alter-xp commented on code in PR #12614: URL: https://github.com/apache/tvm/pull/12614#discussion_r962554794
########## tests/scripts/task_riscv_microtvm.sh: ########## @@ -22,6 +22,13 @@ source tests/scripts/setup-pytest-env.sh make cython3 -# NOTE: this exists to ensure some tests run on RISC-V image. Without it, Jenkins reports a configuration error. -# This line can be removed when RISC-V tests are added. -run_pytest ctypes riscv-platform-minimal-test-0 tests/python/all-platform-minimal-test +#riscv gcc +export PATH=/opt/csi-nn2/tools/gcc-toolchain/bin:$PATH +#riscv qemu +export PATH=/opt/csi-nn2/tools/qemu/bin:$PATH + +# run qemu tvm_rpc +nohup qemu-riscv64 -cpu c906fdv -L /opt/csi-nn2/tools/gcc-toolchain/sysroot/ ./build-c906/tvm_rpc server --host=127.0.0.1 --port=9090 & Review Comment: a very good suggestion. pytest.fixture is very useful for test programs that need preprocessing and post-processing ########## tests/python/contrib/test_csinn/test_conv2d.py: ########## @@ -0,0 +1,158 @@ +# 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. +"""SHL integration conv2d tests.""" + +import numpy as np + +import tvm +from tvm import relay + +from infrastructure import ( + skip_runtime_test, + build_and_run, + verify, +) +from infrastructure import Device + + +def _get_model( + shape, + kernel_h, + kernel_w, + padding, + strides, + dilation, + groups, + dtype, + channels, + var_names, + has_bias=False, +): + """Return a model and any parameters it may have""" + a = relay.var(next(var_names), shape=shape, dtype=dtype) + if len(padding) == 2: + padding = (padding[0], padding[1], padding[0], padding[1]) + shape = (shape[0], shape[1], shape[2] + padding[0] * 2, shape[3] + padding[1] * 2) + + weight_shape = (channels, shape[1] // groups, kernel_h, kernel_w) + w = tvm.nd.array(np.random.uniform(-128, 127, weight_shape).astype(dtype)) + weights = relay.const(w, dtype) + out = relay.nn.conv2d( + a, + weights, + kernel_size=(kernel_h, kernel_w), + data_layout="NCHW", + dilation=dilation, + strides=strides, + padding=padding, + groups=groups, + channels=channels, + out_dtype=dtype, + ) + params = {"w": w} + if has_bias: + bias_shape = weight_shape[0] + b = tvm.nd.array(np.random.uniform(-128, 127, bias_shape).astype(dtype)) + biasc = relay.const(b, dtype) + out = relay.nn.bias_add(out, biasc, axis=1) + params["b"] = b + return out, params + + +def test_conv2d(): + Device.load("test_config.json") + + if skip_runtime_test(): + return + + device = Device() + np.random.seed(0) + + dtype = "float32" + trials = [ + # Normal convolution + [2, 2, (1, 1), (1, 1), (1, 1), 4, (14, 10, 10), False, False], + [2, 1, (2, 2), (1, 1), (1, 1), 7, (16, 12, 15), False, False], + [2, 2, (1, 1), (1, 1), (1, 1), 4, (14, 10, 10), True, False], + [3, 3, (1, 1), (1, 1), (1, 1), 16, (16, 12, 15), False, False], + [5, 5, (1, 1), (2, 2), (1, 1), 4, (14, 10, 10), False, False], + [1, 3, (1, 1), (1, 1), (1, 1), 7, (20, 20, 20), False, False], + [2, 2, (2, 2), (1, 1), (1, 1), 4, (20, 20, 20), True, False], + [5, 5, (1, 1), (2, 2), (1, 1), 4, (14, 10, 10), False, False], + [3, 3, (2, 1), (1, 1), (1, 1), 7, (20, 20, 20), False, False], + [3, 3, (1, 1), (2, 2), (1, 1), 16, (14, 10, 10), True, False], + # # Depth-wise convolution + [3, 3, (1, 1), (1, 1), (1, 1), 20, (20, 20, 20), False, True], + [5, 5, (2, 2), (1, 1), (1, 1), 20, (20, 20, 20), True, True], + [3, 3, (2, 2), (2, 2), (1, 1), 14, (14, 10, 10), False, True], + [5, 5, (0, 0), (1, 1), (1, 1), 20, (20, 20, 20), False, True], + [3, 3, (1, 1), (2, 2), (1, 1), 14, (14, 10, 10), True, True], + ] + cc = 0 + for ( + kernel_h, + kernel_w, + pad, + stride, + dilation, + out_channels, + shape, + has_bias, + is_depthwise, + ) in trials: + cc += 1 + shape = (1, *shape) + if is_depthwise: + groups = shape[1] + else: + groups = 1 + outputs = [] + inputs = { + "a": tvm.nd.array(np.random.uniform(-128, 127, shape).astype(dtype)), + } + + func, params = _get_model( + shape, + kernel_h, + kernel_w, + pad, + stride, + dilation, + groups, + dtype, + out_channels, + iter(inputs), + has_bias, + ) + for csinn in [False, True]: + outputs.append(build_and_run(func, inputs, 1, params, device, enable_csinn=csinn)[0]) + + config = { + "shape": shape, + "groups": groups, + "kernel size": (kernel_h, kernel_w), + "padding": pad, + "stride": stride, + "dilation": dilation, + "out channels": out_channels, + "has bias": has_bias, + } + verify(outputs, atol=0.002, rtol=0.01, config=config) + + +if __name__ == "__main__": + test_conv2d() Review Comment: 👌 -- This is an automated message from the Apache Git Service. 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