Skip to content
Snippets Groups Projects
Commit e9fd4827 authored by Maxence Naud's avatar Maxence Naud
Browse files

Merge branch 'dev' of gitlab.eclipse.org:eclipse/aidge/aidge_backend_cpu into broadcasting

parents ffd6e77a 5e85b482
No related branches found
No related tags found
2 merge requests!50version 0.2.0,!30add broadcasting for Arithmetic operators
Pipeline #39374 failed
Stage: build
    Stage: test
      Hide whitespace changes
      Inline Side-by-side
      aidge_backend_cpu/unit_tests/test_tensor.py deleted 100644 → 0
      + 0
      − 71
      View file @ ffd6e77a
      import unittest
      import aidge_core
      import aidge_backend_cpu
      import numpy as np
      class test_tensor(unittest.TestCase):
      """Test tensor binding
      """
      def setUp(self):
      pass
      def tearDown(self):
      pass
      def test_getavailable_backends(self):
      self.assertTrue("cpu" in aidge_core.Tensor.get_available_backends())
      def test_numpy_int_to_tensor(self):
      np_array = np.arange(9).reshape(1,1,3,3).astype(np.int32)
      # Numpy -> Tensor
      t = aidge_core.Tensor(np_array)
      self.assertEqual(t.dtype(), aidge_core.DataType.Int32)
      for i_t, i_n in zip(t, np_array.flatten()):
      self.assertTrue(i_t == i_n)
      for i,j in zip(t.dims(), np_array.shape):
      self.assertEqual(i,j)
      def test_tensor_int_to_numpy(self):
      np_array = np.arange(9).reshape(1,1,3,3)
      # Numpy -> Tensor
      t = aidge_core.Tensor(np_array)
      # Tensor -> Numpy
      nnarray = np.array(t)
      for i_nn, i_n in zip(nnarray.flatten(), np_array.flatten()):
      self.assertTrue(i_nn == i_n)
      for i,j in zip(t.dims(), nnarray.shape):
      self.assertEqual(i,j)
      def test_numpy_int64_to_tensor(self):
      np_array = np.arange(9).reshape(1,1,3,3).astype(np.int64)
      # Numpy -> Tensor
      t = aidge_core.Tensor(np_array)
      self.assertEqual(t.dtype(), aidge_core.DataType.Int64)
      for i_t, i_n in zip(t, np_array.flatten()):
      self.assertTrue(i_t == i_n)
      for i,j in zip(t.dims(), np_array.shape):
      self.assertEqual(i,j)
      def test_numpy_float_to_tensor(self):
      t = aidge_core.Tensor()
      np_array = np.random.rand(1, 1, 3, 3).astype(np.float32)
      # Numpy -> Tensor
      t = aidge_core.Tensor(np_array)
      self.assertEqual(t.dtype(), aidge_core.DataType.Float32)
      for i_t, i_n in zip(t, np_array.flatten()):
      self.assertTrue(i_t == i_n) # TODO : May need to change this to a difference
      for i,j in zip(t.dims(), np_array.shape):
      self.assertEqual(i,j)
      def test_get_set(self):
      dims = [2,2,2]
      np_array = np.arange(8).reshape(dims).astype(np.int32)
      # Numpy -> Tensor
      t = aidge_core.Tensor(np_array)
      for i in range(8):
      self.assertEqual(t[i], i)
      t[i] = 5
      self.assertEqual(t[i], 5)
      if __name__ == '__main__':
      unittest.main()
      include/aidge/backend/cpu.hpp
      + 4
      − 2
      View file @ e9fd4827
      ...@@ -12,7 +12,6 @@ ...@@ -12,7 +12,6 @@
      #ifndef AIDGE_CPU_IMPORTS_H_ #ifndef AIDGE_CPU_IMPORTS_H_
      #define AIDGE_CPU_IMPORTS_H_ #define AIDGE_CPU_IMPORTS_H_
      #include "aidge/backend/cpu/data/TensorImpl.hpp"
      #include "aidge/backend/cpu/operator/AddImpl.hpp" #include "aidge/backend/cpu/operator/AddImpl.hpp"
      #include "aidge/backend/cpu/operator/AvgPoolingImpl.hpp" #include "aidge/backend/cpu/operator/AvgPoolingImpl.hpp"
      #include "aidge/backend/cpu/operator/MaxPoolingImpl.hpp" #include "aidge/backend/cpu/operator/MaxPoolingImpl.hpp"
      ...@@ -40,4 +39,7 @@ ...@@ -40,4 +39,7 @@
      #include "aidge/backend/cpu/operator/SubImpl.hpp" #include "aidge/backend/cpu/operator/SubImpl.hpp"
      #include "aidge/backend/cpu/operator/TransposeImpl.hpp" #include "aidge/backend/cpu/operator/TransposeImpl.hpp"
      #endif /* AIDGE_CPU_IMPORTS_H_ */ #include "aidge/backend/cpu/data/TensorImpl.hpp"
      \ No newline at end of file
      #endif /* AIDGE_CPU_IMPORTS_H_ */
      include/aidge/backend/cpu/data/GetCPUPtr.h deleted 100644 → 0
      + 0
      − 24
      View file @ ffd6e77a
      /********************************************************************************
      * Copyright (c) 2023 CEA-List
      *
      * This program and the accompanying materials are made available under the
      * terms of the Eclipse Public License 2.0 which is available at
      * http://www.eclipse.org/legal/epl-2.0.
      *
      * SPDX-License-Identifier: EPL-2.0
      *
      ********************************************************************************/
      #ifndef AIDGE_CPU_DATA_GETCPUPTR_H_
      #define AIDGE_CPU_DATA_GETCPUPTR_H_
      #include "aidge/data/Tensor.hpp"
      namespace Aidge {
      inline void *getCPUPtr(std::shared_ptr<Aidge::Data> const &data, const std::size_t offset = 0) {
      const auto tensor = std::static_pointer_cast<Tensor>(data);
      return tensor->getImpl()->hostPtr(tensor->getImplOffset() + offset);
      }
      } // namespace Aidge
      #endif // AIDGE_CPU_DATA_GETCPUPTR_H_
      \ No newline at end of file
      include/aidge/backend/cpu/data/TensorImpl.hpp deleted 100644 → 0
      + 0
      − 200
      View file @ ffd6e77a
      /********************************************************************************
      * Copyright (c) 2023 CEA-List
      *
      * This program and the accompanying materials are made available under the
      * terms of the Eclipse Public License 2.0 which is available at
      * http://www.eclipse.org/legal/epl-2.0.
      *
      * SPDX-License-Identifier: EPL-2.0
      *
      ********************************************************************************/
      #ifndef AIDGE_CPU_DATA_TENSORIMPL_H_
      #define AIDGE_CPU_DATA_TENSORIMPL_H_
      #include "aidge/backend/TensorImpl.hpp"
      #include "aidge/data/Tensor.hpp"
      #include "aidge/data/half.hpp"
      #include "aidge/utils/Registrar.hpp"
      #include "aidge/utils/Types.h"
      #include "aidge/utils/ErrorHandling.hpp"
      #include "aidge/utils/future_std/span.hpp"
      namespace Aidge {
      template <class T>
      class TensorImpl_cpu : public TensorImpl {
      private:
      /// Pointer to the data and its capacity
      future_std::span<T> mData;
      /// If this instance own the data, std::unique_ptr manages it
      std::unique_ptr<T[]> mDataOwner;
      public:
      static constexpr const char *Backend = "cpu";
      TensorImpl_cpu(DeviceIdx_t device, std::vector<DimSize_t> dims) : TensorImpl(Backend, device, dims) {}
      bool operator==(const TensorImpl &otherImpl) const override final {
      const auto& typedOtherImpl = reinterpret_cast<const TensorImpl_cpu<T> &>(otherImpl);
      AIDGE_INTERNAL_ASSERT(typedOtherImpl.size() >= mNbElts);
      std::size_t i = 0;
      for (; i < mNbElts &&
      *(mData.data()+i) == *static_cast<const T*>(typedOtherImpl.rawPtr(i));
      ++i) {
      }
      return i == mNbElts;
      }
      static std::shared_ptr<TensorImpl_cpu> create(DeviceIdx_t device, std::vector<DimSize_t> dims) {
      return std::make_shared<TensorImpl_cpu<T>>(device, dims);
      }
      inline std::size_t scalarSize() const noexcept override final { return sizeof(T); }
      void copy(const void *src, NbElts_t length, NbElts_t offset = 0) override final {
      const T* srcT = static_cast<const T *>(src);
      T* dstT = static_cast<T *>(rawPtr(offset));
      AIDGE_ASSERT(length <= mData.size() || length <= mNbElts, "copy length is above capacity");
      AIDGE_ASSERT(dstT < srcT || dstT >= srcT + length, "overlapping copy is not supported");
      std::copy(srcT, srcT + length, dstT);
      }
      void copyCast(const void *src, const DataType srcDt, NbElts_t length, NbElts_t offset = 0) override final {
      if (length == 0) {
      return;
      }
      T* dstT = static_cast<T *>(rawPtr(offset));
      AIDGE_ASSERT(length <= mData.size() || length <= mNbElts, "copy length is above capacity");
      switch (srcDt)
      {
      case DataType::Float64:
      std::copy(static_cast<const double*>(src), static_cast<const double*>(src) + length,
      dstT);
      break;
      case DataType::Float32:
      std::copy(static_cast<const float*>(src), static_cast<const float*>(src) + length,
      dstT);
      break;
      case DataType::Float16:
      std::copy(static_cast<const half_float::half*>(src), static_cast<const half_float::half*>(src) + length,
      dstT);
      break;
      case DataType::Int64:
      std::copy(static_cast<const int64_t*>(src), static_cast<const int64_t*>(src) + length,
      dstT);
      break;
      case DataType::UInt64:
      std::copy(static_cast<const uint64_t*>(src), static_cast<const uint64_t*>(src) + length,
      dstT);
      break;
      case DataType::Int32:
      std::copy(static_cast<const int32_t*>(src), static_cast<const int32_t*>(src) + length,
      dstT);
      break;
      case DataType::UInt32:
      std::copy(static_cast<const uint32_t*>(src), static_cast<const uint32_t*>(src) + length,
      dstT);
      break;
      case DataType::Int16:
      std::copy(static_cast<const int16_t*>(src), static_cast<const int16_t*>(src) + length,
      dstT);
      break;
      case DataType::UInt16:
      std::copy(static_cast<const uint16_t*>(src), static_cast<const uint16_t*>(src) + length,
      dstT);
      break;
      case DataType::Int8:
      std::copy(static_cast<const int8_t*>(src), static_cast<const int8_t*>(src) + length,
      dstT);
      break;
      case DataType::UInt8:
      std::copy(static_cast<const uint8_t*>(src), static_cast<const uint8_t*>(src) + length,
      dstT);
      break;
      default:
      AIDGE_THROW_OR_ABORT(std::runtime_error, "Unsupported data type.");
      break;
      }
      }
      void copyFromDevice(const void *src, const std::pair<std::string, DeviceIdx_t>& device, NbElts_t length, NbElts_t offset = 0) override final {
      AIDGE_ASSERT(device.first == Backend, "backend must match");
      AIDGE_ASSERT(device.second == 0, "device cannot be != 0 for CPU backend");
      copy(src, length, offset);
      }
      inline void copyFromHost(const void *src, NbElts_t length, NbElts_t offset = 0) override final {
      copy(src, length, offset);
      }
      void copyToHost(void *dst, NbElts_t length, NbElts_t offset = 0) const override final {
      const T* src = static_cast<const T*>(rawPtr(offset));
      AIDGE_ASSERT(length <= mData.size() || length <= mNbElts, "copy length is above capacity");
      std::copy(src, src + length, static_cast<T *>(dst));
      }
      void *rawPtr(NbElts_t offset = 0) override final {
      lazyInit();
      return (mData.data() + offset);
      };
      const void *rawPtr(NbElts_t offset = 0) const override final {
      AIDGE_ASSERT(mData.size() >= mNbElts, "accessing uninitialized const rawPtr");
      return (mData.data() + offset);
      };
      void *hostPtr(NbElts_t offset = 0) override final {
      lazyInit();
      return (mData.data() + offset);
      };
      const void *hostPtr(NbElts_t offset = 0) const override final {
      AIDGE_ASSERT(mData.size() >= mNbElts, "accessing uninitialized const hostPtr");
      return (mData.data() + offset);
      };
      void setRawPtr(void *ptr, NbElts_t length) override final {
      AIDGE_ASSERT(length >= mNbElts, "trying to set raw pointer of insufficient capacity");
      mData = future_std::span<T>(static_cast<T *>(ptr), length);
      mDataOwner.reset();
      };
      virtual ~TensorImpl_cpu() = default;
      private:
      void lazyInit() {
      if (mData.size() < mNbElts) {
      // Need more data, a re-allocation will occur
      AIDGE_ASSERT(mData.empty() || mDataOwner != nullptr, "trying to enlarge non-owned data");
      mDataOwner.reset(new T[mNbElts]);
      mData = future_std::span<T>(mDataOwner.get(), mNbElts);
      }
      }
      };
      namespace {
      static Registrar<Tensor> registrarTensorImpl_cpu_Float64(
      {"cpu", DataType::Float64}, Aidge::TensorImpl_cpu<double>::create);
      static Registrar<Tensor> registrarTensorImpl_cpu_Float32(
      {"cpu", DataType::Float32}, Aidge::TensorImpl_cpu<float>::create);
      static Registrar<Tensor> registrarTensorImpl_cpu_Float16(
      {"cpu", DataType::Float16}, Aidge::TensorImpl_cpu<half_float::half>::create);
      static Registrar<Tensor> registrarTensorImpl_cpu_Int64(
      {"cpu", DataType::Int64}, Aidge::TensorImpl_cpu<long>::create);
      static Registrar<Tensor> registrarTensorImpl_cpu_Int32(
      {"cpu", DataType::Int32}, Aidge::TensorImpl_cpu<int>::create);
      static Registrar<Tensor> registrarTensorImpl_cpu_Int16(
      {"cpu", DataType::Int16}, Aidge::TensorImpl_cpu<int16_t>::create);
      static Registrar<Tensor> registrarTensorImpl_cpu_UInt16(
      {"cpu", DataType::UInt16}, Aidge::TensorImpl_cpu<uint16_t>::create);
      static Registrar<Tensor> registrarTensorImpl_cpu_Int8(
      {"cpu", DataType::Int8}, Aidge::TensorImpl_cpu<int8_t>::create);
      static Registrar<Tensor> registrarTensorImpl_cpu_UInt8(
      {"cpu", DataType::UInt8}, Aidge::TensorImpl_cpu<uint8_t>::create);
      } // namespace
      } // namespace Aidge
      #endif /* AIDGE_CPU_DATA_TENSORIMPL_H_ */
      unit_tests/data/Test_TensorImpl.cpp deleted 100644 → 0
      + 0
      − 126
      View file @ ffd6e77a
      /********************************************************************************
      * Copyright (c) 2023 CEA-List
      *
      * This program and the accompanying materials are made available under the
      * terms of the Eclipse Public License 2.0 which is available at
      * http://www.eclipse.org/legal/epl-2.0.
      *
      * SPDX-License-Identifier: EPL-2.0
      *
      ********************************************************************************/
      #include <array>
      #include <catch2/catch_test_macros.hpp>
      #include "aidge/data/Tensor.hpp"
      #include "aidge/utils/TensorUtils.hpp"
      #include "aidge/backend/cpu/data/TensorImpl.hpp"
      using namespace Aidge;
      TEST_CASE("[backend_cpu/data] Tensor(constructor)","[Tensor]") {
      SECTION("from const array") {
      Tensor x = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
      Tensor xCopy = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
      Tensor xFloat =
      Array3D<float, 2, 2, 2>{{{{1., 2.}, {3., 4.}}, {{5., 6.}, {7., 8.}}}};
      SECTION("Tensor features") {
      REQUIRE(x.nbDims() == 3);
      REQUIRE(x.dims()[0] == 2);
      REQUIRE(x.dims()[1] == 2);
      REQUIRE(x.dims()[2] == 2);
      REQUIRE(x.size() == 8);
      }
      SECTION("Access to array") {
      REQUIRE(static_cast<int *>(x.getImpl()->rawPtr())[0] == 1);
      REQUIRE(static_cast<int *>(x.getImpl()->rawPtr())[7] == 8);
      }
      SECTION("get function") {
      REQUIRE(x.get<int>({0, 0, 0}) == 1);
      REQUIRE(x.get<int>({0, 0, 1}) == 2);
      REQUIRE(x.get<int>({0, 1, 1}) == 4);
      REQUIRE(x.get<int>({1, 1, 0}) == 7);
      x.set<int>({1, 1, 1}, 36);
      REQUIRE(x.get<int>({1, 1, 1}) == 36);
      }
      SECTION("Pretty printing for debug") { REQUIRE_NOTHROW(x.print()); }
      SECTION("Tensor (in)equality") {
      REQUIRE(x == xCopy);
      REQUIRE_FALSE(x == xFloat);
      }
      }
      }
      TEST_CASE("Tensor fill") {
      SECTION("Instantiate batches independantly") {
      // initialization with 0s
      std::shared_ptr<Tensor> concatenatedTensor= std::make_shared<Tensor>(Array2D<int, 3, 5>{});
      //concatenatedTensor->print();
      std::shared_ptr<Tensor> myTensor1 = std::make_shared<Tensor>(Array1D<int, 5>{{1,2,3,4,5}});
      std::shared_ptr<Tensor> myTensor2 = std::make_shared<Tensor>(Array1D<int, 5>{{6,7,8,9,10}});
      std::shared_ptr<Tensor> myTensor3 = std::make_shared<Tensor>(Array1D<int, 5>{{11,12,13,14,15}});
      // use copy function from implementation
      concatenatedTensor->getImpl()->copy(myTensor1->getImpl()->rawPtr(), 5, 0);
      concatenatedTensor->getImpl()->copy(myTensor2->getImpl()->rawPtr(), 5, 5);
      concatenatedTensor->getImpl()->copy(myTensor3->getImpl()->rawPtr(), 5, 10);
      // concatenatedTensor->print();
      std::shared_ptr<Tensor> expectedTensor= std::make_shared<Tensor>(Array2D<int, 3, 5>{
      {{1,2,3,4,5},
      {6,7,8,9,10},
      {11,12,13,14,15}}
      });
      // expectedTensor->print();
      REQUIRE(*concatenatedTensor == *expectedTensor);
      }
      }
      TEST_CASE("[backend_cpu/data] Tensor(methods)", "[Tensor]") {
      Tensor x = Array3D<int, 2, 2, 2>{{
      {{1, 2},
      {3, 4}},
      {{5, 6},
      {7, 8}}
      }};
      Tensor xCopy = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
      Tensor xFloat =
      Array3D<float, 2, 2, 2>{{{{1., 2.}, {3., 4.}}, {{5., 6.}, {7., 8.}}}};
      SECTION("Tensor sharing") {
      Tensor xCopyCtor(x);
      REQUIRE(xCopyCtor.getImpl() == x.getImpl());
      Tensor xEqOp = x;
      REQUIRE(xEqOp.getImpl() == x.getImpl());
      Tensor xCloned = x.clone();
      REQUIRE(xCloned.getImpl() != x.getImpl());
      REQUIRE(xCloned == x);
      }
      SECTION("Tensor extract") {
      Tensor y = x.extract({0, 1});
      REQUIRE(y.getImpl() == x.getImpl());
      REQUIRE(approxEq<int>(y, Array1D<int, 2>{{3, 4}}));
      REQUIRE(y.isContiguous());
      Tensor y2 = x.extract({0, 1, 1}, {2, 1, 1});
      REQUIRE(y2.getImpl() == x.getImpl());
      REQUIRE(!y2.isContiguous());
      Tensor y3 = y2.clone();
      REQUIRE(y3.isContiguous());
      REQUIRE(approxEq<int>(y3, Array3D<int, 2, 1, 1>{{{{4}}, {{8}}}}));
      }
      }
      0% or .
      You are about to add 0 people to the discussion. Proceed with caution.
      Finish editing this message first!
      Please register or to comment

      Copyright © Eclipse Foundation, Inc. All Rights Reserved.     Privacy Policy | Terms of Use | Copyright Agent