#!/usr/bin/env python
# coding=utf-8
"""
Commonly used constants.
"""
[docs]
TEXT_ONLY_DATASET_DESCRIPTION = (
"""
"text_only": a dataset with only raw text instances, with following format:
{
"type": "text_only",
"instances": [
{ "text": "TEXT_1" },
{ "text": "TEXT_2" },
...
]
}
"""
).lstrip("\n")
[docs]
TEXT_TO_SCORED_TEXTLIST_DATASET_DESCRIPTION = (
"""
This kind of dataset is commonly used in reward model training/prediction, as well as rl training.
{
"type": "text_to_scored_textlist",
"instances": [
{
"input": "what's your name?",
"output": [
{"score": 1.0, "text": "My name is John"},
{"score": -0.8, "text": "I'm John"}
]
},
{
"input": "Who are you?",
"output": [
{"score": 1.5, "text": "My name is Amy"},
{"score": 1.0, "text": "I'm Amy"}
]
},
]
}
"""
).lstrip("\n")
[docs]
PAIRED_TEXT_TO_TEXT_DATASET_DESCRIPTION = (
"""
This kind of dataset is commonly used in reward model training as well as rl training.
{
"type": "paired_text_to_text",
"instances": [
{
"prompt": "Who are you?",
"chosen": "My name is Amy.",
"rejected": "I'm Amy",
"margin": 0.6
},
{
"prompt": "what's your name?",
"chosen": "My name is John.",
"rejected": "I'm John",
"margin": 0.5
}
]
}
"""
).lstrip("\n")
[docs]
TEXT_ONLY_DATASET_DETAILS = (
"""
For example,
```python
from lmflow.datasets import Dataset
data_dict = {
"type": "text_only",
"instances": [
{ "text": "Human: Hello. Bot: Hi!" },
{ "text": "Human: How are you today? Bot: Fine, thank you!" },
]
}
dataset = Dataset.create_from_dict(data_dict)
```
You may also save the corresponding format to json,
```python
import json
from lmflow.args import DatasetArguments
from lmflow.datasets import Dataset
data_dict = {
"type": "text_only",
"instances": [
{ "text": "Human: Hello. Bot: Hi!" },
{ "text": "Human: How are you today? Bot: Fine, thank you!" },
]
}
with open("data.json", "w") as fout:
json.dump(data_dict, fout)
data_args = DatasetArgument(dataset_path="data.json")
dataset = Dataset(data_args)
new_data_dict = dataset.to_dict()
# `new_data_dict` Should have the same content as `data_dict`
```
"""
).lstrip("\n")
[docs]
TEXT2TEXT_DATASET_DESCRIPTION = (
"""
"text2text": a dataset with input & output instances, with following format:
{
"type": "text2text",
"instances": [
{ "input": "INPUT_1", "output": "OUTPUT_1" },
{ "input": "INPUT_2", "output": "OUTPUT_2" },
...
]
}
"""
).lstrip("\n")
[docs]
CONVERSATION_DATASET_DESCRIPTION = (
"""
"conversation": a dataset with conversation instances, with following format (`conversation_id`, `system` and `tools` are optional):
{
"type": "conversation",
"instances": [
{
"conversation_id": "CONVERSATION_ID",
"system": "SYSTEM_PROPMT",
"tools": ["TOOL_DESCRIPTION_1","TOOL_DESCRIPTION_2","TOOL_DESCRIPTION_X"],
"messages": [
{
"role": "user",
"content": "USER_INPUT_1"
},
{
"role": "assistant",
"content": "ASSISTANT_RESPONSE_1"
},
{
"role": "user",
"content": "USER_INPUT_2"
},
{
"role": "assistant",
"content": "ASSISTANT_RESPONSE_2"
}
]
},
{
"conversation_id": "CONVERSATION_ID",
"system": "SYSTEM_PROPMT",
"tools": ["TOOL_DESCRIPTION_1"],
"messages": [
{
"role": "user",
"content": "USER_INPUT_1"
},
{
"role": "assistant",
"content": "ASSISTANT_RESPONSE_1"
}
]
}
]
}
"""
).lstrip("\n")
[docs]
PAIRED_CONVERSATION_DATASET_DESCRIPTION = (
"""
"paired_conversation": a dataset with paired conversation instances, with following format:
{
"type": "paired_conversation",
"instances": [
{
"chosen": {
"conversation_id": "CONVERSATION_ID",
"system": "SYSTEM_PROPMT",
"tools": ["TOOL_DESCRIPTION_1","TOOL_DESCRIPTION_2","TOOL_DESCRIPTION_3"],
"messages": [
{
"role": "user",
"content": "USER_INPUT_1"
},
{
"role": "assistant",
"content": "ASSISTANT_RESPONSE_1_GOOD"
},
{
"role": "user",
"content": "USER_INPUT_2"
},
{
"role": "assistant",
"content": "ASSISTANT_RESPONSE_2_GOOD"
}
]
},
"rejected": {
"conversation_id": "CONVERSATION_ID",
"system": "SYSTEM_PROPMT",
"tools": ["TOOL_DESCRIPTION_1","TOOL_DESCRIPTION_2","TOOL_DESCRIPTION_3"],
"messages": [
{
"role": "user",
"content": "USER_INPUT_1"
},
{
"role": "assistant",
"content": "ASSISTANT_RESPONSE_1_BAD"
},
{
"role": "user",
"content": "USER_INPUT_2"
},
{
"role": "assistant",
"content": "ASSISTANT_RESPONSE_2_BAD"
}
]
}
}
]
}
"""
).lstrip("\n")
[docs]
TEXT_TO_TEXTLIST_DATASET_DESCRIPTION = (
"""
This kind of dataset is commonly used in reward model inference.
{
"type": "text_to_textlist",
"instances": [
{
"input": "what's your name?",
"output": [
"My name is John",
"I'm John",
]
},
{
"input": "Who are you?",
"output": [
"My name is Amy",
"I'm Amy",
]
},
]
}
"""
).lstrip("\n")
[docs]
TEXT2TEXT_DATASET_DETAILS = (
"""
For example,
```python
from lmflow.datasets import Dataset
data_dict = {
"type": "text2text",
"instances": [
{
"input": "Human: Hello.",
"output": "Bot: Hi!",
},
{
"input": "Human: How are you today?",
"output": "Bot: Fine, thank you! And you?",
}
]
}
dataset = Dataset.create_from_dict(data_dict)
```
You may also save the corresponding format to json,
```python
import json
from lmflow.args import DatasetArguments
from lmflow.datasets import Dataset
data_dict = {
"type": "text2text",
"instances": [
{
"input": "Human: Hello.",
"output": "Bot: Hi!",
},
{
"input": "Human: How are you today?",
"output": "Bot: Fine, thank you! And you?",
}
]
}
with open("data.json", "w") as fout:
json.dump(data_dict, fout)
data_args = DatasetArgument(dataset_path="data.json")
dataset = Dataset(data_args)
new_data_dict = dataset.to_dict()
# `new_data_dict` Should have the same content as `data_dict`
```
"""
).lstrip("\n")
[docs]
FLOAT_ONLY_DATASET_DESCRIPTION = (
"""
"float_only": a dataset with only float instances, with following format:
{
"type": "float_only",
"instances": [
{ "value": "FLOAT_1" },
{ "value": "FLOAT_2" },
...
]
}
"""
).lstrip("\n")
[docs]
TEXT_ONLY_DATASET_LONG_DESCRITION = (
TEXT_ONLY_DATASET_DESCRIPTION + TEXT_ONLY_DATASET_DETAILS
)
[docs]
TEXT2TEXT_DATASET_LONG_DESCRITION = (
TEXT2TEXT_DATASET_DESCRIPTION + TEXT2TEXT_DATASET_DETAILS
)
[docs]
DATASET_DESCRIPTION_MAP = {
"text_only": TEXT_ONLY_DATASET_DESCRIPTION,
"text2text": TEXT2TEXT_DATASET_DESCRIPTION,
"float_only": FLOAT_ONLY_DATASET_DESCRIPTION,
}
[docs]
INSTANCE_FIELDS_MAP = {
"text_only": ["text"],
"text2text": ["input", "output"],
"conversation": ["messages"], # system, tools and conversation_id are optional
"paired_conversation": ["chosen", "rejected"],
"paired_text_to_text": ["prompt", "chosen", "rejected"],
"float_only": ["value"],
"image_text": ["images", "text"],
"text_to_textlist": ["input", "output"],
"text_to_scored_textlist": ["input", "output"],
}
[docs]
CONVERSATION_ROLE_NAMES = {
"system": "system",
"user": "user",
"assistant": "assistant",
"function": "function",
"observation": "observation"
}
# LLAVA constants
[docs]
CONTROLLER_HEART_BEAT_EXPIRATION = 30
[docs]
WORKER_HEART_BEAT_INTERVAL = 15
# Model Constants
[docs]
IMAGE_TOKEN_INDEX = -200
[docs]
DEFAULT_IMAGE_TOKEN = "<image>"
[docs]
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
[docs]
DEFAULT_IM_START_TOKEN = "<im_start>"
[docs]
DEFAULT_IM_END_TOKEN = "<im_end>"
# Lora
# NOTE: Be careful, when passing lora_target_modules through arg parser, the
# value should be like'--lora_target_modules q_proj, v_proj \', while specifying
# here, it should be in list format.
[docs]
LMFLOW_LORA_TARGET_MODULES_MAPPING = {
'qwen2': ["q_proj", "v_proj"],
'internlm2': ["wqkv"],
}
# vllm inference
[docs]
MEMORY_SAFE_VLLM_INFERENCE_FINISH_FLAG = "MEMORY_SAFE_VLLM_INFERENCE_DONE"
[docs]
RETURN_CODE_ERROR_BUFFER = [
134
]
# return code 134:
# > Fatal Python error: _enter_buffered_busy: could not acquire lock for <_io.BufferedWriter name='<stdout>'>
# > at interpreter shutdown, possibly due to daemon threads
# The above error, by our observation, is due to the kill signal with unfinished
# stdout/stderr writing in the subprocess
[docs]
MEMORY_SAFE_VLLM_INFERENCE_ENV_VAR_TO_REMOVE = [
"OMP_NUM_THREADS",
"LOCAL_RANK",
"RANK",
"GROUP_RANK",
"ROLE_RANK",
"ROLE_NAME",
"LOCAL_WORLD_SIZE",
"WORLD_SIZE",
"GROUP_WORLD_SIZE",
"ROLE_WORLD_SIZE",
"MASTER_ADDR",
"MASTER_PORT",
"TORCHELASTIC_RESTART_COUNT",
"TORCHELASTIC_MAX_RESTARTS",
"TORCHELASTIC_RUN_ID",
"TORCHELASTIC_USE_AGENT_STORE",
"TORCH_NCCL_ASYNC_ERROR_HANDLING",
"TORCHELASTIC_ERROR_FILE",
]
# dpov2 align
[docs]
MEMORY_SAFE_DPOV2_ALIGN_ENV_VAR_TO_REMOVE = [
"OMP_NUM_THREADS",
"LOCAL_RANK",
"RANK",
"GROUP_RANK",
"ROLE_RANK",
"ROLE_NAME",
"LOCAL_WORLD_SIZE",
"WORLD_SIZE",
"GROUP_WORLD_SIZE",
"ROLE_WORLD_SIZE",
"MASTER_ADDR",
"MASTER_PORT",
"TORCHELASTIC_RESTART_COUNT",
"TORCHELASTIC_MAX_RESTARTS",
"TORCHELASTIC_RUN_ID",
"TORCHELASTIC_USE_AGENT_STORE",
"TORCH_NCCL_ASYNC_ERROR_HANDLING",
"TORCHELASTIC_ERROR_FILE",
]
