COMPMID-3510 [Interface change] Fix definition of "axis" in NESoftmaxLayer and CLSoftmaxLayer
* [Interface change] "axis" argument is renamed to "reduce_end_axis"
* Unify the meaning of "axis"(now "reduce_end_axis") to be the last axis
of the first n dimensions (inclusive)to reduce.
This way the meaning of reduce_end_axis stays the same for both
positive and negative values: it selects a dimension before which all
dimensions (including the selected dimension) are reduced.
Change-Id: I4ab03bd8360b1cd8cac4998df0b1571064a9d4ed
Signed-off-by: SiCong Li <sicong.li@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3278
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/runtime/CL/functions/CLSoftmaxLayer.cpp b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
index b0b2117..71ccf9f 100644
--- a/src/runtime/CL/functions/CLSoftmaxLayer.cpp
+++ b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
@@ -42,35 +42,38 @@
}
template <bool IS_LOG>
-void CLSoftmaxLayerGeneric<IS_LOG>::configure_reshape_input_kernel(const ICLTensor *input, const ICLTensor *output, size_t axis)
+void CLSoftmaxLayerGeneric<IS_LOG>::configure_reshape_input_kernel(const ICLTensor *input, const ICLTensor *output, size_t first_n_reduce_axes)
{
- configure_reshape_input_kernel(CLKernelLibrary::get().get_compile_context(), input, output, axis);
+ configure_reshape_input_kernel(CLKernelLibrary::get().get_compile_context(), input, output, first_n_reduce_axes);
}
template <bool IS_LOG>
-void CLSoftmaxLayerGeneric<IS_LOG>::configure_reshape_input_kernel(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *output, size_t axis)
+void CLSoftmaxLayerGeneric<IS_LOG>::configure_reshape_input_kernel(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *output, size_t first_n_reduce_axes)
{
// Flatten the input
- const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input->info(), axis);
+ const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input->info(), first_n_reduce_axes);
// Initialize the flat input
_input_flattened.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten));
// If we need to flatten the input, we can use CLFlattenKernel or CLReshapeKernel
- // If flattening on the third axes, we use CLFlattenKernel.
+ // If the number of reduced axes is 3 (max dimension), which means collapsing all axes except the batch axis, 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(compile_context, input, &_input_flattened);
- _flatten_kernel_ptr = std::move(reshape_kernel_ptr);
- }
- else
+ // Note that the "other cases" include both:
+ // 1. first_n_reduce_axes < 3: Reduce the first 1 (no need to reduce) or 2 dimensions (inclusive)
+ // 2. first_n_reduce_axes == 4: Reduce all 4 dimensions. This can only be handled by CLReshapeKernel instead of CLFlattenKernel.
+ if(first_n_reduce_axes == 3)
{
auto flatten_kernel_ptr = support::cpp14::make_unique<CLFlattenLayerKernel>();
flatten_kernel_ptr->configure(compile_context, input, &_input_flattened);
_flatten_kernel_ptr = std::move(flatten_kernel_ptr);
}
+ else
+ {
+ auto reshape_kernel_ptr = support::cpp14::make_unique<CLReshapeLayerKernel>();
+ reshape_kernel_ptr->configure(compile_context, input, &_input_flattened);
+ _flatten_kernel_ptr = std::move(reshape_kernel_ptr);
+ }
// We need to init the output tensor here. Indeed, the reshape kernel expects
// both tensors to be already initialized
@@ -78,20 +81,23 @@
}
template <bool IS_LOG>
-void CLSoftmaxLayerGeneric<IS_LOG>::configure(const ICLTensor *input, ICLTensor *output, float beta, size_t axis)
+void CLSoftmaxLayerGeneric<IS_LOG>::configure(const ICLTensor *input, ICLTensor *output, float beta, size_t reduce_end_axis)
{
- configure(CLKernelLibrary::get().get_compile_context(), input, output, beta, axis);
+ configure(CLKernelLibrary::get().get_compile_context(), input, output, beta, reduce_end_axis);
}
template <bool IS_LOG>
-void CLSoftmaxLayerGeneric<IS_LOG>::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, float beta, size_t axis)
+void CLSoftmaxLayerGeneric<IS_LOG>::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, float beta, size_t reduce_end_axis)
{
// Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayerGeneric<IS_LOG>::validate(input->info(), output->info(), beta, axis));
+ ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayerGeneric<IS_LOG>::validate(input->info(), output->info(), beta, reduce_end_axis));
- // We don't need flattening only in the case the input is 2D and axis is 1
- _needs_flattening = axis != 1;
+ // Convert reduce-before axis (inclusive) to first n axes to reduce
+ size_t first_n_reduce_axes = dim_index_2_num_dims(reduce_end_axis, input->info()->num_dimensions());
+
+ // We only need flattening when the number of axes to reduce is greater than 1
+ _needs_flattening = first_n_reduce_axes > 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
@@ -102,8 +108,8 @@
// Add to the memory manager _input_flattened
_memory_group.manage(&_input_flattened);
- // Cofigure _flatten_kernel and _input_flattened
- configure_reshape_input_kernel(input, output, axis);
+ // Cofigure _flatten_kernel and _input_flattened
+ configure_reshape_input_kernel(input, output, first_n_reduce_axes);
}
// We want to deal with a 2D input. Either it is the flattened version of the original input (4D case)
@@ -165,11 +171,15 @@
}
template <bool IS_LOG>
-Status CLSoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, size_t axis)
+Status CLSoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, size_t reduce_end_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);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() <= reduce_end_axis);
+
+ // Convert reduce-before axis (inclusive) to first n axes to reduce
+ size_t first_n_reduce_axes = dim_index_2_num_dims(reduce_end_axis, input->num_dimensions());
// Create intermediate tensor info
DataType tmp_data_type = is_data_type_quantized_asymmetric(input->data_type()) ? DataType::S32 : input->data_type();
@@ -180,20 +190,20 @@
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 bool needs_flattening = (axis != 1);
+ const bool needs_flattening = (first_n_reduce_axes > 1);
if(needs_flattening)
{
- const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input, axis);
+ const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input, first_n_reduce_axes);
TensorInfo tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true));
- if(axis != 3)
+ if(first_n_reduce_axes == 3)
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayerKernel::validate(input, &tensor_info_flat));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayerKernel::validate(input, &tensor_info_flat));
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayerKernel::validate(input, &tensor_info_flat));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayerKernel::validate(input, &tensor_info_flat));
}
}