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Unverified Commit df5e9c53 authored by YeAnbang's avatar YeAnbang Committed by GitHub
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[ColossalChat] Update RLHF V2 (#5286) 



* Add dpo. Fix sft, ppo, lora. Refactor all

* fix and tested ppo

* 2 nd round refactor

* add ci tests

* fix ci

* fix ci

* fix readme, style

* fix readme style

* fix style, fix benchmark

* reproduce benchmark result, remove useless files

* rename to ColossalChat

* use new image

* fix ci workflow

* fix ci

* use local model/tokenizer for ci tests

* fix ci

* fix ci

* fix ci

* fix ci timeout

* fix rm progress bar. fix ci timeout

* fix ci

* fix ci typo

* remove 3d plugin from ci temporary

* test environment

* cannot save optimizer

* support chat template

* fix readme

* fix path

* test ci locally

* restore build_or_pr

* fix ci data path

* fix benchmark

* fix ci, move ci tests to 3080, disable fast tokenizer

* move ci to 85

* support flash attention 2

* add all-in-one data preparation script. Fix colossal-llama2-chat chat template

* add hardware requirements

* move ci test data

* fix save_model, add unwrap

* fix missing bos

* fix missing bos; support grad accumulation with gemini

* fix ci

* fix ci

* fix ci

* fix llama2 chat template config

* debug sft

* debug sft

* fix colossalai version requirement

* fix ci

* add sanity check to prevent NaN loss

* fix requirements

* add dummy data generation script

* add dummy data generation script

* add dummy data generation script

* add dummy data generation script

* update readme

* update readme

* update readme and ignore

* fix logger bug

* support parallel_output

* modify data preparation logic

* fix tokenization

* update lr

* fix inference

* run pre-commit

---------
Co-authored-by: default avatarTong Li <tong.li352711588@gmail.com>
parent 36c4bb28
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applications/Chat/coati/models/bloom/bloom_actor.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
from transformers import BloomConfig, BloomForCausalLM
from ..base import Actor
class BLOOMActor(Actor):
"""
BLOOM Actor model.
Args:
pretrained (str): Pretrained model name or path.
config (BloomConfig): Model config.
checkpoint (bool): Enable gradient checkpointing.
lora_rank (int): LoRA rank.
lora_train_bias (str): LoRA bias training mode.
"""
def __init__(
self,
pretrained: str = None,
config: Optional[BloomConfig] = None,
checkpoint: bool = False,
lora_rank: int = 0,
lora_train_bias: str = "none",
) -> None:
if pretrained is not None:
model = BloomForCausalLM.from_pretrained(pretrained)
elif config is not None:
model = BloomForCausalLM(config)
else:
model = BloomForCausalLM(BloomConfig())
if checkpoint:
model.gradient_checkpointing_enable()
super().__init__(model, lora_rank, lora_train_bias)
applications/Chat/coati/models/bloom/bloom_critic.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
import torch.nn as nn
from transformers import BloomConfig, BloomModel
from ..base import Critic
class BLOOMCritic(Critic):
"""
BLOOM Critic model.
Args:
pretrained (str): Pretrained model name or path.
config (BloomConfig): Model config.
lora_rank (int): LoRA rank.
lora_train_bias (str): LoRA bias training mode.
"""
def __init__(
self,
pretrained: str = None,
config: Optional[BloomConfig] = None,
lora_rank: int = 0,
lora_train_bias: str = "none",
**kwargs,
) -> None:
if pretrained is not None:
model = BloomModel.from_pretrained(pretrained)
elif config is not None:
model = BloomModel(config)
else:
model = BloomModel(BloomConfig())
value_head = nn.Linear(model.config.hidden_size, 1)
super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)
applications/Chat/coati/models/bloom/bloom_rm.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
import torch.nn as nn
from transformers import BloomConfig, BloomModel
from ..base import RewardModel
class BLOOMRM(RewardModel):
"""
BLOOM Reward model.
Args:
pretrained (str): Pretrained model name or path.
config (BloomConfig): Model config.
lora_rank (int): LoRA rank.
lora_train_bias (str): LoRA bias training mode.
"""
def __init__(
self,
pretrained: str = None,
config: Optional[BloomConfig] = None,
lora_rank: int = 0,
lora_train_bias: str = "none",
) -> None:
if pretrained is not None:
model = BloomModel.from_pretrained(pretrained)
elif config is not None:
model = BloomModel(config)
else:
model = BloomModel(BloomConfig())
value_head = nn.Linear(model.config.hidden_size, 1)
value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.hidden_size + 1))
super().__init__(model, value_head, lora_rank, lora_train_bias)
applications/Chat/coati/models/chatglm/__init__.py deleted 100644 → 0
View file @ 36c4bb28
from .chatglm_actor import ChatGLMActor
__all__ = ["ChatGLMActor"]
applications/Chat/coati/models/chatglm/chatglm_actor.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
from ..base import Actor
from .configuration_chatglm import ChatGLMConfig
from .modeling_chatglm import ChatGLMForConditionalGeneration
class ChatGLMActor(Actor):
"""
ChatGLM Actor model.
Args:
pretrained (str): Pretrained model name or path.
config (ChatGLMConfig): Model config.
checkpoint (bool): Enable gradient checkpointing.
do not support lora for now.
"""
def __init__(
self, pretrained: str = None, config: Optional[ChatGLMConfig] = None, checkpoint: bool = False
) -> None:
if pretrained is not None:
model = ChatGLMForConditionalGeneration.from_pretrained(pretrained)
elif config is not None:
model = ChatGLMForConditionalGeneration(config)
else:
model = ChatGLMForConditionalGeneration(ChatGLMConfig())
if checkpoint:
model.gradient_checkpointing_enable()
super().__init__(model, lora_rank=0, lora_train_bias="none")
applications/Chat/coati/models/chatglm/chatglm_tokenizer.py deleted 100644 → 0
View file @ 36c4bb28
"""
This code is copied from https://huggingface.co/THUDM/chatglm-6b/blob/main/tokenization_chatglm.py
"""
"""Tokenization classes for ChatGLM."""
import os
from typing import Dict, List, Optional, Union
import numpy as np
import sentencepiece as spm
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import BatchEncoding, EncodedInput
from transformers.utils import PaddingStrategy, logging
logger = logging.get_logger(__name__)
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"THUDM/chatglm-6b": 2048,
}
class TextTokenizer:
def __init__(self, model_path):
self.sp = spm.SentencePieceProcessor()
self.sp.Load(model_path)
self.num_tokens = self.sp.vocab_size()
def encode(self, text):
return self.sp.EncodeAsIds(text)
def decode(self, ids: List[int]):
return self.sp.DecodeIds(ids)
def tokenize(self, text):
return self.sp.EncodeAsPieces(text)
def convert_tokens_to_string(self, tokens):
return self.sp.DecodePieces(tokens)
def convert_tokens_to_ids(self, tokens):
return [self.sp.PieceToId(token) for token in tokens]
def convert_token_to_id(self, token):
return self.sp.PieceToId(token)
def convert_id_to_token(self, idx):
return self.sp.IdToPiece(idx)
def __len__(self):
return self.num_tokens
class SPTokenizer:
def __init__(
self,
vocab_file,
num_image_tokens=20000,
max_blank_length=80,
byte_fallback=True,
):
assert vocab_file is not None
self.vocab_file = vocab_file
self.num_image_tokens = num_image_tokens
self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
self.max_blank_length = max_blank_length
self.byte_fallback = byte_fallback
self.text_tokenizer = TextTokenizer(vocab_file)
def _get_text_tokenizer(self):
return self.text_tokenizer
@staticmethod
def get_blank_token(length: int):
assert length >= 2
return f"<|blank_{length}|>"
@staticmethod
def get_tab_token():
return f"<|tab|>"
@property
def num_text_tokens(self):
return self.text_tokenizer.num_tokens
@property
def num_tokens(self):
return self.num_image_tokens + self.num_text_tokens
@staticmethod
def _encode_whitespaces(text: str, max_len: int = 80):
text = text.replace("\t", SPTokenizer.get_tab_token())
for i in range(max_len, 1, -1):
text = text.replace(" " * i, SPTokenizer.get_blank_token(i))
return text
def _preprocess(self, text: str, linebreak=True, whitespaces=True):
if linebreak:
text = text.replace("\n", "<n>")
if whitespaces:
text = self._encode_whitespaces(text, max_len=self.max_blank_length)
return text
def encode(self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True) -> List[int]:
"""
@param text: Text to encode.
@param linebreak: Whether to encode newline (\n) in text.
@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
"""
text = self._preprocess(text, linebreak, whitespaces)
if not add_dummy_prefix:
text = "<n>" + text
tmp = self._get_text_tokenizer().encode(text)
tokens = [x + self.num_image_tokens for x in tmp]
return tokens if add_dummy_prefix else tokens[2:]
def postprocess(self, text):
text = text.replace("<n>", "\n")
text = text.replace(SPTokenizer.get_tab_token(), "\t")
for i in range(2, self.max_blank_length + 1):
text = text.replace(self.get_blank_token(i), " " * i)
return text
def decode(self, text_ids: List[int]) -> str:
ids = [int(_id) - self.num_image_tokens for _id in text_ids]
ids = [_id for _id in ids if _id >= 0]
text = self._get_text_tokenizer().decode(ids)
text = self.postprocess(text)
return text
def decode_tokens(self, tokens: List[str]) -> str:
text = self._get_text_tokenizer().convert_tokens_to_string(tokens)
text = self.postprocess(text)
return text
def tokenize(self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True) -> List[str]:
"""
@param text: Text to encode.
@param linebreak: Whether to encode newline (\n) in text.
@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
"""
text = self._preprocess(text, linebreak, whitespaces)
if not add_dummy_prefix:
text = "<n>" + text
tokens = self._get_text_tokenizer().tokenize(text)
return tokens if add_dummy_prefix else tokens[2:]
def __getitem__(self, x: Union[int, str]):
if isinstance(x, int):
if x < self.num_image_tokens:
return "<image_{}>".format(x)
else:
return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens)
elif isinstance(x, str):
if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit():
return int(x[7:-1])
else:
return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens
else:
raise ValueError("The key should be str or int.")
class ChatGLMTokenizer(PreTrainedTokenizer):
"""
Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = {"vocab_file": "ice_text.model"}
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask", "position_ids"]
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=False,
bos_token="<sop>",
eos_token="<eop>",
end_token="</s>",
mask_token="[MASK]",
gmask_token="[gMASK]",
padding_side="left",
pad_token="<pad>",
unk_token="<unk>",
num_image_tokens=20000,
**kwargs,
) -> None:
super().__init__(
do_lower_case=do_lower_case,
remove_space=remove_space,
padding_side=padding_side,
bos_token=bos_token,
eos_token=eos_token,
end_token=end_token,
mask_token=mask_token,
gmask_token=gmask_token,
pad_token=pad_token,
unk_token=unk_token,
num_image_tokens=num_image_tokens,
**kwargs,
)
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.vocab_file = vocab_file
self.bos_token = bos_token
self.eos_token = eos_token
self.end_token = end_token
self.mask_token = mask_token
self.gmask_token = gmask_token
self.sp_tokenizer = SPTokenizer(vocab_file, num_image_tokens=num_image_tokens)
""" Initialisation """
@property
def gmask_token_id(self) -> Optional[int]:
if self.gmask_token is None:
return None
return self.convert_tokens_to_ids(self.gmask_token)
@property
def end_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the end of context token in the vocabulary. Returns `None` if the token has not been
set.
"""
if self.end_token is None:
return None
return self.convert_tokens_to_ids(self.end_token)
@property
def vocab_size(self):
"""Returns vocab size"""
return self.sp_tokenizer.num_tokens
def get_vocab(self):
"""Returns vocab as a dict"""
vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def preprocess_text(self, inputs):
if self.remove_space:
outputs = " ".join(inputs.strip().split())
else:
outputs = inputs
if self.do_lower_case:
outputs = outputs.lower()
return outputs
def _tokenize(self, text, **kwargs):
"""Returns a tokenized string."""
text = self.preprocess_text(text)
seq = self.sp_tokenizer.tokenize(text)
return seq
def convert_tokens_to_string(self, tokens: List[str]) -> str:
return self.sp_tokenizer.decode_tokens(tokens)
def _decode(self, token_ids: Union[int, List[int]], **kwargs) -> str:
if isinstance(token_ids, int):
token_ids = [token_ids]
if len(token_ids) == 0:
return ""
if self.pad_token_id in token_ids: # remove pad
token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
return super()._decode(token_ids, **kwargs)
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_tokenizer[token]
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.sp_tokenizer[index]
def save_vocabulary(self, save_directory, filename_prefix=None):
"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
filename_prefix (`str`, *optional*):
An optional prefix to add to the named of the saved files.
Returns:
`Tuple(str)`: Paths to the files saved.
"""
if os.path.isdir(save_directory):
vocab_file = os.path.join(save_directory, self.vocab_files_names["vocab_file"])
else:
vocab_file = save_directory
with open(self.vocab_file, "rb") as fin:
proto_str = fin.read()
with open(vocab_file, "wb") as writer:
writer.write(proto_str)
return (vocab_file,)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
gmask_id = self.sp_tokenizer[self.gmask_token]
self.sp_tokenizer[self.eos_token]
token_ids_0 = token_ids_0 + [gmask_id, self.sp_tokenizer[self.bos_token]]
if token_ids_1 is not None:
token_ids_0 = token_ids_0 + token_ids_1
return token_ids_0
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
Args:
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
bos_token_id = self.sp_tokenizer[self.bos_token]
mask_token_id = self.sp_tokenizer[self.mask_token]
gmask_token_id = self.sp_tokenizer[self.gmask_token]
assert self.padding_side == "left"
required_input = encoded_inputs[self.model_input_names[0]]
seq_length = len(required_input)
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
# Initialize attention mask if not present.
if max_length is not None:
if "attention_mask" not in encoded_inputs:
if bos_token_id in required_input:
context_length = required_input.index(bos_token_id)
else:
context_length = seq_length
attention_mask = np.ones((1, seq_length, seq_length))
attention_mask = np.tril(attention_mask)
attention_mask[:, :, :context_length] = 1
attention_mask = np.bool_(attention_mask < 0.5)
encoded_inputs["attention_mask"] = attention_mask
if "position_ids" not in encoded_inputs:
if bos_token_id in required_input:
context_length = required_input.index(bos_token_id)
else:
context_length = seq_length
position_ids = np.arange(seq_length, dtype=np.int64)
mask_token = mask_token_id if mask_token_id in required_input else gmask_token_id
if mask_token in required_input:
mask_position = required_input.index(mask_token)
position_ids[context_length:] = mask_position
block_position_ids = np.concatenate(
[
np.zeros(context_length, dtype=np.int64),
np.arange(1, seq_length - context_length + 1, dtype=np.int64),
]
)
encoded_inputs["position_ids"] = np.stack([position_ids, block_position_ids], axis=0)
if needs_to_be_padded:
difference = max_length - len(required_input)
if "attention_mask" in encoded_inputs:
encoded_inputs["attention_mask"] = np.pad(
encoded_inputs["attention_mask"],
pad_width=[(0, 0), (difference, 0), (difference, 0)],
mode="constant",
constant_values=True,
)
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
if "position_ids" in encoded_inputs:
encoded_inputs["position_ids"] = np.pad(
encoded_inputs["position_ids"], pad_width=[(0, 0), (difference, 0)]
)
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
return encoded_inputs
applications/Chat/coati/models/chatglm/configuration_chatglm.py deleted 100644 → 0
View file @ 36c4bb28
"""
This code is copied from https://huggingface.co/THUDM/chatglm-6b/resolve/main/configuration_chatglm.py
"""
""" ChatGLM model configuration """
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class ChatGLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`~ChatGLMModel`].
It is used to instantiate an ChatGLM model according to the specified arguments, defining the model
architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
the ChatGLM-6B [THUDM/ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used
to control the model outputs. Read the documentation from [`PretrainedConfig`]
for more information.
Args:
vocab_size (`int`, *optional*, defaults to 150528):
Vocabulary size of the ChatGLM-6B model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~ChatGLMModel`] or
[`~TFChatGLMModel`].
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
inner_hidden_size (`int`, *optional*, defaults to 16384):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
max_sequence_length (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
layernorm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether the model should return the last key/values attentions (not used by all models).
Example:
```python
>>> from configuration_chatglm import ChatGLMConfig
>>> from modeling_chatglm import ChatGLMModel
>>> # Initializing a ChatGLM-6B THUDM/ChatGLM-6B style configuration
>>> configuration = ChatGLMConfig()
>>> # Initializing a model from the THUDM/ChatGLM-6B style configuration
>>> model = ChatGLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "chatglm"
def __init__(
self,
vocab_size=130528,
hidden_size=4096,
num_layers=28,
num_attention_heads=32,
layernorm_epsilon=1e-5,
use_cache=True,
bos_token_id=130004,
eos_token_id=130005,
mask_token_id=130000,
gmask_token_id=130001,
pad_token_id=3,
max_sequence_length=2048,
inner_hidden_size=16384,
position_encoding_2d=True,
quantization_bit=0,
pre_seq_len=None,
prefix_projection=False,
**kwargs,
):
self.num_layers = num_layers
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.max_sequence_length = max_sequence_length
self.layernorm_epsilon = layernorm_epsilon
self.inner_hidden_size = inner_hidden_size
self.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.mask_token_id = mask_token_id
self.gmask_token_id = gmask_token_id
self.position_encoding_2d = position_encoding_2d
self.quantization_bit = quantization_bit
self.pre_seq_len = pre_seq_len
self.prefix_projection = prefix_projection
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
applications/Chat/coati/models/chatglm/modeling_chatglm.py deleted 100644 → 0
View file @ 36c4bb28
"""
This code is copied from https://huggingface.co/THUDM/chatglm-6b/resolve/main/modeling_chatglm.py
"""
""" PyTorch ChatGLM model. """
import copy
import math
import os
import re
import sys
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, LayerNorm
from torch.nn.utils import skip_init
from transformers.generation.logits_process import LogitsProcessor
from transformers.generation.utils import GenerationConfig, LogitsProcessorList, ModelOutput, StoppingCriteriaList
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
)
from .configuration_chatglm import ChatGLMConfig
# flags required to enable jit fusion kernels
if sys.platform != "darwin":
torch._C._jit_set_profiling_mode(False)
torch._C._jit_set_profiling_executor(False)
torch._C._jit_override_can_fuse_on_cpu(True)
torch._C._jit_override_can_fuse_on_gpu(True)
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM-6B"
_CONFIG_FOR_DOC = "ChatGLM6BConfig"
CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
"THUDM/chatglm-6b",
# See all ChatGLM-6B models at https://huggingface.co/models?filter=chatglm
]
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
if torch.isnan(scores).any() or torch.isinf(scores).any():
scores.zero_()
scores[..., 5] = 5e4
return scores
def load_tf_weights_in_chatglm_6b(model, config, tf_checkpoint_path):
"""Load tf checkpoints in a pytorch model."""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
):
logger.info(f"Skipping {'/'.join(name)}")
continue
pointer = model
for m_name in name:
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
scope_names = re.split(r"_(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "kernel" or scope_names[0] == "gamma":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "output_weights":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "squad":
pointer = getattr(pointer, "classifier")
else:
try:
pointer = getattr(pointer, scope_names[0])
except AttributeError:
logger.info(f"Skipping {'/'.join(name)}")
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if m_name[-11:] == "_embeddings":
pointer = getattr(pointer, "weight")
elif m_name == "kernel":
array = np.transpose(array)
try:
assert (
pointer.shape == array.shape
), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
return model
class PrefixEncoder(torch.nn.Module):
"""
The torch.nn model to encode the prefix
Input shape: (batch-size, prefix-length)
Output shape: (batch-size, prefix-length, 2*layers*hidden)
"""
def __init__(self, config):
super().__init__()
self.prefix_projection = config.prefix_projection
if self.prefix_projection:
# Use a two-layer MLP to encode the prefix
self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size)
self.trans = torch.nn.Sequential(
torch.nn.Linear(config.hidden_size, config.hidden_size),
torch.nn.Tanh(),
torch.nn.Linear(config.hidden_size, config.num_layers * config.hidden_size * 2),
)
else:
self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_layers * config.hidden_size * 2)
def forward(self, prefix: torch.Tensor):
if self.prefix_projection:
prefix_tokens = self.embedding(prefix)
past_key_values = self.trans(prefix_tokens)
else:
past_key_values = self.embedding(prefix)
return past_key_values
@torch.jit.script
def gelu_impl(x):
"""OpenAI's gelu implementation."""
return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x * (1.0 + 0.044715 * x * x)))
def gelu(x):
return gelu_impl(x)
class RotaryEmbedding(torch.nn.Module):
def __init__(self, dim, base=10000, precision=torch.half, learnable=False):
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
inv_freq = inv_freq.half()
self.learnable = learnable
if learnable:
self.inv_freq = torch.nn.Parameter(inv_freq)
self.max_seq_len_cached = None
else:
self.register_buffer("inv_freq", inv_freq)
self.max_seq_len_cached = None
self.cos_cached = None
self.sin_cached = None
self.precision = precision
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
pass
def forward(self, x, seq_dim=1, seq_len=None):
if seq_len is None:
seq_len = x.shape[seq_dim]
if self.max_seq_len_cached is None or (seq_len > self.max_seq_len_cached):
self.max_seq_len_cached = None if self.learnable else seq_len
t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
if self.precision == torch.bfloat16:
emb = emb.float()
# [sx, 1 (b * np), hn]
cos_cached = emb.cos()[:, None, :]
sin_cached = emb.sin()[:, None, :]
if self.precision == torch.bfloat16:
cos_cached = cos_cached.bfloat16()
sin_cached = sin_cached.bfloat16()
if self.learnable:
return cos_cached, sin_cached
self.cos_cached, self.sin_cached = cos_cached, sin_cached
return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...]
def _apply(self, fn):
if self.cos_cached is not None:
self.cos_cached = fn(self.cos_cached)
if self.sin_cached is not None:
self.sin_cached = fn(self.sin_cached)
return super()._apply(fn)
def rotate_half(x):
x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions
@torch.jit.script
def apply_rotary_pos_emb_index(q, k, cos, sin, position_id):
# position_id: [sq, b], q, k: [sq, b, np, hn], cos: [sq, 1, hn] -> [sq, b, 1, hn]
cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(2), F.embedding(
position_id, sin.squeeze(1)
).unsqueeze(2)
q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
return q, k
def attention_fn(
self,
query_layer,
key_layer,
value_layer,
attention_mask,
hidden_size_per_partition,
layer_id,
layer_past=None,
scaling_attention_score=True,
use_cache=False,
):
if layer_past is not None:
past_key, past_value = layer_past[0], layer_past[1]
key_layer = torch.cat((past_key, key_layer), dim=0)
value_layer = torch.cat((past_value, value_layer), dim=0)
# seqlen, batch, num_attention_heads, hidden_size_per_attention_head
seq_len, b, nh, hidden_size = key_layer.shape
if use_cache:
present = (key_layer, value_layer)
else:
present = None
query_key_layer_scaling_coeff = float(layer_id + 1)
if scaling_attention_score:
query_layer = query_layer / (math.sqrt(hidden_size) * query_key_layer_scaling_coeff)
# ===================================
# Raw attention scores. [b, np, s, s]
# ===================================
# [b, np, sq, sk]
output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))
# [sq, b, np, hn] -> [sq, b * np, hn]
query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
# [sk, b, np, hn] -> [sk, b * np, hn]
key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)
matmul_result = torch.zeros(
1,
1,
1,
dtype=query_layer.dtype,
device=query_layer.device,
)
matmul_result = torch.baddbmm(
matmul_result,
query_layer.transpose(0, 1), # [b * np, sq, hn]
key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk]
beta=0.0,
alpha=1.0,
)
# change view to [b, np, sq, sk]
attention_scores = matmul_result.view(*output_size)
if self.scale_mask_softmax:
self.scale_mask_softmax.scale = query_key_layer_scaling_coeff
attention_probs = self.scale_mask_softmax(attention_scores, attention_mask.contiguous())
else:
if not (attention_mask == 0).all():
# if auto-regressive, skip
attention_scores.masked_fill_(attention_mask, -10000.0)
dtype = attention_scores.dtype
attention_scores = attention_scores.float()
attention_scores = attention_scores * query_key_layer_scaling_coeff
attention_probs = F.softmax(attention_scores, dim=-1)
attention_probs = attention_probs.type(dtype)
# =========================
# Context layer. [sq, b, hp]
# =========================
# value_layer -> context layer.
# [sk, b, np, hn] --> [b, np, sq, hn]
# context layer shape: [b, np, sq, hn]
output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))
# change view [sk, b * np, hn]
value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)
# change view [b * np, sq, sk]
attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
# matmul: [b * np, sq, hn]
context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))
# change view [b, np, sq, hn]
context_layer = context_layer.view(*output_size)
# [b, np, sq, hn] --> [sq, b, np, hn]
context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
# [sq, b, np, hn] --> [sq, b, hp]
new_context_layer_shape = context_layer.size()[:-2] + (hidden_size_per_partition,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, present, attention_probs)
return outputs
def default_init(cls, *args, **kwargs):
return cls(*args, **kwargs)
class SelfAttention(torch.nn.Module):
def __init__(
self,
hidden_size,
num_attention_heads,
layer_id,
hidden_size_per_attention_head=None,
bias=True,
params_dtype=torch.float,
position_encoding_2d=True,
empty_init=True,
):
if empty_init:
init_method = skip_init
else:
init_method = default_init
super(SelfAttention, self).__init__()
self.layer_id = layer_id
self.hidden_size = hidden_size
self.hidden_size_per_partition = hidden_size
self.num_attention_heads = num_attention_heads
self.num_attention_heads_per_partition = num_attention_heads
self.position_encoding_2d = position_encoding_2d
self.rotary_emb = RotaryEmbedding(
self.hidden_size // (self.num_attention_heads * 2)
if position_encoding_2d
else self.hidden_size // self.num_attention_heads,
base=10000,
precision=torch.half,
learnable=False,
)
self.scale_mask_softmax = None
if hidden_size_per_attention_head is None:
self.hidden_size_per_attention_head = hidden_size // num_attention_heads
else:
self.hidden_size_per_attention_head = hidden_size_per_attention_head
self.inner_hidden_size = num_attention_heads * self.hidden_size_per_attention_head
# Strided linear layer.
self.query_key_value = init_method(
torch.nn.Linear,
hidden_size,
3 * self.inner_hidden_size,
bias=bias,
dtype=params_dtype,
)
self.dense = init_method(
torch.nn.Linear,
self.inner_hidden_size,
hidden_size,
bias=bias,
dtype=params_dtype,
)
@staticmethod
def attention_mask_func(attention_scores, attention_mask):
attention_scores.masked_fill_(attention_mask, -10000.0)
return attention_scores
def split_tensor_along_last_dim(self, tensor, num_partitions, contiguous_split_chunks=False):
"""Split a tensor along its last dimension.
Arguments:
tensor: input tensor.
num_partitions: number of partitions to split the tensor
contiguous_split_chunks: If True, make each chunk contiguous
in memory.
"""
# Get the size and dimension.
last_dim = tensor.dim() - 1
last_dim_size = tensor.size()[last_dim] // num_partitions
# Split.
tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
# Note: torch.split does not create contiguous tensors by default.
if contiguous_split_chunks:
return tuple(chunk.contiguous() for chunk in tensor_list)
return tensor_list
def forward(
self,
hidden_states: torch.Tensor,
position_ids,
attention_mask: torch.Tensor,
layer_id,
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
use_cache: bool = False,
output_attentions: bool = False,
):
"""
hidden_states: [seq_len, batch, hidden_size]
attention_mask: [(1, 1), seq_len, seq_len]
"""
# [seq_len, batch, 3 * hidden_size]
mixed_raw_layer = self.query_key_value(hidden_states)
# [seq_len, batch, 3 * hidden_size] --> [seq_len, batch, num_attention_heads, 3 * hidden_size_per_attention_head]
new_tensor_shape = mixed_raw_layer.size()[:-1] + (
self.num_attention_heads_per_partition,
3 * self.hidden_size_per_attention_head,
)
mixed_raw_layer = mixed_raw_layer.view(*new_tensor_shape)
# [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
(query_layer, key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_raw_layer, 3)
if self.position_encoding_2d:
q1, q2 = query_layer.chunk(2, dim=(query_layer.ndim - 1))
k1, k2 = key_layer.chunk(2, dim=(key_layer.ndim - 1))
cos, sin = self.rotary_emb(q1, seq_len=position_ids.max() + 1)
position_ids, block_position_ids = (
position_ids[:, 0, :].transpose(0, 1).contiguous(),
position_ids[:, 1, :].transpose(0, 1).contiguous(),
)
q1, k1 = apply_rotary_pos_emb_index(q1, k1, cos, sin, position_ids)
q2, k2 = apply_rotary_pos_emb_index(q2, k2, cos, sin, block_position_ids)
query_layer = torch.concat([q1, q2], dim=(q1.ndim - 1))
key_layer = torch.concat([k1, k2], dim=(k1.ndim - 1))
else:
position_ids = position_ids.transpose(0, 1)
cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1)
# [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
query_layer, key_layer = apply_rotary_pos_emb_index(query_layer, key_layer, cos, sin, position_ids)
# [seq_len, batch, hidden_size]
context_layer, present, attention_probs = attention_fn(
self=self,
query_layer=query_layer,
key_layer=key_layer,
value_layer=value_layer,
attention_mask=attention_mask,
hidden_size_per_partition=self.hidden_size_per_partition,
layer_id=layer_id,
layer_past=layer_past,
use_cache=use_cache,
)
output = self.dense(context_layer)
outputs = (output, present)
if output_attentions:
outputs += (attention_probs,)
return outputs # output, present, attention_probs
class GEGLU(torch.nn.Module):
def __init__(self):
super().__init__()
self.activation_fn = F.gelu
def forward(self, x):
# dim=-1 breaks in jit for pt<1.10
x1, x2 = x.chunk(2, dim=(x.ndim - 1))
return x1 * self.activation_fn(x2)
class GLU(torch.nn.Module):
def __init__(
self,
hidden_size,
inner_hidden_size=None,
layer_id=None,
bias=True,
activation_func=gelu,
params_dtype=torch.float,
empty_init=True,
):
super(GLU, self).__init__()
if empty_init:
init_method = skip_init
else:
init_method = default_init
self.layer_id = layer_id
self.activation_func = activation_func
# Project to 4h.
self.hidden_size = hidden_size
if inner_hidden_size is None:
inner_hidden_size = 4 * hidden_size
self.inner_hidden_size = inner_hidden_size
self.dense_h_to_4h = init_method(
torch.nn.Linear,
self.hidden_size,
self.inner_hidden_size,
bias=bias,
dtype=params_dtype,
)
# Project back to h.
self.dense_4h_to_h = init_method(
torch.nn.Linear,
self.inner_hidden_size,
self.hidden_size,
bias=bias,
dtype=params_dtype,
)
def forward(self, hidden_states):
"""
hidden_states: [seq_len, batch, hidden_size]
"""
# [seq_len, batch, inner_hidden_size]
intermediate_parallel = self.dense_h_to_4h(hidden_states)
intermediate_parallel = self.activation_func(intermediate_parallel)
output = self.dense_4h_to_h(intermediate_parallel)
return output
class GLMBlock(torch.nn.Module):
def __init__(
self,
hidden_size,
num_attention_heads,
layernorm_epsilon,
layer_id,
inner_hidden_size=None,
hidden_size_per_attention_head=None,
layernorm=LayerNorm,
use_bias=True,
params_dtype=torch.float,
num_layers=28,
position_encoding_2d=True,
empty_init=True,
):
super(GLMBlock, self).__init__()
# Set output layer initialization if not provided.
self.layer_id = layer_id
# Layernorm on the input data.
self.input_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)
self.position_encoding_2d = position_encoding_2d
# Self attention.
self.attention = SelfAttention(
hidden_size,
num_attention_heads,
layer_id,
hidden_size_per_attention_head=hidden_size_per_attention_head,
bias=use_bias,
params_dtype=params_dtype,
position_encoding_2d=self.position_encoding_2d,
empty_init=empty_init,
)
# Layernorm on the input data.
self.post_attention_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)
self.num_layers = num_layers
# GLU
self.mlp = GLU(
hidden_size,
inner_hidden_size=inner_hidden_size,
bias=use_bias,
layer_id=layer_id,
params_dtype=params_dtype,
empty_init=empty_init,
)
def forward(
self,
hidden_states: torch.Tensor,
position_ids,
attention_mask: torch.Tensor,
layer_id,
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
use_cache: bool = False,
output_attentions: bool = False,
):
"""
hidden_states: [seq_len, batch, hidden_size]
attention_mask: [(1, 1), seq_len, seq_len]
"""
# Layer norm at the begining of the transformer layer.
# [seq_len, batch, hidden_size]
attention_input = self.input_layernorm(hidden_states)
# Self attention.
attention_outputs = self.attention(
attention_input,
position_ids,
attention_mask=attention_mask,
layer_id=layer_id,
layer_past=layer_past,
use_cache=use_cache,
output_attentions=output_attentions,
)
attention_output = attention_outputs[0]
outputs = attention_outputs[1:]
# Residual connection.
alpha = (2 * self.num_layers) ** 0.5
hidden_states = attention_input * alpha + attention_output
mlp_input = self.post_attention_layernorm(hidden_states)
# MLP.
mlp_output = self.mlp(mlp_input)
# Second residual connection.
output = mlp_input * alpha + mlp_output
if use_cache:
outputs = (output,) + outputs
else:
outputs = (output,) + outputs[1:]
return outputs # hidden_states, present, attentions
class ChatGLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
is_parallelizable = False
supports_gradient_checkpointing = True
config_class = ChatGLMConfig
base_model_prefix = "transformer"
_no_split_modules = ["GLMBlock"]
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
def _init_weights(self, module: nn.Module):
"""Initialize the weights."""
return
def get_masks(self, input_ids, device):
batch_size, seq_length = input_ids.shape
context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
attention_mask = torch.ones((batch_size, seq_length, seq_length), device=device)
attention_mask.tril_()
for i, context_length in enumerate(context_lengths):
attention_mask[i, :, :context_length] = 1
attention_mask.unsqueeze_(1)
attention_mask = (attention_mask < 0.5).bool()
return attention_mask
def get_position_ids(self, input_ids, mask_positions, device, use_gmasks=None):
batch_size, seq_length = input_ids.shape
if use_gmasks is None:
use_gmasks = [False] * batch_size
context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
if self.position_encoding_2d:
position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
for i, context_length in enumerate(context_lengths):
position_ids[i, context_length:] = mask_positions[i]
block_position_ids = [
torch.cat(
(
torch.zeros(context_length, dtype=torch.long, device=device),
torch.arange(seq_length - context_length, dtype=torch.long, device=device) + 1,
)
)
for context_length in context_lengths
]
block_position_ids = torch.stack(block_position_ids, dim=0)
position_ids = torch.stack((position_ids, block_position_ids), dim=1)
else:
position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
for i, context_length in enumerate(context_lengths):
if not use_gmasks[i]:
position_ids[i, context_length:] = mask_positions[i]
return position_ids
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, ChatGLMModel):
module.gradient_checkpointing = value
CHATGLM_6B_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
usage and behavior.
Parameters:
config ([`~ChatGLM6BConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
CHATGLM_6B_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`ChatGLM6BTokenizer`].
See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range `[0, config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert *input_ids* indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ChatGLM-6B Model transformer outputting raw hidden-states without any specific head on top.",
CHATGLM_6B_START_DOCSTRING,
)
class ChatGLMModel(ChatGLMPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well
as a decoder, in which case a layer of cross-attention is added between
the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani,
Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the
`is_decoder` argument of the configuration set to `True`.
To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder`
argument and `add_cross_attention` set to `True`; an
`encoder_hidden_states` is then expected as an input to the forward pass.
"""
def __init__(self, config: ChatGLMConfig, empty_init=True):
super().__init__(config)
if empty_init:
init_method = skip_init
else:
init_method = default_init
# recording parameters
self.max_sequence_length = config.max_sequence_length
self.hidden_size = config.hidden_size
self.params_dtype = torch.half
self.num_attention_heads = config.num_attention_heads
self.vocab_size = config.vocab_size
self.num_layers = config.num_layers
self.layernorm_epsilon = config.layernorm_epsilon
self.inner_hidden_size = config.inner_hidden_size
self.hidden_size_per_attention_head = self.hidden_size // self.num_attention_heads
self.position_encoding_2d = config.position_encoding_2d
self.pre_seq_len = config.pre_seq_len
self.prefix_projection = config.prefix_projection
self.word_embeddings = init_method(
torch.nn.Embedding, num_embeddings=self.vocab_size, embedding_dim=self.hidden_size, dtype=self.params_dtype
)
self.gradient_checkpointing = False
def get_layer(layer_id):
return GLMBlock(
self.hidden_size,
self.num_attention_heads,
self.layernorm_epsilon,
layer_id,
inner_hidden_size=self.inner_hidden_size,
hidden_size_per_attention_head=self.hidden_size_per_attention_head,
layernorm=LayerNorm,
use_bias=True,
params_dtype=self.params_dtype,
position_encoding_2d=self.position_encoding_2d,
empty_init=empty_init,
)
self.layers = torch.nn.ModuleList([get_layer(layer_id) for layer_id in range(self.num_layers)])
# Final layer norm before output.
self.final_layernorm = LayerNorm(self.hidden_size, eps=self.layernorm_epsilon)
if self.pre_seq_len is not None:
for param in self.parameters():
param.requires_grad = False
self.prefix_tokens = torch.arange(self.pre_seq_len).long()
self.prefix_encoder = PrefixEncoder(config)
self.dropout = torch.nn.Dropout(0.1)
# total_params = sum(p.numel() for p in self.parameters())
# trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
# print("Using p-tuning v2: # trainable_params = {} / {}".format(trainable_params, total_params))
def get_input_embeddings(self):
return self.word_embeddings
def set_input_embeddings(self, new_embeddings: torch.Tensor):
self.word_embeddings = new_embeddings
def get_prompt(self, batch_size, device, dtype=torch.half):
prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
past_key_values = self.prefix_encoder(prefix_tokens).type(dtype)
past_key_values = past_key_values.view(
batch_size,
self.pre_seq_len,
self.num_layers * 2,
self.num_attention_heads,
self.hidden_size // self.num_attention_heads,
)
# seq_len, b, nh, hidden_size
past_key_values = self.dropout(past_key_values)
past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2)
# past_key_values = [(v[0], v[1]) for v in past_key_values]
return past_key_values
@add_start_docstrings_to_model_forward(CHATGLM_6B_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
inputs_embeds: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape[:2]
elif inputs_embeds is not None:
batch_size, seq_length = inputs_embeds.shape[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if past_key_values is None:
if self.pre_seq_len is not None:
past_key_values = self.get_prompt(
batch_size=input_ids.shape[0], device=input_ids.device, dtype=inputs_embeds.dtype
)
else:
past_key_values = tuple([None] * len(self.layers))
if attention_mask is None:
attention_mask = self.get_masks(input_ids, device=input_ids.device)
if position_ids is None:
MASK, gMASK = self.config.mask_token_id, self.config.gmask_token_id
seqs = input_ids.tolist()
mask_positions, use_gmasks = [], []
for seq in seqs:
mask_token = gMASK if gMASK in seq else MASK
use_gmask = mask_token == gMASK
mask_positions.append(seq.index(mask_token))
use_gmasks.append(use_gmask)
position_ids = self.get_position_ids(
input_ids, mask_positions=mask_positions, device=input_ids.device, use_gmasks=use_gmasks
)
if self.pre_seq_len is not None and attention_mask is not None:
prefix_attention_mask = torch.ones(batch_size, 1, input_ids.size(-1), self.pre_seq_len).to(
attention_mask.device
)
prefix_attention_mask = (prefix_attention_mask < 0.5).bool()
attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=3)
# [seq_len, batch, hidden_size]
hidden_states = inputs_embeds.transpose(0, 1)
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
if attention_mask is None:
attention_mask = torch.zeros(1, 1, device=input_ids.device).bool()
else:
attention_mask = attention_mask.to(hidden_states.device)
for i, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_past = past_key_values[i]
if self.gradient_checkpointing and self.training:
layer_ret = torch.utils.checkpoint.checkpoint(
layer,
hidden_states,
position_ids,
attention_mask,
torch.tensor(i),
layer_past,
use_cache,
output_attentions,
)
else:
layer_ret = layer(
hidden_states,
position_ids=position_ids,
attention_mask=attention_mask,
layer_id=torch.tensor(i),
layer_past=layer_past,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = layer_ret[0]
if use_cache:
presents = presents + (layer_ret[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_ret[2 if use_cache else 1],)
# Final layer norm.
hidden_states = self.final_layernorm(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
def __init__(self, config: ChatGLMConfig, empty_init=True):
super().__init__(config)
if empty_init:
init_method = skip_init
else:
init_method = default_init
# self.hidden_size = config.hidden_size
# self.params_dtype = torch.half
# self.vocab_size = config.vocab_size
self.max_sequence_length = config.max_sequence_length
self.position_encoding_2d = config.position_encoding_2d
self.transformer = ChatGLMModel(config, empty_init=empty_init)
self.lm_head = init_method(nn.Linear, config.hidden_size, config.vocab_size, bias=False, dtype=torch.half)
self.config = config
self.quantized = False
if self.config.quantization_bit:
self.quantize(self.config.quantization_bit, empty_init=True)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def _update_model_kwargs_for_generation(
self,
outputs: ModelOutput,
model_kwargs: Dict[str, Any],
is_encoder_decoder: bool = False,
standardize_cache_format: bool = False,
) -> Dict[str, Any]:
# update past_key_values
model_kwargs["past_key_values"] = self._extract_past_from_model_output(
outputs, standardize_cache_format=standardize_cache_format
)
# update attention mask
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
if attention_mask is not None and attention_mask.dtype == torch.bool:
attention_mask = torch.cat(
[attention_mask, attention_mask.new_ones((*attention_mask.shape[:3], 1))], dim=3
)
new_attention_mask = attention_mask[:, :, -1:].clone()
new_attention_mask[..., -1] = False
model_kwargs["attention_mask"] = torch.cat([attention_mask, new_attention_mask], dim=2)
# update position ids
if "position_ids" in model_kwargs:
position_ids = model_kwargs["position_ids"]
new_position_id = position_ids[..., -1:].clone()
new_position_id[:, 1, :] += 1
model_kwargs["position_ids"] = torch.cat([position_ids, new_position_id], dim=-1)
return model_kwargs
def prepare_inputs_for_generation(
self,
input_ids: torch.LongTensor,
past: Optional[torch.Tensor] = None,
past_key_values: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
**kwargs,
) -> dict:
batch_size, seq_length = input_ids.shape
MASK, gMASK = self.config.mask_token_id, self.config.gmask_token_id
seqs = input_ids.tolist()
mask_positions, use_gmasks = [], []
for seq in seqs:
mask_token = gMASK if gMASK in seq else MASK
use_gmask = mask_token == gMASK
mask_positions.append(seq.index(mask_token))
use_gmasks.append(use_gmask)
# only last token for input_ids if past is not None
if past is not None or past_key_values is not None:
last_token = input_ids[:, -1].unsqueeze(-1)
if attention_mask is not None and attention_mask.dtype == torch.bool:
attention_mask = attention_mask[:, :, -1:]
else:
attention_mask = None
if position_ids is not None:
position_ids = position_ids[..., -1:]
else:
context_lengths = [seq.index(self.config.bos_token_id) for seq in seqs]
if self.position_encoding_2d:
position_ids = torch.tensor(
[
[mask_position, seq_length - context_length]
for mask_position, context_length in zip(mask_positions, context_lengths)
],
dtype=torch.long,
device=input_ids.device,
).unsqueeze(-1)
else:
position_ids = torch.tensor(
[mask_position for mask_position in mask_positions], dtype=torch.long, device=input_ids.device
).unsqueeze(-1)
if past is None:
past = past_key_values
return {
"input_ids": last_token,
"past_key_values": past,
"position_ids": position_ids,
"attention_mask": attention_mask,
}
else:
if attention_mask is not None and attention_mask.dtype != torch.bool:
logger.warning_once(f"The dtype of attention mask ({attention_mask.dtype}) is not bool")
attention_mask = None
if attention_mask is None:
attention_mask = self.get_masks(input_ids, device=input_ids.device)
if position_ids is None:
position_ids = self.get_position_ids(
input_ids, device=input_ids.device, mask_positions=mask_positions, use_gmasks=use_gmasks
)
return {
"input_ids": input_ids,
"past_key_values": past,
"position_ids": position_ids,
"attention_mask": attention_mask,
}
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids=input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states).permute(1, 0, 2).contiguous()
loss = None
if labels is not None:
lm_logits = lm_logits.to(torch.float32)
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss(ignore_index=-100)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
lm_logits = lm_logits.to(hidden_states.dtype)
loss = loss.to(hidden_states.dtype)
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@staticmethod
def _reorder_cache(
past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
Output shares the same memory storage as `past`.
"""
return tuple(
(
layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)),
layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)),
)
for layer_past in past
)
def process_response(self, response):
response = response.strip()
response = response.replace("[[训练时间]]", "2023年")
punkts = [
[",", "，"],
["!", "！"],
[":", "："],
[";", "；"],
["\?", "？"],
]
for item in punkts:
response = re.sub(r"([\u4e00-\u9fff])%s" % item[0], r"\1%s" % item[1], response)
response = re.sub(r"%s([\u4e00-\u9fff])" % item[0], r"%s\1" % item[1], response)
return response
@torch.no_grad()
def chat(
self,
tokenizer,
query: str,
history: List[Tuple[str, str]] = None,
max_length: int = 2048,
num_beams=1,
do_sample=True,
top_p=0.7,
temperature=0.95,
logits_processor=None,
**kwargs,
):
if history is None:
history = []
if logits_processor is None:
logits_processor = LogitsProcessorList()
logits_processor.append(InvalidScoreLogitsProcessor())
gen_kwargs = {
"max_length": max_length,
"num_beams": num_beams,
"do_sample": do_sample,
"top_p": top_p,
"temperature": temperature,
"logits_processor": logits_processor,
**kwargs,
}
if not history:
prompt = query
else:
prompt = ""
for i, (old_query, response) in enumerate(history):
prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response)
prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
inputs = tokenizer([prompt], return_tensors="pt")
inputs = inputs.to(self.device)
outputs = self.generate(**inputs, **gen_kwargs)
outputs = outputs.tolist()[0][len(inputs["input_ids"][0]) :]
response = tokenizer.decode(outputs)
response = self.process_response(response)
history = history + [(query, response)]
return response, history
@torch.no_grad()
def stream_chat(
self,
tokenizer,
query: str,
history: List[Tuple[str, str]] = None,
max_length: int = 2048,
do_sample=True,
top_p=0.7,
temperature=0.95,
logits_processor=None,
**kwargs,
):
if history is None:
history = []
if logits_processor is None:
logits_processor = LogitsProcessorList()
logits_processor.append(InvalidScoreLogitsProcessor())
gen_kwargs = {
"max_length": max_length,
"do_sample": do_sample,
"top_p": top_p,
"temperature": temperature,
"logits_processor": logits_processor,
**kwargs,
}
if not history:
prompt = query
else:
prompt = ""
for i, (old_query, response) in enumerate(history):
prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response)
prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
inputs = tokenizer([prompt], return_tensors="pt")
inputs = inputs.to(self.device)
for outputs in self.stream_generate(**inputs, **gen_kwargs):
outputs = outputs.tolist()[0][len(inputs["input_ids"][0]) :]
response = tokenizer.decode(outputs)
response = self.process_response(response)
new_history = history + [(query, response)]
yield response, new_history
@torch.no_grad()
def stream_generate(
self,
input_ids,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
**kwargs,
):
batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
if generation_config is None:
generation_config = self.generation_config
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs)
bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
if has_default_max_length and generation_config.max_new_tokens is None:
warnings.warn(
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
" recommend using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif generation_config.max_new_tokens is not None:
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
if not has_default_max_length:
logger.warn(
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
"Please refer to the documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
UserWarning,
)
if input_ids_seq_length >= generation_config.max_length:
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
" increasing `max_new_tokens`."
)
# 2. Set generation parameters if not already defined
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
logits_processor = self._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
encoder_input_ids=input_ids,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
logits_processor=logits_processor,
)
stopping_criteria = self._get_stopping_criteria(
generation_config=generation_config, stopping_criteria=stopping_criteria
)
logits_warper = self._get_logits_warper(generation_config)
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
scores = None
while True:
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# forward pass to get next token
outputs = self(
**model_inputs,
return_dict=True,
output_attentions=False,
output_hidden_states=False,
)
next_token_logits = outputs.logits[:, -1, :]
# pre-process distribution
next_token_scores = logits_processor(input_ids, next_token_logits)
next_token_scores = logits_warper(input_ids, next_token_scores)
# sample
probs = nn.functional.softmax(next_token_scores, dim=-1)
if generation_config.do_sample:
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
else:
next_tokens = torch.argmax(probs, dim=-1)
# update generated ids, model inputs, and length for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long())
# stop when each sentence is finished, or if we exceed the maximum length
if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
break
yield input_ids
def quantize(self, bits: int, empty_init=False, **kwargs):
if bits == 0:
return
from .quantization import quantize
if self.quantized:
logger.info("Already quantized.")
return self
self.quantized = True
self.config.quantization_bit = bits
self.transformer = quantize(self.transformer, bits, empty_init=empty_init, **kwargs)
return self
applications/Chat/coati/models/gpt/__init__.py deleted 100644 → 0
View file @ 36c4bb28
from .gpt_actor import GPTActor
from .gpt_critic import GPTCritic
from .gpt_rm import GPTRM
__all__ = ["GPTActor", "GPTCritic", "GPTRM"]
applications/Chat/coati/models/gpt/gpt_actor.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
from transformers.models.gpt2.configuration_gpt2 import GPT2Config
from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
from ..base import Actor
class GPTActor(Actor):
"""
GPT Actor model.
Args:
pretrained (str): Pretrained model name or path.
config (GPT2Config): Model config.
checkpoint (bool): Enable gradient checkpointing.
lora_rank (int): Rank of the LoRa layer.
lora_train_bias (str): Bias training strategy for the LoRa layer.
"""
def __init__(
self,
pretrained: Optional[str] = None,
config: Optional[GPT2Config] = None,
checkpoint: bool = False,
lora_rank: int = 0,
lora_train_bias: str = "none",
**kwargs,
) -> None:
if pretrained is not None:
model = GPT2LMHeadModel.from_pretrained(pretrained)
elif config is not None:
model = GPT2LMHeadModel(config)
else:
model = GPT2LMHeadModel(GPT2Config())
if checkpoint:
model.gradient_checkpointing_enable()
super().__init__(model, lora_rank, lora_train_bias, **kwargs)
applications/Chat/coati/models/gpt/gpt_critic.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
import torch.nn as nn
from transformers.models.gpt2.configuration_gpt2 import GPT2Config
from transformers.models.gpt2.modeling_gpt2 import GPT2Model
from ..base import Critic
class GPTCritic(Critic):
"""
GPT Critic model.
Args:
pretrained (str): Pretrained model name or path.
config (GPT2Config): Model config.
lora_rank (int): Rank of the LO-RA decomposition.
lora_train_bias (str): LoRA bias training mode.
"""
def __init__(
self,
pretrained: Optional[str] = None,
config: Optional[GPT2Config] = None,
lora_rank: int = 0,
lora_train_bias: str = "none",
**kwargs,
) -> None:
if pretrained is not None:
model = GPT2Model.from_pretrained(pretrained)
elif config is not None:
model = GPT2Model(config)
else:
model = GPT2Model(GPT2Config())
value_head = nn.Linear(model.config.n_embd, 1)
super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)
applications/Chat/coati/models/gpt/gpt_rm.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
import torch.nn as nn
from transformers.models.gpt2.configuration_gpt2 import GPT2Config
from transformers.models.gpt2.modeling_gpt2 import GPT2Model
from ..base import RewardModel
class GPTRM(RewardModel):
"""
GPT Reward model.
Args:
pretrained (str): Pretrained model name or path.
config (GPT2Config): Model config.
lora_rank (int): Rank of the low-rank approximation.
lora_train_bias (str): LoRA bias training mode.
"""
def __init__(
self,
pretrained: Optional[str] = None,
config: Optional[GPT2Config] = None,
lora_rank: int = 0,
lora_train_bias: str = "none",
) -> None:
if pretrained is not None:
model = GPT2Model.from_pretrained(pretrained)
elif config is not None:
model = GPT2Model(config)
else:
model = GPT2Model(GPT2Config())
value_head = nn.Linear(model.config.n_embd, 1)
value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.n_embd + 1))
super().__init__(model, value_head, lora_rank, lora_train_bias)
applications/Chat/coati/models/llama/__init__.py deleted 100644 → 0
View file @ 36c4bb28
from .llama_actor import LlamaActor
from .llama_critic import LlamaCritic
from .llama_rm import LlamaRM
__all__ = ["LlamaActor", "LlamaCritic", "LlamaRM"]
applications/Chat/coati/models/llama/llama_actor.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
from transformers import LlamaConfig, LlamaForCausalLM
from ..base import Actor
class LlamaActor(Actor):
"""
Llama Actor model.
Args:
pretrained (str): Pretrained model name or path.
config (LlamaConfig): Model config.
checkpoint (bool): Enable gradient checkpointing.
lora_rank (int): LoRA rank.
lora_train_bias (str): LoRA bias training mode.
"""
def __init__(
self,
pretrained: Optional[str] = None,
config: Optional[LlamaConfig] = None,
checkpoint: bool = False,
lora_rank: int = 0,
lora_train_bias: str = "none",
) -> None:
if pretrained is not None:
model = LlamaForCausalLM.from_pretrained(pretrained)
elif config is not None:
model = LlamaForCausalLM(config)
else:
model = LlamaForCausalLM(LlamaConfig())
if checkpoint:
model.gradient_checkpointing_enable()
super().__init__(model, lora_rank, lora_train_bias)
applications/Chat/coati/models/llama/llama_critic.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
import torch.nn as nn
from transformers import LlamaConfig, LlamaModel
from ..base import Critic
class LlamaCritic(Critic):
"""
Llama Critic model.
Args:
pretrained (str): Pretrained model name or path.
config (LlamaConfig): Model config.
lora_rank (int): LoRA rank.
lora_train_bias (str): LoRA bias training mode.
"""
def __init__(
self,
pretrained: Optional[str] = None,
config: Optional[LlamaConfig] = None,
lora_rank: int = 0,
lora_train_bias: str = "none",
**kwargs,
) -> None:
if pretrained is not None:
model = LlamaModel.from_pretrained(pretrained)
elif config is not None:
model = LlamaModel(config)
else:
model = LlamaModel(LlamaConfig())
value_head = nn.Linear(model.config.hidden_size, 1)
super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)
applications/Chat/coati/models/llama/llama_rm.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
import torch.nn as nn
from transformers import LlamaConfig, LlamaModel
from ..base import RewardModel
class LlamaRM(RewardModel):
"""
Llama Reward model.
Args:
pretrained (str): Pretrained model name or path.
config (LlamaConfig): Model config.
lora_rank (int): LoRA rank.
lora_train_bias (str): LoRA bias training mode.
"""
def __init__(
self,
pretrained: Optional[str] = None,
config: Optional[LlamaConfig] = None,
lora_rank: int = 0,
lora_train_bias: str = "none",
) -> None:
if pretrained is not None:
model = LlamaModel.from_pretrained(pretrained)
elif config is not None:
model = LlamaModel(config)
else:
model = LlamaModel(LlamaConfig())
value_head = nn.Linear(model.config.hidden_size, 1)
value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.hidden_size + 1))
super().__init__(model, value_head, lora_rank, lora_train_bias)
applications/Chat/coati/models/opt/__init__.py deleted 100644 → 0
View file @ 36c4bb28
from .opt_actor import OPTActor
from .opt_critic import OPTCritic
from .opt_rm import OPTRM
__all__ = ["OPTActor", "OPTCritic", "OPTRM"]
applications/Chat/coati/models/opt/opt_actor.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
from transformers.models.opt.configuration_opt import OPTConfig
from transformers.models.opt.modeling_opt import OPTForCausalLM
from ..base import Actor
class OPTActor(Actor):
"""
OPT Actor model.
Args:
pretrained (str): Pretrained model name or path.
config (OPTConfig): Model config.
checkpoint (bool): Enable gradient checkpointing.
lora_rank (int): Rank of the low-rank approximation.
lora_train_bias (str): LoRA bias training mode.
"""
def __init__(
self,
pretrained: Optional[str] = None,
config: Optional[OPTConfig] = None,
checkpoint: bool = False,
lora_rank: int = 0,
lora_train_bias: str = "none",
) -> None:
if pretrained is not None:
model = OPTForCausalLM.from_pretrained(pretrained)
elif config is not None:
model = OPTForCausalLM(config)
else:
model = OPTForCausalLM(OPTConfig())
if checkpoint:
model.gradient_checkpointing_enable()
super().__init__(model, lora_rank, lora_train_bias)
applications/Chat/coati/models/opt/opt_critic.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
import torch.nn as nn
from transformers.models.opt.configuration_opt import OPTConfig
from transformers.models.opt.modeling_opt import OPTModel
from ..base import Critic
class OPTCritic(Critic):
"""
OPT Critic model.
Args:
pretrained (str): Pretrained model name or path.
config (OPTConfig): Model config.
lora_rank (int): Rank of the low-rank approximation.
lora_train_bias (str): LoRA bias training mode.
"""
def __init__(
self,
pretrained: Optional[str] = None,
config: Optional[OPTConfig] = None,
lora_rank: int = 0,
lora_train_bias: str = "none",
**kwargs,
) -> None:
if pretrained is not None:
model = OPTModel.from_pretrained(pretrained)
elif config is not None:
model = OPTModel(config)
else:
model = OPTModel(OPTConfig())
value_head = nn.Linear(model.config.word_embed_proj_dim, 1)
super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)
applications/Chat/coati/models/opt/opt_rm.py deleted 100644 → 0
View file @ 36c4bb28
from typing import Optional
import torch.nn as nn
from transformers import OPTConfig, OPTModel
from ..base import RewardModel
class OPTRM(RewardModel):
"""
OPT Reward model.
Args:
pretrained (str): Pretrained model name or path.
config (OPTConfig): Model config.
lora_rank (int): Rank of the low-rank approximation.
lora_train_bias (str): LoRA bias training mode.
"""
def __init__(
self,
pretrained: Optional[str] = None,
config: Optional[OPTConfig] = None,
lora_rank: int = 0,
lora_train_bias: str = "none",
) -> None:
if pretrained is not None:
model = OPTModel.from_pretrained(pretrained)
elif config is not None:
model = OPTModel(config)
else:
model = OPTModel(OPTConfig())
value_head = nn.Linear(model.config.word_embed_proj_dim, 1)
value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.word_embed_proj_dim + 1))
super().__init__(model, value_head, lora_rank, lora_train_bias)
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