def test_gpu_merged_ksum(self): with xdl.device("GPU"): ksum = xdl.ksum(embeds, idx, values, segs, grps, sidx, sseg) ksum = xdl.execute(ksum) res = np.array([[0.03, 0.03], [0.04, 0.05], [0.05, 0.1]], dtype=np.float) self.assertTrue(np.allclose(ksum, res))
def test_merged_gpu(self): with xdl.device("GPU"): ksum_grad = xdl.ksum_grad(embeds_shape, idx, values, segs, grps, sidx_nogrp, sseg, merged_grads) ksum_grad = xdl.execute(ksum_grad) res = np.array([[7], [5], [12], [3], [5], [6]], dtype=np.float) self.assertTrue(np.allclose(ksum_grad, res))
def test_gpu(self): with xdl.device("GPU"): grps = np.array([],dtype=np.int32) ksum_grad = xdl.ksum_grad(embeds, idx, values, segs, grps, grads) ksum_grad = xdl.execute(ksum_grad) res = np.array([[0.4],[0.3],[0.6],[0.2],[0.3],[0.3]],dtype=np.float) self.assertTrue(np.allclose(ksum_grad, res))
def test_gpu_ksum(self): with xdl.device("GPU"): grps = np.array([], dtype=np.int32) ksum = xdl.ksum(embeds, idx, values, segs, grps, sidx, sseg) ksum = xdl.execute(ksum) res = np.array([[0.06], [0.09], [0.15]], dtype=np.float) self.assertTrue(np.allclose(ksum, res))
def test_gpu_kavg(self): with xdl.device("GPU"): grps = np.array([], dtype=np.int32) ksum = xdl.ksum(embeds, idx, values, segs, grps, average=True) ksum = xdl.execute(ksum) res = np.array([[0.02], [0.03], [0.0375]], dtype=np.float) self.assertTrue(np.allclose(ksum, res))
def test_gpu(self): with xdl.device("GPU"): out = xdl.take_grad(comm_grad, indicator, comm) out = xdl.execute(out) res = np.array([[0.5, 0.7, 0.9], [0.8, 1.0, 1.2], [0.4, 0.5, 0.6]], dtype=np.float) self.assertTrue(np.allclose(out, res))
def test_gpu(self): with xdl.device("GPU"): out = xdl.take_op(comm, indicator) out = xdl.execute(out) res = np.array([[0.1, 0.2, 0.3], [0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]], dtype=np.float) self.assertTrue(np.allclose(out, res))
def compute_sparse_grad(self, sparse_var_grad): inputs = [] outputs = [] in_grads = {} for var, grad in sparse_var_grad: inputs.append(var.grad_tensor) outputs.append(get_embedding_output(var)) if outputs[-1] is None: raise Exception('embedding output is None for var:', var.name) in_grads[outputs[-1]] = grad backend_device_type = get_collection(BACKEND_DEVICE_TYPE)[0] if backend_device_type == 'gpu': with xdl.device('GPU'): return gradient(inputs, outputs, in_grads) else: with xdl.device('CPU'): return gradient(inputs, outputs, in_grads)
def merge_sparse(sparse_inputs, device='CPU', **device_attrs): id_list = [x.ids for x in sparse_inputs] value_list = [x.values for x in sparse_inputs] segment_list = [x.segments for x in sparse_inputs] with xdl.device(device, **device_attrs): ids, values, segments, groups = \ xdl.merge_sparse_op(id_list, value_list, segment_list) return MergedSparseTensor(ids, values, segments, groups)
def test_constant_gpu(self): with xdl.device("GPU"): a = xdl.convert_to_tensor(1) b = xdl.convert_to_tensor([10, 20]) c = xdl.convert_to_tensor(np.array([30, 40])) a, b, c = xdl.execute([a, b, c]) self.assertTrue(a == 1) self.assertTrue((b == np.array([10, 20])).all()) self.assertTrue((c == np.array([30, 40])).all())
def test_unique_gpu_2d(self): with xdl.device("GPU"): res_uniq = np.array([[1, 1], [1, 3], [2, 0], [2, 1], [3, 1], [3, 2]]) res_idx = np.array([4, 3, 4, 2, 1, 0, 2, 1, 5, 5, 3, 2]) uniq, idx = xdl.unique(data.reshape((data.size / 2, 2)), itype=DataType.int32) uniq, idx = xdl.execute([uniq, idx]) self.assertTrue((uniq == res_uniq).all()) self.assertTrue((idx == res_idx).all())
def test_unique_gpu_1d(self): with xdl.device("GPU"): res_uniq = np.array([0, 1, 2, 3]) res_idx = np.array([ 3, 1, 2, 1, 3, 1, 2, 0, 1, 3, 1, 1, 2, 0, 1, 3, 3, 2, 3, 2, 2, 1, 2, 0 ]) uniq, idx = xdl.unique(data, itype=DataType.int32) uniq, idx = xdl.execute([uniq, idx]) self.assertTrue((uniq == res_uniq).all()) self.assertTrue((idx == res_idx).all())
def ksum(embeddings, idx, values, segments, sidx, sseg, device='CPU', **device_attrs): groups = np.array([], dtype=dtype_xdl_2_np(segments.dtype)) with xdl.device(device, **device_attrs): res = xdl.ksum(embeddings, idx, values, segments, groups, sidx, sseg) return res
def merged_ksum(embeddings, idx, values, segments, groups, sidx, sseg, device='CPU', **device_attrs): with xdl.device(device, **device_attrs): res = xdl.ksum(embeddings, idx, values, segments, groups, sidx, sseg) return res
def test_gpu(self): with xdl.device("GPU"): merged_sparse = xdl.merge_sparse_op([ids1, ids2], [val1, val2], [seg1, seg2]) ids, vals, segs, grps = xdl.execute(merged_sparse) res_ids = np.array([1, 2, 6, 3, 7, 8, 9, 4, 5, 10], dtype=np.int64) res_vals = np.array([0, 0, 1, 0, 1, 1, 1, 0, 0, 1], dtype=np.float) res_segs = np.array([3, 7, 10]) res_grps = np.array([2, 3, 4, 7, 9, 10]) self.assertTrue((ids == res_ids).all()) self.assertTrue(np.allclose(vals, res_vals)) self.assertTrue((segs == res_segs).all()) self.assertTrue((grps == res_grps).all())
def test_add_1d_gpu(self): ids1 = np.array([1,2,3,4,5,6],dtype=np.int64) eb1 = np.array([0.1,0.2,0.3,0.4,0.5,0.6],dtype=np.float).reshape((-1,1)) ids2 = np.array([0,2,3,5,7,9],dtype=np.int64) eb2 = np.array([0.,0.2,0.3,0.5,0.7,0.9],dtype=np.float).reshape((-1,1)) ids3 = np.array([1,4,5,7,8],dtype=np.int64) eb3 = np.array([0.1,0.4,0.5,0.7,0.8],dtype=np.float).reshape((-1,1)) res_eb = np.array([0.2,0.4,0.6,0.8,1.5,0.6,0.0,1.4,0.9,0.8],dtype=np.float).reshape((-1,1)) res_id = np.array([1,2,3,4,5,6,0,7,9,8],dtype=np.int64) with xdl.device("GPU"): add_sparse = xdl.sparse_grad_add_op([eb1,eb2,eb3],[ids1,ids2,ids3]) eb, id = xdl.execute(add_sparse) self.assertTrue((res_id == id).all()) self.assertTrue(np.allclose(res_eb, eb))
def test_gpu_2d(self): with xdl.device("GPU"): merged_sparse = xdl.merge_sparse_op([ids3, ids4], [val1, val2], [seg1, seg2]) ids, vals, segs, grps = xdl.execute(merged_sparse) res_ids = np.array([[1, 2], [3, 4], [11, 12], [5, 6], [13, 14], [15, 16], [17, 18], [7, 8], [9, 10], [19, 20]]) res_vals = np.array([0, 0, 1, 0, 1, 1, 1, 0, 0, 1]) res_segs = np.array([3, 7, 10]) res_grps = np.array([2, 3, 4, 7, 9, 10]) self.assertTrue((ids == res_ids).all()) self.assertTrue(np.allclose(vals, res_vals)) self.assertTrue((segs == res_segs).all()) self.assertTrue((grps == res_grps).all())
def test_add_2d_gpu(self): ids1 = np.array([0,1,2,3,4,5],dtype=np.int64).reshape((-1,2)) eb1 = np.array([0.,0.1,0.2,0.3,0.4,0.6],dtype=np.float).reshape((-1,2)) ids2 = np.array([0,1,4,5,6,7],dtype=np.int64).reshape((-1,2)) eb2 = np.array([0.,0.1,0.4,0.5,0.6,0.7],dtype=np.float).reshape((-1,2)) ids3 = np.array([2,3,4,6],dtype=np.int64).reshape((-1,2)) eb3 = np.array([0.2,0.3,0.4,0.6],dtype=np.float).reshape((-1,2)) res_id = np.array([0,1,2,3,4,5,6,7,4,6],dtype=np.int64).reshape((-1,2)) res_eb = np.array([0.,0.2,0.4,0.6,0.8,1.1,0.6,0.7,0.4,0.6],dtype=np.float).reshape((-1,2)) with xdl.device("GPU"): add_sparse = xdl.sparse_grad_add_op([eb1,eb2,eb3],[ids1,ids2,ids3]) eb, id = xdl.execute(add_sparse) self.assertTrue(np.equal(res_id, id).all()) self.assertTrue(np.allclose(res_eb, eb))
def test_gpu_tile(self): with xdl.device("GPU"): res = xdl.tile_grad(embeds, idx, values, segs, grps, grads, length=length, reverse=False) res = xdl.execute(res) res_grad = np.array([[10.7], [3.9], [0.1], [12.1], [4.8], [13.5]], dtype=np.float) self.assertTrue(np.allclose(res, res_grad))
def test_gpu_tile_reverse(self): with xdl.device("GPU"): res = xdl.tile_grad(embeds, idx, values, segs, grps, grads, length=length, reverse=True) res = xdl.execute(res) res_grad = np.array([[1.6], [4.4], [13.3], [12.3], [4.2], [12.6]], dtype=np.float) self.assertTrue(np.allclose(res, res_grad))
def KSumGrad(op, grad): with xdl.device('CPU'): shape = xdl.shape_op(op.inputs[0]) return [ xdl.ksum_grad(shape, op.inputs[1], op.inputs[2], op.inputs[3], op.inputs[4], op.inputs[5], op.inputs[6], grad[0], average=op.attrs['average']) ]
def test_gpu(self): with xdl.device("GPU"): res = xdl.tile(embeds, idx, values, segs, grps, length=length, reverse=False) res = xdl.execute(res) res_tile = np.array([[0.3, 0.4, 0.0, 0.0, 0.0, 0.0], [1.2, 0.0, 0.0, 0.4, 0.0, 0.0], [1.0, 3.0, 0.0, 0.7, 3.2, 5.4]], dtype=np.float) self.assertTrue(np.allclose(res, res_tile))
def test_gpu_tile_empty_value_reverse(self): with xdl.device("GPU"): empty_values = np.array([], dtype=np.float) res = xdl.tile_grad(embeds, idx, empty_values, segs, grps, grads, length=length, reverse=True) res = xdl.execute(res) res_grad = np.array([[0.4], [1.0], [1.6], [1.6], [0.7], [1.4]], dtype=np.float) self.assertTrue(np.allclose(res, res_grad))
def test_gpu_tile_empty_value(self): with xdl.device("GPU"): empty_values = np.array([], dtype=np.float) res = xdl.tile(embeds, idx, empty_values, segs, grps, length=length, reverse=False) res = xdl.execute(res) res_tile = np.array( [[0.3, 0.2, 0.4], [0.1, 0.0, 0.0], [0.2, 0.5, 0.0], [0.0, 0.0, 0.0], [0.1, 0.4, 0.6]], dtype=np.float) self.assertTrue(np.allclose(res, res_tile))
def test_unique_gpu_2d(self): with xdl.device("GPU"): res_uniq = np.array([[1, 1], [1, 3], [2, 0], [2, 1], [3, 1], [3, 2]]) res_idx = np.array([4, 3, 4, 2, 1, 0, 2, 1, 5, 5, 3, 2]) segment = np.array([2, 2, 5, 6, 9, 12], np.int32) res_sidx = np.array([3, 2, 4, 2, 4, 5, 0, 5, 0, 2, 4, 5]) res_sseg = np.array([1, 3, 6, 8, 10, 12]) uniq, idx, sidx, sseg = xdl.unique(data.reshape( (data.size / 2, 2)), segment, itype=DataType.int32) uniq, idx, sidx, sseg = xdl.execute([uniq, idx, sidx, sseg]) self.assertTrue((uniq == res_uniq).all()) self.assertTrue((idx == res_idx).all()) self.assertTrue((sidx == res_sidx).all()) self.assertTrue((sseg == res_sseg).all())
def merged_tile(embeddings, idx, values, segments, groups, length, reverse=False, device='CPU', **device_attrs): with xdl.device(device, **device_attrs): res = xdl.tile(embeddings, idx, values, segments, groups, reverse=reverse, length=length) return res
def tile(embeddings, idx, values, segments, length, reverse=False, device='CPU', **device_attrs): groups = np.array([], dtype=dtype_xdl_2_np(segments.dtype)) with xdl.device(device, **device_attrs): res = xdl.tile(embeddings, idx, values, segments, groups, reverse=reverse, length=length) return res
def test_unique_gpu_1d(self): with xdl.device("GPU"): res_uniq = np.array([0, 1, 2, 3]) res_idx = np.array([ 3, 1, 2, 1, 3, 1, 2, 0, 1, 3, 1, 1, 2, 0, 1, 3, 3, 2, 3, 2, 2, 1, 2, 0 ]) res_sidx = np.array([ 2, 6, 10, 0, 1, 2, 3, 4, 5, 6, 9, 0, 2, 6, 7, 8, 9, 10, 0, 1, 3, 6, 7, 8 ]) res_sseg = np.array([3, 11, 18, 24]) segment = np.array([3, 5, 8, 10, 11, 12, 16, 18, 20, 22, 24], np.int32) uniq, idx, sidx, sseg = xdl.unique(data, segment=segment, itype=DataType.int32) uniq, idx, sidx, sseg = xdl.execute([uniq, idx, sidx, sseg]) self.assertTrue((uniq == res_uniq).all()) self.assertTrue((idx == res_idx).all()) self.assertTrue((sidx == res_sidx).all()) self.assertTrue((sseg == res_sseg).all())
def TakeGrad(op, grad): with xdl.device('CPU'): shape = xdl.shape_op(op.inputs[0]) g = xdl.take_grad(grad[0], op.inputs[1], shape) return [g]
def train(is_training=True): if is_training or xdl.get_task_index() == 0: init() else: return file_type = xdl.parsers.txt if is_training: data_io = xdl.DataIO("tdm", file_type=file_type, fs_type=xdl.fs.hdfs, namenode="hdfs://your/namenode/hdfs/path:9000", enable_state=False) feature_count = 69 for i in xrange(1, feature_count + 1): data_io.feature(name=("item_%s" % i), type=xdl.features.sparse, table=1) data_io.feature(name="unit_id_expand", type=xdl.features.sparse, table=0) data_io.batch_size(intconf('train_batch_size')) data_io.epochs(intconf('train_epochs')) data_io.threads(intconf('train_threads')) data_io.label_count(2) base_path = '%s/%s/' % (conf('upload_url'), conf('data_dir')) data = base_path + conf('train_sample') + '_' + r'[\d]+' sharding = xdl.DataSharding(data_io.fs()) sharding.add_path(data) paths = sharding.partition(rank=xdl.get_task_index(), size=xdl.get_task_num()) print 'train: sharding.partition() =', paths data_io.add_path(paths) iop = xdl.GetIOP("TDMOP") else: data_io = xdl.DataIO("tdm", file_type=file_type, fs_type=xdl.fs.hdfs, namenode="hdfs://your/namenode/hdfs/path:9000", enable_state=False) feature_count = 69 for i in xrange(1, feature_count + 1): data_io.feature(name=("item_%s" % i), type=xdl.features.sparse, table=1) data_io.feature(name="unit_id_expand", type=xdl.features.sparse, table=0) data_io.feature(name="test_unit_id", type=xdl.features.sparse, table=1) data_io.batch_size(intconf('predict_batch_size')) data_io.epochs(intconf('predict_epochs')) data_io.threads(intconf('predict_threads')) data_io.label_count(2) base_path = '%s/%s/' % (conf('upload_url'), conf('data_dir')) data = base_path + conf('test_sample') data_io.add_path(data) print 'predict: add_path =', data iop = xdl.GetIOP("TDMPREDICTOP") #data_io.finish_delay(True) assert iop is not None key_value = {} key_value["key"] = "value" key_value["debug"] = conf('tdmop_debug') key_value["layer_counts"] = conf('tdmop_layer_counts') key_value["start_sample_layer"] = "22" key_value["pr_test_each_layer_retrieve_num"] = "400" key_value["pr_test_final_layer_retrieve_num"] = "200" if not is_training: key_value["expand_mode"] = "vector" iop.init(key_value) data_io.add_op(iop) data_io.split_group(False) data_io.startup() if not is_training: if xdl.get_task_index() == 0: saver = xdl.Saver() saver.restore(conf('saver_ckpt')) batch = data_io.read() emb_combiner = 'mean' # mean | sum if not is_training: gt_ids = batch["_ids"][-1] gt_segments = batch["_segments"][-1] emb = [] emb_dim = 24 if is_training: feature_add_probability = 1. else: feature_add_probability = 0. import xdl.python.sparse_engine.embedding as embedding emb_name = "item_emb" for i in xrange(1, feature_count + 1): eb = xdl.embedding(emb_name, batch["item_%s" % i], xdl.Normal(stddev=0.001), emb_dim, 50000, emb_combiner, vtype="hash", feature_add_probability=feature_add_probability) with xdl.device('GPU'): eb_take = xdl.take_op(eb, batch["indicators"][0]) eb_take.set_shape(eb.shape) emb.append(eb_take) unit_id_expand_emb = xdl.embedding(emb_name, batch["unit_id_expand"], xdl.Normal(stddev=0.001), emb_dim, 50000, emb_combiner, vtype="hash", feature_add_probability=feature_add_probability) @xdl.mxnet_wrapper(is_training=is_training, device_type='gpu') def dnn_model_define(user_input, indicator, unit_id_emb, label, bs, eb_dim, sample_num, fea_groups, active_op='prelu', use_batch_norm=True): # 把用户输入按fea_groups划分窗口,窗口内做avg pooling fea_groups = [int(s) for s in fea_groups.split(',')] total_group_length = np.sum(np.array(fea_groups)) print "fea_groups", fea_groups, "total_group_length", total_group_length, "eb_dim", eb_dim user_input_before_reshape = mx.sym.concat(*user_input) user_input = mx.sym.reshape(user_input_before_reshape, shape=(-1, total_group_length, eb_dim)) idx = 0 for group_length in fea_groups: block_before_sum = mx.sym.slice_axis(user_input, axis=1, begin=idx, end=idx + group_length) block = mx.sym.sum_axis(block_before_sum, axis=1) / group_length if idx == 0: grouped_user_input = block else: grouped_user_input = mx.sym.concat(grouped_user_input, block, dim=1) idx += group_length indicator = mx.symbol.BlockGrad(indicator) label = mx.symbol.BlockGrad(label) grouped_user_input_after_take = grouped_user_input net_version = "e" layer_arr = [] layer1 = mx_dnn_layer(10 * eb_dim, 128, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (1, net_version)) layer_arr.append(layer1) layer2 = mx_dnn_layer(128, 64, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (2, net_version)) layer_arr.append(layer2) layer3 = mx_dnn_layer(64, 24, active_op='', use_batch_norm=False, version="%d_%s" % (3, net_version)) layer_arr.append(layer3) layer_data = [grouped_user_input_after_take] for layer in layer_arr: layer_data.append(layer.call(layer_data[-1])) dout = layer_data[-1] inner_product = mx.sym.sum(dout * unit_id_emb, axis=1) softmax_input = mx.sym.Reshape(inner_product, shape=( bs / sample_num, sample_num ) ) # 用正例的label减1作为softmax的label ph_label_click = mx.sym.slice_axis(label, axis=1, begin=1, end=2) ph_label_click = mx.sym.reshape(ph_label_click, shape=(bs / sample_num, sample_num)) - 1 ph_label_click = mx.sym.slice_axis(ph_label_click, axis=1, begin=0, end=1) ph_label_click = mx.sym.reshape(ph_label_click, shape=(bs / sample_num, )) prop = mx.symbol.SoftmaxOutput(data=softmax_input, label=ph_label_click, normalization='valid', use_ignore=True) positive_prop = mx.sym.slice_axis(prop, axis=1, begin=0, end=1) positive_prop = mx.sym.reshape(positive_prop, shape=(bs / sample_num, ) ) # 实际的有效样本数量是(bs/sample_num)减去需要ignore的label数量 loss = -mx.sym.sum(mx.symbol.log(positive_prop)) / (bs / sample_num + mx.sym.sum(ph_label_click)) user_vector = mx.sym.reshape(dout, shape=(bs / sample_num, sample_num, eb_dim)) user_vector = mx.sym.slice_axis(user_vector, axis=1, begin=0, end=1) user_vector = mx.sym.reshape(user_vector, shape=(bs / sample_num, eb_dim)) return prop, loss, mx.sym.BlockGrad(user_vector) if is_training: re = dnn_model_define(emb, batch["indicators"][0], unit_id_expand_emb, batch["label"], data_io._batch_size, emb_dim, 600, '20,20,10,10,2,2,2,1,1,1') else: re = dnn_model_define(emb, batch["indicators"][0], unit_id_expand_emb, batch["label"], data_io._batch_size, emb_dim, 1, '20,20,10,10,2,2,2,1,1,1') prop = re[0] loss = re[1] if is_training: train_op = xdl.Adam(learning_rate=intconf('learning_rate')).optimize() else: user_vector = re[2] hooks = [] if is_training: if conf("train_mode") == "sync": hooks.append(xdl.SyncRunHook(xdl.get_task_index(), xdl.get_task_num())) if xdl.get_task_index() == 0: ckpt_hook = xdl.CheckpointHook(intconf('save_checkpoint_interval')) hooks.append(ckpt_hook) log_hook = xdl.LoggerHook([loss], "#### loss:{0}") else: log_hook = xdl.LoggerHook([loss], "#### loss:{0}") hooks.append(log_hook) from xdl.python.training.training_utils import get_global_step global_step = get_global_step() sess = xdl.TrainSession(hooks) elapsed_time = 0. statis_begin_loop = 200 loop_num = 0 if not is_training: urun_re = iop.urun({"get_level_ids": key_value["start_sample_layer"]}) item_num = len(urun_re) item_ids = np.array([int(iid) for iid in urun_re.keys()], dtype=np.int64).reshape((item_num, 1)) print 'item_ids shape: ' print item_ids.shape zeros = np.zeros((item_num, 1), dtype=np.int64) hash_ids = np.concatenate((zeros, item_ids), axis=1) item_embeddings = xdl.execute(xdl.ps_sparse_pull_op(hash_ids, var_name="item_emb", var_type="hash", save_ratio=1.0, otype=xdl.DataType.float)) item_embeddings = item_embeddings.transpose() print 'item_embeddings shape: ' print item_embeddings.shape hit_num_list = [] precision_list = [] recall_list = [] gt_num_list = [] user_idx = 1 while not sess.should_stop(): print ">>>>>>>>>>>> %d >>>>>>>>>>>" % loop_num begin_time = time.time() for itr in xrange(200): if is_training: result = sess.run([train_op, xdl.get_collection(xdl.UPDATE_OPS)]) else: result = sess.run([user_vector, global_step.value, gt_ids, gt_segments]) if result is None: print "result is None, finished success." break if not is_training: print "global_step =", result[1] batch_uv = result[0] batch_gt = result[2] batch_seg = result[3] batch_uv = batch_uv[0:len(batch_seg)] batch_scores = np.matmul(batch_uv, item_embeddings) sorted_idx = np.argsort(-batch_scores, axis=1) sorted_idx = sorted_idx[:, :int(key_value["pr_test_final_layer_retrieve_num"])] gt_id_start_idx = 0 for i in xrange(len(batch_seg)): pred_set = set(item_ids[sorted_idx[i, :], 0]) gt_dict = {} for gt in batch_gt[gt_id_start_idx:batch_seg[i], 1]: if gt in gt_dict: gt_dict[gt] += 1 else: gt_dict[gt] = 1 test_gt_list = batch_gt[gt_id_start_idx:batch_seg[i], 1].tolist() test_gt_str = ','.join([str(gtid) for gtid in test_gt_list]) test_pred_list = item_ids[sorted_idx[i, :], 0].tolist() test_pred_str = ','.join([str(gtid) for gtid in test_pred_list]) user_idx += 1 gt_set = set(batch_gt[gt_id_start_idx:batch_seg[i], 1]) comm_set = gt_set.intersection(pred_set) hit_num = sum([float(gt_dict[item]) if item in gt_dict else 0.0 for item in comm_set]) hit_num_list.append(hit_num) if len(pred_set) > 0: precision = hit_num / len(pred_set) else: precision = 0.0 if len(gt_dict) > 0: recall = hit_num / (batch_seg[i] - gt_id_start_idx) else: recall = 0.0 precision_list.append(precision) recall_list.append(recall) gt_num_list.append(float(batch_seg[i] - gt_id_start_idx)) gt_id_start_idx = batch_seg[i] print "==================================================" print 'predicted user num is: %d' % len(hit_num_list) print 'gt num is: %f' % sum(gt_num_list) print 'precision: %f' % (sum(precision_list) / len(hit_num_list)) print 'recall: %f' % (sum(recall_list) / len(hit_num_list)) print 'global recall: %f' % (sum(hit_num_list) / sum(gt_num_list)) print "==================================================" loop_num += 1 if loop_num > statis_begin_loop: elapsed_time += time.time() - begin_time #print 'batch_size = %d, qps = %f batch/s' % (data_io._batch_size, (loop_num - statis_begin_loop) / elapsed_time) if not is_training: print "==================================================" print 'predicted user num is: %d' % len(hit_num_list) print 'gt num is: %f' % sum(gt_num_list) print 'precision: %f' % (sum(precision_list) / len(hit_num_list)) print 'recall: %f' % (sum(recall_list) / len(hit_num_list)) print 'global recall: %f' % (sum(hit_num_list) / sum(gt_num_list)) print "==================================================" if is_training: xdl.execute(xdl.ps_synchronize_leave_op(np.array(xdl.get_task_index(), dtype=np.int32))) if xdl.get_task_index() == 0: print 'start put item_emb' def _string_to_int8(src): return np.array([ord(ch) for ch in src], dtype=np.int8) from xdl.python.utils.config import get_ckpt_dir output_dir = conf('model_url') op = xdl.ps_convert_ckpt_variable_op(checkpoint_dir=_string_to_int8(get_ckpt_dir()), output_dir=_string_to_int8(output_dir), variables=_string_to_int8("item_emb")) xdl.execute(op) shell_cmd("rm -f data/item_emb") shell_cmd("hadoop fs -get %s/item_emb data/item_emb" % output_dir) shell_cmd("sed -i 's/..//' data/item_emb") shell_cmd("hadoop fs -put -f data/item_emb %s" % output_dir) print 'finish put item_emb'
def merged_embedding(name, sparse_inputs, initializer, emb_dim, feature_dim, combiner='sum', vtype=VarType.Index, length=50, reverse=False, batch_read=3000, feature_add_probability=1.0, cbf=0, device='CPU', **device_attr): """xdl embedding Args: name: name for embedding, will be used for declaring variable on ps-plus sparse_inputs: a list of sparse tensors represent input data initializer: intializer for the weights emb_dim: embedding dimension feature_dim: sparse input dimension, for pre-allocate memory combiner: reduce operator, support sum|mean Returns: a tensor represent embedding result Raises: None """ import xdl.python.framework.variable as variable with variable.variable_info(batch_read=batch_read, save_ratio=feature_add_probability, bloom_filter=cbf): var = variable.Variable(name=name, dtype=DataType.float, shape=[feature_dim, emb_dim], initializer=initializer, vtype=vtype, trainable = True) if isinstance(sparse_inputs, (list, tuple)): merged_sparse_inputs = merge_sparse(sparse_inputs) emb_dim *= len(sparse_inputs) else: assert(isinstance(sparse_inputs, MergedSparseTensor)) merged_sparse_inputs = sparse_inputs if merged_sparse_inputs.has_unique_ids(): unique_ids = merged_sparse_inputs.ids idx = merged_sparse_inputs.indices sidx = merged_sparse_inputs.sidx sseg = merged_sparse_inputs.sseg else: with xdl.device(device, **device_attr): unique_ids, idx, sidx, sseg = xdl.unique(ids, merged_sparse_inputs.groups, itype=DataType.int32) embeddings = var.gather(unique_ids) global _EMBEDDING_TENSOR _EMBEDDING_TENSOR[embeddings] = var import xdl.python.sparse_engine.embedding_ops as embedding_ops if combiner == 'sum': embeddings = embedding_ops.merged_ksum( embeddings, idx, merged_sparse_inputs.values, merged_sparse_inputs.segments, merged_sparse_inputs.groups, sidx, sseg, device, **device_attr) elif combiner == 'mean': embeddings = embedding_ops.merged_kmean( embeddings, idx, merged_sparse_inputs.values, merged_sparse_inputs.segments, merged_sparse_inputs.groups, sidx, sseg, device, **device_attr) elif combiner == 'tile': embeddings = embedding_ops.merged_tile( embeddings, idx, merged_sparse_inputs.values, merged_sparse_inputs.segments, merged_sparse_inputs.groups, length, reverse, device, **device_attr) else: raise Exception("Unrecognized combiner:" + str(combiner)) emb_info = EmbeddingInfo(name, feature_dim, emb_dim, combiner, None, var, length, embeddings) set_embedding_info([var], emb_info) return embeddings
def train(is_training=True): #np.set_printoptions(threshold='nan') if is_training or xdl.get_task_index() == 0: init() else: return file_type = xdl.parsers.txt if is_training: data_io = xdl.DataIO("tdm", file_type=file_type, fs_type=xdl.fs.hdfs, namenode="hdfs://your/namenode/hdfs/path:9000", enable_state=False) feature_count = 69 for i in xrange(1, feature_count + 1): data_io.feature(name=("item_%s" % i), type=xdl.features.sparse, table=1) data_io.feature(name="unit_id_expand", type=xdl.features.sparse, table=0) data_io.batch_size(intconf('train_batch_size')) data_io.epochs(intconf('train_epochs')) data_io.threads(intconf('train_threads')) data_io.label_count(2) base_path = '%s/%s/' % (conf('upload_url'), conf('data_dir')) data = base_path + conf('train_sample') + '_' + r'[\d]+' sharding = xdl.DataSharding(data_io.fs()) sharding.add_path(data) paths = sharding.partition(rank=xdl.get_task_index(), size=xdl.get_task_num()) print 'train: sharding.partition() =', paths data_io.add_path(paths) iop = xdl.GetIOP("TDMOP") else: data_io = xdl.DataIO("tdm", file_type=file_type, fs_type=xdl.fs.hdfs, namenode="hdfs://your/namenode/hdfs/path:9000", enable_state=False) feature_count = 69 for i in xrange(1, feature_count + 1): data_io.feature(name=("item_%s" % i), type=xdl.features.sparse, table=1) data_io.feature(name="unit_id_expand", type=xdl.features.sparse, table=0) data_io.batch_size(intconf('predict_batch_size')) data_io.epochs(intconf('predict_epochs')) data_io.threads(intconf('predict_threads')) data_io.label_count(2) base_path = '%s/%s/' % (conf('upload_url'), conf('data_dir')) data = base_path + conf('test_sample') data_io.add_path(data) print 'predict: add_path =', data iop = xdl.GetIOP("TDMPREDICTOP") #data_io.finish_delay(True) assert iop is not None key_value = {} key_value["key"] = "value" key_value["debug"] = conf('tdmop_debug') key_value["layer_counts"] = conf('tdmop_layer_counts') key_value["pr_test_each_layer_retrieve_num"] = "400" key_value["pr_test_final_layer_retrieve_num"] = "200" iop.init(key_value) data_io.add_op(iop) data_io.split_group(False) if not is_training: data_io.keep_sample(True) data_io.pause(intconf('predict_io_pause_num'), True) data_io.startup() if not is_training: if xdl.get_task_index() == 0: saver = xdl.Saver() saver.restore(conf('saver_ckpt')) batch = data_io.read() emb_combiner = 'mean' # mean | sum ind = batch["indicators"][0] ids = batch["_ids"][0] emb = [] emb_dim = 24 if is_training: feature_add_probability = 1. else: feature_add_probability = 0. import xdl.python.sparse_engine.embedding as embedding emb_name = "item_emb" for i in xrange(1, feature_count + 1): #emb_name = "item_%s_emb" % i eb = xdl.embedding(emb_name, batch["item_%s" % i], xdl.Normal(stddev=0.001), emb_dim, 50000, emb_combiner, vtype="hash", feature_add_probability=feature_add_probability) with xdl.device('GPU'): eb_take = xdl.take_op(eb, batch["indicators"][0]) eb_take.set_shape(eb.shape) emb.append(eb_take) #emb_name = "unit_id_expand_emb" unit_id_expand_emb = xdl.embedding(emb_name, batch["unit_id_expand"], xdl.Normal(stddev=0.001), emb_dim, 50000, emb_combiner, vtype="hash", feature_add_probability=feature_add_probability) @xdl.mxnet_wrapper(is_training=is_training, device_type='gpu') def dnn_model_define(user_input, indicator, unit_id_emb, label, bs, eb_dim, fea_groups, active_op='prelu', use_batch_norm=True): # 把用户输入按fea_groups划分窗口,窗口内做avg pooling fea_groups = [int(s) for s in fea_groups.split(',')] total_group_length = np.sum(np.array(fea_groups)) print "fea_groups", fea_groups, "total_group_length", total_group_length, "eb_dim", eb_dim user_input_before_reshape = mx.sym.concat(*user_input) user_input = mx.sym.reshape(user_input_before_reshape, shape=(-1, total_group_length, eb_dim)) layer_data = [] # start att att_user_input = mx.sym.reshape(user_input, (bs, total_group_length, eb_dim)) att_node_input = mx.sym.reshape(unit_id_emb, (bs, 1, eb_dim)) att_node_input = mx.sym.broadcast_to(data=att_node_input, shape=(0, total_group_length, 0)) att_din = mx.sym.concat(att_user_input, att_user_input * att_node_input, att_node_input, dim=2) att_active_op = 'prelu' att_layer_arr = [] att_layer1 = FullyConnected3D(3*eb_dim, 36, active_op=att_active_op, version=1, batch_size=bs) att_layer_arr.append(att_layer1) att_layer2 = FullyConnected3D(36, 1, active_op=att_active_op, version=2, batch_size=bs) att_layer_arr.append(att_layer2) layer_data.append(att_din) for layer in att_layer_arr: layer_data.append(layer.call(layer_data[-1])) att_dout = layer_data[-1] att_dout = mx.sym.broadcast_to(data=att_dout, shape=(0, 0, eb_dim)) user_input = mx.sym.reshape(user_input, shape=(bs, -1, eb_dim)) user_input = user_input * att_dout # end att idx = 0 for group_length in fea_groups: block_before_sum = mx.sym.slice_axis(user_input, axis=1, begin=idx, end=idx+group_length) block = mx.sym.sum_axis(block_before_sum, axis=1) / group_length if idx == 0: grouped_user_input = block else: grouped_user_input = mx.sym.concat(grouped_user_input, block, dim=1) idx += group_length indicator = mx.symbol.BlockGrad(indicator) label = mx.symbol.BlockGrad(label) # 按indicator来扩展user fea,然后过网络 #grouped_user_input_after_take = mx.symbol.take(grouped_user_input, indicator) grouped_user_input_after_take = grouped_user_input din = mx.symbol.concat(*[grouped_user_input_after_take, unit_id_emb], dim=1) net_version = "d" layer_arr = [] layer1 = mx_dnn_layer(11 * eb_dim, 128, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (1, net_version)) layer_arr.append(layer1) layer2 = mx_dnn_layer(128, 64, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (2, net_version)) layer_arr.append(layer2) layer3 = mx_dnn_layer(64, 32, active_op=active_op, use_batch_norm=use_batch_norm, version="%d_%s" % (3, net_version)) layer_arr.append(layer3) layer4 = mx_dnn_layer(32, 2, active_op='', use_batch_norm=False, version="%d_%s" % (4, net_version)) layer_arr.append(layer4) #layer_data = [din] layer_data.append(din) for layer in layer_arr: layer_data.append(layer.call(layer_data[-1])) dout = layer_data[-1] # 正常label两列加和必为1,补全的label为0,故减一之后即可得到-1,作为ignore label ph_label_sum = mx.sym.sum(label, axis=1) ph_label_ignore = ph_label_sum - 1 ph_label_ignore = mx.sym.reshape(ph_label_ignore, shape=(-1, 1)) ph_label_click = mx.sym.slice_axis(label, axis=1, begin=1, end=2) ph_label_click = ph_label_click + ph_label_ignore ph_label_click = mx.sym.reshape(ph_label_click, shape=(bs, )) prop = mx.symbol.SoftmaxOutput(data=dout, label=ph_label_click, grad_scale=1.0, use_ignore=True, normalization='valid') origin_loss = mx.sym.log(prop) * label ph_label_sum = mx.sym.reshape(ph_label_sum, shape=(bs, 1)) origin_loss = mx.sym.broadcast_mul(origin_loss, ph_label_sum) loss = - mx.symbol.sum(origin_loss) / mx.sym.sum(ph_label_sum) return prop, loss re = dnn_model_define(emb, batch["indicators"][0], unit_id_expand_emb, batch["label"], data_io._batch_size, emb_dim, '20,20,10,10,2,2,2,1,1,1') prop = re[0] loss = re[1] if is_training: train_op = xdl.Adam(learning_rate=intconf('learning_rate'), lr_decay=False).optimize() #train_op = xdl.SGD(0.1).optimize() #fc_1_weight_grad = xdl.get_gradient("fc_w_1_d") #fc_1_bias_grad = xdl.get_gradient("fc_b_1_d") else: fin = data_io.set_prop(prop=prop) hooks = [] if is_training: if conf("train_mode") == "sync": hooks.append(xdl.SyncRunHook(xdl.get_task_index(), xdl.get_task_num())) if xdl.get_task_index() == 0: ckpt_hook = xdl.CheckpointHook(intconf('save_checkpoint_interval')) hooks.append(ckpt_hook) log_hook = xdl.LoggerHook([loss], "#### loss:{0}") else: log_hook = xdl.LoggerHook([loss], "#### loss:{0}") hooks.append(log_hook) from xdl.python.training.training_utils import get_global_step global_step = get_global_step() sess = xdl.TrainSession(hooks) elapsed_time = 0. statis_begin_loop = 200 loop_num = 0 while not sess.should_stop(): print ">>>>>>>>>>>> %d >>>>>>>>>>>" % loop_num begin_time = time.time() for itr in xrange(200): if is_training: result = sess.run([train_op, xdl.get_collection(xdl.UPDATE_OPS)]) #result = sess.run([train_op, xdl.get_collection(xdl.UPDATE_OPS), unit_id_expand_emb]) else: result = sess.run([loss, fin, global_step.value]) #result = sess.run([loss, fin, ids, global_step.value]) if result is None: print "result is None, finished success." break if not is_training: print "global_step =", result[-1] #print "batch['_ids'] =", result[-2] #else: # print "unit_id_expand_emb = { mean =", result[-1].mean(), ", std =", result[-1].std(), "}" loop_num += 1 if loop_num > statis_begin_loop: elapsed_time += time.time() - begin_time #print 'batch_size = %d, qps = %f batch/s' % (data_io._batch_size, (loop_num - statis_begin_loop) / elapsed_time) if is_training: xdl.execute(xdl.ps_synchronize_leave_op(np.array(xdl.get_task_index(), dtype=np.int32))) if xdl.get_task_index() == 0: print 'start put item_emb' def _string_to_int8(src): return np.array([ord(ch) for ch in src], dtype=np.int8) from xdl.python.utils.config import get_ckpt_dir output_dir = conf('model_url') op = xdl.ps_convert_ckpt_variable_op(checkpoint_dir=_string_to_int8(get_ckpt_dir()), output_dir=_string_to_int8(output_dir), variables=_string_to_int8("item_emb")) xdl.execute(op) shell_cmd("rm -f data/item_emb") shell_cmd("hadoop fs -get %s/item_emb data/item_emb" % output_dir) shell_cmd("sed -i 's/..//' data/item_emb") shell_cmd("hadoop fs -put -f data/item_emb %s" % output_dir) print 'finish put item_emb'