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Diffstat (limited to 'arm_compute/graph/backends/FunctionHelpers.h')
-rw-r--r--arm_compute/graph/backends/FunctionHelpers.h188
1 files changed, 4 insertions, 184 deletions
diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h
index 803283e20d..a567427bf1 100644
--- a/arm_compute/graph/backends/FunctionHelpers.h
+++ b/arm_compute/graph/backends/FunctionHelpers.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018-2021 Arm Limited.
+ * Copyright (c) 2018-2021, 2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,18 +21,15 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H
-#define ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H
+#ifndef ACL_ARM_COMPUTE_GRAPH_BACKENDS_FUNCTIONHELPERS_H
+#define ACL_ARM_COMPUTE_GRAPH_BACKENDS_FUNCTIONHELPERS_H
-#include "arm_compute/core/experimental/IPostOp.h"
-#include "arm_compute/core/experimental/PostOps.h"
#include "arm_compute/graph/Logger.h"
#include "arm_compute/graph/Tensor.h"
#include "arm_compute/graph/TypePrinter.h"
#include "arm_compute/graph/Types.h"
#include "arm_compute/graph/Utils.h"
#include "arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h"
-#include "arm_compute/graph/backends/FusedConvolutionBatchNormalizationWithPostOpsFunction.h"
#include "arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h"
#include "arm_compute/graph/backends/Utils.h"
#include "arm_compute/graph/nodes/Nodes.h"
@@ -541,183 +538,6 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
return std::move(func);
}
-/** Create a backend convolution layer function with post operator
- *
- * @tparam ConvolutionLayerFunctions Backend convolution functions
- * @tparam TargetInfo Target-specific information
- *
- * @param[in] node Node to create the backend function for
- * @param[in] ctx Graph context
- *
- * @return Backend convolution layer function
- */
-template <typename ConvolutionLayerFunctions, typename TargetInfo>
-std::unique_ptr<IFunction> create_fused_convolution_with_post_op(FusedConvolutionWithPostOpNode &node, GraphContext &ctx)
-{
- validate_node<TargetInfo>(node, 4 /* expected inputs */, 1 /* expected outputs */);
-
- // Extract IO and info
- typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));
- typename TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));
- typename TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2));
- typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));
-
- const bool is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
-
- if(is_quantized)
- {
- biases->info()->set_data_type(DataType::S32);
- }
-
- const PadStrideInfo conv_info = node.convolution_info();
- const unsigned int num_groups = node.num_groups();
- const ActivationLayerInfo fused_act = node.fused_activation();
-
- experimental::PostOpList<typename TargetInfo::TensorType *> post_ops;
-
- auto &post_op_info_list = node.post_op_info_list();
- for(const auto &post_op_info : post_op_info_list)
- {
- switch(post_op_info->type())
- {
- case PostOpType::Activation:
- {
- const auto act_info = utils::cast::polymorphic_downcast<const ConvPostOpInfoActivation *>(post_op_info.get());
- post_ops.template push_back_op<experimental::PostOpAct<typename TargetInfo::TensorType *>>(act_info->_act);
- break;
- }
- case PostOpType::Eltwise_Add:
- {
- typename TargetInfo::TensorType *add_input = get_backing_tensor<TargetInfo>(node.input(3));
- const auto eltwise_info = utils::cast::polymorphic_downcast<const ConvPostOpInfoEltwiseAdd *>(post_op_info.get());
- post_ops.template push_back_op<experimental::PostOpEltwiseAdd<typename TargetInfo::TensorType *>>(add_input, eltwise_info->_prev_op_dst_pos, eltwise_info->_policy);
- break;
- }
- default:
- {
- ARM_COMPUTE_ERROR("Unsupported PostOpType");
- }
- }
- }
-
- // Create and configure function (we assume that functions have been validated before creation)
- std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, TargetInfo::TargetType);
- std::unique_ptr<IFunction> func;
- std::string func_name;
-
- // Fuse convolution with post ops is only supported for conv1x1, which is only implemented as gemmconv2d
- std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::GEMMConvolutionLayer>(
- std::string("GEMMConvolutionLayer"), mm,
- input, weights, biases, output, conv_info,
- WeightsInfo(), Size2D(1U, 1U), fused_act, num_groups, post_ops);
-
- // Log info
- std::ostringstream qss;
- if(is_quantized)
- {
- qss << " Input QuantInfo: " << input->info()->quantization_info()
- << " Weights QuantInfo: " << weights->info()->quantization_info()
- << " Output QuantInfo: " << output->info()->quantization_info();
- }
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << func_name
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Groups: " << num_groups
- << " Input shape: " << input->info()->tensor_shape()
- << " Weights shape: " << weights->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << qss.str()
- << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
- << " Post ops" << post_ops
- << std::endl);
- return std::move(func);
-}
-
-/** Create a backend convolution batch normalization layer function with post operator
- *
- * @tparam FusedLayerTypes Backend convolution functions
- * @tparam TargetInfo Target-specific information
- *
- * @param[in] node Node to create the backend function for
- * @param[in] ctx Graph context
- *
- * @return Backend fused convolution with batch normalization layer function
- */
-template <typename FusedLayerTypes, typename TargetInfo>
-std::unique_ptr<IFunction> create_fused_convolution_batch_normalization_with_post_op(FusedConvolutionBatchNormalizationWithPostOpsNode &node, GraphContext &ctx)
-{
- validate_node<TargetInfo>(node, 8 /* expected inputs */, 1 /* expected outputs */);
-
- // Extract IO and info
- typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0));
- typename TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));
- typename TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2));
- typename TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.input(3));
- typename TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.input(4));
- typename TargetInfo::TensorType *beta = get_backing_tensor<TargetInfo>(node.input(5));
- typename TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.input(6));
-
- typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));
-
- const PadStrideInfo conv_info = node.convolution_info();
- const unsigned int num_groups = node.num_groups();
- const bool fast_math = node.fast_math_hint() == FastMathHint::Enabled;
- const float epsilon = node.epsilon();
-
- experimental::PostOpList<typename TargetInfo::TensorType *> post_ops;
-
- auto &post_op_info_list = node.post_op_info_list();
- for(const auto &post_op_info : post_op_info_list)
- {
- switch(post_op_info->type())
- {
- case PostOpType::Activation:
- {
- const auto act_info = utils::cast::polymorphic_downcast<const ConvPostOpInfoActivation *>(post_op_info.get());
- post_ops.template push_back_op<experimental::PostOpAct<typename TargetInfo::TensorType *>>(act_info->_act);
- break;
- }
- case PostOpType::Eltwise_Add:
- {
- typename TargetInfo::TensorType *add_input = get_backing_tensor<TargetInfo>(node.input(3));
- const auto eltwise_info = utils::cast::polymorphic_downcast<const ConvPostOpInfoEltwiseAdd *>(post_op_info.get());
- post_ops.template push_back_op<experimental::PostOpEltwiseAdd<typename TargetInfo::TensorType *>>(add_input, eltwise_info->_prev_op_dst_pos, eltwise_info->_policy);
- break;
- }
- default:
- {
- ARM_COMPUTE_ERROR("Unsupported PostOpType");
- }
- }
- }
-
- // Create and configure function (we assume that functions have been validated before creation)
- std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, TargetInfo::TargetType);
- std::unique_ptr<IFunction> func;
- std::string func_name;
-
- using FType = FusedConvolutionBatchNormalizationWithPostOpsFunction<TargetInfo, FusedLayerTypes>;
-
- // Create and configure function
- std::tie(func, func_name) = create_named_memory_managed_function<FType>(
- std::string("FusedConvolutionBatchNormalizationLayerWithPostOpsLayer"), mm, input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, num_groups, fast_math, post_ops);
-
- // Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Weights shape: " << weights->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << " Post Ops:" << post_ops
- << std::endl);
- return std::move(func);
-}
-
/** Create a backend deconvolution layer function
*
* @tparam DeconvolutionLayerFunction Backend deconvolution function
@@ -2025,4 +1845,4 @@ std::unique_ptr<IFunction> create_strided_slice_layer(StridedSliceLayerNode &nod
} // namespace graph
} // namespace arm_compute
-#endif /* ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H */
+#endif // ACL_ARM_COMPUTE_GRAPH_BACKENDS_FUNCTIONHELPERS_H
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-15 20:42:21 +0000