Move CPU/GPU files from Core/Runtime to the respective backend folders
Legacy structure contained two libraries core/runtime with two backends
in each.
We reduce the core/runtime libraries to a single library thus merging
the backend files
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I69545765fe7a730368105cdbd067d3135ec7a174
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6155
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/gpu/cl/operators/ClSoftmax.cpp b/src/gpu/cl/operators/ClSoftmax.cpp
new file mode 100644
index 0000000..6b728f5
--- /dev/null
+++ b/src/gpu/cl/operators/ClSoftmax.cpp
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/gpu/cl/operators/ClSoftmax.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/helpers/MemoryHelpers.h"
+#include "src/core/helpers/SoftmaxHelpers.h"
+#include "src/gpu/cl/kernels/ClSoftmaxKernel.h"
+#include "src/gpu/cl/operators/ClPermute.h"
+#include "src/gpu/cl/utils/ClAuxTensorHandler.h"
+#include "support/Cast.h"
+
+using namespace arm_compute::experimental;
+
+namespace arm_compute
+{
+namespace opencl
+{
+ClSoftmax::ClSoftmax()
+ : _permute_input(std::make_unique<ClPermute>()),
+ _permute_output(std::make_unique<ClPermute>()),
+ _max_shift_exp_sum_kernel(std::make_unique<kernels::ClLogits1DMaxShiftExpSumKernel>()),
+ _norm_kernel(std::make_unique<kernels::ClLogits1DNormKernel>()),
+ _max_info(),
+ _sum_info(),
+ _tmp_info(),
+ _permuted_src_info(),
+ _permuted_dst_info(),
+ _aux_mem(InternalTensorIdx::COUNT)
+{
+}
+
+void ClSoftmax::configure(const CLCompileContext &compile_context, const ITensorInfo &src, ITensorInfo &dst, const SoftmaxKernelInfo &info)
+{
+ ARM_COMPUTE_ERROR_THROW_ON(validate(src, dst, info));
+
+ const size_t actual_axis = static_cast<size_t>(wrap_around(info.axis, static_cast<int32_t>(src.num_dimensions())));
+
+ _needs_permute = actual_axis != 0;
+
+ const ITensorInfo &tmp_input_info = _needs_permute ? _permuted_src_info : src;
+ ITensorInfo &tmp_output_info = _needs_permute ? _permuted_dst_info : dst;
+
+ if(_needs_permute)
+ {
+ const auto perm_info = softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis);
+ _permute_input->configure(compile_context, &src, &_permuted_src_info, perm_info);
+ }
+
+ DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input_info.data_type()) ? DataType::S32 : tmp_input_info.data_type();
+ _tmp_info = tmp_input_info.clone()->set_data_type(tmp_data_type);
+
+ TensorShape max_sum_shape = tmp_input_info.tensor_shape();
+ _max_info = tmp_input_info.clone()->set_tensor_shape(max_sum_shape);
+ _sum_info = tmp_input_info.clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type);
+
+ // Set GPU target to kernels
+ _max_shift_exp_sum_kernel->set_target(CLScheduler::get().target());
+
+ _max_shift_exp_sum_kernel->configure(compile_context, tmp_input_info, _max_info, _tmp_info, _sum_info, info);
+ _norm_kernel->configure(compile_context, _tmp_info, _sum_info, tmp_output_info, info);
+
+ if(_needs_permute)
+ {
+ const auto perm_info = softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis);
+ _permute_output->configure(compile_context, &_permuted_dst_info, &dst, perm_info);
+ }
+
+ _aux_mem[InternalTensorIdx::SUM] = MemoryInfo(offset_int_vec(InternalTensorIdx::SUM), MemoryLifetime::Temporary, _sum_info.total_size());
+ _aux_mem[InternalTensorIdx::TMP] = MemoryInfo(offset_int_vec(InternalTensorIdx::TMP), MemoryLifetime::Temporary, _tmp_info.total_size());
+ _aux_mem[InternalTensorIdx::MAX] = MemoryInfo(offset_int_vec(InternalTensorIdx::MAX), MemoryLifetime::Temporary, _max_info.total_size());
+
+ _aux_mem[InternalTensorIdx::PERMUTED_SRC] = MemoryInfo(offset_int_vec(InternalTensorIdx::PERMUTED_SRC), MemoryLifetime::Temporary, _permuted_src_info.total_size());
+ _aux_mem[InternalTensorIdx::PERMUTED_DST] = MemoryInfo(offset_int_vec(InternalTensorIdx::PERMUTED_DST), MemoryLifetime::Temporary, _permuted_dst_info.total_size());
+}
+
+Status ClSoftmax::validate(const ITensorInfo &src, const ITensorInfo &dst, const SoftmaxKernelInfo &info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(src.num_dimensions() > 4, "Only up to 4 dimensions are supported");
+ ARM_COMPUTE_UNUSED(info.beta);
+ ARM_COMPUTE_RETURN_ERROR_ON(info.axis < static_cast<int32_t>(-src.num_dimensions()) || static_cast<int32_t>(src.num_dimensions()) <= info.axis);
+
+ const size_t actual_axis = static_cast<size_t>(wrap_around(info.axis, static_cast<int32_t>(src.num_dimensions())));
+ const bool needs_permute = actual_axis != 0;
+ if(needs_permute)
+ {
+ const PermutationVector permutation_vector = softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis);
+ const TensorShape permuted_shape = misc::shape_calculator::compute_permutation_output_shape(src, permutation_vector);
+ TensorInfo input_permuted(src.clone()->set_tensor_shape(permuted_shape));
+ ARM_COMPUTE_RETURN_ON_ERROR(ClPermute::validate(&src, &input_permuted, permutation_vector));
+ TensorInfo output_permuted(dst.clone()->set_tensor_shape(permuted_shape));
+ ARM_COMPUTE_RETURN_ON_ERROR(ClPermute::validate(&output_permuted, &dst, permutation_vector));
+ }
+
+ // Create intermediate tensor info
+ DataType tmp_data_type = is_data_type_quantized_asymmetric(src.data_type()) ? DataType::S32 : src.data_type();
+ TensorInfo tensor_info_tmp(src.clone()->set_data_type(tmp_data_type).set_is_resizable(true));
+
+ TensorShape max_sum_shape = src.tensor_shape();
+ max_sum_shape.set(0, 1);
+ TensorInfo tensor_info_max(src.clone()->set_tensor_shape(max_sum_shape).set_is_resizable(true));
+ TensorInfo tensor_info_sum(src.clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(QuantizationInfo()).set_is_resizable(true));
+
+ ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClLogits1DMaxShiftExpSumKernel::validate(src, tensor_info_max, tensor_info_tmp, tensor_info_sum));
+ ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClLogits1DNormKernel::validate(tensor_info_tmp, tensor_info_sum, dst, info));
+
+ return Status{};
+}
+
+void ClSoftmax::run(ITensorPack &tensors)
+{
+ auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
+ auto dst = tensors.get_tensor(TensorType::ACL_DST);
+
+ CLAuxTensorHandler sum(offset_int_vec(InternalTensorIdx::SUM), _sum_info, tensors, false);
+ CLAuxTensorHandler tmp(offset_int_vec(InternalTensorIdx::TMP), _tmp_info, tensors, false);
+ CLAuxTensorHandler max(offset_int_vec(InternalTensorIdx::MAX), _max_info, tensors, false);
+
+ CLAuxTensorHandler permuted_src(offset_int_vec(InternalTensorIdx::PERMUTED_SRC), _permuted_src_info, tensors, false);
+ CLAuxTensorHandler permuted_dst(offset_int_vec(InternalTensorIdx::PERMUTED_DST), _permuted_dst_info, tensors, false);
+
+ if(_needs_permute)
+ {
+ ITensorPack pack;
+ pack.add_const_tensor(TensorType::ACL_SRC, src);
+ pack.add_tensor(TensorType::ACL_DST, permuted_src.get());
+ _permute_input.get()->run(pack);
+ }
+
+ ITensorPack sum_pack;
+ ITensorPack norm_pack;
+ if(_needs_permute)
+ {
+ sum_pack.add_const_tensor(TensorType::ACL_SRC, permuted_src.get());
+ norm_pack.add_tensor(TensorType::ACL_DST, permuted_dst.get());
+ }
+ else
+ {
+ sum_pack.add_const_tensor(TensorType::ACL_SRC, src);
+ norm_pack.add_tensor(TensorType::ACL_DST, dst);
+ }
+ sum_pack.add_tensor(TensorType::ACL_DST, tmp.get());
+ sum_pack.add_tensor(TensorType::ACL_INT_0, max.get());
+ sum_pack.add_tensor(TensorType::ACL_INT_1, sum.get());
+
+ norm_pack.add_const_tensor(TensorType::ACL_SRC, tmp.get());
+ norm_pack.add_tensor(TensorType::ACL_INT_0, sum.get());
+
+ CLScheduler::get().enqueue_op(*_max_shift_exp_sum_kernel.get(), sum_pack, false);
+ CLScheduler::get().enqueue_op(*_norm_kernel.get(), norm_pack, false);
+
+ if(_needs_permute)
+ {
+ ITensorPack pack;
+ pack.add_const_tensor(TensorType::ACL_SRC, permuted_dst.get());
+ pack.add_tensor(TensorType::ACL_DST, dst);
+ _permute_output.get()->run(pack);
+ }
+}
+
+experimental::MemoryRequirements ClSoftmax::workspace() const
+{
+ return _aux_mem;
+}
+} // namespace opencl
+} // namespace arm_compute
\ No newline at end of file