diff --git a/include/aidge/backend/cpu/operator/AndImpl.hpp b/include/aidge/backend/cpu/operator/AndImpl.hpp
index 316a2fb922596642088d133a7fec49c988739bb7..8814df2fac36be56332035731679b724b169efe7 100644
--- a/include/aidge/backend/cpu/operator/AndImpl.hpp
+++ b/include/aidge/backend/cpu/operator/AndImpl.hpp
@@ -23,7 +23,7 @@
namespace Aidge {
// Operator implementation entry point for the backend
using AndImpl_cpu = OperatorImpl_cpu<And_Op,
- void(const std::vector<std::size_t>&, const std::vector<std::size_t>&, const std::vector<std::size_t>&, const void*, const void*,void*)>;
+ void(std::vector<std::size_t>, std::vector<std::size_t>, const std::vector<std::size_t>&, const void*, const void*, void*)>;
// Implementation entry point registration to Operator
REGISTRAR(And_Op, "cpu", Aidge::AndImpl_cpu::create);
diff --git a/include/aidge/backend/cpu/operator/AndImpl_kernels.hpp b/include/aidge/backend/cpu/operator/AndImpl_kernels.hpp
index 197e829f3527ce2f36c3ef5ee812a26477633703..dd1e4acc0a977fd40d4c003a2eed4c4431559524 100644
--- a/include/aidge/backend/cpu/operator/AndImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/AndImpl_kernels.hpp
@@ -12,52 +12,153 @@
#ifndef AIDGE_CPU_OPERATOR_ANDIMPL_KERNELS_H_
#define AIDGE_CPU_OPERATOR_ANDIMPL_KERNELS_H_
-#include "aidge/backend/cpu/data/Broadcasting.hpp"
#include "aidge/backend/cpu/operator/AndImpl.hpp"
#include "aidge/utils/Registrar.hpp"
namespace Aidge {
-template <class I1, class I2, class O>
-void AndImpl_cpu_forward_kernel(const std::vector<std::size_t>& input1Dims,
- const std::vector<std::size_t>& input2Dims,
+
+namespace {
+// suppose values are contiguous in memory
+template <class I, class O>
+void equal_contiguous_arrays(const std::size_t input1size,
+ const std::size_t input2size,
+ const std::size_t output1size,
+ const I* input1,
+ const I* input2,
+ O* output)
+{
+ for (std::size_t i = 0; i < output1size; ++i)
+ {
+ const std::size_t in1_id = (input1size != 1) ? i : 0;
+ const std::size_t in2_id = (input2size != 1) ? i : 0;
+ output[i] = static_cast<O>(input1[in1_id] == input2[in2_id]);
+ }
+}
+}
+
+
+template <class I, class O>
+void EqualImpl_cpu_forward_kernel(std::vector<std::size_t> dims0,
+ std::vector<std::size_t> dims1,
const std::vector<std::size_t>& outputDims,
+ const void* input0_,
const void* input1_,
- const void* input2_,
void* output_) {
- const I1* input_1 = static_cast<const I1*>(input1_);
- const I2* input_2 = static_cast<const I2*>(input2_);
+ const I* input_0 = static_cast<const I*>(input0_);
+ const I* input_1 = static_cast<const I*>(input1_);
O* output = static_cast<O*>(output_);
- size_t totalElements = 1;
- for (size_t dimSize : outputDims) {
- totalElements *= dimSize;
+ // [5,2,1,7] & [2,6,7]
+ // 1. Same number of dimensions -> [5,2,1,7] & [1,2,6,7]
+ // 2. Find the highest equal dimension -> 3
+ // Exception: if the first diverging dimension is the last one, then -> 4 (dims.size())
+ // 3. Compute the highest number of contiguous data -> 7
+ // 4. Compute stride and offset step for the broadcast mechanism
+ // 5. Call a simple kernel
+
+ // special case for equal dimensions, the kernel is called with the entire arrays at once
+ if (dims0 == dims1) {
+ const std::size_t input0_contiguous_size = std::accumulate(dims0.cbegin(), dims0.cend(), std::size_t(1), std::multiplies<std::size_t>());
+ for (std::size_t i = 0; i < input0_contiguous_size; ++i)
+ {
+ output[i] = static_cast<O>(input_0[i] + input_1[i]);
+ }
+ return;
}
- for (std::size_t oIndex = 0; oIndex < totalElements; ++oIndex)
- {
- std::vector<size_t> indexes = getMultiDimIndices(outputDims, oIndex);
+ // set dimensions to be of equal size by filling the smallest one with ones.
+ if (dims0.size() > dims1.size()) {
+ dims1.insert(dims1.cbegin(), dims0.size() - dims1.size(), std::size_t(1));
+ }
+ else if (dims1.size() > dims0.size()) {
+ dims0.insert(dims0.cbegin(), dims1.size() - dims0.size(), std::size_t(1));
+ }
- std::size_t idx1 = getFlattenedIndex(input1Dims, indexes);
- std::size_t idx2 = getFlattenedIndex(input2Dims, indexes);
+ const std::size_t nbDims = dims0.size();
- output[oIndex] = static_cast<O>(input_1[idx1] == input_2[idx2]);
+ // Find the highest equal dimension
+ // std::size_t contiguousIdx = nbDims - 1;
+ std::size_t contiguousIdx = nbDims;
+ while (contiguousIdx-- > 0) {
+ // for (; contiguousIdx+1 > 0; --contiguousIdx) {
+ if (dims0[contiguousIdx] != dims1[contiguousIdx]) {
+ if (contiguousIdx == (nbDims -1)) { // last dimensions of one of the input Tensor are of size 1
+ const std::vector<std::size_t>& dims = (dims0[contiguousIdx] == 1) ? dims0 : dims1;
+ while ((contiguousIdx+1 > 0) && (dims[contiguousIdx] == 1)) {
+ --contiguousIdx;
+ }
+ }
+ break;
+ }
+ }
+ ++contiguousIdx;
+
+ // Compute the highest number of contiguous data for each Tensor
+ const std::size_t input0_contiguous_size = std::accumulate(dims0.cbegin()+contiguousIdx, dims0.cend(), std::size_t(1), std::multiplies<std::size_t>());
+ const std::size_t input1_contiguous_size = std::accumulate(dims1.cbegin()+contiguousIdx, dims1.cend(), std::size_t(1), std::multiplies<std::size_t>());
+ const std::size_t output_contiguous_size = std::accumulate(outputDims.cbegin()+contiguousIdx, outputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
+
+ // initialize strides to iterate through data because of broadcasting
+ std::unique_ptr<std::int32_t[]> stride_post0 = std::make_unique<std::int32_t[]>(contiguousIdx);
+ std::unique_ptr<std::int32_t[]> stride_post1 = std::make_unique<std::int32_t[]>(contiguousIdx);
+ std::unique_ptr<std::int32_t[]> stride_step0 = std::make_unique<std::int32_t[]>(contiguousIdx);
+ std::unique_ptr<std::int32_t[]> stride_step1 = std::make_unique<std::int32_t[]>(contiguousIdx);
+ if (contiguousIdx > 0) {
+ stride_post0[contiguousIdx - 1] = 1;
+ stride_post1[contiguousIdx - 1] = 1;
+ for (std::size_t i = contiguousIdx - 2; i != static_cast<std::size_t>(-1); --i) {
+ stride_post0[i] = stride_post0[i+1]*static_cast<std::int32_t>(dims0[i+1]);
+ stride_post1[i] = stride_post1[i+1]*static_cast<std::int32_t>(dims1[i+1]);
+ }
+ for (std::size_t i = 0; i != contiguousIdx; ++i) {
+ stride_step0[i] = (dims0[i] == 1) ? 1 - stride_post0[i] : 1;
+ stride_step1[i] = (dims1[i] == 1) ? 1 - stride_post1[i] : 1;
+ }
+ }
+
+ // variables for arrays offsets
+ std::size_t offsetIn0 = 0;
+ std::size_t offsetIn1 = 0;
+ std::size_t offsetOut = 0;
+
+
+ std::size_t dim = contiguousIdx - 1;
+ const std::size_t nbStacks = std::accumulate(outputDims.cbegin(), outputDims.cbegin() + contiguousIdx, std::size_t(1), std::multiplies<std::size_t>());
+ for (std::size_t stack = 0; stack < nbStacks;) {
+ equal_contiguous_arrays<I,O>(input0_contiguous_size, input1_contiguous_size, output_contiguous_size,
+ input_0 + offsetIn0*input0_contiguous_size,
+ input_1 + offsetIn1*input1_contiguous_size,
+ output + offsetOut*output_contiguous_size);
+ if (++stack < nbStacks) {
+ std::size_t tmp_stack = stack;
+ while(tmp_stack % outputDims[dim] == 0) {
+ tmp_stack /= outputDims[dim];
+ dim--;
+ }
+ offsetIn0 += stride_step0[dim];
+ offsetIn1 += stride_step1[dim];
+ ++offsetOut;
+ dim = contiguousIdx - 1;
+ }
}
}
+} // namespace Aidge
+
// Kernels registration to implementation entry point
REGISTRAR(AndImpl_cpu,
{DataType::Float32},
- {ProdConso::inPlaceModel, Aidge::AndImpl_cpu_forward_kernel<float, float, float>, nullptr});
+ {ProdConso::inPlaceModel, Aidge::EqualImpl_cpu_forward_kernel<float, float, float>, nullptr});
REGISTRAR(AndImpl_cpu,
{DataType::Float64},
- {ProdConso::inPlaceModel, Aidge::AndImpl_cpu_forward_kernel<double, double, double>, nullptr});
+ {ProdConso::inPlaceModel, Aidge::EqualImpl_cpu_forward_kernel<double, double, double>, nullptr});
REGISTRAR(AndImpl_cpu,
{DataType::Int32},
- {ProdConso::inPlaceModel, Aidge::AndImpl_cpu_forward_kernel<std::int32_t, std::int32_t, std::int32_t>, nullptr});
+ {ProdConso::inPlaceModel, Aidge::EqualImpl_cpu_forward_kernel<std::int32_t, std::int32_t, std::int32_t>, nullptr});
REGISTRAR(AndImpl_cpu,
{DataType::Int64},
- {ProdConso::inPlaceModel, Aidge::AndImpl_cpu_forward_kernel<std::int64_t, std::int64_t, std::int64_t>, nullptr});
+ {ProdConso::inPlaceModel, Aidge::EqualImpl_cpu_forward_kernel<std::int64_t, std::int64_t, std::int64_t>, nullptr});
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_ANDIMPL_KERNELS_H_ */
diff --git a/src/operator/AndImpl.cpp b/src/operator/AndImpl.cpp
index 2e0f59769ad86f6e4143ab59d089706e34792244..0cff914a4d03f6ef1ef339d7c7b46e48b6f4c293 100644
--- a/src/operator/AndImpl.cpp
+++ b/src/operator/AndImpl.cpp
@@ -25,22 +25,34 @@
template <>
void Aidge::AndImpl_cpu::forward() {
- const std::vector<std::size_t> inputDims0 = getBroadcastedDims(std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dims(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims());
- const std::vector<std::size_t> inputDims1 = getBroadcastedDims(std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dims(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->dims());
+ const And_Op& op = static_cast<const And_Op&>(mOp);
+ // Check inputs
+ AIDGE_ASSERT(op.getInput(0), "missing input in And operator");
+ AIDGE_ASSERT(op.getInput(0)->hasImpl(), "cannot run And forward because the 0-th input has no implementation.");
+ AIDGE_ASSERT(op.getInput(1), "missing input in And operator");
+ AIDGE_ASSERT(op.getInput(1)->hasImpl(), "cannot run And forward because the 1st input has no implementation.");
+
+ AIDGE_ASSERT(op.getInput(1)->dataType() == op.getInput(0)->dataType(), "Cannot And inputs with two differents data type.");
// Find the correct kernel type
const auto impl = Registrar<AndImpl_cpu>::create(getBestMatch(getRequiredSpec()));
- // Call kernel
- impl.forward(inputDims0,
- inputDims1,
- std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dims(),
- getCPUPtr(mOp.getRawInput(0)),
- getCPUPtr(mOp.getRawInput(1)),
- getCPUPtr(mOp.getRawOutput(0)));
+ // Convert input data (no overhead if not needed!)
+ // TODO: right now, if needed, memory will be allocated/deallocated at each
+ // call to forward(). We might put the following shared_ptr as members of
+ // this class to avoid that.
+ std::shared_ptr<Tensor> input0Fallback, input1Fallback, input2Fallback;
+ const auto& input0 = op.getInput(0)->refCastFrom(input0Fallback, *op.getInput(0));
+ const auto& input1 = op.getInput(1)->refCastFrom(input1Fallback, *op.getInput(1));
+
+
+ impl.forward(op.getInput(0)->dims(),
+ op.getInput(1)->dims(),
+ op.getOutput(0)->dims(),
+ input0.getImpl()->rawPtr(),
+ input1.getImpl()->rawPtr(),
+ getCPUPtr(op.getRawOutput(0)));
}
template <>