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-rw-r--r--arm_compute/core/Types.h12
-rw-r--r--arm_compute/core/utils/misc/ShapeCalculator.h7
-rw-r--r--arm_compute/core/utils/quantization/AsymmHelpers.h4
-rw-r--r--src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp4
-rw-r--r--src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp4
-rw-r--r--src/core/CL/kernels/CLIm2ColKernel.cpp2
-rw-r--r--src/core/utils/quantization/AsymmHelpers.cpp21
-rw-r--r--src/runtime/CL/functions/CLFullyConnectedLayer.cpp2
-rw-r--r--src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp38
-rw-r--r--src/runtime/NEON/functions/NEGEMM.cpp6
-rw-r--r--src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp8
-rw-r--r--src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp2
-rw-r--r--tests/datasets/SmallConvolutionLayerDataset.h25
-rw-r--r--tests/validation/CL/ConvolutionLayer.cpp11
-rw-r--r--tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp6
-rw-r--r--tests/validation/fixtures/GEMMFixture.h4
-rw-r--r--tests/validation/fixtures/GEMMLowpFixture.h2
17 files changed, 104 insertions, 54 deletions
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h
index cb1a212797..0240916da8 100644
--- a/arm_compute/core/Types.h
+++ b/arm_compute/core/Types.h
@@ -1192,7 +1192,7 @@ class GEMMReshapeInfo final
public:
/** Default constructor */
GEMMReshapeInfo()
- : _m(1), _n(1), _k(1), _mult_transpose1xW_width(1), _mult_interleave4x4_height(1), _depth_output_gemm3d(1), _reinterpret_input_as_3d(false)
+ : _m(1), _n(1), _k(1), _mult_transpose1xW_width(1), _mult_interleave4x4_height(1), _depth_output_gemm3d(0), _reinterpret_input_as_3d(false)
{
}
/** Constructor
@@ -1202,11 +1202,12 @@ public:
* @param[in] k Number of matrix A columns or matrix B rows
* @param[in] mult_transpose1xW_width (Optional) Multiplication factor for the width of the 1xW transposed block
* @param[in] mult_interleave4x4_height (Optional) Multiplication factor for the height of the 4x4 interleaved block
- * @param[in] depth_output_gemm3d (Optional) Depth (third dimension) of the output tensor to be used with the GEMM3D kernel
+ * @param[in] depth_output_gemm3d (Optional) Depth (third dimension) of the output tensor to be used with the GEMM3D kernel.
+ * If 0 the output will not be reinterpreted as 3D. Default 0
* @param[in] reinterpret_input_as_3d (Optional) Reinterpret the input as 3D tensor. (i.e. this flag should be set to true when GEMM is used
* to perform 1x1 convolutions with the NHWC data layout)
*/
- GEMMReshapeInfo(int m, int n, int k, int mult_transpose1xW_width = 1, int mult_interleave4x4_height = 1, int depth_output_gemm3d = 1, bool reinterpret_input_as_3d = false)
+ GEMMReshapeInfo(int m, int n, int k, int mult_transpose1xW_width = 1, int mult_interleave4x4_height = 1, int depth_output_gemm3d = 0, bool reinterpret_input_as_3d = false)
: _m(m), _n(n), _k(k), _mult_transpose1xW_width(mult_transpose1xW_width), _mult_interleave4x4_height(mult_interleave4x4_height), _depth_output_gemm3d(depth_output_gemm3d),
_reinterpret_input_as_3d(reinterpret_input_as_3d)
{
@@ -1311,7 +1312,7 @@ class GEMMInfo
public:
/** Default constructor */
GEMMInfo()
- : _is_a_reshaped(false), _is_b_reshaped(false), _reshape_b_only_on_first_run(false), _depth_output_gemm3d(1), _reinterpret_input_as_3d(false), _retain_internal_weights(false), _gemmlowp_output_stage()
+ : _is_a_reshaped(false), _is_b_reshaped(false), _reshape_b_only_on_first_run(false), _depth_output_gemm3d(0), _reinterpret_input_as_3d(false), _retain_internal_weights(false), _gemmlowp_output_stage()
{
}
/** Constructor
@@ -1320,13 +1321,14 @@ public:
* @param[in] is_b_reshaped True if the matrix B has been reshaped
* @param[in] reshape_b_only_on_first_run Reshape matrix B only for the first run
* @param[in] depth_output_gemm3d (Optional) Depth (third dimension) of the output tensor to be used with the GEMM3D kernel
+ * If 0 the output will not be reinterpreted as 3D. Default 0
* @param[in] reinterpret_input_as_3d (Optional) Reinterpret the input as 3D tensor. (i.e. this flag should be set to true when GEMM is used
* to perform 1x1 convolutions with the NHWC data layout)
* @param[in] retain_internal_weights (Optional) Retain the weights tensor from previous run
* @param[in] gemmlowp_output_stage (Optional) GEMMLowp Output stage info
*
*/
- GEMMInfo(bool is_a_reshaped, bool is_b_reshaped, bool reshape_b_only_on_first_run, int depth_output_gemm3d = 1, bool reinterpret_input_as_3d = false, bool retain_internal_weights = false,
+ GEMMInfo(bool is_a_reshaped, bool is_b_reshaped, bool reshape_b_only_on_first_run, int depth_output_gemm3d = 0, bool reinterpret_input_as_3d = false, bool retain_internal_weights = false,
GEMMLowpOutputStageInfo gemmlowp_output_stage = GEMMLowpOutputStageInfo())
: _is_a_reshaped(is_a_reshaped), _is_b_reshaped(is_b_reshaped), _reshape_b_only_on_first_run(reshape_b_only_on_first_run), _depth_output_gemm3d(depth_output_gemm3d),
_reinterpret_input_as_3d(reinterpret_input_as_3d), _retain_internal_weights(retain_internal_weights), _gemmlowp_output_stage(gemmlowp_output_stage)
diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h
index 56f65d0ba8..1f532ca31e 100644
--- a/arm_compute/core/utils/misc/ShapeCalculator.h
+++ b/arm_compute/core/utils/misc/ShapeCalculator.h
@@ -532,13 +532,14 @@ inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo
ARM_COMPUTE_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The first input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true");
const bool reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d();
- const bool reinterpret_output_as_3d = reshape_info.depth_output_gemm3d() != 1;
+ const bool reinterpret_output_as_3d = reshape_info.depth_output_gemm3d() != 0;
+ const int depth_output_gemm3d = reinterpret_output_as_3d ? reshape_info.depth_output_gemm3d() : 1;
const int m = reshape_info.reinterpret_input_as_3d() ? input0.dimension(1) * input0.dimension(2) : input0.dimension(1);
// If the output of GEMM has to be reinterpreted as 3D, the number of input0 rows (M) is obtained collapsing the second and third
// dimension of the output tensor
const int dim0 = is_interleaved_transposed ? reshape_info.n() : input1.dimension(0);
- const int dim1 = is_interleaved_transposed ? reshape_info.m() / reshape_info.depth_output_gemm3d() : m / reshape_info.depth_output_gemm3d();
+ const int dim1 = is_interleaved_transposed ? reshape_info.m() / depth_output_gemm3d : m / depth_output_gemm3d;
const int dim2 = reinterpret_input_as_3d ? input0.tensor_shape()[3] : input0.tensor_shape()[2];
const int dim3 = reinterpret_input_as_3d ? 1 : input0.tensor_shape()[3];
@@ -546,7 +547,7 @@ inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo
output_shape.set(0, dim0);
output_shape.set(1, dim1);
- output_shape.set(2, reinterpret_output_as_3d ? reshape_info.depth_output_gemm3d() : dim2);
+ output_shape.set(2, reinterpret_output_as_3d ? depth_output_gemm3d : dim2);
output_shape.set(3, reinterpret_output_as_3d ? dim2 : dim3);
output_shape.set(4, reinterpret_output_as_3d ? dim3 : 1);
diff --git a/arm_compute/core/utils/quantization/AsymmHelpers.h b/arm_compute/core/utils/quantization/AsymmHelpers.h
index 6fd1d80010..d9f20cee2b 100644
--- a/arm_compute/core/utils/quantization/AsymmHelpers.h
+++ b/arm_compute/core/utils/quantization/AsymmHelpers.h
@@ -38,7 +38,7 @@ namespace quantization
*
* @return a status
*/
-arm_compute::Status calculate_quantized_multiplier_less_than_one(double multiplier, int *quant_multiplier, int *right_shift);
+arm_compute::Status calculate_quantized_multiplier_less_than_one(float multiplier, int *quant_multiplier, int *right_shift);
/** Calculate quantized representation of multiplier having value greater than one.
*
* @param[in] multiplier Real multiplier.
@@ -47,7 +47,7 @@ arm_compute::Status calculate_quantized_multiplier_less_than_one(double multipli
*
* @return a status
*/
-arm_compute::Status calculate_quantized_multiplier_greater_than_one(double multiplier, int *quantized_multiplier, int *left_shift);
+arm_compute::Status calculate_quantized_multiplier_greater_than_one(float multiplier, int *quantized_multiplier, int *left_shift);
} // namespace quantization
} // namespace arm_compute
#endif /* __ARM_COMPUTE_IO_FILE_HANDLER_H__ */
diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
index 73b1d41eb1..b2fb3e0278 100644
--- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
@@ -112,7 +112,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe
unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
bool reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d();
- bool reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 1);
+ bool reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0);
Window win{};
Window win_out{};
@@ -227,7 +227,7 @@ void CLGEMMLowpMatrixMultiplyKernel::configure(const ICLTensor *input0, const IC
_input1 = input1;
_output = output;
_reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d();
- _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 1);
+ _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0);
// In case both input and output have to be reinterpreted as 3D tensors,
// force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
index 715edae606..5e02dda9e3 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
@@ -110,7 +110,7 @@ inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *inpu
unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
bool reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d();
- bool reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 1);
+ bool reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0);
// In case both input and output have to be reinterpreted as 3D tensors,
// force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
@@ -227,7 +227,7 @@ void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTen
_input1 = input1;
_output = output;
_reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d();
- _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 1);
+ _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0);
// In case both input and output have to be reinterpreted as 3D tensors,
// force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp
index 0ba0d0e2f9..54ef23f2a2 100644
--- a/src/core/CL/kernels/CLIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLIm2ColKernel.cpp
@@ -109,7 +109,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
const int yin_end = input->dimension(1);
const int xout_start = 0;
- const int xout_end = input->dimension(0) < num_elems_processed_per_iteration ? ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration) : output->dimension(0);
+ const int xout_end = input->dimension(0) < num_elems_processed_per_iteration ? output->dimension(0) + (num_elems_processed_per_iteration - input->dimension(0)) : output->dimension(0);
const int yout_start = 0;
const int yout_end = output->dimension(1);
diff --git a/src/core/utils/quantization/AsymmHelpers.cpp b/src/core/utils/quantization/AsymmHelpers.cpp
index 8bb6d8e173..ea9ba776a9 100644
--- a/src/core/utils/quantization/AsymmHelpers.cpp
+++ b/src/core/utils/quantization/AsymmHelpers.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -30,21 +30,30 @@
using namespace arm_compute::quantization;
constexpr int64_t fixed_point_one_Q0 = (1ll << 31);
+constexpr float epsilon = 0.00001f;
-arm_compute::Status arm_compute::quantization::calculate_quantized_multiplier_less_than_one(double multiplier,
+arm_compute::Status arm_compute::quantization::calculate_quantized_multiplier_less_than_one(float multiplier,
int *quant_multiplier,
int *right_shift)
{
ARM_COMPUTE_RETURN_ERROR_ON(quant_multiplier == nullptr);
ARM_COMPUTE_RETURN_ERROR_ON(right_shift == nullptr);
- ARM_COMPUTE_RETURN_ERROR_ON(multiplier < 0);
- ARM_COMPUTE_RETURN_ERROR_ON(multiplier >= 1);
- if(multiplier == 0)
+ ARM_COMPUTE_RETURN_ERROR_ON(multiplier < -epsilon);
+ ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f + epsilon);
+ if(std::fabs(1.0f - multiplier) < epsilon)
+ {
+ *quant_multiplier = 1;
+ *right_shift = 0;
+ return arm_compute::Status{};
+ }
+
+ if(std::fabs(0.0f - multiplier) < epsilon)
{
*quant_multiplier = 0;
*right_shift = 0;
return arm_compute::Status{};
}
+
const double q = std::frexp(multiplier, right_shift);
*right_shift *= -1;
auto q_fixed = static_cast<int64_t>(round(q * fixed_point_one_Q0));
@@ -61,7 +70,7 @@ arm_compute::Status arm_compute::quantization::calculate_quantized_multiplier_le
return arm_compute::Status{};
}
-arm_compute::Status arm_compute::quantization::calculate_quantized_multiplier_greater_than_one(double multiplier,
+arm_compute::Status arm_compute::quantization::calculate_quantized_multiplier_greater_than_one(float multiplier,
int *quantized_multiplier,
int *left_shift)
{
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index c5637dba26..6a2aac6457 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -101,7 +101,7 @@ void CLFullyConnectedLayer::configure_mm(const ICLTensor *input, const ICLTensor
else
{
// Configure matrix multiply kernel
- _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */, 1, false, retain_internal_weights));
+ _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */, 0, false, retain_internal_weights));
}
}
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index 67f55d56e2..4825d878f8 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -276,14 +276,16 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
{
const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
- float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
- int output_multiplier, output_shift;
+ const float multiplier = (input->info()->quantization_info().scale * weights->info()->quantization_info().scale) / output_quant_info.scale;
+ int output_multiplier = 0;
+ int output_shift = 0;
quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
int min_activation = 0;
int max_activation = 0;
const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
+ ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
};
@@ -308,7 +310,10 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
}
// Configure and tune GEMM
- configure_mm(gemm_input_to_use, &_weights_reshaped, biases, gemm_output_to_use, gemmlowp_output_stage, (data_layout == DataLayout::NHWC) ? conv_h : 1);
+ // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix
+ const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0;
+
+ configure_mm(gemm_input_to_use, &_weights_reshaped, biases, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth);
if(!_skip_im2col)
{
@@ -454,9 +459,11 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
{
const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input->quantization_info() : output->quantization_info();
- float multiplier = input->quantization_info().scale * weights->quantization_info().scale / output_quant_info.scale;
- int output_multiplier, output_shift;
- quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+ const float multiplier = (input->quantization_info().scale * weights->quantization_info().scale) / output_quant_info.scale;
+ int output_multiplier = 0;
+ int output_shift = 0;
+
+ ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift));
int min_activation = 0;
int max_activation = 0;
@@ -485,17 +492,20 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
// If the activation layer is RELU, BOUNDED_RELU or LU_BOUNDED_RELU, we can use the GEMMLowp output stage to perform this operation
is_activationlayer_enabled = false;
-
- // Set the GEMMLowp output stage info
- gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;
- gemmlowp_output_stage.gemmlowp_multiplier = output_multiplier;
- gemmlowp_output_stage.gemmlowp_shift = output_shift;
- gemmlowp_output_stage.gemmlowp_min_bound = min_activation;
- gemmlowp_output_stage.gemmlowp_max_bound = max_activation;
}
+
+ // Set the GEMMLowp output stage info
+ gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset;
+ gemmlowp_output_stage.gemmlowp_multiplier = output_multiplier;
+ gemmlowp_output_stage.gemmlowp_shift = output_shift;
+ gemmlowp_output_stage.gemmlowp_min_bound = min_activation;
+ gemmlowp_output_stage.gemmlowp_max_bound = max_activation;
}
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, gemmlowp_output_stage, skip_col2im ? conv_h : 1, skip_im2col));
+ // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix
+ const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0;
+
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, skip_im2col));
// Validate Col2Im
if(!skip_col2im)
diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp
index 321ecf85d8..e8bf6732b2 100644
--- a/src/runtime/NEON/functions/NEGEMM.cpp
+++ b/src/runtime/NEON/functions/NEGEMM.cpp
@@ -140,7 +140,7 @@ Status NEGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso
if(c != nullptr)
{
- ARM_COMPUTE_RETURN_ERROR_ON(gemm_info.depth_output_gemm3d() != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(gemm_info.depth_output_gemm3d() != 0);
ARM_COMPUTE_RETURN_ERROR_ON(gemm_info.reinterpret_input_as_3d());
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, c);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != c->dimension(1), "The C matrix must have the same number of rows as the matrix A");
@@ -150,7 +150,7 @@ Status NEGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != output->dimension(0));
- if(gemm_info.depth_output_gemm3d() != 1)
+ if(gemm_info.depth_output_gemm3d() != 0)
{
if(gemm_info.reinterpret_input_as_3d())
{
@@ -174,7 +174,7 @@ Status NEGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso
if(!run_optimised)
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.reinterpret_input_as_3d(), "NEGEMM cannot reinterpret the input tensor as 3D");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.depth_output_gemm3d() != 1, "NEGEMM cannot reinterpret the output tensor as 3D");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.depth_output_gemm3d() != 0, "NEGEMM cannot reinterpret the output tensor as 3D");
// Check if the first input tensor is a vector.
const bool run_vector_matrix_multiplication = a->dimension(1) < 2;
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index fc65469488..02ae1715da 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -276,7 +276,9 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
}
// Configure GEMM
- configure_mm(gemm_input_to_use, &_weights_reshaped, gemm_output_to_use, _skip_col2im ? conv_h : 1);
+ // In case we need to skip col2im, GEMM3D (gemm_3d_depth != 0) must be called in order to avoid reshaping the output matrix
+ const unsigned int gemm_3d_depth = _skip_col2im ? conv_h : 0;
+ configure_mm(gemm_input_to_use, &_weights_reshaped, gemm_output_to_use, gemm_3d_depth);
if(!_skip_im2col)
{
@@ -477,7 +479,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
gemm_output_to_use = &info_gemm;
}
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, gemm_output_to_use, skip_col2im ? conv_h : 1, skip_im2col));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, gemm_output_to_use, skip_col2im ? conv_h : 0, skip_im2col));
if(is_quantized)
{
@@ -518,7 +520,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
}
// Validate output stage for quantized case
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, min, max, skip_reshape ? conv_h : 1);
+ NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, min, max, skip_reshape ? conv_h : 0);
}
// Validate Col2Im/ReshapeLayer
diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
index 80f5ab0c93..16ee3d07fd 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
@@ -198,7 +198,7 @@ Status NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso
ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.reinterpret_input_as_3d(), "NEGEMMLowpMatrixMultiplyCore cannot reinterpret the input tensor as 3D");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.depth_output_gemm3d() != 1, "NEGEMMLowpMatrixMultiplyCore cannot reinterpret the output tensor as 3D");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.depth_output_gemm3d() != 0, "NEGEMMLowpMatrixMultiplyCore cannot reinterpret the output tensor as 3D");
int32_t a_offset = a->quantization_info().offset;
int32_t b_offset = b->quantization_info().offset;
diff --git a/tests/datasets/SmallConvolutionLayerDataset.h b/tests/datasets/SmallConvolutionLayerDataset.h
index bbfc760bf3..df9196fb77 100644
--- a/tests/datasets/SmallConvolutionLayerDataset.h
+++ b/tests/datasets/SmallConvolutionLayerDataset.h
@@ -139,6 +139,7 @@ public:
{
add_config(TensorShape(224U, 224U, 3U), TensorShape(3U, 3U, 3U, 32U), TensorShape(32U), TensorShape(112U, 112U, 32U),
PadStrideInfo(2, 2, /*left*/ 0, /*right*/ 1, /*top*/ 0, /*bottom*/ 1, DimensionRoundingType::FLOOR));
+
// Batch size 1
add_config(TensorShape(23U, 27U, 5U), TensorShape(3U, 3U, 5U, 21U), TensorShape(21U), TensorShape(11U, 25U, 21U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 7U), TensorShape(5U, 5U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U), PadStrideInfo(3, 2, 1, 0));
@@ -146,6 +147,7 @@ public:
add_config(TensorShape(23U, 27U, 5U), TensorShape(3U, 1U, 5U, 21U), TensorShape(21U), TensorShape(11U, 27U, 21U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 7U), TensorShape(5U, 7U, 7U, 16U), TensorShape(16U), TensorShape(11U, 11U, 16U), PadStrideInfo(3, 2, 1, 0));
add_config(TensorShape(17U, 31U, 2U), TensorShape(5U, 3U, 2U, 19U), TensorShape(19U), TensorShape(15U, 16U, 19U), PadStrideInfo(1, 2, 1, 1));
+ add_config(TensorShape(3U, 3U, 1U), TensorShape(2U, 2U, 1U, 11U), TensorShape(11U), TensorShape(2U, 2U, 11U), PadStrideInfo(1, 1, 0, 0));
// Batch size 4
add_config(TensorShape(23U, 27U, 5U, 4U), TensorShape(3U, 3U, 5U, 21U), TensorShape(21U), TensorShape(11U, 25U, 21U, 4U), PadStrideInfo(2, 1, 0, 0));
add_config(TensorShape(33U, 27U, 7U, 4U), TensorShape(5U, 5U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 4U), PadStrideInfo(3, 2, 1, 0));
@@ -164,9 +166,26 @@ public:
add_config(TensorShape(33U, 27U, 7U, 5U), TensorShape(5U, 7U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 5U), PadStrideInfo(3, 2, 1, 3, 0, 2, DimensionRoundingType::FLOOR));
add_config(TensorShape(33U, 27U, 7U, 5U), TensorShape(5U, 7U, 7U, 16U), TensorShape(16U), TensorShape(10U, 11U, 16U, 5U), PadStrideInfo(3, 2, 1, 0, 1, 0, DimensionRoundingType::FLOOR));
add_config(TensorShape(33U, 27U, 7U, 5U), TensorShape(5U, 7U, 7U, 16U), TensorShape(16U), TensorShape(10U, 11U, 16U, 5U), PadStrideInfo(3, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR));
- // TODO (micgio01) - COMPMID-1604: investigate issue in GLES and re-enable the following dataset
- // Single output channel
- //add_config(TensorShape(5U, 4U, 3U, 2U), TensorShape(4U, 4U, 3U, 1U), TensorShape(1U), TensorShape(2U, 1U, 1U, 2U), PadStrideInfo(1, 1, 0, 0, 0, 0, DimensionRoundingType::FLOOR));
+
+ add_config(TensorShape(5U, 4U, 3U, 2U), TensorShape(4U, 4U, 3U, 1U), TensorShape(1U), TensorShape(2U, 1U, 1U, 2U), PadStrideInfo(1, 1, 0, 0, 0, 0, DimensionRoundingType::FLOOR));
+ }
+};
+
+// TODO (COMPMID-1749)
+class SmallConvolutionLayerReducedDataset final : public ConvolutionLayerDataset
+{
+public:
+ SmallConvolutionLayerReducedDataset()
+ {
+ // Batch size 1
+ add_config(TensorShape(23U, 27U, 5U), TensorShape(3U, 3U, 5U, 21U), TensorShape(21U), TensorShape(11U, 25U, 21U), PadStrideInfo(2, 1, 0, 0));
+ add_config(TensorShape(33U, 27U, 7U), TensorShape(5U, 5U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U), PadStrideInfo(3, 2, 1, 0));
+ add_config(TensorShape(17U, 31U, 2U), TensorShape(5U, 5U, 2U, 19U), TensorShape(19U), TensorShape(15U, 15U, 19U), PadStrideInfo(1, 2, 1, 1));
+
+ // Asymmetric padding
+ add_config(TensorShape(33U, 27U, 7U, 5U), TensorShape(5U, 7U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 5U), PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR));
+ add_config(TensorShape(33U, 27U, 7U, 5U), TensorShape(5U, 7U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 5U), PadStrideInfo(3, 2, 1, 1, 0, 2, DimensionRoundingType::FLOOR));
+ add_config(TensorShape(33U, 27U, 7U, 5U), TensorShape(5U, 7U, 7U, 16U), TensorShape(16U), TensorShape(11U, 12U, 16U, 5U), PadStrideInfo(3, 2, 2, 1, 2, 0, DimensionRoundingType::FLOOR));
}
};
diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp
index 0274bed977..5f10b4b9bc 100644
--- a/tests/validation/CL/ConvolutionLayer.cpp
+++ b/tests/validation/CL/ConvolutionLayer.cpp
@@ -268,11 +268,18 @@ const auto QuantizedActivationFunctionsDataset = framework::dataset::make("Activ
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
+const auto QuantizationData = framework::dataset::make("QuantizationInfo",
+{
+ QuantizationInfo(0.5f, 10),
+ QuantizationInfo(0.3f, 3),
+ QuantizationInfo(1.f, 10),
+});
+
FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QASYMM8)),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
+ QuantizationData),
QuantizedActivationFunctionsDataset))
{
// Validate output
@@ -282,7 +289,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerQuantizedFixture<uint8_t>
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QASYMM8)),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 0) })),
+ QuantizationData),
QuantizedActivationFunctionsDataset))
{
// Validate output
diff --git a/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp b/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
index 2961dc9519..85a477b293 100644
--- a/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
+++ b/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
@@ -65,7 +65,7 @@ const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo
TEST_SUITE(GC)
TEST_SUITE(ConvolutionLayer)
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()),
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallConvolutionLayerReducedDataset(), datasets::LargeConvolutionLayerDataset()),
CNNDataTypes),
ActivationFunctionsDataset),
input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, act_info)
@@ -114,7 +114,7 @@ using GCConvolutionLayerFixture = ConvolutionValidationFixture<GCTensor, GCAcces
TEST_SUITE(Float)
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerReducedDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::F16)),
@@ -139,7 +139,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge, GCConvolutionLayerFixture<half>, framework::Dat
TEST_SUITE_END()
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerReducedDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("DataLayout",
diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h
index 255b12c0ed..74205e25d4 100644
--- a/tests/validation/fixtures/GEMMFixture.h
+++ b/tests/validation/fixtures/GEMMFixture.h
@@ -85,9 +85,9 @@ protected:
// Create and configure function
FunctionType gemm;
// The GEMMinfo includes the values of the depth in case of reinterpreted 3d output.
- // If the output shape has the same number of dimensions of the input the method called is a 2D matrix multiplication (depth_output_reinterpreted_as_3D = 1),
+ // If the output shape has the same number of dimensions of the input the method called is a 2D matrix multiplication (depth_output_reinterpreted_as_3D = 0),
// in the other case we have to use the reinterpreted version of GEMM (depth_output_reinterpreted_as_3D = depth of the 3D output).
- gemm.configure(&a, &b, &c, &dst, alpha, beta, GEMMInfo(false, false, false, (reinterpret_ouput_as_3d ? output_shape[2] : 1), reinterpret_input_as_3d));
+ gemm.configure(&a, &b, &c, &dst, alpha, beta, GEMMInfo(false, false, false, (reinterpret_ouput_as_3d ? output_shape[2] : 0), reinterpret_input_as_3d));
ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index b61b4eca38..96debe0eec 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -76,7 +76,7 @@ protected:
// The GEMMinfo includes the values of the depth in case of reinterpreted 3d input/output
FunctionType gemmlowp;
// TODO (COMPMID-1672) - Extending the test to validate add bias in offset contribution
- gemmlowp.configure(&a, &b, nullptr, &c, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_c[2] : 1), reinterpret_input_as_3d));
+ gemmlowp.configure(&a, &b, nullptr, &c, GEMMInfo(false, false, false, (reinterpret_output_as_3d ? shape_c[2] : 0), reinterpret_input_as_3d));
ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-10 15:00:51 +0000