COMPMID-1532: Add DepthwiseConvolution3x3 FP16 on NEON
Change-Id: I780970f317b979b3230e2b471ac01df7fda9ee14
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/148168
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
index 88758b5..7029b06 100644
--- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
+++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
@@ -146,7 +146,7 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, bool is_optimized)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
const DataLayout data_layout = input->data_layout();
@@ -165,8 +165,14 @@
const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
- //ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()) && (output->data_type() != DataType::S32));
- ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_float(input->data_type()) && (output->data_type() != DataType::F32));
+ if(is_data_type_quantized_asymmetric(input->data_type()))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(output->data_type() != DataType::S32);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+ }
}
return Status{};
@@ -229,6 +235,11 @@
case DataType::QASYMM8:
num_elems_read_per_iteration = 16;
break;
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ num_elems_read_per_iteration = 24;
+ break;
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F32:
num_elems_read_per_iteration = 12;
break;
@@ -313,7 +324,7 @@
}
// Check supported data type
- bool supported_datatype = (dt == DataType::F32);
+ bool supported_datatype = is_data_type_float(dt);
// Check for supported strides
const auto &strides = conv_info.stride();
@@ -334,7 +345,7 @@
void NEDepthwiseConvolutionLayer3x3Kernel::generate_convolver()
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(_input, 1, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(_input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(_input, _weights);
ARM_COMPUTE_ERROR_ON(_weights->info()->dimension(1) != 3 || _weights->info()->dimension(2) != 3);
@@ -371,6 +382,11 @@
switch(_input->info()->data_type())
{
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ convolve_3x3<float16_t, float16_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier);
+ break;
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F32:
convolve_3x3<float, float>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier);
break;
@@ -398,6 +414,7 @@
ITensor *out,
bool setup_strides)
{
+ const DataType dt = in->info()->data_type();
const TensorShape shape = in->info()->tensor_shape();
const int in_rows = shape.z();
const int in_cols = shape.y();
@@ -414,34 +431,60 @@
const int output_batch_stride = (setup_strides) ? out->info()->strides_in_bytes()[3] / out->info()->element_size() : 0;
const auto stride_x = conv_info.stride().first;
- switch(stride_x)
+ switch(dt)
{
- case 1:
- return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<4, 4, 3, 3, 1, 1, float, float>>(
- n_batches,
- in_rows,
- in_cols,
- n_channels,
- padding_same,
- reinterpret_cast<const float *>(w->ptr_to_element(Coordinates())),
- reinterpret_cast<float *>(in->ptr_to_element(Coordinates())),
- reinterpret_cast<float *>(out->ptr_to_element(Coordinates())),
- weight_col_stride, weight_row_stride,
- input_col_stride, input_row_stride, input_batch_stride,
- output_col_stride, output_row_stride, output_batch_stride);
- case 2:
- return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<3, 3, 3, 3, 2, 2, float, float>>(
- n_batches,
- in_rows,
- in_cols,
- n_channels,
- padding_same,
- reinterpret_cast<const float *>(w->ptr_to_element(Coordinates())),
- reinterpret_cast<float *>(in->ptr_to_element(Coordinates())),
- reinterpret_cast<float *>(out->ptr_to_element(Coordinates())),
- weight_col_stride, weight_row_stride,
- input_col_stride, input_row_stride, input_batch_stride,
- output_col_stride, output_row_stride, output_batch_stride);
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ {
+ switch(stride_x)
+ {
+ case 1:
+ return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<4, 4, 3, 3, 1, 1, float16_t, float16_t>>(
+ n_batches, in_rows, in_cols, n_channels, padding_same,
+ reinterpret_cast<const float16_t *>(w->ptr_to_element(Coordinates())),
+ reinterpret_cast<float16_t *>(in->ptr_to_element(Coordinates())),
+ reinterpret_cast<float16_t *>(out->ptr_to_element(Coordinates())), weight_col_stride,
+ weight_row_stride, input_col_stride, input_row_stride, input_batch_stride,
+ output_col_stride, output_row_stride, output_batch_stride);
+ case 2:
+ return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<4, 4, 3, 3, 2, 2, float16_t, float16_t>>(
+ n_batches, in_rows, in_cols, n_channels, padding_same,
+ reinterpret_cast<const float16_t *>(w->ptr_to_element(Coordinates())),
+ reinterpret_cast<float16_t *>(in->ptr_to_element(Coordinates())),
+ reinterpret_cast<float16_t *>(out->ptr_to_element(Coordinates())), weight_col_stride,
+ weight_row_stride, input_col_stride, input_row_stride, input_batch_stride,
+ output_col_stride, output_row_stride, output_batch_stride);
+ default:
+ return nullptr;
+ }
+ break;
+ }
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ {
+ switch(stride_x)
+ {
+ case 1:
+ return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<4, 4, 3, 3, 1, 1, float, float>>(
+ n_batches, in_rows, in_cols, n_channels, padding_same,
+ reinterpret_cast<const float *>(w->ptr_to_element(Coordinates())),
+ reinterpret_cast<float *>(in->ptr_to_element(Coordinates())),
+ reinterpret_cast<float *>(out->ptr_to_element(Coordinates())), weight_col_stride,
+ weight_row_stride, input_col_stride, input_row_stride, input_batch_stride,
+ output_col_stride, output_row_stride, output_batch_stride);
+ case 2:
+ return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<3, 3, 3, 3, 2, 2, float, float>>(
+ n_batches, in_rows, in_cols, n_channels, padding_same,
+ reinterpret_cast<const float *>(w->ptr_to_element(Coordinates())),
+ reinterpret_cast<float *>(in->ptr_to_element(Coordinates())),
+ reinterpret_cast<float *>(out->ptr_to_element(Coordinates())), weight_col_stride,
+ weight_row_stride, input_col_stride, input_row_stride, input_batch_stride,
+ output_col_stride, output_row_stride, output_batch_stride);
+ default:
+ return nullptr;
+ }
+ break;
+ }
default:
return nullptr;
}