vllm4emb.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ExPwJ8cVrda",
        "outputId": "f82267ea-6a8b-47de-992e-fb5fa5ce4d03"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Installing collected packages: nvidia-ml-py, ninja, websockets, uvloop, ujson, shellingham, python-multipart, python-dotenv, orjson, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, lark, interegular, httptools, h11, dnspython, diskcache, watchfiles, uvicorn, tiktoken, starlette, nvidia-cusparse-cu12, nvidia-cudnn-cu12, httpcore, email_validator, typer, prometheus-fastapi-instrumentator, nvidia-cusolver-cu12, lm-format-enforcer, httpx, ray, openai, fastapi-cli, xformers, vllm-flash-attn, outlines, fastapi, vllm\n",
            "  Attempting uninstall: typer\n",
            "    Found existing installation: typer 0.9.4\n",
            "    Uninstalling typer-0.9.4:\n",
            "      Successfully uninstalled typer-0.9.4\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n",
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            "\u001b[0mSuccessfully installed diskcache-5.6.3 dnspython-2.6.1 email_validator-2.1.1 fastapi-0.111.0 fastapi-cli-0.0.4 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 interegular-0.3.3 lark-1.1.9 lm-format-enforcer-0.10.1 ninja-1.11.1.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-ml-py-12.555.43 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 openai-1.32.0 orjson-3.10.3 outlines-0.0.34 prometheus-fastapi-instrumentator-7.0.0 python-dotenv-1.0.1 python-multipart-0.0.9 ray-2.24.0 shellingham-1.5.4 starlette-0.37.2 tiktoken-0.7.0 typer-0.12.3 ujson-5.10.0 uvicorn-0.30.1 uvloop-0.19.0 vllm-0.4.3 vllm-flash-attn-2.5.8.post2 watchfiles-0.22.0 websockets-12.0 xformers-0.0.26.post1\n"
          ]
        }
      ],
      "source": [
        "!pip install vllm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1XGkHVkWJMV",
        "outputId": "d54ecfef-cc09-4a0f-8061-8c04e861f9cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "from vllm.model_executor.models import ModelRegistry\n",
        "\n",
        "# 先保存原始的 is_embedding_model 方法，以便之后需要时可以恢复\n",
        "original_is_embedding_model = ModelRegistry.is_embedding_model\n",
        "\n",
        "# 定义一个新的方法，该方法不管传入什么都返回 True\n",
        "def always_true_is_embedding_model(model_arch: str) -> bool:\n",
        "    return True\n",
        "\n",
        "# 将 ModelRegistry 类中的 is_embedding_model 方法替换为 always_true_is_embedding_model\n",
        "ModelRegistry.is_embedding_model = always_true_is_embedding_model\n",
        "\n",
        "# 现在调用 ModelRegistry.is_embedding_model 无论如何都会返回 True\n",
        "print(ModelRegistry.is_embedding_model(\"any_model_architecture\"))  # 输出 True\n",
        "\n",
        "# 如果需要恢复原始方法，可以这样做：\n",
        "# ModelRegistry.is_embedding_model = original_is_embedding_model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "0XWbeBMLXeMu"
      },
      "outputs": [],
      "source": [
        "from typing import Iterable, List, Optional, Tuple\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "from vllm.attention import AttentionMetadata\n",
        "from vllm.model_executor.layers.pooler import Pooler, PoolingType\n",
        "from vllm.model_executor.model_loader.weight_utils import default_weight_loader\n",
        "from vllm.model_executor.models.llama import LlamaModel\n",
        "from vllm.model_executor.pooling_metadata import PoolingMetadata\n",
        "from vllm.sequence import PoolerOutput\n",
        "\n",
        "# not same as mistral one\n",
        "class MyLlamaEmbeddingModel(nn.Module):\n",
        "    \"\"\"A model that uses Llama with additional embedding functionalities.\n",
        "\n",
        "   This class encapsulates the LlamaModel and provides an interface for\n",
        "   embedding operations and customized pooling functions.\n",
        "\n",
        "   Attributes:\n",
        "       model: An instance of LlamaModel used for forward operations.\n",
        "       _pooler: An instance of Pooler used for pooling operations.\n",
        "   \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        **kwargs,\n",
        "    ) -> None:\n",
        "        super().__init__()\n",
        "        self.model = LlamaModel(**kwargs)\n",
        "        self._pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        input_ids: Optional[torch.Tensor],\n",
        "        positions: torch.Tensor,\n",
        "        kv_caches: List[torch.Tensor],\n",
        "        attn_metadata: AttentionMetadata,\n",
        "        inputs_embeds: Optional[torch.Tensor] = None,\n",
        "    ) -> torch.Tensor:\n",
        "        return self.model.forward(input_ids, positions, kv_caches,\n",
        "                                  attn_metadata, inputs_embeds)\n",
        "\n",
        "    def pooler(\n",
        "        self,\n",
        "        hidden_states: torch.Tensor,\n",
        "        pooling_metadata: PoolingMetadata,\n",
        "    ) -> Optional[PoolerOutput]:\n",
        "        return self._pooler(hidden_states, pooling_metadata)\n",
        "\n",
        "    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): # 建议这个直接抄你用的那个模型的，Mistral和LLaMA不太一样\n",
        "        stacked_params_mapping = [\n",
        "            # (param_name, shard_name, shard_id)\n",
        "            (\".qkv_proj\", \".q_proj\", \"q\"),\n",
        "            (\".qkv_proj\", \".k_proj\", \"k\"),\n",
        "            (\".qkv_proj\", \".v_proj\", \"v\"),\n",
        "            (\".gate_up_proj\", \".gate_proj\", 0),\n",
        "            (\".gate_up_proj\", \".up_proj\", 1),\n",
        "        ]\n",
        "        params_dict = dict(self.named_parameters())\n",
        "        for name, loaded_weight in weights:\n",
        "            if \"rotary_emb.inv_freq\" in name:\n",
        "                continue\n",
        "            if (\"rotary_emb.cos_cached\" in name\n",
        "                    or \"rotary_emb.sin_cached\" in name):\n",
        "                # Models trained using ColossalAI may include these tensors in\n",
        "                # the checkpoint. Skip them.\n",
        "                continue\n",
        "            for (param_name, weight_name, shard_id) in stacked_params_mapping:\n",
        "                if weight_name not in name:\n",
        "                    continue\n",
        "                name = name.replace(weight_name, param_name)\n",
        "                # Skip loading extra bias for GPTQ models.\n",
        "                if name.endswith(\".bias\") and name not in params_dict:\n",
        "                    continue\n",
        "                param = params_dict[name]\n",
        "                weight_loader = param.weight_loader\n",
        "                weight_loader(param, loaded_weight, shard_id)\n",
        "                break\n",
        "            else:\n",
        "                # Skip loading extra bias for GPTQ models.\n",
        "                if name.endswith(\".bias\") and name not in params_dict:\n",
        "                    continue\n",
        "                # Remapping the name of FP8 kv-scale.\n",
        "                if name.endswith(\"kv_scale\"):\n",
        "                    remapped_kv_scale_name = name.replace(\n",
        "                        \".kv_scale\", \".attn.kv_scale\")\n",
        "                    if remapped_kv_scale_name not in params_dict:\n",
        "                        # print_warning_once(\n",
        "                        #     f\"Found kv scale in the checkpoint (e.g. {name}), \"\n",
        "                        #     \"but not found the expected name in the model \"\n",
        "                        #     f\"(e.g. {remapped_kv_scale_name}). kv-scale is \"\n",
        "                        #     \"not loaded.\")\n",
        "                        continue\n",
        "                    else:\n",
        "                        name = remapped_kv_scale_name\n",
        "                if \"lm_head\" in name: # 看具体情况 你lm head 可能多了\n",
        "                    continue\n",
        "                param = params_dict[name]\n",
        "                weight_loader = getattr(param, \"weight_loader\",\n",
        "                                        default_weight_loader)\n",
        "                weight_loader(param, loaded_weight)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "GzGMigJFW1qF"
      },
      "outputs": [],
      "source": [
        "from vllm import ModelRegistry\n",
        "\n",
        "ModelRegistry.register_model(\"MyLlamaEmbeddingModel\", MyLlamaEmbeddingModel)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cZhmHkByY7uH",
        "outputId": "a4ec46e7-0533-46b1-8aed-1798650f5de1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Git LFS initialized.\n",
            "Cloning into 'Sheared-LLaMA-1.3B'...\n",
            "remote: Enumerating objects: 58, done.\u001b[K\n",
            "remote: Counting objects: 100% (54/54), done.\u001b[K\n",
            "remote: Compressing objects: 100% (54/54), done.\u001b[K\n",
            "remote: Total 58 (delta 28), reused 0 (delta 0), pack-reused 4 (from 1)\u001b[K\n",
            "Unpacking objects: 100% (58/58), 484.80 KiB | 2.69 MiB/s, done.\n",
            "Filtering content: 100% (2/2), 1.01 GiB | 4.10 MiB/s, done.\n",
            "Encountered 1 file(s) that may not have been copied correctly on Windows:\n",
            "\tpytorch_model.bin\n",
            "\n",
            "See: `git lfs help smudge` for more details.\n"
          ]
        }
      ],
      "source": [
        "!git lfs install\n",
        "!git clone https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g6zjdnLfZPkH",
        "outputId": "52cb0cbf-0928-46c8-95a5-92e8041d64e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"_name_or_path\": \"princeton-nlp/Sheared-LLaMA-1.3B\",\n",
            "  \"architectures\": [\n",
            "    \"LlamaForCausalLM\"\n",
            "  ],\n",
            "  \"bos_token_id\": 1,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"silu\",\n",
            "  \"hidden_size\": 2048,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 5504,\n",
            "  \"max_position_embeddings\": 4096,\n",
            "  \"model_type\": \"llama\",\n",
            "  \"num_attention_heads\": 16,\n",
            "  \"num_hidden_layers\": 24,\n",
            "  \"num_key_value_heads\": 16,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pretraining_tp\": 1,\n",
            "  \"rms_norm_eps\": 1e-05,\n",
            "  \"rope_scaling\": null,\n",
            "  \"tie_word_embeddings\": false,\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.28.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32000\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "!cat ./Sheared-LLaMA-1.3B/config.json"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvLC2ELcZUYm"
      },
      "source": [
        "修改model arth的名字 (Change `architectures` name to)\n",
        "\n",
        "`MyLlamaEmbeddingModel`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LO9N3tyZY4H",
        "outputId": "f31df095-7944-416c-9497-0511e212a2a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"_name_or_path\": \"princeton-nlp/Sheared-LLaMA-1.3B\",\n",
            "  \"architectures\": [\n",
            "    \"MyLlamaEmbeddingModel\"\n",
            "  ],\n",
            "  \"bos_token_id\": 1,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"silu\",\n",
            "  \"hidden_size\": 2048,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 5504,\n",
            "  \"max_position_embeddings\": 4096,\n",
            "  \"model_type\": \"llama\",\n",
            "  \"num_attention_heads\": 16,\n",
            "  \"num_hidden_layers\": 24,\n",
            "  \"num_key_value_heads\": 16,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pretraining_tp\": 1,\n",
            "  \"rms_norm_eps\": 1e-05,\n",
            "  \"rope_scaling\": null,\n",
            "  \"tie_word_embeddings\": false,\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.28.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32000\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "!cat ./Sheared-LLaMA-1.3B/config.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "JFnhKm-0X_xX"
      },
      "outputs": [],
      "source": [
        "from vllm import LLM\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gTkf17djYBOO",
        "outputId": "b92a482d-b2d5-4c42-f02c-da05ab528b34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO 06-07 06:39:53 config.py:1151] Downcasting torch.float32 to torch.float16.\n",
            "INFO 06-07 06:39:53 llm_engine.py:161] Initializing an LLM engine (v0.4.3) with config: model='./Sheared-LLaMA-1.3B', speculative_config=None, tokenizer='./Sheared-LLaMA-1.3B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=./Sheared-LLaMA-1.3B)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO 06-07 06:39:53 selector.py:120] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.\n",
            "INFO 06-07 06:39:53 selector.py:51] Using XFormers backend.\n",
            "INFO 06-07 06:39:57 selector.py:120] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.\n",
            "INFO 06-07 06:39:57 selector.py:51] Using XFormers backend.\n",
            "INFO 06-07 06:40:22 model_runner.py:146] Loading model weights took 2.4211 GB\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Create an LLM.\n",
        "model = LLM(model=\"./Sheared-LLaMA-1.3B\", enforce_eager=True, dtype=\"float16\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pw-Lc0XFZjf0",
        "outputId": "701482f1-a147-4c57-d698-8b40ffb60fbb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Processed prompts: 100%|██████████| 4/4 [00:02<00:00,  1.65it/s, Generation Speed: 0.00 toks/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
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          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Sample prompts.\n",
        "prompts = [\n",
        "    \"Hello, my name is\",\n",
        "    \"The president of the United States is\",\n",
        "    \"The capital of France is\",\n",
        "    \"The future of AI is\",\n",
        "]\n",
        "\n",
        "\n",
        "# Generate embedding. The output is a list of EmbeddingRequestOutputs.\n",
        "\n",
        "outputs = model.encode(prompts)\n",
        "# Print the outputs.\n",
        "for output in outputs:\n",
        "    print(output.outputs.embedding)  # list of 4096 floats"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}