################################################################################ # # Copyright (c) 2023 - 2025 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. # ################################################################################ """ Unittest for mixed types of nodes in SM90 """ import logging import unittest import cutlass_cppgen from cutlass_cppgen.backend import * from cutlass_cppgen.epilogue import * from cutlass_cppgen.swizzle import ThreadblockSwizzleStreamK from utils.evt_testbed import EVTTestBed, EVTTestCaseBase cutlass_cppgen.set_log_level(logging.WARNING) @unittest.skipIf(device_cc() not in [80, 86, 89, 90], "This unittest is only supported on CC [80, 86, 89, 90]") class TestEVTMixed(EVTTestCaseBase): def test_same_variable_used_multiple_times(self): """ The same variable z0 is used multiple times """ def evt_aux_store(accum): z0 = relu(accum) D = z0 + z0 return z0, D for m, n, k, l in self.get_problem_sizes(8): example_inputs = { "accum": self.fake_tensor(self.element, (l, m, n)), "D": self.fake_tensor(self.element, (l, m, n)), "z0": self.fake_tensor(self.element, (l, m, n)), } launcher = EVTTestBed(self.element, evt_aux_store, example_inputs) input_keys = ["accum"] result_keys = ["z0", "D"] launcher.verify((m, n, k), input_keys, result_keys, l) def test_no_lca(self): """ The same variable z0 is used multiple times """ def evt_no_lca(accum, bias): E = relu(accum) F = E + bias tmp_2 = E + 2 D = tmp_2 + E return D for m, n, k, l in self.get_problem_sizes(8): example_inputs = { "accum": self.fake_tensor(self.element, (l, m, n)), "D": self.fake_tensor(self.element, (l, m, n)), "bias": self.fake_tensor(self.element, (m,1), stride=(1,0)), } launcher = EVTTestBed(self.element, evt_no_lca, example_inputs) input_keys = ["accum", "bias"] result_keys = ["D"] launcher.verify((m, n, k), input_keys, result_keys, l) def test_mixed_dag(self): def evt_mixed_dag(accum, alpha, C, beta, aux, cbias, rbias): F = alpha * accum + (beta * C + aux) F_row_max = max(F, dim=[0, 1]) E = relu(F + 1) + cbias + rbias E_col_max = max(E, dim=[0, 2]) D = E + F return D, F, F_row_max, E_col_max if device_cc() == 80: alignments = [2, 4, 8] else: # Sm90 EVT currently only supports 128-bit alignment alignments = [8,] for align in alignments: for m, n, k, l in self.get_problem_sizes(align): example_inputs = { "accum": self.fake_tensor(self.element, (l, m, n)), "alpha": 1.0, "C": self.fake_tensor(self.element, (l, m, n)), "beta": 1.0, "aux": self.fake_tensor(self.element, (l, m, n)), "cbias": self.fake_tensor(self.element, (m, 1)), "rbias": self.fake_tensor(self.element, (n,)), "D": self.fake_tensor(self.element, (l, m, n)), "F": self.fake_tensor(self.element, (l, m, n)), "F_row_max": self.fake_tensor(DataType.f32, (n,)), "E_col_max": self.fake_tensor(DataType.f32, (m, 1)) } launcher = EVTTestBed(self.element, evt_mixed_dag, example_inputs) input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"] result_keys = ["D", "F", "F_row_max", "E_col_max"] launcher.verify((m, n, k), input_keys, result_keys, l) @unittest.skipIf(device_cc() not in [80, 89], "This unittest is for cc 80 and 89 only") def test_mixed_dag_float(self): def evt_mixed_dag(accum, alpha, C, beta, aux, cbias, rbias): F = alpha * accum + (beta * C + aux) F_row_max = max(F, dim=[0, 1]) E = relu(F + 1) + cbias + rbias E_col_max = max(E, dim=[0, 2]) D = E + F return D, F, F_row_max, E_col_max for align in [3, 2, 4]: for m, n, k, l in self.get_problem_sizes(align): example_inputs = { "accum": self.fake_tensor(np.float32, (l, m, n)), "alpha": 1.0, "C": self.fake_tensor(np.float32, (l, m, n)), "beta": 1.0, "aux": self.fake_tensor(np.float32, (l, m, n)), "cbias": self.fake_tensor(np.float32, (m, 1)), "rbias": self.fake_tensor(np.float32, (n,)), "D": self.fake_tensor(np.float32, (l, m, n)), "F": self.fake_tensor(np.float32, (l, m, n)), "F_row_max": self.fake_tensor(np.float32, (n,)), "E_col_max": self.fake_tensor(np.float32, (m, 1)) } launcher = EVTTestBed(DataType.f32, evt_mixed_dag, example_inputs) input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"] result_keys = ["D", "F", "F_row_max", "E_col_max"] launcher.verify((m, n, k), input_keys, result_keys, l) @unittest.skipIf(device_cc() not in [80, 89], "This unittest is for cc 80 and 89 only") def test_mixed_dag_stage2(self): def evt_mixed_dag(accum, alpha, C, beta, aux, cbias, rbias): F = alpha * accum + (beta * C + aux) F_row_max = max(F, dim=[0, 1]) E = relu(F + 1) + cbias + rbias E_col_max = max(E, dim=[0, 2]) D = E + F return D, F, F_row_max, E_col_max for m, n, k, l in self.get_problem_sizes(8): example_inputs = { "accum": self.fake_tensor(self.element, (l, m, n)), "alpha": 1.0, "C": self.fake_tensor(self.element, (l, m, n)), "beta": 1.0, "aux": self.fake_tensor(self.element, (l, m, n)), "cbias": self.fake_tensor(self.element, (m, 1)), "rbias": self.fake_tensor(self.element, (n,)), "D": self.fake_tensor(self.element, (l, m, n)), "F": self.fake_tensor(self.element, (l, m, n)), "F_row_max": self.fake_tensor(DataType.f32, (n,)), "E_col_max": self.fake_tensor(DataType.f32, (m, 1)) } launcher = EVTTestBed(self.element, evt_mixed_dag, example_inputs, epilogue_stages=2) input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"] result_keys = ["D", "F", "F_row_max", "E_col_max"] launcher.verify((m, n, k), input_keys, result_keys, l) @unittest.skipIf(device_cc() not in [80, 89], "This unittest is for cc 80 and 89 only") def test_mixed_dag_partition_k(self): def evt_mixed_dag(accum, alpha, C, beta, aux, cbias, rbias): F = alpha * accum + (beta * C + aux) F_row_max = max(F, dim=[0, 1]) E = relu(F + 1) + cbias + rbias E_col_max = max(E, dim=[0, 2]) D = E + F return D, F, F_row_max, E_col_max for m, n, k, l in self.get_problem_sizes(8): example_inputs = { "accum": self.fake_tensor(self.element, (l, m, n)), "alpha": 1.0, "C": self.fake_tensor(self.element, (l, m, n)), "beta": 1.0, "aux": self.fake_tensor(self.element, (l, m, n)), "cbias": self.fake_tensor(self.element, (m, 1)), "rbias": self.fake_tensor(self.element, (n,)), "D": self.fake_tensor(self.element, (l, m, n)), "F": self.fake_tensor(self.element, (l, m, n)), "F_row_max": self.fake_tensor(DataType.f32, (n,)), "E_col_max": self.fake_tensor(DataType.f32, (m, 1)) } tile_description = { "threadblock_shape": [128, 128, 64], "warp_count": [2, 2, 2] } launcher = EVTTestBed(self.element, evt_mixed_dag, example_inputs, tile_description=tile_description, epilogue_stages=2) input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"] result_keys = ["D", "F", "F_row_max", "E_col_max"] launcher.verify((m, n, k), input_keys, result_keys, l) @unittest.skipIf(device_cc() not in [80, 89], "This unittest is for cc 80 and 89 only") def test_mixed_dag_stream_k(self): def evt_mixed_dag(accum, alpha, C, beta, aux, cbias, rbias): F = alpha * accum + (beta * C + aux) F_row_max = max(F, dim=[0, 1]) E = relu(F + 1) + cbias + rbias E_col_max = max(E, dim=[0, 2]) D = E + F return D, F, F_row_max, E_col_max # High per-sm occupancy tile_description tile_description = { "threadblock_shape": [128, 128, 32], "warp_count": [2, 2, 1], "stages": 3 } tds = [None, tile_description] for td in tds: for m, n, k, l in self.get_problem_sizes(8, k=960, batch_count=[1, 3]): if l == 1: example_inputs = { "accum": self.fake_tensor(self.element, (m, n)), "alpha": 1.0, "C": self.fake_tensor(self.element, (m, n)), "beta": 1.0, "aux": self.fake_tensor(self.element, (m, n)), "cbias": self.fake_tensor(self.element, (m, 1)), "rbias": self.fake_tensor(self.element, (n,)), "D": self.fake_tensor(self.element, (m, n)), "F": self.fake_tensor(self.element, (m, n)), "F_row_max": self.fake_tensor(DataType.f32, (n,)), "E_col_max": self.fake_tensor(DataType.f32, (m, 1)) } else: example_inputs = { "accum": self.fake_tensor(self.element, (l, m, n)), "alpha": 1.0, "C": self.fake_tensor(self.element, (l, m, n)), "beta": 1.0, "aux": self.fake_tensor(self.element, (l, m, n)), "cbias": self.fake_tensor(self.element, (m, 1)), "rbias": self.fake_tensor(self.element, (n,)), "D": self.fake_tensor(self.element, (l, m, n)), "F": self.fake_tensor(self.element, (l, m, n)), "F_row_max": self.fake_tensor(DataType.f32, (n,)), "E_col_max": self.fake_tensor(DataType.f32, (m, 1)) } if td is not None: launcher = EVTTestBed( self.element, evt_mixed_dag, example_inputs, tile_description=td, swizzling_functor=ThreadblockSwizzleStreamK, backend="torch") else: launcher = EVTTestBed( self.element, evt_mixed_dag, example_inputs, swizzling_functor=ThreadblockSwizzleStreamK, backend="torch") input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"] result_keys = ["D", "F", "F_row_max", "E_col_max"] launcher.verify((m, n, k), input_keys, result_keys, l) def test_mixed_dag_no_batch(self): def evt_mixed_dag_no_batch(accum, alpha, C, beta, aux, cbias, rbias): F = alpha * accum + (beta * C + aux) F_row_max = max(F, dim=[0, 1]) E = relu(F + 1) + cbias + rbias E_col_max = max(E, dim=[0, 2]) D = E + F return D, F, F_row_max, E_col_max for m, n, k, _ in self.get_problem_sizes(8): example_inputs = { "accum": self.fake_tensor(self.element, (m, n)), "alpha": 1.0, "C": self.fake_tensor(self.element, (m, n)), "beta": 1.0, "aux": self.fake_tensor(self.element, (m, n)), "cbias": self.fake_tensor(self.element, (m, 1)), "rbias": self.fake_tensor(self.element, (n,)), "D": self.fake_tensor(self.element, (m, n)), "F": self.fake_tensor(self.element, (m, n)), "F_row_max": self.fake_tensor(DataType.f32, (n,)), "E_col_max": self.fake_tensor(DataType.f32, (m, 1)) } launcher = EVTTestBed(self.element, evt_mixed_dag_no_batch, example_inputs) input_keys = ["alpha", "C", "beta", "aux", "cbias", "rbias"] result_keys = ["D", "F", "F_row_max", "E_col_max"] launcher.verify((m, n, k), input_keys, result_keys, 1) if __name__ == '__main__': unittest.main()