llm-finetuning

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11_RLHF_Training_for_CustomDataset_for_AnyModel.ipynb 
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            "Collecting transformers\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m59.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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5170
            "Installing collected packages: tokenizers, safetensors, xxhash, dill, multiprocess, huggingface-hub, transformers, datasets, accelerate, trl\n",
5171
            "Successfully installed accelerate-0.22.0 datasets-2.14.4 dill-0.3.7 huggingface-hub-0.16.4 multiprocess-0.70.15 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.0 trl-0.7.1 xxhash-3.3.0\n"
5172
          ]
5173
        }
5174
      ]
5175
    },
5176
    {
5177
      "cell_type": "code",
5178
      "execution_count": 2,
5179
      "metadata": {
5180
        "id": "hgj2hw_WQecA"
5181
      },
5182
      "outputs": [],
5183
      "source": [
5184
        "## Import libraries\n",
5185
        "import torch\n",
5186
        "import transformers\n",
5187
        "from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling\n",
5188
        "from trl import RewardTrainer, SFTTrainer\n",
5189
        "from datasets import Dataset\n",
5190
        "import json\n",
5191
        "import pandas as pd\n",
5192
        "from transformers import Trainer, TrainingArguments"
5193
      ]
5194
    },
5195
    {
5196
      "cell_type": "code",
5197
      "source": [
5198
        "!wget https://huggingface.co/datasets/CarperAI/openai_summarize_comparisons/resolve/main/data/test-00000-of-00001-0845e2eec675b16a.parquet\n",
5199
        "# after that rename to test.parquet"
5200
      ],
5201
      "metadata": {
5202
        "colab": {
5203
          "base_uri": "https://localhost:8080/"
5204
        },
5205
        "id": "NFgC7kBFRm0P",
5206
        "outputId": "b23a409d-ff09-4f49-a120-083a0be42d4a"
5207
      },
5208
      "execution_count": 3,
5209
      "outputs": [
5210
        {
5211
          "output_type": "stream",
5212
          "name": "stdout",
5213
          "text": [
5214
            "--2023-09-06 12:36:39--  https://huggingface.co/datasets/CarperAI/openai_summarize_comparisons/resolve/main/data/test-00000-of-00001-0845e2eec675b16a.parquet\n",
5215
            "Resolving huggingface.co (huggingface.co)... 18.172.134.24, 18.172.134.4, 18.172.134.88, ...\n",
5216
            "Connecting to huggingface.co (huggingface.co)|18.172.134.24|:443... connected.\n",
5217
            "HTTP request sent, awaiting response... 302 Found\n",
5218
            "Location: https://cdn-lfs.huggingface.co/repos/02/84/02842250b699bbde4e980e9206f5045df542f9ca374595cc86b8d57189641ff5/a8c5d73113a2c7119faacd44e43959f74e5b59e181f3da57caa918c907ac6620?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27test-00000-of-00001-0845e2eec675b16a.parquet%3B+filename%3D%22test-00000-of-00001-0845e2eec675b16a.parquet%22%3B&Expires=1694262999&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5NDI2Mjk5OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8wMi84NC8wMjg0MjI1MGI2OTliYmRlNGU5ODBlOTIwNmY1MDQ1ZGY1NDJmOWNhMzc0NTk1Y2M4NmI4ZDU3MTg5NjQxZmY1L2E4YzVkNzMxMTNhMmM3MTE5ZmFhY2Q0NGU0Mzk1OWY3NGU1YjU5ZTE4MWYzZGE1N2NhYTkxOGM5MDdhYzY2MjA%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=CLl3zBeYW5jzKbX9MZkryL0p-j8lTtx03ksho0Zwpi5ZeKplDz%7EohUhbXaK5OKCteoEJx%7EDjpluciHcFdC%7EQcfdwgRSuAuT%7ECMaPxAmmzR54Zd0AcIPR02OenGpIbf3scib4X-ZCWgSVAgUlF6F8jfbSq1Yo4eoi4iyJj3m%7El1O0NbWVZ7qm%7E4kXfNzBpaMI1l0pDg-9x6UCrVuto2g1guD3Uzwb%7EahiEwCYIcVGLgxo-SXOFO7-9w4eCZ-KkvwNq%7EgEGeUBmilzf1Wwnu6IVPnygYicEbiAIopHZDcm87wuC3j%7EcMwVKQx9JxrfXf3kP-Q6OqrLDUd2ejN0gInvUQ__&Key-Pair-Id=KVTP0A1DKRTAX [following]\n",
5219
            "--2023-09-06 12:36:39--  https://cdn-lfs.huggingface.co/repos/02/84/02842250b699bbde4e980e9206f5045df542f9ca374595cc86b8d57189641ff5/a8c5d73113a2c7119faacd44e43959f74e5b59e181f3da57caa918c907ac6620?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27test-00000-of-00001-0845e2eec675b16a.parquet%3B+filename%3D%22test-00000-of-00001-0845e2eec675b16a.parquet%22%3B&Expires=1694262999&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5NDI2Mjk5OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8wMi84NC8wMjg0MjI1MGI2OTliYmRlNGU5ODBlOTIwNmY1MDQ1ZGY1NDJmOWNhMzc0NTk1Y2M4NmI4ZDU3MTg5NjQxZmY1L2E4YzVkNzMxMTNhMmM3MTE5ZmFhY2Q0NGU0Mzk1OWY3NGU1YjU5ZTE4MWYzZGE1N2NhYTkxOGM5MDdhYzY2MjA%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=CLl3zBeYW5jzKbX9MZkryL0p-j8lTtx03ksho0Zwpi5ZeKplDz%7EohUhbXaK5OKCteoEJx%7EDjpluciHcFdC%7EQcfdwgRSuAuT%7ECMaPxAmmzR54Zd0AcIPR02OenGpIbf3scib4X-ZCWgSVAgUlF6F8jfbSq1Yo4eoi4iyJj3m%7El1O0NbWVZ7qm%7E4kXfNzBpaMI1l0pDg-9x6UCrVuto2g1guD3Uzwb%7EahiEwCYIcVGLgxo-SXOFO7-9w4eCZ-KkvwNq%7EgEGeUBmilzf1Wwnu6IVPnygYicEbiAIopHZDcm87wuC3j%7EcMwVKQx9JxrfXf3kP-Q6OqrLDUd2ejN0gInvUQ__&Key-Pair-Id=KVTP0A1DKRTAX\n",
5220
            "Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 18.154.185.26, 18.154.185.64, 18.154.185.94, ...\n",
5221
            "Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|18.154.185.26|:443... connected.\n",
5222
            "HTTP request sent, awaiting response... 200 OK\n",
5223
            "Length: 20441979 (19M) [binary/octet-stream]\n",
5224
            "Saving to: ‘test-00000-of-00001-0845e2eec675b16a.parquet’\n",
5225
            "\n",
5226
            "test-00000-of-00001 100%[===================>]  19.49M   105MB/s    in 0.2s    \n",
5227
            "\n",
5228
            "2023-09-06 12:36:39 (105 MB/s) - ‘test-00000-of-00001-0845e2eec675b16a.parquet’ saved [20441979/20441979]\n",
5229
            "\n"
5230
          ]
5231
        }
5232
      ]
5233
    },
5234
    {
5235
      "cell_type": "markdown",
5236
      "source": [
5237
        "### Dataset for Training : https://huggingface.co/datasets/CarperAI/openai_summarize_tldr\n",
5238
        "\n",
5239
        "### Dataset for Testing : https://huggingface.co/datasets/CarperAI/openai_summarize_comparisons"
5240
      ],
5241
      "metadata": {
5242
        "id": "r81RnxurR-E2"
5243
      }
5244
    },
5245
    {
5246
      "cell_type": "code",
5247
      "source": [
5248
        "Model_name = \"bigcode/tiny_starcoder_py\"\n",
5249
        "Data_path = \"test.parquet\""
5250
      ],
5251
      "metadata": {
5252
        "id": "sY4LnQyVQl5J"
5253
      },
5254
      "execution_count": 4,
5255
      "outputs": []
5256
    },
5257
    {
5258
      "cell_type": "code",
5259
      "source": [
5260
        "# Read the file\n",
5261
        "df  = pd.read_parquet(Data_path)\n",
5262
        "df = df[:10]\n",
5263
        "raw_dataset = Dataset.from_pandas(df)\n",
5264
        "raw_dataset"
5265
      ],
5266
      "metadata": {
5267
        "colab": {
5268
          "base_uri": "https://localhost:8080/"
5269
        },
5270
        "id": "ReMAX6WySL3s",
5271
        "outputId": "7dbda935-3fa6-47fd-a5a9-3ce66a006f3c"
5272
      },
5273
      "execution_count": 7,
5274
      "outputs": [
5275
        {
5276
          "output_type": "execute_result",
5277
          "data": {
5278
            "text/plain": [
5279
              "Dataset({\n",
5280
              "    features: ['prompt', 'chosen', 'rejected'],\n",
5281
              "    num_rows: 10\n",
5282
              "})"
5283
            ]
5284
          },
5285
          "metadata": {},
5286
          "execution_count": 7
5287
        }
5288
      ]
5289
    },
5290
    {
5291
      "cell_type": "markdown",
5292
      "source": [
5293
        "### Tokenize the dataset to feed in the model"
5294
      ],
5295
      "metadata": {
5296
        "id": "1IPbHllpSeYn"
5297
      }
5298
    },
5299
    {
5300
      "cell_type": "code",
5301
      "source": [
5302
        "tokenizer = AutoTokenizer.from_pretrained(Model_name)\n",
5303
        "model = AutoModelForCausalLM.from_pretrained(Model_name)"
5304
      ],
5305
      "metadata": {
5306
        "colab": {
5307
          "base_uri": "https://localhost:8080/",
5308
          "height": 273,
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          "referenced_widgets": [
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          ]
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        },
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        "id": "daw09BDBSa44",
5401
        "outputId": "38e185ab-cfa4-457c-cb2c-aa9f4cf01532"
5402
      },
5403
      "execution_count": 8,
5404
      "outputs": [
5405
        {
5406
          "output_type": "display_data",
5407
          "data": {
5408
            "text/plain": [
5409
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/677 [00:00<?, ?B/s]"
5410
            ],
5411
            "application/vnd.jupyter.widget-view+json": {
5412
              "version_major": 2,
5413
              "version_minor": 0,
5414
              "model_id": "49fa119988e94491bd0b0934ab1b04dd"
5415
            }
5416
          },
5417
          "metadata": {}
5418
        },
5419
        {
5420
          "output_type": "display_data",
5421
          "data": {
5422
            "text/plain": [
5423
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/777k [00:00<?, ?B/s]"
5424
            ],
5425
            "application/vnd.jupyter.widget-view+json": {
5426
              "version_major": 2,
5427
              "version_minor": 0,
5428
              "model_id": "57f7311ca349467992a24e326a5c8973"
5429
            }
5430
          },
5431
          "metadata": {}
5432
        },
5433
        {
5434
          "output_type": "display_data",
5435
          "data": {
5436
            "text/plain": [
5437
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/442k [00:00<?, ?B/s]"
5438
            ],
5439
            "application/vnd.jupyter.widget-view+json": {
5440
              "version_major": 2,
5441
              "version_minor": 0,
5442
              "model_id": "83063ba407ed4a72b7eb808a8991f063"
5443
            }
5444
          },
5445
          "metadata": {}
5446
        },
5447
        {
5448
          "output_type": "display_data",
5449
          "data": {
5450
            "text/plain": [
5451
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.06M [00:00<?, ?B/s]"
5452
            ],
5453
            "application/vnd.jupyter.widget-view+json": {
5454
              "version_major": 2,
5455
              "version_minor": 0,
5456
              "model_id": "91a1e970c39a45c1a4968936a7ee2b9b"
5457
            }
5458
          },
5459
          "metadata": {}
5460
        },
5461
        {
5462
          "output_type": "display_data",
5463
          "data": {
5464
            "text/plain": [
5465
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/532 [00:00<?, ?B/s]"
5466
            ],
5467
            "application/vnd.jupyter.widget-view+json": {
5468
              "version_major": 2,
5469
              "version_minor": 0,
5470
              "model_id": "09ff7ca1f4ae47cead2d35146e5ffc3f"
5471
            }
5472
          },
5473
          "metadata": {}
5474
        },
5475
        {
5476
          "output_type": "display_data",
5477
          "data": {
5478
            "text/plain": [
5479
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.03k [00:00<?, ?B/s]"
5480
            ],
5481
            "application/vnd.jupyter.widget-view+json": {
5482
              "version_major": 2,
5483
              "version_minor": 0,
5484
              "model_id": "7c52d3a64462430494f26f614f2cb808"
5485
            }
5486
          },
5487
          "metadata": {}
5488
        },
5489
        {
5490
          "output_type": "display_data",
5491
          "data": {
5492
            "text/plain": [
5493
              "Downloading model.safetensors:   0%|          | 0.00/657M [00:00<?, ?B/s]"
5494
            ],
5495
            "application/vnd.jupyter.widget-view+json": {
5496
              "version_major": 2,
5497
              "version_minor": 0,
5498
              "model_id": "5ef2a9736301486caedd3c739f6999a0"
5499
            }
5500
          },
5501
          "metadata": {}
5502
        },
5503
        {
5504
          "output_type": "display_data",
5505
          "data": {
5506
            "text/plain": [
5507
              "Downloading (…)neration_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
5508
            ],
5509
            "application/vnd.jupyter.widget-view+json": {
5510
              "version_major": 2,
5511
              "version_minor": 0,
5512
              "model_id": "c915bcd052904defa7523185e02deef2"
5513
            }
5514
          },
5515
          "metadata": {}
5516
        }
5517
      ]
5518
    },
5519
    {
5520
      "cell_type": "markdown",
5521
      "source": [
5522
        "### Tokenize the dataset to feed in the model"
5523
      ],
5524
      "metadata": {
5525
        "id": "Z15PFpEhS51h"
5526
      }
5527
    },
5528
    {
5529
      "cell_type": "code",
5530
      "source": [
5531
        "tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
5532
        "def formatting_func(examples):\n",
5533
        "    kwargs = {\"padding\": \"max_length\",\n",
5534
        "              \"truncation\": True,\n",
5535
        "              \"max_length\": 256,\n",
5536
        "              \"return_tensors\": \"pt\"\n",
5537
        "              }\n",
5538
        "\n",
5539
        "    # Prepend the prompt and a line break to the original_response and response-1 fields.\n",
5540
        "    prompt_plus_chosen_response = examples[\"prompt\"] + \"\\n\" + examples[\"chosen\"]\n",
5541
        "    prompt_plus_rejected_response = examples[\"prompt\"] + \"\\n\" + examples[\"rejected\"]\n",
5542
        "\n",
5543
        "    # Then tokenize these modified fields.\n",
5544
        "    tokens_chosen = tokenizer.encode_plus(prompt_plus_chosen_response, **kwargs)\n",
5545
        "    tokens_rejected = tokenizer.encode_plus(prompt_plus_rejected_response, **kwargs)\n",
5546
        "\n",
5547
        "    return {\n",
5548
        "        \"input_ids_chosen\": tokens_chosen[\"input_ids\"][0], \"attention_mask_chosen\": tokens_chosen[\"attention_mask\"][0],\n",
5549
        "        \"input_ids_rejected\": tokens_rejected[\"input_ids\"][0], \"attention_mask_rejected\": tokens_rejected[\"attention_mask\"][0]\n",
5550
        "    }"
5551
      ],
5552
      "metadata": {
5553
        "id": "gcAtMRzpS2aC"
5554
      },
5555
      "execution_count": 9,
5556
      "outputs": []
5557
    },
5558
    {
5559
      "cell_type": "markdown",
5560
      "source": [
5561
        "### Map the function to data"
5562
      ],
5563
      "metadata": {
5564
        "id": "qec70IWWVYkU"
5565
      }
5566
    },
5567
    {
5568
      "cell_type": "code",
5569
      "source": [
5570
        "formatted_dataset = raw_dataset.map(formatting_func)\n",
5571
        "formatted_dataset = formatted_dataset.train_test_split()"
5572
      ],
5573
      "metadata": {
5574
        "colab": {
5575
          "base_uri": "https://localhost:8080/",
5576
          "height": 49,
5577
          "referenced_widgets": [
5578
            "6396b18205ab4daa965b76a29a8e8fda",
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            "df34019ab4154eb38dcce6ed6d204fbc",
5580
            "fff7525af65f4bed8f1ef699603f3c07",
5581
            "733550fcf3154e25ba0ace779ceb87a7",
5582
            "cd99115f95bb4282910caf8e4f03612e",
5583
            "4561689ef64b40d2a41ccf58815959bd",
5584
            "0a3eb086e97f459bb3a032ac9ec22dea",
5585
            "9fce344d959f46a6b28c2f8726c704b4",
5586
            "92085ca3991f46b5b626f00d665788ed",
5587
            "be943a11e101479dbbf8f176f3783cd6",
5588
            "6c6448ae3f764b32b99b2a0e21d85184"
5589
          ]
5590
        },
5591
        "id": "b5q-aIpRUCxP",
5592
        "outputId": "4578f0f4-09c3-45ca-f164-d1ace2c2525d"
5593
      },
5594
      "execution_count": 10,
5595
      "outputs": [
5596
        {
5597
          "output_type": "display_data",
5598
          "data": {
5599
            "text/plain": [
5600
              "Map:   0%|          | 0/10 [00:00<?, ? examples/s]"
5601
            ],
5602
            "application/vnd.jupyter.widget-view+json": {
5603
              "version_major": 2,
5604
              "version_minor": 0,
5605
              "model_id": "6396b18205ab4daa965b76a29a8e8fda"
5606
            }
5607
          },
5608
          "metadata": {}
5609
        }
5610
      ]
5611
    },
5612
    {
5613
      "cell_type": "code",
5614
      "source": [
5615
        "model.config"
5616
      ],
5617
      "metadata": {
5618
        "colab": {
5619
          "base_uri": "https://localhost:8080/"
5620
        },
5621
        "id": "9ejl2Q_iVXtn",
5622
        "outputId": "b888e6bc-7fea-4515-bb67-1dc9367fe9cf"
5623
      },
5624
      "execution_count": 11,
5625
      "outputs": [
5626
        {
5627
          "output_type": "execute_result",
5628
          "data": {
5629
            "text/plain": [
5630
              "GPTBigCodeConfig {\n",
5631
              "  \"_name_or_path\": \"bigcode/tiny_starcoder_py\",\n",
5632
              "  \"activation_function\": \"gelu_pytorch_tanh\",\n",
5633
              "  \"architectures\": [\n",
5634
              "    \"GPTBigCodeForCausalLM\"\n",
5635
              "  ],\n",
5636
              "  \"attention_softmax_in_fp32\": true,\n",
5637
              "  \"attn_pdrop\": 0.1,\n",
5638
              "  \"bos_token_id\": 0,\n",
5639
              "  \"embd_pdrop\": 0.1,\n",
5640
              "  \"eos_token_id\": 0,\n",
5641
              "  \"inference_runner\": 0,\n",
5642
              "  \"initializer_range\": 0.02,\n",
5643
              "  \"layer_norm_epsilon\": 1e-05,\n",
5644
              "  \"max_batch_size\": null,\n",
5645
              "  \"max_sequence_length\": null,\n",
5646
              "  \"model_type\": \"gpt_bigcode\",\n",
5647
              "  \"multi_query\": true,\n",
5648
              "  \"n_embd\": 768,\n",
5649
              "  \"n_head\": 12,\n",
5650
              "  \"n_inner\": 3072,\n",
5651
              "  \"n_layer\": 20,\n",
5652
              "  \"n_positions\": 8192,\n",
5653
              "  \"pad_key_length\": true,\n",
5654
              "  \"pre_allocate_kv_cache\": false,\n",
5655
              "  \"resid_pdrop\": 0.1,\n",
5656
              "  \"scale_attention_softmax_in_fp32\": true,\n",
5657
              "  \"scale_attn_weights\": true,\n",
5658
              "  \"summary_activation\": null,\n",
5659
              "  \"summary_first_dropout\": 0.1,\n",
5660
              "  \"summary_proj_to_labels\": true,\n",
5661
              "  \"summary_type\": \"cls_index\",\n",
5662
              "  \"summary_use_proj\": true,\n",
5663
              "  \"torch_dtype\": \"float32\",\n",
5664
              "  \"transformers_version\": \"4.33.0\",\n",
5665
              "  \"use_cache\": true,\n",
5666
              "  \"validate_runner_input\": true,\n",
5667
              "  \"vocab_size\": 49152\n",
5668
              "}"
5669
            ]
5670
          },
5671
          "metadata": {},
5672
          "execution_count": 11
5673
        }
5674
      ]
5675
    },
5676
    {
5677
      "cell_type": "code",
5678
      "source": [
5679
        "### Loading the TRL reward trainer and training the trainer\n",
5680
        "training_args = TrainingArguments(\n",
5681
        "        output_dir=\"tinystarcoder-rlhf-model\",\n",
5682
        "        num_train_epochs=1,\n",
5683
        "        logging_steps=10,\n",
5684
        "        gradient_accumulation_steps=1,\n",
5685
        "        save_strategy=\"steps\",\n",
5686
        "        evaluation_strategy=\"steps\",\n",
5687
        "        per_device_train_batch_size=2,\n",
5688
        "        per_device_eval_batch_size=1,\n",
5689
        "        eval_accumulation_steps=1,\n",
5690
        "        eval_steps=500,\n",
5691
        "        save_steps=500,\n",
5692
        "        warmup_steps=100,\n",
5693
        "        logging_dir=\"./logs\",\n",
5694
        "        learning_rate=1e-5,\n",
5695
        "        save_total_limit=1,\n",
5696
        "        no_cuda=True\n",
5697
        "    )"
5698
      ],
5699
      "metadata": {
5700
        "colab": {
5701
          "base_uri": "https://localhost:8080/"
5702
        },
5703
        "id": "S4UivLiGVgO2",
5704
        "outputId": "cb75c51d-59b6-4fcf-841d-e0b5c62ee074"
5705
      },
5706
      "execution_count": 12,
5707
      "outputs": [
5708
        {
5709
          "output_type": "stream",
5710
          "name": "stderr",
5711
          "text": [
5712
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1257: FutureWarning: using `no_cuda` is deprecated and will be removed in version 5.0 of 🤗 Transformers. Use `use_cpu` instead\n",
5713
            "  warnings.warn(\n"
5714
          ]
5715
        }
5716
      ]
5717
    },
5718
    {
5719
      "cell_type": "code",
5720
      "source": [
5721
        "trainer = RewardTrainer(model=model,\n",
5722
        "                        tokenizer=tokenizer,\n",
5723
        "                        train_dataset=formatted_dataset['train'],\n",
5724
        "                        eval_dataset=formatted_dataset['test'],\n",
5725
        "                        args= training_args\n",
5726
        "                        )\n",
5727
        "trainer.train()"
5728
      ],
5729
      "metadata": {
5730
        "colab": {
5731
          "base_uri": "https://localhost:8080/",
5732
          "height": 252
5733
        },
5734
        "id": "P2ooNlBhVim-",
5735
        "outputId": "259a4a95-1b91-4c2b-cb70-23b64cc267ca"
5736
      },
5737
      "execution_count": 13,
5738
      "outputs": [
5739
        {
5740
          "output_type": "stream",
5741
          "name": "stderr",
5742
          "text": [
5743
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/reward_trainer.py:124: UserWarning: When using RewardDataCollatorWithPadding, you should set `max_length` in the RewardTrainer's init it will be set to `512` by default, but you should do it yourself in the future.\n",
5744
            "  warnings.warn(\n",
5745
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/reward_trainer.py:138: UserWarning: When using RewardDataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments we have set it for you, but you should do it yourself in the future.\n",
5746
            "  warnings.warn(\n",
5747
            "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
5748
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2436: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
5749
            "  warnings.warn(\n",
5750
            "Could not estimate the number of tokens of the input, floating-point operations will not be computed\n"
5751
          ]
5752
        },
5753
        {
5754
          "output_type": "display_data",
5755
          "data": {
5756
            "text/plain": [
5757
              "<IPython.core.display.HTML object>"
5758
            ],
5759
            "text/html": [
5760
              "\n",
5761
              "    <div>\n",
5762
              "      \n",
5763
              "      <progress value='4' max='4' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
5764
              "      [4/4 00:52, Epoch 1/1]\n",
5765
              "    </div>\n",
5766
              "    <table border=\"1\" class=\"dataframe\">\n",
5767
              "  <thead>\n",
5768
              " <tr style=\"text-align: left;\">\n",
5769
              "      <th>Step</th>\n",
5770
              "      <th>Training Loss</th>\n",
5771
              "      <th>Validation Loss</th>\n",
5772
              "    </tr>\n",
5773
              "  </thead>\n",
5774
              "  <tbody>\n",
5775
              "  </tbody>\n",
5776
              "</table><p>"
5777
            ]
5778
          },
5779
          "metadata": {}
5780
        },
5781
        {
5782
          "output_type": "execute_result",
5783
          "data": {
5784
            "text/plain": [
5785
              "TrainOutput(global_step=4, training_loss=0.7674217224121094, metrics={'train_runtime': 101.4459, 'train_samples_per_second': 0.069, 'train_steps_per_second': 0.039, 'total_flos': 0.0, 'train_loss': 0.7674217224121094, 'epoch': 1.0})"
5786
            ]
5787
          },
5788
          "metadata": {},
5789
          "execution_count": 13
5790
        }
5791
      ]
5792
    },
5793
    {
5794
      "cell_type": "code",
5795
      "source": [
5796
        "trainer.evaluate()"
5797
      ],
5798
      "metadata": {
5799
        "colab": {
5800
          "base_uri": "https://localhost:8080/",
5801
          "height": 231
5802
        },
5803
        "id": "ENADz21Gf1XN",
5804
        "outputId": "eecf2422-7dda-498a-c2d8-479f3aab6b72"
5805
      },
5806
      "execution_count": 15,
5807
      "outputs": [
5808
        {
5809
          "output_type": "stream",
5810
          "name": "stderr",
5811
          "text": [
5812
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2436: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
5813
            "  warnings.warn(\n",
5814
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/reward_trainer.py:214: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3571.)\n",
5815
            "  logits = torch.stack(logits).mean(dim=2).softmax(dim=0).T\n"
5816
          ]
5817
        },
5818
        {
5819
          "output_type": "display_data",
5820
          "data": {
5821
            "text/plain": [
5822
              "<IPython.core.display.HTML object>"
5823
            ],
5824
            "text/html": [
5825
              "\n",
5826
              "    <div>\n",
5827
              "      \n",
5828
              "      <progress value='3' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
5829
              "      [3/3 00:14]\n",
5830
              "    </div>\n",
5831
              "    "
5832
            ]
5833
          },
5834
          "metadata": {}
5835
        },
5836
        {
5837
          "output_type": "execute_result",
5838
          "data": {
5839
            "text/plain": [
5840
              "{'eval_loss': 0.6931466460227966,\n",
5841
              " 'eval_accuracy': 0.0,\n",
5842
              " 'eval_runtime': 22.5484,\n",
5843
              " 'eval_samples_per_second': 0.133,\n",
5844
              " 'eval_steps_per_second': 0.133,\n",
5845
              " 'epoch': 1.0}"
5846
            ]
5847
          },
5848
          "metadata": {},
5849
          "execution_count": 15
5850
        }
5851
      ]
5852
    },
5853
    {
5854
      "cell_type": "code",
5855
      "source": [
5856
        "from huggingface_hub import login\n",
5857
        "login()"
5858
      ],
5859
      "metadata": {
5860
        "colab": {
5861
          "base_uri": "https://localhost:8080/",
5862
          "height": 145,
5863
          "referenced_widgets": [
5864
            "70363dc337a84a4e8d9dbe548bb24e5a",
5865
            "b50c4b7e474f4b3ca1b626b8390f6d4e",
5866
            "5b58c1033da8470f82744583306674e4",
5867
            "85c7e1bc8dcb4ee99baeb9f30cfdd2bb",
5868
            "2d293c8541264b0cbaeb7219c2d94ef8",
5869
            "a634b0ccd0a84f53acbb16ede00688b7",
5870
            "2f2cd4fd194444aeb41ab473ce74f744",
5871
            "cbcda32fd6694581a3ea49c2abc83232",
5872
            "f3b324fe716e4abc81fcdd0817387797",
5873
            "9fbdfbeef5ed4880ae68fcde7d12b617",
5874
            "b7653907cb634671b17cde0234bdaf18",
5875
            "9fd5e7396838452da35d5f1240dcfc09",
5876
            "b6fe4d9105d849da8c69966016b52454",
5877
            "37b3bc877baa429e99456abc6e47e551",
5878
            "61e3ec46b6ca4bfaa5cffe684130d13b",
5879
            "1b1bb860813d45bb88f39b1aaa192e91",
5880
            "f8a983b16f3644fca1692d8de9f07f0d",
5881
            "89cc8bd26d154bbd812997f60b0e241d",
5882
            "6511db857f284380a6d93b9115d14479",
5883
            "f387c978d4c24221890d90ac8d22e0b6",
5884
            "79131c98086a4259800d77bd94f81554",
5885
            "b015f478b8b6453fa0f49c115ff572c2",
5886
            "2d9d192cfa124aadaf1b3d31bb9dd0ce",
5887
            "cb52479c2c3844a99388a8e71fd6d0e6",
5888
            "9d2d72d38d5f43fcbd64b43481982ce8",
5889
            "7218cccb90284684b43124ee11adb918",
5890
            "1b93ff678ada4ff5bec69a1bdb36bd5b",
5891
            "1d5e9cd1f7234b899efbfd3cfb23774c",
5892
            "a6295ffea3b546e9a9b62552424dc970",
5893
            "554b5751193b4133992e160b6a938dda",
5894
            "9edaf08284f24cd6bd37039f565315f2",
5895
            "1ff9cb3284ad4ffd9eb7ca376e5e3006"
5896
          ]
5897
        },
5898
        "id": "ZI4Ex-o7WFHc",
5899
        "outputId": "4ca5f4f0-44ac-46be-8976-8697f79018cc"
5900
      },
5901
      "execution_count": 17,
5902
      "outputs": [
5903
        {
5904
          "output_type": "display_data",
5905
          "data": {
5906
            "text/plain": [
5907
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
5908
            ],
5909
            "application/vnd.jupyter.widget-view+json": {
5910
              "version_major": 2,
5911
              "version_minor": 0,
5912
              "model_id": "70363dc337a84a4e8d9dbe548bb24e5a"
5913
            }
5914
          },
5915
          "metadata": {}
5916
        }
5917
      ]
5918
    },
5919
    {
5920
      "cell_type": "code",
5921
      "source": [
5922
        "trainer.push_to_hub(\"RLHF model of StarCoder\")"
5923
      ],
5924
      "metadata": {
5925
        "colab": {
5926
          "base_uri": "https://localhost:8080/",
5927
          "height": 67,
5928
          "referenced_widgets": [
5929
            "7511afb8ae6b4947a2445ef0450d449f",
5930
            "df5badd1ab29425d8ed70a91d618a0e4",
5931
            "c89e2bd3496a46ed83c1678b2f9d1713",
5932
            "2fa045000acd438da96717b13d3c5a0c",
5933
            "a8434eed07314c24935c6ca6a9c4952e",
5934
            "74551940aad54d309c95ce1d549438e0",
5935
            "4fe71e691bc04d609baeb07bfe0bd4e1",
5936
            "cce70368ebaf4d2a83f4bd8fc87b827f",
5937
            "7b6da5b0aa0744bba4041954252279fa",
5938
            "e9a71eebb4084b6fb7b2efd8bfc8fa14",
5939
            "312bca73ede940f280ea3c25206ef3f8"
5940
          ]
5941
        },
5942
        "id": "bUrhJDQLWDuu",
5943
        "outputId": "bb69e358-4c0a-4a0d-9cbc-8bdb35017c9a"
5944
      },
5945
      "execution_count": 18,
5946
      "outputs": [
5947
        {
5948
          "output_type": "display_data",
5949
          "data": {
5950
            "text/plain": [
5951
              "pytorch_model.bin:   0%|          | 0.00/657M [00:00<?, ?B/s]"
5952
            ],
5953
            "application/vnd.jupyter.widget-view+json": {
5954
              "version_major": 2,
5955
              "version_minor": 0,
5956
              "model_id": "7511afb8ae6b4947a2445ef0450d449f"
5957
            }
5958
          },
5959
          "metadata": {}
5960
        },
5961
        {
5962
          "output_type": "execute_result",
5963
          "data": {
5964
            "text/plain": [
5965
              "'https://huggingface.co/ashishpatel26/tinystarcoder-rlhf-model/tree/main/'"
5966
            ],
5967
            "application/vnd.google.colaboratory.intrinsic+json": {
5968
              "type": "string"
5969
            }
5970
          },
5971
          "metadata": {},
5972
          "execution_count": 18
5973
        }
5974
      ]
5975
    },
5976
    {
5977
      "cell_type": "code",
5978
      "source": [
5979
        "trainer.save_model(\"tinystarcoder-rlhf-model\")"
5980
      ],
5981
      "metadata": {
5982
        "id": "jb3_r6pcVqDx"
5983
      },
5984
      "execution_count": 20,
5985
      "outputs": []
5986
    },
5987
    {
5988
      "cell_type": "markdown",
5989
      "source": [
5990
        "### Policy Model"
5991
      ],
5992
      "metadata": {
5993
        "id": "pvXbToUJV7Y9"
5994
      }
5995
    },
5996
    {
5997
      "cell_type": "code",
5998
      "source": [
5999
        "help(trainer.push_to_hub)"
6000
      ],
6001
      "metadata": {
6002
        "id": "8dfAGf2Ha20p"
6003
      },
6004
      "execution_count": null,
6005
      "outputs": []
6006
    },
6007
    {
6008
      "cell_type": "code",
6009
      "source": [
6010
        "## inference the model\n",
6011
        "rm_model = AutoModelForCausalLM.from_pretrained(\"tinystarcoder-rlhf-model\")\n",
6012
        "tokenizer = AutoTokenizer.from_pretrained(\"tinystarcoder-rlhf-model\")"
6013
      ],
6014
      "metadata": {
6015
        "id": "E_JNgUoqV8cL"
6016
      },
6017
      "execution_count": 21,
6018
      "outputs": []
6019
    },
6020
    {
6021
      "cell_type": "code",
6022
      "source": [
6023
        "def get_score(model, tokenizer, prompt, response):\n",
6024
        "\n",
6025
        "    instructions = tokenizer.encode_plus(prompt,\n",
6026
        "                                       response,\n",
6027
        "                                       padding=\"max_length\",\n",
6028
        "                                       max_length=256,\n",
6029
        "                                       return_tensors=\"pt\",\n",
6030
        "                                        truncation=True)\n",
6031
        "    with torch.no_grad():\n",
6032
        "        outputs = model(**instructions)\n",
6033
        "\n",
6034
        "    logits = outputs[0]\n",
6035
        "\n",
6036
        "    return logits"
6037
      ],
6038
      "metadata": {
6039
        "id": "YyFiSSkkZUSt"
6040
      },
6041
      "execution_count": 22,
6042
      "outputs": []
6043
    },
6044
    {
6045
      "cell_type": "code",
6046
      "source": [
6047
        "# usage with prompt\n",
6048
        "prompt = df.iloc[0][\"prompt\"]\n",
6049
        "example_prefered_response = df.iloc[0][\"chosen\"]\n",
6050
        "example_unprefered_response = df.iloc[0][\"rejected\"]"
6051
      ],
6052
      "metadata": {
6053
        "id": "BUNv_muAZcrB"
6054
      },
6055
      "execution_count": 23,
6056
      "outputs": []
6057
    },
6058
    {
6059
      "cell_type": "code",
6060
      "source": [
6061
        "loss1 = get_score(model, tokenizer, prompt, example_prefered_response)\n",
6062
        "loss2= get_score(model, tokenizer, prompt, example_unprefered_response)"
6063
      ],
6064
      "metadata": {
6065
        "id": "uV6LkWkWZePB"
6066
      },
6067
      "execution_count": 24,
6068
      "outputs": []
6069
    },
6070
    {
6071
      "cell_type": "code",
6072
      "source": [
6073
        "from torch import nn\n",
6074
        "loss = -nn.functional.logsigmoid(loss1 - loss2).mean()"
6075
      ],
6076
      "metadata": {
6077
        "id": "0E6NWxhPZgVB"
6078
      },
6079
      "execution_count": 25,
6080
      "outputs": []
6081
    },
6082
    {
6083
      "cell_type": "code",
6084
      "source": [
6085
        "tokenizer.decode(torch.max(loss1, axis=-1).indices[0])"
6086
      ],
6087
      "metadata": {
6088
        "colab": {
6089
          "base_uri": "https://localhost:8080/",
6090
          "height": 87
6091
        },
6092
        "id": "IhbtDog6Zh0s",
6093
        "outputId": "74f7678a-6edc-48ba-804f-16cd63eb4af9"
6094
      },
6095
      "execution_count": 26,
6096
      "outputs": [
6097
        {
6098
          "output_type": "execute_result",
6099
          "data": {
6100
            "text/plain": [
6101
              "'_DDIT_\\n           \"r/#: Relationship RelationshipRelationship0]0ARCH\\nlsriend\\n2//M]\\n [\\n [\\n the was to the [\\n a friends to\\n\\n: https [lfriend [ me am a aumb. my19 minutes.\\n\\n\"\"\"I updated:** girlfriend was through my Facebook..** I my my friends.**.** my  of dayslf.\\n\\n** went dding for my message personirl** I had to makeellpping my my my past** but I was a in\\n\\n** have ali a of ago she  tolirt with my girl. and she had my about my.. me few of gir.1viously). was\\'t find that was).\\n\\n** was it for  twoirl and the had my Facebook. the   she wasand historyirl) was was March,\\n to to find that were flited. I a g.. f.ing on her.\\nirirl wass; I1 girirllfriend is 19 months. through my Facebook.. my permission. I it messages. her.lirty with me coupleirl.\\n found up with me after she through more with\\n'"
6102
            ],
6103
            "application/vnd.google.colaboratory.intrinsic+json": {
6104
              "type": "string"
6105
            }
6106
          },
6107
          "metadata": {},
6108
          "execution_count": 26
6109
        }
6110
      ]
6111
    },
6112
    {
6113
      "cell_type": "markdown",
6114
      "source": [
6115
        "### Policy Model"
6116
      ],
6117
      "metadata": {
6118
        "id": "yIjodhd5hP7g"
6119
      }
6120
    },
6121
    {
6122
      "cell_type": "code",
6123
      "source": [
6124
        "import torch\n",
6125
        "import transformers\n",
6126
        "from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling\n",
6127
        "from trl import RewardTrainer, SFTTrainer\n",
6128
        "from datasets import Dataset\n",
6129
        "import json\n",
6130
        "import pandas as pd\n",
6131
        "from transformers import Trainer, TrainingArguments\n",
6132
        "from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_model"
6133
      ],
6134
      "metadata": {
6135
        "id": "m_7o8be1ZpfJ"
6136
      },
6137
      "execution_count": 54,
6138
      "outputs": []
6139
    },
6140
    {
6141
      "cell_type": "code",
6142
      "source": [
6143
        "##model path\n",
6144
        "MODEL_PATH = \"bigcode/tiny_starcoder_py\"\n",
6145
        "DATA_PATH = \"test.parquet\""
6146
      ],
6147
      "metadata": {
6148
        "id": "7fZZYLbrhRom"
6149
      },
6150
      "execution_count": 55,
6151
      "outputs": []
6152
    },
6153
    {
6154
      "cell_type": "code",
6155
      "source": [
6156
        "df = pd.read_parquet(DATA_PATH)\n",
6157
        "df = df[:1000]\n",
6158
        "dataset = Dataset.from_pandas(df)\n",
6159
        "dataset"
6160
      ],
6161
      "metadata": {
6162
        "colab": {
6163
          "base_uri": "https://localhost:8080/"
6164
        },
6165
        "id": "6vJxLQvYhU23",
6166
        "outputId": "85339387-e6f3-441c-f3f8-5322e87005fe"
6167
      },
6168
      "execution_count": 56,
6169
      "outputs": [
6170
        {
6171
          "output_type": "execute_result",
6172
          "data": {
6173
            "text/plain": [
6174
              "Dataset({\n",
6175
              "    features: ['prompt', 'chosen', 'rejected'],\n",
6176
              "    num_rows: 1000\n",
6177
              "})"
6178
            ]
6179
          },
6180
          "metadata": {},
6181
          "execution_count": 56
6182
        }
6183
      ]
6184
    },
6185
    {
6186
      "cell_type": "code",
6187
      "source": [
6188
        "from transformers import AutoTokenizer, pipeline"
6189
      ],
6190
      "metadata": {
6191
        "id": "o74znYVghvy5"
6192
      },
6193
      "execution_count": 59,
6194
      "outputs": []
6195
    },
6196
    {
6197
      "cell_type": "code",
6198
      "source": [
6199
        "rf_model_path = \"tinystarcoder-rlhf-model\"\n",
6200
        "starcoder_model = AutoModelForCausalLMWithValueHead.from_pretrained(rf_model_path)\n",
6201
        "starcoder_model_ref = AutoModelForCausalLMWithValueHead.from_pretrained(rf_model_path)\n",
6202
        "starcoder_tokenizer = AutoTokenizer.from_pretrained(rf_model_path)\n",
6203
        "\n",
6204
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, padding_side='left')\n",
6205
        "tokenizer.pad_token = tokenizer.eos_token"
6206
      ],
6207
      "metadata": {
6208
        "id": "NCrrq1WEjWYm"
6209
      },
6210
      "execution_count": 62,
6211
      "outputs": []
6212
    },
6213
    {
6214
      "cell_type": "code",
6215
      "source": [
6216
        "txt_in_len = 5\n",
6217
        "txt_out_len = 32\n",
6218
        "seed = 1\n",
6219
        "\n",
6220
        "dataset = dataset.map(\n",
6221
        "    lambda x: {\"input_ids\": tokenizer.encode(\" \" + x[\"chosen\"], return_tensors=\"pt\", truncation=True, padding=\"max_length\", max_length=32)[0]},\n",
6222
        "    batched=False,\n",
6223
        ")\n",
6224
        "dataset = dataset.map(lambda x: {\"query\": tokenizer.decode(x[\"input_ids\"])}, batched=False)\n",
6225
        "dataset = dataset[:20480]\n",
6226
        "from datasets import Dataset\n",
6227
        "\n",
6228
        "dataset = Dataset.from_dict(dataset)\n",
6229
        "dataset.set_format(\"pytorch\")"
6230
      ],
6231
      "metadata": {
6232
        "colab": {
6233
          "base_uri": "https://localhost:8080/",
6234
          "height": 81,
6235
          "referenced_widgets": [
6236
            "1a97f217b8974c71979590b3921142f4",
6237
            "8cf22559c709440da8afa8284f5721c8",
6238
            "e7e2120d9a6d46cc9198d3ec6227d731",
6239
            "f51b130d5bee4a3f9856d786feeb49dd",
6240
            "4ec54d6d87b54e589350489b434035fe",
6241
            "f49f4dc7622a421f8b51e5dbf42b9897",
6242
            "c34cf7b8aae94de9bbae518c124f11be",
6243
            "6cad2953fe054449b1c3c57dc12cb2da",
6244
            "63b5f2cc7f4e4dd889e3e0ce1218abb3",
6245
            "117f42f3c9664da481bf04bbbc3b5021",
6246
            "a922b8c9424b4576824246bff7023f89",
6247
            "d60942cf3d974c71bd1f9d5984e0580c",
6248
            "d4d7add22a154279943428d496ac05ca",
6249
            "42d9369bf5bc4f538f925e0fd0e3048f",
6250
            "bbe71558dbf0434a9334aba784e547ac",
6251
            "5d677887f4074e288eb95c7dba354680",
6252
            "aa6374e9313348bda63dfd8bad97481a",
6253
            "c8c318424b2b4ba0b788d7a15217ddbe",
6254
            "8614f95764c0409da8dd82ec9ad474f1",
6255
            "9820beb8b5d44759b81e6ca97ff7b250",
6256
            "07b8e67462694703a1d9dbed9f9d5c1b",
6257
            "88a6e00a597d4e7394cdd42386d987a7"
6258
          ]
6259
        },
6260
        "id": "WDPj8gqqjdBY",
6261
        "outputId": "a405d2a3-6a76-4781-cc0d-1ca70a4dd688"
6262
      },
6263
      "execution_count": 63,
6264
      "outputs": [
6265
        {
6266
          "output_type": "display_data",
6267
          "data": {
6268
            "text/plain": [
6269
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
6270
            ],
6271
            "application/vnd.jupyter.widget-view+json": {
6272
              "version_major": 2,
6273
              "version_minor": 0,
6274
              "model_id": "1a97f217b8974c71979590b3921142f4"
6275
            }
6276
          },
6277
          "metadata": {}
6278
        },
6279
        {
6280
          "output_type": "display_data",
6281
          "data": {
6282
            "text/plain": [
6283
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
6284
            ],
6285
            "application/vnd.jupyter.widget-view+json": {
6286
              "version_major": 2,
6287
              "version_minor": 0,
6288
              "model_id": "d60942cf3d974c71bd1f9d5984e0580c"
6289
            }
6290
          },
6291
          "metadata": {}
6292
        }
6293
      ]
6294
    },
6295
    {
6296
      "cell_type": "code",
6297
      "source": [
6298
        "dataset"
6299
      ],
6300
      "metadata": {
6301
        "colab": {
6302
          "base_uri": "https://localhost:8080/"
6303
        },
6304
        "id": "jkQnoXkwj96E",
6305
        "outputId": "136d7e64-9d45-4552-97d9-31cbe6a40e85"
6306
      },
6307
      "execution_count": 64,
6308
      "outputs": [
6309
        {
6310
          "output_type": "execute_result",
6311
          "data": {
6312
            "text/plain": [
6313
              "Dataset({\n",
6314
              "    features: ['review', 'chosen', 'rejected', 'input_ids', 'query'],\n",
6315
              "    num_rows: 1000\n",
6316
              "})"
6317
            ]
6318
          },
6319
          "metadata": {},
6320
          "execution_count": 64
6321
        }
6322
      ]
6323
    },
6324
    {
6325
      "cell_type": "code",
6326
      "source": [
6327
        "sentiment_pipe_kwargs = {\"top_k\": None, \"function_to_apply\": \"none\"}\n",
6328
        "\n",
6329
        "config = PPOConfig(\n",
6330
        "    model_name=MODEL_PATH, steps=51200, learning_rate=1.41e-5, remove_unused_columns=True\n",
6331
        ")\n",
6332
        "\n",
6333
        "txt_in_len = 5\n",
6334
        "txt_out_len = 20\n",
6335
        "seed = 1"
6336
      ],
6337
      "metadata": {
6338
        "id": "f9F3y_XDkAvj"
6339
      },
6340
      "execution_count": 98,
6341
      "outputs": []
6342
    },
6343
    {
6344
      "cell_type": "code",
6345
      "source": [
6346
        "import torch\n",
6347
        "optimizer = torch.optim.SGD(starcoder_model.parameters(), lr=config.learning_rate)\n",
6348
        "ppo_trainer = PPOTrainer(config, starcoder_model, starcoder_model, starcoder_tokenizer, dataset=dataset, data_collator=collator, optimizer=optimizer)"
6349
      ],
6350
      "metadata": {
6351
        "id": "bJYnA8hZkTEK"
6352
      },
6353
      "execution_count": 99,
6354
      "outputs": []
6355
    },
6356
    {
6357
      "cell_type": "code",
6358
      "source": [
6359
        "# for i in ppo_trainer.dataloader:\n",
6360
        "#   print(i)"
6361
      ],
6362
      "metadata": {
6363
        "id": "i_RWvbQDkgsx"
6364
      },
6365
      "execution_count": 100,
6366
      "outputs": []
6367
    },
6368
    {
6369
      "cell_type": "code",
6370
      "source": [
6371
        "ctrl_str = [\"[negative]\", \"[positive]\"]\n",
6372
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")  # this should be handled by accelerate\n",
6373
        "ctrl_tokens = dict((s, starcoder_tokenizer.encode(s, return_tensors=\"pt\").squeeze().to(device)) for s in ctrl_str)"
6374
      ],
6375
      "metadata": {
6376
        "id": "YFs55MmokaWm"
6377
      },
6378
      "execution_count": 101,
6379
      "outputs": []
6380
    },
6381
    {
6382
      "cell_type": "code",
6383
      "source": [
6384
        "def pos_logit_to_reward(logit, task):\n",
6385
        "    \"\"\"\n",
6386
        "    Take the positive sentiment logit and scale it for the task.\n",
6387
        "        task [negative]: reward = -logit\n",
6388
        "        task [neutral]: reward = -2*abs(logit)+4\n",
6389
        "        task [positive]: reward = logit\n",
6390
        "    \"\"\"\n",
6391
        "    for i in range(len(logit)):\n",
6392
        "        if task[i] == \"[negative]\":\n",
6393
        "            logit[i] = -logit[i]\n",
6394
        "        elif task[i] == \"[positive]\":\n",
6395
        "            pass\n",
6396
        "        else:\n",
6397
        "            raise ValueError(\"task has to be in [0, 1, 2]!\")\n",
6398
        "    return logit"
6399
      ],
6400
      "metadata": {
6401
        "id": "g6EMveqTkuNW"
6402
      },
6403
      "execution_count": 102,
6404
      "outputs": []
6405
    },
6406
    {
6407
      "cell_type": "code",
6408
      "source": [
6409
        "pos_logit_to_reward(torch.Tensor([4, 4]), ctrl_str)"
6410
      ],
6411
      "metadata": {
6412
        "colab": {
6413
          "base_uri": "https://localhost:8080/"
6414
        },
6415
        "id": "7INpXsiskue5",
6416
        "outputId": "fc0be95d-5b3a-4bb2-8b29-45bb91f66016"
6417
      },
6418
      "execution_count": 103,
6419
      "outputs": [
6420
        {
6421
          "output_type": "execute_result",
6422
          "data": {
6423
            "text/plain": [
6424
              "tensor([-4.,  4.])"
6425
            ]
6426
          },
6427
          "metadata": {},
6428
          "execution_count": 103
6429
        }
6430
      ]
6431
    },
6432
    {
6433
      "cell_type": "code",
6434
      "source": [
6435
        "generation_kwargs = {\n",
6436
        "    \"min_length\": -1,\n",
6437
        "    \"top_k\": 0.0,\n",
6438
        "    \"top_p\": 1.0,\n",
6439
        "    \"do_sample\": True,\n",
6440
        "    \"pad_token_id\": starcoder_tokenizer.eos_token_id,\n",
6441
        "    \"max_new_tokens\": 32,\n",
6442
        "    \"eos_token_id\": -1,\n",
6443
        "}"
6444
      ],
6445
      "metadata": {
6446
        "id": "Y-oelQ_qkv0D"
6447
      },
6448
      "execution_count": 104,
6449
      "outputs": []
6450
    },
6451
    {
6452
      "cell_type": "code",
6453
      "source": [
6454
        "def get_score(model, tokenizer, responses):\n",
6455
        "    positive_logist = []\n",
6456
        "    for i in responses:\n",
6457
        "        instructions = tokenizer.encode_plus(\n",
6458
        "                                           i,\n",
6459
        "                                           padding=\"max_length\",\n",
6460
        "                                           max_length=32,\n",
6461
        "                                           return_tensors=\"pt\")\n",
6462
        "        with torch.no_grad():\n",
6463
        "            outputs = model(**instructions)\n",
6464
        "\n",
6465
        "        logits = outputs[0].mean()\n",
6466
        "        positive_logist.append(logits)\n",
6467
        "\n",
6468
        "    return positive_logist\n",
6469
        ""
6470
      ],
6471
      "metadata": {
6472
        "id": "hs9DCR5Lk0Ut"
6473
      },
6474
      "execution_count": 105,
6475
      "outputs": []
6476
    },
6477
    {
6478
      "cell_type": "code",
6479
      "source": [
6480
        "# responses =[\"ashish is a goo\", \"heelow how are you\", \"__IT_\\nr/\\n: r RelationshipRelationship]]0]\\nlsriend\\n2//M]\\n [ [ a\\n the was to the [. a friends to\\n\\n:\\n [lfriend [ me have a aried in his19 minutes.\\n\\nWhat Modified:** girlfriend was through the Facebook.. I my my friends.**** my  of lf**\\n\\n** was d1ing for my few personirl** I had for findoolpping my my the future** but I was that in\\n\\n** have ali  of to she  tolirt my me girl. and she found my about my.. me few of gir.1viously). was\\'t find her was).\\n\\n** was it about my twoirl and the had  Facebook. the  and she gand historyirl) was in April,\\n to, find, were flirted. I a messages.. f.ing on her.\\n girlM\\n; I1 girirllfriend and the19 months. to my Facebook.. my permission. she her messages. my.lirty with my fewirl.\\n found her with me. I through more with\\n\"]\n",
6481
        "# get_score(starcoder_model, tokenizer, responses)"
6482
      ],
6483
      "metadata": {
6484
        "id": "PV0W3vzzlAK7"
6485
      },
6486
      "execution_count": 113,
6487
      "outputs": []
6488
    },
6489
    {
6490
      "cell_type": "code",
6491
      "source": [
6492
        "# from random import choices\n",
6493
        "# from tqdm import tqdm\n",
6494
        "# import time\n",
6495
        "# import numpy as np\n",
6496
        "\n",
6497
        "# for epoch in range(1):\n",
6498
        "#     for batch in tqdm(ppo_trainer.dataloader):\n",
6499
        "#         (logs, game_data,) = (\n",
6500
        "#             dict(),\n",
6501
        "#             dict(),\n",
6502
        "#         )\n",
6503
        "\n",
6504
        "#         print(ctrl_str)\n",
6505
        "#         #### prepend a random control token\n",
6506
        "#         task_list = choices(ctrl_str, k=config.batch_size)\n",
6507
        "#         game_data[\"query\"] = [t + q for t, q in zip(task_list, batch[\"query\"])]\n",
6508
        "#         query_tensors = [torch.cat((ctrl_tokens[t], input_ids)) for t, input_ids in zip(task_list, batch[\"input_ids\"])]\n",
6509
        "\n",
6510
        "#         #### get response from gpt2\n",
6511
        "#         response_tensors = []\n",
6512
        "#         for query in query_tensors:\n",
6513
        "#             response = ppo_trainer.generate(query, **generation_kwargs)\n",
6514
        "#             response_tensors.append(response.squeeze()[-txt_out_len:])\n",
6515
        "# #         print(response_tensors)\n",
6516
        "#         game_data[\"response\"] = [starcoder_tokenizer.decode(r.squeeze()) for r in response_tensors]\n",
6517
        "\n",
6518
        "#         #### sentiment analysis\n",
6519
        "#         texts = [q + r for q, r in zip(batch[\"query\"], game_data[\"response\"])]\n",
6520
        "#         logits = get_score(starcoder_model,starcoder_tokenizer, texts)\n",
6521
        "#         rewards = pos_logit_to_reward(logits, task_list)\n",
6522
        "\n",
6523
        "#         #### Run PPO training\n",
6524
        "#         t = time.time()\n",
6525
        "#         stats = ppo_trainer.step(query_tensors, response_tensors, rewards)\n",
6526
        "\n",
6527
        "#         for cs in ctrl_str:\n",
6528
        "#             key = \"env/reward_\" + cs.strip(\"[]\")\n",
6529
        "#             stats[key] = np.mean([r.cpu().numpy() for r, t in zip(rewards, task_list) if t == cs])\n",
6530
        "#         ppo_trainer.log_stats(stats, game_data, rewards)"
6531
      ],
6532
      "metadata": {
6533
        "id": "WTiDklXBlT52"
6534
      },
6535
      "execution_count": 115,
6536
      "outputs": []
6537
    },
6538
    {
6539
      "cell_type": "code",
6540
      "source": [
6541
        "###saving the model\n",
6542
        "starcoder_model.save_pretrained(\"StarCoderTinyrhlfmodel/\")\n",
6543
        "starcoder_tokenizer.save_pretrained(\"StarCoderTinyrhlfmodel/\")"
6544
      ],
6545
      "metadata": {
6546
        "colab": {
6547
          "base_uri": "https://localhost:8080/"
6548
        },
6549
        "id": "MNFD6wvSlWux",
6550
        "outputId": "7cda4399-b238-4336-a5aa-480613ece097"
6551
      },
6552
      "execution_count": 109,
6553
      "outputs": [
6554
        {
6555
          "output_type": "execute_result",
6556
          "data": {
6557
            "text/plain": [
6558
              "('StarCoderTinyrhlfmodel/tokenizer_config.json',\n",
6559
              " 'StarCoderTinyrhlfmodel/special_tokens_map.json',\n",
6560
              " 'StarCoderTinyrhlfmodel/vocab.json',\n",
6561
              " 'StarCoderTinyrhlfmodel/merges.txt',\n",
6562
              " 'StarCoderTinyrhlfmodel/added_tokens.json',\n",
6563
              " 'StarCoderTinyrhlfmodel/tokenizer.json')"
6564
            ]
6565
          },
6566
          "metadata": {},
6567
          "execution_count": 109
6568
        }
6569
      ]
6570
    },
6571
    {
6572
      "cell_type": "code",
6573
      "source": [
6574
        "from transformers import pipeline, set_seed\n",
6575
        "model_path = \"StarCoderTinyrhlfmodel/\"\n",
6576
        "set_seed(42)\n",
6577
        "pipe = pipeline(\"text-generation\",model=model_path, tokenizer=model_path, max_length=30, num_return_sequences=5)"
6578
      ],
6579
      "metadata": {
6580
        "colab": {
6581
          "base_uri": "https://localhost:8080/"
6582
        },
6583
        "id": "f9KWg8IWoQqN",
6584
        "outputId": "b175814c-5206-488a-fc35-da972e3719cb"
6585
      },
6586
      "execution_count": 110,
6587
      "outputs": [
6588
        {
6589
          "output_type": "stream",
6590
          "name": "stderr",
6591
          "text": [
6592
            "Some weights of the model checkpoint at StarCoderTinyrhlfmodel/ were not used when initializing GPTBigCodeForCausalLM: ['v_head.summary.weight', 'v_head.summary.bias']\n",
6593
            "- This IS expected if you are initializing GPTBigCodeForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
6594
            "- This IS NOT expected if you are initializing GPTBigCodeForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
6595
          ]
6596
        }
6597
      ]
6598
    }
6599
  ]
6600
}

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