mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-03 12:50:06 +06:00
9fe82793ee
* fix E721 warnings * config.hidden_size is not a tuple * fix copies * fix-copies * not a tuple * undo * undo
646 lines
27 KiB
Plaintext
646 lines
27 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Saving PruneBERT\n",
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"\n",
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"\n",
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"This notebook aims at showcasing how we can leverage standard tools to save (and load) an extremely sparse model fine-pruned with [movement pruning](https://arxiv.org/abs/2005.07683) (or any other unstructured pruning mehtod).\n",
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"\n",
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"In this example, we used BERT (base-uncased, but the procedure described here is not specific to BERT and can be applied to a large variety of models.\n",
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"\n",
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"We first obtain an extremely sparse model by fine-pruning with movement pruning on SQuAD v1.1. We then used the following combination of standard tools:\n",
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"- We reduce the precision of the model with Int8 dynamic quantization using [PyTorch implementation](https://pytorch.org/tutorials/intermediate/dynamic_quantization_bert_tutorial.html). We only quantized the Fully Connected Layers.\n",
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"- Sparse quantized matrices are converted into the [Compressed Sparse Row format](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html).\n",
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"- We use HDF5 with `gzip` compression to store the weights.\n",
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"\n",
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"We experiment with a question answering model with only 6% of total remaining weights in the encoder (previously obtained with movement pruning). **We are able to reduce the memory size of the encoder from 340MB (original dense BERT) to 11MB**, which fits on a [91' floppy disk](https://en.wikipedia.org/wiki/Floptical)!\n",
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"\n",
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"<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/00/Floptical_disk_21MB.jpg/440px-Floptical_disk_21MB.jpg\" width=\"200\">\n",
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"\n",
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"*Note: this notebook is compatible with `torch>=1.5.0` If you are using, `torch==1.4.0`, please refer to [this previous version of the notebook](https://github.com/huggingface/transformers/commit/b11386e158e86e62d4041eabd86d044cd1695737).*"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Includes\n",
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"\n",
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"import json\n",
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"import os\n",
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"from collections import OrderedDict\n",
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"\n",
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"import h5py\n",
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"import numpy as np\n",
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"import torch\n",
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"from scipy import sparse\n",
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"from torch import nn\n",
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"\n",
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"from transformers import BertForQuestionAnswering\n",
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"\n",
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"\n",
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"os.chdir(\"../../\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Saving"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Dynamic quantization induces little or no loss of performance while significantly reducing the memory footprint."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load fine-pruned model and quantize the model\n",
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"\n",
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"model = BertForQuestionAnswering.from_pretrained(\"huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad\")\n",
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"model.to(\"cpu\")\n",
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"\n",
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"quantized_model = torch.quantization.quantize_dynamic(\n",
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" model=model,\n",
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" qconfig_spec={\n",
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" nn.Linear: torch.quantization.default_dynamic_qconfig,\n",
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" },\n",
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" dtype=torch.qint8,\n",
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")\n",
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"# print(quantized_model)\n",
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"\n",
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"qtz_st = quantized_model.state_dict()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Saving the original (encoder + classifier) in the standard torch.save format\n",
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"\n",
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"dense_st = {\n",
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" name: param for name, param in model.state_dict().items() if \"embedding\" not in name and \"pooler\" not in name\n",
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"}\n",
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"torch.save(\n",
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" dense_st,\n",
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" \"dbg/dense_squad.pt\",\n",
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")\n",
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"dense_mb_size = os.path.getsize(\"dbg/dense_squad.pt\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Decompose quantization for bert.encoder.layer.0.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.0.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.0.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.0.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.0.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.0.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.1.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.1.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.1.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.1.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.1.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.1.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.2.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.2.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.2.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.2.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.2.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.2.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.3.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.3.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.3.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.3.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.3.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.3.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.4.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.4.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.4.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.4.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.4.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.4.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.5.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.5.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.5.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.5.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.5.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.5.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.6.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.6.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.6.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.6.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.6.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.6.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.7.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.7.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.7.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.7.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.7.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.7.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.8.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.8.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.8.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.8.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.8.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.8.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.9.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.9.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.9.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.9.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.9.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.9.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.10.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.10.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.10.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.10.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.10.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.10.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.11.attention.self.query._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.11.attention.self.key._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.11.attention.self.value._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.11.attention.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.11.intermediate.dense._packed_params.weight\n",
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"Decompose quantization for bert.encoder.layer.11.output.dense._packed_params.weight\n",
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"Decompose quantization for bert.pooler.dense._packed_params.weight\n",
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"Decompose quantization for qa_outputs._packed_params.weight\n"
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]
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}
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],
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"source": [
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"# Elementary representation: we decompose the quantized tensors into (scale, zero_point, int_repr).\n",
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"# See https://pytorch.org/docs/stable/quantization.html\n",
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"\n",
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"# We further leverage the fact that int_repr is sparse matrix to optimize the storage: we decompose int_repr into\n",
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"# its CSR representation (data, indptr, indices).\n",
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"\n",
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"elementary_qtz_st = {}\n",
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"for name, param in qtz_st.items():\n",
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" if \"dtype\" not in name and param.is_quantized:\n",
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" print(\"Decompose quantization for\", name)\n",
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" # We need to extract the scale, the zero_point and the int_repr for the quantized tensor and modules\n",
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" scale = param.q_scale() # torch.tensor(1,) - float32\n",
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" zero_point = param.q_zero_point() # torch.tensor(1,) - int32\n",
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" elementary_qtz_st[f\"{name}.scale\"] = scale\n",
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" elementary_qtz_st[f\"{name}.zero_point\"] = zero_point\n",
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"\n",
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" # We assume the int_repr is sparse and compute its CSR representation\n",
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" # Only the FCs in the encoder are actually sparse\n",
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" int_repr = param.int_repr() # torch.tensor(nb_rows, nb_columns) - int8\n",
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" int_repr_cs = sparse.csr_matrix(int_repr) # scipy.sparse.csr.csr_matrix\n",
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"\n",
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" elementary_qtz_st[f\"{name}.int_repr.data\"] = int_repr_cs.data # np.array int8\n",
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" elementary_qtz_st[f\"{name}.int_repr.indptr\"] = int_repr_cs.indptr # np.array int32\n",
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" assert max(int_repr_cs.indices) < 65535 # If not, we shall fall back to int32\n",
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" elementary_qtz_st[f\"{name}.int_repr.indices\"] = np.uint16(int_repr_cs.indices) # np.array uint16\n",
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" elementary_qtz_st[f\"{name}.int_repr.shape\"] = int_repr_cs.shape # tuple(int, int)\n",
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" else:\n",
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" elementary_qtz_st[name] = param"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create mapping from torch.dtype to string description (we could also used an int8 instead of string)\n",
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"str_2_dtype = {\"qint8\": torch.qint8}\n",
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"dtype_2_str = {torch.qint8: \"qint8\"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Encoder Size (MB) - Sparse & Quantized - `torch.save`: 21.29\n"
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]
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}
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],
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"source": [
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"# Saving the pruned (encoder + classifier) in the standard torch.save format\n",
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"\n",
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"dense_optimized_st = {\n",
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" name: param for name, param in elementary_qtz_st.items() if \"embedding\" not in name and \"pooler\" not in name\n",
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"}\n",
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"torch.save(\n",
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" dense_optimized_st,\n",
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" \"dbg/dense_squad_optimized.pt\",\n",
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")\n",
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"print(\n",
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" \"Encoder Size (MB) - Sparse & Quantized - `torch.save`:\",\n",
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" round(os.path.getsize(\"dbg/dense_squad_optimized.pt\") / 1e6, 2),\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Skip bert.embeddings.word_embeddings.weight\n",
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"Skip bert.embeddings.position_embeddings.weight\n",
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"Skip bert.embeddings.token_type_embeddings.weight\n",
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"Skip bert.embeddings.LayerNorm.weight\n",
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"Skip bert.embeddings.LayerNorm.bias\n",
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"Skip bert.pooler.dense.scale\n",
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"Skip bert.pooler.dense.zero_point\n",
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"Skip bert.pooler.dense._packed_params.weight.scale\n",
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"Skip bert.pooler.dense._packed_params.weight.zero_point\n",
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"Skip bert.pooler.dense._packed_params.weight.int_repr.data\n",
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"Skip bert.pooler.dense._packed_params.weight.int_repr.indptr\n",
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"Skip bert.pooler.dense._packed_params.weight.int_repr.indices\n",
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"Skip bert.pooler.dense._packed_params.weight.int_repr.shape\n",
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"Skip bert.pooler.dense._packed_params.bias\n",
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"Skip bert.pooler.dense._packed_params.dtype\n",
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"\n",
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"Encoder Size (MB) - Dense: 340.26\n",
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"Encoder Size (MB) - Sparse & Quantized: 11.28\n"
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]
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}
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],
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"source": [
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"# Save the decomposed state_dict with an HDF5 file\n",
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"# Saving only the encoder + QA Head\n",
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"\n",
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"with h5py.File(\"dbg/squad_sparse.h5\", \"w\") as hf:\n",
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" for name, param in elementary_qtz_st.items():\n",
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" if \"embedding\" in name:\n",
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" print(f\"Skip {name}\")\n",
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" continue\n",
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"\n",
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" if \"pooler\" in name:\n",
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" print(f\"Skip {name}\")\n",
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" continue\n",
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"\n",
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" if isinstance(param, torch.Tensor):\n",
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" if param.numel() == 1:\n",
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" # module scale\n",
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" # module zero_point\n",
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" hf.attrs[name] = param\n",
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" continue\n",
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"\n",
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" if param.requires_grad:\n",
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" # LayerNorm\n",
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" param = param.detach().numpy()\n",
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" hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
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"\n",
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" elif isinstance(param, (float, int, tuple)):\n",
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" # float - tensor _packed_params.weight.scale\n",
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" # int - tensor _packed_params.weight.zero_point\n",
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" # tuple - tensor _packed_params.weight.shape\n",
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" hf.attrs[name] = param\n",
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"\n",
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" elif isinstance(param, torch.dtype):\n",
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" # dtype - tensor _packed_params.dtype\n",
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" hf.attrs[name] = dtype_2_str[param]\n",
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"\n",
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" else:\n",
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" hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
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"\n",
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"\n",
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"with open(\"dbg/metadata.json\", \"w\") as f:\n",
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" f.write(json.dumps(qtz_st._metadata))\n",
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"\n",
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"size = os.path.getsize(\"dbg/squad_sparse.h5\") + os.path.getsize(\"dbg/metadata.json\")\n",
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"print(\"\")\n",
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"print(\"Encoder Size (MB) - Dense: \", round(dense_mb_size / 1e6, 2))\n",
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"print(\"Encoder Size (MB) - Sparse & Quantized:\", round(size / 1e6, 2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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|
"text": [
|
|
"\n",
|
|
"Size (MB): 99.41\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Save the decomposed state_dict to HDF5 storage\n",
|
|
"# Save everything in the architecutre (embedding + encoder + QA Head)\n",
|
|
"\n",
|
|
"with h5py.File(\"dbg/squad_sparse_with_embs.h5\", \"w\") as hf:\n",
|
|
" for name, param in elementary_qtz_st.items():\n",
|
|
" # if \"embedding\" in name:\n",
|
|
" # print(f\"Skip {name}\")\n",
|
|
" # continue\n",
|
|
"\n",
|
|
" # if \"pooler\" in name:\n",
|
|
" # print(f\"Skip {name}\")\n",
|
|
" # continue\n",
|
|
"\n",
|
|
" if isinstance(param, torch.Tensor):\n",
|
|
" if param.numel() == 1:\n",
|
|
" # module scale\n",
|
|
" # module zero_point\n",
|
|
" hf.attrs[name] = param\n",
|
|
" continue\n",
|
|
"\n",
|
|
" if param.requires_grad:\n",
|
|
" # LayerNorm\n",
|
|
" param = param.detach().numpy()\n",
|
|
" hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
|
|
"\n",
|
|
" elif isinstance(param, (float, int, tuple)):\n",
|
|
" # float - tensor _packed_params.weight.scale\n",
|
|
" # int - tensor _packed_params.weight.zero_point\n",
|
|
" # tuple - tensor _packed_params.weight.shape\n",
|
|
" hf.attrs[name] = param\n",
|
|
"\n",
|
|
" elif isinstance(param, torch.dtype):\n",
|
|
" # dtype - tensor _packed_params.dtype\n",
|
|
" hf.attrs[name] = dtype_2_str[param]\n",
|
|
"\n",
|
|
" else:\n",
|
|
" hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
|
|
"\n",
|
|
"\n",
|
|
"with open(\"dbg/metadata.json\", \"w\") as f:\n",
|
|
" f.write(json.dumps(qtz_st._metadata))\n",
|
|
"\n",
|
|
"size = os.path.getsize(\"dbg/squad_sparse_with_embs.h5\") + os.path.getsize(\"dbg/metadata.json\")\n",
|
|
"print(\"\\nSize (MB):\", round(size / 1e6, 2))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Loading"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Reconstruct the elementary state dict\n",
|
|
"\n",
|
|
"reconstructed_elementary_qtz_st = {}\n",
|
|
"\n",
|
|
"hf = h5py.File(\"dbg/squad_sparse_with_embs.h5\", \"r\")\n",
|
|
"\n",
|
|
"for attr_name, attr_param in hf.attrs.items():\n",
|
|
" if \"shape\" in attr_name:\n",
|
|
" attr_param = tuple(attr_param)\n",
|
|
" elif \".scale\" in attr_name:\n",
|
|
" if \"_packed_params\" in attr_name:\n",
|
|
" attr_param = float(attr_param)\n",
|
|
" else:\n",
|
|
" attr_param = torch.tensor(attr_param)\n",
|
|
" elif \".zero_point\" in attr_name:\n",
|
|
" if \"_packed_params\" in attr_name:\n",
|
|
" attr_param = int(attr_param)\n",
|
|
" else:\n",
|
|
" attr_param = torch.tensor(attr_param)\n",
|
|
" elif \".dtype\" in attr_name:\n",
|
|
" attr_param = str_2_dtype[attr_param]\n",
|
|
" reconstructed_elementary_qtz_st[attr_name] = attr_param\n",
|
|
" # print(f\"Unpack {attr_name}\")\n",
|
|
"\n",
|
|
"# Get the tensors/arrays\n",
|
|
"for data_name, data_param in hf.items():\n",
|
|
" if \"LayerNorm\" in data_name or \"_packed_params.bias\" in data_name:\n",
|
|
" reconstructed_elementary_qtz_st[data_name] = torch.from_numpy(np.array(data_param))\n",
|
|
" elif \"embedding\" in data_name:\n",
|
|
" reconstructed_elementary_qtz_st[data_name] = torch.from_numpy(np.array(data_param))\n",
|
|
" else: # _packed_params.weight.int_repr.data, _packed_params.weight.int_repr.indices and _packed_params.weight.int_repr.indptr\n",
|
|
" data_param = np.array(data_param)\n",
|
|
" if \"indices\" in data_name:\n",
|
|
" data_param = np.array(data_param, dtype=np.int32)\n",
|
|
" reconstructed_elementary_qtz_st[data_name] = data_param\n",
|
|
" # print(f\"Unpack {data_name}\")\n",
|
|
"\n",
|
|
"\n",
|
|
"hf.close()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Sanity checks\n",
|
|
"\n",
|
|
"for name, param in reconstructed_elementary_qtz_st.items():\n",
|
|
" assert name in elementary_qtz_st\n",
|
|
"for name, param in elementary_qtz_st.items():\n",
|
|
" assert name in reconstructed_elementary_qtz_st, name\n",
|
|
"\n",
|
|
"for name, param in reconstructed_elementary_qtz_st.items():\n",
|
|
" assert isinstance(param, type(elementary_qtz_st[name])), name\n",
|
|
" if isinstance(param, torch.Tensor):\n",
|
|
" assert torch.all(torch.eq(param, elementary_qtz_st[name])), name\n",
|
|
" elif isinstance(param, np.ndarray):\n",
|
|
" assert (param == elementary_qtz_st[name]).all(), name\n",
|
|
" else:\n",
|
|
" assert param == elementary_qtz_st[name], name"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Re-assemble the sparse int_repr from the CSR format\n",
|
|
"\n",
|
|
"reconstructed_qtz_st = {}\n",
|
|
"\n",
|
|
"for name, param in reconstructed_elementary_qtz_st.items():\n",
|
|
" if \"weight.int_repr.indptr\" in name:\n",
|
|
" prefix_ = name[:-16]\n",
|
|
" data = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.data\"]\n",
|
|
" indptr = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.indptr\"]\n",
|
|
" indices = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.indices\"]\n",
|
|
" shape = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.shape\"]\n",
|
|
"\n",
|
|
" int_repr = sparse.csr_matrix(arg1=(data, indices, indptr), shape=shape)\n",
|
|
" int_repr = torch.tensor(int_repr.todense())\n",
|
|
"\n",
|
|
" scale = reconstructed_elementary_qtz_st[f\"{prefix_}.scale\"]\n",
|
|
" zero_point = reconstructed_elementary_qtz_st[f\"{prefix_}.zero_point\"]\n",
|
|
" weight = torch._make_per_tensor_quantized_tensor(int_repr, scale, zero_point)\n",
|
|
"\n",
|
|
" reconstructed_qtz_st[f\"{prefix_}\"] = weight\n",
|
|
" elif (\n",
|
|
" \"int_repr.data\" in name\n",
|
|
" or \"int_repr.shape\" in name\n",
|
|
" or \"int_repr.indices\" in name\n",
|
|
" or \"weight.scale\" in name\n",
|
|
" or \"weight.zero_point\" in name\n",
|
|
" ):\n",
|
|
" continue\n",
|
|
" else:\n",
|
|
" reconstructed_qtz_st[name] = param"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Sanity checks\n",
|
|
"\n",
|
|
"for name, param in reconstructed_qtz_st.items():\n",
|
|
" assert name in qtz_st\n",
|
|
"for name, param in qtz_st.items():\n",
|
|
" assert name in reconstructed_qtz_st, name\n",
|
|
"\n",
|
|
"for name, param in reconstructed_qtz_st.items():\n",
|
|
" assert isinstance(param, type(qtz_st[name])), name\n",
|
|
" if isinstance(param, torch.Tensor):\n",
|
|
" assert torch.all(torch.eq(param, qtz_st[name])), name\n",
|
|
" elif isinstance(param, np.ndarray):\n",
|
|
" assert (param == qtz_st[name]).all(), name\n",
|
|
" else:\n",
|
|
" assert param == qtz_st[name], name"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Sanity checks"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<All keys matched successfully>"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Load the re-constructed state dict into a model\n",
|
|
"\n",
|
|
"dummy_model = BertForQuestionAnswering.from_pretrained(\"bert-base-uncased\")\n",
|
|
"dummy_model.to(\"cpu\")\n",
|
|
"\n",
|
|
"reconstructed_qtz_model = torch.quantization.quantize_dynamic(\n",
|
|
" model=dummy_model,\n",
|
|
" qconfig_spec=None,\n",
|
|
" dtype=torch.qint8,\n",
|
|
")\n",
|
|
"\n",
|
|
"reconstructed_qtz_st = OrderedDict(reconstructed_qtz_st)\n",
|
|
"with open(\"dbg/metadata.json\", \"r\") as read_file:\n",
|
|
" metadata = json.loads(read_file.read())\n",
|
|
"reconstructed_qtz_st._metadata = metadata\n",
|
|
"\n",
|
|
"reconstructed_qtz_model.load_state_dict(reconstructed_qtz_st)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sanity check passed\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Sanity checks on the infernce\n",
|
|
"\n",
|
|
"N = 32\n",
|
|
"\n",
|
|
"for _ in range(25):\n",
|
|
" inputs = torch.randint(low=0, high=30000, size=(N, 128))\n",
|
|
" mask = torch.ones(size=(N, 128))\n",
|
|
"\n",
|
|
" y_reconstructed = reconstructed_qtz_model(input_ids=inputs, attention_mask=mask)[0]\n",
|
|
" y = quantized_model(input_ids=inputs, attention_mask=mask)[0]\n",
|
|
"\n",
|
|
" assert torch.all(torch.eq(y, y_reconstructed))\n",
|
|
"print(\"Sanity check passed\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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": "3.6.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|