diff --git a/PaddleRec/din/README.md b/PaddleRec/din/README.md index 75f21e5d44451ff04d90ed6ceb2f8b01513c5297..3538ba760ff9b80807a6a56aed4b75400c97ae03 100644 --- a/PaddleRec/din/README.md +++ b/PaddleRec/din/README.md @@ -29,7 +29,6 @@ DIN通过一个兴趣激活模块(Activation Unit)，用预估目标Candidate AD 最后我们将这相关的用户兴趣表达、用户静态特征和上下文相关特征，以及ad相关的特征拼接起来，输入到后续的多层DNN网络，最后预测得到用户对当前目标ADs的点击概率。 -**目前模型库下模型均要求使用PaddlePaddle 1.6及以上版本或适当的develop版本。** ## 数据下载及预处理 diff --git a/PaddleRec/din/cluster_train.py b/PaddleRec/din/cluster_train.py index 1683a78ca56bbe551eef9126f8eb67991852b893..6b3272366fa674c2bfaa6454beb2c93de1545a4f 100644 --- a/PaddleRec/din/cluster_train.py +++ b/PaddleRec/din/cluster_train.py @@ -9,7 +9,6 @@ import time import network import reader import random -import sys logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger("fluid") @@ -86,7 +85,7 @@ def train(): #data_reader, max_len = reader.prepare_reader(train_path, args.batch_size) logger.info("reading data completes") - avg_cost, pred = network.network(item_count, cat_count) + avg_cost, pred = network.network(item_count, cat_count, 433) #fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=5.0)) base_lr = args.base_lr boundaries = [410000] @@ -168,21 +167,6 @@ def train(): logger.info("run trainer") train_loop(t.get_trainer_program()) -def check_version(): - """ - Log error and exit when the installed version of paddlepaddle is - not satisfied. - """ - err = "PaddlePaddle version 1.6 or higher is required, " \ - "or a suitable develop version is satisfied as well. \n" \ - "Please make sure the version is good with your code." \ - - try: - fluid.require_version('1.6.0') - except Exception as e: - logger.error(err) - sys.exit(1) if __name__ == "__main__": - check_version() train() diff --git a/PaddleRec/din/infer.py b/PaddleRec/din/infer.py index b6fb972ff27b37a8f3d7d03ad93adcdc3c0da441..fc1484e51ec4fc42b98748723d34b26e52946848 100644 --- a/PaddleRec/din/infer.py +++ b/PaddleRec/din/infer.py @@ -19,7 +19,6 @@ import os import paddle import paddle.fluid as fluid import reader -import sys logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger("fluid") @@ -100,21 +99,6 @@ def infer(): auc = calc_auc(score) logger.info("TEST --> loss: {}, auc: {}".format(loss_sum / count, auc)) -def check_version(): - """ - Log error and exit when the installed version of paddlepaddle is - not satisfied. - """ - err = "PaddlePaddle version 1.6 or higher is required, " \ - "or a suitable develop version is satisfied as well. \n" \ - "Please make sure the version is good with your code." \ - - try: - fluid.require_version('1.6.0') - except Exception as e: - logger.error(err) - sys.exit(1) if __name__ == '__main__': - check_version() infer() diff --git a/PaddleRec/din/network.py b/PaddleRec/din/network.py index 26b9fbb0a4a03275fc224aa3c2b5b340984c0d85..a65e155f22d52680380a46d715ad09f48bf995d6 100644 --- a/PaddleRec/din/network.py +++ b/PaddleRec/din/network.py @@ -14,7 +14,7 @@ import paddle.fluid as fluid -def din_attention(hist, target_expand, mask): +def din_attention(hist, target_expand, max_len, mask): """activation weight""" hidden_size = hist.shape[-1] @@ -45,10 +45,9 @@ def din_attention(hist, target_expand, mask): return out -def network(item_count, cat_count): +def network(item_count, cat_count, max_len): """network definition""" - seq_len = -1 item_emb_size = 64 cat_emb_size = 64 is_sparse = False @@ -57,60 +56,60 @@ def network(item_count, cat_count): item_emb_attr = fluid.ParamAttr(name="item_emb") cat_emb_attr = fluid.ParamAttr(name="cat_emb") - hist_item_seq = fluid.data( - name="hist_item_seq", shape=[None, seq_len], dtype="int64") - hist_cat_seq = fluid.data( - name="hist_cat_seq", shape=[None, seq_len], dtype="int64") - target_item = fluid.data( - name="target_item", shape=[None], dtype="int64") - target_cat = fluid.data( - name="target_cat", shape=[None], dtype="int64") - label = fluid.data( - name="label", shape=[None, 1], dtype="float32") - mask = fluid.data( - name="mask", shape=[None, seq_len, 1], dtype="float32") - target_item_seq = fluid.data( - name="target_item_seq", shape=[None, seq_len], dtype="int64") - target_cat_seq = fluid.data( - name="target_cat_seq", shape=[None, seq_len], dtype="int64") - - hist_item_emb = fluid.embedding( + hist_item_seq = fluid.layers.data( + name="hist_item_seq", shape=[max_len, 1], dtype="int64") + hist_cat_seq = fluid.layers.data( + name="hist_cat_seq", shape=[max_len, 1], dtype="int64") + target_item = fluid.layers.data( + name="target_item", shape=[1], dtype="int64") + target_cat = fluid.layers.data( + name="target_cat", shape=[1], dtype="int64") + label = fluid.layers.data( + name="label", shape=[1], dtype="float32") + mask = fluid.layers.data( + name="mask", shape=[max_len, 1], dtype="float32") + target_item_seq = fluid.layers.data( + name="target_item_seq", shape=[max_len, 1], dtype="int64") + target_cat_seq = fluid.layers.data( + name="target_cat_seq", shape=[max_len, 1], dtype="int64", lod_level=0) + + hist_item_emb = fluid.layers.embedding( input=hist_item_seq, size=[item_count, item_emb_size], param_attr=item_emb_attr, is_sparse=is_sparse) - hist_cat_emb = fluid.embedding( + hist_cat_emb = fluid.layers.embedding( input=hist_cat_seq, size=[cat_count, cat_emb_size], param_attr=cat_emb_attr, is_sparse=is_sparse) - target_item_emb = fluid.embedding( + target_item_emb = fluid.layers.embedding( input=target_item, size=[item_count, item_emb_size], param_attr=item_emb_attr, is_sparse=is_sparse) - target_cat_emb = fluid.embedding( + target_cat_emb = fluid.layers.embedding( input=target_cat, size=[cat_count, cat_emb_size], param_attr=cat_emb_attr, is_sparse=is_sparse) - target_item_seq_emb = fluid.embedding( + target_item_seq_emb = fluid.layers.embedding( input=target_item_seq, size=[item_count, item_emb_size], param_attr=item_emb_attr, is_sparse=is_sparse) - target_cat_seq_emb = fluid.embedding( + target_cat_seq_emb = fluid.layers.embedding( input=target_cat_seq, size=[cat_count, cat_emb_size], param_attr=cat_emb_attr, is_sparse=is_sparse) - item_b = fluid.embedding( + item_b = fluid.layers.embedding( input=target_item, size=[item_count, 1], param_attr=fluid.initializer.Constant(value=0.0)) @@ -121,7 +120,7 @@ def network(item_count, cat_count): target_concat = fluid.layers.concat( [target_item_emb, target_cat_emb], axis=1) - out = din_attention(hist_seq_concat, target_seq_concat, mask) + out = din_attention(hist_seq_concat, target_seq_concat, max_len, mask) out_fc = fluid.layers.fc(name="out_fc", input=out, size=item_emb_size + cat_emb_size, diff --git a/PaddleRec/din/reader.py b/PaddleRec/din/reader.py index 07d1c33c904517e863967b93cbb0e4fe2001e058..a50a90894b3619f8f752c1d18663f1677c0f6154 100644 --- a/PaddleRec/din/reader.py +++ b/PaddleRec/din/reader.py @@ -20,7 +20,7 @@ import pickle def pad_batch_data(input, max_len): res = np.array([x + [0] * (max_len - len(x)) for x in input]) - res = res.astype("int64").reshape([-1, max_len]) + res = res.astype("int64").reshape([-1, max_len, 1]) return res @@ -34,10 +34,10 @@ def make_data(b): [-1, max_len, 1]) target_item_seq = np.array( [[x[2]] * max_len for x in b]).astype("int64").reshape( - [-1, max_len]) + [-1, max_len, 1]) target_cat_seq = np.array( [[x[3]] * max_len for x in b]).astype("int64").reshape( - [-1, max_len]) + [-1, max_len, 1]) res = [] for i in range(len(b)): res.append([ diff --git a/PaddleRec/din/train.py b/PaddleRec/din/train.py index 0c865f7d9ee7e4b0a0d505f26c1dc2d61181d6e1..9697961510c786ddd7f9f4fd4560e3f8d62fe4b1 100644 --- a/PaddleRec/din/train.py +++ b/PaddleRec/din/train.py @@ -78,7 +78,7 @@ def train(): args.num_devices) logger.info("reading data completes") - avg_cost, pred = network.network(item_count, cat_count) + avg_cost, pred = network.network(item_count, cat_count, max_len) fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByGlobalNorm( clip_norm=5.0)) base_lr = args.base_lr @@ -173,21 +173,6 @@ def get_cards(args): else: return args.num_devices -def check_version(): - """ - Log error and exit when the installed version of paddlepaddle is - not satisfied. - """ - err = "PaddlePaddle version 1.6 or higher is required, " \ - "or a suitable develop version is satisfied as well. \n" \ - "Please make sure the version is good with your code." \ - - try: - fluid.require_version('1.6.0') - except Exception as e: - logger.error(err) - sys.exit(1) if __name__ == "__main__": - check_version() train() diff --git a/PaddleRec/gnn/README.md b/PaddleRec/gnn/README.md index e3a5099ea8cb778dc74793204e6e64e9565a3530..e9a32861e2ecfd4e27e0ce6f2d7e08697ff85b6d 100644 --- a/PaddleRec/gnn/README.md +++ b/PaddleRec/gnn/README.md @@ -31,7 +31,6 @@ SR-GNN模型的介绍可以参阅论文[Session-based Recommendation with Graph 我们复现了论文效果，在DIGINETICA数据集上P@20可以达到50.7 -**目前模型库下模型均要求使用PaddlePaddle 1.6及以上版本或适当的develop版本。** 同时推荐用户参考[ IPython Notebook demo](https://aistudio.baidu.com/aistudio/projectDetail/124382) diff --git a/PaddleRec/gnn/infer.py b/PaddleRec/gnn/infer.py index 20125f7253186381b0beb6d5b2bef4d43276a767..6f935745933881a7f9f4b8f7ba923ba787e97b64 100644 --- a/PaddleRec/gnn/infer.py +++ b/PaddleRec/gnn/infer.py @@ -20,7 +20,6 @@ import paddle import paddle.fluid as fluid import reader import network -import sys logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger("fluid") @@ -56,7 +55,7 @@ def infer(args): test_data = reader.Data(args.test_path, False) place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) - loss, acc, py_reader, feed_datas = network.network(items_num, args.hidden_size, args.step, batch_size) + loss, acc, py_reader, feed_datas = network.network(items_num, args.hidden_size, args.step) exe.run(fluid.default_startup_program()) infer_program = fluid.default_main_program().clone(for_test=True) @@ -88,22 +87,6 @@ def infer(args): logger.info("TEST --> error: there is no model in " + model_path) -def check_version(): - """ - Log error and exit when the installed version of paddlepaddle is - not satisfied. - """ - err = "PaddlePaddle version 1.6 or higher is required, " \ - "or a suitable develop version is satisfied as well. \n" \ - "Please make sure the version is good with your code." \ - - try: - fluid.require_version('1.6.0') - except Exception as e: - logger.error(err) - sys.exit(1) - if __name__ == "__main__": - check_version() args = parse_args() infer(args) diff --git a/PaddleRec/gnn/network.py b/PaddleRec/gnn/network.py index 84cea25f06e63cd1e6024568f86f3046e907c404..d445dbe94febc75bfadf1fb5bf11b5090526827e 100644 --- a/PaddleRec/gnn/network.py +++ b/PaddleRec/gnn/network.py @@ -19,36 +19,36 @@ import paddle.fluid as fluid import paddle.fluid.layers as layers -def network(items_num, hidden_size, step, bs): +def network(items_num, hidden_size, step): stdv = 1.0 / math.sqrt(hidden_size) - items = fluid.data( + items = layers.data( name="items", - shape=[bs, -1], - dtype="int64") #[batch_size, uniq_max] - seq_index = fluid.data( + shape=[1, 1], + dtype="int64") #[batch_size, uniq_max, 1] + seq_index = layers.data( name="seq_index", - shape=[bs, -1, 2], - dtype="int32") #[batch_size, seq_max, 2] - last_index = fluid.data( + shape=[1], + dtype="int32") #[batch_size, seq_max] + last_index = layers.data( name="last_index", - shape=[bs, 2], - dtype="int32") #[batch_size, 2] - adj_in = fluid.data( + shape=[1], + dtype="int32") #[batch_size, 1] + adj_in = layers.data( name="adj_in", - shape=[bs, -1, -1], + shape=[1,1], dtype="float32") #[batch_size, seq_max, seq_max] - adj_out = fluid.data( + adj_out = layers.data( name="adj_out", - shape=[bs, -1, -1], + shape=[1,1], dtype="float32") #[batch_size, seq_max, seq_max] - mask = fluid.data( + mask = layers.data( name="mask", - shape=[bs, -1, 1], + shape=[1, 1], dtype="float32") #[batch_size, seq_max, 1] - label = fluid.data( + label = layers.data( name="label", - shape=[bs, 1], + shape=[1], dtype="int64") #[batch_size, 1] datas = [items, seq_index, last_index, adj_in, adj_out, mask, label] @@ -57,17 +57,19 @@ def network(items_num, hidden_size, step, bs): feed_datas = fluid.layers.read_file(py_reader) items, seq_index, last_index, adj_in, adj_out, mask, label = feed_datas - items_emb = fluid.embedding( + items_emb = layers.embedding( input=items, param_attr=fluid.ParamAttr( name="emb", initializer=fluid.initializer.Uniform( low=-stdv, high=stdv)), size=[items_num, hidden_size]) #[batch_size, uniq_max, h] + items_emb_shape = layers.shape(items_emb) pre_state = items_emb for i in range(step): - pre_state = layers.reshape(x=pre_state, shape=[bs, -1, hidden_size]) + pre_state = layers.reshape( + x=pre_state, shape=[-1, 1, hidden_size], actual_shape=items_emb_shape) state_in = layers.fc( input=pre_state, name="state_in", @@ -102,12 +104,24 @@ def network(items_num, hidden_size, step, bs): bias_attr=False) pre_state, _, _ = fluid.layers.gru_unit( input=gru_fc, - hidden=layers.reshape(x=pre_state, shape=[-1, hidden_size]), + hidden=layers.reshape( + x=pre_state, shape=[-1, hidden_size]), size=3 * hidden_size) - final_state = layers.reshape(pre_state, shape=[bs, -1, hidden_size]) - seq = layers.gather_nd(final_state, seq_index) - last = layers.gather_nd(final_state, last_index) + final_state = pre_state #[batch_size * uniq_max, h] + + seq_origin_shape = layers.assign(np.array([0,0,hidden_size-1]).astype("int32")) + seq_origin_shape += layers.shape(layers.unsqueeze(seq_index,[2])) #value: [batch_size, seq_max, h] + seq_origin_shape.stop_gradient = True + + seq_index = layers.reshape(seq_index, shape=[-1]) + seq = layers.gather(final_state, seq_index) #[batch_size * seq_max, h] + last = layers.gather(final_state, last_index) #[batch_size, h] + + seq = layers.reshape( + seq, shape=[-1, 1, hidden_size], actual_shape=seq_origin_shape) #[batch_size, seq_max, h] + last = layers.reshape( + last, shape=[-1, hidden_size]) #[batch_size, h] seq_fc = layers.fc( input=seq, @@ -170,13 +184,13 @@ def network(items_num, hidden_size, step, bs): low=-stdv, high=stdv))) #[batch_size, h] all_vocab = layers.create_global_var( - shape=[items_num - 1], + shape=[items_num - 1, 1], value=0, dtype="int64", persistable=True, name="all_vocab") - all_emb = fluid.embedding( + all_emb = layers.embedding( input=all_vocab, param_attr=fluid.ParamAttr( name="emb", diff --git a/PaddleRec/gnn/reader.py b/PaddleRec/gnn/reader.py index e1adb8f38d17ba6efb286f4eaecfdac0b173a8f9..effcc2425940916f6550de97f6329b4499bd8261 100644 --- a/PaddleRec/gnn/reader.py +++ b/PaddleRec/gnn/reader.py @@ -64,19 +64,19 @@ class Data(): adj_out.append(np.divide(adj.transpose(), u_deg_out).transpose()) seq_index.append( - [[id, np.where(node == i)[0][0]] for i in e[0]]) + [np.where(node == i)[0][0] + id * max_uniq_len for i in e[0]]) last_index.append( - [id, np.where(node == e[0][last_id[id]])[0][0]]) + np.where(node == e[0][last_id[id]])[0][0] + id * max_uniq_len) label.append(e[1] - 1) mask.append([[1] * (last_id[id] + 1) + [0] * (max_seq_len - last_id[id] - 1)]) id += 1 - items = np.array(items).astype("int64").reshape((batch_size, -1)) + items = np.array(items).astype("int64").reshape((batch_size, -1, 1)) seq_index = np.array(seq_index).astype("int32").reshape( - (batch_size, -1, 2)) + (batch_size, -1)) last_index = np.array(last_index).astype("int32").reshape( - (batch_size, 2)) + (batch_size)) adj_in = np.array(adj_in).astype("float32").reshape( (batch_size, max_uniq_len, max_uniq_len)) adj_out = np.array(adj_out).astype("float32").reshape( @@ -110,10 +110,8 @@ class Data(): cur_batch = remain_data[i:i + batch_size] yield self.make_data(cur_batch, batch_size) else: - # Due to fixed batch_size, discard the remaining ins - return - #cur_batch = remain_data[i:] - #yield self.make_data(cur_batch, group_remain % batch_size) + cur_batch = remain_data[i:] + yield self.make_data(cur_batch, group_remain % batch_size) return _reader diff --git a/PaddleRec/gnn/train.py b/PaddleRec/gnn/train.py index d82db868f6068110d81c99dd4cc0efbb471c5fa5..d07b11bf56d4ad2e1121dc0e15f39dd0b5c0e6c1 100644 --- a/PaddleRec/gnn/train.py +++ b/PaddleRec/gnn/train.py @@ -72,7 +72,7 @@ def train(): batch_size = args.batch_size items_num = reader.read_config(args.config_path) loss, acc, py_reader, feed_datas = network.network(items_num, args.hidden_size, - args.step, batch_size) + args.step) data_reader = reader.Data(args.train_path, True) logger.info("load data complete") @@ -96,7 +96,7 @@ def train(): all_vocab = fluid.global_scope().var("all_vocab").get_tensor() all_vocab.set( - np.arange(1, items_num).astype("int64").reshape((-1)), place) + np.arange(1, items_num).astype("int64").reshape((-1, 1)), place) feed_list = [e.name for e in feed_datas] @@ -170,21 +170,6 @@ def get_cards(args): num = len(cards.split(",")) return num -def check_version(): - """ - Log error and exit when the installed version of paddlepaddle is - not satisfied. - """ - err = "PaddlePaddle version 1.6 or higher is required, " \ - "or a suitable develop version is satisfied as well. \n" \ - "Please make sure the version is good with your code." \ - - try: - fluid.require_version('1.6.0') - except Exception as e: - logger.error(err) - sys.exit(1) if __name__ == "__main__": - check_version() train()