Merge pull request #2875 from CarryFun/add_MiniCPM_1.2b

Add MiniCPM-1.2b and MiniCPM-2.4b.

transformers/llm/engine/include/llm.hpp

@@ -231,6 +231,31 @@ class Llama2_7b : public Llm {
     virtual VARP gen_position_ids(int seq_len) override;
     virtual bool is_stop(int token_id) override;
 };
+
+class MiniCPM_1_2b : public Llama2_7b {
+public:
+    MiniCPM_1_2b() {
+        model_name_ = "MiniCPM_1_2b";
+        layer_nums_ = 52;
+        key_value_shape_ = {2, 1, 8, 0, 64};
+        hidden_size_ = 1536;
+    }
+private:
+    virtual std::vector<int> tokenizer(const std::string& query) override;
+};
+
+class MiniCPM_2_4b : public Llama2_7b {
+public:
+    MiniCPM_2_4b() {
+        model_name_ = "MiniCPM_1_2b";
+        layer_nums_ = 40;
+        key_value_shape_ = {2, 1, 36, 0, 64};
+        hidden_size_ = 2304;
+    }
+private:
+    virtual std::vector<int> tokenizer(const std::string& query) override;
+};
+
 class Llama3_8b : public Llama2_7b {
 public:
     Llama3_8b() {

transformers/llm/engine/src/llm.cpp

@@ -90,6 +90,10 @@ Llm* Llm::createLLM(const std::string& path, std::string model_type, int forward
     } else if (model_type.find("llama3") != std::string::npos) {
         llm = new Llama3_8b;
         llm->model_name_ = "Llama3_8b";
+    } else if (model_type.find("MiniCPM_1_2b") != std::string::npos) {
+        llm = new MiniCPM_1_2b;
+    } else if (model_type.find("MiniCPM_2_4b") != std::string::npos) {
+        llm = new MiniCPM_2_4b;
     }
     if (!llm) {
         std::cerr << "model type can't judge!" << std::endl;
@@ -789,6 +793,22 @@ bool Llama2_7b::is_stop(int token_id) {
     return token_id == 2;
 }
 
+std::vector<int> MiniCPM_1_2b::tokenizer(const std::string& query) {
+    auto ids = tokenizer_encode(query);
+    // auto prompt = "<用户>" + query + "<AI>";
+    ids.insert(ids.begin(), {59396, 4194, 59388});
+    ids.insert(ids.end(), {59396, 10850, 59388});
+    return ids;
+}
+
+std::vector<int> MiniCPM_2_4b::tokenizer(const std::string& query) {
+    auto ids = tokenizer_encode(query);
+    // auto prompt = "<用户>" + query + "<AI>";
+    ids.insert(ids.begin(), {95396, 4194, 95388});
+    ids.insert(ids.end(), {95396, 10850, 95388});
+    return ids;
+}
+
 std::vector<int> Qwen2::tokenizer(const std::string& query) {
     auto ids = tokenizer_encode(query);
     // auto prompt = "<|im_start|>user\n" + query + "<|im_end|>\n<|im_start|>assistant\n";

transformers/llm/export/llm_export.py

@@ -1206,6 +1206,8 @@ def export(self):
         'TinyLlama-1_1B-Chat': Llama2_7b_Chat,
         'Yi-6B-Chat': Llama2_7b_Chat,
         'deepseek-llm-7b-chat': Llama2_7b_Chat,
+        'MiniCPM-1.2b': Llama2_7b_Chat,
+        'MiniCPM-2.4b': Llama2_7b_Chat,
         'phi-2': phi_2,
         'bge-large-zh': bge,
         'lora': LoraModule

transformers/llm/export/llm_models/MiniCPM-1.2b/config.json

@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "auto_map": {
+    "AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM"
+  },
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.1,
+  "intermediate_size": 3840,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 52,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.31.0.dev0",
+  "use_cache": true,
+  "vocab_size": 73440
+}

transformers/llm/export/llm_models/MiniCPM-1.2b/configuration_llama.py

@@ -0,0 +1,174 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" LLaMA model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class LlamaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    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 LLaMA-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 LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LlamaModel`]
+        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 encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        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`.
+        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).
+        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. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        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-12):
+            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`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
+            strategies: linear and dynamic. Their scaling factor must be an 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/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+
+        Example:
+
+    ```python
+    >>> from transformers import LlamaModel, LlamaConfig
+
+    >>> # Initializing a LLaMA llama-7b style configuration
+    >>> configuration = LlamaConfig()
+
+    >>> # Initializing a model from the llama-7b style configuration
+    >>> model = LlamaModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "llama"
+    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=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_scaling=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_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        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,
+        )
+
+    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, `name` 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 name 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 an float > 1, got {rope_scaling_factor}")

transformers/llm/export/llm_models/MiniCPM-1.2b/convert_minicpm_to_llama.py

@@ -0,0 +1,38 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import math
+#torch.manual_seed(0)
+
+path = "path-to-MiniCPM-1B-sft-bf16"
+tokenizer = AutoTokenizer.from_pretrained(path)
+model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, trust_remote_code=True)
+
+responds, history = model.chat(tokenizer, "山东省最高的山是哪座山, 它比黄山高还是矮？差距多少？", temperature=0.3, top_p=0.5)
+print(responds)
+
+
+state_dict = model.state_dict()
+print(state_dict.keys())
+
+scale_emb = 12
+dim_model_base = 256
+scale_depth = 1.4
+num_layers = 52
+hidden_size = 1536
+
+new_emb = state_dict["model.embed_tokens.weight"] * scale_emb
+state_dict["model.embed_tokens.weight"] = new_emb
+
+new_emb = state_dict["lm_head.weight"] / (hidden_size / dim_model_base)
+state_dict["lm_head.weight"] = new_emb
+
+for i in range(num_layers):
+    attn_out_name = f"model.layers.{i}.self_attn.o_proj.weight"
+    new_weight = state_dict[attn_out_name] * (scale_depth / math.sqrt(num_layers))
+    state_dict[attn_out_name] = new_weight
+
+    ffn_down_proj_name = f"model.layers.{i}.mlp.down_proj.weight"
+    new_weight = state_dict[ffn_down_proj_name] * (scale_depth / math.sqrt(num_layers))
+    state_dict[ffn_down_proj_name] = new_weight
+
+torch.save(state_dict, "pytorch_model_llama.bin")