Remove padding from ClDirectConv2dKernel
- Delete old NCHW ClDirectConv2d kernels.
- Merge all kernels on a single file.
- Removed padding from ClDirectConv2dKernel
Resolves COMPMID-4721
Signed-off-by: Adnan AlSinan <adnan.alsinan@arm.com>
Change-Id: I624d218fb770e7b5f3c0acd4e85a21ae48470f55
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6779
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/Android.bp b/Android.bp
index 727e7c1..b6006be 100644
--- a/Android.bp
+++ b/Android.bp
@@ -81,10 +81,7 @@
"src/core/CL/cl_kernels/nchw/channel_shuffle.cl",
"src/core/CL/cl_kernels/nchw/depth_to_space.cl",
"src/core/CL/cl_kernels/nchw/dequantization_layer.cl",
- "src/core/CL/cl_kernels/nchw/direct_convolution1x1.cl",
- "src/core/CL/cl_kernels/nchw/direct_convolution3x3.cl",
- "src/core/CL/cl_kernels/nchw/direct_convolution5x5.cl",
- "src/core/CL/cl_kernels/nchw/direct_convolution_quantized.cl",
+ "src/core/CL/cl_kernels/nchw/direct_convolution.cl",
"src/core/CL/cl_kernels/nchw/im2col.cl",
"src/core/CL/cl_kernels/nchw/normalization_layer.cl",
"src/core/CL/cl_kernels/nchw/normalize_planar_yuv_layer.cl",
diff --git a/SConscript b/SConscript
index a8995ac..7e90101 100644
--- a/SConscript
+++ b/SConscript
@@ -353,10 +353,7 @@
'src/core/CL/cl_kernels/nchw/batchnormalization_layer.cl',
'src/core/CL/cl_kernels/nchw/channel_shuffle.cl',
'src/core/CL/cl_kernels/nchw/depth_to_space.cl',
- 'src/core/CL/cl_kernels/nchw/direct_convolution_quantized.cl',
- 'src/core/CL/cl_kernels/nchw/direct_convolution1x1.cl',
- 'src/core/CL/cl_kernels/nchw/direct_convolution3x3.cl',
- 'src/core/CL/cl_kernels/nchw/direct_convolution5x5.cl',
+ 'src/core/CL/cl_kernels/nchw/direct_convolution.cl',
'src/core/CL/cl_kernels/nchw/dequantization_layer.cl',
'src/core/CL/cl_kernels/nchw/im2col.cl',
'src/core/CL/cl_kernels/nchw/normalization_layer.cl',
diff --git a/src/core/CL/cl_kernels/nchw/direct_convolution.cl b/src/core/CL/cl_kernels/nchw/direct_convolution.cl
new file mode 100644
index 0000000..866f62d
--- /dev/null
+++ b/src/core/CL/cl_kernels/nchw/direct_convolution.cl
@@ -0,0 +1,147 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "helpers.h"
+#include "helpers_asymm.h"
+
+/** This kernel performs a direct convolution to convolve the low three dimensions.
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The data size must be passed at compile time using -DDATA_SIZE e.g. -DDATA_SIZE=32
+ * @note The convolution stride x must be passed at compile time using -DSTRIDE_X e.g. -DSTRIDE_X=1
+ * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
+ * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
+ * @note The output quantization multiplier must be passed at compile time using -DOUTPUT_MULTIPLIER e.g. -DOUTPUT_MULTIPLIER=1234
+ * @note The output quantization shift must be passed at compile time using -DOUTPUT_SHIFT e.g. -DOUTPUT_SHIFT=4
+ * @note The input offset quantization parameter must be passed at compile time using -DINPUT_OFFSET e.g. -DINPUT_OFFSET=3
+ * @note The weights offset quantization parameter must be passed at compile time using -DWEIGHTS_OFFSET e.g. -DWEIGHTS_OFFSET=3
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
+ * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
+ * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
+ * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
+ * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
+ * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
+ * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
+ * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
+ * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
+ * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
+ */
+__kernel void direct_convolution_nchw(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+ TENSOR3D_DECLARATION(weights),
+#ifdef HAS_BIAS
+ VECTOR_DECLARATION(biases),
+#endif /* defined(HAS_BIAS) */
+ unsigned int weights_stride_w)
+{
+ const int id0 = get_global_id(0);
+ const int id1 = get_global_id(1);
+ const int id2 = get_global_id(2);
+
+ const int x_coords = (id0 * STRIDE_X) - PAD_LEFT;
+ const int y_coords = (id1 * STRIDE_Y) - PAD_TOP;
+
+ const int x_offs = max((int)(get_global_id(0) * VEC_SIZE - (VEC_SIZE - VEC_SIZE_LEFTOVER) % VEC_SIZE), 0) * sizeof(DATA_TYPE);
+
+ __global uchar *src_addr = (__global uchar *)(src_ptr + src_offset_first_element_in_bytes);
+ __global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + id2 * weights_stride_w);
+ __global uchar *dst_addr = (__global uchar *)dst_ptr + dst_offset_first_element_in_bytes + x_offs + id1 * dst_stride_y + id2 * dst_stride_z;
+
+#ifdef IS_QUANTIZED
+ int acc_value = 0;
+#else /* IS_QUANTIZED */
+ DATA_TYPE acc_value = 0;
+#endif /* IS_QUANTIZED */
+ for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
+ {
+ for(int y = 0; y < WEI_HEIGHT; ++y)
+ {
+ for(int x = 0; x < WEI_WIDTH; ++x)
+ {
+ const int idx_x = (x_coords + x);
+ const int idx_y = (y_coords + y);
+ if((idx_x >= 0 && idx_x < SRC_WIDTH) && (idx_y >= 0 && idx_y < SRC_HEIGHT))
+ {
+ const int weight_offset = x + (WEI_HEIGHT * y);
+ const int input_offset = idx_x + SRC_WIDTH * idx_y;
+#ifdef IS_QUANTIZED
+ int weight = convert_int(*((__global DATA_TYPE *)weights_addr + weight_offset));
+ int input = convert_int(*((__global DATA_TYPE *)src_addr + input_offset));
+ acc_value += (input + INPUT_OFFSET) * (weight + WEIGHTS_OFFSET);
+#else /* IS_QUANTIZED */
+ DATA_TYPE weight = *((__global DATA_TYPE *)weights_addr + weight_offset);
+ DATA_TYPE input = *((__global DATA_TYPE *)src_addr + input_offset);
+ acc_value += input * weight;
+#endif /* IS_QUANTIZED */
+ }
+ }
+ }
+ src_addr += src_stride_z;
+ weights_addr += weights_stride_z;
+ }
+
+#ifdef HAS_BIAS
+
+ Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
+#ifdef IS_QUANTIZED
+ int bias = *((__global int *)(vector_offset(&biases, id2)));
+#else /* IS_QUANTIZED */
+ DATA_TYPE bias = *((__global DATA_TYPE *)(vector_offset(&biases, id2)));
+#endif /* IS_QUANTIZED */
+ acc_value += bias;
+
+#endif /* defined(HAS_BIAS) */
+
+#ifdef IS_QUANTIZED
+
+#if OUTPUT_SHIFT < 0
+ acc_value = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(acc_value, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 1);
+#else // OUTPUT_SHIFT < 0
+ acc_value = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(acc_value, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 1);
+#endif // OUTPUT_SHIFT < 0
+ acc_value = acc_value + OUTPUT_OFFSET;
+#endif /* IS_QUANTIZED */
+
+ *(__global DATA_TYPE *)dst_addr = CONVERT_SAT(acc_value, DATA_TYPE);
+}
\ No newline at end of file
diff --git a/src/core/CL/cl_kernels/nchw/direct_convolution1x1.cl b/src/core/CL/cl_kernels/nchw/direct_convolution1x1.cl
deleted file mode 100644
index 8ab2d1d..0000000
--- a/src/core/CL/cl_kernels/nchw/direct_convolution1x1.cl
+++ /dev/null
@@ -1,316 +0,0 @@
-/*
- * Copyright (c) 2016-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "helpers.h"
-
-#undef CONVERT_SAT
-
-#define ADD_OP(a, b) ((a) + (b))
-#define MUL_OP(a, b) ((a) * (b))
-#define CONVERT_SAT(a, b) ((a))
-
-#if defined(DATA_TYPE) && defined(DATA_SIZE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
-
-#if STRIDE_X == 3
-#define INPUT_PIXEL_STR(data_size) extract_input_stride3_##data_size
-#define INPUT_PIXEL(data_size) INPUT_PIXEL_STR(data_size)
-#elif STRIDE_X == 2
-#define INPUT_PIXEL(data_size) extract_input_stride2
-#elif STRIDE_X == 1
-#define INPUT_PIXEL(data_size) extract_input_stride1
-#else /* STRIDE_X not equals 1, 2 or 3 */
-#error "Only support strides 1, 2 and 3"
-#endif /* STRIDE_X == 3 */
-
-/** Extracts a 1D horizontal vector from the input tensor with stride as 1.
- *
- * @param[in] input_pixel Pointer to the first pixel.
- *
- * @return extracted input values.
- */
-inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride1(__global const DATA_TYPE *input_pixel)
-{
- return vload8(0, input_pixel);
-}
-
-/** Extracts a 1D horizontal vector from the input tensor with stride as 2.
- *
- * @param[in] input_pixel Pointer to the first pixel.
- *
- * @return extracted input values.
- */
-inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride2(__global const DATA_TYPE *input_pixel)
-{
- VEC_DATA_TYPE(DATA_TYPE, 16)
- temp = vload16(0, input_pixel);
- return temp.s02468ace;
-}
-
-/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 32-bit data size.
- *
- * @param[in] input_pixel Pointer to the first pixel.
- *
- * @return extracted input values.
- */
-inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_32(__global const DATA_TYPE *input_pixel)
-{
- VEC_DATA_TYPE(DATA_TYPE, 4)
- temp1 = vload4(0, input_pixel);
- VEC_DATA_TYPE(DATA_TYPE, 4)
- temp2 = vload4(0, input_pixel + 6);
- VEC_DATA_TYPE(DATA_TYPE, 4)
- temp3 = vload4(0, input_pixel + 12);
- VEC_DATA_TYPE(DATA_TYPE, 4)
- temp4 = vload4(0, input_pixel + 18);
- return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s03, temp2.s03, temp3.s03, temp4.s03);
-}
-
-/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 16-bit data size.
- *
- * @param[in] input_pixel Pointer to the first pixel.
- *
- * @return extracted input values.
- */
-inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_16(__global const DATA_TYPE *input_pixel)
-{
- VEC_DATA_TYPE(DATA_TYPE, 8)
- temp1 = vload8(0, input_pixel);
- VEC_DATA_TYPE(DATA_TYPE, 8)
- temp2 = vload8(0, input_pixel + 8);
- VEC_DATA_TYPE(DATA_TYPE, 8)
- temp3 = vload8(0, input_pixel + 16);
- return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s036, temp2.s147, temp3.s25);
-}
-
-/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 8-bit data size.
- *
- * @param[in] input_pixel Pointer to the first pixel.
- *
- * @return extracted input values.
- */
-inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_8(__global const DATA_TYPE *input_pixel)
-{
- VEC_DATA_TYPE(DATA_TYPE, 16)
- temp1 = vload16(0, input_pixel);
- VEC_DATA_TYPE(DATA_TYPE, 16)
- temp2 = vload16(0, input_pixel + 12);
- return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s0369, temp2.s0369);
-}
-
-/** This kernel performs a direct convolution to convolve the low three dimensions.
- *
- * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
- * @note The data size must be passed at compile time using -DDATA_SIZE e.g. -DDATA_SIZE=32
- * @note The convolution stride x must be passed at compile time using -DSTRIDE_X e.g. -DSTRIDE_X=1
- * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
- * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
- * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
- * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
- * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
- * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
- * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
- * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
- * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
- * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
- * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
- */
-__kernel void direct_convolution1x1(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst),
- TENSOR3D_DECLARATION(weights),
-#ifdef HAS_BIAS
- VECTOR_DECLARATION(biases),
-#endif /* defined(HAS_BIAS) */
- unsigned int weights_stride_w)
-{
- Image src = CONVERT_TO_IMAGE_STRUCT(src);
- Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
- Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
-
-#ifdef HAS_BIAS
- Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
-#endif /* defined(HAS_BIAS) */
-
- VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)
- values = 0;
-
- const uint z_index = get_global_id(2);
-
- weights.ptr += z_index * weights_stride_w;
- for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
- {
- DATA_TYPE weight = *(__global DATA_TYPE *)weights.ptr;
- VEC_DATA_TYPE(DATA_TYPE, 8)
- input_pixel = INPUT_PIXEL(DATA_SIZE)((__global DATA_TYPE *)src.ptr);
- values = ADD_OP(values, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))weight, input_pixel));
- src.ptr += src_stride_z;
- weights.ptr += weights_stride_z;
- }
-
-#ifdef HAS_BIAS
- values = ADD_OP(values, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, z_index))));
-#endif /* defined(HAS_BIAS) */
-
- vstore8(CONVERT_SAT(values, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr);
-}
-#endif // defined(DATA_TYPE) && defined(DATA_SIZE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
-
-#if defined(WEIGHTS_DEPTH)
-
-#define CONVOLUTION1x1_BIFROST(acc, src, weight_value) \
- ({ \
- acc.s0 = mad(src.s0, weight_value, acc.s0); \
- acc.s1 = mad(src.s1, weight_value, acc.s1); \
- acc.s2 = mad(src.s2, weight_value, acc.s2); \
- acc.s3 = mad(src.s3, weight_value, acc.s3); \
- })
-
-/** An optimized direct convolution 1x1 OpenCL kernel for Bifrost architectures when the data type is F32
- *
- * @note This OpenCL kernel works only with stride_x and stride_y equal to 1
- * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
- * @note In case biases, -DHAS_BIAS must to be passed at compile
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
- * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
- * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
- * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
- * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
- * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
- * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
- * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
- * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
- * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
- */
-__kernel void direct_convolution1x1_f32_bifrost(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst),
- TENSOR3D_DECLARATION(weights),
-#ifdef HAS_BIAS
- VECTOR_DECLARATION(biases),
-#endif /* defined(HAS_BIAS) */
- unsigned int weights_stride_w)
-{
- // Get the kernel index
- const int kernel_index = get_global_id(2);
-
- Image src = CONVERT_TO_IMAGE_STRUCT(src);
- Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
-
- float4 acc0 = 0.0f;
- float4 acc1 = 0.0f;
- float4 acc2 = 0.0f;
- float4 acc3 = 0.0f;
-
- __global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + kernel_index * weights_stride_w);
- __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);
-
- for(ushort d = 0; d < (ushort)WEIGHTS_DEPTH; ++d)
- {
- // Load the weights
- float weight = *((__global float *)weights_addr);
-
- // Load values from row0 of input tensor
- float4 src0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
- float4 src1 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
- float4 src2 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
- float4 src3 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
-
- CONVOLUTION1x1_BIFROST(acc0, src0, weight);
- CONVOLUTION1x1_BIFROST(acc1, src1, weight);
- CONVOLUTION1x1_BIFROST(acc2, src2, weight);
- CONVOLUTION1x1_BIFROST(acc3, src3, weight);
-
- src_addr += src_stride_z;
- weights_addr += weights_stride_z;
- }
-
-#ifdef HAS_BIAS
- Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
-
- float bias = (float) * ((__global float *)(vector_offset(&biases, kernel_index)));
-
- acc0.s0 += bias;
- acc0.s1 += bias;
- acc0.s2 += bias;
- acc0.s3 += bias;
- acc1.s0 += bias;
- acc1.s1 += bias;
- acc1.s2 += bias;
- acc1.s3 += bias;
- acc2.s0 += bias;
- acc2.s1 += bias;
- acc2.s2 += bias;
- acc2.s3 += bias;
- acc3.s0 += bias;
- acc3.s1 += bias;
- acc3.s2 += bias;
- acc3.s3 += bias;
-#endif /* defined(HAS_BIAS) */
-
- vstore4(acc0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y));
- vstore4(acc1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y));
- vstore4(acc2, 0, (__global float *)(dst.ptr + 2 * dst_stride_y));
- vstore4(acc3, 0, (__global float *)(dst.ptr + 3 * dst_stride_y));
-}
-#endif // defined(WEIGHTS_DEPTH)
diff --git a/src/core/CL/cl_kernels/nchw/direct_convolution3x3.cl b/src/core/CL/cl_kernels/nchw/direct_convolution3x3.cl
deleted file mode 100644
index 811df05..0000000
--- a/src/core/CL/cl_kernels/nchw/direct_convolution3x3.cl
+++ /dev/null
@@ -1,291 +0,0 @@
-/*
- * Copyright (c) 2016-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "helpers.h"
-
-#undef CONVERT_SAT
-
-#define ADD_OP(a, b) ((a) + (b))
-#define MUL_OP(a, b) ((a) * (b))
-#define CONVERT_SAT(a, b) ((a))
-
-#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
-
-#if STRIDE_X == 1
-#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr)
-#elif STRIDE_X == 2 /* STRIDE_X == 1 */
-#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr)
-#else /* STRIDE_X not equals 1 or 2 */
-#error "STRIDE_X larger than 2 is not supported"
-#endif /* STRIDE_X == 2 */
-
-#define CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr) \
- ({ \
- VEC_DATA_TYPE(DATA_TYPE, 3) \
- weights_values0 = vload3(0, weights_row_ptr); \
- VEC_DATA_TYPE(DATA_TYPE, 8) \
- src0 = vload8(0, src_row_ptr); \
- VEC_DATA_TYPE(DATA_TYPE, 2) \
- src1 = vload2(0, src_row_ptr + 8); \
- \
- acc = ADD_OP(acc, MUL_OP(src0, (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0)); \
- acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1)); \
- acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2)); \
- })
-
-#define CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr) \
- ({ \
- VEC_DATA_TYPE(DATA_TYPE, 3) \
- weights_values0 = vload3(0, weights_row_ptr); \
- VEC_DATA_TYPE(DATA_TYPE, 16) \
- src0 = vload16(0, src_row_ptr); \
- DATA_TYPE src1 = *(src_row_ptr + 16); \
- \
- acc = ADD_OP(acc, MUL_OP(src0.even, (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0)); \
- acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1)); \
- acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2)); \
- })
-
-/** This kernel performs a direct convolution to convolve the low three dimensions.
- *
- * @note This OpenCL kernel works with stride_x = 1 and 2
- * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
- * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
- * @note If biases are used then -DHAS_BIAS has to be passed at compile time
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
- * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
- * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
- * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
- * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
- * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
- * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
- * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
- * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
- * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
- */
-__kernel void direct_convolution3x3(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst),
- TENSOR3D_DECLARATION(weights),
-#ifdef HAS_BIAS
- VECTOR_DECLARATION(biases),
-#endif /* defined(HAS_BIAS) */
- unsigned int weights_stride_w)
-{
- Image src = CONVERT_TO_IMAGE_STRUCT(src);
- Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
- Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
-
- VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)
- values0 = 0;
-
- __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);
- __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);
-
- const int kernel_index = get_global_id(2);
- weights_addr += kernel_index * weights_stride_w;
-
- for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
- {
- CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y));
- CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
- CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
-
- src_addr += src_stride_z;
- weights_addr += weights_stride_z;
- }
-
-#ifdef HAS_BIAS
- Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
-
- values0 = ADD_OP(values0, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index))));
-#endif /* defined(HAS_BIAS) */
-
- vstore8(CONVERT_SAT(values0, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr);
-}
-#endif //defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
-
-#if defined(WEIGHTS_DEPTH)
-
-#define CONVOLUTION1x3_BIFROST(acc, src0, src1, weights_row0) \
- ({ \
- acc.s0 = mad(src0.s0, weights_row0.s0, acc.s0); \
- acc.s1 = mad(src0.s1, weights_row0.s0, acc.s1); \
- acc.s2 = mad(src0.s2, weights_row0.s0, acc.s2); \
- acc.s3 = mad(src0.s3, weights_row0.s0, acc.s3); \
- acc.s0 = mad(src0.s1, weights_row0.s1, acc.s0); \
- acc.s1 = mad(src0.s2, weights_row0.s1, acc.s1); \
- acc.s2 = mad(src0.s3, weights_row0.s1, acc.s2); \
- acc.s3 = mad(src1.s0, weights_row0.s1, acc.s3); \
- acc.s0 = mad(src0.s2, weights_row0.s2, acc.s0); \
- acc.s1 = mad(src0.s3, weights_row0.s2, acc.s1); \
- acc.s2 = mad(src1.s0, weights_row0.s2, acc.s2); \
- acc.s3 = mad(src1.s1, weights_row0.s2, acc.s3); \
- })
-
-/** An optimized direct convolution 3x3 OpenCL kernel for Bifrost architectures when the data type is F32
- *
- * @note This OpenCL kernel works only with stride_x and stride_y equal to 1
- * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
- * @note In case biases, -DHAS_BIAS must to be passed at compile
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
- * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
- * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
- * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
- * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
- * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
- * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
- * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
- * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
- * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
- */
-__kernel void direct_convolution3x3_f32_bifrost(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst),
- TENSOR3D_DECLARATION(weights),
-#ifdef HAS_BIAS
- VECTOR_DECLARATION(biases),
-#endif /* defined(HAS_BIAS) */
- unsigned int weights_stride_w)
-{
- // Get the kernel index
- const int kernel_index = get_global_id(2);
-
- Image src = CONVERT_TO_IMAGE_STRUCT(src);
- Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
-
- float4 values0 = 0;
- float4 values1 = 0;
- float4 values2 = 0;
-
- __global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + kernel_index * weights_stride_w);
- __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);
-
- // Note: Since each work-item computes 4x3 elements, we need to load 5 rows from the input tensor
-
- for(ushort d = 0; d < (ushort)WEIGHTS_DEPTH; ++d)
- {
- // Load the weights
- float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y));
- float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y));
- float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y));
- float4 src0;
- float2 src1;
-
- // Load values from row0 of input tensor
- src0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
- src1 = vload2(0, (__global float *)(src_addr + 0 * src_stride_y) + 4);
-
- CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row0);
-
- // Load values from row1 of input tensor
- src0 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
- src1 = vload2(0, (__global float *)(src_addr + 1 * src_stride_y) + 4);
-
- // Accumulate
- CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row1);
- CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row0);
-
- // Load values from row2 of input tensor
- src0 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
- src1 = vload2(0, (__global float *)(src_addr + 2 * src_stride_y) + 4);
-
- // Accumulate
- CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row2);
- CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row1);
- CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row0);
-
- // Load values from row3 of input tensor
- src0 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
- src1 = vload2(0, (__global float *)(src_addr + 3 * src_stride_y) + 4);
-
- // Accumulate
- CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row2);
- CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row1);
-
- // Row4
- src0 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y));
- src1 = vload2(0, (__global float *)(src_addr + 4 * src_stride_y) + 4);
-
- // Accumulate
- CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row2);
-
- src_addr += src_stride_z;
- weights_addr += weights_stride_z;
- }
-
-#ifdef HAS_BIAS
- Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
-
- float bias = (float) * ((__global float *)(vector_offset(&biases, kernel_index)));
-
- values0 += (float4)bias;
- values1 += (float4)bias;
- values2 += (float4)bias;
-#endif /* defined(HAS_BIAS) */
-
- vstore4(values0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y));
- vstore4(values1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y));
- vstore4(values2, 0, (__global float *)(dst.ptr + 2 * dst_stride_y));
-}
-#endif // defined(WEIGHTS_DEPTH)
diff --git a/src/core/CL/cl_kernels/nchw/direct_convolution5x5.cl b/src/core/CL/cl_kernels/nchw/direct_convolution5x5.cl
deleted file mode 100644
index 59d668f..0000000
--- a/src/core/CL/cl_kernels/nchw/direct_convolution5x5.cl
+++ /dev/null
@@ -1,313 +0,0 @@
-/*
- * Copyright (c) 2016-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "helpers.h"
-
-#undef CONVERT_SAT
-
-#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
-
-#if STRIDE_X == 1
-#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr)
-#elif STRIDE_X == 2 /* STRIDE_X == 1 */
-#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr)
-#else /* STRIDE_X not equals 1 or 2 */
-#error "STRIDE_X larger than 2 is not supported"
-#endif /* STRIDE_X == 2 */
-
-#define CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr) \
- ({ \
- VEC_DATA_TYPE(DATA_TYPE, 4) \
- weights_values0 = vload4(0, weights_row_ptr); \
- DATA_TYPE weights_value1 = *(weights_row_ptr + 4); \
- VEC_DATA_TYPE(DATA_TYPE, 8) \
- src0 = vload8(0, src_row_ptr); \
- VEC_DATA_TYPE(DATA_TYPE, 4) \
- src1 = vload4(0, src_row_ptr + 8); \
- \
- acc += src0 * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \
- acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \
- acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \
- acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s345, src0.s67, src1.s012) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \
- acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s45, src0.s67, src1.s0123) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \
- })
-
-#define CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr) \
- ({ \
- VEC_DATA_TYPE(DATA_TYPE, 4) \
- weights_values0 = vload4(0, weights_row_ptr); \
- DATA_TYPE weights_value1 = *(weights_row_ptr + 4); \
- VEC_DATA_TYPE(DATA_TYPE, 16) \
- src0 = vload16(0, src_row_ptr); \
- VEC_DATA_TYPE(DATA_TYPE, 4) \
- src1 = vload4(0, src_row_ptr + 16); \
- acc += src0.even * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \
- acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \
- acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \
- \
- acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s3579, src0.sBDF, src1.s1) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \
- acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468a, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \
- })
-
-/** This kernel performs a direct convolution to convolve the low three dimensions.
- *
- * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
- * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
- * @note If biases are used then -DHAS_BIAS has to be passed at compile time
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
- * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
- * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
- * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
- * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
- * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
- * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
- * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
- * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
- * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
- */
-__kernel void direct_convolution5x5(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst),
- TENSOR3D_DECLARATION(weights),
-#ifdef HAS_BIAS
- VECTOR_DECLARATION(biases),
-#endif /* defined(HAS_BIAS) */
- unsigned int weights_stride_w)
-{
- Image src = CONVERT_TO_IMAGE_STRUCT(src);
- Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
- Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
-
- VEC_DATA_TYPE(DATA_TYPE, 8)
- values0 = 0;
-
- __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);
- __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);
-
- const int kernel_index = get_global_id(2);
- weights_addr += kernel_index * weights_stride_w;
-
- for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
- {
- CONVOLUTION1x5(values0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr);
- CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
- CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
- CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));
- CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));
-
- src_addr += src_stride_z;
- weights_addr += weights_stride_z;
- }
-
-#ifdef HAS_BIAS
- Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
-
- values0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index)));
-#endif /* defined(HAS_BIAS) */
-
- vstore8(values0, 0, (__global DATA_TYPE *)dst.ptr);
-}
-#endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
-
-#if defined(WEIGHTS_DEPTH)
-
-#define CONVOLUTION1x5_BIFROST(acc, src0, weights_row00, weights_row01) \
- ({ \
- acc.s0 = mad(src0.s0, weights_row00.s0, acc.s0); \
- acc.s1 = mad(src0.s1, weights_row00.s0, acc.s1); \
- acc.s2 = mad(src0.s2, weights_row00.s0, acc.s2); \
- acc.s3 = mad(src0.s3, weights_row00.s0, acc.s3); \
- acc.s0 = mad(src0.s1, weights_row00.s1, acc.s0); \
- acc.s1 = mad(src0.s2, weights_row00.s1, acc.s1); \
- acc.s2 = mad(src0.s3, weights_row00.s1, acc.s2); \
- acc.s3 = mad(src0.s4, weights_row00.s1, acc.s3); \
- acc.s0 = mad(src0.s2, weights_row00.s2, acc.s0); \
- acc.s1 = mad(src0.s3, weights_row00.s2, acc.s1); \
- acc.s2 = mad(src0.s4, weights_row00.s2, acc.s2); \
- acc.s3 = mad(src0.s5, weights_row00.s2, acc.s3); \
- acc.s0 = mad(src0.s3, weights_row00.s3, acc.s0); \
- acc.s1 = mad(src0.s4, weights_row00.s3, acc.s1); \
- acc.s2 = mad(src0.s5, weights_row00.s3, acc.s2); \
- acc.s3 = mad(src0.s6, weights_row00.s3, acc.s3); \
- acc.s0 = mad(src0.s4, weights_row01, acc.s0); \
- acc.s1 = mad(src0.s5, weights_row01, acc.s1); \
- acc.s2 = mad(src0.s6, weights_row01, acc.s2); \
- acc.s3 = mad(src0.s7, weights_row01, acc.s3); \
- })
-
-/** An optimized direct convolution 5x5 OpenCL kernel for Bifrost architectures when the data type is F32
- *
- * @note This OpenCL kernel works only with stride_x and stride_y equal to 1
- * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
- * @note If biases are used then -DHAS_BIAS has to be passed at compile time
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
- * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
- * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
- * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
- * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
- * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
- * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
- * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
- * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
- * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
- */
-__kernel void direct_convolution5x5_f32_bifrost(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst),
- TENSOR3D_DECLARATION(weights),
-#ifdef HAS_BIAS
- VECTOR_DECLARATION(biases),
-#endif /* defined(HAS_BIAS) */
- unsigned int weights_stride_w)
-{
- // Get the kernel index
- const int kernel_index = get_global_id(2);
-
- Image src = CONVERT_TO_IMAGE_STRUCT(src);
- Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
-
- float4 values0 = 0.0f;
- float4 values1 = 0.0f;
-
- __global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + kernel_index * weights_stride_w);
- __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);
-
- // Note: Since each work-item computes 4x2 elements, we need to load 6 rows from the input tensor
-
- for(ushort d = 0; d < (ushort)WEIGHTS_DEPTH; ++d)
- {
- // Load the weights from row0 and row1
- float4 weights_row00 = vload4(0, (__global float *)(weights_addr + 0 * weights_stride_y));
- float weights_row01 = *((__global float *)(weights_addr + 0 * weights_stride_y) + 4);
- float4 weights_row10 = vload4(0, (__global float *)(weights_addr + 1 * weights_stride_y));
- float weights_row11 = *((__global float *)(weights_addr + 1 * weights_stride_y) + 4);
- float8 src0;
-
- // Load values from row0 of input tensor
- src0 = vload8(0, (__global float *)(src_addr + 0 * src_stride_y));
-
- // Accumulate
- CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);
-
- // Load values from row1 of input tensor
- src0 = vload8(0, (__global float *)(src_addr + 1 * src_stride_y));
-
- // Accumulate
- CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11);
- CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);
-
- // Load values from row2 of input tensor
- src0 = vload8(0, (__global float *)(src_addr + 2 * src_stride_y));
-
- // Load weights from row2
- weights_row00 = vload4(0, (__global float *)(weights_addr + 2 * weights_stride_y));
- weights_row01 = *((__global float *)(weights_addr + 2 * weights_stride_y) + 4);
-
- // Accumulate
- CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);
- CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11);
-
- // Load values from row3 of input tensor
- src0 = vload8(0, (__global float *)(src_addr + 3 * src_stride_y));
-
- // Load weights from row3
- weights_row10 = vload4(0, (__global float *)(weights_addr + 3 * weights_stride_y));
- weights_row11 = *((__global float *)(weights_addr + 3 * weights_stride_y) + 4);
-
- // Accumulate
- CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11);
- CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);
-
- // Load values from row4 of input tensor
- src0 = vload8(0, (__global float *)(src_addr + 4 * src_stride_y));
-
- // Load weights from row4
- weights_row00 = vload4(0, (__global float *)(weights_addr + 4 * weights_stride_y));
- weights_row01 = *((__global float *)(weights_addr + 4 * weights_stride_y) + 4);
-
- CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);
- CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11);
-
- // Load values from row5 of input tensor
- src0 = vload8(0, (__global float *)(src_addr + 5 * src_stride_y));
-
- // Accumulate
- CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);
-
- src_addr += src_stride_z;
- weights_addr += weights_stride_z;
- }
-
-#ifdef HAS_BIAS
- Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
-
- float4 bias = (float4) * ((__global float *)(vector_offset(&biases, kernel_index)));
-
- values0 += bias;
- values1 += bias;
-#endif /* defined(HAS_BIAS) */
-
- vstore4(values0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y));
- vstore4(values1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y));
-}
-#endif // defined(WEIGHTS_DEPTH)
diff --git a/src/core/CL/cl_kernels/nchw/direct_convolution_quantized.cl b/src/core/CL/cl_kernels/nchw/direct_convolution_quantized.cl
deleted file mode 100644
index b80d4f5..0000000
--- a/src/core/CL/cl_kernels/nchw/direct_convolution_quantized.cl
+++ /dev/null
@@ -1,308 +0,0 @@
-/*
- * Copyright (c) 2017-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "helpers_asymm.h"
-
-#undef CONVERT_SAT_STR
-#undef CONVERT_SAT
-
-#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT)
-
-#define CONVERT_SAT_STR(x, type) (convert_##type##8_sat((x)))
-#define CONVERT_SAT(x, type) CONVERT_SAT_STR(x, type)
-
-#if KERNEL_SIZE == 9
-
-#if STRIDE_X == 1
-#define CONVOLUTION1x9(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x9_STRIDE1(acc, src_row_ptr, weights_row_ptr)
-#elif STRIDE_X == 2
-#define CONVOLUTION1x9(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x9_STRIDE2(acc, src_row_ptr, weights_row_ptr)
-#else /* STRIDE_X not equals 1 or 2 */
-#error "STRIDE_X larger than 2 is not supported"
-#endif /* STRIDE_X */
-
-#define CONVOLUTION1x9_STRIDE1(acc, src_row_ptr, weights_row_ptr) \
- ({ \
- int8 weights_values0 = convert_int8(vload8(0, weights_row_ptr)); \
- int weights_value1 = convert_int(*(weights_row_ptr + 8)); \
- int16 src0 = convert_int16(vload16(0, src_row_ptr)); \
- acc += (src0.lo + INPUT_OFFSET) * ((int8)weights_values0.s0 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s1234, src0.s5678) + INPUT_OFFSET) * ((int8)weights_values0.s1 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s2345, src0.s6789) + INPUT_OFFSET) * ((int8)weights_values0.s2 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s3456, src0.s789A) + INPUT_OFFSET) * ((int8)weights_values0.s3 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s4567, src0.s89AB) + INPUT_OFFSET) * ((int8)weights_values0.s4 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s5678, src0.s9ABC) + INPUT_OFFSET) * ((int8)weights_values0.s5 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s6789, src0.sABCD) + INPUT_OFFSET) * ((int8)weights_values0.s6 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s789A, src0.sBCDE) + INPUT_OFFSET) * ((int8)weights_values0.s7 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s89AB, src0.sCDEF) + INPUT_OFFSET) * ((int8)weights_value1 + WEIGHTS_OFFSET); \
- })
-
-#define CONVOLUTION1x9_STRIDE2(acc, src_row_ptr, weights_row_ptr) \
- ({ \
- int8 weights_values0 = convert_int8(vload8(0, weights_row_ptr)); \
- int weights_value1 = convert_int(*(weights_row_ptr + 8)); \
- int16 src0 = convert_int16(vload16(0, src_row_ptr)); \
- int8 src1 = convert_int8(vload8(0, src_row_ptr + 16)); \
- acc += (src0.even + INPUT_OFFSET) * ((int8)weights_values0.s0 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s1357, src0.s9BDF) + INPUT_OFFSET) * ((int8)weights_values0.s1 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s2468, src0.sACE, src1.s0) + INPUT_OFFSET) * ((int8)weights_values0.s2 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s3579, src0.sBDF, src1.s1) + INPUT_OFFSET) * ((int8)weights_values0.s3 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s468A, src0.sCE, src1.s02) + INPUT_OFFSET) * ((int8)weights_values0.s4 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s579B, src0.sDF, src1.s13) + INPUT_OFFSET) * ((int8)weights_values0.s5 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s68AC, src0.sE, src1.s024) + INPUT_OFFSET) * ((int8)weights_values0.s6 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s79BD, src0.sF, src1.s135) + INPUT_OFFSET) * ((int8)weights_values0.s7 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s8ACE, src1.s0246) + INPUT_OFFSET) * ((int8)weights_value1 + WEIGHTS_OFFSET); \
- })
-
-#elif KERNEL_SIZE == 5
-
-#if STRIDE_X == 1
-#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr)
-#elif STRIDE_X == 2
-#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr)
-#else /* STRIDE_X not equals 1 or 2 */
-#error "STRIDE_X larger than 2 is not supported"
-#endif /* STRIDE_X */
-
-#define CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr) \
- ({ \
- int4 weights_values0 = convert_int4(vload4(0, weights_row_ptr)); \
- int weights_value1 = convert_int(*(weights_row_ptr + 4)); \
- int8 src0 = convert_int8(vload8(0, src_row_ptr)); \
- int4 src1 = convert_int4(vload4(0, src_row_ptr + 8)); \
- acc += (src0 + INPUT_OFFSET) * ((int8)weights_values0.s0 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s1234, src0.s567, src1.s0) + INPUT_OFFSET) * ((int8)weights_values0.s1 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s234, src0.s567, src1.s01) + INPUT_OFFSET) * ((int8)weights_values0.s2 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s345, src0.s67, src1.s012) + INPUT_OFFSET) * ((int8)weights_values0.s3 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s45, src0.s67, src1.s0123) + INPUT_OFFSET) * ((int8)weights_value1 + WEIGHTS_OFFSET); \
- })
-
-#define CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr) \
- ({ \
- int4 weights_values0 = convert_int4(vload4(0, weights_row_ptr)); \
- int weights_value1 = convert_int(*(weights_row_ptr + 4)); \
- int16 src0 = convert_int16(vload16(0, src_row_ptr)); \
- int4 src1 = convert_int4(vload4(0, src_row_ptr + 16)); \
- acc += (src0.even + INPUT_OFFSET) * ((int8)weights_values0.s0 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s1357, src0.s9BDF) + INPUT_OFFSET) * ((int8)weights_values0.s1 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s2468, src0.sACE, src1.s0) + INPUT_OFFSET) * ((int8)weights_values0.s2 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s3579, src0.sBDF, src1.s1) + INPUT_OFFSET) * ((int8)weights_values0.s3 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s468a, src0.sCE, src1.s02) + INPUT_OFFSET) * ((int8)weights_value1 + WEIGHTS_OFFSET); \
- })
-
-#elif KERNEL_SIZE == 3
-
-#if STRIDE_X == 1
-#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr)
-#elif STRIDE_X == 2
-#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr)
-#else /* STRIDE_X not equals 1 or 2 */
-#error "STRIDE_X larger than 2 is not supported"
-#endif /* STRIDE_X */
-
-#define CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr) \
- ({ \
- int3 weights_values0 = convert_int3(vload3(0, weights_row_ptr)); \
- int8 src0 = convert_int8(vload8(0, src_row_ptr)); \
- int2 src1 = convert_int2(vload2(0, src_row_ptr + 8)); \
- acc += (src0 + INPUT_OFFSET) * ((int8)weights_values0.s0 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s1234, src0.s567, src1.s0) + INPUT_OFFSET) * ((int8)weights_values0.s1 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s234, src0.s567, src1.s01) + INPUT_OFFSET) * ((int8)weights_values0.s2 + WEIGHTS_OFFSET); \
- })
-
-#define CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr) \
- ({ \
- int3 weights_values0 = convert_int3(vload3(0, weights_row_ptr)); \
- int16 src0 = convert_int16(vload16(0, src_row_ptr)); \
- int src1 = convert_int(*(src_row_ptr + 16)); \
- acc += (src0.even + INPUT_OFFSET) * ((int8)weights_values0.s0 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s1357, src0.s9BDF) + INPUT_OFFSET) * ((int8)weights_values0.s1 + WEIGHTS_OFFSET); \
- acc += ((int8)(src0.s2468, src0.sACE, src1) + INPUT_OFFSET) * ((int8)weights_values0.s2 + WEIGHTS_OFFSET); \
- })
-
-#elif KERNEL_SIZE == 1
-
-#if STRIDE_X == 3
-#define INPUT_VALUE extract_input_stride3
-#elif STRIDE_X == 2
-#define INPUT_VALUE extract_input_stride2
-#elif STRIDE_X == 1
-#define INPUT_VALUE extract_input_stride1
-
-#else /* STRIDE_X not equals 1, 2 or 3 */
-#error "Only support strides 1, 2 and 3"
-#endif /* STRIDE_X */
-
-/** Extracts a 1D horizontal vector from the input tensor with stride as 1.
- *
- * @param[in] input_value Pointer to the first value.
- *
- * @return extracted input values.
- */
-inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride1(__global const DATA_TYPE *input_value)
-{
- return vload8(0, input_value);
-}
-
-/** Extracts a 1D horizontal vector from the input tensor with stride as 2.
- *
- * @param[in] input_value Pointer to the first value.
- *
- * @return extracted input values.
- */
-inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride2(__global const DATA_TYPE *input_value)
-{
- VEC_DATA_TYPE(DATA_TYPE, 16)
- temp = vload16(0, input_value);
- return temp.s02468ace;
-}
-
-/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 8-bit data size.
- *
- * @param[in] input_value Pointer to the first value.
- *
- * @return extracted input values.
- */
-inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3(__global const DATA_TYPE *input_value)
-{
- VEC_DATA_TYPE(DATA_TYPE, 16)
- temp1 = vload16(0, input_value);
- VEC_DATA_TYPE(DATA_TYPE, 16)
- temp2 = vload16(0, input_value + 12);
- return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s0369, temp2.s0369);
-}
-
-#else /* KERNEL_SIZE not equals 1, 3 , 5, 9 */
-#error "Only kernel sizes 1, 3, 5 and 9 are supported"
-#endif /* KERNEL_SIZE */
-
-/** This kernel performs a direct convolution to convolve the low three dimensions.
- *
- * @note The convolution stride x must be passed at compile time using -DSTRIDE_X e.g. -DSTRIDE_X=1
- * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
- * @note If biases are used then -DHAS_BIAS has to be passed at compile time
- * @note The output quantization multiplier must be passed at compile time using -DOUTPUT_MULTIPLIER e.g. -DOUTPUT_MULTIPLIER=1234
- * @note The output quantization shift must be passed at compile time using -DOUTPUT_SHIFT e.g. -DOUTPUT_SHIFT=4
- * @note The input offset quantization parameter must be passed at compile time using -DINPUT_OFFSET e.g. -DINPUT_OFFSET=3
- * @note The weights offset quantization parameter must be passed at compile time using -DWEIGHTS_OFFSET e.g. -DWEIGHTS_OFFSET=3
- * @note The destination offset quantization parameter must be passed at compile time using -DOUTPUT_OFFSET e.g. -DOUTPUT_OFFSET=3
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/QASYMM8_SIGNED
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
- * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
- * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
- * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
- * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
- * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
- * @param[in] biases_ptr Pointer to the biases tensor. Supported data types: S32
- * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
- * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
- * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
- */
-__kernel void direct_convolution_quantized(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst),
- TENSOR3D_DECLARATION(weights),
-#ifdef HAS_BIAS
- VECTOR_DECLARATION(biases),
-#endif /* defined(HAS_BIAS) */
- unsigned int weights_stride_w)
-{
- Image src = CONVERT_TO_IMAGE_STRUCT(src);
- Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
- Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
-
- int8 values0 = 0;
-
- __global DATA_TYPE *weights_addr = (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 0, 0);
- __global DATA_TYPE *src_addr = (__global DATA_TYPE *)offset(&src, 0, 0);
-
- const int kernel_index = get_global_id(2);
- weights_addr += kernel_index * weights_stride_w;
-
- for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
- {
-#if KERNEL_SIZE == 9
- CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y));
- CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
- CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
- CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));
- CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));
- CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 5 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 5 * weights_stride_y));
- CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 6 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 6 * weights_stride_y));
- CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 7 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 7 * weights_stride_y));
- CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 8 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 8 * weights_stride_y));
-#elif KERNEL_SIZE == 5
- CONVOLUTION1x5(values0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr);
- CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
- CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
- CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));
- CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));
-#elif KERNEL_SIZE == 3
- CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y));
- CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
- CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
-#elif KERNEL_SIZE == 1
- int weight = convert_int(*(__global DATA_TYPE *)weights_addr);
- int8 input_value = convert_int8(INPUT_VALUE((__global DATA_TYPE *)src_addr));
- values0 += (input_value + INPUT_OFFSET) * ((int8)weight + WEIGHTS_OFFSET);
-#endif /* (KERNEL_SIZE == 1) || (KERNEL_SIZE == 3) || (KERNEL_SIZE == 5) */
-
- src_addr += src_stride_z;
- weights_addr += weights_stride_z;
- }
-
-#ifdef HAS_BIAS
- Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
- __global int *bias_addr = ((__global int *)(vector_offset(&biases, kernel_index)));
- values0 += (int8)(*bias_addr);
-#endif /* defined(HAS_BIAS) */
-
-#if OUTPUT_SHIFT < 0
- values0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(values0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8);
-#else // OUTPUT_SHIFT < 0
- values0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(values0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8);
-#endif // OUTPUT_SHIFT < 0
- values0 = values0 + OUTPUT_OFFSET;
-
- vstore8(CONVERT_SAT(values0, DATA_TYPE), 0, (__global DATA_TYPE *)dst.ptr);
-}
-#endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT)
diff --git a/src/gpu/cl/ClKernelLibrary.cpp b/src/gpu/cl/ClKernelLibrary.cpp
index f87b226..92a9d9c 100644
--- a/src/gpu/cl/ClKernelLibrary.cpp
+++ b/src/gpu/cl/ClKernelLibrary.cpp
@@ -363,12 +363,8 @@
{ "depth_to_space_nchw", "nchw/depth_to_space.cl" },
{ "dequantization_layer_per_channel_nchw", "nchw/dequantization_layer.cl" },
{ "direct_convolution1x1", "nchw/direct_convolution1x1.cl" },
- { "direct_convolution1x1_f32_bifrost", "nchw/direct_convolution1x1.cl" },
- { "direct_convolution3x3", "nchw/direct_convolution3x3.cl" },
- { "direct_convolution3x3_f32_bifrost", "nchw/direct_convolution3x3.cl" },
- { "direct_convolution5x5", "nchw/direct_convolution5x5.cl" },
- { "direct_convolution5x5_f32_bifrost", "nchw/direct_convolution5x5.cl" },
- { "direct_convolution_quantized", "nchw/direct_convolution_quantized.cl" },
+ { "direct_convolution_nchw", "nchw/direct_convolution.cl" },
+
{ "im2col1x1_stridex1_nchw", "nchw/im2col.cl" },
{ "im2col3x3_nchw", "nchw/im2col.cl" },
{ "im2col5x5_nchw", "nchw/im2col.cl" },
@@ -767,20 +763,8 @@
#include "./cl_kernels/nchw/dequantization_layer.clembed"
},
{
- "nchw/direct_convolution1x1.cl",
-#include "./cl_kernels/nchw/direct_convolution1x1.clembed"
- },
- {
- "nchw/direct_convolution3x3.cl",
-#include "./cl_kernels/nchw/direct_convolution3x3.clembed"
- },
- {
- "nchw/direct_convolution5x5.cl",
-#include "./cl_kernels/nchw/direct_convolution5x5.clembed"
- },
- {
- "nchw/direct_convolution_quantized.cl",
-#include "./cl_kernels/nchw/direct_convolution_quantized.clembed"
+ "nchw/direct_convolution.cl",
+#include "./cl_kernels/nchw/direct_convolution.clembed"
},
{
"nchw/im2col.cl",
diff --git a/src/gpu/cl/kernels/ClDirectConv2dKernel.cpp b/src/gpu/cl/kernels/ClDirectConv2dKernel.cpp
index 5af7aa9..ff8c2c3 100644
--- a/src/gpu/cl/kernels/ClDirectConv2dKernel.cpp
+++ b/src/gpu/cl/kernels/ClDirectConv2dKernel.cpp
@@ -122,209 +122,6 @@
return Status{};
}
-inline bool can_run_optimized_kernel_for_bifrost_nchw(GPUTarget gpu_target, unsigned int conv_stride_x, unsigned int conv_stride_y, unsigned int kernel_size,
- DataType data_type, DataLayout data_layout)
-{
- return gpu_target_is_in(gpu_target,
- GPUTarget::G71, GPUTarget::G72, GPUTarget::G76,
- GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT,
- GPUTarget::G52, GPUTarget::G52LIT)
- && (kernel_size <= 5)
- && (conv_stride_x == 1) && (conv_stride_y == 1)
- && (data_type == DataType::F32)
- && (data_layout == DataLayout::NCHW);
-}
-
-inline void setup_num_elems_nchw(unsigned int &num_elems_read_per_iteration_x, unsigned int &num_elems_read_per_iteration_y,
- unsigned int &num_elems_written_per_iteration_x, unsigned int &num_elems_written_per_iteration_y,
- unsigned int kernel_size, const PadStrideInfo &conv_info, const GPUTarget target, ITensorInfo *src)
-{
- const DataType data_type = src->data_type();
- const DataLayout data_layout = src->data_layout();
- unsigned int conv_stride_x = std::get<0>(conv_info.stride());
- unsigned int conv_stride_y = std::get<1>(conv_info.stride());
-
- const bool run_optimized_bifrost = can_run_optimized_kernel_for_bifrost_nchw(target, conv_stride_x, conv_stride_y, kernel_size, data_type, data_layout);
-
- if(run_optimized_bifrost)
- {
- // Configure kernel window
- switch(kernel_size)
- {
- case 1:
- {
- num_elems_read_per_iteration_x = 4;
- num_elems_read_per_iteration_y = 4;
- num_elems_written_per_iteration_x = 4;
- num_elems_written_per_iteration_y = 4;
- break;
- }
- case 3:
- {
- num_elems_read_per_iteration_x = 6;
- num_elems_read_per_iteration_y = 5;
- num_elems_written_per_iteration_x = 4;
- num_elems_written_per_iteration_y = 3;
- break;
- }
- case 5:
- {
- num_elems_read_per_iteration_x = 8;
- num_elems_read_per_iteration_y = 6;
- num_elems_written_per_iteration_x = 4;
- num_elems_written_per_iteration_y = 2;
- break;
- }
- default:
- {
- ARM_COMPUTE_ERROR("Kernel size not optimized for Bifrost");
- }
- }
- }
- else
- {
- num_elems_read_per_iteration_y = kernel_size;
- num_elems_written_per_iteration_x = 8;
- num_elems_written_per_iteration_y = 1;
- switch(kernel_size)
- {
- case 1:
- switch(conv_stride_x)
- {
- case 1:
- num_elems_read_per_iteration_x = 8;
- break;
- case 2:
- num_elems_read_per_iteration_x = 16;
- break;
- case 3:
- switch(src->element_size())
- {
- case 1:
- num_elems_read_per_iteration_x = 28;
- break;
- case 2:
- num_elems_read_per_iteration_x = 24;
- break;
- case 4:
- num_elems_read_per_iteration_x = 22;
- break;
- default:
- ARM_COMPUTE_ERROR("Invalid data size");
- }
- break;
- default:
- ARM_COMPUTE_ERROR("Invalid convolution stride X");
- }
- break;
- case 3:
- switch(conv_stride_x)
- {
- case 1:
- num_elems_read_per_iteration_x = 10;
- break;
- case 2:
- num_elems_read_per_iteration_x = 17;
- break;
- default:
- ARM_COMPUTE_ERROR("Invalid convolution stride X");
- }
- break;
- case 5:
- switch(conv_stride_x)
- {
- case 1:
- num_elems_read_per_iteration_x = 12;
- break;
- case 2:
- num_elems_read_per_iteration_x = 20;
- break;
- default:
- ARM_COMPUTE_ERROR("Invalid convolution stride X");
- }
- break;
- case 9:
- switch(conv_stride_x)
- {
- case 1:
- num_elems_read_per_iteration_x = 16;
- break;
- case 2:
- num_elems_read_per_iteration_x = 24;
- break;
- default:
- ARM_COMPUTE_ERROR("Invalid convolution stride X");
- }
- break;
- default:
- ARM_COMPUTE_ERROR("Invalid direct convolution size");
- }
- }
-}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *weights, ITensorInfo *dst, const PadStrideInfo &conv_info, const GPUTarget target)
-{
- const DataLayout data_layout = src->data_layout();
-
- // Get dst shape
- TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info);
-
- // Output auto inizialitation if not yet initialized
- auto_init_if_empty(*dst, output_shape,
- 1,
- src->data_type(),
- src->quantization_info());
-
- if(data_layout == DataLayout::NHWC)
- {
- const unsigned int vec_size = std::min(static_cast<unsigned int>(dst->tensor_shape()[0]), 4u);
- unsigned int num_rows = 1U;
- if(dst->tensor_shape()[0] > 16)
- {
- num_rows = src->data_type() == DataType::F32 ? 2U : 4U;
- }
-
- // Create window and update padding
- Window win = calculate_max_window(output_shape, Steps(vec_size, num_rows));
- return std::make_pair(Status{}, win);
- }
- else if(data_layout == DataLayout::NCHW)
- {
- const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
- const unsigned int kernel_size = weights->dimension(width_idx);
-
- unsigned int num_elems_read_per_iteration_x = 0;
- unsigned int num_elems_read_per_iteration_y = 0;
- unsigned int num_elems_written_per_iteration_x = 0;
- unsigned int num_elems_written_per_iteration_y = 0;
-
- unsigned int conv_pad_left = conv_info.pad_left();
- unsigned int conv_pad_top = conv_info.pad_top();
- unsigned int conv_stride_x = std::get<0>(conv_info.stride());
- unsigned int conv_stride_y = std::get<1>(conv_info.stride());
-
- setup_num_elems_nchw(num_elems_read_per_iteration_x, num_elems_read_per_iteration_y,
- num_elems_written_per_iteration_x, num_elems_written_per_iteration_y,
- kernel_size, conv_info, target, src);
-
- // Create window and update padding
- bool window_changed = false;
- Window win = calculate_max_window(*dst, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
-
- AccessWindowRectangle input_access(src, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration_x, num_elems_read_per_iteration_y, conv_stride_x, conv_stride_y);
- AccessWindowStatic weights_access(weights, 0, 0, kernel_size, kernel_size);
- AccessWindowRectangle output_access(dst, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
- window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
- output_access.set_valid_region(win, ValidRegion(Coordinates(), dst->tensor_shape()));
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_pair(err, win);
- }
- else
- {
- ARM_COMPUTE_ERROR("Not supported");
- }
-}
-
bool export_to_cl_image_support(ITensorInfo *tensor, GPUTarget gpu_target, DataLayout data_layout)
{
if(tensor->tensor_shape()[0] % 4 || (data_layout != DataLayout::NHWC))
@@ -370,11 +167,6 @@
} // namespace
-BorderSize ClDirectConv2dKernel::border_size() const
-{
- return _border_size;
-}
-
ClDirectConv2dKernel::ClDirectConv2dKernel()
{
_type = CLKernelType::DIRECT;
@@ -400,24 +192,49 @@
const unsigned int kernel_size = weights->dimension(width_idx);
const DataType data_type = src->data_type();
- const GPUTarget gpu_target = get_target();
+ const GPUTarget gpu_target = get_target();
+ unsigned int _num_elems_processed_per_iteration = 0;
+
+ // Get dst shape
+ TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info);
+
+ // Output auto inizialitation if not yet initialized
+ auto_init_if_empty(*dst, output_shape,
+ 1,
+ src->data_type(),
+ src->quantization_info());
// Configure kernel window
- auto win_config = validate_and_configure_window(src, weights, dst, conv_info, gpu_target);
- ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
- ICLKernel::configure_internal(win_config.second);
+ Window win;
+ if(_data_layout == DataLayout::NHWC)
+ {
+ const unsigned int vec_size = std::min(static_cast<unsigned int>(dst->tensor_shape()[0]), 4u);
+ unsigned int num_rows = 1U;
+ if(dst->tensor_shape()[0] > 16)
+ {
+ num_rows = src->data_type() == DataType::F32 ? 2U : 4U;
+ }
+
+ // Create window and update padding
+ win = calculate_max_window(output_shape, Steps(vec_size, num_rows));
+ }
+ else if(_data_layout == DataLayout::NCHW)
+ {
+ _num_elems_processed_per_iteration = 1u;
+ win = calculate_max_window(*dst, Steps(_num_elems_processed_per_iteration));
+ }
+
+ ICLKernel::configure_internal(win);
std::stringstream kernel_name;
CLBuildOptions build_options;
if(_data_layout == DataLayout::NHWC)
{
- _border_size = BorderSize();
-
kernel_name << "direct_convolution_nhwc";
- const unsigned int n0 = win_config.second.x().step();
- const unsigned int m0 = win_config.second.y().step();
+ const unsigned int n0 = win.x().step();
+ const unsigned int m0 = win.y().step();
const unsigned int k0 = adjust_vec_size(is_data_type_quantized(data_type) ? 16u : 8u, src->dimension(channel_idx));
const unsigned int partial_store_n0 = dst->dimension(channel_idx) % n0;
const unsigned int pad_left = conv_info.pad_left();
@@ -492,47 +309,42 @@
}
else
{
- _border_size = BorderSize(src->padding());
-
- kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
-
+ kernel_name << "direct_convolution_nchw";
build_options.add_option_if(biases != nullptr, std::string("-DHAS_BIAS"));
+ build_options.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(width_idx)));
+ build_options.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(height_idx)));
+ build_options.add_option("-DSRC_CHANNELS=" + support::cpp11::to_string(src->dimension(channel_idx)));
+ build_options.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
+ build_options.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
+ build_options.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_stride_x));
+ build_options.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_stride_y));
+ build_options.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(weights->dimension(width_idx)));
+ build_options.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(weights->dimension(height_idx)));
+ build_options.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
+ build_options.add_option(std::string("-DDATA_SIZE=" + get_data_size_from_data_type(data_type)));
+ build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(weights->dimension(channel_idx))));
+ build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(conv_stride_x)));
+ build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(data_type)));
+ build_options.add_option(std::string("-DVEC_SIZE=" + support::cpp11::to_string(_num_elems_processed_per_iteration)));
+ build_options.add_option(std::string("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration)));
- const bool run_optimized_for_bifrost = can_run_optimized_kernel_for_bifrost_nchw(gpu_target, conv_stride_x, conv_stride_y, kernel_size, data_type, _data_layout);
-
- if(run_optimized_for_bifrost)
+ if(is_data_type_quantized(data_type))
{
- build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(weights->dimension(channel_idx))));
+ const UniformQuantizationInfo iqinfo = src->quantization_info().uniform();
+ const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform();
+ const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform();
- kernel_name << "_f32_bifrost";
- }
- else
- {
- build_options.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
- build_options.add_option(std::string("-DDATA_SIZE=" + get_data_size_from_data_type(data_type)));
- build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(weights->dimension(channel_idx))));
- build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(conv_stride_x)));
- build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(data_type)));
-
- if(is_data_type_quantized(data_type))
- {
- const UniformQuantizationInfo iqinfo = src->quantization_info().uniform();
- const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform();
- const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform();
-
- float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale;
- int output_multiplier = 0;
- int output_shift = 0;
- quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
- build_options.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
- build_options.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
- build_options.add_option("-DKERNEL_SIZE=" + support::cpp11::to_string(kernel_size));
- build_options.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iqinfo.offset));
- build_options.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wqinfo.offset));
- build_options.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oqinfo.offset));
-
- kernel_name.str("direct_convolution_quantized");
- }
+ float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale;
+ int output_multiplier = 0;
+ int output_shift = 0;
+ quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
+ build_options.add_option("-DIS_QUANTIZED");
+ build_options.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
+ build_options.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
+ build_options.add_option("-DKERNEL_SIZE=" + support::cpp11::to_string(kernel_size));
+ build_options.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iqinfo.offset));
+ build_options.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wqinfo.offset));
+ build_options.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oqinfo.offset));
}
}
@@ -565,11 +377,9 @@
}
Status ClDirectConv2dKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
- const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, const GPUTarget target)
+ const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info, act_info));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), weights->clone().get(), dst->clone().get(), conv_info, target).first);
-
return Status{};
}
@@ -623,22 +433,7 @@
}
else
{
- Window win_in = window;
-
- win_in.adjust(Window::DimX, -_conv_info.pad_left(), true);
- win_in.adjust(Window::DimY, -_conv_info.pad_top(), true);
-
- const int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
- const int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
-
- const int conv_stride_x = std::get<0>(_conv_info.stride());
- const int conv_stride_y = std::get<1>(_conv_info.stride());
-
- win_in.set_dimension_step(width_idx, window[width_idx].step() * conv_stride_x);
- win_in.set_dimension_step(height_idx, window[height_idx].step() * conv_stride_y);
-
- Window slice_in = win_in.first_slice_window_3D();
- unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
+ unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
add_3D_tensor_argument(idx1, weights, slice);
if(biases != nullptr)
@@ -653,11 +448,11 @@
do
{
unsigned int idx = 0;
- add_3D_tensor_argument(idx, src, slice_in);
+ add_3D_tensor_argument(idx, src, slice);
add_3D_tensor_argument(idx, dst, slice);
enqueue(queue, *this, slice, lws_hint());
}
- while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));
+ while(window.slide_window_slice_3D(slice));
}
}
} // namespace kernels
diff --git a/src/gpu/cl/kernels/ClDirectConv2dKernel.h b/src/gpu/cl/kernels/ClDirectConv2dKernel.h
index 5624f3a..5681927 100644
--- a/src/gpu/cl/kernels/ClDirectConv2dKernel.h
+++ b/src/gpu/cl/kernels/ClDirectConv2dKernel.h
@@ -72,15 +72,13 @@
* @return a status
*/
static Status validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
- const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, const GPUTarget target);
+ const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info);
// Inherited methods overridden:
void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
- BorderSize border_size() const override;
public:
DataLayout _data_layout{};
- BorderSize _border_size{};
PadStrideInfo _conv_info{};
};
} // namespace kernels
diff --git a/src/gpu/cl/operators/ClDirectConv2d.cpp b/src/gpu/cl/operators/ClDirectConv2d.cpp
index d2e4049..53de6fc 100644
--- a/src/gpu/cl/operators/ClDirectConv2d.cpp
+++ b/src/gpu/cl/operators/ClDirectConv2d.cpp
@@ -83,7 +83,7 @@
Status ClDirectConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClDirectConv2dKernel::validate(src, weights, biases, dst, conv_info, ActivationLayerInfo(), CLScheduler::get().target()));
+ ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClDirectConv2dKernel::validate(src, weights, biases, dst, conv_info, ActivationLayerInfo()));
if(act_info.enabled())
{
ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClActivationKernel::validate(dst, dst, act_info));