[COMPMID-386] Github: Support SoftmaxLayer on different number of dimensions?

Change-Id: I7422b977538ff29930a90f078badc2edee78af93
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/146638
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

1 files changed, 57 insertions, 26 deletions

diff --git a/src/runtime/CL/functions/CLSoftmaxLayer.cpp b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
index 3a7d6c770b..d6718467d5 100644
--- a/src/runtime/CL/functions/CLSoftmaxLayer.cpp
+++ b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
@@ -36,34 +36,48 @@
 namespace arm_compute
 {
 CLSoftmaxLayer::CLSoftmaxLayer(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _max_shift_exp_sum_kernel(), _norm_kernel(), _flatten_kernel(), _reshape_kernel(), _max(), _sum(), _tmp(), _input_flat(), _output_flat(),
+    : _memory_group(std::move(memory_manager)), _max_shift_exp_sum_kernel(), _norm_kernel(), _flatten_kernel_ptr(), _reshape_kernel(), _max(), _sum(), _tmp(), _input_flattened(), _output_flattened(),
       _needs_flattening(false)
 {
 }
 
-void CLSoftmaxLayer::configure_flatten_kernel(const ICLTensor *input, const ICLTensor *output)
+void CLSoftmaxLayer::configure_reshape_input_kernel(const ICLTensor *input, const ICLTensor *output, size_t axis)
 {
     // Flatten the input
-    const TensorShape shape_flatten = misc::shape_calculator::compute_flatten_shape(input->info());
+    const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input->info(), axis);
 
     // Initialize the flat input
-    _input_flat.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten));
+    _input_flattened.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten));
 
-    // Configure the flatten_kernel
-    _flatten_kernel.configure(input, &_input_flat);
+    // If we need to flatten the input, we can use CLFlattenKernel or CLReshapeKernel
+    // If flattening on the third axes, we use CLFlattenKernel.
+    // In all other cases we have to use CLReshapeKernel
+    if(axis != 3)
+    {
+        auto reshape_kernel_ptr = support::cpp14::make_unique<CLReshapeLayerKernel>();
+        reshape_kernel_ptr->configure(input, &_input_flattened);
+        _flatten_kernel_ptr = std::move(reshape_kernel_ptr);
+    }
+    else
+    {
+        auto flatten_kernel_ptr = support::cpp14::make_unique<CLFlattenLayerKernel>();
+        flatten_kernel_ptr->configure(input, &_input_flattened);
+        _flatten_kernel_ptr = std::move(flatten_kernel_ptr);
+    }
 
     // We need to init the output tensor here. Indeed, the reshape kernel expects
     // both tensors to be already initialized
     auto_init_if_empty(*output->info(), *input->info()->clone());
 }
 
-void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float beta)
+void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float beta, size_t axis)
 {
     // Perform validation step
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayer::validate(input->info(), output->info()));
+    ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayer::validate(input->info(), output->info(), beta, axis));
 
-    _needs_flattening = input->info()->num_dimensions() > 2;
+    // We don't need flattening only in the case the input is 2D and axis is 1
+    _needs_flattening = axis != 1;
 
     // If we are dealing with a 4D tensor, we will:
     // - Flatten the input, so that we end up with a [width*height*depth] * batches 2D tensor
@@ -71,16 +85,16 @@ void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float
     // - Reshape the flattened output into the real output
     if(_needs_flattening)
     {
-        // Add to the memory manager _input_flat
-        _memory_group.manage(&_input_flat);
+        // Add to the memory manager _input_flattened
+        _memory_group.manage(&_input_flattened);
 
-        // Cofigure  _flatten_kernel and _input_flat
-        configure_flatten_kernel(input, output);
+        // Cofigure  _flatten_kernel and _input_flattened
+        configure_reshape_input_kernel(input, output, axis);
     }
 
     // We want to deal with a 2D input. Either it is the flattened version of the original input (4D case)
     // or it is the original input case (2D case)
-    const ICLTensor *input_2D = (_needs_flattening ? &_input_flat : input);
+    const ICLTensor *input_2D = (_needs_flattening ? &_input_flattened : input);
 
     // Create intermediate tensors shapes
     TensorInfo input_info    = input_2D->info()->clone()->reset_padding().set_is_resizable(true);
@@ -106,18 +120,18 @@ void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float
 
     if(_needs_flattening)
     {
-        // Add to the memory manager _output_flat
-        _memory_group.manage(&_output_flat);
+        // Add to the memory manager _output_flattened
+        _memory_group.manage(&_output_flattened);
 
         // The normalization kernel stores the result in a flat output tensor
-        _norm_kernel.configure(&_tmp, &_sum, &_output_flat, beta);
+        _norm_kernel.configure(&_tmp, &_sum, &_output_flattened, beta);
 
         // Reshape the flat output into a the requested (4D) output
-        _reshape_kernel.configure(&_output_flat, output);
+        _reshape_kernel.configure(&_output_flattened, output);
 
         // Allocate the intermediate flat tensors
-        _input_flat.allocator()->allocate();
-        _output_flat.allocator()->allocate();
+        _input_flattened.allocator()->allocate();
+        _output_flattened.allocator()->allocate();
     }
     else
     {
@@ -131,10 +145,11 @@ void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float
     _sum.allocator()->allocate();
 }
 
-Status CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *output)
+Status CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, size_t axis)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 4, "Only up to 4 dimensions are supported");
+    ARM_COMPUTE_UNUSED(beta);
 
     // Create intermediate tensor info
     DataType   tmp_data_type = is_data_type_quantized_asymmetric(input->data_type()) ? DataType::S32 : input->data_type();
@@ -145,26 +160,42 @@ Status CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *out
     TensorInfo tensor_info_max(input->clone()->set_tensor_shape(max_sum_shape).set_is_resizable(true));
     TensorInfo tensor_info_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(QuantizationInfo()).set_is_resizable(true));
 
-    const TensorShape shape_flatten = misc::shape_calculator::compute_flatten_shape(input);
-    TensorInfo        tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true));
+    const bool needs_flattening = (axis != 1);
 
-    if(input->num_dimensions() > 2) // needs flattening
+    if(needs_flattening)
     {
-        ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayerKernel::validate(input, &tensor_info_flat));
+        const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input, axis);
+        TensorInfo        tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true));
+
+        if(axis != 3)
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayerKernel::validate(input, &tensor_info_flat));
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayerKernel::validate(input, &tensor_info_flat));
+        }
     }
 
     ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DMaxShiftExpSumKernel::validate(input, &tensor_info_max, &tensor_info_tmp, &tensor_info_sum));
     ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DNormKernel::validate(&tensor_info_tmp, &tensor_info_sum, output));
 
+    if(needs_flattening)
+    {
+        const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input);
+        TensorInfo        tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true));
+    }
+
     return Status{};
 }
 
 void CLSoftmaxLayer::run()
 {
     _memory_group.acquire();
+
     if(_needs_flattening)
     {
-        CLScheduler::get().enqueue(_flatten_kernel, false);
+        CLScheduler::get().enqueue(*_flatten_kernel_ptr, false);
     }
 
     CLScheduler::get().enqueue(_max_shift_exp_sum_kernel, false);