# 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 numpy as np import tvm from tvm import autotvm from tvm.autotvm.task.space import FallbackConfigEntity from tvm.contrib import nnpack from tvm.contrib.pickle_memoize import memoize import topi import topi.testing from topi.util import get_const_tuple from pytest import skip def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1, add_bias=False, add_relu=False, devices=['cuda', 'llvm -device=arm_cpu', 'opencl -device=mali']): print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) in_height = in_width = in_size A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A') W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W') bias = tvm.placeholder((num_filter, 1, 1), name='bias') a_shape = get_const_tuple(A.shape) w_shape = get_const_tuple(W.shape) bias_shape = get_const_tuple(bias.shape) dtype = A.dtype @memoize("topi.tests.test_topi_conv2d_nchw.verify_conv2d_nchw") def get_ref_data(): a_np = np.random.uniform(size=a_shape).astype(dtype) w_np = np.random.uniform(size=w_shape).astype(dtype) b_np = np.random.uniform(size=bias_shape).astype(dtype) dw_np = topi.testing.dilate_python(w_np, (1, 1, dilation, dilation)) c_np = topi.testing.conv2d_nchw_python(a_np, dw_np, stride, padding) if add_bias: b_np = np.random.uniform(size=bias_shape).astype(dtype) c_np += b_np if add_relu: c_np = np.maximum(c_np, 0) return a_np, w_np, b_np, c_np a_np, w_np, b_np, c_np = get_ref_data() def check_device(device): ctx = tvm.context(device, 0) if not ctx.exist: skip("s is not enabled" % device) print("Running on target: %s" % device) with tvm.target.create(device): C = topi.nn.conv2d(A, W, stride, padding, dilation, layout='NCHW', out_dtype=dtype) if add_bias: C = topi.add(C, bias) if add_relu: C = topi.nn.relu(C) s = topi.generic.schedule_conv2d_nchw([C]) a = tvm.nd.array(a_np, ctx) w = tvm.nd.array(w_np, ctx) b = tvm.nd.array(b_np, ctx) c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx) if add_bias: func = tvm.build(s, [A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) func(a, w, b, c) else: func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) func(a, w, c) tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-4) for device in devices: check_device(device) class WinogradFallback(autotvm.FallbackContext): def _query_inside(self, target, workload): key = (target, workload) if key in self.memory: return self.memory[key] cfg = FallbackConfigEntity() cfg.template_key = 'winograd_nnpack_fp32' self.memory[key] = cfg return cfg def test_conv2d_nchw(): if not tvm.get_global_func("tvm.contrib.nnpack.convolution_inference_without_weight_transform", True): skip("extern function is not available") if not nnpack.is_available(): skip("nnpack is not available") devices = ['llvm -device=arm_cpu'] autotvm.DispatchContext.current.silent = True with WinogradFallback(): # resnet 18 workloads verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, devices=devices) verify_conv2d_nchw(1, 128, 28, 128, 3, 1, 1, devices=devices) verify_conv2d_nchw(1, 256, 14, 256, 3, 1, 1, devices=devices) verify_conv2d_nchw(1, 512, 7, 512, 3, 1, 1, devices=devices) # unet workloads verify_conv2d_nchw(1, 3, 192, 12, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 4, 192, 12, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 12, 96, 24, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 24, 48, 48, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 48, 24, 96, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 96, 12, 180, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 180, 6, 220, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 220, 6, 180, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 180, 12, 96, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 96, 24, 48, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 48, 48, 24, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 24, 96, 12, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 12, 192, 1, 3, 1, 1, add_bias=True, devices=devices) # relu, bias verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, add_bias=True, devices=devices) verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, add_relu=True, devices=devices) verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, add_relu=True, add_bias=True, devices=devices) # werid workloads verify_conv2d_nchw(1, 3, 3, 3, 3, 1, 1, devices=devices) verify_conv2d_nchw(1, 13, 71, 59, 3, 1, 1, devices=devices) if __name__ == "__main__": import pytest pytest.main()