tools/generate-dwconv2d-chw-test.py - platform/external/XNNPACK - Gitiles

 #!/usr/bin/env python
 # Copyright 2020 Google LLC
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.

 import argparse
 import codecs
 import os
 import re
 import sys
 import yaml

 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 from primes import next_prime
 import xngen
 import xnncommon


 parser = argparse.ArgumentParser(
   description='Test generator for DWCONV2D CHW micro-kernels')
 parser.add_argument("-s", "--spec", metavar="FILE", required=True,
                     help="Spec (YAML) file")
 parser.add_argument("-o", "--output", metavar="FILE", required=True,
                     help='Output (C++ source) file')
 parser.set_defaults(defines=list())


 TEST_TEMPLATE = """\
 $if SUBSAMPLING == 1:
   TEST(${TEST_NAME}, output_width_eq_${WIDTH_TILE}) {
     $if ISA_CHECK:
       ${ISA_CHECK};
     DWConv2DMicrokernelTester()
       .input_width(${(WIDTH_TILE - 1) * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING})
       .input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
       .kernel_height(${KERNEL_HEIGHT})
       .kernel_width(${KERNEL_WIDTH})
       .subsampling(${SUBSAMPLING})
       .padding_left(${PADDING})
       .padding_right(${PADDING})
       .padding_top(${PADDING})
       .padding_bottom(${PADDING})
       .Test(${", ".join(TEST_ARGS)});
   }
 $else:
   TEST(${TEST_NAME}, output_width_eq_${WIDTH_TILE}) {
     $if ISA_CHECK:
       ${ISA_CHECK};
     for (size_t input_width = ${(WIDTH_TILE - 1) * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width < ${WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width++) {
       DWConv2DMicrokernelTester()
         .input_width(input_width)
         .input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
         .kernel_height(${KERNEL_HEIGHT})
         .kernel_width(${KERNEL_WIDTH})
         .subsampling(${SUBSAMPLING})
         .padding_left(${PADDING})
         .padding_right(${PADDING})
         .padding_top(${PADDING})
         .padding_bottom(${PADDING})
         .Test(${", ".join(TEST_ARGS)});
     }
   }

 $if WIDTH_TILE > 1:
   TEST(${TEST_NAME}, output_width_div_${WIDTH_TILE}) {
     $if ISA_CHECK:
       ${ISA_CHECK};
     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 += ${WIDTH_TILE * SUBSAMPLING}) {
       DWConv2DMicrokernelTester()
         .input_width(input_width)
         .input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
         .kernel_height(${KERNEL_HEIGHT})
         .kernel_width(${KERNEL_WIDTH})
         .subsampling(${SUBSAMPLING})
         .padding_left(${PADDING})
         .padding_right(${PADDING})
         .padding_top(${PADDING})
         .padding_bottom(${PADDING})
         .Test(${", ".join(TEST_ARGS)});
     }
   }

   TEST(${TEST_NAME}, output_width_lt_${WIDTH_TILE}) {
     $if ISA_CHECK:
       ${ISA_CHECK};
     for (size_t input_width = ${max(1, KERNEL_WIDTH - 2 * PADDING)}; input_width < ${(WIDTH_TILE - 1) * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width++) {
       DWConv2DMicrokernelTester()
         .input_width(${WIDTH_TILE * SUBSAMPLING})
         .input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
         .kernel_height(${KERNEL_HEIGHT})
         .kernel_width(${KERNEL_WIDTH})
         .subsampling(${SUBSAMPLING})
         .padding_left(${PADDING})
         .padding_right(${PADDING})
         .padding_top(${PADDING})
         .padding_bottom(${PADDING})
         .Test(${", ".join(TEST_ARGS)});
     }
   }

 TEST(${TEST_NAME}, output_width_gt_${WIDTH_TILE}) {
   $if ISA_CHECK:
     ${ISA_CHECK};
   for (size_t input_width = ${WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width < ${(5 if WIDTH_TILE == 1 else 2) * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width++) {
     DWConv2DMicrokernelTester()
       .input_width(input_width)
       .input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
       .kernel_height(${KERNEL_HEIGHT})
       .kernel_width(${KERNEL_WIDTH})
       .subsampling(${SUBSAMPLING})
       .padding_left(${PADDING})
       .padding_right(${PADDING})
       .padding_top(${PADDING})
       .padding_bottom(${PADDING})
       .Test(${", ".join(TEST_ARGS)});
   }
 }

 $if SUBSAMPLING > 1:
   TEST(${TEST_NAME}, output_height_eq_${HEIGHT_TILE}) {
     $if ISA_CHECK:
       ${ISA_CHECK};
     for (size_t input_height = ${(HEIGHT_TILE - 1) * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING}; input_height < ${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING}; input_height++) {
       for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
         DWConv2DMicrokernelTester()
           .input_width(input_width)
           .input_height(input_height)
           .kernel_height(${KERNEL_HEIGHT})
           .kernel_width(${KERNEL_WIDTH})
           .subsampling(${SUBSAMPLING})
           .padding_left(${PADDING})
           .padding_right(${PADDING})
           .padding_top(${PADDING})
           .padding_bottom(${PADDING})
           .Test(${", ".join(TEST_ARGS)});
       }
     }
   }

 $if HEIGHT_TILE > 1:
   TEST(${TEST_NAME}, output_height_div_${HEIGHT_TILE}) {
     $if ISA_CHECK:
       ${ISA_CHECK};
     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}) {
       for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
         DWConv2DMicrokernelTester()
           .input_width(input_width)
           .input_height(input_height)
           .kernel_height(${KERNEL_HEIGHT})
           .kernel_width(${KERNEL_WIDTH})
           .subsampling(${SUBSAMPLING})
           .padding_left(${PADDING})
           .padding_right(${PADDING})
           .padding_top(${PADDING})
           .padding_bottom(${PADDING})
           .Test(${", ".join(TEST_ARGS)});
       }
     }
   }

   TEST(${TEST_NAME}, output_height_lt_${HEIGHT_TILE}) {
     $if ISA_CHECK:
       ${ISA_CHECK};
     for (size_t input_height = ${max(1, KERNEL_HEIGHT - 2 * PADDING)}; input_height < ${(HEIGHT_TILE - 1) * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING}; input_height++) {
       for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
         DWConv2DMicrokernelTester()
           .input_width(input_width)
           .input_height(input_height)
           .kernel_height(${KERNEL_HEIGHT})
           .kernel_width(${KERNEL_WIDTH})
           .subsampling(${SUBSAMPLING})
           .padding_left(${PADDING})
           .padding_right(${PADDING})
           .padding_top(${PADDING})
           .padding_bottom(${PADDING})
           .Test(${", ".join(TEST_ARGS)});
       }
     }
   }

 TEST(${TEST_NAME}, output_height_gt_${HEIGHT_TILE}) {
   $if ISA_CHECK:
     ${ISA_CHECK};
   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++) {
     for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
       DWConv2DMicrokernelTester()
         .input_width(input_width)
         .input_height(input_height)
         .kernel_height(${KERNEL_HEIGHT})
         .kernel_width(${KERNEL_WIDTH})
         .subsampling(${SUBSAMPLING})
         .padding_left(${PADDING})
         .padding_right(${PADDING})
         .padding_top(${PADDING})
         .padding_bottom(${PADDING})
         .Test(${", ".join(TEST_ARGS)});
     }
   }
 }

 $if SUBSAMPLING > 1:
   TEST(${TEST_NAME}, padding_top_eq_${SUBSAMPLING - 1}) {
     $if ISA_CHECK:
       ${ISA_CHECK};
     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++) {
       for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
         DWConv2DMicrokernelTester()
           .input_width(input_width)
           .input_height(input_height)
           .kernel_height(${KERNEL_HEIGHT})
           .kernel_width(${KERNEL_WIDTH})
           .subsampling(${SUBSAMPLING})
           .padding_left(${PADDING})
           .padding_right(${PADDING})
           .padding_top(${PADDING - 1})
           .padding_bottom(${PADDING})
           .Test(${", ".join(TEST_ARGS)});
       }
     }
   }
 """

 def split_ukernel_name(name):
   match = re.match(r"^xnn_(f16|f32)_dwconv2d_chw_ukernel_(\d+)x(\d+)(s2)?p(\d+)__(.+)_(\d+)x(\d+)(_acc\d+)?$", name)
   assert match is not None
   kernel_height, kernel_width = int(match.group(2)), int(match.group(3))
   if match.group(4):
     assert match.group(4).startswith("s")
     stride = int(match.group(4)[1:])
   else:
     stride = 1
   padding = int(match.group(5))

   height_tile = int(match.group(7))
   width_tile = int(match.group(8))

   arch, isa = xnncommon.parse_target_name(target_name=match.group(6))
   return kernel_height, kernel_width, stride, padding, arch, isa, \
          height_tile, width_tile


 def generate_test_cases(ukernel, kernel_height, kernel_width, subsampling, \
   padding, isa, height_tile, width_tile):
   """Generates all tests cases for a DWCONV2D CHW micro-kernel.

   Args:
     ukernel: C name of the micro-kernel function.
     kernel_height: convolution kernel height assumed by the micro-kernel.
     kernel_width: convolution kernel width assumed by the micro-kernel.
     subsampling: convolution subsampling (stride) assumed by the micro-kernel.
                  The same subsampling factor is assumed for both horizontal and
                  vertical directions.
     padding: convolution padding value assumed by the micro-kernel for right,
              bottom, and left padding. If convolution stride is 1, the same
              padding value is assumed for the top padding. If convolution stride
              is different than 1, top padding is specified via micro-kernel
              parameter, and can be either padding or (padding - 1).
     isa: instruction set required to run the micro-kernel. Generated unit test
          will skip execution if the host processor doesn't support this ISA.
     height_tile: number of output rows processed in one iteration of the main
                  loop of the micro-kernel.
     width_tile: number of output columns processed in one iteration of the main
                 loop of the micro-kernel.

   Returns:
     Code for the test case.
   """
   _, test_name = ukernel.split("_", 1)
   _, datatype, ukernel_type, _ = ukernel.split("_", 3)
   test_args = [ukernel]
   if not isa or isa == "psimd":
     test_args.append("DWConv2DMicrokernelTester::Variant::Scalar")
   return xngen.preprocess(TEST_TEMPLATE, {
       "TEST_NAME": test_name.upper().replace("UKERNEL_", ""),
       "TEST_ARGS": test_args,
       "UKERNEL_TYPE": ukernel_type.upper(),
       "DATATYPE": datatype,
       "KERNEL_HEIGHT": kernel_height,
       "KERNEL_WIDTH": kernel_width,
       "SUBSAMPLING": subsampling,
       "PADDING": padding,
       "HEIGHT_TILE": height_tile,
       "WIDTH_TILE": width_tile,
       "ISA_CHECK": xnncommon.generate_isa_check_macro(isa),
       "next_prime": next_prime,
     })


 def main(args):
   options = parser.parse_args(args)

   with codecs.open(options.spec, "r", encoding="utf-8") as spec_file:
     spec_yaml = yaml.safe_load(spec_file)
     if not isinstance(spec_yaml, list):
       raise ValueError("expected a list of micro-kernels in the spec")

     tests = """\
 // Copyright 2020 Google LLC
 //
 // This source code is licensed under the BSD-style license found in the
 // LICENSE file in the root directory of this source tree.
 //
 // Auto-generated file. Do not edit!
 //   Specification: {specification}
 //   Generator: {generator}


 #include <gtest/gtest.h>

 #include <xnnpack/common.h>
 #include <xnnpack/isa-checks.h>

 #include <xnnpack/dwconv.h>
 #include "dwconv2d-microkernel-tester.h"
 """.format(specification=options.spec, generator=sys.argv[0])

     for ukernel_spec in spec_yaml:
       name = ukernel_spec["name"]
       pipelined = bool(ukernel_spec.get("pipelined", False))
       assembly = bool(ukernel_spec.get("assembly", False))
       kernel_height, kernel_width, subsampling, padding, arch, isa, \
         height_tile, width_tile = split_ukernel_name(name)

       # specification can override architecture
       arch = ukernel_spec.get("arch", arch)

       test_case = generate_test_cases(name, kernel_height, kernel_width, \
                                       subsampling, padding, isa, \
                                       height_tile, width_tile)
       tests += "\n\n" + xnncommon.postprocess_test_case(test_case, arch, isa, assembly)

     with codecs.open(options.output, "w", encoding="utf-8") as output_file:
       output_file.write(tests)


 if __name__ == "__main__":
   main(sys.argv[1:])
	#!/usr/bin/env python
	# Copyright 2020 Google LLC
	#
	# This source code is licensed under the BSD-style license found in the
	# LICENSE file in the root directory of this source tree.

	import argparse
	import codecs
	import os
	import re
	import sys
	import yaml

	sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
	from primes import next_prime
	import xngen
	import xnncommon


	parser = argparse.ArgumentParser(
	description='Test generator for DWCONV2D CHW micro-kernels')
	parser.add_argument("-s", "--spec", metavar="FILE", required=True,
	help="Spec (YAML) file")
	parser.add_argument("-o", "--output", metavar="FILE", required=True,
	help='Output (C++ source) file')
	parser.set_defaults(defines=list())


	TEST_TEMPLATE = """\
	$if SUBSAMPLING == 1:
	TEST(${TEST_NAME}, output_width_eq_${WIDTH_TILE}) {
	$if ISA_CHECK:
	${ISA_CHECK};
	DWConv2DMicrokernelTester()
	.input_width(${(WIDTH_TILE - 1) * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING})
	.input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
	.kernel_height(${KERNEL_HEIGHT})
	.kernel_width(${KERNEL_WIDTH})
	.subsampling(${SUBSAMPLING})
	.padding_left(${PADDING})
	.padding_right(${PADDING})
	.padding_top(${PADDING})
	.padding_bottom(${PADDING})
	.Test(${", ".join(TEST_ARGS)});
	}
	$else:
	TEST(${TEST_NAME}, output_width_eq_${WIDTH_TILE}) {
	$if ISA_CHECK:
	${ISA_CHECK};
	for (size_t input_width = ${(WIDTH_TILE - 1) * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width < ${WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width++) {
	DWConv2DMicrokernelTester()
	.input_width(input_width)
	.input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
	.kernel_height(${KERNEL_HEIGHT})
	.kernel_width(${KERNEL_WIDTH})
	.subsampling(${SUBSAMPLING})
	.padding_left(${PADDING})
	.padding_right(${PADDING})
	.padding_top(${PADDING})
	.padding_bottom(${PADDING})
	.Test(${", ".join(TEST_ARGS)});
	}
	}

	$if WIDTH_TILE > 1:
	TEST(${TEST_NAME}, output_width_div_${WIDTH_TILE}) {
	$if ISA_CHECK:
	${ISA_CHECK};
	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 += ${WIDTH_TILE * SUBSAMPLING}) {
	DWConv2DMicrokernelTester()
	.input_width(input_width)
	.input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
	.kernel_height(${KERNEL_HEIGHT})
	.kernel_width(${KERNEL_WIDTH})
	.subsampling(${SUBSAMPLING})
	.padding_left(${PADDING})
	.padding_right(${PADDING})
	.padding_top(${PADDING})
	.padding_bottom(${PADDING})
	.Test(${", ".join(TEST_ARGS)});
	}
	}

	TEST(${TEST_NAME}, output_width_lt_${WIDTH_TILE}) {
	$if ISA_CHECK:
	${ISA_CHECK};
	for (size_t input_width = ${max(1, KERNEL_WIDTH - 2 * PADDING)}; input_width < ${(WIDTH_TILE - 1) * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width++) {
	DWConv2DMicrokernelTester()
	.input_width(${WIDTH_TILE * SUBSAMPLING})
	.input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
	.kernel_height(${KERNEL_HEIGHT})
	.kernel_width(${KERNEL_WIDTH})
	.subsampling(${SUBSAMPLING})
	.padding_left(${PADDING})
	.padding_right(${PADDING})
	.padding_top(${PADDING})
	.padding_bottom(${PADDING})
	.Test(${", ".join(TEST_ARGS)});
	}
	}

	TEST(${TEST_NAME}, output_width_gt_${WIDTH_TILE}) {
	$if ISA_CHECK:
	${ISA_CHECK};
	for (size_t input_width = ${WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width < ${(5 if WIDTH_TILE == 1 else 2) * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width++) {
	DWConv2DMicrokernelTester()
	.input_width(input_width)
	.input_height(${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING - 1})
	.kernel_height(${KERNEL_HEIGHT})
	.kernel_width(${KERNEL_WIDTH})
	.subsampling(${SUBSAMPLING})
	.padding_left(${PADDING})
	.padding_right(${PADDING})
	.padding_top(${PADDING})
	.padding_bottom(${PADDING})
	.Test(${", ".join(TEST_ARGS)});
	}
	}

	$if SUBSAMPLING > 1:
	TEST(${TEST_NAME}, output_height_eq_${HEIGHT_TILE}) {
	$if ISA_CHECK:
	${ISA_CHECK};
	for (size_t input_height = ${(HEIGHT_TILE - 1) * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING}; input_height < ${HEIGHT_TILE * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING}; input_height++) {
	for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
	DWConv2DMicrokernelTester()
	.input_width(input_width)
	.input_height(input_height)
	.kernel_height(${KERNEL_HEIGHT})
	.kernel_width(${KERNEL_WIDTH})
	.subsampling(${SUBSAMPLING})
	.padding_left(${PADDING})
	.padding_right(${PADDING})
	.padding_top(${PADDING})
	.padding_bottom(${PADDING})
	.Test(${", ".join(TEST_ARGS)});
	}
	}
	}

	$if HEIGHT_TILE > 1:
	TEST(${TEST_NAME}, output_height_div_${HEIGHT_TILE}) {
	$if ISA_CHECK:
	${ISA_CHECK};
	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}) {
	for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
	DWConv2DMicrokernelTester()
	.input_width(input_width)
	.input_height(input_height)
	.kernel_height(${KERNEL_HEIGHT})
	.kernel_width(${KERNEL_WIDTH})
	.subsampling(${SUBSAMPLING})
	.padding_left(${PADDING})
	.padding_right(${PADDING})
	.padding_top(${PADDING})
	.padding_bottom(${PADDING})
	.Test(${", ".join(TEST_ARGS)});
	}
	}
	}

	TEST(${TEST_NAME}, output_height_lt_${HEIGHT_TILE}) {
	$if ISA_CHECK:
	${ISA_CHECK};
	for (size_t input_height = ${max(1, KERNEL_HEIGHT - 2 * PADDING)}; input_height < ${(HEIGHT_TILE - 1) * SUBSAMPLING + KERNEL_HEIGHT - 2 * PADDING}; input_height++) {
	for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
	DWConv2DMicrokernelTester()
	.input_width(input_width)
	.input_height(input_height)
	.kernel_height(${KERNEL_HEIGHT})
	.kernel_width(${KERNEL_WIDTH})
	.subsampling(${SUBSAMPLING})
	.padding_left(${PADDING})
	.padding_right(${PADDING})
	.padding_top(${PADDING})
	.padding_bottom(${PADDING})
	.Test(${", ".join(TEST_ARGS)});
	}
	}
	}

	TEST(${TEST_NAME}, output_height_gt_${HEIGHT_TILE}) {
	$if ISA_CHECK:
	${ISA_CHECK};
	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++) {
	for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
	DWConv2DMicrokernelTester()
	.input_width(input_width)
	.input_height(input_height)
	.kernel_height(${KERNEL_HEIGHT})
	.kernel_width(${KERNEL_WIDTH})
	.subsampling(${SUBSAMPLING})
	.padding_left(${PADDING})
	.padding_right(${PADDING})
	.padding_top(${PADDING})
	.padding_bottom(${PADDING})
	.Test(${", ".join(TEST_ARGS)});
	}
	}
	}

	$if SUBSAMPLING > 1:
	TEST(${TEST_NAME}, padding_top_eq_${SUBSAMPLING - 1}) {
	$if ISA_CHECK:
	${ISA_CHECK};
	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++) {
	for (size_t input_width = 1; input_width < ${5 * WIDTH_TILE * SUBSAMPLING + KERNEL_WIDTH - 2 * PADDING}; input_width += ${max(1, WIDTH_TILE * SUBSAMPLING - 1)}) {
	DWConv2DMicrokernelTester()
	.input_width(input_width)
	.input_height(input_height)
	.kernel_height(${KERNEL_HEIGHT})
	.kernel_width(${KERNEL_WIDTH})
	.subsampling(${SUBSAMPLING})
	.padding_left(${PADDING})
	.padding_right(${PADDING})
	.padding_top(${PADDING - 1})
	.padding_bottom(${PADDING})
	.Test(${", ".join(TEST_ARGS)});
	}
	}
	}
	"""

	def split_ukernel_name(name):
	match = re.match(r"^xnn_(f16\|f32)_dwconv2d_chw_ukernel_(\d+)x(\d+)(s2)?p(\d+)__(.+)_(\d+)x(\d+)(_acc\d+)?$", name)
	assert match is not None
	kernel_height, kernel_width = int(match.group(2)), int(match.group(3))
	if match.group(4):
	assert match.group(4).startswith("s")
	stride = int(match.group(4)[1:])
	else:
	stride = 1
	padding = int(match.group(5))

	height_tile = int(match.group(7))
	width_tile = int(match.group(8))

	arch, isa = xnncommon.parse_target_name(target_name=match.group(6))
	return kernel_height, kernel_width, stride, padding, arch, isa, \
	height_tile, width_tile


	def generate_test_cases(ukernel, kernel_height, kernel_width, subsampling, \
	padding, isa, height_tile, width_tile):
	"""Generates all tests cases for a DWCONV2D CHW micro-kernel.

	Args:
	ukernel: C name of the micro-kernel function.
	kernel_height: convolution kernel height assumed by the micro-kernel.
	kernel_width: convolution kernel width assumed by the micro-kernel.
	subsampling: convolution subsampling (stride) assumed by the micro-kernel.
	The same subsampling factor is assumed for both horizontal and
	vertical directions.
	padding: convolution padding value assumed by the micro-kernel for right,
	bottom, and left padding. If convolution stride is 1, the same
	padding value is assumed for the top padding. If convolution stride
	is different than 1, top padding is specified via micro-kernel
	parameter, and can be either padding or (padding - 1).
	isa: instruction set required to run the micro-kernel. Generated unit test
	will skip execution if the host processor doesn't support this ISA.
	height_tile: number of output rows processed in one iteration of the main
	loop of the micro-kernel.
	width_tile: number of output columns processed in one iteration of the main
	loop of the micro-kernel.

	Returns:
	Code for the test case.
	"""
	_, test_name = ukernel.split("_", 1)
	_, datatype, ukernel_type, _ = ukernel.split("_", 3)
	test_args = [ukernel]
	if not isa or isa == "psimd":
	test_args.append("DWConv2DMicrokernelTester::Variant::Scalar")
	return xngen.preprocess(TEST_TEMPLATE, {
	"TEST_NAME": test_name.upper().replace("UKERNEL_", ""),
	"TEST_ARGS": test_args,
	"UKERNEL_TYPE": ukernel_type.upper(),
	"DATATYPE": datatype,
	"KERNEL_HEIGHT": kernel_height,
	"KERNEL_WIDTH": kernel_width,
	"SUBSAMPLING": subsampling,
	"PADDING": padding,
	"HEIGHT_TILE": height_tile,
	"WIDTH_TILE": width_tile,
	"ISA_CHECK": xnncommon.generate_isa_check_macro(isa),
	"next_prime": next_prime,
	})


	def main(args):
	options = parser.parse_args(args)

	with codecs.open(options.spec, "r", encoding="utf-8") as spec_file:
	spec_yaml = yaml.safe_load(spec_file)
	if not isinstance(spec_yaml, list):
	raise ValueError("expected a list of micro-kernels in the spec")

	tests = """\
	// Copyright 2020 Google LLC
	//
	// This source code is licensed under the BSD-style license found in the
	// LICENSE file in the root directory of this source tree.
	//
	// Auto-generated file. Do not edit!
	// Specification: {specification}
	// Generator: {generator}


	#include <gtest/gtest.h>

	#include <xnnpack/common.h>
	#include <xnnpack/isa-checks.h>

	#include <xnnpack/dwconv.h>
	#include "dwconv2d-microkernel-tester.h"
	""".format(specification=options.spec, generator=sys.argv[0])

	for ukernel_spec in spec_yaml:
	name = ukernel_spec["name"]
	pipelined = bool(ukernel_spec.get("pipelined", False))
	assembly = bool(ukernel_spec.get("assembly", False))
	kernel_height, kernel_width, subsampling, padding, arch, isa, \
	height_tile, width_tile = split_ukernel_name(name)

	# specification can override architecture
	arch = ukernel_spec.get("arch", arch)

	test_case = generate_test_cases(name, kernel_height, kernel_width, \
	subsampling, padding, isa, \
	height_tile, width_tile)
	tests += "\n\n" + xnncommon.postprocess_test_case(test_case, arch, isa, assembly)

	with codecs.open(options.output, "w", encoding="utf-8") as output_file:
	output_file.write(tests)


	if __name__ == "__main__":
	main(sys.argv[1:])