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import sys
sys.path.append("./")
sys.path.append("../")
sys.path.append("../../")
sys.path.append("../../../")
import torch
import torch.nn as nn
import transformers
import torch.nn.functional as F
from typing import Optional, Tuple, Union
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
from transformers.models.gpt2.modeling_gpt2 import GPT2PreTrainedModel, GPT2Model, GPT2LMHeadModel
from tools.runner_utils.log_util import logging
from tools.model_utils.parameter_freeze import ParameterFreeze
logger = logging.getLogger(__name__)
freezer = ParameterFreeze()
"""
Function: Use Causal LM to prompt for cls
Notes:
- For classification, the model only calculate the loss at the position of label, the other position is set as -100
- During inference, generate result at the last position.
"""
class GPT2ForInContextLearning(GPT2PreTrainedModel):
_keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias", r"lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = GPT2Model(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
# if self.config.use_freezing:
# self.transformer = freezer.freeze_lm(self.transformer)
# Model parallel
self.model_parallel = False
self.device_map = None
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kwargs
if past:
input_ids = input_ids[:, -1].unsqueeze(-1)
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past:
position_ids = position_ids[:, -1].unsqueeze(-1)
else:
position_ids = None
return {
"input_ids": input_ids,
"past_key_values": past,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
# Set device for model parallelism
if self.model_parallel:
torch.cuda.set_device(self.transformer.first_device)
hidden_states = hidden_states.to(self.lm_head.weight.device)
lm_logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# print("shift_labels=", shift_labels)
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
cross_attentions=transformer_outputs.cross_attentions,
)
@staticmethod
def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[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.
"""
return tuple(
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
for layer_past in past
)
if __name__ == "__main__":
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("/Users/wangjianing/Desktop/开源代码与数据模型/模型/gpt2")
model = GPT2ForInContextLearning.from_pretrained("/Users/wangjianing/Desktop/开源代码与数据模型/模型/gpt2")
# In-Context Learning for classification
# input_text = "The capital city of China is Beijing. \n\n The capital city of Japan is Tokyo. \n\n The capital city of America is"
input_text = "What are follows emotions? \n\n Input: The book is very nice.\n Output: Great. \n\n Input: I never eat chocolate!\n Output:"
# input_text = "This film is wonderful.\n Great."
tokenizer.pad_token = tokenizer.eos_token
inputs = tokenizer(input_text, return_tensors="pt")
input_len = inputs["input_ids"].shape[-1]
gen_output = model.generate(**inputs, max_length=input_len + 10)
gen_result = tokenizer.decode(gen_output[0])
print("classification result:\n", gen_result)
# In-Context Learning for generation
input_text = "Please tell me what is the transformer? "
# input_text = "This film is wonderful.\n Great."
tokenizer.pad_token = tokenizer.eos_token
inputs = tokenizer(input_text, return_tensors="pt")
input_len = inputs["input_ids"].shape[-1]
gen_output = model.generate(**inputs, max_length=input_len + 60)
gen_result = tokenizer.decode(gen_output[0])
print("generation result:\n", gen_result)
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