
- mask_threshold = torch.kthvalue(
- torch.cat(all_score),
- k=self.init_bgt - budget,
- )[0].item()
-
- rank_pattern = {}
- # Mask the unimportant triplets
- with torch.no_grad():
- for n, p in model.named_parameters():
- if f"lora_E.{self.adapter_name}" in n:
- p.masked_fill_(triplet_ipt[n] <= mask_threshold, 0.0)
- rank_pattern[n] = (~(triplet_ipt[n] <= mask_threshold)).view(-1).tolist()
- return rank_pattern
-
- def update_and_allocate(self, model, global_step, force_mask=False):
- # # Update the importance score and allocate the budget
- if global_step < self.peft_config.total_step - self.peft_config.tfinal:
- self.update_ipt(model)
- budget, mask_ind = self.budget_schedule(global_step)
- # Allocate the budget according to importance scores
- if mask_ind or force_mask:
- rank_pattern = self.mask_to_budget(model, budget)
- else:
- rank_pattern = None
- return budget, rank_pattern
-
- def mask_using_rank_pattern(self, model, rank_pattern):
- # Mask the unimportant triplets
- is_adapter_name_truncated = False
- if self.adapter_name not in next(iter(rank_pattern.keys())):
- is_adapter_name_truncated = True
-
- with torch.no_grad():
- for n, p in model.named_parameters():
- if f"lora_E.{self.adapter_name}" in n:
- key = n if not is_adapter_name_truncated else n.replace(f".{self.adapter_name}", "")
- mask = torch.Tensor(rank_pattern[key]).unsqueeze(-1).to(p.device)
- p.masked_fill_(~mask.bool(), 0.0)
\ No newline at end of file
diff --git a/mftcoder_atorch/model/peft/tuner/bitfit.py b/mftcoder_atorch/model/peft/tuner/bitfit.py
deleted file mode 100644
index 2e2a12f..0000000
--- a/mftcoder_atorch/model/peft/tuner/bitfit.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import sys
-sys.path.append("..")
-sys.path.append("../..")
-import torch
-import importlib
-from enum import Enum
-from peft.utils import PeftType
-from dataclasses import dataclass, field, asdict
-from typing import Optional, List
-
-from .pe_base_model import PEBaseModel
-from model.peft.utils import PetuningConfig
-from model.peft.utils.others import _freeze_model
-
-
-def is_alps_available():
- return importlib.util.find_spec("alps") is not None
-
-
-if is_alps_available():
- from alps.util import logger
-else:
- import logging
- logger = logging.getLogger(__file__)
-
-
-class PEBitfitModel(PEBaseModel):
- """
- 只训练模型bias：参考 https://arxiv.org/pdf/2106.10199.pdf
- model: huggingface transformers model
- tokenizer: huggingface transformers tokenizer
- """
-
- def __init__(self, model):
- self.model = model
-
- def get_model(self):
- not_freeze_param_name = ["bias"]
- set_parameter_requires_grad(self.model, not_freeze_param_name)
- return self.model
-
- @classmethod
- def restore(self, model=None, path=None):
- logger.info("bitfit不需要额外加载参数")
- return model
-
-
-# 根据名称锁定参数层
-def set_parameter_requires_grad(model, freeze_param_name=[]):
- if not isinstance(freeze_param_name, list):
- freeze_param_name = [freeze_param_name]
-
- for idx, (name, param) in enumerate(model.named_parameters()):
- for p in freeze_param_name:
- if p not in name:
- param.requires_grad = False
- # 打印参数层名
- for idx, (name, param) in enumerate(model.named_parameters()):
- for p in freeze_param_name:
- if p in name:
- print("trainable parameter name is:")
- print(name)
- param.requires_grad = True
-
-
-@dataclass
-class PeftBitfitConfig(PetuningConfig):
- """
- This is the configuration class to store the configuration of a [`PeftBitfitModel`].
-
- Args:
- modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable
- and saved in the final checkpoint.
- """
-
- modules_to_save: Optional[List[str]] = field(
- default=None,
- metadata={
- "help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. "
- "For example, in Sequence Classification or Token Classification tasks, "
- "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
- },
- )
-
- def __post_init__(self):
- self.peft_type = PeftType.BITFIT
-
-
-class PeftBitfitModel(torch.nn.Module):
- """
- Creates Bitfit model for ant peft.
-
- Args:
- model ([`~transformers.PreTrainedModel`]): The model to be freeze with some layers.
- config ([`PeftBitfitConfig`]): The configuration of the Bitfit model.
-
- Returns:
- `torch.nn.Module`: The Bitfit model.
-
- **Attributes**:
- - **model** ([`~transformers.PreTrainedModel`]) -- The model to be freezed.
- - **peft_config** ([`PeftBitfitConfig`]): The configuration of the Bitfit model.
- """
-
- def __init__(self, model, config, adapter_name):
- super().__init__()
- self.model = model
-
- self.forward = self.model.forward
- self.peft_config = config
- self.add_adapter(adapter_name, self.peft_config[adapter_name])
-
- def add_adapter(self, adapter_name, config=None):
- if not isinstance(config, PeftBitfitConfig):
- raise ValueError(
- f"The PeftBitfitModel need PeftBitfitConfig, but get {type(config)}."
- )
-
- if config is not None:
- config = self._prepare_lora_config(config)
- self.peft_config[adapter_name] = config
-
- if len(self.peft_config) > 1:
- raise ValueError(
- "BitfitModel supports only 1 peft config or name."
- "Because it only freeze the shallow layers without any additional parameters."
- )
-
- self.model = PEBitfitModel(self.model).get_model()
-
- if self.peft_config[adapter_name].inference_mode:
- _freeze_model(self.model)
-
- @staticmethod
- def _prepare_lora_config(peft_config):
- if peft_config.inference_mode:
- peft_config.merge_weights = True
- return peft_config
-
- def __getattr__(self, name: str):
- """Forward missing attributes to the wrapped module."""
- try:
- return super().__getattr__(name) # defer to nn.Module's logic
- except AttributeError:
- return getattr(self.model, name)
-
- def get_peft_config_as_dict(self, inference: bool = False):
- config_dict = {}
- for key, value in self.peft_config.items():
- config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(value).items()}
- if inference:
- config["inference_mode"] = True
- config_dict[key] = config
- return config
\ No newline at end of file
diff --git a/mftcoder_atorch/model/peft/tuner/pe_base_model.py b/mftcoder_atorch/model/peft/tuner/pe_base_model.py
deleted file mode 100644
index bd4082a..0000000
--- a/mftcoder_atorch/model/peft/tuner/pe_base_model.py
+++ /dev/null
@@ -1,19 +0,0 @@
-class PEBaseModel:
- """PEtuning的基类模型，定义了PEtuning模型都该有的方法"""
-
- def __init__():
- return
-
- def get_model(self):
- """对模型进行修改，冻结参数或者插入可训模块"""
- pass
-
- @classmethod
- def restore(self, model=None, path=None):
- """从path恢复PE模型
-
- Args:
- model (_type_, optional): 原始模型. Defaults to None.
- path (_type_, optional): 增量路径. Defaults to None.
- """
- pass
diff --git a/mftcoder_atorch/model/peft/tuner/roem.py b/mftcoder_atorch/model/peft/tuner/roem.py
deleted file mode 100644
index 9b455bf..0000000
--- a/mftcoder_atorch/model/peft/tuner/roem.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import sys
-sys.path.append("..")
-sys.path.append("../..")
-import torch
-import importlib
-from enum import Enum
-from peft.utils import PeftType
-from dataclasses import dataclass, field, asdict
-from typing import Optional, List, Union
-
-from .pe_base_model import PEBaseModel
-from model.peft.utils import (
- PetuningConfig,
- TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING
-)
-from model.peft.utils.others import _freeze_model
-
-
-def is_alps_available():
- return importlib.util.find_spec("alps") is not None
-
-
-if is_alps_available():
- from alps.util import logger
-else:
- import logging
- logger = logging.getLogger(__file__)
-
-
-class PEROEMModel(PEBaseModel):
- """
- 只训练模型中间偏上层mlp：参考 https://arxiv.org/pdf/2202.05262.pdf ； https://arxiv.org/abs/2012.14913
- model: huggingface transformers model
- tokenizer: huggingface transformers tokenizer
- """
-
- def __init__(self, model, model_name, task_type=None):
- self.model = model
- self.model_name = model_name
-
- def get_model(self):
- layer_mapping = TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING[self.model_name]
- assert len(layer_mapping) == 2
- not_freeze_param_name = []
- for i in range(layer_mapping[0], layer_mapping[1]):
- no_freeze_name = str(i) + ".mlp"
- logger.info(f"Freeze the {no_freeze_name} layer of model")
- not_freeze_param_name.append(no_freeze_name)
- set_parameter_requires_grad(self.model, not_freeze_param_name)
- return self.model
-
- @classmethod
- def restore(self, model=None, path=None):
- logger.info("roem不需要额外加载参数")
- return model
-
-
-# 根据名称锁定参数层
-def set_parameter_requires_grad(model, freeze_param_name=[]):
- if not isinstance(freeze_param_name, list):
- freeze_param_name = [freeze_param_name]
-
- for idx, (name, param) in enumerate(model.named_parameters()):
- for p in freeze_param_name:
- if p not in name:
- param.requires_grad = False
- # 打印参数层名
- for idx, (name, param) in enumerate(model.named_parameters()):
- for p in freeze_param_name:
- if p in name:
- print("The name of used parameter used by ROEM is:")
- print(name)
- param.requires_grad = True
-
-
-@dataclass
-class PeftROEMConfig(PetuningConfig):
- """
- This is the configuration class to store the configuration of a [`PeftROEMModel`].
-
- Args:
- target_layers (`Union[List[int], int]`): The names of the modules to apply Lora to.
- """
-
- target_layers: Optional[Union[List[int], int]] = field(
- default=None,
- metadata={
- "help": "List of layers of the model to freeze the parameters."
- "For example, [20, 30] or '30' "
- },
- )
-
- def __post_init__(self):
- self.peft_type = PeftType.ROEM
-
-
-class PeftROEMModel(torch.nn.Module):
- """
- Creates ROEM model for ant peft.
-
- Args:
- model ([`~transformers.PreTrainedModel`]): The model to be freeze with some layers.
- config ([`PeftROEMConfig`]): The configuration of the ROEM model.
-
- Returns:
- `torch.nn.Module`: The ROEM model.
-
- **Attributes**:
- - **model** ([`~transformers.PreTrainedModel`]) -- The model to be freezed.
- - **peft_config** ([`PeftROEMConfig`]): The configuration of the ROEM model.
- """
-
- def __init__(self, model, config, adapter_name):
- super().__init__()
- self.model = model
-
- self.forward = self.model.forward
- self.peft_config = config
- self.add_adapter(adapter_name, self.peft_config[adapter_name])
-
- def add_adapter(self, adapter_name, config=None):
- if not isinstance(config, PeftROEMConfig):
- raise ValueError(
- f"The PeftROEMModel need PeftROEMConfig, but get {type(config)}."
- )
-
- model_config = self.model.config.to_dict() if hasattr(self.model.config, "to_dict") else self.model.config
- if config is not None:
- config = self._prepare_lora_config(config, model_config)
- self.peft_config[adapter_name] = config
-
- if len(self.peft_config) > 1:
- raise ValueError(
- "ROEMModel supports only 1 peft config or name."
- "Because it only freeze the shallow layers without any additional parameters."
- )
-
- model_name = model_config["model_type"]
- self.model = PEROEMModel(self.model, model_name).get_model()
-
- if self.peft_config[adapter_name].inference_mode:
- _freeze_model(self.model)
-
- @staticmethod
- def _prepare_lora_config(peft_config, model_config):
- if peft_config.target_layers is None:
- if model_config["model_type"] not in TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING:
- raise ValueError("Please specify `target_layers` in `peft_config`")
- peft_config.target_layers = TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING[model_config["model_type"]]
- if peft_config.inference_mode:
- peft_config.merge_weights = True
- return peft_config
-
- def __getattr__(self, name: str):
- """Forward missing attributes to the wrapped module."""
- try:
- return super().__getattr__(name) # defer to nn.Module's logic
- except AttributeError:
- return getattr(self.model, name)
-
- def get_peft_config_as_dict(self, inference: bool = False):
- config_dict = {}
- for key, value in self.peft_config.items():
- config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(value).items()}
- if inference:
- config["inference_mode"] = True
- config_dict[key] = config
- return config
\ No newline at end of file
diff --git a/mftcoder_atorch/model/peft/tuner/routelora.py b/mftcoder_atorch/model/peft/tuner/routelora.py
deleted file mode 100644
index ccbe46f..0000000
--- a/mftcoder_atorch/model/peft/tuner/routelora.py
+++ /dev/null
@@ -1,622 +0,0 @@
-import sys
-sys.path.append("..")
-sys.path.append("../..")
-import importlib
-import re
-import math
-import warnings
-from dataclasses import dataclass, field
-from typing import Optional, Dict, List, Any, Union # noqa
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from peft.utils import transpose
-from transformers.pytorch_utils import Conv1D
-from model.peft.utils import TRANSFORMERS_MODELS_TO_ROUTELORA_TARGET_MODULES_MAPPING
-from model.peft.tuner import PeftType
-
-from peft.tuners.lora import (
- LoraConfig,
- LoraLayer,
- LoraModel
-)
-
-
-def is_bnb_available():
- return importlib.util.find_spec("bitsandbytes") is not None
-
-
-if is_bnb_available():
- import bitsandbytes as bnb
-
-
-@dataclass
-class RouteLoraConfig(LoraConfig):
- """
- This is the configuration class to store the configuration of a [`~peft.RouteLora`].
-
- Args:
- - r (`int`): Lora attention dimension
- - route_size (`int`): The number of router models.
- - target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to.
- - lora_alpha (`float`): The alpha parameter for Lora scaling.
- - lora_dropout (`float`): The dropout probability for Lora layers.
- - merge_weights (`bool`):
- Whether to merge the weights of the Lora layers with the base transformer model in `eval` mode.
- - fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out)
- - enable_lora ( `List[bool]`): Used with `lora.MergedLinear`.
- - bias (`str`): Bias type for Lora. Can be 'none', 'all' or 'lora_only'
- - modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable
- and saved in the final checkpoint.
- """
-
- route_size: int = field(default=1, metadata={"help": "The size of router"})
-
- def __post_init__(self):
- self.peft_type = PeftType.ROUTELORA
-
-
-class RouteLoraModel(LoraModel):
- """
- Creates Low Rank Adapter (Lora) model from a pretrained transformers model.
-
- Args:
- model ([`transformers.PreTrainedModel`]): The model to be adapted.
- config ([`LoraConfig`]): The configuration of the Lora model.
-
- Returns:
- `torch.nn.Module`: The Lora model.
-
- Example::
-
- >>> from transformers import AutoModelForSeq2SeqLM, LoraConfig >>> from peft import LoraModel, LoraConfig >>>
- config = LoraConfig(
- peft_type="LORA", task_type="SEQ_2_SEQ_LM", r=8, lora_alpha=32, target_modules=["q", "v"],
- lora_dropout=0.01, )
- >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> lora_model = LoraModel(config, model)
-
- **Attributes**:
- - **model** ([`transformers.PreTrainedModel`]) -- The model to be adapted.
- - **peft_config** ([`LoraConfig`]): The configuration of the Lora model.
- """
-
- def __init__(self, config, model):
- super(RouteLoraModel, self).__init__(config, model)
-
- self.route_size = self.peft_config.route_size
- if self.route_size > 0:
- self.activate_route_lora(self.route_size - 1)
-
- def _find_and_replace(self):
- loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
- if loaded_in_8bit and not is_bnb_available():
- raise ImportError(
- "To use Lora with 8-bit quantization, please install the `bitsandbytes` package. "
- "You can install it with `pip install bitsandbytes`."
- )
- is_target_modules_in_base_model = False
- kwargs = {
- "r": self.peft_config.r,
- "lora_alpha": self.peft_config.lora_alpha,
- "lora_dropout": self.peft_config.lora_dropout,
- "fan_in_fan_out": self.peft_config.fan_in_fan_out,
- "merge_weights": self.peft_config.merge_weights or self.peft_config.inference_mode,
- }
- key_list = [key for key, _ in self.model.named_modules()]
- for key in key_list:
- if isinstance(self.peft_config.target_modules, str):
- target_module_found = re.fullmatch(self.peft_config.target_modules, key)
- else:
- target_module_found = any(key.endswith(target_key) for target_key in self.peft_config.target_modules)
- if target_module_found:
- if not is_target_modules_in_base_model:
- is_target_modules_in_base_model = True
- parent, target, target_name = self._get_submodules(key)
- bias = target.bias is not None
- if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt):
- kwargs.update(
- {
- "has_fp16_weights": target.state.has_fp16_weights,
- "memory_efficient_backward": target.state.memory_efficient_backward,
- "threshold": target.state.threshold,
- "index": target.index,
- }
- )
- if self.peft_config.enable_lora is None:
- new_module = Linear8bitLt(target.in_features, target.out_features, bias=bias, **kwargs)
- else:
- kwargs.update({
- "enable_lora": self.peft_config.enable_lora,
- "route_size": self.peft_config.route_size
- })
- new_module = MergedLinear8bitLt(target.in_features, target.out_features, bias=bias, **kwargs)
- elif isinstance(target, torch.nn.Linear) and self.peft_config.enable_lora is None:
- new_module = RouteLinear(target.in_features, target.out_features, bias=bias, **kwargs)
- elif self.peft_config.enable_lora is not None:
- kwargs.update({
- "enable_lora": self.peft_config.enable_lora,
- "route_size": self.peft_config.route_size
- })
- if isinstance(target, Conv1D):
- in_features, out_features = (
- target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape
- )
- else:
- in_features, out_features = target.in_features, target.out_features
- if kwargs["fan_in_fan_out"]:
- warnings.warn(
- "fan_in_fan_out is set to True but the target module is not a Conv1D. "
- "Setting fan_in_fan_out to False."
- )
- kwargs["fan_in_fan_out"] = False
- new_module = MergedRouteLinear(in_features, out_features, bias=bias, **kwargs)
- self._replace_module(parent, target_name, new_module, target)
- if not is_target_modules_in_base_model:
- raise ValueError(
- f"Target modules {self.peft_config.target_modules} not found in the base model. "
- f"Please check the target modules and try again."
- )
-
- def expand_external_router(self, weight_path: str):
- loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
- if loaded_in_8bit:
- raise NotImplementedError(
- "The route lora method is not support for int quantization, "
- "we will implement this fuction in the future."
- )
-
- states_dict = torch.load(weight_path)
- external_key_list = states_dict.keys()
- if "0" in external_key_list[0]:
- raise NotImplementedError("The merge with other router is not support, pls wait.")
- self.peft_config.route_size += 1
-
- key_list = [key for key, _ in self.model.named_modules()]
- for key in key_list:
- if isinstance(self.peft_config.target_modules, str):
- target_module_found = re.fullmatch(self.peft_config.target_modules, key)
- else:
- target_module_found = any(key.endswith(target_key) for target_key in self.peft_config.target_modules)
-
- if target_module_found:
- expand_cnt = 0
- for external_key in external_key_list:
- if external_key.beginwith(key):
- if "0" in external_key:
- raise NotImplementedError("The merge with other router is not support, pls wait.")
- else:
- _, target, _ = self._get_submodules(key)
- target.route_size += 1
- weights = states_dict[external_key]
- new_linear_moudle = nn.Linear(weights.size(0), weights.size(1))
- new_linear_moudle.weight.data = weights
- new_linear_moudle.to(target.weights.device)
-
- if "lora_A" in external_key:
- target.lora_A.append(new_linear_moudle)
- else:
- target.lora_B.append(new_linear_moudle)
- expand_cnt += 1
-
- assert expand_cnt == 2, ValueError("123")
-
- def activate_route_lora(self, route_id: int):
- loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
- if loaded_in_8bit:
- raise NotImplementedError(
- "The route lora method is not support for int quantization, "
- "we will implement this fuction in the future."
- )
-
- if route_id < self.route_size:
- key_list = [key for key, _ in self.model.named_modules()]
- for key in key_list:
- if isinstance(self.peft_config.target_modules, str):
- target_module_found = re.fullmatch(self.peft_config.target_modules, key)
- else:
- target_module_found = any(
- key.endswith(target_key) for target_key in self.peft_config.target_modules)
- if target_module_found:
- _, target, _ = self._get_submodules(key)
- target.activate_route_lora(route_id)
- else:
- warnings.warn("The route id need less than the route size,"
- f"but the route id is {route_id} "
- f"and the route size is {self.route_size}.")
-
- @staticmethod
- def _prepare_lora_config(peft_config, model_config):
- if peft_config.target_modules is None:
- if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_ROUTELORA_TARGET_MODULES_MAPPING:
- raise ValueError("Please specify `target_modules` in `peft_config`")
- peft_config.target_modules = TRANSFORMERS_MODELS_TO_ROUTELORA_TARGET_MODULES_MAPPING[
- model_config["model_type"]]
- if peft_config.inference_mode:
- peft_config.merge_weights = True
- return peft_config
-
-
-# 以下代码基于https://github.com/microsoft/LoRA/blob/main/loralib/layers.py改进，
-# 用于扩展RouteLora方法并适配AntNLP框架
-
-
-class RouteLinear(nn.Linear, LoraLayer):
- # Lora implemented in a dense layer
- def __init__(
- self,
- in_features: int,
- out_features: int,
- r: int = 0,
- route_size: int = 1,
- lora_alpha: int = 1,
- lora_dropout: float = 0.0,
- fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
- merge_weights: bool = True,
- **kwargs,
- ):
- nn.Linear.__init__(self, in_features, out_features, **kwargs)
- LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights)
-
- self.fan_in_fan_out = fan_in_fan_out
- self.route_size = route_size
-
- # Actual trainable parameters
- self.active_route_id = None
- if r > 0 and route_size > 0:
- self.lora_A = nn.ParameterList()
- self.lora_B = nn.ParameterList()
- for _ in range(route_size):
- self.lora_A.append(nn.Linear(in_features, r, bias=False))
- self.lora_B.append(nn.Linear(r, out_features, bias=False))
- self.scaling = self.lora_alpha / self.r
-
- # Freezing the pre-trained weight matrix
- self.weight.requires_grad = False
- self.reset_parameters()
- if fan_in_fan_out:
- self.weight.data = self.weight.data.T
-
- def reset_parameters(self):
- nn.Linear.reset_parameters(self)
- if hasattr(self, "lora_A"):
- # initialize A the same way as the default for nn.Linear and B to zero
- for sub_lora_A, sub_lora_B in zip(self.lora_A, self.lora_B):
- nn.init.kaiming_uniform_(sub_lora_A, a=math.sqrt(5))
- nn.init.zeros_(sub_lora_B.weight)
-
- def activate_route_lora(self, route_id: int):
- if route_id != self.active_route_id:
- if route_id >= self.route_size:
- warnings.warn(f"The choice route id is great than route size,"
- f"where the route id is {route_id} and route size is {self.route_size}.")
- elif not self.merged:
- self.active_route_id = route_id
- elif self.merged and self.r > 0 and self.active_route_id is not None:
- self.weight.data -= (
- transpose(
- self.lora_B[self.active_route_id].weight @ self.lora_A[self.active_route_id].weight,
- self.fan_in_fan_out
- ) * self.scaling
- )
- self.merged = False
-
- def train(self, mode: bool = True):
- nn.Linear.train(self, mode)
- self.lora_A.train(mode)
- self.lora_B.train(mode)
- if not mode and self.merge_weights and not self.merged and self.active_route_id is not None:
- # Merge the weights and mark it
- if self.r > 0:
- self.weight.data += (
- transpose(
- self.lora_B[self.active_route_id].weight @ self.lora_A[self.active_route_id].weight,
- self.fan_in_fan_out
- ) * self.scaling
- )
- self.merged = True
- elif self.merge_weights and self.merged and self.active_route_id is not None:
- # Make sure that the weights are not merged
- if self.r > 0:
- self.weight.data -= (
- transpose(
- self.lora_B[self.active_route_id].weight @ self.lora_A[self.active_route_id].weight,
- self.fan_in_fan_out
- ) * self.scaling
- )
- self.merged = False
-
- def eval(self):
- nn.Linear.eval(self)
- self.lora_A.eval()
- self.lora_B.eval()
-
- def forward(self, x: torch.Tensor):
- if self.disable_adapters:
- if self.r > 0 and self.merged and self.active_route_id is not None:
- self.weight.data -= (
- transpose(
- self.lora_B[self.active_route_id].weight @ self.lora_A[self.active_route_id].weight,
- self.fan_in_fan_out
- ) * self.scaling
- )
- self.merged = False
- return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
- elif self.r > 0 and not self.merged and self.active_route_id is not None:
- result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
- if self.r > 0:
- result += self.lora_B[self.active_route_id](
- self.lora_A[self.active_route_id](self.lora_dropout(x))
- ) * self.scaling
- return result
- else:
- return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
-
-
-class MergedRouteLinear(nn.Linear, LoraLayer):
- # Lora implemented in a dense layer
- def __init__(
- self,
- in_features: int,
- out_features: int,
- r: int = 0,
- route_size: int = 1,
- lora_alpha: int = 1,
- lora_dropout: float = 0.0,
- enable_lora: List[bool] = [False],
- fan_in_fan_out: bool = False,
- merge_weights: bool = True,
- **kwargs,
- ):
- nn.Linear.__init__(self, in_features, out_features, **kwargs)
- LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights)
- if out_features % len(enable_lora) != 0:
- raise ValueError("The length of enable_lora must divide out_features")
- self.enable_lora = enable_lora
- self.fan_in_fan_out = fan_in_fan_out
- self.route_size = route_size
-
- # Actual trainable parameters
- self.active_route_id = None
- if r > 0 and route_size > 0 and any(enable_lora):
- self.lora_A = nn.ParameterList()
- self.lora_B = nn.ParameterList()
- for _ in range(route_size):
- self.lora_A.append(nn.Linear(in_features, r * sum(enable_lora), bias=False))
- self.lora_B.append(nn.Conv1d(
- r * sum(enable_lora),
- out_features // len(enable_lora) * sum(enable_lora),
- kernel_size=1,
- groups=2,
- bias=False,
- ))
- self.scaling = self.lora_alpha / self.r
-
- # Freezing the pre-trained weight matrix
- self.weight.requires_grad = False
-
- # Compute the indices
- self.lora_ind = self.weight.new_zeros((out_features,), dtype=torch.bool).view(len(enable_lora), -1)
- self.lora_ind[enable_lora, :] = True
- self.lora_ind = self.lora_ind.view(-1)
- self.reset_parameters()
- if fan_in_fan_out:
- self.weight.data = self.weight.data.T
-
- def reset_parameters(self):
- nn.Linear.reset_parameters(self)
- if hasattr(self, "lora_A"):
- # initialize A the same way as the default for nn.Linear and B to zero
- for sub_lora_A, sub_lora_B in zip(self.lora_A, self.lora_B):
- nn.init.kaiming_uniform_(sub_lora_A.weight, a=math.sqrt(5))
- nn.init.zeros_(sub_lora_B.weight)
-
- def zero_pad(self, x):
- result = x.new_zeros((*x.shape[:-1], self.out_features))
- result = result.view(-1, self.out_features)
- result[:, self.lora_ind] = x.reshape(-1, self.out_features // len(self.enable_lora) * sum(self.enable_lora))
- return result.view((*x.shape[:-1], self.out_features))
-
- def activate_route_lora(self, route_id: int):
- if route_id != self.active_route_id:
- if route_id >= self.route_size:
- warnings.warn(f"The choice route id is great than route size,"
- f"where the route id is {route_id} and route size is {self.route_size}.")
- elif not self.merged:
- self.active_route_id = route_id
- elif self.merged and self.r > 0 and self.active_route_id is not None:
- delta_w = F.conv1d(
- self.lora_A[self.active_route_id].weight.data.unsqueeze(0),
- self.lora_B[self.active_route_id].weight.data.unsqueeze(-1),
- groups=sum(self.enable_lora),
- ).squeeze(0)
- self.weight.data -= self.zero_pad(transpose(delta_w * self.scaling, self.fan_in_fan_out))
- self.merged = False
-
- def train(self, mode: bool = True):
- nn.Linear.train(self, mode)
- self.lora_A.train(mode)
- self.lora_B.train(mode)
- if not mode and self.merge_weights and not self.merged:
- # Merge the weights and mark it
- if self.r > 0 and self.active_route_id is not None and any(self.enable_lora):
- delta_w = F.conv1d(
- self.lora_A[self.active_route_id].weight.data.unsqueeze(0),
- self.lora_B[self.active_route_id].weight.data.unsqueeze(-1),
- groups=sum(self.enable_lora),
- ).squeeze(0)
- self.weight.data += self.zero_pad(transpose(delta_w * self.scaling, self.fan_in_fan_out))
- self.merged = True
- elif self.merge_weights and self.merged:
- # Make sure that the weights are not merged
- if self.r > 0 and self.active_route_id is not None and any(self.enable_lora):
- delta_w = F.conv1d(
- self.lora_A[self.active_route_id].weight.data.unsqueeze(0),
- self.lora_B[self.active_route_id].weight.data.unsqueeze(-1),
- groups=sum(self.enable_lora),
- ).squeeze(0)
- self.weight.data -= self.zero_pad(transpose(delta_w * self.scaling, self.fan_in_fan_out))
- self.merged = False
-
- def eval(self):
- nn.Linear.eval(self)
- self.lora_A.eval()
- self.lora_B.eval()
-
- def forward(self, x: torch.Tensor):
- if self.disable_adapters:
- if self.r > 0 and self.merged and any(self.enable_lora) and self.active_route_id is not None:
- delta_w = F.conv1d(
- self.lora_A[self.active_route_id].weight.data.unsqueeze(0),
- self.lora_B[self.active_route_id].weight.data.unsqueeze(-1),
- groups=sum(self.enable_lora),
- ).squeeze(0)
- self.weight.data -= self.zero_pad(transpose(delta_w * self.scaling, self.fan_in_fan_out))
- self.merged = False
- return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
- elif self.merged:
- return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
- else:
- result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
- if self.r > 0 and self.active_route_id is not None:
- after_A = self.lora_A[self.active_route_id](self.lora_dropout(x))
- after_B = self.lora_B[self.active_route_id](after_A.transpose(-2, -1)).transpose(-2, -1)
- result += self.zero_pad(after_B) * self.scaling
- return result
-
-
-if is_bnb_available():
-
- class Linear8bitLt(bnb.nn.Linear8bitLt, LoraLayer):
- # Lora implemented in a dense layer
- def __init__(
- self,
- in_features,
- out_features,
- r: int = 0,
- lora_alpha: int = 1,
- lora_dropout: float = 0.0,
- **kwargs,
- ):
- bnb.nn.Linear8bitLt.__init__(
- self,
- in_features,
- out_features,
- bias=kwargs.get("bias", True),
- has_fp16_weights=kwargs.get("has_fp16_weights", True),
- memory_efficient_backward=kwargs.get("memory_efficient_backward", False),
- threshold=kwargs.get("threshold", 0.0),
- index=kwargs.get("index", None),
- )
- LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=False)
- # Actual trainable parameters
- if r > 0:
- self.lora_A = nn.Linear(in_features, r, bias=False)
- self.lora_B = nn.Linear(r, out_features, bias=False)
- self.scaling = self.lora_alpha / self.r
- # Freezing the pre-trained weight matrix
- self.weight.requires_grad = False
- self.reset_parameters()
-
- def reset_parameters(self):
- if hasattr(self, "lora_A"):
- # initialize A the same way as the default for nn.Linear and B to zero
- nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
- nn.init.zeros_(self.lora_B.weight)
-
- def forward(self, x: torch.Tensor):
- result = super().forward(x)
-
- if self.disable_adapters:
- return result
- elif self.r > 0:
- if not torch.is_autocast_enabled():
- expected_dtype = result.dtype
-
- if x.dtype != torch.float32:
- x = x.float()
- output = self.lora_B(self.lora_A(self.lora_dropout(x))).to(expected_dtype) * self.scaling
- result += output
- else:
- output = self.lora_B(self.lora_A(self.lora_dropout(x))) * self.scaling
- result += output
- return result
-
- class MergedLinear8bitLt(bnb.nn.Linear8bitLt, LoraLayer):
- # Lora implemented in a dense layer
- def __init__(
- self,
- in_features: int,
- out_features: int,
- r: int = 0,
- lora_alpha: int = 1,
- lora_dropout: float = 0.0,
- enable_lora: List[bool] = [False],
- **kwargs,
- ):
- bnb.nn.Linear8bitLt.__init__(
- self,
- in_features,
- out_features,
- bias=kwargs.get("bias", True),
- has_fp16_weights=kwargs.get("has_fp16_weights", True),
- memory_efficient_backward=kwargs.get("memory_efficient_backward", False),
- threshold=kwargs.get("threshold", 0.0),
- index=kwargs.get("index", None),
- )
- LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=False)
- if out_features % len(enable_lora) != 0:
- raise ValueError("The length of enable_lora must divide out_features")
- self.enable_lora = enable_lora
- # Actual trainable parameters
- if r > 0 and any(enable_lora):
- self.lora_A = nn.Linear(in_features, r * sum(enable_lora), bias=False)
- self.lora_B = nn.Conv1d(
- r * sum(enable_lora),
- out_features // len(enable_lora) * sum(enable_lora),
- kernel_size=1,
- groups=2,
- bias=False,
- )
- self.scaling = self.lora_alpha / self.r
- # Freezing the pre-trained weight matrix
- self.weight.requires_grad = False
- # Compute the indices
- self.lora_ind = self.weight.new_zeros((out_features,), dtype=torch.bool).view(len(enable_lora), -1)
- self.lora_ind[enable_lora, :] = True
- self.lora_ind = self.lora_ind.view(-1)
- self.reset_parameters()
-
- def reset_parameters(self):
- if hasattr(self, "lora_A"):
- # initialize A the same way as the default for nn.Linear and B to zero
- nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
- nn.init.zeros_(self.lora_B.weight)
-
- def zero_pad(self, x):
- result = x.new_zeros((*x.shape[:-1], self.out_features))
- result = result.view(-1, self.out_features)
- result[:, self.lora_ind] = x.reshape(
- -1, self.out_features // len(self.enable_lora) * sum(self.enable_lora)
- )
- return result.view((*x.shape[:-1], self.out_features))
-
- def forward(self, x: torch.Tensor):
- result = super().forward(x)
- if self.disable_adapters:
- return result
- elif self.r > 0:
- if not torch.is_autocast_enabled():
- expected_dtype = result.dtype
- if x.dtype != torch.float32:
- x = x.float()
- after_A = self.lora_A(self.lora_dropout(x))
- after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1)
- output = self.zero_pad(after_B).to(expected_dtype) * self.scaling
- result += output
- else:
- after_A = self.lora_A(self.lora_dropout(x))
- after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1)
- output = self.zero_pad(after_B) * self.scaling
- result += output
- return result
\ No newline at end of file
diff --git a/mftcoder_atorch/model/peft/tuner/unipelt.py b/mftcoder_atorch/model/peft/tuner/unipelt.py
deleted file mode 100644
index a8445c3..0000000
--- a/mftcoder_atorch/model/peft/tuner/unipelt.py
+++ /dev/null
@@ -1,1021 +0,0 @@
-import sys
-sys.path.append("..")
-sys.path.append("../..")
-from peft import get_peft_model, PeftModel
-import math
-import re
-import warnings
-import os
-from dataclasses import asdict, dataclass, field
-from enum import Enum
-from typing import List, Optional, Union
-from accelerate import dispatch_model, infer_auto_device_map
-from accelerate.utils import get_balanced_memory
-import inspect
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers.pytorch_utils import Conv1D
-
-from accelerate.hooks import (
- AlignDevicesHook,
- add_hook_to_module,
- remove_hook_from_submodules,
-)
-from peft.utils import (
- TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING,
- ModulesToSaveWrapper,
- PeftConfig,
- WEIGHTS_NAME,
- _freeze_adapter,
- _get_submodules,
- transpose,
-)
-from model.peft.tuner import PeftType
-from peft.mapping import PEFT_TYPE_TO_CONFIG_MAPPING
-
-
-from .pe_base_model import PEBaseModel
-
-
-# coding=utf-8
-# Copyright 2023-present the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-class UniPELTModel(torch.nn.Module):
- r"""
- 改编自LoraModel
- Args:
- model ([`~transformers.PreTrainedModel`]): The model to be adapted.
- config ([`LoraConfig`]): The configuration of the Lora model.
-
- Returns:
- `torch.nn.Module`: The Lora model.
-
- **Attributes**:
- - **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted.
- - **peft_config** ([`LoraConfig`]): The configuration of the Lora model.
- """
-
- def __init__(self, model, config, adapter_name):
- super().__init__()
- self.model = model
- self.forward = self.model.forward
- self.peft_config = config
- self.add_adapter(adapter_name, self.peft_config[adapter_name])
-
- def add_adapter(self, adapter_name, config=None):
- if config is not None:
- model_config = (
- self.model.config.to_dict()
- if hasattr(self.model.config, "to_dict")
- else self.model.config
- )
- config = self._prepare_lora_config(config, model_config)
- self.peft_config[adapter_name] = config
- self._find_and_replace(adapter_name)
- if len(self.peft_config) > 1 and self.peft_config[adapter_name].bias != "none":
- raise ValueError(
- "LoraModel supports only 1 adapter with bias. \
- When using multiple adapters, set bias to 'none' for all adapters."
- )
- mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias)
- if self.peft_config[adapter_name].inference_mode:
- _freeze_adapter(self.model, adapter_name)
-
- def _find_and_replace(self, adapter_name):
- lora_config = self.peft_config[adapter_name]
-
- is_target_modules_in_base_model = False
- kwargs = {
- "r": lora_config.r,
- "lora_alpha": lora_config.lora_alpha,
- "lora_dropout": lora_config.lora_dropout,
- "fan_in_fan_out": lora_config.fan_in_fan_out,
- "init_lora_weights": lora_config.init_lora_weights,
- }
- key_list = [key for key, _ in self.model.named_modules()]
- for key in key_list:
- if isinstance(lora_config.target_modules, str):
- target_module_found = re.fullmatch(lora_config.target_modules, key)
- else:
- target_module_found = any(
- key.endswith(target_key)
- for target_key in lora_config.target_modules
- )
- if target_module_found:
- if not is_target_modules_in_base_model:
- is_target_modules_in_base_model = True
- parent, target, target_name = _get_submodules(self.model, key)
- if hasattr(target, "bias"):
- bias = target.bias is not None
-
- if isinstance(target, GatedLoraLayer):
- target.update_layer(
- adapter_name,
- lora_config.r,
- lora_config.lora_alpha,
- lora_config.lora_dropout,
- lora_config.init_lora_weights,
- )
- else:
- if True: # lazy modification
- if isinstance(target, torch.nn.Linear):
- in_features, out_features = (
- target.in_features,
- target.out_features,
- )
- if kwargs["fan_in_fan_out"]:
- warnings.warn(
- "fan_in_fan_out is set to True but the target module is `torch.nn.Linear`. "
- "Setting fan_in_fan_out to False."
- )
- kwargs[
- "fan_in_fan_out"
- ] = lora_config.fan_in_fan_out = False
- elif isinstance(target, Conv1D):
- in_features, out_features = (
- target.weight.ds_shape
- if hasattr(target.weight, "ds_shape")
- else target.weight.shape
- )
- if not kwargs["fan_in_fan_out"]:
- warnings.warn(
- "fan_in_fan_out is set to False but the target module is `Conv1D`. "
- "Setting fan_in_fan_out to True."
- )
- kwargs[
- "fan_in_fan_out"
- ] = lora_config.fan_in_fan_out = True
- else:
- raise ValueError(
- f"Target module {target} is not supported. "
- f"Currently, only `torch.nn.Linear` and `Conv1D` are supported."
- )
- new_module = PELTLinear(
- adapter_name, in_features, out_features, bias=bias, **kwargs
- )
-
- self._replace_module(parent, target_name, new_module, target)
- if not is_target_modules_in_base_model:
- raise ValueError(
- f"Target modules {lora_config.target_modules} not found in the base model. "
- f"Please check the target modules and try again."
- )
-
- def _replace_module(self, parent_module, child_name, new_module, old_module):
- setattr(parent_module, child_name, new_module)
- new_module.weight = old_module.weight
- if hasattr(old_module, "bias"):
- if old_module.bias is not None:
- new_module.bias = old_module.bias
-
- if getattr(old_module, "state", None) is not None:
- new_module.state = old_module.state
- new_module.to(old_module.weight.device)
-
- # dispatch to correct device
- for name, module in new_module.named_modules():
- if "lora_" in name:
- module.to(old_module.weight.device)
-
- def __getattr__(self, name: str):
- """Forward missing attributes to the wrapped module."""
- try:
- return super().__getattr__(name) # defer to nn.Module's logic
- except AttributeError:
- return getattr(self.model, name)
-
- def get_peft_config_as_dict(self, inference: bool = False):
- config_dict = {}
- for key, value in self.peft_config.items():
- config = {
- k: v.value if isinstance(v, Enum) else v
- for k, v in asdict(value).items()
- }
- if inference:
- config["inference_mode"] = True
- config_dict[key] = config
- return config
-
- def _set_adapter_layers(self, enabled=True):
- for module in self.model.modules():
- if isinstance(module, GatedLoraLayer):
- module.disable_adapters = False if enabled else True
-
- def enable_adapter_layers(self):
- self._set_adapter_layers(enabled=True)
-
- def disable_adapter_layers(self):
- self._set_adapter_layers(enabled=False)
-
- def set_adapter(self, adapter_name):
- for module in self.model.modules():
- if isinstance(module, GatedLoraLayer):
- if module.merged:
- warnings.warn(
- "Adapter cannot be set when the model is merged. Unmerging the model first."
- )
- module.unmerge()
- module.active_adapter = adapter_name
-
- def merge_adapter(self):
- for module in self.model.modules():
- if isinstance(module, GatedLoraLayer):
- module.merge()
-
- def unmerge_adapter(self):
- for module in self.model.modules():
- if isinstance(module, GatedLoraLayer):
- module.unmerge()
-
- @staticmethod
- def _prepare_lora_config(peft_config, model_config):
- if peft_config.target_modules is None:
- if (
- model_config["model_type"]
- not in TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING
- ):
- raise ValueError("Please specify `target_modules` in `peft_config`")
- peft_config.target_modules = (
- TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING[
- model_config["model_type"]
- ]
- )
- if peft_config.inference_mode:
- peft_config.merge_weights = True
- return peft_config
-
- def merge_and_unload(self):
- r"""
- This method merges the LoRa layers into the base model. This is needed if someone wants to use the base model
- as a standalone model.
- """
- if getattr(self.config, "model_type", None) == "gpt2":
- raise ValueError("GPT2 models are not supported for merging LORA layers")
-
- if getattr(self.model, "is_loaded_in_8bit", False):
- raise ValueError(
- "Cannot merge LORA layers when the model is loaded in 8-bit mode"
- )
-
- key_list = [key for key, _ in self.model.named_modules() if "lora" not in key]
- for key in key_list:
- try:
- parent, target, target_name = _get_submodules(self.model, key)
- except AttributeError:
- continue
- if isinstance(target, GatedLoraLayer):
- bias = target.bias is not None
- new_module = torch.nn.Linear(
- target.in_features, target.out_features, bias=bias
- )
- target.merge()
- self._replace_module(parent, target_name, new_module, target)
-
- # save any additional trainable modules part of `modules_to_save`
- if isinstance(target, ModulesToSaveWrapper):
- setattr(
- parent, target_name, target.modules_to_save[target.active_adapter]
- )
-
- return self.model
-
- def add_weighted_adapter(self, adapters, weights, adapter_name):
- if len({self.peft_config[adapter].r for adapter in adapters}) != 1:
- raise ValueError("All adapters must have the same r value")
- self.peft_config[adapter_name] = self.peft_config[adapters[0]]
- self.peft_config[adapter_name].lora_alpha = self.peft_config[adapters[0]].r
- self._find_and_replace(adapter_name)
- mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias)
- _freeze_adapter(self.model, adapter_name)
- key_list = [key for key, _ in self.model.named_modules() if "lora" not in key]
- for key in key_list:
- _, target, _ = _get_submodules(self.model, key)
- if isinstance(target, GatedLoraLayer):
- if adapter_name in target.lora_A:
- target.lora_A[adapter_name].weight.data = (
- target.lora_A[adapter_name].weight.data * 0.0
- )
- target.lora_B[adapter_name].weight.data = (
- target.lora_B[adapter_name].weight.data * 0.0
- )
- for adapter, weight in zip(adapters, weights):
- if adapter not in target.lora_A:
- continue
- target.lora_A[adapter_name].weight.data += (
- target.lora_A[adapter].weight.data
- * weight
- * target.scaling[adapter]
- )
- target.lora_B[adapter_name].weight.data += (
- target.lora_B[adapter].weight.data * weight
- )
-
- elif adapter_name in target.lora_embedding_A:
- target.lora_embedding_A[adapter_name].data = (
- target.lora_embedding_A[adapter_name].data * 0.0
- )
- target.lora_embedding_B[adapter_name].data = (
- target.lora_embedding_B[adapter_name].data * 0.0
- )
- for adapter, weight in zip(adapters, weights):
- if adapter not in target.lora_embedding_A:
- continue
- target.lora_embedding_A[adapter_name].data += (
- target.lora_embedding_A[adapter].data
- * weight
- * target.scaling[adapter]
- )
- target.lora_embedding_B[adapter_name].data += (
- target.lora_embedding_B[adapter].data * weight
- )
-
-
-# Below code is based on https://github.com/microsoft/LoRA/blob/main/loralib/layers.py
-# and modified to work with PyTorch FSDP
-
-
-# ------------------------------------------------------------------------------------------
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
-# ------------------------------------------------------------------------------------------
-
-
-# had to adapt it for `lora_only` to work
-def mark_only_lora_as_trainable(model: nn.Module, bias: str = "none") -> None:
- for n, p in model.named_parameters():
- if "lora_" not in n:
- p.requires_grad = False
- if bias == "none":
- return
- elif bias == "all":
- for n, p in model.named_parameters():
- if "bias" in n:
- p.requires_grad = True
- elif bias == "lora_only":
- for m in model.modules():
- if (
- isinstance(m, GatedLoraLayer)
- and hasattr(m, "bias")
- and m.bias is not None
- ):
- m.bias.requires_grad = True
- else:
- raise NotImplementedError
-
-
-class GatedLoraLayer:
- def __init__(
- self,
- in_features: int,
- out_features: int,
- ):
- """UniPELT里使用了带gate的Lora，在peft Lora上增加了`self.lora_gate`作为门控
-
- Args:
- in_features (int): _description_
- out_features (int): _description_
- """
- self.r = {}
- self.lora_alpha = {}
- self.scaling = {}
- self.lora_dropout = nn.ModuleDict({})
- self.lora_A = nn.ModuleDict({})
- self.lora_B = nn.ModuleDict({})
- self.lora_gate = nn.ModuleDict({})
- # For Embedding layer
- self.lora_embedding_A = nn.ParameterDict({})
- self.lora_embedding_B = nn.ParameterDict({})
- # Mark the weight as unmerged
- self.merged = False
- self.disable_adapters = False
- self.in_features = in_features
- self.out_features = out_features
-
- def update_layer(
- self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights
- ):
- """在lora dict里添加新的实例。
-
- Args:
- adapter_name (_type_): _description_
- r (_type_): _description_
- lora_alpha (_type_): _description_
- lora_dropout (_type_): _description_
- init_lora_weights (_type_): _description_
- """
- self.r[adapter_name] = r
- self.lora_alpha[adapter_name] = lora_alpha
- if lora_dropout > 0.0:
- lora_dropout_layer = nn.Dropout(p=lora_dropout)
- else:
- lora_dropout_layer = nn.Identity()
-
- self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer}))
- # Actual trainable parameters
- if r > 0:
- self.lora_A.update(
- nn.ModuleDict(
- {adapter_name: nn.Linear(self.in_features, r, bias=False)}
- )
- )
- self.lora_B.update(
- nn.ModuleDict(
- {adapter_name: nn.Linear(r, self.out_features, bias=False)}
- )
- )
- self.scaling[adapter_name] = lora_alpha / r
- self.lora_gate.update(
- nn.ModuleDict(
- {adapter_name: nn.Linear(self.in_features, 1, bias=False)}
- )
- )
- if init_lora_weights:
- self.reset_lora_parameters(adapter_name)
- self.to(self.weight.device)
-
- def update_layer_embedding(
- self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights
- ):
- self.r[adapter_name] = r
- self.lora_alpha[adapter_name] = lora_alpha
- if lora_dropout > 0.0:
- lora_dropout_layer = nn.Dropout(p=lora_dropout)
- else:
- lora_dropout_layer = nn.Identity()
-
- self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer}))
- # Actual trainable parameters
- if r > 0:
- self.lora_embedding_A.update(
- nn.ParameterDict(
- {
- adapter_name: nn.Parameter(
- self.weight.new_zeros((r, self.in_features))
- )
- }
- )
- )
- self.lora_embedding_B.update(
- nn.ParameterDict(
- {
- adapter_name: nn.Parameter(
- self.weight.new_zeros((self.out_features, r))
- )
- }
- )
- )
- self.scaling[adapter_name] = lora_alpha / r
- if init_lora_weights:
- self.reset_lora_parameters(adapter_name)
- self.to(self.weight.device)
-
- def reset_lora_parameters(self, adapter_name):
- if adapter_name in self.lora_A.keys():
- # initialize A the same way as the default for nn.Linear and B to zero
- nn.init.kaiming_uniform_(self.lora_A[adapter_name].weight, a=math.sqrt(5))
- nn.init.kaiming_uniform_(self.lora_gate[adapter_name].weight)
- nn.init.zeros_(self.lora_B[adapter_name].weight)
- if adapter_name in self.lora_embedding_A.keys():
- # initialize a the same way as the default for nn.linear and b to zero
- nn.init.zeros_(self.lora_embedding_A[adapter_name])
- nn.init.normal_(self.lora_embedding_B[adapter_name])
-
-
-class PELTLinear(nn.Linear, GatedLoraLayer):
- # GatedLora implemented in a dense layer
- def __init__(
- self,
- adapter_name: str,
- in_features: int,
- out_features: int,
- r: int = 0,
- lora_alpha: int = 1,
- lora_dropout: float = 0.0,
- fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
- **kwargs,
- ):
- init_lora_weights = kwargs.pop("init_lora_weights", True)
-
- nn.Linear.__init__(self, in_features, out_features, **kwargs)
- GatedLoraLayer.__init__(
- self, in_features=in_features, out_features=out_features
- )
- # Freezing the pre-trained weight matrix
- self.weight.requires_grad = False
-
- self.fan_in_fan_out = fan_in_fan_out
- if fan_in_fan_out:
- self.weight.data = self.weight.data.T
-
- nn.Linear.reset_parameters(self)
- self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
- self.active_adapter = adapter_name
-
- def merge(self):
- if self.active_adapter not in self.lora_A.keys():
- return
- if self.merged:
- warnings.warn("Already merged. Nothing to do.")
- return
- if self.r[self.active_adapter] > 0:
- self.weight.data += (
- transpose(
- self.lora_B[self.active_adapter].weight
- @ self.lora_A[self.active_adapter].weight,
- self.fan_in_fan_out,
- )
- * self.scaling[self.active_adapter]
- )
- self.merged = True
-
- def unmerge(self):
- if self.active_adapter not in self.lora_A.keys():
- return
- if not self.merged:
- warnings.warn("Already unmerged. Nothing to do.")
- return
- if self.r[self.active_adapter] > 0:
- self.weight.data -= (
- transpose(
- self.lora_B[self.active_adapter].weight
- @ self.lora_A[self.active_adapter].weight,
- self.fan_in_fan_out,
- )
- * self.scaling[self.active_adapter]
- )
- self.merged = False
-
- def forward(self, x: torch.Tensor):
- previous_dtype = x.dtype
-
- if self.active_adapter not in self.lora_A.keys():
- return F.linear(
- x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias
- )
- if self.disable_adapters:
- if self.r[self.active_adapter] > 0 and self.merged:
- self.unmerge()
- result = F.linear(
- x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias
- )
- elif self.r[self.active_adapter] > 0 and not self.merged:
- result = F.linear(
- x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias
- )
-
- x = x.to(self.lora_A[self.active_adapter].weight.dtype)
-
- gate = self.lora_gate[self.active_adapter](x)
-
- result += (
- gate
- * self.lora_B[self.active_adapter](
- self.lora_A[self.active_adapter](
- self.lora_dropout[self.active_adapter](x)
- )
- )
- * self.scaling[self.active_adapter]
- )
- else:
- result = F.linear(
- x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias
- )
-
- result = result.to(previous_dtype)
-
- return result
-
-
-@dataclass
-class UniPELTConfig(PeftConfig):
- """
- 因为是在Lor上增加门控，所以其他结构不变
- This is the configuration class to store the configuration of a [`LoraModel`].
-
- Args:
- r (`int`): Lora attention dimension.
- target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to.
- lora_alpha (`float`): The alpha parameter for Lora scaling.
- lora_dropout (`float`): The dropout probability for Lora layers.
- fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out).
- For example, gpt-2 uses `Conv1D` which stores weights like (fan_in, fan_out)
- and hence this should be set to `True`.:
- bias (`str`): Bias type for Lora. Can be 'none', 'all' or 'lora_only'
- modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable
- and saved in the final checkpoint.
- """
-
- r: int = field(default=8, metadata={"help": "Lora attention dimension"})
- target_modules: Optional[Union[List[str], str]] = field(
- default=None,
- metadata={
- "help": "List of module names or regex expression of the module names to replace with Lora."
- "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
- },
- )
- lora_alpha: int = field(default=None, metadata={"help": "Lora alpha"})
- lora_dropout: float = field(default=None, metadata={"help": "Lora dropout"})
- fan_in_fan_out: bool = field(
- default=False,
- metadata={
- "help": "Set this to True if the layer to replace stores weight like (fan_in, fan_out)"
- },
- )
- bias: str = field(
- default="none",
- metadata={"help": "Bias type for Lora. Can be 'none', 'all' or 'lora_only'"},
- )
- modules_to_save: Optional[List[str]] = field(
- default=None,
- metadata={
- "help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. "
- "For example, in Sequence Classification or Token Classification tasks, "
- "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
- },
- )
- init_lora_weights: bool = field(
- default=True,
- metadata={"help": "Whether to initialize the weights of the Lora layers."},
- )
-
- def __post_init__(self):
- self.peft_type = PeftType.UNIPELT
-
-
-class PEUniPELTModel(PEBaseModel):
- def __init__(self, model, task_type, r, lora_alpha, lora_dropout, model_name):
- """
- 实现了UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuninghttps://arxiv.org/abs/2110.07577
-
-
- Args:
- model (_type_): huggingface transformers model
- task_type (_type_): "SEQ_CLS", "SEQ_2_SEQ_LM","CAUSAL_LM","TOKEN_CLS"
- r (_type_): lora rank
- lora_alpha (_type_): lora alpha
- lora_dropout (_type_): The dropout probability for Lora layers.
- model_name (_type_): model_name
-
- Raises:
- NotImplementedError: _description_
- """
- self.base_model = model
- if task_type not in ["SEQ_CLS", "SEQ_2_SEQ_LM", "CAUSAL_LM", "TOKEN_CLS"]:
- raise NotImplementedError("this task_type is not supported")
- from solutions.antllm.antllm.models.peft.utils import (
- TRANSFORMERS_MODELS_TO_LORA_LAGE_TARGET_MODULES_MAPPING,
- )
-
- self.config = UniPELTConfig(
- task_type=task_type,
- target_modules=TRANSFORMERS_MODELS_TO_LORA_LAGE_TARGET_MODULES_MAPPING[
- model_name
- ],
- inference_mode=False,
- lora_alpha=lora_alpha,
- r=r,
- lora_dropout=lora_dropout,
- )
-
- def get_model(self):
- self.pe_model = get_peft_model(model=self.base_model, peft_config=self.config)
- return self.pe_model
-
- def get_model_state_dict(self, model, state_dict=None, adapter_name="default"):
- """
- 改了PeftModel.save_pretrained，使其支持UniPELT
- Get the state dict of the Peft model.
-
- Args:
- model ([`PeftModel`]): The Peft model. When using torch.nn.DistributedDataParallel, DeepSpeed or FSDP,
- the model should be the underlying model/unwrapped model (i.e. model.module).
- state_dict (`dict`, *optional*, defaults to `None`):
- The state dict of the model. If not provided, the state dict of the model
- will be used.
- """
- config = model.peft_config[adapter_name]
- if state_dict is None:
- state_dict = model.state_dict()
- # if config.peft_type in (PeftType.LORA, PeftType.ADALORA):
- # to_return = lora_state_dict(model, bias=model.peft_config.bias)
- # adapted from `https://github.com/microsoft/LoRA/blob/main/loralib/utils.py`
- # to be used directly with the state dict which is necessary when using DeepSpeed or FSDP
- bias = config.bias
- if bias == "none":
- to_return = {k: state_dict[k] for k in state_dict if "lora_" in k}
- elif bias == "all":
- to_return = {
- k: state_dict[k] for k in state_dict if "lora_" in k or "bias" in k
- }
- elif bias == "lora_only":
- to_return = {}
- for k in state_dict:
- if "lora_" in k:
- to_return[k] = state_dict[k]
- bias_name = k.split("lora_")[0] + "bias"
- if bias_name in state_dict:
- to_return[bias_name] = state_dict[bias_name]
- else:
- raise NotImplementedError
- to_return = {
- k: v
- for k, v in to_return.items()
- if (("lora_" in k and adapter_name in k) or ("bias" in k))
- }
- if config.peft_type == PeftType.ADALORA:
- rank_pattern = config.rank_pattern
- if rank_pattern is not None:
- rank_pattern = {
- k.replace(f".{adapter_name}", ""): v
- for k, v in rank_pattern.items()
- }
- config.rank_pattern = rank_pattern
- to_return = model.resize_state_dict_by_rank_pattern(
- rank_pattern, to_return, adapter_name
- )
- if model.modules_to_save is not None:
- for key, value in state_dict.items():
- if any(
- f"{module_name}.modules_to_save.{adapter_name}" in key
- for module_name in model.modules_to_save
- ):
- to_return[key.replace("modules_to_save.", "")] = value
-
- to_return = {k.replace(f".{adapter_name}", ""): v for k, v in to_return.items()}
- return to_return
-
- def save(self, save_directory, **kwargs):
- r"""
- 改了PeftModel.save_pretrained，使其支持UniPELT
- This function saves the adapter model and the adapter configuration files to a directory, so that it can be
- reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`]
- method.
-
- Args:
- save_directory (`str`):
- Directory where the adapter model and configuration files will be saved (will be created if it does not
- exist).
- kwargs (additional keyword arguments, *optional*):
- Additional keyword arguments passed along to the `push_to_hub` method.
- """
- if os.path.isfile(save_directory):
- raise ValueError(
- f"Provided path ({save_directory}) should be a directory, not a file"
- )
- os.makedirs(save_directory, exist_ok=True)
-
- for adapter_name, peft_config in self.pe_model.peft_config.items():
- # save only the trainable weights
- output_state_dict = self.get_model_state_dict(
- self.pe_model,
- state_dict=kwargs.get("state_dict", None),
- adapter_name=adapter_name,
- )
- # logger.info(output_state_dict)
- output_dir = (
- os.path.join(save_directory, adapter_name)
- if adapter_name != "default"
- else save_directory
- )
- os.makedirs(output_dir, exist_ok=True)
- torch.save(output_state_dict, os.path.join(output_dir, WEIGHTS_NAME))
-
- # save the config and change the inference mode to `True`
- if peft_config.base_model_name_or_path is None:
- peft_config.base_model_name_or_path = (
- self.base_model.model.__dict__.get("name_or_path", None)
- )
- inference_mode = peft_config.inference_mode
- peft_config.inference_mode = True
- peft_config.save_pretrained(output_dir)
- peft_config.inference_mode = inference_mode
-
- @classmethod
- def restore(
- cls,
- model=None,
- path=None,
- adapter_name="default",
- is_trainable=False,
- **kwargs,
- ):
- r"""
- 改写了PeftModel.from_pretrained，使其支持UniPELT
- Instantiate a [`LoraModel`] from a pretrained Lora configuration and weights.
-
- Args:
- model ([`~transformers.PreTrainedModel`]):
- The model to be adapted. The model should be initialized with the
- [`~transformers.PreTrainedModel.from_pretrained`] method from the 🤗 Transformers library.
- model_id (`str` or `os.PathLike`):
- The name of the Lora configuration to use. Can be either:
- - A string, the `model id` of a Lora configuration hosted inside a model repo on the Hugging Face
- Hub.
- - A path to a directory containing a Lora configuration file saved using the `save_pretrained`
- method (`./my_lora_config_directory/`).
- """
- from peft.mapping import (
- MODEL_TYPE_TO_PEFT_MODEL_MAPPING,
- )
-
- # load the config
- config = PEFT_TYPE_TO_CONFIG_MAPPING[
- PeftConfig.from_pretrained(
- path, subfolder=kwargs.get("subfolder", None)
- ).peft_type
- ].from_pretrained(path, subfolder=kwargs.get("subfolder", None))
-
- if (getattr(model, "hf_device_map", None) is not None) and len(
- set(model.hf_device_map.values()).intersection({"cpu", "disk"})
- ) > 0:
- remove_hook_from_submodules(model)
-
- config.inference_mode = not is_trainable
-
- if config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys():
- peft_model = cls(model, config, adapter_name)
- else:
- # for example model is of PeftModelForSeq2SeqLM
- peft_model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](
- model, config, adapter_name
- )
- peft_model = load_adapter(peft_model, path, adapter_name, **kwargs)
- return peft_model
-
-
-def load_adapter(
- peft_model: PeftModel,
- path,
- adapter_name,
- is_trainable=False,
- **kwargs,
-):
- """改写了PeftModel.load_adapter，使其支持UniPELT
-
- Args:
- peft_model (PeftModel): _description_
- path (_type_): _description_
- adapter_name (_type_): _description_
- is_trainable (bool, optional): _description_. Defaults to False.
-
- Returns:
- _type_: _description_
- """
- if adapter_name not in peft_model.peft_config:
- # load the config
- peft_config = UniPELTConfig.from_pretrained(
- path, subfolder=kwargs.get("subfolder", None)
- )
- peft_config.inference_mode = not is_trainable
- # base model is pretrained model
- peft_model.base_model.add_adapter(adapter_name, peft_config)
-
- peft_model.set_additional_trainable_modules(peft_config, adapter_name)
-
- # load weights if any
- path = (
- os.path.join(path, kwargs["subfolder"])
- if kwargs.get("subfolder", None) is not None
- else path
- )
-
- if os.path.exists(os.path.join(path, WEIGHTS_NAME)):
- filename = os.path.join(path, WEIGHTS_NAME)
- else:
- pass
-
- adapters_weights = torch.load(
- filename,
- map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
- )
- # load the weights into the model
- set_model_state_dict(peft_model, adapters_weights, adapter_name=adapter_name)
- if (
- (getattr(peft_model, "hf_device_map", None) is not None)
- and (
- len(set(peft_model.hf_device_map.values()).intersection({"cpu", "disk"}))
- > 0
- )
- and len(peft_model.peft_config) == 1
- ):
- device_map = kwargs.get("device_map", "auto")
- max_memory = kwargs.get("max_memory", None)
- offload_dir = kwargs.get("offload_folder", None)
- offload_index = kwargs.get("offload_index", None)
-
- dispatch_model_kwargs = {}
- # Safety checker for previous `accelerate` versions
- # `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/
- if "offload_index" in inspect.signature(dispatch_model).parameters:
- dispatch_model_kwargs["offload_index"] = offload_index
-
- no_split_module_classes = peft_model._no_split_modules
-
- if device_map != "sequential":
- max_memory = get_balanced_memory(
- peft_model,
- max_memory=max_memory,
- no_split_module_classes=no_split_module_classes,
- low_zero=(device_map == "balanced_low_0"),
- )
- if isinstance(device_map, str):
- device_map = infer_auto_device_map(
- peft_model,
- max_memory=max_memory,
- no_split_module_classes=no_split_module_classes,
- )
- dispatch_model(
- peft_model,
- device_map=device_map,
- offload_dir=offload_dir,
- **dispatch_model_kwargs,
- )
- hook = AlignDevicesHook(io_same_device=True)
- add_hook_to_module(peft_model.get_base_model(), hook)
-
- # Set model in evaluation mode to deactivate Dropout modules by default
- peft_model.eval()
- return peft_model
-
-
-def set_model_state_dict(
- model: PeftModel, peft_model_state_dict, adapter_name="default"
-):
- """
- 改写了peft.uitls下的set_peft_model_state_dict，使其支持UniPELT
- Set the state dict of the Peft model.
-
- Args:
- model ([`PeftModel`]): The Peft model.
- peft_model_state_dict (`dict`): The state dict of the Peft model.
- """
- config = model.peft_config[adapter_name]
- state_dict = {}
- if model.modules_to_save is not None:
- for key, value in peft_model_state_dict.items():
- if any(module_name in key for module_name in model.modules_to_save):
- for module_name in model.modules_to_save:
- if module_name in key:
- key = key.replace(
- module_name, f"{module_name}.modules_to_save.{adapter_name}"
- )
- break
- state_dict[key] = value
- else:
- state_dict = peft_model_state_dict
-
- if config.peft_type in (PeftType.LORA, PeftType.ADALORA, PeftType.UNIPELT):
- peft_model_state_dict = {}
- for k, v in state_dict.items():
- if "lora_" in k:
- suffix = k.split("lora_")[1]
- if "." in suffix:
- suffix_to_replace = ".".join(suffix.split(".")[1:])
- k = k.replace(
- suffix_to_replace, f"{adapter_name}.{suffix_to_replace}"
- )
- else:
- k = f"{k}.{adapter_name}"
- peft_model_state_dict[k] = v
- else:
- peft_model_state_dict[k] = v
- if config.peft_type == PeftType.ADALORA:
- rank_pattern = config.rank_pattern
- if rank_pattern is not None:
- model.resize_modules_by_rank_pattern(rank_pattern, adapter_name)
- model.load_state_dict(peft_model_state_dict, strict=False)
-
-
-def print_lora_parameters(model):
- """debug 用，查看权重是否正确加载
-
- Args:
- model (_type_): _description_
- """
- for n, p in model.named_parameters():
- if "lora_B" in n:
- print(n)
- print(p)
- # break
- if "lora_A" in n:
- print(n)
- print(p)
- # break
- if "lora_gate" in n:
- print(n)
- print(p)
- break
diff --git a/mftcoder_atorch/model/peft/utils/__init__.py b/mftcoder_atorch/model/peft/utils/__init__.py
deleted file mode 100644
index 240c9e9..0000000
--- a/mftcoder_atorch/model/peft/utils/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""peft utils interface."""
-
-from .config import PeftConfig, PetuningConfig
-
-from .mapping import TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING
-from .mapping import TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING
-from .mapping import TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING
-from .mapping import TRANSFORMERS_MODELS_TO_LORA_LAGE_TARGET_MODULES_MAPPING
-from .mapping import TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING
-from .mapping import TRANSFORMERS_MODELS_TO_ROUTELORA_TARGET_MODULES_MAPPING
-from .mapping import WEIGHTS_NAME, CONFIG_NAME
-from .mapping import bloom_model_postprocess_past_key_value
-
-from .others import get_peft_model_state_dict, set_peft_model_state_dict, _freeze_model, prepare_model_for_kbit_training
\ No newline at end of file
diff --git a/mftcoder_atorch/model/peft/utils/config.py b/mftcoder_atorch/model/peft/utils/config.py
deleted file mode 100644
index cc316a6..0000000
--- a/mftcoder_atorch/model/peft/utils/config.py
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-
-import sys
-sys.path.append("..")
-sys.path.append("../..")
-from typing import List, Optional
-from dataclasses import dataclass, field
-from peft.utils import PeftConfig
-
-
-@dataclass
-class PetuningConfig(PeftConfig):
- """
- This is the base configuration class to store the configuration of [`ROEM`], or [`BitFit`].
-
- Args:
- modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable
- and saved in the final checkpoint.
- """
-
- modules_to_save: Optional[List[str]] = field(
- default=None,
- metadata={
- "help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. "
- "For example, in Sequence Classification or Token Classification tasks, "
- "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
- },
- )
\ No newline at end of file
diff --git a/mftcoder_atorch/model/peft/utils/mapping.py b/mftcoder_atorch/model/peft/utils/mapping.py
deleted file mode 100644
index fc14125..0000000
--- a/mftcoder_atorch/model/peft/utils/mapping.py
+++ /dev/null
@@ -1,141 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-
-import sys
-sys.path.append("..")
-sys.path.append("../..")
-import torch
-from peft.utils import (
- TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING,
- TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING
-)
-
-
-# needed for prefix-tuning of bloom model
-def bloom_model_postprocess_past_key_value(past_key_values):
- past_key_values = torch.cat(past_key_values)
- (
- total_layers,
- batch_size,
- num_attention_heads,
- num_virtual_tokens,
- head_dim,
- ) = past_key_values.shape
- keys = past_key_values[: total_layers // 2]
- keys = keys.transpose(2, 3).reshape(
- total_layers // 2,
- batch_size * num_attention_heads,
- head_dim,
- num_virtual_tokens,
- )
- values = past_key_values[total_layers // 2 :]
- values = values.reshape(
- total_layers // 2,
- batch_size * num_attention_heads,
- num_virtual_tokens,
- head_dim,
- )
-
- return tuple(zip(keys, values))
-
-
-NEW_TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING = {
- "t5": ["q", "v"],
- "mt5": ["q", "v"],
- "bart": ["q_proj", "v_proj"],
- "gpt2": ["c_attn"],
- "bloom": ["query_key_value"],
- "bloomz": ["query_key_value"],
- "blip-2": ["q", "v", "q_proj", "v_proj"],
- "opt": ["q_proj", "v_proj"],
- "gptj": ["q_proj", "v_proj"],
- "gpt_neox": ["query_key_value"],
- "gpt_neo": ["q_proj", "v_proj"],
- "bert": ["query", "value"],
- "roberta": ["query", "value"],
- "xlm-roberta": ["query", "value"],
- "electra": ["query", "value"],
- "deberta-v2": ["query_proj", "value_proj"],
- "deberta": ["in_proj"],
- "layoutlm": ["query", "value"],
- "llama": ["q_proj", "v_proj"],
- "chatglm": ["query_key_value"],
- "glm": ["query_key_value"],
-}
-
-NEW_TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING = {
- "t5": ["q", "k", "v", "o", "wi", "wo"],
- "mt5": ["q", "k", "v", "o", "wi_0", "wi_1", "wo"],
- "bart": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
- # "gpt2": ["c_attn"],
- "bloom": ["query_key_value"],
- "bloomz": ["query_key_value"],
- "opt": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
- # "gptj": ["q_proj", "v_proj"],
- # "gpt_neox": ["query_key_value"],
- # "gpt_neo": ["q_proj", "v_proj"],
- # "bert": ["query", "value"],
- "roberta": ["query", "key", "value", "dense"],
- # "xlm-roberta": ["query", "value"],
- # "electra": ["query", "value"],
- "deberta-v2": ["query_proj", "key_proj", "value_proj", "dense"],
- "chatglm": ["query_key_value"],
- "glm": ["query_key_value"],
- # "deberta": ["in_proj"],
- # "layoutlm": ["query", "value"],
-}
-
-TRANSFORMERS_MODELS_TO_LORA_LAGE_TARGET_MODULES_MAPPING = {
- "t5": ["q", "k", "v", "o", "wi", "wo"],
- "mt5": ["q", "k", "v", "o", "wi_0", "wi_1", "wo"],
- "bart": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
- # "gpt2": ["c_attn"],
- "bloom": ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"],
- "bloomz": ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"],
- "opt": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
- # "gptj": ["q_proj", "v_proj"],
- # "gpt_neox": ["query_key_value"],
- # "gpt_neo": ["q_proj", "v_proj"],
- # "bert": ["query", "value"],
- "roberta": ["query", "key", "value", "dense"],
- # "xlm-roberta": ["query", "value"],
- # "electra": ["query", "value"],
- "llama": ["q_proj", "v_proj"],
- "deberta-v2": ["query_proj", "key_proj", "value_proj", "dense"],
- "glm": ["query_key_value", "dense"]
- # "deberta": ["in_proj"],
- # "layoutlm": ["query", "value"],
-}
-
-TRANSFORMERS_MODELS_TO_ROUTELORA_TARGET_MODULES_MAPPING = {
- "t5": ["q", "k", "v", "o", "wi", "wo"],
- "mt5": ["q", "k", "v", "o", "wi_0", "wi_1", "wo"],
- "bart": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
- "opt": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
- "roberta": ["query", "key", "value", "dense"],
- "deberta-v2": ["query_proj", "key_proj", "value_proj", "dense"],
- "chatglm": ["query_key_value"],
- "glm": ["query_key_value"]
-}
-
-TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING = {
- "glm": [0, 22],
- "bloom": [17, 22],
- "bloomz": [17, 22],
-}
-
-TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING = {
- "bloom": bloom_model_postprocess_past_key_value,
- "bloomz": bloom_model_postprocess_past_key_value,
-}
-
-WEIGHTS_NAME = "adapter_model.bin"
-CONFIG_NAME = "adapter_config.json"
-
-
-TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING.update(
- NEW_TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING
-)
-TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING.update(
- NEW_TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING
-)
diff --git a/mftcoder_atorch/model/peft/utils/others.py b/mftcoder_atorch/model/peft/utils/others.py
deleted file mode 100644
index 65da580..0000000
--- a/mftcoder_atorch/model/peft/utils/others.py
+++ /dev/null
@@ -1,318 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-
-import sys
-sys.path.append("..")
-sys.path.append("../..")
-import copy
-
-import torch
-from .config import PetuningConfig
-from peft.utils import PromptLearningConfig, PeftType
-
-
-def prepare_model_for_int8_training(model, use_gradient_checkpointing=True):
- r"""
- This method wraps the entire protocol for preparing a model before running a training. This includes:
- 1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm
- head to fp32
-
- Args:
- model, (`transformers.PreTrainedModel`):
- The loaded model from `transformers`
- """
- loaded_in_8bit = getattr(model, "is_loaded_in_8bit", False)
-
- for name, param in model.named_parameters():
- # freeze base model's layers
- param.requires_grad = False
-
- # cast all non INT8 parameters to fp32
- for param in model.parameters():
- if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):
- param.data = param.data.to(torch.float32)
-
- if loaded_in_8bit and use_gradient_checkpointing:
- # For backward compatibility
- if hasattr(model, "enable_input_require_grads"):
- model.enable_input_require_grads()
- else:
-
- def make_inputs_require_grad(module, input, output):
- output.requires_grad_(True)
-
- model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
-
- # enable gradient checkpointing for memory efficiency
- model.gradient_checkpointing_enable()
-
- return model
-
-
-def prepare_model_for_kbit_training(model, use_gradient_checkpointing=True):
- r"""
- This method wraps the entire protocol for preparing a model before running a training. This includes:
- 1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm
- head to fp32
-
- Args:
- model, (`transformers.PreTrainedModel`):
- The loaded model from `transformers`
- """
- loaded_in_kbit = getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False)
-
- for name, param in model.named_parameters():
- # freeze base model's layers
- param.requires_grad = False
-
- # cast all non INT8 parameters to fp32
- for param in model.parameters():
- if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):
- param.data = param.data.to(torch.float32)
-
- if loaded_in_kbit and use_gradient_checkpointing:
- # For backward compatibility
- if hasattr(model, "enable_input_require_grads"):
- model.enable_input_require_grads()
- else:
-
- def make_inputs_require_grad(module, input, output):
- output.requires_grad_(True)
-
- model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
-
- # enable gradient checkpointing for memory efficiency
- model.gradient_checkpointing_enable()
-
- return model
-
-
-# copied from transformers.models.bart.modeling_bart
-def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
- """
- Shift input ids one token to the right.
-
- Args:
- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): input ids
- pad_token_id (`int`): The id of the `padding` token.
- decoder_start_token_id (`int`): The id of the `start` token.
- """
- shifted_input_ids = input_ids.new_zeros(input_ids.shape)
- shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
- shifted_input_ids[:, 0] = decoder_start_token_id
-
- if pad_token_id is None:
- raise ValueError("self.model.config.pad_token_id has to be defined.")
- # replace possible -100 values in labels by `pad_token_id`
- shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
-
- return shifted_input_ids
-
-
-class ModulesToSaveWrapper(torch.nn.Module):
- def __init__(self, module_to_save, adapter_name):
- super().__init__()
- self.original_module = module_to_save
- self.modules_to_save = torch.nn.ModuleDict({})
- self.update(adapter_name)
- self.active_adapter = adapter_name
-
- def update(self, adapter_name):
- self.modules_to_save.update(torch.nn.ModuleDict({adapter_name: copy.deepcopy(self.original_module)}))
-
- def forward(self, *args, **kwargs):
- if self.active_adapter not in self.modules_to_save:
- return self.original_module(*args, **kwargs)
- return self.modules_to_save[self.active_adapter](*args, **kwargs)
-
-
-def _get_submodules(model, key):
- parent = model.get_submodule(".".join(key.split(".")[:-1]))
- target_name = key.split(".")[-1]
- target = model.get_submodule(key)
- return parent, target, target_name
-
-
-def _freeze_adapter(model, adapter_name):
- for n, p in model.named_parameters():
- if adapter_name in n:
- p.requires_grad = False
-
-
-def _freeze_model(model):
- for n, p in model.named_parameters():
- p.requires_grad = False
-
-
-def _set_trainable(model, adapter_name):
- key_list = [key for key, _ in model.named_modules()]
- for key in key_list:
- target_module_found = any(key.endswith(target_key) for target_key in model.modules_to_save)
- if target_module_found:
- parent, target, target_name = _get_submodules(model, key)
- if isinstance(target, ModulesToSaveWrapper):
- target.update(adapter_name)
- else:
- for param in target.parameters():
- param.requires_grad = True
- setattr(parent, target_name, ModulesToSaveWrapper(target, adapter_name))
-
-
-def _set_adapter(model, adapter_name):
- for module in model.modules():
- if isinstance(module, ModulesToSaveWrapper):
- module.active_adapter = adapter_name
-
-
-def fsdp_auto_wrap_policy(model):
- import functools
- import os
-
- from accelerate import FullyShardedDataParallelPlugin
- from torch.distributed.fsdp.wrap import _or_policy, lambda_auto_wrap_policy, transformer_auto_wrap_policy
-
- from peft.tuners import PrefixEncoder, PromptEmbedding, PromptEncoder
-
- def lambda_policy_fn(module):
- if (
- len(list(module.named_children())) == 0
- and getattr(module, "weight", None) is not None
- and module.weight.requires_grad
- ):
- return True
- return False
-
- lambda_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=lambda_policy_fn)
- transformer_wrap_policy = functools.partial(
- transformer_auto_wrap_policy,
- transformer_layer_cls=(
- PrefixEncoder,
- PromptEncoder,
- PromptEmbedding,
- FullyShardedDataParallelPlugin.get_module_class_from_name(
- model, os.environ.get("FSDP_TRANSFORMER_CLS_TO_WRAP", "")
- ),
- ),
- )
-
- auto_wrap_policy = functools.partial(_or_policy, policies=[lambda_policy, transformer_wrap_policy])
- return auto_wrap_policy
-
-
-def transpose(weight, fan_in_fan_out):
- return weight.T if fan_in_fan_out else weight
-
-
-def get_peft_model_state_dict(model, state_dict=None, adapter_name="default"):
- """
- Get the state dict of the Peft model.
-
- Args:
- model ([`PeftModel`]): The Peft model. When using torch.nn.DistributedDataParallel, DeepSpeed or FSDP,
- the model should be the underlying model/unwrapped model (i.e. model.module).
- state_dict (`dict`, *optional*, defaults to `None`):
- The state dict of the model. If not provided, the state dict of the model
- will be used.
- """
- config = model.peft_config[adapter_name]
- if state_dict is None:
- state_dict = model.state_dict()
- if config.peft_type in (PeftType.LORA, PeftType.ADALORA, PeftType.ROUTELORA, PeftType.UNIPELT):
- # to_return = lora_state_dict(model, bias=model.peft_config.bias)
- # adapted from `https://github.com/microsoft/LoRA/blob/main/loralib/utils.py`
- # to be used directly with the state dict which is necessary when using DeepSpeed or FSDP
- bias = config.bias
- if bias == "none":
- to_return = {k: state_dict[k] for k in state_dict if "lora_" in k}
- elif bias == "all":
- to_return = {k: state_dict[k] for k in state_dict if "lora_" in k or "bias" in k}
- elif bias == "lora_only":
- to_return = {}
- for k in state_dict:
- if "lora_" in k:
- to_return[k] = state_dict[k]
- bias_name = k.split("lora_")[0] + "bias"
- if bias_name in state_dict:
- to_return[bias_name] = state_dict[bias_name]
- else:
- raise NotImplementedError
- to_return = {k: v for k, v in to_return.items() if (("lora_" in k and adapter_name in k) or ("bias" in k))}
- if config.peft_type == PeftType.ADALORA:
- rank_pattern = config.rank_pattern
- if rank_pattern is not None:
- rank_pattern = {k.replace(f".{adapter_name}", ""): v for k, v in rank_pattern.items()}
- config.rank_pattern = rank_pattern
- to_return = model.resize_state_dict_by_rank_pattern(rank_pattern, to_return, adapter_name)
-
- elif config.peft_type == PeftType.ADAPTION_PROMPT:
- to_return = {k: state_dict[k] for k in state_dict if k.split(".")[-1].startswith("adaption_")}
- elif isinstance(config, PromptLearningConfig):
- to_return = {}
- if config.inference_mode:
- prompt_embeddings = model.prompt_encoder[adapter_name].embedding.weight
- else:
- prompt_embeddings = model.get_prompt_embedding_to_save(adapter_name)
- to_return["prompt_embeddings"] = prompt_embeddings
- elif isinstance(config, PetuningConfig):
- to_return = state_dict
- else:
- raise NotImplementedError
- if model.modules_to_save is not None:
- for key, value in state_dict.items():
- if any(f"{module_name}.modules_to_save.{adapter_name}" in key for module_name in model.modules_to_save):
- to_return[key.replace("modules_to_save.", "")] = value
-
- to_return = {k.replace(f".{adapter_name}", ""): v for k, v in to_return.items()}
- return to_return
-
-
-def set_peft_model_state_dict(model, peft_model_state_dict, adapter_name="default"):
- """
- Set the state dict of the Peft model.
-
- Args:
- model ([`PeftModel`]): The Peft model.
- peft_model_state_dict (`dict`): The state dict of the Peft model.
- """
- config = model.peft_config[adapter_name]
- state_dict = {}
- if model.modules_to_save is not None:
- for key, value in peft_model_state_dict.items():
- if any(module_name in key for module_name in model.modules_to_save):
- for module_name in model.modules_to_save:
- if module_name in key:
- key = key.replace(module_name, f"{module_name}.modules_to_save.{adapter_name}")
- break
- state_dict[key] = value
- else:
- state_dict = peft_model_state_dict
-
- if config.peft_type in (PeftType.LORA, PeftType.ADALORA, PeftType.ROUTELORA, PeftType.UNIPELT):
- peft_model_state_dict = {}
- for k, v in state_dict.items():
- if "lora_" in k:
- suffix = k.split("lora_")[1]
- if "." in suffix:
- suffix_to_replace = ".".join(suffix.split(".")[1:])
- k = k.replace(suffix_to_replace, f"{adapter_name}.{suffix_to_replace}")
- else:
- k = f"{k}.{adapter_name}"
- peft_model_state_dict[k] = v
- else:
- peft_model_state_dict[k] = v
- if config.peft_type == PeftType.ADALORA:
- rank_pattern = config.rank_pattern
- if rank_pattern is not None:
- model.resize_modules_by_rank_pattern(rank_pattern, adapter_name)
- elif isinstance(config, PromptLearningConfig) or config.peft_type == PeftType.ADAPTION_PROMPT:
- peft_model_state_dict = state_dict
- elif isinstance(config, PetuningConfig):
- peft_model_state_dict = state_dict
- else:
- raise NotImplementedError
-
- model.load_state_dict(peft_model_state_dict, strict=False)
- if isinstance(config, PromptLearningConfig):
- model.prompt_encoder[adapter_name].embedding.load_state_dict(
- {"weight": peft_model_state_dict["prompt_embeddings"]}, strict=True
- )
diff --git a/mftcoder_atorch/tokenizer/__init__.py b/mftcoder_atorch/tokenizer/__init__.py
deleted file mode 100644
index 22b0f7b..0000000
--- a/mftcoder_atorch/tokenizer/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from .tokenizer import build_tokenizer
diff --git a/mftcoder_atorch/tokenizer/gpt2_tokenization.py b/mftcoder_atorch/tokenizer/gpt2_tokenization.py
deleted file mode 100644
index f8a1128..0000000
--- a/mftcoder_atorch/tokenizer/gpt2_tokenization.py
+++ /dev/null
@@ -1,368 +0,0 @@
-# Copyright (c) 2021, EleutherAI
-# This file is based on code by the authors denoted below and has been modified from its original version.
-#
-# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Tokenization classes for OpenAI GPT."""
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import sys
-import json
-import logging
-import os
-import regex as re
-from io import open
-
-from functools import lru_cache
-
-
-logger = logging.getLogger(__name__)
-
-PRETRAINED_VOCAB_ARCHIVE_MAP = {
- "gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
-}
-PRETRAINED_MERGES_ARCHIVE_MAP = {
- "gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
-}
-PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
- "gpt2": 1024,
-}
-
-VOCAB_NAME = "vocab.json"
-MERGES_NAME = "merges.txt"
-SPECIAL_TOKENS_NAME = "special_tokens.txt"
-
-
-@lru_cache()
-def bytes_to_unicode():
- """
- Returns list of utf-8 byte and a corresponding list of unicode strings.
- The reversible bpe codes work on unicode strings.
- This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
- When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
- This is a significant percentage of your normal, say, 32K bpe vocab.
- To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
- And avoids mapping to whitespace/control characters the bpe code barfs on.
- """
- _chr = unichr if sys.version_info[0] == 2 else chr
- bs = (
- list(range(ord("!"), ord("~") + 1))
- + list(range(ord("¡"), ord("¬") + 1))
- + list(range(ord("®"), ord("ÿ") + 1))
- )
- cs = bs[:]
- n = 0
- for b in range(2**8):
- if b not in bs:
- bs.append(b)
- cs.append(2**8 + n)
- n += 1
- cs = [_chr(n) for n in cs]
- return dict(zip(bs, cs))
-
-
-def get_pairs(word):
- """Return set of symbol pairs in a word.
-
- Word is represented as tuple of symbols (symbols being variable-length strings).
- """
- pairs = set()
- prev_char = word[0]
- for char in word[1:]:
- pairs.add((prev_char, char))
- prev_char = char
- return pairs
-
-
-class GPT2Tokenizer(object):
- """
- GPT-2 BPE tokenizer. Peculiarities:
- - Byte-level BPE
- """
-
- @classmethod
- def from_pretrained(
- cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs
- ):
- """
- Instantiate a PreTrainedBertModel from a pre-trained model file.
- Download and cache the pre-trained model file if needed.
- """
- if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
- vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
- merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
- special_tokens_file = None
- else:
- vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
- merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
- special_tokens_file = os.path.join(
- pretrained_model_name_or_path, SPECIAL_TOKENS_NAME
- )
- if not os.path.exists(special_tokens_file):
- special_tokens_file = None
- else:
- logger.info(
- "loading special tokens file {}".format(special_tokens_file)
- )
- # redirect to the cache, if necessary
- try:
- from .file_utils import cached_path
-
- resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
- resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir)
- except EnvironmentError:
- logger.error(
- "Model name '{}' was not found in model name list ({}). "
- "We assumed '{}' was a path or url but couldn't find files {} and {} "
- "at this path or url.".format(
- pretrained_model_name_or_path,
- ", ".join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
- pretrained_model_name_or_path,
- vocab_file,
- merges_file,
- )
- )
- return None
- if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
- logger.info("loading vocabulary file {}".format(vocab_file))
- logger.info("loading merges file {}".format(merges_file))
- else:
- logger.info(
- "loading vocabulary file {} from cache at {}".format(
- vocab_file, resolved_vocab_file
- )
- )
- logger.info(
- "loading merges file {} from cache at {}".format(
- merges_file, resolved_merges_file
- )
- )
- if (
- pretrained_model_name_or_path
- in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP
- ):
- # if we're using a pretrained model, ensure the tokenizer won't index sequences longer
- # than the number of positional embeddings
- max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[
- pretrained_model_name_or_path
- ]
- kwargs["max_len"] = min(kwargs.get("max_len", int(1e12)), max_len)
- # Instantiate tokenizer.
- if special_tokens_file and "special_tokens" not in kwargs:
- special_tokens = (
- open(special_tokens_file, encoding="utf-8").read().split("\n")[:-1]
- )
- else:
- special_tokens = kwargs.pop("special_tokens", [])
- tokenizer = cls(
- resolved_vocab_file,
- resolved_merges_file,
- special_tokens=special_tokens,
- *inputs,
- **kwargs
- )
- return tokenizer
-
- def __init__(
- self,
- vocab_file,
- merges_file,
- errors="replace",
- special_tokens=None,
- max_len=None,
- ):
- self.max_len = max_len if max_len is not None else int(1e12)
- self.encoder = json.load(open(vocab_file))
- self.decoder = {v: k for k, v in self.encoder.items()}
- self.errors = errors # how to handle errors in decoding
- self.byte_encoder = bytes_to_unicode()
- self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
- bpe_data = open(merges_file, encoding="utf-8").read().split("\n")[1:-1]
- bpe_merges = [tuple(merge.split()) for merge in bpe_data]
- self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-
- # Should haved added re.IGNORECASE so BPE merges can happen for
- # capitalized versions of contractions
- self.pat = re.compile(
- r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
- )
-
- self.special_tokens = {}
- self.special_tokens_decoder = {}
- self.set_special_tokens(special_tokens)
-
- def __len__(self):
- return len(self.encoder) + len(self.special_tokens)
-
- def set_special_tokens(self, special_tokens):
- """Add a list of additional tokens to the encoder.
- The additional tokens are indexed starting from the last index of the
- current vocabulary in the order of the `special_tokens` list.
- """
- if not special_tokens:
- self.special_tokens = {}
- self.special_tokens_decoder = {}
- return
- self.special_tokens = dict(
- (tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens)
- )
- self.special_tokens_decoder = {v: k for k, v in self.special_tokens.items()}
- logger.info("Special tokens {}".format(self.special_tokens))
-
- @lru_cache(maxsize=131072)
- def bpe(self, token):
- word = tuple(token)
- pairs = get_pairs(word)
-
- if not pairs:
- return token
-
- while True:
- bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
- if bigram not in self.bpe_ranks:
- break
- first, second = bigram
- new_word = []
- i = 0
- while i < len(word):
- try:
- j = word.index(first, i)
- new_word.extend(word[i:j])
- i = j
- except BaseException:
- new_word.extend(word[i:])
- break
-
- if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
- new_word.append(first + second)
- i += 2
- else:
- new_word.append(word[i])
- i += 1
- new_word = tuple(new_word)
- word = new_word
- if len(word) == 1:
- break
- else:
- pairs = get_pairs(word)
- word = " ".join(word)
- return word
-
- def tokenize(self, text):
- """Tokenize a string."""
- bpe_tokens = []
- for token in re.findall(self.pat, text):
- if sys.version_info[0] == 2:
- token = "".join(self.byte_encoder[ord(b)] for b in token)
- else:
- token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
- bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
- return bpe_tokens
-
- def convert_tokens_to_ids(self, tokens):
- """Converts a sequence of tokens into ids using the vocab."""
- ids = []
- if isinstance(tokens, str) or (
- sys.version_info[0] == 2 and isinstance(tokens, unicode)
- ):
- if tokens in self.special_tokens:
- return self.special_tokens[tokens]
- else:
- return self.encoder.get(tokens, 0)
- for token in tokens:
- if token in self.special_tokens:
- ids.append(self.special_tokens[token])
- else:
- ids.append(self.encoder.get(token, 0))
- if len(ids) > self.max_len:
- logger.warning(
- "Token indices sequence length is longer than the specified maximum "
- " sequence length for this OpenAI GPT model ({} > {}). Running this"
- " sequence through the model will result in indexing errors".format(
- len(ids), self.max_len
- )
- )
- return ids
-
- def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
- """Converts a sequence of ids in BPE tokens using the vocab."""
- tokens = []
- for i in ids:
- if i in self.special_tokens_decoder:
- if not skip_special_tokens:
- tokens.append(self.special_tokens_decoder[i])
- else:
- tokens.append(self.decoder[i])
- return tokens
-
- def encode(self, text):
- return self.convert_tokens_to_ids(self.tokenize(text))
-
- def decode(self, tokens):
- text = "".join([self.decoder[token] for token in tokens])
- text = bytearray([self.byte_decoder[c] for c in text]).decode(
- "utf-8", errors=self.errors
- )
- return text
-
- def save_vocabulary(self, vocab_path):
- """Save the tokenizer vocabulary and merge files to a directory."""
- if not os.path.isdir(vocab_path):
- logger.error(
- "Vocabulary path ({}) should be a directory".format(vocab_path)
- )
- return
- vocab_file = os.path.join(vocab_path, VOCAB_NAME)
- merge_file = os.path.join(vocab_path, MERGES_NAME)
- special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)
-
- with open(vocab_file, "w", encoding="utf-8") as f:
- f.write(json.dumps(self.encoder, ensure_ascii=False))
-
- index = 0
- with open(merge_file, "w", encoding="utf-8") as writer:
- writer.write("#version: 0.2\n")
- for bpe_tokens, token_index in sorted(
- self.bpe_ranks.items(), key=lambda kv: kv[1]
- ):
- if index != token_index:
- logger.warning(
- "Saving vocabulary to {}: BPE merge indices are not consecutive."
- " Please check that the tokenizer is not corrupted!".format(
- merge_file
- )
- )
- index = token_index
- writer.write(" ".join(bpe_tokens) + "\n")
- index += 1
-
- index = len(self.encoder)
- with open(special_tokens_file, "w", encoding="utf-8") as writer:
- for token, token_index in sorted(
- self.special_tokens.items(), key=lambda kv: kv[1]
- ):
- if index != token_index:
- logger.warning(
- "Saving special tokens vocabulary to {}: BPE indices are not consecutive."
- " Please check that the tokenizer is not corrupted!".format(
- special_tokens_file
- )
- )
- index = token_index
- writer.write(token + "\n")
- index += 1
-
- return vocab_file, merge_file, special_tokens_file
diff --git a/mftcoder_atorch/tokenizer/tokenizer.py b/mftcoder_atorch/tokenizer/tokenizer.py
deleted file mode 100644
index 12b1bda..0000000
--- a/mftcoder_atorch/tokenizer/tokenizer.py
+++ /dev/null
@@ -1,409 +0,0 @@
-# Copyright (c) 2021, EleutherAI
-# This file is based on code by the authors denoted below and has been modified from its original version.
-#
-# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Megatron tokenizers."""
-
-from abc import ABC
-from abc import abstractmethod
-
-from tokenizers import Tokenizer
-from transformers import GPT2Tokenizer, GPT2TokenizerFast
-import numpy as np
-import sentencepiece as spm
-from typing import List, Union
-from .gpt2_tokenization import GPT2Tokenizer
-from utils.common_utils import print_rank_0, is_old_version
-
-def build_tokenizer(args):
- """Initialize tokenizer."""
- print_rank_0("> building {} tokenizer ...".format(args.tokenizer_type))
- # if args.rank == 0:
- # print("> building {} tokenizer ...".format(args.tokenizer_type), flush=True)
-
- # Select and instantiate the tokenizer.
- if args.tokenizer_type.lower() == "GPT2BPETokenizer".lower():
- assert args.vocab_file is not None
- assert args.merge_file is not None
- tokenizer = _GPT2BPETokenizer(args.vocab_file, args.merge_file)
- elif args.tokenizer_type.lower() == "SPMTokenizer".lower():
- assert args.vocab_file is not None
- tokenizer = SentencePieceTokenizer(args.vocab_file)
- elif args.tokenizer_type.lower() == "HFTokenizer".lower():
- assert args.vocab_file is not None
- tokenizer = HFTokenizer(args.vocab_file)
- elif args.tokenizer_type.lower() == "HFGPT2Tokenizer".lower():
- if args.vocab_file is None:
- print(
- "WARNING: No vocab file found, loading Huggingface's pretrained GPT2Tokenizer"
- )
- tokenizer = HFGPT2Tokenizer(args.vocab_file)
- elif args.tokenizer_type.lower() == "CharLevelTokenizer".lower():
- tokenizer = CharLevelTokenizer(vocab_size=512)
- elif args.tokenizer_type.lower() == "TiktokenTokenizer".lower():
- assert args.vocab_file is not None
- tokenizer = TiktokenTokenizer(args.vocab_file)
- else:
- raise NotImplementedError(
- "{} tokenizer is not " "implemented.".format(args.tokenizer_type)
- )
-
- # Add vocab size.
- args.padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size, args)
-
- return tokenizer
-
-
-def _vocab_size_with_padding(orig_vocab_size, args):
- """Pad vocab size so it is divisible by model parallel size and
- still having GPU friendly size."""
-
- after = orig_vocab_size
- multiple = args.make_vocab_size_divisible_by * args.model_parallel_size
- while (after % multiple) != 0:
- after += 1
- print_rank_0(
- " > padded vocab (size: {}) with {} dummy tokens "
- "(new size: {})".format(orig_vocab_size, after - orig_vocab_size, after)
- )
- return after
-
-
-class AbstractTokenizer(ABC):
- """Abstract class for tokenizer."""
-
- def __init__(self, name):
- self.name = name
- super().__init__()
-
- @property
- @abstractmethod
- def vocab_size(self):
- pass
-
- @property
- @abstractmethod
- def vocab(self):
- """Dictionary from vocab text token to id token."""
- pass
-
- @property
- @abstractmethod
- def inv_vocab(self):
- """Dictionary from vocab id token to text token."""
- pass
-
- @abstractmethod
- def tokenize(self, text):
- pass
-
- def detokenize(self, token_ids):
- raise NotImplementedError(
- "detokenizer is not implemented for {} " "tokenizer".format(self.name)
- )
-
- @property
- def cls(self):
- raise NotImplementedError(
- "CLS is not provided for {} " "tokenizer".format(self.name)
- )
-
- @property
- def sep(self):
- raise NotImplementedError(
- "SEP is not provided for {} " "tokenizer".format(self.name)
- )
-
- @property
- def pad(self):
- raise NotImplementedError(
- "PAD is not provided for {} " "tokenizer".format(self.name)
- )
-
- @property
- def eod(self):
- raise NotImplementedError(
- "EOD is not provided for {} " "tokenizer".format(self.name)
- )
-
- @property
- def mask(self):
- raise NotImplementedError(
- "MASK is not provided for {} " "tokenizer".format(self.name)
- )
-
-
-class _GPT2BPETokenizer(AbstractTokenizer):
- """Original GPT2 BPE tokenizer."""
-
- def __init__(self, vocab_file, merge_file):
- name = "GPT2 BPE"
- super().__init__(name)
-
- self.tokenizer = GPT2Tokenizer(
- vocab_file, merge_file, errors="replace", special_tokens=[], max_len=None
- )
- self.eod_id = self.tokenizer.encoder["<|endoftext|>"]
-
- @property
- def vocab_size(self):
- return len(self.tokenizer.encoder)
-
- @property
- def vocab(self):
- return self.tokenizer.encoder
-
- @property
- def inv_vocab(self):
- return self.tokenizer.decoder
-
- def tokenize(self, text):
- return self.tokenizer.encode(text)
-
- def detokenize(self, token_ids):
- return self.tokenizer.decode(token_ids)
-
- @property
- def eod(self):
- return self.eod_id
-
-
-class SentencePieceTokenizer(AbstractTokenizer):
- """Designed to Integrate SP's Tokenizer."""
-
- def __init__(self, vocab_file):
- name = "SPM"
- super().__init__(name)
-
- self.tokenizer = spm.SentencePieceProcessor(model_file=vocab_file)
- # self.eod_id = self.tokenizer.piece_to_id("<|endoftext|>")
- self.eod_id = self.tokenizer.piece_to_id("</s>")
- self.pad_id = self.tokenizer.piece_to_id("[PAD]")
- self.unk_id = self.tokenizer.piece_to_id("<unk>")
-
- @property
- def vocab_size(self):
- return self.tokenizer.get_piece_size()
-
- @property
- def vocab(self):
- return {
- self.tokenizer.id_to_piece(idx): idx
- for idx in range(self.tokenizer.get_piece_size())
- }
-
- @property
- def inv_vocab(self):
- return {
- idx: self.tokenizer.id_to_piece(idx)
- for idx in range(self.tokenizer.get_piece_size())
- }
-
- def tokenize(self, text):
- return self.tokenizer.encode(text)
-
- def detokenize(self, token_ids):
- return self.tokenizer.decode(token_ids)
-
- @property
- def eod(self):
- return self.eod_id
-
-
-class HFTokenizer(AbstractTokenizer):
- """Designed to Integrate HF's Tokenizer library."""
-
- def __init__(self, vocab_file):
- name = "HFTokenizer"
- super().__init__(name)
-
- self.tokenizer = Tokenizer.from_file(vocab_file)
- # self.eod_id = self.tokenizer.token_to_id("<|endoftext|>")
- self.eod_id = self.tokenizer.token_to_id("<|end|>")
- # self.pad_id = self.tokenizer.token_to_id("<|padding|>")
-
- # 新词表没有<|padding|>, 用<|extratoken_1|>代替，和tokenization一致
- # self.pad_id = self.tokenizer.token_to_id("<|extratoken_1|>")
- self.pad_id = self.tokenizer.token_to_id("<|pad|>")
-
- @property
- def vocab_size(self):
- return self.tokenizer.get_vocab_size()
-
- @property
- def vocab(self):
- return self.tokenizer.get_vocab()
-
- @property
- def inv_vocab(self):
- return self.tokenizer.decoder
-
- def tokenize(self, text: str):
- return self.tokenizer.encode(text).ids
-
- def tokenize_batch(self, text_batch: Union[List[str], str]):
- return self.tokenizer.encode_batch(text_batch)
-
- def detokenize(self, token_ids):
- return self.tokenizer.decode(token_ids)
-
- @property
- def eod(self):
- return self.eod_id
-
-
-class HFGPT2Tokenizer(AbstractTokenizer):
- """Designed to Integrate the pretrained OpenAI GPT2 Tokenizers from HF"""
-
- def __init__(self, vocab_file=None, fast=True):
- name = "HFGPT2Tokenizer"
- if fast:
- name += "Fast"
- super().__init__(name)
- if vocab_file is None:
- vocab_file = "gpt2"
- if fast:
- self.tokenizer = GPT2TokenizerFast.from_pretrained(vocab_file)
- else:
- self.tokenizer = GPT2Tokenizer.from_pretrained(vocab_file)
-
- self.tokenizer.add_special_tokens({"pad_token": "<|padding|>"})
- self.eod_id = self.tokenizer.eos_token_id
- self.pad_id = self.tokenizer.pad_token_id
-
- @property
- def vocab_size(self):
- return len(self.tokenizer)
-
- @property
- def vocab(self):
- return self.tokenizer.get_vocab()
-
- @property
- def inv_vocab(self):
- return self.tokenizer._tokenizer.decoder
-
- def tokenize(self, text: str):
- return self.tokenizer.encode(text)
-
- def tokenize_batch(self, text_batch: Union[List[str], str]):
- if isinstance(text_batch, str):
- text_batch = [text_batch]
- return [self.tokenize(t) for t in text_batch]
-
- def detokenize(self, token_ids):
- return self.tokenizer.decode(token_ids)
-
- @property
- def eod(self):
- return self.eod_id
-
-
-class CharLevelTokenizer(AbstractTokenizer):
- """Character Level Tokenizer"""
-
- def __init__(self, vocab_size):
- name = "CharLevelTokenizer"
- super().__init__(name)
- self._vocab_size = vocab_size
- self.eod_id = 0
- self.pad_id = 1
-
- def clamp(self, n):
- return max(32, min(n, self.vocab_size))
-
- @property
- def vocab_size(self):
- return self._vocab_size
-
- @property
- def vocab(self):
- raise NotImplementedError
-
- @property
- def inv_vocab(self):
- raise NotImplementedError
-
- def decode_token(self, token: int):
- return str(chr(self.clamp(token)))
-
- def tokenize(self, text: str):
- return list(np.fromstring(text, dtype=np.uint8))
-
- def tokenize_batch(self, text_batch: Union[List[str], str]):
- if isinstance(text_batch, list):
- return [self.tokenize(s) for s in text_batch]
- else:
- return self.tokenize(text_batch)
-
- def detokenize(self, token_ids):
- return "".join(list(map(self.decode_token, token_ids)))
-
- @property
- def eod(self):
- return self.eod_id
-
-
-class TiktokenTokenizer(AbstractTokenizer):
- """Tokenizer from OpenAI's tiktoken implementation"""
-
- def __init__(self, vocab_file):
- try:
- import tiktoken
- except ModuleNotFoundError:
- print("Please install tiktoken: (https://github.com/openai/tiktoken)")
- raise Exception
-
- name = "TiktokenTokenizer"
- super().__init__(name)
-
- self.tokenizer = tiktoken.get_encoding(vocab_file)
- self.eod_id = self.tokenizer.eot_token
- self.pad_id = None
-
- @property
- def vocab_size(self):
- return self.tokenizer.n_vocab
-
- @property
- def vocab(self):
- raise NotImplementedError(
- "TiktokenTokenizer does not implement vocabulary access."
- )
-
- @property
- def inv_vocab(self):
- raise NotImplementedError(
- "TiktokenTokenizer does not implement vocabulary access. \
- To get the idx-th token in vocabulary, use tokenizer.decode([idx]) ."
- )
-
- def tokenize(self, text: str):
- return self.tokenizer.encode(text) # , allowed_special="all")
-
- def tokenize_batch(self, text_batch: List[str]):
- return self.tokenizer.encode_batch(text_batch, allowed_special="all")
-
- def detokenize(self, token_ids):
- return self.tokenizer.decode(tokens=token_ids, errors="strict")
-
- @property
- def eod(self):
- return self.eod_id
-
- @property
- def pad(self):
- raise NotImplementedError
diff --git a/mftcoder_atorch/tokenizer/train_tokenizer.py b/mftcoder_atorch/tokenizer/train_tokenizer.py
deleted file mode 100644
index 14a8e29..0000000
--- a/mftcoder_atorch/tokenizer/train_tokenizer.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# Copyright (c) 2021, EleutherAI
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Assumes a dataset of jsonl files in the same format as the neox training set.
-"""
-
-from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers
-from tokenizers.normalizers import NFKC
-
-from glob import glob
-import os
-import json
-import argparse
-
-
-def load_jsonl(input_path, quiet=True) -> list:
- """
- Read list of objects from a JSON lines file.
- """
- data = []
- with open(input_path, "r", encoding="utf-8") as f:
- for line in f:
- data.append(json.loads(line.rstrip("\n|\r")))
- if not quiet:
- print("Loaded {} records from {}".format(len(data), input_path))
- return data
-
-
-def json_iterator(input_dir, text_key="text"):
- all_jsonls = glob(f"{input_dir}/*.jsonl") + glob(f"{input_dir}/*.json")
- for j in all_jsonls:
- data = load_jsonl(j)
- for doc in data:
- yield doc[text_key]
-
-
-def train_tokenizer(
- input_dir: str, save_path: str, tokenizer_type: str = "BPE", vocab_size: int = 52000
-):
- """
- Trains a tokenizer on all the json files in `input_dir` and saves it to `save_path`
-
- :param input_dir: input directory containing jsonl files
- :param save_path: path to save tokenizer to
- :param tokenizer_type: type of tokenizer to train.
- :param vocab_size: int, size of tokenizer's vocab
- :return:
- """
-
- if tokenizer_type == "BPE":
- model = models.BPE()
- else:
- raise NotImplementedError(f"Tokenizer type {tokenizer_type} not implemented")
- tokenizer = Tokenizer(model)
-
- # Customize pre-tokenization and decoding
- tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=True)
- tokenizer.decoder = decoders.ByteLevel()
- tokenizer.post_processor = processors.ByteLevel(trim_offsets=True)
- tokenizer.normalizer = NFKC()
-
- # And then train
- trainer = trainers.BpeTrainer(
- vocab_size=vocab_size, special_tokens=["<|endoftext|>", "<|padding|>"]
- )
- tokenizer.train_from_iterator(json_iterator(input_dir), trainer)
-
- # And Save it
- tokenizer.save(save_path, pretty=True)
- print(f"Tokenizer saved at {save_path}")
-
-
-def parse_args():
- parser = argparse.ArgumentParser(
- description="script for training a multilingual "
- "HF tokenizer on CC dumps with upweighting for low resource languages"
- )
- parser.add_argument(
- "--json_input_dir",
- type=str,
- help="Path to folder containing tokenizer training data in jsonl format",
- )
- parser.add_argument(
- "--tokenizer_output_path",
- type=str,
- help="Path to which your trained tokenizer will be saved (should end in .json)",
- )
- parser.add_argument(
- "--tokenizer_type",
- type=str,
- help="type of tokenizer to train, currently only BPE is supported",
- choices=["BPE"],
- default=["BPE"],
- )
- parser.add_argument(
- "-v",
- "--vocab_size",
- help="vocabulary size of tokenizer, default=52k",
- type=int,
- default=52000,
- )
- return parser.parse_args()
-
-
-if __name__ == "__main__":
-
- args = parse_args()
-
- train_tokenizer(
- args.json_input_dir,
- save_path=args.tokenizer_output_path,
- tokenizer_type=args.tokenizer_type,
- vocab_size=args.vocab_size,
- )
diff --git a/mftcoder_atorch/train/__init__.py b/mftcoder_atorch/train/__init__.py
deleted file mode 100644
index d2045bb..0000000
--- a/mftcoder_atorch/train/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .run_train import *
\ No newline at end of file
diff --git a/mftcoder_atorch/train/run_gpt_mft.sh b/mftcoder_atorch/train/run_gpt_mft.sh
deleted file mode 100644
index de0a58b..0000000
--- a/mftcoder_atorch/train/run_gpt_mft.sh
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/bin/bash
-LOAD_RAW_DATASET=True
-if [ ${LOAD_RAW_DATASET} = "True" ]; then
- LOAD_RAW_DATASET="--load_raw_dataset"
- DATA_PATHS="$DATA_PATHS"
- DATA_WEIGHTS="[1.,1.,...,1.]"
- DATA_SPLIT="95,5,0"
- SHUFFLE_BEFORE_SPLIT=""
- USE_RANDOM_SAMPLER=""
- USE_WEIGHTED_LOSS=""
- WEIGHT_BY_NUM_DOCUMENTS=""
-else
- LOAD_RAW_DATASET=""
- DATA_PATHS="$DATA_PATHS"
- DATA_WEIGHTS="[1.,1.,...,1.]"
- DATA_SPLIT="95,5,0"
- SHUFFLE_BEFORE_SPLIT="--shuffle_before_split"
- USE_RANDOM_SAMPLER="--use_random_sampler"
- USE_WEIGHTED_LOSS="--use_weighted_loss"
- WEIGHT_BY_NUM_DOCUMENTS="--weight_by_num_documents"
-fi
-
-VOCAB_FILE="../utils/vocab.json"
-MODEL_TYPE="gpt_neox"
-
-PRETRAINED_MODEL_PATH="$MODEL_NAME_OR_PATH"
-RESUME_FROM_CHECKPOINT="false"
-
-PER_DEVICE_BATCH_SIZE=$1
-TP=$2
-DP=$3
-EPOCH=$4
-TOTAL_TRAIN_BATCH_SIZE=$(($PER_DEVICE_BATCH_SIZE * $TP * $DP))
-GPU=$(($TP * $DP))
-OUTPUT="$OUTPUT_DIR"
-TENSORBOARD_PATH="$TensorBoard_DIR"
-PREFIX="master-0"
-mkdir -p $OUTPUT || true
-echo "output to $OUTPUT"
-mkdir -p $TENSORBOARD_PATH
-chmod 777 $OUTPUT
-chmod 777 $TENSORBOARD_PATH
-
-python -m atorch.distributed.launch \
- --nproc_per_node=$(nvidia-smi -L | wc -l) \
- run_train.py \
- ${LOAD_RAW_DATASET} \
- --tokenize_mode 'sft' \
- --train_mode 'sft' \
- --padding_mode 'padding' \
- --pretrained_model_path $PRETRAINED_MODEL_PATH \
- --vocab_file $VOCAB_FILE \
- --model_type $MODEL_TYPE \
- --padding \
- --data_paths $DATA_PATHS \
- --data_weights $DATA_WEIGHTS \
- --data_split $DATA_SPLIT \
- ${SHUFFLE_BEFORE_SPLIT} \
- ${USE_RANDOM_SAMPLER} \
- ${USE_WEIGHTED_LOSS} \
- ${WEIGHT_BY_NUM_DOCUMENTS} \
- --train_iters 100 \
- --num_warmup_steps 500 \
- --custom_lr_scheduler_type 'cosine' \
- --learning_rate 1.0e-4 \
- --min_lr 1.0e-5 \
- --valid_iters 10 \
- --valid_interval 2000 \
- --num_train_epochs $EPOCH \
- --seq_length 4096 \
- --total_train_batch_size $TOTAL_TRAIN_BATCH_SIZE \
- --per_device_valid_batch_size $PER_DEVICE_BATCH_SIZE \
- --seed 42 \
- --preprocessing_num_workers 6 \
- --num_workers 8 \
- --output_dir $OUTPUT \
- --tensorboard_dir $TENSORBOARD_PATH \
- --ignore_mismatched_sizes \
- --skip_atorch_autoacc_dryrun \
- --tp $TP \
- --dp $DP \
- --bf16 \
- --checkpointing_steps 2000 \
- --log_interval 10 \
- --make_vocab_size_divisible_by 128 \
- --weighted_loss_mode 'case3' \
- --checkpoint_activations \
- --resume_from_checkpoint $RESUME_FROM_CHECKPOINT \
- --max_grad_norm 1 \
- --evaluation_strategy "steps,epoch" \
- --save_strategy "steps" \
- --save_total_limit 2 \
- --extra_save_by_epoch \
- --metric_for_best_model 'loss' \
- --greater_is_better 'false' \
- --early_stopping_patience 10 2>&1 | tee $OUTPUT/$PREFIX-output.txt
\ No newline at end of file
diff --git a/mftcoder_atorch/train/run_gpt_mft_peft.sh b/mftcoder_atorch/train/run_gpt_mft_peft.sh
deleted file mode 100644
index d43405c..0000000
--- a/mftcoder_atorch/train/run_gpt_mft_peft.sh
+++ /dev/null
@@ -1,97 +0,0 @@
-#!/bin/bash
-LOAD_RAW_DATASET=True
-if [ ${LOAD_RAW_DATASET} = "True" ]; then
- LOAD_RAW_DATASET="--load_raw_dataset"
- DATA_PATHS="$DATA_PATHS"
- DATA_WEIGHTS="[1.,1.,...,1.]"
- DATA_SPLIT="90,10,0"
- SHUFFLE_BEFORE_SPLIT=""
- USE_RANDOM_SAMPLER=""
- USE_WEIGHTED_LOSS=""
- WEIGHT_BY_NUM_DOCUMENTS=""
-else
- LOAD_RAW_DATASET=""
- DATA_PATHS="$DATA_PATHS"
- DATA_WEIGHTS="[1.,1.,...,1.]"
- DATA_SPLIT="95,5,0"
- SHUFFLE_BEFORE_SPLIT="--shuffle_before_split"
- USE_RANDOM_SAMPLER="--use_random_sampler"
- USE_WEIGHTED_LOSS="--use_weighted_loss"
- WEIGHT_BY_NUM_DOCUMENTS="--weight_by_num_documents"
-fi
-
-VOCAB_FILE="../utils/vocab.json"
-MODEL_TYPE="gpt_neox"
-
-PRETRAINED_MODEL_PATH="$MODEL_NAME_OR_PATH"
-RESUME_FROM_CHECKPOINT="false"
-
-PER_DEVICE_BATCH_SIZE=$1
-TP=$2
-DP=$3
-EPOCH=$4
-TOTAL_TRAIN_BATCH_SIZE=$(($PER_DEVICE_BATCH_SIZE * $TP * $DP))
-GPU=$(($TP * $DP))
-OUTPUT="$OUTPUT_DIR"
-TENSORBOARD_PATH="$TensorBoard_DIR"
-PREFIX="master-0"
-mkdir -p $OUTPUT || true
-echo "output to $OUTPUT"
-mkdir -p $TENSORBOARD_PATH
-chmod 777 $OUTPUT
-chmod 777 $TENSORBOARD_PATH
-
-python -m atorch.distributed.launch \
- --nproc_per_node=$(nvidia-smi -L | wc -l) \
- run_train.py \
- ${LOAD_RAW_DATASET} \
- --tokenize_mode 'sft' \
- --padding_mode 'padding' \
- --pretrained_model_path $PRETRAINED_MODEL_PATH \
- --vocab_file $VOCAB_FILE \
- --model_type $MODEL_TYPE \
- --padding \
- --data_paths $DATA_PATHS \
- --data_weights $DATA_WEIGHTS \
- --data_split $DATA_SPLIT \
- ${SHUFFLE_BEFORE_SPLIT} \
- ${USE_RANDOM_SAMPLER} \
- ${USE_WEIGHTED_LOSS} \
- ${WEIGHT_BY_NUM_DOCUMENTS} \
- --train_iters 100 \
- --num_warmup_steps 30 \
- --custom_lr_scheduler_type 'cosine' \
- --learning_rate 5.0e-5 \
- --min_lr 1.0e-6 \
- --valid_iters 400 \
- --valid_interval 500 \
- --num_train_epochs $EPOCH \
- --seq_length 4096 \
- --total_train_batch_size $TOTAL_TRAIN_BATCH_SIZE \
- --per_device_valid_batch_size $PER_DEVICE_BATCH_SIZE \
- --seed 42 \
- --preprocessing_num_workers 6 \
- --num_workers 8 \
- --output_dir $OUTPUT \
- --tensorboard_dir $TENSORBOARD_PATH \
- --ignore_mismatched_sizes \
- --skip_atorch_autoacc_dryrun \
- --tp $TP \
- --dp $DP \
- --bf16 \
- --checkpointing_steps 500 \
- --log_interval 10 \
- --make_vocab_size_divisible_by 128 \
- --weighted_loss_mode 'case3' \
- --peft_type 'lora' \
- --checkpoint_activations \
- --resume_from_checkpoint $RESUME_FROM_CHECKPOINT \
- --max_grad_norm 1 \
- --evaluation_strategy "steps,epoch" \
- --save_strategy "steps" \
- --save_total_limit 2 \
- --extra_save_by_epoch \
- --metric_for_best_model 'loss' \
- --greater_is_better 'false' \
- --early_stopping_patience 5 \
- --use_dynamic_padding 2>&1 | tee $OUTPUT/$PREFIX-output.txt
diff --git a/mftcoder_atorch/train/run_train.py b/mftcoder_atorch/train/run_train.py
deleted file mode 100644
index 9e4fcbe..0000000
--- a/mftcoder_atorch/train/run_train.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import sys
-sys.path.append("..")
-import json
-import logging
-import math
-import os
-import numpy as np
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-class cb:
- def __init__(self, path):
- self.path = path
- def __call__(self, s):
- with open(f"{self.path}/fsdp_mapping.html", "w") as f:
- f.write(s)
-
-# handle multi-processing writing
-os.environ["HF_MODULES_CACHE"] = os.path.join("/root/.cache/huggingface/modules", os.getenv("RANK", ""))
-import random # noqa: E402
-import datasets # noqa: E402
-import transformers # noqa: E402
-from torch.utils.data import DataLoader # noqa: E402
-from torch.utils.data.distributed import DistributedSampler # noqa: E402
-from transformers import ( # noqa: E402
- default_data_collator,
- # get_scheduler,
- set_seed,
-)
-from transformers.utils.versions import require_version # noqa: E402
-from atorch.utils.meta_model_utils import init_empty_weights_with_disk_offload # noqa: E402
-
-from transformers import AutoTokenizer
-
-from torch.distributed.fsdp import (
- FullyShardedDataParallel as FSDP,
-)
-
-from utils.common_utils import (
- is_local_main_process, generate_task_id, print_rank_0, is_old_version,
- atorch_init_distributed, atorch_reset_distributed, TASK2ID, ID2TASK,
- get_rank, get_world_size
-)
-from utils.auto_accelerate_utils import DataCollatorForMFTDataset, loss_func_mft
-from arguments.get_arguments import parse_args
-from model.build_model import setup_model
-from data.gpt2_multi_task_dataset import load_dataset_from_jsonl
-from train.trainer.atorch_trainer import AtorchTrainer
-from pathlib import Path
-
-
-def main():
- args = parse_args()
-
- # Make one log on every process with the configuration for debugging.
- logging.basicConfig(
- # format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- format="%(asctime)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO,
- )
- logger = logging.getLogger(__name__)
- if is_local_main_process():
- datasets.utils.logging.set_verbosity_warning()
- transformers.utils.logging.set_verbosity_info()
- else:
- datasets.utils.logging.set_verbosity_error()
- transformers.utils.logging.set_verbosity_error()
-
- # If passed along, set the training seed now.
- if args.seed is not None:
- set_seed(args.seed)
-
- generate_task_id(args.data_paths, args.train_mode) # generate TASK2ID, ID2TASK mapping
- print(TASK2ID)
- print(ID2TASK)
-
- model, model_config, tokenizer = setup_model(args, logger, use_cache=False)
- print(f'args.total_model_param: {args.total_model_param}')
-
- train_dataset, dataloader_args = None, None
- train_dataloader, valid_dataloader, test_dataloader = None, None, None
-
- args.world_size = get_world_size()
- global_rank = get_rank()
- print(f'world_size: {args.world_size}, global_rank: {global_rank}')
- args.per_device_train_batch_size = args.total_train_batch_size // args.world_size
- if args.load_raw_dataset:
- print_rank_0('load raw dataset')
- if args.model_type in ['gpt_neox']:
- train_dataset, valid_dataset = load_dataset_from_jsonl(args, tokenizer, shard_data=True, world_size=args.world_size, global_rank=global_rank)
-
- if train_dataset is not None:
- args.do_train = True
- if valid_dataset is not None:
- args.do_valid = True
- else:
- print_rank_0('please set load_raw_dataset to True and rerun')
-
- if args.resume_from_checkpoint == 'true':
- logger.info(f'Resume from {args.output_dir}')
- resume_from_checkpoint = True
- else:
- logger.info(f'Train from scratch')
- resume_from_checkpoint = False
- if args.model_type in ['gpt_neox']:
- gpt_data = True
- else:
- gpt_data = False
- data_collator = DataCollatorForMFTDataset(args.model_type, args.weighted_loss_mode, args.use_dynamic_padding)
- my_loss_function = loss_func_mft
- trainer = AtorchTrainer(
- model=model,
- args=args,
- train_dataset=train_dataset,
- valid_dataset=valid_dataset,
- tokenizer=tokenizer,
- # files_to_save=files_to_save,
- args_to_save={
- # 'max_length': args.max_length,
- 'max_length': args.seq_length,
- 'peft_type': args.peft_type,
- 'gpt_model': gpt_data
- },
- data_collator=data_collator,
- my_loss_func=my_loss_function,
- custom_lr_scheduler_type=args.custom_lr_scheduler_type,
- rank=global_rank
- )
- if args.do_train:
- trainer.train(resume_from_checkpoint=resume_from_checkpoint)
-
-
-if __name__ == "__main__":
- atorch_init_distributed("nccl")
- main()
- atorch_reset_distributed()
diff --git a/mftcoder_atorch/train/trainer/atorch_trainer.py b/mftcoder_atorch/train/trainer/atorch_trainer.py
deleted file mode 100644
index 110a060..0000000
--- a/mftcoder_atorch/train/trainer/atorch_trainer.py
+++ /dev/null
@@ -1,1041 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-
-import datetime
-import json
-import logging
-import math
-import os
-import random
-import re
-import shutil
-import time
-import warnings
-from functools import partial
-from pathlib import Path
-import gc
-
-import numpy as np
-
-import atorch
-import torch
-from deepspeed.ops.adam import DeepSpeedCPUAdam
-from torch.distributed.fsdp import FullStateDictConfig
-from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
-from torch.distributed.fsdp import StateDictType
-from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR, CosineAnnealingWarmRestarts
-from torch.utils.data import DataLoader
-from torch.utils.data.distributed import DistributedSampler
-from torch.utils.tensorboard import SummaryWriter
-from tqdm.auto import tqdm
-from transformers import get_scheduler as get_scheduler_trans
-from transformers.modeling_utils import PreTrainedModel, unwrap_model
-from transformers.trainer import (
- OPTIMIZER_NAME,
- SCHEDULER_NAME,
- TRAINER_STATE_NAME,
- TRAINING_ARGS_NAME
-)
-from transformers.trainer_pt_utils import reissue_pt_warnings
-from transformers.trainer_utils import (
- PREFIX_CHECKPOINT_DIR,
-)
-from transformers.utils import WEIGHTS_NAME
-from torch.nn import CrossEntropyLoss
-from utils.common_utils import print_rank_0, get_tflops_megatron, get_computation_speed, TASK2ID, ID2TASK, EarlyStopping
-from utils.auto_accelerate_utils import get_ltor_masks_and_position_ids
-
-from atorch.auto import auto_accelerate
-from atorch.utils.version import torch_version
-from model.gpt_neox.modeling_gpt_neox import GPTNeoXLayer, GPTNeoXAttention, GPTNeoXMLP
-from model.peft.modeling_peft import PeftModel
-
-HYPER_PARAMETER_NAME = 'hyper_parameters.json'
-ATORCH_CHECKPOINT_NAME = 'atorch_checkpoint.bin'
-EPOCH_CHECKPOINT_NAME = 'epoch'
-
-logger = logging.getLogger(__name__)
-
-
-def is_local_main_process():
- return atorch.local_rank() == 0
-
-
-def is_global_main_process():
- return atorch.rank() == 0
-
-
-def has_inf_or_nan(x):
- try:
- # if x is half, the .float() incurs an additional deep copy, but it's necessary if
- # Pytorch's .sum() creates a one-element tensor of the same type as x
- # (which is true for some recent version of pytorch).
- cpu_sum = float(x.float().sum())
- # More efficient version that can be used if .sum() returns a Python scalar
- # cpu_sum = float(x.sum())
- except RuntimeError as instance:
- # We want to check if inst is actually an overflow exception.
- # RuntimeError could come from a different error.
- # If so, we still want the exception to propagate.
- if "value cannot be converted" not in instance.args[0]:
- raise
- return True
- else:
- if cpu_sum == float('inf') or cpu_sum == -float('inf') or cpu_sum != cpu_sum:
- return True
- return False
-
-
-def count_model_params(model):
- trainable_params = 0
- all_params = 0
- for param in model.parameters():
- num_params = param.numel()
- all_params += num_params
- if param.requires_grad:
- trainable_params += num_params
- return all_params, trainable_params
-
-
-class AtorchArguments:
- def __init__(self, **kwargs):
- for key, value in kwargs.items():
- setattr(self, key, value)
-
-
-def get_linear_schedule_with_log_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
-
- def lr_lambda(current_step: int):
- inverse_log_warm_up = 1.0 / math.log(num_warmup_steps)
- if current_step == 0:
- return 0.0
- if current_step < num_warmup_steps:
- return inverse_log_warm_up * math.log(current_step)
- return max(
- 0.0, float(num_training_steps - current_step) /
- float(max(1, num_training_steps - num_warmup_steps))
- )
-
- return LambdaLR(optimizer, lr_lambda, last_epoch)
-
-
-def get_scheduler(name, optimizer, num_warmup_steps, num_training_steps):
- scheduler_map = {
- 'log_warmup_linear_decay': get_linear_schedule_with_log_warmup}
- try:
- lr_scheduler = get_scheduler_trans(
- name, optimizer, num_warmup_steps, num_training_steps)
- return lr_scheduler
- except Exception:
- schedule_func = scheduler_map[name]
- return schedule_func(optimizer, num_warmup_steps, num_training_steps)
-
-
-class AtorchTrainer:
- def __init__(self,
- model,
- args,
- train_dataset,
- valid_dataset,
- tokenizer=None,
- callbacks=None,
- no_save_atorch_checkpoint=None,
- save_pytorch_model_bin_checkpoint=True,
- train_peft=False,
- rank=0,
- max_shard_size='10GB',
- files_to_save=None,
- args_to_save=None,
- data_collator=None,
- my_loss_func=None,
- **kwargs,
- ):
- self.args = args
- self.TASK2ID = TASK2ID
- self.ID2TASK = ID2TASK
- print('in atorch trainer')
- print(TASK2ID)
- print(ID2TASK)
- self.model = model
- self.no_save_atorch_checkpoint = no_save_atorch_checkpoint
- self.save_pytorch_model_bin_checkpoint = save_pytorch_model_bin_checkpoint
- self.train_peft = train_peft
- self.rank = rank
- self.kwargs = kwargs
- self.train_dataset = train_dataset
- self.valid_dataset = valid_dataset
- self.tokenizer = tokenizer
- self.max_shard_size = max_shard_size
- self.files_to_save = files_to_save
- self.args_to_save = args_to_save
- self.best_metric = None
- self.best_model_checkpoint = None
- self.no_save_base_model = True
-
- self.device = f"cuda:{atorch.local_rank()}"
-
- self.total_train_batch_size = self.args.per_device_train_batch_size * \
- self.args.gradient_accumulation_steps * \
- atorch.world_size()
-
- self.data_collator = data_collator
- self.my_loss_func = my_loss_func
- if self.args.early_stopping_patience > 0:
- print(f'early_stopping_patience: {self.args.early_stopping_patience}')
- patience = self.args.early_stopping_patience
- self.early_stopping = EarlyStopping(patience, verbose=True)
-
- self.train_dataloader_args = {
- "shuffle": True,
- "batch_size": self.total_train_batch_size,
- "pin_memory": True,
- "collate_fn": data_collator,
- "drop_last": True,
- # "num_workers": args.num_workers,
- # "persistent_workers": args.num_workers > 0,
- }
-
- self.valid_dataloader = DataLoader(
- valid_dataset,
- sampler=DistributedSampler(valid_dataset, shuffle=True),
- batch_size=args.per_device_valid_batch_size,
- pin_memory=True,
- collate_fn=data_collator
- )
- self.valid_dataloader_length = len(self.valid_dataloader)
-
- if self.args.resume_from_checkpoint == 'true':
- self.resume_checkpoint_dir = self.get_last_checkpoint(
- self.args.output_dir)
-
- self.atorch_args = AtorchArguments(
- lr=args.learning_rate,
- weight_decay=args.weight_decay,
- adam_eps=args.adam_epsilon,
- adam_beta1=args.adam_beta1,
- adam_beta2=args.adam_beta2)
-
- self.atorch_init()
-
- self.num_update_steps_per_epoch = math.ceil(
- len(self.train_dataloader) / self.args.gradient_accumulation_steps)
- print(f'number of update steps per epoch: {self.num_update_steps_per_epoch}')
- if self.args.max_steps == -1:
- self.args.max_steps = int(
- self.args.num_train_epochs * self.num_update_steps_per_epoch)
- else:
- self.args.num_train_epochs = math.ceil(
- self.args.max_steps / self.num_update_steps_per_epoch)
-
- custom_lr_scheduler_type = self.kwargs.get(
- 'custom_lr_scheduler_type', None)
- self.lr_scheduler = get_scheduler(
- name=custom_lr_scheduler_type if custom_lr_scheduler_type else self.args.lr_scheduler_type,
- optimizer=self.optimizer,
- num_warmup_steps=self.args.num_warmup_steps,
- num_training_steps=self.args.max_steps,
- )
- print_rank_0(f'lr_scheduler{self.lr_scheduler}')
- if self.args.resume_from_checkpoint == 'true':
- with warnings.catch_warnings(record=True):
- self.lr_scheduler.load_state_dict(torch.load(
- os.path.join(self.resume_checkpoint_dir, SCHEDULER_NAME)))
- self._load_rng_state(self.resume_checkpoint_dir)
- torch.distributed.barrier()
- now_datetime = datetime.datetime.now()
- timestr = datetime.datetime.strftime(now_datetime, '%Y%m%d-%H%M%S')
- self.log_dir = os.path.join(self.args.output_dir, 'runs', timestr)
- self.summary_writer = None
- if torch.distributed.get_rank() == 0:
- self.summary_writer = SummaryWriter(log_dir=self.log_dir)
-
- def get_last_checkpoint(self, folder):
- _re_checkpoint = re.compile(r"^" + PREFIX_CHECKPOINT_DIR + r"\-(\d+)")
- content = sorted(os.listdir(folder))
- checkpoints = [
- path
- for path in content
- if _re_checkpoint.search(path) is not None and os.path.isdir(os.path.join(folder, path))
- ]
- if len(checkpoints) == 0:
- return
- return os.path.join(folder, max(checkpoints, key=lambda x: int(_re_checkpoint.search(x).groups()[0])))
-
- def _load_rng_state(self, resume_checkpoint_dir):
- # Load RNG states from `checkpoint`
- if resume_checkpoint_dir is None:
- return
-
- if self.args.world_size > 1:
- rng_file = os.path.join(
- resume_checkpoint_dir, f"rng_state_{self.rank}.pth")
- if not os.path.isfile(rng_file):
- logger.info(
- f"Didn't find an RNG file for process {self.rnak}, if you are resuming a training that "
- "wasn't launched in a distributed fashion, reproducibility is not guaranteed."
- )
- return
- else:
- rng_file = os.path.join(resume_checkpoint_dir, "rng_state.pth")
- if not os.path.isfile(rng_file):
- logger.info(
- "Didn't find an RNG file, if you are resuming a training that was launched in a distributed "
- "fashion, reproducibility is not guaranteed."
- )
- return
-
- checkpoint_rng_state = torch.load(rng_file)
- random.setstate(checkpoint_rng_state["python"])
- np.random.set_state(checkpoint_rng_state["numpy"])
- torch.random.set_rng_state(checkpoint_rng_state["cpu"])
- if torch.cuda.is_available():
- if self.args.local_rank != -1:
- torch.cuda.random.set_rng_state(checkpoint_rng_state["cuda"])
- else:
- try:
- torch.cuda.random.set_rng_state_all(
- checkpoint_rng_state["cuda"])
- except Exception as e:
- logger.info(
- f"Didn't manage to set back the RNG states of the GPU because of the following error:\n {e}"
- "\nThis won't yield the same results as if the training had not been interrupted."
- )
-
- def load_atorch_model_state(self, model_state_dict, **kwargs):
- print('resume atorch model state')
- if self.is_rank0():
- self.model.load_state_dict(model_state_dict)
- # load on rank 0，then distribute params via sync_module_states
- torch.distributed.barrier()
- # self.model = FSDP(self.model, sync_module_states=True, **kwargs)
-
- def load_atorch_optim_state(self, optim_state_dict):
-
- print('resume optimizer state')
- optim_state_dict = FSDP.scatter_full_optim_state_dict(
- optim_state_dict, self.model) # may be removed after PyTorch 2.2
-
- def move_optim_state_to_cpu(optim_state_dict):
- for k in optim_state_dict:
- if isinstance(optim_state_dict[k], torch.Tensor):
- optim_state_dict[k] = optim_state_dict[k].cpu()
- elif isinstance(optim_state_dict[k], dict):
- move_optim_state_to_cpu(optim_state_dict[k])
-
- move_optim_state_to_cpu(optim_state_dict)
-
- self.optimizer.load_state_dict(optim_state_dict)
-
-
- def atorch_init(self):
- assert torch_version() >= (2, 0, 0), "use pt2.0 for use orig param if fsdp"
-
- if self.args.model_type == 'gpt_neox':
- # wrap_class = (GPTNeoXAttention, GPTNeoXMLP)
- wrap_class = (GPTNeoXLayer,)
-
- parallel_mode = []
- if self.args.dp:
- parallel_mode.append(("data", self.args.dp))
- if self.args.tp:
- parallel_mode.append(("tensor_parallel", self.args.tp))
- strategy = [
- ("parallel_mode", (parallel_mode, None)),
- "module_replace",
- ]
- if self.args.peft_type is None or self.args.peft_type == 'lora':
- cpu_offload = False
- fsdp_config = {
- "atorch_wrap_cls": wrap_class,
- "sync_module_states": True,
- "use_orig_params": True,
- "limit_all_gathers": True,
- # "cpu_offload": True,
- }
- print(fsdp_config)
- fsdp_opt = ("fsdp", fsdp_config)
- strategy.append(fsdp_opt)
- self.args.atorch_opt = "fsdp"
- else:
- # TODO: qlora
- self.args.atorch_opt = "ddp"
-
- if self.args.bf16 or self.args.fp16:
- if self.args.bf16:
- amp_config = {"dtype": torch.bfloat16, "skip_if_nonfinite": True}
- # TODO: qlora
- elif self.args.fp16:
- amp_config = {"dtype": torch.float16}
- strategy.append(("amp_native", amp_config))
-
- if self.args.checkpoint_activations:
- strategy.append(("checkpoint", wrap_class))
- print(f"Manually loaded auto acc strategy: {strategy}")
-
- def prepare_input(batch, device):
- batch = {k: v.to(device=device, non_blocking=True) if v is not None else None
- for k, v in batch.items()}
- return batch
-
- def optim_param_func(model, args):
- no_decay = ["bias", "LayerNorm.weight", "layernorm.weight"]
- optimizer_grouped_parameters = [
- {
- "params": [
- p
- for n, p in model.named_parameters()
- if not any(nd in n for nd in no_decay)
- ],
- "weight_decay": args.weight_decay,
- },
- {
- "params": [
- p
- for n, p in model.named_parameters()
- if any(nd in n for nd in no_decay)
- ],
- "weight_decay": 0.0,
- },
- ]
- return optimizer_grouped_parameters
-
- # load fsdp checkpoint
- if self.args.resume_from_checkpoint == 'true':
- logger.info(f'Resume training from {self.resume_checkpoint_dir}')
- if self.is_rank0():
- sd = torch.load(os.path.join(
- self.resume_checkpoint_dir, ATORCH_CHECKPOINT_NAME), map_location='cpu')
- model_state_dict, optim_state_dict = sd['model_state_dict'], sd['optimizer_state_dict']
- else:
- model_state_dict, optim_state_dict = None, None
- torch.distributed.barrier() # other rank waiting
- ##########
- self.load_atorch_model_state(model_state_dict)
- ##########
-
- if self.is_rank0():
- print(f'GPU mem before fsdp:')
- print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
- optim_func = torch.optim.AdamW
- print(f'optimizer before fsdp: {optim_func}')
-
- ddp_find_unused_parameters = None
- if self.args.atorch_opt == "ddp" and not (self.args.peft_type in ["lora", "qlora"] and self.args.checkpoint_activations):
- ddp_find_unused_parameters = True
-
- status, result, best_strategy = auto_accelerate(
- self.model,
- optim_func,
- self.train_dataset,
- dataloader_args=self.train_dataloader_args,
- loss_func=self.my_loss_func,
- prepare_input=prepare_input,
- optim_args={
- "lr": self.atorch_args.lr,
- "weight_decay": self.atorch_args.weight_decay,
- "eps": self.atorch_args.adam_eps,
- "betas": (self.atorch_args.adam_beta1, self.atorch_args.adam_beta2),
- },
- optim_param_func=partial(
- optim_param_func, args=self.atorch_args),
- load_strategy=strategy,
- ignore_dryrun_on_load_strategy=True,
- find_unused_parameters=ddp_find_unused_parameters,
- )
- assert (
- status
- ), f"auto_accelerate failed. status: {status}, result: {result}, best_strategy: {best_strategy}"
- print(f"Best strategy is: {best_strategy}")
-
- self.model = result.model
- self.optimizer = result.optim
- print(f'optimizer after fsdp: {self.optimizer}')
- self.loss_func = result.loss_func
- self.train_dataloader = result.dataloader
- self.prepare_input = result.prepare_input
-
- if self.args.resume_from_checkpoint == 'true':
- self.load_atorch_optim_state(optim_state_dict)
- print(f"atorch use optimizer: {self.optimizer}")
- if self.is_rank0():
- print(f'GPU mem after fsdp:')
- print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
-
- def evaluate(self):
- logger.info(f"Start evaluation")
- if self.is_rank0():
- print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
- print(f'valid dataset length is: {len(self.valid_dataset)}')
- print(f'valid dataloader length is: {len(self.valid_dataloader)}')
- print(f'per device batch size: {self.args.per_device_valid_batch_size}')
- progress_bar = tqdm(range(len(self.valid_dataloader)),
- disable=not is_local_main_process(),
- smoothing=0)
- self.model.eval()
- losses = []
- accumulated_task_loss_np = np.zeros(len(self.ID2TASK))
- accumulated_task_num_np = np.zeros(len(self.ID2TASK))
- accumulated_step = 0
- for step, batch in enumerate(self.valid_dataloader):
- if step >= self.args.valid_iters:
- break
- with torch.no_grad():
- outputs = self.model(
- input_ids=batch['input_ids'].to(self.device),
- attention_mask=batch['attention_mask'].to(self.device),
- position_ids=batch['position_ids'].to(self.device)
- )
- loss, task_loss, task_num = self.loss_func(outputs, batch, self.args.weighted_loss_mode)
-
- repeated_loss = loss.repeat(
- self.args.per_device_valid_batch_size)
- if repeated_loss.ndim == 0:
- repeated_loss = repeated_loss.clone()[None]
- output_tensors = [repeated_loss.clone()
- for _ in range(atorch.world_size())]
- torch.distributed.all_gather(output_tensors, repeated_loss)
- for tensor in output_tensors:
- if torch.isnan(tensor).any() or torch.isinf(tensor).any():
- accumulated_step -= 1
- continue
- losses.append(torch.cat(output_tensors, dim=0).cpu())
- task_loss = task_loss.cpu().numpy()
- task_num = task_num.cpu().numpy()
- accumulated_task_loss_np += task_loss
- accumulated_task_num_np += task_num
-
- accumulated_step += 1
- progress_bar.update(1)
-
- losses = torch.cat(losses)
- losses = losses[: len(self.valid_dataset)]
- mean_loss = torch.mean(losses).item()
- accumulated_task_loss = torch.tensor(accumulated_task_loss_np).to(self.device)
- accumulated_task_num = torch.tensor(accumulated_task_num_np).to(self.device)
- torch.distributed.all_reduce(accumulated_task_loss, op=torch.distributed.ReduceOp.SUM)
- torch.distributed.all_reduce(accumulated_task_num, op=torch.distributed.ReduceOp.SUM)
- accumulated_task_loss /= torch.distributed.get_world_size()
- valid_task_loss = accumulated_task_loss / (accumulated_step - 1)
- logs = {'valid_loss': mean_loss}
- per_task_valid_loss = {self.ID2TASK[i]+'_loss': valid_task_loss[i].item() for i in range(len(self.ID2TASK))}
- logs.update(per_task_valid_loss)
- if is_global_main_process():
- logger.info('log point')
- for i in range(len(self.ID2TASK)):
- if accumulated_task_num[i] != 0:
- logger.info(f"{self.ID2TASK[i]}_loss: {valid_task_loss[i]}, sample nums: {accumulated_task_num[i]}")
- self.log(logs, step=self.global_steps, phase='Evaluation')
- metrics = {'valid_loss': mean_loss, 'valid_task_loss': valid_task_loss}
- logger.info(f"Finish evaluation")
- if self.is_rank0():
- print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
-
- return metrics
-
- def log(self, logs, step, phase='Train'):
- if not self.summary_writer:
- return
- logger.info(json.dumps(logs))
- for key, value in logs.items():
- self.summary_writer.add_scalar(f'{phase}/{key}', value, step)
-
- def _sorted_checkpoints(
- self,
- output_dir=None,
- checkpoint_prefix=PREFIX_CHECKPOINT_DIR,
- checkpoint_name_pattern='([0-9]+)',
- use_mtime=False
- ):
- ordering_and_checkpoint_path = []
-
- glob_checkpoints = [str(x) for x in Path(output_dir).glob(
- f"{checkpoint_prefix}-*") if os.path.isdir(x)]
-
- for path in glob_checkpoints:
- if use_mtime:
- ordering_and_checkpoint_path.append(
- (os.path.getmtime(path), path))
- else:
- regex_match = re.search(
- f".*{checkpoint_prefix}-({checkpoint_name_pattern})", path)
- if regex_match is not None and regex_match.groups() is not None:
- ordering_and_checkpoint_path.append(
- (int(regex_match.groups()[0]), path))
-
- checkpoints_sorted = sorted(ordering_and_checkpoint_path)
- checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
- # Make sure we don't delete the best model.
- if self.best_model_checkpoint is not None:
- best_model_index = checkpoints_sorted.index(str(Path(self.best_model_checkpoint)))
- for i in range(best_model_index, len(checkpoints_sorted) - 1):
- checkpoints_sorted[i], checkpoints_sorted[i + 1] = checkpoints_sorted[i + 1], checkpoints_sorted[i]
- print_rank_0(f'checkpoints sorted list: {checkpoints_sorted}')
- return checkpoints_sorted
-
- def _rotate_checkpoints(
- self,
- use_mtime=False,
- output_dir=None,
- prefix=PREFIX_CHECKPOINT_DIR,
- checkpoint_name_pattern='.*') -> None:
- if self.args.save_total_limit is None or self.args.save_total_limit <= 0:
- return
-
- # Check if we should delete older checkpoint(s)
- checkpoints_sorted = self._sorted_checkpoints(
- use_mtime=use_mtime,
- output_dir=output_dir,
- checkpoint_prefix=prefix,
- checkpoint_name_pattern=checkpoint_name_pattern)
- if len(checkpoints_sorted) <= self.args.save_total_limit:
- return
-
- # If save_total_limit=1 with load_best_model_at_end=True, we could end up deleting the last checkpoint, which
- # we don't do to allow resuming.
- save_total_limit = self.args.save_total_limit
-
- number_of_checkpoints_to_delete = max(
- 0, len(checkpoints_sorted) - save_total_limit)
- checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
- for checkpoint in checkpoints_to_be_deleted:
- logger.info(
- f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
- shutil.rmtree(checkpoint, ignore_errors=True)
-
- def _clean_atorch_checkpoints(self, output_dir=None, prefix=PREFIX_CHECKPOINT_DIR):
- # Check if we should delete older checkpoint(s)
- checkpoints_sorted = self._sorted_checkpoints(
- output_dir=output_dir,
- checkpoint_prefix=prefix,
- checkpoint_name_pattern='([0-9]+)')
-
- # If save_total_limit=1 with load_best_model_at_end=True, we could end up deleting the last checkpoint, which
- # we don't do to allow resuming.
-
- for checkpoint in checkpoints_sorted[:-1]:
- logger.info(
- f"Deleting older atorch checkpoint [{checkpoint}] due to self.args.save_total_limit")
- try:
- os.remove(os.path.join(checkpoint, ATORCH_CHECKPOINT_NAME))
- except Exception:
- continue
-
- def _save_peft_model(self, output_dir, state_dict=None):
- logger.info(f"Start saving peft model to {output_dir}")
- output_dir = output_dir if output_dir is not None else self.args.output_dir
- os.makedirs(output_dir, exist_ok=True)
- model = unwrap_model(self.model)
- if isinstance(model, PeftModel):
- if state_dict is None:
- state_dict = model.state_dict()
- model.save_pretrained(
- output_dir, state_dict=state_dict, is_main_process=self.is_rank0())
- else:
- if state_dict is None:
- state_dict = self.model.state_dict()
- if self.is_rank0():
- torch.save(state_dict, os.path.join(
- output_dir, "pytorch_model.bin"))
- logger.info(f"Saving peft model done.")
-
- def _save_model(self, output_dir=None, state_dict=None):
- # If we are executing this function, we are the process zero, so we don't check for that.
- output_dir = output_dir if output_dir is not None else self.args.output_dir
- os.makedirs(output_dir, exist_ok=True)
- logger.info(f"Saving model checkpoint to {output_dir}")
- # Save a trained model and configuration using `save_pretrained()`.
- # They can then be reloaded using `from_pretrained()`
- if not isinstance(self.model, PreTrainedModel):
- if isinstance(unwrap_model(self.model), PreTrainedModel):
- print_rank_0('save in not pretrained model~~~~~~~')
- if state_dict is None:
- state_dict = self.model.state_dict()
- state_dict = {key: value.bfloat16() if self.args.bf16 else value.half() for key, value in state_dict.items()}
- model = unwrap_model(self.model)
- model.save_pretrained(
- output_dir,
- state_dict=state_dict,
- max_shard_size=self.max_shard_size,
- is_main_process=self.is_rank0())
- elif isinstance(unwrap_model(self.model), PeftModel):
- if state_dict is None:
- state_dict = unwrap_model(self.model).base_model.model.state_dict()
- # Filter the peft params ...
- param_keys = list(state_dict.keys())
- base_model_state_dict = {}
- for key in param_keys:
- if LORA_KEY in key:
- # state_dict.pop(key)
- continue
- elif PEFT_PARAM_PREFIX in key:
- value = state_dict[key]
- new_key = key.replace(PEFT_PARAM_PREFIX, "")
- base_model_state_dict[new_key] = value
- else:
- base_model_state_dict[key] = value
- if self.is_rank0():
- torch.save(base_model_state_dict,
- os.path.join(output_dir, WEIGHTS_NAME))
- else:
- logger.info(
- "Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
- if state_dict is None:
- state_dict = self.model.state_dict()
- state_dict = {key: value.bfloat16() if self.args.bf16 else value.half() for key, value in state_dict.items()}
- torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
- else:
- print(f'save in pretrained model!!!!!!')
- if state_dict is None:
- state_dict = self.model.state_dict()
- state_dict = {key: value.bfloat16() if self.args.bf16 else value.half() for key, value in state_dict.items()}
- self.model.save_pretrained(
- output_dir,
- state_dict=state_dict,
- max_shard_size=self.max_shard_size)
- if self.tokenizer is not None:
- self.tokenizer.save_pretrained(output_dir)
-
- # Good practice: save your training arguments together with the trained model
- torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
-
- def is_rank0(self):
- return self.rank == 0
-
- def _save_atorch_checkpoint(self, output_dir):
-
- # StateDictType.FULL_STATE_DICT
- # FullStateDictConfig save to CPU，save on rank 0 only
- save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
- with FSDP.state_dict_type(self.model, StateDictType.FULL_STATE_DICT, save_policy):
- model_state_dict = self.model.state_dict()
- optim_state_dict = FSDP.full_optim_state_dict(self.model, self.optimizer) # may be removed after PyTorch 2.2
-
- if self.is_rank0():
- os.makedirs(output_dir, exist_ok=True)
- torch.save(
- {
- "model_state_dict": model_state_dict,
- "optimizer_state_dict": optim_state_dict,
- "global_steps": self.global_steps,
- },
- os.path.join(output_dir, ATORCH_CHECKPOINT_NAME),
- )
- torch.distributed.barrier() # other rank waiting
-
-
- def save(self, suffix=None, metrics=None):
- logger.info('Save start')
- if self.is_rank0():
- print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
- if not self.save_pytorch_model_bin_checkpoint:
- return
- if suffix is None:
- checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.global_steps}"
- else:
- checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{suffix}"
-
- run_dir = self.args.output_dir
- output_dir = os.path.join(run_dir, checkpoint_folder)
- # get state_dict, by every rank
- if isinstance(self.model, FSDP):
- save_policy = FullStateDictConfig(offload_to_cpu=atorch.world_size() > 1, rank0_only=atorch.world_size() > 1)
- with FSDP.state_dict_type(self.model, StateDictType.FULL_STATE_DICT, save_policy):
- model_state_dict = self.model.state_dict()
- optim_state_dict = FSDP.full_optim_state_dict(self.model, self.optimizer) # may be removed after PyTorch 2.2
- else:
- model_state_dict = unwrap_model(self.model).state_dict()
- optim_state_dict = self.optimizer.state_dict()
- if not self.no_save_atorch_checkpoint:
- if self.args.peft_type is None or not self.no_save_base_model:
- if self.is_rank0():
- os.makedirs(output_dir, exist_ok=True)
- torch.save(
- {
- "model_state_dict": model_state_dict,
- "optimizer_state_dict": optim_state_dict,
- "global_steps": self.global_steps,
- },
- os.path.join(output_dir, ATORCH_CHECKPOINT_NAME),
- )
- torch.distributed.barrier() # other rank waiting
- if self.args.peft_type is not None:
- print(f'no_save_base_model: {self.no_save_base_model}')
- if not self.no_save_base_model:
- self._save_model(output_dir=output_dir)
- self._save_peft_model(output_dir=output_dir)
- else:
- self._save_model(output_dir=output_dir)
-
- with warnings.catch_warnings(record=True) as caught_warnings:
- torch.save(self.lr_scheduler.state_dict(),
- os.path.join(output_dir, SCHEDULER_NAME))
- reissue_pt_warnings(caught_warnings)
-
- # Save RNG state in non-distributed training
- rng_states = {
- "python": random.getstate(),
- "numpy": np.random.get_state(),
- "cpu": torch.random.get_rng_state(),
- }
- if torch.cuda.is_available():
- if self.args.local_rank == -1:
- # In non distributed, we save the global CUDA RNG state (will take care of DataParallel)
- rng_states["cuda"] = torch.cuda.random.get_rng_state_all()
- else:
- rng_states["cuda"] = torch.cuda.random.get_rng_state()
-
- os.makedirs(output_dir, exist_ok=True)
-
- if torch.distributed.get_world_size() <= 1:
- torch.save(rng_states, os.path.join(output_dir, "rng_state.pth"))
- else:
- torch.save(rng_states, os.path.join(
- output_dir, f"rng_state_{self.rank}.pth"))
-
- if self.args_to_save:
- json.dump(self.args_to_save, open(os.path.join(output_dir,
- HYPER_PARAMETER_NAME), 'w'), ensure_ascii=False, indent=2)
- # save state
- state = {'global_steps': self.global_steps}
- json.dump(state, open(os.path.join(
- output_dir, TRAINER_STATE_NAME), 'w'), ensure_ascii=False, indent=2)
-
- # Determine the new best metric / best model checkpoint
- if metrics is not None and self.args.metric_for_best_model is not None:
- metric_to_check = self.args.metric_for_best_model
- if not metric_to_check.startswith("valid_"):
- metric_to_check = f"valid_{metric_to_check}"
- metric_value = metrics[metric_to_check]
-
- operator = np.greater if self.args.greater_is_better == 'true' else np.less
- if (
- self.best_metric is None
- or self.best_model_checkpoint is None
- or operator(metric_value, self.best_metric)
- ):
- self.best_metric = metric_value
- self.best_model_checkpoint = output_dir
- print_rank_0(f'current best model checkpoint is: {self.best_model_checkpoint}, valid_loss: {self.best_metric}')
-
- if self.is_rank0():
- if self.args.extra_save_by_epoch:
- print('extra_save_by_epoch')
- # for epoch saving，checkpoint will not be deleted，not affected by save_total_limit
- # for step saving，only keep save_total_limit ckpts
- self._rotate_checkpoints(
- output_dir=run_dir, prefix=PREFIX_CHECKPOINT_DIR, checkpoint_name_pattern='([0-9]+)$')
- else:
- self._rotate_checkpoints(
- output_dir=run_dir, prefix=PREFIX_CHECKPOINT_DIR)
- # only save atorch checkpoint of the latest checkpoint
- self._clean_atorch_checkpoints(
- output_dir=run_dir, prefix=PREFIX_CHECKPOINT_DIR)
-
- print(torch.cuda.memory_summary(device=self.device, abbreviated=False))
- torch.distributed.barrier()
- logger.info('Save finished')
-
-
- def train(self, **kwargs):
- logger.info("***** Running training *****")
- logger.info(f" Num examples = {len(self.train_dataset)}")
- logger.info(f" Num Epochs = {self.args.num_train_epochs}")
- logger.info(
- f" Instantaneous batch size per device = {self.args.per_device_train_batch_size}")
- logger.info(
- f" Total train batch size (w. parallel, distributed & accumulation) = {self.total_train_batch_size}")
- logger.info(
- f" Gradient Accumulation steps = {self.args.gradient_accumulation_steps}")
- logger.info(f" Total optimization steps = {self.args.max_steps}")
-
- progress_bar = tqdm(range(self.args.max_steps),
- disable=not is_local_main_process(),
- smoothing=0)
- training_time = 0
-
- self.global_steps = 0
- start_epoch = 0
- steps_trained_in_current_epoch = 0
- exit_flag = False
- if self.args.resume_from_checkpoint == 'true':
- state = json.load(
- open(os.path.join(self.resume_checkpoint_dir, TRAINER_STATE_NAME), 'r'))
- self.global_steps = state.get('global_steps', 0)
- progress_bar = tqdm(range(self.args.max_steps),
- disable=not is_local_main_process(),
- initial=self.global_steps,
- smoothing=0)
- start_epoch = self.global_steps // self.num_update_steps_per_epoch
- steps_trained_in_current_epoch = self.global_steps % self.num_update_steps_per_epoch
- steps_trained_in_current_epoch *= self.args.gradient_accumulation_steps
- print(f'Start training at step {self.global_steps}')
- self.last_step_logged = self.global_steps
- self.skipped_steps = 0
- self.accumulated_loss = 0
- self.accumulated_task_loss = np.zeros(len(self.ID2TASK))
- self.accumulated_task_num = np.zeros(len(self.ID2TASK))
-
- self.train_task_loss_prev = None
- self.valid_task_loss_prev = None # L_valid at step t-1
- self.ema_valid_task_loss_prev = None # L_valid_ema at step t-2
- self.ratio_valid_task_loss_prev = torch.zeros(len(self.ID2TASK)).to(self.device) # ema ratio at step t-1
-
- for epoch in range(start_epoch, int(self.args.num_train_epochs)):
- self.train_dataloader.set_epoch(epoch)
- self.model.train()
- start_time = time.time()
-
- valid_iterator = iter(self.valid_dataloader)
- for step, batch in enumerate(self.train_dataloader):
- if step == 0:
- print_rank_0(f"step 1 batch shape: {batch['input_ids'].shape},\n"
- f"last 10 tokens: {batch['input_ids'][:, -10:]}")
- print_rank_0(f"first 1000 tokens")
- for pt in range(10):
- print_rank_0(f"{batch['input_ids'][:, 10 * pt:10 * pt + 10]}")
- skipped = False
- self.model.train()
- step_start = time.time()
- if steps_trained_in_current_epoch and step < steps_trained_in_current_epoch:
- continue
- steps_trained_in_current_epoch = 0
- outputs = self.model(
- input_ids=batch['input_ids'].to(self.device),
- attention_mask=batch['attention_mask'].to(self.device),
- position_ids=batch['position_ids'].to(self.device),
- )
-
- loss, task_loss, task_num = self.loss_func(outputs, batch, self.args.weighted_loss_mode)
-
- loss = loss / self.args.gradient_accumulation_steps
- loss_tensor = torch.zeros(
- [1], device=loss.device, dtype=loss.dtype)
- loss_tensor[0] = loss.item()
- torch.distributed.all_reduce(loss_tensor)
- torch.distributed.all_reduce(task_loss, op=torch.distributed.ReduceOp.SUM)
- torch.distributed.all_reduce(task_num, op=torch.distributed.ReduceOp.SUM)
- reduce_loss = loss_tensor.sum() / torch.distributed.get_world_size()
- if has_inf_or_nan(reduce_loss):
- print_rank_0(f'There have nan loss.')
- self.skipped_steps += 1
- skipped = True
- else:
- self.accumulated_loss += reduce_loss.item()
- mean_task_loss = task_loss / torch.distributed.get_world_size()
- self.accumulated_task_loss += mean_task_loss.cpu().numpy()
- self.accumulated_task_num += task_num.cpu().numpy()
- loss.backward()
- self.global_steps += 1
- if step % self.args.gradient_accumulation_steps == 0 or step == len(self.train_dataloader) - 1:
- if self.args.max_grad_norm is not None and self.args.max_grad_norm > 0:
- # unscale for fp16
- try:
- self.optimizer.unscale_()
- except Exception:
- pass
- if isinstance(self.model, FSDP):
- self.model.clip_grad_norm_(self.args.max_grad_norm)
- else:
- torch.nn.utils.clip_grad_norm_(
- self.model.parameters(), self.args.max_grad_norm)
- self.optimizer.step()
- overflow = hasattr(self.optimizer, "step_was_skipped") and self.optimizer.step_was_skipped
- if skipped != overflow:
- print(f'skipped != overflow!!!!!!!!!!!!!!!!')
- if not overflow:
- self.lr_scheduler.step()
-
- self.optimizer.zero_grad()
-
- step_time = time.time() - step_start
- step_tflops = get_tflops_megatron(self.args.total_model_param, self.args.hidden_size, self.args.num_hidden_layers,
- self.args.per_device_train_batch_size, self.args.seq_length, step_time)
- step_speed = get_computation_speed(self.args.per_device_train_batch_size, self.args.seq_length, step_time)
- progress_bar.update(1)
-
- if self.global_steps % self.args.log_interval == 0:
- print_rank_0(f'max memory allocated: {torch.cuda.max_memory_allocated()}')
- if (self.global_steps - self.last_step_logged - self.skipped_steps) == 0:
- self.accumulated_loss = 0
- self.skipped_steps = 0
- self.accumulated_task_loss = torch.zeros(len(self.ID2TASK)).to(self.device)
- self.accumulated_task_num = torch.zeros(len(self.ID2TASK)).to(self.device)
- self.last_step_logged = self.global_steps
- self.epoch = self.global_steps / self.num_update_steps_per_epoch
- print_rank_0(f'this log interval is skipped!')
- continue
-
- train_loss = round(
- self.accumulated_loss / (self.global_steps - self.last_step_logged - self.skipped_steps), 4)
-
- train_task_loss = self.accumulated_task_loss / (self.global_steps - self.last_step_logged - self.skipped_steps)
-
- if is_global_main_process():
- logger.info('log point')
- logger.info(f'skipped steps: {self.skipped_steps}')
- for i in range(len(self.ID2TASK)):
- if self.accumulated_task_num[i] != 0:
- logger.info(f"{self.ID2TASK[i]}_loss: {train_task_loss[i]}, sample nums: {self.accumulated_task_num[i]}")
- self.accumulated_loss = 0
- self.skipped_steps = 0
- self.accumulated_task_loss = np.zeros(len(self.ID2TASK))
- self.accumulated_task_num = np.zeros(len(self.ID2TASK))
- self.last_step_logged = self.global_steps
- self.epoch = self.global_steps / self.num_update_steps_per_epoch
- learning_rate = self.lr_scheduler.get_last_lr()[0]
- if torch.is_tensor(learning_rate):
- learning_rate = learning_rate.item()
- logs = {'train_loss': train_loss,
- 'epoch': self.epoch,
- 'learning_rate': learning_rate,
- }
- per_task_train_loss = {self.ID2TASK[i]+'_loss': train_task_loss[i].item() for i in range(len(self.ID2TASK))}
- logs.update(per_task_train_loss)
- if is_global_main_process():
- compute_mode = 'labels = -100' if batch['loss_mask'] is None else 'loss mask'
- logger.info(f'weighted loss mode: {self.args.weighted_loss_mode}, compute mode: {compute_mode}')
- self.log(logs, step=self.global_steps,
- phase='train')
- logger.info(f"tflops: {step_tflops} | token speed: {step_speed:.2f} tokens/gpu/s | sample speed: {step_speed / self.args.seq_length:.2f}")
-
- if 'steps' in self.args.evaluation_strategy.split(',') and self.global_steps % self.args.valid_interval == 0:
- exit_flag = False
- del loss, outputs
- metrics = self.evaluate()
- print_rank_0(f'Global steps: {self.global_steps} evaluate metrics: {metrics}')
- if self.args.early_stopping_patience > 0:
- self.early_stopping(metrics['valid_loss'], self.model)
- # if early stopping
- if self.early_stopping.early_stop:
- exit_flag = True
- print("Early stopping")
- # exit model training
- break
- if 'steps' in self.args.save_strategy.split(',') and self.global_steps % self.args.checkpointing_steps == 0:
- self.save(metrics=metrics)
-
- if self.global_steps >= self.args.max_steps:
- break
-
- if exit_flag:
- print("Early stopping")
- break
- logger.info(f"Training of epoch {epoch + 1} finished")
-
- training_time += time.time() - start_time
- if 'epoch' in self.args.evaluation_strategy.split(','):
- metrics = self.evaluate()
- if self.args.early_stopping_patience > 0:
- self.early_stopping(metrics['valid_loss'], self.model)
- # if early stopping
- if self.early_stopping.early_stop:
- exit_flag = True
- print("Early stopping")
- # exit model training
- break
- if 'epoch' in self.args.save_strategy.split(',') or self.args.extra_save_by_epoch:
- if epoch + 1 < (int(self.args.num_train_epochs) // 3) and self.args.total_model_param < 1e9:
- continue
- print_rank_0(f'Global steps: {self.global_steps} | Epoch {epoch + 1} checkpoint metrics: {metrics}')
- self.save(
- suffix=f'{self.global_steps}-{EPOCH_CHECKPOINT_NAME}-{epoch + 1}')
diff --git a/mftcoder_atorch/utils/__init__.py b/mftcoder_atorch/utils/__init__.py
deleted file mode 100644
index 0cf9434..0000000
--- a/mftcoder_atorch/utils/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .common_utils import *
-from .auto_accelerate_utils import *
\ No newline at end of file
diff --git a/mftcoder_atorch/utils/auto_accelerate_utils.py b/mftcoder_atorch/utils/auto_accelerate_utils.py
deleted file mode 100644
index ea1cc13..0000000
--- a/mftcoder_atorch/utils/auto_accelerate_utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import sys
-sys.path.append("..")
-import torch
-import atorch
-from utils.common_utils import print_rank_0, TASK2ID, ID2TASK
-from torch.nn import CrossEntropyLoss
-from dataclasses import dataclass
-import numpy as np
-import torch.nn.functional as F
-from typing import Dict, List, Tuple, Union
-
-torch.set_printoptions(threshold=np.inf)
-
-
-def get_task_mask(task_id):
- task_num = len(TASK2ID)
- task_mask = torch.zeros(task_id.shape[0], task_num)
- task_mask[torch.arange(task_id.size(0)).unsqueeze(1), task_id] = 1
-
- return task_mask
-
-
-def get_task_loss(task_losses, task_id): # TODO
- # Fix a printing order.
- task_loss_per_batch = torch.zeros(len(ID2TASK)).to(device=task_id.device)
- # Count the occurrences of each task.
- task_num_per_batch = torch.zeros(len(ID2TASK)).to(device=task_id.device)
- for i in range(len(task_id)):
- task_num_per_batch[task_id[i][0]] += 1
- task_loss_per_batch[task_id[i][0]] = task_losses[task_id[i][0]]
-
- return task_loss_per_batch, task_num_per_batch
-
-
-def loss_func_mft(outputs, inputs, weighted_loss_mode):
- # task_id shape: [[1], [2], [4], [3], ..., [1]]
- labels, task_mask, task_id, loss_mask, weights = inputs['labels'], inputs['task_mask'], inputs['task_id'], inputs['loss_mask'], inputs['weights']
- weighted = weighted_loss_mode
- lm_logits = outputs["logits"]
- labels = labels.to(device=lm_logits.device)
- task_mask = task_mask.to(device=lm_logits.device)
- task_id = task_id.to(device=lm_logits.device)
- bsz, seq_len = labels.shape
- if loss_mask is None:
- ineffective_tokens_per_sample = (labels==-100).sum(dim=1)
- effective_tokens_per_sample = - (ineffective_tokens_per_sample - seq_len)
- effective_tokens = bsz * seq_len - ineffective_tokens_per_sample.sum()
- loss_fct = CrossEntropyLoss(reduction='none', ignore_index=-100)
- else:
- loss_mask = loss_mask.to(device=lm_logits.device)
- effective_tokens_per_sample = torch.sum(loss_mask, dim=1, dtype=torch.int)
- effective_tokens = torch.sum(loss_mask).item()
- loss_fct = CrossEntropyLoss(reduction='none')
- if weighted_loss_mode.endswith('focalloss'):
- losses = loss_fct(lm_logits, labels)
- else:
- losses = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) # [B * L, 1]
- # losses = losses.contiguous().view(bsz, -1)
- losses = losses.view(bsz, -1)
- token_losses = losses.clone().detach().float() if loss_mask is None else losses.clone().detach().float() # [B, L]
- task_mask_trans = torch.transpose(task_mask, 0, 1)
- unique_weights = torch.unique(weights)
- unique_id = torch.unique(task_id)
- if weighted_loss_mode == "case3" or weighted_loss_mode == "case4":
- loss = 0.0
- for i, w in enumerate(unique_weights):
- row_idx = torch.squeeze(weights) == w.item()
- if weighted_loss_mode == "case3":
- if loss_mask is None:
- loss += torch.sum(losses[row_idx, :]) / torch.sum(effective_tokens_per_sample[row_idx])
- else:
- loss += torch.sum((losses * loss_mask)[row_idx, :]) / torch.sum(loss_mask[row_idx, :])
- elif weighted_loss_mode == "case4":
- if loss_mask is None:
- loss += torch.mean(torch.sum(losses, dim=1)[row_idx] / effective_tokens_per_sample[row_idx])
- else:
- loss += torch.mean(torch.sum(losses * loss_mask, dim=1)[row_idx] / torch.sum(loss_mask, dim=1)[row_idx])
- loss /= len(unique_weights)
- elif weighted_loss_mode == "case2":
- if loss_mask is None:
- loss = torch.mean(torch.sum(losses, dim=1) / effective_tokens_per_sample)
- else:
- loss = torch.mean(torch.sum(losses * loss_mask, dim=1) / torch.sum(loss_mask, dim=1))
- elif weighted_loss_mode == "case1":
- # flatten losses & loss_mask tensor
- if loss_mask is None:
- loss = torch.sum(losses.view(-1)) / effective_tokens
- else:
- loss = torch.sum(losses.view(-1) * loss_mask.view(-1)) / loss_mask.sum()
-
- # Fix a printing order.
- task_loss = torch.zeros(len(ID2TASK)).to(device=task_id.device) # per task loss
- task_num = torch.zeros(len(ID2TASK)).to(device=task_id.device)
- # unique_id = torch.unique(task_id)
- for i, w in enumerate(unique_id):
- row_idx = torch.squeeze(task_id) == w.item()
- if loss_mask is None:
- task_loss[w] = torch.sum(token_losses[row_idx, :]) / torch.sum(effective_tokens_per_sample[row_idx])
- task_num[w] = len(effective_tokens_per_sample[row_idx])
- else: # TODO:
- task_loss[w] = torch.sum((token_losses * loss_mask)[row_idx, :]) / torch.sum(loss_mask[row_idx, :])
- task_num[w] = len(torch.sum(loss_mask, dim=1)[row_idx])
-
- return loss, task_loss, task_num
-
-
-def get_attn_mask(seq_length, device):
- """
- Get triangular attention mask for a given sequence length / device.
- """
- # lower triangular attention mask
- mask = torch.tril(torch.ones((1, seq_length, seq_length), device=device)).view(
- 1, 1, seq_length, seq_length
- )
-
- # convert to binary
- return mask < 0.5
-
-
-def get_ltor_masks_and_position_ids(data):
- """Build masks and position id for left to right model."""
-
- # Extract batch size and sequence length.
- batch_size, seq_length = data.size()
-
- # Attention mask (lower triangular).
- # attention_mask = get_attn_mask(
- # seq_length=seq_length,
- # device=data.device,
- # )
- attention_mask = torch.ones((batch_size, seq_length), device=data.device)
-
- # Position ids.
- position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device)
- position_ids = position_ids.unsqueeze(0).expand_as(data).clone()
-
- return attention_mask, position_ids
-
-
-def prepare_gpt_input(batch, device):
- batch = {k: v.to(device=device, non_blocking=True) for k, v in batch.items()}
-
- if 'loss_mask' in batch and 'labels' not in batch:
- # if 'loss_mask' in batch:
- print_rank_0('loss mask in batch')
- input_ids = batch['input_ids'].long()
- batch['input_ids'] = input_ids[:, :-1].contiguous().to(device=device)
- batch['labels'] = input_ids[:, 1:].contiguous().to(device=device)
- batch['loss_mask'] = batch['loss_mask'].float()[:, 1:].contiguous()
- else:
- batch['input_ids'] = batch['input_ids'].long()
- batch['labels'] = batch['labels'].long()
- batch['loss_mask'] = None
-
- # Get the masks and position ids.
- batch['attention_mask'], batch['position_ids'] = get_ltor_masks_and_position_ids(data=batch['input_ids'])
-
- if self.args.weighted_loss_mode:
- weights = batch['weight'].float().to(device=device) # [2, 4, 6, 3, ..., 2]
- # batch['loss_mask'] *= weights
-
- if 'task_id' in batch and batch['task_id'] is not None: # task_id: bsz * 1, [[1], [2], [4], [3], ..., [1]]
- batch['task_mask'] = get_task_mask(batch['task_id']).to(device=device) # bsz * task_num
-
- return batch
-
-
-@dataclass
-class DataCollatorForMFTDataset(object):
- def __init__(self, model_type, weighted_loss_mode, use_dynamic_padding):
- self.model_type = model_type
- self.weighted_loss_mode = weighted_loss_mode
- self.use_dynamic_padding = use_dynamic_padding
-
- # tokenizer: None
-
- def __call__(self, instances):
- input_ids, attention_mask, position_ids, labels, loss_mask, weights, task_id = tuple(
- [instance[key] if key in instance else None for instance in instances] for key in
- ("input_ids", "attention_mask", "position_ids", "labels", "loss_mask", "weight", "task_id"))
- # input_ids, loss_mask, weights, task_id = tuple(instances[key] for key in ("input_ids", "loss_mask", "weight", "task_id"))
-
- result_batch = {}
- '''
- outputs = model(
- input_ids=batch['input_ids'],
- attention_mask=batch['attention_mask'],
- # labels=(batch['labels'], batch['loss_mask'], batch['task_mask']),
- # labels=(batch['labels'], batch['loss_mask']),
- position_ids=batch['position_ids'],
- )
- '''
-
- input_ids = torch.tensor(np.array(input_ids)).long()
- # input_ids = input_ids.long()
- if loss_mask[0] is None:
- result_batch['input_ids'] = input_ids.contiguous()
- labels = torch.tensor(np.array(labels)).long()
- result_batch['labels'] = labels.contiguous()
- result_batch['loss_mask'] = None
- else:
- loss_mask = torch.tensor(np.array(loss_mask))
- if self.use_dynamic_padding:
- last_one_pos = (loss_mask == 1).long().cumsum(dim=1).argmax(dim=1)
- max_pos = last_one_pos.max().item() + 1
- else:
- max_pos = loss_mask.shape[-1]
- result_batch['input_ids'] = input_ids[:, :max_pos-1].contiguous() # [B, L + 1] -> [B, L]
- result_batch['labels'] = input_ids[:, 1:max_pos].contiguous()
- result_batch['loss_mask'] = loss_mask.float()[:, 1:max_pos].contiguous()
-
- if self.weighted_loss_mode and weights is not None:
- weights = torch.tensor(np.array(weights))
- result_batch['weights'] = weights
- # if result_batch['loss_mask'] is not None:
- # result_batch['loss_mask'] *= weights
-
- # Get the masks and position ids.
- result_batch['attention_mask'], result_batch['position_ids'] = get_ltor_masks_and_position_ids(data=result_batch['input_ids'])
-
- if task_id is not None:
- task_id = torch.tensor(np.array(task_id))
- result_batch['task_mask'] = get_task_mask(task_id) # bsz * task_num
- result_batch['task_id'] = task_id
-
- return result_batch
diff --git a/mftcoder_atorch/utils/common_utils.py b/mftcoder_atorch/utils/common_utils.py
deleted file mode 100644
index 7d36ad5..0000000
--- a/mftcoder_atorch/utils/common_utils.py
+++ /dev/null
@@ -1,371 +0,0 @@
-import os
-import math
-import torch
-import atorch
-import numpy as np
-from collections.abc import Mapping # noqa: E402
-from contextlib import contextmanager # noqa: E402
-from torch.distributed.fsdp import (
- FullyShardedDataParallel as FSDP,
- # BackwardPrefetch,
- FullStateDictConfig,
- StateDictType,
-)
-from transformers import get_scheduler
-from utils.learning_rates import AnnealingLR
-TASK2ID = {}
-ID2TASK = {}
-
-
-def get_rank():
- return atorch.rank()
-
-
-def get_local_rank():
- return atorch.local_rank()
-
-
-def is_main_process():
- return atorch.rank() == 0
-
-
-def is_local_main_process():
- return atorch.local_rank() == 0
-
-
-def print_rank_0(*message):
- """If distributed is initialized print only on rank 0."""
- if torch.distributed.is_initialized():
- if torch.distributed.get_rank() == 0:
- print(*message, flush=True)
- else:
- print(*message, flush=True)
-
-
-def get_world_size():
- return atorch.world_size()
-
-
-def wait_for_everyone():
- torch.distributed.barrier()
-
-
-def atorch_init_distributed(backend="nccl"):
- atorch.init_distributed(backend, set_cuda_device_using_local_rank=True)
- # atorch.init_distributed(backend)
-
-
-def atorch_reset_distributed():
- atorch.reset_distributed()
-
-
-def _goes_first(is_main):
- if is_main is False:
- wait_for_everyone()
- yield
- if is_main is True:
- wait_for_everyone()
-
-
-def get_model_params_num(model):
- """
- Get params number of the model
- Args:
- model: model(required)
- Returns:
- the number of parameters of model
- """
- num = 0
- for _, param in model.named_parameters():
- num += param.nelement()
- return num
-
-
-@contextmanager
-def main_process_first():
- yield from _goes_first(is_main_process())
-
-
-def unwrap_model(model):
- """
- Recursively unwraps a model from potential containers (as used in distributed training).
-
- Args:
- model (`torch.nn.Module`): The model to unwrap.
- """
- # since there could be multiple levels of wrapping, unwrap recursively
- if hasattr(model, "module"):
- return unwrap_model(model.module)
- else:
- return model
-
-
-def honor_type(obj, generator):
- """
- Cast a generator to the same type as obj (list, tuple or namedtuple)
- """
- try:
- return type(obj)(generator)
- except TypeError:
- # Some objects may not be able to instantiate from a generator directly
- return type(obj)(*list(generator))
-
-
-def recursively_apply(
- func,
- data,
- *args,
- test_type=lambda t: isinstance(t, torch.Tensor),
- error_on_other_type=False,
- **kwargs,
-):
- if isinstance(data, (tuple, list)):
- return honor_type(
- data,
- (
- recursively_apply(
- func,
- o,
- *args,
- test_type=test_type,
- error_on_other_type=error_on_other_type,
- **kwargs,
- )
- for o in data
- ),
- )
- elif isinstance(data, Mapping):
- return type(data)(
- {
- k: recursively_apply(
- func,
- v,
- *args,
- test_type=test_type,
- error_on_other_type=error_on_other_type,
- **kwargs,
- )
- for k, v in data.items()
- }
- )
- elif test_type(data):
- return func(data, *args, **kwargs)
- elif error_on_other_type:
- raise TypeError(
- f"Can't apply {func.__name__} on object of type {type(data)}, only of nested list/tuple/dicts of objects "
- f"that satisfy {test_type.__name__}."
- )
- return data
-
-
-def gather(tensor):
- def _gpu_gather_one(tensor):
- if tensor.ndim == 0:
- tensor = tensor.clone()[None]
- output_tensors = [tensor.clone() for _ in range(torch.distributed.get_world_size())]
- torch.distributed.all_gather(output_tensors, tensor)
- return torch.cat(output_tensors, dim=0)
-
- return recursively_apply(_gpu_gather_one, tensor, error_on_other_type=True)
-
-
-def save_ckpt(model, optimizer, lr_scheduler, epoch, steps, save_path, logger):
- if isinstance(model, FSDP):
- print('Saving a FSDP model')
- optim_state_dict = FSDP.full_optim_state_dict(model, optimizer)
- save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
- with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, save_policy):
- model_state_dict = model.state_dict()
- lrs_state_dict = lr_scheduler.state_dict()
- else:
- print('Saving a normal model')
- model_state_dict = model.state_dict()
- optim_state_dict = optimizer.state_dict()
- lrs_state_dict = lr_scheduler.state_dict()
- # rank0 save
- if is_main_process():
- torch.save(
- {
- "epoch": epoch + 1,
- "step": steps,
- "state_dict": model_state_dict,
- "optimizer": optim_state_dict,
- "lrs_state_dict": lrs_state_dict,
- },
- save_path,
- )
- logger.info(f"Saved checkpoint {save_path} (epoch {epoch + 1} @ {steps} steps)")
- wait_for_everyone()
- # torch.distributed.barrier() # other rank waiting
-
-
-def scheduler_and_resume(args, train_dataloader, model, optimizer, checkpoint):
- # Scheduler and math around the number of training steps.
- overrode_max_steps = False
- args.num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- if args.max_steps == -1:
- args.max_steps = args.num_train_epochs * args.num_update_steps_per_epoch
- overrode_max_steps = True
-
- lr_scheduler = AnnealingLR(
- optimizer,
- start_lr=args.learning_rate,
- warmup_iter=args.num_warmup_steps,
- total_iters=args.max_steps * args.gradient_accumulation_steps,
- decay_style=args.lr_scheduler_type,
- last_iter=0,
- min_lr=args.min_lr,
- use_checkpoint_lr_scheduler=True,
- )
-
- if args.resume_from_checkpoint is not None:
- if os.path.isfile(args.resume_from_checkpoint):
- starting_epoch = checkpoint["epoch"] - 1
- steps = checkpoint["step"]
- args.resume_step = steps
- # Restore the optim state
- if optimizer is not None:
- if isinstance(model, FSDP):
- print('Loading optimizer for a FSDP model')
- full_osd = checkpoint["optimizer"]
- sharded_osd = FSDP.scatter_full_optim_state_dict(full_osd, model)
- optimizer.load_state_dict(sharded_osd)
- else:
- print('Loading optimizer for a normal model')
- optimizer.load_state_dict(checkpoint["optimizer"])
- logging.info("Optimizer state is restored from the checkpoint")
- if lr_scheduler is not None:
- lr_scheduler.load_state_dict(checkpoint["lrs_state_dict"])
- logging.info(f"Loaded checkpoint '{args.resume_from_checkpoint}' (epoch {checkpoint['epoch']} @ {steps} steps)")
- else:
- logger.info(f"No optimizer and lr scheduler checkpoint found at '{args.resume_from_checkpoint}'")
-
- # We need to recalculate our total training steps as the size of the training dataloader may have changed.
- args.num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- if overrode_max_steps:
- args.max_steps = args.num_train_epochs * args.num_update_steps_per_epoch
- # Afterwards we recalculate our number of training epochs
- args.num_train_epochs = math.ceil(args.max_steps / args.num_update_steps_per_epoch)
-
- return args, lr_scheduler, optimizer
-
-
-def get_computation_speed(batch_size_per_device, seq_len, step_time):
-
- return batch_size_per_device * seq_len / (step_time + 1e-12)
-
-
-def human_readable_flops(num):
- for unit in [
- "",
- "KFLOPS",
- "MFLOPS",
- "GFLOPS",
- "TFLOPS",
- "PFLOPS",
- "EFLOPS",
- "ZFLOPS",
- ]:
- if abs(num) < 1000.0:
- return "%3.1f%s" % (num, unit)
- num /= 1000.0
- return "%.1f%s" % (num, "Yi")
-
-
-def get_tflops_new(args, batch_size, seq_len, step_time):
- sl = seq_len
- L = args.num_hidden_layers
- h = args.hidden_size
- V = args.vocab_size
- flops = (96 * batch_size * sl * L * h * h * (1 + sl / (6 * h) + V / (16 * L * h)) / step_time)
- return human_readable_flops(flops)
-
-
-def get_tflops_megatron(total_model_param, hidden_size, num_hidden_layers,
- batch_size_per_device, seq_len, step_time):
-
- ff = total_model_param * 6
- attn = seq_len * hidden_size * num_hidden_layers * 60
- flops = (
- batch_size_per_device
- * seq_len
- * (ff + attn)
- / step_time
- )
- return human_readable_flops(flops)
-
-
-def is_old_version(path):
- new_vocab_files = ['merge.model']
- new_vocab_file_exists = []
- for filename in new_vocab_files:
- if not os.path.exists(os.path.join(path, filename)):
- new_vocab_file_exists.append(False)
- else:
- new_vocab_file_exists.append(True)
- if all(new_vocab_file_exists):
- return False
- if any(new_vocab_file_exists):
- return 'new_version_file_absent'
- else:
- return True
-
-
-def generate_task_id(data_paths, train_mode):
- data_prefixes = list(data_paths[1:-1].split(','))
- print("data paths: ")
- print(data_prefixes)
-
- for i, prefix in enumerate(data_prefixes):
- if train_mode == 'sft':
- task_name = prefix.split('/')[-1]
- else:
- task_name = prefix.split('/')[-2]
- TASK2ID[task_name] = i
- ID2TASK[i] = task_name
-
-
-class EarlyStopping:
- """Early stops the training if validation loss doesn't improve after a given patience."""
- def __init__(self, patience=7, verbose=False, delta=0):
- """
- Args:
- patience (int): How long to wait after last time validation loss improved.
- Default: 7
- verbose (bool): If True, prints a message for each validation loss improvement.
- Default: False
- delta (float): Minimum change in the monitored quantity to qualify as an improvement.
- Default: 0
- """
- self.patience = patience
- self.verbose = verbose
- self.counter = 0
- self.best_score = None
- self.early_stop = False
- self.val_loss_min = np.Inf
- self.delta = delta
-
- def __call__(self, val_loss, model):
-
- score = -val_loss
-
- if self.best_score is None:
- self.best_score = score
- # self.save_checkpoint(val_loss, model)
- elif score < self.best_score + self.delta:
- self.counter += 1
- print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
- if self.counter >= self.patience:
- self.early_stop = True
- else:
- self.best_score = score
- # self.save_checkpoint(val_loss, model)
- self.counter = 0
-
- def save_checkpoint(self, val_loss, model):
- '''Saves model when validation loss decrease.'''
- if self.verbose:
- print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
- torch.save(model.state_dict(), 'checkpoint.pt')
- self.val_loss_min = val_loss
-
diff --git a/mftcoder_atorch/utils/learning_rates.py b/mftcoder_atorch/utils/learning_rates.py
deleted file mode 100644
index eb644e1..0000000
--- a/mftcoder_atorch/utils/learning_rates.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright (c) 2021, EleutherAI
-# This file is based on code by the authors denoted below and has been modified from its original version.
-#
-# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Learning rate decay functions."""
-
-import math
-
-# from .common_utils import print_rank_0
-
-
-class AnnealingLR(object):
- """Anneals the learning rate."""
-
- def __init__(
- self,
- optimizer,
- start_lr,
- warmup_iter,
- total_iters,
- decay_style,
- last_iter,
- min_lr=0.0,
- use_checkpoint_lr_scheduler=True,
- override_lr_scheduler=False,
- use_mup=False,
- ):
-
- # Class values.
- self.optimizer = optimizer
- self.start_lr = start_lr
- self.min_lr = min_lr
- self.warmup_iter = warmup_iter
- self.num_iters = last_iter
- self.end_iter = total_iters
- assert self.end_iter > 0
- self.decay_style = decay_style
- self.override_lr_scheduler = override_lr_scheduler
- self.use_checkpoint_lr_scheduler = use_checkpoint_lr_scheduler
- self.use_mup = use_mup
- if self.override_lr_scheduler:
- assert not self.use_checkpoint_lr_scheduler, (
- "both override and " "use-checkpoint are set."
- )
- # Set the learning rate
- self.step(self.num_iters)
-
- print("> learning rate decay style: {}".format(self.decay_style))
-
- def update_lr(self, lr):
- self.start_lr = lr
-
- def get_lr(self):
- """Learning rate decay functions from:
- https://openreview.net/pdf?id=BJYwwY9ll pg. 4"""
-
- num_iters_ = min(self.num_iters, self.end_iter - self.warmup_iter)
- # Warmup.
- if self.warmup_iter > 0 and self.num_iters <= self.warmup_iter:
- return float(self.start_lr) * num_iters_ / self.warmup_iter
-
- num_iters_ = num_iters_ - self.warmup_iter
- if self.decay_style == "linear":
- lr = self.start_lr * (self.end_iter - num_iters_) / self.end_iter
- elif self.decay_style == "cosine":
- lr = (
- self.start_lr
- / 2.0
- * (math.cos(math.pi * num_iters_ / self.end_iter) + 1)
- )
- elif self.decay_style == "exponential":
- # exp(-0.693) = 1/2
- lr = self.start_lr * math.exp(-0.693 * num_iters_ / self.end_iter)
- else:
- lr = self.start_lr
- return max(lr, self.min_lr)
-
- def step(self, step_num=None):
- """Set lr for all parameters groups."""
- if step_num is None:
- step_num = self.num_iters + 1
- self.num_iters = step_num
- new_lr = self.get_lr()
- for group in self.optimizer.param_groups:
- if self.use_mup and "width_mult" in group:
- group["lr"] = new_lr / group["width_mult"]
- else:
- group["lr"] = new_lr
-
- def state_dict(self):
- state_dict = {
- "start_lr": self.start_lr,
- "warmup_iter": self.warmup_iter,
- "num_iters": self.num_iters,
- "decay_style": self.decay_style,
- "end_iter": self.end_iter,
- "min_lr": self.min_lr,
- }
- return state_dict
-
- def _check_and_set(self, cls_value, sd_value, name):
- """Auxiliary function for checking the values in the checkpoint and
- setting them."""
- if self.override_lr_scheduler:
- print_rank_0(" > overriding {} value to {}".format(name, cls_value))
- return cls_value
-
- if not self.use_checkpoint_lr_scheduler:
- assert cls_value == sd_value, (
- "AnnealingLR: class input value"
- "and checkpoint values for {} do not match".format(name)
- )
- print_rank_0(" > using checkpoint value {} for {}".format(sd_value, name))
- return sd_value
-
- def load_state_dict(self, sd):
-
- self.start_lr = self._check_and_set(
- self.start_lr, sd["start_lr"], "learning rate"
- )
- self.min_lr = self._check_and_set(
- self.min_lr, sd["min_lr"], "minimum learning rate"
- )
- self.warmup_iter = self._check_and_set(
- self.warmup_iter, sd["warmup_iter"], "warmup iterations"
- )
- self.end_iter = self._check_and_set(
- self.end_iter, sd["end_iter"], "total number of iterations"
- )
- self.decay_style = self._check_and_set(
- self.decay_style, sd["decay_style"], "decay style"
- )
-
- self.num_iters = sd["num_iters"]
- self.step(self.num_iters)
diff --git a/mftcoder_atorch/utils/merge_base_and_lora_to_hf.py b/mftcoder_atorch/utils/merge_base_and_lora_to_hf.py
deleted file mode 100644
index 29378e3..0000000
--- a/mftcoder_atorch/utils/merge_base_and_lora_to_hf.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import os
-import sys
-import time
-import shutil
-import torch
-import transformers
-sys.path.append("..")
-from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel
-from peft import LoraConfig, get_peft_model
-from peft import PeftModel
-from model_mapping import MODEL_SPECIAL_TOKENS
-
-
-model_path='path to base model'
-lora_adapter='path to lora adaptor ckpt'
-save_path='path to new merged model'
-model_type = 'gpt_neox'
-
-t0 = time.time()
-config = {"model_type": model_type}
-tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-
-base_model = AutoModelForCausalLM.from_pretrained(
- model_path,
- trust_remote_code=True,
- torch_dtype=torch.bfloat16,
- return_dict=True,
- device_map="auto"
-)
-print(base_model)
-
-# merge, save model and tokenizer
-model_to_merge = PeftModel.from_pretrained(base_model, lora_adapter)
-merged_model = model_to_merge.merge_and_unload()
-print(merged_model.config)
-merged_model.save_pretrained(save_path)
-tokenizer.save_pretrained(save_path)
-print(f"Merge finised: {save_path} saved, Cost {time.time()-t0:.2f}s")
\ No newline at end of file
diff --git a/mftcoder_atorch/utils/vocab.json b/mftcoder_atorch/utils/vocab.json
deleted file mode 100644
index fb3b7bb..0000000
--- a/mftcoder_atorch/utils/vocab.json
+++ /dev/null
@@ -1,205457 +0,0 @@
-{
- "version": "1.0",
- "truncation": null,
- "padding": null,
- "added_tokens": [
- {
- "id": 100256,
- "content": "<|endoftext|>",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": false,
- "special": true
- },
- {
- "id": 100257,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100258,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100259,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100260,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100261,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100262,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100263,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100264,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100265,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100266,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100267,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100268,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100269,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100270,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100271,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100272,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100273,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100274,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100275,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100276,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100277,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100278,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100279,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
- "normalized": true,
- "special": false
- },
- {
- "id": 100280,
- "content": " ",
- "single_word": false,
- "lstrip": false,
- "rstrip": false,
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- ".g roup",
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- "ool s",
- "y cl",
- "enc ode",
- "ol t",
- "com ment",
- ". api",
- "D ir",
- "\u0120un e",
- "iz ont",
- ".p osition",
- "\u0120des igned",
- "_ val",
- "av i",
- "ir ing",
- "t ab",
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- "\u0120re ve",
- "ra el",
- "\u0120O N",
- "r ics",
- "16 0",
- "n p",
- "\u0120c ore",
- "() );\u010d\u010a",
- "M ain",
- "\u0120exp ert",
- "\u0109\u0109 \u010d\u010a",
- "_ en",
- "\u0120/ >",
- "ut ter",
- "I AL",
- "ail s",
- "\u0120K ing",
- "*/ \u010a\u010a",
- "\u0120M et",
- "_ end",
- "add r",
- "or a",
- "\u0120 ir",
- "M in",
- "\u0120sur pr",
- "\u0120re pe",
- "\u0120direct ory",
- "P UT",
- "- S",
- "\u0120e lection",
- "h aps",
- ".p re",
- "c m",
- "Val ues",
- "\u0120\" \u010a",
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- "9 3",
- "\u0120beaut iful",
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- "(e vent",
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- "um s",
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- "( Q",
- "con s",
- "it a",
- "\u0120' .",
- "{ }",
- "\u0109 C",
- "\u0120st uff",
- "19 4",
- "\u0120 :\u010a",
- "\u0120A R",
- "T ask",
- "h idden",
- "er os",
- "IG N",
- "at io",
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- "Ent er",
- "' >",
- "\u0120T witter",
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- "f it",
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- "re quired",
- "n on",
- "boot strap",
- "h old",
- "r im",
- "- old",
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- "od ay",
- "\u0120to wn",
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- "u ate",
- "ell ing",
- "] ->",
- "ot es",
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- "_ EN",
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- ". un",
- "in st",
- "en ge",
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- "ic ens",
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- "Se lected",
- "_ pl",
- "ue ue",
- "\u0120d ark",
- "// \u010a\u010a",
- "s i",
- "ther n",
- "\u0120J apan",
- "/ w",
- "P U",
- "\u0120E ast",
- "ov ie",
- "\u0120p ackage",
- "\u0120n or",
- "\u0120ap i",
- "b ot",
- "\" ];\u010a",
- "_p ost",
- "ul ate",
- "\u0120cl ub",
- "') );\u010a",
- "\u0120lo op",
- "PI O",
- "ion e",
- "sh ot",
- "In itial",
- "\u0120play ed",
- "reg ister",
- "rou ght",
- "_m ax",
- "ac ement",
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- "raph ics",
- "A ST",
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- "D A",
- ".C h",
- ".com mon",
- "m o",
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- "it o",
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- "id x",
- "\u00c3 \u0142",
- "ong o",
- "\u0120Eng lish",
- "< !--",
- "\u0120comput er",
- "EN SE",
- "\u0120p as",
- "\u0120r ais",
- "H ash",
- "\u0120m obile",
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- "F IG",
- "\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120 \u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120",
- "th es",
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- "m ore",
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- "FF FF",
- "( error",
- ". ui",
- "at ar",
- "ad ers",
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- "T arget",
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- "\u0120i o",
- "par se",
- "$ _",
- "he st",
- ". int",
- "- item",
- "as y",
- "S p",
- "\u0120sh ift",
- "N T",
- "\u0120t f",
- "_T R",
- ". web",
- "C S",
- "\u0120} )",
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- "12 5",
- "10 5",
- "_ z",
- "' );\u010d\u010a",
- "if orn",
- "\u0120{ @",
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- ".l ist",
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- "\u0120f loor",
- "\u0120red irect",
- "\u0120U K",
- "( ['",
- "\u0120w ish",
- "\u0120cap t",
- "leg al",
- "\u0120I O",
- "\u0120st age",
- ". String",
- "\u0120A fr",
- "ig en",
- "\u0120S H",
- "De lete",
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- "\u00d0 \u00bc",
- "_ read",
- ". Id",
- "e ff",
- "Off set",
- "ch a",
- "US ER",
- "\u0109\u0109 \u0120\u0120\u0120",
- "ipp ed",
- "\u0120d ict",
- "\u0120R un",
- ".h pp",
- "\u0120an g",
- "x ml",
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- "_m essage",
- "U ID",
- "G r",
- "and ed",
- "_C H",
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- "am ing",
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- "Rec ord",
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- ".W eb",
- "ond on",
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- "_p os",
- "P R",
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- "C ollection",
- "_m sg",
- "C D",
- "\u0120L o",
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- "ib ly",
- ".e quals",
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- "# pragma",
- "oo th",
- "W riter",
- "i ate",
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- "iv o",
- "iv ery",
- "r d",
- "HE CK",
- "\u0120c ache",
- ".c ount",
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- ".Re ad",
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- "RE D",
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- "\u0120Date Time",
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- "( url",
- "\u0120\" -",
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- "As ync",
- "\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120 \u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120",
- "\u0120Oct ober",
- "_l ine",
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- "\u0120im ag",
- "T ree",
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- "et te",
- "_n o",
- "IV E",
- "\u0120v on",
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- ".p ost",
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- "pr imary",
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- "is ode",
- "Tr ace",
- "\u0120g ave",
- "\u0120sh ot",
- "\u0120A ir",
- "\u0120m other",
- "pr ice",
- "\u0120mor ning",
- ")) {\u010a",
- "- x",
- "\u0120tr ade",
- "\u0120des c",
- "\u0120&& \u010a",
- "\u0120parent s",
- "A pi",
- "\u00e5 \u012a",
- "t ed",
- "w er",
- "\u0120 \u00e6",
- "\u0120s y",
- "\u0120K e",
- "Par ser",
- "\u00e5 \u0127",
- "anc y",
- "\u0120pie ce",
- "iforn ia",
- "to String",
- "r an",
- "id ing",
- "PT ION",
- "com es",
- "/ lic",
- ".c lient",
- "E l",
- "L ong",
- "\u0120profession al",
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- "v a",
- "\u0120complet ely",
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- "00 2",
- "\u0120se lection",
- "R em",
- "in i",
- "\u0120c am",
- "RE E",
- "\u0120sit es",
- "p a",
- "AT US",
- "\u00d1\u0123 \u00d1\u0124",
- "arr ant",
- "* (",
- "_ KEY",
- "\u0120B utton",
- "\u0120F riday",
- "se qu",
- "\u0120re ader",
- "\u0120m essages",
- "\u00e8 \u00af",
- "\u0120bu f",
- "K e",
- "\u0120n ov",
- "H P",
- "M sg",
- "al ign",
- "ar ily",
- "\u0120' ,",
- "_w ith",
- "\u0120d as",
- "\u0120he ard",
- "at omic",
- "ri al",
- ") [",
- "\u0120dis e",
- "@ end",
- "\u0120g old",
- "\u0120f air",
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- ". Button",
- "str ict",
- "s ave",
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- "View Controller",
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- ".p aram",
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- "IN FO",
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- "IC ENSE",
- "cc ording",
- "k i",
- "\u0120U N",
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- "\u0120man ager",
- "\u0120p ain",
- "\u0120F ire",
- "rom e",
- "\u0120pl ans",
- "F ound",
- "l ay",
- "\u0120Dec ember",
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- "\u00c3 \u00ba",
- "ren ch",
- "\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120 \u0120",
- "az ing",
- "b rief",
- "c all",
- "wo od",
- "\u0120load ed",
- "\u0120gr and",
- "/ f",
- "im p",
- "_ U",
- "12 7",
- "ST R",
- "\u00e2\u0122 \u00a2",
- "\u0120cred it",
- ".C olor",
- "or ge",
- "QUE ST",
- "\u0120differ ence",
- "\u0120P C",
- "w args",
- "\u0120p ub",
- "und ay",
- "\u0120f ra",
- ".m ax",
- "\u0120tri ed",
- "ann els",
- "s end",
- "\u0120report s",
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- "\u00e4 \u00ba",
- "\u0120cons ist",
- "\u0120St reet",
- "\u0120Pro gram",
- "S QL",
- "M atrix",
- "ounc il",
- "- A",
- "\u0109 w",
- "\u0120who se",
- "\u0120rel ig",
- "\u0120S ex",
- "\u0120g ives",
- "n one",
- ".m essage",
- "( G",
- ".aw t",
- "- right",
- "\u0120Nov ember",
- "ell ig",
- "3 60",
- "ut ive",
- "\u00c4 \u0125",
- "over n",
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- "\u0120ide as",
- "10 4",
- "\u0120\u00d0 \u00bd",
- "/c ss",
- "ly ing",
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- "C an",
- "_c olor",
- "\u00d0\u00be\u00d0 \u00b2",
- "\u0120p air",
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- "\u0120s plit",
- "14 0",
- "d rop",
- "art y",
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- "R el",
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- "R ender",
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- "E O",
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- "fr ame",
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- "\u0120P ART",
- "ed s",
- "; ;",
- "\u0120A M",
- "\u0120bas ic",
- "\u0120L et",
- "lic h",
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- "V ENT",
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- "M apping",
- "- content",
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- "- de",
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- "+ '",
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- "> ::",
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- "\u0120p ow",
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- "m ake",
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- "\u00ef\u00bd\u0140 \u010a\u010a",
- "\u00e9\u0122\u0123 \u00e6\u0138\u013b",
- "\u00e2\u0122\u00a6 it",
- "Se ats",
- "Cl ar",
- "W ars",
- "\u0120Hutch inson",
- "\u0120Has an",
- "! ')\u010a\u010a",
- "\u0120Rich ie",
- "che iden",
- "($ ('",
- "Y ork",
- "\u0120l ids",
- "\u0120al phanumeric",
- "\u0120G lock",
- ".sh apes",
- "\u0120spark ing",
- "_ epsilon",
- "uplic ated",
- ".dir ty",
- "]) ==",
- "\u0120\u00ec\u013e\u0126 \u00ec\u00b9\u013a",
- "\u0120sc n",
- "\u0120/ ****************************************************************",
- "_PRE VIEW",
- "_H C",
- "ield ing",
- "f gets",
- "\u0120Add ison",
- "\u0120product Service",
- "- figure",
- "(ret val",
- "z ano",
- "\u0120aut ob",
- "\u0109s d",
- "_n umer",
- "\u0120Set LastError",
- "\u0120F ior",
- "ific ance",
- "Unt itled",
- "\u0120in field",
- "\u0120{} ));\u010a",
- "\u0120sp ac",
- "\u0120ro okies",
- "(des cribing",
- "ng en",
- "\u00e0\u00ae\u00bf \u00e0\u00ae",
- ".r df",
- ".M utex",
- "\u0120kne eling",
- "\u0120Q E",
- "set Max",
- "Read Stream",
- "\u0120vent as",
- "s ut",
- "cm peq",
- ".WriteAll Text",
- "\u0120Ex perienced",
- "$ __",
- "\u0120ka um",
- "\u0120L IS",
- "\u0120document os",
- "_HE ALTH",
- "icont ains",
- "\u0120art isans",
- "OWN ER",
- "\u0120blink ed",
- "get Display",
- "\u0120to en",
- "\u0120row Num",
- "\u0120av ril",
- "\u0120inv is",
- "\u0120K ear",
- "toBe InTheDocument",
- "ap ur",
- "\u0120r acked",
- "\u0120Mc Master",
- "_ATTR IB",
- "H az",
- "\u0120fact ura",
- "/ ts",
- "\u0120\u00d1\u0122\u00d0\u00b0\u00d0\u00b7 \u00d0\u00bc\u00d0\u00b5\u00d1\u0122",
- "\u0120z f",
- "\u0120short fall",
- ".f asta",
- "\u0120CONST ANT",
- ".man aged",
- "g ems",
- "Shared Pointer",
- "\u0120blur ry",
- "b rightness",
- "( components",
- "\u0120... \"\u010a\u010a",
- "SE LL",
- "\u0120Illustr ator",
- ".get Channel",
- "\u0120trou v\u00c3\u00a9",
- "yst ers",
- "\u0120vo is",
- "\u0120Lind en",
- "\u0120em ojis",
- "\u0120b rawl",
- "\u0120MS R",
- "\u0120E lo",
- "\u0120Croat ian",
- "Popup Menu",
- "L ewis",
- ".J WT",
- "\u0120aston ished",
- "B ush",
- "(item Id",
- "\u0120det achment",
- "\u0120Enc ore",
- "\u00e5\u00b0 \u0136",
- "\u0120re kl",
- "\u0120cr am",
- ")$ /",
- ".get Host",
- "_re commend",
- "- HT",
- "_cal ibration",
- "Auth enticate",
- ".firebase app",
- "UN IX",
- "\u0109C amera",
- "\u0120HE AP",
- "I deal",
- ". office",
- "\u0120goof y",
- "(S ymbol",
- "\u0120jou er",
- "_part itions",
- "\u0120rapid ement",
- "\u0120GN UNET",
- "id User",
- "\u0120superv ise",
- "( Contact",
- "AW N",
- "\u00e3\u0123 \u013a",
- "\u0120na am",
- "\u0120a ust",
- "\u00e5\u013e\u00a8 \u00e7\u00ba\u00bf",
- "_soft max",
- "Allow Anonymous",
- "amm able",
- "RO UTE",
- "* D",
- "\u0120ad en",
- "\u0120Crist ina",
- "\u0120Crist iano",
- "\u0120blood stream",
- "sub class",
- "_person a",
- "CH ILD",
- "-k now",
- "\u0120navigation Options",
- "\u0120Zuk unft",
- "\u0120Pix ar",
- "Ty ler",
- "\u0120under world",
- "\u0120sincer ity",
- "\u0120dispens er",
- "\u0120k ter",
- "idd ers",
- ".add Node",
- "- checked",
- "\u0120ke yst",
- "\u0120W TO",
- ".sign als",
- "\u0120advent urer",
- "\u0120P ang",
- "\\ R",
- "= pos",
- "\u0120dispens aries",
- "\u0120Clo set",
- "(\"{ \\\"",
- "ide on",
- "\u0120n\u00c3\u00a9cess aire",
- "() \"\u010a",
- "_RECE IVED",
- "\u0120r\u00c3\u00a9sult ats",
- "\u0120mod en",
- "\u0120Iceland ic",
- "; d",
- ". allowed",
- "(new User",
- "\u0120merc iless",
- ".Wait For",
- "\u0120day care",
- "\u0120Con veyor"
- ]
- }
-}
\ No newline at end of file
From 03edd3bcecc4764fa01b5cc92bf0aeeb6d175a01 Mon Sep 17 00:00:00 2001
From: "chris.ccy" <chris.ccy@antgroup.com>
Date: Mon, 16 Dec 2024 13:39:14 +0800
Subject: [PATCH 4/5] infer launch
---
.../inference/acceelerate_infer_launch.sh | 53 +++++++++++++++++++
1 file changed, 53 insertions(+)
create mode 100644 mftcoder_accelerate/inference/acceelerate_infer_launch.sh
diff --git a/mftcoder_accelerate/inference/acceelerate_infer_launch.sh b/mftcoder_accelerate/inference/acceelerate_infer_launch.sh
new file mode 100644
index 0000000..7df51c1
--- /dev/null
+++ b/mftcoder_accelerate/inference/acceelerate_infer_launch.sh
@@ -0,0 +1,53 @@
+#######################################################################################################################
+MODEL_PATH="path/to/hf/model"
+DATA_FILE="path/to/input/file/jsonl"
+BATCH_SIZE=2
+
+N_NODE=1
+N_GPU_PER_NODE=2
+#######################################################################################################################
+# you could add some specific commnads here if you need.
+pip install transformers==4.44.2
+pip install -U accelerate
+
+
+export OMP_NUM_THREADS=16
+
+
+# One Node inference
+# accelerate launch \
+# --num_machines 1 \
+# --num_processes 2 \
+# --machine_rank 0 \
+# --dynamo_backend 'no' \
+# --same_network \
+# --rdzv_backend 'static' \
+# infer_accelerate.py \
+# --model_path $MODEL_PATH \
+# --data_file $DATA_FILE \
+# --batch_size $BATCH_SIZE \
+# --output_dir "." \
+# > "path/to/local/log" 2>&1 &
+
+# You need to export $MACHINE_RANK, $MASTER_ADDR, $MASTER_PORT automatically for each Node.
+
+echo "RANK: $RANK"
+echo "MASTER_ADDR: $MASTER_ADDR"
+echo "MASTER_PORT: $MASTER_PORT"
+
+
+# launch by multi-node, multi-gpu
+accelerate launch \
+ --num_machines $N_NODE \
+ --num_processes $(($N_NODE*$N_GPU_PER_NODE)) \
+ --machine_rank $RANK \
+ --main_process_ip $MASTER_ADDR \
+ --main_process_port $MASTER_PORT \
+ --dynamo_backend 'no' \
+ --same_network \
+ --rdzv_backend 'static' \
+ infer_accelerate.py \
+ --model_path $MODEL_PATH \
+ --data_file $DATA_FILE \
+ --batch_size $BATCH_SIZE \
+ --output_dir "path/to/output/directory"
\ No newline at end of file
From d0ff90cf05bc932cd75731753c7c63689266053b Mon Sep 17 00:00:00 2001
From: "chris.ccy" <chris.ccy@antgroup.com>
Date: Mon, 30 Dec 2024 14:01:44 +0800
Subject: [PATCH 5/5] drop dpo samples which exceed max_length
---
init_env.sh | 1 +
mftcoder_accelerate/inference/infer_utils.py | 46 +++++++
mftcoder_accelerate/inference/split.sh | 23 ++++
.../training/train_utils/model_mapping.py | 4 +-
.../training/train_utils/watchdog.py | 120 ++++++++++++++++++
.../training/xxpo_accelerate.py | 9 +-
6 files changed, 197 insertions(+), 6 deletions(-)
create mode 100644 mftcoder_accelerate/inference/split.sh
create mode 100644 mftcoder_accelerate/training/train_utils/watchdog.py
diff --git a/init_env.sh b/init_env.sh
index 834b38d..251ab97 100644
--- a/init_env.sh
+++ b/init_env.sh
@@ -1,4 +1,5 @@
pip install torch==2.1.0 && \
pip install tensorboard==2.11.0 && \
pip install packaging && \
+MAX_JOBS=4 pip install flash-attn==2.3.6 --no-build-isolation && \
pip install -r requirements.txt
diff --git a/mftcoder_accelerate/inference/infer_utils.py b/mftcoder_accelerate/inference/infer_utils.py
index 2fe3be2..6d5f626 100644
--- a/mftcoder_accelerate/inference/infer_utils.py
+++ b/mftcoder_accelerate/inference/infer_utils.py
@@ -5,6 +5,8 @@
import json
import os
import gzip
+import re
+import ast
from tqdm import tqdm
from typing import Iterable, Dict, List
@@ -74,3 +76,47 @@ def write_jsonl(filename: str, data: Iterable[Dict], total, append: bool = False
with open(filename, mode) as fp:
for x in tqdm(data, total=total):
fp.write((json.dumps(x) + "\n").encode("utf-8"))
+
+
+def is_compilable(code):
+ try:
+ ast.parse(code)
+ return True
+ except SyntaxError:
+ return False
+
+
+def is_tests(code):
+ return code.strip().startswith("assert")
+
+
+def extract_python_code_block(response):
+ # pattern = r"^```[Pp]ython\s*\n(.*?)(?=^```)"
+ pattern = r"^```\s*(?:[Pp]ython)?\s*\n(.*?)(?=^```)"
+ result = re.findall(pattern, response, re.DOTALL | re.MULTILINE)
+ return "\n".join([x for x in result if is_compilable(x) and not is_tests(x)])
+
+
+def extract_python_test_block(response):
+ # pattern = r"^```[Pp]ython\s*\n(.*?)(?=^```)"
+ pattern = r"^```\s*(?:[Pp]ython)?\s*\n(.*?)(?=^```)"
+ result = re.findall(pattern, response, re.DOTALL | re.MULTILINE)
+ return "\n".join([x for x in result if is_compilable(x) and is_tests(x)])
+
+
+def extract_code_with_lang(text):
+ """
+ 使用正则表达式从文本中提取任意语言的代码块。
+
+ 参数:
+ - text: 包含Markdown代码块的字符串。
+
+ 返回:
+ - 一个包含所有匹配的代码块及其语言的列表，每一个元组都是 (language, code_block)。
+ """
+ # 编译正则表达式模式，匹配任意语言的代码块
+ pattern = re.compile(r"```([\w+#-]*)\s*\n(.*?)```", re.DOTALL | re.MULTILINE)
+
+ # 使用findall方法找出所有匹配的代码块及其语言标识
+ # 返回的是一个元组列表，每个元组包含 (language, code_block)
+ return pattern.findall(text)
\ No newline at end of file
diff --git a/mftcoder_accelerate/inference/split.sh b/mftcoder_accelerate/inference/split.sh
new file mode 100644
index 0000000..665ad83
--- /dev/null
+++ b/mftcoder_accelerate/inference/split.sh
@@ -0,0 +1,23 @@
+# 输入文件的完整路径
+input_file_path="$1"
+
+# 获取输入文件的目录
+input_directory=$(dirname "$input_file_path")
+
+# 获取文件名，不带扩展名
+input_filename=$(basename "$input_file_path" .jsonl)
+
+# 计算原始jsonl文件的总行数
+total_lines=$(wc -l < "$input_file_path")
+echo "total lines: "$total_lines
+# 要分成的文件数K，例如我们假设为4
+K=$2
+
+# 计算每个文件应该有多少行（向上取整）
+lines_per_file=$(( (total_lines + K - 1) / K ))
+echo "lines_per_file: "$lines_per_file
+# 检查输出目录是否存在，如果不存在则创建它
+mkdir -p "$input_directory"
+
+# 使用split命令等分文件，并将结果文件放在输入文件的目录中，前缀为输入文件的名字
+split -l $lines_per_file -d --additional-suffix=.jsonl "$input_file_path" "${input_directory}/${input_filename}-part-"
\ No newline at end of file
diff --git a/mftcoder_accelerate/training/train_utils/model_mapping.py b/mftcoder_accelerate/training/train_utils/model_mapping.py
index 4dfd80b..067caea 100644
--- a/mftcoder_accelerate/training/train_utils/model_mapping.py
+++ b/mftcoder_accelerate/training/train_utils/model_mapping.py
@@ -1,6 +1,6 @@
"""
- @author qumu
- transformers==4.40 is stable now
+ @author Chaoyu Chen
+ transformers==4.44.2 is stable now
"""
# Models that Transformers support Code and FA2 when flash_attn>=2.1.0
diff --git a/mftcoder_accelerate/training/train_utils/watchdog.py b/mftcoder_accelerate/training/train_utils/watchdog.py
new file mode 100644
index 0000000..1e576b2
--- /dev/null
+++ b/mftcoder_accelerate/training/train_utils/watchdog.py
@@ -0,0 +1,120 @@
+"""
+ @author Chaoyu Chen
+ watchdog which supports callback functions for checkpointing
+"""
+import time
+import os
+import argparse
+import json
+import logging
+import sys
+import pprint
+
+# 配置logger
+# 设置日志级别为DEBUG，这意味着DEBUG及以上级别的所有日志都会被捕获
+# 默认的日志格式包含了时间戳、日志级别、消息和日志发生地点的相关信息
+logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
+
+# 获取一个logger实例
+logger = logging.getLogger(__name__)
+
+
+def func(ckpt_path, ckpt_name, base_model_path, model_type):
+ pass
+
+
+def ckpt_callback(callback, ckpt_path, ckpt_name, base_model_path, model_type):
+ lora = os.path.isfile(os.path.join(ckpt_path, "adapter_config.json"))
+ logger.info(f"use lora inference: {lora}")
+ logger.info(f"submit: {ckpt_name}")
+ callback(ckpt_path, ckpt_name, base_model_path, model_type)
+
+
+def main(ckpt_dir: str, base_model_path: str, model_type):
+ # 单ckpt评测
+ if ckpt_dir.split("/")[-1].startswith(("epoch_", "step_", "checkpoint-")):
+ ckpt_path = ckpt_dir
+ ckpt_name = ckpt_dir.split("/")[-2] + "-" + ckpt_dir.split("/")[-1]
+ ckpt_callback(func, ckpt_path, ckpt_name, base_model_path, model_type)
+ return
+
+ # 进入监控循环
+ stop_limit = 100
+ stop_num = 0
+ # 读取history
+ history_path = os.path.join(ckpt_dir, "watchdog")
+ if os.path.exists(history_path):
+ with open(history_path, "r") as f:
+ history = json.load(f)
+ known_dirs = set(history["submitted"])
+ logger.info(f"submitted: {known_dirs}")
+ else:
+ logger.info(f"history: {history_path} 不存在")
+ # 初始化已知目录状态
+ # known_dirs = {name for name in os.listdir(ckpt_dir) if os.path.isdir(os.path.join(ckpt_dir, name)) and name.startswith(('epoch_', 'step_', "checkpoint-"))}
+ known_dirs = set()
+ logger.info(f"initialized: {known_dirs}")
+
+ # 主监控循环
+ try:
+ while True:
+ # 兼容ckpt_dir没有被创建
+ if os.path.exists(ckpt_dir):
+ current_dirs = {
+ name
+ for name in os.listdir(ckpt_dir)
+ if
+ os.path.isdir(os.path.join(ckpt_dir, name)) and name.startswith(("epoch_", "step_", "checkpoint-"))
+ }
+ # ckpt_dir存在，但还没有任何submits的时候，打印一次训练的args
+ if not os.path.exists(history_path) and os.path.exists(os.path.join(ckpt_dir, "args.json")):
+ with open(os.path.join(ckpt_dir, "args.json"), "r") as f:
+ pprint.pprint(json.load(f))
+ sys.stdout.flush()
+ else:
+ logger.info("ckpt dir not created, training may not strart")
+ time.sleep(300) # 定时间隔 5min
+ continue
+ # 检测新添加的文件夹
+ new_dirs = current_dirs - known_dirs
+ logger.info(f"new_dirs: {new_dirs}")
+ if not new_dirs:
+ stop_num += 1
+ else:
+ stop_num = 0
+ time.sleep(60)
+ if stop_num > stop_limit:
+ break
+ for new_dir in new_dirs:
+ # 检查是否以 'epoch_' 或 'step_' 开头
+ if new_dir.startswith(("epoch_", "step_", "checkpoint-")):
+ ckpt_path = os.path.join(ckpt_dir, new_dir)
+ ckpt_name = ckpt_dir.split("/")[-1] + "-" + new_dir
+ ckpt_callback(func, ckpt_path, ckpt_name, base_model_path, model_type)
+
+ # 更新已知目录列表
+ known_dirs = current_dirs
+ history = {"submitted": list(known_dirs)}
+ with open(history_path, "w") as f:
+ json.dump(history, f, indent=2)
+ time.sleep(300) # 定时间隔 5min
+ except KeyboardInterrupt:
+ print("监控停止")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description="watchdog参数.")
+ parser.add_argument(
+ "--base_model_path",
+ type=str,
+ )
+ parser.add_argument("--ckpt_dir", type=str)
+
+ parser.add_argument("--model_type", type=str)
+
+ args = parser.parse_args()
+ main(
+ ckpt_dir=args.ckpt_dir,
+ base_model_path=args.base_model_path,
+ model_type=args.model_type,
+ )
diff --git a/mftcoder_accelerate/training/xxpo_accelerate.py b/mftcoder_accelerate/training/xxpo_accelerate.py
index dcfeacf..e0a48f1 100644
--- a/mftcoder_accelerate/training/xxpo_accelerate.py
+++ b/mftcoder_accelerate/training/xxpo_accelerate.py
@@ -326,10 +326,11 @@ def main():
all_datasets.append(ds)
all_dataset = concatenate_datasets(all_datasets)
- # all_dataset = all_dataset.filter(
- # lambda x: len(x["prompt"]) + len(x["chosen"]) <= args.max_length
- # and len(x["prompt"]) + len(x["rejected"]) <= args.max_length
- # )
+ # drop samples which exceed max_length
+ all_dataset = all_dataset.filter(
+ lambda x: len(x["prompt"]) + len(x["chosen"]) <= args.max_length
+ and len(x["prompt"]) + len(x["rejected"]) <= args.max_length
+ )
accelerator.print(f"Length of all_dataset: {len(all_dataset)}")
# split train/eval dataset