def __init__(self, save_interval_step, is_training=True):
super(CheckpointHook, self).__init__()
self._global_step = get_global_step()
self._save_interval = save_interval_step
self._ckpt_dir = get_ckpt_dir()
self._saver = Saver(self._ckpt_dir)
self._is_training = is_training
self._save_cnt = 0
self._first_run = True
def __init__(self, save_interval_step, is_training=True, export_graph=True, as_text=True):
super(CheckpointHook, self).__init__()
self._global_step = get_global_step()
self._save_interval = save_interval_step
self._ckpt_dir = get_ckpt_dir()
self._saver = Saver()
self._is_training = is_training
self._save_cnt = 0
self._first_run = True
self._export_graph = export_graph
self._as_text = as_text
def __init__(self, save_interval_step=None, save_interval_secs=None,
is_training=True, meta=None, tf_backend=False, max_to_keep=5,
tf_graph_name=None):
super(CheckpointHook, self).__init__(priority=3000)
self._global_step = get_global_step()
self._save_interval_step = save_interval_step
self._save_interval_secs = save_interval_secs
self._ckpt_dir = get_ckpt_dir()
self._saver = Saver(self._ckpt_dir, tf_graph_name)
self._is_training = is_training
self._last_save_step = 0
self._last_save_time = time.time()
self._meta = meta
self._max_to_keep = max_to_keep
self._ckpt_queue = []
if self._save_interval_step is None and self._save_interval_secs is None:
print("Checkpoint interval_steps and interval_secs both not set, use default 10000 steps.")
self._save_interval_step = 10000
elif self._save_interval_step is not None and self._save_interval_secs is not None:
raise ValueError("Checkpoint interval_steps and interval_secs can't be both set.")
self.gstep_val = 0
self.meta_val = None
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'
def __init__(self, ckp_model):
super(RestoreFromHook, self).__init__(priority=1000)
self._ckp_model = ckp_model
self._saver = Saver(get_ckpt_dir())
def __init__(self, ckpt_dir=None, tf_graph_name=None):
self._ckpt_dir = ckpt_dir
if self._ckpt_dir is None:
self._ckpt_dir = get_ckpt_dir()
self._graph_def = _graphdef_to_pb(current_graph()._graph_def)
self._tf_graph_name = tf_graph_name
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 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'