hybrid_parallel_qat.py

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import division
from __future__ import print_function

import os
import paddle
import numpy as np
import random
import paddle.distributed as dist
import paddle.fluid as fluid
import paddle.distributed.fleet as fleet
from paddle.io import DataLoader, Dataset
import unittest
import paddle.nn as nn
from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
from paddle.distributed.utils import find_free_ports, watch_local_trainers, get_cluster, TrainerProc


def set_random_seed(seed, dp_id, rank_id):
    """Set random seed for reproducability."""
    random.seed(seed)
    np.random.seed(seed + dp_id)
    paddle.seed(seed + rank_id)


vocab_size = 20
hidden_size = 10
inner_size = 8
output_size = 10
seq_length = 2
batch_size = 4


def get_attr(layer, name):
    if getattr(layer, name, None) is not None:
        return getattr(layer, name, None)
    else:
        return get_attr(layer._layer, name)


def get_gpus(selected_gpus):
    selected_gpus = [x.strip() for x in selected_gpus.split(',')]
    return selected_gpus


def get_cluster_from_args(selected_gpus):
    cluster_node_ips = '127.0.0.1'
    node_ip = '127.0.0.1'

    node_ips = [x.strip() for x in cluster_node_ips.split(',')]

    node_ips.index(node_ip)

    free_ports = None

    free_ports = find_free_ports(len(selected_gpus))
    if free_ports is not None:
        free_ports = list(free_ports)

    trainer_endpoints = []
    for ip in node_ips:
        trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports])
    return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus)


def parallel_matmul(lm_output, logit_weights, parallel_output):
    hcg = fleet.get_hybrid_communicate_group()
    model_parallel_group = hcg.get_model_parallel_group()
    world_size = hcg.get_model_parallel_world_size()
    rank = hcg.get_model_parallel_rank()

    if world_size > 1:
        input_parallel = paddle.distributed.collective._c_identity(
            lm_output, group=model_parallel_group)

        logits = paddle.matmul(input_parallel, logit_weights, transpose_y=True)

        if parallel_output:
            return logits

        return paddle.distributed.collective._c_concat(
            logits, group=model_parallel_group)
    else:
        logits = paddle.matmul(lm_output, logit_weights, transpose_y=True)
        return logits


class PACT(nn.Layer):

    def __init__(self, init_value=20):
        super(PACT, self).__init__()
        alpha_attr = paddle.ParamAttr(
            name=self.full_name() + ".pact",
            initializer=paddle.nn.initializer.Constant(value=init_value))
        self.alpha = self.create_parameter(shape=[1],
                                           attr=alpha_attr,
                                           dtype='float32')

    def forward(self, x):
        out_left = paddle.nn.functional.relu(x - self.alpha)
        out_right = paddle.nn.functional.relu(-self.alpha - x)
        x = x - out_left + out_right
        return x


class SimpleMPNet(nn.Layer):

    def __init__(self, vocab_size, hidden_size, inner_size, output_size, np_fc1,
                 np_fc2, mp_id):
        super(SimpleMPNet, self).__init__()

        if mp_id == 0:
            init_fc1_data = np_fc1[:, :(inner_size // 2)]
            init_fc2_data = np_fc2[:(inner_size // 2), :]
        else:
            init_fc1_data = np_fc1[:, (inner_size // 2):]
            init_fc2_data = np_fc2[(inner_size // 2):, :]

        self.linear1 = fleet.meta_parallel.ColumnParallelLinear(
            hidden_size,
            inner_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Assign(init_fc1_data)),
            gather_output=False,
            has_bias=True)

        self.linear2 = fleet.meta_parallel.RowParallelLinear(
            inner_size,
            hidden_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Assign(init_fc2_data)),
            input_is_parallel=True,
            has_bias=True)

        self.linear3 = paddle.nn.Linear(
            hidden_size,
            output_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)),
            bias_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)))

        self.embedding = fleet.meta_parallel.VocabParallelEmbedding(
            vocab_size,
            hidden_size,
            weight_attr=paddle.nn.initializer.Constant(value=1.))

    def forward(self, x):
        x = self.embedding(x)
        x = self.linear1(x)
        x = self.linear2(x)
        x = self.linear3(x)
        x = parallel_matmul(x, get_attr(self.embedding, "weight"), False)
        return x


class SimpleDPNet(nn.Layer):

    def __init__(self, vocab_size, hidden_size, inner_size, output_size, np_fc1,
                 np_fc2):

        super(SimpleDPNet, self).__init__()
        self.linear1 = paddle.nn.Linear(
            hidden_size,
            inner_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Assign(np_fc1)),
            bias_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)))

        self.linear2 = paddle.nn.Linear(
            inner_size,
            hidden_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Assign(np_fc2)),
            bias_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)))

        self.linear3 = paddle.nn.Linear(
            hidden_size,
            output_size,
            weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)),
            bias_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(0.0)))

        self.embedding = paddle.nn.Embedding(
            vocab_size,
            hidden_size,
            weight_attr=paddle.nn.initializer.Constant(value=1.))

    def forward(self, x):
        x = self.embedding(x)
        x = self.linear1(x)
        x = self.linear2(x)
        x = self.linear3(x)
        x = paddle.matmul(x,
                          get_attr(self.embedding, "weight"),
                          transpose_y=True)
        return x


class TestDistMPTraning(unittest.TestCase):

    def setUp(self):
        strategy = fleet.DistributedStrategy()
        self.model_parallel_size = 2
        self.data_parallel_size = 1
        strategy.hybrid_configs = {
            "dp_degree": self.data_parallel_size,
            "mp_degree": self.model_parallel_size,
            "pp_degree": 1
        }
        fleet.init(is_collective=True, strategy=strategy)
        self.onnx_format = False
        self.check_export_model_accuracy = True
        self.diff_threshold = 0.01
        self.fuse_conv_bn = False

    def train_batch(self, batch, model, optimizer, is_mp):
        output = model(batch)
        loss = output.mean()
        loss.backward()  # do backward
        optimizer.step()  # update parameters
        optimizer.clear_grad()
        return loss

    def build_optimizer(self, model):
        optimizer = paddle.optimizer.SGD(learning_rate=0.001,
                                         parameters=model.parameters())
        return optimizer

    def build_model_optimizer(self,
                              weight_quantize_type,
                              activation_quantize_type,
                              use_pact=False):
        hcg = fleet.get_hybrid_communicate_group()
        word_size = hcg.get_model_parallel_world_size()
        mp_id = hcg.get_model_parallel_rank()
        dp_id = hcg.get_data_parallel_rank()
        rank_id = dist.get_rank()
        imperative_qat = ImperativeQuantAware(
            weight_quantize_type=weight_quantize_type,
            activation_quantize_type=activation_quantize_type,
            fuse_conv_bn=self.fuse_conv_bn,
            act_preprocess_layer=PACT if use_pact else None)
        set_random_seed(1024, dp_id, rank_id)

        np_fc1 = np.ones((hidden_size, inner_size))
        np_fc2 = np.ones(
            (inner_size,
             hidden_size))  #np.random.random_sample((inner_size, hidden_size))

        model_a = SimpleMPNet(vocab_size, hidden_size, inner_size, output_size,
                              np_fc1, np_fc2, mp_id)
        model_a = imperative_qat.quantize(model_a)
        optimizer_a = self.build_optimizer(model_a)
        model_a = fleet.distributed_model(model_a)
        optimizer_a = fleet.distributed_optimizer(optimizer_a)

        model_b = SimpleDPNet(vocab_size, hidden_size, inner_size, output_size,
                              np_fc1, np_fc2)
        model_b = imperative_qat.quantize(model_b)
        optimizer_b = self.build_optimizer(model_b)

        return model_a, optimizer_a, model_b, optimizer_b

    def train(self, model_a, optimizer_a, model_b, optimizer_b):

        for epoch in range(5):

            np_data = np.random.randint(0, vocab_size, (
                batch_size,
                seq_length,
            ))
            batch = paddle.to_tensor(np_data)
            loss_a = self.train_batch(batch, model_a, optimizer_a, True)
            loss_b = self.train_batch(batch, model_b, optimizer_b, False)

            np.testing.assert_allclose(loss_a.numpy(),
                                       loss_b.numpy(),
                                       rtol=1e-6)

    def test_mp_model_1(self):
        if not fluid.core.is_compiled_with_cuda(
        ) or fluid.core.get_cuda_device_count() == 0:
            return
        selected_gpus = get_gpus('0,1')
        cluster = None
        pod = None

        model_a, optimizer_a, model_b, optimizer_b = self.build_model_optimizer(
            weight_quantize_type='abs_max',
            activation_quantize_type='moving_average_abs_max')
        self.train(model_a, optimizer_a, model_b, optimizer_b)

    def test_mp_model_2(self):
        if not fluid.core.is_compiled_with_cuda(
        ) or fluid.core.get_cuda_device_count() == 0:
            return
        selected_gpus = get_gpus('0,1')
        cluster = None
        pod = None

        model_a, optimizer_a, model_b, optimizer_b = self.build_model_optimizer(
            weight_quantize_type='channel_wise_abs_max',
            activation_quantize_type='moving_average_abs_max',
            use_pact=True)
        self.train(model_a, optimizer_a, model_b, optimizer_b)


if __name__ == "__main__":
    unittest.main()