COMPMID-1741: Implement NEFuseBatchNormalizationKernel
Change-Id: Ib3ba4b22804a94a1e8ef6d7076e28c2fc1cd2fa2
Reviewed-on: https://review.mlplatform.org/385
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Anthony Barbier <Anthony.barbier@arm.com>
diff --git a/src/core/NEON/kernels/NEFuseBatchNormalizationKernel.cpp b/src/core/NEON/kernels/NEFuseBatchNormalizationKernel.cpp
new file mode 100644
index 0000000..25a0848
--- /dev/null
+++ b/src/core/NEON/kernels/NEFuseBatchNormalizationKernel.cpp
@@ -0,0 +1,286 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INNEUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY NEAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/NEON/kernels/NEFuseBatchNormalizationKernel.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 "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Window.h"
+
+#include "support/ToolchainSupport.h"
+
+#include "arm_compute/core/NEON/wrapper/wrapper.h"
+#include "utils/TypePrinter.h"
+namespace arm_compute
+{
+namespace
+{
+Status validate_arguments(const ITensorInfo *conv_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)
+{
+ ARM_COMPUTE_UNUSED(epsilon);
+ //ARM_COMPUTE_RETURN_ERROR_ON_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_MISMATCHING_SHAPES(bn_mean, bn_var);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, bn_mean, bn_var);
+
+ 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)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, conv_bias);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, conv_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);
+ }
+ // 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);
+ }
+
+ // 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);
+ }
+ // 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);
+ }
+
+ 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)
+{
+ using ExactTagType = typename wrapper::traits::neon_vector<ScalarType, size>::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);
+
+ // 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 conv_w_in(conv_weights, win);
+ Iterator conv_w_out(run_in_place_weights ? conv_weights : fused_weights, win);
+
+ 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;
+ 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 conv_bias_in_scalar = ScalarType(0.0);
+ execute_window_loop(win, [&](const Coordinates & id)
+ {
+ if(slice != id[3])
+ {
+ slice = id[3];
+ mean = input_mean[slice];
+ var = input_var[slice];
+ gamma = ScalarType(1.0);
+ beta = ScalarType(0.0);
+
+ // 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(input_beta != nullptr)
+ {
+ beta = input_beta[slice];
+ beta_vec = wrapper::vdup_n(beta, ExactTagType{});
+ }
+ if(conv_bias_in != nullptr)
+ {
+ conv_bias_in_scalar = conv_bias_in[slice];
+ }
+ 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));
+ }
+
+ 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());
+
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ auto wn = wrapper::vloadq(conv_w_in_ptr + x);
+ wn = wrapper::vmul(wn, rvar_vec);
+ wn = wrapper::vmul(wn, gamma_vec);
+
+ // Store results
+ wrapper::vstore(conv_w_out_ptr + x, wn);
+ }
+
+ // 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_in, conv_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(),
+ _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,
+ ITensor *fused_weights, ITensor *fused_bias,
+ const ITensor *conv_bias, const ITensor *bn_beta, const ITensor *bn_gamma,
+ float epsilon)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(conv_weights, bn_mean, bn_var);
+
+ _conv_weights = conv_weights;
+ _conv_bias = conv_bias;
+ _bn_mean = bn_mean;
+ _bn_var = bn_var;
+ _bn_beta = bn_beta;
+ _bn_gamma = bn_gamma;
+ _fused_weights = fused_weights;
+ _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);
+
+ // 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());
+ }
+ if(_fused_bias != nullptr)
+ {
+ // Output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*_fused_bias->info(), *_bn_mean->info()->clone());
+ _fused_bias->info()->set_valid_region(bn_mean->info()->valid_region());
+ }
+
+ // Validate arguments
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(conv_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,
+ (bn_beta != nullptr) ? bn_beta->info() : nullptr,
+ (bn_gamma != nullptr) ? bn_gamma->info() : nullptr,
+ epsilon));
+
+ // Configure kernel window
+ Window win = calculate_max_window(*conv_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)
+ {
+ case DataType::F32:
+ _func = &fused_batch_normmalization<float, 4>;
+ break;
+#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;
+ }
+}
+
+Status NEFuseBatchNormalizationKernel::validate(const ITensorInfo *conv_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)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(conv_weights, bn_mean, bn_var, fused_weights, fused_bias, conv_bias, bn_beta, bn_gamma, epsilon));
+ return Status{};
+}
+
+void NEFuseBatchNormalizationKernel::run(const Window &window, const ThreadInfo &info)
+{
+ 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);
+}
+} // namespace arm_compute