Make GpuWorkloadContext own all tensor info objects
* The tensor info objects created by calling create_tensor_info
is now solely owned by the context object. The user only receives
pointers to those objects.
- Internally pointers to tensor info objects are used in various
places. It's safer for dynamic fusion to manage these objects
directly rather than relying on the users.
- The validation test is updated to use the modified API.
* Make various changes in dynamic fusion API to make it more
friendly (e.g. making some of the objects moveable).
Partially resolves: COMPMID-6707
Signed-off-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Change-Id: Ifee70e53c05f8e7b72bf9ef123701ff291c5ee80
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10990
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h b/arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h
index 3deaff7..6b92f12 100644
--- a/arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h
+++ b/arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef ARM_COMPUTE_DYNAMIC_FUSION_RUNTIME_GPU_CL_CLWORKLOADRUNTIME
-#define ARM_COMPUTE_DYNAMIC_FUSION_RUNTIME_GPU_CL_CLWORKLOADRUNTIME
+#ifndef ACL_ARM_COMPUTE_DYNAMIC_FUSION_RUNTIME_GPU_CL_CLWORKLOADRUNTIME_H
+#define ACL_ARM_COMPUTE_DYNAMIC_FUSION_RUNTIME_GPU_CL_CLWORKLOADRUNTIME_H
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/dynamic_fusion/sketch/MemoryDescriptor.h"
@@ -46,8 +46,18 @@
class ClWorkloadRuntime
{
public:
+ /** Default constructor. */
ClWorkloadRuntime();
+
+ /** Destructor */
~ClWorkloadRuntime();
+
+ /** Move constructor */
+ ClWorkloadRuntime(ClWorkloadRuntime &&);
+
+ /** Move assignment */
+ ClWorkloadRuntime &operator=(ClWorkloadRuntime &&);
+
/** Configure @ref ClWorkloadRuntime
* @note A runtime cannot be re-configured
*
@@ -78,4 +88,4 @@
} // namespace dynamic_fusion
} // namespace experimental
} // namespace arm_compute
-#endif /* ARM_COMPUTE_DYNAMIC_FUSION_RUNTIME_GPU_CL_CLWORKLOADRUNTIME */
+#endif // ACL_ARM_COMPUTE_DYNAMIC_FUSION_RUNTIME_GPU_CL_CLWORKLOADRUNTIME_H
diff --git a/arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h b/arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h
index 38b350c..76e4255 100644
--- a/arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h
+++ b/arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADCONTEXT
-#define ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADCONTEXT
+#ifndef ACL_ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADCONTEXT_H
+#define ACL_ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADCONTEXT_H
#include "arm_compute/core/GPUTarget.h"
#include "arm_compute/core/TensorInfo.h"
@@ -85,11 +85,14 @@
* @return TensorInfo Newly created tensor info
*/
template <typename... TArgs>
- TensorInfo create_tensor_info(TArgs &&...args)
+ ITensorInfo *create_tensor_info(TArgs &&...args)
{
- auto tensor_info = TensorInfo(std::forward<TArgs>(args)...);
- register_user_tensor(tensor_info);
- return tensor_info;
+ auto tensor_info = std::make_unique<TensorInfo>(std::forward<TArgs>(args)...);
+ auto *tensor_info_ptr = tensor_info.get();
+
+ register_user_tensor(std::move(tensor_info));
+
+ return tensor_info_ptr;
}
/** Get the internal implementation */
@@ -101,9 +104,11 @@
private:
/** Set a new ID to the tensor info and register its memory descriptor to the context.
*
- * @param[in,out] tensor_info @ref ITensorInfo to be registered.
+ * The ownership of the tensor info object will be transfered to this context object.
+ *
+ * @param[in] tensor_info @ref TensorInfo to be registered.
*/
- void register_user_tensor(ITensorInfo &tensor_info);
+ void register_user_tensor(std::unique_ptr<TensorInfo> &&tensor_info);
/** Internal implementation */
std::unique_ptr<Impl> _impl;
@@ -113,4 +118,4 @@
} // namespace experimental
} // namespace arm_compute
-#endif /* ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADCONTEXT */
+#endif // ACL_ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADCONTEXT_H
diff --git a/arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h b/arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h
index 75c2b1f..1c738bd 100644
--- a/arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h
+++ b/arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCH
-#define ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCH
+#ifndef ACL_ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCH_H
+#define ACL_ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCH_H
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h"
@@ -53,15 +53,28 @@
* @param[in] context Gpu context for the creation of a workload
*/
explicit GpuWorkloadSketch(GpuWorkloadContext *context);
+
/** Destructor */
~GpuWorkloadSketch();
+
+ /** Move constructor */
+ GpuWorkloadSketch(GpuWorkloadSketch &&);
+
+ /** Move assignment */
+ GpuWorkloadSketch &operator=(GpuWorkloadSketch &&);
+
/** Get the implementation */
Implementation &implementation();
+
/** Get the implementation */
const Implementation &implementation() const;
+
/** Get the gpu workload context of this sketch */
const GpuWorkloadContext *gpu_context() const;
+ /** Get the gpu workload context of this sketch */
+ GpuWorkloadContext *gpu_context();
+
private:
std::unique_ptr<Implementation> _impl; /**< Internal opaque implementation*/
};
@@ -69,4 +82,4 @@
} // namespace dynamic_fusion
} // namespace experimental
} // namespace arm_compute
-#endif /* ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCH */
+#endif // ACL_ARM_COMPUTE_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCH_H
diff --git a/src/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.cpp b/src/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.cpp
index ba39ff4..3500a0e 100644
--- a/src/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.cpp
+++ b/src/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -289,6 +289,10 @@
ClWorkloadRuntime::~ClWorkloadRuntime() = default;
+ClWorkloadRuntime::ClWorkloadRuntime(ClWorkloadRuntime &&) = default;
+
+ClWorkloadRuntime &ClWorkloadRuntime::operator=(ClWorkloadRuntime &&) = default;
+
Status ClWorkloadRuntime::configure(const GpuWorkloadSketch &sketch)
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(_impl->_is_configured, "ClWorkloadRuntime cannot be re-configured");
diff --git a/src/dynamic_fusion/sketch/gpu/GpuWorkloadContext.cpp b/src/dynamic_fusion/sketch/gpu/GpuWorkloadContext.cpp
index 36cad79..fab18aa 100644
--- a/src/dynamic_fusion/sketch/gpu/GpuWorkloadContext.cpp
+++ b/src/dynamic_fusion/sketch/gpu/GpuWorkloadContext.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -60,9 +60,9 @@
return _impl->cl_compile_context();
}
-void GpuWorkloadContext::register_user_tensor(ITensorInfo &tensor_info)
+void GpuWorkloadContext::register_user_tensor(std::unique_ptr<TensorInfo> &&tensor_info)
{
- _impl->register_user_tensor(tensor_info);
+ _impl->register_user_tensor(std::move(tensor_info));
}
GpuWorkloadContext::Impl &GpuWorkloadContext::implementation()
@@ -99,17 +99,17 @@
return _mem_map;
}
-void GpuWorkloadContext::Impl::register_user_tensor(ITensorInfo &tensor_info)
+void GpuWorkloadContext::Impl::register_user_tensor(std::unique_ptr<TensorInfo> &&tensor_info)
{
- ARM_COMPUTE_ERROR_ON(tensor_info.has_valid_id());
+ ARM_COMPUTE_ERROR_ON(tensor_info->has_valid_id());
const auto tensor_id = next_tensor_id();
- tensor_info.set_id(tensor_id);
+ tensor_info->set_id(tensor_id);
_mem_map[tensor_id] = MemoryDescriptor{MemoryType::User};
// Save a *copy* of the user tensor info in workload context for future reference
// Note that this means if the user modifies the @p tensor_info, the change will not be reflected in the context
- _managed_tensor_info.emplace(tensor_info.id(), std::make_unique<TensorInfo>(tensor_info));
+ _managed_tensor_info.emplace(tensor_info->id(), std::move(tensor_info));
}
ITensorInfo *GpuWorkloadContext::Impl::create_virtual_tensor()
diff --git a/src/dynamic_fusion/sketch/gpu/GpuWorkloadContextImpl.h b/src/dynamic_fusion/sketch/gpu/GpuWorkloadContextImpl.h
index 7d96990..b3571a6 100644
--- a/src/dynamic_fusion/sketch/gpu/GpuWorkloadContextImpl.h
+++ b/src/dynamic_fusion/sketch/gpu/GpuWorkloadContextImpl.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -64,9 +64,11 @@
/** Set a new ID and register the user tensor info.
*
- * @param[in, out] tensor_info The tensor info to be registered.
+ * The ownership of the tensor info object will be transfered to this context object.
+ *
+ * @param[in] tensor_info The tensor info to be registered.
*/
- void register_user_tensor(ITensorInfo &tensor_info);
+ void register_user_tensor(std::unique_ptr<TensorInfo> &&tensor_info);
/** Create a virtual (see @ref MemoryType) tensor info and save it
*
diff --git a/src/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.cpp b/src/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.cpp
index 973f7c7..357cb48 100644
--- a/src/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.cpp
+++ b/src/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -31,22 +31,34 @@
{
namespace dynamic_fusion
{
+
GpuWorkloadSketch::GpuWorkloadSketch(Context *context) : _impl{std::make_unique<Implementation>(context)}
{
}
+
GpuWorkloadSketch::~GpuWorkloadSketch()
{
}
+GpuWorkloadSketch::GpuWorkloadSketch(GpuWorkloadSketch &&) = default;
+
+GpuWorkloadSketch &GpuWorkloadSketch::operator=(GpuWorkloadSketch &&) = default;
+
const GpuWorkloadSketch::Context *GpuWorkloadSketch::gpu_context() const
{
return _impl->context();
}
+GpuWorkloadSketch::Context *GpuWorkloadSketch::gpu_context()
+{
+ return _impl->context();
+}
+
GpuWorkloadSketch::Implementation &GpuWorkloadSketch::implementation()
{
return *_impl;
}
+
const GpuWorkloadSketch::Implementation &GpuWorkloadSketch::implementation() const
{
return *_impl;
diff --git a/src/dynamic_fusion/sketch/gpu/GpuWorkloadSketchImpl.h b/src/dynamic_fusion/sketch/gpu/GpuWorkloadSketchImpl.h
index fea4fe9..04e294e 100644
--- a/src/dynamic_fusion/sketch/gpu/GpuWorkloadSketchImpl.h
+++ b/src/dynamic_fusion/sketch/gpu/GpuWorkloadSketchImpl.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef SRC_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCHIMPL
-#define SRC_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCHIMPL
+#ifndef ACL_SRC_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCHIMPL_H
+#define ACL_SRC_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCHIMPL_H
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
#include "arm_compute/dynamic_fusion/sketch/MemoryDescriptor.h"
@@ -63,6 +63,11 @@
{
return _context;
}
+ /** Get workload context */
+ Context *context()
+ {
+ return _context;
+ }
/** Get component graph */
const GpuKernelComponentGraph &component_graph() const
{
@@ -126,4 +131,4 @@
} // namespace dynamic_fusion
} // namespace experimental
} // namespace arm_compute
-#endif /* SRC_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCHIMPL */
+#endif // ACL_SRC_DYNAMIC_FUSION_SKETCH_GPU_GPUWORKLOADSKETCHIMPL_H
diff --git a/tests/validation/dynamic_fusion/gpu/Integration.cpp b/tests/validation/dynamic_fusion/gpu/Integration.cpp
index 89cca5c..bb9c008 100644
--- a/tests/validation/dynamic_fusion/gpu/Integration.cpp
+++ b/tests/validation/dynamic_fusion/gpu/Integration.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -37,11 +37,10 @@
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuDepthwiseConv2d.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuMul.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
-
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuSigmoid.h"
+
#include "tests/CL/CLAccessor.h"
#include "tests/framework/Macros.h"
-#include "tests/validation/Validation.h"
#include "tests/validation/dynamic_fusion/Utils.h"
#include "tests/validation/reference/ActivationLayer.h"
#include "tests/validation/reference/ConvolutionLayer.h"
@@ -50,6 +49,7 @@
#include "tests/validation/reference/ElementwiseOperations.h"
#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/PixelWiseMultiplication.h"
+#include "tests/validation/Validation.h"
using namespace arm_compute::experimental::dynamic_fusion;
using namespace arm_compute::test::validation::utils;
@@ -79,18 +79,18 @@
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Fuse conv2d
Conv2dAttributes conv2d_attr{};
- TensorInfo input_info = context.create_tensor_info(t_input_shape, 1, data_type, data_layout);
- TensorInfo weight_info = context.create_tensor_info(TensorInfo(t_weight_shape, 1, data_type, data_layout));
+ ITensorInfo *input_info = context.create_tensor_info(t_input_shape, 1, data_type, data_layout);
+ ITensorInfo *weight_info = context.create_tensor_info(TensorInfo(t_weight_shape, 1, data_type, data_layout));
- ITensorInfo *conv_out_info = GpuConv2d::create_op(sketch, &input_info, &weight_info, nullptr, conv2d_attr);
+ ITensorInfo *conv_out_info = GpuConv2d::create_op(sketch, input_info, weight_info, nullptr, conv2d_attr);
- TensorInfo dst_info = context.create_tensor_info();
- GpuOutput::create_op(sketch, conv_out_info, &dst_info);
+ ITensorInfo *dst_info = context.create_tensor_info();
+ GpuOutput::create_op(sketch, conv_out_info, dst_info);
// Configure runtime
ClWorkloadRuntime runtime;
@@ -98,7 +98,7 @@
// (Important) Allocate auxiliary tensor memory if there are any
// Instead of using ACL allocated memory, the user can choose to import memory into the tensors
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -115,9 +115,9 @@
CLTensor t_dst{};
// Initialize user tensors
- t_input.allocator()->init(input_info);
- t_weight.allocator()->init(weight_info);
- t_dst.allocator()->init(dst_info);
+ t_input.allocator()->init(*input_info);
+ t_weight.allocator()->init(*weight_info);
+ t_dst.allocator()->init(*dst_info);
// Allocate and fill user tensors
// Instead of using ACL allocator, the user can choose to import memory into the tensors
@@ -128,12 +128,12 @@
fill<float>(CLAccessor(t_weight), 1, library.get());
// Run runtime
- runtime.run({ &t_input, &t_weight, &t_dst });
+ runtime.run({&t_input, &t_weight, &t_dst});
// Create reference
- SimpleTensor<float> ref_t_input{ t_input_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC };
- SimpleTensor<float> ref_t_weight{ t_weight_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC };
- SimpleTensor<float> ref_t_bias_placeholder{ t_dst_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC };
+ SimpleTensor<float> ref_t_input{t_input_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC};
+ SimpleTensor<float> ref_t_weight{t_weight_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC};
+ SimpleTensor<float> ref_t_bias_placeholder{t_dst_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC};
// Fill reference
fill<float>(ref_t_input, 0, library.get());
@@ -145,12 +145,15 @@
auto t_dst_shape_nchw = t_dst_shape;
permute(t_dst_shape_nchw, PermutationVector(1U, 2U, 0U));
- PadStrideInfo legacy_pad_stride(conv2d_attr.stride().x(), conv2d_attr.stride().y(), conv2d_attr.pad().left, conv2d_attr.pad().right, conv2d_attr.pad().top, conv2d_attr.pad().bottom,
+ PadStrideInfo legacy_pad_stride(conv2d_attr.stride().x(), conv2d_attr.stride().y(), conv2d_attr.pad().left,
+ conv2d_attr.pad().right, conv2d_attr.pad().top, conv2d_attr.pad().bottom,
DimensionRoundingType{});
- auto ref_t_dst_nchw = reference::convolution_layer(ref_t_input_nchw, ref_t_weight_nchw, ref_t_bias_placeholder_nchw, t_dst_shape_nchw, legacy_pad_stride, conv2d_attr.dilation());
- const auto ref_t_dst = reference::permute(ref_t_dst_nchw, PermutationVector(2U, 0U, 1U));
+ auto ref_t_dst_nchw = reference::convolution_layer(ref_t_input_nchw, ref_t_weight_nchw, ref_t_bias_placeholder_nchw,
+ t_dst_shape_nchw, legacy_pad_stride, conv2d_attr.dilation());
+ const auto ref_t_dst = reference::permute(ref_t_dst_nchw, PermutationVector(2U, 0U, 1U));
- RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
+ RelativeTolerance<float> tolerance_f32(
+ 0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
validate(CLAccessor(t_dst), ref_t_dst_nchw, tolerance_f32);
}
#endif // ACL_INTERNAL_TEST_CKW_IN_DF
@@ -167,20 +170,20 @@
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
- TensorInfo in_0_info = context.create_tensor_info(t_input_shape, 1, data_type);
- TensorInfo in_1_info = context.create_tensor_info(t_input_shape, 1, data_type);
- TensorInfo in_2_info = context.create_tensor_info(t_input_shape, 1, data_type);
+ ITensorInfo *in_0_info = context.create_tensor_info(t_input_shape, 1, data_type);
+ ITensorInfo *in_1_info = context.create_tensor_info(t_input_shape, 1, data_type);
+ ITensorInfo *in_2_info = context.create_tensor_info(t_input_shape, 1, data_type);
- TensorInfo out_0_info = context.create_tensor_info();
- TensorInfo out_1_info = context.create_tensor_info();
+ ITensorInfo *out_0_info = context.create_tensor_info();
+ ITensorInfo *out_1_info = context.create_tensor_info();
- ITensorInfo *ans_0_info = GpuAdd::create_op(sketch, &in_0_info, &in_1_info);
- GpuOutput::create_op(sketch, ans_0_info, &out_0_info);
- ITensorInfo *ans_1_info = GpuAdd::create_op(sketch, ans_0_info, &in_2_info);
- GpuOutput::create_op(sketch, ans_1_info, &out_1_info);
+ ITensorInfo *ans_0_info = GpuAdd::create_op(sketch, in_0_info, in_1_info);
+ GpuOutput::create_op(sketch, ans_0_info, out_0_info);
+ ITensorInfo *ans_1_info = GpuAdd::create_op(sketch, ans_0_info, in_2_info);
+ GpuOutput::create_op(sketch, ans_1_info, out_1_info);
// Configure runtime
ClWorkloadRuntime runtime;
@@ -188,7 +191,7 @@
// (Important) Allocate auxiliary tensor memory if there are any
// Instead of using ACL allocated memory, the user can choose to import memory into the tensors
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -208,12 +211,12 @@
CLTensor t_out_1{};
// Initialize user tensors
- t_in_0.allocator()->init(in_0_info);
- t_in_1.allocator()->init(in_1_info);
- t_in_2.allocator()->init(in_2_info);
+ t_in_0.allocator()->init(*in_0_info);
+ t_in_1.allocator()->init(*in_1_info);
+ t_in_2.allocator()->init(*in_2_info);
- t_out_0.allocator()->init(out_0_info);
- t_out_1.allocator()->init(out_1_info);
+ t_out_0.allocator()->init(*out_0_info);
+ t_out_1.allocator()->init(*out_1_info);
// Allocate and fill user tensors
// Instead of using ACL allocator, the user can choose to import memory into the tensors
@@ -229,15 +232,15 @@
fill<float>(CLAccessor(t_in_2), 2, library.get());
// Run runtime
- runtime.run({ &t_in_0, &t_in_1, &t_in_2, &t_out_0, &t_out_1 });
+ runtime.run({&t_in_0, &t_in_1, &t_in_2, &t_out_0, &t_out_1});
// Create reference
- SimpleTensor<float> ref_t_in_0{ t_input_shape, data_type, 1, QuantizationInfo() };
- SimpleTensor<float> ref_t_in_1{ t_input_shape, data_type, 1, QuantizationInfo() };
- SimpleTensor<float> ref_t_in_2{ t_input_shape, data_type, 1, QuantizationInfo() };
+ SimpleTensor<float> ref_t_in_0{t_input_shape, data_type, 1, QuantizationInfo()};
+ SimpleTensor<float> ref_t_in_1{t_input_shape, data_type, 1, QuantizationInfo()};
+ SimpleTensor<float> ref_t_in_2{t_input_shape, data_type, 1, QuantizationInfo()};
- SimpleTensor<float> ref_t_out_0{ t_input_shape, data_type, 1, QuantizationInfo() };
- SimpleTensor<float> ref_t_out_1{ t_input_shape, data_type, 1, QuantizationInfo() };
+ SimpleTensor<float> ref_t_out_0{t_input_shape, data_type, 1, QuantizationInfo()};
+ SimpleTensor<float> ref_t_out_1{t_input_shape, data_type, 1, QuantizationInfo()};
// Fill reference
fill<float>(ref_t_in_0, 0, library.get());
@@ -245,9 +248,11 @@
fill<float>(ref_t_in_2, 2, library.get());
reference::arithmetic_operation(ArithmeticOperation::ADD, ref_t_in_0, ref_t_in_1, ref_t_out_0, ConvertPolicy::WRAP);
- reference::arithmetic_operation(ArithmeticOperation::ADD, ref_t_out_0, ref_t_in_2, ref_t_out_1, ConvertPolicy::WRAP);
+ reference::arithmetic_operation(ArithmeticOperation::ADD, ref_t_out_0, ref_t_in_2, ref_t_out_1,
+ ConvertPolicy::WRAP);
- RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
+ RelativeTolerance<float> tolerance_f32(
+ 0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
validate(CLAccessor(t_out_0), ref_t_out_0, tolerance_f32);
validate(CLAccessor(t_out_1), ref_t_out_1, tolerance_f32);
}
@@ -264,15 +269,15 @@
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
- TensorInfo in_0_info = context.create_tensor_info(t_input_shape, 1, data_type);
- TensorInfo in_1_info = context.create_tensor_info(t_input_shape, 1, data_type);
- TensorInfo in_2_info = context.create_tensor_info(t_input_shape, 1, data_type);
+ ITensorInfo *in_0_info = context.create_tensor_info(t_input_shape, 1, data_type);
+ ITensorInfo *in_1_info = context.create_tensor_info(t_input_shape, 1, data_type);
+ ITensorInfo *in_2_info = context.create_tensor_info(t_input_shape, 1, data_type);
- TensorInfo out_0_info = context.create_tensor_info();
- TensorInfo out_1_info = context.create_tensor_info();
+ ITensorInfo *out_0_info = context.create_tensor_info();
+ ITensorInfo *out_1_info = context.create_tensor_info();
CastAttributes cast_0_attr;
cast_0_attr.data_type(DataType::S32).convert_policy(ConvertPolicy::SATURATE);
@@ -280,12 +285,12 @@
CastAttributes cast_1_attr;
cast_1_attr.data_type(DataType::F32).convert_policy(ConvertPolicy::SATURATE);
- ITensorInfo *ans_0_info = GpuAdd::create_op(sketch, &in_0_info, &in_1_info);
- GpuOutput::create_op(sketch, ans_0_info, &out_0_info);
- ITensorInfo *ans_1_info = GpuAdd::create_op(sketch, ans_0_info, &in_2_info);
+ ITensorInfo *ans_0_info = GpuAdd::create_op(sketch, in_0_info, in_1_info);
+ GpuOutput::create_op(sketch, ans_0_info, out_0_info);
+ ITensorInfo *ans_1_info = GpuAdd::create_op(sketch, ans_0_info, in_2_info);
ITensorInfo *ans_2_info = GpuCast::create_op(sketch, ans_1_info, cast_0_attr);
ITensorInfo *ans_3_info = GpuCast::create_op(sketch, ans_2_info, cast_1_attr);
- GpuOutput::create_op(sketch, ans_3_info, &out_1_info);
+ GpuOutput::create_op(sketch, ans_3_info, out_1_info);
// Configure runtime
ClWorkloadRuntime runtime;
@@ -293,7 +298,7 @@
// (Important) Allocate auxiliary tensor memory if there are any
// Instead of using ACL allocated memory, the user can choose to import memory into the tensors
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -313,12 +318,12 @@
CLTensor t_out_1{};
// Initialize user tensors
- t_in_0.allocator()->init(in_0_info);
- t_in_1.allocator()->init(in_1_info);
- t_in_2.allocator()->init(in_2_info);
+ t_in_0.allocator()->init(*in_0_info);
+ t_in_1.allocator()->init(*in_1_info);
+ t_in_2.allocator()->init(*in_2_info);
- t_out_0.allocator()->init(out_0_info);
- t_out_1.allocator()->init(out_1_info);
+ t_out_0.allocator()->init(*out_0_info);
+ t_out_1.allocator()->init(*out_1_info);
// Allocate and fill user tensors
// Instead of using ACL allocator, the user can choose to import memory into the tensors
@@ -334,15 +339,15 @@
fill<float>(CLAccessor(t_in_2), 2, library.get());
// Run runtime
- runtime.run({ &t_in_0, &t_in_1, &t_in_2, &t_out_0, &t_out_1 });
+ runtime.run({&t_in_0, &t_in_1, &t_in_2, &t_out_0, &t_out_1});
// Create reference
- SimpleTensor<float> ref_t_in_0{ t_input_shape, data_type, 1, QuantizationInfo() };
- SimpleTensor<float> ref_t_in_1{ t_input_shape, data_type, 1, QuantizationInfo() };
- SimpleTensor<float> ref_t_in_2{ t_input_shape, data_type, 1, QuantizationInfo() };
+ SimpleTensor<float> ref_t_in_0{t_input_shape, data_type, 1, QuantizationInfo()};
+ SimpleTensor<float> ref_t_in_1{t_input_shape, data_type, 1, QuantizationInfo()};
+ SimpleTensor<float> ref_t_in_2{t_input_shape, data_type, 1, QuantizationInfo()};
- SimpleTensor<float> ref_t_out_0{ t_input_shape, data_type, 1, QuantizationInfo() };
- SimpleTensor<float> ref_t_ans_1{ t_input_shape, data_type, 1, QuantizationInfo() };
+ SimpleTensor<float> ref_t_out_0{t_input_shape, data_type, 1, QuantizationInfo()};
+ SimpleTensor<float> ref_t_ans_1{t_input_shape, data_type, 1, QuantizationInfo()};
// Fill reference
fill<float>(ref_t_in_0, 0, library.get());
@@ -350,9 +355,12 @@
fill<float>(ref_t_in_2, 2, library.get());
reference::arithmetic_operation(ArithmeticOperation::ADD, ref_t_in_0, ref_t_in_1, ref_t_out_0, ConvertPolicy::WRAP);
- reference::arithmetic_operation(ArithmeticOperation::ADD, ref_t_out_0, ref_t_in_2, ref_t_ans_1, ConvertPolicy::WRAP);
- const auto ref_t_ans_2 = reference::depth_convert<float, int32_t>(ref_t_ans_1, DataType::S32, ConvertPolicy::SATURATE, 0);
- const auto ref_t_out_1 = reference::depth_convert<int32_t, float>(ref_t_ans_2, DataType::F32, ConvertPolicy::SATURATE, 0);
+ reference::arithmetic_operation(ArithmeticOperation::ADD, ref_t_out_0, ref_t_in_2, ref_t_ans_1,
+ ConvertPolicy::WRAP);
+ const auto ref_t_ans_2 =
+ reference::depth_convert<float, int32_t>(ref_t_ans_1, DataType::S32, ConvertPolicy::SATURATE, 0);
+ const auto ref_t_out_1 =
+ reference::depth_convert<int32_t, float>(ref_t_ans_2, DataType::F32, ConvertPolicy::SATURATE, 0);
RelativeTolerance<float> tolerance_add_f32(0.001f);
AbsoluteTolerance<float> tolerance_cast_f32(1.0f);
@@ -436,20 +444,22 @@
Conv2dAttributes conv2d_attr;
auto tensor1_info = context.create_tensor_info(conv2d_wei_shape, 1, DataType::F32, DataLayout::NHWC);
auto tensor2_info = context.create_tensor_info(conv2d_bia_shape, 1, DataType::F32, DataLayout::NHWC);
- ARM_COMPUTE_EXPECT(GpuConv2d::validate_op(sketch0, &tensor0_info, &tensor1_info, &tensor2_info, conv2d_attr), framework::LogLevel::ERRORS);
- auto ans_info = GpuConv2d::create_op(sketch0, &tensor0_info, &tensor1_info, &tensor2_info, conv2d_attr);
+ ARM_COMPUTE_EXPECT(GpuConv2d::validate_op(sketch0, tensor0_info, tensor1_info, tensor2_info, conv2d_attr),
+ framework::LogLevel::ERRORS);
+ auto ans_info = GpuConv2d::create_op(sketch0, tensor0_info, tensor1_info, tensor2_info, conv2d_attr);
ARM_COMPUTE_EXPECT(GpuSigmoid::validate_op(sketch0, ans_info), framework::LogLevel::ERRORS);
ans_info = GpuSigmoid::create_op(sketch0, ans_info);
DepthwiseConv2dAttributes dwc_attr;
- auto tensor3_info = context.create_tensor_info(dwc_wei_shape, 1, DataType::F32, DataLayout::NHWC);
- auto tensor4_info = context.create_tensor_info(dwc_bia_shape, 1, DataType::F32, DataLayout::NHWC);
- ARM_COMPUTE_EXPECT(!GpuDepthwiseConv2d::validate_op(sketch0, ans_info, &tensor3_info, &tensor4_info, dwc_attr), framework::LogLevel::ERRORS);
+ auto tensor3_info = context.create_tensor_info(dwc_wei_shape, 1, DataType::F32, DataLayout::NHWC);
+ auto tensor4_info = context.create_tensor_info(dwc_bia_shape, 1, DataType::F32, DataLayout::NHWC);
+ ARM_COMPUTE_EXPECT(!GpuDepthwiseConv2d::validate_op(sketch0, ans_info, tensor3_info, tensor4_info, dwc_attr),
+ framework::LogLevel::ERRORS);
auto tensor5_info = context.create_tensor_info();
- ARM_COMPUTE_EXPECT(GpuOutput::validate_op(sketch0, ans_info, &tensor5_info), framework::LogLevel::ERRORS);
- GpuOutput::create_op(sketch0, ans_info, &tensor5_info);
+ ARM_COMPUTE_EXPECT(GpuOutput::validate_op(sketch0, ans_info, tensor5_info), framework::LogLevel::ERRORS);
+ GpuOutput::create_op(sketch0, ans_info, tensor5_info);
// Create the first workload runtime.
ClWorkloadRuntime runtime0;
@@ -458,15 +468,16 @@
// Create the second sketch: dwc + sigmoid + output.
GpuWorkloadSketch sketch1(&context);
- ARM_COMPUTE_EXPECT(GpuDepthwiseConv2d::validate_op(sketch1, &tensor5_info, &tensor3_info, &tensor4_info, dwc_attr), framework::LogLevel::ERRORS);
- ans_info = GpuDepthwiseConv2d::create_op(sketch1, &tensor5_info, &tensor3_info, &tensor4_info, dwc_attr);
+ ARM_COMPUTE_EXPECT(GpuDepthwiseConv2d::validate_op(sketch1, tensor5_info, tensor3_info, tensor4_info, dwc_attr),
+ framework::LogLevel::ERRORS);
+ ans_info = GpuDepthwiseConv2d::create_op(sketch1, tensor5_info, tensor3_info, tensor4_info, dwc_attr);
- ARM_COMPUTE_EXPECT(GpuMul::validate_op(sketch1, ans_info, &tensor2_info), framework::LogLevel::ERRORS);
- ans_info = GpuMul::create_op(sketch1, ans_info, &tensor2_info);
+ ARM_COMPUTE_EXPECT(GpuMul::validate_op(sketch1, ans_info, tensor2_info), framework::LogLevel::ERRORS);
+ ans_info = GpuMul::create_op(sketch1, ans_info, tensor2_info);
auto tensor6_info = context.create_tensor_info();
- ARM_COMPUTE_EXPECT(GpuOutput::validate_op(sketch1, ans_info, &tensor6_info), framework::LogLevel::ERRORS);
- GpuOutput::create_op(sketch1, ans_info, &tensor6_info);
+ ARM_COMPUTE_EXPECT(GpuOutput::validate_op(sketch1, ans_info, tensor6_info), framework::LogLevel::ERRORS);
+ GpuOutput::create_op(sketch1, ans_info, tensor6_info);
// Create the second workload runtime.
ClWorkloadRuntime runtime1;
@@ -481,13 +492,13 @@
CLTensor tensor5;
CLTensor tensor6;
- tensor0.allocator()->init(tensor0_info);
- tensor1.allocator()->init(tensor1_info);
- tensor2.allocator()->init(tensor2_info);
- tensor3.allocator()->init(tensor3_info);
- tensor4.allocator()->init(tensor4_info);
- tensor5.allocator()->init(tensor5_info);
- tensor6.allocator()->init(tensor6_info);
+ tensor0.allocator()->init(*tensor0_info);
+ tensor1.allocator()->init(*tensor1_info);
+ tensor2.allocator()->init(*tensor2_info);
+ tensor3.allocator()->init(*tensor3_info);
+ tensor4.allocator()->init(*tensor4_info);
+ tensor5.allocator()->init(*tensor5_info);
+ tensor6.allocator()->init(*tensor6_info);
tensor0.allocator()->allocate();
tensor1.allocator()->allocate();
@@ -498,7 +509,7 @@
tensor6.allocator()->allocate();
// Allocate the auxiliary tensors.
- for(auto &data : runtime0.get_auxiliary_tensors())
+ for (auto &data : runtime0.get_auxiliary_tensors())
{
auto tensor = std::get<0>(data);
auto &tensor_info = std::get<1>(data);
@@ -508,7 +519,7 @@
tensor->allocator()->allocate();
}
- for(auto &data : runtime1.get_auxiliary_tensors())
+ for (auto &data : runtime1.get_auxiliary_tensors())
{
auto tensor = std::get<0>(data);
auto &tensor_info = std::get<1>(data);
@@ -526,8 +537,8 @@
fill<float>(CLAccessor(tensor4), 4, library.get());
// Run each runtime.
- runtime0.run({ &tensor0, &tensor1, &tensor2, &tensor5 });
- runtime1.run({ &tensor5, &tensor3, &tensor4, &tensor2, &tensor6 });
+ runtime0.run({&tensor0, &tensor1, &tensor2, &tensor5});
+ runtime1.run({&tensor5, &tensor3, &tensor4, &tensor2, &tensor6});
// Compute the reference result.
SimpleTensor<float> ref_conv2d_src(conv2d_src_shape, DataType::F32, 1, QuantizationInfo(), DataLayout::NHWC);
@@ -549,18 +560,22 @@
const auto ref_conv2d_src_nchw = reference::permute(ref_conv2d_src, nhwc_to_nchw);
const auto ref_conv2d_wei_nchw = reference::permute(ref_conv2d_wei, nhwc_to_nchw);
const auto ref_conv2d_bia_nchw = reference::permute(ref_conv2d_bia, nhwc_to_nchw);
- const auto ref_conv2d_dst_nchw = reference::convolution_layer(ref_conv2d_src_nchw, ref_conv2d_wei_nchw, ref_conv2d_bia_nchw, conv2d_dst_shape_nchw, PadStrideInfo());
+ const auto ref_conv2d_dst_nchw = reference::convolution_layer(
+ ref_conv2d_src_nchw, ref_conv2d_wei_nchw, ref_conv2d_bia_nchw, conv2d_dst_shape_nchw, PadStrideInfo());
- const auto ref_sigmoid_dst_nchw = reference::activation_layer(ref_conv2d_dst_nchw, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+ const auto ref_sigmoid_dst_nchw = reference::activation_layer(
+ ref_conv2d_dst_nchw, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
auto dwc_dst_shape_nchw = dwc_dst_shape;
permute(dwc_dst_shape_nchw, nhwc_to_nchw);
const auto ref_dwc_wei_nchw = reference::permute(ref_dwc_wei, nhwc_to_nchw);
const auto ref_dwc_bia_nchw = reference::permute(ref_dwc_bia, nhwc_to_nchw);
- const auto ref_dwc_dst_nchw = reference::depthwise_convolution(ref_sigmoid_dst_nchw, ref_dwc_wei_nchw, ref_dwc_bia_nchw, dwc_dst_shape_nchw, PadStrideInfo(), 1);
+ const auto ref_dwc_dst_nchw = reference::depthwise_convolution(
+ ref_sigmoid_dst_nchw, ref_dwc_wei_nchw, ref_dwc_bia_nchw, dwc_dst_shape_nchw, PadStrideInfo(), 1);
- const auto ref_mul_dst_nchw = reference::pixel_wise_multiplication<float, float, float>(ref_dwc_dst_nchw, ref_conv2d_bia_nchw, 1.0, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_UP,
- DataType::F32);
+ const auto ref_mul_dst_nchw = reference::pixel_wise_multiplication<float, float, float>(
+ ref_dwc_dst_nchw, ref_conv2d_bia_nchw, 1.0, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_UP,
+ DataType::F32);
constexpr RelativeTolerance<float> tolerance(0.001f);
validate(CLAccessor(tensor6), ref_mul_dst_nchw, tolerance);
@@ -587,34 +602,35 @@
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Create tensor infos
- TensorInfo input_info = context.create_tensor_info(t_input_shape, 1, data_type, data_layout);
- TensorInfo weight_info = context.create_tensor_info(TensorInfo(t_weight_shape, 1, data_type, data_layout));
+ ITensorInfo *input_info = context.create_tensor_info(t_input_shape, 1, data_type, data_layout);
+ ITensorInfo *weight_info = context.create_tensor_info(TensorInfo(t_weight_shape, 1, data_type, data_layout));
ITensorInfo *dst_info;
// Fuse conv2d into the workload
{
// Validate operator
- const Status success = GpuConv2d::validate_op(sketch, &input_info, &weight_info, nullptr, conv2d_attr);
+ const Status success = GpuConv2d::validate_op(sketch, input_info, weight_info, nullptr, conv2d_attr);
ARM_COMPUTE_EXPECT(bool(success), framework::LogLevel::ERRORS);
- dst_info = GpuConv2d::create_op(sketch, &input_info, &weight_info, nullptr, conv2d_attr);
+ dst_info = GpuConv2d::create_op(sketch, input_info, weight_info, nullptr, conv2d_attr);
}
// Create tensor infos
- TensorInfo weight_info_2 = context.create_tensor_info(t_weight_info);
+ ITensorInfo *weight_info_2 = context.create_tensor_info(t_weight_info);
// Fuse conv2d into the workload
{
// Validate operator, should fail
- const Status success = GpuConv2d::validate_op(sketch, dst_info, &weight_info_2, nullptr, conv2d_attr);
- const auto expected_error_str = "Operator fusion test failed. This operator cannot be fused into the workload";
+ const Status success = GpuConv2d::validate_op(sketch, dst_info, weight_info_2, nullptr, conv2d_attr);
+ const auto expected_error_str = "Operator fusion test failed. This operator cannot be fused into the workload";
ARM_COMPUTE_EXPECT(!bool(success), framework::LogLevel::ERRORS);
- ARM_COMPUTE_EXPECT((success.error_description().find(expected_error_str) != std::string::npos), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT((success.error_description().find(expected_error_str) != std::string::npos),
+ framework::LogLevel::ERRORS);
}
}
TEST_SUITE_END() // Invalid_Fusion_Should_Fail
diff --git a/tests/validation/dynamic_fusion/gpu/cl/Add.cpp b/tests/validation/dynamic_fusion/gpu/cl/Add.cpp
index 09a8f3f..a358d47 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/Add.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/Add.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -29,14 +29,13 @@
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuAdd.h"
#include "tests/CL/CLAccessor.h"
-#include "tests/framework/Fixture.h"
-#include "tests/framework/Macros.h"
-#include "tests/framework/datasets/Datasets.h"
-#include "tests/validation/Validation.h"
-
#include "tests/datasets/DynamicFusionDataset.h"
#include "tests/datasets/ShapeDatasets.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/framework/Fixture.h"
+#include "tests/framework/Macros.h"
#include "tests/validation/fixtures/dynamic_fusion/gpu/cl/ElementwiseBinaryFixture.h"
+#include "tests/validation/Validation.h"
namespace arm_compute
{
@@ -97,32 +96,36 @@
auto lhs_info = context.create_tensor_info(input1_info);
auto rhs_info = context.create_tensor_info(input2_info);
- bool res = bool(GpuAdd::validate_op(sketch, &lhs_info, &rhs_info));
+ bool res = bool(GpuAdd::validate_op(sketch, lhs_info, rhs_info));
ARM_COMPUTE_EXPECT(res == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*
-constexpr AbsoluteTolerance<float> tolerance_f(0.0001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 and DataType::F16 */
-constexpr float tolerance_num = 0.0001f; /**< Tolerance number */
+constexpr AbsoluteTolerance<float> tolerance_f(
+ 0.0001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 and DataType::F16 */
+constexpr float tolerance_num = 0.0001f; /**< Tolerance number */
template <typename T>
-using DynamicFusionCLAddFixture = DynamicFusionGpuElementwiseBinaryOneOpValidationFixture<CLTensor, CLAccessor, GpuAdd, T>;
+using DynamicFusionCLAddFixture =
+ DynamicFusionGpuElementwiseBinaryOneOpValidationFixture<CLTensor, CLAccessor, GpuAdd, T>;
template <typename T>
-using DynamicFusionCLAddBroadcastFixture = DynamicFusionGpuElementwiseBinaryBroadcastOneOpValidationFixture<CLTensor, CLAccessor, GpuAdd, T>;
+using DynamicFusionCLAddBroadcastFixture =
+ DynamicFusionGpuElementwiseBinaryBroadcastOneOpValidationFixture<CLTensor, CLAccessor, GpuAdd, T>;
template <typename T>
-using DynamicFusionCLAddTwoOpsFixture = DynamicFusionGpuElementwiseBinaryTwoOpsValidationFixture<CLTensor, CLAccessor, GpuAdd, T>;
+using DynamicFusionCLAddTwoOpsFixture =
+ DynamicFusionGpuElementwiseBinaryTwoOpsValidationFixture<CLTensor, CLAccessor, GpuAdd, T>;
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmallOneOp,
DynamicFusionCLAddFixture<float>,
framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::ADD }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::ADD}),
datasets::SmallShapes()),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f);
@@ -130,10 +133,10 @@
FIXTURE_DATA_TEST_CASE(RunLargeOneOp,
DynamicFusionCLAddFixture<float>,
framework::DatasetMode::NIGHTLY,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::ADD }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::ADD}),
datasets::LargeShapes()),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f);
@@ -141,10 +144,10 @@
FIXTURE_DATA_TEST_CASE(RunSmallBroadcastOneOp,
DynamicFusionCLAddBroadcastFixture<float>,
framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::ADD }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::ADD}),
datasets::TemporaryLimitedSmallShapesBroadcast()),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f);
@@ -153,22 +156,23 @@
FIXTURE_DATA_TEST_CASE(RunLargeBroadcastOneOp,
DynamicFusionCLAddBroadcastFixture<float>,
framework::DatasetMode::NIGHTLY,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::ADD }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::ADD}),
datasets::TemporaryLimitedLargeShapesBroadcast()),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f);
}
-FIXTURE_DATA_TEST_CASE(RunSmallTwoOps,
- DynamicFusionCLAddTwoOpsFixture<float>,
- framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::ADD }),
- datasets::DynamicFusionElementwiseBinaryTwoOpsSmallShapes()),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })),
- framework::dataset::make("FuseTwoOps", { true })))
+FIXTURE_DATA_TEST_CASE(
+ RunSmallTwoOps,
+ DynamicFusionCLAddTwoOpsFixture<float>,
+ framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::ADD}),
+ datasets::DynamicFusionElementwiseBinaryTwoOpsSmallShapes()),
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})),
+ framework::dataset::make("FuseTwoOps", {true})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f);
@@ -179,10 +183,10 @@
FIXTURE_DATA_TEST_CASE(RunSmallOneOp,
DynamicFusionCLAddFixture<half>,
framework::DatasetMode::ALL,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::ADD }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::ADD}),
datasets::SmallShapes()),
- framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F16})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f, tolerance_num);
@@ -191,10 +195,10 @@
FIXTURE_DATA_TEST_CASE(RunSmallBroadcastOneOp,
DynamicFusionCLAddBroadcastFixture<half>,
framework::DatasetMode::ALL,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::ADD }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::ADD}),
datasets::TemporaryLimitedSmallShapesBroadcast()),
- framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F16})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f, tolerance_num);
@@ -206,10 +210,10 @@
FIXTURE_DATA_TEST_CASE(RunSmall,
DynamicFusionCLAddFixture<int32_t>,
framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::ADD }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::ADD}),
datasets::SmallShapes()),
- framework::dataset::make("DataType", { DataType::S32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::S32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -220,10 +224,10 @@
FIXTURE_DATA_TEST_CASE(RunSmall,
DynamicFusionCLAddFixture<int16_t>,
framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::ADD }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::ADD}),
datasets::SmallShapes()),
- framework::dataset::make("DataType", { DataType::S16 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::S16})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -231,10 +235,10 @@
FIXTURE_DATA_TEST_CASE(RunLarge,
DynamicFusionCLAddFixture<int16_t>,
framework::DatasetMode::NIGHTLY,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::ADD }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::ADD}),
datasets::LargeShapes()),
- framework::dataset::make("DataType", { DataType::S16 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::S16})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -245,10 +249,10 @@
FIXTURE_DATA_TEST_CASE(RunSmall,
DynamicFusionCLAddFixture<uint8_t>,
framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::ADD }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::ADD}),
datasets::SmallShapes()),
- framework::dataset::make("DataType", { DataType::U8 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::U8})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
diff --git a/tests/validation/dynamic_fusion/gpu/cl/Clamp.cpp b/tests/validation/dynamic_fusion/gpu/cl/Clamp.cpp
index 285c0d6..cef8b87 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/Clamp.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/Clamp.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -29,10 +29,10 @@
#include "tests/CL/CLAccessor.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/framework/Asserts.h"
-#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
-#include "tests/validation/Validation.h"
+#include "tests/framework/Macros.h"
#include "tests/validation/fixtures/dynamic_fusion/operators/ClampFixture.h"
+#include "tests/validation/Validation.h"
namespace arm_compute
{
@@ -73,13 +73,13 @@
GpuWorkloadSketch sketch{ &context };
// Fuse Clamp
- const TensorInfo src_info = context.create_tensor_info(input_info);
+ const ITensorInfo* src_info = context.create_tensor_info(input_info);
ClampAttributes attributes {};
attributes.min_val(min_val)
.max_val(max_val);
- const bool res = static_cast<bool>(GpuClamp::validate_op(sketch, &src_info, attributes));
+ const bool res = static_cast<bool>(GpuClamp::validate_op(sketch, src_info, attributes));
ARM_COMPUTE_EXPECT(res == expected, framework::LogLevel::ERRORS);
}
// clang-format on
@@ -94,8 +94,9 @@
DynamicFusionClampOpFixture<half>,
framework::DatasetMode::ALL,
combine(combine(combine(datasets::SmallShapes(),
- framework::dataset::make("ClampAttributes", { ClampAttributes().min_val(0.1f).max_val(0.6f) })),
- framework::dataset::make("Fuse", { false })),
+ framework::dataset::make(
+ "ClampAttributes", {ClampAttributes().min_val(0.1f).max_val(0.6f)})),
+ framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
@@ -106,8 +107,9 @@
DynamicFusionClampOpFixture<half>,
framework::DatasetMode::ALL,
combine(combine(combine(datasets::Small5dShapes(),
- framework::dataset::make("ClampAttributes", { ClampAttributes().min_val(0.1f).max_val(0.6f) })),
- framework::dataset::make("Fuse", { false })),
+ framework::dataset::make(
+ "ClampAttributes", {ClampAttributes().min_val(0.1f).max_val(0.6f)})),
+ framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
@@ -119,8 +121,9 @@
DynamicFusionClampOpFixture<half>,
framework::DatasetMode::ALL,
combine(combine(combine(datasets::SmallShapes(),
- framework::dataset::make("ClampAttributes", { ClampAttributes().min_val(0.2f).max_val(0.4f) })),
- framework::dataset::make("Fuse", { true })),
+ framework::dataset::make(
+ "ClampAttributes", {ClampAttributes().min_val(0.2f).max_val(0.4f)})),
+ framework::dataset::make("Fuse", {true})),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
@@ -134,8 +137,9 @@
DynamicFusionClampOpFixture<float>,
framework::DatasetMode::ALL,
combine(combine(combine(datasets::SmallShapes(),
- framework::dataset::make("ClampAttributes", { ClampAttributes().min_val(0.3f).max_val(0.7f) })),
- framework::dataset::make("Fuse", { false })),
+ framework::dataset::make(
+ "ClampAttributes", {ClampAttributes().min_val(0.3f).max_val(0.7f)})),
+ framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
@@ -146,8 +150,9 @@
DynamicFusionClampOpFixture<float>,
framework::DatasetMode::ALL,
combine(combine(combine(datasets::Small5dShapes(),
- framework::dataset::make("ClampAttributes", { ClampAttributes().min_val(0.3f).max_val(0.7f) })),
- framework::dataset::make("Fuse", { false })),
+ framework::dataset::make(
+ "ClampAttributes", {ClampAttributes().min_val(0.3f).max_val(0.7f)})),
+ framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
@@ -159,8 +164,9 @@
DynamicFusionClampOpFixture<float>,
framework::DatasetMode::ALL,
combine(combine(combine(datasets::SmallShapes(),
- framework::dataset::make("ClampAttributes", { ClampAttributes().min_val(0.1f).max_val(0.9f) })),
- framework::dataset::make("Fuse", { true })),
+ framework::dataset::make(
+ "ClampAttributes", {ClampAttributes().min_val(0.1f).max_val(0.9f)})),
+ framework::dataset::make("Fuse", {true})),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
diff --git a/tests/validation/dynamic_fusion/gpu/cl/DepthwiseConv2d.cpp b/tests/validation/dynamic_fusion/gpu/cl/DepthwiseConv2d.cpp
index aec1306..40e1ea8 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/DepthwiseConv2d.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/DepthwiseConv2d.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -28,11 +28,11 @@
#include "tests/datasets/DepthwiseConvolutionLayerDataset.h"
#include "tests/datasets/DilatedDepthwiseConvolutionLayerDataset.h"
#include "tests/framework/Asserts.h"
+#include "tests/framework/datasets/Datasets.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
-#include "tests/framework/datasets/Datasets.h"
-#include "tests/validation/Validation.h"
#include "tests/validation/fixtures/dynamic_fusion/gpu/cl/DepthwiseConv2dFixture.h"
+#include "tests/validation/Validation.h"
namespace arm_compute
{
@@ -40,16 +40,18 @@
{
namespace validation
{
-const auto depth_multipliers = framework::dataset::make("DepthMultiplier", { 1U, 4U });
-const auto large_depth_multipliers = framework::dataset::make("DepthMultiplier", { 1, 2, 5, 8 });
+const auto depth_multipliers = framework::dataset::make("DepthMultiplier", {1U, 4U});
+const auto large_depth_multipliers = framework::dataset::make("DepthMultiplier", {1, 2, 5, 8});
TEST_SUITE(CL)
TEST_SUITE(DYNAMIC_FUSION)
TEST_SUITE(DEPTHWISE_CONV2D)
-RelativeTolerance<float> tolerance_f32(0.01f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
-RelativeTolerance<half_float::half> tolerance_f16(half_float::half(0.1)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
-constexpr float tolerance_num = 0.02f; /**< Tolerance number */
+RelativeTolerance<float> tolerance_f32(
+ 0.01f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
+RelativeTolerance<half_float::half> tolerance_f16(half_float::half(
+ 0.1)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
+constexpr float tolerance_num = 0.02f; /**< Tolerance number */
// *INDENT-OFF*
// clang-format off
@@ -245,9 +247,9 @@
GpuWorkloadContext context = GpuWorkloadContext{ &cl_compile_ctx };
GpuWorkloadSketch sketch{ &context };
- const TensorInfo sketch_input_info = context.create_tensor_info(input_info);
- const TensorInfo sketch_weights_info = context.create_tensor_info(weights_info);
- const TensorInfo sketch_biases_info = context.create_tensor_info(biases_info);
+ const ITensorInfo* sketch_input_info = context.create_tensor_info(input_info);
+ const ITensorInfo* sketch_weights_info = context.create_tensor_info(weights_info);
+ const ITensorInfo* sketch_biases_info = context.create_tensor_info(biases_info);
DepthwiseConv2dAttributes attributes {};
attributes.pad(padding)
@@ -255,7 +257,7 @@
.dilation(dilation)
.depth_multiplier(depth_multiplier);
- const Status status = GpuDepthwiseConv2d::validate_op(sketch, &sketch_input_info, &sketch_weights_info, &sketch_biases_info, attributes);
+ const Status status = GpuDepthwiseConv2d::validate_op(sketch, sketch_input_info, sketch_weights_info, sketch_biases_info, attributes);
const bool res = bool(status);
ARM_COMPUTE_EXPECT(res == expected, framework::LogLevel::ERRORS);
}
@@ -263,40 +265,50 @@
// *INDENT-ON*
template <typename T>
-using DynamicFusionGpuDepthwiseConv2dFixture = DynamicFusionGpuDepthwiseConv2dValidationFixture<CLTensor, CLAccessor, GpuDepthwiseConv2d, T>;
+using DynamicFusionGpuDepthwiseConv2dFixture =
+ DynamicFusionGpuDepthwiseConv2dValidationFixture<CLTensor, CLAccessor, GpuDepthwiseConv2d, T>;
TEST_SUITE(Float)
TEST_SUITE(FP16)
TEST_SUITE(W3x3)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDepthwiseConv2dFixture<half>, framework::DatasetMode::ALL,
- combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset3x3(),
- depth_multipliers),
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuDepthwiseConv2dFixture<half>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset3x3(), depth_multipliers),
framework::dataset::make("DataType", DataType::F16)),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_f16);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDepthwiseConv2dFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseConvolutionLayerDataset3x3(),
- large_depth_multipliers),
- framework::dataset::make("DataType", DataType::F16)),
- framework::dataset::make("DataLayout", DataLayout::NHWC)))
+FIXTURE_DATA_TEST_CASE(RunLarge,
+ DynamicFusionGpuDepthwiseConv2dFixture<half>,
+ framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeDepthwiseConvolutionLayerDataset3x3(),
+ large_depth_multipliers),
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_f16);
}
#ifndef ACL_INTERNAL_TEST_CKW_IN_DF // Do not include this test as dilation not supported yet in DepthwiseConv2d CKW kernel
TEST_SUITE(Dilation)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDepthwiseConv2dFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset3x3(),
- depth_multipliers),
- framework::dataset::make("DataType", DataType::F16)),
- framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuDepthwiseConv2dFixture<half>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset3x3(),
+ depth_multipliers),
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})))
{
validate(CLAccessor(_target), _reference, tolerance_f16);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDepthwiseConv2dFixture<half>, framework::DatasetMode::NIGHTLY,
+FIXTURE_DATA_TEST_CASE(RunLarge,
+ DynamicFusionGpuDepthwiseConv2dFixture<half>,
+ framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(),
large_depth_multipliers),
framework::dataset::make("DataType", DataType::F16)),
- framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})))
{
validate(CLAccessor(_target), _reference, tolerance_f16);
}
@@ -305,34 +317,44 @@
TEST_SUITE_END() // W3x3
TEST_SUITE(Generic)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDepthwiseConv2dFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset(),
- depth_multipliers),
- framework::dataset::make("DataType", DataType::F16)),
- framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuDepthwiseConv2dFixture<half>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset(), depth_multipliers),
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})))
{
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDepthwiseConv2dFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseConvolutionLayerDataset(),
- large_depth_multipliers),
- framework::dataset::make("DataType", DataType::F16)),
- framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+FIXTURE_DATA_TEST_CASE(RunLarge,
+ DynamicFusionGpuDepthwiseConv2dFixture<half>,
+ framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeDepthwiseConvolutionLayerDataset(),
+ large_depth_multipliers),
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})))
{
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
#ifndef ACL_INTERNAL_TEST_CKW_IN_DF // Do not include this test as dilation not supported yet in DepthwiseConv2d CKW kernel
TEST_SUITE(Dilation)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDepthwiseConv2dFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset(),
- depth_multipliers),
- framework::dataset::make("DataType", DataType::F16)),
- framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuDepthwiseConv2dFixture<half>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset(),
+ depth_multipliers),
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})))
{
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDepthwiseConv2dFixture<half>, framework::DatasetMode::NIGHTLY,
+FIXTURE_DATA_TEST_CASE(RunLarge,
+ DynamicFusionGpuDepthwiseConv2dFixture<half>,
+ framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset(),
large_depth_multipliers),
framework::dataset::make("DataType", DataType::F16)),
- framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})))
{
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
@@ -343,15 +365,18 @@
TEST_SUITE(FP32)
TEST_SUITE(W3x3)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDepthwiseConv2dFixture<float>, framework::DatasetMode::ALL,
- combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset3x3(),
- depth_multipliers),
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuDepthwiseConv2dFixture<float>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset3x3(), depth_multipliers),
framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDepthwiseConv2dFixture<float>, framework::DatasetMode::NIGHTLY,
+FIXTURE_DATA_TEST_CASE(RunLarge,
+ DynamicFusionGpuDepthwiseConv2dFixture<float>,
+ framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeDepthwiseConvolutionLayerDataset3x3(),
large_depth_multipliers),
framework::dataset::make("DataType", DataType::F32)),
@@ -363,7 +388,9 @@
#ifndef ACL_INTERNAL_TEST_CKW_IN_DF // Do not include this test as dilation not supported yet in DepthwiseConv2d CKW kernel
TEST_SUITE(Dilation)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDepthwiseConv2dFixture<float>, framework::DatasetMode::ALL,
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuDepthwiseConv2dFixture<float>,
+ framework::DatasetMode::ALL,
combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset3x3(),
depth_multipliers),
framework::dataset::make("DataType", DataType::F32)),
@@ -371,7 +398,9 @@
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDepthwiseConv2dFixture<float>, framework::DatasetMode::NIGHTLY,
+FIXTURE_DATA_TEST_CASE(RunLarge,
+ DynamicFusionGpuDepthwiseConv2dFixture<float>,
+ framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(),
large_depth_multipliers),
framework::dataset::make("DataType", DataType::F32)),
@@ -384,47 +413,57 @@
TEST_SUITE_END() // W3x3
TEST_SUITE(Generic)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDepthwiseConv2dFixture<float>, framework::DatasetMode::ALL,
- combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset(),
- depth_multipliers),
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuDepthwiseConv2dFixture<float>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset(), depth_multipliers),
framework::dataset::make("DataType", DataType::F32)),
- framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDepthwiseConv2dFixture<float>, framework::DatasetMode::NIGHTLY,
+FIXTURE_DATA_TEST_CASE(RunLarge,
+ DynamicFusionGpuDepthwiseConv2dFixture<float>,
+ framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeDepthwiseConvolutionLayerDataset(),
large_depth_multipliers),
framework::dataset::make("DataType", DataType::F32)),
- framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLargeKernelSize, DynamicFusionGpuDepthwiseConv2dFixture<float>, framework::DatasetMode::ALL,
+FIXTURE_DATA_TEST_CASE(RunLargeKernelSize,
+ DynamicFusionGpuDepthwiseConv2dFixture<float>,
+ framework::DatasetMode::ALL,
combine(combine(combine(datasets::LargeKernelSizeDepthwiseConvolutionLayerNHWCDataset(),
- framework::dataset::make("DepthMultiplier", { 1 })),
+ framework::dataset::make("DepthMultiplier", {1})),
framework::dataset::make("DataType", DataType::F32)),
- framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
#ifndef ACL_INTERNAL_TEST_CKW_IN_DF // Do not include this test as dilation not supported yet in DepthwiseConv2d CKW kernel
TEST_SUITE(Dilation)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDepthwiseConv2dFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset(),
- depth_multipliers),
- framework::dataset::make("DataType", DataType::F32)),
- framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuDepthwiseConv2dFixture<float>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset(),
+ depth_multipliers),
+ framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDepthwiseConv2dFixture<float>, framework::DatasetMode::NIGHTLY,
+FIXTURE_DATA_TEST_CASE(RunLarge,
+ DynamicFusionGpuDepthwiseConv2dFixture<float>,
+ framework::DatasetMode::NIGHTLY,
combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(),
large_depth_multipliers),
framework::dataset::make("DataType", DataType::F32)),
- framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
diff --git a/tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp b/tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp
index bae8cbf..dae5500 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,14 +24,13 @@
#include "tests/AssetsLibrary.h"
#include "tests/CL/CLAccessor.h"
+#include "tests/datasets/SmallConvolutionLayerDataset.h"
+#include "tests/framework/datasets/Datasets.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
-#include "tests/framework/datasets/Datasets.h"
-#include "tests/validation/Validation.h"
-#include "tests/validation/reference/ConvolutionLayer.h"
-
-#include "tests/datasets/SmallConvolutionLayerDataset.h"
#include "tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h"
+#include "tests/validation/reference/ConvolutionLayer.h"
+#include "tests/validation/Validation.h"
namespace arm_compute
{
@@ -43,10 +42,12 @@
{
/** Tolerances from tests/validation/CL/DirectConvolutionLayer.cpp
*/
-RelativeTolerance<float> tolerance_f32(0.05f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
-RelativeTolerance<half_float::half> tolerance_f16(half_float::half(0.2)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
-constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/
-constexpr float tolerance_num = 0.07f; /**< Tolerance number */
+RelativeTolerance<float> tolerance_f32(
+ 0.05f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
+RelativeTolerance<half_float::half> tolerance_f16(half_float::half(
+ 0.2)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
+constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/
+constexpr float tolerance_num = 0.07f; /**< Tolerance number */
} // namespace
TEST_SUITE(CL)
@@ -69,8 +70,13 @@
template <typename T>
using DynamicFusionGpuConv2dFixture = DynamicFusionGpuConv2dValidationFixture<CLTensor, CLAccessor, GpuConv2d, T>;
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuConv2dFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
- framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NHWC })), framework::dataset::make("QuantizationInfo", QuantizationInfo())))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuConv2dFixture<float>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+ framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})),
+ framework::dataset::make("QuantizationInfo", QuantizationInfo())))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -78,8 +84,13 @@
TEST_SUITE_END() // FP32
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuConv2dFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
- framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("DataLayout", { DataLayout::NHWC })), framework::dataset::make("QuantizationInfo", QuantizationInfo())))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuConv2dFixture<half>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", {DataLayout::NHWC})),
+ framework::dataset::make("QuantizationInfo", QuantizationInfo())))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
@@ -156,10 +167,10 @@
auto context = GpuWorkloadContext{ &cl_compile_ctx };
GpuWorkloadSketch sketch{ &context };
- const TensorInfo sketch_input_info = context.create_tensor_info(input_info);
- const TensorInfo sketch_weights_info = context.create_tensor_info(weights_info);
- const TensorInfo sketch_biases_info = context.create_tensor_info(biases_info);
- bool is_valid = bool(GpuConv2d::validate_op(sketch, &sketch_input_info, &sketch_weights_info, &sketch_biases_info, conv2d_attrs));
+ const ITensorInfo* sketch_input_info = context.create_tensor_info(input_info);
+ const ITensorInfo* sketch_weights_info = context.create_tensor_info(weights_info);
+ const ITensorInfo* sketch_biases_info = context.create_tensor_info(biases_info);
+ bool is_valid = bool(GpuConv2d::validate_op(sketch, sketch_input_info, sketch_weights_info, sketch_biases_info, conv2d_attrs));
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
template <typename T>
diff --git a/tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp b/tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp
index 38c3a0c..d714a2f 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,16 +24,15 @@
#ifdef ACL_INTERNAL_TEST_CKW_IN_DF
#include "tests/AssetsLibrary.h"
#include "tests/CL/CLAccessor.h"
-#include "tests/framework/Fixture.h"
-#include "tests/framework/Macros.h"
-#include "tests/framework/datasets/Datasets.h"
#include "tests/datasets/LargeMatMulDataset.h"
#include "tests/datasets/SmallMatMulDataset.h"
-#include "tests/validation/Validation.h"
-#include "tests/validation/reference/Permute.h"
-#include "tests/validation/reference/GEMM.h"
-
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/framework/Fixture.h"
+#include "tests/framework/Macros.h"
#include "tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.h"
+#include "tests/validation/reference/GEMM.h"
+#include "tests/validation/reference/Permute.h"
+#include "tests/validation/Validation.h"
#include <tuple>
@@ -45,35 +44,37 @@
{
namespace
{
- RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
-constexpr float abs_tolerance_f32(
+RelativeTolerance<float> tolerance_f32(
+ 0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
+constexpr float abs_tolerance_f32(
0.0001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for floating point data types in case using relative tolerance fails because of small values */
constexpr float abs_tolerance_f16(
- 0.001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for fp16 data types in case using relative tolerance fails because of small values */
- RelativeTolerance<half_float::half> tolerance_f16(half(0.02)); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
-}
+ 0.001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for fp16 data types in case using relative tolerance fails because of small values */
+RelativeTolerance<half_float::half> tolerance_f16(half(
+ 0.02)); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
+} // namespace
/** M0 values to test --precommit*/
-const auto m0_values_precommit = framework::dataset::make("M0", { 1, 3 });
+const auto m0_values_precommit = framework::dataset::make("M0", {1, 3});
/** N0 values to test --precommit*/
-const auto n0_values_precommit = framework::dataset::make("N0", { 1, 2, 4 });
+const auto n0_values_precommit = framework::dataset::make("N0", {1, 2, 4});
/** K0 values to test --precommit*/
-const auto k0_values_precommit = framework::dataset::make("K0", { 1, 2, 3 });
+const auto k0_values_precommit = framework::dataset::make("K0", {1, 2, 3});
/** M0 values to test --nightly*/
-const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", { 1, 2, 3, 4, 5, 6, 7, 8 });
-const auto m0_values_nightly_lhs_t = framework::dataset::make("M0", { 1, 2, 3, 4, 8 });
+const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", {1, 2, 3, 4, 5, 6, 7, 8});
+const auto m0_values_nightly_lhs_t = framework::dataset::make("M0", {1, 2, 3, 4, 8});
/** N0 values to test --nightly*/
-const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", { 1, 2, 3, 4, 8, 16 });
-const auto n0_values_nightly_rhs_t = framework::dataset::make("N0", { 1, 2, 3, 4, 8 });
+const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", {1, 2, 3, 4, 8, 16});
+const auto n0_values_nightly_rhs_t = framework::dataset::make("N0", {1, 2, 3, 4, 8});
/** K0 values to test --nightly*/
-const auto k0_values_nightly_lhs_nt_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 8, 16 });
-const auto k0_values_nightly_rhs_t = framework::dataset::make("K0", { 1, 2, 3, 4, 8 });
-const auto k0_values_nightly_lhs_t_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 5, 6, 7, 8 });
+const auto k0_values_nightly_lhs_nt_rhs_nt = framework::dataset::make("K0", {1, 2, 3, 4, 8, 16});
+const auto k0_values_nightly_rhs_t = framework::dataset::make("K0", {1, 2, 3, 4, 8});
+const auto k0_values_nightly_lhs_t_rhs_nt = framework::dataset::make("K0", {1, 2, 3, 4, 5, 6, 7, 8});
TEST_SUITE(CL)
TEST_SUITE(DYNAMIC_FUSION)
@@ -85,45 +86,43 @@
{
using MatMulConfigurationPair = std::pair<MatMulKernelInfo, bool>;
- const std::vector<MatMulConfigurationPair> supported_block_sizes =
- {
+ const std::vector<MatMulConfigurationPair> supported_block_sizes = {
// MatMulKernelInfo(adj_lhs, adj_rhs, M0, N0, K0, export_rhs_to_cl_image = false)
// Lhs not-transposed, Rhs transposed
- { MatMulKernelInfo(false, true, 0, 1, 1), false }, // M0 should be > 0
- { MatMulKernelInfo(false, true, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
- { MatMulKernelInfo(false, true, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
- { MatMulKernelInfo(false, true, 3, 3, 12), false }, // K0 not in {1, 2, 3, 4, 8, 16}
- { MatMulKernelInfo(false, true, 3, 3, 6), false }, // K0 not in {1, 2, 3, 4, 8, 16}
- { MatMulKernelInfo(false, true, 5, 1, 2), true },
- { MatMulKernelInfo(false, true, 3, 3, 3), true },
- { MatMulKernelInfo(false, true, 2, 4, 8), true },
+ {MatMulKernelInfo(false, true, 0, 1, 1), false}, // M0 should be > 0
+ {MatMulKernelInfo(false, true, 3, 11, 1), false}, // N0 not in {1, 2, 3, 4, 8, 16}
+ {MatMulKernelInfo(false, true, 3, 7, 1), false}, // N0 not in {1, 2, 3, 4, 8, 16}
+ {MatMulKernelInfo(false, true, 3, 3, 12), false}, // K0 not in {1, 2, 3, 4, 8, 16}
+ {MatMulKernelInfo(false, true, 3, 3, 6), false}, // K0 not in {1, 2, 3, 4, 8, 16}
+ {MatMulKernelInfo(false, true, 5, 1, 2), true}, {MatMulKernelInfo(false, true, 3, 3, 3), true},
+ {MatMulKernelInfo(false, true, 2, 4, 8), true},
};
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Set big enough shapes so that block sizes are not truncated. Also, set all dimensions equal
// so that it doesn't fail for different NT/T configurations. We aim to test the block sizes here,
// not the shapes themselves.
- const TensorInfo lhs_info = context.create_tensor_info(TensorInfo(TensorShape(100U, 100U), 1, DataType::F32));
- const TensorInfo rhs_info = context.create_tensor_info(TensorInfo(TensorShape(100U, 100U), 1, DataType::F32));
+ const ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(TensorShape(100U, 100U), 1, DataType::F32));
+ const ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(TensorShape(100U, 100U), 1, DataType::F32));
- for(auto &pair : supported_block_sizes)
+ for (auto &pair : supported_block_sizes)
{
- MatMulAttributes matmul_attr {};
+ MatMulAttributes matmul_attr{};
matmul_attr.adj_lhs(pair.first.adj_lhs);
matmul_attr.adj_rhs(pair.first.adj_rhs);
- GpuMatMulSettings matmul_settings {};
+ GpuMatMulSettings matmul_settings{};
matmul_settings.m0(pair.first.m0);
matmul_settings.n0(pair.first.n0);
matmul_settings.k0(pair.first.k0);
- Status status = GpuMatMul::validate_op(sketch, &lhs_info, &rhs_info, matmul_attr, matmul_settings);
+ Status status = GpuMatMul::validate_op(sketch, lhs_info, rhs_info, matmul_attr, matmul_settings);
ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
}
}
@@ -132,117 +131,110 @@
{
// Create a sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
- using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>;
- const std::vector<ShapeConfigurationTuple> shape_configurations =
- {
- { TensorShape(5U, 1U), TensorShape(3U, 5U), true },
- { TensorShape(10U, 12U), TensorShape(3U, 10U), true },
- { TensorShape(8U, 4U), TensorShape(2U, 8U), true },
- { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension
- { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension
- { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true },
- { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting
- { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension
+ using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>;
+ const std::vector<ShapeConfigurationTuple> shape_configurations = {
+ {TensorShape(5U, 1U), TensorShape(3U, 5U), true},
+ {TensorShape(10U, 12U), TensorShape(3U, 10U), true},
+ {TensorShape(8U, 4U), TensorShape(2U, 8U), true},
+ {TensorShape(8U, 4U), TensorShape(2U, 5U), false}, // Mismatch in the K dimension
+ {TensorShape(5U, 0U), TensorShape(2U, 5U), false}, // Invalid dimension
+ {TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true},
+ {TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false}, // no batch broadcasting
+ {TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U),
+ false}, // mismatch in batch dimension
};
- for(auto &tuple : shape_configurations)
+ for (auto &tuple : shape_configurations)
{
const bool expected = std::get<2>(tuple);
- for(bool adj_lhs :
- {
- false
- })
+ for (bool adj_lhs : {false})
{
- for(bool adj_rhs :
- {
- true
- })
+ for (bool adj_rhs : {true})
{
TensorShape lhs_shape = std::get<0>(tuple);
TensorShape rhs_shape = std::get<1>(tuple);
- if(adj_lhs)
+ if (adj_lhs)
{
permute(lhs_shape, PermutationVector(1U, 0U));
}
- if(adj_rhs)
+ if (adj_rhs)
{
permute(rhs_shape, PermutationVector(1U, 0U));
}
- const TensorInfo lhs_info = context.create_tensor_info(TensorInfo(lhs_shape, 1, DataType::F32));
- const TensorInfo rhs_info = context.create_tensor_info(TensorInfo(rhs_shape, 1, DataType::F32));
+ const ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(lhs_shape, 1, DataType::F32));
+ const ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(rhs_shape, 1, DataType::F32));
- MatMulAttributes matmul_attr {};
+ MatMulAttributes matmul_attr{};
matmul_attr.adj_lhs(adj_lhs);
matmul_attr.adj_rhs(adj_rhs);
- GpuMatMulSettings matmul_settings {};
+ GpuMatMulSettings matmul_settings{};
matmul_settings.m0(1);
matmul_settings.n0(1);
matmul_settings.k0(1);
- Status status = GpuMatMul::validate_op(sketch, &lhs_info, &rhs_info, matmul_attr, matmul_settings);
+ Status status = GpuMatMul::validate_op(sketch, lhs_info, rhs_info, matmul_attr, matmul_settings);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
}
}
-
TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL)
{
// Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>;
- const std::vector<DataTypeConfigurationTuple> data_type_configurations =
- {
- { DataType::F32, DataType::F32, DataType::F32, true },
- { DataType::F16, DataType::F16, DataType::F16, true },
- { DataType::F16, DataType::F32, DataType::F32, false }, // no mixed precision
- { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
- { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, false }, // no quantized types
- { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, false }, // no quantized types
- { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types
- { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false }, // no quantized types
- { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false }, // no quantized types
- { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false }, // no quantized types
- { DataType::S64, DataType::S64, DataType::S64, false }, // no integral types
- { DataType::S32, DataType::S32, DataType::S32, false }, // no integral types
- { DataType::S16, DataType::S16, DataType::S16, false }, // no integral types
- { DataType::S8, DataType::S8, DataType::S8, false }, // no integral types
- { DataType::U64, DataType::U64, DataType::U64, false }, // no integral types
- { DataType::U32, DataType::U32, DataType::U32, false }, // no integral types
- { DataType::U16, DataType::U16, DataType::U16, false }, // no integral types
- { DataType::U8, DataType::U8, DataType::U8, false }, // no integral types
+ const std::vector<DataTypeConfigurationTuple> data_type_configurations = {
+ {DataType::F32, DataType::F32, DataType::F32, true},
+ {DataType::F16, DataType::F16, DataType::F16, true},
+ {DataType::F16, DataType::F32, DataType::F32, false}, // no mixed precision
+ {DataType::F64, DataType::F64, DataType::F64, false}, // no double precision
+ {DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, false}, // no quantized types
+ {DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, false}, // no quantized types
+ {DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL,
+ false}, // no quantized types
+ {DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false}, // no quantized types
+ {DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false}, // no quantized types
+ {DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false}, // no quantized types
+ {DataType::S64, DataType::S64, DataType::S64, false}, // no integral types
+ {DataType::S32, DataType::S32, DataType::S32, false}, // no integral types
+ {DataType::S16, DataType::S16, DataType::S16, false}, // no integral types
+ {DataType::S8, DataType::S8, DataType::S8, false}, // no integral types
+ {DataType::U64, DataType::U64, DataType::U64, false}, // no integral types
+ {DataType::U32, DataType::U32, DataType::U32, false}, // no integral types
+ {DataType::U16, DataType::U16, DataType::U16, false}, // no integral types
+ {DataType::U8, DataType::U8, DataType::U8, false}, // no integral types
};
// Create a sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
const TensorShape shape = TensorShape(10U, 10U);
- MatMulAttributes matmul_attr {};
+ MatMulAttributes matmul_attr{};
matmul_attr.adj_lhs(false);
matmul_attr.adj_rhs(false);
- GpuMatMulSettings matmul_settings {};
+ GpuMatMulSettings matmul_settings{};
matmul_settings.m0(1);
matmul_settings.n0(1);
matmul_settings.k0(1);
- for(auto &tuple : data_type_configurations)
+ for (auto &tuple : data_type_configurations)
{
const bool expected = std::get<3>(tuple);
- const TensorInfo lhs_info = context.create_tensor_info(TensorInfo(shape, 1, std::get<0>(tuple)));
- const TensorInfo rhs_info = context.create_tensor_info(TensorInfo(shape, 1, std::get<1>(tuple)));
+ const ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(shape, 1, std::get<0>(tuple)));
+ const ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(shape, 1, std::get<1>(tuple)));
- Status status = GpuMatMul::validate_op(sketch, &lhs_info, &rhs_info, matmul_attr, matmul_settings);
+ Status status = GpuMatMul::validate_op(sketch, lhs_info, rhs_info, matmul_attr, matmul_settings);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
@@ -250,59 +242,75 @@
TEST_SUITE_END() // Validate
template <typename T>
-using DynamicFusionGpuMatmulFixture = DynamicFusionGpuMatMulValidationFixture<CLTensor, CLAccessor,GpuMatMul, T>;
+using DynamicFusionGpuMatmulFixture = DynamicFusionGpuMatMulValidationFixture<CLTensor, CLAccessor, GpuMatMul, T>;
TEST_SUITE(Float)
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunTiny, DynamicFusionGpuMatmulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(),
- framework::dataset::make("TransposeA", { false })),
- framework::dataset::make("TransposeB", { true })),
- m0_values_precommit),
- n0_values_precommit),
- k0_values_precommit),
- framework::dataset::make("ExportRhsToCLImage", { false })),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(
+ RunTiny,
+ DynamicFusionGpuMatmulFixture<float>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(),
+ framework::dataset::make("TransposeA", {false})),
+ framework::dataset::make("TransposeB", {true})),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", {false})),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuMatmulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
- framework::dataset::make("TransposeA", { false })),
- framework::dataset::make("TransposeB", { true })),
- m0_values_precommit),
- n0_values_precommit),
- k0_values_precommit),
- framework::dataset::make("ExportRhsToCLImage", { false })),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(
+ RunSmall,
+ DynamicFusionGpuMatmulFixture<float>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
+ framework::dataset::make("TransposeA", {false})),
+ framework::dataset::make("TransposeB", {true})),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", {false})),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, DynamicFusionGpuMatmulFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
- framework::dataset::make("TransposeA", { false })),
- framework::dataset::make("TransposeB", { true })),
- m0_values_nightly_lhs_nt),
- n0_values_nightly_rhs_t),
- k0_values_nightly_rhs_t),
- framework::dataset::make("ExportRhsToCLImage", { false })),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(
+ RunLargeRhsTransposed,
+ DynamicFusionGpuMatmulFixture<float>,
+ framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("TransposeA", {false})),
+ framework::dataset::make("TransposeB", {true})),
+ m0_values_nightly_lhs_nt),
+ n0_values_nightly_rhs_t),
+ k0_values_nightly_rhs_t),
+ framework::dataset::make("ExportRhsToCLImage", {false})),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
// Running High Dimensional test is enough for FP32, because we're stressing the number of dimensions, not data type or M0/N0/K0
-FIXTURE_DATA_TEST_CASE(RunHighDimensional, DynamicFusionGpuMatmulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(),
- framework::dataset::make("TransposeA", { false })),
- framework::dataset::make("TransposeB", { true })),
- framework::dataset::make("M0", { 2 })),
- framework::dataset::make("N0", { 2 })),
- framework::dataset::make("K0", { 2 })),
- framework::dataset::make("ExportRhsToCLImage", { false })),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(
+ RunHighDimensional,
+ DynamicFusionGpuMatmulFixture<float>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(),
+ framework::dataset::make("TransposeA", {false})),
+ framework::dataset::make("TransposeB", {true})),
+ framework::dataset::make("M0", {2})),
+ framework::dataset::make("N0", {2})),
+ framework::dataset::make("K0", {2})),
+ framework::dataset::make("ExportRhsToCLImage", {false})),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
@@ -311,28 +319,35 @@
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuMatmulFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
- framework::dataset::make("TransposeA", { false })),
- framework::dataset::make("TransposeB", { true })),
- m0_values_precommit),
- n0_values_precommit),
- k0_values_precommit),
- framework::dataset::make("ExportRhsToCLImage", { false })),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(
+ RunSmall,
+ DynamicFusionGpuMatmulFixture<half>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
+ framework::dataset::make("TransposeA", {false})),
+ framework::dataset::make("TransposeB", {true})),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", {false})),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
-
-FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, DynamicFusionGpuMatmulFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
- framework::dataset::make("TransposeA", { false })),
- framework::dataset::make("TransposeB", { true })),
- m0_values_nightly_lhs_nt),
- n0_values_nightly_rhs_t),
- k0_values_nightly_rhs_t),
- framework::dataset::make("ExportRhsToCLImage", { false })),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(
+ RunLargeRhsTransposed,
+ DynamicFusionGpuMatmulFixture<half>,
+ framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("TransposeA", {false})),
+ framework::dataset::make("TransposeB", {true})),
+ m0_values_nightly_lhs_nt),
+ n0_values_nightly_rhs_t),
+ k0_values_nightly_rhs_t),
+ framework::dataset::make("ExportRhsToCLImage", {false})),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
diff --git a/tests/validation/dynamic_fusion/gpu/cl/Mul.cpp b/tests/validation/dynamic_fusion/gpu/cl/Mul.cpp
index b69479f..c11bffe 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/Mul.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/Mul.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -29,14 +29,13 @@
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuMul.h"
#include "tests/CL/CLAccessor.h"
-#include "tests/framework/Fixture.h"
-#include "tests/framework/Macros.h"
-#include "tests/framework/datasets/Datasets.h"
-#include "tests/validation/Validation.h"
-
#include "tests/datasets/DynamicFusionDataset.h"
#include "tests/datasets/ShapeDatasets.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/framework/Fixture.h"
+#include "tests/framework/Macros.h"
#include "tests/validation/fixtures/dynamic_fusion/operators/MulFixture.h"
+#include "tests/validation/Validation.h"
namespace arm_compute
{
@@ -58,8 +57,10 @@
*/
namespace
{
-constexpr AbsoluteTolerance<float> tolerance_f16(0.0001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
-constexpr AbsoluteTolerance<float> tolerance_f32(0.0001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
+constexpr AbsoluteTolerance<float> tolerance_f16(
+ 0.0001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
+constexpr AbsoluteTolerance<float> tolerance_f32(
+ 0.0001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
} // namespace
TEST_SUITE(CL)
TEST_SUITE(DYNAMIC_FUSION)
@@ -112,7 +113,7 @@
auto lhs_info = context.create_tensor_info(input1_info);
auto rhs_info = context.create_tensor_info(input2_info);
- bool res = bool(GpuMul::validate_op(sketch, &lhs_info, &rhs_info));
+ bool res = bool(GpuMul::validate_op(sketch, lhs_info, rhs_info));
ARM_COMPUTE_EXPECT(res == expected, framework::LogLevel::ERRORS);
}
// clang-format on
@@ -129,9 +130,8 @@
FIXTURE_DATA_TEST_CASE(RunSmallOneOp,
DynamicFusionCLMulFixture<half>,
framework::DatasetMode::ALL,
- combine(combine(datasets::SmallShapes(),
- framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("InPlace", { false })))
+ combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", {DataType::F16})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16);
@@ -141,8 +141,8 @@
DynamicFusionCLMulBroadcastFixture<half>,
framework::DatasetMode::PRECOMMIT,
combine(combine(datasets::TemporaryLimitedSmallShapesBroadcast(),
- framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F16})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16);
@@ -152,8 +152,8 @@
DynamicFusionCLMulBroadcastFixture<half>,
framework::DatasetMode::NIGHTLY,
combine(combine(datasets::TemporaryLimitedLargeShapesBroadcast(),
- framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F16})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16);
@@ -164,9 +164,8 @@
FIXTURE_DATA_TEST_CASE(RunSmallOneOp,
DynamicFusionCLMulFixture<float>,
framework::DatasetMode::PRECOMMIT,
- combine(combine(datasets::SmallShapes(),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -175,9 +174,8 @@
FIXTURE_DATA_TEST_CASE(RunLargeOneOp,
DynamicFusionCLMulFixture<float>,
framework::DatasetMode::NIGHTLY,
- combine(combine(datasets::LargeShapes(),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -187,8 +185,8 @@
DynamicFusionCLMulBroadcastFixture<float>,
framework::DatasetMode::PRECOMMIT,
combine(combine(datasets::TemporaryLimitedSmallShapesBroadcast(),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -198,8 +196,8 @@
DynamicFusionCLMulBroadcastFixture<float>,
framework::DatasetMode::NIGHTLY,
combine(combine(datasets::TemporaryLimitedLargeShapesBroadcast(),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -209,9 +207,9 @@
DynamicFusionCLMulTwoOpsFixture<float>,
framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::DynamicFusionElementwiseBinaryTwoOpsSmallShapes(),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })),
- framework::dataset::make("FuseTwoOps", { true })))
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})),
+ framework::dataset::make("FuseTwoOps", {true})))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
diff --git a/tests/validation/dynamic_fusion/gpu/cl/Pool2d.cpp b/tests/validation/dynamic_fusion/gpu/cl/Pool2d.cpp
index 411e31b..f894ce3 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/Pool2d.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/Pool2d.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -25,13 +25,13 @@
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuPool2d.h"
#include "tests/CL/CLAccessor.h"
-#include "tests/datasets/ShapeDatasets.h"
#include "tests/datasets/dynamic_fusion/PoolingLayerDataset.h"
+#include "tests/datasets/ShapeDatasets.h"
+#include "tests/framework/datasets/Datasets.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
-#include "tests/framework/datasets/Datasets.h"
-#include "tests/validation/Validation.h"
#include "tests/validation/fixtures/dynamic_fusion/gpu/cl/Pool2dFixture.h"
+#include "tests/validation/Validation.h"
namespace arm_compute
{
@@ -43,15 +43,19 @@
TEST_SUITE(DYNAMIC_FUSION)
TEST_SUITE(POOL2D)
-constexpr AbsoluteTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for 32-bit floating-point type */
-constexpr AbsoluteTolerance<float> tolerance_f16(0.01f); /**< Tolerance value for comparing reference's output against implementation's output for 16-bit floating-point type */
+constexpr AbsoluteTolerance<float> tolerance_f32(
+ 0.001f); /**< Tolerance value for comparing reference's output against implementation's output for 32-bit floating-point type */
+constexpr AbsoluteTolerance<float> tolerance_f16(
+ 0.01f); /**< Tolerance value for comparing reference's output against implementation's output for 16-bit floating-point type */
-const auto PoolingLayerDatasetFP = combine(combine(combine(combine(framework::dataset::make("PoolingType", { PoolingType::MAX, PoolingType::AVG }), framework::dataset::make("PoolingSize", { Size2D(2, 2), Size2D(3, 3) })),
- framework::dataset::make("Pad", { Padding2D() })),
- framework::dataset::make("Stride", { Size2D(1, 1), Size2D(2, 1), Size2D(5, 7) })),
- framework::dataset::make("ExcludePadding", { true }));
+const auto PoolingLayerDatasetFP =
+ combine(combine(combine(combine(framework::dataset::make("PoolingType", {PoolingType::MAX, PoolingType::AVG}),
+ framework::dataset::make("PoolingSize", {Size2D(2, 2), Size2D(3, 3)})),
+ framework::dataset::make("Pad", {Padding2D()})),
+ framework::dataset::make("Stride", {Size2D(1, 1), Size2D(2, 1), Size2D(5, 7)})),
+ framework::dataset::make("ExcludePadding", {true}));
-const auto pool_fp_mixed_precision_dataset = framework::dataset::make("FpMixedPrecision", { true, false });
+const auto pool_fp_mixed_precision_dataset = framework::dataset::make("FpMixedPrecision", {true, false});
template <typename T>
using DynamicFusionGpuPool2dFixture = DynamicFusionGpuPool2dValidationFixture<CLTensor, CLAccessor, GpuPool2d, T>;
@@ -60,7 +64,8 @@
using DFSpecialGpuPool2dFixture = DynamicFusionGpuPool2dSpecialValidationFixture<CLTensor, CLAccessor, GpuPool2d, T>;
template <typename T>
-using DFPoolMixedPrecisionFixture = DynamicFusionGpuPool2dMixedPrecisionValidationFixture<CLTensor, CLAccessor, GpuPool2d, T>;
+using DFPoolMixedPrecisionFixture =
+ DynamicFusionGpuPool2dMixedPrecisionValidationFixture<CLTensor, CLAccessor, GpuPool2d, T>;
// *INDENT-OFF*
// clang-format off
@@ -91,7 +96,7 @@
// Validate Pool2d Configuration
auto src_info = context.create_tensor_info(input_info);
- bool res = bool(GpuPool2d::validate_op(sketch, &src_info, pool2d_attr, settings));
+ bool res = bool(GpuPool2d::validate_op(sketch, src_info, pool2d_attr, settings));
ARM_COMPUTE_EXPECT(res == expected, framework::LogLevel::ERRORS);
}
@@ -100,53 +105,68 @@
TEST_SUITE(Float)
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuPool2dFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallNoneUnitShapes(), PoolingLayerDatasetFP),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuPool2dFixture<float>,
+ framework::DatasetMode::PRECOMMIT,
+ combine(combine(datasets::SmallNoneUnitShapes(), PoolingLayerDatasetFP),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuPool2dFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), PoolingLayerDatasetFP),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunLarge,
+ DynamicFusionGpuPool2dFixture<float>,
+ framework::DatasetMode::NIGHTLY,
+ combine(combine(datasets::LargeShapes(), PoolingLayerDatasetFP),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunSpecial, DFSpecialGpuPool2dFixture<float>, framework::DatasetMode::ALL, combine(datasets::PoolingLayerDatasetSpecialDynamicFusion(),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunSpecial,
+ DFSpecialGpuPool2dFixture<float>,
+ framework::DatasetMode::ALL,
+ combine(datasets::PoolingLayerDatasetSpecialDynamicFusion(),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
TEST_SUITE(GlobalPooling)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuPool2dFixture<float>, framework::DatasetMode::ALL,
- combine(combine(combine(combine(combine(combine(
- framework::dataset::make("InputShape", { TensorShape(27U, 13U, 2U),
- TensorShape(27U, 13U, 2U, 4U)
- }),
- framework::dataset::make("PoolingType", { PoolingType::AVG, PoolingType::MAX })),
- framework::dataset::make("PoolingSize", { Size2D(27, 13) })),
- framework::dataset::make("Pad", { Padding2D() })),
- framework::dataset::make("Stride", { Size2D(1, 1) })),
- framework::dataset::make("ExcludePadding", true)),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(
+ RunSmall,
+ DynamicFusionGpuPool2dFixture<float>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(framework::dataset::make("InputShape",
+ {TensorShape(27U, 13U, 2U),
+ TensorShape(27U, 13U, 2U, 4U)}),
+ framework::dataset::make("PoolingType",
+ {PoolingType::AVG, PoolingType::MAX})),
+ framework::dataset::make("PoolingSize", {Size2D(27, 13)})),
+ framework::dataset::make("Pad", {Padding2D()})),
+ framework::dataset::make("Stride", {Size2D(1, 1)})),
+ framework::dataset::make("ExcludePadding", true)),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuPool2dFixture<float>, framework::DatasetMode::NIGHTLY,
- combine(combine(combine(combine(combine(combine(
- framework::dataset::make("InputShape", { TensorShape(79U, 37U, 11U),
- TensorShape(79U, 37U, 11U, 4U)
- }),
- framework::dataset::make("PoolingType", { PoolingType::AVG, PoolingType::MAX })),
- framework::dataset::make("PoolingSize", { Size2D(79, 37) })),
- framework::dataset::make("Pad", { Padding2D() })),
- framework::dataset::make("Stride", { Size2D(1, 1) })),
- framework::dataset::make("ExcludePadding", true)),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(
+ RunLarge,
+ DynamicFusionGpuPool2dFixture<float>,
+ framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(framework::dataset::make("InputShape",
+ {TensorShape(79U, 37U, 11U),
+ TensorShape(79U, 37U, 11U, 4U)}),
+ framework::dataset::make("PoolingType",
+ {PoolingType::AVG, PoolingType::MAX})),
+ framework::dataset::make("PoolingSize", {Size2D(79, 37)})),
+ framework::dataset::make("Pad", {Padding2D()})),
+ framework::dataset::make("Stride", {Size2D(1, 1)})),
+ framework::dataset::make("ExcludePadding", true)),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -155,49 +175,61 @@
TEST_SUITE_END() // FP32
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, DFPoolMixedPrecisionFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallNoneUnitShapes(), PoolingLayerDatasetFP),
- framework::dataset::make("DataType", DataType::F16)),
- pool_fp_mixed_precision_dataset))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DFPoolMixedPrecisionFixture<half>,
+ framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(datasets::SmallNoneUnitShapes(), PoolingLayerDatasetFP),
+ framework::dataset::make("DataType", DataType::F16)),
+ pool_fp_mixed_precision_dataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DFPoolMixedPrecisionFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeShapes(), PoolingLayerDatasetFP),
- framework::dataset::make("DataType", DataType::F16)),
- pool_fp_mixed_precision_dataset))
+FIXTURE_DATA_TEST_CASE(RunLarge,
+ DFPoolMixedPrecisionFixture<half>,
+ framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeShapes(), PoolingLayerDatasetFP),
+ framework::dataset::make("DataType", DataType::F16)),
+ pool_fp_mixed_precision_dataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16);
}
TEST_SUITE(GlobalPooling)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuPool2dFixture<half>, framework::DatasetMode::ALL,
- combine(combine(combine(combine(combine(combine(
- framework::dataset::make("InputShape", { TensorShape(27U, 13U, 2U),
- TensorShape(27U, 13U, 2U, 4U)
- }),
- framework::dataset::make("PoolingType", { PoolingType::AVG, PoolingType::MAX })),
- framework::dataset::make("PoolingSize", { Size2D(27, 13) })),
- framework::dataset::make("Pad", { Padding2D() })),
- framework::dataset::make("Stride", { Size2D(1, 1) })),
- framework::dataset::make("ExcludePadding", true)),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(
+ RunSmall,
+ DynamicFusionGpuPool2dFixture<half>,
+ framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(framework::dataset::make("InputShape",
+ {TensorShape(27U, 13U, 2U),
+ TensorShape(27U, 13U, 2U, 4U)}),
+ framework::dataset::make("PoolingType",
+ {PoolingType::AVG, PoolingType::MAX})),
+ framework::dataset::make("PoolingSize", {Size2D(27, 13)})),
+ framework::dataset::make("Pad", {Padding2D()})),
+ framework::dataset::make("Stride", {Size2D(1, 1)})),
+ framework::dataset::make("ExcludePadding", true)),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuPool2dFixture<half>, framework::DatasetMode::NIGHTLY,
- combine(combine(combine(combine(combine(combine(
- framework::dataset::make("InputShape", { TensorShape(79U, 37U, 11U),
- TensorShape(79U, 37U, 11U, 4U)
- }),
- framework::dataset::make("PoolingType", { PoolingType::AVG, PoolingType::MAX })),
- framework::dataset::make("PoolingSize", { Size2D(79, 37) })),
- framework::dataset::make("Pad", { Padding2D() })),
- framework::dataset::make("Stride", { Size2D(1, 1) })),
- framework::dataset::make("ExcludePadding", true)),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(
+ RunLarge,
+ DynamicFusionGpuPool2dFixture<half>,
+ framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(framework::dataset::make("InputShape",
+ {TensorShape(79U, 37U, 11U),
+ TensorShape(79U, 37U, 11U, 4U)}),
+ framework::dataset::make("PoolingType",
+ {PoolingType::AVG, PoolingType::MAX})),
+ framework::dataset::make("PoolingSize", {Size2D(79, 37)})),
+ framework::dataset::make("Pad", {Padding2D()})),
+ framework::dataset::make("Stride", {Size2D(1, 1)})),
+ framework::dataset::make("ExcludePadding", true)),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16);
@@ -209,7 +241,7 @@
TEST_SUITE_END() // POOL2D
TEST_SUITE_END() // DYNAMIC_FUSION
TEST_SUITE_END() // CL
-}
-}
-}
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
#endif // ACL_INTERNAL_TEST_CKW_IN_DF
diff --git a/tests/validation/dynamic_fusion/gpu/cl/Reshape.cpp b/tests/validation/dynamic_fusion/gpu/cl/Reshape.cpp
index 4d038b2..43617fe 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/Reshape.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/Reshape.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,10 +24,10 @@
#ifndef ACL_INTERNAL_TEST_CKW_IN_DF // Do not include this test if ACL_INTERNAL_TEST_CKW_IN_DF and the op has not been ported to ckw
#include "tests/CL/CLAccessor.h"
#include "tests/datasets/ReshapeLayerDataset.h"
-#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
-#include "tests/validation/Validation.h"
+#include "tests/framework/Macros.h"
#include "tests/validation/fixtures/dynamic_fusion/operators/ReshapeFixture.h"
+#include "tests/validation/Validation.h"
namespace arm_compute
{
@@ -39,41 +39,52 @@
TEST_SUITE(DYNAMIC_FUSION)
TEST_SUITE(RESHAPE)
-DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(framework::dataset::make("InputInfo",
-{
- TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32), TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32), TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32) /*mismatching dimensions*/,
-}),
-framework::dataset::make("OutputShape",
-{
- TensorShape(9U, 5U, 21U),
- TensorShape(8U, 24U, 4U),
- TensorShape(192U, 192U),
-})),
-framework::dataset::make("Expected", { true, true, false })),
-input_info, output_shape, expected)
+DATA_TEST_CASE(Validate,
+ framework::DatasetMode::ALL,
+ zip(zip(framework::dataset::make(
+ "InputInfo",
+ {
+ TensorInfo(TensorShape(9U, 5U, 7U, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(8U, 4U, 6U, 4U), 1, DataType::F32) /*mismatching dimensions*/,
+ }),
+ framework::dataset::make("OutputShape",
+ {
+ TensorShape(9U, 5U, 21U),
+ TensorShape(8U, 24U, 4U),
+ TensorShape(192U, 192U),
+ })),
+ framework::dataset::make("Expected", {true, true, false})),
+ input_info,
+ output_shape,
+ expected)
{
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Create sketch tensors
TensorShape input_shape = input_info.tensor_shape();
ARM_COMPUTE_UNUSED(input_shape);
- TensorInfo src_info = context.create_tensor_info(input_info);
+ ITensorInfo *src_info = context.create_tensor_info(input_info);
ReshapeAttributes attributes;
attributes.shape(output_shape);
- Status status = GpuReshape::validate_op(sketch, &src_info, attributes);
+ Status status = GpuReshape::validate_op(sketch, src_info, attributes);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
template <typename T>
-using DynamicFusionGpuReshapeLayerFixture = DynamicFusionGpuReshapeLayerValidationFixture<CLTensor, CLAccessor, GpuReshape, T>;
+using DynamicFusionGpuReshapeLayerFixture =
+ DynamicFusionGpuReshapeLayerValidationFixture<CLTensor, CLAccessor, GpuReshape, T>;
TEST_SUITE(F32)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuReshapeLayerFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallReshapeLayerDataset(), framework::dataset::make("DataType",
- DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuReshapeLayerFixture<float>,
+ framework::DatasetMode::ALL,
+ combine(datasets::SmallReshapeLayerDataset(),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -81,8 +92,11 @@
TEST_SUITE_END() // F32
TEST_SUITE(F16)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuReshapeLayerFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallReshapeLayerDataset(), framework::dataset::make("DataType",
- DataType::F16)))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuReshapeLayerFixture<half>,
+ framework::DatasetMode::ALL,
+ combine(datasets::SmallReshapeLayerDataset(),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -90,8 +104,11 @@
TEST_SUITE_END() // F16
TEST_SUITE(U8)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuReshapeLayerFixture<uint8_t>, framework::DatasetMode::ALL, combine(datasets::SmallReshapeLayerDataset(), framework::dataset::make("DataType",
- DataType::U8)))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuReshapeLayerFixture<uint8_t>,
+ framework::DatasetMode::ALL,
+ combine(datasets::SmallReshapeLayerDataset(),
+ framework::dataset::make("DataType", DataType::U8)))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -99,8 +116,11 @@
TEST_SUITE_END() // U8
TEST_SUITE(S8)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuReshapeLayerFixture<int8_t>, framework::DatasetMode::ALL, combine(datasets::SmallReshapeLayerDataset(), framework::dataset::make("DataType",
- DataType::S8)))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuReshapeLayerFixture<int8_t>,
+ framework::DatasetMode::ALL,
+ combine(datasets::SmallReshapeLayerDataset(),
+ framework::dataset::make("DataType", DataType::S8)))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -108,8 +128,11 @@
TEST_SUITE_END() // S8
TEST_SUITE(S16)
-FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuReshapeLayerFixture<int16_t>, framework::DatasetMode::ALL, combine(datasets::SmallReshapeLayerDataset(), framework::dataset::make("DataType",
- DataType::S16)))
+FIXTURE_DATA_TEST_CASE(RunSmall,
+ DynamicFusionGpuReshapeLayerFixture<int16_t>,
+ framework::DatasetMode::ALL,
+ combine(datasets::SmallReshapeLayerDataset(),
+ framework::dataset::make("DataType", DataType::S16)))
{
// Validate output
validate(CLAccessor(_target), _reference);
diff --git a/tests/validation/dynamic_fusion/gpu/cl/Resize.cpp b/tests/validation/dynamic_fusion/gpu/cl/Resize.cpp
index 5f99cd6..10915ac 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/Resize.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/Resize.cpp
@@ -1,5 +1,5 @@
/*
-* Copyright (c) 2022-2023 Arm Limited.
+* Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -29,8 +29,8 @@
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
-#include "tests/validation/Validation.h"
#include "tests/validation/fixtures/dynamic_fusion/operators/ResizeFixture.h"
+#include "tests/validation/Validation.h"
using namespace arm_compute::experimental::dynamic_fusion;
namespace arm_compute
@@ -41,10 +41,10 @@
{
namespace
{
-using datasets::ScaleShapesBaseDataSet;
+using datasets::ScaleAlignCornersSamplingPolicySet;
using datasets::ScaleInterpolationPolicySet;
using datasets::ScaleSamplingPolicySet;
-using datasets::ScaleAlignCornersSamplingPolicySet;
+using datasets::ScaleShapesBaseDataSet;
/** We consider vector size in byte 16 since the maximum size of
* a vector used by @ref CLScaleKernel is currently 16-byte (float4).
@@ -59,9 +59,9 @@
/** Quantization information data set */
const auto QuantizationInfoSet = framework::dataset::make("QuantizationInfo",
-{
- QuantizationInfo(0.5f, -1),
-});
+ {
+ QuantizationInfo(0.5f, -1),
+ });
/** Tolerance */
constexpr AbsoluteTolerance<uint8_t> tolerance_q8(1);
@@ -83,22 +83,20 @@
TEST_SUITE(Validate)
-const auto default_input_shape = TensorShape{ 2, 3, 3, 2 };
-const auto default_output_shape = TensorShape{ 4, 6, 3, 2 };
+const auto default_input_shape = TensorShape{2, 3, 3, 2};
+const auto default_output_shape = TensorShape{4, 6, 3, 2};
constexpr auto default_data_type = DataType::U8;
constexpr auto default_data_layout = DataLayout::NHWC;
TEST_CASE(NullPtr, framework::DatasetMode::ALL)
{
- const TensorInfo input_info = TensorInfo{ default_input_shape, 1, default_data_type, default_data_layout };
- const TensorInfo output_info = TensorInfo{ default_output_shape, 1, default_data_type, default_data_layout };
+ const TensorInfo input_info = TensorInfo{default_input_shape, 1, default_data_type, default_data_layout};
+ const TensorInfo output_info = TensorInfo{default_output_shape, 1, default_data_type, default_data_layout};
CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- GpuWorkloadContext context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
-
- const TensorInfo sketch_input_info = context.create_tensor_info(input_info);
+ GpuWorkloadContext context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// nullptr is given as input
Status status = GpuResize::validate_op(sketch, nullptr, ResizeAttributes());
@@ -107,44 +105,43 @@
TEST_CASE(SupportDataType, framework::DatasetMode::ALL)
{
- const std::map<DataType, bool> supported_data_types =
- {
- { DataType::U8, true },
- { DataType::S8, false },
- { DataType::QSYMM8, false },
- { DataType::QASYMM8, true },
- { DataType::QASYMM8_SIGNED, true },
- { DataType::QSYMM8_PER_CHANNEL, false },
- { DataType::U16, false },
- { DataType::S16, true },
- { DataType::QSYMM16, false },
- { DataType::QASYMM16, false },
- { DataType::U32, false },
- { DataType::S32, false },
- { DataType::U64, false },
- { DataType::S64, false },
- { DataType::BFLOAT16, false },
- { DataType::F16, true },
- { DataType::F32, true },
- { DataType::F64, false },
- { DataType::SIZET, false },
+ const std::map<DataType, bool> supported_data_types = {
+ {DataType::U8, true},
+ {DataType::S8, false},
+ {DataType::QSYMM8, false},
+ {DataType::QASYMM8, true},
+ {DataType::QASYMM8_SIGNED, true},
+ {DataType::QSYMM8_PER_CHANNEL, false},
+ {DataType::U16, false},
+ {DataType::S16, true},
+ {DataType::QSYMM16, false},
+ {DataType::QASYMM16, false},
+ {DataType::U32, false},
+ {DataType::S32, false},
+ {DataType::U64, false},
+ {DataType::S64, false},
+ {DataType::BFLOAT16, false},
+ {DataType::F16, true},
+ {DataType::F32, true},
+ {DataType::F64, false},
+ {DataType::SIZET, false},
};
- for(auto &kv : supported_data_types)
+ for (auto &kv : supported_data_types)
{
- const TensorInfo input_info = TensorInfo{ default_input_shape, 1, kv.first, default_data_layout };
+ const TensorInfo input_info = TensorInfo{default_input_shape, 1, kv.first, default_data_layout};
CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- GpuWorkloadContext context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ GpuWorkloadContext context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
- const TensorInfo sketch_input_info = context.create_tensor_info(input_info);
+ const ITensorInfo *sketch_input_info = context.create_tensor_info(input_info);
ResizeAttributes attributes;
attributes.output_width(default_output_shape[0]); // shape is not important unless it's empty
attributes.output_height(default_output_shape[1]);
- Status status = GpuResize::validate_op(sketch, &sketch_input_info, attributes);
+ Status status = GpuResize::validate_op(sketch, sketch_input_info, attributes);
ARM_COMPUTE_EXPECT(bool(status) == kv.second, framework::LogLevel::ERRORS);
}
}
@@ -153,16 +150,16 @@
{
constexpr DataType non_default_data_type = DataType::F32;
- const TensorInfo input_info = TensorInfo{ default_input_shape, 1, default_data_type, default_data_layout };
- const TensorInfo output_info = TensorInfo{ default_output_shape, 1, non_default_data_type, default_data_layout };
+ const TensorInfo input_info = TensorInfo{default_input_shape, 1, default_data_type, default_data_layout};
+ const TensorInfo output_info = TensorInfo{default_output_shape, 1, non_default_data_type, default_data_layout};
CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- GpuWorkloadContext context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ GpuWorkloadContext context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
- const TensorInfo sketch_input_info = context.create_tensor_info(input_info);
+ const ITensorInfo *sketch_input_info = context.create_tensor_info(input_info);
- Status status = GpuResize::validate_op(sketch, &sketch_input_info, ResizeAttributes());
+ Status status = GpuResize::validate_op(sketch, sketch_input_info, ResizeAttributes());
ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
}
@@ -173,59 +170,57 @@
constexpr bool align_corners = true;
constexpr SamplingPolicy sampling_policy = SamplingPolicy::CENTER;
- const TensorInfo input_info = TensorInfo{ default_input_shape, 1, default_data_type, default_data_layout };
- const TensorInfo output_info = TensorInfo{ default_output_shape, 1, default_data_type, default_data_layout };
+ const TensorInfo input_info = TensorInfo{default_input_shape, 1, default_data_type, default_data_layout};
+ const TensorInfo output_info = TensorInfo{default_output_shape, 1, default_data_type, default_data_layout};
CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- GpuWorkloadContext context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ GpuWorkloadContext context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
- const TensorInfo sketch_input_info = context.create_tensor_info(input_info);
+ const ITensorInfo *sketch_input_info = context.create_tensor_info(input_info);
ResizeAttributes attributes{};
- attributes.interpolation_policy(interpolation_policy)
- .sampling_policy(sampling_policy)
- .align_corners(align_corners);
+ attributes.interpolation_policy(interpolation_policy).sampling_policy(sampling_policy).align_corners(align_corners);
- Status status = GpuResize::validate_op(sketch, &sketch_input_info, attributes);
+ Status status = GpuResize::validate_op(sketch, sketch_input_info, attributes);
ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
}
TEST_CASE(UnsupportedInterpolationPolicy, framework::DatasetMode::ALL)
{
- const TensorInfo input_info = TensorInfo{ TensorShape(28U, 33U, 2U), 1, DataType::F32, default_data_layout };
- const TensorInfo output_info = TensorInfo{ TensorShape(26U, 21U, 2U), 1, DataType::F32, default_data_layout };
+ const TensorInfo input_info = TensorInfo{TensorShape(28U, 33U, 2U), 1, DataType::F32, default_data_layout};
+ const TensorInfo output_info = TensorInfo{TensorShape(26U, 21U, 2U), 1, DataType::F32, default_data_layout};
constexpr auto interpolation_policy = InterpolationPolicy::AREA;
CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- GpuWorkloadContext context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ GpuWorkloadContext context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
- const TensorInfo sketch_input_info = context.create_tensor_info(input_info);
+ const ITensorInfo *sketch_input_info = context.create_tensor_info(input_info);
ResizeAttributes attributes{};
attributes.interpolation_policy(interpolation_policy);
- Status status = GpuResize::validate_op(sketch, &sketch_input_info, attributes);
+ Status status = GpuResize::validate_op(sketch, sketch_input_info, attributes);
ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
}
TEST_CASE(UnsupportedLayout, framework::DatasetMode::ALL)
{
- const TensorInfo input_info = TensorInfo{ default_input_shape, 1, default_data_type, DataLayout::NCHW };
- const TensorInfo output_info = TensorInfo{ default_output_shape, 1, default_data_type, DataLayout::NCHW };
+ const TensorInfo input_info = TensorInfo{default_input_shape, 1, default_data_type, DataLayout::NCHW};
+ const TensorInfo output_info = TensorInfo{default_output_shape, 1, default_data_type, DataLayout::NCHW};
constexpr auto interpolation_policy = InterpolationPolicy::BILINEAR;
CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- GpuWorkloadContext context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ GpuWorkloadContext context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
- const TensorInfo sketch_input_info = context.create_tensor_info(input_info);
+ const ITensorInfo *sketch_input_info = context.create_tensor_info(input_info);
ResizeAttributes attributes{};
attributes.interpolation_policy(interpolation_policy);
- Status status = GpuResize::validate_op(sketch, &sketch_input_info, attributes);
+ Status status = GpuResize::validate_op(sketch, sketch_input_info, attributes);
ARM_COMPUTE_EXPECT(bool(status) == false, framework::LogLevel::ERRORS);
}
@@ -237,43 +232,60 @@
TEST_SUITE(Float)
TEST_SUITE(FP32)
-const auto f32_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<float>())), framework::dataset::make("DataType", DataType::F32));
+const auto f32_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<float>())),
+ framework::dataset::make("DataType", DataType::F32));
-FIXTURE_DATA_TEST_CASE(Run, DynamicFusionResizeFixture<float>, framework::DatasetMode::ALL, ASSEMBLE_DATASET_DYNAMIC_FUSION(f32_shape, ScaleSamplingPolicySet))
+FIXTURE_DATA_TEST_CASE(Run,
+ DynamicFusionResizeFixture<float>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(f32_shape, ScaleSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_f32, tolerance_num_f32, tolerance_f32_absolute);
}
-FIXTURE_DATA_TEST_CASE(RunAlignCorners, DynamicFusionResizeFixture<float>, framework::DatasetMode::ALL, ASSEMBLE_DATASET_DYNAMIC_FUSION(f32_shape, ScaleAlignCornersSamplingPolicySet))
+FIXTURE_DATA_TEST_CASE(RunAlignCorners,
+ DynamicFusionResizeFixture<float>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(f32_shape, ScaleAlignCornersSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_f32, tolerance_num_f32, tolerance_f32_absolute);
}
-const auto f32_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<float>())), framework::dataset::make("DataType", DataType::F32));
-FIXTURE_DATA_TEST_CASE(RunNightly, DynamicFusionResizeFixture<float>, framework::DatasetMode::NIGHTLY, ASSEMBLE_DATASET_DYNAMIC_FUSION(f32_nightly_shape, ScaleSamplingPolicySet))
+const auto f32_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<float>())),
+ framework::dataset::make("DataType", DataType::F32));
+FIXTURE_DATA_TEST_CASE(RunNightly,
+ DynamicFusionResizeFixture<float>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(f32_nightly_shape, ScaleSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_f32, tolerance_num_f32, tolerance_f32_absolute);
}
-FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners, DynamicFusionResizeFixture<float>, framework::DatasetMode::NIGHTLY, ASSEMBLE_DATASET_DYNAMIC_FUSION(f32_nightly_shape,
- ScaleAlignCornersSamplingPolicySet))
+FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners,
+ DynamicFusionResizeFixture<float>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(f32_nightly_shape, ScaleAlignCornersSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_f32, tolerance_num_f32, tolerance_f32_absolute);
@@ -281,41 +293,58 @@
TEST_SUITE_END() // FP32
TEST_SUITE(FP16)
-const auto f16_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<half>())), framework::dataset::make("DataType", DataType::F16));
-FIXTURE_DATA_TEST_CASE(Run, DynamicFusionResizeFixture<half>, framework::DatasetMode::ALL, ASSEMBLE_DATASET_DYNAMIC_FUSION(f16_shape, ScaleSamplingPolicySet))
+const auto f16_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<half>())),
+ framework::dataset::make("DataType", DataType::F16));
+FIXTURE_DATA_TEST_CASE(Run,
+ DynamicFusionResizeFixture<half>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(f16_shape, ScaleSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_f16, 0.0f, abs_tolerance_f16);
}
-FIXTURE_DATA_TEST_CASE(RunAlignCorners, DynamicFusionResizeFixture<half>, framework::DatasetMode::ALL, ASSEMBLE_DATASET_DYNAMIC_FUSION(f16_shape, ScaleAlignCornersSamplingPolicySet))
+FIXTURE_DATA_TEST_CASE(RunAlignCorners,
+ DynamicFusionResizeFixture<half>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(f16_shape, ScaleAlignCornersSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_f16, 0.0f, abs_tolerance_f16);
}
-const auto f16_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<half>())), framework::dataset::make("DataType", DataType::F16));
-FIXTURE_DATA_TEST_CASE(RunNightly, DynamicFusionResizeFixture<half>, framework::DatasetMode::NIGHTLY, ASSEMBLE_DATASET_DYNAMIC_FUSION(f16_nightly_shape, ScaleSamplingPolicySet))
+const auto f16_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<half>())),
+ framework::dataset::make("DataType", DataType::F16));
+FIXTURE_DATA_TEST_CASE(RunNightly,
+ DynamicFusionResizeFixture<half>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(f16_nightly_shape, ScaleSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_f16, 0.0f, abs_tolerance_f16);
}
-FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners, DynamicFusionResizeFixture<half>, framework::DatasetMode::NIGHTLY, ASSEMBLE_DATASET_DYNAMIC_FUSION(f16_nightly_shape,
- ScaleAlignCornersSamplingPolicySet))
+FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners,
+ DynamicFusionResizeFixture<half>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(f16_nightly_shape, ScaleAlignCornersSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_f16, 0.0f, abs_tolerance_f16);
@@ -325,41 +354,58 @@
TEST_SUITE(Integer)
TEST_SUITE(U8)
-const auto u8_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<uint8_t>())), framework::dataset::make("DataType", DataType::U8));
-FIXTURE_DATA_TEST_CASE(Run, DynamicFusionResizeFixture<uint8_t>, framework::DatasetMode::ALL, ASSEMBLE_DATASET_DYNAMIC_FUSION(u8_shape, ScaleSamplingPolicySet))
+const auto u8_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<uint8_t>())),
+ framework::dataset::make("DataType", DataType::U8));
+FIXTURE_DATA_TEST_CASE(Run,
+ DynamicFusionResizeFixture<uint8_t>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(u8_shape, ScaleSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_q8);
}
-FIXTURE_DATA_TEST_CASE(RunAlignCorners, DynamicFusionResizeFixture<uint8_t>, framework::DatasetMode::ALL, ASSEMBLE_DATASET_DYNAMIC_FUSION(u8_shape, ScaleAlignCornersSamplingPolicySet))
+FIXTURE_DATA_TEST_CASE(RunAlignCorners,
+ DynamicFusionResizeFixture<uint8_t>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(u8_shape, ScaleAlignCornersSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_q8);
}
-const auto u8_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<uint8_t>())), framework::dataset::make("DataType", DataType::U8));
-FIXTURE_DATA_TEST_CASE(RunNightly, DynamicFusionResizeFixture<uint8_t>, framework::DatasetMode::NIGHTLY, ASSEMBLE_DATASET_DYNAMIC_FUSION(u8_nightly_shape, ScaleSamplingPolicySet))
+const auto u8_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<uint8_t>())),
+ framework::dataset::make("DataType", DataType::U8));
+FIXTURE_DATA_TEST_CASE(RunNightly,
+ DynamicFusionResizeFixture<uint8_t>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(u8_nightly_shape, ScaleSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_q8);
}
-FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners, DynamicFusionResizeFixture<uint8_t>, framework::DatasetMode::NIGHTLY, ASSEMBLE_DATASET_DYNAMIC_FUSION(u8_nightly_shape,
- ScaleAlignCornersSamplingPolicySet))
+FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners,
+ DynamicFusionResizeFixture<uint8_t>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(u8_nightly_shape, ScaleAlignCornersSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_q8);
@@ -367,41 +413,58 @@
TEST_SUITE_END() // U8
TEST_SUITE(S16)
-const auto s16_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<int16_t>())), framework::dataset::make("DataType", DataType::S16));
-FIXTURE_DATA_TEST_CASE(Run, DynamicFusionResizeFixture<int16_t>, framework::DatasetMode::ALL, ASSEMBLE_DATASET_DYNAMIC_FUSION(s16_shape, ScaleSamplingPolicySet))
+const auto s16_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<int16_t>())),
+ framework::dataset::make("DataType", DataType::S16));
+FIXTURE_DATA_TEST_CASE(Run,
+ DynamicFusionResizeFixture<int16_t>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(s16_shape, ScaleSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_s16);
}
-FIXTURE_DATA_TEST_CASE(RunAlignCorners, DynamicFusionResizeFixture<int16_t>, framework::DatasetMode::ALL, ASSEMBLE_DATASET_DYNAMIC_FUSION(s16_shape, ScaleAlignCornersSamplingPolicySet))
+FIXTURE_DATA_TEST_CASE(RunAlignCorners,
+ DynamicFusionResizeFixture<int16_t>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(s16_shape, ScaleAlignCornersSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_s16);
}
-const auto s16_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<int16_t>())), framework::dataset::make("DataType", DataType::S16));
-FIXTURE_DATA_TEST_CASE(RunNightly, DynamicFusionResizeFixture<int16_t>, framework::DatasetMode::NIGHTLY, ASSEMBLE_DATASET_DYNAMIC_FUSION(s16_nightly_shape, ScaleSamplingPolicySet))
+const auto s16_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<int16_t>())),
+ framework::dataset::make("DataType", DataType::S16));
+FIXTURE_DATA_TEST_CASE(RunNightly,
+ DynamicFusionResizeFixture<int16_t>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(s16_nightly_shape, ScaleSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_s16);
}
-FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners, DynamicFusionResizeFixture<int16_t>, framework::DatasetMode::NIGHTLY, ASSEMBLE_DATASET_DYNAMIC_FUSION(s16_nightly_shape,
- ScaleAlignCornersSamplingPolicySet))
+FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners,
+ DynamicFusionResizeFixture<int16_t>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_DATASET_DYNAMIC_FUSION(s16_nightly_shape, ScaleAlignCornersSamplingPolicySet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_s16);
@@ -410,50 +473,70 @@
TEST_SUITE_END() // Integer
template <typename T>
-using DynamicFusionResizeQuantizedFixture = DynamicFusionResizeQuantizedValidationFixture<CLTensor, CLAccessor, GpuResize, T>;
+using DynamicFusionResizeQuantizedFixture =
+ DynamicFusionResizeQuantizedValidationFixture<CLTensor, CLAccessor, GpuResize, T>;
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
-const auto qasymm8_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<uint8_t>())), framework::dataset::make("DataType", DataType::QASYMM8));
-FIXTURE_DATA_TEST_CASE(Run, DynamicFusionResizeQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_shape, ScaleSamplingPolicySet,
- QuantizationInfoSet))
+const auto qasymm8_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<uint8_t>())),
+ framework::dataset::make("DataType", DataType::QASYMM8));
+FIXTURE_DATA_TEST_CASE(Run,
+ DynamicFusionResizeQuantizedFixture<uint8_t>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_shape,
+ ScaleSamplingPolicySet,
+ QuantizationInfoSet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_q8);
}
-FIXTURE_DATA_TEST_CASE(RunAlignCorners, DynamicFusionResizeQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_shape,
- ScaleAlignCornersSamplingPolicySet,
- QuantizationInfoSet))
+FIXTURE_DATA_TEST_CASE(RunAlignCorners,
+ DynamicFusionResizeQuantizedFixture<uint8_t>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_shape,
+ ScaleAlignCornersSamplingPolicySet,
+ QuantizationInfoSet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_q8);
}
-const auto qasymm8_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<uint8_t>())), framework::dataset::make("DataType", DataType::QASYMM8));
-FIXTURE_DATA_TEST_CASE(RunNightly, DynamicFusionResizeQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_nightly_shape,
- ScaleSamplingPolicySet,
- QuantizationInfoSet))
+const auto qasymm8_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<uint8_t>())),
+ framework::dataset::make("DataType", DataType::QASYMM8));
+FIXTURE_DATA_TEST_CASE(RunNightly,
+ DynamicFusionResizeQuantizedFixture<uint8_t>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_nightly_shape,
+ ScaleSamplingPolicySet,
+ QuantizationInfoSet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_q8);
}
-FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners, DynamicFusionResizeQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_nightly_shape,
- ScaleAlignCornersSamplingPolicySet,
- QuantizationInfoSet))
+FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners,
+ DynamicFusionResizeQuantizedFixture<uint8_t>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_nightly_shape,
+ ScaleAlignCornersSamplingPolicySet,
+ QuantizationInfoSet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_q8);
@@ -461,47 +544,66 @@
TEST_SUITE_END() // QASYMM8
TEST_SUITE(QASYMM8_SIGNED)
-const auto qasymm8_signed_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<int8_t>())), framework::dataset::make("DataType", DataType::QASYMM8_SIGNED));
-FIXTURE_DATA_TEST_CASE(Run, DynamicFusionResizeQuantizedFixture<int8_t>, framework::DatasetMode::ALL, ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_signed_shape, ScaleSamplingPolicySet,
- QuantizationInfoSet))
+const auto qasymm8_signed_shape = combine((SCALE_PRECOMMIT_SHAPE_DATASET(num_elements_per_vector<int8_t>())),
+ framework::dataset::make("DataType", DataType::QASYMM8_SIGNED));
+FIXTURE_DATA_TEST_CASE(Run,
+ DynamicFusionResizeQuantizedFixture<int8_t>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_signed_shape,
+ ScaleSamplingPolicySet,
+ QuantizationInfoSet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_qs8);
}
-FIXTURE_DATA_TEST_CASE(RunAlignCorners, DynamicFusionResizeQuantizedFixture<int8_t>, framework::DatasetMode::ALL, ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_signed_shape,
- ScaleAlignCornersSamplingPolicySet,
- QuantizationInfoSet))
+FIXTURE_DATA_TEST_CASE(RunAlignCorners,
+ DynamicFusionResizeQuantizedFixture<int8_t>,
+ framework::DatasetMode::ALL,
+ ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_signed_shape,
+ ScaleAlignCornersSamplingPolicySet,
+ QuantizationInfoSet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_qs8);
}
-const auto qasymm8_signed_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<int8_t>())), framework::dataset::make("DataType", DataType::QASYMM8_SIGNED));
-FIXTURE_DATA_TEST_CASE(RunNightly, DynamicFusionResizeQuantizedFixture<int8_t>, framework::DatasetMode::NIGHTLY, ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_signed_nightly_shape,
- ScaleSamplingPolicySet,
- QuantizationInfoSet))
+const auto qasymm8_signed_nightly_shape = combine((SCALE_NIGHTLY_SHAPE_DATASET(num_elements_per_vector<int8_t>())),
+ framework::dataset::make("DataType", DataType::QASYMM8_SIGNED));
+FIXTURE_DATA_TEST_CASE(RunNightly,
+ DynamicFusionResizeQuantizedFixture<int8_t>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_signed_nightly_shape,
+ ScaleSamplingPolicySet,
+ QuantizationInfoSet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_qs8);
}
-FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners, DynamicFusionResizeQuantizedFixture<int8_t>, framework::DatasetMode::NIGHTLY, ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_signed_nightly_shape,
- ScaleAlignCornersSamplingPolicySet,
- QuantizationInfoSet))
+FIXTURE_DATA_TEST_CASE(RunNightlyAlignCorners,
+ DynamicFusionResizeQuantizedFixture<int8_t>,
+ framework::DatasetMode::NIGHTLY,
+ ASSEMBLE_QUANTIZED_DATASET_DYNAMIC_FUSION(qasymm8_signed_nightly_shape,
+ ScaleAlignCornersSamplingPolicySet,
+ QuantizationInfoSet))
{
//Create valid region
TensorInfo src_info(_shape, 1, _data_type);
- const ValidRegion valid_region = calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
+ const ValidRegion valid_region =
+ calculate_valid_region_scale(src_info, _reference.shape(), _interpolation_policy, _sampling_policy, false);
// Validate output
validate(CLAccessor(_target), _reference, valid_region, tolerance_qs8);
diff --git a/tests/validation/dynamic_fusion/gpu/cl/Sigmoid.cpp b/tests/validation/dynamic_fusion/gpu/cl/Sigmoid.cpp
index e995511..0134a7c 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/Sigmoid.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/Sigmoid.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -29,10 +29,10 @@
#include "tests/CL/CLAccessor.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/framework/Asserts.h"
-#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
-#include "tests/validation/Validation.h"
+#include "tests/framework/Macros.h"
#include "tests/validation/fixtures/dynamic_fusion/operators/ActivationFixture.h"
+#include "tests/validation/Validation.h"
namespace arm_compute
{
@@ -65,9 +65,9 @@
GpuWorkloadSketch sketch{ &context };
// Fuse sigmoid
- const TensorInfo src_info = context.create_tensor_info(input_info);
+ const ITensorInfo *src_info = context.create_tensor_info(input_info);
- const bool res = static_cast<bool>(GpuSigmoid::validate_op(sketch, &src_info));
+ const bool res = static_cast<bool>(GpuSigmoid::validate_op(sketch, src_info));
ARM_COMPUTE_EXPECT(res == expected, framework::LogLevel::ERRORS);
}
// clang-format on
@@ -81,8 +81,7 @@
FIXTURE_DATA_TEST_CASE(RunSmallOneOp,
DynamicFusionSigmoidOpFixture<half>,
framework::DatasetMode::ALL,
- combine(combine(datasets::SmallShapes(),
- framework::dataset::make("Fuse", { false })),
+ combine(combine(datasets::SmallShapes(), framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
@@ -92,8 +91,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall5dOneOp,
DynamicFusionSigmoidOpFixture<half>,
framework::DatasetMode::ALL,
- combine(combine(datasets::Small5dShapes(),
- framework::dataset::make("Fuse", { false })),
+ combine(combine(datasets::Small5dShapes(), framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
@@ -104,8 +102,7 @@
FIXTURE_DATA_TEST_CASE(RunSmallTwoOps,
DynamicFusionSigmoidOpFixture<half>,
framework::DatasetMode::ALL,
- combine(combine(datasets::SmallShapes(),
- framework::dataset::make("Fuse", { true })),
+ combine(combine(datasets::SmallShapes(), framework::dataset::make("Fuse", {true})),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
@@ -118,8 +115,7 @@
FIXTURE_DATA_TEST_CASE(RunSmallOneOp,
DynamicFusionSigmoidOpFixture<float>,
framework::DatasetMode::ALL,
- combine(combine(datasets::SmallShapes(),
- framework::dataset::make("Fuse", { false })),
+ combine(combine(datasets::SmallShapes(), framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
@@ -129,8 +125,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall5dOneOp,
DynamicFusionSigmoidOpFixture<float>,
framework::DatasetMode::ALL,
- combine(combine(datasets::Small5dShapes(),
- framework::dataset::make("Fuse", { false })),
+ combine(combine(datasets::Small5dShapes(), framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
@@ -141,8 +136,7 @@
FIXTURE_DATA_TEST_CASE(RunSmallTwoOps,
DynamicFusionSigmoidOpFixture<float>,
framework::DatasetMode::ALL,
- combine(combine(datasets::SmallShapes(),
- framework::dataset::make("Fuse", { true })),
+ combine(combine(datasets::SmallShapes(), framework::dataset::make("Fuse", {true})),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
diff --git a/tests/validation/dynamic_fusion/gpu/cl/Softmax.cpp b/tests/validation/dynamic_fusion/gpu/cl/Softmax.cpp
index 340f5dc..b7cb6ba 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/Softmax.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/Softmax.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -28,11 +28,11 @@
#include "tests/CL/CLAccessor.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/framework/Asserts.h"
+#include "tests/framework/datasets/Datasets.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
-#include "tests/framework/datasets/Datasets.h"
-#include "tests/validation/Validation.h"
#include "tests/validation/fixtures/dynamic_fusion/operators/SoftmaxFixture.h"
+#include "tests/validation/Validation.h"
using namespace arm_compute::experimental::dynamic_fusion;
@@ -110,9 +110,9 @@
SoftmaxAttributes softmax_attr{};
softmax_attr.axis(axis).beta(beta).is_log_softmax(false);
- TensorInfo src_info = context.create_tensor_info(input_info);
- TensorInfo dst_info = context.create_tensor_info(output_info);
- const bool res = static_cast<bool>(GpuSoftmax::validate_op(sketch, &src_info, &dst_info, softmax_attr));
+ ITensorInfo* src_info = context.create_tensor_info(input_info);
+ ITensorInfo* dst_info = context.create_tensor_info(output_info);
+ const bool res = static_cast<bool>(GpuSoftmax::validate_op(sketch, src_info, dst_info, softmax_attr));
ARM_COMPUTE_EXPECT(res == expected, framework::LogLevel::ERRORS);
}
diff --git a/tests/validation/dynamic_fusion/gpu/cl/Sub.cpp b/tests/validation/dynamic_fusion/gpu/cl/Sub.cpp
index 022c9b4..ef9f75b 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/Sub.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/Sub.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -29,14 +29,13 @@
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuSub.h"
#include "tests/CL/CLAccessor.h"
-#include "tests/framework/Fixture.h"
-#include "tests/framework/Macros.h"
-#include "tests/framework/datasets/Datasets.h"
-#include "tests/validation/Validation.h"
-
#include "tests/datasets/DynamicFusionDataset.h"
#include "tests/datasets/ShapeDatasets.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/framework/Fixture.h"
+#include "tests/framework/Macros.h"
#include "tests/validation/fixtures/dynamic_fusion/gpu/cl/ElementwiseBinaryFixture.h"
+#include "tests/validation/Validation.h"
namespace arm_compute
{
@@ -99,29 +98,32 @@
auto lhs_info = context.create_tensor_info(input1_info);
auto rhs_info = context.create_tensor_info(input2_info);
- bool res = bool(GpuSub::validate_op(sketch, &lhs_info, &rhs_info));
+ bool res = bool(GpuSub::validate_op(sketch, lhs_info, rhs_info));
ARM_COMPUTE_EXPECT(res == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*
template <typename T>
-using DynamicFusionCLSubFixture = DynamicFusionGpuElementwiseBinaryOneOpValidationFixture<CLTensor, CLAccessor, GpuSub, T>;
+using DynamicFusionCLSubFixture =
+ DynamicFusionGpuElementwiseBinaryOneOpValidationFixture<CLTensor, CLAccessor, GpuSub, T>;
template <typename T>
-using DynamicFusionCLSubBroadcastFixture = DynamicFusionGpuElementwiseBinaryBroadcastOneOpValidationFixture<CLTensor, CLAccessor, GpuSub, T>;
+using DynamicFusionCLSubBroadcastFixture =
+ DynamicFusionGpuElementwiseBinaryBroadcastOneOpValidationFixture<CLTensor, CLAccessor, GpuSub, T>;
template <typename T>
-using DynamicFusionCLSubTwoOpsFixture = DynamicFusionGpuElementwiseBinaryTwoOpsValidationFixture<CLTensor, CLAccessor, GpuSub, T>;
+using DynamicFusionCLSubTwoOpsFixture =
+ DynamicFusionGpuElementwiseBinaryTwoOpsValidationFixture<CLTensor, CLAccessor, GpuSub, T>;
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmallOneOp,
DynamicFusionCLSubFixture<float>,
framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::SUB }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::SUB}),
datasets::SmallShapes()),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -129,10 +131,10 @@
FIXTURE_DATA_TEST_CASE(RunLargeOneOp,
DynamicFusionCLSubFixture<float>,
framework::DatasetMode::NIGHTLY,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::SUB }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::SUB}),
datasets::LargeShapes()),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -140,10 +142,10 @@
FIXTURE_DATA_TEST_CASE(RunSmallBroadcastOneOp,
DynamicFusionCLSubBroadcastFixture<float>,
framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::SUB }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::SUB}),
datasets::TemporaryLimitedSmallShapesBroadcast()),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -152,22 +154,23 @@
FIXTURE_DATA_TEST_CASE(RunLargeBroadcastOneOp,
DynamicFusionCLSubBroadcastFixture<float>,
framework::DatasetMode::NIGHTLY,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::SUB }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::SUB}),
datasets::TemporaryLimitedLargeShapesBroadcast()),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
}
-FIXTURE_DATA_TEST_CASE(RunSmallTwoOps,
- DynamicFusionCLSubTwoOpsFixture<float>,
- framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::SUB }),
- datasets::DynamicFusionElementwiseBinaryTwoOpsSmallShapes()),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("InPlace", { false })),
- framework::dataset::make("FuseTwoOps", { true })))
+FIXTURE_DATA_TEST_CASE(
+ RunSmallTwoOps,
+ DynamicFusionCLSubTwoOpsFixture<float>,
+ framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::SUB}),
+ datasets::DynamicFusionElementwiseBinaryTwoOpsSmallShapes()),
+ framework::dataset::make("DataType", {DataType::F32})),
+ framework::dataset::make("InPlace", {false})),
+ framework::dataset::make("FuseTwoOps", {true})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -178,10 +181,10 @@
FIXTURE_DATA_TEST_CASE(RunSmallOneOp,
DynamicFusionCLSubFixture<half>,
framework::DatasetMode::ALL,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::SUB }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::SUB}),
datasets::SmallShapes()),
- framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F16})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -190,10 +193,10 @@
FIXTURE_DATA_TEST_CASE(RunSmallBroadcastOneOp,
DynamicFusionCLSubBroadcastFixture<half>,
framework::DatasetMode::ALL,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::SUB }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::SUB}),
datasets::TemporaryLimitedSmallShapesBroadcast()),
- framework::dataset::make("DataType", { DataType::F16 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::F16})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -205,10 +208,10 @@
FIXTURE_DATA_TEST_CASE(RunSmall,
DynamicFusionCLSubFixture<int32_t>,
framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::SUB }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::SUB}),
datasets::SmallShapes()),
- framework::dataset::make("DataType", { DataType::S32 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::S32})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -219,10 +222,10 @@
FIXTURE_DATA_TEST_CASE(RunSmall,
DynamicFusionCLSubFixture<int16_t>,
framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::SUB }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::SUB}),
datasets::SmallShapes()),
- framework::dataset::make("DataType", { DataType::S16 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::S16})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -230,10 +233,10 @@
FIXTURE_DATA_TEST_CASE(RunLarge,
DynamicFusionCLSubFixture<int16_t>,
framework::DatasetMode::NIGHTLY,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::SUB }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::SUB}),
datasets::LargeShapes()),
- framework::dataset::make("DataType", { DataType::S16 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::S16})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -244,10 +247,10 @@
FIXTURE_DATA_TEST_CASE(RunSmall,
DynamicFusionCLSubFixture<uint8_t>,
framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(framework::dataset::make("ElementwiseOp", { ArithmeticOperation::SUB }),
+ combine(combine(combine(framework::dataset::make("ElementwiseOp", {ArithmeticOperation::SUB}),
datasets::SmallShapes()),
- framework::dataset::make("DataType", { DataType::U8 })),
- framework::dataset::make("InPlace", { false })))
+ framework::dataset::make("DataType", {DataType::U8})),
+ framework::dataset::make("InPlace", {false})))
{
// Validate output
validate(CLAccessor(_target), _reference);
diff --git a/tests/validation/dynamic_fusion/gpu/cl/Tanh.cpp b/tests/validation/dynamic_fusion/gpu/cl/Tanh.cpp
index 12f3677..2560f3a 100644
--- a/tests/validation/dynamic_fusion/gpu/cl/Tanh.cpp
+++ b/tests/validation/dynamic_fusion/gpu/cl/Tanh.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -29,10 +29,10 @@
#include "tests/CL/CLAccessor.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/framework/Asserts.h"
-#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
-#include "tests/validation/Validation.h"
+#include "tests/framework/Macros.h"
#include "tests/validation/fixtures/dynamic_fusion/operators/ActivationFixture.h"
+#include "tests/validation/Validation.h"
namespace arm_compute
{
@@ -65,9 +65,9 @@
GpuWorkloadSketch sketch{ &context };
// Fuse tanh
- const TensorInfo src_info = context.create_tensor_info(input_info);
+ const ITensorInfo* src_info = context.create_tensor_info(input_info);
- const bool res = static_cast<bool>(GpuTanh::validate_op(sketch, &src_info));
+ const bool res = static_cast<bool>(GpuTanh::validate_op(sketch, src_info));
ARM_COMPUTE_EXPECT(res == expected, framework::LogLevel::ERRORS);
}
// clang-format on
@@ -81,8 +81,7 @@
FIXTURE_DATA_TEST_CASE(RunSmallOneOp,
DynamicFusionTanhOpFixture<half>,
framework::DatasetMode::ALL,
- combine(combine(datasets::SmallShapes(),
- framework::dataset::make("Fuse", { false })),
+ combine(combine(datasets::SmallShapes(), framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
@@ -92,8 +91,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall5dOneOp,
DynamicFusionTanhOpFixture<half>,
framework::DatasetMode::ALL,
- combine(combine(datasets::Small5dShapes(),
- framework::dataset::make("Fuse", { false })),
+ combine(combine(datasets::Small5dShapes(), framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
@@ -104,8 +102,7 @@
FIXTURE_DATA_TEST_CASE(RunSmallTwoOps,
DynamicFusionTanhOpFixture<half>,
framework::DatasetMode::ALL,
- combine(combine(datasets::SmallShapes(),
- framework::dataset::make("Fuse", { true })),
+ combine(combine(datasets::SmallShapes(), framework::dataset::make("Fuse", {true})),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
@@ -118,8 +115,7 @@
FIXTURE_DATA_TEST_CASE(RunSmallOneOp,
DynamicFusionTanhOpFixture<float>,
framework::DatasetMode::ALL,
- combine(combine(datasets::SmallShapes(),
- framework::dataset::make("Fuse", { false })),
+ combine(combine(datasets::SmallShapes(), framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
@@ -129,8 +125,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall5dOneOp,
DynamicFusionTanhOpFixture<float>,
framework::DatasetMode::ALL,
- combine(combine(datasets::Small5dShapes(),
- framework::dataset::make("Fuse", { false })),
+ combine(combine(datasets::Small5dShapes(), framework::dataset::make("Fuse", {false})),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
@@ -141,8 +136,7 @@
FIXTURE_DATA_TEST_CASE(RunSmallTwoOps,
DynamicFusionTanhOpFixture<float>,
framework::DatasetMode::ALL,
- combine(combine(datasets::SmallShapes(),
- framework::dataset::make("Fuse", { true })),
+ combine(combine(datasets::SmallShapes(), framework::dataset::make("Fuse", {true})),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
diff --git a/tests/validation/fixtures/dynamic_fusion/gpu/cl/DepthwiseConv2dFixture.h b/tests/validation/fixtures/dynamic_fusion/gpu/cl/DepthwiseConv2dFixture.h
index 6498a06..ca4de11 100644
--- a/tests/validation/fixtures/dynamic_fusion/gpu/cl/DepthwiseConv2dFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/gpu/cl/DepthwiseConv2dFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,14 +21,13 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE
-#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE
+#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE_H
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-
#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
#include "arm_compute/dynamic_fusion/sketch/attributes/DepthwiseConv2dAttributes.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
@@ -36,13 +35,11 @@
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
#include "tests/CL/CLAccessor.h"
-
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
-
-#include "tests/validation/Validation.h"
#include "tests/validation/reference/DepthwiseConvolutionLayer.h"
+#include "tests/validation/Validation.h"
using namespace arm_compute::experimental::dynamic_fusion;
@@ -56,22 +53,30 @@
class DynamicFusionGpuDepthwiseConv2dValidationGenericFixture : public framework::Fixture
{
public:
- using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value
- || std::is_same<typename std::decay<T>::type, int8_t>::value,
- int32_t, T >::type; // If T: uint8_t or int8_t then TBias: int32_t, otherwise TBias: T
+ using TBias = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value ||
+ std::is_same<typename std::decay<T>::type, int8_t>::value,
+ int32_t,
+ T>::type; // If T: uint8_t or int8_t then TBias: int32_t, otherwise TBias: T
- void setup(TensorShape input_shape, Size2D kernel_size, const PadStrideInfo &pad_stride, const Size2D &dilation,
- const unsigned int depth_multiplier, const DataType data_type, const DataLayout data_layout)
+ void setup(TensorShape input_shape,
+ Size2D kernel_size,
+ const PadStrideInfo &pad_stride,
+ const Size2D &dilation,
+ const unsigned int depth_multiplier,
+ const DataType data_type,
+ const DataLayout data_layout)
{
- ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion depthwise conv2d only supports NHWC layout
+ ARM_COMPUTE_ERROR_ON(data_layout !=
+ DataLayout::NHWC); // Dynamic fusion depthwise conv2d only supports NHWC layout
DepthwiseConv2dAttributes dwc_conv2d_attr;
- const Padding2D padding_2d(pad_stride.pad_left(), pad_stride.pad_right(), pad_stride.pad_top(), pad_stride.pad_bottom());
+ const Padding2D padding_2d(pad_stride.pad_left(), pad_stride.pad_right(), pad_stride.pad_top(),
+ pad_stride.pad_bottom());
dwc_conv2d_attr.pad(padding_2d)
- .stride(Size2D(pad_stride.stride().first, pad_stride.stride().second))
- .dilation(dilation)
- .depth_multiplier(depth_multiplier)
- .dimension_rounding_type(pad_stride.round());
+ .stride(Size2D(pad_stride.stride().first, pad_stride.stride().second))
+ .dilation(dilation)
+ .depth_multiplier(depth_multiplier)
+ .dimension_rounding_type(pad_stride.round());
// Calculate Output and Weight Shapes
TensorShape weights_shape = TensorShape(kernel_size.width, kernel_size.height);
@@ -79,8 +84,9 @@
const TensorInfo in_info(input_shape, 1, data_type);
const TensorInfo we_info(weights_shape, 1, data_type);
- const ConvolutionInfo info{ pad_stride, depth_multiplier, ActivationLayerInfo(), dilation };
- const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(in_info, we_info, info);
+ const ConvolutionInfo info{pad_stride, depth_multiplier, ActivationLayerInfo(), dilation};
+ const TensorShape output_shape =
+ misc::shape_calculator::compute_depthwise_convolution_shape(in_info, we_info, info);
weights_shape.set(2, output_shape.z());
const TensorShape bias_shape = TensorShape(weights_shape[2]);
@@ -95,11 +101,11 @@
template <typename U>
void fill(U &&tensor, int i)
{
- switch(tensor.data_type())
+ switch (tensor.data_type())
{
case DataType::F16:
{
- arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
+ arm_compute::utils::uniform_real_distribution_16bit<half> distribution{-1.0f, 1.0f};
library->fill(tensor, distribution, i);
break;
}
@@ -115,7 +121,10 @@
}
// Given input is in nchw format
- TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, const DepthwiseConv2dAttributes dwc_conv2d_attr)
+ TensorType compute_target(TensorShape input_shape,
+ TensorShape weights_shape,
+ const TensorShape &bias_shape,
+ const DepthwiseConv2dAttributes dwc_conv2d_attr)
{
ARM_COMPUTE_ERROR_ON(_data_layout != DataLayout::NHWC);
@@ -125,24 +134,24 @@
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Create sketch tensors
- TensorInfo input_info = context.create_tensor_info(TensorInfo(input_shape, 1, _data_type, _data_layout));
- TensorInfo weight_info = context.create_tensor_info(TensorInfo(weights_shape, 1, _data_type, _data_layout));
- TensorInfo bias_info = context.create_tensor_info(TensorInfo(bias_shape, 1, _data_type, _data_layout));
- TensorInfo dst_info = context.create_tensor_info();
+ ITensorInfo *input_info = context.create_tensor_info(TensorInfo(input_shape, 1, _data_type, _data_layout));
+ ITensorInfo *weight_info = context.create_tensor_info(TensorInfo(weights_shape, 1, _data_type, _data_layout));
+ ITensorInfo *bias_info = context.create_tensor_info(TensorInfo(bias_shape, 1, _data_type, _data_layout));
+ ITensorInfo *dst_info = context.create_tensor_info();
- ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, dwc_conv2d_attr);
- GpuOutput::create_op(sketch, ans_info, &dst_info);
+ ITensorInfo *ans_info = FunctionType::create_op(sketch, input_info, weight_info, bias_info, dwc_conv2d_attr);
+ GpuOutput::create_op(sketch, ans_info, dst_info);
// Configure runtime
ClWorkloadRuntime runtime;
runtime.configure(sketch);
// (Important) Allocate auxiliary tensor memory if there are any
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -158,10 +167,10 @@
TensorType t_dst{};
// Initialize user tensors
- t_input.allocator()->init(input_info);
- t_weight.allocator()->init(weight_info);
- t_bias.allocator()->init(bias_info);
- t_dst.allocator()->init(dst_info);
+ t_input.allocator()->init(*input_info);
+ t_weight.allocator()->init(*weight_info);
+ t_bias.allocator()->init(*bias_info);
+ t_dst.allocator()->init(*dst_info);
// Allocate and fill user tensors
t_input.allocator()->allocate();
@@ -174,17 +183,20 @@
fill(AccessorType(t_bias), 2);
// Run runtime
- runtime.run({ &t_input, &t_weight, &t_bias, &t_dst });
+ runtime.run({&t_input, &t_weight, &t_bias, &t_dst});
return t_dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape,
- const TensorShape &output_shape, DepthwiseConv2dAttributes dwc_conv2d_attr)
+ SimpleTensor<T> compute_reference(const TensorShape &input_shape,
+ const TensorShape &weights_shape,
+ const TensorShape &bias_shape,
+ const TensorShape &output_shape,
+ DepthwiseConv2dAttributes dwc_conv2d_attr)
{
// Create reference
- SimpleTensor<T> src{ input_shape, _data_type, 1 };
- SimpleTensor<T> weight{ weights_shape, _data_type, 1 };
- SimpleTensor<TBias> bias{ bias_shape, _data_type, 1 };
+ SimpleTensor<T> src{input_shape, _data_type, 1};
+ SimpleTensor<T> weight{weights_shape, _data_type, 1};
+ SimpleTensor<TBias> bias{bias_shape, _data_type, 1};
fill(src, 0);
fill(weight, 1);
@@ -195,10 +207,13 @@
auto bias_nchw = bias;
auto output_shape_nchw = output_shape;
- PadStrideInfo legacy_pad_stride(dwc_conv2d_attr.stride().x(), dwc_conv2d_attr.stride().y(), dwc_conv2d_attr.pad().left, dwc_conv2d_attr.pad().right, dwc_conv2d_attr.pad().top,
- dwc_conv2d_attr.pad().bottom,
+ PadStrideInfo legacy_pad_stride(dwc_conv2d_attr.stride().x(), dwc_conv2d_attr.stride().y(),
+ dwc_conv2d_attr.pad().left, dwc_conv2d_attr.pad().right,
+ dwc_conv2d_attr.pad().top, dwc_conv2d_attr.pad().bottom,
DimensionRoundingType{});
- auto dst_nchw = reference::depthwise_convolution(src_nchw, weights_nchw, bias_nchw, output_shape_nchw, legacy_pad_stride, dwc_conv2d_attr.depth_multiplier(), dwc_conv2d_attr.dilation());
+ auto dst_nchw =
+ reference::depthwise_convolution(src_nchw, weights_nchw, bias_nchw, output_shape_nchw, legacy_pad_stride,
+ dwc_conv2d_attr.depth_multiplier(), dwc_conv2d_attr.dilation());
return dst_nchw;
}
@@ -209,16 +224,23 @@
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionGpuDepthwiseConv2dValidationFixture : public DynamicFusionGpuDepthwiseConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionGpuDepthwiseConv2dValidationFixture
+ : public DynamicFusionGpuDepthwiseConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(TensorShape input_shape, Size2D kernel_size, const PadStrideInfo &info, const Size2D &dilation, const unsigned int depth_multiplier, DataType data_type, DataLayout data_layout)
+ void setup(TensorShape input_shape,
+ Size2D kernel_size,
+ const PadStrideInfo &info,
+ const Size2D &dilation,
+ const unsigned int depth_multiplier,
+ DataType data_type,
+ DataLayout data_layout)
{
- DynamicFusionGpuDepthwiseConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, kernel_size, info, dilation,
- depth_multiplier, data_type, data_layout);
+ DynamicFusionGpuDepthwiseConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ input_shape, kernel_size, info, dilation, depth_multiplier, data_type, data_layout);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
-#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE */
+#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DEPTHWISECONV2DFIXTURE_H
diff --git a/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h b/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h
index e30a564..1f4e223 100644
--- a/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,13 +21,12 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE
-#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE
+#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE_H
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
-
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
#include "arm_compute/dynamic_fusion/sketch/attributes/Conv2dAttributes.h"
@@ -38,9 +37,9 @@
#include "tests/CL/CLAccessor.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
-#include "tests/validation/Validation.h"
#include "tests/validation/reference/ConvolutionLayer.h"
#include "tests/validation/reference/Permute.h"
+#include "tests/validation/Validation.h"
using namespace arm_compute::experimental::dynamic_fusion;
@@ -55,11 +54,11 @@
template <typename U>
void fill(U &&tensor, int i)
{
- switch(tensor.data_type())
+ switch (tensor.data_type())
{
case DataType::F16:
{
- arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
+ arm_compute::utils::uniform_real_distribution_16bit<half> distribution{-1.0f, 1.0f};
library->fill(tensor, distribution, i);
break;
}
@@ -84,12 +83,21 @@
class DynamicFusionGpuConv2dValidationGenericFixture : public framework::Fixture
{
public:
- using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value
- || std::is_same<typename std::decay<T>::type, int8_t>::value,
- int32_t, T >::type; // If T: uint8_t or int8_t then TBias: int32_t, otherwise TBias: T
+ using TBias = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value ||
+ std::is_same<typename std::decay<T>::type, int8_t>::value,
+ int32_t,
+ T>::type; // If T: uint8_t or int8_t then TBias: int32_t, otherwise TBias: T
- void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, const PadStrideInfo &info, const Size2D &dilation, DataType data_type,
- DataLayout data_layout, QuantizationInfo quantization_info, QuantizationInfo weight_quantization_info)
+ void setup(TensorShape input_shape,
+ TensorShape weights_shape,
+ TensorShape bias_shape,
+ TensorShape output_shape,
+ const PadStrideInfo &info,
+ const Size2D &dilation,
+ DataType data_type,
+ DataLayout data_layout,
+ QuantizationInfo quantization_info,
+ QuantizationInfo weight_quantization_info)
{
ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout
const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, dilation);
@@ -100,12 +108,15 @@
_weight_quantization_info = weight_quantization_info;
_bias_data_type = _is_quantized ? DataType::S32 : data_type;
_target = compute_target(input_shape, weights_shape, bias_shape, conv2d_attr);
- _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr);
+ _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr);
}
protected:
// Given input is in nchw format
- TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, Conv2dAttributes conv2d_attr)
+ TensorType compute_target(TensorShape input_shape,
+ TensorShape weights_shape,
+ const TensorShape &bias_shape,
+ Conv2dAttributes conv2d_attr)
{
ARM_COMPUTE_ERROR_ON(_data_layout != DataLayout::NHWC);
permute(input_shape, PermutationVector(2U, 0U, 1U));
@@ -114,23 +125,23 @@
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Create sketch tensors
- TensorInfo input_info = context.create_tensor_info(TensorInfo(input_shape, 1, _data_type, _data_layout));
- TensorInfo weight_info = context.create_tensor_info(TensorInfo(weights_shape, 1, _data_type, _data_layout));
- TensorInfo bias_info = context.create_tensor_info(TensorInfo(bias_shape, 1, _data_type, _data_layout));
- TensorInfo dst_info = context.create_tensor_info();
+ ITensorInfo *input_info = context.create_tensor_info(TensorInfo(input_shape, 1, _data_type, _data_layout));
+ ITensorInfo *weight_info = context.create_tensor_info(TensorInfo(weights_shape, 1, _data_type, _data_layout));
+ ITensorInfo *bias_info = context.create_tensor_info(TensorInfo(bias_shape, 1, _data_type, _data_layout));
+ ITensorInfo *dst_info = context.create_tensor_info();
- ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, conv2d_attr);
- GpuOutput::create_op(sketch, ans_info, &dst_info);
+ ITensorInfo *ans_info = FunctionType::create_op(sketch, input_info, weight_info, bias_info, conv2d_attr);
+ GpuOutput::create_op(sketch, ans_info, dst_info);
// Configure runtime
ClWorkloadRuntime runtime;
runtime.configure(sketch);
// (Important) Allocate auxiliary tensor memory if there are any
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -145,10 +156,10 @@
TensorType t_dst{};
// Initialize user tensors
- t_input.allocator()->init(input_info);
- t_weight.allocator()->init(weight_info);
- t_bias.allocator()->init(bias_info);
- t_dst.allocator()->init(dst_info);
+ t_input.allocator()->init(*input_info);
+ t_weight.allocator()->init(*weight_info);
+ t_bias.allocator()->init(*bias_info);
+ t_dst.allocator()->init(*dst_info);
// Allocate and fill user tensors
t_input.allocator()->allocate();
@@ -161,17 +172,20 @@
fill(AccessorType(t_bias), 2);
// Run runtime
- runtime.run({ &t_input, &t_weight, &t_bias, &t_dst });
+ runtime.run({&t_input, &t_weight, &t_bias, &t_dst});
return t_dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape,
- const TensorShape &output_shape, Conv2dAttributes conv2d_attr)
+ SimpleTensor<T> compute_reference(const TensorShape &input_shape,
+ const TensorShape &weights_shape,
+ const TensorShape &bias_shape,
+ const TensorShape &output_shape,
+ Conv2dAttributes conv2d_attr)
{
// Create reference
- SimpleTensor<T> src{ input_shape, _data_type, 1, _quantization_info };
- SimpleTensor<T> weight{ weights_shape, _data_type, 1, _weight_quantization_info };
- SimpleTensor<TBias> bias{ bias_shape, _data_type, 1, _quantization_info };
+ SimpleTensor<T> src{input_shape, _data_type, 1, _quantization_info};
+ SimpleTensor<T> weight{weights_shape, _data_type, 1, _weight_quantization_info};
+ SimpleTensor<TBias> bias{bias_shape, _data_type, 1, _quantization_info};
fill(src, 0);
fill(weight, 1);
@@ -182,9 +196,11 @@
auto bias_nchw = bias;
auto output_shape_nchw = output_shape;
- PadStrideInfo legacy_pad_stride(conv2d_attr.stride().x(), conv2d_attr.stride().y(), conv2d_attr.pad().left, conv2d_attr.pad().right, conv2d_attr.pad().top, conv2d_attr.pad().bottom,
+ PadStrideInfo legacy_pad_stride(conv2d_attr.stride().x(), conv2d_attr.stride().y(), conv2d_attr.pad().left,
+ conv2d_attr.pad().right, conv2d_attr.pad().top, conv2d_attr.pad().bottom,
DimensionRoundingType{});
- auto dst_nchw = reference::convolution_layer(src_nchw, weights_nchw, bias_nchw, output_shape_nchw, legacy_pad_stride, conv2d_attr.dilation());
+ auto dst_nchw = reference::convolution_layer(src_nchw, weights_nchw, bias_nchw, output_shape_nchw,
+ legacy_pad_stride, conv2d_attr.dilation());
return dst_nchw;
}
@@ -199,14 +215,23 @@
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionGpuConv2dValidationFixture : public DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionGpuConv2dValidationFixture
+ : public DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape output_shape, TensorShape bias_shape,
- const PadStrideInfo &info, const Size2D &dialation, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
+ void setup(TensorShape input_shape,
+ TensorShape weights_shape,
+ TensorShape output_shape,
+ TensorShape bias_shape,
+ const PadStrideInfo &info,
+ const Size2D &dialation,
+ DataType data_type,
+ DataLayout data_layout,
+ QuantizationInfo quantization_info)
{
- DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, output_shape, bias_shape, info, dialation,
- data_type, data_layout, quantization_info, quantization_info);
+ DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ input_shape, weights_shape, output_shape, bias_shape, info, dialation, data_type, data_layout,
+ quantization_info, quantization_info);
}
};
@@ -218,10 +243,19 @@
class DynamicFusionDirectConv2dValidationGenericFixture : public framework::Fixture
{
public:
- using TBias = typename std::conditional < std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int32_t, T >::type;
+ using TBias =
+ typename std::conditional<std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int32_t, T>::type;
- void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels,
- DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout)
+ void setup(TensorShape input_shape,
+ int stride_x,
+ int stride_y,
+ int pad_x,
+ int pad_y,
+ unsigned int kernel_size,
+ unsigned int num_kernels,
+ DataType data_type,
+ QuantizationInfo quantization_info,
+ DataLayout data_layout)
{
ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout
@@ -230,20 +264,30 @@
const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
- const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, { 1U, 1U } /* dilation */);
+ const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, {1U, 1U} /* dilation */);
TensorInfo input_info = TensorInfo(input_shape, 1, data_type);
TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type);
- const TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_info, weights_info, info);
+ const TensorShape output_shape =
+ misc::shape_calculator::compute_deep_convolution_shape(input_info, weights_info, info);
- _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr, data_type, bias_data_type, quantization_info, data_layout);
- _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info);
+ _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr, data_type,
+ bias_data_type, quantization_info, data_layout);
+ _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type,
+ bias_data_type, quantization_info);
}
protected:
- TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const Conv2dAttributes &conv2d_attr,
- DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info, const DataLayout &data_layout)
+ TensorType compute_target(TensorShape input_shape,
+ TensorShape weights_shape,
+ const TensorShape &bias_shape,
+ TensorShape output_shape,
+ const Conv2dAttributes &conv2d_attr,
+ DataType data_type,
+ DataType bias_data_type,
+ QuantizationInfo quantization_info,
+ const DataLayout &data_layout)
{
ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC);
ARM_COMPUTE_UNUSED(quantization_info);
@@ -253,8 +297,8 @@
permute(output_shape, PermutationVector(2U, 0U, 1U));
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Create sketch tensors
auto input_info = context.create_tensor_info(TensorInfo(input_shape, 1, data_type, data_layout));
@@ -262,14 +306,14 @@
auto bias_info = context.create_tensor_info(TensorInfo(bias_shape, 1, bias_data_type, data_layout));
auto dst_info = context.create_tensor_info();
- ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, conv2d_attr);
- GpuOutput::create_op(sketch, ans_info, &dst_info);
+ ITensorInfo *ans_info = FunctionType::create_op(sketch, input_info, weight_info, bias_info, conv2d_attr);
+ GpuOutput::create_op(sketch, ans_info, dst_info);
// Configure runtime
ClWorkloadRuntime runtime;
runtime.configure(sketch);
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -284,10 +328,10 @@
TensorType t_dst{};
// Initialize user tensors
- t_input.allocator()->init(input_info);
- t_weight.allocator()->init(weight_info);
- t_bias.allocator()->init(bias_info);
- t_dst.allocator()->init(dst_info);
+ t_input.allocator()->init(*input_info);
+ t_weight.allocator()->init(*weight_info);
+ t_bias.allocator()->init(*bias_info);
+ t_dst.allocator()->init(*dst_info);
ARM_COMPUTE_ASSERT(t_input.info()->is_resizable());
ARM_COMPUTE_ASSERT(t_weight.info()->is_resizable());
@@ -310,17 +354,23 @@
fill(AccessorType(t_bias), 2);
// Run runtime
- runtime.run({ &t_input, &t_weight, &t_bias, &t_dst });
+ runtime.run({&t_input, &t_weight, &t_bias, &t_dst});
return t_dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
- DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info)
+ SimpleTensor<T> compute_reference(const TensorShape &input_shape,
+ const TensorShape &weights_shape,
+ const TensorShape &bias_shape,
+ const TensorShape &output_shape,
+ const PadStrideInfo &info,
+ DataType data_type,
+ DataType bias_data_type,
+ QuantizationInfo quantization_info)
{
// Create reference
- SimpleTensor<T> src{ input_shape, data_type, 1, quantization_info };
- SimpleTensor<T> weights{ weights_shape, data_type, 1, quantization_info };
- SimpleTensor<TBias> bias{ bias_shape, bias_data_type, 1, quantization_info };
+ SimpleTensor<T> src{input_shape, data_type, 1, quantization_info};
+ SimpleTensor<T> weights{weights_shape, data_type, 1, quantization_info};
+ SimpleTensor<TBias> bias{bias_shape, bias_data_type, 1, quantization_info};
// Fill reference
fill(src, 0);
@@ -335,19 +385,27 @@
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionDirectConv2dValidationFixture : public DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionDirectConv2dValidationFixture
+ : public DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type,
- DataLayout data_layout)
+ void setup(TensorShape input_shape,
+ int stride_x,
+ int stride_y,
+ int pad_x,
+ int pad_y,
+ unsigned int kernel_size,
+ unsigned int num_kernels,
+ DataType data_type,
+ DataLayout data_layout)
{
- DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type,
- QuantizationInfo(),
- data_layout);
+ DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, QuantizationInfo(),
+ data_layout);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
-#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE */
+#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE_H
diff --git a/tests/validation/fixtures/dynamic_fusion/gpu/cl/ElementwiseBinaryFixture.h b/tests/validation/fixtures/dynamic_fusion/gpu/cl/ElementwiseBinaryFixture.h
index 567322f..69bd0ef 100644
--- a/tests/validation/fixtures/dynamic_fusion/gpu/cl/ElementwiseBinaryFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/gpu/cl/ElementwiseBinaryFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_ELEMENTWISEBINARYFIXTURE
-#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_ELEMENTWISEBINARYFIXTURE
+#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_ELEMENTWISEBINARYFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_ELEMENTWISEBINARYFIXTURE_H
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
@@ -47,9 +47,15 @@
class DynamicFusionGpuElementwiseBinaryValidationGenericFixture : public framework::Fixture
{
public:
- void setup(ArithmeticOperation ref_op, const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2, DataType data_type, bool is_inplace, bool fuse_two_ops = false)
+ void setup(ArithmeticOperation ref_op,
+ const TensorShape &shape0,
+ const TensorShape &shape1,
+ const TensorShape &shape2,
+ DataType data_type,
+ bool is_inplace,
+ bool fuse_two_ops = false)
{
- _ref_op = ref_op;
+ _ref_op = ref_op;
_is_inplace = is_inplace;
_data_type = data_type;
_fuse = fuse_two_ops;
@@ -63,12 +69,12 @@
template <typename U>
void fill(U &&tensor, int i)
{
- if(is_data_type_float(tensor.data_type()))
+ if (is_data_type_float(tensor.data_type()))
{
- switch(_ref_op)
+ switch (_ref_op)
{
case ArithmeticOperation::DIV:
- library->fill_tensor_uniform_ranged(tensor, i, { std::pair<float, float>(-0.001f, 0.001f) });
+ library->fill_tensor_uniform_ranged(tensor, i, {std::pair<float, float>(-0.001f, 0.001f)});
break;
case ArithmeticOperation::POWER:
library->fill_tensor_uniform(tensor, i, 0.0f, 5.0f);
@@ -77,12 +83,12 @@
library->fill_tensor_uniform(tensor, i);
}
}
- else if(tensor.data_type() == DataType::S32)
+ else if (tensor.data_type() == DataType::S32)
{
- switch(_ref_op)
+ switch (_ref_op)
{
case ArithmeticOperation::DIV:
- library->fill_tensor_uniform_ranged(tensor, i, { std::pair<int32_t, int32_t>(-1U, 1U) });
+ library->fill_tensor_uniform_ranged(tensor, i, {std::pair<int32_t, int32_t>(-1U, 1U)});
break;
default:
library->fill_tensor_uniform(tensor, i);
@@ -98,27 +104,27 @@
{
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Fuse first element wise binary Op
- TensorInfo lhs_info = context.create_tensor_info(TensorInfo(shape0, 1, _data_type));
- TensorInfo rhs_info = context.create_tensor_info(TensorInfo(shape1, 1, _data_type));
- TensorInfo dst_info = context.create_tensor_info();
+ ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(shape0, 1, _data_type));
+ ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(shape1, 1, _data_type));
+ ITensorInfo *dst_info = context.create_tensor_info();
- TensorInfo rhs_info_fuse;
+ ITensorInfo *rhs_info_fuse = nullptr;
- ITensorInfo *ans_info = FunctionType::create_op(sketch, &lhs_info, &rhs_info);
+ ITensorInfo *ans_info = FunctionType::create_op(sketch, lhs_info, rhs_info);
- if(_fuse)
+ if (_fuse)
{
rhs_info_fuse = context.create_tensor_info(TensorInfo(shape2, 1, _data_type));
- ITensorInfo *ans2_info = FunctionType::create_op(sketch, ans_info, &rhs_info_fuse);
- GpuOutput::create_op(sketch, ans2_info, &dst_info);
+ ITensorInfo *ans2_info = FunctionType::create_op(sketch, ans_info, rhs_info_fuse);
+ GpuOutput::create_op(sketch, ans2_info, dst_info);
}
else
{
- GpuOutput::create_op(sketch, ans_info, &dst_info);
+ GpuOutput::create_op(sketch, ans_info, dst_info);
}
// Configure runtime
@@ -126,7 +132,7 @@
runtime.configure(sketch);
// (Important) Allocate auxiliary tensor memory if there are any
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -142,12 +148,12 @@
TensorType t_dst{};
// Initialize user tensors
- t_lhs.allocator()->init(lhs_info);
- t_rhs.allocator()->init(rhs_info);
- t_dst.allocator()->init(dst_info);
- if(_fuse)
+ t_lhs.allocator()->init(*lhs_info);
+ t_rhs.allocator()->init(*rhs_info);
+ t_dst.allocator()->init(*dst_info);
+ if (_fuse)
{
- t_rhs_fuse.allocator()->init(rhs_info_fuse);
+ t_rhs_fuse.allocator()->init(*rhs_info_fuse);
}
// Allocate and fill user tensors
@@ -155,26 +161,26 @@
t_lhs.allocator()->allocate();
t_rhs.allocator()->allocate();
t_dst.allocator()->allocate();
- if(_fuse)
+ if (_fuse)
{
t_rhs_fuse.allocator()->allocate();
}
fill(AccessorType(t_lhs), 0);
fill(AccessorType(t_rhs), 1);
- if(_fuse)
+ if (_fuse)
{
fill(AccessorType(t_rhs_fuse), 2);
}
// Run runtime
- if(_fuse)
+ if (_fuse)
{
- runtime.run({ &t_lhs, &t_rhs, &t_rhs_fuse, &t_dst });
+ runtime.run({&t_lhs, &t_rhs, &t_rhs_fuse, &t_dst});
}
else
{
- runtime.run({ &t_lhs, &t_rhs, &t_dst });
+ runtime.run({&t_lhs, &t_rhs, &t_dst});
}
return t_dst;
@@ -186,18 +192,18 @@
const TensorShape out_shape_fuse = TensorShape::broadcast_shape(out_shape, shape1);
// Create reference
- SimpleTensor<T> ref_lhs{ shape0, _data_type, 1, QuantizationInfo() };
- SimpleTensor<T> ref_rhs{ shape1, _data_type, 1, QuantizationInfo() };
- SimpleTensor<T> ref_rhs_fuse{ shape2, _data_type, 1, QuantizationInfo() };
- SimpleTensor<T> ref_dst{ out_shape, _data_type, 1, QuantizationInfo() };
- SimpleTensor<T> ref_dst_fuse{ out_shape_fuse, _data_type, 1, QuantizationInfo() };
+ SimpleTensor<T> ref_lhs{shape0, _data_type, 1, QuantizationInfo()};
+ SimpleTensor<T> ref_rhs{shape1, _data_type, 1, QuantizationInfo()};
+ SimpleTensor<T> ref_rhs_fuse{shape2, _data_type, 1, QuantizationInfo()};
+ SimpleTensor<T> ref_dst{out_shape, _data_type, 1, QuantizationInfo()};
+ SimpleTensor<T> ref_dst_fuse{out_shape_fuse, _data_type, 1, QuantizationInfo()};
// Fill reference
fill(ref_lhs, 0);
fill(ref_rhs, 1);
reference::arithmetic_operation<T>(_ref_op, ref_lhs, ref_rhs, ref_dst, ConvertPolicy::WRAP);
- if(_fuse)
+ if (_fuse)
{
fill(ref_rhs_fuse, 2);
reference::arithmetic_operation<T>(_ref_op, ref_dst, ref_rhs_fuse, ref_dst_fuse, ConvertPolicy::WRAP);
@@ -206,46 +212,62 @@
return *ret;
}
- ArithmeticOperation _ref_op{ ArithmeticOperation::ADD };
+ ArithmeticOperation _ref_op{ArithmeticOperation::ADD};
TensorType _target{};
SimpleTensor<T> _reference{};
DataType _data_type{};
DataLayout _data_layout{};
- bool _is_inplace{ false };
- bool _fuse{ false };
+ bool _is_inplace{false};
+ bool _fuse{false};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionGpuElementwiseBinaryOneOpValidationFixture : public DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionGpuElementwiseBinaryOneOpValidationFixture
+ : public DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
void setup(ArithmeticOperation ref_op, const TensorShape &shape0, DataType data_type, bool is_inplace)
{
- DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ref_op, shape0, shape0, TensorShape(), data_type, is_inplace);
+ DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ ref_op, shape0, shape0, TensorShape(), data_type, is_inplace);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionGpuElementwiseBinaryBroadcastOneOpValidationFixture : public DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionGpuElementwiseBinaryBroadcastOneOpValidationFixture
+ : public DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(ArithmeticOperation ref_op, const TensorShape &shape0, const TensorShape &shape1, DataType data_type, bool is_inplace)
+ void setup(ArithmeticOperation ref_op,
+ const TensorShape &shape0,
+ const TensorShape &shape1,
+ DataType data_type,
+ bool is_inplace)
{
- DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ref_op, shape0, shape1, TensorShape(), data_type, is_inplace);
+ DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ ref_op, shape0, shape1, TensorShape(), data_type, is_inplace);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionGpuElementwiseBinaryTwoOpsValidationFixture : public DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionGpuElementwiseBinaryTwoOpsValidationFixture
+ : public DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(ArithmeticOperation ref_op, const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2, DataType data_type, bool is_inplace, bool fuse_two_ops)
+ void setup(ArithmeticOperation ref_op,
+ const TensorShape &shape0,
+ const TensorShape &shape1,
+ const TensorShape &shape2,
+ DataType data_type,
+ bool is_inplace,
+ bool fuse_two_ops)
{
- DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ref_op, shape0, shape1, shape2, data_type, is_inplace, fuse_two_ops);
+ DynamicFusionGpuElementwiseBinaryValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ ref_op, shape0, shape1, shape2, data_type, is_inplace, fuse_two_ops);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
-#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_ELEMENTWISEBINARYFIXTURE */
+#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_ELEMENTWISEBINARYFIXTURE_H
diff --git a/tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.h b/tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.h
index c6ac4b9..65a3363 100644
--- a/tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -28,7 +28,6 @@
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-
#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
#include "arm_compute/dynamic_fusion/sketch/attributes/MatMulAttributes.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
@@ -39,10 +38,10 @@
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
#include "tests/validation/Helpers.h"
-#include "tests/validation/Validation.h"
#include "tests/validation/reference/GEMM.h"
#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/ReshapeLayer.h"
+#include "tests/validation/Validation.h"
using namespace arm_compute::experimental::dynamic_fusion;
@@ -57,11 +56,11 @@
template <typename U>
void fill(U &&tensor, int i)
{
- switch(tensor.data_type())
+ switch (tensor.data_type())
{
case DataType::F16:
{
- arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
+ arm_compute::utils::uniform_real_distribution_16bit<half> distribution{-1.0f, 1.0f};
library->fill(tensor, distribution, i);
break;
}
@@ -80,67 +79,83 @@
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionGpuMatMulValidationGenericFixture : public framework::Fixture
{
-
public:
- void setup(TensorShape lhs_shape, TensorShape rhs_shape, TensorShape output_shape, bool transpose_a, bool transpose_b,
- int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
+ void setup(TensorShape lhs_shape,
+ TensorShape rhs_shape,
+ TensorShape output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ int M0,
+ int N0,
+ int K0,
+ bool export_rhs_to_cl_image,
+ DataType data_type)
{
//For brevity, the input shapes are assumed to be not-transposed for both a and b matrices.
- if(transpose_a)
+ if (transpose_a)
{
permute(lhs_shape, PermutationVector(1U, 0U));
}
- if(transpose_b)
+ if (transpose_b)
{
permute(rhs_shape, PermutationVector(1U, 0U));
}
// Skip configurations unsupported by the device.
_device_supports_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device());
- if(!_device_supports_export_to_cl_image && export_rhs_to_cl_image)
+ if (!_device_supports_export_to_cl_image && export_rhs_to_cl_image)
{
ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
framework::ARM_COMPUTE_PRINT_INFO();
return; // Note: Also need to skip the validate in corresponding FIXTURE_DATA_TEST_CASEs.
}
- _target = compute_target(lhs_shape, rhs_shape, transpose_a, transpose_b, M0, N0, K0, export_rhs_to_cl_image, data_type);
+ _target = compute_target(lhs_shape, rhs_shape, transpose_a, transpose_b, M0, N0, K0, export_rhs_to_cl_image,
+ data_type);
_reference = compute_reference(lhs_shape, rhs_shape, output_shape, transpose_a, transpose_b, data_type);
}
protected:
- TensorType compute_target(TensorShape &shape_a, TensorShape &shape_b, bool transpose_a, bool transpose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
+ TensorType compute_target(TensorShape &shape_a,
+ TensorShape &shape_b,
+ bool transpose_a,
+ bool transpose_b,
+ int M0,
+ int N0,
+ int K0,
+ bool export_rhs_to_cl_image,
+ DataType data_type)
{
ARM_COMPUTE_UNUSED(export_rhs_to_cl_image);
CLScheduler::get().default_reinit();
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Create sketch tensors
- TensorInfo lhs_info = context.create_tensor_info(TensorInfo(shape_a, 1, data_type));
- TensorInfo rhs_info = context.create_tensor_info(TensorInfo(shape_b, 1, data_type));
- TensorInfo dst_info = context.create_tensor_info();
+ ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(shape_a, 1, data_type));
+ ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(shape_b, 1, data_type));
+ ITensorInfo *dst_info = context.create_tensor_info();
- MatMulAttributes matmul_attr {};
+ MatMulAttributes matmul_attr{};
matmul_attr.adj_lhs(transpose_a);
matmul_attr.adj_rhs(transpose_b);
- GpuMatMulSettings matmul_settings {};
+ GpuMatMulSettings matmul_settings{};
matmul_settings.m0(M0);
matmul_settings.n0(N0);
matmul_settings.k0(K0);
- ITensorInfo *ans_info = FunctionType::create_op(sketch, &lhs_info, &rhs_info, matmul_attr, matmul_settings);
- GpuOutput::create_op(sketch, ans_info, &dst_info);
+ ITensorInfo *ans_info = FunctionType::create_op(sketch, lhs_info, rhs_info, matmul_attr, matmul_settings);
+ GpuOutput::create_op(sketch, ans_info, dst_info);
// Configure runtime
ClWorkloadRuntime runtime;
runtime.configure(sketch);
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -155,9 +170,9 @@
TensorType t_dst{};
// Initialize user tensors
- t_lhs.allocator()->init(lhs_info);
- t_rhs.allocator()->init(rhs_info);
- t_dst.allocator()->init(dst_info);
+ t_lhs.allocator()->init(*lhs_info);
+ t_rhs.allocator()->init(*rhs_info);
+ t_dst.allocator()->init(*dst_info);
ARM_COMPUTE_ASSERT(t_lhs.info()->is_resizable());
ARM_COMPUTE_ASSERT(t_rhs.info()->is_resizable());
@@ -176,12 +191,17 @@
fill(AccessorType(t_rhs), 1);
// Run runtime
- runtime.run({ &t_lhs, &t_rhs, &t_dst });
+ runtime.run({&t_lhs, &t_rhs, &t_dst});
return t_dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, DataType data_type)
+ SimpleTensor<T> compute_reference(const TensorShape &shape_a,
+ const TensorShape &shape_b,
+ const TensorShape &output_shape,
+ bool pretranspose_a,
+ bool pretranspose_b,
+ DataType data_type)
{
// We collapse dimensions > 3 onto dimension 3, i.e. 5D+ tensors will look like 4D
// This is necessary unless we choose to extend gemm reference for 5D+ tensors
@@ -190,9 +210,9 @@
TensorShape shape_b_collapsed = shape_b.collapsed_from(Window::DimZ);
// Create reference
- SimpleTensor<T> a{ shape_a_collapsed, data_type, 1 };
- SimpleTensor<T> b{ shape_b_collapsed, data_type, 1 };
- SimpleTensor<T> c{ output_shape_collapsed, data_type, 1 };
+ SimpleTensor<T> a{shape_a_collapsed, data_type, 1};
+ SimpleTensor<T> b{shape_b_collapsed, data_type, 1};
+ SimpleTensor<T> c{output_shape_collapsed, data_type, 1};
// Fill reference
fill(a, 0);
@@ -213,27 +233,27 @@
b_transposed_shape.set(1, b.shape().x());
// Define transposed tensors
- SimpleTensor<T> a_transposed{ a_transposed_shape, data_type };
- SimpleTensor<T> b_transposed{ b_transposed_shape, data_type };
+ SimpleTensor<T> a_transposed{a_transposed_shape, data_type};
+ SimpleTensor<T> b_transposed{b_transposed_shape, data_type};
//pretranspose a if necessary
- if(pretranspose_a)
+ if (pretranspose_a)
{
a_transposed = reference::permute<T>(a, PermutationVector(1U, 0U));
}
// pretranspose b if necessary
- if(pretranspose_b)
+ if (pretranspose_b)
{
b_transposed = reference::permute<T>(b, PermutationVector(1U, 0U));
}
// Use transposed tensors if boolean enabled else use original tensors
- SimpleTensor<T> result = reference::gemm<T>((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c, 1.0f, 0.f);
-
+ SimpleTensor<T> result =
+ reference::gemm<T>((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c, 1.0f, 0.f);
// We reshape the gemm output back if the tensor is high dimensional
- if(output_shape_collapsed != output_shape)
+ if (output_shape_collapsed != output_shape)
{
// std::cout << "called reshape: \n";
result = reference::reshape_layer(result, output_shape);
@@ -244,20 +264,30 @@
CLTensor _target{};
SimpleTensor<T> _reference{};
- bool _device_supports_export_to_cl_image{ false };
- bool _device_supports_mmul{ false };
+ bool _device_supports_export_to_cl_image{false};
+ bool _device_supports_mmul{false};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionGpuMatMulValidationFixture : public DynamicFusionGpuMatMulValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionGpuMatMulValidationFixture
+ : public DynamicFusionGpuMatMulValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
- public:
- void setup(TensorShape lhs_shape, TensorShape rhs_shape, TensorShape output_shape, bool transpose_a, bool transpose_b,
- int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
+public:
+ void setup(TensorShape lhs_shape,
+ TensorShape rhs_shape,
+ TensorShape output_shape,
+ bool transpose_a,
+ bool transpose_b,
+ int M0,
+ int N0,
+ int K0,
+ bool export_rhs_to_cl_image,
+ DataType data_type)
{
ARM_COMPUTE_UNUSED(export_rhs_to_cl_image);
- DynamicFusionGpuMatMulValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(lhs_shape, rhs_shape, output_shape, transpose_a, transpose_b, M0,
- N0, K0, false /* export_rhs_to_cl_image bias */, data_type);
+ DynamicFusionGpuMatMulValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ lhs_shape, rhs_shape, output_shape, transpose_a, transpose_b, M0, N0, K0,
+ false /* export_rhs_to_cl_image bias */, data_type);
}
};
diff --git a/tests/validation/fixtures/dynamic_fusion/gpu/cl/Pool2dFixture.h b/tests/validation/fixtures/dynamic_fusion/gpu/cl/Pool2dFixture.h
index 34f2647..dd3519b 100644
--- a/tests/validation/fixtures/dynamic_fusion/gpu/cl/Pool2dFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/gpu/cl/Pool2dFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -28,14 +28,13 @@
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-
#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
#include "arm_compute/dynamic_fusion/sketch/attributes/Pool2dAttributes.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
-#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuPool2d.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
-#include "src/dynamic_fusion/utils/Utils.h"
+#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuPool2d.h"
+#include "src/dynamic_fusion/utils/Utils.h"
#include "tests/CL/CLAccessor.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/reference/PoolingLayer.h"
@@ -54,19 +53,20 @@
public:
void setup(TensorShape input_shape, const Pool2dAttributes &pool_attr, DataType data_type, bool mixed_precision)
{
- _target = compute_target(input_shape, pool_attr, data_type, mixed_precision);
- _reference = compute_reference(input_shape, convert_pool_attr_to_pool_info(pool_attr, mixed_precision), data_type);
+ _target = compute_target(input_shape, pool_attr, data_type, mixed_precision);
+ _reference =
+ compute_reference(input_shape, convert_pool_attr_to_pool_info(pool_attr, mixed_precision), data_type);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
- switch(tensor.data_type())
+ switch (tensor.data_type())
{
case DataType::F16:
{
- arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
+ arm_compute::utils::uniform_real_distribution_16bit<half> distribution{-1.0f, 1.0f};
library->fill(tensor, distribution, i);
break;
}
@@ -82,7 +82,10 @@
}
// Given input is in nchw format
- TensorType compute_target(TensorShape input_shape, const Pool2dAttributes &pool_attr, const DataType data_type, bool mixed_precision)
+ TensorType compute_target(TensorShape input_shape,
+ const Pool2dAttributes &pool_attr,
+ const DataType data_type,
+ bool mixed_precision)
{
CLScheduler::get().default_reinit();
@@ -91,8 +94,8 @@
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Create sketch tensors
auto input_info = context.create_tensor_info(TensorInfo(input_shape, 1, data_type, DataLayout::NHWC));
@@ -101,14 +104,14 @@
// Create Pool2dSettings
GpuPool2dSettings pool_settings = GpuPool2dSettings().mixed_precision(mixed_precision);
- ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, pool_attr, pool_settings);
- GpuOutput::create_op(sketch, ans_info, &dst_info);
+ ITensorInfo *ans_info = FunctionType::create_op(sketch, input_info, pool_attr, pool_settings);
+ GpuOutput::create_op(sketch, ans_info, dst_info);
// Configure runtime
ClWorkloadRuntime runtime;
runtime.configure(sketch);
// (Important) Allocate auxiliary tensor memory if there are any
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -121,8 +124,8 @@
TensorType t_dst{};
// Initialize user tensors
- t_input.allocator()->init(input_info);
- t_dst.allocator()->init(dst_info);
+ t_input.allocator()->init(*input_info);
+ t_dst.allocator()->init(*dst_info);
// Allocate and fill user tensors
t_input.allocator()->allocate();
@@ -131,7 +134,7 @@
fill(AccessorType(t_input), 0);
// Run runtime
- runtime.run({ &t_input, &t_dst });
+ runtime.run({&t_input, &t_dst});
return t_dst;
}
@@ -149,36 +152,57 @@
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionGpuPool2dValidationFixture : public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionGpuPool2dValidationFixture
+ : public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(TensorShape input_shape, PoolingType pool_type, Size2D pool_size, Padding2D pad, Size2D stride, bool exclude_padding, DataType data_type)
+ void setup(TensorShape input_shape,
+ PoolingType pool_type,
+ Size2D pool_size,
+ Padding2D pad,
+ Size2D stride,
+ bool exclude_padding,
+ DataType data_type)
{
- DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape,
- Pool2dAttributes().pool_type(pool_type).pool_size(pool_size).pad(pad).stride(stride).exclude_padding(exclude_padding),
- data_type, false);
+ DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ input_shape,
+ Pool2dAttributes().pool_type(pool_type).pool_size(pool_size).pad(pad).stride(stride).exclude_padding(
+ exclude_padding),
+ data_type, false);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionGpuPool2dMixedPrecisionValidationFixture : public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionGpuPool2dMixedPrecisionValidationFixture
+ : public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(TensorShape input_shape, PoolingType pool_type, Size2D pool_size, Padding2D pad, Size2D stride, bool exclude_padding, DataType data_type, bool mixed_precision)
+ void setup(TensorShape input_shape,
+ PoolingType pool_type,
+ Size2D pool_size,
+ Padding2D pad,
+ Size2D stride,
+ bool exclude_padding,
+ DataType data_type,
+ bool mixed_precision)
{
- DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape,
- Pool2dAttributes().pool_type(pool_type).pool_size(pool_size).pad(pad).stride(stride).exclude_padding(exclude_padding),
- data_type, mixed_precision);
+ DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ input_shape,
+ Pool2dAttributes().pool_type(pool_type).pool_size(pool_size).pad(pad).stride(stride).exclude_padding(
+ exclude_padding),
+ data_type, mixed_precision);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionGpuPool2dSpecialValidationFixture : public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionGpuPool2dSpecialValidationFixture
+ : public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
void setup(TensorShape input_shape, Pool2dAttributes pool_attr, DataType data_type)
{
- DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, pool_attr, data_type, false);
+ DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ input_shape, pool_attr, data_type, false);
}
};
diff --git a/tests/validation/fixtures/dynamic_fusion/operators/ActivationFixture.h b/tests/validation/fixtures/dynamic_fusion/operators/ActivationFixture.h
index 18c3b6b..2f0b133 100644
--- a/tests/validation/fixtures/dynamic_fusion/operators/ActivationFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/operators/ActivationFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -22,8 +22,8 @@
* SOFTWARE.
*/
-#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_ACTIVATIONFIXTURE
-#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_ACTIVATIONFIXTURE
+#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_ACTIVATIONFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_ACTIVATIONFIXTURE_H
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
@@ -49,11 +49,11 @@
public:
void setup(TensorShape shape, bool fuse, DataType data_type, ActivationLayerInfo act_info, TArgs... args)
{
- _fuse = fuse;
- _data_type = data_type;
- _function = act_info.activation();
- _target = compute_target(shape, args...);
- _reference = compute_reference(shape, act_info);
+ _fuse = fuse;
+ _data_type = data_type;
+ _function = act_info.activation();
+ _target = compute_target(shape, args...);
+ _reference = compute_reference(shape, act_info);
}
protected:
@@ -73,17 +73,19 @@
// To ensure all the inserted values are within the given range after subtracing/adding delta
auto insert_values = [&boundary_values, &min, &max](const std::initializer_list<T> &new_values)
{
- for(auto &v : new_values)
+ for (auto &v : new_values)
{
- if(v >= min && v <= max)
+ if (v >= min && v <= max)
{
boundary_values.emplace_back(v);
}
}
};
- insert_values({ min, static_cast<T>(min + delta), static_cast<T>(lower_quarter), static_cast<T>(center_value - delta) }); // lower partition
- insert_values({ static_cast<T>(center_value), static_cast<T>(center_value + delta), static_cast<T>(upper_quarter), static_cast<T>(max - delta), max }); // upper partition
+ insert_values({min, static_cast<T>(min + delta), static_cast<T>(lower_quarter),
+ static_cast<T>(center_value - delta)}); // lower partition
+ insert_values({static_cast<T>(center_value), static_cast<T>(center_value + delta),
+ static_cast<T>(upper_quarter), static_cast<T>(max - delta), max}); // upper partition
return boundary_values;
}
@@ -91,8 +93,8 @@
template <typename U>
void fill(U &&tensor)
{
- float min_bound = 0;
- float max_bound = 0;
+ float min_bound = 0;
+ float max_bound = 0;
std::tie(min_bound, max_bound) = get_activation_layer_test_bounds<T>(_function, _data_type);
library->fill_static_values(tensor, get_boundary_values(static_cast<T>(min_bound), static_cast<T>(max_bound)));
}
@@ -101,22 +103,22 @@
{
// Create a new workload sketch
CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- GpuWorkloadContext context{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ GpuWorkloadContext context{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Create sketch tensors
- TensorInfo src_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type));
- TensorInfo dst_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type));
+ ITensorInfo *src_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type));
+ ITensorInfo *dst_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type));
- ITensorInfo *ans_0_info = FunctionType::create_op(sketch, &src_info, args...);
- if(_fuse)
+ ITensorInfo *ans_0_info = FunctionType::create_op(sketch, src_info, args...);
+ if (_fuse)
{
ITensorInfo *ans_1_info = FunctionType::create_op(sketch, ans_0_info, args...);
- GpuOutput::create_op(sketch, ans_1_info, &dst_info);
+ GpuOutput::create_op(sketch, ans_1_info, dst_info);
}
else
{
- GpuOutput::create_op(sketch, ans_0_info, &dst_info);
+ GpuOutput::create_op(sketch, ans_0_info, dst_info);
}
// Configure runtime
@@ -128,8 +130,8 @@
TensorType t_dst{};
// Initialize user tensors
- t_src.allocator()->init(src_info);
- t_dst.allocator()->init(dst_info);
+ t_src.allocator()->init(*src_info);
+ t_dst.allocator()->init(*dst_info);
// Allocate and fill user tensors
t_src.allocator()->allocate();
@@ -138,7 +140,7 @@
fill(AccessorType(t_src));
// Run runtime
- runtime.run({ &t_src, &t_dst });
+ runtime.run({&t_src, &t_dst});
return t_dst;
}
@@ -146,14 +148,14 @@
SimpleTensor<T> compute_reference(const TensorShape &shape, ActivationLayerInfo act_info)
{
// Create reference
- SimpleTensor<T> src{ shape, _data_type, 1 };
+ SimpleTensor<T> src{shape, _data_type, 1};
// Fill reference
fill(src);
auto tmp = reference::activation_layer<T>(src, act_info);
- if(_fuse)
+ if (_fuse)
{
auto dst = reference::activation_layer<T>(tmp, act_info);
return dst;
@@ -166,31 +168,35 @@
protected:
ActivationLayerInfo::ActivationFunction _function{};
- bool _fuse{ false };
+ bool _fuse{false};
DataType _data_type{};
TensorType _target{};
SimpleTensor<T> _reference{};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionSigmoidValidationFixture : public DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionSigmoidValidationFixture
+ : public DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
void setup(TensorShape shape, bool fuse, DataType data_type)
{
- ActivationLayerInfo act_info{ ActivationLayerInfo::ActivationFunction::LOGISTIC };
- DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, fuse, data_type, act_info);
+ ActivationLayerInfo act_info{ActivationLayerInfo::ActivationFunction::LOGISTIC};
+ DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, fuse,
+ data_type, act_info);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionTanhValidationFixture : public DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionTanhValidationFixture
+ : public DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
void setup(TensorShape shape, bool fuse, DataType data_type)
{
- ActivationLayerInfo act_info{ ActivationLayerInfo::ActivationFunction::TANH };
- DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, fuse, data_type, act_info);
+ ActivationLayerInfo act_info{ActivationLayerInfo::ActivationFunction::TANH};
+ DynamicFusionActivationValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, fuse,
+ data_type, act_info);
}
};
@@ -198,4 +204,4 @@
} // namespace test
} // namespace arm_compute
-#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_ACTIVATIONFIXTURE */
+#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_ACTIVATIONFIXTURE_H
diff --git a/tests/validation/fixtures/dynamic_fusion/operators/CastFixture.h b/tests/validation/fixtures/dynamic_fusion/operators/CastFixture.h
index d8e250c..edf0dff 100644
--- a/tests/validation/fixtures/dynamic_fusion/operators/CastFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/operators/CastFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CASTFIXTURE
-#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CASTFIXTURE
+#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CASTFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CASTFIXTURE_H
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
@@ -58,14 +58,14 @@
void fill(U &&tensor, int i, DataType dt_in, DataType dt_out)
{
// Restricting range to avoid inf values
- if(dt_out == DataType::F16)
+ if (dt_out == DataType::F16)
{
constexpr int signed_min = -32000;
constexpr int signed_max = 32000;
constexpr int unsigned_min = 0;
constexpr int unsigned_max = 65000;
- switch(dt_in)
+ switch (dt_in)
{
case DataType::U8:
case DataType::QASYMM8:
@@ -78,22 +78,26 @@
}
case DataType::U16:
{
- library->fill_tensor_uniform(tensor, i, static_cast<uint16_t>(unsigned_min), static_cast<uint16_t>(unsigned_max));
+ library->fill_tensor_uniform(tensor, i, static_cast<uint16_t>(unsigned_min),
+ static_cast<uint16_t>(unsigned_max));
break;
}
case DataType::S16:
{
- library->fill_tensor_uniform(tensor, i, static_cast<int16_t>(signed_min), static_cast<int16_t>(signed_max));
+ library->fill_tensor_uniform(tensor, i, static_cast<int16_t>(signed_min),
+ static_cast<int16_t>(signed_max));
break;
}
case DataType::U32:
{
- library->fill_tensor_uniform(tensor, i, static_cast<uint32_t>(unsigned_min), static_cast<uint32_t>(unsigned_max));
+ library->fill_tensor_uniform(tensor, i, static_cast<uint32_t>(unsigned_min),
+ static_cast<uint32_t>(unsigned_max));
break;
}
case DataType::S32:
{
- library->fill_tensor_uniform(tensor, i, static_cast<int32_t>(signed_min), static_cast<int32_t>(signed_max));
+ library->fill_tensor_uniform(tensor, i, static_cast<int32_t>(signed_min),
+ static_cast<int32_t>(signed_max));
break;
}
default:
@@ -107,29 +111,31 @@
}
// Given input is in nchw format
- TensorType compute_target(const TensorShape &shape, const DataType dt_in, const DataType dt_out, const ConvertPolicy policy)
+ TensorType
+ compute_target(const TensorShape &shape, const DataType dt_in, const DataType dt_out, const ConvertPolicy policy)
{
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Create sketch tensors
- TensorInfo src_info = context.create_tensor_info(TensorInfo(shape, 1, dt_in, DataLayout::NCHW)); // layout is not important
- TensorInfo dst_info = context.create_tensor_info();
+ ITensorInfo *src_info =
+ context.create_tensor_info(TensorInfo(shape, 1, dt_in, DataLayout::NCHW)); // layout is not important
+ ITensorInfo *dst_info = context.create_tensor_info();
CastAttributes attributes;
attributes.convert_policy(policy).data_type(dt_out);
- ITensorInfo *ans_info = FunctionType::create_op(sketch, &src_info, attributes);
- GpuOutput::create_op(sketch, ans_info, &dst_info);
+ ITensorInfo *ans_info = FunctionType::create_op(sketch, src_info, attributes);
+ GpuOutput::create_op(sketch, ans_info, dst_info);
// Configure runtime
ClWorkloadRuntime runtime;
runtime.configure(sketch);
// (Important) Allocate auxiliary tensor memory if there are any
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -143,8 +149,8 @@
TensorType t_dst{};
// Initialize user tensors
- t_src.allocator()->init(src_info);
- t_dst.allocator()->init(dst_info);
+ t_src.allocator()->init(*src_info);
+ t_dst.allocator()->init(*dst_info);
// Allocate and fill user tensors
t_src.allocator()->allocate();
@@ -153,14 +159,15 @@
fill(AccessorType(t_src), 0, dt_in, dt_out);
// Run runtime
- runtime.run({ &t_src, &t_dst });
+ runtime.run({&t_src, &t_dst});
return t_dst;
}
- SimpleTensor<T2> compute_reference(const TensorShape &shape, const DataType dt_in, const DataType dt_out, const ConvertPolicy policy)
+ SimpleTensor<T2>
+ compute_reference(const TensorShape &shape, const DataType dt_in, const DataType dt_out, const ConvertPolicy policy)
{
// Create reference
- SimpleTensor<T1> src{ shape, dt_in, 1 };
+ SimpleTensor<T1> src{shape, dt_in, 1};
// Fill reference
fill(src, 0, dt_in, dt_out);
@@ -174,4 +181,4 @@
} // namespace validation
} // namespace test
} // namespace arm_compute
-#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CASTFIXTURE */
+#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CASTFIXTURE_H
diff --git a/tests/validation/fixtures/dynamic_fusion/operators/ClampFixture.h b/tests/validation/fixtures/dynamic_fusion/operators/ClampFixture.h
index 3c325d7..e8f6f83 100644
--- a/tests/validation/fixtures/dynamic_fusion/operators/ClampFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/operators/ClampFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2022-2023 Arm Limited.
+ * Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CLAMPFIXTURE
-#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CLAMPFIXTURE
+#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CLAMPFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CLAMPFIXTURE_H
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
@@ -107,18 +107,18 @@
GpuWorkloadSketch sketch{ &context };
// Create sketch tensors
- TensorInfo src_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type));
- TensorInfo dst_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type));
+ ITensorInfo* src_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type));
+ ITensorInfo* dst_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type));
- ITensorInfo *ans_0_info = FunctionType::create_op(sketch, &src_info, attributes);
+ ITensorInfo *ans_0_info = FunctionType::create_op(sketch, src_info, attributes);
if(_fuse)
{
ITensorInfo *ans_1_info = FunctionType::create_op(sketch, ans_0_info, attributes);
- GpuOutput::create_op(sketch, ans_1_info, &dst_info);
+ GpuOutput::create_op(sketch, ans_1_info, dst_info);
}
else
{
- GpuOutput::create_op(sketch, ans_0_info, &dst_info);
+ GpuOutput::create_op(sketch, ans_0_info, dst_info);
}
// Configure runtime
@@ -130,8 +130,8 @@
TensorType t_dst{};
// Initialize user tensors
- t_src.allocator()->init(src_info);
- t_dst.allocator()->init(dst_info);
+ t_src.allocator()->init(*src_info);
+ t_dst.allocator()->init(*dst_info);
// Allocate and fill user tensors
t_src.allocator()->allocate();
@@ -168,4 +168,4 @@
} // namespace validation
} // namespace test
} // namespace arm_compute
-#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CLAMPFIXTURE */
+#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CLAMPFIXTURE_H
diff --git a/tests/validation/fixtures/dynamic_fusion/operators/MulFixture.h b/tests/validation/fixtures/dynamic_fusion/operators/MulFixture.h
index 02dc996..f02aa5e 100644
--- a/tests/validation/fixtures/dynamic_fusion/operators/MulFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/operators/MulFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_MULFIXTURE
-#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_MULFIXTURE
+#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_MULFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_MULFIXTURE_H
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
@@ -31,9 +31,9 @@
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
-#include "tests/Globals.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
+#include "tests/Globals.h"
#include "tests/validation/reference/PixelWiseMultiplication.h"
using namespace arm_compute::experimental::dynamic_fusion;
@@ -52,180 +52,188 @@
class DynamicFusionMulValidationFixture : public framework::Fixture
{
public:
- void setup(const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2, DataType data_type, bool is_inplace, bool fuse_two_ops = false)
- {
- _data_type = data_type;
- _is_inplace = is_inplace;
- _fuse = fuse_two_ops;
- ARM_COMPUTE_ERROR_ON_MSG(_fuse && shape2.total_size() == 0, "No shape2 provided for fusion of two ops.");
- ARM_COMPUTE_ERROR_ON_MSG(_fuse && _is_inplace, "In place for fusing case not supported yet.");
- _target = compute_target(shape0, shape1, shape2);
- _reference = compute_reference(shape0, shape1, shape2);
- }
+ void setup(const TensorShape &shape0,
+ const TensorShape &shape1,
+ const TensorShape &shape2,
+ DataType data_type,
+ bool is_inplace,
+ bool fuse_two_ops = false)
+ {
+ _data_type = data_type;
+ _is_inplace = is_inplace;
+ _fuse = fuse_two_ops;
+ ARM_COMPUTE_ERROR_ON_MSG(_fuse && shape2.total_size() == 0, "No shape2 provided for fusion of two ops.");
+ ARM_COMPUTE_ERROR_ON_MSG(_fuse && _is_inplace, "In place for fusing case not supported yet.");
+ _target = compute_target(shape0, shape1, shape2);
+ _reference = compute_reference(shape0, shape1, shape2);
+ }
protected:
- template <typename U>
- void fill(U &&tensor, int i)
- {
- library->fill_tensor_uniform(tensor, i);
- }
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ library->fill_tensor_uniform(tensor, i);
+ }
- TensorType compute_target(const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2)
- {
- // Create a new workload sketch
- auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ TensorType compute_target(const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2)
+ {
+ // Create a new workload sketch
+ auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
- // Fuse first multiplication op
- TensorInfo lhs_info = context.create_tensor_info(TensorInfo(shape0, 1, _data_type));
- TensorInfo rhs_info = context.create_tensor_info(TensorInfo(shape1, 1, _data_type));
- TensorInfo dst_info = context.create_tensor_info();
+ // Fuse first multiplication op
+ ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(shape0, 1, _data_type));
+ ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(shape1, 1, _data_type));
+ ITensorInfo *dst_info = context.create_tensor_info();
- TensorInfo rhs_info_fuse;
+ ITensorInfo *rhs_info_fuse = nullptr;
- ITensorInfo *ans_info = FunctionType::create_op(sketch, &lhs_info, &rhs_info);
+ ITensorInfo *ans_info = FunctionType::create_op(sketch, lhs_info, rhs_info);
- if(_fuse)
- {
- rhs_info_fuse = context.create_tensor_info(TensorInfo(shape2, 1, _data_type));
- ITensorInfo *ans2_info = FunctionType::create_op(sketch, ans_info, &rhs_info_fuse);
- GpuOutput::create_op(sketch, ans2_info, &dst_info);
- }
- else
- {
- GpuOutput::create_op(sketch, ans_info, &dst_info);
- }
+ if (_fuse)
+ {
+ rhs_info_fuse = context.create_tensor_info(TensorInfo(shape2, 1, _data_type));
+ ITensorInfo *ans2_info = FunctionType::create_op(sketch, ans_info, rhs_info_fuse);
+ GpuOutput::create_op(sketch, ans2_info, dst_info);
+ }
+ else
+ {
+ GpuOutput::create_op(sketch, ans_info, dst_info);
+ }
- // Configure runtime
- ClWorkloadRuntime runtime;
- runtime.configure(sketch);
+ // Configure runtime
+ ClWorkloadRuntime runtime;
+ runtime.configure(sketch);
- // (Important) Allocate auxiliary tensor memory if there are any
- for(auto &data : runtime.get_auxiliary_tensors())
- {
- CLTensor *tensor = std::get<0>(data);
- TensorInfo info = std::get<1>(data);
- AuxMemoryInfo aux_mem_req = std::get<2>(data);
- tensor->allocator()->init(info, aux_mem_req.alignment);
- tensor->allocator()->allocate(); // Use ACL allocated memory
- }
+ // (Important) Allocate auxiliary tensor memory if there are any
+ for (auto &data : runtime.get_auxiliary_tensors())
+ {
+ CLTensor *tensor = std::get<0>(data);
+ TensorInfo info = std::get<1>(data);
+ AuxMemoryInfo aux_mem_req = std::get<2>(data);
+ tensor->allocator()->init(info, aux_mem_req.alignment);
+ tensor->allocator()->allocate(); // Use ACL allocated memory
+ }
- // Construct user tensors
- TensorType t_lhs{};
- TensorType t_rhs{};
- TensorType t_rhs_fuse{};
- TensorType t_dst{};
+ // Construct user tensors
+ TensorType t_lhs{};
+ TensorType t_rhs{};
+ TensorType t_rhs_fuse{};
+ TensorType t_dst{};
- // Initialize user tensors
- t_lhs.allocator()->init(lhs_info);
- t_rhs.allocator()->init(rhs_info);
- t_dst.allocator()->init(dst_info);
- if(_fuse)
- {
- t_rhs_fuse.allocator()->init(rhs_info_fuse);
- }
+ // Initialize user tensors
+ t_lhs.allocator()->init(*lhs_info);
+ t_rhs.allocator()->init(*rhs_info);
+ t_dst.allocator()->init(*dst_info);
+ if (_fuse)
+ {
+ t_rhs_fuse.allocator()->init(*rhs_info_fuse);
+ }
- // Allocate and fill user tensors
- // Instead of using ACL allocator, the user can choose to import memory into the tensors
- t_lhs.allocator()->allocate();
- t_rhs.allocator()->allocate();
- t_dst.allocator()->allocate();
- if(_fuse)
- {
- t_rhs_fuse.allocator()->allocate();
- }
+ // Allocate and fill user tensors
+ // Instead of using ACL allocator, the user can choose to import memory into the tensors
+ t_lhs.allocator()->allocate();
+ t_rhs.allocator()->allocate();
+ t_dst.allocator()->allocate();
+ if (_fuse)
+ {
+ t_rhs_fuse.allocator()->allocate();
+ }
- fill(AccessorType(t_lhs), 0);
- fill(AccessorType(t_rhs), 1);
- if(_fuse)
- {
- fill(AccessorType(t_rhs_fuse), 2);
- }
+ fill(AccessorType(t_lhs), 0);
+ fill(AccessorType(t_rhs), 1);
+ if (_fuse)
+ {
+ fill(AccessorType(t_rhs_fuse), 2);
+ }
- // Run runtime
- if(_fuse)
- {
- runtime.run({ &t_lhs, &t_rhs, &t_rhs_fuse, &t_dst });
- }
- else
- {
- runtime.run({ &t_lhs, &t_rhs, &t_dst });
- }
+ // Run runtime
+ if (_fuse)
+ {
+ runtime.run({&t_lhs, &t_rhs, &t_rhs_fuse, &t_dst});
+ }
+ else
+ {
+ runtime.run({&t_lhs, &t_rhs, &t_dst});
+ }
- return t_dst;
- }
+ return t_dst;
+ }
- SimpleTensor<T> compute_reference(const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2)
- {
- // Create reference
- SimpleTensor<T> ref_lhs{ shape0, _data_type, 1, QuantizationInfo() };
- SimpleTensor<T> ref_rhs{ shape1, _data_type, 1, QuantizationInfo() };
- SimpleTensor<T> ref_rhs_fuse{ shape2, _data_type, 1, QuantizationInfo() };
+ SimpleTensor<T> compute_reference(const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2)
+ {
+ // Create reference
+ SimpleTensor<T> ref_lhs{shape0, _data_type, 1, QuantizationInfo()};
+ SimpleTensor<T> ref_rhs{shape1, _data_type, 1, QuantizationInfo()};
+ SimpleTensor<T> ref_rhs_fuse{shape2, _data_type, 1, QuantizationInfo()};
- // Fill reference
- fill(ref_lhs, 0);
- fill(ref_rhs, 1);
- SimpleTensor<T> ref_dst = reference::pixel_wise_multiplication<T, T, T>(ref_lhs,
- ref_rhs,
- 1.f,
- ConvertPolicy::SATURATE,
- RoundingPolicy::TO_NEAREST_UP,
- _data_type,
- QuantizationInfo());
- if(_fuse)
- {
- fill(ref_rhs_fuse, 2);
- SimpleTensor<T> ref_dst_fuse = reference::pixel_wise_multiplication<T, T, T>(ref_dst,
- ref_rhs_fuse,
- 1.f,
- ConvertPolicy::SATURATE,
- RoundingPolicy::TO_NEAREST_UP,
- _data_type,
- QuantizationInfo());
- return ref_dst_fuse;
- }
- return ref_dst;
- }
+ // Fill reference
+ fill(ref_lhs, 0);
+ fill(ref_rhs, 1);
+ SimpleTensor<T> ref_dst = reference::pixel_wise_multiplication<T, T, T>(
+ ref_lhs, ref_rhs, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_UP, _data_type,
+ QuantizationInfo());
+ if (_fuse)
+ {
+ fill(ref_rhs_fuse, 2);
+ SimpleTensor<T> ref_dst_fuse = reference::pixel_wise_multiplication<T, T, T>(
+ ref_dst, ref_rhs_fuse, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_UP, _data_type,
+ QuantizationInfo());
+ return ref_dst_fuse;
+ }
+ return ref_dst;
+ }
- TensorType _target{};
- SimpleTensor<T> _reference{};
- DataType _data_type{};
- bool _is_inplace{ false };
- bool _fuse{ false };
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+ DataType _data_type{};
+ bool _is_inplace{false};
+ bool _fuse{false};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionMulOneOpValidationFixture : public DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionMulOneOpValidationFixture
+ : public DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(const TensorShape &shape0, DataType data_type, bool is_inplace)
- {
- DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape0, shape0, TensorShape(), data_type, is_inplace);
- }
+ void setup(const TensorShape &shape0, DataType data_type, bool is_inplace)
+ {
+ DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ shape0, shape0, TensorShape(), data_type, is_inplace);
+ }
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionMulBroadcastValidationFixture : public DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionMulBroadcastValidationFixture
+ : public DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(const TensorShape &shape0, const TensorShape &shape1, DataType data_type, bool is_inplace)
- {
- DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape0, shape1, TensorShape(), data_type, is_inplace);
- }
+ void setup(const TensorShape &shape0, const TensorShape &shape1, DataType data_type, bool is_inplace)
+ {
+ DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ shape0, shape1, TensorShape(), data_type, is_inplace);
+ }
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionMulTwoOpsValidationFixture : public DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionMulTwoOpsValidationFixture
+ : public DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2, DataType data_type, bool is_inplace, bool fuse_two_ops)
- {
- DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape0, shape1, shape2, data_type, is_inplace, fuse_two_ops);
- }
+ void setup(const TensorShape &shape0,
+ const TensorShape &shape1,
+ const TensorShape &shape2,
+ DataType data_type,
+ bool is_inplace,
+ bool fuse_two_ops)
+ {
+ DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ shape0, shape1, shape2, data_type, is_inplace, fuse_two_ops);
+ }
};
} // namespace validation
} // namespace test
} // namespace arm_compute
-#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_MULFIXTURE */
+#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_MULFIXTURE_H
diff --git a/tests/validation/fixtures/dynamic_fusion/operators/ReshapeFixture.h b/tests/validation/fixtures/dynamic_fusion/operators/ReshapeFixture.h
index abfc645..bde3360 100644
--- a/tests/validation/fixtures/dynamic_fusion/operators/ReshapeFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/operators/ReshapeFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESHAPEFIXTURE
-#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESHAPEFIXTURE
+#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESHAPEFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESHAPEFIXTURE_H
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
@@ -33,9 +33,9 @@
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuReshape.h"
-#include "tests/Globals.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
+#include "tests/Globals.h"
#include "tests/validation/reference/ReshapeLayer.h"
using namespace arm_compute::experimental::dynamic_fusion;
@@ -70,24 +70,24 @@
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- auto context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ auto context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Create sketch tensors
- TensorInfo src_info = context.create_tensor_info(TensorInfo(input_shape, 1, data_type));
- TensorInfo dst_info = context.create_tensor_info(TensorInfo(output_shape, 1, data_type));
+ ITensorInfo *src_info = context.create_tensor_info(TensorInfo(input_shape, 1, data_type));
+ ITensorInfo *dst_info = context.create_tensor_info(TensorInfo(output_shape, 1, data_type));
ReshapeAttributes attributes;
attributes.shape(output_shape);
- ITensorInfo *ans_info = FunctionType::create_op(sketch, &src_info, attributes);
- GpuOutput::create_op(sketch, ans_info, &dst_info);
+ ITensorInfo *ans_info = FunctionType::create_op(sketch, src_info, attributes);
+ GpuOutput::create_op(sketch, ans_info, dst_info);
// Configure runtime
ClWorkloadRuntime runtime;
runtime.configure(sketch);
// (Important) Allocate auxiliary tensor memory if there are any
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -100,8 +100,8 @@
TensorType t_src{};
TensorType t_dst{};
// Initialize user tensors
- t_src.allocator()->init(src_info);
- t_dst.allocator()->init(dst_info);
+ t_src.allocator()->init(*src_info);
+ t_dst.allocator()->init(*dst_info);
// Allocate and fill user tensors
t_src.allocator()->allocate();
@@ -110,15 +110,16 @@
fill(AccessorType(t_src), 0);
// Run runtime
- runtime.run({ &t_src, &t_dst });
+ runtime.run({&t_src, &t_dst});
return t_dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, DataType data_type)
+ SimpleTensor<T>
+ compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, DataType data_type)
{
// Create reference
- SimpleTensor<T> src{ input_shape, data_type };
+ SimpleTensor<T> src{input_shape, data_type};
// Fill reference
fill(src, 0);
@@ -133,4 +134,4 @@
} // namespace validation
} // namespace test
} // namespace arm_compute
-#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESHAPEFIXTURE */
+#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESHAPEFIXTURE_H
diff --git a/tests/validation/fixtures/dynamic_fusion/operators/ResizeFixture.h b/tests/validation/fixtures/dynamic_fusion/operators/ResizeFixture.h
index c44f037..711767b 100644
--- a/tests/validation/fixtures/dynamic_fusion/operators/ResizeFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/operators/ResizeFixture.h
@@ -1,5 +1,5 @@
/*
-* Copyright (c) 2022-2023 Arm Limited.
+* Copyright (c) 2022-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE
-#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE
+#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
@@ -33,12 +33,12 @@
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
#include "tests/CL/CLAccessor.h"
-#include "tests/SimpleTensor.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
-#include "tests/validation/Validation.h"
+#include "tests/SimpleTensor.h"
#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/Scale.h"
+#include "tests/validation/Validation.h"
using namespace arm_compute::experimental::dynamic_fusion;
@@ -52,9 +52,14 @@
class DynamicFusionResizeGenericValidationFixture : public framework::Fixture
{
public:
- void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout,
- InterpolationPolicy interpolation_policy, SamplingPolicy sampling_policy,
- bool align_corners, QuantizationInfo output_quantization_info)
+ void setup(TensorShape shape,
+ DataType data_type,
+ QuantizationInfo quantization_info,
+ DataLayout data_layout,
+ InterpolationPolicy interpolation_policy,
+ SamplingPolicy sampling_policy,
+ bool align_corners,
+ QuantizationInfo output_quantization_info)
{
_shape = shape;
_interpolation_policy = interpolation_policy;
@@ -79,13 +84,13 @@
protected:
void generate_scale(const TensorShape &shape)
{
- static constexpr float _min_scale{ 0.25f };
- static constexpr float _max_scale{ 3.f };
+ static constexpr float _min_scale{0.25f};
+ static constexpr float _max_scale{3.f};
- constexpr float max_width{ 8192.0f };
- constexpr float max_height{ 6384.0f };
- constexpr float min_width{ 1.f };
- constexpr float min_height{ 1.f };
+ constexpr float max_width{8192.0f};
+ constexpr float max_height{6384.0f};
+ constexpr float min_width{1.f};
+ constexpr float min_height{1.f};
std::mt19937 generator(library->seed());
std::uniform_real_distribution<float> distribution_float(_min_scale, _max_scale);
@@ -93,7 +98,8 @@
auto generate = [&](size_t input_size, float min_output, float max_output) -> int
{
const float generated_scale = distribution_float(generator);
- const int output_size = static_cast<int>(utility::clamp(static_cast<float>(input_size) * generated_scale, min_output, max_output));
+ const int output_size = static_cast<int>(
+ utility::clamp(static_cast<float>(input_size) * generated_scale, min_output, max_output));
return output_size;
};
@@ -108,17 +114,17 @@
template <typename U>
void fill(U &&tensor)
{
- if(tensor.data_type() == DataType::F32)
+ if (tensor.data_type() == DataType::F32)
{
std::uniform_real_distribution<float> distribution(-5.0f, 5.0f);
library->fill(tensor, distribution, 0);
}
- else if(tensor.data_type() == DataType::F16)
+ else if (tensor.data_type() == DataType::F16)
{
- arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -5.0f, 5.0f };
+ arm_compute::utils::uniform_real_distribution_16bit<half> distribution{-5.0f, 5.0f};
library->fill(tensor, distribution, 0);
}
- else if(is_data_type_quantized(tensor.data_type()))
+ else if (is_data_type_quantized(tensor.data_type()))
{
std::uniform_int_distribution<> distribution(0, 100);
library->fill(tensor, distribution, 0);
@@ -136,26 +142,30 @@
// Create a new workload sketch
CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- GpuWorkloadContext context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ GpuWorkloadContext context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
// Create sketch tensors
- TensorInfo src_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type, _data_layout));
- src_info.set_quantization_info(_input_quantization_info);
- TensorInfo dst_info = context.create_tensor_info();
+ ITensorInfo *src_info = context.create_tensor_info(TensorInfo(shape, 1, _data_type, _data_layout));
+ src_info->set_quantization_info(_input_quantization_info);
+ ITensorInfo *dst_info = context.create_tensor_info();
ResizeAttributes attributes;
- attributes.align_corners(_align_corners).sampling_policy(_sampling_policy).interpolation_policy(_interpolation_policy).output_width(_output_width).output_height(_output_height);
+ attributes.align_corners(_align_corners)
+ .sampling_policy(_sampling_policy)
+ .interpolation_policy(_interpolation_policy)
+ .output_width(_output_width)
+ .output_height(_output_height);
- ITensorInfo *scale_result_info = FunctionType::create_op(sketch, &src_info, attributes);
- GpuOutput::create_op(sketch, scale_result_info, &dst_info);
+ ITensorInfo *scale_result_info = FunctionType::create_op(sketch, src_info, attributes);
+ GpuOutput::create_op(sketch, scale_result_info, dst_info);
// Configure runtime
ClWorkloadRuntime runtime;
runtime.configure(sketch);
// (Important) Allocate auxiliary tensor memory if there are any
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -169,8 +179,8 @@
TensorType t_dst{};
// Initialize user tensors
- t_src.allocator()->init(src_info);
- t_dst.allocator()->init(dst_info);
+ t_src.allocator()->init(*src_info);
+ t_dst.allocator()->init(*dst_info);
// Allocate and fill user tensors
t_src.allocator()->allocate();
@@ -179,7 +189,7 @@
fill(AccessorType(t_src));
// Run runtime
- runtime.run({ &t_src, &t_dst });
+ runtime.run({&t_src, &t_dst});
return t_dst;
}
@@ -187,7 +197,7 @@
SimpleTensor<T> compute_reference(const TensorShape &shape)
{
// Create reference
- SimpleTensor<T> src{ shape, _data_type, 1, _input_quantization_info };
+ SimpleTensor<T> src{shape, _data_type, 1, _input_quantization_info};
// Reference code is NCHW, so the input shapes are NCHW
const int idx_width = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
@@ -199,9 +209,9 @@
// Fill reference
fill(src);
- return reference::scale<T>(src, scale_x, scale_y, _interpolation_policy,
- BorderMode::REPLICATE, static_cast<T>(0), _sampling_policy, /* ceil_policy_scale */ false,
- _align_corners, _output_quantization_info);
+ return reference::scale<T>(src, scale_x, scale_y, _interpolation_policy, BorderMode::REPLICATE,
+ static_cast<T>(0), _sampling_policy, /* ceil_policy_scale */ false, _align_corners,
+ _output_quantization_info);
}
TensorType _target{};
@@ -213,43 +223,45 @@
DataLayout _data_layout{};
QuantizationInfo _input_quantization_info{};
QuantizationInfo _output_quantization_info{};
- bool _align_corners{ false };
- int _output_width{ 0 };
- int _output_height{ 0 };
+ bool _align_corners{false};
+ int _output_width{0};
+ int _output_height{0};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionResizeValidationFixture : public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionResizeValidationFixture
+ : public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(TensorShape shape, DataType data_type, DataLayout data_layout, InterpolationPolicy policy, SamplingPolicy sampling_policy, bool align_corners)
+ void setup(TensorShape shape,
+ DataType data_type,
+ DataLayout data_layout,
+ InterpolationPolicy policy,
+ SamplingPolicy sampling_policy,
+ bool align_corners)
{
- DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
- data_type,
- QuantizationInfo(),
- data_layout,
- policy,
- sampling_policy,
- align_corners,
- QuantizationInfo());
+ DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ shape, data_type, QuantizationInfo(), data_layout, policy, sampling_policy, align_corners,
+ QuantizationInfo());
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
-class DynamicFusionResizeQuantizedValidationFixture : public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionResizeQuantizedValidationFixture
+ : public DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
- void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout, InterpolationPolicy policy, SamplingPolicy sampling_policy,
- bool align_corners)
+ void setup(TensorShape shape,
+ DataType data_type,
+ QuantizationInfo quantization_info,
+ DataLayout data_layout,
+ InterpolationPolicy policy,
+ SamplingPolicy sampling_policy,
+ bool align_corners)
{
- DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
- data_type,
- quantization_info,
- data_layout,
- policy,
- sampling_policy,
- align_corners,
- quantization_info);
+ DynamicFusionResizeGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ shape, data_type, quantization_info, data_layout, policy, sampling_policy, align_corners,
+ quantization_info);
}
};
@@ -257,4 +269,4 @@
} // namespace test
} // namespace arm_compute
-#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE */
+#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_RESIZEFIXTURE_H
diff --git a/tests/validation/fixtures/dynamic_fusion/operators/SoftmaxFixture.h b/tests/validation/fixtures/dynamic_fusion/operators/SoftmaxFixture.h
index 1ed133d..175d4ff 100644
--- a/tests/validation/fixtures/dynamic_fusion/operators/SoftmaxFixture.h
+++ b/tests/validation/fixtures/dynamic_fusion/operators/SoftmaxFixture.h
@@ -1,5 +1,5 @@
/*
-* Copyright (c) 2023 Arm Limited.
+* Copyright (c) 2023-2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,18 +21,18 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_SOFTMAXFIXTURE
-#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_SOFTMAXFIXTURE
+#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_SOFTMAXFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_SOFTMAXFIXTURE_H
#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
#include "arm_compute/dynamic_fusion/sketch/attributes/SoftmaxAttributes.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
-#include "tests/SimpleTensor.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
-#include "tests/validation/Validation.h"
+#include "tests/SimpleTensor.h"
#include "tests/validation/reference/SoftmaxLayer.h"
+#include "tests/validation/Validation.h"
using namespace arm_compute::experimental::dynamic_fusion;
@@ -56,17 +56,17 @@
template <typename U>
void fill(U &&tensor)
{
- if(tensor.data_type() == DataType::F32)
+ if (tensor.data_type() == DataType::F32)
{
std::uniform_real_distribution<float> distribution(-10.0f, 10.0f);
library->fill(tensor, distribution, 0);
}
- else if(tensor.data_type() == DataType::F16)
+ else if (tensor.data_type() == DataType::F16)
{
- arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -10.0f, 10.0f };
+ arm_compute::utils::uniform_real_distribution_16bit<half> distribution{-10.0f, 10.0f};
library->fill(tensor, distribution, 0);
}
- else if(!is_data_type_quantized(tensor.data_type()))
+ else if (!is_data_type_quantized(tensor.data_type()))
{
std::uniform_int_distribution<> distribution(0, 100);
library->fill(tensor, distribution, 0);
@@ -81,14 +81,14 @@
{
// Create a new workload sketch
CLCompileContext cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
- GpuWorkloadContext context = GpuWorkloadContext{ &cl_compile_ctx };
- GpuWorkloadSketch sketch{ &context };
+ GpuWorkloadContext context = GpuWorkloadContext{&cl_compile_ctx};
+ GpuWorkloadSketch sketch{&context};
SoftmaxAttributes softmax_attr{};
softmax_attr.axis(axis).beta(beta).is_log_softmax(is_log);
- TensorInfo src_info = context.create_tensor_info(shape, 1, data_type);
- TensorInfo dst_info = context.create_tensor_info(shape, 1, data_type);
- FunctionType::create_op(sketch, &src_info, &dst_info, softmax_attr);
+ ITensorInfo *src_info = context.create_tensor_info(shape, 1, data_type);
+ ITensorInfo *dst_info = context.create_tensor_info(shape, 1, data_type);
+ FunctionType::create_op(sketch, src_info, dst_info, softmax_attr);
// Configure runtime
ClWorkloadRuntime runtime;
@@ -96,7 +96,7 @@
// (Important) Allocate auxiliary tensor memory if there are any
// Instead of using ACL allocated memory, the user can choose to import memory into the tensors
- for(auto &data : runtime.get_auxiliary_tensors())
+ for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
@@ -109,8 +109,8 @@
TensorType dst{};
// Initialize user tensors
- src.allocator()->init(src_info);
- dst.allocator()->init(dst_info);
+ src.allocator()->init(*src_info);
+ dst.allocator()->init(*dst_info);
// Allocate and fill user tensors
src.allocator()->allocate();
@@ -118,15 +118,16 @@
fill(AccessorType(src));
// Run runtime
- runtime.run({ &src, &dst });
+ runtime.run({&src, &dst});
return dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &shape, DataType data_type, float beta, int32_t axis, bool is_log)
+ SimpleTensor<T>
+ compute_reference(const TensorShape &shape, DataType data_type, float beta, int32_t axis, bool is_log)
{
// Create reference
- SimpleTensor<T> src{ shape, data_type, 1 };
+ SimpleTensor<T> src{shape, data_type, 1};
// Fill reference
fill(src);
@@ -139,16 +140,14 @@
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class DynamicFusionSoftmaxValidationFixture : public DynamicFusionSoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+class DynamicFusionSoftmaxValidationFixture
+ : public DynamicFusionSoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
{
public:
void setup(TensorShape shape, DataType data_type, float beta, size_t axis, bool is_log)
{
- DynamicFusionSoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
- data_type,
- beta,
- axis,
- is_log);
+ DynamicFusionSoftmaxValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
+ shape, data_type, beta, axis, is_log);
}
};
@@ -156,4 +155,4 @@
} // namespace test
} // namespace arm_compute
-#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_SOFTMAXFIXTURE */
+#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_SOFTMAXFIXTURE_H