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#################################################################################################
#
# Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
#################################################################################################
"""
Low-level functionality tests for Conv2d opreations on SM80
"""
import logging
import unittest
import cutlass
from cutlass.backend.utils.device import device_cc
from conv2d_test_utils import *
cutlass.set_log_level(logging.WARNING)
cc = 80
@unittest.skipIf(device_cc() < cc, 'Device compute capability is invalid for SM80 tests.')
class Conv2dSm80(unittest.TestCase):
"""
Wrapper class to which tests will be added dynamically in __main__
"""
pass
conv_problems = get_conv_problems()
# Tests for optimized & analytic
for conv_kind in ["fprop", "wgrad", "dgrad"]:
# F16, simt
add_test(
Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
opclass="simt", threadblock_shape=[128, 128, 8],
warp_count=[4, 2, 1], stages=2, instruction_shape=[1, 1, 1])
# F16, tensor op
add_test(
Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
opclass="tensor_op", threadblock_shape=[128, 128, 64],
warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16])
# F16, tensor op, analytic iterator
add_test(
Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f16,
opclass="tensor_op", threadblock_shape=[128, 128, 64],
warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], iterator_algorithm="analytic")
# F16, tensor op, f32 output
add_test(
Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f32,
opclass="tensor_op", threadblock_shape=[128, 128, 64],
warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16])
# F16, tensor op, different tile description
add_test(
Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
opclass="tensor_op", threadblock_shape=[128, 64, 32],
warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 8])
# F32, simt
add_test(
Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f32,
opclass="simt", threadblock_shape=[128, 128, 8],
warp_count=[4, 2, 1], stages=4, instruction_shape=[1, 1, 1])
# Tf32, tensorop
add_test(
Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f32,
opclass="tensor_op", threadblock_shape=[128, 128, 16],
warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 8]
)
# Split-K
add_test(
Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
opclass="tensor_op", threadblock_shape=[128, 128, 64],
warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], split_k_mode="serial",
split_k_slices=2)
add_test(
Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
opclass="tensor_op", threadblock_shape=[128, 128, 64],
warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], split_k_mode="parallel",
split_k_slices=5)
# Swizzling functor
add_test(
Conv2dSm80, cc, conv_kind, conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
opclass="tensor_op", threadblock_shape=[128, 64, 32],
warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 8], swizzle=4)
# Tests for few channels and fixed channels
# F16, tensor op, few channels
for c, tb, stage, inst in zip([2, 1],
[[128, 128, 64], [128, 128, 32]],
[3, 2],
[[16, 8, 16], [16, 8, 8]]):
add_test(
Conv2dSm80, cc, "fprop", conv2d_few_channel_problemsizes(c), cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
opclass="tensor_op", threadblock_shape=tb,
warp_count=[2, 2, 1], stages=stage, instruction_shape=inst, iterator_algorithm="few_channels"
)
# F16, tensor op, fixed channels
for c in [8, 4, 2]:
add_test(
Conv2dSm80, cc, "fprop", conv2d_few_channel_problemsizes(c), cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
opclass="tensor_op", threadblock_shape=[128, 128, 64],
warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], iterator_algorithm="fixed_channels"
)
# Test activations
for activation in ["relu", "leaky_relu"]:
for split_k_mode, split_k_slices in zip(["parallel", "serial", "parallel"], [1, 7, 5]):
add_test(
Conv2dSm80, cc, "fprop", conv_problems, cutlass.DataType.f16, cutlass.DataType.f32, cutlass.DataType.f16,
opclass="tensor_op", threadblock_shape=[128, 128, 64],
warp_count=[2, 2, 1], stages=3, instruction_shape=[16, 8, 16], split_k_mode=split_k_mode,
split_k_slices=split_k_slices, activation=activation)
if __name__ == '__main__':
unittest.main()
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