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"0f34fb0b6e974bf4a332173d4a026ee9", | |
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"c6bd0339dda7447cb55c33c40a991281", | |
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"0b590305e3d8436ab2ede0c9ae7a1947" | |
] | |
}, | |
"outputId": "ad429399-4989-4cb8-acc1-77fce9d55b68" | |
}, | |
"execution_count": 1, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stderr", | |
"text": [ | |
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", | |
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n", | |
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", | |
"You will be able to reuse this secret in all of your notebooks.\n", | |
"Please note that authentication is recommended but still optional to access public models or datasets.\n", | |
" warnings.warn(\n" | |
] | |
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} | |
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"metadata": {} | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"tokenizer.padding_side = \"left\"\n", | |
"tokenizer.pad_token = tokenizer.eos_token # to avoid an error" | |
], | |
"metadata": { | |
"id": "BqgvvdID1For" | |
}, | |
"execution_count": 2, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"texts = [\"this\", \"this is\", \"this is a\", \"this is a first\", \"this is a first test\"]" | |
], | |
"metadata": { | |
"id": "u6EMTbJkDHSD" | |
}, | |
"execution_count": 3, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"%%timeit\n", | |
"responses = []\n", | |
"for text in texts:\n", | |
" token = tokenizer(text, return_tensors='pt')\n", | |
" outputs = model.generate(token.input_ids, max_new_tokens=1, output_scores=True, return_dict_in_generate=True, pad_token_id=tokenizer.eos_token_id)\n", | |
" response = tokenizer.decode(outputs.sequences[0])\n", | |
" responses.append(response)" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "hz1U0zYg2FXj", | |
"outputId": "5a19169e-7fd5-4c7f-de0d-be2881f3ad78" | |
}, | |
"execution_count": 4, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"404 ms ± 4.02 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"responses = []\n", | |
"for text in texts:\n", | |
" token = tokenizer(text, return_tensors='pt')\n", | |
" outputs = model.generate(token.input_ids, max_new_tokens=1, output_scores=True, return_dict_in_generate=True, pad_token_id=tokenizer.eos_token_id)\n", | |
" response = tokenizer.decode(outputs.sequences[0])\n", | |
" responses.append(response)" | |
], | |
"metadata": { | |
"id": "827l806oEoKQ" | |
}, | |
"execution_count": 5, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"responses" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "CnHIX1R_3fc6", | |
"outputId": "02829059-8564-487c-85c3-0f2dc544c515" | |
}, | |
"execution_count": 6, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"['this.',\n", | |
" 'this is a',\n", | |
" 'this is a very',\n", | |
" 'this is a first-',\n", | |
" 'this is a first test of']" | |
] | |
}, | |
"metadata": {}, | |
"execution_count": 6 | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"%%timeit\n", | |
"responses = []\n", | |
"token = tokenizer.batch_encode_plus(texts, padding=True, return_tensors='pt')\n", | |
"outputs = model.generate(**token, max_new_tokens=1, output_scores=True, return_dict_in_generate=True, pad_token_id=tokenizer.eos_token_id)\n", | |
"response = tokenizer.batch_decode(outputs.sequences, skip_special_tokens=True)\n", | |
"responses.append(response)" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "SWRaYKLJE0BX", | |
"outputId": "3f2943b3-6317-49e4-d107-bb3062e4d876" | |
}, | |
"execution_count": 7, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"221 ms ± 64.6 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"responses = []\n", | |
"token = tokenizer.batch_encode_plus(texts, padding=True, return_tensors='pt')\n", | |
"outputs = model.generate(**token, max_new_tokens=1, output_scores=True, return_dict_in_generate=True, pad_token_id=tokenizer.eos_token_id)\n", | |
"response = tokenizer.batch_decode(outputs.sequences, skip_special_tokens=True)\n", | |
"responses.append(response)" | |
], | |
"metadata": { | |
"id": "gVCvmPhPFS4v" | |
}, | |
"execution_count": 8, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"%%timeit\n", | |
"full_tokens = tokenizer.encode(\"this is a first test\")\n", | |
"n = len(full_tokens)\n", | |
"encoding_copy = {'input_ids': torch.tensor([[tokenizer.eos_token_id for _ in range(n-m-1)] + full_tokens[:m+1] for m in range(n)]),\n", | |
" 'attention_mask': torch.tensor([[0 for i in range(n-m-1)]+ [1 for i in range(m+1)] for m in range(n)])}\n", | |
"with torch.no_grad():\n", | |
" generated_ids = model.generate(**encoding_copy, max_new_tokens=1, output_scores=True, return_dict_in_generate=True, pad_token_id=tokenizer.eos_token_id)\n", | |
"generated_texts = tokenizer.batch_decode(generated_ids.sequences, skip_special_tokens=True)" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "sNSTQQWGBgMQ", | |
"outputId": "c67233ef-74bd-4cef-b8aa-a4f69c4384e3" | |
}, | |
"execution_count": null, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"280 ms ± 22.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"full_tokens = tokenizer.encode(\"this is a first test\")\n", | |
"n = len(full_tokens)\n", | |
"encoding_copy = {'input_ids': torch.tensor([[tokenizer.eos_token_id for _ in range(n-m-1)] + full_tokens[:m+1] for m in range(n)]),\n", | |
" 'attention_mask': torch.tensor([[0 for i in range(n-m-1)]+ [1 for i in range(m+1)] for m in range(n)])}\n", | |
"with torch.no_grad():\n", | |
" generated_ids = model.generate(**encoding_copy, max_new_tokens=1, output_scores=True, return_dict_in_generate=True, pad_token_id=tokenizer.eos_token_id)\n", | |
"generated_texts = tokenizer.batch_decode(generated_ids.sequences, skip_special_tokens=True)" | |
], | |
"metadata": { | |
"id": "FiCziRtdOD3i" | |
}, | |
"execution_count": null, | |
"outputs": [] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"generated_texts" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "2LnOLLfoAxb6", | |
"outputId": "07bd5759-b07f-4527-dd1f-df5beef6dee3" | |
}, | |
"execution_count": null, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"['this.',\n", | |
" 'this is a',\n", | |
" 'this is a very',\n", | |
" 'this is a first-',\n", | |
" 'this is a first test of']" | |
] | |
}, | |
"metadata": {}, | |
"execution_count": 12 | |
} | |
] | |
} | |
] | |
} |
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