COMPMID-811 Add NHWC data format support for CL depthwise convolution
Change-Id: I574f7945f0be009c638d860028bce8b52b4120fd
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/136484
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h b/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h
index 62b9a86..85fbaae 100644
--- a/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h
+++ b/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h
@@ -72,6 +72,7 @@
private:
unsigned int _num_rows_processed_per_iteration;
+ unsigned int _num_planes_processed_per_iteration;
};
} // namespace arm_compute
#endif /*__ARM_COMPUTE_CLDEPTHWISECONVOLUTIONNHWCKERNEL3x3_H__ */
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 2bcacad..fb688b5 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -197,6 +197,8 @@
{ "deconvolution_upsample", "deconvolution_layer.cl" },
{ "depthwise_convolution_3x3", "depthwise_convolution.cl" },
{ "depthwise_convolution_3x3_f16", "depthwise_convolution.cl" },
+ { "depthwise_convolution_3x3_nhwc", "depthwise_convolution.cl" },
+ { "depthwise_convolution_3x3_nhwc_stride1", "depthwise_convolution.cl" },
{ "depthwise_convolution_3x3_quantized_nchw", "depthwise_convolution_quantized.cl" },
{ "depthwise_convolution_3x3_quantized_nhwc_stride1", "depthwise_convolution_quantized.cl" },
{ "depthwise_convolution_3x3_quantized_nhwc_stride2", "depthwise_convolution_quantized.cl" },
diff --git a/src/core/CL/cl_kernels/depthwise_convolution.cl b/src/core/CL/cl_kernels/depthwise_convolution.cl
index 5f4247e..f3aa0d6 100644
--- a/src/core/CL/cl_kernels/depthwise_convolution.cl
+++ b/src/core/CL/cl_kernels/depthwise_convolution.cl
@@ -451,6 +451,22 @@
#endif // defined(DEPTH_MULTIPLIER)
+#if defined(NCHW)
+#define in_stride_x src_stride_x
+#define in_stride_y src_stride_y
+#define in_stride_z src_stride_z
+#define out_stride_x dst_stride_x
+#define out_stride_y dst_stride_y
+#define out_stride_z dst_stride_z
+#else //defined(NCHW)
+#define in_stride_x src_stride_y
+#define in_stride_y src_stride_z
+#define in_stride_z src_stride_x
+#define out_stride_x dst_stride_y
+#define out_stride_y dst_stride_z
+#define out_stride_z dst_stride_x
+#endif //defined(NCHW)
+
#if defined(SRC_WIDTH) && defined(DATA_TYPE)
/** This kernel reshapes each of the tensor's low three dimensions to single rows.
*
@@ -484,17 +500,16 @@
#endif /* HAS_BIAS */
)
{
- Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
#ifdef HAS_BIAS
Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
#endif /* HAS_BIAS */
- __global DATA_TYPE *input_ptr = (__global DATA_TYPE *)src.ptr;
- __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + get_global_id(1) * SRC_WIDTH * dst_stride_x + get_global_id(2) * dst_stride_y;
+ __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + get_global_id(1) * in_stride_y + get_global_id(2) * in_stride_z;
+ __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + get_global_id(1) * SRC_WIDTH * dst_stride_x + get_global_id(2) * dst_stride_y;
- for(int i = 0; i < SRC_WIDTH; ++i, ++input_ptr)
+ for(int i = 0; i < SRC_WIDTH; ++i, input_ptr += in_stride_x)
{
- *((__global DATA_TYPE *)(output_ptr + i * dst_stride_x)) = *input_ptr;
+ *((__global DATA_TYPE *)(output_ptr + i * dst_stride_x)) = *((__global DATA_TYPE *)input_ptr);
}
#if defined(HAS_BIAS)
@@ -541,7 +556,7 @@
const int src_y = -PAD_TOP + src_pixel_linear / max_initial_x * STRIDE_Y;
const int src_z = get_global_id(2) / DEPTH_MULTIPLIER;
- __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + src_z * src_stride_z;
+ __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + src_z * in_stride_z;
__global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst.ptr));
for(int y = src_y; y < src_y + KERNEL_HEIGHT; ++y)
@@ -554,7 +569,7 @@
}
else
{
- *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
+ *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * in_stride_x + y * in_stride_y));
}
}
}
@@ -596,7 +611,7 @@
const int z = id0 / patch_size;
const int index2D = id0 - z * patch_size;
- __global uchar *out_ptr = dst_ptr + dst_offset_first_element_in_bytes + index2D % CONV_WIDTH * dst_stride_x + index2D / CONV_WIDTH * dst_stride_y + z * dst_stride_z;
+ __global uchar *out_ptr = dst_ptr + dst_offset_first_element_in_bytes + index2D % CONV_WIDTH * out_stride_x + index2D / CONV_WIDTH * out_stride_y + z * out_stride_z;
*((__global DATA_TYPE *)out_ptr) = *((__global DATA_TYPE *)src.ptr);
}
@@ -980,3 +995,335 @@
vstore4(pixels1, 0, (__global half *)(dst.ptr + 1 * dst_stride_y));
}
#endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(DEPTH_MULTIPLIER)
+
+#if defined(VEC_SIZE) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT)
+
+#define VEC_FLOAT VEC_DATA_TYPE(float, VEC_SIZE)
+
+#if defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y)
+/** This function computes the depthwise convolution for NHWC data layout when the stride along the width or height is not 1.
+ *
+ * @note The number of elements read per thread must be passed at compile time using -DVEC_SIZE (e.g. -DVEC_SIZE=2)
+ * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112)
+ * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1)
+ * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1)
+ * @note The convolution stride along the width must be passed at compile time using -DCONV_STRIDE_X (e.g. -DCONV_STRIDE_Y=X)
+ * @note The convolution stride along the height must be passed at compile time using -DCONV_STRIDE_Y (e.g. -DCONV_STRIDE_Y=1)
+ *
+ * @param[in] src_ptr Pointer to the source image. Supported data types: FP32
+ * @param[in] src_stride_x Stride of the source image 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 image 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_offset_first_element_in_bytes The offset of the first element in the source image
+ * @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 Y processed per workitem(in bytes)
+ * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as 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 Y 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 Y 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: QASYMM8
+ * @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 Y 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] max_offset Max offset for the input tensor
+ * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr
+ * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)
+ * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector
+ */
+__kernel void depthwise_convolution_3x3_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+ TENSOR3D_DECLARATION(weights),
+#if defined(HAS_BIAS)
+ VECTOR_DECLARATION(biases),
+#endif /* defined(HAS_BIAS) */
+ int max_offset)
+{
+ int x = get_global_id(0); // channels
+ int y = get_global_id(1); // spatial coordinate x
+ int z = get_global_id(2); // spatial coordinate y
+
+ Vector weights = CONVERT_TO_VECTOR_STRUCT(weights);
+
+ __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(float) * VEC_SIZE;
+
+ int z_coord = 0;
+ int4 offset = 0;
+ int4 y_offset = ((int4)(y * CONV_STRIDE_X) + (int4)(0, 1, 2, 3) - CONV_PAD_LEFT) * (int4)src_stride_y;
+
+ // We compute 2x1x1 [C,W,H] elements
+ VEC_FLOAT acc = 0;
+
+ // Load weights
+ VEC_FLOAT w0 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 0 * weights_stride_z));
+ VEC_FLOAT w1 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 0 * weights_stride_z));
+ VEC_FLOAT w2 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 0 * weights_stride_z));
+ VEC_FLOAT w3 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 1 * weights_stride_z));
+ VEC_FLOAT w4 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 1 * weights_stride_z));
+ VEC_FLOAT w5 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 1 * weights_stride_z));
+ VEC_FLOAT w6 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 2 * weights_stride_z));
+ VEC_FLOAT w7 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 2 * weights_stride_z));
+ VEC_FLOAT w8 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 2 * weights_stride_z));
+
+ // Load input values
+ // z == 0
+ // Clamp z_coord as for z = 0, it can be negative
+ // z_coord is casted to unsigned int in order to use just a min() operation
+ // A "-1" 32 bit signed variable converted to unsigned gives 4294967295
+ z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP;
+ z_coord = min((uint)z_coord, (uint)SRC_DIM_2);
+ offset = y_offset + (int4)(z_coord * src_stride_z);
+ offset = min(offset, max_offset);
+
+ VEC_FLOAT values0 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0));
+ VEC_FLOAT values1 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1));
+ VEC_FLOAT values2 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2));
+
+ // z == 1
+ // z_coord can be only negative for z = 0 so we do not need to clamp it
+ // Moreover z_coord cannot be out-of-bound for z = 1 so we do not need to clamp the offset
+ z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP + 1;
+ offset = y_offset + (int4)(z_coord * src_stride_z);
+ VEC_FLOAT values3 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0));
+ VEC_FLOAT values4 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1));
+ VEC_FLOAT values5 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2));
+
+ // z == 2
+ // After z = 1 we can simply add src_stride_z to offset without updating z_coord
+ // However offset can be out-of-bound so we need to check if it is greater than max_offset
+ offset += (int4)src_stride_z;
+ offset = min(offset, max_offset);
+ VEC_FLOAT values6 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0));
+ VEC_FLOAT values7 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1));
+ VEC_FLOAT values8 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2));
+
+ acc = fma(values0, w0, acc);
+ acc = fma(values1, w1, acc);
+ acc = fma(values2, w2, acc);
+
+ acc = fma(values3, w3, acc);
+ acc = fma(values4, w4, acc);
+ acc = fma(values5, w5, acc);
+
+ acc = fma(values6, w6, acc);
+ acc = fma(values7, w7, acc);
+ acc = fma(values8, w8, acc);
+
+#if defined(HAS_BIAS)
+ Vector biases = CONVERT_TO_VECTOR_STRUCT(biases);
+ VEC_FLOAT bias_values = VLOAD(VEC_SIZE)(0, (__global float *)biases.ptr);
+ acc += bias_values;
+#endif // defined(HAS_BIAS)
+
+ Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
+ VSTORE(VEC_SIZE)
+ (acc, 0, (__global float *)(dst.ptr));
+}
+#endif // defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y)
+
+#if defined(NUM_ROWS_PROCESSED) && defined(NUM_PLANES_PROCESSED)
+/** This function computes the depthwise convolution for NHWC data layout when the stride along the width and height is 1.
+ *
+ * @note The number of elements read per thread must be passed at compile time using -DVEC_SIZE (e.g. -DVEC_SIZE=2)
+ * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112)
+ * @note The number of rows processed per thread must be passed at compile time using -DNUM_ROWS_PROCESSED (i.e. -DNUM_ROWS_PROCESSED=2)
+ * @note The number of planes processed per thread must be passed at compile time using -DNUM_PLANES_PROCESSED (i.e. -DNUM_PLANES_PROCESSED=2)
+ * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1)
+ * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1)
+ *
+ * @param[in] src_ptr Pointer to the source image. Supported data types: FP32
+ * @param[in] src_stride_x Stride of the source image 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 image 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_offset_first_element_in_bytes The offset of the first element in the source image
+ * @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 Y processed per workitem(in bytes)
+ * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as 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 Y 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 Y 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: QASYMM8
+ * @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 Y 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] max_offset Max offset for the input tensor
+ * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr
+ * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)
+ * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector
+ */
+__kernel void depthwise_convolution_3x3_nhwc_stride1(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+ TENSOR3D_DECLARATION(weights),
+#if defined(HAS_BIAS)
+ VECTOR_DECLARATION(biases),
+#endif /* defined(HAS_BIAS) */
+ int max_offset)
+{
+ int x = get_global_id(0); // channels
+ int y = get_global_id(1); // spatial coordinate x
+ int z = get_global_id(2); // spatial coordinate y
+
+ Vector weights = CONVERT_TO_VECTOR_STRUCT(weights);
+
+ __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(float) * VEC_SIZE;
+
+ int z_coord = 0;
+ int4 offset = 0;
+ int4 y_offset = ((int4)(y * NUM_ROWS_PROCESSED) + (int4)(0, 1, 2, 3) - CONV_PAD_LEFT) * (int4)src_stride_y;
+
+ // We compute 2x2x2 [C,W,H] elements
+ VEC_FLOAT acc0 = 0;
+ VEC_FLOAT acc1 = 0;
+ VEC_FLOAT acc2 = 0;
+ VEC_FLOAT acc3 = 0;
+
+ // Load weights
+ VEC_FLOAT w0 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 0 * weights_stride_z));
+ VEC_FLOAT w1 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 0 * weights_stride_z));
+ VEC_FLOAT w2 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 0 * weights_stride_z));
+ VEC_FLOAT w3 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 1 * weights_stride_z));
+ VEC_FLOAT w4 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 1 * weights_stride_z));
+ VEC_FLOAT w5 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 1 * weights_stride_z));
+ VEC_FLOAT w6 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 0 * weights_stride_y + 2 * weights_stride_z));
+ VEC_FLOAT w7 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 1 * weights_stride_y + 2 * weights_stride_z));
+ VEC_FLOAT w8 = VLOAD(VEC_SIZE)(0, (__global float *)(weights.ptr + 2 * weights_stride_y + 2 * weights_stride_z));
+
+ // Load input values
+ // z == 0
+ // Clamp z_coord as for z = 0, it can be negative
+ // z_coord is casted to unsigned int in order to use just a min() operation
+ // A "-1" 32 bit signed variable converted to unsigned gives 4294967295
+ z_coord = z * NUM_PLANES_PROCESSED - (int)CONV_PAD_TOP;
+ z_coord = min((uint)z_coord, (uint)SRC_DIM_2);
+ offset = y_offset + (int4)(z_coord * src_stride_z);
+ offset = min(offset, max_offset);
+
+ VEC_FLOAT values0 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0));
+ VEC_FLOAT values1 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1));
+ VEC_FLOAT values2 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2));
+ VEC_FLOAT values3 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s3));
+
+ // z == 1
+ // z_coord can be only negative for z = 0 so we do not need to clamp it
+ // Moreover z_coord cannot be out-of-bound for z = 1 so we do not need to clamp the offset
+ z_coord = z * NUM_PLANES_PROCESSED - (int)CONV_PAD_TOP + 1;
+ offset = y_offset + (int4)(z_coord * src_stride_z);
+ VEC_FLOAT values4 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0));
+ VEC_FLOAT values5 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1));
+ VEC_FLOAT values6 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2));
+ VEC_FLOAT values7 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s3));
+
+ // z == 2
+ // After z = 1 we can simply add src_stride_z to offset without updating z_coord
+ // However offset can be out-of-bound so we need to check if it is greater than max_offset
+ offset += (int4)src_stride_z;
+ offset = min(offset, max_offset);
+ VEC_FLOAT values8 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0));
+ VEC_FLOAT values9 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1));
+ VEC_FLOAT values10 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2));
+ VEC_FLOAT values11 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s3));
+
+ // z == 3
+ // After z = 1 we can simply add src_stride_z to offset without updating z_coord
+ // However offset can be out-of-bound so we need to check if it is greater than max_offset
+ offset += (int4)(src_stride_z);
+ offset = min(offset, max_offset);
+ VEC_FLOAT values12 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s0));
+ VEC_FLOAT values13 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s1));
+ VEC_FLOAT values14 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s2));
+ VEC_FLOAT values15 = VLOAD(VEC_SIZE)(0, (__global float *)(src_addr + offset.s3));
+
+ acc0 = fma(values0, w0, acc0);
+ acc0 = fma(values1, w1, acc0);
+ acc0 = fma(values2, w2, acc0);
+ acc1 = fma(values1, w0, acc1);
+ acc1 = fma(values2, w1, acc1);
+ acc1 = fma(values3, w2, acc1);
+
+ acc0 = fma(values4, w3, acc0);
+ acc0 = fma(values5, w4, acc0);
+ acc0 = fma(values6, w5, acc0);
+ acc1 = fma(values5, w3, acc1);
+ acc1 = fma(values6, w4, acc1);
+ acc1 = fma(values7, w5, acc1);
+
+ acc0 = fma(values8, w6, acc0);
+ acc0 = fma(values9, w7, acc0);
+ acc0 = fma(values10, w8, acc0);
+ acc1 = fma(values9, w6, acc1);
+ acc1 = fma(values10, w7, acc1);
+ acc1 = fma(values11, w8, acc1);
+
+ acc2 = fma(values4, w0, acc2);
+ acc2 = fma(values5, w1, acc2);
+ acc2 = fma(values6, w2, acc2);
+ acc3 = fma(values5, w0, acc3);
+ acc3 = fma(values6, w1, acc3);
+ acc3 = fma(values7, w2, acc3);
+
+ acc2 = fma(values8, w3, acc2);
+ acc2 = fma(values9, w4, acc2);
+ acc2 = fma(values10, w5, acc2);
+ acc3 = fma(values9, w3, acc3);
+ acc3 = fma(values10, w4, acc3);
+ acc3 = fma(values11, w5, acc3);
+
+ acc2 = fma(values12, w6, acc2);
+ acc2 = fma(values13, w7, acc2);
+ acc2 = fma(values14, w8, acc2);
+ acc3 = fma(values13, w6, acc3);
+ acc3 = fma(values14, w7, acc3);
+ acc3 = fma(values15, w8, acc3);
+
+#if defined(HAS_BIAS)
+ Vector biases = CONVERT_TO_VECTOR_STRUCT(biases);
+
+ VEC_FLOAT bias_values = VLOAD(VEC_SIZE)(0, (__global float *)biases.ptr);
+
+ acc0 += bias_values;
+ acc1 += bias_values;
+ acc2 += bias_values;
+ acc3 += bias_values;
+#endif // defined(HAS_BIAS)
+
+ __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * dst_step_x + y * dst_step_y + (z * NUM_PLANES_PROCESSED) * dst_step_z;
+
+ VSTORE(VEC_SIZE)
+ (acc0, 0, (__global float *)(dst_addr + 0 * dst_stride_y));
+ VSTORE(VEC_SIZE)
+ (acc1, 0, (__global float *)(dst_addr + 1 * dst_stride_y));
+
+#if((DST_DIM_2 % NUM_PLANES_PROCESSED) != 0)
+ if((z * NUM_PLANES_PROCESSED + 1) < DST_DIM_2)
+#endif // ((DST_DIM_2 % NUM_PLANES_PROCESSED) != 0)
+ {
+ VSTORE(VEC_SIZE)
+ (acc2, 0, (__global float *)(dst_addr + 0 * dst_stride_y + 1 * dst_stride_z));
+ VSTORE(VEC_SIZE)
+ (acc3, 0, (__global float *)(dst_addr + 1 * dst_stride_y + 1 * dst_stride_z));
+ }
+}
+
+#endif // defined(NUM_ROWS_PROCESSED) && defined(NUM_PLANES_PROCESSED)
+#endif // defined(VEC_SIZE) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT)
\ No newline at end of file
diff --git a/src/core/CL/cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.cl b/src/core/CL/cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.cl
index b58dc7a..ae87420 100644
--- a/src/core/CL/cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.cl
+++ b/src/core/CL/cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.cl
@@ -296,7 +296,12 @@
#if defined(HAS_BIAS)
// Load and add bias
+#if defined(NCHW)
int bias_value = *((__global int *)(vector_offset(&bias, get_global_id(2))));
+#else // defined(NCHW)
+ int16 bias_value = vload16(0, ((__global int *)(vector_offset(&bias, get_global_id(0) * 16))));
+#endif // defined(NCHW)
+
vals += (int16)(bias_value);
#endif //defined(HAS_BIAS)
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
index d24ef0f..1de08aa 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
@@ -44,18 +44,27 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier,
const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.enabled()) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
- && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
- && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU),
- "For QASYMM8 only relu, lower bounded relu and lower-upper bounded relu are supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::QASYMM8);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.enabled()) && (input->data_type() == DataType::F32 || ((act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
+ && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
+ && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU))),
+ "For QASYMM8 only relu, lower bounded relu and lower-upper bounded relu are supported"); //COMPMID-1317 add fused activation for F32
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier > 1); // COMPMID-1071 Add depth multiplier support for NHWC
ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(1) != 3 || weights->dimension(2) != 3);
+ const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type());
+
if(biases != nullptr)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+ if(is_qasymm)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+ }
ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(0));
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
@@ -72,12 +81,23 @@
std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *output,
const PadStrideInfo &conv_info)
{
- const unsigned int num_rows_processed_per_iteration = 4;
- const unsigned int num_elems_accessed_per_iteration = 4;
- const unsigned int num_rows_read_per_iteration = num_rows_processed_per_iteration + 2;
- const unsigned int num_rows_written_per_iteration = num_rows_processed_per_iteration / conv_info.stride().first;
+ const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type());
+ const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
- const BorderSize border_size(std::max(conv_info.pad_left(), conv_info.pad_top()), 0, std::max(conv_info.pad_right(), conv_info.pad_bottom()), 0);
+ const unsigned int num_rows_processed_per_iteration = is_qasymm ? 4 : (is_stride_1 ? 2 : 1);
+ const unsigned int num_elems_accessed_per_iteration = is_qasymm ? 4 : 2;
+ const unsigned int num_rows_read_per_iteration = num_rows_processed_per_iteration + 2;
+ const unsigned int num_rows_written_per_iteration = std::ceil(num_rows_processed_per_iteration / static_cast<float>(conv_info.stride().first));
+
+ BorderSize border_size;
+ if(is_qasymm)
+ {
+ border_size = BorderSize(std::max(conv_info.pad_left(), conv_info.pad_top()), 0, std::max(conv_info.pad_right(), conv_info.pad_bottom()), 0);
+ }
+ else
+ {
+ border_size = BorderSize(conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0);
+ }
// Configure kernel window
Window win = calculate_max_window(*output, Steps(num_elems_accessed_per_iteration, num_rows_written_per_iteration));
@@ -103,7 +123,7 @@
} // namespace
CLDepthwiseConvolutionLayer3x3NHWCKernel::CLDepthwiseConvolutionLayer3x3NHWCKernel()
- : _num_rows_processed_per_iteration(1)
+ : _num_rows_processed_per_iteration(1), _num_planes_processed_per_iteration(1)
{
}
@@ -135,66 +155,97 @@
ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 2);
ARM_COMPUTE_ERROR_ON(std::max(conv_info.pad_top(), conv_info.pad_bottom()) > 1);
- _input = input;
- _output = output;
- _weights = weights;
- _biases = biases;
- _conv_stride_y = conv_info.stride().second;
- _num_rows_processed_per_iteration = 4;
+ const bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type());
+ const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
- const unsigned int num_elems_accessed_per_iteration = 4;
+ _input = input;
+ _output = output;
+ _weights = weights;
+ _biases = biases;
+ _conv_stride_y = conv_info.stride().second;
+ _num_rows_processed_per_iteration = is_qasymm ? 4 : (is_stride_1 ? 2 : 1);
+ _num_planes_processed_per_iteration = (is_stride_1 && !is_qasymm) ? 2 : 1;
- _border_size = BorderSize(std::max(conv_info.pad_left(), conv_info.pad_top()), 0, std::max(conv_info.pad_right(), conv_info.pad_bottom()), 0);
+ const unsigned int num_elems_accessed_per_iteration = is_qasymm ? 4 : 2;
- float multiplier = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale;
- int output_multiplier = 0;
- int output_shift = 0;
- quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+ if(is_qasymm)
+ {
+ _border_size = BorderSize(std::max(conv_info.pad_left(), conv_info.pad_top()), 0, std::max(conv_info.pad_right(), conv_info.pad_bottom()), 0);
+ }
+ else
+ {
+ _border_size = BorderSize(conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0);
+ }
CLBuildOptions build_opts;
build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS");
- build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-_input->info()->quantization_info().offset));
- build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-_weights->info()->quantization_info().offset));
- build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(_output->info()->quantization_info().offset));
- build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * input->info()->quantization_info().offset * weights->info()->quantization_info().offset));
- build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
- build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_accessed_per_iteration));
- build_opts.add_option("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1)));
build_opts.add_option("-DSRC_DIM_2=" + support::cpp11::to_string(_input->info()->dimension(2)));
build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
build_opts.add_option("-DCONV_PAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
- if(act_info.enabled())
+ if(is_qasymm)
{
- const int a_val = input->info()->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
- const int b_val = input->info()->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
- const int o1 = input->info()->quantization_info().offset;
+ float multiplier = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale;
+ int output_multiplier = 0;
+ int output_shift = 0;
+ quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
- build_opts.add_option("-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation())));
- build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val));
- build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val));
- build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1));
+ build_opts.add_option("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1)));
+ build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-_input->info()->quantization_info().offset));
+ build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-_weights->info()->quantization_info().offset));
+ build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(_output->info()->quantization_info().offset));
+ build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * input->info()->quantization_info().offset * weights->info()->quantization_info().offset));
+ build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
+ build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
- if(output != nullptr)
+ if(act_info.enabled())
{
- const float s1 = input->info()->quantization_info().scale;
- const float s2 = output->info()->quantization_info().scale;
- const int o2 = output->info()->quantization_info().offset;
+ const int a_val = input->info()->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
+ const int b_val = input->info()->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
+ const int o1 = input->info()->quantization_info().offset;
- if(o1 != o2 || s1 != s2)
+ build_opts.add_option("-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation())));
+ build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val));
+ build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val));
+ build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1));
+
+ if(output != nullptr)
{
- build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1));
- build_opts.add_option("-DS2_VAL=" + float_to_string_with_full_precision(s2));
- build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1));
- build_opts.add_option("-DO2_VAL=" + support::cpp11::to_string(o2));
+ const float s1 = input->info()->quantization_info().scale;
+ const float s2 = output->info()->quantization_info().scale;
+ const int o2 = output->info()->quantization_info().offset;
+
+ if(o1 != o2 || s1 != s2)
+ {
+ build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1));
+ build_opts.add_option("-DS2_VAL=" + float_to_string_with_full_precision(s2));
+ build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1));
+ build_opts.add_option("-DO2_VAL=" + support::cpp11::to_string(o2));
+ }
}
}
}
+ else if(is_stride_1)
+ {
+ build_opts.add_option("-DNUM_ROWS_PROCESSED=" + support::cpp11::to_string(_num_rows_processed_per_iteration));
+ build_opts.add_option("-DNUM_PLANES_PROCESSED=" + support::cpp11::to_string(_num_planes_processed_per_iteration));
+ build_opts.add_option("-DDST_DIM_2=" + support::cpp11::to_string(_output->info()->dimension(2)));
+ }
+ else
+ {
+ build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(conv_stride_x));
+ build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y));
+ }
// Create kernel
- std::string kernel_name = std::string("depthwise_convolution_3x3_quantized_nhwc_stride") + support::cpp11::to_string(conv_stride_x);
- _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
+ std::string kernel_name = std::string("depthwise_convolution_3x3") + (is_qasymm ? std::string("_quantized") : std::string()) + std::string("_nhwc");
+ if(is_qasymm || is_stride_1)
+ {
+ kernel_name += std::string("_stride") + support::cpp11::to_string(conv_stride_x);
+ }
+
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
// Configure kernel window
auto win_config = validate_and_configure_window(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info);
@@ -213,6 +264,8 @@
_config_id += support::cpp11::to_string(output->info()->dimension(0));
_config_id += "_";
_config_id += support::cpp11::to_string(output->info()->dimension(1));
+ _config_id += "_";
+ _config_id += string_from_data_type(input->info()->data_type());
}
Status CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
@@ -233,15 +286,18 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+ Window win = window;
+ win.set(Window::DimZ, Window::Dimension(0, std::ceil(_output->info()->dimension(2) / static_cast<float>(_num_planes_processed_per_iteration)), 1));
+
// Create input window and adjust
- Window win_in = window;
+ Window win_in = win;
win_in.set_dimension_step(Window::DimY, _num_rows_processed_per_iteration);
win_in.set_dimension_step(Window::DimZ, _conv_stride_y);
ARM_COMPUTE_ERROR_ON((win_in.y().step() < window.y().step()) || (win_in.z().step() < window.z().step()));
Window slice_in = win_in.first_slice_window_3D();
- Window slice_out = window.first_slice_window_3D();
+ Window slice_out = win.first_slice_window_3D();
if(_biases != nullptr)
{
@@ -252,6 +308,15 @@
add_1D_tensor_argument(idx, _biases, win_biases);
}
+ if(!(is_data_type_quantized_asymmetric(_input->info()->data_type())))
+ {
+ unsigned int idx = 3 * num_arguments_per_3D_tensor() + ((_biases != nullptr) ? num_arguments_per_1D_tensor() : 0);
+ const int max_offset = _input->info()->strides_in_bytes().z() * _input->info()->dimension(2) - (_input->info()->padding().bottom + _input->info()->padding().top) *
+ _input->info()->strides_in_bytes().y();
+
+ _kernel.setArg(idx, max_offset);
+ }
+
do
{
unsigned int idx = 0;
diff --git a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
index c89b16e..bef13f9 100644
--- a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
@@ -47,13 +47,15 @@
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier)
{
+ const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+
ARM_COMPUTE_UNUSED(conv_info);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()) && has_bias);
- ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(2) * depth_multiplier) != output->dimension(2));
+ ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(idx_c) * depth_multiplier) != output->dimension(2));
ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (kernel_dims.width * kernel_dims.height + ((has_bias) ? 1 : 0)));
return Status{};
@@ -68,6 +70,10 @@
_input = input;
_output = output;
+ const DataLayout data_layout = input->info()->data_layout();
+ const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+
// Create kernel
CLBuildOptions build_opts;
@@ -78,11 +84,12 @@
build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
build_opts.add_option("-DPAD_RIGHT=" + support::cpp11::to_string(conv_info.pad_right()));
build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom()));
- build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0)));
- build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
+ build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_w)));
+ build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_h)));
build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier));
+ build_opts.add_option("-D" + string_from_data_layout(input->info()->data_layout()));
build_opts.add_option_if(has_bias, "-DHAS_BIAS");
build_opts.add_option_if_else(is_data_type_quantized_asymmetric(input->info()->data_type()),
"-DPAD_VALUE=" + support::cpp11::to_string(input->info()->quantization_info().offset),
diff --git a/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp b/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp
index 0d158f1..c97ecaf 100644
--- a/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp
@@ -37,12 +37,16 @@
namespace
{
-TensorShape compute_output_shape(const TensorShape &input, size_t conv_w, size_t conv_h)
+TensorShape compute_output_shape(const TensorShape &input, size_t conv_w, size_t conv_h, const DataLayout &data_layout)
{
+ const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const size_t idx_c = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+
TensorShape output_shape(input);
- output_shape.set(0, conv_w);
- output_shape.set(1, conv_h);
- output_shape.set(2, input.x() / (conv_w * conv_h));
+ output_shape.set(idx_w, conv_w);
+ output_shape.set(idx_h, conv_h);
+ output_shape.set(idx_c, input.x() / (conv_w * conv_h));
return output_shape;
}
@@ -54,7 +58,7 @@
if(output->total_size() != 0)
{
- TensorShape output_shape = compute_output_shape(input->tensor_shape(), conv_w, conv_h);
+ TensorShape output_shape = compute_output_shape(input->tensor_shape(), conv_w, conv_h, output->data_layout());
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
@@ -74,7 +78,7 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Output auto inizialitation if not yet initialized
- TensorShape output_shape = compute_output_shape(input->info()->tensor_shape(), conv_w, conv_h);
+ TensorShape output_shape = compute_output_shape(input->info()->tensor_shape(), conv_w, conv_h, output->info()->data_layout());
auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), conv_w, conv_h));
@@ -87,6 +91,7 @@
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
build_opts.add_option("-DCONV_WIDTH=" + support::cpp11::to_string(conv_w));
build_opts.add_option("-DCONV_HEIGHT=" + support::cpp11::to_string(conv_h));
+ build_opts.add_option("-D" + string_from_data_layout(output->info()->data_layout()));
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("depthwise_vector_to_tensor", build_opts.options()));
diff --git a/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp b/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp
index 59c45ad..fd3b754 100644
--- a/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp
@@ -39,19 +39,23 @@
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *biases)
{
+ const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+ const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()) && (biases != nullptr));
- ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(2) != output->dimension(1));
- ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (input->dimension(0) * input->dimension(1) + ((biases != nullptr) ? 1 : 0)));
+ ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_c) != output->dimension(1));
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (input->dimension(idx_w) * input->dimension(idx_h) + ((biases != nullptr) ? 1 : 0)));
if(biases != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
- ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != input->dimension(2));
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != input->dimension(idx_c));
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
@@ -73,11 +77,14 @@
_biases = biases;
_output = output;
+ const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
+
// Create kernel
std::set<std::string> build_opts;
build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
- build_opts.emplace("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0)));
+ build_opts.emplace("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_w)));
+ build_opts.emplace("-D" + string_from_data_layout(input->info()->data_layout()));
if(_biases != nullptr)
{
build_opts.emplace("-DHAS_BIAS");
@@ -107,10 +114,14 @@
Window slice = window.first_slice_window_3D();
Window slice_out = window.first_slice_window_2D();
+ const size_t idx_w = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+ const size_t idx_c = get_data_layout_dimension_index(_input->info()->data_layout(), DataLayoutDimension::CHANNEL);
+
// Setup slice
- slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _input->info()->dimension(0)));
- slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), 1));
- slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), 1));
+ slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(idx_w), _input->info()->dimension(idx_w)));
+ slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(idx_h), 1));
+ slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(idx_c), 1));
// Setup output slice
// The first two dimensions of the output are increased by the inner loops
diff --git a/src/core/CL/kernels/CLDirectConvolutionOutputStageKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionOutputStageKernel.cpp
index 1a6dc14..c6147ee 100644
--- a/src/core/CL/kernels/CLDirectConvolutionOutputStageKernel.cpp
+++ b/src/core/CL/kernels/CLDirectConvolutionOutputStageKernel.cpp
@@ -168,6 +168,7 @@
// Create kernel
CLBuildOptions build_opts;
build_opts.add_option_if(bias != nullptr, "-DHAS_BIAS");
+ build_opts.add_option("-D" + string_from_data_layout(input->info()->data_layout()));
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("output_stage_quantized", build_opts.options()));
// Set static kernel arguments
diff --git a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
index 1815361..1d89b84 100644
--- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
@@ -99,10 +99,15 @@
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
- const size_t weights_w = weights->info()->dimension(0);
- const size_t weights_h = weights->info()->dimension(1);
- const size_t weights_z = weights->info()->dimension(2);
+ const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+ const size_t idx_c = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::CHANNEL);
+
+ const size_t weights_w = weights->info()->dimension(idx_w);
+ const size_t weights_h = weights->info()->dimension(idx_h);
+ const size_t weights_z = weights->info()->dimension(idx_c);
_is_prepared = false;
_original_weights = weights;
@@ -119,8 +124,8 @@
ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
// Output width and height
- const unsigned int conv_w = output_shape.x();
- const unsigned int conv_h = output_shape.y();
+ const unsigned int conv_w = output_shape[idx_w];
+ const unsigned int conv_h = output_shape[idx_h];
// Set up intermediate tensors
const size_t patch_size = weights_w * weights_h + ((append_bias) ? 1 : 0);
@@ -188,17 +193,21 @@
Status CLDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
unsigned int depth_multiplier)
{
+ const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+ const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+ const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(2) * depth_multiplier) != weights->dimension(2));
+ ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(idx_c) * depth_multiplier) != weights->dimension(idx_c));
const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
const bool append_bias = (biases != nullptr) && !is_quantized;
const TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
- const size_t weights_w = weights->dimension(0);
- const size_t weights_h = weights->dimension(1);
- const size_t weights_z = weights->dimension(2);
- const unsigned int conv_w = output_shape.x();
- const unsigned int conv_h = output_shape.y();
+ const size_t weights_w = weights->dimension(idx_w);
+ const size_t weights_h = weights->dimension(idx_h);
+ const size_t weights_z = weights->dimension(idx_c);
+ const unsigned int conv_w = output_shape[idx_w];
+ const unsigned int conv_h = output_shape[idx_h];
const size_t patch_size = weights_w * weights_h + ((append_bias) ? 1 : 0);
const size_t conv_size = conv_w * conv_h;
diff --git a/tests/datasets/DepthwiseConvolutionLayerDataset.h b/tests/datasets/DepthwiseConvolutionLayerDataset.h
index 4b6d85b..889473e 100644
--- a/tests/datasets/DepthwiseConvolutionLayerDataset.h
+++ b/tests/datasets/DepthwiseConvolutionLayerDataset.h
@@ -153,8 +153,18 @@
{
add_config(TensorShape(3U, 3U, 2U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 0));
add_config(TensorShape(7U, 7U, 3U, 2U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 0));
+ add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(1, 1, 1, 0));
+ add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 1));
+ add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 1, 1, 1));
add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(1, 2, 1, 0));
add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 2, 0, 1));
+ add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 2, 1, 1));
+ add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(2, 1, 1, 0));
+ add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 1, 0, 1));
+ add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 1, 1, 1));
+ add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(2, 2, 1, 0));
+ add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 1));
+ add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 2, 1, 1));
// Asymmetric padding
add_config(TensorShape(33U, 27U, 11U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR));
}
diff --git a/tests/validation/CL/DepthwiseConvolutionLayer.cpp b/tests/validation/CL/DepthwiseConvolutionLayer.cpp
index 093d342..5b18f59 100644
--- a/tests/validation/CL/DepthwiseConvolutionLayer.cpp
+++ b/tests/validation/CL/DepthwiseConvolutionLayer.cpp
@@ -251,7 +251,7 @@
FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset(), depth_multipliers),
framework::dataset::make("DataType",
DataType::F16)),
- framework::dataset::make("DataLayout", DataLayout::NCHW)))
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
@@ -259,7 +259,7 @@
depth_multipliers),
framework::dataset::make("DataType",
DataType::F16)),
- framework::dataset::make("DataLayout", DataLayout::NCHW)))
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
@@ -268,6 +268,7 @@
TEST_SUITE(FP32)
TEST_SUITE(W3x3)
+TEST_SUITE(NCHW)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture3x3<float>, framework::DatasetMode::ALL,
combine(combine(combine(framework::dataset::concat(datasets::SmallDepthwiseConvolutionLayerDataset3x3(),
datasets::SmallDepthwiseConvolutionLayerDataset3x3NCHW()),
@@ -287,12 +288,32 @@
validate(CLAccessor(_target), _reference, tolerance_f32);
}
TEST_SUITE_END()
+TEST_SUITE(NHWC)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture3x3<float>, framework::DatasetMode::ALL,
+ combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset3x3(),
+ framework::dataset::make("DepthMultiplier", 1)), // COMPMID-1071 Add depth multiplier support for NHWC
+ framework::dataset::make("DataType",
+ DataType::F32)),
+ framework::dataset::make("DataLayout", DataLayout::NHWC)))
+{
+ validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture3x3<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseConvolutionLayerDataset3x3(),
+ framework::dataset::make("DepthMultiplier", 1)), // COMPMID-1071 Add depth multiplier support for NHWC
+ framework::dataset::make("DataType",
+ DataType::F32)),
+ framework::dataset::make("DataLayout", DataLayout::NHWC)))
+{
+ validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
TEST_SUITE(Generic)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset(), depth_multipliers),
framework::dataset::make("DataType",
DataType::F32)),
- framework::dataset::make("DataLayout", DataLayout::NCHW)))
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
@@ -300,7 +321,7 @@
depth_multipliers),
framework::dataset::make("DataType",
DataType::F32)),
- framework::dataset::make("DataLayout", DataLayout::NCHW)))
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
validate(CLAccessor(_target), _reference, tolerance_f32);
}
@@ -321,7 +342,7 @@
depth_multipliers),
framework::dataset::make("DataType", DataType::QASYMM8)),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10) })),
- framework::dataset::make("DataLayout", DataLayout::NCHW)))
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
@@ -330,7 +351,7 @@
depth_multipliers),
framework::dataset::make("DataType", DataType::QASYMM8)),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10) })),
- framework::dataset::make("DataLayout", DataLayout::NCHW)))
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}