COMPMID-2398: Add test for CLFuseBatchNormalizationLayer
Change-Id: I786df628ce15fc33fc42c9437fe82972e02e3b16
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1317
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 904575a..b734fd2 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -298,7 +298,7 @@
{ "finalize", "optical_flow_pyramid_lk.cl" },
{ "flatten", "flatten.cl" },
{ "floor_layer", "floor.cl" },
- { "fuse_batchnormalization_layer", "batchnormalization_layer.cl" },
+ { "fuse_batchnormalization_conv_layer", "batchnormalization_layer.cl" },
{ "gather", "gather.cl" },
{ "gaussian1x5_sub_x", "gaussian_pyramid.cl" },
{ "gaussian5x1_sub_y", "gaussian_pyramid.cl" },
diff --git a/src/core/CL/cl_kernels/batchnormalization_layer.cl b/src/core/CL/cl_kernels/batchnormalization_layer.cl
index 66d371c..a532131 100644
--- a/src/core/CL/cl_kernels/batchnormalization_layer.cl
+++ b/src/core/CL/cl_kernels/batchnormalization_layer.cl
@@ -259,161 +259,145 @@
}
#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DATA_TYPE)*/
-#if defined(NUM_CHANNELS) && defined(DATA_TYPE) && defined(EPSILON)
-/** Fuse batchnorm parameters to convolution layer parameters
+#if defined(DIM2) && defined(DATA_TYPE) && defined(EPSILON)
+/** OpenCL kernel to fuse the weights of convolution layer with batch normalization when the data layout is either NCHW or NHWC
*
- * @attention Data type should be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float
- * @attention Input tensor depth should be given as a preprocessor argument using -DNUM_CHANNELS=size. e.g. -DNUM_CHANNELS=16
- * @attention Batch normalization epsilon parameter should be given as a preprocessor argument with -DEPSILON=value. e.g. -DEPSILON=0.001f
+ * @note The input weights tensor is assumed 4D with the OFMs in the fourth dimension
+ * @note Data type should be passed at compile time using the -DDATA_TYPE, e.g. -DDATA_TYPE=float
+ * @note The third dimension of the input tensor should be passed at compile time using -DNUM_CHANNELS=size. e.g. -DNUM_CHANNELS=16
+ * @note Batch normalization epsilon parameter should be passed at compile time using -DEPSILON=value. e.g. -DEPSILON=0.001f
*
- * @param[in] conv_w_ptr Pointer to the source tensor. Supported data types: F16/F32
- * @param[in] conv_w_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] conv_w_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] conv_w_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] conv_w_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] conv_w_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] conv_w_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] conv_w_stride_w Stride of the source tensor in W dimension (in bytes)
- * @param[in] conv_w_step_w input_stride_w * number of elements along W processed per workitem(in bytes)
- * @param[in] conv_w_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in] bn_mean_ptr Pointer to the mean source tensor. Supported data types: same as @p input_ptr
- * @param[in] bn_mean_stride_x Stride of the mean source tensor in X dimension (in bytes)
- * @param[in] bn_mean_step_x bn_mean_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] bn_mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor
- * @param[in] bn_var_ptr Pointer to the var tensor. Supported data types: same as @p input_ptr
- * @param[in] bn_var_stride_x Stride of the var tensor in X dimension (in bytes)
- * @param[in] bn_var_step_x bn_var_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] bn_var_offset_first_element_in_bytes The offset of the first element in the var source tensor
- * @param[out] fused_w_ptr Pointer to the destination weights tensors. Supported data types: same as @p input_ptr
- * @param[in] fused_w_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] fused_w_step_x fused_w_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] fused_w_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] fused_w_step_y fused_w_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] fused_w_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] fused_w_step_z fused_w_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] fused_w_stride_w Stride of the destination tensor in W dimension (in bytes)
- * @param[in] fused_w_step_w fused_w_stride_w * number of elements along W processed per workitem(in bytes)
- * @param[in] fused_w_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in] fused_b_ptr Pointer to the destination bias tensor. Supported data types: same as @p input_ptr
- * @param[in] fused_b_stride_x Stride of the bias source tensor in X dimension (in bytes)
- * @param[in] fused_b_step_x fused_b_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] fused_b_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in] conv_b_ptr Pointer to the source bias tensor. Supported data types: same as @p input_ptr
- * @param[in] conv_b_stride_x Stride of the beta source tensor in X dimension (in bytes)
- * @param[in] conv_b_step_x conv_b_beta_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] conv_b_offset_first_element_in_bytes The offset of the first element in the source bias tensor
- * @param[in] bn_beta_ptr Pointer to the beta source tensor. Supported data types: same as @p input_ptr
- * @param[in] bn_beta_stride_x Stride of the beta source tensor in X dimension (in bytes)
- * @param[in] bn_beta_step_x bn_beta_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] bn_beta_offset_first_element_in_bytes The offset of the first element in the beta source tensor
- * @param[in] bn_gamma_ptr Pointer to the gamma source tensor. Supported data types: same as @p input_ptr
- * @param[in] bn_gamma_stride_x Stride of the gamma source tensor in X dimension (in bytes)
- * @param[in] bn_gamma_step_x bn_gamma_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] bn_gamma_offset_first_element_in_bytes The offset of the first element in the gamma source tensor
- * @param[in] epsilon Epsilon parameter in the batch normalization equation
+ * @param[in] w_ptr Pointer to the weights tensor. Supported data types: F16/F32
+ * @param[in] w_stride_x Stride of the weights tensor in X dimension (in bytes)
+ * @param[in] w_step_x w_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] w_stride_y Stride of the weights tensor in Y dimension (in bytes)
+ * @param[in] w_step_y w_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] w_stride_z Stride of the weights tensor in Z dimension (in bytes)
+ * @param[in] w_step_z w_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] w_offset_first_element_in_bytes The offset of the first element in the weights tensor
+ * @param[in] b_ptr (Optional) Pointer to the bias tensor. Supported data types: same as @p w_ptr
+ * @param[in] b_stride_x (Optional) Stride of the bias tensor in X dimension (in bytes)
+ * @param[in] b_step_x (Optional) b_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] b_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] b_step_y (Optional) b_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] b_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] b_step_z (Optional) b_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] b_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[in] mean_ptr Pointer to the mean source tensor. Supported data types: same as @p w_ptr
+ * @param[in] mean_stride_x Stride of the mean source tensor in X dimension (in bytes)
+ * @param[in] mean_step_x mean_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor
+ * @param[in] var_ptr Pointer to the var tensor. Supported data types: same as @p w_ptr
+ * @param[in] var_stride_x Stride of the var tensor in X dimension (in bytes)
+ * @param[in] var_step_x var_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] var_offset_first_element_in_bytes The offset of the first element in the var source tensor
+ * @param[out] w_fused_ptr (Optional) Pointer to the destination weights tensors. Supported data types: same as @p w_ptr
+ * @param[in] w_fused_stride_x (Optional) Stride of the destination weights tensor in X dimension (in bytes)
+ * @param[in] w_fused_step_x (Optional) w_fused_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] w_fused_stride_y (Optional) Stride of the destination weights tensor in Y dimension (in bytes)
+ * @param[in] w_fused_step_y (Optional) w_fused_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] w_fused_stride_z (Optional) Stride of the destination weights tensor in Z dimension (in bytes)
+ * @param[in] w_fused_step_z (Optional) w_fused_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] w_fused_offset_first_element_in_bytes (Optional) The offset of the first element in the destination weights tensor
+ * @param[in] b_fused_ptr (Optional) Pointer to the destination bias tensor. Supported data types: same as @p w_ptr
+ * @param[in] b_fused_stride_x (Optional) Stride of the destination bias tensor in X dimension (in bytes)
+ * @param[in] b_fused_step_x (Optional) b_fused_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] b_fused_offset_first_element_in_bytes (Optional) The offset of the first element in the destination bias tensor
+ * @param[in] beta_ptr (Optional) Pointer to the beta source tensor. Supported data types: same as @p w_ptr
+ * @param[in] beta_stride_x (Optional) Stride of the beta source tensor in X dimension (in bytes)
+ * @param[in] beta_step_x (Optional) beta_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] beta_offset_first_element_in_bytes (Optional) The offset of the first element in the beta source tensor
+ * @param[in] gamma_ptr (Optional) Pointer to the gamma source tensor. Supported data types: same as @p w_ptr
+ * @param[in] gamma_stride_x (Optional) Stride of the gamma source tensor in X dimension (in bytes)
+ * @param[in] gamma_step_x (Optional) gamma_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] gamma_offset_first_element_in_bytes (Optional) The offset of the first element in the gamma source tensor
*/
-__kernel void fuse_batchnormalization_layer(TENSOR4D_DECLARATION(conv_w),
- VECTOR_DECLARATION(bn_mean),
- VECTOR_DECLARATION(bn_var)
+__kernel void fuse_batchnormalization_conv_layer(TENSOR3D_DECLARATION(w),
+#if defined(BIAS)
+ VECTOR_DECLARATION(b),
+#endif // defined(BIAS)
+ VECTOR_DECLARATION(mean),
+ VECTOR_DECLARATION(var)
#ifndef IN_PLACE_W
- ,
- TENSOR4D_DECLARATION(fused_w)
-#endif /* not IN_PLACE_W */
+ ,
+ TENSOR3D_DECLARATION(w_fused)
+#endif // ifndef IN_PLACE_W
#ifndef IN_PLACE_B
- ,
- VECTOR_DECLARATION(fused_b)
-#endif /* not IN_PLACE_B */
-#ifdef HAS_BIAS
- ,
- VECTOR_DECLARATION(conv_b)
-#endif /* HAS_BIAS */
-#ifndef USE_DEFAULT_BETA
- ,
- VECTOR_DECLARATION(bn_beta)
-#endif /* USE_DEFAULT_BETA */
-#ifndef USE_DEFAULT_GAMMA
- ,
- VECTOR_DECLARATION(bn_gamma)
-#endif /* USE_DEFAULT_GAMMA */
- )
+ ,
+ VECTOR_DECLARATION(b_fused)
+#endif // ifndef IN_PLACE_B
+#if defined(BETA)
+ ,
+ VECTOR_DECLARATION(beta)
+#endif // defined(BETA)
+#if defined(GAMMA)
+ ,
+ VECTOR_DECLARATION(gamma)
+#endif // defined(GAMMA)
+ )
{
- Tensor4D conv_w = CONVERT_TO_TENSOR4D_STRUCT(conv_w, NUM_CHANNELS);
- Vector bn_mean = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_mean);
- Vector bn_var = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_var);
+ int x = get_global_id(0);
+ int y = get_global_id(1);
+ int z = get_global_id(2);
+ int c0 = z % DIM2;
+ int c1 = z / DIM2;
- // Conditional ops
-#ifdef HAS_BIAS
- Vector conv_b = CONVERT_TO_VECTOR_STRUCT_NO_STEP(conv_b);
-#endif /* HAS_BIAS */
-#ifndef USE_DEFAULT_BETA
- Vector bn_beta = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_beta);
-#endif /* USE_DEFAULT_BETA */
-#ifndef USE_DEFAULT_GAMMA
- Vector bn_gamma = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_gamma);
-#endif /* USE_DEFAULT_GAMMA */
+ int w_offset = x * sizeof(DATA_TYPE) + y * w_stride_y + z * w_stride_z;
+ int v_offset = c1 * sizeof(DATA_TYPE);
- // In-place ops
-#ifdef IN_PLACE_W
- Tensor4D fused_w = conv_w;
- uint fused_w_stride_x = conv_w_stride_x;
-#else /* IN_PLACE_W */
- Tensor4D fused_w = CONVERT_TO_TENSOR4D_STRUCT(fused_w, NUM_CHANNELS);
-#endif /* IN_PLACE_W */
-#ifdef IN_PLACE_B
- Vector fused_b = conv_b;
-#else /* IN_PLACE_B */
- Vector fused_b = CONVERT_TO_VECTOR_STRUCT_NO_STEP(fused_b);
-#endif /* IN_PLACE_B */
+ DATA_TYPE w_old = 0.0f;
+ DATA_TYPE b_old = 0.0f;
+ DATA_TYPE w_new = 0.0f;
+ DATA_TYPE b_new = 0.0f;
+ DATA_TYPE gamma = 1.0f;
+ DATA_TYPE mean = 0.0f;
+ DATA_TYPE var = 1.0f;
+ DATA_TYPE beta = 0.0f;
- const int current_slice = get_global_id(2) / NUM_CHANNELS;
+ w_old = *((__global DATA_TYPE *)(w_ptr + w_offset + w_offset_first_element_in_bytes));
+ var = *((__global DATA_TYPE *)(var_ptr + v_offset + var_offset_first_element_in_bytes));
+ mean = *((__global DATA_TYPE *)(mean_ptr + v_offset + mean_offset_first_element_in_bytes));
-#if defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
- // Check if access on width gets out of bounds
- // If it does shift access vector to access elements within bounds
- const int xi = (int)(get_global_id(0) * VEC_SIZE);
- conv_w.ptr -= max(xi - (int)LAST_ACCESSED_X, 0) * conv_w_stride_x;
- fused_w.ptr -= max(xi - (int)LAST_ACCESSED_X, 0) * fused_w_stride_x;
+#if defined(GAMMA)
+ gamma = *((__global DATA_TYPE *)(gamma_ptr + v_offset + gamma_offset_first_element_in_bytes));
+#endif // defined(GAMMA)
- // Load W
- VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
- wn = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)conv_w.ptr);
-#else // !defined(VEC_SIZE) || !defined(LAST_ACCESSED_X)
- DATA_TYPE wn = *((__global DATA_TYPE *)(conv_w.ptr));
-#endif // defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
+ // Compute new weight
+ w_new = (gamma * w_old) / (sqrt(var + EPSILON));
- // rvar = 1 / sqrt(var + epsilon)
- const DATA_TYPE var = *((__global DATA_TYPE *)(bn_var.ptr + current_slice * bn_var.stride_x));
- const DATA_TYPE rvar = INVSQRT_OP(ADD_OP(var, SQCVT_SAT((float)EPSILON)));
- wn *= rvar;
+#if defined(IN_PLACE_W)
+ *((__global DATA_TYPE *)(w_ptr + w_offset + w_offset_first_element_in_bytes)) = w_new;
+#else // defined(IN_PLACE_W)
+ *((__global DATA_TYPE *)(w_fused_ptr + w_offset + w_fused_offset_first_element_in_bytes)) = w_new;
+#endif // defined(IN_PLACE_W)
- // Load b
- const DATA_TYPE mean = *((__global DATA_TYPE *)(bn_mean.ptr + current_slice * bn_mean.stride_x));
- DATA_TYPE bn = 0;
-#ifdef HAS_BIAS
- bn = *((__global DATA_TYPE *)(conv_b.ptr + current_slice * conv_b.stride_x));
-#endif /* HAS_BIAS */
- bn = (bn - mean) * rvar;
+ // Compute bias
+ if(x == 0 && y == 0 && c0 == 0)
+ {
+#if defined(BIAS)
+ b_old = *((__global DATA_TYPE *)(b_ptr + v_offset + b_offset_first_element_in_bytes));
+#endif // defined(BIAS)
+#if defined(BETA)
+ beta = *((__global DATA_TYPE *)(beta_ptr + v_offset + beta_offset_first_element_in_bytes));
+#endif // defined(BETA)
-#ifndef USE_DEFAULT_GAMMA
- const DATA_TYPE gamma_scalar = *((__global DATA_TYPE *)(bn_gamma.ptr + current_slice * bn_gamma.stride_x));
- wn *= gamma_scalar;
- bn *= gamma_scalar;
-#endif /* USE_DEFAULT_GAMMA */
+ b_new = ((gamma * (b_old - mean)) / (sqrt(var + EPSILON))) + beta;
-#ifndef USE_DEFAULT_BETA
- const DATA_TYPE beta_scalar = *((__global DATA_TYPE *)(bn_beta.ptr + current_slice * bn_beta.stride_x));
- bn += beta_scalar;
-#endif /* USE_DEFAULT_BETA */
+#if defined(BIAS)
-#if defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
- // Store updated weights
- VSTORE(VEC_SIZE)
- (wn, 0, (__global DATA_TYPE *)fused_w.ptr);
-#else // !defined(VEC_SIZE) || !defined(LAST_ACCESSED_X)
- *((__global DATA_TYPE *)(fused_w.ptr)) = wn;
-#endif // defined(VEC_SIZE) && defined(LAST_ACCESSED_X)
+#if defined(IN_PLACE_B)
+ *((__global DATA_TYPE *)(b_ptr + v_offset + b_offset_first_element_in_bytes)) = b_new;
+#else // defined(IN_PLACE_B)
+ *((__global DATA_TYPE *)(b_fused_ptr + v_offset + b_fused_offset_first_element_in_bytes)) = b_new;
+#endif // defined(IN_PLACE_B)
- // Store updated bias
- *((__global DATA_TYPE *)(fused_b.ptr + current_slice * fused_b.stride_x)) = bn;
+#else // defined(BIAS)
+
+#ifndef IN_PLACE_B
+ *((__global DATA_TYPE *)(b_fused_ptr + v_offset + b_fused_offset_first_element_in_bytes)) = b_new;
+#endif // ifndef IN_PLACE_B
+
+#endif // defined(BIAS)
+ }
}
-#endif /* defined(NUM_CHANNELS) && defined(DATA_TYPE) && defined(EPSILON) */
+#endif // defined(DIM2) && defined(DATA_TYPE) && defined(EPSILON)
\ No newline at end of file
diff --git a/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp b/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp
index 150d9b6..16ad7d9 100644
--- a/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp
+++ b/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp
@@ -48,9 +48,9 @@
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));
+ ARM_COMPUTE_RETURN_ERROR_ON(conv_bias == nullptr && fused_bias == nullptr);
+ ARM_COMPUTE_RETURN_ERROR_ON(conv_weights->dimension(3) != bn_mean->dimension(0));
+ ARM_COMPUTE_RETURN_ERROR_ON(bn_mean->num_dimensions() > 1);
// Validate bias
if(conv_bias != nullptr)
@@ -70,7 +70,6 @@
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)
{
@@ -113,20 +112,18 @@
_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_bias = (conv_bias != nullptr && 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
@@ -139,35 +136,22 @@
epsilon));
// Configure kernel window
- const unsigned int num_elems_processed_per_iteration_x = 4;
- const int output_width_x = conv_weights->info()->tensor_shape().x();
- const bool multi_access_x = (output_width_x / num_elems_processed_per_iteration_x > 0);
-
Window win = calculate_max_window(*conv_weights->info());
- if(multi_access_x)
- {
- win.set(Window::DimX, Window::Dimension(win.x().start(),
- ceil_to_multiple(win.x().end(), num_elems_processed_per_iteration_x),
- num_elems_processed_per_iteration_x));
- }
ICLKernel::configure_internal(win);
// Set build options
CLBuildOptions build_opts;
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(conv_weights->info()->data_type()));
- build_opts.add_option("-DSELECT_DATA_TYPE=" + get_cl_select_type_from_data_type(conv_weights->info()->data_type()));
- build_opts.add_option("-DNUM_CHANNELS=" + support::cpp11::to_string(conv_weights->info()->dimension(2)));
+ build_opts.add_option("-DDIM2=" + support::cpp11::to_string(conv_weights->info()->dimension(2)));
build_opts.add_option("-DEPSILON=" + float_to_string_with_full_precision(epsilon));
- build_opts.add_option_if(multi_access_x, "-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration_x));
- build_opts.add_option_if(multi_access_x, "-DLAST_ACCESSED_X=" + support::cpp11::to_string(std::max<int>(output_width_x - num_elems_processed_per_iteration_x, 0)));
build_opts.add_option_if(_run_in_place_weights, "-DIN_PLACE_W");
build_opts.add_option_if(_run_in_place_bias, "-DIN_PLACE_B");
- build_opts.add_option_if(conv_bias != nullptr, "-DHAS_BIAS");
- build_opts.add_option_if(bn_beta == nullptr, "-DUSE_DEFAULT_BETA");
- build_opts.add_option_if(bn_gamma == nullptr, "-DUSE_DEFAULT_GAMMA");
+ build_opts.add_option_if(conv_bias != nullptr, "-DBIAS");
+ build_opts.add_option_if(bn_beta != nullptr, "-DBETA");
+ build_opts.add_option_if(bn_gamma != nullptr, "-DGAMMA");
// Create kernel
- _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("fuse_batchnormalization_layer", build_opts.options()));
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("fuse_batchnormalization_conv_layer", build_opts.options()));
}
Status CLFuseBatchNormalizationKernel::validate(const ITensorInfo *conv_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var,
@@ -185,37 +169,35 @@
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
// Create window slice
- Window collapsed_window = window.collapse_if_possible(window, Window::DimZ);
- Window slice = collapsed_window.first_slice_window_4D();
-
- Window vector_slice = window.first_slice_window_1D();
- vector_slice.set(Window::DimX, Window::Dimension(0, 0, 0));
+ Window collapsed_window = window.collapse(window, Window::DimZ);
+ Window slice_1d = window.first_slice_window_1D();
+ Window slice_3d = collapsed_window.first_slice_window_3D();
// Add kernel arguments
unsigned int idx = 0;
- add_4D_tensor_argument(idx, _conv_weights, slice);
- add_1D_tensor_argument(idx, _bn_mean, vector_slice);
- add_1D_tensor_argument(idx, _bn_var, vector_slice);
+ add_3D_tensor_argument(idx, _conv_weights, slice_3d);
+ if(_conv_bias != nullptr)
+ {
+ add_1D_tensor_argument(idx, _conv_bias, slice_1d);
+ }
+ add_1D_tensor_argument(idx, _bn_mean, slice_1d);
+ add_1D_tensor_argument(idx, _bn_var, slice_1d);
if(!_run_in_place_weights)
{
- add_4D_tensor_argument(idx, _fused_weights, slice);
+ add_3D_tensor_argument(idx, _fused_weights, slice_3d);
}
if(!_run_in_place_bias)
{
- add_1D_tensor_argument(idx, _fused_bias, vector_slice);
- }
- if(_conv_bias != nullptr)
- {
- add_1D_tensor_argument(idx, _conv_bias, vector_slice);
+ add_1D_tensor_argument(idx, _fused_bias, slice_1d);
}
if(_bn_beta != nullptr)
{
- add_1D_tensor_argument(idx, _bn_beta, vector_slice);
+ add_1D_tensor_argument(idx, _bn_beta, slice_1d);
}
if(_bn_gamma != nullptr)
{
- add_1D_tensor_argument(idx, _bn_gamma, vector_slice);
+ add_1D_tensor_argument(idx, _bn_gamma, slice_1d);
}
- enqueue(queue, *this, slice);
+ enqueue(queue, *this, slice_3d);
}
} // namespace arm_compute