(prototype) FX Graph Mode Post Training Dynamic Quantization

(prototype) FX Graph Mode Post Training Dynamic Quantization#

This tutorial introduces the steps to do post training dynamic quantization in graph mode based on torch.fx. We have a separate tutorial for FX Graph Mode Post Training Static Quantization, comparison between FX Graph Mode Quantization and Eager Mode Quantization can be found in the quantization docs

tldr; The FX Graph Mode API for dynamic quantization looks like the following:

… code:: python

import torch
from torch.ao.quantization import default_dynamic_qconfig, QConfigMapping
# Note that this is temporary, we'll expose these functions to torch.ao.quantization after official releasee
from torch.quantization.quantize_fx import prepare_fx, convert_fx

float_model.eval()
# The old 'fbgemm' is still available but 'x86' is the recommended default.
qconfig = get_default_qconfig("x86")
qconfig_mapping = QConfigMapping().set_global(qconfig)
prepared_model = prepare_fx(float_model, qconfig_mapping, example_inputs)  # fuse modules and insert observers
# no calibration is required for dynamic quantization
quantized_model = convert_fx(prepared_model)  # convert the model to a dynamically quantized model

In this tutorial, we’ll apply dynamic quantization to an LSTM-based next word-prediction model, closely following the word language model from the PyTorch examples. We will copy the code from Dynamic Quantization on an LSTM Word Language Model and omit the descriptions.

1. Define the Model, Download Data and Model#

Download the data and unzip to data folder

… code::

mkdir data
cd data
wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip
unzip wikitext-2-v1.zip

Download model to the data folder:

… code::

wget https://s3.amazonaws.com/pytorch-tutorial-assets/word_language_model_quantize.pth

Define the model:

# imports
import os
from io import open
import time
import copy

import torch
import torch.nn as nn
import torch.nn.functional as F

# Model Definition
class LSTMModel(nn.Module):
    """Container module with an encoder, a recurrent module, and a decoder."""

    def __init__(self, ntoken, ninp, nhid, nlayers, dropout=0.5):
        super(LSTMModel, self).__init__()
        self.drop = nn.Dropout(dropout)
        self.encoder = nn.Embedding(ntoken, ninp)
        self.rnn = nn.LSTM(ninp, nhid, nlayers, dropout=dropout)
        self.decoder = nn.Linear(nhid, ntoken)

        self.init_weights()

        self.nhid = nhid
        self.nlayers = nlayers

    def init_weights(self):
        initrange = 0.1
        self.encoder.weight.data.uniform_(-initrange, initrange)
        self.decoder.bias.data.zero_()
        self.decoder.weight.data.uniform_(-initrange, initrange)

    def forward(self, input, hidden):
        emb = self.drop(self.encoder(input))
        output, hidden = self.rnn(emb, hidden)
        output = self.drop(output)
        decoded = self.decoder(output)
        return decoded, hidden


def init_hidden(lstm_model, bsz):
    # get the weight tensor and create hidden layer in the same device
    weight = lstm_model.encoder.weight
    # get weight from quantized model
    if not isinstance(weight, torch.Tensor):
        weight = weight()
    device = weight.device
    nlayers = lstm_model.rnn.num_layers
    nhid = lstm_model.rnn.hidden_size
    return (torch.zeros(nlayers, bsz, nhid, device=device),
            torch.zeros(nlayers, bsz, nhid, device=device))


# Load Text Data
class Dictionary(object):
    def __init__(self):
        self.word2idx = {}
        self.idx2word = []

    def add_word(self, word):
        if word not in self.word2idx:
            self.idx2word.append(word)
            self.word2idx[word] = len(self.idx2word) - 1
        return self.word2idx[word]

    def __len__(self):
        return len(self.idx2word)


class Corpus(object):
    def __init__(self, path):
        self.dictionary = Dictionary()
        self.train = self.tokenize(os.path.join(path, 'wiki.train.tokens'))
        self.valid = self.tokenize(os.path.join(path, 'wiki.valid.tokens'))
        self.test = self.tokenize(os.path.join(path, 'wiki.test.tokens'))

    def tokenize(self, path):
        """Tokenizes a text file."""
        assert os.path.exists(path)
        # Add words to the dictionary
        with open(path, 'r', encoding="utf8") as f:
            for line in f:
                words = line.split() + ['<eos>']
                for word in words:
                    self.dictionary.add_word(word)

        # Tokenize file content
        with open(path, 'r', encoding="utf8") as f:
            idss = []
            for line in f:
                words = line.split() + ['<eos>']
                ids = []
                for word in words:
                    ids.append(self.dictionary.word2idx[word])
                idss.append(torch.tensor(ids).type(torch.int64))
            ids = torch.cat(idss)

        return ids

model_data_filepath = 'data/'

corpus = Corpus(model_data_filepath + 'wikitext-2')

ntokens = len(corpus.dictionary)

# Load Pretrained Model
model = LSTMModel(
    ntoken = ntokens,
    ninp = 512,
    nhid = 256,
    nlayers = 5,
)

model.load_state_dict(
    torch.load(
        model_data_filepath + 'word_language_model_quantize.pth',
        map_location=torch.device('cpu')
        )
    )

model.eval()
print(model)

bptt = 25
criterion = nn.CrossEntropyLoss()
eval_batch_size = 1

# create test data set
def batchify(data, bsz):
    # Work out how cleanly we can divide the dataset into bsz parts.
    nbatch = data.size(0) // bsz
    # Trim off any extra elements that wouldn't cleanly fit (remainders).
    data = data.narrow(0, 0, nbatch * bsz)
    # Evenly divide the data across the bsz batches.
    return data.view(bsz, -1).t().contiguous()

test_data = batchify(corpus.test, eval_batch_size)
example_inputs = (next(iter(test_data))[0])

# Evaluation functions
def get_batch(source, i):
    seq_len = min(bptt, len(source) - 1 - i)
    data = source[i:i+seq_len]
    target = source[i+1:i+1+seq_len].reshape(-1)
    return data, target

def repackage_hidden(h):
  """Wraps hidden states in new Tensors, to detach them from their history."""

  if isinstance(h, torch.Tensor):
      return h.detach()
  else:
      return tuple(repackage_hidden(v) for v in h)

def evaluate(model_, data_source):
    # Turn on evaluation mode which disables dropout.
    model_.eval()
    total_loss = 0.
    hidden = init_hidden(model_, eval_batch_size)
    with torch.no_grad():
        for i in range(0, data_source.size(0) - 1, bptt):
            data, targets = get_batch(data_source, i)
            output, hidden = model_(data, hidden)
            hidden = repackage_hidden(hidden)
            output_flat = output.view(-1, ntokens)
            total_loss += len(data) * criterion(output_flat, targets).item()
    return total_loss / (len(data_source) - 1)

2. Post Training Dynamic Quantization#

Now we can dynamically quantize the model. We can use the same function as post training static quantization but with a dynamic qconfig.

from torch.quantization.quantize_fx import prepare_fx, convert_fx
from torch.ao.quantization import default_dynamic_qconfig, float_qparams_weight_only_qconfig, QConfigMapping

# Full docs for supported qconfig for floating point modules/ops can be found in `quantization docs <https://pytorch.org/docs/stable/quantization.html#module-torch.quantization>`_
# Full docs for `QConfigMapping <https://pytorch.org/docs/stable/generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html#torch.ao.quantization.qconfig_mapping.QConfigMapping>`_
qconfig_mapping = (QConfigMapping()
    .set_object_type(nn.Embedding, float_qparams_weight_only_qconfig)
    .set_object_type(nn.LSTM, default_dynamic_qconfig)
    .set_object_type(nn.Linear, default_dynamic_qconfig)
)
# Deepcopying the original model because quantization api changes the model inplace and we want
# to keep the original model for future comparison
model_to_quantize = copy.deepcopy(model)
prepared_model = prepare_fx(model_to_quantize, qconfig_mapping, example_inputs)
print("prepared model:", prepared_model)
quantized_model = convert_fx(prepared_model)
print("quantized model", quantized_model)

For dynamically quantized objects, we didn’t do anything in prepare_fx for modules, but will insert observers for weight for dynamically quantizable forunctionals and torch ops. We also fuse the modules like Conv + Bn, Linear + ReLU.

In convert we’ll convert the float modules to dynamically quantized modules and convert float ops to dynamically quantized ops. We can see in the example model, nn.Embedding, nn.Linear and nn.LSTM are dynamically quantized.

Now we can compare the size and runtime of the quantized model.

def print_size_of_model(model):
    torch.save(model.state_dict(), "temp.p")
    print('Size (MB):', os.path.getsize("temp.p")/1e6)
    os.remove('temp.p')

print_size_of_model(model)
print_size_of_model(quantized_model)

There is a 4x size reduction because we quantized all the weights in the model (nn.Embedding, nn.Linear and nn.LSTM) from float (4 bytes) to quantized int (1 byte).

torch.set_num_threads(1)

def time_model_evaluation(model, test_data):
    s = time.time()
    loss = evaluate(model, test_data)
    elapsed = time.time() - s
    print('''loss: {0:.3f}\nelapsed time (seconds): {1:.1f}'''.format(loss, elapsed))

time_model_evaluation(model, test_data)
time_model_evaluation(quantized_model, test_data)

There is a roughly 2x speedup for this model. Also note that the speedup may vary depending on model, device, build, input batch sizes, threading etc.

3. Conclusion#

This tutorial introduces the api for post training dynamic quantization in FX Graph Mode, which dynamically quantizes the same modules as Eager Mode Quantization.

(prototype) FX Graph Mode Post Training Dynamic Quantization

目录

(prototype) FX Graph Mode Post Training Dynamic Quantization#

1. Define the Model, Download Data and Model#

2. Post Training Dynamic Quantization#

3. Conclusion#