COMPMID-3174: Remove padding from NEDirectConvolutionLayerOutputStageKernel

Signed-off-by: Michalis Spyrou <michalis.spyrou@arm.com>
Change-Id: I3c5cfe50e9cee30b66f4094da105d383c077aaf9
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4044
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 208 insertions, 151 deletions

diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.cpp
index 2814c67f70..c2c85f81ef 100644
--- a/src/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.cpp
+++ b/src/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.cpp
@@ -86,78 +86,6 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con
     return Status{};
 }
 
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output,
-                                                        const DirectConvolutionLayerOutputStageKernelInfo &info)
-{
-    ARM_COMPUTE_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
-
-    const DataType data_type = input->data_type();
-
-    // Auto-initialize output output if required
-    if(output != nullptr)
-    {
-        // Work out expected output data type
-        const DataType output_dt = (data_type == DataType::S32) ? info.output_data_type : data_type;
-        // Output tensor auto initialization if not yet initialized
-        auto_init_if_empty(*output, input->clone()->set_data_type(output_dt));
-    }
-
-    bool         window_changed                    = false;
-    unsigned int num_elems_processed_per_iteration = 16 / element_size_from_data_type(data_type);
-
-    // Update processed elements when input is S32 (comes from quantization input)
-    if(data_type == DataType::S32)
-    {
-        num_elems_processed_per_iteration = 16;
-    }
-
-    // Configure kernel window
-    Window                 win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
-    AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
-
-    if(output != nullptr && (output->total_size() != 0))
-    {
-        AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
-
-        if(bias == nullptr)
-        {
-            window_changed = update_window_and_padding(win, input_access, output_access);
-        }
-        else
-        {
-            AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
-            window_changed = update_window_and_padding(win, input_access, output_access, bias_access);
-        }
-
-        output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
-    }
-    else
-    {
-        if(bias == nullptr)
-        {
-            window_changed = update_window_and_padding(win, input_access);
-        }
-        else
-        {
-            if(input->data_layout() == DataLayout::NCHW)
-            {
-                AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
-                window_changed = update_window_and_padding(win, input_access, bias_access);
-            }
-            else
-            {
-                AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration);
-                window_changed = update_window_and_padding(win, input_access, bias_access);
-            }
-        }
-
-        input_access.set_valid_region(win, ValidRegion(Coordinates(), input->tensor_shape()));
-    }
-
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
-}
-
 template <typename T, bool has_bias>
 typename std::enable_if<arm_compute::utils::traits::is_floating_point<T>::value, void>::type
 output_stage_nchw(ITensor *input, const ITensor *bias, const Window &window, ITensor *output,
@@ -171,23 +99,50 @@ output_stage_nchw(ITensor *input, const ITensor *bias, const Window &window, ITe
     ARM_COMPUTE_UNUSED(result_shift);
     ARM_COMPUTE_UNUSED(result_offset_after_shift);
 
-    Iterator in(input, window);
-    Iterator out(output, window);
-    execute_window_loop(window, [&](const Coordinates & id)
+    const int window_start_x = window.x().start();
+    const int window_end_x   = window.x().end();
+    const int window_step_x  = 16 / input->info()->element_size();
+    Window    win            = window;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator in(input, win);
+    Iterator out(output, win);
+    execute_window_loop(win, [&](const Coordinates & id)
     {
-        // Get bias and pointer to input
-        const auto in_ptr = reinterpret_cast<const T *>(in.ptr());
-        auto       v_in   = wrapper::vloadq(in_ptr);
+        int x = window_start_x;
+        for(; x < (window_end_x - window_step_x); x += window_step_x)
+        {
+            // Get bias and pointer to input
+            const auto in_ptr = reinterpret_cast<const T *>(in.ptr()) + x;
+            auto       v_in   = wrapper::vloadq(in_ptr);
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto vb = wrapper::vdup_n(*reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(id.z()))), ExactTagType{});
+                v_in          = wrapper::vadd(v_in, vb);
+            }
+
+            const auto out_ptr = reinterpret_cast<T *>(out.ptr()) + x;
+            wrapper::vstore(out_ptr, v_in);
+        }
 
-        // Accumulate bias
-        if(has_bias)
+        // Left-overs loop
+        for(; x < window_end_x; ++x)
         {
-            const auto vb = wrapper::vdup_n(*reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(id.z()))), ExactTagType{});
-            v_in          = wrapper::vadd(v_in, vb);
+            // Get bias and pointer to input
+            auto s_in = *(reinterpret_cast<const T *>(in.ptr()) + x);
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto b = *reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(id.z())));
+                s_in += b;
+            }
+
+            *(reinterpret_cast<T *>(out.ptr()) + x) = s_in;
         }
 
-        const auto out_ptr = reinterpret_cast<T *>(out.ptr());
-        wrapper::vstore(out_ptr, v_in);
     },
     in, out);
 }
@@ -202,29 +157,57 @@ output_stage_nhwc(ITensor *input, const ITensor *bias, const Window &window, ITe
     ARM_COMPUTE_UNUSED(result_offset_after_shift);
 
     Window window_bias = window;
+    window_bias.set(Window::DimX, Window::Dimension(0, 1, 1));
     window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
     window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
     window_bias.set(3, Window::Dimension(0, 0, 0));
 
-    Iterator in(input, window);
+    const int window_start_x = window.x().start();
+    const int window_end_x   = window.x().end();
+    const int window_step_x  = 16 / input->info()->element_size();
+    Window    win            = window;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator in(input, win);
     Iterator bi(bias, window_bias);
-    Iterator out(output, window);
-    execute_window_loop(window, [&](const Coordinates &)
-    {
-        // Get bias and pointer to input
-        const auto in_ptr = reinterpret_cast<const T *>(in.ptr());
-        auto       v_in   = wrapper::vloadq(in_ptr);
+    Iterator out(output, win);
 
-        // Accumulate bias
-        if(has_bias)
+    execute_window_loop(win, [&](const Coordinates &)
+    {
+        int x = window_start_x;
+        for(; x < (window_end_x - window_step_x); x += window_step_x)
         {
-            const auto bias_ptr = reinterpret_cast<T *>(bi.ptr());
-            v_in                = wrapper::vadd(v_in, wrapper::vloadq(bias_ptr));
+            // Get bias and pointer to input
+            const auto in_ptr = reinterpret_cast<const T *>(in.ptr());
+            auto       v_in   = wrapper::vloadq(in_ptr + x);
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto bias_ptr = reinterpret_cast<T *>(bi.ptr()) + x;
+                v_in                = wrapper::vadd(v_in, wrapper::vloadq(bias_ptr));
+            }
+
+            const auto out_ptr = reinterpret_cast<T *>(out.ptr());
+            wrapper::vstore(out_ptr + x, v_in);
         }
 
-        const auto out_ptr = reinterpret_cast<T *>(out.ptr());
-        wrapper::vstore(out_ptr, v_in);
+        // Left-overs loop
+        for(; x < window_end_x; ++x)
+        {
+            // Get bias and pointer to input
+            auto s_in = *(reinterpret_cast<const T *>(in.ptr()) + x);
 
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto bias_ptr = reinterpret_cast<T *>(bi.ptr()) + x;
+                s_in += *bias_ptr;
+            }
+
+            const auto out_ptr = reinterpret_cast<T *>(out.ptr());
+            *(out_ptr + x)     = s_in;
+        }
     },
     in, bi, out);
 }
@@ -242,40 +225,70 @@ void output_stage_nchw(ITensor *input, const ITensor *bias, const Window &window
     const VectorType min = wrapper::vdup_n(std::numeric_limits<TOut>::lowest(), TagType{});
     const VectorType max = wrapper::vdup_n(std::numeric_limits<TOut>::max(), TagType{});
 
-    Iterator in(input, window);
-    Iterator out(output, window);
+    const int window_start_x = window.x().start();
+    const int window_end_x   = window.x().end();
+    const int window_step_x  = 16 / input->info()->element_size();
+    Window    win            = window;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
 
-    execute_window_loop(window, [&](const Coordinates & id)
+    Iterator in(input, win);
+    Iterator out(output, win);
+
+    execute_window_loop(win, [&](const Coordinates & id)
     {
-        // Get bias and pointer to input
-        const auto  in_ptr = reinterpret_cast<int32_t *>(in.ptr());
-        int32x4x4_t v_in =
-        {
-            {
-                wrapper::vloadq(in_ptr),
-                wrapper::vloadq(in_ptr + 4),
-                wrapper::vloadq(in_ptr + 8),
-                wrapper::vloadq(in_ptr + 12)
-            }
-        };
 
-        // Accumulate bias
-        if(has_bias)
+        int x = window_start_x;
+        for(; x < (window_end_x - window_step_x); x += window_step_x)
         {
-            const auto vb = wrapper::vdup_n(*reinterpret_cast<const int32_t *>(bias->ptr_to_element(Coordinates(id.z()))), TagType{});
-            v_in =
+            // Get bias and pointer to input
+            const auto  in_ptr = reinterpret_cast<int32_t *>(in.ptr()) + x;
+            int32x4x4_t v_in =
             {
                 {
-                    wrapper::vadd(v_in.val[0], vb),
-                    wrapper::vadd(v_in.val[1], vb),
-                    wrapper::vadd(v_in.val[2], vb),
-                    wrapper::vadd(v_in.val[3], vb)
+                    wrapper::vloadq(in_ptr),
+                    wrapper::vloadq(in_ptr + 4),
+                    wrapper::vloadq(in_ptr + 8),
+                    wrapper::vloadq(in_ptr + 12)
                 }
             };
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto vb = wrapper::vdup_n(*reinterpret_cast<const int32_t *>(bias->ptr_to_element(Coordinates(id.z()))), TagType{});
+                v_in =
+                {
+                    {
+                        wrapper::vadd(v_in.val[0], vb),
+                        wrapper::vadd(v_in.val[1], vb),
+                        wrapper::vadd(v_in.val[2], vb),
+                        wrapper::vadd(v_in.val[3], vb)
+                    }
+                };
+            }
+
+            const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x;
+            wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32,
+                                                           min, max, false));
         }
 
-        const auto out_ptr = reinterpret_cast<TOut *>(out.ptr());
-        wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max, false));
+        // Left-overs loop
+        for(; x < window_end_x; ++x)
+        {
+            // Get bias and pointer to input
+            int32_t s_in = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto b = *reinterpret_cast<const int32_t *>(bias->ptr_to_element(Coordinates(id.z())));
+                s_in += b;
+            }
+
+            const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x;
+            *out_ptr           = finalize_quantization(s_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift,
+                                                       std::numeric_limits<TOut>::lowest(), std::numeric_limits<TOut>::max(), false);
+        }
     },
     in, out);
 }
@@ -292,41 +305,71 @@ void output_stage_nhwc(ITensor *input, const ITensor *bias, const Window &window
     const VectorType max = wrapper::vdup_n(std::numeric_limits<TOut>::max(), TagType{});
 
     Window window_bias = window;
+    window_bias.set(Window::DimX, Window::Dimension(0, 1, 1));
     window_bias.set(Window::DimY, Window::Dimension(0, 0, 0));
     window_bias.set(Window::DimZ, Window::Dimension(0, 0, 0));
     window_bias.set(3, Window::Dimension(0, 0, 0));
 
-    Iterator in(input, window);
+    const int window_start_x = window.x().start();
+    const int window_end_x   = window.x().end();
+    const int window_step_x  = 16 / input->info()->element_size();
+    Window    win            = window;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Iterator in(input, win);
     Iterator bi(bias, window_bias);
+    Iterator out(output, win);
 
-    Iterator out(output, window);
-    execute_window_loop(window, [&](const Coordinates &)
+    execute_window_loop(win, [&](const Coordinates &)
     {
-        // Get bias and pointer to input
-        const auto  in_ptr = reinterpret_cast<int32_t *>(in.ptr());
-        int32x4x4_t v_in =
+        int x = window_start_x;
+        for(; x < (window_end_x - window_step_x); x += window_step_x)
         {
+            // Get bias and pointer to input
+            const auto  in_ptr = reinterpret_cast<int32_t *>(in.ptr()) + x;
+            int32x4x4_t v_in =
             {
-                wrapper::vloadq(in_ptr),
-                wrapper::vloadq(in_ptr + 4),
-                wrapper::vloadq(in_ptr + 8),
-                wrapper::vloadq(in_ptr + 12),
+                {
+                    wrapper::vloadq(in_ptr),
+                    wrapper::vloadq(in_ptr + 4),
+                    wrapper::vloadq(in_ptr + 8),
+                    wrapper::vloadq(in_ptr + 12),
+                }
+            };
+
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto bias_ptr = reinterpret_cast<int32_t *>(bi.ptr()) + x;
+
+                wrapper::vadd(v_in.val[0], wrapper::vloadq(bias_ptr));
+                wrapper::vadd(v_in.val[1], wrapper::vloadq(bias_ptr + 4));
+                wrapper::vadd(v_in.val[2], wrapper::vloadq(bias_ptr + 8));
+                wrapper::vadd(v_in.val[3], wrapper::vloadq(bias_ptr + 12));
             }
-        };
 
-        // Accumulate bias
-        if(has_bias)
+            const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x;
+            wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max, false));
+        }
+
+        // Left-overs loop
+        for(; x < window_end_x; ++x)
         {
-            const auto bias_ptr = reinterpret_cast<int32_t *>(bi.ptr());
+            // Get bias and pointer to input
+            const auto in_ptr = reinterpret_cast<int32_t *>(in.ptr()) + x;
+            int32_t    s_in   = *in_ptr;
 
-            wrapper::vadd(v_in.val[0], wrapper::vloadq(bias_ptr));
-            wrapper::vadd(v_in.val[1], wrapper::vloadq(bias_ptr + 4));
-            wrapper::vadd(v_in.val[2], wrapper::vloadq(bias_ptr + 8));
-            wrapper::vadd(v_in.val[3], wrapper::vloadq(bias_ptr + 12));
-        }
+            // Accumulate bias
+            if(has_bias)
+            {
+                const auto bias_ptr = reinterpret_cast<int32_t *>(bi.ptr()) + x;
+                s_in += *bias_ptr;
+            }
 
-        const auto out_ptr = reinterpret_cast<TOut *>(out.ptr());
-        wrapper::vstore(out_ptr, finalize_quantization(v_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift_s32, min, max, false));
+            const auto out_ptr = reinterpret_cast<TOut *>(out.ptr()) + x;
+            *out_ptr           = finalize_quantization(s_in, result_fixedpoint_multiplier, result_shift, result_offset_after_shift,
+                                                       std::numeric_limits<TOut>::lowest(), std::numeric_limits<TOut>::max(), false);
+        }
     },
     in, bi, out);
 }
@@ -352,10 +395,29 @@ void NEDirectConvolutionLayerOutputStageKernel::configure(ITensor *input, const
     _result_shift                 = info.result_shift;
     _result_offset_after_shift    = info.result_offset_after_shift;
 
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(input->info(), (bias == nullptr) ? nullptr : bias->info(), (output == nullptr) ? nullptr : output->info(), info);
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-    INEKernel::configure(win_config.second);
+    // Auto-initialize output output if required
+    if(output != nullptr && output->info() != nullptr)
+    {
+        // Work out expected output data type
+        const DataType output_dt = (input->info()->data_type() == DataType::S32) ? info.output_data_type : DataType::S32;
+        // Output tensor auto initialization if not yet initialized
+        auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(output_dt));
+    }
+
+    Window      win = calculate_max_window(*input->info(), Steps());
+    Coordinates coord;
+    coord.set_num_dimensions(input->info()->num_dimensions());
+
+    if(output != nullptr && (output->info()->total_size() != 0))
+    {
+        output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));
+    }
+    else
+    {
+        input->info()->set_valid_region(ValidRegion(coord, input->info()->tensor_shape()));
+    }
+
+    INEKernel::configure(win);
 
     const bool has_bias          = bias != nullptr;
     const bool is_qasymm8_signed = (output != nullptr) ? is_data_type_quantized_asymmetric_signed(output->info()->data_type()) : false;
@@ -435,11 +497,6 @@ Status NEDirectConvolutionLayerOutputStageKernel::validate(const ITensorInfo *in
                                                            const DirectConvolutionLayerOutputStageKernelInfo &info)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, info));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
-                                                              bias == nullptr ? nullptr : bias->clone().get(),
-                                                              output == nullptr ? nullptr : output->clone().get(),
-                                                              info)
-                                .first);
 
     return Status{};
 }