echuraev commented on a change in pull request #7299: URL: https://github.com/apache/tvm/pull/7299#discussion_r587434650
########## File path: tests/python/contrib/test_bnns/test_pooling.py ########## @@ -0,0 +1,297 @@ +# 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. +"""BNNS integration pooling tests.""" + +import numpy as np + +import tvm +from tvm import relay +from tvm import testing +from .infrastructure import ( + skip_runtime_test, + skip_codegen_test, + build_and_run, + verify, + verify_codegen, +) +from .infrastructure import Device + + +def _calculate_output_shape(shape, sizes, padding, strides): + """Calculate pooling output shape.""" + output_height = ((shape[2] - sizes[0] + padding[0] + padding[2]) / strides[0]) + 1 + output_width = ((shape[3] - sizes[1] + padding[1] + padding[3]) / strides[1]) + 1 + return 1, shape[1], int(output_height), int(output_width) + + +def _get_pooling_model( + shape, dtype, typef, sizes, strides, padding, ceil_mode, count_include_pad, var_names +): + """Return a model and any parameters it may have.""" + if len(padding) == 2: + padding = (padding[0], padding[1], padding[0], padding[1]) + out = relay.var(next(var_names), shape=shape, dtype=dtype) + + if typef == "nn.max_pool2d": + out = relay.nn.max_pool2d( + out, + pool_size=sizes, + strides=strides, + padding=padding, + ceil_mode=ceil_mode, + ) + elif typef == "nn.avg_pool2d": + out = relay.nn.avg_pool2d( + out, + pool_size=sizes, + strides=strides, + padding=padding, + ceil_mode=ceil_mode, + count_include_pad=count_include_pad, + ) + else: + raise ValueError("Function not supported") + + return out + + +def _get_global_pooling_model(shape, dtype, typef, var_names): + """Return a model and any parameters it may have.""" + out = relay.var(next(var_names), shape=shape, dtype=dtype) + + if typef == "nn.global_max_pool2d": + out = relay.nn.global_max_pool2d(out) + elif typef == "nn.global_avg_pool2d": + out = relay.nn.global_avg_pool2d(out) + else: + raise ValueError("Function not supported") + + return out + + +def _get_expected_pooling_codegen( + shape, dtype, typef, sizes, strides, padding, ceil_mode, count_include_pad +): + if len(padding) == 2: + padding = (padding[0], padding[1], padding[0], padding[1]) + output_shape = _calculate_output_shape(shape, sizes, padding, strides) + + node = { + "op": "kernel", + "name": typef, + "inputs": [[0, 0, 0]], + "attrs": { + "num_inputs": "1", + "num_outputs": "1", + "layout": [["NCHW"]], + "shape": [[list(output_shape)]], + "dtype": [[dtype]], + "padding": [[str(p) for p in padding]], + "strides": [[str(s) for s in strides]], + "pool_size": [[str(s) for s in sizes]], + "ceil_mode": [[str(1 if ceil_mode else 0)]], + }, + } + + if typef == "nn.avg_pool2d" or typef == "nn.l2_pool2d": + node["attrs"]["count_include_pad"] = [["1" if count_include_pad else "0"]] + + input = {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}} + return [input, node] + + +def _get_expected_global_pooling_codegen(shape, dtype, typef): + node = { + "op": "kernel", + "name": typef, + "inputs": [[0, 0, 0]], + "attrs": { + "num_inputs": "1", + "num_outputs": "1", + "layout": [["NCHW"]], + "shape": [[[1, shape[1], 1, 1]]], + "dtype": [[dtype]], + }, + } + + input = {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}} + return [input, node] + + +def test_pooling(): + if skip_runtime_test(): + return + + device = Device() + np.random.seed(0) + + dtype = "float32" + trials = [ + ["nn.max_pool2d", (3, 3), (2, 2), (0, 0), False, False, (27, 27, 512)], + ["nn.max_pool2d", (2, 2), (2, 2), (0, 0), False, True, (16, 16, 16)], + ["nn.max_pool2d", (3, 3), (2, 2), (1, 1), True, True, (15, 15, 16)], + ["nn.max_pool2d", (2, 2), (2, 2), (0, 1), False, False, (16, 16, 16)], + ["nn.avg_pool2d", (2, 2), (2, 2), (1, 1), False, False, (16, 16, 16)], + ["nn.avg_pool2d", (2, 2), (2, 2), (0, 0), False, True, (16, 16, 16)], + ["nn.avg_pool2d", (3, 3), (2, 2), (0, 1), True, False, (15, 15, 16)], + ] + + for ( + typef, + size, + stride, + pad, + ceil_mode, + count_include_pad, + input_shape, + ) in trials: + shape = (1, *input_shape) + outputs = [] + inputs = { + "a": tvm.nd.array(np.random.uniform(-127, 128, shape).astype(dtype)), + } + + func = _get_pooling_model( + shape, dtype, typef, size, stride, pad, ceil_mode, count_include_pad, iter(inputs) + ) + + config = { + "size": size, + "stride": stride, + "shape": shape, + "pooling type": typef, + "dtype": dtype, + "padding": pad, + "ceil_mode": ceil_mode, + "count_include_pad": count_include_pad, + "inputs": inputs, + } + + params = None + for enable_bnns in [False, True]: + outputs.append( + build_and_run( + func, inputs, 1, params, device, enable_bnns=enable_bnns, config=config + )[0] + ) + + verify(outputs, atol=0.001, rtol=0.001, config=config) + + +def test_global_pooling(): + if skip_runtime_test(): + return + + device = Device() + np.random.seed(0) + + dtype = "float32" + + trials = [ + ["nn.global_max_pool2d", (8, 8, 16)], + ["nn.global_max_pool2d", (9, 9, 16)], + ["nn.global_max_pool2d", (8, 8, 16)], + ["nn.global_avg_pool2d", (8, 8, 16)], + ["nn.global_avg_pool2d", (8, 8, 16)], + ["nn.global_avg_pool2d", (9, 9, 16)], + ] + + for typef, input_shape in trials: + shape = (1, *input_shape) + outputs = [] + inputs = { + "a": tvm.nd.array(np.random.uniform(-127, 128, shape).astype(dtype)), + } + + func = _get_global_pooling_model(shape, dtype, typef, iter(inputs)) + config = { + "shape": shape, + "pooling type": typef, + "dtype": dtype, + } + + for enable_bnns in [False, True]: + outputs.append( + build_and_run( + func, inputs, 1, None, device, enable_bnns=enable_bnns, config=config + )[0] + ) + + verify(outputs, atol=0.001, rtol=0.001, config=config) + + +def test_codegen_pooling(): + if skip_codegen_test(): + return Review comment: Done ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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