497 lines
17 KiB
Python
Executable File
497 lines
17 KiB
Python
Executable File
#!/usr/bin/env python
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# Copyright 2023 Google LLC
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import argparse
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import codecs
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import os
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import re
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import sys
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import yaml
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from primes import next_prime
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import xngen
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import xnncommon
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parser = argparse.ArgumentParser(
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description="Test generator for CONV HWC2CHW micro-kernels"
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)
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parser.add_argument(
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"-s", "--spec", metavar="FILE", required=True, help="Spec (YAML) file"
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)
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parser.add_argument(
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"-o",
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"--output",
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metavar="FILE",
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required=True,
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help="Output (C++ source) file",
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)
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parser.set_defaults(defines=list())
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TEST_TEMPLATE = """\
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TEST(${TEST_NAME}, input_width_eq_${INPUT_WIDTH}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(${OUTPUT_CHANNELS_TILE})
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.input_width(${INPUT_WIDTH})
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.input_height(${KERNEL_SIZE})
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.Test(${", ".join(TEST_ARGS)});
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}
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$if INPUT_WIDTH > 1:
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TEST(${TEST_NAME}, input_width_div_${INPUT_WIDTH}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_width = ${INPUT_WIDTH*2}; input_width <= ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*3}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(${OUTPUT_CHANNELS_TILE})
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.input_width(input_width)
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.input_height(${KERNEL_SIZE})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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TEST(${TEST_NAME}, input_width_lt_${INPUT_WIDTH}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH}; input_width++) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(${OUTPUT_CHANNELS_TILE})
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.input_width(input_width)
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.input_height(${KERNEL_SIZE})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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TEST(${TEST_NAME}, input_width_gt_${INPUT_WIDTH}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_width = ${INPUT_WIDTH+1}; input_width < ${33 if INPUT_WIDTH == 1 else INPUT_WIDTH*2}; input_width++) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(${OUTPUT_CHANNELS_TILE})
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.input_width(input_width)
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.input_height(${KERNEL_SIZE})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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TEST(${TEST_NAME}, output_channels_lt_${OUTPUT_CHANNELS_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t output_channels = 1; output_channels < ${OUTPUT_CHANNELS_TILE}; output_channels++) {
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*2-1}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(output_channels)
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.input_width(input_width)
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.input_height(${KERNEL_SIZE})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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TEST(${TEST_NAME}, output_channels_div_${OUTPUT_CHANNELS_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t output_channels = ${OUTPUT_CHANNELS_TILE*2}; output_channels <= ${OUTPUT_CHANNELS_TILE*4}; output_channels += ${OUTPUT_CHANNELS_TILE}) {
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*2-1}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(output_channels)
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.input_width(input_width)
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.input_height(${KERNEL_SIZE})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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TEST(${TEST_NAME}, output_channels_gt_${OUTPUT_CHANNELS_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t output_channels = ${OUTPUT_CHANNELS_TILE+1}; output_channels < ${OUTPUT_CHANNELS_TILE*2}; output_channels++) {
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*2-1}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(output_channels)
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.input_width(input_width)
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.input_height(${KERNEL_SIZE})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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TEST(${TEST_NAME}, input_height_lt_3) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_height = 1; input_height < 3; input_height++) {
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for (size_t output_channels = 1; output_channels < ${OUTPUT_CHANNELS_TILE*2}; output_channels += ${OUTPUT_CHANNELS_TILE-1}) {
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*2-1}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding(1) // padded input height of at least 3 required
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(output_channels)
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.input_width(input_width)
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.input_height(input_height)
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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}
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TEST(${TEST_NAME}, input_height_gt_3) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_height = 4; input_height <= 9; input_height++) {
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for (size_t output_channels = 1; output_channels < ${OUTPUT_CHANNELS_TILE*2}; output_channels += ${OUTPUT_CHANNELS_TILE-1}) {
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*2-1}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(output_channels)
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.input_width(input_width)
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.input_height(input_height)
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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}
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TEST(${TEST_NAME}, padding_top) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t padding_top = 0; padding_top <= 1; padding_top++) {
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for (size_t output_channels = 1; output_channels < ${OUTPUT_CHANNELS_TILE*4}; output_channels += ${OUTPUT_CHANNELS_TILE*2-1}) {
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*2-1}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.padding_top(padding_top)
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(output_channels)
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.input_width(input_width)
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.input_height(9)
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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}
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TEST(${TEST_NAME}, padding_bottom) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t padding_bottom = 0; padding_bottom <= 1; padding_bottom++) {
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for (size_t output_channels = 1; output_channels < ${OUTPUT_CHANNELS_TILE*4}; output_channels += ${OUTPUT_CHANNELS_TILE*2-1}) {
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*2-1}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.padding_bottom(padding_bottom)
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(output_channels)
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.input_width(input_width)
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.input_height(9)
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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}
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TEST(${TEST_NAME}, output_y_start) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t output_y_start = 1; output_y_start <= 3; output_y_start++) {
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for (size_t output_channels = 1; output_channels < ${OUTPUT_CHANNELS_TILE*2}; output_channels += ${OUTPUT_CHANNELS_TILE-1}) {
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*2-1}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(output_channels)
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.input_width(input_width)
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.input_height(9)
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.output_y_start(output_y_start)
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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}
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TEST(${TEST_NAME}, output_y_end) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t output_y_end = 2; output_y_end < 5; output_y_end++) {
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for (size_t output_channels = 1; output_channels < ${OUTPUT_CHANNELS_TILE*2}; output_channels += ${OUTPUT_CHANNELS_TILE-1}) {
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*2-1}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(output_channels)
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.input_width(input_width)
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.input_height(9)
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.output_y_end(output_y_end)
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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}
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TEST(${TEST_NAME}, qmin) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t output_channels = 1; output_channels < ${OUTPUT_CHANNELS_TILE*2}; output_channels += ${OUTPUT_CHANNELS_TILE-1}) {
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*2-1}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(output_channels)
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.input_width(input_width)
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.input_height(6)
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.qmin(128)
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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TEST(${TEST_NAME}, qmax) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t output_channels = 1; output_channels < ${OUTPUT_CHANNELS_TILE*2}; output_channels += ${OUTPUT_CHANNELS_TILE-1}) {
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for (size_t input_width = ${1 if PADDING_LEFT else 2}; input_width < ${INPUT_WIDTH*8}; input_width += ${INPUT_WIDTH*2-1}) {
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ConvHWC2CHWMicrokernelTester()
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.kernel_size(${KERNEL_SIZE})
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.subsampling(${SUBSAMPLING})
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.padding_width(${PADDING_RIGHT})
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.input_channels(${INPUT_CHANNELS})
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.output_channels_tile(${OUTPUT_CHANNELS_TILE})
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.output_channels(output_channels)
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.input_width(input_width)
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.input_height(6)
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.qmax(128)
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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"""
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def split_ukernel_name(name):
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match = re.fullmatch(
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r"xnn_(f16|f32)_conv_hwc2chw_ukernel_(\d+)x(\d+)s(\d+)(p1)c(\d+)x(\d+)__(.+)_(\d+)x(\d+)?",
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name,
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)
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assert match is not None
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kernel_height, kernel_width = int(match.group(2)), int(match.group(3))
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assert kernel_height == kernel_width
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subsampling = int(match.group(4))
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padding_right = 1
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padding_left = 1
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input_channels = int(match.group(6))
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channel_tile = int(match.group(7))
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height_tile = int(match.group(9))
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width_tile = int(match.group(10))
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arch, isa, assembly = xnncommon.parse_target_name(target_name=match.group(8))
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return (
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kernel_height,
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kernel_width,
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subsampling,
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padding_left,
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padding_right,
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input_channels,
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channel_tile,
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height_tile,
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width_tile,
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arch,
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isa,
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)
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def generate_test_cases(
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ukernel,
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kernel_height,
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kernel_width,
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subsampling,
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padding_left,
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padding_right,
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input_channels,
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channel_tile,
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height_tile,
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width_tile,
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init_fn,
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isa,
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):
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"""Generates all tests cases for a CONV HWC2CHW micro-kernel.
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Args:
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ukernel: C name of the micro-kernel function.
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kernel_height: convolution kernel height assumed by the micro-kernel.
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kernel_width: convolution kernel width assumed by the micro-kernel.
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subsampling: convolution subsampling (stride) assumed by the micro-kernel.
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The same subsampling factor is assumed for both horizontal and vertical
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directions.
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padding_left: input padding on the left assumed by micro-kernel, 0 or 1
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padding_right: input padding on the right assumed by micro-kernel, current
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microkernels always assume this is 1
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input_channels: number of input channels assumed by micro-kernel.
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channel_tile: number of output channels processed in one iteration of the
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main loop of the micro-kernel.
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height_tile: number of output rows processed in one iteration of the main
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loop of the micro-kernel.
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width_tile: number of output columns processed in one iteration of the main
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loop of the micro-kernel.
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init_fn: C name of the function to initialize microkernel parameters.
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isa: instruction set required to run the micro-kernel. Generated unit test
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will skip execution if the host processor doesn't support this ISA.
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Returns:
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Code for the test case.
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"""
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_, test_name = ukernel.split("_", 1)
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_, datatype, ukernel_type, _ = ukernel.split("_", 3)
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test_args = [ukernel, init_fn]
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return xngen.preprocess(
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TEST_TEMPLATE,
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{
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"TEST_NAME": test_name.upper().replace("UKERNEL_", ""),
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"TEST_ARGS": test_args,
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"UKERNEL_TYPE": ukernel_type.upper(),
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"DATATYPE": datatype,
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"INPUT_WIDTH": width_tile * height_tile,
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"KERNEL_SIZE": kernel_height,
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"KERNEL_HEIGHT": kernel_height,
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"KERNEL_WIDTH": kernel_width,
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"SUBSAMPLING": subsampling,
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"PADDING_LEFT": padding_left,
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"PADDING_RIGHT": padding_right,
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"INPUT_CHANNELS": input_channels,
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"OUTPUT_CHANNELS_TILE": channel_tile,
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"HEIGHT_TILE": height_tile,
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"WIDTH_TILE": width_tile,
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"ISA_CHECK": xnncommon.generate_isa_check_macro(isa),
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"next_prime": next_prime,
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},
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)
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def main(args):
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options = parser.parse_args(args)
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with codecs.open(options.spec, "r", encoding="utf-8") as spec_file:
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spec_yaml = yaml.safe_load(spec_file)
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if not isinstance(spec_yaml, list):
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raise ValueError("expected a list of micro-kernels in the spec")
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tests = """\
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// Copyright 2023 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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//
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// Auto-generated file. Do not edit!
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// Specification: {specification}
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// Generator: {generator}
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#include <gtest/gtest.h>
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#include "xnnpack/common.h"
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#include "xnnpack/conv.h"
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#include "xnnpack/isa-checks.h"
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#include "xnnpack/microparams-init.h"
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#include "conv-hwc2chw-microkernel-tester.h"
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""".format(specification=options.spec, generator=sys.argv[0])
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for ukernel_spec in spec_yaml:
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name = ukernel_spec["name"]
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init_fn = ukernel_spec["init"]
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(
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kernel_height,
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kernel_width,
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subsampling,
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padding_left,
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padding_right,
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input_channels,
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channel_tile,
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height_tile,
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width_tile,
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arch,
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isa,
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) = split_ukernel_name(name)
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test_case = generate_test_cases(
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name,
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kernel_height,
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kernel_width,
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subsampling,
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padding_left,
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padding_right,
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input_channels,
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channel_tile,
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height_tile,
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width_tile,
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init_fn,
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isa,
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)
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tests += "\n\n" + xnncommon.postprocess_test_case(test_case, arch, isa)
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xnncommon.overwrite_if_changed(options.output, tests)
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if __name__ == "__main__":
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main(sys.argv[1:])
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