From 5e69d591a6d49463a7e257939d58c8202f975d69 Mon Sep 17 00:00:00 2001 From: Cherrytest Date: Mon, 8 Sep 2025 18:26:13 +0000 Subject: [PATCH] Upload folder using ModelScope SDK --- .gitattributes | 4 +- README.md | 74 +- added_tokens.json | 10 + chat_template.jinja | 7 + config.json | 174 +++ configuration.json | 1 + configuration_minicpm.py | 203 +++ generation_config.json | 12 + model-00001-of-00004.safetensors | 3 + model-00002-of-00004.safetensors | 3 + model-00003-of-00004.safetensors | 3 + model-00004-of-00004.safetensors | 3 + model.safetensors.index.json | 299 ++++ modeling_minicpm.py | 2221 ++++++++++++++++++++++++++++++ special_tokens_map.json | 33 + tokenizer.json | 3 + tokenizer.model | 3 + tokenizer_config.json | 117 ++ 18 files changed, 3131 insertions(+), 42 deletions(-) create mode 100644 added_tokens.json create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 configuration.json create mode 100644 configuration_minicpm.py create mode 100644 generation_config.json create mode 100644 model-00001-of-00004.safetensors create mode 100644 model-00002-of-00004.safetensors create mode 100644 model-00003-of-00004.safetensors create mode 100644 model-00004-of-00004.safetensors create mode 100644 model.safetensors.index.json create mode 100644 modeling_minicpm.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/.gitattributes b/.gitattributes index 53d7257..21b3632 100644 --- a/.gitattributes +++ b/.gitattributes @@ -44,4 +44,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.tar filter=lfs diff=lfs merge=lfs -text *.wasm filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text \ No newline at end of file +*tfevents* filter=lfs diff=lfs merge=lfs -text + +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md index 97838c0..7802162 100644 --- a/README.md +++ b/README.md @@ -1,47 +1,39 @@ --- -license: Apache License 2.0 - -#model-type: -##如 gpt、phi、llama、chatglm、baichuan 等 -#- gpt - -#domain: -##如 nlp、cv、audio、multi-modal -#- nlp - -#language: -##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa -#- cn - -#metrics: -##如 CIDEr、Blue、ROUGE 等 -#- CIDEr - -#tags: -##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他 -#- pretrained - -#tools: -##如 vllm、fastchat、llamacpp、AdaSeq 等 -#- vllm +license: apache-2.0 +language: +- zh +- en +pipeline_tag: text-generation +library_name: mlx +base_model: openbmb/MiniCPM4.1-8B +tags: +- mlx --- -### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。 -#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型 -SDK下载 +# mlx-community/MiniCPM4.1-8B-bf16 + +This model [mlx-community/MiniCPM4.1-8B-bf16](https://huggingface.co/mlx-community/MiniCPM4.1-8B-bf16) was +converted to MLX format from [openbmb/MiniCPM4.1-8B](https://huggingface.co/openbmb/MiniCPM4.1-8B) +using mlx-lm version **0.26.0**. + +## Use with mlx + ```bash -#安装ModelScope -pip install modelscope -``` -```python -#SDK模型下载 -from modelscope import snapshot_download -model_dir = snapshot_download('mlx-community/MiniCPM4.1-8B-bf16') -``` -Git下载 -``` -#Git模型下载 -git clone https://www.modelscope.cn/mlx-community/MiniCPM4.1-8B-bf16.git +pip install mlx-lm ``` -

如果您是本模型的贡献者，我们邀请您根据模型贡献文档，及时完善模型卡片内容。

\ No newline at end of file +```python +from mlx_lm import load, generate + +model, tokenizer = load("mlx-community/MiniCPM4.1-8B-bf16") + +prompt = "hello" + +if tokenizer.chat_template is not None: + messages = [{"role": "user", "content": prompt}] + prompt = tokenizer.apply_chat_template( + messages, add_generation_prompt=True + ) + +response = generate(model, tokenizer, prompt=prompt, verbose=True) +``` diff --git a/added_tokens.json b/added_tokens.json new file mode 100644 index 0000000..7f0cedc --- /dev/null +++ b/added_tokens.json @@ -0,0 +1,10 @@ +{ + "<|execute_end|>": 73444, + "<|execute_start|>": 73443, + "<|fim_middle|>": 73446, + "<|fim_prefix|>": 73445, + "<|fim_suffix|>": 73447, + "<|im_end|>": 73440, + "<|im_start|>": 73441, + "<|tool_call|>": 73442 +} diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..f37bc08 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,7 @@ +{% for message in messages %}{{'<|im_start|>' + message['role'] + ' +' + message['content'] + '<|im_end|>' + ' +'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant +' }}{% if enable_thinking is defined and enable_thinking is false %}{{ ' + + +' }}{% endif %}{% endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..98a920a --- /dev/null +++ b/config.json @@ -0,0 +1,174 @@ +{ + "architectures": [ + "MiniCPMForCausalLM" + ], + "auto_map": { + "AutoConfig": "configuration_minicpm.MiniCPMConfig", + "AutoModel": "modeling_minicpm.MiniCPMModel", + "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM", + "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM", + "AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification" + }, + "bos_token_id": 1, + "dim_model_base": 256, + "eos_token_id": [ + 2, + 73440 + ], + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.1, + "intermediate_size": 16384, + "max_position_embeddings": 65536, + "model_type": "minicpm", + "mup_denominator": 32, + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 2, + "pad_token_id": 2, + "rms_norm_eps": 1e-06, + "rope_scaling": { + "rope_type": "longrope", + "long_factor": [ + 0.9982316082870437, + 1.033048153422584, + 1.0749920956484724, + 1.1255096879436193, + 1.1863348602111476, + 1.259543828902579, + 1.3476188888731149, + 1.4535223827776373, + 1.5807816745852985, + 1.7335856049489526, + 1.9168922912975785, + 2.1365471404135326, + 2.3994084200118646, + 2.713475511863602, + 3.0880118452194134, + 3.533650295140154, + 4.062463396503134, + 4.687974098908333, + 5.425075306704039, + 6.289818967956352, + 7.29902962722721, + 8.469695779093664, + 9.81809877306655, + 11.358657902065282, + 13.102505860712087, + 15.055862949967128, + 17.218348131364184, + 19.581439255386453, + 22.127353314656723, + 24.828633849376587, + 27.6486820771775, + 30.54334096108829, + 33.46345345363812, + 36.358112337548896, + 39.17816056534983, + 41.879441100069684, + 44.425355159339965, + 46.78844628336223, + 48.95093146475928, + 50.90428855401433, + 52.648136512661125, + 54.18869564165987, + 55.537098635632745, + 56.7077647874992, + 57.71697544677006, + 58.58171910802236, + 59.31882031581807, + 59.94433101822328, + 60.47314411958625, + 60.918782569507, + 61.29331890286281, + 61.60738599471455, + 61.87024727431288, + 62.089902123428836, + 62.27320880977746, + 62.42601274014111, + 62.55327203194878, + 62.65917552585329, + 62.74725058582382, + 62.82045955451526, + 62.88128472678279, + 62.931802319077946, + 62.97374626130382, + 63.008562806439365 + ], + "short_factor": [ + 0.9982316082870437, + 1.033048153422584, + 1.0749920956484724, + 1.1255096879436193, + 1.1863348602111476, + 1.259543828902579, + 1.3476188888731149, + 1.4535223827776373, + 1.5807816745852985, + 1.7335856049489526, + 1.9168922912975785, + 2.1365471404135326, + 2.3994084200118646, + 2.713475511863602, + 3.0880118452194134, + 3.533650295140154, + 4.062463396503134, + 4.687974098908333, + 5.425075306704039, + 6.289818967956352, + 7.29902962722721, + 8.469695779093664, + 9.81809877306655, + 11.358657902065282, + 13.102505860712087, + 15.055862949967128, + 17.218348131364184, + 19.581439255386453, + 22.127353314656723, + 24.828633849376587, + 27.6486820771775, + 30.54334096108829, + 33.46345345363812, + 36.358112337548896, + 39.17816056534983, + 41.879441100069684, + 44.425355159339965, + 46.78844628336223, + 48.95093146475928, + 50.90428855401433, + 52.648136512661125, + 54.18869564165987, + 55.537098635632745, + 56.7077647874992, + 57.71697544677006, + 58.58171910802236, + 59.31882031581807, + 59.94433101822328, + 60.47314411958625, + 60.918782569507, + 61.29331890286281, + 61.60738599471455, + 61.87024727431288, + 62.089902123428836, + 62.27320880977746, + 62.42601274014111, + 62.55327203194878, + 62.65917552585329, + 62.74725058582382, + 62.82045955451526, + 62.88128472678279, + 62.931802319077946, + 62.97374626130382, + 63.008562806439365 + ], + "original_max_position_embeddings": 65536 + }, + "rope_theta": 10000.0, + "scale_depth": 1.4, + "scale_emb": 12, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.46.3", + "use_cache": true, + "vocab_size": 73448 +} \ No newline at end of file diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..bbeeda1 --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"framework": "pytorch", "task": "text-generation", "allow_remote": true} \ No newline at end of file diff --git a/configuration_minicpm.py b/configuration_minicpm.py new file mode 100644 index 0000000..6d8fa07 --- /dev/null +++ b/configuration_minicpm.py @@ -0,0 +1,203 @@ +# coding=utf-8 +# Copyright 2025 The OpenBMB Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" 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 MiniCPMConfig(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, + mup_denominator=32, + sparse_config=None, + **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 + # only used for Eagle Head + self.mup_denominator = mup_denominator + + # sparse config + self.sparse_config = sparse_config + + 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..cf2547d --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "bos_token_id": 1, + "do_sample": true, + "eos_token_id": [ + 2, + 73440 + ], + "pad_token_id": 2, + "temperature": 0.8, + "top_p": 0.8, + "transformers_version": "4.46.1" +} diff --git 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch MiniCPM model.""" +import math +import re +import warnings +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache, CacheLayerMixin, DynamicLayer +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_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from transformers.utils.import_utils import is_torch_fx_available + +from .configuration_minicpm import MiniCPMConfig + +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 + from infllm_v2 import ( + infllmv2_attn_stage1, + infllmv2_attn_varlen_func, + infllmv2_attn_with_kvcache, + max_pooling_1d, + max_pooling_1d_varlen + ) +except: + pass + +from functools import lru_cache + + +def compressed_attention( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + kernel_size: int, + kernel_stride: int, + block_size: int, + topk: int, + cu_seqlens_q: torch.Tensor, + cu_seqlens_k: torch.Tensor, + max_seqlen_q: int, + max_seqlen_k: int, + sm_scale: float = None, + init_blocks: int = 1, + local_blocks: int = 2, + cache_lens: torch.Tensor = None, +) -> Tuple[torch.Tensor, torch.Tensor]: + """Attention between query and compressed key and value. Compute attention output and topk block idx used in topk_sparse_attention. + + Args: + q (torch.Tensor): shape [total_q_len, num_q_heads, head_dim] + k (torch.Tensor): shape [total_kv_len, num_kv_heads, head_dim] + v (torch.Tensor): shape [total_kv_len, num_kv_heads, head_dim] + kernel_size (int): kernel size in compress_key_value + kernel_stride (int): stride of compress_key_value + block_size (int): key value block size for topk sparse attention. + topk (int): number of blocks for each query. + cu_seqlens_q (torch.Tensor): shape [batch_size + 1], similar to cu_seqlens_q in flash_attn_func_varlen. + cu_seqlens_k (torch.Tensor): shape [batch_size + 1], similar to cu_seqlens_k in flash_attn_func_varlen. + max_seqlen_q (int): max q len of the batch. + max_seqlen_k (int): max k len of the batch. + sm_scale (float, optional): softmax scale. Defaults to None, means 1/sqrt(head_dim). + init_blocks (int, optional): Number of init blocks for each query. Defaults to 1. + local_blocks (int, optional): Number of local blocks for each query. Defaults to 2. + cache_lens (torch.Tensor, optional): shape [batch_size], used to record the cache length of each query. Defaults to None. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: attention output and topk_idx used in topk_sparse_attention + """ + with torch.no_grad(): + batch_size = cu_seqlens_q.shape[0] - 1 + + # Check if it's prefilling stage + is_prefilling = cache_lens is None or (cache_lens == 0).all().item() + + # prefilling stage + if is_prefilling: + # Calculate q_idx for each query position in each batch + cache_lens = torch.zeros(batch_size, dtype=torch.int32, device=q.device) + q_idx = torch.cat([ + (torch.arange(cu_seqlens_q[i + 1] - cu_seqlens_q[i], device=q.device) + + max_seqlen_q - (cu_seqlens_q[i + 1] - cu_seqlens_q[i])) // block_size + for i in range(batch_size) + ], dim=0) # shape: [total_q_len] + # decoding stage + else: + # Each batch has only one query (last position). Shape: [batch_size] = [total_q_len] in decoding + q_idx = cache_lens // block_size + + # compute attention score + score = infllmv2_attn_stage1( + q.contiguous(), + k.contiguous(), + v.contiguous(), + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k, + causal=is_prefilling) + # Shape: [num_heads, total_q_len, num_blocks] + score = score[:, :q_idx.shape[0], :] + + # Shape: [num_heads, total_q_len, num_blocks] + block_score = max_pooling_1d_varlen( + score.contiguous(), + cu_seqlens_q, + cu_seqlens_k, + cache_lens, + max_seqlen_q, + max_seqlen_k, + local_blocks=local_blocks, + init_blocks=init_blocks, + block_size=block_size, + stride=kernel_stride) + + # get topk + topk = min(topk, block_score.shape[-1]) + topk_idx = block_score.topk(topk, dim=-1).indices.sort(-1).values + topk_idx[topk_idx > q_idx[None, :, None]] = -1 + topk_idx = topk_idx.to(torch.int32) + + return topk_idx + + +@lru_cache(maxsize=16) +def calc_chunks_with_stride(cu_seqlen, chunk_size, kernel_stride): + """ + Compute the chunks that require Sparse attention, with stride support. + + Args: + cu_seqlen (torch.Tensor): Cumulative sequence lengths for each sample. + chunk_size (int): Chunk size used for Sparse attention. + kernel_stride (int): Stride size when sliding over the sequence. + + Returns: + filtered_indices (torch.Tensor): Indices used to directly index into the key/value tensors. + cu_seqlens_compressed (torch.Tensor): Cumulative sequence lengths after compression. + """ + # 1. Compute the length of each sequence + batch_sizes = cu_seqlen[1:] - cu_seqlen[:-1] + + # 2. Compute the start positions of chunks for each sequence (with stride) + max_seq_len = torch.max(batch_sizes) + max_num_chunks_per_seq = (max_seq_len - chunk_size) // kernel_stride + 1 + chunk_start_offsets = torch.arange(0, max_num_chunks_per_seq * kernel_stride, kernel_stride, device=cu_seqlen.device) + seq_starts = cu_seqlen[:-1] + chunk_start_in_seq = seq_starts[:, None] + chunk_start_offsets[None, :] # [batch_size, max_num_chunks_per_seq] + + # 3. Filter out chunks that exceed sequence length or are smaller than the full chunk size + chunk_end_in_seq = chunk_start_in_seq + chunk_size + valid_chunk_mask = (chunk_end_in_seq <= (seq_starts[:, None] + batch_sizes[:, None])) + + # 4. Filter valid chunk start positions using the valid_chunk_mask + valid_chunk_starts = chunk_start_in_seq[valid_chunk_mask] # [num_valid_chunks] + del chunk_start_in_seq + # 5. Generate filtered_indices + chunk_indices = torch.arange( + 0, chunk_size, device=cu_seqlen.device + )[None, :] # [1, chunk_size] + filtered_indices = valid_chunk_starts[:, None] + chunk_indices # [num_valid_chunks, chunk_size] + filtered_indices = filtered_indices.view(-1) # Flatten to 1D indices + + # 6. Compute compressed cumulative sequence lengths + num_filtered_chunks_per_batch = valid_chunk_mask.sum(dim=1) # Number of valid chunks per batch + cu_seqlens_compressed = torch.zeros( + len(cu_seqlen), dtype=torch.int32, device=cu_seqlen.device + ) + cu_seqlens_compressed[1:] = num_filtered_chunks_per_batch.cumsum(dim=0) + del num_filtered_chunks_per_batch, chunk_start_offsets, seq_starts, chunk_end_in_seq, valid_chunk_mask, chunk_indices + return filtered_indices, cu_seqlens_compressed + + +class CompressK(torch.nn.Module): + def __init__(self, head_num_k, head_dim, kernel_size, kernel_stride=16): + """ + Module for compressing key (K) representations. + + Args: + head_num_k (int): Number of key attention heads. + head_dim (int): Dimension of each attention head. + kernel_size (int): Size of each chunk used for compression. + kernel_stride (int, optional): Stride used when dividing input into chunks. Default is 16. + """ + super().__init__() + self.kernel_size = kernel_size + self.head_num_k = head_num_k + self.head_dim = head_dim + self.kernel_stride = kernel_stride + + def forward(self, k: torch.Tensor, cu_seqlens): + """ + Forward pass for compressing the key (K) tensor. + + Args: + k (torch.Tensor): Input key tensor of shape (total_seq_len, num_heads, head_dim). + cu_seqlens (torch.Tensor): Cumulative sequence lengths for each sample in the batch, typically used for handling variable-length sequences. + + Returns: + compress_k (torch.Tensor): Compressed key tensor. + cu_seqlens_compressed (torch.Tensor): Updated cumulative sequence lengths after compression. + + """ + # Compute chunk-related metadata, with stride support + filtered_k_indices, cu_seqlens_compressed = calc_chunks_with_stride( + cu_seqlens, self.kernel_size, self.kernel_stride + ) + + # Extract filtered key vectors + filtered_k = k.index_select(0, filtered_k_indices.view(-1)) + + # split + filtered_k = filtered_k.view(filtered_k.shape[0] // self.kernel_size, self.kernel_size, self.head_num_k, self.head_dim) # [l, block_size,h,d] + + compressed_k = filtered_k.mean(dim=1) + return compressed_k, cu_seqlens_compressed + + + +class InfLLMv2CacheLayer(DynamicLayer): + def __init__(self): + super().__init__() + # Initialize any additional attributes specific to InfLLMv2CacheLayer + self.no_rope_keys = torch.tensor([], dtype=torch.float32) + self.compress_k_cache = [] + self.no_compress_k_cache = [] + self.cached_compressed_cu_seqlens = torch.tensor([], dtype=torch.int32) + self.compress_k_cache_varlen = torch.tensor([], dtype=torch.float32) + + def update_no_rope_key(self, key_states): + if self.no_rope_keys.numel() == 0: + self.no_rope_keys = key_states + else: + self.no_rope_keys = torch.cat([self.no_rope_keys, key_states], dim=1) + return self.no_rope_keys + + def update_compress_k(self, key_states, cu_seqlens=None): + if len(self.compress_k_cache) == 0: + if cu_seqlens is not None: + self.cached_compressed_cu_seqlens = cu_seqlens.clone() + self.compress_k_cache_varlen = key_states + split_sizes = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist() + self.compress_k_cache = list(torch.split(key_states, split_sizes)) + else: + for index, k in enumerate(key_states): + if k is not None: + self.compress_k_cache[index] = torch.cat([self.compress_k_cache[index], k], dim=0) + new_seq_lens = torch.tensor([tensor.shape[0] for tensor in self.compress_k_cache], dtype=torch.int32) + new_cumsum = torch.cumsum(new_seq_lens, dim=0, dtype=torch.int32) + + self.compress_k_cache_varlen = torch.cat(self.compress_k_cache, dim=0) + self.cached_compressed_cu_seqlens = torch.cat([torch.tensor([0], dtype=torch.int32), new_cumsum]).to(self.compress_k_cache_varlen.device) + return self.compress_k_cache_varlen, self.cached_compressed_cu_seqlens + + def update_no_compress_k(self, key_states, kernel_size=32, kernel_stride=16): + k_chunk_list = [] + for index, k in enumerate(key_states): + if len(self.no_compress_k_cache) <= index: + self.no_compress_k_cache.append(k) + else: + self.no_compress_k_cache[index] = torch.cat([self.no_compress_k_cache[index], k], dim=0) + current_len = self.no_compress_k_cache[index].shape[0] + if current_len >= kernel_size: + k_chunk_list.append(self.no_compress_k_cache[index][:kernel_size]) + self.no_compress_k_cache[index] = self.no_compress_k_cache[index][kernel_stride:] + else: + k_chunk_list.append(None) + return k_chunk_list + +class InfLLMv2Cache(DynamicCache): + def __init__(self, + config,num_hidden_layers: Optional[int] = None) -> None: + super().__init__(config=config) + self.layers = [InfLLMv2CacheLayer() for _ in range(num_hidden_layers)] if num_hidden_layers else [] + self._seen_tokens = 0 + + def update(self, key_states, value_states, layer_idx, cache_kwargs=None): + if layer_idx == 0: + self._seen_tokens += key_states.shape[-2] + return self.layers[layer_idx].update(key_states, value_states, cache_kwargs) + + def update_no_rope_key(self, key_states, layer_idx, cache_kwargs=None): + return self.layers[layer_idx].update_no_rope_key(key_states) + + def update_compress_k(self, key_states, layer_idx, cu_seqlens=None, cache_kwargs=None): + return self.layers[layer_idx].update_compress_k(key_states, cu_seqlens) + + def update_no_compress_k(self, key_states, layer_idx, kernel_size=32, kernel_stride=16, cache_kwargs=None): + return self.layers[layer_idx].update_no_compress_k(key_states, kernel_size, kernel_stride) + + def crop(self, max_length): + for layer in self.layers: + layer.crop(max_length) + + def batch_repeat_interleave(self, repeats): + for layer in self.layers: + layer.batch_repeat_interleave(repeats) + + def batch_select_indices(self, indices): + for layer in self.layers: + layer.batch_select_indices(indices) + + +# 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 = 'MiniCPMConfig' + + +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, + ) + + + + +# @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 MiniCPMLongRoPE(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, short_factor=None, long_factor=None, original_max_position_embeddings=None): + self.short_factor = short_factor + self.long_factor = long_factor + self.original_max_position_embeddings = original_max_position_embeddings + scale = (max_position_embeddings / self.original_max_position_embeddings) + self.scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings)) + 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) + if seq_len > self.original_max_position_embeddings: + ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=device) + else: + ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=device) + + freqs = torch.mul( + torch.outer(t, 1.0 / ext_factors).to(device=device), + self.inv_freq.to(device=device).to(dtype) + ) + # 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) * self.scaling_factor, persistent=False) + self.register_buffer('sin_cached', emb.sin().to(dtype) * self.scaling_factor, persistent=False) + + +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 _unpad_one_tensor(hidden_states, attention_mask): + # Unpad the hidden states using the indices + indices, cu_seqlens, max_seqlen_in_batch = _get_unpad_data(attention_mask) + batch_size, seq_len = hidden_states.shape[:2] + + # Get the remaining dimensions + remaining_dims = hidden_states.shape[2:] + + # Reshape to (batch_size * seq_len, *remaining_dims) + reshaped_states = hidden_states.reshape(batch_size * seq_len, *remaining_dims) + + # Apply unpadding using indices + unpadded_states = index_first_axis(reshaped_states, indices) + + return unpadded_states, indices, cu_seqlens, max_seqlen_in_batch + +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: MiniCPMConfig, 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['rope_type'] + scaling_factor = self.config.rope_scaling.get('factor', None) + 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, + ) + elif scaling_type == 'longrope': + self.rotary_emb = MiniCPMLongRoPE( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + short_factor=self.config.rope_scaling['short_factor'], + long_factor=self.config.rope_scaling['long_factor'], + base=self.rope_theta, + original_max_position_embeddings=self.config.rope_scaling['original_max_position_embeddings'] + ) + 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 = position_ids.max().item() + 1 + 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 alignment, 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 = position_ids.max().item() + 1 + 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 MiniCPMInfLLMv2Attention(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) + assert self.config._attn_implementation == 'flash_attention_2', 'Only flash_attention_2 is supported for sparse attention' + # 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 alignment, 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() + + # -------sparse------- + self.kernel_size = self.config.sparse_config.get('kernel_size', 32) + self.kernel_stride = self.config.sparse_config.get('kernel_stride', 16) + self.init_blocks = self.config.sparse_config.get('init_blocks', 1) + self.block_size = self.config.sparse_config.get('block_size', 64) + self.window_size = self.config.sparse_config.get('window_size', 2048) + self.dense_len = self.config.sparse_config.get('dense_len', 8192) + + self.local_blocks = self.window_size // self.block_size # local_blocks + self.topk = self.config.sparse_config.get('topk', 64) + (self.window_size//self.block_size) + self.use_nope = self.config.sparse_config.get('use_nope', False) + self.compress_k = CompressK(self.num_key_value_heads, self.head_dim, kernel_size=self.kernel_size, kernel_stride=self.kernel_stride) + + 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) + + # !save no rope + if self.use_nope: + query_states_no_rope = query_states.view(bsz, q_len, self.num_heads, self.head_dim) + key_states_no_rope = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim) + + # 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 = position_ids.max().item() + 1 + 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) + if self.use_nope: + key_states_no_rope = past_key_value.update_no_rope_key(key_states_no_rope, self.layer_idx) + no_rope_param = { + 'key_states_no_rope': key_states_no_rope, + 'query_states_no_rope': query_states_no_rope, + } + else: + no_rope_param = None + + 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) + if kv_seq_len < self.dense_len: + attn_output = self._flash_attention_forward_dense( + query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate) + else: + attn_output = self._sparse_attention_forward( + query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, + no_rope_param=no_rope_param, # if past_key_value is not None else None, + past_key_value=past_key_value) + 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 _sparse_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + no_rope_param=None, + past_key_value=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] + # assert batch_size == 1, 'Only batch_size=1 is supported at the moment.' + if past_key_value!=None: + compressed_k, compressed_cu_seqlens = self.get_compress_k( + key_states=key_states if self.use_nope ==False else no_rope_param['key_states_no_rope'], # This can be optimized a bit; + attention_mask=attention_mask, + past_key_value=past_key_value) + + 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 + if no_rope_param != None: + if max_seqlen_in_batch_q == 1: + no_rope_param['query_states_no_rope'] = no_rope_param['query_states_no_rope'].squeeze(1) + else: + no_rope_param['query_states_no_rope'],_, _, _ = _unpad_one_tensor(no_rope_param['query_states_no_rope'],attention_mask=attention_mask) + if past_key_value==None: + # compress_k use varlen form + compressed_k, compressed_cu_seqlens = self.compress_k(key_states,cu_seqlens_k) + + attn_output_unpad = self.sparse_forward( + query_states, + key_states, + value_states, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_in_batch_q, + max_seqlen_in_batch_k, + no_rope_param=no_rope_param, + compressed_k=compressed_k, + compressed_cu_seqlens=compressed_cu_seqlens) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + raise ValueError('Need attention mask') + + return attn_output + + def get_compress_k(self, key_states, attention_mask, past_key_value): + """ + Get compressed key states and corresponding cumulative sequence lengths. + + Args: + key_states: Key states tensor + cu_seqlens_k: Cumulative sequence lengths for keys + past_key_value: Past key-value cache + no_rope_param: Optional parameter containing key states without rope + + Returns: + Tuple of (compressed_k, compressed_cu_seqlens) + """ + # Check if this is prefilling or initial compression condition + is_prefilling = ( + key_states.shape[1] >= self.dense_len and + ( + not past_key_value.layers[self.layer_idx].compress_k_cache + ) + ) + + if is_prefilling: + unpadded_key_states, indices, cu_seqlens, max_seqlen_in_batch = _unpad_one_tensor(key_states,attention_mask=attention_mask) + # Compress the keys + compressed_k, compressed_cu_seqlens = self.compress_k(unpadded_key_states, cu_seqlens) + + past_key_value.update_compress_k( + compressed_k, self.layer_idx, compressed_cu_seqlens) + + no_compress_k_list = [] + # Compute and update no_compress_k + for i in range(len(compressed_cu_seqlens)-1): + no_compress_k_start = (compressed_cu_seqlens[i+1]- compressed_cu_seqlens[i]) * self.kernel_stride + + no_compress_k_list.append(unpadded_key_states[cu_seqlens[i]+no_compress_k_start:cu_seqlens[i+1]].clone()) + + past_key_value.update_no_compress_k( + no_compress_k_list, self.layer_idx,kernel_stride=self.kernel_stride, + kernel_size=self.kernel_size) + else: + # Decode case: incremental update + batch_size = key_states.shape[0] # key_states.shape = [batch_size, seq, k_head_num, head_dim] + key_states_split = list(torch.split( + key_states[:,-1:].squeeze(1), #[batch_size, seq, k_head_num, head_dim]->[batch_size, 1, k_head_num, head_dim]-> [batch_size, k_head_num, head_dim] + [1] * batch_size,dim=0, + )) + # Try to update no_compress_k buffer + no_compress_k_list = past_key_value.update_no_compress_k( + key_states_split, self.layer_idx, + kernel_stride=self.kernel_stride, + kernel_size=self.kernel_size) + new_compressed_k_list = [] + for no_compress_k in no_compress_k_list: + if no_compress_k is not None: + # We have enough tokens to compress + new_compressed_k = no_compress_k.mean(dim=0, keepdim=True) # [1, n_heads_k, head_dim] + new_compressed_k_list.append(new_compressed_k) + else: + new_compressed_k_list.append(None) + compressed_k, compressed_cu_seqlens = past_key_value.update_compress_k(new_compressed_k_list, self.layer_idx,) + + return compressed_k, compressed_cu_seqlens + + def sparse_forward(self, + query_layer, + key_layer, + value_layer, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_in_batch_q, + max_seqlen_in_batch_k, + no_rope_param=None, + compressed_k=None, + compressed_cu_seqlens=None): + compressed_seqlens = compressed_cu_seqlens[1:] - compressed_cu_seqlens[:-1] + cache_lens = None + if max_seqlen_in_batch_q==1 and max_seqlen_in_batch_k>1: #decoding + seq_lens_k = cu_seqlens_k[1:] - cu_seqlens_k[:-1] + cache_lens = seq_lens_k-1 + + topk_idx = compressed_attention( + query_layer if no_rope_param is None else no_rope_param['query_states_no_rope'], + compressed_k, + compressed_k.clone(), + self.kernel_size, + self.kernel_stride, + self.block_size, + self.topk, + cu_seqlens_q, + compressed_cu_seqlens, + max_seqlen_in_batch_q, + compressed_seqlens.max().item(), + None, + init_blocks=self.init_blocks, + local_blocks=self.local_blocks, + cache_lens=cache_lens + ) + topk_attn_output = infllmv2_attn_varlen_func( + query_layer, + key_layer, + value_layer, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_in_batch_q, + max_seqlen_in_batch_k, + dropout_p=0.0, + deterministic=False, + softmax_scale=None, + causal=max_seqlen_in_batch_q != 1, + return_attn_probs=False, + # block_window_size=self.window_size // self.block_size, + topk_idx=topk_idx + ) + + return topk_attn_output + + def _flash_attention_forward_dense( + 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 = position_ids.max().item() + 1 + 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: MiniCPMConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + if config.sparse_config is not None and torch.cuda.is_available(): + self.self_attn = MiniCPMInfLLMv2Attention(config=config, layer_idx=layer_idx) + else: + 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 ([`MiniCPMConfig`]): + 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 = MiniCPMConfig + 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 MiniCPMModel(MiniCPMPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`] + + Args: + config: MiniCPMConfig + """ + + def __init__(self, config: MiniCPMConfig): + 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, + ) -> 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: + raise ValueError( + 'You must use the new past_key_values format, such as the Cache class, instead of the old tuple format.' + ) + + # Calculate the usable length of past key values + past_key_values_length = past_key_values.get_seq_length() if isinstance(past_key_values, InfLLMv2Cache) else 0 + + # Initialize InfLLMv2Cache if needed + if self.config.sparse_config is not None and torch.cuda.is_available() and past_key_values_length == 0: + past_key_values = InfLLMv2Cache(config = self.config, num_hidden_layers=self.config.num_hidden_layers) + + 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 + if attention_mask is None: + raise ValueError( + f'need attention_mask for flash attention, but got {attention_mask}.' + ) + 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 + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + 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: + 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 MiniCPMForCausalLM(MiniCPMPreTrainedModel): + _tied_weights_keys = ['lm_head.weight'] + + def __init__(self, config): + super().__init__(config) + self.model = MiniCPMModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # 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, + logits_to_keep: Union[int, torch.Tensor] = 0, + **kwargs, + ) -> 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 + + # 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=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + hidden_states = hidden_states[:, slice_indices, :].contiguous() + 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(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)] + logits = torch.cat(logits, dim=-1) + else: + logits = self.lm_head(hidden_states / (self.config.hidden_size / self.config.dim_model_base)) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + 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) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=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): + # Use the new Cache class methods + cache_length = past_key_values.get_seq_length() + + if self.config.sparse_config is not None and torch.cuda.is_available() and cache_length == 0: + past_key_values = InfLLMv2Cache(config = self.config, num_hidden_layers=self.config.num_hidden_layers) + past_length = cache_length + max_cache_length = None + else: + raise ValueError( + 'You must use the new past_key_values format, such as the Cache class, instead of the old tuple format.' + ) + + # 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 exclusively 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, + } + ) + # Forward ALL kwargs that are uninitialized (e.g. `use_cache`). + for key, value in kwargs.items(): + if key not in model_inputs: + model_inputs[key] = value + 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 + + +@add_start_docstrings( + """ + The MiniCPM Model transformer with a sequence classification head on top (linear layer). + + [`MiniCPMForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + MINICPM_START_DOCSTRING, +) +class MiniCPMForSequenceClassification(MiniCPMPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = MiniCPMModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # 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 + + @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, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + 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=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError('Cannot handle batch sizes > 1 if no padding token is defined.') + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to( + logits.device + ) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = 'regression' + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = 'single_label_classification' + else: + self.config.problem_type = 'multi_label_classification' + + if self.config.problem_type == 'regression': + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == 'single_label_classification': + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == 'multi_label_classification': + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..2fcea2d --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,33 @@ +{ + "additional_special_tokens": [ + "<|im_end|>", + "<|im_start|>", + "<|tool_call|>", + "<|execute_start|>", + "<|execute_end|>", + "<|fim_prefix|>", + "<|fim_middle|>", + "<|fim_suffix|>" + ], + "bos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "<|im_end|>", + "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..d433aed --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:adf7208af154a5ca065d2eda4e5419e02aac58c2c00627874748b75ec6769094 +size 6701371 diff --git a/tokenizer.model b/tokenizer.model new file mode 100644 index 0000000..d99b569 --- /dev/null +++ b/tokenizer.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76bc1bf4aa8b378aade9380334f789a5c191d4d0d1639339441f64a221a0eb4c +size 132 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..ab86075 --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,117 @@ +{ + "add_bos_token": true, + "add_eos_token": false, + "add_prefix_space": null, + "added_tokens_decoder": { + "0": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "1": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "2": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73440": { + "content": "<|im_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73441": { + "content": "<|im_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73442": { + "content": "<|tool_call|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73443": { + "content": "<|execute_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73444": { + "content": "<|execute_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73445": { + "content": "<|fim_prefix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73446": { + "content": "<|fim_middle|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "73447": { + "content": "<|fim_suffix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "additional_special_tokens": [ + "<|im_end|>", + "<|im_start|>", + "<|tool_call|>", + "<|execute_start|>", + "<|execute_end|>", + "<|fim_prefix|>", + "<|fim_middle|>", + "<|fim_suffix|>" + ], + "bos_token": "", + "clean_up_tokenization_spaces": false, + "eos_token": "<|im_end|>", + "extra_special_tokens": {}, + "legacy": true, + "model_max_length": 1000000000000000019884624838656, + "pad_token": null, + "sp_model_kwargs": {}, + "spaces_between_special_tokens": false, + "tokenizer_class": "LlamaTokenizer", + "unk_token": "", + "use_default_system_prompt": false +}