MichaelJKlaiber commented on code in PR #12087: URL: https://github.com/apache/tvm/pull/12087#discussion_r925589594
########## tests/python/contrib/test_uma/test_uma_lowering_with_umalower.py: ########## @@ -0,0 +1,115 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +import pytest +import pathlib + +import tvm +from tests.python.contrib.test_uma.test_uma_utils import _create_schedule, _generate_io_arrays +from tvm import topi +from tvm.relay.backend.contrib.uma._template.passes import MyAiHwConv2dPass +import tvm.testing +from tvm import te +from tvm.relay.backend.contrib.uma.api.lower import UMALower +from tvm.relay.backend.contrib.uma.api.utils import PassPhase + + +def _conv2d_te_definition(shapes: dict) -> list: + n, w, h, ci, kw, kh, co = ( + shapes["n"], + shapes["w"], + shapes["h"], + shapes["ci"], + shapes["kw"], + shapes["kh"], + shapes["co"], + ) + ifmap = te.placeholder((n, ci, w, h), dtype="float32", name="ifmap") + weights = te.placeholder((co, ci, kw, kh), dtype="float32", name="weights") + result = topi.nn.conv2d_nchw(ifmap, weights, stride=1, padding=[kw // 2, kh // 2], dilation=1) + return [ifmap, weights, result] + + +def _pepare_conv2d_schedule(shapes, use_external_conv2d_impl=True): + placeholders = _conv2d_te_definition(shapes) + + uma_path = pathlib.Path(str(tvm.relay.backend.contrib.uma.__file__)).parent.absolute() + conv2d_file = uma_path / "_template" / "conv2dnchw.cc" + + with conv2d_file.open() as f: + sch_tir = _create_schedule( + placeholders, f, use_external_conv2d_impl=use_external_conv2d_impl + ) + return placeholders, sch_tir + + +def _run_external_conv2d(dut_io_arrays, conv2d_shapes, target): + # Run conv2d with external function + placeholders, schedule = _pepare_conv2d_schedule(conv2d_shapes) + + uma_lower = UMALower("lower_test") + uma_lower._tir_passes.append((PassPhase.TIR_PHASE_0, MyAiHwConv2dPass())) + with tvm.transform.PassContext(): + tir_mod = uma_lower._lower_stir_to_nstir(schedule.mod["main"]) + + ifmap_data, weight_data, result_data = dut_io_arrays + + llvm_conv2d_mod = tvm.build(tir_mod, placeholders, target=target, name="test_external_conv2d") + llvm_conv2d_mod(ifmap_data, weight_data, result_data) + + +def _run_reference_conv2d(reference_io_arrays, conv2d_shapes, target): + placeholders, schedule = _pepare_conv2d_schedule(conv2d_shapes) + ref_mod = tvm.build(schedule.mod, placeholders, target=target, name="test_reference_conv2d") + ifmap, weights, result = reference_io_arrays + ref_mod(ifmap, weights, result) + + +def _prepare_io_arrays(conv2d_shapes, dev): + dut_io_arrays = _generate_io_arrays(conv2d_shapes, dev) + _, _, ref_result = _generate_io_arrays(conv2d_shapes, dev) + reference_io_arrays = [dut_io_arrays[0], dut_io_arrays[1], ref_result] + return dut_io_arrays, reference_io_arrays + + [email protected]( + "n, w, h, ci, kw, kh, co", + [ + (1, 224, 224, 3, 3, 3, 4), + (1, 224, 224, 3, 5, 5, 4), + (1, 224, 224, 3, 7, 7, 4), + (1, 224, 320, 3, 7, 7, 4), + (1, 224, 224, 3, 7, 7, 4), + ], +) +def test_lower_with_uma(n, w, h, ci, kw, kh, co): + target = tvm.target.Target(target="llvm", host="llvm") + dev = tvm.device(target.kind.name, 0) + conv2d_shapes = dict(n=n, w=w, h=h, ci=ci, kw=kw, kh=kh, co=co) + + dut_io_arrays, reference_io_arrays = _prepare_io_arrays(conv2d_shapes, dev) + + _run_external_conv2d(dut_io_arrays, conv2d_shapes, target) + _run_reference_conv2d(reference_io_arrays, conv2d_shapes, target) + + # compare results + dut_results = dut_io_arrays[2].numpy() + ref_results = reference_io_arrays[2].numpy() + tvm.testing.assert_allclose(dut_results, ref_results, rtol=1e-5) + + +if __name__ == "__main__": + test_lower_with_uma(1, 224, 224, 3, 3, 3, 4) Review Comment: done -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
