Rework OpenCL Depthwise Convolution
- Remove dedicated kernels for NCHW. Now we only use NHWC with permute
- Remove specialized kernels for 3x3 NHWC
- Simplify CLDepthwiseConvolutionLayer.cpp to call just the native
implementation for both floating-point and quantized data types
- Develop two parametric opencl kernels for depthwise convolution layer NHWC
(floating-point and quantized)
- Add support to export the weights to cl_image
- Extend test for depthwise convolution on opencl
Resolves COMPMID-4417
Change-Id: I253dd5d959a70783c82e62b1771a5e9f91621cb0
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5806
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
diff --git a/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h b/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h
index 19ec6b2..8e187bf 100644
--- a/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h
@@ -275,7 +275,7 @@
if(padding_valid)
{
- _conv_info = PadStrideInfo();
+ _conv_info = PadStrideInfo(stride.width, stride.height);
}
else
{
@@ -399,14 +399,15 @@
public:
template <typename...>
void setup(size_t width, size_t height, size_t channel, size_t batch, Size2D kernel_size, size_t depth_multiplier, Size2D dilation, Size2D stride, bool padding_valid, DataType data_type,
- DataLayout data_layout, const ActivationLayerInfo &act_info, unsigned int n0)
+ DataLayout data_layout, const ActivationLayerInfo &act_info, unsigned int n0, bool export_to_cl_image)
{
- _dilation = dilation;
- _depth_multiplier = depth_multiplier;
- _data_type = data_type;
- _data_layout = data_layout;
- _act_info = act_info;
- _n0 = n0;
+ _dilation = dilation;
+ _depth_multiplier = depth_multiplier;
+ _data_type = data_type;
+ _data_layout = data_layout;
+ _act_info = act_info;
+ _n0 = n0;
+ _export_to_cl_image = export_to_cl_image;
_input_shape = TensorShape(width, height, channel, batch);
_weights_shape = TensorShape(kernel_size.width, kernel_size.height, channel * _depth_multiplier);
@@ -414,11 +415,11 @@
if(padding_valid)
{
- _conv_info = PadStrideInfo();
+ _conv_info = calculate_same_pad(_input_shape, _weights_shape, PadStrideInfo(stride.width, stride.height), DataLayout::NCHW, _dilation);
}
else
{
- _conv_info = calculate_same_pad(_input_shape, _weights_shape, PadStrideInfo(stride.width, stride.height), DataLayout::NCHW, _dilation);
+ _conv_info = PadStrideInfo(stride.width, stride.height);
}
}
@@ -439,14 +440,26 @@
_biases = create_tensor<TensorType>(_biases_shape, _data_type, 1, QuantizationInfo(), _data_layout);
_target = create_tensor<TensorType>(TensorShape(), _data_type, 1, QuantizationInfo(), _data_layout);
- DWCWeightsKernelInfo dwc_weights_info;
- dwc_weights_info.n0 = _n0;
+ DWCComputeKernelInfo dwc_info;
+ dwc_info.n0 = _n0;
+ dwc_info.m0 = _conv_info.stride().first == 1 && _dilation.x() == 1 ? 8 : 1;
+ dwc_info.export_weights_to_cl_image = _export_to_cl_image;
- DWCKernelInfo dwc_info;
- dwc_info.activation_info = _act_info;
+#if defined(ARM_COMPUTE_OPENCL_ENABLED)
+ if(_export_to_cl_image)
+ {
+ _validate_output |= image2d_from_buffer_supported(CLKernelLibrary::get().get_device());
+ _validate_output |= (get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) != 0);
+ }
+#endif // ARM_COMPUTE_OPENCL_ENABLED
+
+ const ConvolutionInfo conv_kernel_info
+ {
+ _conv_info, _depth_multiplier, _act_info, _dilation
+ };
// Create Depthwise Convolution configure function
- _dwc.configure(&_src, &_weights, &_biases, &_target, dwc_weights_info, dwc_info, _conv_info, _depth_multiplier, _dilation);
+ _dwc.configure(&_src, &_weights, &_biases, &_target, dwc_info, conv_kernel_info);
ARM_COMPUTE_ASSERT(_src.info()->is_resizable());
ARM_COMPUTE_ASSERT(_weights.info()->is_resizable());
@@ -456,7 +469,8 @@
void allocate_and_run_target()
{
- add_padding_x({ &_src, &_weights, &_biases, &_target }, _data_layout);
+ add_padding_x({ &_src, &_biases, &_target }, _data_layout);
+ add_padding_x({ &_weights }, _data_layout, _export_to_cl_image); // Don't add left padding if cl image will be used
// Allocate tensors
_src.allocator()->allocate();
@@ -479,7 +493,10 @@
_target.info()->set_data_layout(_data_layout == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW);
// Compute function
- _dwc.run();
+ if(_validate_output)
+ {
+ _dwc.run();
+ }
// Reinstating original data layout for the test suite to properly check the values
_target.info()->set_data_layout(_data_layout);
@@ -497,7 +514,10 @@
const ConvolutionInfo info{ _conv_info, _depth_multiplier, _act_info, _dilation };
const TensorShape dst_shape = compute_depthwise_convolution_shape(TensorInfo(_input_shape, 1, _data_type), TensorInfo(_weights_shape, 1, _data_type), info);
- _reference = reference::activation_layer(reference::depthwise_convolution(src, weights, biases, dst_shape, _conv_info, _depth_multiplier, _dilation), _act_info);
+ if(_validate_output)
+ {
+ _reference = reference::activation_layer(reference::depthwise_convolution(src, weights, biases, dst_shape, _conv_info, _depth_multiplier, _dilation), _act_info);
+ }
}
protected:
@@ -541,6 +561,8 @@
Size2D _dilation{};
unsigned int _depth_multiplier{};
unsigned int _n0{};
+ bool _export_to_cl_image{};
+ bool _validate_output{ true };
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>