COMPMID-2245: Extend NEFuseBatchNormalization to support DepthwiseConvolution weights
Change-Id: I2ee4aebfd69865290ed6c78dd17ff1299353317e
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1371
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/src/core/NEON/kernels/NEFuseBatchNormalizationKernel.cpp b/src/core/NEON/kernels/NEFuseBatchNormalizationKernel.cpp
index d45e3ce..836e429 100644
--- a/src/core/NEON/kernels/NEFuseBatchNormalizationKernel.cpp
+++ b/src/core/NEON/kernels/NEFuseBatchNormalizationKernel.cpp
@@ -26,74 +26,86 @@
#include "arm_compute/core/CPP/Validate.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
-
#include "support/ToolchainSupport.h"
-#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "utils/TypePrinter.h"
+#include <map>
+
namespace arm_compute
{
namespace
{
-Status validate_arguments(const ITensorInfo *conv_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var,
+Status validate_arguments(const ITensorInfo *input_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var,
const ITensorInfo *fused_weights, const ITensorInfo *fused_bias,
- const ITensorInfo *conv_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma,
- float epsilon)
+ const ITensorInfo *input_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma,
+ float epsilon, FuseBatchNormalizationType fbn_type)
{
ARM_COMPUTE_UNUSED(epsilon);
- ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(conv_weights);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(conv_weights, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input_weights, bn_mean, bn_var);
+ ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input_weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_weights, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_var);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, bn_mean, bn_var);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_mean, bn_var);
+ ARM_COMPUTE_RETURN_ERROR_ON(input_bias == nullptr && fused_bias == nullptr);
+ ARM_COMPUTE_RETURN_ERROR_ON(bn_mean->num_dimensions() > 1);
- unsigned int kernels_idx = get_data_layout_dimension_index(conv_weights->data_layout(), DataLayoutDimension::BATCHES);
- ARM_COMPUTE_RETURN_ERROR_ON(conv_weights->dimension(kernels_idx) != bn_mean->dimension(0));
-
- // Validate bias
- if(conv_bias != nullptr)
+ if(fbn_type == FuseBatchNormalizationType::CONVOLUTION)
{
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, conv_bias);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, conv_bias);
+ ARM_COMPUTE_RETURN_ERROR_ON(input_weights->dimension(3) != bn_mean->dimension(0));
+ }
+ else
+ {
+ const size_t channel_idx = get_data_layout_dimension_index(input_weights->data_layout(), DataLayoutDimension::CHANNEL);
+ ARM_COMPUTE_RETURN_ERROR_ON(input_weights->dimension(channel_idx) != bn_mean->dimension(0));
+ }
+ // Validate bias
+ if(input_bias != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, input_bias);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, input_bias);
}
// Validate beta
if(bn_beta != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_beta);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, bn_beta);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_beta);
}
// Validate gamma
if(bn_gamma != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_gamma);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, bn_gamma);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_gamma);
}
// Validate output weights
if(fused_weights != nullptr && fused_weights->total_size() != 0)
{
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(conv_weights, fused_weights);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(conv_weights, fused_weights);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, fused_weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_weights, fused_weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input_weights, fused_weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, fused_weights);
}
// Validate output bias
if(fused_bias != nullptr && fused_bias->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, fused_bias);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, fused_bias);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, fused_bias);
}
return Status{};
}
-template <typename ScalarType, int size>
-void fused_batch_normmalization(const ITensor *conv_weights, const ITensor *conv_bias, ITensor *fused_weights, ITensor *fused_bias,
- const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window)
+template <typename VectorType>
+void fused_batch_normalization_conv(const ITensor *conv_weights, const ITensor *conv_bias, ITensor *fused_weights, ITensor *fused_bias,
+ const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window)
{
- using ExactTagType = typename wrapper::traits::neon_vector<ScalarType, size>::tag_type;
+ using ScalarType = typename VectorType::scalar_type;
+ const int size = 16 / conv_weights->info()->element_size();
+ using ExactTagType = typename VectorType::tag_type;
const bool run_in_place_weights = (fused_weights == nullptr) || (fused_weights == conv_weights);
const bool run_in_place_bias = (fused_bias == nullptr) || (conv_bias != nullptr && fused_bias == conv_bias);
@@ -112,8 +124,6 @@
const auto conv_bias_in = (conv_bias != nullptr ? reinterpret_cast<ScalarType *>(conv_bias->ptr_to_element(Coordinates(0, 0))) : nullptr);
auto conv_bias_out = (run_in_place_bias ? conv_bias_in : reinterpret_cast<ScalarType *>(fused_bias->ptr_to_element(Coordinates(0, 0))));
- int slice = -1;
-
const auto input_mean = reinterpret_cast<const ScalarType *>(bn_mean->ptr_to_element(Coordinates(0, 0)));
const auto input_var = reinterpret_cast<const ScalarType *>(bn_var->ptr_to_element(Coordinates(0, 0)));
const auto input_gamma = (bn_gamma != nullptr) ? reinterpret_cast<const ScalarType *>(bn_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr;
@@ -133,45 +143,38 @@
auto conv_bias_in_scalar = ScalarType(0.0);
execute_window_loop(win, [&](const Coordinates & id)
{
- if(slice != id[3])
+ var = input_var[id[3]];
+ if(input_gamma != nullptr)
{
- slice = id[3];
- mean = input_mean[slice];
- var = input_var[slice];
- gamma = ScalarType(1.0);
- beta = ScalarType(0.0);
+ gamma = input_gamma[id[3]];
+ }
- // Construct vectors
- mean_vec = wrapper::vdup_n(mean, ExactTagType{});
- var_vec = wrapper::vdup_n(var, ExactTagType{});
- if(input_gamma != nullptr)
- {
- gamma = input_gamma[slice];
- gamma_vec = wrapper::vdup_n(gamma, ExactTagType{});
- }
+ if((id[0] == 0) && (id[1] == 0) && (id[2] == 0))
+ {
if(input_beta != nullptr)
{
- beta = input_beta[slice];
+ beta = input_beta[id[3]];
beta_vec = wrapper::vdup_n(beta, ExactTagType{});
}
+
+ // Construct vectors
+ mean = input_mean[id[3]];
+ mean_vec = wrapper::vdup_n(mean, ExactTagType{});
+
if(conv_bias_in != nullptr)
{
- conv_bias_in_scalar = conv_bias_in[slice];
+ conv_bias_in_scalar = conv_bias_in[id[3]];
}
- else
- {
- conv_bias_in_scalar = ScalarType(0);
- }
-
- conv_bias_in_scalar = (conv_bias_in_scalar - mean) / sqrt(var + ScalarType(epsilon));
- conv_bias_in_scalar = (conv_bias_in_scalar * gamma) + beta;
- conv_bias_out[slice] = conv_bias_in_scalar;
- rvar_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec));
+ auto conv_bias_tmp_scalar = (conv_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon));
+ conv_bias_out[id[3]] = (conv_bias_tmp_scalar * gamma) + beta;
}
int x = window_start_x;
auto conv_w_in_ptr = reinterpret_cast<const ScalarType *>(conv_w_in.ptr());
auto conv_w_out_ptr = reinterpret_cast<ScalarType *>(conv_w_out.ptr());
+ var_vec = wrapper::vdup_n(var, ExactTagType{});
+ gamma_vec = wrapper::vdup_n(gamma, ExactTagType{});
+ rvar_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec));
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
@@ -186,28 +189,245 @@
// Compute left-over elements
for(; x < window_end_x; ++x)
{
- *(conv_w_out_ptr + x) = *(conv_w_in_ptr + x) / sqrt(var + ScalarType(epsilon)) * gamma;
+ *(conv_w_out_ptr + x) = *(conv_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma;
}
},
conv_w_in, conv_w_out);
}
+
+template <typename VectorType>
+void fused_batch_normalization_dwc_nhwc(const ITensor *dwc_weights, const ITensor *dwc_bias, ITensor *fused_weights, ITensor *fused_bias,
+ const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window)
+{
+ using ScalarType = typename VectorType::scalar_type;
+ const int size = 16 / dwc_weights->info()->element_size();
+ using ExactTagType = typename VectorType::tag_type;
+
+ const bool run_in_place_weights = (fused_weights == nullptr) || (fused_weights == dwc_weights);
+ const bool run_in_place_bias = (fused_bias == nullptr) || (dwc_bias != nullptr && fused_bias == dwc_bias);
+
+ // Set build options
+ Window win = window;
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ const int window_step_x = size;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ Iterator dwc_w_in(dwc_weights, win);
+ Iterator dwc_w_out(run_in_place_weights ? dwc_weights : fused_weights, win);
+
+ const auto dwc_bias_in = (dwc_bias != nullptr ? reinterpret_cast<ScalarType *>(dwc_bias->ptr_to_element(Coordinates(0, 0))) : nullptr);
+ auto dwc_bias_out = (run_in_place_bias ? dwc_bias_in : reinterpret_cast<ScalarType *>(fused_bias->ptr_to_element(Coordinates(0, 0))));
+
+ const auto input_mean = reinterpret_cast<const ScalarType *>(bn_mean->ptr_to_element(Coordinates(0, 0)));
+ const auto input_var = reinterpret_cast<const ScalarType *>(bn_var->ptr_to_element(Coordinates(0, 0)));
+ const auto input_gamma = (bn_gamma != nullptr) ? reinterpret_cast<const ScalarType *>(bn_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr;
+ const auto input_beta = (bn_beta != nullptr) ? reinterpret_cast<const ScalarType *>(bn_beta->ptr_to_element(Coordinates(0, 0))) : nullptr;
+
+ auto mean_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+ auto var_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+ auto gamma_vec = wrapper::vdup_n(ScalarType(1), ExactTagType{});
+ auto beta_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+ auto rvar_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+ auto dwc_bias_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+ const auto epsilon_vec = wrapper::vdup_n(ScalarType(epsilon), ExactTagType{});
+
+ auto gamma = ScalarType(1.0);
+ auto beta = ScalarType(0.0);
+ auto dwc_bias_in_scalar = ScalarType(0);
+
+ execute_window_loop(win, [&](const Coordinates & id)
+ {
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ var_vec = wrapper::vloadq(input_var + x);
+ if(input_gamma != nullptr)
+ {
+ gamma_vec = wrapper::vloadq(input_gamma + x);
+ }
+
+ if((id[2] == 0) && (id[1] == 0))
+ {
+ mean_vec = wrapper::vloadq(input_mean + x);
+
+ // Construct vectors
+ if(input_beta != nullptr)
+ {
+ beta_vec = wrapper::vloadq(input_beta + x);
+ }
+
+ if(dwc_bias_in != nullptr)
+ {
+ dwc_bias_vec = wrapper::vloadq(dwc_bias_in + x);
+ }
+
+ auto dwc_bias_tmp_vec = wrapper::vmul(wrapper::vsub(dwc_bias_vec, mean_vec), wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec)));
+ dwc_bias_tmp_vec = wrapper::vadd(wrapper::vmul(dwc_bias_tmp_vec, gamma_vec), beta_vec);
+ wrapper::vstore(dwc_bias_out + x, dwc_bias_tmp_vec);
+ }
+
+ auto dwc_w_in_ptr = reinterpret_cast<const ScalarType *>(dwc_w_in.ptr());
+ auto dwc_w_out_ptr = reinterpret_cast<ScalarType *>(dwc_w_out.ptr());
+
+ auto wn = wrapper::vloadq(dwc_w_in_ptr + x);
+ rvar_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec));
+ wn = wrapper::vmul(wn, rvar_vec);
+ wn = wrapper::vmul(wn, gamma_vec);
+
+ // Store results
+ wrapper::vstore(dwc_w_out_ptr + x, wn);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ auto var = input_var[x];
+ if(input_gamma != nullptr)
+ {
+ gamma = input_gamma[x];
+ }
+
+ if(id[2] == 0 && id[1] == 0)
+ {
+ auto mean = input_mean[x];
+ if(input_beta != nullptr)
+ {
+ beta = input_beta[x];
+ }
+ if(dwc_bias_in != nullptr)
+ {
+ dwc_bias_in_scalar = dwc_bias_in[x];
+ }
+
+ auto dwc_bias_tmp_scalar = (dwc_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon));
+ dwc_bias_out[x] = (dwc_bias_tmp_scalar * gamma) + beta;
+ }
+
+ const auto dwc_w_in_ptr = reinterpret_cast<const ScalarType *>(dwc_w_in.ptr());
+ auto dwc_w_out_ptr = reinterpret_cast<ScalarType *>(dwc_w_out.ptr());
+
+ *(dwc_w_out_ptr + x) = *(dwc_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma;
+ }
+ },
+ dwc_w_in, dwc_w_out);
+}
+
+template <typename VectorType>
+void fused_batch_normalization_dwc_nchw(const ITensor *dwc_weights, const ITensor *dwc_bias, ITensor *fused_weights, ITensor *fused_bias,
+ const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window)
+{
+ using ScalarType = typename VectorType::scalar_type;
+ const int size = 16 / dwc_weights->info()->element_size();
+ using ExactTagType = typename VectorType::tag_type;
+
+ const bool run_in_place_weights = (fused_weights == nullptr) || (fused_weights == dwc_weights);
+ const bool run_in_place_bias = (fused_bias == nullptr) || (dwc_bias != nullptr && fused_bias == dwc_bias);
+
+ // Set build options
+ Window win = window;
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ const int window_step_x = size;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ Iterator dwc_w_in(dwc_weights, win);
+ Iterator dwc_w_out(run_in_place_weights ? dwc_weights : fused_weights, win);
+
+ const auto dwc_bias_in = (dwc_bias != nullptr ? reinterpret_cast<ScalarType *>(dwc_bias->ptr_to_element(Coordinates(0, 0))) : nullptr);
+ auto dwc_bias_out = (run_in_place_bias ? dwc_bias_in : reinterpret_cast<ScalarType *>(fused_bias->ptr_to_element(Coordinates(0, 0))));
+
+ const auto input_mean = reinterpret_cast<const ScalarType *>(bn_mean->ptr_to_element(Coordinates(0, 0)));
+ const auto input_var = reinterpret_cast<const ScalarType *>(bn_var->ptr_to_element(Coordinates(0, 0)));
+ const auto input_gamma = (bn_gamma != nullptr) ? reinterpret_cast<const ScalarType *>(bn_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr;
+ const auto input_beta = (bn_beta != nullptr) ? reinterpret_cast<const ScalarType *>(bn_beta->ptr_to_element(Coordinates(0, 0))) : nullptr;
+
+ auto mean_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+ auto var_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+ auto gamma_vec = wrapper::vdup_n(ScalarType(1), ExactTagType{});
+ auto beta_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+ auto rvar_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+ const auto epsilon_vec = wrapper::vdup_n(ScalarType(epsilon), ExactTagType{});
+
+ auto mean = ScalarType(0.0);
+ auto var = ScalarType(0.0);
+ auto gamma = ScalarType(1.0);
+ auto beta = ScalarType(0.0);
+ auto dwc_bias_in_scalar = ScalarType(0.0);
+ execute_window_loop(win, [&](const Coordinates & id)
+ {
+ var = input_var[id[2]];
+ if(input_gamma != nullptr)
+ {
+ gamma = input_gamma[id[2]];
+ }
+
+ if(id[1] == 0)
+ {
+ mean = input_mean[id[2]];
+
+ // Construct vectors
+ mean_vec = wrapper::vdup_n(mean, ExactTagType{});
+ if(input_beta != nullptr)
+ {
+ beta = input_beta[id[2]];
+ beta_vec = wrapper::vdup_n(beta, ExactTagType{});
+ }
+
+ if(dwc_bias_in != nullptr)
+ {
+ dwc_bias_in_scalar = dwc_bias_in[id[2]];
+ }
+
+ auto dwc_bias_tmp_scalar = (dwc_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon));
+ dwc_bias_out[id[2]] = (dwc_bias_tmp_scalar * gamma) + beta;
+ }
+
+ int x = window_start_x;
+ auto dwc_w_in_ptr = reinterpret_cast<const ScalarType *>(dwc_w_in.ptr());
+ auto dwc_w_out_ptr = reinterpret_cast<ScalarType *>(dwc_w_out.ptr());
+ var_vec = wrapper::vdup_n(var, ExactTagType{});
+ gamma_vec = wrapper::vdup_n(gamma, ExactTagType{});
+ rvar_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec));
+
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ auto wn = wrapper::vloadq(dwc_w_in_ptr + x);
+ wn = wrapper::vmul(wn, rvar_vec);
+ wn = wrapper::vmul(wn, gamma_vec);
+
+ // Store results
+ wrapper::vstore(dwc_w_out_ptr + x, wn);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ *(dwc_w_out_ptr + x) = *(dwc_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma;
+ }
+ },
+ dwc_w_in, dwc_w_out);
+}
+
} // namespace
NEFuseBatchNormalizationKernel::NEFuseBatchNormalizationKernel()
- : _conv_weights(nullptr), _conv_bias(nullptr), _bn_mean(nullptr), _bn_var(nullptr), _bn_gamma(nullptr), _bn_beta(nullptr), _fused_weights(nullptr), _fused_bias(nullptr), _epsilon(),
+ : _input_weights(nullptr), _input_bias(nullptr), _bn_mean(nullptr), _bn_var(nullptr), _bn_gamma(nullptr), _bn_beta(nullptr), _fused_weights(nullptr), _fused_bias(nullptr), _epsilon(),
_run_in_place_weights(false), _run_in_place_bias(false), _func(nullptr)
{
}
-void NEFuseBatchNormalizationKernel::configure(const ITensor *conv_weights, const ITensor *bn_mean, const ITensor *bn_var,
+void NEFuseBatchNormalizationKernel::configure(const ITensor *input_weights, const ITensor *bn_mean, const ITensor *bn_var,
ITensor *fused_weights, ITensor *fused_bias,
- const ITensor *conv_bias, const ITensor *bn_beta, const ITensor *bn_gamma,
- float epsilon)
+ const ITensor *input_bias, const ITensor *bn_beta, const ITensor *bn_gamma,
+ float epsilon, FuseBatchNormalizationType fbn_type)
{
- ARM_COMPUTE_ERROR_ON_NULLPTR(conv_weights, bn_mean, bn_var);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input_weights, bn_mean, bn_var);
- _conv_weights = conv_weights;
- _conv_bias = conv_bias;
+ _input_weights = input_weights;
+ _input_bias = input_bias;
_bn_mean = bn_mean;
_bn_var = bn_var;
_bn_beta = bn_beta;
@@ -216,15 +436,15 @@
_fused_bias = fused_bias;
_epsilon = epsilon;
- _run_in_place_weights = (fused_weights == nullptr) || (fused_weights == conv_weights);
- _run_in_place_bias = (fused_bias == nullptr) || (conv_bias != nullptr && fused_bias == conv_bias);
+ _run_in_place_weights = (fused_weights == nullptr) || (fused_weights == input_weights);
+ _run_in_place_bias = (fused_bias == nullptr) || (input_bias != nullptr && fused_bias == input_bias);
// Auto initialize outputs
if(_fused_weights != nullptr)
{
// Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*_fused_weights->info(), *_conv_weights->info()->clone());
- fused_weights->info()->set_valid_region(conv_weights->info()->valid_region());
+ auto_init_if_empty(*_fused_weights->info(), *_input_weights->info()->clone());
+ fused_weights->info()->set_valid_region(input_weights->info()->valid_region());
}
if(_fused_bias != nullptr)
{
@@ -234,42 +454,53 @@
}
// Validate arguments
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(conv_weights->info(), bn_mean->info(), bn_var->info(),
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input_weights->info(), bn_mean->info(), bn_var->info(),
(fused_weights != nullptr) ? fused_weights->info() : nullptr,
(fused_bias != nullptr) ? fused_bias->info() : nullptr,
- (conv_bias != nullptr) ? conv_bias->info() : nullptr,
+ (input_bias != nullptr) ? input_bias->info() : nullptr,
(bn_beta != nullptr) ? bn_beta->info() : nullptr,
(bn_gamma != nullptr) ? bn_gamma->info() : nullptr,
- epsilon));
+ epsilon, fbn_type));
// Configure kernel window
- Window win = calculate_max_window(*conv_weights->info());
+ Window win = calculate_max_window(*input_weights->info());
INEKernel::configure(win);
- // Configure function to run based on different data types
- const DataType data_type = _conv_weights->info()->data_type();
- switch(data_type)
+ // Configure function
+ static std::map<std::string, FuseBatchNormFunction *> map_function =
{
- case DataType::F32:
- _func = &fused_batch_normmalization<float, 4>;
- break;
+ { "fused_batch_normalization_conv_NHWC_F32", &fused_batch_normalization_conv<wrapper::traits::neon_vector<float, 4>> },
+ { "fused_batch_normalization_conv_NCHW_F32", &fused_batch_normalization_conv<wrapper::traits::neon_vector<float, 4>> },
+ { "fused_batch_normalization_dwc_NHWC_F32", &fused_batch_normalization_dwc_nhwc<wrapper::traits::neon_vector<float, 4>> },
+ { "fused_batch_normalization_dwc_NCHW_F32", &fused_batch_normalization_dwc_nchw<wrapper::traits::neon_vector<float, 4>> },
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- case DataType::F16:
- _func = &fused_batch_normmalization<float16_t, 8>;
- break;
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- default:
- ARM_COMPUTE_ERROR("Not Supported");
- break;
+ { "fused_batch_normalization_conv_NHWC_F16", &fused_batch_normalization_conv<wrapper::traits::neon_vector<float16_t, 8>> },
+ { "fused_batch_normalization_conv_NCHW_F16", &fused_batch_normalization_conv<wrapper::traits::neon_vector<float16_t, 8>> },
+ { "fused_batch_normalization_dwc_NHWC_F16", &fused_batch_normalization_dwc_nhwc<wrapper::traits::neon_vector<float16_t, 8>> },
+ { "fused_batch_normalization_dwc_NCHW_F16", &fused_batch_normalization_dwc_nchw<wrapper::traits::neon_vector<float16_t, 8>> },
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+ };
+
+ std::string function_to_call("fused_batch_normalization_");
+ function_to_call += fbn_type == FuseBatchNormalizationType::CONVOLUTION ? "conv_" : "dwc_";
+ function_to_call += string_from_data_layout(_input_weights->info()->data_layout());
+ function_to_call += "_";
+ function_to_call += string_from_data_type(_input_weights->info()->data_type());
+
+ auto it = map_function.find(function_to_call);
+
+ if(it != map_function.end())
+ {
+ _func = it->second;
}
}
-Status NEFuseBatchNormalizationKernel::validate(const ITensorInfo *conv_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var,
+Status NEFuseBatchNormalizationKernel::validate(const ITensorInfo *input_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var,
const ITensorInfo *fused_weights, const ITensorInfo *fused_bias,
- const ITensorInfo *conv_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma,
- float epsilon)
+ const ITensorInfo *input_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma,
+ float epsilon, FuseBatchNormalizationType fbn_type)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(conv_weights, bn_mean, bn_var, fused_weights, fused_bias, conv_bias, bn_beta, bn_gamma, epsilon));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input_weights, bn_mean, bn_var, fused_weights, fused_bias, input_bias, bn_beta, bn_gamma, epsilon, fbn_type));
return Status{};
}
@@ -278,6 +509,6 @@
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
- (*_func)(_conv_weights, _conv_bias, _fused_weights, _fused_bias, _bn_mean, _bn_var, _bn_beta, _bn_gamma, _epsilon, window);
+ (*_func)(_input_weights, _input_bias, _fused_weights, _fused_bias, _bn_mean, _bn_var, _bn_beta, _bn_gamma, _epsilon, window);
}
} // namespace arm_compute