COMPMID-601: Add GraphContext
GraphContext hold all the information about the hints that need to be
passed in the nodes. As these might expand, it serves as a centralized
class for such information.
Change-Id: I0b5527630fb97cc5fa500db0bac8307ff2ea36e6
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/90300
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/graph/nodes/ConvolutionLayer.cpp b/src/graph/nodes/ConvolutionLayer.cpp
index ce9f096..a992095 100644
--- a/src/graph/nodes/ConvolutionLayer.cpp
+++ b/src/graph/nodes/ConvolutionLayer.cpp
@@ -65,7 +65,7 @@
}
// Instantiate GEMM based convolution layer
-template <typename ConvolutionType, typename TensorType, Hint hint>
+template <typename ConvolutionType, typename TensorType, TargetHint target_hint>
std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
{
auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>();
@@ -79,7 +79,7 @@
}
// Instantiate direct convolution layer
-template <typename ConvolutionType, typename TensorType, Hint hint>
+template <typename ConvolutionType, typename TensorType, TargetHint target_hint>
std::unique_ptr<arm_compute::IFunction> instantiate_direct_function(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
{
auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>();
@@ -92,35 +92,37 @@
return std::move(conv);
}
-template <Hint hint>
+template <TargetHint target_hint>
std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
ConvolutionMethodHint conv_method);
template <>
-std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
- ConvolutionMethodHint conv_method)
+std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
+ const WeightsInfo &weights_info,
+ ConvolutionMethodHint conv_method)
{
if(conv_method == ConvolutionMethodHint::GEMM)
{
- return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::ICLTensor, Hint::OPENCL>(input, weights, biases, output, conv_info, weights_info);
+ return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info, weights_info);
}
else
{
- return instantiate_direct_function<arm_compute::CLDirectConvolutionLayer, arm_compute::ICLTensor, Hint::OPENCL>(input, weights, biases, output, conv_info);
+ return instantiate_direct_function<arm_compute::CLDirectConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info);
}
}
template <>
-std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
- ConvolutionMethodHint conv_method)
+std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
+ const WeightsInfo &weights_info,
+ ConvolutionMethodHint conv_method)
{
if(conv_method == ConvolutionMethodHint::GEMM)
{
- return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::ITensor, Hint::NEON>(input, weights, biases, output, conv_info, weights_info);
+ return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info, weights_info);
}
else
{
- return instantiate_direct_function<arm_compute::NEDirectConvolutionLayer, arm_compute::ITensor, Hint::NEON>(input, weights, biases, output, conv_info);
+ return instantiate_direct_function<arm_compute::NEDirectConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info);
}
}
} // namespace
@@ -166,7 +168,7 @@
std::vector<std::unique_ptr<IFunction>> _convolutions;
};
-std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output)
+std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output)
{
// Set weights and biases info
if(_weights.tensor() == nullptr)
@@ -181,17 +183,18 @@
}
std::unique_ptr<arm_compute::IFunction> func;
- _hint = hint;
- _input = input;
- _output = output;
+ _target_hint = ctx.hints().target_hint();
+ _input = input;
+ _output = output;
+ const ConvolutionMethodHint conv_method_hint = ctx.hints().convolution_method_hint();
// Check if the weights and biases are loaded
bool weights_are_loaded = _weights.tensor() != nullptr;
bool biases_are_loaded = _weights.tensor() != nullptr;
// Set bias and weights target
- _weights.set_target(_hint);
- _biases.set_target(_hint);
+ _weights.set_target(_target_hint);
+ _biases.set_target(_target_hint);
// Calculate output shape
TensorShape output_shape = calculate_convolution_layer_output_shape(_input->info()->tensor_shape(), _weights.info().tensor_shape(), _conv_info);
@@ -200,14 +203,13 @@
arm_compute::auto_init_if_empty(*_output->info(), output_shape, 1, _input->info()->data_type(), _input->info()->fixed_point_position());
// Create appropriate convolution function
- // TODO(geopin01): Fix convolution layer hints once the GraphContext has been added
if(_num_groups == 1)
{
- func = instantiate_convolution(ConvolutionMethodHint::GEMM);
+ func = instantiate_convolution(conv_method_hint);
}
else
{
- func = instantiate_grouped_convolution(ConvolutionMethodHint::GEMM);
+ func = instantiate_grouped_convolution(conv_method_hint);
}
// Fill weights
@@ -226,7 +228,7 @@
void ConvolutionLayer::print_info()
{
- if(_hint == Hint::OPENCL)
+ if(_target_hint == TargetHint::OPENCL)
{
std::cout << "Instantiating CLConvolutionLayer";
}
@@ -248,13 +250,13 @@
std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_convolution(ConvolutionMethodHint conv_method_hint)
{
std::unique_ptr<arm_compute::IFunction> func;
- if(_hint == Hint::OPENCL)
+ if(_target_hint == TargetHint::OPENCL)
{
- func = instantiate<Hint::OPENCL>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint);
+ func = instantiate<TargetHint::OPENCL>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint);
}
else
{
- func = instantiate<Hint::NEON>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint);
+ func = instantiate<TargetHint::NEON>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint);
}
return func;
}
@@ -306,20 +308,20 @@
Coordinates biases_coord(biases_split * i);
// Create sub-tensors for input, output, weights and bias
- auto hint_to_use = (_hint == Hint::OPENCL) ? Hint::OPENCL : Hint::NEON;
+ auto hint_to_use = (_target_hint == TargetHint::OPENCL) ? TargetHint::OPENCL : TargetHint::NEON;
_is[i] = SubTensor(_input, input_shape, input_coord, hint_to_use);
_os[i] = SubTensor(_output, output_shape, output_coord, hint_to_use);
_ws[i] = SubTensor(_weights.tensor(), weights_shape, weights_coord, hint_to_use);
_bs[i] = SubTensor(_biases.tensor(), biases_shape, biases_coord, hint_to_use);
// Instantiate convolution function
- if(_hint == Hint::OPENCL)
+ if(_target_hint == TargetHint::OPENCL)
{
- func = instantiate<Hint::OPENCL>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint);
+ func = instantiate<TargetHint::OPENCL>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint);
}
else
{
- func = instantiate<Hint::NEON>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint);
+ func = instantiate<TargetHint::NEON>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint);
}
// Add convolution function to the list of convolutions for the grouped convolution