self_RAG.ipynb

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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: lit\n",
            "  Building wheel for lit (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lit: filename=lit-17.0.4-py3-none-any.whl size=93257 sha256=3f551285f3d291328b22852675a0205e6aada052d3ea4e029cfbbf0c50aa4f15\n",
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            "Installing collected packages: sentencepiece, ninja, lit, websockets, uvloop, typing-extensions, safetensors, python-dotenv, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, httptools, h11, watchfiles, uvicorn, starlette, nvidia-cusolver-cu11, nvidia-cudnn-cu11, huggingface-hub, tokenizers, fastapi, transformers, ray, triton, torch, xformers, vllm\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.5.0\n",
            "    Uninstalling typing_extensions-4.5.0:\n",
            "      Successfully uninstalled typing_extensions-4.5.0\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 2.1.0\n",
            "    Uninstalling triton-2.1.0:\n",
            "      Successfully uninstalled triton-2.1.0\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.1.0+cu118\n",
            "    Uninstalling torch-2.1.0+cu118:\n",
            "      Successfully uninstalled torch-2.1.0+cu118\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",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\n",
            "torchaudio 2.1.0+cu118 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n",
            "torchvision 0.16.0+cu118 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed fastapi-0.104.1 h11-0.14.0 httptools-0.6.1 huggingface-hub-0.17.3 lit-17.0.4 ninja-1.11.1.1 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 python-dotenv-1.0.0 ray-2.8.0 safetensors-0.4.0 sentencepiece-0.1.99 starlette-0.27.0 tokenizers-0.14.1 torch-2.0.1 transformers-4.35.0 triton-2.0.0 typing-extensions-4.8.0 uvicorn-0.24.0.post1 uvloop-0.19.0 vllm-0.2.1.post1 watchfiles-0.21.0 websockets-12.0 xformers-0.0.22\n"
          ]
        }
      ],
      "source": [
        "!pip install vllm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from vllm import LLM, SamplingParams\n",
        "\n",
        "model = LLM(\"selfrag/selfrag_llama2_7b\", download_dir=\"/gscratch/h2lab/akari/model_cache\", dtype=\"half\")\n",
        "sampling_params = SamplingParams(temperature=0.0, top_p=1.0, max_tokens=100, skip_special_tokens=False)\n",
        "\n",
        "def format_prompt(input, paragraph=None):\n",
        "  prompt = \"### Instruction:\\n{0}\\n\\n### Response:\\n\".format(input)\n",
        "  if paragraph is not None:\n",
        "    prompt += \"[Retrieval]<paragraph>{0}</paragraph>\".format(paragraph)\n",
        "  return prompt\n",
        "\n",
        "query_1 = \"Leave odd one out: twitter, instagram, whatsapp.\"\n",
        "query_2 = \"Can you tell me the difference between llamas and alpacas?\"\n",
        "queries = [query_1, query_2]\n",
        "\n",
        "# for a query that doesn't require retrieval\n",
        "preds = model.generate([format_prompt(query) for query in queries], sampling_params)\n",
        "for pred in preds:\n",
        "  print(\"Model prediction: {0}\".format(pred.outputs[0].text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487,
          "referenced_widgets": [
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        },
        "id": "LGINT5PKZFfy",
        "outputId": "becae360-372f-4898-cf59-51dac158feb9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/635 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e891009ad0c41d4996efa418fed1ff8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING 11-10 14:58:43 config.py:346] Casting torch.bfloat16 to torch.float16.\n",
            "INFO 11-10 14:58:43 llm_engine.py:72] Initializing an LLM engine with config: model='selfrag/selfrag_llama2_7b', tokenizer='selfrag/selfrag_llama2_7b', tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=4096, download_dir='/gscratch/h2lab/akari/model_cache', load_format=auto, tensor_parallel_size=1, quantization=None, seed=0)\n",
            "INFO 11-10 14:58:43 tokenizer.py:31] For some LLaMA V1 models, initializing the fast tokenizer may take a long time. To reduce the initialization time, consider using 'hf-internal-testing/llama-tokenizer' instead of the original tokenizer.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/855 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c628f154c8d414b826915356576ede8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d3d003398d04d87911d314c8e45dc33"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/433 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "979f833d537f4665939607ec9c1b85d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/470 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "099625052e344fd28fc713a439e0c258"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)l-00001-of-00002.bin:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "323e5d9deab044e6a2a9bb5bc33b8059"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)l-00002-of-00002.bin:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eef804748e4f46c68d7d540f7495c3cf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO 11-10 15:01:47 llm_engine.py:207] # GPU blocks: 26, # CPU blocks: 512\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processed prompts: 100%|██████████| 2/2 [00:06<00:00,  3.26s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model prediction: Twitter, Instagram, and WhatsApp are all social media platforms.[No Retrieval]However, WhatsApp is a messaging app, while Twitter and Instagram are both primarily used for sharing photos and videos.[No Retrieval]Therefore, WhatsApp is the odd one out in this group.[Utility:5]</s>\n",
            "Model prediction: Sure![Retrieval]<paragraph>\n",
            "\n",
            "* Alpaca (left) and llama (right) in the Andes of southern Peru.\n",
            "\n",
            "Alpacas and llamas are both domesticated species of South American camelids.[Continue to Use Evidence]Alpacas are a much smaller than llamas, with a shoulder height of 3 to 4 feet.[Continue to Use Evidence]They are also bred specifically for their fiber, which is used to make all sorts of textiles and clothing.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "paragraph=\"\"\"Llamas range from 200 to 350 lbs., while alpacas weigh in at 100 to 175 lbs.\"\"\""
      ],
      "metadata": {
        "id": "_LiWwVakZO-Z"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def format_prompt_p(input, paragraph=paragraph):\n",
        "  prompt = \"### Instruction:\\n{0}\\n\\n### Response:\\n\".format(input)\n",
        "  if paragraph is not None:\n",
        "    prompt += \"[Retrieval]<paragraph>{0}</paragraph>\".format(paragraph)\n",
        "  return prompt\n",
        "\n",
        "query_1 = \"Leave odd one out: twitter, instagram, whatsapp.\"\n",
        "query_2 = \"Can you tell me the differences between llamas and alpacas?\"\n",
        "queries = [query_1, query_2]\n",
        "\n",
        "# for a query that doesn't require retrieval\n",
        "preds = model.generate([format_prompt_p(query) for query in queries], sampling_params)\n",
        "for pred in preds:\n",
        "  print(\"Model prediction: {0}\".format(pred.outputs[0].text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2G6G-e7qbDsa",
        "outputId": "b7c53b1e-06bc-4cb5-9d5c-6f36b60ffdc7"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processed prompts: 100%|██████████| 2/2 [00:02<00:00,  1.20s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model prediction: [Irrelevant]Whatsapp is the odd one out.[No Retrieval]Twitter and Instagram are both social media platforms, while Whatsapp is a messaging app.[Utility:5]</s>\n",
            "Model prediction: [Relevant]Llamas are larger than alpacas, with males weighing up to 350 pounds.[Partially supported][Utility:5]</s>\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "paragraph=\"\"\"I like Avocado.\"\"\""
      ],
      "metadata": {
        "id": "ml5Aqvc5bTqo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def format_prompt_p(input, paragraph=paragraph):\n",
        "  prompt = \"### Instruction:\\n{0}\\n\\n### Response:\\n\".format(input)\n",
        "  if paragraph is not None:\n",
        "    prompt += \"[Retrieval]<paragraph>{0}</paragraph>\".format(paragraph)\n",
        "  return prompt\n",
        "\n",
        "query_1 = \"Leave odd one out: twitter, instagram, whatsapp.\"\n",
        "query_2 = \"Can you tell me the difference between llamas and alpacas?\"\n",
        "queries = [query_1, query_2]\n",
        "\n",
        "# for a query that doesn't require retrieval\n",
        "preds = model.generate([format_prompt_p(query) for query in queries], sampling_params)\n",
        "for pred in preds:\n",
        "  print(\"Model prediction: {0}\".format(pred.outputs[0].text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1HEv-4ICb3Nh",
        "outputId": "d3f5011a-e6ea-45fb-b778-7494aa11e19c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Processed prompts:   0%|          | 0/2 [00:00<?, ?it/s]\u001b[A\n",
            "Processed prompts:  50%|█████     | 1/2 [00:00<00:00,  1.37it/s]\u001b[A\n",
            "Processed prompts: 100%|██████████| 2/2 [00:01<00:00,  1.06it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model prediction: [Irrelevant]Twitter is the odd one out.[Utility:5]</s>\n",
            "Model prediction: [Irrelevant]Sure![Continue to Use Evidence]Alpacas are a much smaller than llamas.They are also bred specifically for their fiber.[Utility:5]</s>\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jAUp-GVvb4KO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}