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339 lines
13 KiB
339 lines
13 KiB
#!/usr/bin/env python
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# Copyright 2020 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 DWCONV2D CHW micro-kernels')
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parser.add_argument("-s", "--spec", metavar="FILE", required=True,
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help="Spec (YAML) file")
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parser.add_argument("-o", "--output", metavar="FILE", required=True,
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help='Output (C++ source) file')
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parser.set_defaults(defines=list())
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TEST_TEMPLATE = """\
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$if SUBSAMPLING == 1:
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TEST(${TEST_NAME}, output_width_eq_${WIDTH_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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DWConv2DMicrokernelTester()
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.input_width(${(WIDTH_TILE - 1) * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING})
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.input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
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.kernel_height(${KERNEL_HEIGHT})
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.kernel_width(${KERNEL_WIDTH})
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.subsampling(${SUBSAMPLING})
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.padding_left(${PADDING})
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.padding_right(${PADDING})
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.padding_top(${PADDING})
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.padding_bottom(${PADDING})
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.Test(${", ".join(TEST_ARGS)});
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}
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$else:
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TEST(${TEST_NAME}, output_width_eq_${WIDTH_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_width = ${(WIDTH_TILE - 1) * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width < ${WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width++) {
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DWConv2DMicrokernelTester()
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.input_width(input_width)
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.input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
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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})
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.padding_right(${PADDING})
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.padding_top(${PADDING})
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.padding_bottom(${PADDING})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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$if WIDTH_TILE > 1:
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TEST(${TEST_NAME}, output_width_div_${WIDTH_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_width = ${2 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING - 1}; input_width < ${8 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING - 1}; input_width += ${WIDTH_TILE * SUBSAMPLING}) {
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DWConv2DMicrokernelTester()
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.input_width(input_width)
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.input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
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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})
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.padding_right(${PADDING})
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.padding_top(${PADDING})
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.padding_bottom(${PADDING})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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TEST(${TEST_NAME}, output_width_lt_${WIDTH_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_width = ${max(1, KERNEL_WIDTH - 2 * PADDING)}; input_width < ${(WIDTH_TILE - 1) * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width++) {
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DWConv2DMicrokernelTester()
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.input_width(${WIDTH_TILE * SUBSAMPLING})
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.input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
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.kernel_height(${KERNEL_HEIGHT})
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.kernel_width(${KERNEL_WIDTH})
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.subsampling(${SUBSAMPLING})
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.padding_left(${PADDING})
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.padding_right(${PADDING})
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.padding_top(${PADDING})
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.padding_bottom(${PADDING})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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TEST(${TEST_NAME}, output_width_gt_${WIDTH_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_width = ${WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width < ${(5 if WIDTH_TILE == 1 else 2) * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width++) {
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DWConv2DMicrokernelTester()
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.input_width(input_width)
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.input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
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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})
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.padding_right(${PADDING})
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.padding_top(${PADDING})
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.padding_bottom(${PADDING})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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$if SUBSAMPLING > 1:
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TEST(${TEST_NAME}, output_height_eq_${HEIGHT_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_height = ${(HEIGHT_TILE - 1) * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING}; input_height < ${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING}; input_height++) {
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for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
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DWConv2DMicrokernelTester()
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.input_width(input_width)
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.input_height(input_height)
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.kernel_height(${KERNEL_HEIGHT})
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.kernel_width(${KERNEL_WIDTH})
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.subsampling(${SUBSAMPLING})
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.padding_left(${PADDING})
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.padding_right(${PADDING})
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.padding_top(${PADDING})
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.padding_bottom(${PADDING})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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$if HEIGHT_TILE > 1:
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TEST(${TEST_NAME}, output_height_div_${HEIGHT_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_height = ${2 * HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1}; input_height < ${8 * HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1}; input_height += ${HEIGHT_TILE * SUBSAMPLING}) {
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for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
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DWConv2DMicrokernelTester()
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.input_width(input_width)
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.input_height(input_height)
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.kernel_height(${KERNEL_HEIGHT})
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.kernel_width(${KERNEL_WIDTH})
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.subsampling(${SUBSAMPLING})
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.padding_left(${PADDING})
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.padding_right(${PADDING})
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.padding_top(${PADDING})
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.padding_bottom(${PADDING})
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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_height_lt_${HEIGHT_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_height = ${max(1, KERNEL_HEIGHT - 2 * PADDING)}; input_height < ${(HEIGHT_TILE - 1) * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING}; input_height++) {
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for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
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DWConv2DMicrokernelTester()
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.input_width(input_width)
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.input_height(input_height)
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.kernel_height(${KERNEL_HEIGHT})
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.kernel_width(${KERNEL_WIDTH})
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.subsampling(${SUBSAMPLING})
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.padding_left(${PADDING})
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.padding_right(${PADDING})
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.padding_top(${PADDING})
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.padding_bottom(${PADDING})
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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_height_gt_${HEIGHT_TILE}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_height = ${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING}; input_height < ${(5 if WIDTH_TILE == 1 else 2) * HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING}; input_height++) {
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for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
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DWConv2DMicrokernelTester()
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.input_width(input_width)
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.input_height(input_height)
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.kernel_height(${KERNEL_HEIGHT})
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.kernel_width(${KERNEL_WIDTH})
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.subsampling(${SUBSAMPLING})
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.padding_left(${PADDING})
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.padding_right(${PADDING})
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.padding_top(${PADDING})
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.padding_bottom(${PADDING})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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$if SUBSAMPLING > 1:
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TEST(${TEST_NAME}, padding_top_eq_${SUBSAMPLING - 1}) {
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$if ISA_CHECK:
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${ISA_CHECK};
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for (size_t input_height = ${max(1, KERNEL_HEIGHT - 2 * PADDING + 1)}; input_height < ${3 * HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING + 1}; input_height++) {
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for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
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DWConv2DMicrokernelTester()
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.input_width(input_width)
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.input_height(input_height)
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.kernel_height(${KERNEL_HEIGHT})
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.kernel_width(${KERNEL_WIDTH})
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.subsampling(${SUBSAMPLING})
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.padding_left(${PADDING})
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.padding_right(${PADDING})
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.padding_top(${PADDING - 1})
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.padding_bottom(${PADDING})
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.Test(${", ".join(TEST_ARGS)});
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}
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}
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}
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"""
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def split_ukernel_name(name):
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match = re.match(r"^xnn_(f16|f32)_dwconv2d_chw_ukernel_(\d+)x(\d+)(s2)?p(\d+)__(.+)_(\d+)x(\d+)(_acc\d+)?$", name)
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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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if match.group(4):
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assert match.group(4).startswith("s")
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stride = int(match.group(4)[1:])
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else:
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stride = 1
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padding = int(match.group(5))
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height_tile = int(match.group(7))
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width_tile = int(match.group(8))
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arch, isa = xnncommon.parse_target_name(target_name=match.group(6))
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return kernel_height, kernel_width, stride, padding, arch, isa, \
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height_tile, width_tile
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def generate_test_cases(ukernel, kernel_height, kernel_width, subsampling, \
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padding, isa, height_tile, width_tile):
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"""Generates all tests cases for a DWCONV2D CHW 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
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vertical directions.
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padding: convolution padding value assumed by the micro-kernel for right,
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bottom, and left padding. If convolution stride is 1, the same
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padding value is assumed for the top padding. If convolution stride
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is different than 1, top padding is specified via micro-kernel
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parameter, and can be either padding or (padding - 1).
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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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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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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]
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if not isa:
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test_args.append("DWConv2DMicrokernelTester::Variant::Scalar")
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return xngen.preprocess(TEST_TEMPLATE, {
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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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"KERNEL_HEIGHT": kernel_height,
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"KERNEL_WIDTH": kernel_width,
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"SUBSAMPLING": subsampling,
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"PADDING": padding,
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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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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 2020 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/isa-checks.h>
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#include <xnnpack/dwconv.h>
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#include "dwconv2d-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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pipelined = bool(ukernel_spec.get("pipelined", False))
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assembly = bool(ukernel_spec.get("assembly", False))
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kernel_height, kernel_width, subsampling, padding, arch, isa, \
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height_tile, width_tile = split_ukernel_name(name)
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# specification can override architecture
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arch = ukernel_spec.get("arch", arch)
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test_case = generate_test_cases(name, kernel_height, kernel_width, \
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subsampling, padding, isa, \
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height_tile, width_tile)
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tests += "\n\n" + xnncommon.postprocess_test_case(test_case, arch, isa, assembly)
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with codecs.open(options.output, "w", encoding="utf-8") as output_file:
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output_file.write(tests)
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if __name__ == "__main__":
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main(sys.argv[1:])
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