CumSum reference implementation revision

docs/template_plugin/tests/functional/op_reference/cum_sum.cpp

@@ -0,0 +1,193 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <ie_core.hpp>
+#include <ie_ngraph_utils.hpp>
+#include <ngraph/ngraph.hpp>
+#include <shared_test_classes/base/layer_test_utils.hpp>
+#include <tuple>
+
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ngraph;
+using namespace InferenceEngine;
+
+namespace {
+struct CumSumParams {
+    // Custom axis input and attributes
+    template <class IT, class AT>
+    CumSumParams(const PartialShape& shape, const element::Type& iType, const std::vector<IT>& iValues, const std::vector<IT>& oValues, const bool execlusive,
+                 const bool reverse, const element::Type& axisType, AT axisVal, const PartialShape& axisShape)
+        : execlusive(execlusive),
+          reverse(reverse),
+          axisValue(axisVal),
+          axisShape(axisShape),
+          inShape(shape),
+          axisType(axisType),
+          inType(iType),
+          outType(iType),
+          axisData(CreateBlob(axisType, std::vector<AT> {axisVal})),
+          inputData(CreateBlob(iType, iValues)),
+          refData(CreateBlob(iType, oValues)),
+          testDefaults(false) {}
+
+    // Default axis input and attributes
+    template <class IT>
+    CumSumParams(const PartialShape& shape, const element::Type& iType, const std::vector<IT>& iValues, const std::vector<IT>& oValues)
+        : inShape(shape),
+          axisType(element::i32),
+          inType(iType),
+          outType(iType),
+          inputData(CreateBlob(iType, iValues)),
+          refData(CreateBlob(iType, oValues)),
+          testDefaults(true) {}
+
+    bool execlusive = false;
+    bool reverse = false;
+    int64_t axisValue = 0;
+
+    PartialShape axisShape;
+    PartialShape inShape;
+    element::Type axisType;
+    element::Type inType;
+    element::Type outType;
+    Blob::Ptr axisData;
+    Blob::Ptr inputData;
+    Blob::Ptr refData;
+
+    bool testDefaults = false;
+};
+
+class ReferenceCumSumLayerTest : public testing::TestWithParam<CumSumParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        if (params.testDefaults) {
+            function = CreateFunction(params.inShape, params.inType);
+            inputData = {params.inputData};
+            refOutData = {params.refData};
+        } else {
+            function = CreateFunction(params.inShape, params.inType, params.axisShape, params.axisType, params.execlusive, params.reverse);
+            inputData = {params.inputData, params.axisData};
+            refOutData = {params.refData};
+        }
+    }
+    static std::string getTestCaseName(const testing::TestParamInfo<CumSumParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "testDefaults=" << param.testDefaults << "_";
+        result << "axisValue=" << param.axisValue << "_";
+        result << "execlusive=" << param.execlusive << "_";
+        result << "reverse=" << param.reverse << "_";
+        result << "inShape=" << param.inShape << "_";
+        result << "iType=" << param.inType << "_";
+        result << "axisType=" << param.axisType << "_";
+        result << "oType=" << param.outType;
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const PartialShape& data_shape, const element::Type& data_type, const PartialShape& axis_shape,
+                                                    const element::Type& axis_type, const bool execlusive, const bool reverse) {
+        const auto data_param = std::make_shared<op::Parameter>(data_type, data_shape);
+        const auto axis_param = std::make_shared<op::Parameter>(axis_type, axis_shape);
+        const auto cum_sum = std::make_shared<op::v0::CumSum>(data_param, axis_param, execlusive, reverse);
+        return std::make_shared<Function>(NodeVector {cum_sum}, ParameterVector {data_param, axis_param});
+    }
+
+    static std::shared_ptr<Function> CreateFunction(const PartialShape& data_shape, const element::Type& data_type) {
+        const auto data_param = std::make_shared<op::Parameter>(data_type, data_shape);
+        const auto cum_sum = std::make_shared<op::v0::CumSum>(data_param);
+        return std::make_shared<Function>(NodeVector {cum_sum}, ParameterVector {data_param});
+    }
+};
+
+TEST_P(ReferenceCumSumLayerTest, CompareWithHardcodedRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<CumSumParams> generateCumSumParams(const element::Type& type) {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<CumSumParams> opParams {
+        // Default axis input and attributes
+        CumSumParams(PartialShape {1}, type, std::vector<T> {3}, std::vector<T> {3}),
+        CumSumParams(PartialShape {6}, type, std::vector<T> {1, 2, 3, 4, 5, 6}, std::vector<T> {1, 3, 6, 10, 15, 21}),
+        CumSumParams(PartialShape {2, 4}, type, std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7}, std::vector<T> {0, 1, 2, 3, 4, 6, 8, 10}),
+        // Custom axis input and attributes
+        CumSumParams(PartialShape {6}, type, std::vector<T> {1, 2, 3, 4, 5, 6}, std::vector<T> {1, 3, 6, 10, 15, 21}, false, false, element::i32, int32_t(0),
+                     PartialShape {}),  // axis i32
+        CumSumParams(PartialShape {6}, type, std::vector<T> {1, 2, 3, 4, 5, 6}, std::vector<T> {1, 3, 6, 10, 15, 21}, false, false, element::i64, int64_t(0),
+                     PartialShape {}),  // axis i64
+        CumSumParams(PartialShape {6}, type, std::vector<T> {1, 2, 3, 4, 5, 6}, std::vector<T> {21, 20, 18, 15, 11, 6}, false, true, element::i64, int64_t(0),
+                     PartialShape {}),
+        CumSumParams(PartialShape {6}, type, std::vector<T> {1, 2, 3, 4, 5, 6}, std::vector<T> {0, 1, 3, 6, 10, 15}, true, false, element::i64, int64_t(0),
+                     PartialShape {}),
+        CumSumParams(PartialShape {6}, type, std::vector<T> {1, 2, 3, 4, 5, 6}, std::vector<T> {20, 18, 15, 11, 6, 0}, true, true, element::i64, int64_t(0),
+                     PartialShape {}),
+
+        CumSumParams(PartialShape {2, 4}, type, std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7}, std::vector<T> {0, 1, 2, 3, 4, 6, 8, 10}, false, false, element::i32,
+                    int32_t(0), PartialShape {}),
+        CumSumParams(PartialShape {2, 4}, type, std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7}, std::vector<T> {4, 6, 8, 10, 4, 5, 6, 7}, false, true, element::i32,
+                    int32_t(0), PartialShape {}),
+        CumSumParams(PartialShape {2, 4}, type, std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7}, std::vector<T> {0, 0, 0, 0, 0, 1, 2, 3}, true, false, element::i32,
+                    int32_t(0), PartialShape {}),
+        CumSumParams(PartialShape {2, 4}, type, std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7}, std::vector<T> {4, 5, 6, 7, 0, 0, 0, 0}, true, true, element::i32,
+                    int32_t(0), PartialShape {}),
+        CumSumParams(PartialShape {2, 4}, type, std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7}, std::vector<T> {0, 1, 3, 6, 4, 9, 15, 22}, false, false, element::i32,
+                     int32_t(1), PartialShape {}),
+        CumSumParams(PartialShape {2, 4}, type, std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7}, std::vector<T> {0, 0, 1, 3, 0, 4, 9, 15}, true, false, element::i32,
+                     int32_t(1), PartialShape {}),
+        CumSumParams(PartialShape {2, 4}, type, std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7}, std::vector<T> {6, 6, 5, 3, 22, 18, 13, 7}, false, true, element::i32,
+                     int32_t(1), PartialShape {}),
+        CumSumParams(PartialShape {2, 4}, type, std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7}, std::vector<T> {6, 5, 3, 0, 18, 13, 7, 0}, true, true, element::i32,
+                     int32_t(1), PartialShape {}),
+
+        CumSumParams(PartialShape {3, 2, 4}, type,
+                    std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7,
+                                    8, 9, 10, 11, 12, 13, 14, 15,
+                                    16, 17, 18, 19, 20, 21, 22, 23},
+                    std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7,
+                                    8, 10, 12, 14, 16, 18, 20, 22,
+                                    24, 27, 30, 33, 36, 39, 42, 45},
+                    false, false, element::i32, int32_t(0), PartialShape {}),
+        CumSumParams(PartialShape {3, 2, 4}, type,
+                    std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7,
+                                    8, 9, 10, 11, 12, 13, 14, 15,
+                                    16, 17, 18, 19, 20, 21, 22, 23},
+                    std::vector<T> {0,  1,  2,  3,  4,  6,  8, 10,
+                                    8,  9,  10, 11, 20, 22, 24, 26,
+                                    16, 17, 18, 19, 36, 38, 40, 42},
+                    false, false, element::i32, int32_t(1), PartialShape {}),
+        CumSumParams(PartialShape {3, 2, 4}, type,
+                    std::vector<T> {0, 1, 2, 3, 4, 5, 6, 7,
+                                    8, 9, 10, 11, 12, 13, 14, 15,
+                                    16, 17, 18, 19, 20, 21, 22, 23},
+                    std::vector<T> {0,  1,  3,  6,  4,  9,  15, 22,
+                                    8,  17, 27, 38, 12, 25, 39, 54,
+                                    16, 33, 51, 70, 20, 41, 63, 86},
+                    false, false, element::i32, int32_t(2), PartialShape {}),
+    };
+    return opParams;
+}
+
+std::vector<CumSumParams> generateCumSumCombinedParams() {
+    const std::vector<std::vector<CumSumParams>> opTypeParams {
+        generateCumSumParams<element::Type_t::bf16>(element::bf16), generateCumSumParams<element::Type_t::f16>(element::f16),
+        generateCumSumParams<element::Type_t::f32>(element::f32),   generateCumSumParams<element::Type_t::i32>(element::i32),
+        generateCumSumParams<element::Type_t::i64>(element::i64),   generateCumSumParams<element::Type_t::u32>(element::u32),
+        generateCumSumParams<element::Type_t::i8>(element::i8)};
+    std::vector<CumSumParams> combinedParams;
+    std::for_each(opTypeParams.begin(), opTypeParams.end(), [&](std::vector<CumSumParams> params) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    });
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_CumSum_With_Hardcoded_Refs, ReferenceCumSumLayerTest, ::testing::ValuesIn(generateCumSumCombinedParams()),
+                         ReferenceCumSumLayerTest::getTestCaseName);
+}  // namespace

inference-engine/tests/functional/shared_test_classes/src/single_layer/cum_sum.cpp

@@ -30,17 +30,12 @@ void CumSumLayerTest::SetUp() {
     bool exclusive, reverse;
     int64_t axis;
     std::tie(inputShapes, inputPrecision, axis, exclusive, reverse, targetDevice) = this->GetParam();
-    auto inType = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(inputPrecision);
-    ngraph::ParameterVector paramVector;
-    auto paramData = std::make_shared<ngraph::opset1::Parameter>(inType, ngraph::Shape(inputShapes));
-    paramVector.push_back(paramData);
+    const auto inType = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(inputPrecision);
+    const auto paramData = std::make_shared<ngraph::op::Parameter>(inType, ngraph::Shape(inputShapes));
+    const auto axisNode = std::make_shared<ngraph::op::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{}, std::vector<int64_t>{axis})->output(0);
+    const auto cumSum = std::make_shared<ngraph::op::v0::CumSum>(paramData, axisNode, exclusive, reverse);
 
-    auto axisNode = std::make_shared<ngraph::op::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{}, std::vector<int64_t>{axis})->output(0);
-
-    auto paramOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(paramVector));
-    auto cumSum = std::dynamic_pointer_cast<ngraph::op::CumSum>(ngraph::builder::makeCumSum(paramOuts[0], axisNode, exclusive, reverse));
-
-    ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(cumSum)};
-    function = std::make_shared<ngraph::Function>(results, paramVector, "cumsum");
+    ngraph::ResultVector results{std::make_shared<ngraph::op::Result>(cumSum)};
+    function = std::make_shared<ngraph::Function>(results, ngraph::ParameterVector{paramData}, "cumsum");
 }
 }  // namespace LayerTestsDefinitions

ngraph/core/reference/include/ngraph/runtime/reference/cum_sum.hpp

@@ -5,107 +5,41 @@
 #pragma once
 
 #include <cmath>
-#include <map>
-#include <utility>
-#include <vector>
-
-#include "ngraph/coordinate_transform.hpp"
-#include "ngraph/type/bfloat16.hpp"
-#include "ngraph/type/float16.hpp"
 
 namespace ngraph {
 namespace runtime {
 namespace reference {
+
 template <typename T, typename P>
 void cumsum(const T* arg,
             const P* axis_tensor,
             T* out,
             const Shape& tensor_shape,
             const bool exclusive,
             const bool reverse) {
-    NGRAPH_SUPPRESS_DEPRECATED_START
-    CoordinateTransform temp_transform(tensor_shape);
-    for (const Coordinate& output_coord : temp_transform) {
-        out[temp_transform.index(output_coord)] = 0;
-    }
-
-    P axis = axis_tensor[0];
-    P rank = tensor_shape.size();
-
-    if (axis < -rank || axis > rank) {
-        throw ngraph_error("axis must be in the range [-rank, rank]");
-    }
-    axis = axis < 0 ? rank + axis : axis;
-
-    auto get_key = [&, axis](const Coordinate& coord) -> Coordinate {
-        Coordinate result(coord.size(), 0);
-        result[axis] = coord[axis];
-
-        for (size_t i = 0; i < coord.size(); i++) {
-            result[i] = coord[i] - result[i];
-        }
-        return result;
-    };
-
-    auto update_output_buffer =
-        [&](size_t input_index, size_t output_index, T& prev, std::vector<std::pair<size_t, T>>& tensor_vec) -> void {
-        tensor_vec[input_index].second = prev + tensor_vec[input_index].second;
-        out[tensor_vec[output_index].first] = tensor_vec[input_index].second;
-
-        // update prev to hold the last result value to compute ruuning sum for
-        // subsequent iter
-        prev = out[tensor_vec[output_index].first];
-    };
-
-    auto cum_sum = [&, exclusive, reverse](std::vector<std::pair<size_t, T>>& tensor_vec) {
-        if (!reverse) {
-            T prev = 0;
-            for (size_t i = 0; i < tensor_vec.size(); i++) {
-                if (exclusive && i == 0) {
-                    out[tensor_vec[i].first] = prev;
-                    continue;
-                }
-                // we will compute running sum of j-1 elements if exlusive=1 or else
-                // for j elements if exclusive = 0
-                size_t arg_index = exclusive == 1 ? i - 1 : i;
-                update_output_buffer(arg_index, i, prev, tensor_vec);
+    const auto rank = tensor_shape.size();
+    const auto axis = axis_tensor[0] >= 0 ? axis_tensor[0] : rank + axis_tensor[0];
+    const auto axis_dim = tensor_shape[axis];
+
+    const auto size_before_axis = shape_size(Shape(tensor_shape.begin(), tensor_shape.begin() + axis));
+    const auto size_after_axis = shape_size(Shape(tensor_shape.begin() + axis + 1, tensor_shape.end()));
+
+    const auto reverse_shift = reverse ? -1 : 1;
+    const auto element_shift = exclusive ? size_after_axis * reverse_shift : 0;
+
+    for (size_t i = 0; i < size_before_axis; ++i) {
+        const auto slice_idx = i * axis_dim * size_after_axis + reverse * size_after_axis * (axis_dim - 1);
+        for (size_t j = 0; j < size_after_axis; ++j) {
+            const auto sequence_start_idx = slice_idx + j;
+            out[sequence_start_idx] = exclusive ? T{0} : arg[sequence_start_idx];
+            for (size_t k = 1; k < axis_dim; ++k) {
+                const auto element_idx = sequence_start_idx + (k * size_after_axis) * reverse_shift;
+                const auto in_idx = element_idx - element_shift;
+                const auto previous_sum_idx = element_idx - size_after_axis * reverse_shift;
+                out[element_idx] = out[previous_sum_idx] + arg[in_idx];
             }
-        } else  // reverse == true
-        {
-            T prev = 0;
-            for (size_t i = tensor_vec.size(); i-- > 0;) {
-                if (exclusive && i == tensor_vec.size() - 1) {
-                    out[tensor_vec[i].first] = prev;
-                    continue;
-                }
-                // we will compute running sum of j-1 elements if exlusive=1 or else
-                // for j elements if exclusive = 0
-                size_t arg_index = exclusive == 1 ? i + 1 : i;
-                update_output_buffer(arg_index, i, prev, tensor_vec);
-            }
-        }
-    };
-
-    // Map to collect tensor elements belonging to the same axis
-    std::map<Coordinate, std::vector<std::pair<size_t, T>>> map_cooord_to_val;
-    CoordinateTransform input_transform(tensor_shape);
-    for (const Coordinate& input_coord : input_transform) {
-        // points to the current element in the input tensor
-        T current = arg[input_transform.index(input_coord)];
-        auto key = get_key(input_coord);
-        auto index = input_transform.index(input_coord);
-        if (map_cooord_to_val.find(key) != map_cooord_to_val.end()) {
-            map_cooord_to_val[key].push_back(std::make_pair(index, current));
-        } else {
-            map_cooord_to_val.insert({key, std::vector<std::pair<size_t, T>>()});
-            map_cooord_to_val[key].push_back(std::make_pair(index, current));
         }
     }
-    // iterate the map and perform cumulative sum over the give axis
-    for (auto& it : map_cooord_to_val) {
-        cum_sum(it.second);
-    }
-    NGRAPH_SUPPRESS_DEPRECATED_END
 }
 }  // namespace reference
 }  // namespace runtime

ngraph/test/CMakeLists.txt

@@ -427,7 +427,6 @@ set(MULTI_TEST_SRC
     backend/cosh.in.cpp
     backend/ctc_greedy_decoder.in.cpp
     backend/ctc_greedy_decoder_seq_len.in.cpp
-    backend/cum_sum.in.cpp
     backend/deformable_psroi_pooling.in.cpp
     backend/detection_output.in.cpp
     backend/dft.in.cpp