Remove legacy PostOps code

PostOps was the experimental interface for Dynamic Fusion. It is now
replaced by the new Dynamic Fusion interface with code generation using
the Compute Kernel Writer.

Resolves: COMPMID-6190

Change-Id: I813b48facef2fd6f3aee332588886b4f9b3d33d8
Signed-off-by: Jakub Sujak <jakub.sujak@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10219
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: SiCong Li <sicong.li@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

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