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fix-l3-lor
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.github/workflows/lint.yml
vendored
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.github/workflows/lint.yml
vendored
@@ -7,7 +7,6 @@ on:
|
||||
- 'requirements.txt'
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- '.github/workflows/*.yml'
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||||
- "*.md"
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||||
- "examples/**/*.y[a]?ml"
|
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workflow_dispatch:
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|
||||
jobs:
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|
||||
723
README.md
723
README.md
@@ -35,16 +35,16 @@ Features:
|
||||
- [Google Colab](#google-colab)
|
||||
- [Launching on public clouds via SkyPilot](#launching-on-public-clouds-via-skypilot)
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||||
- [Dataset](#dataset)
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||||
- [How to Add Custom Prompts](#how-to-add-custom-prompts)
|
||||
- [How to Use Custom Pretokenized Dataset](#how-to-use-your-custom-pretokenized-dataset)
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||||
- [Config](#config)
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||||
- [Train](#train)
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||||
- [Inference](#inference-playground)
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||||
- [Merge LORA to Base](#merge-lora-to-base)
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- [Special Tokens](#special-tokens)
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||||
- [All Config Options](#all-config-options)
|
||||
- Advanced Topics
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||||
- [Multipack](./docs/multipack.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
|
||||
- [RLHF & DPO](./docs/rlhf.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
|
||||
- [Dataset Pre-Processing](./docs/dataset_preprocessing.qmd)<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 13.5v6H5v-12h6m3-3h6v6m0-6-9 9" class="icon_svg-stroke" stroke="#666" stroke-width="1.5" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg>
|
||||
- [Common Errors](#common-errors-)
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||||
- [Tokenization Mismatch b/w Training & Inference](#tokenization-mismatch-bw-inference--training)
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||||
- [Debugging Axolotl](#debugging-axolotl)
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||||
@@ -82,7 +82,6 @@ Features:
|
||||
| llama | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| Mistral | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| Mixtral-MoE | ✅ | ✅ | ✅ | ❓ | ❓ | ❓ | ❓ |
|
||||
| Mixtral8X22 | ✅ | ✅ | ✅ | ❓ | ❓ | ❓ | ❓ |
|
||||
| Pythia | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
|
||||
| cerebras | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
|
||||
| btlm | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❓ |
|
||||
@@ -109,7 +108,7 @@ Get started with Axolotl in just a few steps! This quickstart guide will walk yo
|
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git clone https://github.com/OpenAccess-AI-Collective/axolotl
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cd axolotl
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|
||||
pip3 install packaging ninja
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||||
pip3 install packaging
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||||
pip3 install -e '.[flash-attn,deepspeed]'
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||||
```
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||||
|
||||
@@ -223,17 +222,23 @@ For cloud GPU providers that support docker images, use [`winglian/axolotl-cloud
|
||||
python get-pip.py
|
||||
```
|
||||
|
||||
3. Install Pytorch https://pytorch.org/get-started/locally/
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||||
|
||||
4. Follow instructions on quickstart.
|
||||
|
||||
5. Run
|
||||
3. Install torch
|
||||
```bash
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||||
pip3 install -U torch --index-url https://download.pytorch.org/whl/cu118
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||||
```
|
||||
|
||||
4. Axolotl
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||||
```bash
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git clone https://github.com/OpenAccess-AI-Collective/axolotl
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||||
cd axolotl
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||||
|
||||
pip3 install packaging
|
||||
pip3 install -e '.[flash-attn,deepspeed]'
|
||||
pip3 install protobuf==3.20.3
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||||
pip3 install -U --ignore-installed requests Pillow psutil scipy
|
||||
```
|
||||
|
||||
6. Set path
|
||||
5. Set path
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||||
```bash
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||||
export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
|
||||
```
|
||||
@@ -294,9 +299,186 @@ HF_TOKEN=xx BUCKET=<unique-name> sky spot launch axolotl-spot.yaml --env HF_TOKE
|
||||
|
||||
### Dataset
|
||||
|
||||
Axolotl supports a variety of dataset formats. It is recommended to use a JSONL. The schema of the JSONL depends upon the task and the prompt template you wish to use. Instead of a JSONL, you can also use a HuggingFace dataset with columns for each JSONL field.
|
||||
Axolotl supports a variety of dataset formats. Below are some of the formats you can use.
|
||||
Have dataset(s) in one of the following format (JSONL recommended):
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||||
|
||||
See [these docs](https://openaccess-ai-collective.github.io/axolotl/docs/dataset-formats/) for more information on how to use different dataset formats.
|
||||
#### Pretraining
|
||||
|
||||
- `completion`: raw corpus
|
||||
```json
|
||||
{"text": "..."}
|
||||
```
|
||||
|
||||
Note: Axolotl usually loads the entire dataset into memory. This will be challenging for large datasets. Use the following config to enable streaming:
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||||
|
||||
```yaml
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pretraining_dataset: # hf path only
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||||
```
|
||||
|
||||
#### Supervised finetuning
|
||||
|
||||
##### Instruction
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||||
|
||||
- `alpaca`: instruction; input(optional)
|
||||
```json
|
||||
{"instruction": "...", "input": "...", "output": "..."}
|
||||
```
|
||||
|
||||
<details>
|
||||
|
||||
<summary>See other formats</summary>
|
||||
|
||||
- `jeopardy`: question and answer
|
||||
```json
|
||||
{"question": "...", "category": "...", "answer": "..."}
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||||
```
|
||||
- `oasst`: instruction
|
||||
```json
|
||||
{"INSTRUCTION": "...", "RESPONSE": "..."}
|
||||
```
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||||
- `gpteacher`: instruction; input(optional)
|
||||
```json
|
||||
{"instruction": "...", "input": "...", "response": "..."}
|
||||
```
|
||||
- `reflection`: instruction with reflect; input(optional)
|
||||
```json
|
||||
{"instruction": "...", "input": "...", "output": "...", "reflection": "...", "corrected": "..."}
|
||||
```
|
||||
- `explainchoice`: question, choices, (solution OR explanation)
|
||||
```json
|
||||
{"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
|
||||
```
|
||||
- `concisechoice`: question, choices, (solution OR explanation)
|
||||
```json
|
||||
{"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
|
||||
```
|
||||
- `summarizetldr`: article and summary
|
||||
```json
|
||||
{"article": "...", "summary": "..."}
|
||||
```
|
||||
- `alpaca_chat`: basic instruct for alpaca chat
|
||||
```json
|
||||
{"instruction": "...", "input": "...", "response": "..."}
|
||||
```
|
||||
- `alpaca_chat.load_qa`: question and answer for alpaca chat
|
||||
```json
|
||||
{"question": "...", "answer": "..."}
|
||||
```
|
||||
- `alpaca_chat.load_concise`: question and answer for alpaca chat, for concise answers
|
||||
```json
|
||||
{"instruction": "...", "input": "...", "response": "..."}
|
||||
```
|
||||
- `alpaca_chat.load_camel_ai`: question and answer for alpaca chat, for load_camel_ai
|
||||
```json
|
||||
{"message_1": "...", "message_2": "..."}
|
||||
```
|
||||
- `alpaca_w_system.load_open_orca`: support for open orca datasets with included system prompts, instruct
|
||||
```json
|
||||
{"system_prompt": "...", "question": "...", "response": "..."}
|
||||
```
|
||||
- `context_qa`: in context question answering from an article
|
||||
```json
|
||||
{"article": "...", "question": "...", "answer": "..."}
|
||||
```
|
||||
- `context_qa.load_v2`: in context question answering (alternate)
|
||||
```json
|
||||
{"context": "...", "question": "...", "answer": "..."}
|
||||
```
|
||||
- `context_qa.load_404`: in context question answering from an article, with default response for no answer from context
|
||||
```json
|
||||
{"article": "...", "unanswerable_question": "..."}
|
||||
```
|
||||
- `creative_acr.load_answer`: instruction and revision
|
||||
```json
|
||||
{"instruction": "...", "revision": "..."}
|
||||
```
|
||||
- `creative_acr.load_critique`: critique
|
||||
```json
|
||||
{"scores": "...", "critiques": "...", "instruction": "...", "answer": "..."}
|
||||
```
|
||||
- `creative_acr.load_revise`: critique and revise
|
||||
```json
|
||||
{"scores": "...", "critiques": "...", "instruction": "...", "answer": "...", "revision": "..."}
|
||||
```
|
||||
- `metharme`: instruction, adds additional eos tokens
|
||||
```json
|
||||
{"prompt": "...", "generation": "..."}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
##### Template-Free
|
||||
|
||||
- `input_output`: template-free prompt construction
|
||||
```json
|
||||
{"segments": [{"label": true|false, "text": "..."}]}
|
||||
```
|
||||
|
||||
This is a special format that allows you to construct prompts without using templates. This is for advanced users who want more freedom with prompt construction. See [these docs](docs/input_output.qmd) for more details.
|
||||
|
||||
##### Conversation
|
||||
|
||||
- `sharegpt`: conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)
|
||||
```json
|
||||
{"conversations": [{"from": "...", "value": "..."}]}
|
||||
```
|
||||
|
||||
<details>
|
||||
|
||||
<summary>See other formats</summary>
|
||||
|
||||
- `pygmalion`: pygmalion
|
||||
```json
|
||||
{"conversations": [{"role": "...", "value": "..."}]}
|
||||
```
|
||||
- `sharegpt.load_role`: conversations where `role` is used instead of `from`
|
||||
```json
|
||||
{"conversations": [{"role": "...", "value": "..."}]}
|
||||
```
|
||||
- `sharegpt.load_guanaco`: conversations where `from` is `prompter`/`assistant` instead of default sharegpt
|
||||
```json
|
||||
{"conversations": [{"from": "...", "value": "..."}]}
|
||||
```
|
||||
- `sharegpt_jokes`: creates a chat where bot is asked to tell a joke, then explain why the joke is funny
|
||||
```json
|
||||
{"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
Note: `type: sharegpt` opens a special config `conversation:` that enables conversions to many Conversation types. See dataset section under [all yaml options](#all-yaml-options).
|
||||
|
||||
#### How to add custom prompts
|
||||
|
||||
For a dataset that is preprocessed for instruction purposes:
|
||||
|
||||
```json
|
||||
{"input": "...", "output": "..."}
|
||||
```
|
||||
|
||||
You can use this example in your YAML config:
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
- path: repo
|
||||
type:
|
||||
system_prompt: ""
|
||||
field_system: system
|
||||
field_instruction: input
|
||||
field_output: output
|
||||
format: "[INST] {instruction} [/INST]"
|
||||
no_input_format: "[INST] {instruction} [/INST]"
|
||||
```
|
||||
See full config options under [all yaml options](#all-yaml-options).
|
||||
|
||||
#### How to use your custom pretokenized dataset
|
||||
|
||||
- Do not pass a `type:`
|
||||
- Columns in Dataset must be exactly `input_ids`, `attention_mask`, `labels`
|
||||
|
||||
```yaml
|
||||
- path: ...
|
||||
```
|
||||
|
||||
### Config
|
||||
|
||||
@@ -381,9 +563,512 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
|
||||
- v_proj
|
||||
```
|
||||
|
||||
#### All Config Options
|
||||
<details id="all-yaml-options">
|
||||
|
||||
See [these docs](docs/config.qmd) for all config options.
|
||||
<summary>All yaml options (click to expand)</summary>
|
||||
|
||||
```yaml
|
||||
# This is the huggingface model that contains *.pt, *.safetensors, or *.bin files
|
||||
# This can also be a relative path to a model on disk
|
||||
base_model: ./llama-7b-hf
|
||||
# You can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
|
||||
base_model_ignore_patterns:
|
||||
# If the base_model repo on hf hub doesn't include configuration .json files,
|
||||
# You can set that here, or leave this empty to default to base_model
|
||||
base_model_config: ./llama-7b-hf
|
||||
# You can specify to choose a specific model revision from huggingface hub
|
||||
revision_of_model:
|
||||
# Optional tokenizer configuration path in case you want to use a different tokenizer
|
||||
# than the one defined in the base model
|
||||
tokenizer_config:
|
||||
# If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
|
||||
model_type: AutoModelForCausalLM
|
||||
# Corresponding tokenizer for the model AutoTokenizer is a good choice
|
||||
tokenizer_type: AutoTokenizer
|
||||
# Trust remote code for untrusted source
|
||||
trust_remote_code:
|
||||
# use_fast option for tokenizer loading from_pretrained, default to True
|
||||
tokenizer_use_fast:
|
||||
# Whether to use the legacy tokenizer setting, defaults to True
|
||||
tokenizer_legacy:
|
||||
# Resize the model embeddings when new tokens are added to multiples of 32
|
||||
# This is reported to improve training speed on some models
|
||||
resize_token_embeddings_to_32x:
|
||||
|
||||
# (Internal use only)
|
||||
# Used to identify which the model is based on
|
||||
is_falcon_derived_model:
|
||||
is_llama_derived_model:
|
||||
is_qwen_derived_model:
|
||||
# Please note that if you set this to true, `padding_side` will be set to "left" by default
|
||||
is_mistral_derived_model:
|
||||
|
||||
# optional overrides to the base model configuration
|
||||
overrides_of_model_config:
|
||||
# RoPE Scaling https://github.com/huggingface/transformers/pull/24653
|
||||
rope_scaling:
|
||||
type: # linear | dynamic
|
||||
factor: # float
|
||||
|
||||
# optional overrides to the bnb 4bit quantization configuration
|
||||
# https://huggingface.co/docs/transformers/main/main_classes/quantization#transformers.BitsAndBytesConfig
|
||||
bnb_config_kwargs:
|
||||
# These are default values
|
||||
llm_int8_has_fp16_weight: false
|
||||
bnb_4bit_quant_type: nf4
|
||||
bnb_4bit_use_double_quant: true
|
||||
|
||||
|
||||
# Whether you are training a 4-bit GPTQ quantized model
|
||||
gptq: true
|
||||
|
||||
# This will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
|
||||
load_in_8bit: true
|
||||
# Use bitsandbytes 4 bit
|
||||
load_in_4bit:
|
||||
|
||||
# Use CUDA bf16
|
||||
bf16: true # bool or 'full' for `bf16_full_eval`. require >=ampere
|
||||
# Use CUDA fp16
|
||||
fp16: true
|
||||
# Use CUDA tf32
|
||||
tf32: true # require >=ampere
|
||||
|
||||
# No AMP (automatic mixed precision)
|
||||
bfloat16: true # require >=ampere
|
||||
float16: true
|
||||
|
||||
# Limit the memory for all available GPUs to this amount (if an integer, expressed in gigabytes); default: unset
|
||||
gpu_memory_limit: 20GiB
|
||||
# Do the LoRA/PEFT loading on CPU -- this is required if the base model is so large it takes up most or all of the available GPU VRAM, e.g. during a model and LoRA merge
|
||||
lora_on_cpu: true
|
||||
|
||||
# A list of one or more datasets to finetune the model with
|
||||
datasets:
|
||||
# HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
|
||||
- path: vicgalle/alpaca-gpt4
|
||||
# The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
|
||||
type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
|
||||
ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
|
||||
data_files: # Optional[str] path to source data files
|
||||
shards: # Optional[int] number of shards to split data into
|
||||
name: # Optional[str] name of dataset configuration to load
|
||||
train_on_split: train # Optional[str] name of dataset split to load from
|
||||
|
||||
# Optional[str] fastchat conversation type, only used with type: sharegpt
|
||||
conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
|
||||
field_human: # Optional[str]. Human key to use for conversation.
|
||||
field_model: # Optional[str]. Assistant key to use for conversation.
|
||||
# Add additional keys from your dataset as input or output roles
|
||||
roles:
|
||||
input: # Optional[List[str]]. These will be masked based on train_on_input
|
||||
output: # Optional[List[str]].
|
||||
|
||||
# Custom user instruction prompt
|
||||
- path: repo
|
||||
type:
|
||||
# The below are defaults. only set what's needed if you use a different column name.
|
||||
system_prompt: ""
|
||||
system_format: "{system}"
|
||||
field_system: system
|
||||
field_instruction: instruction
|
||||
field_input: input
|
||||
field_output: output
|
||||
|
||||
# Customizable to be single line or multi-line
|
||||
# Use {instruction}/{input} as key to be replaced
|
||||
# 'format' can include {input}
|
||||
format: |-
|
||||
User: {instruction} {input}
|
||||
Assistant:
|
||||
# 'no_input_format' cannot include {input}
|
||||
no_input_format: "{instruction} "
|
||||
|
||||
# For `completion` datsets only, uses the provided field instead of `text` column
|
||||
field:
|
||||
|
||||
# If false, the datasets will not be shuffled and will keep their original order in `datasets`.
|
||||
# The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
|
||||
shuffle_merged_datasets: true
|
||||
|
||||
# A list of one or more datasets to eval the model with.
|
||||
# You can use either test_datasets, or val_set_size, but not both.
|
||||
test_datasets:
|
||||
- path: /workspace/data/eval.jsonl
|
||||
ds_type: json
|
||||
# You need to specify a split. For "json" datasets the default split is called "train".
|
||||
split: train
|
||||
type: completion
|
||||
data_files:
|
||||
- /workspace/data/eval.jsonl
|
||||
|
||||
# use RL training: 'dpo', 'ipo', 'kto_pair'
|
||||
rl:
|
||||
|
||||
# Saves the desired chat template to the tokenizer_config.json for easier inferencing
|
||||
# Currently supports chatml and inst (mistral/mixtral)
|
||||
chat_template: chatml
|
||||
# Changes the default system message
|
||||
default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
|
||||
# Axolotl attempts to save the dataset as an arrow after packing the data together so
|
||||
# subsequent training attempts load faster, relative path
|
||||
dataset_prepared_path: data/last_run_prepared
|
||||
# Push prepared dataset to hub
|
||||
push_dataset_to_hub: # repo path
|
||||
# The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
|
||||
# if not set.
|
||||
dataset_processes: # defaults to os.cpu_count() if not set
|
||||
# Keep dataset in memory while preprocessing
|
||||
# Only needed if cached dataset is taking too much storage
|
||||
dataset_keep_in_memory:
|
||||
# push checkpoints to hub
|
||||
hub_model_id: # private repo path to push finetuned model
|
||||
# how to push checkpoints to hub
|
||||
# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
|
||||
hub_strategy:
|
||||
# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
|
||||
# Required to be true when used in combination with `push_dataset_to_hub`
|
||||
hf_use_auth_token: # boolean
|
||||
# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc. 0 for no eval.
|
||||
val_set_size: 0.04
|
||||
# Num shards for whole dataset
|
||||
dataset_shard_num:
|
||||
# Index of shard to use for whole dataset
|
||||
dataset_shard_idx:
|
||||
|
||||
# The maximum length of an input to train with, this should typically be less than 2048
|
||||
# as most models have a token/context limit of 2048
|
||||
sequence_len: 2048
|
||||
# Pad inputs so each step uses constant sized buffers
|
||||
# This will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
|
||||
pad_to_sequence_len:
|
||||
# Use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
|
||||
sample_packing:
|
||||
# Set to 'false' if getting errors during eval with sample_packing on.
|
||||
eval_sample_packing:
|
||||
# You can set these packing optimizations AFTER starting a training at least once.
|
||||
# The trainer will provide recommended values for these values.
|
||||
sample_packing_eff_est:
|
||||
total_num_tokens:
|
||||
|
||||
# Passed through to transformers when loading the model when launched without accelerate
|
||||
# Use `sequential` when training w/ model parallelism to limit memory
|
||||
device_map:
|
||||
# Defines the max memory usage per gpu on the system. Passed through to transformers when loading the model.
|
||||
max_memory:
|
||||
|
||||
# If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
|
||||
adapter: lora
|
||||
# If you already have a lora model trained that you want to load, put that here.
|
||||
# This means after training, if you want to test the model, you should set this to the value of `output_dir`.
|
||||
# Note that if you merge an adapter to the base model, a new subdirectory `merged` will be created under the `output_dir`.
|
||||
lora_model_dir:
|
||||
|
||||
# LoRA hyperparameters
|
||||
# For more details about the following options, see:
|
||||
# https://www.anyscale.com/blog/fine-tuning-llms-lora-or-full-parameter-an-in-depth-analysis-with-llama-2
|
||||
lora_r: 8
|
||||
lora_alpha: 16
|
||||
lora_dropout: 0.05
|
||||
lora_target_modules:
|
||||
- q_proj
|
||||
- v_proj
|
||||
# - k_proj
|
||||
# - o_proj
|
||||
# - gate_proj
|
||||
# - down_proj
|
||||
# - up_proj
|
||||
lora_target_linear: # If true, will target all linear modules
|
||||
peft_layers_to_transform: # The layer indices to transform, otherwise, apply to all layers
|
||||
|
||||
# If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
|
||||
# For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
|
||||
# `embed_tokens` converts tokens to embeddings, and `lm_head` converts embeddings to token probabilities.
|
||||
# https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
|
||||
lora_modules_to_save:
|
||||
# - embed_tokens
|
||||
# - lm_head
|
||||
|
||||
lora_fan_in_fan_out: false
|
||||
|
||||
peft:
|
||||
# Configuration options for loftq initialization for LoRA
|
||||
# https://huggingface.co/docs/peft/developer_guides/quantization#loftq-initialization
|
||||
loftq_config:
|
||||
loftq_bits: # typically 4 bits
|
||||
|
||||
# ReLoRA configuration
|
||||
# Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
|
||||
relora_steps: # Number of steps per ReLoRA restart
|
||||
relora_warmup_steps: # Number of per-restart warmup steps
|
||||
relora_anneal_steps: # Number of anneal steps for each relora cycle
|
||||
relora_prune_ratio: # threshold for optimizer magnitude when pruning
|
||||
relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings
|
||||
|
||||
# wandb configuration if you're using it
|
||||
# Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
|
||||
wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
|
||||
wandb_project: # Your wandb project name
|
||||
wandb_entity: # A wandb Team name if using a Team
|
||||
wandb_watch:
|
||||
wandb_name: # Set the name of your wandb run
|
||||
wandb_run_id: # Set the ID of your wandb run
|
||||
wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
|
||||
|
||||
# mlflow configuration if you're using it
|
||||
mlflow_tracking_uri: # URI to mlflow
|
||||
mlflow_experiment_name: # Your experiment name
|
||||
hf_mlflow_log_artifacts: # set to true to copy each saved checkpoint on each save to mlflow artifact registry
|
||||
|
||||
# Where to save the full-finetuned model to
|
||||
output_dir: ./completed-model
|
||||
|
||||
# Whether to use torch.compile and which backend to use
|
||||
torch_compile: # bool
|
||||
torch_compile_backend: # Optional[str]
|
||||
|
||||
# Training hyperparameters
|
||||
|
||||
# If greater than 1, backpropagation will be skipped and the gradients will be accumulated for the given number of steps.
|
||||
gradient_accumulation_steps: 1
|
||||
# The number of samples to include in each batch. This is the number of samples sent to each GPU.
|
||||
micro_batch_size: 2
|
||||
eval_batch_size:
|
||||
num_epochs: 4
|
||||
warmup_steps: 100 # cannot use with warmup_ratio
|
||||
warmup_ratio: 0.05 # cannot use with warmup_steps
|
||||
learning_rate: 0.00003
|
||||
lr_quadratic_warmup:
|
||||
logging_steps:
|
||||
eval_steps: # Leave empty to eval at each epoch, integers for every N steps. decimal for fraction of total steps
|
||||
evals_per_epoch: # number of times per epoch to run evals, mutually exclusive with eval_steps
|
||||
save_strategy: # Set to `no` to skip checkpoint saves
|
||||
save_steps: # Leave empty to save at each epoch
|
||||
saves_per_epoch: # number of times per epoch to save a checkpoint, mutually exclusive with save_steps
|
||||
save_total_limit: # Checkpoints saved at a time
|
||||
# Maximum number of iterations to train for. It precedes num_epochs which means that
|
||||
# if both are set, num_epochs will not be guaranteed.
|
||||
# e.g., when 1 epoch is 1000 steps => `num_epochs: 2` and `max_steps: 100` will train for 100 steps
|
||||
max_steps:
|
||||
|
||||
eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
|
||||
eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
|
||||
eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", chrf]
|
||||
|
||||
loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
|
||||
loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)
|
||||
|
||||
# Save model as safetensors (require safetensors package)
|
||||
save_safetensors:
|
||||
|
||||
# Whether to mask out or include the human's prompt from the training labels
|
||||
train_on_inputs: false
|
||||
# Group similarly sized data to minimize padding.
|
||||
# May be slower to start, as it must download and sort the entire dataset.
|
||||
# Note that training loss may have an oscillating pattern with this enabled.
|
||||
group_by_length: false
|
||||
|
||||
# Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
|
||||
gradient_checkpointing: false
|
||||
# additional kwargs to pass to the trainer for gradient checkpointing
|
||||
# gradient_checkpointing_kwargs:
|
||||
# use_reentrant: true
|
||||
|
||||
# Stop training after this many evaluation losses have increased in a row
|
||||
# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
|
||||
early_stopping_patience: 3
|
||||
|
||||
# Specify a scheduler and kwargs to use with the optimizer
|
||||
lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
|
||||
lr_scheduler_kwargs:
|
||||
cosine_min_lr_ratio: # decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of peak lr
|
||||
cosine_constant_lr_ratio: # freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means start cosine_min_lr at 80% of training step (https://arxiv.org/pdf/2308.04014.pdf)
|
||||
|
||||
# For one_cycle optim
|
||||
lr_div_factor: # Learning rate div factor
|
||||
|
||||
# Specify optimizer
|
||||
# Valid values are driven by the Transformers OptimizerNames class, see:
|
||||
# https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
|
||||
#
|
||||
# Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
|
||||
# torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
|
||||
# in the examples/ for your model and fine-tuning use case.
|
||||
#
|
||||
# Valid values for 'optimizer' include:
|
||||
# - adamw_hf
|
||||
# - adamw_torch
|
||||
# - adamw_torch_fused
|
||||
# - adamw_torch_xla
|
||||
# - adamw_apex_fused
|
||||
# - adafactor
|
||||
# - adamw_anyprecision
|
||||
# - sgd
|
||||
# - adagrad
|
||||
# - adamw_bnb_8bit
|
||||
# - lion_8bit
|
||||
# - lion_32bit
|
||||
# - paged_adamw_32bit
|
||||
# - paged_adamw_8bit
|
||||
# - paged_lion_32bit
|
||||
# - paged_lion_8bit
|
||||
# - galore_adamw
|
||||
# - galore_adamw_8bit
|
||||
# - galore_adafactor
|
||||
# - galore_adamw_layerwise
|
||||
# - galore_adamw_8bit_layerwise
|
||||
# - galore_adafactor_layerwise
|
||||
optimizer:
|
||||
# Dictionary of arguments to pass to the optimizer
|
||||
optim_args:
|
||||
# For Galore Optimizers the following optim_args are available
|
||||
# rank: # type: int
|
||||
# update_proj_gap # type: int
|
||||
# scale # type: float
|
||||
# proj_type: # type: str, default = std
|
||||
|
||||
# The target modules to optimize, i.e. the module names that you would like to train, right now this is used only for GaLore algorithm
|
||||
optim_target_modules:
|
||||
# - self_attn # for llama
|
||||
# - mlp
|
||||
|
||||
# Specify weight decay
|
||||
weight_decay:
|
||||
# adamw hyperparams
|
||||
adam_beta1:
|
||||
adam_beta2:
|
||||
adam_epsilon:
|
||||
# Gradient clipping max norm
|
||||
max_grad_norm:
|
||||
|
||||
# Augmentation techniques
|
||||
# NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to add noise to embeddings
|
||||
# currently only supported on Llama and Mistral
|
||||
neftune_noise_alpha:
|
||||
|
||||
# Whether to bettertransformers
|
||||
flash_optimum:
|
||||
# Whether to use xformers attention patch https://github.com/facebookresearch/xformers:
|
||||
xformers_attention:
|
||||
# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
|
||||
flash_attention:
|
||||
flash_attn_cross_entropy: # Whether to use flash-attention cross entropy implementation - advanced use only
|
||||
flash_attn_rms_norm: # Whether to use flash-attention rms norm implementation - advanced use only
|
||||
flash_attn_fuse_qkv: # Whether to fuse QKV into a single operation
|
||||
flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
|
||||
# Whether to use scaled-dot-product attention
|
||||
# https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
|
||||
sdp_attention:
|
||||
# Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
|
||||
s2_attention:
|
||||
# Resume from a specific checkpoint dir
|
||||
resume_from_checkpoint:
|
||||
# If resume_from_checkpoint isn't set and you simply want it to start where it left off.
|
||||
# Be careful with this being turned on between different models.
|
||||
auto_resume_from_checkpoints: false
|
||||
|
||||
# Don't mess with this, it's here for accelerate and torchrun
|
||||
local_rank:
|
||||
|
||||
# Add or change special tokens.
|
||||
# If you add tokens here, you don't need to add them to the `tokens` list.
|
||||
special_tokens:
|
||||
# bos_token: "<s>"
|
||||
# eos_token: "</s>"
|
||||
# unk_token: "<unk>"
|
||||
|
||||
# Add extra tokens.
|
||||
tokens:
|
||||
|
||||
# FSDP
|
||||
fsdp:
|
||||
fsdp_config:
|
||||
|
||||
# Deepspeed config path. e.g., deepspeed_configs/zero3.json
|
||||
deepspeed:
|
||||
|
||||
# Advanced DDP Arguments
|
||||
ddp_timeout:
|
||||
ddp_bucket_cap_mb:
|
||||
ddp_broadcast_buffers:
|
||||
|
||||
# Path to torch distx for optim 'adamw_anyprecision'
|
||||
torchdistx_path:
|
||||
|
||||
# Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
|
||||
pretraining_dataset:
|
||||
|
||||
# Debug mode
|
||||
debug:
|
||||
|
||||
# Seed
|
||||
seed:
|
||||
|
||||
# Allow overwrite yml config using from cli
|
||||
strict:
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary> Understanding of batch size and gradient accumulation steps </summary>
|
||||
<br/>
|
||||
Gradient accumulation means accumulating gradients over several mini-batches and updating the model weights afterward. When the samples in each batch are diverse, this technique doesn't significantly impact learning.
|
||||
|
||||
This method allows for effective training with larger effective batch sizes without needing proportionally larger memory. Here's why:
|
||||
|
||||
1. **Memory Consumption with Batch Size**: The primary reason increasing the batch size impacts memory is due to the storage requirements for intermediate activations. When you forward propagate a batch through a network, you have to store the activations at each layer for each sample in the batch, because these activations are used during backpropagation to compute gradients. Therefore, larger batches mean more activations, leading to greater GPU memory consumption.
|
||||
|
||||
2. **Gradient Accumulation**: With gradient accumulation, you're effectively simulating a larger batch size by accumulating gradients over several smaller batches (or micro-batches). However, at any given time, you're only forward and backward propagating a micro-batch. This means you only store activations for the micro-batch, not the full accumulated batch. As a result, you can simulate the effect of a larger batch size without the memory cost of storing activations for a large batch.
|
||||
|
||||
**Example 1:**
|
||||
Micro batch size: 3
|
||||
Gradient accumulation steps: 2
|
||||
Number of GPUs: 3
|
||||
Total batch size = 3 * 2 * 3 = 18
|
||||
|
||||
```
|
||||
| GPU 1 | GPU 2 | GPU 3 |
|
||||
|----------------|----------------|----------------|
|
||||
| S1, S2, S3 | S4, S5, S6 | S7, S8, S9 |
|
||||
| e1, e2, e3 | e4, e5, e6 | e7, e8, e9 |
|
||||
|----------------|----------------|----------------|
|
||||
| → (accumulate) | → (accumulate) | → (accumulate) |
|
||||
|----------------|----------------|----------------|
|
||||
| S10, S11, S12 | S13, S14, S15 | S16, S17, S18 |
|
||||
| e10, e11, e12 | e13, e14, e15 | e16, e17, e18 |
|
||||
|----------------|----------------|----------------|
|
||||
| → (apply) | → (apply) | → (apply) |
|
||||
|
||||
Accumulated gradient for the weight w1 after the second iteration (considering all GPUs):
|
||||
Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6 + e7 + e8 + e9 + e10 + e11 + e12 + e13 + e14 + e15 + e16 + e17 + e18
|
||||
|
||||
Weight update for w1:
|
||||
w1_new = w1_old - learning rate x (Total gradient for w1 / 18)
|
||||
```
|
||||
|
||||
**Example 2:**
|
||||
Micro batch size: 2
|
||||
Gradient accumulation steps: 1
|
||||
Number of GPUs: 3
|
||||
Total batch size = 2 * 1 * 3 = 6
|
||||
|
||||
```
|
||||
| GPU 1 | GPU 2 | GPU 3 |
|
||||
|-----------|-----------|-----------|
|
||||
| S1, S2 | S3, S4 | S5, S6 |
|
||||
| e1, e2 | e3, e4 | e5, e6 |
|
||||
|-----------|-----------|-----------|
|
||||
| → (apply) | → (apply) | → (apply) |
|
||||
|
||||
Accumulated gradient for the weight w1 (considering all GPUs):
|
||||
Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6
|
||||
|
||||
Weight update for w1:
|
||||
w1_new = w1_old - learning rate × (Total gradient for w1 / 6)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
### Train
|
||||
|
||||
@@ -427,7 +1112,7 @@ deepspeed: deepspeed_configs/zero1.json
|
||||
```
|
||||
|
||||
```shell
|
||||
accelerate launch -m axolotl.cli.train examples/llama-2/config.yml --deepspeed deepspeed_configs/zero1.json
|
||||
accelerate launch -m axolotl.cli.train examples/llama-2/config.py --deepspeed deepspeed_configs/zero1.json
|
||||
```
|
||||
|
||||
##### FSDP
|
||||
@@ -614,8 +1299,14 @@ Bugs? Please check the [open issues](https://github.com/OpenAccess-AI-Collective
|
||||
|
||||
PRs are **greatly welcome**!
|
||||
|
||||
Please run the quickstart instructions followed by the below to setup env:
|
||||
Please run below to setup env
|
||||
```bash
|
||||
git clone https://github.com/OpenAccess-AI-Collective/axolotl
|
||||
cd axolotl
|
||||
|
||||
pip3 install packaging
|
||||
pip3 install -e '.[flash-attn,deepspeed]'
|
||||
|
||||
pip3 install -r requirements-dev.txt -r requirements-tests.txt
|
||||
pre-commit install
|
||||
|
||||
|
||||
@@ -30,20 +30,20 @@ website:
|
||||
# TODO Edit folder structure after we have more docs.
|
||||
- docs/debugging.qmd
|
||||
- docs/multipack.qmd
|
||||
- docs/fsdp_qlora.qmd
|
||||
- docs/fdsp_qlora.qmd
|
||||
- docs/input_output.qmd
|
||||
- docs/rlhf.qmd
|
||||
- docs/nccl.qmd
|
||||
- docs/mac.qmd
|
||||
- docs/multi-node.qmd
|
||||
- section: "Dataset Formats"
|
||||
contents: docs/dataset-formats/*
|
||||
- section: "Reference"
|
||||
contents:
|
||||
- docs/config.qmd
|
||||
- docs/faq.qmd
|
||||
|
||||
|
||||
|
||||
|
||||
format:
|
||||
html:
|
||||
theme: materia
|
||||
|
||||
@@ -1,41 +0,0 @@
|
||||
{
|
||||
"zero_force_ds_cpu_optimizer": false,
|
||||
"zero_allow_untested_optimizer": true,
|
||||
"zero_optimization": {
|
||||
"stage": 3,
|
||||
"offload_optimizer": {
|
||||
"device": "cpu",
|
||||
"pin_memory": true
|
||||
},
|
||||
"offload_param": {
|
||||
"device": "cpu",
|
||||
"pin_memory": true
|
||||
},
|
||||
"overlap_comm": true,
|
||||
"contiguous_gradients": true,
|
||||
"sub_group_size": 0,
|
||||
"reduce_bucket_size": "auto",
|
||||
"stage3_prefetch_bucket_size": "auto",
|
||||
"stage3_param_persistence_threshold": "auto",
|
||||
"stage3_max_live_parameters": 0,
|
||||
"stage3_max_reuse_distance": 0,
|
||||
"stage3_gather_16bit_weights_on_model_save": true
|
||||
},
|
||||
"bf16": {
|
||||
"enabled": true
|
||||
},
|
||||
"fp16": {
|
||||
"enabled": "auto",
|
||||
"auto_cast": false,
|
||||
"loss_scale": 0,
|
||||
"initial_scale_power": 32,
|
||||
"loss_scale_window": 1000,
|
||||
"hysteresis": 2,
|
||||
"min_loss_scale": 1
|
||||
},
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"wall_clock_breakdown": false
|
||||
}
|
||||
@@ -1,37 +0,0 @@
|
||||
{
|
||||
"zero_force_ds_cpu_optimizer": false,
|
||||
"zero_allow_untested_optimizer": true,
|
||||
"zero_optimization": {
|
||||
"stage": 3,
|
||||
"offload_param": {
|
||||
"device": "cpu",
|
||||
"pin_memory": true
|
||||
},
|
||||
"overlap_comm": true,
|
||||
"contiguous_gradients": true,
|
||||
"sub_group_size": 0,
|
||||
"reduce_bucket_size": "auto",
|
||||
"stage3_prefetch_bucket_size": "auto",
|
||||
"stage3_param_persistence_threshold": "auto",
|
||||
"stage3_max_live_parameters": 0,
|
||||
"stage3_max_reuse_distance": 0,
|
||||
"stage3_gather_16bit_weights_on_model_save": true
|
||||
},
|
||||
"bf16": {
|
||||
"enabled": true
|
||||
},
|
||||
"fp16": {
|
||||
"enabled": "auto",
|
||||
"auto_cast": false,
|
||||
"loss_scale": 0,
|
||||
"initial_scale_power": 32,
|
||||
"loss_scale_window": 1000,
|
||||
"hysteresis": 2,
|
||||
"min_loss_scale": 1
|
||||
},
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"wall_clock_breakdown": false
|
||||
}
|
||||
@@ -1,59 +0,0 @@
|
||||
---
|
||||
title: Batch size vs Gradient accumulation
|
||||
description: Understanding of batch size and gradient accumulation steps
|
||||
---
|
||||
|
||||
Gradient accumulation means accumulating gradients over several mini-batches and updating the model weights afterward. When the samples in each batch are diverse, this technique doesn't significantly impact learning.
|
||||
|
||||
This method allows for effective training with larger effective batch sizes without needing proportionally larger memory. Here's why:
|
||||
|
||||
1. **Memory Consumption with Batch Size**: The primary reason increasing the batch size impacts memory is due to the storage requirements for intermediate activations. When you forward propagate a batch through a network, you have to store the activations at each layer for each sample in the batch, because these activations are used during backpropagation to compute gradients. Therefore, larger batches mean more activations, leading to greater GPU memory consumption.
|
||||
|
||||
2. **Gradient Accumulation**: With gradient accumulation, you're effectively simulating a larger batch size by accumulating gradients over several smaller batches (or micro-batches). However, at any given time, you're only forward and backward propagating a micro-batch. This means you only store activations for the micro-batch, not the full accumulated batch. As a result, you can simulate the effect of a larger batch size without the memory cost of storing activations for a large batch.
|
||||
|
||||
**Example 1:**
|
||||
Micro batch size: 3
|
||||
Gradient accumulation steps: 2
|
||||
Number of GPUs: 3
|
||||
Total batch size = 3 * 2 * 3 = 18
|
||||
|
||||
```
|
||||
| GPU 1 | GPU 2 | GPU 3 |
|
||||
|----------------|----------------|----------------|
|
||||
| S1, S2, S3 | S4, S5, S6 | S7, S8, S9 |
|
||||
| e1, e2, e3 | e4, e5, e6 | e7, e8, e9 |
|
||||
|----------------|----------------|----------------|
|
||||
| → (accumulate) | → (accumulate) | → (accumulate) |
|
||||
|----------------|----------------|----------------|
|
||||
| S10, S11, S12 | S13, S14, S15 | S16, S17, S18 |
|
||||
| e10, e11, e12 | e13, e14, e15 | e16, e17, e18 |
|
||||
|----------------|----------------|----------------|
|
||||
| → (apply) | → (apply) | → (apply) |
|
||||
|
||||
Accumulated gradient for the weight w1 after the second iteration (considering all GPUs):
|
||||
Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6 + e7 + e8 + e9 + e10 + e11 + e12 + e13 + e14 + e15 + e16 + e17 + e18
|
||||
|
||||
Weight update for w1:
|
||||
w1_new = w1_old - learning rate x (Total gradient for w1 / 18)
|
||||
```
|
||||
|
||||
**Example 2:**
|
||||
Micro batch size: 2
|
||||
Gradient accumulation steps: 1
|
||||
Number of GPUs: 3
|
||||
Total batch size = 2 * 1 * 3 = 6
|
||||
|
||||
```
|
||||
| GPU 1 | GPU 2 | GPU 3 |
|
||||
|-----------|-----------|-----------|
|
||||
| S1, S2 | S3, S4 | S5, S6 |
|
||||
| e1, e2 | e3, e4 | e5, e6 |
|
||||
|-----------|-----------|-----------|
|
||||
| → (apply) | → (apply) | → (apply) |
|
||||
|
||||
Accumulated gradient for the weight w1 (considering all GPUs):
|
||||
Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6
|
||||
|
||||
Weight update for w1:
|
||||
w1_new = w1_old - learning rate × (Total gradient for w1 / 6)
|
||||
```
|
||||
449
docs/config.qmd
449
docs/config.qmd
@@ -3,444 +3,15 @@ title: Config options
|
||||
description: A complete list of all configuration options.
|
||||
---
|
||||
|
||||
```yaml
|
||||
# This is the huggingface model that contains *.pt, *.safetensors, or *.bin files
|
||||
# This can also be a relative path to a model on disk
|
||||
base_model: ./llama-7b-hf
|
||||
# You can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
|
||||
base_model_ignore_patterns:
|
||||
# If the base_model repo on hf hub doesn't include configuration .json files,
|
||||
# You can set that here, or leave this empty to default to base_model
|
||||
base_model_config: ./llama-7b-hf
|
||||
# You can specify to choose a specific model revision from huggingface hub
|
||||
revision_of_model:
|
||||
# Optional tokenizer configuration path in case you want to use a different tokenizer
|
||||
# than the one defined in the base model
|
||||
tokenizer_config:
|
||||
# If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too
|
||||
model_type: AutoModelForCausalLM
|
||||
# Corresponding tokenizer for the model AutoTokenizer is a good choice
|
||||
tokenizer_type: AutoTokenizer
|
||||
# Trust remote code for untrusted source
|
||||
trust_remote_code:
|
||||
# use_fast option for tokenizer loading from_pretrained, default to True
|
||||
tokenizer_use_fast:
|
||||
# Whether to use the legacy tokenizer setting, defaults to True
|
||||
tokenizer_legacy:
|
||||
# Resize the model embeddings when new tokens are added to multiples of 32
|
||||
# This is reported to improve training speed on some models
|
||||
resize_token_embeddings_to_32x:
|
||||
```{python}
|
||||
#|echo: false
|
||||
#|output: asis
|
||||
import re
|
||||
# Regex pattern to match the YAML block including its code fence
|
||||
pattern = r'<details[^>]*id="all-yaml-options"[^>]*>.*?<summary>All yaml options.*?```yaml(.*?)```.*?</details>'
|
||||
|
||||
# (Internal use only)
|
||||
# Used to identify which the model is based on
|
||||
is_falcon_derived_model:
|
||||
is_llama_derived_model:
|
||||
is_qwen_derived_model:
|
||||
# Please note that if you set this to true, `padding_side` will be set to "left" by default
|
||||
is_mistral_derived_model:
|
||||
|
||||
# optional overrides to the base model configuration
|
||||
overrides_of_model_config:
|
||||
# RoPE Scaling https://github.com/huggingface/transformers/pull/24653
|
||||
rope_scaling:
|
||||
type: # linear | dynamic
|
||||
factor: # float
|
||||
|
||||
# optional overrides to the bnb 4bit quantization configuration
|
||||
# https://huggingface.co/docs/transformers/main/main_classes/quantization#transformers.BitsAndBytesConfig
|
||||
bnb_config_kwargs:
|
||||
# These are default values
|
||||
llm_int8_has_fp16_weight: false
|
||||
bnb_4bit_quant_type: nf4
|
||||
bnb_4bit_use_double_quant: true
|
||||
|
||||
|
||||
# Whether you are training a 4-bit GPTQ quantized model
|
||||
gptq: true
|
||||
|
||||
# This will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
|
||||
load_in_8bit: true
|
||||
# Use bitsandbytes 4 bit
|
||||
load_in_4bit:
|
||||
|
||||
# Use CUDA bf16
|
||||
bf16: true # bool or 'full' for `bf16_full_eval`. require >=ampere
|
||||
# Use CUDA fp16
|
||||
fp16: true
|
||||
# Use CUDA tf32
|
||||
tf32: true # require >=ampere
|
||||
|
||||
# No AMP (automatic mixed precision)
|
||||
bfloat16: true # require >=ampere
|
||||
float16: true
|
||||
|
||||
# Limit the memory for all available GPUs to this amount (if an integer, expressed in gigabytes); default: unset
|
||||
gpu_memory_limit: 20GiB
|
||||
# Do the LoRA/PEFT loading on CPU -- this is required if the base model is so large it takes up most or all of the available GPU VRAM, e.g. during a model and LoRA merge
|
||||
lora_on_cpu: true
|
||||
|
||||
# A list of one or more datasets to finetune the model with
|
||||
datasets:
|
||||
# HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
|
||||
- path: vicgalle/alpaca-gpt4
|
||||
# The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
|
||||
type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
|
||||
ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
|
||||
data_files: # Optional[str] path to source data files
|
||||
shards: # Optional[int] number of shards to split data into
|
||||
name: # Optional[str] name of dataset configuration to load
|
||||
train_on_split: train # Optional[str] name of dataset split to load from
|
||||
|
||||
# Optional[str] fastchat conversation type, only used with type: sharegpt
|
||||
conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
|
||||
field_human: # Optional[str]. Human key to use for conversation.
|
||||
field_model: # Optional[str]. Assistant key to use for conversation.
|
||||
# Add additional keys from your dataset as input or output roles
|
||||
roles:
|
||||
input: # Optional[List[str]]. These will be masked based on train_on_input
|
||||
output: # Optional[List[str]].
|
||||
|
||||
# Custom user instruction prompt
|
||||
- path: repo
|
||||
type:
|
||||
# The below are defaults. only set what's needed if you use a different column name.
|
||||
system_prompt: ""
|
||||
system_format: "{system}"
|
||||
field_system: system
|
||||
field_instruction: instruction
|
||||
field_input: input
|
||||
field_output: output
|
||||
|
||||
# Customizable to be single line or multi-line
|
||||
# Use {instruction}/{input} as key to be replaced
|
||||
# 'format' can include {input}
|
||||
format: |-
|
||||
User: {instruction} {input}
|
||||
Assistant:
|
||||
# 'no_input_format' cannot include {input}
|
||||
no_input_format: "{instruction} "
|
||||
|
||||
# For `completion` datsets only, uses the provided field instead of `text` column
|
||||
field:
|
||||
|
||||
# If false, the datasets will not be shuffled and will keep their original order in `datasets`.
|
||||
# The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
|
||||
shuffle_merged_datasets: true
|
||||
|
||||
# A list of one or more datasets to eval the model with.
|
||||
# You can use either test_datasets, or val_set_size, but not both.
|
||||
test_datasets:
|
||||
- path: /workspace/data/eval.jsonl
|
||||
ds_type: json
|
||||
# You need to specify a split. For "json" datasets the default split is called "train".
|
||||
split: train
|
||||
type: completion
|
||||
data_files:
|
||||
- /workspace/data/eval.jsonl
|
||||
|
||||
# use RL training: 'dpo', 'ipo', 'kto_pair'
|
||||
rl:
|
||||
|
||||
# Saves the desired chat template to the tokenizer_config.json for easier inferencing
|
||||
# Currently supports chatml and inst (mistral/mixtral)
|
||||
chat_template: chatml
|
||||
# Changes the default system message
|
||||
default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
|
||||
# Axolotl attempts to save the dataset as an arrow after packing the data together so
|
||||
# subsequent training attempts load faster, relative path
|
||||
dataset_prepared_path: data/last_run_prepared
|
||||
# Push prepared dataset to hub
|
||||
push_dataset_to_hub: # repo path
|
||||
# The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
|
||||
# if not set.
|
||||
dataset_processes: # defaults to os.cpu_count() if not set
|
||||
# Keep dataset in memory while preprocessing
|
||||
# Only needed if cached dataset is taking too much storage
|
||||
dataset_keep_in_memory:
|
||||
# push checkpoints to hub
|
||||
hub_model_id: # private repo path to push finetuned model
|
||||
# how to push checkpoints to hub
|
||||
# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
|
||||
hub_strategy:
|
||||
# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
|
||||
# Required to be true when used in combination with `push_dataset_to_hub`
|
||||
hf_use_auth_token: # boolean
|
||||
# How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc. 0 for no eval.
|
||||
val_set_size: 0.04
|
||||
# Num shards for whole dataset
|
||||
dataset_shard_num:
|
||||
# Index of shard to use for whole dataset
|
||||
dataset_shard_idx:
|
||||
|
||||
# The maximum length of an input to train with, this should typically be less than 2048
|
||||
# as most models have a token/context limit of 2048
|
||||
sequence_len: 2048
|
||||
# Pad inputs so each step uses constant sized buffers
|
||||
# This will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
|
||||
pad_to_sequence_len:
|
||||
# Use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
|
||||
sample_packing:
|
||||
# Set to 'false' if getting errors during eval with sample_packing on.
|
||||
eval_sample_packing:
|
||||
# You can set these packing optimizations AFTER starting a training at least once.
|
||||
# The trainer will provide recommended values for these values.
|
||||
sample_packing_eff_est:
|
||||
total_num_tokens:
|
||||
|
||||
# Passed through to transformers when loading the model when launched without accelerate
|
||||
# Use `sequential` when training w/ model parallelism to limit memory
|
||||
device_map:
|
||||
# Defines the max memory usage per gpu on the system. Passed through to transformers when loading the model.
|
||||
max_memory:
|
||||
|
||||
# If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
|
||||
adapter: lora
|
||||
# If you already have a lora model trained that you want to load, put that here.
|
||||
# This means after training, if you want to test the model, you should set this to the value of `output_dir`.
|
||||
# Note that if you merge an adapter to the base model, a new subdirectory `merged` will be created under the `output_dir`.
|
||||
lora_model_dir:
|
||||
|
||||
# LoRA hyperparameters
|
||||
# For more details about the following options, see:
|
||||
# https://www.anyscale.com/blog/fine-tuning-llms-lora-or-full-parameter-an-in-depth-analysis-with-llama-2
|
||||
lora_r: 8
|
||||
lora_alpha: 16
|
||||
lora_dropout: 0.05
|
||||
lora_target_modules:
|
||||
- q_proj
|
||||
- v_proj
|
||||
# - k_proj
|
||||
# - o_proj
|
||||
# - gate_proj
|
||||
# - down_proj
|
||||
# - up_proj
|
||||
lora_target_linear: # If true, will target all linear modules
|
||||
peft_layers_to_transform: # The layer indices to transform, otherwise, apply to all layers
|
||||
|
||||
# If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
|
||||
# For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
|
||||
# `embed_tokens` converts tokens to embeddings, and `lm_head` converts embeddings to token probabilities.
|
||||
# https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
|
||||
lora_modules_to_save:
|
||||
# - embed_tokens
|
||||
# - lm_head
|
||||
|
||||
lora_fan_in_fan_out: false
|
||||
|
||||
peft:
|
||||
# Configuration options for loftq initialization for LoRA
|
||||
# https://huggingface.co/docs/peft/developer_guides/quantization#loftq-initialization
|
||||
loftq_config:
|
||||
loftq_bits: # typically 4 bits
|
||||
|
||||
# ReLoRA configuration
|
||||
# Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
|
||||
relora_steps: # Number of steps per ReLoRA restart
|
||||
relora_warmup_steps: # Number of per-restart warmup steps
|
||||
relora_anneal_steps: # Number of anneal steps for each relora cycle
|
||||
relora_prune_ratio: # threshold for optimizer magnitude when pruning
|
||||
relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings
|
||||
|
||||
# wandb configuration if you're using it
|
||||
# Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
|
||||
wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
|
||||
wandb_project: # Your wandb project name
|
||||
wandb_entity: # A wandb Team name if using a Team
|
||||
wandb_watch:
|
||||
wandb_name: # Set the name of your wandb run
|
||||
wandb_run_id: # Set the ID of your wandb run
|
||||
wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
|
||||
|
||||
# mlflow configuration if you're using it
|
||||
mlflow_tracking_uri: # URI to mlflow
|
||||
mlflow_experiment_name: # Your experiment name
|
||||
hf_mlflow_log_artifacts: # set to true to copy each saved checkpoint on each save to mlflow artifact registry
|
||||
|
||||
# Where to save the full-finetuned model to
|
||||
output_dir: ./completed-model
|
||||
|
||||
# Whether to use torch.compile and which backend to use
|
||||
torch_compile: # bool
|
||||
torch_compile_backend: # Optional[str]
|
||||
|
||||
# Training hyperparameters
|
||||
|
||||
# If greater than 1, backpropagation will be skipped and the gradients will be accumulated for the given number of steps.
|
||||
gradient_accumulation_steps: 1
|
||||
# The number of samples to include in each batch. This is the number of samples sent to each GPU.
|
||||
micro_batch_size: 2
|
||||
eval_batch_size:
|
||||
num_epochs: 4
|
||||
warmup_steps: 100 # cannot use with warmup_ratio
|
||||
warmup_ratio: 0.05 # cannot use with warmup_steps
|
||||
learning_rate: 0.00003
|
||||
lr_quadratic_warmup:
|
||||
logging_steps:
|
||||
eval_steps: # Leave empty to eval at each epoch, integers for every N steps. decimal for fraction of total steps
|
||||
evals_per_epoch: # number of times per epoch to run evals, mutually exclusive with eval_steps
|
||||
save_strategy: # Set to `no` to skip checkpoint saves
|
||||
save_steps: # Leave empty to save at each epoch
|
||||
saves_per_epoch: # number of times per epoch to save a checkpoint, mutually exclusive with save_steps
|
||||
save_total_limit: # Checkpoints saved at a time
|
||||
# Maximum number of iterations to train for. It precedes num_epochs which means that
|
||||
# if both are set, num_epochs will not be guaranteed.
|
||||
# e.g., when 1 epoch is 1000 steps => `num_epochs: 2` and `max_steps: 100` will train for 100 steps
|
||||
max_steps:
|
||||
|
||||
eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
|
||||
eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
|
||||
eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", chrf]
|
||||
|
||||
loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
|
||||
loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)
|
||||
|
||||
# Save model as safetensors (require safetensors package)
|
||||
save_safetensors:
|
||||
|
||||
# Whether to mask out or include the human's prompt from the training labels
|
||||
train_on_inputs: false
|
||||
# Group similarly sized data to minimize padding.
|
||||
# May be slower to start, as it must download and sort the entire dataset.
|
||||
# Note that training loss may have an oscillating pattern with this enabled.
|
||||
group_by_length: false
|
||||
|
||||
# Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
|
||||
gradient_checkpointing: false
|
||||
# additional kwargs to pass to the trainer for gradient checkpointing
|
||||
# gradient_checkpointing_kwargs:
|
||||
# use_reentrant: true
|
||||
|
||||
# Stop training after this many evaluation losses have increased in a row
|
||||
# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
|
||||
early_stopping_patience: 3
|
||||
|
||||
# Specify a scheduler and kwargs to use with the optimizer
|
||||
lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
|
||||
lr_scheduler_kwargs:
|
||||
cosine_min_lr_ratio: # decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of peak lr
|
||||
cosine_constant_lr_ratio: # freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means start cosine_min_lr at 80% of training step (https://arxiv.org/pdf/2308.04014.pdf)
|
||||
|
||||
# For one_cycle optim
|
||||
lr_div_factor: # Learning rate div factor
|
||||
|
||||
# Specify optimizer
|
||||
# Valid values are driven by the Transformers OptimizerNames class, see:
|
||||
# https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
|
||||
#
|
||||
# Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
|
||||
# torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
|
||||
# in the examples/ for your model and fine-tuning use case.
|
||||
#
|
||||
# Valid values for 'optimizer' include:
|
||||
# - adamw_hf
|
||||
# - adamw_torch
|
||||
# - adamw_torch_fused
|
||||
# - adamw_torch_xla
|
||||
# - adamw_apex_fused
|
||||
# - adafactor
|
||||
# - adamw_anyprecision
|
||||
# - sgd
|
||||
# - adagrad
|
||||
# - adamw_bnb_8bit
|
||||
# - lion_8bit
|
||||
# - lion_32bit
|
||||
# - paged_adamw_32bit
|
||||
# - paged_adamw_8bit
|
||||
# - paged_lion_32bit
|
||||
# - paged_lion_8bit
|
||||
# - galore_adamw
|
||||
# - galore_adamw_8bit
|
||||
# - galore_adafactor
|
||||
# - galore_adamw_layerwise
|
||||
# - galore_adamw_8bit_layerwise
|
||||
# - galore_adafactor_layerwise
|
||||
optimizer:
|
||||
# Dictionary of arguments to pass to the optimizer
|
||||
optim_args:
|
||||
# For Galore Optimizers the following optim_args are available
|
||||
# rank: # type: int
|
||||
# update_proj_gap # type: int
|
||||
# scale # type: float
|
||||
# proj_type: # type: str, default = std
|
||||
|
||||
# The target modules to optimize, i.e. the module names that you would like to train, right now this is used only for GaLore algorithm
|
||||
optim_target_modules:
|
||||
# - self_attn # for llama
|
||||
# - mlp
|
||||
|
||||
# Specify weight decay
|
||||
weight_decay:
|
||||
# adamw hyperparams
|
||||
adam_beta1:
|
||||
adam_beta2:
|
||||
adam_epsilon:
|
||||
# Gradient clipping max norm
|
||||
max_grad_norm:
|
||||
|
||||
# Augmentation techniques
|
||||
# NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to add noise to embeddings
|
||||
# currently only supported on Llama and Mistral
|
||||
neftune_noise_alpha:
|
||||
|
||||
# Whether to bettertransformers
|
||||
flash_optimum:
|
||||
# Whether to use xformers attention patch https://github.com/facebookresearch/xformers:
|
||||
xformers_attention:
|
||||
# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
|
||||
flash_attention:
|
||||
flash_attn_cross_entropy: # Whether to use flash-attention cross entropy implementation - advanced use only
|
||||
flash_attn_rms_norm: # Whether to use flash-attention rms norm implementation - advanced use only
|
||||
flash_attn_fuse_qkv: # Whether to fuse QKV into a single operation
|
||||
flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
|
||||
# Whether to use scaled-dot-product attention
|
||||
# https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
|
||||
sdp_attention:
|
||||
# Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
|
||||
s2_attention:
|
||||
# Resume from a specific checkpoint dir
|
||||
resume_from_checkpoint:
|
||||
# If resume_from_checkpoint isn't set and you simply want it to start where it left off.
|
||||
# Be careful with this being turned on between different models.
|
||||
auto_resume_from_checkpoints: false
|
||||
|
||||
# Don't mess with this, it's here for accelerate and torchrun
|
||||
local_rank:
|
||||
|
||||
# Add or change special tokens.
|
||||
# If you add tokens here, you don't need to add them to the `tokens` list.
|
||||
special_tokens:
|
||||
# bos_token: "<s>"
|
||||
# eos_token: "</s>"
|
||||
# unk_token: "<unk>"
|
||||
# pad_token: "[PAD]"
|
||||
|
||||
# Add extra tokens.
|
||||
tokens:
|
||||
|
||||
# FSDP
|
||||
fsdp:
|
||||
fsdp_config:
|
||||
|
||||
# Deepspeed config path. e.g., deepspeed_configs/zero3.json
|
||||
deepspeed:
|
||||
|
||||
# Advanced DDP Arguments
|
||||
ddp_timeout:
|
||||
ddp_bucket_cap_mb:
|
||||
ddp_broadcast_buffers:
|
||||
|
||||
# Path to torch distx for optim 'adamw_anyprecision'
|
||||
torchdistx_path:
|
||||
|
||||
# Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
|
||||
pretraining_dataset:
|
||||
|
||||
# Debug mode
|
||||
debug:
|
||||
|
||||
# Seed
|
||||
seed:
|
||||
|
||||
# Allow overwrite yml config using from cli
|
||||
strict:
|
||||
with open('../README.md', 'r') as f:
|
||||
doc = f.read()
|
||||
match = re.search(pattern, doc, re.DOTALL)
|
||||
print("```yaml", match.group(1).strip(), "```", sep="\n")
|
||||
```
|
||||
|
||||
@@ -1,63 +0,0 @@
|
||||
---
|
||||
title: Conversation
|
||||
description: Conversation format for supervised fine-tuning.
|
||||
order: 3
|
||||
---
|
||||
|
||||
## sharegpt
|
||||
|
||||
conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"conversations": [{"from": "...", "value": "..."}]}
|
||||
```
|
||||
|
||||
Note: `type: sharegpt` opens special configs:
|
||||
- `conversation`: enables conversions to many Conversation types. Refer to the 'name' [here](https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py) for options.
|
||||
- `roles`: allows you to specify the roles for input and output. This is useful for datasets with custom roles such as `tool` etc to support masking.
|
||||
- `field_human`: specify the key to use instead of `human` in the conversation.
|
||||
- `field_model`: specify the key to use instead of `gpt` in the conversation.
|
||||
|
||||
```yaml
|
||||
datasets:
|
||||
path: ...
|
||||
type: sharegpt
|
||||
|
||||
conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
|
||||
field_human: # Optional[str]. Human key to use for conversation.
|
||||
field_model: # Optional[str]. Assistant key to use for conversation.
|
||||
# Add additional keys from your dataset as input or output roles
|
||||
roles:
|
||||
input: # Optional[List[str]]. These will be masked based on train_on_input
|
||||
output: # Optional[List[str]].
|
||||
```
|
||||
|
||||
## pygmalion
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"conversations": [{"role": "...", "value": "..."}]}
|
||||
```
|
||||
|
||||
## sharegpt.load_role
|
||||
|
||||
conversations where `role` is used instead of `from`
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"conversations": [{"role": "...", "value": "..."}]}
|
||||
```
|
||||
|
||||
## sharegpt.load_guanaco
|
||||
|
||||
conversations where `from` is `prompter` `assistant` instead of default sharegpt
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"conversations": [{"from": "...", "value": "..."}]}
|
||||
```
|
||||
|
||||
## sharegpt_jokes
|
||||
|
||||
creates a chat where bot is asked to tell a joke, then explain why the joke is funny
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
|
||||
```
|
||||
@@ -1,14 +0,0 @@
|
||||
---
|
||||
title: Dataset Formats
|
||||
description: Supported dataset formats.
|
||||
listing:
|
||||
fields: [title, description]
|
||||
type: table
|
||||
sort-ui: false
|
||||
filter-ui: false
|
||||
max-description-length: 250
|
||||
---
|
||||
|
||||
Axolotl supports a variety of dataset formats. It is recommended to use a JSONL format. The schema of the JSONL depends upon the task and the prompt template you wish to use. Instead of a JSONL, you can also use a HuggingFace dataset with columns for each JSONL field.
|
||||
|
||||
Below are these various formats organized by task:
|
||||
@@ -1,189 +0,0 @@
|
||||
---
|
||||
title: Instruction Tuning
|
||||
description: Instruction tuning formats for supervised fine-tuning.
|
||||
order: 2
|
||||
---
|
||||
|
||||
## alpaca
|
||||
|
||||
instruction; input(optional)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "input": "...", "output": "..."}
|
||||
```
|
||||
|
||||
## jeopardy
|
||||
|
||||
question and answer
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"question": "...", "category": "...", "answer": "..."}
|
||||
```
|
||||
|
||||
## oasst
|
||||
|
||||
instruction
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"INSTRUCTION": "...", "RESPONSE": "..."}
|
||||
```
|
||||
|
||||
## gpteacher
|
||||
|
||||
instruction; input(optional)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "input": "...", "response": "..."}
|
||||
```
|
||||
|
||||
## reflection
|
||||
|
||||
instruction with reflect; input(optional)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "input": "...", "output": "...", "reflection": "...", "corrected": "..."}
|
||||
```
|
||||
|
||||
## explainchoice
|
||||
|
||||
question, choices, (solution OR explanation)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
|
||||
```
|
||||
|
||||
## concisechoice
|
||||
|
||||
question, choices, (solution OR explanation)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"question": "...", "choices": ["..."], "solution": "...", "explanation": "..."}
|
||||
```
|
||||
|
||||
## summarizetldr
|
||||
|
||||
article and summary
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"article": "...", "summary": "..."}
|
||||
```
|
||||
|
||||
## alpaca_chat
|
||||
|
||||
basic instruct for alpaca chat
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "input": "...", "response": "..."}
|
||||
```
|
||||
|
||||
## alpaca_chat.load_qa
|
||||
|
||||
question and answer for alpaca chat
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"question": "...", "answer": "..."}
|
||||
```
|
||||
|
||||
## alpaca_chat.load_concise
|
||||
|
||||
question and answer for alpaca chat, for concise answers
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "input": "...", "response": "..."}
|
||||
```
|
||||
|
||||
## alpaca_chat.load_camel_ai
|
||||
|
||||
question and answer for alpaca chat, for load_camel_ai
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"message_1": "...", "message_2": "..."}
|
||||
```
|
||||
|
||||
## alpaca_w_system.load_open_orca
|
||||
|
||||
support for open orca datasets with included system prompts, instruct
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"system_prompt": "...", "question": "...", "response": "..."}
|
||||
```
|
||||
|
||||
## context_qa
|
||||
|
||||
in context question answering from an article
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"article": "...", "question": "...", "answer": "..."}
|
||||
```
|
||||
|
||||
## context_qa.load_v2
|
||||
|
||||
in context question answering (alternate)
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"context": "...", "question": "...", "answer": "..."}
|
||||
```
|
||||
|
||||
## context_qa.load_404
|
||||
|
||||
in context question answering from an article, with default response for no answer from context
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"article": "...", "unanswerable_question": "..."}
|
||||
```
|
||||
|
||||
## creative_acr.load_answer
|
||||
|
||||
instruction and revision
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"instruction": "...", "revision": "..."}
|
||||
```
|
||||
|
||||
## creative_acr.load_critique
|
||||
|
||||
critique
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"scores": "...", "critiques": "...", "instruction": "...", "answer": "..."}
|
||||
```
|
||||
|
||||
## creative_acr.load_revise
|
||||
|
||||
critique and revise
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"scores": "...", "critiques": "...", "instruction": "...", "answer": "...", "revision": "..."}
|
||||
```
|
||||
|
||||
## metharme
|
||||
|
||||
instruction, adds additional eos tokens
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"prompt": "...", "generation": "..."}
|
||||
```
|
||||
|
||||
## How to add custom prompt format
|
||||
|
||||
For a dataset that is preprocessed for instruction purposes:
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"input": "...", "output": "..."}
|
||||
```
|
||||
|
||||
You can use this example in your YAML config:
|
||||
|
||||
```{.yaml filename="config.yaml"}
|
||||
datasets:
|
||||
- path: repo
|
||||
type:
|
||||
system_prompt: ""
|
||||
field_system: system
|
||||
field_instruction: input
|
||||
field_output: output
|
||||
format: "[INST] {instruction} [/INST]"
|
||||
no_input_format: "[INST] {instruction} [/INST]"
|
||||
```
|
||||
|
||||
See full config options under [here](../config.qmd).
|
||||
@@ -1,26 +0,0 @@
|
||||
---
|
||||
title: Pre-training
|
||||
description: Data format for a pre-training completion task.
|
||||
order: 1
|
||||
---
|
||||
|
||||
For pretraining, there is no prompt template or roles. The only required field is `text`:
|
||||
|
||||
```{.json filename="data.jsonl"}
|
||||
{"text": "first row"}
|
||||
{"text": "second row"}
|
||||
...
|
||||
```
|
||||
|
||||
:::{.callout-note}
|
||||
|
||||
### Streaming is recommended for large datasets
|
||||
|
||||
Axolotl usually loads the entire dataset into memory. This will be challenging for large datasets. Use the following config to enable streaming:
|
||||
|
||||
```{.yaml filename="config.yaml"}
|
||||
pretraining_dataset: # hf path only
|
||||
...
|
||||
```
|
||||
|
||||
:::
|
||||
@@ -1,7 +0,0 @@
|
||||
---
|
||||
title: Template-Free
|
||||
description: Construct prompts without a template.
|
||||
order: 4
|
||||
---
|
||||
|
||||
See [these docs](../input_output.qmd).
|
||||
@@ -1,12 +0,0 @@
|
||||
---
|
||||
title: Custom Pre-Tokenized Dataset
|
||||
description: How to use a custom pre-tokenized dataset.
|
||||
order: 5
|
||||
---
|
||||
|
||||
- Do not pass a `type:` in your axolotl config.
|
||||
- Columns in Dataset must be exactly `input_ids`, `attention_mask`, `labels`
|
||||
|
||||
```{.yaml filename="config.yml"}
|
||||
- path: ...
|
||||
```
|
||||
@@ -1,35 +0,0 @@
|
||||
---
|
||||
title: Dataset Preprocessing
|
||||
description: How datasets are processed
|
||||
---
|
||||
|
||||
Dataset pre-processing is the step where Axolotl takes each dataset you've configured alongside
|
||||
the (dataset format)[../dataset-formats/] and prompt strategies to:
|
||||
- parse the dataset based on the *dataset format*
|
||||
- transform the dataset to how you would interact with the model based on the *prompt strategy*
|
||||
- tokenize the dataset based on the configured model & tokenizer
|
||||
- shuffle and merge multiple datasets together if using more than one
|
||||
|
||||
The processing of the datasets can happen one of two ways:
|
||||
|
||||
1. Before kicking off training by calling `python -m axolotl.cli.preprocess /path/to/your.yaml --debug`
|
||||
2. When training is started
|
||||
|
||||
What are the benefits of pre-processing? When training interactively or for sweeps
|
||||
(e.g. you are restarting the trainer often), processing the datasets can oftentimes be frustratingly
|
||||
slow. Pre-processing will cache the tokenized/formatted datasets according to a hash of dependent
|
||||
training parameters so that it will intelligently pull from its cache when possible.
|
||||
|
||||
The path of the cache is controlled by `dataset_prepared_path:` and is often left blank in example
|
||||
YAMLs as this leads to a more robust solution that prevents unexpectedly reusing cached data.
|
||||
|
||||
If `dataset_prepared_path:` is left empty, when training, the processed dataset will be cached in a
|
||||
default path of `./last_run_prepared/`, but will ignore anything already cached there. By explicitly
|
||||
setting `dataset_prepared_path: ./last_run_prepared`, the trainer will use whatever pre-processed
|
||||
data is in the cache.
|
||||
|
||||
What are the edge cases? Let's say you are writing a custom prompt strategy or using a user-defined
|
||||
prompt template. Because the trainer cannot readily detect these changes, we cannot change the
|
||||
calculated hash value for the pre-processed dataset. If you have `dataset_prepared_path: ...` set
|
||||
and change your prompt templating logic, it may not pick up the changes you made and you will be
|
||||
training over the old prompt.
|
||||
@@ -1,5 +1,5 @@
|
||||
---
|
||||
title: "FDSP + QLoRA"
|
||||
title: FDSP + QLoRA
|
||||
description: Use FSDP with QLoRA to fine-tune large LLMs on consumer GPUs.
|
||||
format:
|
||||
html:
|
||||
|
||||
@@ -43,7 +43,7 @@ labels so that your model can focus on predicting the outputs only.
|
||||
### You may not want prompt templates
|
||||
|
||||
However, there are many situations where you don't want to use one of
|
||||
these formats or templates. This is because they can:
|
||||
these formats or templates (I usually don't!). This is because they can:
|
||||
|
||||
- Add unnecessary boilerplate to your prompts.
|
||||
- Create artifacts like special delimiters `<|im_start|>` that can
|
||||
@@ -91,9 +91,8 @@ format into a jsonl file (below is the first row from the file
|
||||
|
||||
```bash
|
||||
$ head -n1 output.jsonl | python -m json.tool
|
||||
```
|
||||
|
||||
:::{.cell-output .cell-output-stdout}
|
||||
{.cell-output .cell-output-stdout}
|
||||
{
|
||||
"segments": [
|
||||
{
|
||||
@@ -114,7 +113,7 @@ $ head -n1 output.jsonl | python -m json.tool
|
||||
}
|
||||
]
|
||||
}
|
||||
:::
|
||||
```
|
||||
|
||||
Set `label:false` when you want to mask a segment of text so that the
|
||||
model isn't trained on it. Some things to keep in mind:
|
||||
@@ -239,9 +238,8 @@ version is repeated below for reference):
|
||||
|
||||
```bash
|
||||
$ head -n1 output.jsonl | python -m json.tool
|
||||
```
|
||||
|
||||
:::{.cell-output .cell-output-stdout}
|
||||
{.cell-output .cell-output-stdout}
|
||||
{
|
||||
"segments": [
|
||||
{
|
||||
@@ -262,4 +260,4 @@ $ head -n1 output.jsonl | python -m json.tool
|
||||
}
|
||||
]
|
||||
}
|
||||
:::
|
||||
```
|
||||
|
||||
@@ -1,81 +0,0 @@
|
||||
base_model: LnL-AI/dbrx-base-converted-v2
|
||||
trust_remote_code: true
|
||||
|
||||
load_in_8bit: false
|
||||
load_in_4bit: false
|
||||
strict: false
|
||||
|
||||
datasets:
|
||||
- path: tatsu-lab/alpaca
|
||||
type: alpaca
|
||||
dataset_prepared_path: last_run_prepared
|
||||
val_set_size: 0.0
|
||||
output_dir: ./out
|
||||
|
||||
sequence_len: 512
|
||||
sample_packing: false
|
||||
pad_to_sequence_len: false
|
||||
|
||||
wandb_project:
|
||||
wandb_entity:
|
||||
wandb_watch:
|
||||
wandb_name:
|
||||
wandb_log_model:
|
||||
|
||||
adapter: lora
|
||||
lora_model_dir:
|
||||
lora_r: 8
|
||||
lora_alpha: 16
|
||||
lora_dropout: 0.05
|
||||
# w1, w2, & v1 will hang the trainer
|
||||
lora_target_modules:
|
||||
- q_proj # attn
|
||||
- k_proj # attn
|
||||
- v_proj # attn
|
||||
- out_proj # attn
|
||||
- layer # router
|
||||
# - w1
|
||||
# - w2
|
||||
# - v1
|
||||
|
||||
gradient_accumulation_steps: 1
|
||||
micro_batch_size: 1
|
||||
num_epochs: 1
|
||||
optimizer: paged_adamw_8bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.0002
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: false # don't use with fsdp_activation_checkpointing
|
||||
gradient_checkpointing_kwargs:
|
||||
use_reentrant: false
|
||||
early_stopping_patience:
|
||||
resume_from_checkpoint:
|
||||
local_rank:
|
||||
logging_steps: 1
|
||||
xformers_attention:
|
||||
flash_attention: true
|
||||
|
||||
warmup_steps: 10
|
||||
evals_per_epoch:
|
||||
saves_per_epoch: 1
|
||||
debug:
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
- full_shard
|
||||
- auto_wrap
|
||||
fsdp_config:
|
||||
fsdp_limit_all_gathers: true
|
||||
fsdp_sync_module_states: true
|
||||
fsdp_offload_params: false
|
||||
fsdp_use_orig_params: false
|
||||
fsdp_cpu_ram_efficient_loading: true
|
||||
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
|
||||
fsdp_transformer_layer_cls_to_wrap: DbrxBlock
|
||||
fsdp_state_dict_type: FULL_STATE_DICT
|
||||
fsdp_activation_checkpointing: true
|
||||
@@ -1,81 +0,0 @@
|
||||
base_model: LnL-AI/dbrx-base-converted-v2
|
||||
trust_remote_code: true
|
||||
|
||||
load_in_8bit: true
|
||||
load_in_4bit: false
|
||||
strict: false
|
||||
|
||||
datasets:
|
||||
- path: tatsu-lab/alpaca
|
||||
type: alpaca
|
||||
dataset_prepared_path: last_run_prepared
|
||||
val_set_size: 0.0
|
||||
output_dir: ./out
|
||||
|
||||
sequence_len: 512
|
||||
sample_packing: false
|
||||
pad_to_sequence_len: false
|
||||
|
||||
wandb_project:
|
||||
wandb_entity:
|
||||
wandb_watch:
|
||||
wandb_name:
|
||||
wandb_log_model:
|
||||
|
||||
adapter: lora
|
||||
lora_model_dir:
|
||||
lora_r: 8
|
||||
lora_alpha: 16
|
||||
lora_dropout: 0.05
|
||||
# w1, w2, & v1 will hang the trainer
|
||||
lora_target_modules:
|
||||
- q_proj # attn
|
||||
- k_proj # attn
|
||||
- v_proj # attn
|
||||
- out_proj # attn
|
||||
- layer # router
|
||||
# - w1
|
||||
# - w2
|
||||
# - v1
|
||||
|
||||
gradient_accumulation_steps: 1
|
||||
micro_batch_size: 1
|
||||
num_epochs: 1
|
||||
optimizer: paged_adamw_8bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.0002
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: false # don't use with fsdp_activation_checkpointing
|
||||
gradient_checkpointing_kwargs:
|
||||
use_reentrant: false
|
||||
early_stopping_patience:
|
||||
resume_from_checkpoint:
|
||||
local_rank:
|
||||
logging_steps: 1
|
||||
xformers_attention:
|
||||
flash_attention: true
|
||||
|
||||
warmup_steps: 10
|
||||
evals_per_epoch:
|
||||
saves_per_epoch: 1
|
||||
debug:
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
- full_shard
|
||||
- auto_wrap
|
||||
fsdp_config:
|
||||
fsdp_limit_all_gathers: true
|
||||
fsdp_sync_module_states: true
|
||||
fsdp_offload_params: false
|
||||
fsdp_use_orig_params: false
|
||||
fsdp_cpu_ram_efficient_loading: true
|
||||
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
|
||||
fsdp_transformer_layer_cls_to_wrap: DbrxBlock
|
||||
fsdp_state_dict_type: FULL_STATE_DICT
|
||||
fsdp_activation_checkpointing: true
|
||||
@@ -1,26 +0,0 @@
|
||||
# DBRX MoE
|
||||
|
||||
Currently, for LoRA, only the `q_proj`, `k_proj`, `v_proj` `out_proj` and `layer` Linear layers are trainable.
|
||||
|
||||
We are using the "converted" base models based on [this issue](https://huggingface.co/databricks/dbrx-instruct/discussions/10)
|
||||
where the Experts are fused as an `nn.Parameter` rather than a `nn.Linear` layer. However, the implementation
|
||||
is still a bit buggy and attempting to train a LoRA adapter over those `w1`, `w2` and `v1` layers
|
||||
results in the trainer hanging.
|
||||
|
||||
|
||||
### FSDP
|
||||
We've tested using the [`LnL-AI/dbrx-base-converted-v2`](https://huggingface.co/LnL-AI/dbrx-base-converted-v2) model as the base model for FSDP.
|
||||
|
||||
The high memory usage seen w/ FSDP is due to FSDP not supporting 8bit optimizers.
|
||||
|
||||
- 16-bit LoRA w/ FSDP
|
||||
- ✅ w/o CPU Offload - 8x80GB uses ~80GiB/gpu
|
||||
- ❌ w/ CPU Offload - `paged_adamw_8bit` optimizer errors from being on cpu
|
||||
- ✅ 8-bit LoRA w/ FSDP
|
||||
- ❌ 4-bit QLoRA w/ FSDP - errors w/: `Error an illegal memory access was encountered at line 90 in file /src/csrc/ops.cu`
|
||||
- ✅ bf16 full finetune w/ FSDP, freezing all but first 8 layers (8x80GB uses ~78GiB/gpu)
|
||||
|
||||
|
||||
### Deepspeed
|
||||
|
||||
WIP
|
||||
@@ -1,56 +0,0 @@
|
||||
base_model: LnL-AI/dbrx-base-converted-v2
|
||||
trust_remote_code: true
|
||||
|
||||
load_in_8bit: false
|
||||
load_in_4bit: false
|
||||
strict: false
|
||||
|
||||
datasets:
|
||||
- path: tatsu-lab/alpaca
|
||||
type: alpaca
|
||||
dataset_prepared_path: last_run_prepared
|
||||
val_set_size: 0.0
|
||||
output_dir: ./out
|
||||
|
||||
sequence_len: 512
|
||||
sample_packing: false
|
||||
pad_to_sequence_len: false
|
||||
|
||||
unfrozen_parameters:
|
||||
- transformer.blocks.[0-7].
|
||||
|
||||
wandb_project:
|
||||
wandb_entity:
|
||||
wandb_watch:
|
||||
wandb_name:
|
||||
wandb_log_model:
|
||||
|
||||
gradient_accumulation_steps: 1
|
||||
micro_batch_size: 1
|
||||
num_epochs: 1
|
||||
optimizer: paged_adamw_8bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.0002
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: true
|
||||
gradient_checkpointing_kwargs:
|
||||
use_reentrant: false
|
||||
early_stopping_patience:
|
||||
resume_from_checkpoint:
|
||||
local_rank:
|
||||
logging_steps: 1
|
||||
xformers_attention:
|
||||
flash_attention: true
|
||||
|
||||
warmup_steps: 10
|
||||
evals_per_epoch:
|
||||
saves_per_epoch: 1
|
||||
debug:
|
||||
weight_decay: 0.0
|
||||
deepspeed: deepspeed_configs/zero3_bf16.json
|
||||
@@ -25,7 +25,7 @@ lora_dropout:
|
||||
lora_target_linear:
|
||||
lora_fan_in_fan_out:
|
||||
|
||||
lisa_n_layers: 4
|
||||
lisa_n_layers: 2
|
||||
lisa_step_interval: 20
|
||||
lisa_layers_attribute: model.layers
|
||||
|
||||
@@ -35,7 +35,7 @@ wandb_watch:
|
||||
wandb_name:
|
||||
wandb_log_model:
|
||||
|
||||
gradient_accumulation_steps: 2
|
||||
gradient_accumulation_steps: 1
|
||||
micro_batch_size: 1
|
||||
num_epochs: 1
|
||||
optimizer: adamw_bnb_8bit
|
||||
|
||||
@@ -65,14 +65,12 @@ deepspeed:
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
- full_shard
|
||||
- auto_wrap
|
||||
fsdp_config:
|
||||
fsdp_limit_all_gathers: true
|
||||
fsdp_sync_module_states: true
|
||||
fsdp_offload_params: true
|
||||
fsdp_use_orig_params: false
|
||||
fsdp_cpu_ram_efficient_loading: true
|
||||
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
|
||||
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
|
||||
fsdp_state_dict_type: FULL_STATE_DICT
|
||||
fsdp_state_dict_type: SHARDED_STATE_DICT
|
||||
special_tokens:
|
||||
|
||||
@@ -1,13 +0,0 @@
|
||||
# Llama-3
|
||||
|
||||
https://llama.meta.com/llama3/
|
||||
|
||||
[8B Base Model](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
|
||||
- [Full Fine Tune](./fft-8b.yaml)
|
||||
- Single GPU @ 48GB VRAM
|
||||
- [LoRA](./lora-8b.yml)
|
||||
- Single GPU @ 11GB VRAM
|
||||
|
||||
[70B Base Model](https://huggingface.co/meta-llama/Meta-Llama-3-70B)
|
||||
- [QLORA+FSDP](./qlora-fsdp-70b.yaml)
|
||||
- Dual GPU @ 21GB VRAM
|
||||
@@ -1,58 +0,0 @@
|
||||
base_model: meta-llama/Meta-Llama-3-8B
|
||||
model_type: LlamaForCausalLM
|
||||
tokenizer_type: AutoTokenizer
|
||||
|
||||
load_in_8bit: false
|
||||
load_in_4bit: false
|
||||
strict: false
|
||||
|
||||
datasets:
|
||||
- path: tatsu-lab/alpaca
|
||||
type: alpaca
|
||||
dataset_prepared_path: last_run_prepared
|
||||
val_set_size: 0.05
|
||||
output_dir: ./out
|
||||
|
||||
sequence_len: 8192
|
||||
sample_packing: true
|
||||
pad_to_sequence_len: true
|
||||
|
||||
wandb_project:
|
||||
wandb_entity:
|
||||
wandb_watch:
|
||||
wandb_name:
|
||||
wandb_log_model:
|
||||
|
||||
gradient_accumulation_steps: 8
|
||||
micro_batch_size: 1
|
||||
num_epochs: 1
|
||||
optimizer: paged_adamw_8bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 2e-5
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: true
|
||||
gradient_checkpointing_kwargs:
|
||||
use_reentrant: false
|
||||
early_stopping_patience:
|
||||
resume_from_checkpoint:
|
||||
logging_steps: 1
|
||||
xformers_attention:
|
||||
flash_attention: true
|
||||
|
||||
warmup_steps: 100
|
||||
evals_per_epoch: 2
|
||||
eval_table_size:
|
||||
saves_per_epoch: 1
|
||||
debug:
|
||||
deepspeed:
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
fsdp_config:
|
||||
special_tokens:
|
||||
pad_token: <|end_of_text|>
|
||||
@@ -1,67 +0,0 @@
|
||||
base_model: NousResearch/Llama-2-7b-hf
|
||||
model_type: LlamaForCausalLM
|
||||
tokenizer_type: AutoTokenizer
|
||||
|
||||
load_in_8bit: true
|
||||
load_in_4bit: false
|
||||
strict: false
|
||||
|
||||
datasets:
|
||||
- path: mhenrichsen/alpaca_2k_test
|
||||
type: alpaca
|
||||
dataset_prepared_path:
|
||||
val_set_size: 0.05
|
||||
output_dir: ./lora-out
|
||||
|
||||
sequence_len: 4096
|
||||
sample_packing: true
|
||||
pad_to_sequence_len: true
|
||||
|
||||
adapter: lora
|
||||
lora_model_dir:
|
||||
lora_r: 32
|
||||
lora_alpha: 16
|
||||
lora_dropout: 0.05
|
||||
lora_target_linear: true
|
||||
lora_fan_in_fan_out:
|
||||
|
||||
wandb_project:
|
||||
wandb_entity:
|
||||
wandb_watch:
|
||||
wandb_name:
|
||||
wandb_log_model:
|
||||
|
||||
gradient_accumulation_steps: 4
|
||||
micro_batch_size: 2
|
||||
num_epochs: 4
|
||||
optimizer: adamw_bnb_8bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.0002
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: true
|
||||
early_stopping_patience:
|
||||
resume_from_checkpoint:
|
||||
local_rank:
|
||||
logging_steps: 1
|
||||
xformers_attention:
|
||||
flash_attention: true
|
||||
s2_attention:
|
||||
|
||||
warmup_steps: 10
|
||||
evals_per_epoch: 4
|
||||
eval_table_size:
|
||||
eval_max_new_tokens: 128
|
||||
saves_per_epoch: 1
|
||||
debug:
|
||||
deepspeed:
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
fsdp_config:
|
||||
special_tokens:
|
||||
pad_token: <|end_of_text|>
|
||||
@@ -1,80 +0,0 @@
|
||||
base_model: casperhansen/llama-3-70b-fp16
|
||||
model_type: LlamaForCausalLM
|
||||
tokenizer_type: AutoTokenizer # PreTrainedTokenizerFast
|
||||
|
||||
load_in_8bit: false
|
||||
load_in_4bit: true
|
||||
strict: false
|
||||
|
||||
datasets:
|
||||
- path: tatsu-lab/alpaca
|
||||
type: alpaca
|
||||
dataset_prepared_path: last_run_prepared
|
||||
val_set_size: 0.05
|
||||
output_dir: ./out/qlora-llama3-70b
|
||||
|
||||
adapter: qlora
|
||||
lora_model_dir:
|
||||
|
||||
sequence_len: 512
|
||||
sample_packing: false
|
||||
pad_to_sequence_len: true
|
||||
|
||||
lora_r: 8
|
||||
lora_alpha: 16
|
||||
lora_dropout: 0.05
|
||||
lora_target_modules:
|
||||
lora_target_linear: true
|
||||
lora_fan_in_fan_out:
|
||||
|
||||
wandb_project:
|
||||
wandb_entity:
|
||||
wandb_watch:
|
||||
wandb_name:
|
||||
wandb_log_model:
|
||||
|
||||
gradient_accumulation_steps: 4
|
||||
micro_batch_size: 1
|
||||
num_epochs: 4
|
||||
optimizer: adamw_torch
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.00001
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: true
|
||||
gradient_checkpointing_kwargs:
|
||||
use_reentrant: true
|
||||
early_stopping_patience:
|
||||
resume_from_checkpoint:
|
||||
local_rank:
|
||||
logging_steps: 1
|
||||
xformers_attention:
|
||||
flash_attention: true
|
||||
|
||||
warmup_steps: 10
|
||||
evals_per_epoch: 4
|
||||
eval_table_size:
|
||||
saves_per_epoch: 1
|
||||
debug:
|
||||
deepspeed:
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
- full_shard
|
||||
- auto_wrap
|
||||
fsdp_config:
|
||||
fsdp_limit_all_gathers: true
|
||||
fsdp_sync_module_states: true
|
||||
fsdp_offload_params: true
|
||||
fsdp_use_orig_params: false
|
||||
fsdp_cpu_ram_efficient_loading: true
|
||||
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
|
||||
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
|
||||
fsdp_state_dict_type: FULL_STATE_DICT
|
||||
fsdp_sharding_strategy: FULL_SHARD
|
||||
special_tokens:
|
||||
pad_token: <|end_of_text|>
|
||||
@@ -1,67 +0,0 @@
|
||||
base_model: meta-llama/Meta-Llama-3-8B
|
||||
model_type: AutoModelForCausalLM
|
||||
tokenizer_type: AutoTokenizer
|
||||
|
||||
load_in_8bit: false
|
||||
load_in_4bit: true
|
||||
strict: false
|
||||
|
||||
datasets:
|
||||
- path: aaditya/alpaca_subset_1
|
||||
type: alpaca
|
||||
dataset_prepared_path:
|
||||
val_set_size: 0
|
||||
output_dir: ./qlora-out
|
||||
|
||||
adapter: qlora
|
||||
lora_model_dir:
|
||||
|
||||
sequence_len: 4096
|
||||
sample_packing: true
|
||||
pad_to_sequence_len: true
|
||||
|
||||
lora_r: 32
|
||||
lora_alpha: 16
|
||||
lora_dropout: 0.05
|
||||
lora_target_modules:
|
||||
lora_target_linear: true
|
||||
lora_fan_in_fan_out:
|
||||
|
||||
wandb_project:
|
||||
wandb_entity:
|
||||
wandb_watch:
|
||||
wandb_name:
|
||||
wandb_log_model:
|
||||
|
||||
gradient_accumulation_steps: 4
|
||||
micro_batch_size: 2
|
||||
num_epochs: 4
|
||||
optimizer: paged_adamw_32bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.0002
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: true
|
||||
early_stopping_patience:
|
||||
resume_from_checkpoint:
|
||||
local_rank:
|
||||
logging_steps: 1
|
||||
xformers_attention:
|
||||
flash_attention: true
|
||||
|
||||
warmup_steps: 10
|
||||
evals_per_epoch: 4
|
||||
eval_table_size:
|
||||
saves_per_epoch: 1
|
||||
debug:
|
||||
deepspeed:
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
fsdp_config:
|
||||
special_tokens:
|
||||
pad_token: "<|end_of_text|>"
|
||||
@@ -1,63 +0,0 @@
|
||||
base_model: mistral-community/Mixtral-8x22B-v0.1
|
||||
model_type: AutoModelForCausalLM
|
||||
tokenizer_type: LlamaTokenizer
|
||||
trust_remote_code: true
|
||||
|
||||
load_in_8bit: false
|
||||
load_in_4bit: false
|
||||
strict: false
|
||||
|
||||
unfrozen_parameters:
|
||||
- ^lm_head.weight$
|
||||
- ^model.embed_tokens.weight$
|
||||
- model.layers.4[4-9]+.block_sparse_moe.gate
|
||||
- model.layers.4[4-9]+.block_sparse_moe.experts
|
||||
- model.layers.5[0-5]+.block_sparse_moe.gate
|
||||
- model.layers.5[0-5]+.block_sparse_moe.experts
|
||||
|
||||
model_config:
|
||||
output_router_logits: true
|
||||
|
||||
datasets:
|
||||
- path: tatsu-lab/alpaca
|
||||
type: alpaca
|
||||
dataset_prepared_path: last_run_prepared
|
||||
val_set_size: 0.05
|
||||
output_dir: ./out
|
||||
|
||||
sequence_len: 2048
|
||||
sample_packing: true
|
||||
pad_to_sequence_len: true
|
||||
|
||||
gradient_accumulation_steps: 1
|
||||
micro_batch_size: 1
|
||||
num_epochs: 3
|
||||
optimizer: adamw_bnb_8bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.0001
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: true
|
||||
early_stopping_patience:
|
||||
resume_from_checkpoint:
|
||||
local_rank:
|
||||
logging_steps: 1
|
||||
xformers_attention:
|
||||
flash_attention: true
|
||||
|
||||
save_total_limit: 1
|
||||
save_steps:
|
||||
debug:
|
||||
deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
fsdp_config:
|
||||
special_tokens:
|
||||
eos_token: "<|im_end|>"
|
||||
tokens:
|
||||
- "<|im_start|>"
|
||||
@@ -1,82 +0,0 @@
|
||||
base_model: mistralai/Mixtral-8x7B-v0.1
|
||||
model_type: AutoModelForCausalLM
|
||||
tokenizer_type: LlamaTokenizer
|
||||
trust_remote_code: true
|
||||
|
||||
load_in_8bit: false
|
||||
load_in_4bit: true
|
||||
strict: false
|
||||
|
||||
datasets:
|
||||
- path: tatsu-lab/alpaca
|
||||
type: alpaca
|
||||
dataset_prepared_path: last_run_prepared
|
||||
val_set_size: 0.02
|
||||
output_dir: ./qlora-out
|
||||
|
||||
model_config:
|
||||
output_router_logits: true
|
||||
|
||||
adapter: qlora
|
||||
lora_model_dir:
|
||||
|
||||
sequence_len: 1024
|
||||
sample_packing: false
|
||||
pad_to_sequence_len: false
|
||||
|
||||
lora_r: 32
|
||||
lora_alpha: 16
|
||||
lora_dropout: 0.05
|
||||
lora_target_linear: true
|
||||
lora_fan_in_fan_out:
|
||||
|
||||
wandb_project:
|
||||
wandb_entity:
|
||||
wandb_watch:
|
||||
wandb_name:
|
||||
wandb_log_model:
|
||||
|
||||
gradient_accumulation_steps: 4
|
||||
micro_batch_size: 2
|
||||
num_epochs: 1
|
||||
optimizer: paged_adamw_8bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.0002
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: true
|
||||
early_stopping_patience:
|
||||
resume_from_checkpoint:
|
||||
local_rank:
|
||||
logging_steps: 1
|
||||
xformers_attention:
|
||||
flash_attention: true
|
||||
|
||||
loss_watchdog_threshold: 5.0
|
||||
loss_watchdog_patience: 3
|
||||
|
||||
warmup_steps: 10
|
||||
evals_per_epoch: 4
|
||||
eval_table_size:
|
||||
eval_max_new_tokens: 128
|
||||
saves_per_epoch: 1
|
||||
debug:
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
- full_shard
|
||||
- auto_wrap
|
||||
fsdp_config:
|
||||
fsdp_limit_all_gathers: true
|
||||
fsdp_sync_module_states: true
|
||||
fsdp_offload_params: false
|
||||
fsdp_use_orig_params: false
|
||||
fsdp_cpu_ram_efficient_loading: false
|
||||
fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer
|
||||
fsdp_state_dict_type: FULL_STATE_DICT
|
||||
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
|
||||
special_tokens:
|
||||
@@ -1,81 +0,0 @@
|
||||
base_model: mistral-community/Mixtral-8x22B-v0.1
|
||||
model_type: AutoModelForCausalLM
|
||||
tokenizer_type: LlamaTokenizer
|
||||
|
||||
load_in_8bit: false
|
||||
load_in_4bit: true
|
||||
strict: false
|
||||
|
||||
datasets:
|
||||
- path: tatsu-lab/alpaca
|
||||
type: alpaca
|
||||
dataset_prepared_path: last_run_prepared
|
||||
val_set_size: 0.02
|
||||
output_dir: ./qlora-out
|
||||
|
||||
model_config:
|
||||
output_router_logits: true
|
||||
|
||||
adapter: qlora
|
||||
lora_model_dir:
|
||||
|
||||
sequence_len: 1024
|
||||
sample_packing: false
|
||||
pad_to_sequence_len: false
|
||||
|
||||
lora_r: 32
|
||||
lora_alpha: 16
|
||||
lora_dropout: 0.05
|
||||
lora_target_linear: true
|
||||
lora_fan_in_fan_out:
|
||||
|
||||
wandb_project:
|
||||
wandb_entity:
|
||||
wandb_watch:
|
||||
wandb_name:
|
||||
wandb_log_model:
|
||||
|
||||
gradient_accumulation_steps: 4
|
||||
micro_batch_size: 2
|
||||
num_epochs: 1
|
||||
optimizer: adamw_torch
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.0002
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: true
|
||||
|
||||
gradient_checkpointing: true
|
||||
early_stopping_patience:
|
||||
resume_from_checkpoint:
|
||||
local_rank:
|
||||
logging_steps: 1
|
||||
xformers_attention:
|
||||
flash_attention: true
|
||||
|
||||
loss_watchdog_threshold: 5.0
|
||||
loss_watchdog_patience: 3
|
||||
|
||||
warmup_steps: 10
|
||||
evals_per_epoch: 4
|
||||
eval_table_size:
|
||||
eval_max_new_tokens: 128
|
||||
saves_per_epoch: 1
|
||||
debug:
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
- full_shard
|
||||
- auto_wrap
|
||||
fsdp_config:
|
||||
fsdp_limit_all_gathers: true
|
||||
fsdp_sync_module_states: true
|
||||
fsdp_offload_params: true
|
||||
fsdp_use_orig_params: false
|
||||
fsdp_cpu_ram_efficient_loading: true
|
||||
fsdp_transformer_layer_cls_to_wrap: MixtralSparseMoeBlock
|
||||
fsdp_state_dict_type: FULL_STATE_DICT
|
||||
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
|
||||
special_tokens:
|
||||
@@ -39,7 +39,7 @@ wandb_log_model:
|
||||
gradient_accumulation_steps: 4
|
||||
micro_batch_size: 2
|
||||
num_epochs: 1
|
||||
optimizer: adamw_torch
|
||||
optimizer: paged_adamw_8bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.0002
|
||||
|
||||
@@ -47,7 +47,7 @@ train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: true
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: true
|
||||
early_stopping_patience:
|
||||
@@ -69,17 +69,6 @@ debug:
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
- full_shard
|
||||
- auto_wrap
|
||||
fsdp_config:
|
||||
fsdp_limit_all_gathers: true
|
||||
fsdp_sync_module_states: true
|
||||
fsdp_offload_params: true
|
||||
fsdp_use_orig_params: false
|
||||
fsdp_cpu_ram_efficient_loading: true
|
||||
fsdp_transformer_layer_cls_to_wrap: MixtralSparseMoeBlock
|
||||
fsdp_state_dict_type: FULL_STATE_DICT
|
||||
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
|
||||
fsdp_sharding_strategy: FULL_SHARD
|
||||
fsdp_forward_prefetch: false
|
||||
fsdp_backward_prefetch: BACKWARD_PRE
|
||||
special_tokens:
|
||||
|
||||
@@ -1,61 +0,0 @@
|
||||
base_model: mistral-community/Mixtral-8x22B-v0.1
|
||||
model_type: AutoModelForCausalLM
|
||||
tokenizer_type: LlamaTokenizer
|
||||
trust_remote_code: true
|
||||
|
||||
load_in_8bit: false
|
||||
load_in_4bit: false
|
||||
strict: false
|
||||
|
||||
unfrozen_parameters:
|
||||
- ^lm_head.weight$
|
||||
- ^model.embed_tokens.weight$
|
||||
- model.layers.4[4-9]+.block_sparse_moe.gate
|
||||
- model.layers.4[4-9]+.block_sparse_moe.experts
|
||||
- model.layers.5[0-5]+.block_sparse_moe.gate
|
||||
- model.layers.5[0-5]+.block_sparse_moe.experts
|
||||
|
||||
model_config:
|
||||
output_router_logits: true
|
||||
|
||||
datasets:
|
||||
- path: yahma/alpaca-cleaned
|
||||
type: alpaca
|
||||
output_dir: ./out
|
||||
|
||||
sequence_len: 8000
|
||||
sample_packing: true
|
||||
pad_to_sequence_len: true
|
||||
|
||||
gradient_accumulation_steps: 1
|
||||
micro_batch_size: 1
|
||||
num_epochs: 3
|
||||
optimizer: adamw_bnb_8bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.0001
|
||||
|
||||
train_on_inputs: false
|
||||
group_by_length: false
|
||||
bf16: auto
|
||||
fp16:
|
||||
tf32: false
|
||||
|
||||
gradient_checkpointing: true
|
||||
early_stopping_patience:
|
||||
resume_from_checkpoint:
|
||||
local_rank:
|
||||
logging_steps: 1
|
||||
xformers_attention:
|
||||
flash_attention: true
|
||||
|
||||
save_total_limit: 1
|
||||
save_steps:
|
||||
debug:
|
||||
deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_all.json
|
||||
weight_decay: 0.0
|
||||
fsdp:
|
||||
fsdp_config:
|
||||
special_tokens:
|
||||
eos_token: "<|im_end|>"
|
||||
tokens:
|
||||
- "<|im_start|>"
|
||||
@@ -16,7 +16,7 @@ sequence_len: 1024 # supports up to 32k
|
||||
sample_packing: false
|
||||
pad_to_sequence_len: false
|
||||
|
||||
adapter: qlora
|
||||
adapter: lora
|
||||
lora_model_dir:
|
||||
lora_r: 32
|
||||
lora_alpha: 16
|
||||
|
||||
@@ -1,8 +1,4 @@
|
||||
---
|
||||
toc-location: right-body
|
||||
toc-title: Table Of Contents
|
||||
toc-expand: 2
|
||||
---
|
||||
|
||||
|
||||
```{python}
|
||||
#|output: asis
|
||||
|
||||
@@ -40,5 +40,3 @@ gcsfs
|
||||
# adlfs
|
||||
|
||||
trl @ git+https://github.com/huggingface/trl.git@0ee349dcd43b0f4b3169449f16751c38ac4a609f
|
||||
zstandard==0.22.0
|
||||
fastcore
|
||||
|
||||
@@ -24,7 +24,6 @@ from huggingface_hub import HfApi
|
||||
from huggingface_hub.utils import LocalTokenNotFoundError
|
||||
from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
|
||||
from transformers.utils import is_torch_bf16_gpu_available
|
||||
from transformers.utils.import_utils import _is_package_available
|
||||
|
||||
from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
|
||||
from axolotl.logging_config import configure_logging
|
||||
@@ -63,20 +62,6 @@ def print_axolotl_text_art(suffix=None):
|
||||
if is_main_process():
|
||||
print(ascii_art)
|
||||
|
||||
print_dep_versions()
|
||||
|
||||
|
||||
def print_dep_versions():
|
||||
packages = ["accelerate", "peft", "transformers", "trl", "torch", "bitsandbytes"]
|
||||
max_len = max(len(pkg) for pkg in packages)
|
||||
if is_main_process():
|
||||
print("*" * 40)
|
||||
print("**** Axolotl Dependency Versions *****")
|
||||
for pkg in packages:
|
||||
version = _is_package_available(pkg, return_version=True)
|
||||
print(f"{pkg: >{max_len}}: {version[1]: <15}")
|
||||
print("*" * 40)
|
||||
|
||||
|
||||
def check_remote_config(config: Union[str, Path]):
|
||||
# Check if the config is a valid HTTPS URL to a .yml or .yaml file
|
||||
|
||||
@@ -38,8 +38,6 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
|
||||
parsed_cfg.load_in_4bit = False
|
||||
parsed_cfg.load_in_8bit = False
|
||||
parsed_cfg.flash_attention = False
|
||||
parsed_cfg.deepspeed = None
|
||||
parsed_cfg.fsdp = None
|
||||
|
||||
do_merge_lora(cfg=parsed_cfg, cli_args=parsed_cli_args)
|
||||
|
||||
|
||||
@@ -23,7 +23,6 @@ from torch.optim.lr_scheduler import OneCycleLR
|
||||
from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
|
||||
from transformers import (
|
||||
EarlyStoppingCallback,
|
||||
PreTrainedModel,
|
||||
Trainer,
|
||||
TrainerCallback,
|
||||
TrainingArguments,
|
||||
@@ -36,7 +35,6 @@ from trl.trainer.utils import pad_to_length
|
||||
from axolotl.loraplus import create_loraplus_optimizer
|
||||
from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
|
||||
from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
|
||||
from axolotl.utils import is_mlflow_available
|
||||
from axolotl.utils.callbacks import (
|
||||
EvalFirstStepCallback,
|
||||
GPUStatsCallback,
|
||||
@@ -72,6 +70,10 @@ except ImportError:
|
||||
LOG = logging.getLogger("axolotl.core.trainer_builder")
|
||||
|
||||
|
||||
def is_mlflow_available():
|
||||
return importlib.util.find_spec("mlflow") is not None
|
||||
|
||||
|
||||
def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
|
||||
if isinstance(tag_names, str):
|
||||
tag_names = [tag_names]
|
||||
@@ -800,15 +802,6 @@ class AxolotlDPOTrainer(DPOTrainer):
|
||||
|
||||
return super().push_to_hub(*args, **kwargs)
|
||||
|
||||
def tokenize_row(
|
||||
self, feature, model: Optional[Union[PreTrainedModel, torch.nn.Module]] = None
|
||||
) -> Dict:
|
||||
res = super().tokenize_row(feature, model=model)
|
||||
if self.tokenizer.bos_token_id is None and res["prompt_input_ids"][0] is None:
|
||||
for key in res.keys():
|
||||
res[key] = res[key][1:]
|
||||
return res
|
||||
|
||||
|
||||
class TrainerBuilderBase(abc.ABC):
|
||||
"""
|
||||
@@ -918,6 +911,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
|
||||
):
|
||||
callbacks.append(SaveBetterTransformerModelCallback())
|
||||
|
||||
if self.cfg.use_wandb:
|
||||
callbacks.append(
|
||||
SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
|
||||
)
|
||||
if self.cfg.use_mlflow and is_mlflow_available():
|
||||
from axolotl.utils.callbacks.mlflow_ import (
|
||||
SaveAxolotlConfigtoMlflowCallback,
|
||||
@@ -936,16 +933,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
|
||||
callbacks = []
|
||||
if self.cfg.use_wandb and self.cfg.eval_table_size > 0:
|
||||
LogPredictionCallback = log_prediction_callback_factory(
|
||||
trainer, self.tokenizer, "wandb"
|
||||
)
|
||||
callbacks.append(LogPredictionCallback(self.cfg))
|
||||
if (
|
||||
self.cfg.use_mlflow
|
||||
and is_mlflow_available()
|
||||
and self.cfg.eval_table_size > 0
|
||||
):
|
||||
LogPredictionCallback = log_prediction_callback_factory(
|
||||
trainer, self.tokenizer, "mlflow"
|
||||
trainer, self.tokenizer
|
||||
)
|
||||
callbacks.append(LogPredictionCallback(self.cfg))
|
||||
|
||||
|
||||
@@ -516,18 +516,24 @@ def mistral_model_forward(
|
||||
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = (
|
||||
self._gradient_checkpointing_func( # pylint: disable=protected-access
|
||||
decoder_layer.__call__,
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
past_key_value,
|
||||
output_attentions,
|
||||
None,
|
||||
cu_seqlens,
|
||||
max_seqlen,
|
||||
)
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
# None for past_key_value
|
||||
return module(*inputs)
|
||||
|
||||
return custom_forward
|
||||
|
||||
layer_outputs = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(decoder_layer),
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
past_key_value,
|
||||
output_attentions,
|
||||
None,
|
||||
cu_seqlens,
|
||||
max_seqlen,
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
|
||||
@@ -20,11 +20,10 @@ class PretrainTokenizationStrategy(PromptTokenizingStrategy):
|
||||
def supports_batched(self):
|
||||
return True
|
||||
|
||||
def __init__(self, *args, max_length=None, text_column="text", **kwargs):
|
||||
def __init__(self, *args, max_length=None, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
if max_length:
|
||||
self.max_length = max_length
|
||||
self.text_column = text_column
|
||||
|
||||
def _tokenize(
|
||||
self, prompt: str, add_eos_token: bool = True, strip_bos_token: bool = False
|
||||
@@ -45,7 +44,7 @@ class PretrainTokenizationStrategy(PromptTokenizingStrategy):
|
||||
return res
|
||||
|
||||
def tokenize_prompt(self, prompt):
|
||||
return self._tokenize(prompt[self.text_column])
|
||||
return self._tokenize(prompt["text"])
|
||||
|
||||
|
||||
def load(tokenizer, cfg):
|
||||
@@ -54,7 +53,6 @@ def load(tokenizer, cfg):
|
||||
tokenizer,
|
||||
cfg.train_on_inputs,
|
||||
cfg.sequence_len,
|
||||
text_column=cfg.pretraining_dataset[0]["text_column"] or "text",
|
||||
max_length=cfg.sequence_len * 64,
|
||||
)
|
||||
return strat
|
||||
|
||||
@@ -9,7 +9,6 @@ from typing import Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
import transformers.modelcard
|
||||
from accelerate import Accelerator
|
||||
from accelerate.logging import get_logger
|
||||
from datasets import Dataset
|
||||
from peft import PeftModel
|
||||
@@ -82,8 +81,6 @@ def train(
|
||||
if cfg.adapter:
|
||||
msg += " and peft_config..."
|
||||
LOG.debug(msg)
|
||||
# we wait unitl the last possible moment to setup Accelerator
|
||||
Accelerator()
|
||||
model, peft_config = load_model(cfg, tokenizer, inference=cli_args.inference)
|
||||
model.generation_config.do_sample = True
|
||||
|
||||
|
||||
@@ -1,8 +0,0 @@
|
||||
"""
|
||||
Basic utils for Axolotl
|
||||
"""
|
||||
import importlib
|
||||
|
||||
|
||||
def is_mlflow_available():
|
||||
return importlib.util.find_spec("mlflow") is not None
|
||||
|
||||
@@ -6,7 +6,7 @@ import logging
|
||||
import os
|
||||
from shutil import copyfile
|
||||
from tempfile import NamedTemporaryFile
|
||||
from typing import TYPE_CHECKING, Any, Dict, List
|
||||
from typing import TYPE_CHECKING, Dict, List
|
||||
|
||||
import evaluate
|
||||
import numpy as np
|
||||
@@ -27,9 +27,7 @@ from transformers import (
|
||||
)
|
||||
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
|
||||
|
||||
from axolotl.utils import is_mlflow_available
|
||||
from axolotl.utils.bench import log_gpu_memory_usage
|
||||
from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
|
||||
from axolotl.utils.distributed import (
|
||||
barrier,
|
||||
broadcast_dict,
|
||||
@@ -542,7 +540,7 @@ def causal_lm_bench_eval_callback_factory(trainer: Trainer, tokenizer):
|
||||
return CausalLMBenchEvalCallback
|
||||
|
||||
|
||||
def log_prediction_callback_factory(trainer: Trainer, tokenizer, logger: str):
|
||||
def log_prediction_callback_factory(trainer: Trainer, tokenizer):
|
||||
class LogPredictionCallback(TrainerCallback):
|
||||
"""Callback to log prediction values during each evaluation"""
|
||||
|
||||
@@ -599,13 +597,15 @@ def log_prediction_callback_factory(trainer: Trainer, tokenizer, logger: str):
|
||||
return ranges
|
||||
|
||||
def log_table_from_dataloader(name: str, table_dataloader):
|
||||
table_data: Dict[str, List[Any]] = {
|
||||
"id": [],
|
||||
"Prompt": [],
|
||||
"Correct Completion": [],
|
||||
"Predicted Completion (model.generate)": [],
|
||||
"Predicted Completion (trainer.prediction_step)": [],
|
||||
}
|
||||
table = wandb.Table( # type: ignore[attr-defined]
|
||||
columns=[
|
||||
"id",
|
||||
"Prompt",
|
||||
"Correct Completion",
|
||||
"Predicted Completion (model.generate)",
|
||||
"Predicted Completion (trainer.prediction_step)",
|
||||
]
|
||||
)
|
||||
row_index = 0
|
||||
|
||||
for batch in tqdm(table_dataloader):
|
||||
@@ -709,29 +709,16 @@ def log_prediction_callback_factory(trainer: Trainer, tokenizer, logger: str):
|
||||
) in zip(
|
||||
prompt_texts, completion_texts, predicted_texts, pred_step_texts
|
||||
):
|
||||
table_data["id"].append(row_index)
|
||||
table_data["Prompt"].append(prompt_text)
|
||||
table_data["Correct Completion"].append(completion_text)
|
||||
table_data["Predicted Completion (model.generate)"].append(
|
||||
prediction_text
|
||||
table.add_data(
|
||||
row_index,
|
||||
prompt_text,
|
||||
completion_text,
|
||||
prediction_text,
|
||||
pred_step_text,
|
||||
)
|
||||
table_data[
|
||||
"Predicted Completion (trainer.prediction_step)"
|
||||
].append(pred_step_text)
|
||||
row_index += 1
|
||||
if logger == "wandb":
|
||||
wandb.run.log({f"{name} - Predictions vs Ground Truth": pd.DataFrame(table_data)}) # type: ignore[attr-defined]
|
||||
elif logger == "mlflow" and is_mlflow_available():
|
||||
import mlflow
|
||||
|
||||
tracking_uri = AxolotlInputConfig(
|
||||
**self.cfg.to_dict()
|
||||
).mlflow_tracking_uri
|
||||
mlflow.log_table(
|
||||
data=table_data,
|
||||
artifact_file="PredictionsVsGroundTruth.json",
|
||||
tracking_uri=tracking_uri,
|
||||
)
|
||||
wandb.run.log({f"{name} - Predictions vs Ground Truth": table}) # type: ignore[attr-defined]
|
||||
|
||||
if is_main_process():
|
||||
log_table_from_dataloader("Eval", eval_dataloader)
|
||||
@@ -761,11 +748,6 @@ class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
|
||||
mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
|
||||
) as temp_file:
|
||||
copyfile(self.axolotl_config_path, temp_file.name)
|
||||
artifact = wandb.Artifact(
|
||||
f"config-{wandb.run.id}", type="axolotl-config"
|
||||
)
|
||||
artifact.add_file(temp_file.name)
|
||||
wandb.log_artifact(artifact)
|
||||
wandb.save(temp_file.name)
|
||||
LOG.info(
|
||||
"The Axolotl config has been saved to the WandB run under files."
|
||||
|
||||
@@ -23,7 +23,6 @@ def chat_templates(user_choice: str):
|
||||
"inst": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}", # I don't know what this one is called. Used by Mistral/Mixtral.
|
||||
"chatml": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||
"gemma": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
|
||||
"cohere": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",
|
||||
}
|
||||
|
||||
if user_choice in templates:
|
||||
|
||||
@@ -217,24 +217,13 @@ class PretrainingBatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
|
||||
Collator for multipack specific to the using the BatchSampler
|
||||
"""
|
||||
|
||||
def __init__(self, *args, multipack_attn=True, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.multipack_attn = multipack_attn
|
||||
|
||||
def __call__(self, features, return_tensors=None):
|
||||
chunked_data = {}
|
||||
for feature in features.keys():
|
||||
if feature == "length":
|
||||
continue
|
||||
if feature == "attention_mask":
|
||||
if self.multipack_attn:
|
||||
arrays = [
|
||||
(i + 1) * np.array(item[feature])
|
||||
for i, item in enumerate(features[feature])
|
||||
if feature in item
|
||||
]
|
||||
else:
|
||||
arrays = [(1) * np.array(item) for item in features[feature]]
|
||||
arrays = [(1) * np.array(item) for item in features[feature]]
|
||||
chunked_data[feature] = np.concatenate(arrays)
|
||||
else:
|
||||
arrays = [np.array(item) for item in features[feature]]
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
"""
|
||||
Module for pydantic models for configuration
|
||||
"""
|
||||
|
||||
# pylint: disable=too-many-lines
|
||||
|
||||
import logging
|
||||
@@ -62,11 +61,7 @@ class RemappedParameters(BaseModel):
|
||||
class PretrainingDataset(BaseModel):
|
||||
"""pretraining dataset configuration subset"""
|
||||
|
||||
name: Optional[str] = None
|
||||
path: Optional[str] = None
|
||||
split: Optional[str] = "train"
|
||||
text_column: Optional[str] = "text"
|
||||
type: Optional[str] = "pretrain"
|
||||
|
||||
|
||||
class UserDefinedPrompterType(BaseModel):
|
||||
@@ -98,7 +93,6 @@ class SFTDataset(BaseModel):
|
||||
ds_type: Optional[str] = None
|
||||
train_on_split: Optional[str] = None
|
||||
|
||||
field: Optional[str] = None
|
||||
field_human: Optional[str] = None
|
||||
field_model: Optional[str] = None
|
||||
|
||||
@@ -142,7 +136,6 @@ class ChatTemplate(str, Enum):
|
||||
chatml = "chatml" # pylint: disable=invalid-name
|
||||
inst = "inst" # pylint: disable=invalid-name
|
||||
gemma = "gemma" # pylint: disable=invalid-name
|
||||
cohere = "cohere" # pylint: disable=invalid-name
|
||||
|
||||
|
||||
class LoftQConfig(BaseModel):
|
||||
@@ -243,6 +236,17 @@ class LoraConfig(BaseModel):
|
||||
raise ValueError("Require cfg.load_in_4bit to be True for qlora")
|
||||
return self
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def validate_quantized_dora(cls, data):
|
||||
if data.get("peft_use_dora") and (
|
||||
data.get("load_in_8bit") or data.get("load_in_4bit")
|
||||
):
|
||||
raise ValueError(
|
||||
"`peft_use_dora` is not currently compatible with quantized weights."
|
||||
)
|
||||
return data
|
||||
|
||||
|
||||
class ReLoRAConfig(BaseModel):
|
||||
"""ReLoRA configuration subset"""
|
||||
@@ -259,7 +263,6 @@ class ModelInputConfig(BaseModel):
|
||||
|
||||
base_model: str
|
||||
base_model_config: Optional[str] = None
|
||||
cls_model_config: Optional[str] = None
|
||||
tokenizer_config: Optional[str] = None
|
||||
tokenizer_use_fast: Optional[bool] = None
|
||||
tokenizer_legacy: Optional[bool] = None
|
||||
@@ -445,7 +448,7 @@ class AxolotlInputConfig(
|
||||
dataset_shard_idx: Optional[int] = None
|
||||
|
||||
pretraining_dataset: Optional[ # type: ignore
|
||||
conlist(Union[PretrainingDataset, SFTDataset], min_length=1)
|
||||
conlist(Union[SFTDataset, PretrainingDataset], min_length=1)
|
||||
] = Field(
|
||||
default=None, metadata={"help": {"streaming dataset to use for pretraining"}}
|
||||
)
|
||||
@@ -479,7 +482,6 @@ class AxolotlInputConfig(
|
||||
eval_causal_lm_metrics: Optional[List[str]] = None
|
||||
do_bench_eval: Optional[bool] = None
|
||||
bench_dataset: Optional[str] = None
|
||||
bench_split: Optional[str] = None
|
||||
metric_for_best_model: Optional[str] = None
|
||||
greater_is_better: Optional[bool] = None
|
||||
|
||||
@@ -495,9 +497,7 @@ class AxolotlInputConfig(
|
||||
|
||||
# torch_dtype: Optional[torch.dtype]
|
||||
|
||||
gradient_checkpointing: Optional[Union[Literal["unsloth"], bool]] = Field(
|
||||
default=False
|
||||
)
|
||||
gradient_checkpointing: Optional[bool] = Field(default=False)
|
||||
gradient_checkpointing_kwargs: Optional[Dict[str, Any]] = None
|
||||
|
||||
unfrozen_parameters: Optional[List[str]] = None
|
||||
@@ -507,14 +507,6 @@ class AxolotlInputConfig(
|
||||
eval_sample_packing: Optional[bool] = None
|
||||
pad_to_sequence_len: Optional[bool] = None
|
||||
|
||||
pretrain_multipack_buffer_size: Optional[int] = 10_000
|
||||
pretrain_multipack_attn: Optional[bool] = Field(
|
||||
default=True,
|
||||
metadata={
|
||||
"help": "whether to prevent cross attention for packed sequences during pretraining",
|
||||
},
|
||||
)
|
||||
|
||||
xformers_attention: Optional[bool] = None
|
||||
sdp_attention: Optional[bool] = None
|
||||
s2_attention: Optional[bool] = None
|
||||
@@ -650,20 +642,6 @@ class AxolotlInputConfig(
|
||||
|
||||
return data
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def check_sample_packing_wo_flash(cls, data):
|
||||
if (
|
||||
data.get("sample_packing")
|
||||
and not data.get("flash_attention")
|
||||
and not data.get("sdp_attention")
|
||||
):
|
||||
LOG.warning(
|
||||
"sample_packing without flash_attention or sdp_attention does not handle cross-attention."
|
||||
)
|
||||
|
||||
return data
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def check_sample_packing_w_rl(cls, data):
|
||||
@@ -975,16 +953,9 @@ class AxolotlInputConfig(
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def check_fsdp_offload_w_8bit_optimizer(cls, data):
|
||||
if (
|
||||
data.get("fsdp")
|
||||
and "8bit" in data.get("optimizer", "")
|
||||
and data.get("fsdp_config")
|
||||
and data["fsdp_config"].get("fsdp_offload_params")
|
||||
):
|
||||
raise ValueError(
|
||||
f"FSDP Offload not compatible with {data.get('optimizer')}"
|
||||
)
|
||||
def check_fsdp_w_8bit_optimizer(cls, data):
|
||||
if data.get("fsdp") and "bnb" in data.get("optimizer", ""):
|
||||
raise ValueError(f"FSDP not compatible with {data.get('optimizer')}")
|
||||
return data
|
||||
|
||||
@model_validator(mode="before")
|
||||
|
||||
@@ -1,10 +1,14 @@
|
||||
"""data handling specific to SFT"""
|
||||
"""Module containing data utilities"""
|
||||
|
||||
import functools
|
||||
import hashlib
|
||||
import logging
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
from typing import List, Optional, Tuple, Union
|
||||
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
import yaml
|
||||
from datasets import (
|
||||
Dataset,
|
||||
DatasetDict,
|
||||
@@ -14,11 +18,13 @@ from datasets import (
|
||||
)
|
||||
from huggingface_hub import hf_hub_download
|
||||
from huggingface_hub.utils import HFValidationError
|
||||
from torch.utils.data import RandomSampler
|
||||
from transformers import PreTrainedTokenizerBase
|
||||
|
||||
from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
|
||||
from axolotl.datasets import TokenizedPromptDataset
|
||||
from axolotl.prompt_strategies import load
|
||||
from axolotl.prompt_strategies.dpo import load as load_dpo
|
||||
from axolotl.prompt_tokenizers import (
|
||||
AlpacaMultipleChoicePromptTokenizingStrategy,
|
||||
AlpacaPromptTokenizingStrategy,
|
||||
@@ -39,18 +45,26 @@ from axolotl.prompters import (
|
||||
SummarizeTLDRPrompter,
|
||||
UnsupportedPrompter,
|
||||
)
|
||||
from axolotl.utils.data.pretraining import wrap_pretraining_dataset
|
||||
from axolotl.utils.data.utils import md5
|
||||
from axolotl.utils.collators import PretrainingBatchSamplerDataCollatorForSeq2Seq
|
||||
from axolotl.utils.dict import DictDefault
|
||||
from axolotl.utils.distributed import is_main_process, zero_first
|
||||
from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
|
||||
from axolotl.utils.trainer import (
|
||||
calculate_total_num_steps,
|
||||
process_datasets_for_packing,
|
||||
process_pretraining_datasets_for_packing,
|
||||
)
|
||||
|
||||
LOG = logging.getLogger("axolotl")
|
||||
|
||||
|
||||
def md5(to_hash: str, encoding: str = "utf-8") -> str:
|
||||
try:
|
||||
return hashlib.md5(to_hash.encode(encoding), usedforsecurity=False).hexdigest()
|
||||
except TypeError:
|
||||
return hashlib.md5(to_hash.encode(encoding)).hexdigest() # nosec
|
||||
|
||||
|
||||
def prepare_dataset(cfg, tokenizer):
|
||||
prompters = []
|
||||
if not cfg.pretraining_dataset:
|
||||
@@ -68,15 +82,12 @@ def prepare_dataset(cfg, tokenizer):
|
||||
)
|
||||
else:
|
||||
path = cfg.pretraining_dataset
|
||||
split = "train"
|
||||
name = None
|
||||
if isinstance(cfg.pretraining_dataset, list) and isinstance(
|
||||
cfg.pretraining_dataset[0], dict
|
||||
):
|
||||
path = cfg.pretraining_dataset[0]["path"]
|
||||
name = cfg.pretraining_dataset[0]["name"]
|
||||
if "split" in cfg.pretraining_dataset[0]:
|
||||
split = cfg.pretraining_dataset[0]["split"]
|
||||
|
||||
ds_wrapper_partial = functools.partial(
|
||||
get_dataset_wrapper,
|
||||
@@ -87,14 +98,13 @@ def prepare_dataset(cfg, tokenizer):
|
||||
)
|
||||
|
||||
train_dataset = wrap_pretraining_dataset(
|
||||
load_dataset(path, streaming=True, split=split, name=name),
|
||||
load_dataset(path, streaming=True, split="train", name=name),
|
||||
tokenizer,
|
||||
cfg,
|
||||
ds_wrapper_partial,
|
||||
max_tokens=cfg.sequence_len,
|
||||
batch_size=cfg.micro_batch_size,
|
||||
seed=cfg.seed or 42,
|
||||
buffer_size=cfg.pretrain_multipack_buffer_size or 10_000,
|
||||
)
|
||||
# https://discuss.huggingface.co/t/how-to-use-huggingface-trainer-streaming-datasets-without-wrapping-it-with-torchdatas-iterablewrapper/25230
|
||||
train_dataset = train_dataset.with_format("torch")
|
||||
@@ -168,7 +178,6 @@ def load_tokenized_prepared_datasets(
|
||||
except Exception: # pylint: disable=broad-except # nosec
|
||||
pass
|
||||
|
||||
# pylint: disable=duplicate-code
|
||||
if dataset:
|
||||
...
|
||||
elif (
|
||||
@@ -379,15 +388,14 @@ def load_tokenized_prepared_datasets(
|
||||
d_base_type = d_type_split[0]
|
||||
d_prompt_style = d_type_split[1] if len(d_type_split) > 1 else None
|
||||
|
||||
if isinstance(ds, DatasetDict):
|
||||
if config_dataset.split and config_dataset.split in ds:
|
||||
ds = ds[config_dataset.split]
|
||||
elif split in ds:
|
||||
ds = ds[split]
|
||||
else:
|
||||
raise ValueError(
|
||||
f"no {split} split found for dataset {config_dataset.path}, you may specify a split with 'split: `"
|
||||
)
|
||||
if config_dataset.split and config_dataset.split in ds:
|
||||
ds = ds[config_dataset.split]
|
||||
elif split in ds:
|
||||
ds = ds[split]
|
||||
elif isinstance(ds, DatasetDict):
|
||||
raise ValueError(
|
||||
f"no {split} split found for dataset {config_dataset.path}, you may specify a split with 'split: `"
|
||||
)
|
||||
|
||||
# support for using a subset of the data
|
||||
if config_dataset.shards:
|
||||
@@ -679,3 +687,301 @@ def get_dataset_wrapper(
|
||||
)
|
||||
|
||||
return dataset_wrapper, dataset_prompter
|
||||
|
||||
|
||||
def encode_pretraining(
|
||||
tokenizer: PreTrainedTokenizerBase, max_tokens: int, examples: List[str]
|
||||
) -> Dict[str, List]:
|
||||
res = tokenizer(
|
||||
examples,
|
||||
truncation=True,
|
||||
max_length=max_tokens - 2,
|
||||
add_special_tokens=True,
|
||||
)
|
||||
# Convert to PyTorch tensors
|
||||
input_ids = [torch.tensor(seq) for seq in res["input_ids"]]
|
||||
attention_mask = [torch.tensor(seq) for seq in res["attention_mask"]]
|
||||
new_input_ids = []
|
||||
new_attention_mask = []
|
||||
# Append EOS and PAD tokens to input_ids, and correct attention_mask
|
||||
for i, _ in enumerate(input_ids):
|
||||
input_ids[i] = torch.cat(
|
||||
(
|
||||
input_ids[i],
|
||||
torch.tensor([tokenizer.eos_token_id, tokenizer.pad_token_id]),
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
attention_mask[i] = torch.cat((attention_mask[i], torch.tensor([1, 0])), dim=0)
|
||||
|
||||
# Concatenate tokens so that their lengths are less than max_tokens
|
||||
buffer_input_ids = torch.tensor([], dtype=torch.long)
|
||||
buffer_attention_mask = torch.tensor([], dtype=torch.long)
|
||||
|
||||
for ids, mask in zip(input_ids, attention_mask):
|
||||
if buffer_input_ids.numel() == max_tokens:
|
||||
new_input_ids.append(buffer_input_ids)
|
||||
new_attention_mask.append(buffer_attention_mask)
|
||||
buffer_input_ids = torch.tensor([], dtype=torch.long)
|
||||
buffer_attention_mask = torch.tensor([], dtype=torch.long)
|
||||
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
|
||||
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
|
||||
elif buffer_input_ids.numel() + ids.numel() <= max_tokens:
|
||||
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
|
||||
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
|
||||
else:
|
||||
buffer_input_ids = torch.cat(
|
||||
(
|
||||
buffer_input_ids,
|
||||
torch.full(
|
||||
(max_tokens - buffer_input_ids.numel(),),
|
||||
tokenizer.pad_token_id,
|
||||
dtype=torch.long,
|
||||
),
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
buffer_attention_mask = torch.cat(
|
||||
(
|
||||
buffer_attention_mask,
|
||||
torch.full(
|
||||
(max_tokens - buffer_attention_mask.numel(),),
|
||||
0,
|
||||
dtype=torch.long,
|
||||
),
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
new_input_ids.append(buffer_input_ids)
|
||||
new_attention_mask.append(buffer_attention_mask)
|
||||
buffer_input_ids = torch.tensor([], dtype=torch.long)
|
||||
buffer_attention_mask = torch.tensor([], dtype=torch.long)
|
||||
|
||||
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
|
||||
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
|
||||
|
||||
if buffer_input_ids.numel() > 0: # for any leftover tokens
|
||||
while buffer_input_ids.numel() < max_tokens: # make all sequences equal in size
|
||||
buffer_input_ids = torch.cat(
|
||||
(
|
||||
buffer_input_ids,
|
||||
torch.full(
|
||||
(max_tokens - buffer_input_ids.numel(),),
|
||||
tokenizer.pad_token_id,
|
||||
dtype=torch.long,
|
||||
),
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
buffer_attention_mask = torch.cat(
|
||||
(
|
||||
buffer_attention_mask,
|
||||
torch.full(
|
||||
(max_tokens - buffer_attention_mask.numel(),),
|
||||
0,
|
||||
dtype=torch.long,
|
||||
),
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
new_input_ids.append(buffer_input_ids)
|
||||
new_attention_mask.append(buffer_attention_mask)
|
||||
|
||||
ret = {
|
||||
"input_ids": [seq.tolist() for seq in new_input_ids],
|
||||
"labels": [seq.tolist() for seq in new_input_ids],
|
||||
"attention_mask": [seq.tolist() for seq in new_attention_mask],
|
||||
}
|
||||
|
||||
LOG.debug(len(ret["input_ids"]))
|
||||
return ret
|
||||
|
||||
|
||||
def wrap_pretraining_dataset(
|
||||
dataset,
|
||||
tokenizer,
|
||||
cfg,
|
||||
ds_wrapper_fn,
|
||||
max_tokens=2048,
|
||||
batch_size=1,
|
||||
seed=42,
|
||||
buffer_size=10_000,
|
||||
):
|
||||
if cfg.sample_packing:
|
||||
collate_fn = PretrainingBatchSamplerDataCollatorForSeq2Seq(
|
||||
tokenizer,
|
||||
return_tensors="pt",
|
||||
padding=True,
|
||||
pad_to_multiple_of=max_tokens * batch_size,
|
||||
)
|
||||
encode = functools.partial(
|
||||
encode_packed_pretraining,
|
||||
collate_fn,
|
||||
ds_wrapper_fn,
|
||||
max_seq_length=max_tokens,
|
||||
batch_size=batch_size,
|
||||
)
|
||||
# set this to 1 so downstream data_loader doesn't try to increase the batch again
|
||||
cfg.micro_batch_size = 1
|
||||
else:
|
||||
encode = functools.partial(encode_pretraining, tokenizer, max_tokens)
|
||||
|
||||
if cfg.shuffle_merged_datasets:
|
||||
dataset = dataset.shuffle(seed=seed, buffer_size=buffer_size)
|
||||
else:
|
||||
LOG.debug("NOT shuffling merged pretraining datasets")
|
||||
|
||||
dataset = dataset.map(
|
||||
encode,
|
||||
batched=True,
|
||||
batch_size=buffer_size,
|
||||
# input_columns="text",
|
||||
# remove all the existing columns after mapping since they end up having
|
||||
# a different length than the encoded/tokenized column
|
||||
remove_columns=dataset.features.keys(),
|
||||
)
|
||||
return dataset
|
||||
|
||||
|
||||
def encode_packed_pretraining(
|
||||
collate_fn,
|
||||
ds_wrapper: Callable,
|
||||
examples: Dict[str, List],
|
||||
max_seq_length: int = 2048,
|
||||
batch_size: int = 4,
|
||||
) -> Dict[str, List]:
|
||||
# pylint: disable=duplicate-code
|
||||
# tokenize all the examples
|
||||
# rows get split with stride (overlap)
|
||||
train_dataset = ds_wrapper(Dataset.from_dict(examples))[0]
|
||||
|
||||
train_dataset = process_pretraining_datasets_for_packing(
|
||||
train_dataset, max_seq_length
|
||||
)
|
||||
|
||||
sampler = MultipackBatchSampler(
|
||||
RandomSampler(train_dataset),
|
||||
batch_size=1,
|
||||
drop_last=True,
|
||||
batch_max_len=batch_size * max_seq_length,
|
||||
lengths=get_dataset_lengths(train_dataset),
|
||||
)
|
||||
|
||||
chunked_data = defaultdict(list)
|
||||
|
||||
for batch in sampler:
|
||||
for data in batch:
|
||||
features = train_dataset[data]
|
||||
if "num_truncated_tokens" in features:
|
||||
del features["num_truncated_tokens"]
|
||||
if "num_truncated_tokens" in features:
|
||||
del features["num_truncated_tokens"]
|
||||
if "overflow_to_sample_mapping" in features:
|
||||
del features["overflow_to_sample_mapping"]
|
||||
if "labels" not in features:
|
||||
features["labels"] = features["input_ids"].copy()
|
||||
collated_features = collate_fn(features)
|
||||
|
||||
for feature in features.keys():
|
||||
if feature == "length":
|
||||
continue
|
||||
chunked_data[feature].append(collated_features[feature].squeeze(0))
|
||||
|
||||
return chunked_data
|
||||
|
||||
|
||||
def _get_path(ds_hash, cfg):
|
||||
prepared_ds_path = (
|
||||
Path(cfg.dataset_prepared_path) / ds_hash
|
||||
if cfg.dataset_prepared_path
|
||||
else Path(DEFAULT_DATASET_PREPARED_PATH) / ds_hash
|
||||
)
|
||||
|
||||
return prepared_ds_path
|
||||
|
||||
|
||||
def _load_preprocessed_ds(cfg, sub_cfg):
|
||||
ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
|
||||
prepared_ds_path = _get_path(ds_hash, cfg)
|
||||
dataset = None
|
||||
|
||||
if (
|
||||
cfg.dataset_prepared_path
|
||||
and any(prepared_ds_path.glob("*"))
|
||||
and not cfg.is_preprocess
|
||||
):
|
||||
LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
|
||||
dataset = load_from_disk(str(prepared_ds_path))
|
||||
|
||||
return dataset
|
||||
|
||||
|
||||
def _save_preprocessed_ds(cfg, sub_cfg, dataset):
|
||||
ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
|
||||
prepared_ds_path = _get_path(ds_hash, cfg)
|
||||
|
||||
if cfg.is_preprocess and is_main_process():
|
||||
LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
|
||||
dataset.save_to_disk(str(prepared_ds_path))
|
||||
|
||||
|
||||
def load_prepare_dpo_datasets(cfg):
|
||||
def load_split(dataset_cfgs, _cfg):
|
||||
split_datasets: List[Any] = []
|
||||
for i, ds_cfg in enumerate(dataset_cfgs):
|
||||
if ds_cfg["ds_type"] == "json":
|
||||
for data_file in ds_cfg["data_files"]:
|
||||
data_files = {ds_cfg["split"]: data_file}
|
||||
ds = load_dataset( # pylint: disable=invalid-name
|
||||
"json",
|
||||
data_files=data_files,
|
||||
split=ds_cfg["split"],
|
||||
)
|
||||
split_datasets.insert(i, ds)
|
||||
else:
|
||||
ds = load_dataset( # pylint: disable=invalid-name
|
||||
ds_cfg["path"],
|
||||
split=ds_cfg["split"],
|
||||
)
|
||||
split_datasets.insert(i, ds)
|
||||
|
||||
for i, data_set in enumerate(split_datasets):
|
||||
_type = dataset_cfgs[i]["type"]
|
||||
if _type:
|
||||
if isinstance(_type, DictDefault):
|
||||
_type = "user_defined.default"
|
||||
ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
|
||||
split_datasets[i] = data_set.map(
|
||||
ds_transform_fn,
|
||||
desc="Mapping RL Dataset",
|
||||
)
|
||||
else:
|
||||
# If no `type` is provided, assume the dataset is already in the expected format with
|
||||
# "prompt", "chosen" and "rejected" already preprocessed
|
||||
split_datasets[i] = data_set
|
||||
|
||||
return concatenate_datasets(split_datasets)
|
||||
|
||||
with zero_first(is_main_process()):
|
||||
train_is_preprocessed = False
|
||||
eval_is_preprocessed = False
|
||||
if train_dataset := _load_preprocessed_ds(cfg, cfg.datasets):
|
||||
train_is_preprocessed = True
|
||||
else:
|
||||
train_dataset = load_split(cfg.datasets, cfg)
|
||||
|
||||
eval_dataset = None
|
||||
if cfg.test_datasets:
|
||||
if eval_dataset := _load_preprocessed_ds(cfg, cfg.test_datasets):
|
||||
eval_is_preprocessed = True
|
||||
else:
|
||||
eval_dataset = load_split(cfg.test_datasets, cfg)
|
||||
if not eval_dataset:
|
||||
eval_dataset = None
|
||||
|
||||
if not train_is_preprocessed:
|
||||
_save_preprocessed_ds(cfg, cfg.datasets, train_dataset)
|
||||
if eval_dataset and not eval_is_preprocessed:
|
||||
_save_preprocessed_ds(cfg, cfg.test_datasets, eval_dataset)
|
||||
|
||||
return train_dataset, eval_dataset
|
||||
@@ -1,15 +0,0 @@
|
||||
"""
|
||||
Data processing modules
|
||||
"""
|
||||
from axolotl.utils.data.dpo import load_prepare_dpo_datasets # noqa: F401
|
||||
from axolotl.utils.data.pretraining import ( # noqa: F401
|
||||
encode_pretraining,
|
||||
wrap_pretraining_dataset,
|
||||
)
|
||||
from axolotl.utils.data.sft import ( # noqa: F401
|
||||
get_dataset_wrapper,
|
||||
load_prepare_datasets,
|
||||
load_tokenized_prepared_datasets,
|
||||
prepare_dataset,
|
||||
)
|
||||
from axolotl.utils.data.utils import md5 # noqa: F401
|
||||
@@ -1,114 +0,0 @@
|
||||
"""data handling specific to DPO"""
|
||||
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Any, List
|
||||
|
||||
import yaml
|
||||
from datasets import concatenate_datasets, load_dataset, load_from_disk
|
||||
|
||||
from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
|
||||
from axolotl.prompt_strategies.dpo import load as load_dpo
|
||||
from axolotl.utils.data.utils import md5
|
||||
from axolotl.utils.dict import DictDefault
|
||||
from axolotl.utils.distributed import is_main_process, zero_first
|
||||
|
||||
LOG = logging.getLogger("axolotl")
|
||||
|
||||
|
||||
def _get_path(ds_hash, cfg):
|
||||
prepared_ds_path = (
|
||||
Path(cfg.dataset_prepared_path) / ds_hash
|
||||
if cfg.dataset_prepared_path
|
||||
else Path(DEFAULT_DATASET_PREPARED_PATH) / ds_hash
|
||||
)
|
||||
|
||||
return prepared_ds_path
|
||||
|
||||
|
||||
def _load_preprocessed_ds(cfg, sub_cfg):
|
||||
ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
|
||||
prepared_ds_path = _get_path(ds_hash, cfg)
|
||||
dataset = None
|
||||
|
||||
# pylint: disable=duplicate-code
|
||||
if (
|
||||
cfg.dataset_prepared_path
|
||||
and any(prepared_ds_path.glob("*"))
|
||||
and not cfg.is_preprocess
|
||||
):
|
||||
LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
|
||||
dataset = load_from_disk(str(prepared_ds_path))
|
||||
|
||||
return dataset
|
||||
|
||||
|
||||
def _save_preprocessed_ds(cfg, sub_cfg, dataset):
|
||||
ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
|
||||
prepared_ds_path = _get_path(ds_hash, cfg)
|
||||
|
||||
if cfg.is_preprocess and is_main_process():
|
||||
LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
|
||||
dataset.save_to_disk(str(prepared_ds_path))
|
||||
|
||||
|
||||
def load_prepare_dpo_datasets(cfg):
|
||||
def load_split(dataset_cfgs, _cfg):
|
||||
split_datasets: List[Any] = []
|
||||
for i, ds_cfg in enumerate(dataset_cfgs):
|
||||
if ds_cfg["ds_type"] == "json":
|
||||
for data_file in ds_cfg["data_files"]:
|
||||
data_files = {ds_cfg["split"]: data_file}
|
||||
ds = load_dataset( # pylint: disable=invalid-name
|
||||
"json",
|
||||
data_files=data_files,
|
||||
split=ds_cfg["split"],
|
||||
)
|
||||
split_datasets.insert(i, ds)
|
||||
else:
|
||||
ds = load_dataset( # pylint: disable=invalid-name
|
||||
ds_cfg["path"],
|
||||
split=ds_cfg["split"],
|
||||
)
|
||||
split_datasets.insert(i, ds)
|
||||
|
||||
for i, data_set in enumerate(split_datasets):
|
||||
_type = dataset_cfgs[i]["type"]
|
||||
if _type:
|
||||
if isinstance(_type, DictDefault):
|
||||
_type = "user_defined.default"
|
||||
ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
|
||||
split_datasets[i] = data_set.map(
|
||||
ds_transform_fn,
|
||||
desc="Mapping RL Dataset",
|
||||
)
|
||||
else:
|
||||
# If no `type` is provided, assume the dataset is already in the expected format with
|
||||
# "prompt", "chosen" and "rejected" already preprocessed
|
||||
split_datasets[i] = data_set
|
||||
|
||||
return concatenate_datasets(split_datasets)
|
||||
|
||||
with zero_first(is_main_process()):
|
||||
train_is_preprocessed = False
|
||||
eval_is_preprocessed = False
|
||||
if train_dataset := _load_preprocessed_ds(cfg, cfg.datasets):
|
||||
train_is_preprocessed = True
|
||||
else:
|
||||
train_dataset = load_split(cfg.datasets, cfg)
|
||||
|
||||
eval_dataset = None
|
||||
if cfg.test_datasets:
|
||||
if eval_dataset := _load_preprocessed_ds(cfg, cfg.test_datasets):
|
||||
eval_is_preprocessed = True
|
||||
else:
|
||||
eval_dataset = load_split(cfg.test_datasets, cfg)
|
||||
if not eval_dataset:
|
||||
eval_dataset = None
|
||||
|
||||
if not train_is_preprocessed:
|
||||
_save_preprocessed_ds(cfg, cfg.datasets, train_dataset)
|
||||
if eval_dataset and not eval_is_preprocessed:
|
||||
_save_preprocessed_ds(cfg, cfg.test_datasets, eval_dataset)
|
||||
|
||||
return train_dataset, eval_dataset
|
||||
@@ -1,232 +0,0 @@
|
||||
"""data handling specific to pretraining"""
|
||||
|
||||
import functools
|
||||
import logging
|
||||
from collections import defaultdict
|
||||
from typing import Callable, Dict, List, Optional
|
||||
|
||||
import torch
|
||||
from datasets import Dataset
|
||||
from torch.utils.data import RandomSampler
|
||||
from transformers import PreTrainedTokenizerBase
|
||||
|
||||
from axolotl.utils.collators import PretrainingBatchSamplerDataCollatorForSeq2Seq
|
||||
from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
|
||||
from axolotl.utils.trainer import process_pretraining_datasets_for_packing
|
||||
|
||||
LOG = logging.getLogger("axolotl")
|
||||
|
||||
|
||||
def encode_pretraining(
|
||||
tokenizer: PreTrainedTokenizerBase, max_tokens: int, examples: List[str]
|
||||
) -> Dict[str, List]:
|
||||
res = tokenizer(
|
||||
examples,
|
||||
truncation=True,
|
||||
max_length=max_tokens - 2,
|
||||
add_special_tokens=True,
|
||||
)
|
||||
# Convert to PyTorch tensors
|
||||
input_ids = [torch.tensor(seq) for seq in res["input_ids"]]
|
||||
attention_mask = [torch.tensor(seq) for seq in res["attention_mask"]]
|
||||
new_input_ids = []
|
||||
new_attention_mask = []
|
||||
# Append EOS and PAD tokens to input_ids, and correct attention_mask
|
||||
for i, _ in enumerate(input_ids):
|
||||
input_ids[i] = torch.cat(
|
||||
(
|
||||
input_ids[i],
|
||||
torch.tensor([tokenizer.eos_token_id, tokenizer.pad_token_id]),
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
attention_mask[i] = torch.cat((attention_mask[i], torch.tensor([1, 0])), dim=0)
|
||||
|
||||
# Concatenate tokens so that their lengths are less than max_tokens
|
||||
buffer_input_ids = torch.tensor([], dtype=torch.long)
|
||||
buffer_attention_mask = torch.tensor([], dtype=torch.long)
|
||||
|
||||
for ids, mask in zip(input_ids, attention_mask):
|
||||
if buffer_input_ids.numel() == max_tokens:
|
||||
new_input_ids.append(buffer_input_ids)
|
||||
new_attention_mask.append(buffer_attention_mask)
|
||||
buffer_input_ids = torch.tensor([], dtype=torch.long)
|
||||
buffer_attention_mask = torch.tensor([], dtype=torch.long)
|
||||
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
|
||||
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
|
||||
elif buffer_input_ids.numel() + ids.numel() <= max_tokens:
|
||||
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
|
||||
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
|
||||
else:
|
||||
buffer_input_ids = torch.cat(
|
||||
(
|
||||
buffer_input_ids,
|
||||
torch.full(
|
||||
(max_tokens - buffer_input_ids.numel(),),
|
||||
tokenizer.pad_token_id,
|
||||
dtype=torch.long,
|
||||
),
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
buffer_attention_mask = torch.cat(
|
||||
(
|
||||
buffer_attention_mask,
|
||||
torch.full(
|
||||
(max_tokens - buffer_attention_mask.numel(),),
|
||||
0,
|
||||
dtype=torch.long,
|
||||
),
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
new_input_ids.append(buffer_input_ids)
|
||||
new_attention_mask.append(buffer_attention_mask)
|
||||
buffer_input_ids = torch.tensor([], dtype=torch.long)
|
||||
buffer_attention_mask = torch.tensor([], dtype=torch.long)
|
||||
|
||||
buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
|
||||
buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
|
||||
|
||||
if buffer_input_ids.numel() > 0: # for any leftover tokens
|
||||
while buffer_input_ids.numel() < max_tokens: # make all sequences equal in size
|
||||
buffer_input_ids = torch.cat(
|
||||
(
|
||||
buffer_input_ids,
|
||||
torch.full(
|
||||
(max_tokens - buffer_input_ids.numel(),),
|
||||
tokenizer.pad_token_id,
|
||||
dtype=torch.long,
|
||||
),
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
buffer_attention_mask = torch.cat(
|
||||
(
|
||||
buffer_attention_mask,
|
||||
torch.full(
|
||||
(max_tokens - buffer_attention_mask.numel(),),
|
||||
0,
|
||||
dtype=torch.long,
|
||||
),
|
||||
),
|
||||
dim=0,
|
||||
)
|
||||
new_input_ids.append(buffer_input_ids)
|
||||
new_attention_mask.append(buffer_attention_mask)
|
||||
|
||||
ret = {
|
||||
"input_ids": [seq.tolist() for seq in new_input_ids],
|
||||
"labels": [seq.tolist() for seq in new_input_ids],
|
||||
"attention_mask": [seq.tolist() for seq in new_attention_mask],
|
||||
}
|
||||
|
||||
LOG.debug(len(ret["input_ids"]))
|
||||
return ret
|
||||
|
||||
|
||||
def wrap_pretraining_dataset(
|
||||
dataset,
|
||||
tokenizer,
|
||||
cfg,
|
||||
ds_wrapper_fn,
|
||||
max_tokens=2048,
|
||||
batch_size=1,
|
||||
seed=42,
|
||||
buffer_size=10_000,
|
||||
):
|
||||
if cfg.sample_packing:
|
||||
collate_fn = PretrainingBatchSamplerDataCollatorForSeq2Seq(
|
||||
tokenizer,
|
||||
return_tensors="pt",
|
||||
padding=True,
|
||||
pad_to_multiple_of=max_tokens * batch_size,
|
||||
multipack_attn=cfg.pretrain_multipack_attn,
|
||||
)
|
||||
encode = functools.partial(
|
||||
encode_packed_pretraining,
|
||||
collate_fn,
|
||||
ds_wrapper_fn,
|
||||
max_seq_length=max_tokens,
|
||||
batch_size=batch_size,
|
||||
multipack_attn=cfg.pretrain_multipack_attn,
|
||||
)
|
||||
# set this to 1 so downstream data_loader doesn't try to increase the batch again
|
||||
cfg.micro_batch_size = 1
|
||||
else:
|
||||
encode = functools.partial(encode_pretraining, tokenizer, max_tokens)
|
||||
|
||||
if cfg.shuffle_merged_datasets:
|
||||
dataset = dataset.shuffle(seed=seed, buffer_size=buffer_size)
|
||||
else:
|
||||
LOG.debug("NOT shuffling merged pretraining datasets")
|
||||
|
||||
# remove all the existing columns after mapping since they end up having
|
||||
# a different length than the encoded/tokenized column
|
||||
# this is empty during streaming/pretraining
|
||||
remove_columns = []
|
||||
if dataset.features is None:
|
||||
for first_row in dataset:
|
||||
remove_columns = first_row.keys()
|
||||
break
|
||||
else:
|
||||
remove_columns = dataset.features.keys()
|
||||
|
||||
dataset = dataset.map(
|
||||
encode,
|
||||
batched=True,
|
||||
batch_size=buffer_size,
|
||||
# input_columns="text",
|
||||
remove_columns=remove_columns,
|
||||
)
|
||||
return dataset
|
||||
|
||||
|
||||
def encode_packed_pretraining(
|
||||
collate_fn,
|
||||
ds_wrapper: Callable,
|
||||
examples: Dict[str, List],
|
||||
max_seq_length: int = 2048,
|
||||
batch_size: int = 4,
|
||||
multipack_attn: Optional[bool] = False,
|
||||
) -> Dict[str, List]:
|
||||
# pylint: disable=duplicate-code
|
||||
# tokenize all the examples
|
||||
# rows get split with stride (overlap)
|
||||
train_dataset = ds_wrapper(Dataset.from_dict(examples))[0]
|
||||
|
||||
train_dataset = process_pretraining_datasets_for_packing(
|
||||
train_dataset,
|
||||
max_seq_length,
|
||||
skip_position_ids=not multipack_attn,
|
||||
)
|
||||
|
||||
sampler = MultipackBatchSampler(
|
||||
RandomSampler(train_dataset),
|
||||
batch_size=1,
|
||||
drop_last=True,
|
||||
batch_max_len=batch_size * max_seq_length,
|
||||
lengths=get_dataset_lengths(train_dataset),
|
||||
)
|
||||
|
||||
chunked_data = defaultdict(list)
|
||||
|
||||
for batch in sampler:
|
||||
for data in batch:
|
||||
features = train_dataset[data]
|
||||
if "num_truncated_tokens" in features:
|
||||
del features["num_truncated_tokens"]
|
||||
if "num_truncated_tokens" in features:
|
||||
del features["num_truncated_tokens"]
|
||||
if "overflow_to_sample_mapping" in features:
|
||||
del features["overflow_to_sample_mapping"]
|
||||
if "labels" not in features:
|
||||
features["labels"] = features["input_ids"].copy()
|
||||
collated_features = collate_fn(features)
|
||||
|
||||
for feature in features.keys():
|
||||
if feature == "length":
|
||||
continue
|
||||
chunked_data[feature].append(collated_features[feature].squeeze(0))
|
||||
|
||||
return chunked_data
|
||||
@@ -1,10 +0,0 @@
|
||||
"""data handling helpers"""
|
||||
|
||||
import hashlib
|
||||
|
||||
|
||||
def md5(to_hash: str, encoding: str = "utf-8") -> str:
|
||||
try:
|
||||
return hashlib.md5(to_hash.encode(encoding), usedforsecurity=False).hexdigest()
|
||||
except TypeError:
|
||||
return hashlib.md5(to_hash.encode(encoding)).hexdigest() # nosec
|
||||
@@ -4,25 +4,27 @@ utility helpers for distributed checks
|
||||
import os
|
||||
import pickle # nosec
|
||||
from contextlib import contextmanager
|
||||
from datetime import timedelta
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
from accelerate import PartialState
|
||||
from accelerate import Accelerator
|
||||
|
||||
distributed_state = None # pylint: disable=invalid-name
|
||||
accelerate = None # pylint: disable=invalid-name
|
||||
|
||||
|
||||
def load_accelerate():
|
||||
global accelerate # pylint: disable=global-statement
|
||||
accelerate = Accelerator()
|
||||
|
||||
|
||||
def is_distributed():
|
||||
"""
|
||||
Check if distributed training is initialized.
|
||||
"""
|
||||
global distributed_state # pylint: disable=global-statement
|
||||
if not distributed_state:
|
||||
timeout = int(os.environ.get("AXOLOTL_NCCL_TIMEOUT", 1800))
|
||||
distributed_state = PartialState(timeout=timedelta(seconds=timeout))
|
||||
|
||||
return distributed_state.use_distributed and distributed_state.initialized
|
||||
global accelerate # pylint: disable=global-statement
|
||||
if not accelerate:
|
||||
accelerate = Accelerator()
|
||||
return dist.is_available() and dist.is_initialized()
|
||||
|
||||
|
||||
def barrier():
|
||||
|
||||
@@ -1,13 +0,0 @@
|
||||
"""custom checkpointing utils"""
|
||||
from axolotl.utils.gradient_checkpointing.unsloth import (
|
||||
Unsloth_Offloaded_Gradient_Checkpointer,
|
||||
)
|
||||
|
||||
|
||||
def hf_grad_checkpoint_unsloth_wrapper(
|
||||
decoder_layer, *args, use_reentrant=None
|
||||
): # pylint: disable=unused-argument
|
||||
return Unsloth_Offloaded_Gradient_Checkpointer.apply(
|
||||
decoder_layer.__self__,
|
||||
*args,
|
||||
)
|
||||
@@ -1,52 +0,0 @@
|
||||
"""Unsloth checkpointing"""
|
||||
|
||||
# Copyright 2023-present Daniel Han-Chen & the Unsloth 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.
|
||||
import torch
|
||||
|
||||
|
||||
class Unsloth_Offloaded_Gradient_Checkpointer( # pylint: disable=invalid-name
|
||||
torch.autograd.Function
|
||||
):
|
||||
"""
|
||||
Saves VRAM by smartly offloading to RAM.
|
||||
Tiny hit to performance, since we mask the movement via non blocking calls.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
@torch.cuda.amp.custom_fwd
|
||||
def forward(ctx, forward_function, hidden_states, *args):
|
||||
saved_hidden_states = hidden_states.to("cpu", non_blocking=True)
|
||||
with torch.no_grad():
|
||||
output = forward_function(hidden_states, *args)
|
||||
ctx.save_for_backward(saved_hidden_states)
|
||||
ctx.forward_function = forward_function
|
||||
ctx.args = args
|
||||
return output
|
||||
|
||||
@staticmethod
|
||||
@torch.cuda.amp.custom_bwd
|
||||
def backward(ctx, dY):
|
||||
(hidden_states,) = ctx.saved_tensors
|
||||
hidden_states = hidden_states.to("cuda", non_blocking=True).detach()
|
||||
hidden_states.requires_grad = True
|
||||
with torch.enable_grad():
|
||||
(output,) = ctx.forward_function(hidden_states, *ctx.args)
|
||||
torch.autograd.backward(output, dY)
|
||||
return (
|
||||
None,
|
||||
hidden_states.grad,
|
||||
) + (
|
||||
None,
|
||||
) * len(ctx.args)
|
||||
@@ -1,259 +0,0 @@
|
||||
"""
|
||||
module to handle loading model on cpu/meta device for FSDP
|
||||
"""
|
||||
import os
|
||||
import time
|
||||
from typing import List, Optional, Type, Union
|
||||
|
||||
import safetensors
|
||||
import torch
|
||||
from accelerate import init_empty_weights
|
||||
from bitsandbytes.nn import Linear4bit, Params4bit
|
||||
from fastcore.parallel import parallel
|
||||
from torch import Tensor, nn
|
||||
from tqdm import tqdm
|
||||
from transformers import AutoModelForCausalLM
|
||||
from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, hub
|
||||
|
||||
|
||||
def _replace_linear(
|
||||
model: nn.Module,
|
||||
linear_replacement: Type[nn.Module],
|
||||
quant_config: Union[dict, None] = None,
|
||||
skip_modules=None,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Replace linear modules with a new Linear module.
|
||||
Parameters:
|
||||
model (`torch.nn.Module`):
|
||||
Input model or `torch.nn.Module` as the function is run recursively.
|
||||
linear_replacement (`torch.nn.Module`):
|
||||
The linear module that replaces the old one. Only expects standard arguments.
|
||||
If other arguments need to be passed, use a lambda.
|
||||
skip_modules (`List[str]`, *optional*, defaults to `lm_head`):
|
||||
List of modules names not to convert. Defaults to `lm_head`.
|
||||
"""
|
||||
if skip_modules is None:
|
||||
skip_modules = ["lm_head"]
|
||||
for name, module in model.named_children():
|
||||
if len(list(module.children())) > 0:
|
||||
_replace_linear(
|
||||
module, linear_replacement, quant_config, skip_modules, **kwargs
|
||||
)
|
||||
|
||||
if isinstance(module, torch.nn.Linear) and name not in skip_modules:
|
||||
if issubclass(linear_replacement, Linear4bit):
|
||||
model._modules[ # pylint: disable=protected-access
|
||||
name
|
||||
] = linear_replacement(
|
||||
module.in_features,
|
||||
module.out_features,
|
||||
module.bias is not None,
|
||||
**kwargs,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported linear replacement: {type(linear_replacement)}"
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def load_and_quantize(
|
||||
module: nn.Module,
|
||||
name: str,
|
||||
value: Tensor,
|
||||
device: torch.device = None,
|
||||
dtype: torch.dtype = None,
|
||||
skip_names: Optional[List[str]] = None,
|
||||
to_cpu: bool = False,
|
||||
to_meta: bool = False,
|
||||
verbose: bool = False,
|
||||
quant_method: str = "bnb",
|
||||
):
|
||||
"""
|
||||
Loads `value` tensor into submodule of `module`, optionally skipping `skip_names` and converting to `dtype`.
|
||||
|
||||
Quantizes `Params4bit` on `device` then places on "cpu" if to_cpu=True or "meta" if to_meta=True.
|
||||
"""
|
||||
|
||||
if not skip_names:
|
||||
skip_names = []
|
||||
|
||||
def place_on_device(value):
|
||||
if to_meta:
|
||||
device = "meta"
|
||||
elif to_cpu:
|
||||
device = "cpu"
|
||||
return value.to(device=device, dtype=dtype)
|
||||
|
||||
if any(skip_name in name for skip_name in skip_names):
|
||||
if verbose:
|
||||
print(f"Skipping {name} because it is in skip_names")
|
||||
return
|
||||
|
||||
module_key, _, value_key = name.rpartition(".")
|
||||
try:
|
||||
submodule = module.get_submodule(module_key)
|
||||
except AttributeError as exc:
|
||||
print(f"Module {module_key} not found:\n{exc}")
|
||||
return
|
||||
|
||||
try:
|
||||
if quant_method == "bnb":
|
||||
param = submodule.get_parameter(value_key)
|
||||
if isinstance(param, Params4bit):
|
||||
# With `sync_module_states=True`, a meta device Params4bit needs to be the same
|
||||
# shape as the quantized Params4bit with an initialized quant_state. However,
|
||||
# FSDP only syncs parameters and buffers, so the quant_state isn't copied. This
|
||||
# workaround quantizes Params4bit to initialize quant_state on all ranks, then
|
||||
# replaces Params4bit's data with a meta tensor to free memory on non-rank 0.
|
||||
value = type(param)(
|
||||
value.to(device=device, dtype=dtype).data, **param.__dict__
|
||||
).cuda(device)
|
||||
if to_meta:
|
||||
value = type(param)(value.data.to("meta"), **value.__dict__)
|
||||
elif to_cpu:
|
||||
value = type(param)(value.data.to("cpu"), **value.__dict__)
|
||||
else:
|
||||
value = type(param)(place_on_device(value).data)
|
||||
|
||||
except AttributeError:
|
||||
# it's a buffer
|
||||
value = place_on_device(value)
|
||||
|
||||
setattr(submodule, value_key, value)
|
||||
|
||||
|
||||
def n_loading_workers(quant_method: str, param_count: float):
|
||||
devprops = torch.cuda.get_device_properties(torch.cuda.current_device())
|
||||
left = int(os.cpu_count() / torch.cuda.device_count())
|
||||
model_params_b = 70
|
||||
right = int(
|
||||
(4 if quant_method == "hqq" else 8)
|
||||
* (devprops.total_memory / 1e9 / 40)
|
||||
* (model_params_b / (param_count / 1e9))
|
||||
)
|
||||
return min(left, right)
|
||||
|
||||
|
||||
def load_sharded_model(
|
||||
model_name,
|
||||
model_config,
|
||||
cfg,
|
||||
torch_dtype=torch.bfloat16,
|
||||
low_memory=True,
|
||||
):
|
||||
if (low_memory and cfg.local_rank == 0) or not low_memory:
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name,
|
||||
use_cache=False,
|
||||
torch_dtype=torch.float32,
|
||||
_attn_implementation=model_config._attn_implementation, # pylint: disable=protected-access
|
||||
trust_remote_code=cfg.trust_remote_code,
|
||||
)
|
||||
dtype = torch_dtype if not cfg.float32 else None
|
||||
model.to(dtype=dtype, device="cpu" if low_memory else cfg.local_rank)
|
||||
else:
|
||||
with init_empty_weights():
|
||||
model = AutoModelForCausalLM.from_config(
|
||||
model_config,
|
||||
torch_dtype=torch_dtype,
|
||||
trust_remote_code=cfg.trust_remote_code,
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def load_sharded_model_quant(
|
||||
model_name,
|
||||
model_config,
|
||||
cfg,
|
||||
compute_dtype=torch.bfloat16,
|
||||
quant_storage=torch.float32,
|
||||
low_memory=True,
|
||||
verbose=False,
|
||||
loading_workers=2,
|
||||
):
|
||||
with init_empty_weights():
|
||||
model = AutoModelForCausalLM.from_config(
|
||||
model_config,
|
||||
trust_remote_code=cfg.trust_remote_code,
|
||||
)
|
||||
if hasattr(model, "transformer"):
|
||||
model.transformer = _replace_linear(
|
||||
model.transformer,
|
||||
Linear4bit,
|
||||
compute_dtype=compute_dtype,
|
||||
quant_type="nf4",
|
||||
quant_storage=quant_storage,
|
||||
)
|
||||
else:
|
||||
# this is the more common case with HF transformers
|
||||
model.model = _replace_linear(
|
||||
model.model,
|
||||
Linear4bit,
|
||||
compute_dtype=compute_dtype,
|
||||
quant_type="nf4",
|
||||
quant_storage=quant_storage,
|
||||
)
|
||||
model.is_loaded_in_4bit = True
|
||||
|
||||
# Grab the safetensors files that hold the weights
|
||||
try:
|
||||
idx = hub.cached_file(model_name, SAFE_WEIGHTS_INDEX_NAME)
|
||||
files, _ = hub.get_checkpoint_shard_files(model_name, idx)
|
||||
except OSError:
|
||||
try:
|
||||
# This means the model doesn't have a model.safetensors.index.json because it is not sharded
|
||||
files = []
|
||||
files.append(hub.cached_file(model_name, SAFE_WEIGHTS_NAME))
|
||||
except OSError as exc:
|
||||
# This means the model probably doesn't have a safetensors file
|
||||
raise exc
|
||||
|
||||
# Load in the weights, using our custom load_and_quantize method which quantizes Params4bit on the fly
|
||||
# and then places each layer on CPU or meta if using low_memory to minimize GPU memory usage
|
||||
def load_and_quantize_parallel(name_param, model, **kwargs):
|
||||
name, param = name_param
|
||||
load_and_quantize(model, name, param, **kwargs)
|
||||
|
||||
quant_method = "bnb"
|
||||
param_count = sum((p.numel() for n, p in model.named_parameters()))
|
||||
|
||||
n_workers = (
|
||||
n_loading_workers(quant_method, param_count)
|
||||
if loading_workers == -1
|
||||
else loading_workers
|
||||
)
|
||||
if cfg.local_rank == 0 and verbose:
|
||||
print(f"Using n_workers: {n_workers} for loading")
|
||||
|
||||
start = time.time()
|
||||
for filename in tqdm(
|
||||
files,
|
||||
desc="Loading & Quantizing Model Shards",
|
||||
disable=cfg.local_rank != 0,
|
||||
position=0,
|
||||
):
|
||||
weights = safetensors.torch.load_file(filename)
|
||||
parallel(
|
||||
load_and_quantize_parallel,
|
||||
iter(weights.items()),
|
||||
n_workers=n_workers,
|
||||
threadpool=True,
|
||||
model=model,
|
||||
dtype=quant_storage,
|
||||
device=cfg.local_rank,
|
||||
skip_names=[],
|
||||
to_cpu=(low_memory and cfg.local_rank == 0),
|
||||
to_meta=(low_memory and cfg.local_rank != 0),
|
||||
verbose=verbose,
|
||||
quant_method=quant_method,
|
||||
)
|
||||
|
||||
if cfg.local_rank == 0 and verbose:
|
||||
print(f"Loaded model weights in {time.time()-start:.3f} seconds")
|
||||
# cleanup any extra memory usage from parallel loading
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
return model
|
||||
@@ -11,7 +11,6 @@ import addict
|
||||
import bitsandbytes as bnb
|
||||
import torch
|
||||
import transformers
|
||||
import transformers.modeling_utils
|
||||
from accelerate import init_empty_weights
|
||||
from bitsandbytes.nn import Params4bit
|
||||
from peft import (
|
||||
@@ -44,38 +43,11 @@ from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
|
||||
from axolotl.utils.bench import log_gpu_memory_usage
|
||||
from axolotl.utils.chat_templates import chat_templates
|
||||
from axolotl.utils.dict import DictDefault
|
||||
from axolotl.utils.distributed import zero_only
|
||||
from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_unsloth_wrapper
|
||||
from axolotl.utils.lora_embeddings import get_linear_embedding_layers
|
||||
from axolotl.utils.model_shard_quant import load_sharded_model, load_sharded_model_quant
|
||||
|
||||
LOG = logging.getLogger("axolotl")
|
||||
|
||||
|
||||
# copied from accelerator.FullyShardedDataParallelPlugin
|
||||
def get_module_class_from_name(module, name):
|
||||
"""
|
||||
Gets a class from a module by its name.
|
||||
|
||||
Args:
|
||||
module (`torch.nn.Module`): The module to get the class from.
|
||||
name (`str`): The name of the class.
|
||||
"""
|
||||
modules_children = list(module.children())
|
||||
if module.__class__.__name__ == name:
|
||||
return module.__class__
|
||||
|
||||
if len(modules_children) == 0:
|
||||
return None
|
||||
|
||||
for child_module in modules_children:
|
||||
module_class = get_module_class_from_name(child_module, name)
|
||||
if module_class is not None:
|
||||
return module_class
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def check_model_config(cfg: DictDefault, model_config: Union[AutoConfig, DictDefault]):
|
||||
quant_config_exists = (
|
||||
hasattr(model_config, "quantization_config")
|
||||
@@ -275,11 +247,10 @@ def load_tokenizer(cfg):
|
||||
{"additional_special_tokens": additional_special_tokens}
|
||||
)
|
||||
|
||||
with zero_only():
|
||||
LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
|
||||
LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
|
||||
LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
|
||||
LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
|
||||
LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
|
||||
LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
|
||||
LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
|
||||
LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
|
||||
|
||||
if cfg.chat_template:
|
||||
chat_template_string = chat_templates(cfg.chat_template)
|
||||
@@ -312,9 +283,6 @@ def load_model(
|
||||
# TODO refactor as a kwarg
|
||||
load_in_8bit = cfg.load_in_8bit
|
||||
|
||||
if cfg.gradient_checkpointing == "unsloth":
|
||||
transformers.modeling_utils.checkpoint = hf_grad_checkpoint_unsloth_wrapper
|
||||
|
||||
if hasattr(model_config, "model_type") and model_config.model_type == "btlm":
|
||||
if cfg.flash_attention:
|
||||
from axolotl.monkeypatch.btlm_attn_hijack_flash import (
|
||||
@@ -467,7 +435,6 @@ def load_model(
|
||||
|
||||
if cfg.revision_of_model:
|
||||
model_kwargs["revision"] = cfg.revision_of_model
|
||||
|
||||
if cfg.gptq:
|
||||
if not hasattr(model_config, "quantization_config"):
|
||||
LOG.warning("model config does not contain quantization_config information")
|
||||
@@ -489,7 +456,7 @@ def load_model(
|
||||
"bnb_4bit_quant_type": "nf4",
|
||||
"bnb_4bit_quant_storage": torch.bfloat16,
|
||||
}
|
||||
if cfg.model_config_type in ["jamba", "qwen2_moe"] and not cfg.deepspeed:
|
||||
if not cfg.deepspeed:
|
||||
# for some reason, this causes the loss to be off by an order of magnitude
|
||||
# but deepspeed needs this still in bfloat16
|
||||
bnb_config["bnb_4bit_quant_storage"] = torch.float32
|
||||
@@ -500,13 +467,6 @@ def load_model(
|
||||
model_kwargs["quantization_config"] = BitsAndBytesConfig(
|
||||
**bnb_config,
|
||||
)
|
||||
elif cfg.adapter == "lora" and cfg.load_in_8bit:
|
||||
bnb_config = {
|
||||
"load_in_8bit": True,
|
||||
}
|
||||
model_kwargs["quantization_config"] = BitsAndBytesConfig(
|
||||
**bnb_config,
|
||||
)
|
||||
|
||||
if cfg.load_in_8bit and cfg.adapter is not None:
|
||||
model_kwargs["load_in_8bit"] = True
|
||||
@@ -554,31 +514,7 @@ def load_model(
|
||||
qlora_fsdp = cfg.fsdp and cfg.adapter == "qlora"
|
||||
|
||||
try:
|
||||
skip_move_to_device = False
|
||||
if (
|
||||
cfg.fsdp and cfg.fsdp_config.fsdp_cpu_ram_efficient_loading
|
||||
) and not qlora_fsdp:
|
||||
model = load_sharded_model(
|
||||
base_model,
|
||||
model_config,
|
||||
cfg,
|
||||
torch_dtype=cfg.torch_dtype,
|
||||
)
|
||||
skip_move_to_device = True
|
||||
elif (
|
||||
qlora_fsdp
|
||||
and cfg.fsdp_config.fsdp_cpu_ram_efficient_loading
|
||||
and cfg.model_config_type == "dbrx"
|
||||
):
|
||||
quant_storage = cfg.torch_dtype
|
||||
model = load_sharded_model_quant(
|
||||
base_model,
|
||||
model_config,
|
||||
cfg,
|
||||
quant_storage=quant_storage,
|
||||
)
|
||||
skip_move_to_device = True
|
||||
elif (
|
||||
model_config.model_type == "llama"
|
||||
and not cfg.trust_remote_code
|
||||
and not cfg.gptq
|
||||
@@ -658,11 +594,6 @@ def load_model(
|
||||
**model_kwargs,
|
||||
)
|
||||
else:
|
||||
if qlora_fsdp and cfg.fsdp_config.fsdp_cpu_ram_efficient_loading:
|
||||
skip_move_to_device = True
|
||||
if "device_map" in model_kwargs:
|
||||
del model_kwargs["device_map"]
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
base_model,
|
||||
config=model_config,
|
||||
@@ -736,17 +667,13 @@ def load_model(
|
||||
needs_fa2_dtype = cfg.adapter or cfg.fsdp
|
||||
skip_prepare_model_for_kbit_training = False
|
||||
|
||||
if is_deepspeed_zero3_enabled():
|
||||
if cfg.model_config_type == "mixtral" and is_deepspeed_zero3_enabled():
|
||||
from deepspeed.utils import ( # pylint: disable=no-name-in-module
|
||||
set_z3_leaf_modules,
|
||||
)
|
||||
from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
|
||||
|
||||
if cfg.model_config_type == "mixtral":
|
||||
moe_block = get_module_class_from_name(model, "MixtralSparseMoeBlock")
|
||||
set_z3_leaf_modules(model, [moe_block])
|
||||
elif cfg.model_config_type == "dbrx":
|
||||
moe_block = get_module_class_from_name(model, "DbrxFFN")
|
||||
set_z3_leaf_modules(model, [moe_block])
|
||||
set_z3_leaf_modules(model, [MixtralSparseMoeBlock])
|
||||
|
||||
if cfg.model_config_type == "qwen" and cfg.adapter == "lora":
|
||||
# Qwen doesn't play nicely with LoRA if this is enabled
|
||||
@@ -756,8 +683,7 @@ def load_model(
|
||||
if cfg.adapter == "lora" and loftq_bits:
|
||||
skip_prepare_model_for_kbit_training = True
|
||||
|
||||
if qlora_fsdp or (cfg.fsdp and cfg.fsdp_config.fsdp_cpu_ram_efficient_loading):
|
||||
# make sure everything is in the same dtype
|
||||
if qlora_fsdp:
|
||||
skip_prepare_model_for_kbit_training = True
|
||||
|
||||
if cfg.adapter in ["lora", "qlora"]:
|
||||
@@ -798,7 +724,7 @@ def load_model(
|
||||
cfg.ddp
|
||||
and not load_in_8bit
|
||||
and not (cfg.rl and cfg.load_in_4bit)
|
||||
and not skip_move_to_device
|
||||
and not qlora_fsdp
|
||||
):
|
||||
# TODO revaldate this conditional
|
||||
model.to(f"cuda:{cfg.local_rank}")
|
||||
@@ -954,12 +880,7 @@ def load_lora(model, cfg, inference=False, config_only=False):
|
||||
|
||||
rank = int(os.environ.get("LOCAL_RANK", 0))
|
||||
|
||||
if (
|
||||
cfg.fsdp
|
||||
and cfg.adapter
|
||||
and cfg.fsdp_config.fsdp_cpu_ram_efficient_loading
|
||||
and rank != 0
|
||||
):
|
||||
if cfg.fsdp and cfg.adapter == "qlora" and rank != 0:
|
||||
setup_quantized_meta_for_peft(model)
|
||||
|
||||
if cfg.lora_model_dir:
|
||||
@@ -978,18 +899,8 @@ def load_lora(model, cfg, inference=False, config_only=False):
|
||||
model = get_peft_model(model, lora_config)
|
||||
|
||||
if rank == 0:
|
||||
try:
|
||||
model.print_trainable_parameters()
|
||||
except AttributeError as exc:
|
||||
LOG.warning(
|
||||
"Exception caught during model.print_trainable_parameters(): %s", exc
|
||||
)
|
||||
elif (
|
||||
cfg.fsdp
|
||||
and cfg.adapter
|
||||
and cfg.fsdp_config.fsdp_cpu_ram_efficient_loading
|
||||
and rank != 0
|
||||
):
|
||||
model.print_trainable_parameters()
|
||||
elif cfg.fsdp and cfg.adapter == "qlora":
|
||||
setup_quantized_peft_meta_for_training(model)
|
||||
|
||||
return model, lora_config
|
||||
|
||||
@@ -172,21 +172,17 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
|
||||
return train_dataset, eval_dataset
|
||||
|
||||
|
||||
def process_pretraining_datasets_for_packing(
|
||||
train_dataset, sequence_len, skip_position_ids=True
|
||||
):
|
||||
def process_pretraining_datasets_for_packing(train_dataset, sequence_len):
|
||||
drop_long = partial(drop_long_seq, sequence_len=sequence_len)
|
||||
|
||||
train_dataset = train_dataset.filter(
|
||||
drop_long,
|
||||
desc="Dropping Long Sequences",
|
||||
)
|
||||
if skip_position_ids:
|
||||
train_dataset = train_dataset.map(
|
||||
add_position_ids,
|
||||
desc="Add position_id column (Pretraining Sample Packing)",
|
||||
)
|
||||
|
||||
train_dataset = train_dataset.map(
|
||||
add_position_ids,
|
||||
desc="Add position_id column (Pretraining Sample Packing)",
|
||||
)
|
||||
return train_dataset
|
||||
|
||||
|
||||
@@ -198,7 +194,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
|
||||
.apply(lambda x: len(x)) # pylint: disable=unnecessary-lambda
|
||||
.values
|
||||
)
|
||||
LOG.debug(f"total_num_tokens: {total_num_tokens:_}", main_process_only=True)
|
||||
LOG.debug(f"total_num_tokens: {total_num_tokens}", main_process_only=True)
|
||||
if update:
|
||||
cfg.total_num_tokens = total_num_tokens
|
||||
|
||||
@@ -212,7 +208,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
|
||||
.sum()
|
||||
)
|
||||
LOG.debug(
|
||||
f"`total_supervised_tokens: {total_supervised_tokens:_}`",
|
||||
f"`total_supervised_tokens: {total_supervised_tokens}`",
|
||||
main_process_only=True,
|
||||
)
|
||||
if update:
|
||||
@@ -239,7 +235,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
|
||||
* cfg.num_epochs
|
||||
)
|
||||
LOG.debug(
|
||||
f"total_num_tokens: {cfg.total_num_tokens:_}, total_num_steps: {total_num_steps:_}",
|
||||
f"total_num_tokens: {cfg.total_num_tokens}, total_num_steps: {total_num_steps}",
|
||||
main_process_only=True,
|
||||
)
|
||||
else:
|
||||
@@ -306,8 +302,6 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
|
||||
|
||||
def setup_fsdp_envs(cfg):
|
||||
os.environ["ACCELERATE_USE_FSDP"] = "true"
|
||||
if cfg.fsdp_config.fsdp_activation_checkpointing:
|
||||
os.environ["FSDP_ACTIVATION_CHECKPOINTING"] = "true"
|
||||
if cfg.fsdp_config.fsdp_offload_params:
|
||||
os.environ["FSDP_OFFLOAD_PARAMS"] = "true"
|
||||
if cfg.fsdp_config.fsdp_sync_module_states:
|
||||
|
||||
@@ -30,7 +30,7 @@ class TestMixtral(unittest.TestCase):
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "hf-internal-testing/Mixtral-tiny",
|
||||
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
|
||||
"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
|
||||
"flash_attention": True,
|
||||
"sample_packing": True,
|
||||
"sequence_len": 2048,
|
||||
@@ -74,7 +74,7 @@ class TestMixtral(unittest.TestCase):
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "hf-internal-testing/Mixtral-tiny",
|
||||
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
|
||||
"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
|
||||
"flash_attention": True,
|
||||
"sample_packing": True,
|
||||
"sequence_len": 2048,
|
||||
|
||||
@@ -22,7 +22,7 @@ class TestModelPatches(unittest.TestCase):
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "hf-internal-testing/Mixtral-tiny",
|
||||
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
|
||||
"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
|
||||
"flash_attention": True,
|
||||
"sample_packing": True,
|
||||
"sequence_len": 2048,
|
||||
|
||||
@@ -33,7 +33,7 @@ class TestMixtral(unittest.TestCase):
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "hf-internal-testing/Mixtral-tiny",
|
||||
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
|
||||
"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
|
||||
"flash_attention": True,
|
||||
"sequence_len": 1024,
|
||||
"load_in_4bit": True,
|
||||
@@ -87,7 +87,7 @@ class TestMixtral(unittest.TestCase):
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "hf-internal-testing/Mixtral-tiny",
|
||||
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
|
||||
"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
|
||||
"flash_attention": False,
|
||||
"sequence_len": 1024,
|
||||
"load_in_4bit": True,
|
||||
@@ -141,7 +141,7 @@ class TestMixtral(unittest.TestCase):
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "hf-internal-testing/Mixtral-tiny",
|
||||
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
|
||||
"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
|
||||
"flash_attention": True,
|
||||
"sequence_len": 1024,
|
||||
"adapter": "lora",
|
||||
@@ -198,7 +198,7 @@ class TestMixtral(unittest.TestCase):
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "hf-internal-testing/Mixtral-tiny",
|
||||
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
|
||||
"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
|
||||
"flash_attention": False,
|
||||
"sequence_len": 1024,
|
||||
"adapter": "lora",
|
||||
@@ -255,7 +255,7 @@ class TestMixtral(unittest.TestCase):
|
||||
cfg = DictDefault(
|
||||
{
|
||||
"base_model": "hf-internal-testing/Mixtral-tiny",
|
||||
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
|
||||
"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
|
||||
"flash_attention": True,
|
||||
"sequence_len": 1024,
|
||||
"val_set_size": 0.1,
|
||||
|
||||
@@ -27,9 +27,7 @@ def fixture_alpaca_dataset():
|
||||
@pytest.fixture(name="tokenizer")
|
||||
def fixture_tokenizer():
|
||||
# pylint: disable=all
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
"casperhansen/mistral-7b-instruct-v0.1-awq"
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
|
||||
tokenizer.add_special_tokens(
|
||||
{
|
||||
"eos_token": AddedToken(
|
||||
|
||||
@@ -43,9 +43,7 @@ def fixture_sharegpt_dataset():
|
||||
|
||||
@pytest.fixture(name="tokenizer")
|
||||
def fixture_tokenizer():
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
"casperhansen/mistral-7b-instruct-v0.1-awq"
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
|
||||
tokenizer.add_tokens(
|
||||
[
|
||||
AddedToken("<eot>", rstrip=False, lstrip=False, normalized=False),
|
||||
|
||||
@@ -96,9 +96,7 @@ def fixture_multi_role_dataset():
|
||||
|
||||
@pytest.fixture(name="tokenizer")
|
||||
def fixture_tokenizer():
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
"casperhansen/mistral-7b-instruct-v0.1-awq"
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
|
||||
tokenizer.add_special_tokens(
|
||||
{
|
||||
"eos_token": AddedToken(
|
||||
|
||||
@@ -454,9 +454,7 @@ class OrpoTokenizationTest(unittest.TestCase):
|
||||
|
||||
def setUp(self) -> None:
|
||||
# pylint: disable=duplicate-code
|
||||
tokenizer = LlamaTokenizer.from_pretrained(
|
||||
"casperhansen/mistral-7b-instruct-v0.1-awq"
|
||||
)
|
||||
tokenizer = LlamaTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
|
||||
tokenizer.add_special_tokens(
|
||||
{
|
||||
"eos_token": AddedToken(
|
||||
|
||||
@@ -600,7 +600,6 @@ class TestValidation(BaseValidation):
|
||||
{
|
||||
"sample_packing": True,
|
||||
"pad_to_sequence_len": None,
|
||||
"flash_attention": True,
|
||||
}
|
||||
)
|
||||
| minimal_cfg
|
||||
@@ -902,7 +901,6 @@ class TestValidation(BaseValidation):
|
||||
{
|
||||
"sample_packing": True,
|
||||
"eval_table_size": 100,
|
||||
"flash_attention": True,
|
||||
}
|
||||
)
|
||||
| minimal_cfg
|
||||
@@ -918,7 +916,6 @@ class TestValidation(BaseValidation):
|
||||
{
|
||||
"sample_packing": True,
|
||||
"eval_sample_packing": False,
|
||||
"flash_attention": True,
|
||||
}
|
||||
)
|
||||
| minimal_cfg
|
||||
@@ -931,7 +928,6 @@ class TestValidation(BaseValidation):
|
||||
{
|
||||
"sample_packing": False,
|
||||
"eval_table_size": 100,
|
||||
"flash_attention": True,
|
||||
}
|
||||
)
|
||||
| minimal_cfg
|
||||
@@ -945,7 +941,6 @@ class TestValidation(BaseValidation):
|
||||
"sample_packing": True,
|
||||
"eval_table_size": 100,
|
||||
"eval_sample_packing": False,
|
||||
"flash_attention": True,
|
||||
}
|
||||
)
|
||||
| minimal_cfg
|
||||
|
||||
Reference in New Issue
Block a user