From c6563764ed39fbd1002b8fe4e16b44e09ea872f8 Mon Sep 17 00:00:00 2001
From: xxl <505279206@qq.com>
Date: Thu, 14 Nov 2024 11:20:23 +0800
Subject: [PATCH] first commit
---
README.md | 389 +-
config.json | 38 +
configuration.json | 1 +
configuration_minicpm_reranker.py | 209 +
generation_config.json | 8 +
model-00001-of-00003.safetensors | 3 +
model-00002-of-00003.safetensors | 3 +
model-00003-of-00003.safetensors | 3 +
model.safetensors.index.json | 402 +
modeling_minicpm_reranker.py | 1496 +
special_tokens_map.json | 30 +
tokenizer.json | 294435 +++++++++++++++++++++++++++
tokenizer.model | 3 +
tokenizer_config.json | 42 +
14 files changed, 297060 insertions(+), 2 deletions(-)
create mode 100644 config.json
create mode 100644 configuration.json
create mode 100644 configuration_minicpm_reranker.py
create mode 100644 generation_config.json
create mode 100644 model-00001-of-00003.safetensors
create mode 100644 model-00002-of-00003.safetensors
create mode 100644 model-00003-of-00003.safetensors
create mode 100644 model.safetensors.index.json
create mode 100644 modeling_minicpm_reranker.py
create mode 100644 special_tokens_map.json
create mode 100644 tokenizer.json
create mode 100644 tokenizer.model
create mode 100644 tokenizer_config.json
diff --git a/README.md b/README.md
index 46c6e69..1a57918 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,388 @@
-# bge-reranker-v2-minicpm-layerwise_a13590573183266816374692
+---
+license: apache-2.0
+pipeline_tag: text-classification
+tags:
+- transformers
+- sentence-transformers
+language:
+- multilingual
+---
-bge-reranker-v2-minicpm-layerwise
\ No newline at end of file
+# Reranker
+
+**More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/tree/master).**
+
+- [Model List](#model-list)
+- [Usage](#usage)
+- [Fine-tuning](#fine-tune)
+- [Evaluation](#evaluation)
+- [Citation](#citation)
+
+Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
+You can get a relevance score by inputting query and passage to the reranker.
+And the score can be mapped to a float value in [0,1] by sigmoid function.
+
+
+## Model List
+
+| Model | Base model | Language | layerwise | feature |
+|:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|
+| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. |
+| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | [xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. |
+| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | [bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | - | Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference. |
+| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | [gemma-2b](https://huggingface.co/google/gemma-2b) | Multilingual | - | Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. |
+| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | [MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) | Multilingual | 8-40 | Suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. |
+
+
+You can select the model according your senario and resource.
+- For **multilingual**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma)
+
+- For **Chinese or English**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise).
+
+- For **efficiency**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and the low layer of [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise).
+
+- For better performance, recommand [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma)
+
+## Usage
+### Using FlagEmbedding
+
+```
+pip install -U FlagEmbedding
+```
+
+#### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 )
+
+Get relevance scores (higher scores indicate more relevance):
+
+```python
+from FlagEmbedding import FlagReranker
+reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+
+score = reranker.compute_score(['query', 'passage'])
+print(score) # -5.65234375
+
+# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
+score = reranker.compute_score(['query', 'passage'], normalize=True)
+print(score) # 0.003497010252573502
+
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
+print(scores) # [-8.1875, 5.26171875]
+
+# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True)
+print(scores) # [0.00027803096387751553, 0.9948403768236574]
+```
+
+#### For LLM-based reranker
+
+```python
+from FlagEmbedding import FlagLLMReranker
+reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+# reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation
+
+score = reranker.compute_score(['query', 'passage'])
+print(score)
+
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
+print(scores)
+```
+
+#### For LLM-based layerwise reranker
+
+```python
+from FlagEmbedding import LayerWiseFlagLLMReranker
+reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+# reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation
+
+score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.
+print(score)
+
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], cutoff_layers=[28])
+print(scores)
+```
+
+### Using Huggingface transformers
+
+#### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 )
+
+Get relevance scores (higher scores indicate more relevance):
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-m3')
+model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-v2-m3')
+model.eval()
+
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+ inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
+ scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
+ print(scores)
+```
+
+#### For LLM-based reranker
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def get_inputs(pairs, tokenizer, prompt=None, max_length=1024):
+ if prompt is None:
+ prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'."
+ sep = "\n"
+ prompt_inputs = tokenizer(prompt,
+ return_tensors=None,
+ add_special_tokens=False)['input_ids']
+ sep_inputs = tokenizer(sep,
+ return_tensors=None,
+ add_special_tokens=False)['input_ids']
+ inputs = []
+ for query, passage in pairs:
+ query_inputs = tokenizer(f'A: {query}',
+ return_tensors=None,
+ add_special_tokens=False,
+ max_length=max_length * 3 // 4,
+ truncation=True)
+ passage_inputs = tokenizer(f'B: {passage}',
+ return_tensors=None,
+ add_special_tokens=False,
+ max_length=max_length,
+ truncation=True)
+ item = tokenizer.prepare_for_model(
+ [tokenizer.bos_token_id] + query_inputs['input_ids'],
+ sep_inputs + passage_inputs['input_ids'],
+ truncation='only_second',
+ max_length=max_length,
+ padding=False,
+ return_attention_mask=False,
+ return_token_type_ids=False,
+ add_special_tokens=False
+ )
+ item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs
+ item['attention_mask'] = [1] * len(item['input_ids'])
+ inputs.append(item)
+ return tokenizer.pad(
+ inputs,
+ padding=True,
+ max_length=max_length + len(sep_inputs) + len(prompt_inputs),
+ pad_to_multiple_of=8,
+ return_tensors='pt',
+ )
+
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-gemma')
+model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-gemma')
+yes_loc = tokenizer('Yes', add_special_tokens=False)['input_ids'][0]
+model.eval()
+
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+ inputs = get_inputs(pairs, tokenizer)
+ scores = model(**inputs, return_dict=True).logits[:, -1, yes_loc].view(-1, ).float()
+ print(scores)
+```
+
+#### For LLM-based layerwise reranker
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def get_inputs(pairs, tokenizer, prompt=None, max_length=1024):
+ if prompt is None:
+ prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'."
+ sep = "\n"
+ prompt_inputs = tokenizer(prompt,
+ return_tensors=None,
+ add_special_tokens=False)['input_ids']
+ sep_inputs = tokenizer(sep,
+ return_tensors=None,
+ add_special_tokens=False)['input_ids']
+ inputs = []
+ for query, passage in pairs:
+ query_inputs = tokenizer(f'A: {query}',
+ return_tensors=None,
+ add_special_tokens=False,
+ max_length=max_length * 3 // 4,
+ truncation=True)
+ passage_inputs = tokenizer(f'B: {passage}',
+ return_tensors=None,
+ add_special_tokens=False,
+ max_length=max_length,
+ truncation=True)
+ item = tokenizer.prepare_for_model(
+ [tokenizer.bos_token_id] + query_inputs['input_ids'],
+ sep_inputs + passage_inputs['input_ids'],
+ truncation='only_second',
+ max_length=max_length,
+ padding=False,
+ return_attention_mask=False,
+ return_token_type_ids=False,
+ add_special_tokens=False
+ )
+ item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs
+ item['attention_mask'] = [1] * len(item['input_ids'])
+ inputs.append(item)
+ return tokenizer.pad(
+ inputs,
+ padding=True,
+ max_length=max_length + len(sep_inputs) + len(prompt_inputs),
+ pad_to_multiple_of=8,
+ return_tensors='pt',
+ )
+
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16)
+model = model.to('cuda')
+model.eval()
+
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+ inputs = get_inputs(pairs, tokenizer).to(model.device)
+ all_scores = model(**inputs, return_dict=True, cutoff_layers=[28])
+ all_scores = [scores[:, -1].view(-1, ).float() for scores in all_scores[0]]
+ print(all_scores)
+```
+
+## Fine-tune
+
+### Data Format
+
+Train data should be a json file, where each line is a dict like this:
+
+```
+{"query": str, "pos": List[str], "neg":List[str], "prompt": str}
+```
+
+`query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts, `prompt` indicates the relationship between query and texts. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives.
+
+See [toy_finetune_data.jsonl](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker/toy_finetune_data.jsonl) for a toy data file.
+
+### Train
+
+You can fine-tune the reranker with the following code:
+
+**For llm-based reranker**
+
+```shell
+torchrun --nproc_per_node {number of gpus} \
+-m FlagEmbedding.llm_reranker.finetune_for_instruction.run \
+--output_dir {path to save model} \
+--model_name_or_path google/gemma-2b \
+--train_data ./toy_finetune_data.jsonl \
+--learning_rate 2e-4 \
+--num_train_epochs 1 \
+--per_device_train_batch_size 1 \
+--gradient_accumulation_steps 16 \
+--dataloader_drop_last True \
+--query_max_len 512 \
+--passage_max_len 512 \
+--train_group_size 16 \
+--logging_steps 1 \
+--save_steps 2000 \
+--save_total_limit 50 \
+--ddp_find_unused_parameters False \
+--gradient_checkpointing \
+--deepspeed stage1.json \
+--warmup_ratio 0.1 \
+--bf16 \
+--use_lora True \
+--lora_rank 32 \
+--lora_alpha 64 \
+--use_flash_attn True \
+--target_modules q_proj k_proj v_proj o_proj
+```
+
+**For llm-based layerwise reranker**
+
+```shell
+torchrun --nproc_per_node {number of gpus} \
+-m FlagEmbedding.llm_reranker.finetune_for_layerwise.run \
+--output_dir {path to save model} \
+--model_name_or_path openbmb/MiniCPM-2B-dpo-bf16 \
+--train_data ./toy_finetune_data.jsonl \
+--learning_rate 2e-4 \
+--num_train_epochs 1 \
+--per_device_train_batch_size 1 \
+--gradient_accumulation_steps 16 \
+--dataloader_drop_last True \
+--query_max_len 512 \
+--passage_max_len 512 \
+--train_group_size 16 \
+--logging_steps 1 \
+--save_steps 2000 \
+--save_total_limit 50 \
+--ddp_find_unused_parameters False \
+--gradient_checkpointing \
+--deepspeed stage1.json \
+--warmup_ratio 0.1 \
+--bf16 \
+--use_lora True \
+--lora_rank 32 \
+--lora_alpha 64 \
+--use_flash_attn True \
+--target_modules q_proj k_proj v_proj o_proj \
+--start_layer 8 \
+--head_multi True \
+--head_type simple \
+--lora_extra_parameters linear_head
+```
+
+Our rerankers are initialized from [google/gemma-2b](https://huggingface.co/google/gemma-2b) (for llm-based reranker) and [openbmb/MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) (for llm-based layerwise reranker), and we train it on a mixture of multilingual datasets:
+
+- [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data)
+- [quora train data](https://huggingface.co/datasets/quora)
+- [fever train data](https://fever.ai/dataset/fever.html)
+
+## Evaluation
+
+- llama-index.
+
+
+
+
+- BEIR.
+
+rereank the top 100 results from bge-en-v1.5 large.
+
+
+
+rereank the top 100 results from e5 mistral 7b instruct.
+
+
+
+- CMTEB-retrieval.
+It rereank the top 100 results from bge-zh-v1.5 large.
+
+
+
+- miracl (multi-language).
+It rereank the top 100 results from bge-m3.
+
+
+
+
+
+## Citation
+
+If you find this repository useful, please consider giving a star and citation
+
+```bibtex
+@misc{li2023making,
+ title={Making Large Language Models A Better Foundation For Dense Retrieval},
+ author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao},
+ year={2023},
+ eprint={2312.15503},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL}
+}
+@misc{chen2024bge,
+ title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
+ author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
+ year={2024},
+ eprint={2402.03216},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL}
+}
+```
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..c6e7c4b
--- /dev/null
+++ b/config.json
@@ -0,0 +1,38 @@
+{
+ "_name_or_path": "BAAI/bge-reranker-v2-minicpm-layerwise",
+ "architectures": [
+ "LayerWiseMiniCPMForCausalLM"
+ ],
+ "attention_bias": false,
+ "attention_dropout": 0.0,
+ "auto_map": {
+ "AutoConfig": "BAAI/bge-reranker-v2-minicpm-layerwise--configuration_minicpm_reranker.LayerWiseMiniCPMConfig",
+ "AutoModel": "BAAI/bge-reranker-v2-minicpm-layerwise--modeling_minicpm_reranker.LayerWiseMiniCPMModel",
+ "AutoModelForCausalLM": "BAAI/bge-reranker-v2-minicpm-layerwise--modeling_minicpm_reranker.LayerWiseMiniCPMForCausalLM"
+ },
+ "bos_token_id": 1,
+ "dim_model_base": 256,
+ "eos_token_id": 2,
+ "head_multi": true,
+ "head_type": "simple",
+ "hidden_act": "silu",
+ "hidden_size": 2304,
+ "initializer_range": 0.1,
+ "intermediate_size": 5760,
+ "max_position_embeddings": 2048,
+ "model_type": "minicpm",
+ "num_attention_heads": 36,
+ "num_hidden_layers": 40,
+ "num_key_value_heads": 36,
+ "pretraining_tp": 1,
+ "rms_norm_eps": 1e-05,
+ "rope_scaling": null,
+ "rope_theta": 10000.0,
+ "scale_depth": 1.4,
+ "scale_emb": 12,
+ "start_layer": 8,
+ "torch_dtype": "bfloat16",
+ "transformers_version": "4.38.1",
+ "use_cache": false,
+ "vocab_size": 122753
+}
diff --git a/configuration.json b/configuration.json
new file mode 100644
index 0000000..c2f5f76
--- /dev/null
+++ b/configuration.json
@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-classification", "allow_remote": true}
\ No newline at end of file
diff --git a/configuration_minicpm_reranker.py b/configuration_minicpm_reranker.py
new file mode 100644
index 0000000..a9feb42
--- /dev/null
+++ b/configuration_minicpm_reranker.py
@@ -0,0 +1,209 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" MiniCPM model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class LayerWiseMiniCPMConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
+ 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 MiniCPM-7B.
+
+ 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 32000):
+ Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MiniCPMModel`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 11008):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details checkout [this
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+ `num_attention_heads`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
+ MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ End of stream token id.
+ pretraining_tp (`int`, *optional*, defaults to 1):
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+ issue](https://github.com/pytorch/pytorch/issues/76232).
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+ these scaling strategies behave:
+ https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+ experimental feature, subject to breaking API changes in future versions.
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+
+ ```python
+ >>> from transformers import MiniCPMModel, MiniCPMConfig
+
+ >>> # Initializing a MiniCPM minicpm-7b style configuration
+ >>> configuration = MiniCPMConfig()
+
+ >>> # Initializing a model from the minicpm-7b style configuration
+ >>> model = MiniCPMModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "minicpm"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=32000,
+ hidden_size=4096,
+ intermediate_size=11008,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=None,
+ hidden_act="silu",
+ max_position_embeddings=2048,
+ initializer_range=0.02,
+ rms_norm_eps=1e-6,
+ use_cache=True,
+ pad_token_id=None,
+ bos_token_id=1,
+ eos_token_id=2,
+ pretraining_tp=1,
+ tie_word_embeddings=True,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ attention_bias=False,
+ attention_dropout=0.0,
+ scale_emb=1,
+ dim_model_base=1,
+ scale_depth=1,
+ start_layer=8,
+ head_multi=True,
+ head_type="simple",
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.pretraining_tp = pretraining_tp
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ self._rope_scaling_validation()
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.scale_emb = scale_emb
+ self.dim_model_base = dim_model_base
+ self.scale_depth = scale_depth
+
+ self.start_layer = start_layer
+ self.head_multi = head_multi
+ self.head_type = head_type
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+ try:
+ import flash_attn
+ self._attn_implementation = "flash_attention_2"
+ except:
+ pass
+
+ def _rope_scaling_validation(self):
+ """
+ Validate the `rope_scaling` configuration.
+ """
+ if self.rope_scaling is None:
+ return
+
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+ raise ValueError(
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+ f"got {self.rope_scaling}"
+ )
+ rope_scaling_type = self.rope_scaling.get("type", None)
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+ raise ValueError(
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+ )
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000..375c429
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,8 @@
+{
+ "bos_token_id": 1,
+ "do_sample": true,
+ "eos_token_id": 2,
+ "temperature": 0.8,
+ "top_p": 0.8,
+ "transformers_version": "4.38.1"
+}
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+ "model.layers.8.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
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+ "model.layers.9.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+ "model.layers.9.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+ "model.layers.9.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+ "model.layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+ "model.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+ "model.norm.weight": "model-00003-of-00003.safetensors"
+ }
+}
diff --git a/modeling_minicpm_reranker.py b/modeling_minicpm_reranker.py
new file mode 100644
index 0000000..2dfdffd
--- /dev/null
+++ b/modeling_minicpm_reranker.py
@@ -0,0 +1,1496 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch MiniCPM model."""
+import sys
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union, Dict
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import (
+ AttentionMaskConverter,
+ _prepare_4d_attention_mask,
+ _prepare_4d_causal_attention_mask,
+ _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, \
+ SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
+from transformers.utils import (
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ is_flash_attn_2_available,
+ is_flash_attn_greater_or_equal_2_10,
+ logging,
+ replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_minicpm_reranker import LayerWiseMiniCPMConfig
+import re
+
+try:
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
+except:
+ pass
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+ if not is_torch_greater_or_equal_than_1_13:
+ import torch.fx
+
+ _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LayerWiseMiniCPMConfig"
+
+
+def _get_unpad_data(attention_mask):
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+ return (
+ indices,
+ cu_seqlens,
+ max_seqlen_in_batch,
+ )
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+ warnings.warn(
+ "Calling `transformers.models.minicpm.modeling_minicpm._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
+ )
+ return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
+
+
+def _make_causal_mask(
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+ warnings.warn(
+ "Calling `transformers.models.minicpm.modeling_minicpm._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.minicpm.modeling_minicpm.AttentionMaskConverter._make_causal_mask"
+ )
+ return AttentionMaskConverter._make_causal_mask(
+ input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
+ )
+
+
+# @torch.jit.script # type: ignore
+def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):
+ old_dtype = hidden.dtype
+ variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
+ hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)
+ return hidden * weight
+
+
+class MiniCPMRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ MiniCPMRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ return rms_layernorm(hidden_states, self.weight, self.variance_epsilon)
+
+
+ALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm)
+
+
+class MiniCPMRotaryEmbedding(nn.Module):
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+ super().__init__()
+
+ self.dim = dim
+ self.max_position_embeddings = max_position_embeddings
+ self.base = base
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ # Build here to make `torch.jit.trace` work.
+ self._set_cos_sin_cache(
+ # seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32
+ )
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+ def forward(self, x, seq_len=None):
+ # x: [bs, num_attention_heads, seq_len, head_size]
+ if seq_len > self.max_seq_len_cached:
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+ return (
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
+ )
+
+
+class MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
+ """MiniCPMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+ t = t / self.scaling_factor
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+class MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
+ """MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+
+ if seq_len > self.max_position_embeddings:
+ base = self.base * (
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+ ) ** (self.dim / (self.dim - 2))
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2:]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`):
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+ used to pass offsetted position ids when working with a KV-cache.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ # cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+ # sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+ # q_embed = (q * cos) + (rotate_half(q) * sin)
+ # k_embed = (k * cos) + (rotate_half(k) * sin)
+ orig_dtype = k.dtype
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]
+ q_fp32 = q.to(dtype=torch.float32, device=q.device)
+ k_fp32 = k.to(dtype=torch.float32, device=k.device)
+ q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin)
+ k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin)
+ return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype)
+
+
+class MiniCPMMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ if self.config.pretraining_tp > 1:
+ slice = self.intermediate_size // self.config.pretraining_tp
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+ gate_proj = torch.cat(
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
+ )
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
+
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+ down_proj = [
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+ ]
+ down_proj = sum(down_proj)
+ else:
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+ return down_proj
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class MiniCPMAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: LayerWiseMiniCPMConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.rope_theta = config.rope_theta
+ self.is_causal = True
+
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+ self._init_rope()
+
+ def _init_rope(self):
+ if self.config.rope_scaling is None:
+ self.rotary_emb = MiniCPMRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ base=self.rope_theta,
+ )
+ else:
+ scaling_type = self.config.rope_scaling["type"]
+ scaling_factor = self.config.rope_scaling["factor"]
+ if scaling_type == "linear":
+ self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ elif scaling_type == "dynamic":
+ self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ else:
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ if self.config.pretraining_tp > 1:
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+ query_slices = self.q_proj.weight.split(
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+ )
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+ query_states = torch.cat(query_states, dim=-1)
+
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+ key_states = torch.cat(key_states, dim=-1)
+
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+ value_states = torch.cat(value_states, dim=-1)
+
+ else:
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ if self.layer_idx is None:
+ raise ValueError(
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+ "with a layer index."
+ )
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
+
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ if self.config.pretraining_tp > 1:
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+ else:
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+
+class MiniCPMFlashAttention2(MiniCPMAttention):
+ """
+ MiniCPM flash attention module. This module inherits from `MiniCPMAttention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ # MiniCPMFlashAttention2 attention does not support output_attentions
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+
+ # overwrite attention_mask with padding_mask
+ attention_mask = kwargs.pop("padding_mask")
+
+ output_attentions = False
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ # Flash attention requires the input to have the shape
+ # batch_size x seq_length x head_dim x hidden_dim
+ # therefore we just need to keep the original shape
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+ # to be able to avoid many of these transpose/reshape/view.
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ dropout_rate = self.attention_dropout if self.training else 0.0
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in the correct dtype just to be sure everything works as expected.
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+ # in fp32. (MiniCPMRMSNorm handles it correctly)
+
+ input_dtype = query_states.dtype
+ if input_dtype == torch.float32:
+ # Handle the case where the model is quantized
+ if hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ else:
+ target_dtype = self.q_proj.weight.dtype
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ attn_output = self._flash_attention_forward(
+ query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+ def _flash_attention_forward(
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+ ):
+ """
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+ first unpad the input, then computes the attention scores and pad the final attention scores.
+
+ Args:
+ query_states (`torch.Tensor`):
+ Input query states to be passed to Flash Attention API
+ key_states (`torch.Tensor`):
+ Input key states to be passed to Flash Attention API
+ value_states (`torch.Tensor`):
+ Input value states to be passed to Flash Attention API
+ attention_mask (`torch.Tensor`):
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+ position of padding tokens and 1 for the position of non-padding tokens.
+ dropout (`int`, *optional*):
+ Attention dropout
+ softmax_scale (`float`, *optional*):
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+ """
+ if not self._flash_attn_uses_top_left_mask:
+ causal = self.is_causal
+ else:
+ # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__.
+ causal = self.is_causal and query_length != 1
+ # Contains at least one padding token in the sequence
+ if attention_mask is not None:
+ batch_size = query_states.shape[0]
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+ query_states, key_states, value_states, attention_mask, query_length
+ )
+
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+ attn_output_unpad = flash_attn_varlen_func(
+ query_states,
+ key_states,
+ value_states,
+ cu_seqlens_q=cu_seqlens_q,
+ cu_seqlens_k=cu_seqlens_k,
+ max_seqlen_q=max_seqlen_in_batch_q,
+ max_seqlen_k=max_seqlen_in_batch_k,
+ dropout_p=dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ )
+
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+ else:
+ attn_output = flash_attn_func(
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+ )
+
+ return attn_output
+
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+ key_layer = index_first_axis(
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+ )
+ value_layer = index_first_axis(
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+ )
+ if query_length == kv_seq_len:
+ query_layer = index_first_axis(
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+ )
+ cu_seqlens_q = cu_seqlens_k
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
+ indices_q = indices_k
+ elif query_length == 1:
+ max_seqlen_in_batch_q = 1
+ cu_seqlens_q = torch.arange(
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
+ ) # There is a memcpy here, that is very bad.
+ indices_q = cu_seqlens_q[:-1]
+ query_layer = query_layer.squeeze(1)
+ else:
+ # The -q_len: slice assumes left padding.
+ attention_mask = attention_mask[:, -query_length:]
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+ return (
+ query_layer,
+ key_layer,
+ value_layer,
+ indices_q,
+ (cu_seqlens_q, cu_seqlens_k),
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+ )
+
+
+class MiniCPMSdpaAttention(MiniCPMAttention):
+ """
+ MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+ SDPA API.
+ """
+
+ # Adapted from MiniCPMAttention.forward
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ if output_attentions:
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+ logger.warning_once(
+ "MiniCPMModel is using MiniCPMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ return super().forward(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ if query_states.device.type == "cuda" and attention_mask is not None:
+ query_states = query_states.contiguous()
+ key_states = key_states.contiguous()
+ value_states = value_states.contiguous()
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=attention_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+ is_causal=self.is_causal and attention_mask is None and q_len > 1,
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, None, past_key_value
+
+
+MINICPM_ATTENTION_CLASSES = {
+ "eager": MiniCPMAttention,
+ "flash_attention_2": MiniCPMFlashAttention2,
+ "sdpa": MiniCPMSdpaAttention,
+}
+
+
+class MiniCPMDecoderLayer(nn.Module):
+ def __init__(self, config: LayerWiseMiniCPMConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+ self.mlp = MiniCPMMLP(config)
+ self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.scale_depth = config.scale_depth
+ self.num_hidden_layers = config.num_hidden_layers
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ **kwargs,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ """
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+ # Self Attention
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ **kwargs,
+ )
+
+ hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ return outputs
+
+
+MINICPM_START_DOCSTRING = r"""
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`LayerWiseMiniCPMConfig`]):
+ 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.
+"""
+
+
+@add_start_docstrings(
+ "The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
+ MINICPM_START_DOCSTRING,
+)
+class MiniCPMPreTrainedModel(PreTrainedModel):
+ config_class = LayerWiseMiniCPMConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MiniCPMDecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_flash_attn_2 = True
+ _supports_sdpa = True
+ _supports_cache_class = True
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+
+MINICPM_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *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)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ Two formats are allowed:
+ - a [`~cache_utils.Cache`] instance;
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+ cache format.
+
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+ legacy cache format will be returned.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+ of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, 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.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`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 MiniCPM Model outputting raw hidden-states without any specific head on top.",
+ MINICPM_START_DOCSTRING,
+)
+class LayerWiseMiniCPMModel(MiniCPMPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]
+
+ Args:
+ config: LayerWiseMiniCPMConfig
+ """
+
+ def __init__(self, config: LayerWiseMiniCPMConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self._use_sdpa = config._attn_implementation == "sdpa"
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+
+ self.norm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cutoff_layers: Optional[Union[int, List]] = None,
+ ) -> Union[Tuple, 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
+
+ # retrieve input_ids and inputs_embeds
+ 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 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
+
+ past_key_values_length = 0
+ if use_cache:
+ use_legacy_cache = not isinstance(past_key_values, Cache)
+ if use_legacy_cache:
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+ if position_ids is None:
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ position_ids = torch.arange(
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+ )
+ position_ids = position_ids.unsqueeze(0)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids) * self.config.scale_emb
+
+ if self._use_flash_attention_2:
+ # 2d mask is passed through the layers
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+ elif self._use_sdpa and not output_attentions:
+ # output_attentions=True can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+ attention_mask,
+ (batch_size, seq_length),
+ inputs_embeds,
+ past_key_values_length,
+ )
+ else:
+ # 4d mask is passed through the layers
+ attention_mask = _prepare_4d_causal_attention_mask(
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+ )
+
+ # embed positions
+ hidden_states = inputs_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ next_decoder_cache = None
+
+ if cutoff_layers is None:
+ max_layer = self.config.num_hidden_layers
+ cutoff_layers = [max_layer]
+ if isinstance(cutoff_layers, int):
+ max_layer = cutoff_layers
+ cutoff_layers = [cutoff_layers]
+ else:
+ max_layer = max(cutoff_layers)
+
+ for idx, decoder_layer in enumerate(self.layers):
+ if idx in cutoff_layers and output_hidden_states:
+ all_hidden_states += (self.norm(hidden_states),)
+
+ if idx == max_layer:
+ break
+
+ if self.gradient_checkpointing and self.training:
+ layer_outputs = self._gradient_checkpointing_func(
+ decoder_layer.__call__,
+ hidden_states,
+ attention_mask,
+ position_ids,
+ past_key_values,
+ output_attentions,
+ use_cache,
+ )
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states and self.config.num_hidden_layers == max_layer:
+ all_hidden_states += (hidden_states,)
+
+ next_cache = None
+ if use_cache:
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+ if not return_dict:
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class LayerWiseHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(self, input_size, output_size):
+ super().__init__()
+ self.linear_head = nn.Linear(input_size, output_size, bias=False)
+
+ def forward(self, **kwargs):
+ return self.linear_head(**kwargs)
+
+class LayerWiseMiniCPMForCausalLM(MiniCPMPreTrainedModel):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = LayerWiseMiniCPMModel(config)
+ self.vocab_size = config.vocab_size
+
+ if self.config.head_type == 'raw':
+ if not self.config.head_multi:
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ else:
+ self.lm_head = nn.ModuleList([nn.Linear(
+ config.hidden_size, config.vocab_size, bias=False) for _ in range(
+ self.config.start_layer,
+ self.model.config.num_hidden_layers + 1)])
+ elif self.config.head_type == 'complex':
+ if not self.config.head_multi:
+ # self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ self.lm_head = LayerWiseHead(config.hidden_size, config.vocab_size)
+ else:
+ # self.lm_head = nn.ModuleList([nn.Linear(
+ # config.hidden_size, config.vocab_size, bias=False) for _ in range(
+ # self.config.start_layer,
+ # self.model.config.num_hidden_layers + 1)])
+ self.lm_head = nn.ModuleList([LayerWiseHead(
+ config.hidden_size, config.vocab_size) for _ in range(
+ self.config.start_layer,
+ self.model.config.num_hidden_layers + 1)])
+ else:
+ if not self.config.head_multi:
+ # self.lm_head = nn.Linear(config.hidden_size, 1, bias=False)
+ self.lm_head = LayerWiseHead(config.hidden_size, 1)
+ else:
+ # self.lm_head = nn.ModuleList([nn.Linear(
+ # config.hidden_size, 1, bias=False) for _ in range(
+ # self.config.start_layer,
+ # self.model.config.num_hidden_layers + 1)])
+ self.lm_head = nn.ModuleList([LayerWiseHead(
+ config.hidden_size, 1) for _ in range(
+ self.config.start_layer,
+ self.model.config.num_hidden_layers + 1)])
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: 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,
+ cutoff_layers: Optional[Union[int, List]] = None,
+ only_for_one_logit: Optional[int] = None
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MiniCPMForCausalLM
+
+ >>> model = MiniCPMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if cutoff_layers is None:
+ cutoff_layers = [self.config.num_hidden_layers]
+ elif isinstance(cutoff_layers, int):
+ cutoff_layers = [cutoff_layers]
+
+ remove_layers = [i for i in cutoff_layers if self.config.start_layer > i or i > self.config.num_hidden_layers]
+ if len(remove_layers) > 0:
+ logger.warning_once(
+ f"layers {remove_layers} are incompatible with the setting. They will be removed..."
+ )
+
+ cutoff_layers = [i for i in cutoff_layers if i not in remove_layers]
+ if len(cutoff_layers) == 0:
+ raise ValueError(f"Your cutoff layers must in [{self.config.start_layer}, {self.config.num_hidden_layers}]")
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=True,
+ return_dict=return_dict,
+ cutoff_layers=cutoff_layers
+ )
+
+ hidden_states = outputs[0]
+
+ all_logits = ()
+ if only_for_one_logit is None and (self.config.head_type == 'complex' or self.config.head_type == 'raw'):
+ if self.config.head_type == 'raw':
+ for i in range(len(outputs.hidden_states)):
+ if self.config.head_multi == False:
+ if self.config.pretraining_tp > 1:
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+ logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+ logits = torch.cat(logits, dim=-1)
+ else:
+ logits = self.lm_head(outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))
+ else:
+ if self.config.pretraining_tp > 1:
+ lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+ logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+ logits = torch.cat(logits, dim=-1)
+ else:
+ logits = self.lm_head[cutoff_layers[i] - self.config.start_layer](outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))
+ logits = logits.float()
+ logits = logits.reshape(input_ids.shape[0], -1)
+ all_logits = all_logits + (logits, )
+ else:
+ for i in range(len(outputs.hidden_states)):
+ if self.config.head_multi == False:
+ if self.config.pretraining_tp > 1:
+ lm_head_slices = self.lm_head.linear_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+ logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+ logits = torch.cat(logits, dim=-1)
+ else:
+ logits = self.lm_head.linear_head(outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))
+ else:
+ if self.config.pretraining_tp > 1:
+ lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+ logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+ logits = torch.cat(logits, dim=-1)
+ else:
+ logits = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head(outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))
+ logits = logits.float()
+ logits = logits.reshape(input_ids.shape[0], -1)
+ all_logits = all_logits + (logits, )
+ else:
+ if self.config.head_type == 'raw':
+ if only_for_one_logit is None:
+ raise ValueError("Cannot handle `only_for_one_logit` is None if the head type is complex.")
+
+ if self.config.head_multi == False:
+ lm_head_slices = self.lm_head.weight.split(1, dim=0)
+ for i in range(len(outputs.hidden_states)):
+ logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])
+ logits = logits.float()
+ logits = logits.reshape(input_ids.shape[0], -1)
+ all_logits = all_logits + (logits,)
+ else:
+ for i in range(len(outputs.hidden_states)):
+ lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].weight.split(1, dim=0)
+ logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])
+ logits = logits.float()
+ logits = logits.reshape(input_ids.shape[0], -1)
+ all_logits = all_logits + (logits, )
+ elif self.config.head_type == 'complex':
+ if only_for_one_logit is None:
+ raise ValueError("Cannot handle `only_for_one_logit` is None if the head type is complex.")
+
+ if self.config.head_multi == False:
+ lm_head_slices = self.lm_head.linear_head.weight.split(1, dim=0)
+ for i in range(len(outputs.hidden_states)):
+ logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])
+ logits = logits.float()
+ logits = logits.reshape(input_ids.shape[0], -1)
+ all_logits = all_logits + (logits,)
+ else:
+ for i in range(len(outputs.hidden_states)):
+ lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head.weight.split(1, dim=0)
+ logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])
+ logits = logits.float()
+ logits = logits.reshape(input_ids.shape[0], -1)
+ all_logits = all_logits + (logits, )
+ else:
+ if self.config.head_multi == False:
+ for i in range(len(outputs.hidden_states)):
+ logits = self.lm_head.linear_head(outputs.hidden_states[i])
+ logits = logits.float()
+ logits = logits.reshape(input_ids.shape[0], -1)
+ all_logits = all_logits + (logits,)
+ else:
+ for i in range(len(outputs.hidden_states)):
+ logits = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head(outputs.hidden_states[i])
+ logits = logits.float()
+ logits = logits.reshape(input_ids.shape[0], -1)
+ all_logits = all_logits + (logits,)
+
+ loss = None
+ if labels is not None and not only_for_one_logit and self.config.head_type == 'complex':
+ # Shift so that tokens < n predict n
+ loss = 0
+ for logits in all_logits:
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss += loss_fct(shift_logits, shift_labels)
+
+ outputs.hidden_states = None if not output_hidden_states else outputs.hidden_states
+
+ if not return_dict:
+ output = (all_logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=all_logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+ ):
+ if past_key_values is not None:
+ if isinstance(past_key_values, Cache):
+ cache_length = past_key_values.get_seq_length()
+ past_length = past_key_values.seen_tokens
+ max_cache_length = past_key_values.get_max_length()
+ else:
+ cache_length = past_length = past_key_values[0][0].shape[2]
+ max_cache_length = None
+
+ # Keep only the unprocessed tokens:
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+ # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+ # input)
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+ # input_ids based on the past_length.
+ elif past_length < input_ids.shape[1]:
+ input_ids = input_ids[:, past_length:]
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+ if (
+ max_cache_length is not None
+ and attention_mask is not None
+ and cache_length + input_ids.shape[1] > max_cache_length
+ ):
+ attention_mask = attention_mask[:, -max_cache_length:]
+
+ 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_key_values:
+ position_ids = position_ids[:, -input_ids.shape[1]:]
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ }
+ )
+ return model_inputs
+
+ @staticmethod
+ def _reorder_cache(past_key_values, beam_idx):
+ reordered_past = ()
+ for layer_past in past_key_values:
+ reordered_past += (
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+ )
+ return reordered_past
+
+ @torch.inference_mode()
+ def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user",
+ max_length: int = 4096, num_beams=1, do_sample=True, top_p=0.8, temperature=0.3, logits_processor=None,
+ **kwargs):
+ if history is None:
+ history = []
+ if logits_processor:
+ 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}
+ else:
+ 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}
+
+ history.append({"role": role, "content": query})
+ history_str = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=False)
+ inputs = tokenizer(history_str, return_tensors='pt').to(self.device)
+ outputs = self.generate(**inputs, **gen_kwargs)
+ outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
+ response = tokenizer.decode(outputs)
+ pattern = re.compile(r".*?(?=|<用户>)", re.DOTALL)
+ matches = pattern.findall(response)
+ if len(matches) > 0:
+ response = matches[0]
+ history.append({"role": "assistant", "content": response})
+ return response, history
\ No newline at end of file
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000..8bedc05
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,30 @@
+{
+ "bos_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ },
+ "unk_token": {
+ "content": "",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/tokenizer.json b/tokenizer.json
new file mode 100644
index 0000000..209e947
--- /dev/null
+++ b/tokenizer.json
@@ -0,0 +1,294435 @@
+{
+ "version": "1.0",
+ "truncation": null,
+ "padding": null,
+ "added_tokens": [
+ {
+ "id": 0,
+ "content": "",
+ "single_word": false,
+ "lstrip": false,
+ "rstrip": false,
+ "normalized": false,
+ "special": true
+ },
+ {
+ "id": 1,
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