# Copyright (c) 2016-present, Facebook, Inc. # # Licensed 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. ############################################################################## ## @package convnet_benchmarks # Module caffe2.experiments.python.convnet_benchmarks """ Benchmark for common convnets. (NOTE: Numbers below prior with missing parameter=update step, TODO to update) Speed on Titan X, with 10 warmup steps and 10 main steps and with different versions of cudnn, are as follows (time reported below is per-batch time, forward / forward+backward): CuDNN V3 CuDNN v4 AlexNet 32.5 / 108.0 27.4 / 90.1 OverFeat 113.0 / 342.3 91.7 / 276.5 Inception 134.5 / 485.8 125.7 / 450.6 VGG (batch 64) 200.8 / 650.0 164.1 / 551.7 Speed on Inception with varied batch sizes and CuDNN v4 is as follows: Batch Size Speed per batch Speed per image 16 22.8 / 72.7 1.43 / 4.54 32 38.0 / 127.5 1.19 / 3.98 64 67.2 / 233.6 1.05 / 3.65 128 125.7 / 450.6 0.98 / 3.52 Speed on Tesla M40, which 10 warmup steps and 10 main steps and with cudnn v4, is as follows: AlexNet 68.4 / 218.1 OverFeat 210.5 / 630.3 Inception 300.2 / 1122.2 VGG (batch 64) 405.8 / 1327.7 (Note that these numbers involve a "full" backprop, i.e. the gradient with respect to the input image is also computed.) To get the numbers, simply run: for MODEL in AlexNet OverFeat Inception; do PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \ --batch_size 128 --model $MODEL --forward_only True done for MODEL in AlexNet OverFeat Inception; do PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \ --batch_size 128 --model $MODEL done PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \ --batch_size 64 --model VGGA --forward_only True PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \ --batch_size 64 --model VGGA for BS in 16 32 64 128; do PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \ --batch_size $BS --model Inception --forward_only True PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \ --batch_size $BS --model Inception done Note that VGG needs to be run at batch 64 due to memory limit on the backward pass. """ import argparse import time from caffe2.python import cnn, workspace, core import caffe2.python.SparseTransformer as SparseTransformer # type: ignore[import] def MLP(order): model = cnn.CNNModelHelper() d = 256 depth = 20 width = 3 for i in range(depth): for j in range(width): current = "fc_{}_{}".format(i, j) if i > 0 else "data" next_ = "fc_{}_{}".format(i + 1, j) model.FC( current, next_, dim_in=d, dim_out=d, weight_init=model.XavierInit, bias_init=model.XavierInit) model.Sum(["fc_{}_{}".format(depth, j) for j in range(width)], ["sum"]) model.FC("sum", "last", dim_in=d, dim_out=1000, weight_init=model.XavierInit, bias_init=model.XavierInit) xent = model.LabelCrossEntropy(["last", "label"], "xent") model.AveragedLoss(xent, "loss") return model, d def AlexNet(order): model = cnn.CNNModelHelper(order, name="alexnet", use_cudnn=True, cudnn_exhaustive_search=True) conv1 = model.Conv( "data", "conv1", 3, 64, 11, ('XavierFill', {}), ('ConstantFill', {}), stride=4, pad=2 ) relu1 = model.Relu(conv1, "conv1") pool1 = model.MaxPool(relu1, "pool1", kernel=3, stride=2) conv2 = model.Conv( pool1, "conv2", 64, 192, 5, ('XavierFill', {}), ('ConstantFill', {}), pad=2 ) relu2 = model.Relu(conv2, "conv2") pool2 = model.MaxPool(relu2, "pool2", kernel=3, stride=2) conv3 = model.Conv( pool2, "conv3", 192, 384, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu3 = model.Relu(conv3, "conv3") conv4 = model.Conv( relu3, "conv4", 384, 256, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu4 = model.Relu(conv4, "conv4") conv5 = model.Conv( relu4, "conv5", 256, 256, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu5 = model.Relu(conv5, "conv5") pool5 = model.MaxPool(relu5, "pool5", kernel=3, stride=2) fc6 = model.FC( pool5, "fc6", 256 * 6 * 6, 4096, ('XavierFill', {}), ('ConstantFill', {}) ) relu6 = model.Relu(fc6, "fc6") fc7 = model.FC( relu6, "fc7", 4096, 4096, ('XavierFill', {}), ('ConstantFill', {}) ) relu7 = model.Relu(fc7, "fc7") fc8 = model.FC( relu7, "fc8", 4096, 1000, ('XavierFill', {}), ('ConstantFill', {}) ) pred = model.Softmax(fc8, "pred") xent = model.LabelCrossEntropy([pred, "label"], "xent") model.AveragedLoss(xent, "loss") return model, 224 def OverFeat(order): model = cnn.CNNModelHelper(order, name="overfeat", use_cudnn=True, cudnn_exhaustive_search=True) conv1 = model.Conv( "data", "conv1", 3, 96, 11, ('XavierFill', {}), ('ConstantFill', {}), stride=4 ) relu1 = model.Relu(conv1, "conv1") pool1 = model.MaxPool(relu1, "pool1", kernel=2, stride=2) conv2 = model.Conv( pool1, "conv2", 96, 256, 5, ('XavierFill', {}), ('ConstantFill', {}) ) relu2 = model.Relu(conv2, "conv2") pool2 = model.MaxPool(relu2, "pool2", kernel=2, stride=2) conv3 = model.Conv( pool2, "conv3", 256, 512, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu3 = model.Relu(conv3, "conv3") conv4 = model.Conv( relu3, "conv4", 512, 1024, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu4 = model.Relu(conv4, "conv4") conv5 = model.Conv( relu4, "conv5", 1024, 1024, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu5 = model.Relu(conv5, "conv5") pool5 = model.MaxPool(relu5, "pool5", kernel=2, stride=2) fc6 = model.FC( pool5, "fc6", 1024 * 6 * 6, 3072, ('XavierFill', {}), ('ConstantFill', {}) ) relu6 = model.Relu(fc6, "fc6") fc7 = model.FC( relu6, "fc7", 3072, 4096, ('XavierFill', {}), ('ConstantFill', {}) ) relu7 = model.Relu(fc7, "fc7") fc8 = model.FC( relu7, "fc8", 4096, 1000, ('XavierFill', {}), ('ConstantFill', {}) ) pred = model.Softmax(fc8, "pred") xent = model.LabelCrossEntropy([pred, "label"], "xent") model.AveragedLoss(xent, "loss") return model, 231 def VGGA(order): model = cnn.CNNModelHelper(order, name='vgg-a', use_cudnn=True, cudnn_exhaustive_search=True) conv1 = model.Conv( "data", "conv1", 3, 64, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu1 = model.Relu(conv1, "conv1") pool1 = model.MaxPool(relu1, "pool1", kernel=2, stride=2) conv2 = model.Conv( pool1, "conv2", 64, 128, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu2 = model.Relu(conv2, "conv2") pool2 = model.MaxPool(relu2, "pool2", kernel=2, stride=2) conv3 = model.Conv( pool2, "conv3", 128, 256, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu3 = model.Relu(conv3, "conv3") conv4 = model.Conv( relu3, "conv4", 256, 256, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu4 = model.Relu(conv4, "conv4") pool4 = model.MaxPool(relu4, "pool4", kernel=2, stride=2) conv5 = model.Conv( pool4, "conv5", 256, 512, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu5 = model.Relu(conv5, "conv5") conv6 = model.Conv( relu5, "conv6", 512, 512, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu6 = model.Relu(conv6, "conv6") pool6 = model.MaxPool(relu6, "pool6", kernel=2, stride=2) conv7 = model.Conv( pool6, "conv7", 512, 512, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu7 = model.Relu(conv7, "conv7") conv8 = model.Conv( relu7, "conv8", 512, 512, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu8 = model.Relu(conv8, "conv8") pool8 = model.MaxPool(relu8, "pool8", kernel=2, stride=2) fcix = model.FC( pool8, "fcix", 512 * 7 * 7, 4096, ('XavierFill', {}), ('ConstantFill', {}) ) reluix = model.Relu(fcix, "fcix") fcx = model.FC( reluix, "fcx", 4096, 4096, ('XavierFill', {}), ('ConstantFill', {}) ) relux = model.Relu(fcx, "fcx") fcxi = model.FC( relux, "fcxi", 4096, 1000, ('XavierFill', {}), ('ConstantFill', {}) ) pred = model.Softmax(fcxi, "pred") xent = model.LabelCrossEntropy([pred, "label"], "xent") model.AveragedLoss(xent, "loss") return model, 231 def net_DAG_Builder(model): print("====================================================") print(" Start Building DAG ") print("====================================================") net_root = SparseTransformer.netbuilder(model) return net_root def _InceptionModule( model, input_blob, input_depth, output_name, conv1_depth, conv3_depths, conv5_depths, pool_depth ): # path 1: 1x1 conv conv1 = model.Conv( input_blob, output_name + ":conv1", input_depth, conv1_depth, 1, ('XavierFill', {}), ('ConstantFill', {}) ) conv1 = model.Relu(conv1, conv1) # path 2: 1x1 conv + 3x3 conv conv3_reduce = model.Conv( input_blob, output_name + ":conv3_reduce", input_depth, conv3_depths[0], 1, ('XavierFill', {}), ('ConstantFill', {}) ) conv3_reduce = model.Relu(conv3_reduce, conv3_reduce) conv3 = model.Conv( conv3_reduce, output_name + ":conv3", conv3_depths[0], conv3_depths[1], 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) conv3 = model.Relu(conv3, conv3) # path 3: 1x1 conv + 5x5 conv conv5_reduce = model.Conv( input_blob, output_name + ":conv5_reduce", input_depth, conv5_depths[0], 1, ('XavierFill', {}), ('ConstantFill', {}) ) conv5_reduce = model.Relu(conv5_reduce, conv5_reduce) conv5 = model.Conv( conv5_reduce, output_name + ":conv5", conv5_depths[0], conv5_depths[1], 5, ('XavierFill', {}), ('ConstantFill', {}), pad=2 ) conv5 = model.Relu(conv5, conv5) # path 4: pool + 1x1 conv pool = model.MaxPool( input_blob, output_name + ":pool", kernel=3, stride=1, pad=1 ) pool_proj = model.Conv( pool, output_name + ":pool_proj", input_depth, pool_depth, 1, ('XavierFill', {}), ('ConstantFill', {}) ) pool_proj = model.Relu(pool_proj, pool_proj) output = model.Concat([conv1, conv3, conv5, pool_proj], output_name) return output def Inception(order): model = cnn.CNNModelHelper(order, name="inception", use_cudnn=True, cudnn_exhaustive_search=True) conv1 = model.Conv( "data", "conv1", 3, 64, 7, ('XavierFill', {}), ('ConstantFill', {}), stride=2, pad=3 ) relu1 = model.Relu(conv1, "conv1") pool1 = model.MaxPool(relu1, "pool1", kernel=3, stride=2, pad=1) conv2a = model.Conv( pool1, "conv2a", 64, 64, 1, ('XavierFill', {}), ('ConstantFill', {}) ) conv2a = model.Relu(conv2a, conv2a) conv2 = model.Conv( conv2a, "conv2", 64, 192, 3, ('XavierFill', {}), ('ConstantFill', {}), pad=1 ) relu2 = model.Relu(conv2, "conv2") pool2 = model.MaxPool(relu2, "pool2", kernel=3, stride=2, pad=1) # Inception modules inc3 = _InceptionModule( model, pool2, 192, "inc3", 64, [96, 128], [16, 32], 32 ) inc4 = _InceptionModule( model, inc3, 256, "inc4", 128, [128, 192], [32, 96], 64 ) pool5 = model.MaxPool(inc4, "pool5", kernel=3, stride=2, pad=1) inc5 = _InceptionModule( model, pool5, 480, "inc5", 192, [96, 208], [16, 48], 64 ) inc6 = _InceptionModule( model, inc5, 512, "inc6", 160, [112, 224], [24, 64], 64 ) inc7 = _InceptionModule( model, inc6, 512, "inc7", 128, [128, 256], [24, 64], 64 ) inc8 = _InceptionModule( model, inc7, 512, "inc8", 112, [144, 288], [32, 64], 64 ) inc9 = _InceptionModule( model, inc8, 528, "inc9", 256, [160, 320], [32, 128], 128 ) pool9 = model.MaxPool(inc9, "pool9", kernel=3, stride=2, pad=1) inc10 = _InceptionModule( model, pool9, 832, "inc10", 256, [160, 320], [32, 128], 128 ) inc11 = _InceptionModule( model, inc10, 832, "inc11", 384, [192, 384], [48, 128], 128 ) pool11 = model.AveragePool(inc11, "pool11", kernel=7, stride=1) fc = model.FC( pool11, "fc", 1024, 1000, ('XavierFill', {}), ('ConstantFill', {}) ) # It seems that Soumith's benchmark does not have softmax on top # for Inception. We will add it anyway so we can have a proper # backward pass. pred = model.Softmax(fc, "pred") xent = model.LabelCrossEntropy([pred, "label"], "xent") model.AveragedLoss(xent, "loss") return model, 224 def AddInput(model, batch_size, db, db_type): """Adds the data input part.""" data_uint8, label = model.TensorProtosDBInput( [], ["data_uint8", "label"], batch_size=batch_size, db=db, db_type=db_type ) data = model.Cast(data_uint8, "data_nhwc", to=core.DataType.FLOAT) data = model.NHWC2NCHW(data, "data") data = model.Scale(data, data, scale=float(1. / 256)) data = model.StopGradient(data, data) return data, label def AddParameterUpdate(model): """ Simple plain SGD update -- not tuned to actually train the models """ ITER = model.Iter("iter") LR = model.LearningRate( ITER, "LR", base_lr=-1e-8, policy="step", stepsize=10000, gamma=0.999) ONE = model.param_init_net.ConstantFill([], "ONE", shape=[1], value=1.0) for param in model.params: param_grad = model.param_to_grad[param] model.WeightedSum([param, ONE, param_grad, LR], param) def Benchmark(model_gen, arg): model, input_size = model_gen(arg.order) model.Proto().type = arg.net_type model.Proto().num_workers = arg.num_workers # In order to be able to run everything without feeding more stuff, let's # add the data and label blobs to the parameter initialization net as well. if arg.order == "NCHW": input_shape = [arg.batch_size, 3, input_size, input_size] else: input_shape = [arg.batch_size, input_size, input_size, 3] if arg.model == "MLP": input_shape = [arg.batch_size, input_size] model.param_init_net.GaussianFill( [], "data", shape=input_shape, mean=0.0, std=1.0 ) model.param_init_net.UniformIntFill( [], "label", shape=[arg.batch_size, ], min=0, max=999 ) if arg.forward_only: print('{}: running forward only.'.format(arg.model)) else: print('{}: running forward-backward.'.format(arg.model)) model.AddGradientOperators(["loss"]) AddParameterUpdate(model) if arg.order == 'NHWC': print( '==WARNING==\n' 'NHWC order with CuDNN may not be supported yet, so I might\n' 'exit suddenly.' ) if not arg.cpu: model.param_init_net.RunAllOnGPU() model.net.RunAllOnGPU() if arg.dump_model: # Writes out the pbtxt for benchmarks on e.g. Android with open( "{0}_init_batch_{1}.pbtxt".format(arg.model, arg.batch_size), "w" ) as fid: fid.write(str(model.param_init_net.Proto())) with open("{0}.pbtxt".format(arg.model), "w") as fid: fid.write(str(model.net.Proto())) workspace.RunNetOnce(model.param_init_net) workspace.CreateNet(model.net) for i in range(arg.warmup_iterations): workspace.RunNet(model.net.Proto().name) plan = core.Plan("plan") plan.AddStep(core.ExecutionStep("run", model.net, arg.iterations)) start = time.time() workspace.RunPlan(plan) print('Spent: {}'.format((time.time() - start) / arg.iterations)) if arg.layer_wise_benchmark: print('Layer-wise benchmark.') workspace.BenchmarkNet(model.net.Proto().name, 1, arg.iterations, True) def GetArgumentParser(): parser = argparse.ArgumentParser(description="Caffe2 benchmark.") parser.add_argument( "--batch_size", type=int, default=128, help="The batch size." ) parser.add_argument("--model", type=str, help="The model to benchmark.") parser.add_argument( "--order", type=str, default="NCHW", help="The order to evaluate." ) parser.add_argument( "--cudnn_ws", type=int, default=-1, help="The cudnn workspace size." ) parser.add_argument( "--iterations", type=int, default=10, help="Number of iterations to run the network." ) parser.add_argument( "--warmup_iterations", type=int, default=10, help="Number of warm-up iterations before benchmarking." ) parser.add_argument( "--forward_only", action='store_true', help="If set, only run the forward pass." ) parser.add_argument( "--layer_wise_benchmark", action='store_true', help="If True, run the layer-wise benchmark as well." ) parser.add_argument( "--cpu", action='store_true', help="If True, run testing on CPU instead of GPU." ) parser.add_argument( "--dump_model", action='store_true', help="If True, dump the model prototxts to disk." ) parser.add_argument("--net_type", type=str, default="dag") parser.add_argument("--num_workers", type=int, default=2) return parser if __name__ == '__main__': args = GetArgumentParser().parse_args() if ( not args.batch_size or not args.model or not args.order or not args.cudnn_ws ): GetArgumentParser().print_help() workspace.GlobalInit(['caffe2', '--caffe2_log_level=0']) model_map = { 'AlexNet': AlexNet, 'OverFeat': OverFeat, 'VGGA': VGGA, 'Inception': Inception, 'MLP': MLP, } Benchmark(model_map[args.model], args)