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78c57f10 · Louis Feng · 2fa8a678 · 78c57f10
Commit 78c57f10 authored Mar 07, 2018 by Louis Feng
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cpu_fusion.cpp src/ngraph/runtime/cpu/pass/cpu_fusion.cpp +551 -551

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--- a/src/ngraph/runtime/cpu/pass/cpu_fusion.cpp
+++ b/src/ngraph/runtime/cpu/pass/cpu_fusion.cpp
 /*******************************************************************************
 * Copyright 2017-2018 Intel Corporation
 *
 * 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.
 *******************************************************************************/
 #include "cpu_fusion.hpp"
 #include <algorithm>
 #include <iostream>
 #include <numeric>
 #include <unordered_set>
 #include "ngraph/graph_util.hpp"
 #include "ngraph/log.hpp"
 #include "ngraph/ops/add.hpp"
 #include "ngraph/ops/add.hpp"
 #include "ngraph/ops/batch_norm.hpp"
 #include "ngraph/ops/broadcast.hpp"
 #include "ngraph/ops/broadcast.hpp"
 #include "ngraph/ops/constant.hpp"
 #include "ngraph/ops/convolution.hpp"
 #include "ngraph/ops/divide.hpp"
 #include "ngraph/ops/dot.hpp"
 #include "ngraph/ops/multiply.hpp"
 #include "ngraph/ops/pad.hpp"
 #include "ngraph/ops/parameter.hpp"
 #include "ngraph/ops/reshape.hpp"
 #include "ngraph/ops/sqrt.hpp"
 #include "ngraph/ops/subtract.hpp"
 #include "ngraph/ops/sum.hpp"
 #include "ngraph/pattern/matcher.hpp"
 #include "ngraph/pattern/op/any.hpp"
 #include "ngraph/pattern/op/label.hpp"
 #include "ngraph/runtime/cpu/ops/conv_bias.hpp"
 #include "ngraph/runtime/cpu/ops/matmul_bias.hpp"
 static bool init_cblas_arg(std::shared_ptr<ngraph::Node> reshape,
                           std::shared_ptr<ngraph::Node> arg,
                           bool& transpose_w,
                           ngraph::Shape& shape_w)
 {
    auto r_w = std::dynamic_pointer_cast<ngraph::op::Reshape>(reshape);
    if (!r_w)
    {
        if (arg->get_shape().size() != 2)
        {
            NGRAPH_DEBUG << arg->get_name() << " 's rank != 2 "
                         << ngraph::vector_to_string(arg->get_shape());
            return false;
        }
        return true; //nth to do; reshape isn't a reshape
    }
    if (r_w->get_shape().size() != 2)
    {
        NGRAPH_DEBUG << "Reshape for " << reshape->get_name() << " doesn't reshape into matrix"
                     << ngraph::vector_to_string(r_w->get_shape());
        return false;
    }
    auto io = r_w->get_input_order();
    if (r_w->get_shape().size() != arg->get_shape().size()) //reshape
    {
        ngraph::AxisVector dio(io.size());
        std::iota(begin(dio), end(dio), 0);
        if (io != dio) //we can't reshape and transpose at the same time
        {
            NGRAPH_DEBUG << "Reshape for " << reshape->get_name() << " is not in default order "
                         << ngraph::vector_to_string(io);
            NGRAPH_DEBUG << "r_w shape = " << ngraph::vector_to_string(r_w->get_shape());
            NGRAPH_DEBUG << "arg shape = " << ngraph::vector_to_string(arg->get_shape());
            return false;
        }
        shape_w = r_w->get_shape();
    }
    else
    {
        if (io == ngraph::AxisVector{1, 0})
        {
            transpose_w = true;
        }
        //otherwise no-op reshape
    }
    return true;
 }
 template <typename T>
 static std::vector<T> apply_permutation(std::vector<T> input, ngraph::AxisVector order)
 {
    if (input.size() != order.size())
    {
        throw "input and order sizes don't match!";
    }
    std::vector<T> output(input.size());
    for (size_t i = 0; i < order.size(); i++)
    {
        output[i] = input.at(order.at(i));
    }
    return output;
 }
 void ngraph::runtime::cpu::pass::CPUFusion::construct_matmulbias_pattern()
 {
    Shape shape_w{2, 4};
    Shape shape_x{4, 1};
    Shape shape_b{1};
    auto W = std::make_shared<pattern::op::Label>(element::f32, shape_w);
    auto x = std::make_shared<pattern::op::Label>(element::f32, shape_x);
    auto b = std::make_shared<pattern::op::Label>(element::f32, shape_b);
    auto pmmb = std::make_shared<op::MatmulBias>(
        W, x, nullptr, W->get_shape(), x->get_shape(), false, false);
    auto pbroadcast = std::make_shared<op::Broadcast>(b, pmmb->get_shape(), AxisSet{0});
    auto padd = pmmb + pbroadcast;
    ngraph::pattern::gr_callback_fn callback = [W, x](pattern::Matcher& m) {
        NGRAPH_DEBUG << "In callback for construct_matmulbias_pattern against node = "
                     << m.match_root()->get_name();
        auto mpattern = m.match_root(); //add
        auto m_matmul = mpattern->get_input_op(0);
        auto m_broadcast = mpattern->get_input_op(1);
        auto pattern_map = m.get_pattern_map();
        return m_matmul->copy_with_new_args(
            NodeVector{pattern_map[W], pattern_map[x], m_broadcast});
    };
    auto m = std::make_shared<ngraph::pattern::Matcher>(padd, callback);
    this->add_matcher(m);
 }
 void ngraph::runtime::cpu::pass::CPUFusion::construct_matmul_pattern()
 {
    Shape shape_w{2, 4};
    Shape shape_x{4, 1};
    Shape shape_b{1};
    Shape shape_dot{2, 1};
    auto W = std::make_shared<pattern::op::Label>(element::f32, shape_w);
    auto x = std::make_shared<pattern::op::Label>(element::f32, shape_x);
    auto reshape_pred = [](std::shared_ptr<Node> n) {
        return static_cast<bool>(std::dynamic_pointer_cast<op::Reshape>(n));
    };
    auto skip_w = std::make_shared<pattern::op::Any>(W, reshape_pred);
    auto skip_x = std::make_shared<pattern::op::Any>(x, reshape_pred);
    auto pdot = std::make_shared<op::Dot>(skip_w, skip_x);
    ngraph::pattern::gr_callback_fn callback = [W, x](pattern::Matcher& m) {
        NGRAPH_DEBUG << "In callback for construct_matmul_pattern against node = "
                     << m.match_root()->get_name();
        auto pattern_map = m.get_pattern_map();
        std::shared_ptr<Node> nn;
        auto mpattern = m.match_root();
        auto dot = m.match_root();
        if (mpattern->get_element_type() != element::f32)
        {
            NGRAPH_DEBUG << "mpattern = " << mpattern->get_name() << " type is not float!";
            return nn;
        }
        if (dot->get_shape().size() != 2)
        {
            NGRAPH_DEBUG << "dot = " << dot->get_name() << " shape is not equal to 2!";
            return nn;
        }
        if (shape_size(dot->get_shape()) == 0)
        {
            NGRAPH_DEBUG << "dot has a zero dimension";
            return nn;
        }
        bool transpose_w = false;
        Shape shape_arg0{pattern_map[W]->get_shape()};
        if (!init_cblas_arg(dot->get_input_op(0), pattern_map[W], transpose_w, shape_arg0))
        {
            return nn;
        }
        bool transpose_x = false;
        Shape shape_arg1{pattern_map[x]->get_shape()};
        if (!init_cblas_arg(dot->get_input_op(1), pattern_map[x], transpose_x, shape_arg1))
        {
            return nn;
        }
        auto cg = std::shared_ptr<Node>(new op::MatmulBias(pattern_map[W],
                                                           pattern_map[x],
                                                           nullptr,
                                                           shape_arg0,
                                                           shape_arg1,
                                                           transpose_w,
                                                           transpose_x));
        return cg;
    };
    auto m = std::make_shared<ngraph::pattern::Matcher>(pdot, callback);
    this->add_matcher(m);
 }
 void ngraph::runtime::cpu::pass::CPUFusion::construct_fprop_bn()
 {
    // construct varaiance
    auto N = op::Constant::create(element::f32, Shape{3}, {2, 2, 2});
    auto input = std::make_shared<pattern::op::Label>(element::f32, Shape{2, 3});
    auto input_sq = std::make_shared<op::Multiply>(input, input);
    auto sum_input = std::make_shared<op::Sum>(input, AxisSet{0});
    auto square_sumed_input = std::make_shared<op::Multiply>(sum_input, sum_input);
    auto sum_squared_input = std::make_shared<op::Sum>(input_sq, AxisSet{0});
    auto avg_input_sum_sq = std::make_shared<op::Divide>(square_sumed_input, N);
    auto xmu = std::make_shared<op::Subtract>(sum_squared_input, avg_input_sum_sq);
    auto variance = std::make_shared<op::Divide>(xmu, N);
    auto variance_label =
        std::make_shared<pattern::op::Label>(variance, nullptr, NodeVector{variance});
    auto variance_with_broadcast =
        std::make_shared<op::Broadcast>(variance_label, Shape{2, 3}, AxisSet{0});
    // construct mean
    auto sum_input1 = std::make_shared<op::Sum>(input, AxisSet{0});
    auto mean = std::make_shared<op::Divide>(sum_input1, N);
    auto mean_label = std::make_shared<pattern::op::Label>(mean, nullptr, NodeVector{mean});
    auto mean_with_broadcast = std::make_shared<op::Broadcast>(mean_label, Shape{2, 3}, AxisSet{0});
    auto input_diff_mean = std::make_shared<op::Subtract>(input, mean_with_broadcast);
    // Eps
    auto eps_label = std::make_shared<pattern::op::Label>(element::f32, Shape{3});
    auto eps_with_broadcast = std::make_shared<op::Broadcast>(eps_label, Shape{2, 3}, AxisSet{0});
    auto add1 = std::make_shared<op::Add>(eps_with_broadcast, variance_with_broadcast);
    auto sqrt_variance_eps = std::make_shared<op::Sqrt>(add1);
    auto divide_mean_variance = std::make_shared<op::Divide>(input_diff_mean, sqrt_variance_eps);
    //Gamma
    auto gamma_label = std::make_shared<pattern::op::Label>(element::f32, Shape{3});
    auto gamma_with_broadcast =
        std::make_shared<op::Broadcast>(gamma_label, Shape{2, 3}, AxisSet{0});
    auto multiply_gamma =
        std::make_shared<op::Multiply>(gamma_with_broadcast, divide_mean_variance);
    //Beta
    auto beta_label = std::make_shared<pattern::op::Label>(element::f32, Shape{3});
    auto beta_with_broadcast = std::make_shared<op::Broadcast>(beta_label, Shape{2, 3}, AxisSet{0});
    auto add_beta = std::make_shared<op::Add>(beta_with_broadcast, multiply_gamma);
    // This completes fprop bn pattern
    //Define a call back that needs to called once the DFG matches the pattern
    ngraph::pattern::gr_callback_fn callback =
        [variance_label, mean_label, input, eps_label, gamma_label, beta_label](
            pattern::Matcher& m) {
            NGRAPH_DEBUG << "In a callback for construct_fprop_bn pattern against "
                         << m.match_root()->get_name();
            std::shared_ptr<Node> nn = nullptr;
            //TODO - add assert's based on the matched node
            auto pattern_map = m.get_pattern_map();
            NGRAPH_DEBUG << "Input: " << pattern_map[input]->get_name() << " "
                         << pattern_map[input]->get_shape().size();
            NGRAPH_DEBUG << "Variance: " << pattern_map[variance_label]->get_name() << " "
                         << pattern_map[variance_label]->get_shape().size();
            NGRAPH_DEBUG << "Mean: " << pattern_map[mean_label]->get_name() << " "
                         << pattern_map[mean_label]->get_shape().size();
            NGRAPH_DEBUG << "eps: " << pattern_map[eps_label]->get_name() << " "
                         << pattern_map[eps_label]->get_shape().size();
            NGRAPH_DEBUG << "gamma: " << pattern_map[gamma_label]->get_name() << " "
                         << pattern_map[gamma_label]->get_shape().size();
            NGRAPH_DEBUG << "beta: " << pattern_map[beta_label]->get_name() << " "
                         << pattern_map[beta_label]->get_shape().size();
            // dont fuse if the inout doesnt have 4dims
            if (pattern_map[input]->get_shape().size() != 4)
            {
                NGRAPH_DEBUG << "Input to bn doesnt not have a rank=4, so not fusing";
                return nn;
            }
            Shape bn_output_shape{m.match_root()->get_shape()};
            Shape m_bn_mean_shape{pattern_map[mean_label]->get_shape()};
            Shape m_bn_variance_shape{pattern_map[variance_label]->get_shape()};
            // get epsilon value
            auto eps_ptr = std::dynamic_pointer_cast<op::Constant>(pattern_map[eps_label]);
            double epsilon = *(reinterpret_cast<const double*>(eps_ptr->get_data_ptr()));
            auto bn_node = std::shared_ptr<Node>(new op::BatchNorm(epsilon,
                                                                   pattern_map[gamma_label],
                                                                   pattern_map[beta_label],
                                                                   pattern_map[input],
                                                                   pattern_map[mean_label],
                                                                   pattern_map[variance_label]));
            return bn_node;
        };
    auto m = std::make_shared<ngraph::pattern::Matcher>(add_beta, callback);
    this->add_matcher(m);
 }
 static bool
    zero_padded_conv_consistency_check(const std::shared_ptr<ngraph::Node>& match_root,
                                       const std::shared_ptr<ngraph::op::Constant>& pad_value_op,
                                       const std::shared_ptr<ngraph::Node>& pad_input,
                                       const std::shared_ptr<ngraph::op::Pad>& matched_pad,
                                       const std::shared_ptr<ngraph::op::Convolution>& matched_conv,
                                       size_t batch_index,
                                       size_t channel_index)
 {
    // Only match float32 convolutions
    if (match_root->get_element_type() != ngraph::element::f32)
    {
        return false;
    }
    // Only match zero padding
    if (pad_value_op->get_vector<float>().at(0) != 0.0f)
    {
        return false;
    }
    // Only match 4D tensors
    if (pad_input->get_shape().size() != 4)
    {
        return false;
    }
    // Only match no interior padding
    if (matched_pad->get_padding_interior() != ngraph::Shape(pad_input->get_shape().size()))
    {
        return false;
    }
    // Only match convolutions with no padding specification
    if (matched_conv->get_padding_below() != ngraph::CoordinateDiff(2) ||
        matched_conv->get_padding_above() != ngraph::CoordinateDiff(2))
    {
        return false;
    }
    // Only match no padding in the batch dimension
    if (matched_pad->get_padding_above().at(batch_index) != 0 ||
        matched_pad->get_padding_below().at(batch_index) != 0)
    {
        return false;
    }
    // Only match no padding in the channel dimension
    if (matched_pad->get_padding_above().at(channel_index) != 0 ||
        matched_pad->get_padding_below().at(channel_index) != 0)
    {
        return false;
    }
    return true;
 }
 void ngraph::runtime::cpu::pass::CPUFusion::construct_zero_padded_reshaped_conv()
 {
    auto pad_input = std::make_shared<pattern::op::Label>(element::f32, Shape{});
    auto pad_value = std::make_shared<pattern::op::Label>(element::f32, Shape{});
    auto pad = std::make_shared<op::Pad>(pad_input, pad_value, Shape{}, Shape{}, Shape{});
    auto pad_label = std::make_shared<pattern::op::Label>(pad, nullptr, NodeVector{pad});
    auto reshape = std::make_shared<op::Reshape>(pad_label, AxisVector{}, Shape{1, 1, 1, 1});
    auto reshape_label =
        std::make_shared<pattern::op::Label>(reshape, nullptr, NodeVector{reshape});
    auto conv_filter = std::make_shared<pattern::op::Label>(element::f32, Shape{1, 1, 1, 1});
    auto conv = std::make_shared<op::Convolution>(reshape_label,
                                                  conv_filter,
                                                  Strides{1, 1},
                                                  Strides{1, 1},
                                                  CoordinateDiff{1, 1},
                                                  CoordinateDiff{1, 1},
                                                  Strides{1, 1});
    auto conv_label = std::make_shared<pattern::op::Label>(conv, nullptr, NodeVector{conv});
    ngraph::pattern::gr_callback_fn callback =
        [pad_input, pad_value, pad_label, reshape_label, conv_filter, conv_label](
            pattern::Matcher& m) -> std::shared_ptr<Node> {
        auto pattern_map = m.get_pattern_map();
        auto pad_value_op = std::dynamic_pointer_cast<op::Constant>(pattern_map[pad_value]);
        const auto& matched_conv =
            std::dynamic_pointer_cast<op::Convolution>(pattern_map[conv_label]);
        const auto& matched_pad = std::dynamic_pointer_cast<op::Pad>(pattern_map[pad_label]);
        const auto& matched_reshape =
            std::dynamic_pointer_cast<op::Reshape>(pattern_map[reshape_label]);
        const auto& input_order = matched_reshape->get_input_order();
        auto hoisted_reshape_output_shape =
            apply_permutation<Shape::value_type>(pattern_map[pad_input]->get_shape(), input_order);
        auto hoisted_reshape = std::make_shared<op::Reshape>(
            pattern_map[pad_input],
            input_order,
            Shape(hoisted_reshape_output_shape.begin(), hoisted_reshape_output_shape.end()));
        if (!zero_padded_conv_consistency_check(m.match_root(),
                                                pad_value_op,
                                                pattern_map[pad_input],
                                                matched_pad,
                                                matched_conv,
                                                input_order[0],
                                                input_order[1]))
        {
            return nullptr;
        }
        CoordinateDiff padding_below{static_cast<CoordinateDiff::value_type>(
                                         matched_pad->get_padding_below().at(input_order[2])),
                                     static_cast<CoordinateDiff::value_type>(
                                         matched_pad->get_padding_below().at(input_order[3]))};
        CoordinateDiff padding_above{static_cast<CoordinateDiff::value_type>(
                                         matched_pad->get_padding_above().at(input_order[2])),
                                     static_cast<CoordinateDiff::value_type>(
                                         matched_pad->get_padding_above().at(input_order[3]))};
        auto zero_padded_conv =
            std::make_shared<op::Convolution>(hoisted_reshape,
                                              pattern_map[conv_filter],
                                              matched_conv->get_window_movement_strides(),
                                              matched_conv->get_window_dilation_strides(),
                                              padding_below,
                                              padding_above,
                                              matched_conv->get_data_dilation_strides());
        return zero_padded_conv;
    };
    this->add_matcher(std::make_shared<ngraph::pattern::Matcher>(conv_label, callback));
 }
 void ngraph::runtime::cpu::pass::CPUFusion::construct_zero_padded_conv()
 {
    auto pad_input = std::make_shared<pattern::op::Label>(element::f32, Shape{1, 1, 1, 1});
    auto pad_value = std::make_shared<pattern::op::Label>(element::f32, Shape{});
    auto pad = std::make_shared<op::Pad>(
        pad_input, pad_value, Shape{0, 0, 0, 0}, Shape{0, 0, 0, 0}, Shape{0, 0, 0, 0});
    auto pad_label = std::make_shared<pattern::op::Label>(pad, nullptr, NodeVector{pad});
    auto conv_filter = std::make_shared<pattern::op::Label>(element::f32, Shape{1, 1, 1, 1});
    auto conv = std::make_shared<op::Convolution>(pad_label,
                                                  conv_filter,
                                                  Strides{1, 1},
                                                  Strides{1, 1},
                                                  CoordinateDiff{1, 1},
                                                  CoordinateDiff{1, 1},
                                                  Strides{1, 1});
    auto conv_label = std::make_shared<pattern::op::Label>(conv, nullptr, NodeVector{conv});
    ngraph::pattern::gr_callback_fn callback =
        [pad_input, pad_value, pad_label, conv_filter, conv_label](
            pattern::Matcher& m) -> std::shared_ptr<Node> {
        auto pattern_map = m.get_pattern_map();
        auto pad_value_op = std::dynamic_pointer_cast<op::Constant>(pattern_map[pad_value]);
        const auto& matched_conv =
            std::dynamic_pointer_cast<op::Convolution>(pattern_map[conv_label]);
        const auto& matched_pad = std::dynamic_pointer_cast<op::Pad>(pattern_map[pad_label]);
        if (!zero_padded_conv_consistency_check(m.match_root(),
                                                pad_value_op,
                                                pattern_map[pad_input],
                                                matched_pad,
                                                matched_conv,
                                                0,
                                                1))
        {
            return nullptr;
        }
        CoordinateDiff padding_below{
            static_cast<CoordinateDiff::value_type>(matched_pad->get_padding_below().at(2)),
            static_cast<CoordinateDiff::value_type>(matched_pad->get_padding_below().at(3))};
        CoordinateDiff padding_above{
            static_cast<CoordinateDiff::value_type>(matched_pad->get_padding_above().at(2)),
            static_cast<CoordinateDiff::value_type>(matched_pad->get_padding_above().at(3))};
        auto zero_padded_conv =
            std::make_shared<op::Convolution>(pattern_map[pad_input],
                                              pattern_map[conv_filter],
                                              matched_conv->get_window_movement_strides(),
                                              matched_conv->get_window_dilation_strides(),
                                              padding_below,
                                              padding_above,
                                              matched_conv->get_data_dilation_strides());
        return zero_padded_conv;
    };
    this->add_matcher(std::make_shared<ngraph::pattern::Matcher>(conv_label, callback));
 }
 void ngraph::runtime::cpu::pass::CPUFusion::construct_conv_bias()
 {
    Shape shape{2, 2, 1, 1};
    auto data_batch = std::make_shared<pattern::op::Label>(element::f32, shape);
    auto filters = std::make_shared<pattern::op::Label>(element::f32, shape);
    auto pbias = std::make_shared<pattern::op::Label>(element::f32, Shape{});
    auto pbroadcast = std::make_shared<op::Broadcast>(pbias, shape, AxisSet{0, 1, 2, 3});
    auto pconv1 = std::make_shared<op::Convolution>(data_batch,
                                                    filters,
                                                    Strides{1, 1},
                                                    Strides{1, 1},
                                                    CoordinateDiff{0, 0},
                                                    CoordinateDiff{0, 0},
                                                    Strides{1, 1});
    auto p_conv_bias = pbroadcast + pconv1;
    ngraph::pattern::gr_callback_fn callback = [](pattern::Matcher& m) {
        NGRAPH_DEBUG << "In callback for construct_conv_bias against node = "
                     << m.match_root()->get_name();
        auto pattern_map = m.get_pattern_map();
        std::shared_ptr<Node> nn;
        auto conv = std::dynamic_pointer_cast<op::Convolution>(m.match_root()->get_input_op(0));
        auto bias = m.match_root()->get_input_op(1)->get_input_op(0);
        auto conv_bias = std::shared_ptr<Node>(new op::ConvolutionBias(conv, bias));
        return conv_bias;
    };
    auto m = std::make_shared<ngraph::pattern::Matcher>(p_conv_bias, callback);
    this->add_matcher(m);
 }