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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pytorch RoBERTa to ONNX"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook documents how to export the PyTorch NLP model into ONNX format and then use it to make predictions using the ONNX runtime.\n",
"\n",
"The model uses the `simpletransformers` library which is a Python wrappers around the `transformers` library which contains PyTorch NLP transformer architectures and weights."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import numpy as np\n",
"from simpletransformers.model import TransformerModel\n",
"from transformers import RobertaForSequenceClassification, RobertaTokenizer\n",
"import onnx\n",
"import onnxruntime"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Load pretrained PyTorch model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download the model weights from https://storage.googleapis.com/seldon-models/pytorch/moviesentiment_roberta/pytorch_model.bin"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"model = TransformerModel('roberta', 'roberta-base', args=({'fp16': False}))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.model.load_state_dict(torch.load('pytorch_model.bin'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Export as ONNX"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PyTorch supports exporting to ONNX, you just need to specify a valid input tensor for the model."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n",
"input_ids = torch.tensor(tokenizer.encode(\"This film is so bad\", add_special_tokens=True)).unsqueeze(0) # Batch size 1"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 0, 713, 822, 16, 98, 1099, 2]])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"input_ids"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Export as ONNX, we specify dynamic axes for batch dimension and sequence length as sentences come in various lengths."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/janis/.conda/envs/py37/lib/python3.7/site-packages/transformers/modeling_roberta.py:172: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if input_ids[:, 0].sum().item() != 0:\n"
]
}
],
"source": [
"torch.onnx.export(model.model,\n",
" (input_ids),\n",
" \"roberta.onnx\",\n",
" input_names=['input'],\n",
" output_names=['output'],\n",
" dynamic_axes={'input' :{0 : 'batch_size',\n",
" 1: 'sentence_length'},\n",
" 'output': {0: 'batch_size'}})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3: Test predictions are the same using ONNX runtime"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"onnx_model = onnx.load(\"roberta.onnx\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# checks the exported model, may crash ipython kernel if run together with the PyTorch model in memory\n",
"# onnx.checker.check_model(onnx_model)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import onnxruntime\n",
"\n",
"ort_session = onnxruntime.InferenceSession(\"roberta.onnx\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def to_numpy(tensor):\n",
" return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"input_ids = torch.tensor(tokenizer.encode(\"This film is so bad\", add_special_tokens=True)).unsqueeze(0) # Batch size 1"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# compute ONNX Runtime output prediction\n",
"ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(input_ids)}\n",
"ort_out = ort_session.run(None, ort_inputs)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"out = model.model(input_ids)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((tensor([[ 2.3067, -2.6440]], grad_fn=<AddmmBackward>),),\n",
" [array([[ 2.3066945, -2.6439788]], dtype=float32)])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out, ort_out"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"np.testing.assert_allclose(to_numpy(out[0]), ort_out[0], rtol=1e-03, atol=1e-05)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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