def dc_criteo(dense_input, sparse_input, y_): feature_dimension = 33762577 embedding_size = 8 learning_rate = 0.001 Embedding = init.random_normal([feature_dimension, embedding_size], stddev=0.01, name="snd_order_embedding") sparse_input = ad.embedding_lookup_op(Embedding, sparse_input) sparse_input = ad.array_reshape_op(sparse_input, (-1, 26 * embedding_size)) ## dc_model x = ad.concat_op(sparse_input, dense_input, axis=1) input_dim = 26 * 8 + 13 hidden_dim = input_dim residual_out = build_residual_layers(x, input_dim, hidden_dim, num_layers=5) W4 = init.random_normal([26 * embedding_size + 13, 1], stddev=0.1, name="W4") y = ad.matmul_op(residual_out, W4) y = ad.sigmoid_op(y) loss = ad.binarycrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) opt = optimizer.SGDOptimizer(learning_rate=learning_rate) train_op = opt.minimize(loss) return loss, y, y_, train_op
def dfm_criteo(dense_input, sparse_input, y_): feature_dimension = 33762577 embedding_size = 128 learning_rate = 0.01 # FM Embedding1 = init.random_normal([feature_dimension, 1], stddev=0.01, name="fst_order_embedding", ctx=ndarray.cpu(0)) FM_W = init.random_normal([13, 1], stddev=0.01, name="dense_parameter") sparse_1dim_input = ad.embedding_lookup_op(Embedding1, sparse_input, ctx=ndarray.cpu(0)) fm_dense_part = ad.matmul_op(dense_input, FM_W) fm_sparse_part = ad.reduce_sum_op(sparse_1dim_input, axes=1) """ fst order output""" y1 = fm_dense_part + fm_sparse_part Embedding2 = init.random_normal([feature_dimension, embedding_size], stddev=0.01, name="snd_order_embedding", ctx=ndarray.cpu(0)) sparse_2dim_input = ad.embedding_lookup_op(Embedding2, sparse_input, ctx=ndarray.cpu(0)) sparse_2dim_sum = ad.reduce_sum_op(sparse_2dim_input, axes=1) sparse_2dim_sum_square = ad.mul_op(sparse_2dim_sum, sparse_2dim_sum) sparse_2dim_square = ad.mul_op(sparse_2dim_input, sparse_2dim_input) sparse_2dim_square_sum = ad.reduce_sum_op(sparse_2dim_square, axes=1) sparse_2dim = sparse_2dim_sum_square + -1 * sparse_2dim_square_sum sparse_2dim_half = sparse_2dim * 0.5 """snd order output""" y2 = ad.reduce_sum_op(sparse_2dim_half, axes=1, keepdims=True) #DNN flatten = ad.array_reshape_op(sparse_2dim_input, (-1, 26 * embedding_size)) W1 = init.random_normal([26 * embedding_size, 256], stddev=0.01, name="W1") W2 = init.random_normal([256, 256], stddev=0.01, name="W2") W3 = init.random_normal([256, 1], stddev=0.01, name="W3") fc1 = ad.matmul_op(flatten, W1) relu1 = ad.relu_op(fc1) fc2 = ad.matmul_op(relu1, W2) relu2 = ad.relu_op(fc2) y3 = ad.matmul_op(relu2, W3) y4 = y1 + y2 y = y4 + y3 y = ad.sigmoid_op(y) loss = ad.binarycrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) opt = optimizer.SGDOptimizer(learning_rate=learning_rate) train_op = opt.minimize(loss) return loss, y, y_, train_op
def rnn(x, y_): ''' RNN model, for MNIST dataset. Parameters: x: Variable(hetu.gpu_ops.Node.Node), shape (N, dims) y_: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) Return: loss: Variable(hetu.gpu_ops.Node.Node), shape (1,) y: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) ''' print("Building RNN model...") diminput = 28 dimhidden = 128 dimoutput = 10 nsteps = 28 weight1 = init.random_normal(shape=(diminput, dimhidden), stddev=0.1, name='rnn_weight1') bias1 = init.random_normal(shape=(dimhidden, ), stddev=0.1, name='rnn_bias1') weight2 = init.random_normal(shape=(dimhidden + dimhidden, dimhidden), stddev=0.1, name='rnn_weight2') bias2 = init.random_normal(shape=(dimhidden, ), stddev=0.1, name='rnn_bias2') weight3 = init.random_normal(shape=(dimhidden, dimoutput), stddev=0.1, name='rnn_weight3') bias3 = init.random_normal(shape=(dimoutput, ), stddev=0.1, name='rnn_bias3') last_state = ad.Variable(value=np.zeros((1, )).astype(np.float32), name='initial_state', trainable=False) for i in range(nsteps): cur_x = ad.slice_op(x, (0, i * diminput), (-1, diminput)) h = ad.matmul_op(cur_x, weight1) h = h + ad.broadcastto_op(bias1, h) if i == 0: last_state = ad.broadcastto_op(last_state, h) s = ad.concat_op(h, last_state, axis=1) s = ad.matmul_op(s, weight2) s = s + ad.broadcastto_op(bias2, s) last_state = ad.relu_op(s) final_state = last_state x = ad.matmul_op(final_state, weight3) y = x + ad.broadcastto_op(bias3, x) loss = ad.softmaxcrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) return loss, y
def neural_mf(user_input, item_input, y_, num_users, num_items): batch_size = 256 embed_dim = 8 layers = [64, 32, 16, 8] learning_rate = 0.01 User_Embedding = init.random_normal( (num_users, embed_dim + layers[0] // 2), stddev=0.01, name="user_embed", ctx=ndarray.cpu(0)) Item_Embedding = init.random_normal( (num_items, embed_dim + layers[0] // 2), stddev=0.01, name="item_embed", ctx=ndarray.cpu(0)) # MLP_User_Embedding = init.random_normal((num_users, layers[0] // 2), stddev=0.01, name="mlp_user_embed", ctx=ndarray.cpu(0)) # MLP_Item_Embedding = init.random_normal((num_items, layers[0] // 2), stddev=0.01, name="mlp_item_embed", ctx=ndarray.cpu(0)) user_latent = ad.embedding_lookup_op(User_Embedding, user_input, ctx=ndarray.cpu(0)) item_latent = ad.embedding_lookup_op(Item_Embedding, item_input, ctx=ndarray.cpu(0)) mf_user_latent = ad.slice_op(user_latent, (0, 0), (-1, embed_dim)) mlp_user_latent = ad.slice_op(user_latent, (0, embed_dim), (-1, -1)) mf_item_latent = ad.slice_op(item_latent, (0, 0), (-1, embed_dim)) mlp_item_latent = ad.slice_op(item_latent, (0, embed_dim), (-1, -1)) # mf_user_latent = ad.embedding_lookup_op(MF_User_Embedding, user_input, ctx=ndarray.cpu(0)) # mf_item_latent = ad.embedding_lookup_op(MF_Item_Embedding, item_input, ctx=ndarray.cpu(0)) # mlp_user_latent = ad.embedding_lookup_op(MLP_User_Embedding, user_input, ctx=ndarray.cpu(0)) # mlp_item_latent = ad.embedding_lookup_op(MLP_Item_Embedding, item_input, ctx=ndarray.cpu(0)) W1 = init.random_normal((layers[0], layers[1]), stddev=0.1, name='W1') W2 = init.random_normal((layers[1], layers[2]), stddev=0.1, name='W2') W3 = init.random_normal((layers[2], layers[3]), stddev=0.1, name='W3') W4 = init.random_normal((embed_dim + layers[3], 1), stddev=0.1, name='W4') mf_vector = ad.mul_op(mf_user_latent, mf_item_latent) mlp_vector = ad.concat_op(mlp_user_latent, mlp_item_latent, axis=1) fc1 = ad.matmul_op(mlp_vector, W1) relu1 = ad.relu_op(fc1) fc2 = ad.matmul_op(relu1, W2) relu2 = ad.relu_op(fc2) fc3 = ad.matmul_op(relu2, W3) relu3 = ad.relu_op(fc3) concat_vector = ad.concat_op(mf_vector, relu3, axis=1) y = ad.matmul_op(concat_vector, W4) y = ad.sigmoid_op(y) loss = ad.binarycrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) opt = optimizer.SGDOptimizer(learning_rate=learning_rate) # opt = optimizer.AdamOptimizer(learning_rate=learning_rate) train_op = opt.minimize(loss) return loss, y, train_op
def train_hetu(args): with open(os.path.join(args.path, "meta.yml"), 'rb') as f: meta = yaml.load(f.read(), Loader=yaml.FullLoader) hidden_layer_size = args.hidden_size num_epoch = args.num_epoch rank = int(os.environ["WORKER_ID"]) nrank = int(os.environ["DMLC_NUM_WORKER"]) ctx = ndarray.gpu(rank) x_ = ad.Variable(name="x_") y_ = ad.Variable(name="y_") mask_ = ad.Variable(name="mask_") gcn1 = GraphSage(meta["feature"], hidden_layer_size, activation="relu", dropout=0.1) gcn2 = GraphSage(2*hidden_layer_size, hidden_layer_size, activation="relu", dropout=0.1) x = gcn1(x_) x = gcn2(x) W = initializers.xavier_uniform(shape=(2*hidden_layer_size, meta["class"])) B = initializers.zeros(shape=(meta["class"],)) x = ad.matmul_op(x, W) y = x + ad.broadcastto_op(B, x) loss = ad.softmaxcrossentropy_op(y, y_) loss = ad.mul_op(loss, mask_) loss = ad.reduce_mean_op(loss, [0]) opt = optimizer.SGDOptimizer(0.1) train_op = opt.minimize(loss) executor = ad.Executor([loss, y, train_op], ctx=ctx, comm_mode='PS') distributed.ps_init(rank, nrank) batch_size = 4000 with DistributedGraphSageSampler(args.path, batch_size, 2, 2, rank=rank, nrank=nrank) as sampler: epoch = 0 nnodes = 0 start = time.time() while True: g_sample, mask = sampler.sample() mp_val = mp_matrix(g_sample, ndarray.gpu(rank)) feed_dict = { gcn1.mp : mp_val, gcn2.mp : mp_val, mask_ : ndarray.array(mask, ctx=ctx), x_ : ndarray.array(g_sample.x, ctx=ctx), y_ : ndarray.array(convert_to_one_hot(g_sample.y, max_val=g_sample.num_classes), ctx=ctx) } loss_val, y_predicted, _ = executor.run(feed_dict = feed_dict) y_predicted = y_predicted.asnumpy().argmax(axis=1) acc = ((y_predicted == g_sample.y) * mask).sum() distributed.ps_get_worker_communicator().BarrierWorker() nnodes += batch_size if nnodes > meta["partition"]["nodes"][rank]: nnodes = 0 epoch += 1 print("Epoch :", epoch, time.time() - start) print("Train accuracy:", acc/mask.sum()) start = time.time() if epoch >= num_epoch: break
def test_ReduceMean(): X = ad.Variable(name="X") y = ad.reduce_mean_op(X, 1, keepdims=True) executor = ad.Executor([y], ctx=ctx) X_val = rand.normal(scale=0.1, size=(2, 2)).astype(np.float32) res = executor.run(feed_dict={X: X_val}) Check(executor, res, [X], [y], [X_val]) print(sys._getframe().f_code.co_name, 'pass!')
def wdl_criteo(dense, sparse, labels): batch_size = 128 feature_dimension = 33762577 embedding_size = 128 learning_rate = 0.01 if isinstance(dense, tuple): dense_input = dl.dataloader_op([[dense[0], batch_size, 'train'], [dense[1], batch_size, 'validate']]) sparse_input = dl.dataloader_op([[sparse[0], batch_size, 'train'], [sparse[1], batch_size, 'validate']]) y_ = dl.dataloader_op([[labels[0], batch_size, 'train'], [labels[1], batch_size, 'validate']]) else: dense_input = dl.dataloader_op([[dense, batch_size, 'train']]) sparse_input = dl.dataloader_op([[sparse, batch_size, 'train']]) y_ = dl.dataloader_op([[labels, batch_size, 'train']]) print("Data loaded.") Embedding = init.random_normal([feature_dimension, embedding_size], stddev=0.01, name="snd_order_embedding", ctx=ndarray.cpu(0)) sparse_input = ad.embedding_lookup_op(Embedding, sparse_input, ctx=ndarray.cpu(0)) sparse_input = ad.array_reshape_op(sparse_input, (-1, 26 * embedding_size)) #DNN flatten = dense_input W1 = init.random_normal([13, 256], stddev=0.01, name="W1") W2 = init.random_normal([256, 256], stddev=0.01, name="W2") W3 = init.random_normal([256, 256], stddev=0.01, name="W3") W4 = init.random_normal([256 + 26 * embedding_size, 1], stddev=0.01, name="W4") fc1 = ad.matmul_op(flatten, W1) relu1 = ad.relu_op(fc1) fc2 = ad.matmul_op(relu1, W2) relu2 = ad.relu_op(fc2) y3 = ad.matmul_op(relu2, W3) y4 = ad.concat_op(sparse_input, y3, axis=1) y = ad.matmul_op(y4, W4) y = ad.sigmoid_op(y) loss = ad.binarycrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) opt = optimizer.SGDOptimizer(learning_rate=learning_rate) train_op = opt.minimize(loss) return loss, y, y_, train_op
def wdl_adult(X_deep, X_wide, y_): lr = 5 / 128 dim_wide = 809 dim_deep = 68 W = init.random_normal([dim_wide+20, 2], stddev=0.1, name="W") W1 = init.random_normal([dim_deep, 50], stddev=0.1, name="W1") b1 = init.random_normal([50], stddev=0.1, name="b1") W2 = init.random_normal([50, 20], stddev=0.1, name="W2") b2 = init.random_normal([20], stddev=0.1, name="b2") #deep Embedding = [] X_deep_input = None for i in range(8): Embedding_name = "Embedding_deep_" + str(i) Embedding.append(init.random_normal([50, 8], stddev=0.1, name=Embedding_name)) now = ad.embedding_lookup_op(Embedding[i], X_deep[i]) now = ad.array_reshape_op(now, (-1, 8)) if X_deep_input is None: X_deep_input = now else: X_deep_input = ad.concat_op(X_deep_input, now, 1) for i in range(4): now = ad.array_reshape_op(X_deep[i + 8], (-1, 1)) X_deep_input = ad.concat_op(X_deep_input, now, 1) mat1 = ad.matmul_op(X_deep_input, W1) add1 = mat1 + ad.broadcastto_op(b1, mat1) relu1= ad.relu_op(add1) dropout1 = relu1 #ad.dropout_op(relu1, 0.5) mat2 = ad.matmul_op(dropout1, W2) add2 = mat2 + ad.broadcastto_op(b2, mat2) relu2= ad.relu_op(add2) dropout2 = relu2 #ad.dropout_op(relu2, 0.5) dmodel=dropout2 # wide wmodel = ad.concat_op(X_wide, dmodel, 1) wmodel = ad.matmul_op(wmodel, W) prediction = wmodel loss = ad.softmaxcrossentropy_op(prediction, y_) loss = ad.reduce_mean_op(loss, [0]) opt = optimizer.SGDOptimizer(learning_rate=lr) train_op = opt.minimize(loss) return loss, prediction, y_, train_op
def alexnet(x, y_): ''' AlexNet model, for MNIST dataset. Parameters: x: Variable(hetu.gpu_ops.Node.Node), shape (N, dims) y_: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) Return: loss: Variable(hetu.gpu_ops.Node.Node), shape (1,) y: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) ''' print('Building AlexNet model...') x = ad.array_reshape_op(x, [-1, 1, 28, 28]) x = conv_bn_relu_pool(x, 1, 32, 'alexnet_conv1', with_relu=True, with_pool=True) x = conv_bn_relu_pool(x, 32, 64, 'alexnet_conv2', with_relu=True, with_pool=True) x = conv_bn_relu_pool(x, 64, 128, 'alexnet_conv3', with_relu=True, with_pool=False) x = conv_bn_relu_pool(x, 128, 256, 'alexnet_conv4', with_relu=True, with_pool=False) x = conv_bn_relu_pool(x, 256, 256, 'alexnet_conv5', with_relu=False, with_pool=True) x = ad.array_reshape_op(x, (-1, 256 * 3 * 3)) x = fc(x, (256 * 3 * 3, 1024), name='alexnet_fc1', with_relu=True) x = fc(x, (1024, 512), name='alexnet_fc2', with_relu=True) y = fc(x, (512, 10), name='alexnet_fc3', with_relu=False) loss = ad.softmaxcrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) return loss, y
def train_hetu(args): with open(os.path.join(args.path, "meta.yml"), 'rb') as f: meta = yaml.load(f.read(), Loader=yaml.FullLoader) hidden_layer_size = args.hidden_size num_epoch = args.num_epoch rank = int(os.environ["WORKER_ID"]) nrank = int(os.environ["DMLC_NUM_WORKER"]) hosts, ports = load_ip_config(args.ip_config) ctx = ndarray.gpu(rank) distributed.grpc_init(hosts=hosts, ports=ports, rank=rank, nrank=nrank) x_ = ad.Variable(name="x_") y_ = ad.Variable(name="y_") gcn1 = GCN(meta["feature"], hidden_layer_size, activation="relu") gcn2 = GCN(hidden_layer_size, meta["class"]) x = gcn1(x_) y = gcn2(x) loss = ad.softmaxcrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) opt = optimizer.SGDOptimizer(0.1) train_op = opt.minimize(loss) executor = ad.Executor([loss, y, train_op], ctx=ctx, comm_mode='PS') def transform(graph): mp_val = mp_matrix(graph, ndarray.gpu(rank)) return graph, mp_val with DistributedSubgraphSampler(args.path, 4000, 2, rank=rank, nrank=nrank ,transformer=transform, backend="grpc") as sampler: epoch = 0 nnodes = 0 start = time.time() while True: g_sample, mp_val = sampler.sample() feed_dict = { gcn1.mp : mp_val, gcn2.mp : mp_val, x_ : ndarray.array(g_sample.x, ctx=ctx), y_ : ndarray.array(convert_to_one_hot(g_sample.y, max_val=g_sample.num_classes), ctx=ctx) } loss_val, y_predicted, _ = executor.run(feed_dict = feed_dict) y_predicted = y_predicted.asnumpy().argmax(axis=1) acc = (y_predicted == g_sample.y).sum() distributed.ps_get_worker_communicator().BarrierWorker() nnodes += g_sample.num_nodes if nnodes > meta["partition"]["nodes"][rank]: nnodes = 0 epoch += 1 print("Epoch :", epoch, time.time() - start) print("Train accuracy:", acc/len(y_predicted)) start = time.time() if epoch >= num_epoch: break
def dc_criteo(dense, sparse, labels): batch_size = 128 feature_dimension = 33762577 embedding_size = 8 learning_rate = 0.001 if isinstance(dense, tuple): dense_input = dl.dataloader_op([[dense[0], batch_size, 'train'], [dense[1], batch_size, 'validate']]) sparse_input = dl.dataloader_op([[sparse[0], batch_size, 'train'], [sparse[1], batch_size, 'validate']]) y_ = dl.dataloader_op([[labels[0], batch_size, 'train'], [labels[1], batch_size, 'validate']]) else: dense_input = dl.dataloader_op([[dense, batch_size, 'train']]) sparse_input = dl.dataloader_op([[sparse, batch_size, 'train']]) y_ = dl.dataloader_op([[labels, batch_size, 'train']]) print("Data loaded.") Embedding = init.random_normal([feature_dimension, embedding_size], stddev=0.01, name="snd_order_embedding") sparse_input = ad.embedding_lookup_op(Embedding, sparse_input) sparse_input = ad.array_reshape_op(sparse_input, (-1, 26 * embedding_size)) ## dc_model x = ad.concat_op(sparse_input, dense_input, axis=1) input_dim = 26 * 8 + 13 hidden_dim = input_dim residual_out = build_residual_layers(x, input_dim, hidden_dim, num_layers=5) W4 = init.random_normal([26 * embedding_size + 13, 1], stddev=0.1, name="W4") y = ad.matmul_op(residual_out, W4) y = ad.sigmoid_op(y) loss = ad.binarycrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) opt = optimizer.SGDOptimizer(learning_rate=learning_rate) train_op = opt.minimize(loss) return loss, y, y_, train_op
def dcn_criteo(dense_input, sparse_input, y_): feature_dimension = 33762577 embedding_size = 128 learning_rate = 0.003 Embedding = init.random_normal([feature_dimension, embedding_size], stddev=0.01, name="snd_order_embedding", ctx=ndarray.cpu(0)) sparse_input = ad.embedding_lookup_op(Embedding, sparse_input, ctx=ndarray.cpu(0)) sparse_input = ad.array_reshape_op(sparse_input, (-1, 26 * embedding_size)) x = ad.concat_op(sparse_input, dense_input, axis=1) # Cross Network cross_output = build_cross_layer(x, num_layers=3) #DNN flatten = x W1 = init.random_normal([26 * embedding_size + 13, 256], stddev=0.01, name="W1") W2 = init.random_normal([256, 256], stddev=0.01, name="W2") W3 = init.random_normal([256, 256], stddev=0.01, name="W3") W4 = init.random_normal([256 + 26 * embedding_size + 13, 1], stddev=0.01, name="W4") fc1 = ad.matmul_op(flatten, W1) relu1 = ad.relu_op(fc1) fc2 = ad.matmul_op(relu1, W2) relu2 = ad.relu_op(fc2) y3 = ad.matmul_op(relu2, W3) y4 = ad.concat_op(cross_output, y3, axis=1) y = ad.matmul_op(y4, W4) y = ad.sigmoid_op(y) loss = ad.binarycrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) opt = optimizer.SGDOptimizer(learning_rate=learning_rate) train_op = opt.minimize(loss) return loss, y, y_, train_op
def mlp(x, y_): ''' MLP model, for MNIST dataset. Parameters: x: Variable(hetu.gpu_ops.Node.Node), shape (N, dims) y_: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) Return: loss: Variable(hetu.gpu_ops.Node.Node), shape (1,) y: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) ''' print("Building MLP model...") x = fc(x, (784, 256), 'mlp_fc1', with_relu=True) x = fc(x, (256, 256), 'mlp_fc2', with_relu=True) y = fc(x, (256, 10), 'mlp_fc3', with_relu=False) loss = ad.softmaxcrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) return loss, y
def cnn_3_layers(x, y_): ''' 3-layer-CNN model, for MNIST dataset. Parameters: x: Variable(hetu.gpu_ops.Node.Node), shape (N, dims) y_: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) Return: loss: Variable(hetu.gpu_ops.Node.Node), shape (1,) y: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) ''' print('Building 3-layer-CNN model...') x = ad.array_reshape_op(x, [-1, 1, 28, 28]) x = conv_relu_avg(x, (32, 1, 5, 5)) x = conv_relu_avg(x, (64, 32, 5, 5)) y = fc(x, (7 * 7 * 64, 10)) loss = ad.softmaxcrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) return loss, y
def test_reduce_mean(shape=(2, 3, 4), axes=[2]): ctx = ndarray.gpu(1) x = np.random.random(shape).astype(np.float32) ath_x = ad.Variable(name='x', value=x) ath_y = ad.reduce_mean_op(ath_x, axes, keepdims=False) ath_grad = ad.gradients(ath_y, [ath_x])[0] executor = ad.Executor([ath_y, ath_grad], ctx=ctx) ath_results = [var.asnumpy() for var in executor.run()] import tensorflow as tf tf_x = tf.convert_to_tensor(x) tf_y = tf.reduce_mean(tf_x, axes) tf_grad = tf.gradients(tf_y, tf_x) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) tf_results = sess.run([tf_y, tf_grad]) np.testing.assert_allclose(ath_results[0], np.reshape(tf_results[0], ath_results[0].shape), rtol=1e-6) np.testing.assert_allclose(ath_results[1], np.reshape(tf_results[1], ath_results[1].shape), rtol=1e-6) print('Passed reduce mean op test with shape and axes ', shape, axes)
def logreg(x, y_): ''' Logistic Regression model, for MNIST dataset. Parameters: x: Variable(hetu.gpu_ops.Node.Node), shape (N, dims) y_: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) Return: loss: Variable(hetu.gpu_ops.Node.Node), shape (1,) y: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) ''' print("Build logistic regression model...") weight = init.zeros((784, 10), name='logreg_weight') bias = init.zeros((10, ), name='logreg_bias') x = ad.matmul_op(x, weight) y = x + ad.broadcastto_op(bias, x) loss = ad.softmaxcrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) return loss, y
def vgg(x, y_, num_layers): ''' VGG model, for CIFAR10 dataset. Parameters: x: Variable(hetu.gpu_ops.Node.Node), shape (N, C, H, W) y_: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) num_layers: 16 or 19 Return: loss: Variable(hetu.gpu_ops.Node.Node), shape (1,) y: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) ''' if num_layers == 16: print('Building VGG-16 model...') x = vgg_2block(x, 3, 64, 'vgg_block1') x = vgg_2block(x, 64, 128, 'vgg_block2') x = vgg_3block(x, 128, 256, 'vgg_block3') x = vgg_3block(x, 256, 512, 'vgg_block4') x = vgg_3block(x, 512, 512, 'vgg_block5') elif num_layers == 19: print('Building VGG-19 model...') x = vgg_2block(x, 3, 64, 'vgg_block1') x = vgg_2block(x, 64, 128, 'vgg_block2') x = vgg_4block(x, 128, 256, 'vgg_block3') x = vgg_4block(x, 256, 512, 'vgg_block4') x = vgg_4block(x, 512, 512, 'vgg_block5') else: assert False, 'VGG model should have 16 or 19 layers!' x = ad.array_reshape_op(x, (-1, 512)) x = vgg_fc(x, 512, 4096, 'vgg_fc1') x = vgg_fc(x, 4096, 4096, 'vgg_fc2') y = vgg_fc(x, 4096, 10, 'vgg_fc3') loss = ad.softmaxcrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) return loss, y
def lenet(x, y_): ''' LeNet model, for MNIST dataset. Parameters: x: Variable(hetu.gpu_ops.Node.Node), shape (N, dims) y_: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) Return: loss: Variable(hetu.gpu_ops.Node.Node), shape (1,) y: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) ''' print('Building LeNet model...') x = ad.array_reshape_op(x, (-1, 1, 28, 28)) x = conv_pool(x, 1, 6, name='lenet_conv1') x = conv_pool(x, 6, 16, name='lenet_conv2') x = ad.array_reshape_op(x, (-1, 7 * 7 * 16)) x = fc(x, (7 * 7 * 16, 120), name='lenet_fc1', with_relu=True) x = fc(x, (120, 84), name='lenet_fc2', with_relu=True) y = fc(x, (84, 10), name='lenet_fc3', with_relu=False) loss = ad.softmaxcrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) return loss, y
def test_csrmm_op(executor_ctx): X = ad.Variable(name="X") W = ad.Variable(name="W") Y = ad.csrmm_op(X, W) Y_ = ad.Variable(name="Y_") loss = ad.softmaxcrossentropy_op(Y, Y_) loss = ad.reduce_mean_op(loss, [0]) grads = ad.gradients(loss, [W, Y]) executor = ad.Executor( [loss, grads[0], grads[1]], ctx=executor_ctx) rand = np.random.RandomState(seed=123) W_val = rand.normal(scale=0.1, size=[70000, 2]).astype(np.float32) if ndarray.is_gpu_ctx(executor_ctx): W_val = ndarray.array(W_val, ctx=executor_ctx) X_val = scipy.sparse.rand(500, 70000, density=1e-5,format='coo',dtype=np.float32) Y_val = np.random.uniform(0, 10, size=(500, 2)).astype(np.float32) loss_val = executor.run(feed_dict={X: X_val, Y_: Y_val, W: W_val}) if ndarray.is_gpu_ctx(executor_ctx): W_val = W_val.asnumpy() loss_val = [val.asnumpy() for val in loss_val] y_groundtruth = X_val.dot(W_val) loss_groundtruth = np.mean( -np.sum(Y_val * np.log(softmax_func(y_groundtruth)), axis=1), keepdims=True) Y_grad_groundtruth = (softmax_func(y_groundtruth) + -1 * Y_val) * np.ones(loss_groundtruth.shape) / 500 W_grad_groundtruth = X_val.T.dot(Y_grad_groundtruth) np.testing.assert_allclose(loss_val[0], loss_groundtruth, rtol=1e-4) np.testing.assert_allclose(loss_val[1], W_grad_groundtruth, rtol=1e-4) np.testing.assert_allclose(loss_val[2], Y_grad_groundtruth, rtol=1e-4)
def resnet(x, y_, num_layers=18): ''' ResNet model, for CIFAR10 dataset. Parameters: x: Variable(hetu.gpu_ops.Node.Node), shape (N, C, H, W) y_: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) num_layers: 18 or 34 Return: loss: Variable(hetu.gpu_ops.Node.Node), shape (1,) y: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) ''' base_size = 16 x = conv2d(x, 3, base_size, stride=1, padding=1, name='resnet_initial_conv') x = batch_norm_with_relu(x, base_size, 'resnet_initial_bn') if num_layers == 18: print("Building ResNet-18 model...") x = resnet_block(x, base_size, num_blocks=2, is_first=True, name='resnet_block1') x = resnet_block(x, base_size, num_blocks=2, is_first=False, name='resnet_block2') x = resnet_block(x, 2 * base_size, num_blocks=2, is_first=False, name='resnet_block3') x = resnet_block(x, 4 * base_size, num_blocks=2, is_first=False, name='resnet_block4') elif num_layers == 34: print("Building ResNet-34 model...") x = resnet_block(x, base_size, num_blocks=3, is_first=True, name='resnet_block1') x = resnet_block(x, base_size, num_blocks=4, is_first=False, name='resnet_block2') x = resnet_block(x, 2 * base_size, num_blocks=6, is_first=False, name='resnet_block3') x = resnet_block(x, 4 * base_size, num_blocks=3, is_first=False, name='resnet_block4') else: assert False, "Number of layers should be 18 or 34 !" x = batch_norm_with_relu(x, 8 * base_size, 'resnet_final_bn') x = ad.array_reshape_op(x, (-1, 128 * base_size)) y = fc(x, (128 * base_size, 10), name='resnet_final_fc') # here we don't use cudnn for softmax crossentropy to avoid overflows loss = ad.softmaxcrossentropy_op(y, y_, use_cudnn=False) loss = ad.reduce_mean_op(loss, [0]) return loss, y
def wdl_adult(whatever): batch_size = 128 lr=5 dim_wide = 809 lr_ = lr / batch_size dim_deep = 68 from .load_data import load_adult_data x_train_deep, x_train_wide, y_train, x_test_deep, x_test_wide, y_test = load_adult_data() W = init.random_normal([dim_wide+20, 2], stddev=0.1, name="W") W1 = init.random_normal([dim_deep, 50], stddev=0.1, name="W1") b1 = init.random_normal([50], stddev=0.1, name="b1") W2 = init.random_normal([50, 20], stddev=0.1, name="W2") b2 = init.random_normal([20], stddev=0.1, name="b2") X_wide = dl.dataloader_op([ [x_train_wide, batch_size, 'train'], [x_test_wide, batch_size, 'validate'], ]) y_ = dl.dataloader_op([ [y_train, batch_size, 'train'], [y_test, batch_size, 'validate'], ]) #deep Embedding = [] X_deep = [] X_deep_input = None for i in range(8): X_deep_name = "x_deep_" + str(i) Embedding_name = "Embedding_deep_" + str(i) X_deep.append(dl.dataloader_op([ [x_train_deep[:,i], batch_size, 'train'], [x_test_deep[:,i], batch_size, 'validate'], ])) Embedding.append(init.random_normal([50, 8], stddev=0.1, name=Embedding_name)) now = ad.embedding_lookup_op(Embedding[i], X_deep[i]) now = ad.array_reshape_op(now, (-1, 8)) if X_deep_input is None: X_deep_input = now else: X_deep_input = ad.concat_op(X_deep_input, now, 1) for i in range(4): X_deep_name = "x_deep_" + str(8+i) X_deep.append(dl.dataloader_op([ [x_train_deep[:,8+i], batch_size, 'train'], [x_test_deep[:,8+i], batch_size, 'validate'], ])) now = ad.array_reshape_op(X_deep[i + 8], (batch_size, 1)) X_deep_input = ad.concat_op(X_deep_input, now, 1) mat1 = ad.matmul_op(X_deep_input, W1) add1 = mat1 + ad.broadcastto_op(b1, mat1) relu1= ad.relu_op(add1) dropout1 = relu1 #ad.dropout_op(relu1, 0.5) mat2 = ad.matmul_op(dropout1, W2) add2 = mat2 + ad.broadcastto_op(b2, mat2) relu2= ad.relu_op(add2) dropout2 = relu2 #ad.dropout_op(relu2, 0.5) dmodel=dropout2 # wide wmodel = ad.concat_op(X_wide, dmodel, 1) wmodel = ad.matmul_op(wmodel, W) prediction = wmodel loss = ad.softmaxcrossentropy_op(prediction, y_) loss = ad.reduce_mean_op(loss, [0]) opt = optimizer.SGDOptimizer(learning_rate=lr_) train_op = opt.minimize(loss) return loss, prediction, y_, train_op
def train_main(args): with open(os.path.join(args.path, "meta.yml"), 'rb') as f: meta = yaml.load(f.read(), Loader=yaml.FullLoader) hidden_layer_size = args.hidden_size num_epoch = args.num_epoch rank = ad.get_worker_communicate().rank() nrank = int(os.environ["DMLC_NUM_WORKER"]) ctx = ndarray.gpu(rank % args.num_local_worker) embedding_width = args.hidden_size extract_width = embedding_width * (meta["feature"] - 1) y_ = dl.GNNDataLoaderOp(lambda g: ndarray.array( convert_to_one_hot(g.y, max_val=g.num_classes), ctx=ndarray.cpu())) mask_ = ad.Variable(name="mask_") gcn1 = GCN(extract_width, hidden_layer_size, activation="relu") gcn2 = GCN(hidden_layer_size, meta["class"]) index = dl.GNNDataLoaderOp( lambda g: ndarray.array(g.x[:, 0:-1], ctx=ndarray.cpu()), ctx=ndarray.cpu()) embedding = initializers.random_normal([meta["idx_max"], embedding_width], stddev=0.1) embed = ad.embedding_lookup_op(embedding, index) embed = ad.array_reshape_op(embed, (-1, extract_width)) # embed = ad.reduce_mean_op(embed, axes=1) # x = ad.concat_op(x_, embed, axis=1) x = gcn1(embed) y = gcn2(x) loss = ad.softmaxcrossentropy_op(y, y_) train_loss = loss * mask_ train_loss = ad.reduce_mean_op(train_loss, [0]) opt = optimizer.SGDOptimizer(args.learning_rate) train_op = opt.minimize(train_loss) ad.worker_init() distributed.ps_init(rank, nrank) ngraph = meta["partition"]["nodes"][rank] // args.batch_size graphs = prepare_data(ngraph) idx = 0 g_sample, mp_val, mask, mask_eval = graphs[idx] idx = (idx + 1) % ngraph dl.GNNDataLoaderOp.step(g_sample) dl.GNNDataLoaderOp.step(g_sample) epoch = 0 nnodes = 0 executor = ad.Executor([loss, y, train_op], ctx=ctx, comm_mode='PS', use_sparse_pull=False, cstable_policy=args.cache) while True: g_sample_nxt, mp_val_nxt, mask_nxt, mask_eval_nxt = graphs[idx] idx = (idx + 1) % ngraph dl.GNNDataLoaderOp.step(g_sample_nxt) feed_dict = {gcn1.mp: mp_val, gcn2.mp: mp_val, mask_: mask} loss_val, y_predicted, _ = executor.run(feed_dict=feed_dict) y_predicted = y_predicted.asnumpy().argmax(axis=1) acc = np.sum((y_predicted == g_sample.y) * mask_eval) train_acc = np.sum((y_predicted == g_sample.y) * mask) stat.update(acc, mask_eval.sum(), np.sum(loss_val.asnumpy() * mask_eval) / mask_eval.sum()) stat.update_train(train_acc, mask.sum(), np.sum(loss_val.asnumpy() * mask) / mask.sum()) # distributed.ps_get_worker_communicator().BarrierWorker() nnodes += mask.sum() + mask_eval.sum() if nnodes > meta["partition"]["nodes"][rank]: nnodes = 0 epoch += 1 if rank == 0: stat.print(epoch) if epoch >= num_epoch: break g_sample, mp_val, mask, mask_eval = g_sample_nxt, mp_val_nxt, mask_nxt, mask_eval_nxt
def lstm(x, y_): ''' LSTM model, for MNIST dataset. Parameters: x: Variable(hetu.gpu_ops.Node.Node), shape (N, dims) y_: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) Return: loss: Variable(hetu.gpu_ops.Node.Node), shape (1,) y: Variable(hetu.gpu_ops.Node.Node), shape (N, num_classes) ''' print("Building LSTM model...") diminput = 28 dimhidden = 128 dimoutput = 10 nsteps = 28 forget_gate_w = init.random_normal(shape=(diminput, dimhidden), stddev=0.1, name="lstm_forget_gate_w") forget_gate_u = init.random_normal(shape=(dimhidden, dimhidden), stddev=0.1, name="lstm_forget_gate_u") forget_gate_b = init.random_normal(shape=(dimhidden, ), stddev=0.1, name="lstm_forget_gate_b") input_gate_w = init.random_normal(shape=(diminput, dimhidden), stddev=0.1, name="lstm_input_gate_w") input_gate_u = init.random_normal(shape=(dimhidden, dimhidden), stddev=0.1, name="lstm_input_gate_u") input_gate_b = init.random_normal(shape=(dimhidden, ), stddev=0.1, name="lstm_input_gate_b") output_gate_w = init.random_normal(shape=(diminput, dimhidden), stddev=0.1, name="lstm_output_gate_w") output_gate_u = init.random_normal(shape=(dimhidden, dimhidden), stddev=0.1, name="lstm_output_gate_u") output_gate_b = init.random_normal(shape=(dimhidden, ), stddev=0.1, name="lstm_output_gate_b") tanh_w = init.random_normal(shape=(diminput, dimhidden), stddev=0.1, name="lstm_tanh_w") tanh_u = init.random_normal(shape=(dimhidden, dimhidden), stddev=0.1, name="lstm_tanh_u") tanh_b = init.random_normal(shape=(dimhidden, ), stddev=0.1, name="lstm_tanh_b") out_weights = init.random_normal(shape=(dimhidden, dimoutput), stddev=0.1, name="lstm_out_weight") out_bias = init.random_normal(shape=(dimoutput, ), stddev=0.1, name="lstm_out_bias") initial_state = ad.Variable(value=np.zeros((1, )).astype(np.float32), name='initial_state', trainable=False) for i in range(nsteps): cur_x = ad.slice_op(x, (0, i * diminput), (-1, diminput)) # forget gate if i == 0: temp = ad.matmul_op(cur_x, forget_gate_w) last_c_state = ad.broadcastto_op(initial_state, temp) last_h_state = ad.broadcastto_op(initial_state, temp) cur_forget = ad.matmul_op(last_h_state, forget_gate_u) + temp else: cur_forget = ad.matmul_op(last_h_state, forget_gate_u) + ad.matmul_op( cur_x, forget_gate_w) cur_forget = cur_forget + ad.broadcastto_op(forget_gate_b, cur_forget) cur_forget = ad.sigmoid_op(cur_forget) # input gate cur_input = ad.matmul_op(last_h_state, input_gate_u) + ad.matmul_op( cur_x, input_gate_w) cur_input = cur_input + ad.broadcastto_op(input_gate_b, cur_input) cur_input = ad.sigmoid_op(cur_input) # output gate cur_output = ad.matmul_op(last_h_state, output_gate_u) + ad.matmul_op( cur_x, output_gate_w) cur_output = cur_output + ad.broadcastto_op(output_gate_b, cur_output) cur_output = ad.sigmoid_op(cur_output) # tanh cur_tanh = ad.matmul_op(last_h_state, tanh_u) + ad.matmul_op( cur_x, tanh_w) cur_tanh = cur_tanh + ad.broadcastto_op(tanh_b, cur_tanh) cur_tanh = ad.tanh_op(cur_tanh) last_c_state = ad.mul_op(last_c_state, cur_forget) + ad.mul_op( cur_input, cur_tanh) last_h_state = ad.tanh_op(last_c_state) * cur_output x = ad.matmul_op(last_h_state, out_weights) y = x + ad.broadcastto_op(out_bias, x) loss = ad.softmaxcrossentropy_op(y, y_) loss = ad.reduce_mean_op(loss, [0]) return loss, y