Add experimental PostOp interface to ClGemmMatrixMultiplyReshapedKernel Part 1
This interface supports the fusion of multiple elementwise operations
Partially resolves: COMPMID-4435
Change-Id: If68dd7dd98dcf239fde7cb1f0a4a6d4d1e899a6f
Signed-off-by: SiCongLi <sicong.li@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6483
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h
index 5f5fa3b..e119158 100644
--- a/tests/validation/fixtures/GEMMFixture.h
+++ b/tests/validation/fixtures/GEMMFixture.h
@@ -27,6 +27,8 @@
#include "arm_compute/core/KernelDescriptors.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
+#include "arm_compute/core/experimental/IPostOp.h"
+#include "src/core/experimental/PostOp.h"
#include "tests/AssetsLibrary.h"
#include "tests/Globals.h"
#include "tests/IAccessor.h"
@@ -34,7 +36,9 @@
#include "tests/framework/Fixture.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/ActivationLayer.h"
+#include "tests/validation/reference/ElementwiseOperations.h"
#include "tests/validation/reference/GEMM.h"
+#include "tests/validation/reference/PostOps.h"
#include <random>
@@ -915,6 +919,263 @@
SimpleTensor<T> _reference{};
};
+/** (EXPERIMENTAL_POST_OPS)*/
+template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMOperatorType, bool fp_mixed_precision = false>
+class GEMMMatrixMultiplyReshapedWithPostOpsValidationFixture : public framework::Fixture
+{
+public:
+ using PostOpArgBroadcast = std::tuple<bool, bool, bool>; // Instruct fixture if we need broadcasting in dimension 0, 1, 2 of each PostOp argument
+public:
+ template <typename...>
+ void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, bool interleave_lhs,
+ bool interleave_rhs, bool export_to_cl_image, DataType data_type, float alpha, float beta, bool broadcast_bias, bool lhs_transpose, const ActivationLayerInfo &act_info,
+ const experimental::PostOpList<PostOpArgBroadcast> &post_ops)
+ {
+ GEMMLHSMatrixInfo lhs_info;
+ lhs_info.m0 = m0;
+ lhs_info.k0 = k0;
+ lhs_info.v0 = v0;
+ lhs_info.interleave = interleave_lhs;
+ lhs_info.transpose = lhs_transpose;
+
+ GEMMRHSMatrixInfo rhs_info;
+ rhs_info.n0 = n0;
+ rhs_info.k0 = k0;
+ rhs_info.h0 = h0;
+ rhs_info.interleave = interleave_rhs;
+ rhs_info.transpose = !lhs_transpose;
+ rhs_info.export_to_cl_image = export_to_cl_image;
+
+ // Set the tensor shapes for LHS and RHS matrices
+ const TensorShape lhs_shape(k, m, batch_size);
+ const TensorShape rhs_shape(n, k, batch_size);
+ const TensorShape bias_shape(n,
+ broadcast_bias ? 1 : m,
+ broadcast_bias ? 1 : batch_size);
+ auto post_ops_with_shapes = experimental::transform_post_op_list_arguments<PostOpArgBroadcast, TensorShape>(post_ops,
+ [ = ](auto broadcast)
+ {
+ return TensorShape
+ {
+ std::get<0>(broadcast) ? 1 : n,
+ std::get<1>(broadcast) ? 1 : m,
+ std::get<2>(broadcast) ? 1 : batch_size,
+ };
+ });
+
+ _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes);
+ if(validate_result)
+ {
+ _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, beta, broadcast_bias, act_info, post_ops_with_shapes);
+ }
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported.");
+ using DistributionType = typename std::conditional<std::is_same<T, half>::value, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type;
+
+ DistributionType distribution{ T(-1.0f), T(1.0f) };
+ library->fill(tensor, distribution, i);
+
+ // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
+ DistributionType distribution_inf{ T(std::numeric_limits<float>::infinity()), T(std::numeric_limits<float>::infinity()) };
+ library->fill_borders_with_garbage(tensor, distribution_inf, i);
+ }
+
+ TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+ DataType data_type, float alpha, float beta, bool broadcast_bias, const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops)
+ {
+ // Create tensors
+ TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
+ TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
+ TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
+
+ // Create post op tensors and populate post op with them
+ std::vector<TensorType> post_op_tensors_holder{};
+ auto populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, ITensorInfo *>(post_ops,
+ [&post_op_tensors_holder, &data_type](auto shape)
+ {
+ auto t = create_tensor<TensorType>(shape, data_type, 1);
+ post_op_tensors_holder.push_back(std::move(t));
+ return post_op_tensors_holder.back().info();
+ });
+ TensorType lhs_reshaped;
+ TensorType rhs_reshaped;
+ TensorType dst;
+
+ const unsigned int M = lhs_shape[1];
+ const unsigned int N = rhs_shape[0];
+ const unsigned int K = lhs_shape[0];
+ GEMMKernelInfo kernel_info;
+ kernel_info.m = M;
+ kernel_info.n = N;
+ kernel_info.k = K;
+ kernel_info.depth_output_gemm3d = 0;
+ kernel_info.reinterpret_input_as_3d = false;
+ kernel_info.broadcast_bias = broadcast_bias;
+ kernel_info.activation_info = act_info;
+ kernel_info.fp_mixed_precision = fp_mixed_precision;
+ kernel_info.post_ops = populated_post_ops;
+
+ // The output tensor will be auto-initialized within the function
+
+ // Create and configure function
+ ReshapeLHSOperatorType reshape_lhs;
+ ReshapeRHSOperatorType reshape_rhs;
+ GEMMOperatorType gemm;
+
+ validate_result = bool(reshape_rhs.validate(rhs.info(), rhs_reshaped.info(), rhs_info));
+ validate_result = validate_result || !rhs_info.export_to_cl_image;
+ if(!validate_result)
+ {
+ return nullptr;
+ }
+
+ reshape_lhs.configure(lhs.info(), lhs_reshaped.info(), lhs_info);
+ reshape_rhs.configure(rhs.info(), rhs_reshaped.info(), rhs_info);
+ gemm.configure(lhs_reshaped.info(), rhs_reshaped.info(), bias.info(), dst.info(), alpha, beta, lhs_info, rhs_info, kernel_info);
+
+ ARM_COMPUTE_ASSERT(lhs.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(rhs.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(bias.info()->is_resizable());
+ for(const auto &tensor : post_op_tensors_holder)
+ {
+ ARM_COMPUTE_ASSERT(tensor.info()->is_resizable());
+ }
+
+ // We do not pad when using image as it needs to comply to strict pitch alignment restrictions
+ if(!rhs_info.export_to_cl_image)
+ {
+ add_padding_x({ &lhs, &rhs, &lhs_reshaped, &rhs_reshaped, &bias, &dst });
+ for(auto &tensor : post_op_tensors_holder)
+ {
+ add_padding_x({ &tensor });
+ }
+ }
+
+ // Allocate tensors
+ lhs.allocator()->allocate();
+ rhs.allocator()->allocate();
+ lhs_reshaped.allocator()->allocate();
+ rhs_reshaped.allocator()->allocate();
+ bias.allocator()->allocate();
+ dst.allocator()->allocate();
+ for(auto &tensor : post_op_tensors_holder)
+ {
+ tensor.allocator()->allocate();
+ }
+
+ ARM_COMPUTE_ASSERT(!lhs.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(!rhs.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(!lhs_reshaped.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(!rhs_reshaped.info()->is_resizable());
+ ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
+ for(const auto &tensor : post_op_tensors_holder)
+ {
+ ARM_COMPUTE_ASSERT(!tensor.info()->is_resizable());
+ }
+
+ // Fill tensors
+ fill(AccessorType(lhs), 0);
+ fill(AccessorType(rhs), 1);
+ fill(AccessorType(bias), 2);
+ for(size_t i = 0; i < post_op_tensors_holder.size(); ++i)
+ {
+ fill(AccessorType(post_op_tensors_holder.at(i)), 3 + i);
+ }
+
+ // Compute GEMM
+ ITensorPack reshape_lhs_pack = { { ACL_SRC, &lhs }, { ACL_DST, &lhs_reshaped } };
+ reshape_lhs.run(reshape_lhs_pack);
+ ITensorPack reshape_rhs_pack = { { ACL_SRC, &rhs }, { ACL_DST, &rhs_reshaped } };
+ reshape_rhs.run(reshape_rhs_pack);
+ ITensorPack gemm_pack({ { ACL_SRC_0, &lhs_reshaped },
+ { ACL_SRC_1, &rhs_reshaped },
+ { ACL_SRC_2, &bias },
+ { ACL_DST, &dst }
+ });
+ for(size_t i = 0; i < post_op_tensors_holder.size(); ++i)
+ {
+ gemm_pack.add_tensor(experimental::get_post_op_arg_type(i), &post_op_tensors_holder.at(i));
+ }
+ gemm.run(gemm_pack);
+
+ return dst;
+ }
+
+ SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, float beta, bool broadcast_bias,
+ const ActivationLayerInfo &act_info, const experimental::PostOpList<TensorShape> &post_ops)
+ {
+ TensorShape dst_shape = lhs_shape;
+ dst_shape[0] = rhs_shape[0];
+ dst_shape[1] = lhs_shape[1];
+
+ // Create reference
+ SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
+ SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
+ SimpleTensor<T> bias{ dst_shape, data_type, 1 };
+ // Create post op tensors and populate post op with them
+ auto populated_post_ops = experimental::transform_post_op_list_arguments<TensorShape, SimpleTensor<T>>(post_ops, [&data_type](auto shape)
+ {
+ return SimpleTensor<T> { shape, data_type, 1 };
+ });
+
+ const int n = rhs_shape[0];
+ const int m = lhs_shape[1];
+ const int batch_size = lhs_shape[2];
+
+ // Fill reference
+ int tensor_idx = 0;
+ fill(lhs, tensor_idx++);
+ fill(rhs, tensor_idx++);
+ fill(bias, tensor_idx++);
+ for(auto &op : populated_post_ops.get_list())
+ {
+ for(auto tensor : op->arguments())
+ {
+ fill(*tensor, tensor_idx++);
+ }
+ }
+
+ if(broadcast_bias)
+ {
+ // In case of broadcast, we need simply copy the first into the following "M" ones
+ for(int i = 1; i < m * batch_size; i++)
+ {
+ memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
+ }
+ }
+
+ SimpleTensor<T> out;
+ if(fp_mixed_precision)
+ {
+ out = reference::gemm_mixed_precision<T>(lhs, rhs, bias, alpha, beta);
+ }
+ else
+ {
+ out = reference::gemm<T>(lhs, rhs, bias, alpha, beta);
+ }
+ // Ignore activation info if post ops are used instead
+ if(populated_post_ops.size() > 0)
+ {
+ out = reference::post_ops<T>(out, populated_post_ops);
+ }
+ else
+ {
+ out = reference::activation_layer(out, act_info);
+ }
+ return out;
+ }
+
+ bool validate_result = true;
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+};
+
template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSOperatorType, typename ReshapeRHSOperatorType, typename GEMMOperatorType, bool fp_mixed_precision = false>
class GEMMMatrixMultiplyReshaped3DValidationFixture : public framework::Fixture
{