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Created April 29, 2024 12:16
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Batch-gpt2.ipynb
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"import torch\n",
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"\n",
"tokenizer = AutoTokenizer.from_pretrained('gpt2')\n",
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{
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"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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"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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