From d6ae2c5f1863764786d896a7e0145c9796292ac5 Mon Sep 17 00:00:00 2001
From: Jerome Hue <jerome.hue@cea.fr>
Date: Fri, 28 Feb 2025 15:10:49 +0100
Subject: [PATCH] Fix leaky test
---
.../cpu/operator/.nfs000000030e7b5d24000000ad | 223 ++++++++++++++++++
unit_tests/operator/Test_MetaOperator.cpp | 4 +-
2 files changed, 225 insertions(+), 2 deletions(-)
create mode 100644 include/aidge/backend/cpu/operator/.nfs000000030e7b5d24000000ad
diff --git a/include/aidge/backend/cpu/operator/.nfs000000030e7b5d24000000ad b/include/aidge/backend/cpu/operator/.nfs000000030e7b5d24000000ad
new file mode 100644
index 00000000..fb7981cb
--- /dev/null
+++ b/include/aidge/backend/cpu/operator/.nfs000000030e7b5d24000000ad
@@ -0,0 +1,223 @@
+/********************************************************************************
+ * 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_OPERATOR_SUBIMPL_KERNELS_H_
+#define AIDGE_CPU_OPERATOR_SUBIMPL_KERNELS_H_
+
+#include "aidge/utils/Registrar.hpp"
+
+#include <cstddef> // std::size_t
+#include <cstdint> // std::int32_t, std::int64_t
+#include <vector>
+
+#include "aidge/backend/cpu/data/Broadcasting.hpp"
+#include "aidge/backend/cpu/operator/SubImpl.hpp"
+
+namespace {
+// suppose values are contiguous in memory
+template <class I1, class I2, class O>
+void sub_contiguous_arrays(const std::size_t input1size,
+ const std::size_t input2size,
+ const std::size_t output1size,
+ const I1* input1,
+ const I2* 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]);
+ }
+}
+}
+
+
+namespace Aidge {
+
+template <class I1, class I2, class O>
+void SubImpl_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_,
+ void* output_) {
+
+ const I1* input_0 = static_cast<const I1*>(input0_);
+ const I2* input_1 = static_cast<const I2*>(input1_);
+ O* output = static_cast<O*>(output_);
+
+ // [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;
+ }
+
+ // 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));
+ }
+
+ const std::size_t nbDims = dims0.size();
+
+ // 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;) {
+ sub_contiguous_arrays<I1,I2,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;
+ }
+ }
+}
+
+template <class I1, class I2, class O>
+void SubImpl_cpu_backward_kernel(const std::size_t input0Length,
+ const std::size_t input1Length,
+ const std::size_t gradOutputLength,
+ const std::vector<std::size_t>& dims0,
+ const std::vector<std::size_t>& dims1,
+ const std::vector<std::size_t>& outputDims,
+ const void* input0_,
+ const void* input1_,
+ const void* grad_output_,
+ void* gradientInput0_,
+ void* gradientInput1_)
+{
+ const I1* input0 = static_cast<const I1*>(input0_);
+ const I2* input1 = static_cast<const I2*>(input1_);
+ const O* grad_output = static_cast<const O*>(grad_output_);
+ auto* grad_input_0 = static_cast<I1*>(gradientInput0_);
+ auto* grad_input_1 = static_cast<I2*>(gradientInput1_);
+
+ std::fill_n(grad_input_0, input0Length, static_cast<I1>(0));
+ std::fill_n(grad_input_1, input1Length, static_cast<I2>(0));
+
+ auto broadcastedDims0 = getBroadcastedDims(outputDims, dims0);
+ auto broadcastedDims1 = getBroadcastedDims(outputDims, dims1);
+
+ for (std::size_t i = 0; i < gradOutputLength; ++i) {
+ auto idxOutputGrad = getMultiDimIndices(outputDims, i);
+ std::vector<std::size_t> idxInput0(broadcastedDims0.size());
+ std::vector<std::size_t> idxInput1(broadcastedDims1.size());
+
+ for (std::size_t dimension = 0; dimension < broadcastedDims0.size(); ++dimension) {
+ idxInput0[dimension] = (broadcastedDims0[dimension] == 1) ? 0 : idxOutputGrad[dimension];
+ }
+
+ for (std::size_t dimension = 0; dimension < broadcastedDims1.size(); ++dimension) {
+ idxInput1[dimension] = (broadcastedDims1[dimension] == 1) ? 0 : idxOutputGrad[dimension];
+ }
+
+ auto idx0 = getFlattenedIndex(broadcastedDims0, idxInput0);
+ auto idx1 = getFlattenedIndex(broadcastedDims1, idxInput1);
+
+ // For subtraction: gradient of first input is 1 * grad_output
+ grad_input_0[idx0] += static_cast<I1>(grad_output[i]);
+ // For subtraction: gradient of second input is -1 * grad_output
+ grad_input_1[idx1] += static_cast<I2>(-grad_output[i]);
+ }
+}
+
+
+// Kernels registration to implementation entry point
+REGISTRAR(SubImpl_cpu,
+ {DataType::Float32},
+ {ProdConso::inPlaceModel, Aidge::SubImpl_cpu_forward_kernel<float, float, float>, Aidge::SubImpl_cpu_backward_kernel<float,float,float>});
+REGISTRAR(SubImpl_cpu,
+ {DataType::Float64},
+ {ProdConso::inPlaceModel, Aidge::SubImpl_cpu_forward_kernel<double, double, double>, nullptr});
+REGISTRAR(SubImpl_cpu,
+ {DataType::Int8},
+ {ProdConso::inPlaceModel, Aidge::SubImpl_cpu_forward_kernel<std::int8_t, std::int8_t, std::int8_t>, nullptr});
+REGISTRAR(SubImpl_cpu,
+ {DataType::UInt8},
+ {ProdConso::inPlaceModel, Aidge::SubImpl_cpu_forward_kernel<std::uint8_t, std::uint8_t, std::uint8_t>, nullptr});
+REGISTRAR(SubImpl_cpu,
+ {DataType::Int32},
+ {ProdConso::inPlaceModel, Aidge::SubImpl_cpu_forward_kernel<std::int32_t, std::int32_t, std::int32_t>, nullptr});
+REGISTRAR(SubImpl_cpu,
+ {DataType::Int64},
+ {ProdConso::inPlaceModel, Aidge::SubImpl_cpu_forward_kernel<std::int64_t, std::int64_t, std::int64_t>, nullptr});
+} // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_SUBIMPL_KERNELS_H_ */
diff --git a/unit_tests/operator/Test_MetaOperator.cpp b/unit_tests/operator/Test_MetaOperator.cpp
index 4fe39630..0c4a64bb 100644
--- a/unit_tests/operator/Test_MetaOperator.cpp
+++ b/unit_tests/operator/Test_MetaOperator.cpp
@@ -705,7 +705,7 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
auto fc2 = FC(outChannels, inChannels, true, "fc2");
// NOTE: Account for init step by adding 1 to the max timestep
// parameter.
- auto lif1 = Leaky(nbTimeSteps + 1, beta, threshold, "leaky");
+ auto lif1 = Leaky(nbTimeSteps + 1, beta, threshold, LeakyReset::Subtraction, "leaky");
// associateInput() does not work
fc1->input(1).first->getOperator()->setOutput(0, myWeights);
@@ -774,7 +774,7 @@ TEST_CASE("[cpu/operator] MetaOperator", "[MetaOperator][CPU]") {
const auto nbTimeSteps = dims[0];
const auto beta = betaDist(gen);
- auto myLeaky = Leaky(nbTimeSteps, beta, 1.0, "leaky");
+ auto myLeaky = Leaky(nbTimeSteps, beta, 1.0, LeakyReset::Subtraction, "leaky");
auto op =
std::static_pointer_cast<MetaOperator_Op>(myLeaky->getOperator());
// auto stack = Stack(2);
--
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