# 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 tvm from tvm import autotvm import topi import topi.testing import numpy as np from topi.util import get_const_tuple from topi.nn.util import get_pad_tuple from tvm.contrib.pickle_memoize import memoize from common import get_all_backend def depthwise_conv2d_with_workload_nchw(batch, in_channel, in_height, channel_multiplier, filter_height, stride, padding, dilation=1): in_width = in_height filter_channel = in_channel filter_width = filter_height stride_h = stride_w = stride if dilation == 1: # here we transform the padding argument from 'str' to 'tuple' , # because we need this to match the "workload" tuple to the records in TopHub pad_h, pad_w, _, _ = get_pad_tuple(padding, (filter_height, filter_width)) padding_args = (pad_h, pad_w) else: padding_args = padding # placeholder Input = tvm.placeholder((batch, in_channel, in_height, in_width), name='Input') Filter = tvm.placeholder((filter_channel, channel_multiplier, filter_height, filter_width), name='Filter') Scale = tvm.placeholder((in_channel * channel_multiplier,), name='Scale') Shift = tvm.placeholder((in_channel * channel_multiplier,), name='Shift') dtype = 'float32' def check_device(device): ctx = tvm.context(device, 0) if not ctx.exist: print("Skip because %s is not enabled" % device) return print("Running on target: %s" % device) with tvm.target.create(device): # declare DepthwiseConv2d = topi.nn.depthwise_conv2d_nchw(Input, Filter, (stride_h, stride_w), padding_args, dilation, dtype) ScaleShift = topi.nn.scale_shift_nchw(DepthwiseConv2d, Scale, Shift) Relu = topi.nn.relu(ScaleShift) # schedule s1 = topi.generic.schedule_depthwise_conv2d_nchw(DepthwiseConv2d) s2 = topi.generic.schedule_depthwise_conv2d_nchw(ScaleShift) s3 = topi.generic.schedule_depthwise_conv2d_nchw(Relu) # build the kernels f1 = tvm.build(s1, [Input, Filter, DepthwiseConv2d], device) f2 = tvm.build(s2, [Input, Filter, Scale, Shift, ScaleShift], device) f3 = tvm.build(s3, [Input, Filter, Scale, Shift, Relu], device) # Prepare pod type for test data closure input_shape = get_const_tuple(Input.shape) filter_shape = get_const_tuple(Filter.shape) scale_shape = get_const_tuple(Scale.shape) shift_shape = get_const_tuple(Shift.shape) scale_shift_shape = get_const_tuple(ScaleShift.shape) # Use memoize, pickle the test data for next time use. @memoize("topi.tests.test_topi_depthwise_conv2d.nchw") def get_ref_data(): input_np = np.random.uniform(size=input_shape).astype(dtype) filter_np = np.random.uniform(size=filter_shape).astype(dtype) dilated_filter_np = topi.testing.dilate_python(filter_np, (1, 1, dilation, dilation)) scale_np = np.random.uniform(size=scale_shape).astype(dtype) shift_np = np.random.uniform(size=shift_shape).astype(dtype) # correctness with scipy depthwise_conv2d_scipy = topi.testing.depthwise_conv2d_python_nchw( input_np, dilated_filter_np, stride, padding) scale_shift_scipy = np.zeros(shape=scale_shift_shape) for c in range(in_channel * channel_multiplier): scale_shift_scipy[:,c,:,:] = depthwise_conv2d_scipy[:,c,:,:] * scale_np[c] + shift_np[c] relu_scipy = np.maximum(scale_shift_scipy, 0) return (input_np, filter_np, scale_np, shift_np, depthwise_conv2d_scipy, scale_shift_scipy, relu_scipy) # Get the test data (input_np, filter_np, scale_np, shift_np, depthwise_conv2d_scipy, scale_shift_scipy, relu_scipy) = get_ref_data() input_tvm = tvm.nd.array(input_np, ctx) filter_tvm = tvm.nd.array(filter_np, ctx) scale_tvm = tvm.nd.array(scale_np, ctx) shift_tvm = tvm.nd.array(shift_np, ctx) depthwise_conv2d_tvm = tvm.nd.array(np.zeros(shape=get_const_tuple(DepthwiseConv2d.shape), dtype=DepthwiseConv2d.dtype), ctx) scale_shift_tvm = tvm.nd.array(np.zeros(shape=get_const_tuple(ScaleShift.shape), dtype=ScaleShift.dtype), ctx) relu_tvm = tvm.nd.array(np.zeros(shape=get_const_tuple(Relu.shape), dtype=Relu.dtype), ctx) # launch kernel 1 (depthwise_conv2d) timer_1 = f1.time_evaluator(f1.entry_name, ctx, number=1) tcost_1 = timer_1(input_tvm, filter_tvm, depthwise_conv2d_tvm).mean # launch kernel 2 (depthwise_conv2d + scale_shift) timer_2 = f2.time_evaluator(f2.entry_name, ctx, number=1) tcost_2 = timer_2(input_tvm, filter_tvm, scale_tvm, shift_tvm, scale_shift_tvm).mean # launch kernel 3 (depthwise_conv2d + scale_shift + relu) timer_3 = f3.time_evaluator(f3.entry_name, ctx, number=1) tcost_3 = timer_3(input_tvm, filter_tvm, scale_tvm, shift_tvm, relu_tvm).mean tvm.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5) tvm.testing.assert_allclose(scale_shift_tvm.asnumpy(), scale_shift_scipy, rtol=1e-5) tvm.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5) for device in get_all_backend(): with autotvm.tophub.context(device): # load tophub pre-tuned parameters check_device(device) def depthwise_conv2d_with_workload_nhwc(batch, in_channel, in_height, channel_multiplier, filter_height, stride_h, padding, dilation=1): in_width = in_height filter_channel = in_channel filter_width = filter_height stride_w = stride_h if dilation == 1: # here we transform the padding argument from 'str' to 'tuple' , # because we need this to match the "workload" tuple to the records in TopHub pad_h, pad_w, _, _ = get_pad_tuple(padding, (filter_height, filter_width)) padding_args = (pad_h, pad_w) else: padding_args = padding # placeholder Input = tvm.placeholder((batch, in_height, in_width, in_channel), name='Input') Filter = tvm.placeholder((filter_height, filter_width,filter_channel, channel_multiplier), name='Filter') Scale = tvm.placeholder((in_channel * channel_multiplier,), name='Scale') Shift = tvm.placeholder((in_channel * channel_multiplier,), name='Shift') dtype = 'float32' def check_device(device): ctx = tvm.context(device, 0) if not ctx.exist: print("Skip because %s is not enabled" % device) return print("Running on target: %s" % device) with tvm.target.create(device): # declare DepthwiseConv2d = topi.nn.depthwise_conv2d_nhwc(Input, Filter, (stride_h, stride_w), padding_args, dilation, dtype) ScaleShift = topi.nn.scale_shift_nhwc(DepthwiseConv2d, Scale, Shift) Relu = topi.nn.relu(ScaleShift) # schedule s1 = topi.generic.schedule_depthwise_conv2d_nhwc(DepthwiseConv2d) s2 = topi.generic.schedule_depthwise_conv2d_nhwc(ScaleShift) s3 = topi.generic.schedule_depthwise_conv2d_nhwc(Relu) # build the kernels f1 = tvm.build(s1, [Input, Filter, DepthwiseConv2d], device) f2 = tvm.build(s2, [Input, Filter, Scale, Shift, ScaleShift], device) f3 = tvm.build(s3, [Input, Filter, Scale, Shift, Relu], device) # Prepare pod type for test data closure input_shape = get_const_tuple(Input.shape) filter_shape = get_const_tuple(Filter.shape) scale_shape = get_const_tuple(Scale.shape) shift_shape = get_const_tuple(Shift.shape) scale_shift_shape = get_const_tuple(ScaleShift.shape) # Use memoize, pickle the test data for next time use. @memoize("topi.tests.test_topi_depthwise_conv2d.nhwc.v2") def get_ref_data(): input_np = np.random.uniform(size=input_shape).astype(dtype) filter_np = np.random.uniform(size=filter_shape).astype(dtype) dilated_filter_np = topi.testing.dilate_python(filter_np, (dilation, dilation, 1, 1)) scale_np = np.random.uniform(size=scale_shape).astype(dtype) shift_np = np.random.uniform(size=shift_shape).astype(dtype) # correctness with scipy depthwise_conv2d_scipy = topi.testing.depthwise_conv2d_python_nhwc( input_np, dilated_filter_np, stride=[stride_h, stride_w], padding=padding) scale_shift_scipy = np.zeros(shape=scale_shift_shape) for c in range(in_channel * channel_multiplier): scale_shift_scipy[:,:,:,c] = depthwise_conv2d_scipy[:,:,:,c] * scale_np[c] + shift_np[c] relu_scipy = np.maximum(scale_shift_scipy, 0) return (input_np, filter_np, scale_np, shift_np, depthwise_conv2d_scipy, scale_shift_scipy, relu_scipy) # Get the test data (input_np, filter_np, scale_np, shift_np, depthwise_conv2d_scipy, scale_shift_scipy, relu_scipy) = get_ref_data() # prepare data input_tvm = tvm.nd.array(input_np, ctx) filter_tvm = tvm.nd.array(filter_np, ctx) scale_tvm = tvm.nd.array(scale_np, ctx) shift_tvm = tvm.nd.array(shift_np, ctx) depthwise_conv2d_tvm = tvm.nd.array(np.zeros(shape=get_const_tuple(DepthwiseConv2d.shape), dtype=DepthwiseConv2d.dtype), ctx) scale_shift_tvm = tvm.nd.array(np.zeros(shape=get_const_tuple(ScaleShift.shape), dtype=ScaleShift.dtype), ctx) relu_tvm = tvm.nd.array(np.zeros(shape=get_const_tuple(Relu.shape), dtype=Relu.dtype), ctx) # launch kernel 1 (depthwise_conv2d) timer_1 = f1.time_evaluator(f1.entry_name, ctx, number=1) tcost_1 = timer_1(input_tvm, filter_tvm, depthwise_conv2d_tvm).mean # launch kernel 2 (depthwise_conv2d + scale_shift) timer_2 = f2.time_evaluator(f2.entry_name, ctx, number=1) tcost_2 = timer_2(input_tvm, filter_tvm, scale_tvm, shift_tvm, scale_shift_tvm).mean # launch kernel 3 (depthwise_conv2d + scale_shift + relu) timer_3 = f3.time_evaluator(f3.entry_name, ctx, number=1) tcost_3 = timer_3(input_tvm, filter_tvm, scale_tvm, shift_tvm, relu_tvm).mean relu_scipy = np.maximum(scale_shift_scipy, 0) tvm.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5) tvm.testing.assert_allclose(scale_shift_tvm.asnumpy(), scale_shift_scipy, rtol=1e-5) tvm.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5) for device in get_all_backend(): with autotvm.tophub.context(device): # load tophub pre-tuned parameters check_device(device) def _transform_data(data, bn): # NCHW -> NCHW[x]c batch_size, channel, height, width = data.shape data = np.reshape(data, (batch_size, channel//bn, bn, height, width)) data = np.transpose(data, (0, 1, 3, 4, 2)) return data def _transform_kernel(kernel, bn): # channel, channel_multiplier, kh, kw -> out_channel_chunk, kh, kw, out_channel_block channel, channel_multiplier, kh, kw = kernel.shape out_channel = channel * channel_multiplier kernel = np.reshape(kernel, (out_channel//bn, bn, kh, kw)) kernel = np.transpose(kernel, (0, 2, 3, 1)) out_channel_chunk, kh, kw, out_channel_block = kernel.shape return kernel.reshape(out_channel_chunk, 1, kh, kw, 1, out_channel_block) def depthwise_conv2d_with_workload_NCHWc(batch, in_channel, in_height, channel_multiplier, filter_height, stride, padding, dilation=1): in_width = in_height filter_channel = in_channel filter_width = filter_height stride_h = stride_w = stride assert dilation == 1, "depthwise_conv2d_NCHWc currently does not support dilation." pad_h, pad_w, _, _ = get_pad_tuple(padding, (filter_height, filter_width)) padding_args = (pad_h, pad_w) out_channel = filter_channel * channel_multiplier # for testing functionality, # we choose arbitrary block size that can divide the channel, # regardless of the performance. oc_block = 1 for bn in range(16, 0, -1): if out_channel % bn == 0: oc_block = bn break ic_block = 1 for bn in range(oc_block, 0, -1): if in_channel % bn == 0: ic_block = bn break # placeholder Input = tvm.placeholder((batch, in_channel//ic_block, in_height, in_width, ic_block), name='Input') Filter = tvm.placeholder((out_channel//oc_block, 1, filter_height, filter_width, 1, oc_block), name='Filter') in_layout = "NCHW%dc" % ic_block out_layout = "NCHW%dc" % oc_block dtype = 'float32' def check_device(device): ctx = tvm.context(device, 0) if not ctx.exist: print("Skip because %s is not enabled" % device) return print("Running on target: %s" % device) with tvm.target.create(device): # declare DepthwiseConv2d = topi.nn.depthwise_conv2d_NCHWc(Input, Filter, (stride_h, stride_w), padding_args, (dilation, dilation), in_layout, out_layout, dtype) # TODO: add scale_shift implement for NCHWc and add test here Relu = topi.nn.relu(DepthwiseConv2d) # schedule s1 = topi.generic.schedule_depthwise_conv2d_nchw(DepthwiseConv2d) s2 = topi.generic.schedule_depthwise_conv2d_nchw(Relu) # build the kernels f1 = tvm.build(s1, [Input, Filter, DepthwiseConv2d], device) f2 = tvm.build(s2, [Input, Filter, Relu], device) # Prepare pod type for test data closure input_shape = (batch, in_channel, in_height, in_width) filter_shape = (filter_channel, channel_multiplier, filter_height, filter_width) # Use memoize, pickle the test data for next time use. @memoize("topi.tests.test_topi_depthwise_conv2d.NCHWc") def get_ref_data(): input_np = np.random.uniform(size=input_shape).astype(dtype) filter_np = np.random.uniform(size=filter_shape).astype(dtype) # correctness with scipy depthwise_conv2d_scipy = topi.testing.depthwise_conv2d_python_nchw( input_np, filter_np, stride, padding) relu_scipy = np.maximum(depthwise_conv2d_scipy, 0) return (_transform_data(input_np, ic_block), _transform_kernel(filter_np, oc_block), _transform_data(depthwise_conv2d_scipy, oc_block), _transform_data(relu_scipy, oc_block)) # Get the test data (input_np, filter_np, depthwise_conv2d_scipy, relu_scipy) = get_ref_data() input_tvm = tvm.nd.array(input_np, ctx) filter_tvm = tvm.nd.array(filter_np, ctx) depthwise_conv2d_tvm = tvm.nd.array(np.zeros(shape=get_const_tuple(DepthwiseConv2d.shape), dtype=DepthwiseConv2d.dtype), ctx) relu_tvm = tvm.nd.array(np.zeros(shape=get_const_tuple(Relu.shape), dtype=Relu.dtype), ctx) # launch kernel 1 (depthwise_conv2d) print(filter_tvm.shape) f1(input_tvm, filter_tvm, depthwise_conv2d_tvm) # launch kernel 2 (depthwise_conv2d + relu) f2(input_tvm, filter_tvm, relu_tvm) tvm.testing.assert_allclose(depthwise_conv2d_tvm.asnumpy(), depthwise_conv2d_scipy, rtol=1e-5) tvm.testing.assert_allclose(relu_tvm.asnumpy(), relu_scipy, rtol=1e-5) # test llvm only for now since depthwise_conv2d_NCHWc implement is missing in other backend. for device in ["llvm"]: with autotvm.tophub.context(device): # load tophub pre-tuned parameters check_device(device) def test_depthwise_conv2d(): # mobilenet workloads depthwise_conv2d_with_workload_nchw(1, 32, 112, 1, 3, 1, "SAME") depthwise_conv2d_with_workload_nchw(1, 64, 112, 1, 3, 2, "SAME") depthwise_conv2d_with_workload_nchw(1, 128, 56, 1, 3, 1, "SAME") depthwise_conv2d_with_workload_nchw(1, 128, 56, 1, 3, 2, "SAME") depthwise_conv2d_with_workload_nchw(1, 256, 28, 1, 3, 1, "SAME") depthwise_conv2d_with_workload_nchw(1, 256, 28, 1, 3, 2, "SAME") depthwise_conv2d_with_workload_nchw(1, 512, 14, 1, 3, 1, "SAME") depthwise_conv2d_with_workload_nchw(1, 512, 14, 1, 3, 2, "SAME") depthwise_conv2d_with_workload_nchw(1, 1024, 7, 1, 3, 1, "SAME") # NCHW depthwise_conv2d_with_workload_nchw(1, 728, 32, 1, 3, 1, "SAME") depthwise_conv2d_with_workload_nchw(4, 256, 64, 2, 5, 2, "SAME") depthwise_conv2d_with_workload_nchw(1, 728, 32, 1, 3, 1, "VALID") depthwise_conv2d_with_workload_nchw(4, 256, 64, 2, 5, 2, "VALID") # dilation = 2 depthwise_conv2d_with_workload_nchw(1, 728, 64, 1, 3, 1, "SAME", dilation=2) # NHWC depthwise_conv2d_with_workload_nhwc(1, 728, 32, 1, 3, 1, "SAME") depthwise_conv2d_with_workload_nhwc(4, 256, 64, 2, 5, 2, "SAME") depthwise_conv2d_with_workload_nhwc(1, 728, 32, 1, 3, 1, "VALID") depthwise_conv2d_with_workload_nhwc(4, 256, 64, 2, 5, 2, "VALID") # dilation = 2 # disabled because it uses too large shared memory on cuda # depthwise_conv2d_with_workload_nhwc(1, 728, 64, 1, 3, 1, "SAME", dilation=2) # NCHW[x]c depthwise_conv2d_with_workload_NCHWc(1, 728, 32, 1, 3, 1, "SAME") depthwise_conv2d_with_workload_NCHWc(4, 256, 64, 2, 5, 2, "SAME") depthwise_conv2d_with_workload_NCHWc(1, 728, 32, 1, 3, 1, "VALID") depthwise_conv2d_with_workload_NCHWc(4, 256, 64, 2, 5, 2, "VALID") if __name__ == "__main__": test_depthwise_conv2d()