ashutosh-arm commented on code in PR #13643: URL: https://github.com/apache/tvm/pull/13643#discussion_r1119029716
########## tests/python/contrib/test_cmsisnn/test_last_error.py: ########## @@ -0,0 +1,164 @@ +# 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. + +"""CMSIS-NN integration tests: debug_last_error""" + +import re +import numpy as np +import pytest +import tvm +from tvm import relay +from tvm.relay.op.contrib import cmsisnn + +from tvm.testing.aot import ( + get_dtype_range, + generate_ref_data, + AOTTestModel, + compile_and_run, +) +from .utils import ( + make_module, + get_same_padding, + make_qnn_relu, + assert_partitioned_function, + create_test_runner, +) + + +def make_model( + pool_op, + shape, + pool_size, + strides, + padding, + dtype, + scale, + zero_point, + relu_type, + layout, + input_op, +): + """Create a Relay Function / network model""" + if input_op: + op = input_op + else: + op = relay.var("input", shape=shape, dtype=dtype) + pad_ = (0, 0, 0, 0) + if padding == "SAME": + dilation = (1, 1) + pad_ = get_same_padding((shape[1], shape[2]), pool_size, dilation, strides) + op = relay.nn.pad( + op, + pad_width=[(0, 0), (pad_[0], pad_[2]), (pad_[1], pad_[3]), (0, 0)], + pad_value=zero_point, + pad_mode="constant", + ) + if pool_op.__name__ == relay.nn.avg_pool2d.__name__: + op = relay.cast(op, "int32") + op = pool_op( + op, pool_size=pool_size, strides=strides, padding=pad_, ceil_mode=True, layout=layout + ) + if pool_op.__name__ == relay.nn.avg_pool2d.__name__: + op = relay.cast(op, dtype) + op = make_qnn_relu(op, relu_type, scale, zero_point, dtype) + return op + + [email protected]_cmsisnn [email protected]("debug_last_error", [True, False]) +def test_last_error(debug_last_error): + """Tests debug_last_error""" + dtype = "int16" + in_shape = (1, 28, 28, 12) + pool_size = (3, 3) + strides = (2, 2) + padding = "SAME" + relu_type = "NONE" + pool_type = relay.nn.avg_pool2d + zero_point = -34 + scale = 0.0256 + compiler_cpu = "cortex-m55" + cpu_flags = "+nomve" + layout = "NHWC" + input_op = None + + interface_api = "c" + use_unpacked_api = True + + model = make_model( + pool_op=pool_type, + shape=in_shape, + pool_size=pool_size, + strides=strides, + padding=padding, + dtype=dtype, + scale=scale, + zero_point=zero_point, + relu_type=relu_type, + layout=layout, + input_op=input_op, + ) + orig_mod = make_module(model) + + cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod) + + # validate pattern matching + assert_partitioned_function(orig_mod, cmsisnn_mod) + + # validate the output + in_min, in_max = get_dtype_range(dtype) + inputs = { + "input": np.random.randint(in_min, high=in_max, size=in_shape, dtype=dtype), + } + output_list = generate_ref_data(orig_mod["main"], inputs) + + def checker(base_path: str) -> bool: + def read_file(path): + with open(path) as f: + return f.read() + + test = read_file(base_path + "/build/test.c") + test_check = "TVMGetLastError" in test + + default_lib2 = read_file(base_path + "/codegen/host/src/default_lib2.c") + regex = ( + r"(?s)arm_avgpool_s16(.*?)" + r'ARM_CMSIS_NN_ARG_ERROR: TVMAPISetLastError\("ARM_CMSIS_NN_ARG_ERROR(.*?)' + r'ARM_CMSIS_NN_NO_IMPL_ERROR: TVMAPISetLastError\("ARM_CMSIS_NN_NO_IMPL_ERROR' + ) + default_lib2_check = re.search(regex, default_lib2) is not None + + if debug_last_error: + return test_check and default_lib2_check + else: + return not (test_check or default_lib2_check) + + result = compile_and_run( + AOTTestModel( + module=cmsisnn_mod, + inputs=inputs, + outputs=output_list, + params=None, + output_tolerance=1, + ), + create_test_runner(compiler_cpu, cpu_flags, debug_last_error=debug_last_error), + interface_api, + use_unpacked_api, + debug_last_error=debug_last_error, + checker=checker, Review Comment: From offline discussion, I understand that the `checker` provides consistency to the CMSIS-NN testing and also allows for conditional execution if required in future. -- 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]
