1.pad remove; 2.cpu mode

examples/erniesage/config/erniesage_v2_cpu.yaml

@@ -4,9 +4,9 @@
 learner_type: "cpu"
 optimizer_type: "adam"
 lr: 0.00005
-batch_size: 2
-CPU_NUM: 10
-epoch: 20
+batch_size: 4
+CPU_NUM: 16
+epoch: 3
 log_per_step: 1
 save_per_step: 100
 output_path: "./output"
@@ -31,6 +31,7 @@ final_fc: true
 final_l2_norm: true
 loss_type: "hinge"
 margin: 0.3
+neg_type: "random_neg"
 
 # infer config ------
 infer_model: "./output/last"

examples/erniesage/infer.py

@@ -183,5 +183,6 @@ if __name__ == "__main__":
     parser.add_argument("--conf", type=str, default="./config.yaml")
     args = parser.parse_args()
     config = edict(yaml.load(open(args.conf), Loader=yaml.FullLoader))
+    config.loss_type = "hinge"
     print(config)
     main(config)

examples/erniesage/job.sh

-
-unset http_proxy https_proxy
-set -x
-mode=${1:-local}
-config=${2:-"./config.yaml"}
-
-function parse_yaml {
-   local prefix=$2
-   local s='[[:space:]]*' w='[a-zA-Z0-9_]*' fs=$(echo @|tr @ '\034')
-   sed -ne "s|^\($s\):|\1|" \
-        -e "s|^\($s\)\($w\)$s:$s[\"']\(.*\)[\"']$s\$|\1$fs\2$fs\3|p" \
-        -e "s|^\($s\)\($w\)$s:$s\(.*\)$s\$|\1$fs\2$fs\3|p"  $1 |
-   awk -F$fs '{
-      indent = length($1)/2;
-      vname[indent] = $2;
-      for (i in vname) {if (i > indent) {delete vname[i]}}
-      if (length($3) > 0) {
-         vn=""; for (i=0; i<indent; i++) {vn=(vn)(vname[i])("_")}
-         printf("%s%s%s=\"%s\"\n", "'$prefix'",vn, $2, $3);
-      }
-   }'
-}
-eval $(parse_yaml $config)
-
-export CPU_NUM=$CPU_NUM
-export FLAGS_rpc_deadline=3000000 
-export FLAGS_rpc_retry_times=1000
-
-if [[ $async_mode == "True" ]];then
-    echo "async_mode is True"
-else
-    export FLAGS_communicator_send_queue_size=1
-    export FLAGS_communicator_min_send_grad_num_before_recv=0
-    export FLAGS_communicator_max_merge_var_num=1 # important! 
-    export FLAGS_communicator_merge_sparse_grad=0
-fi
-
-export FLAGS_communicator_recv_wait_times=5000000
-
-mkdir -p output
-
-python ./train.py --conf $config
-if [[ $TRAINING_ROLE == "TRAINER" ]];then
-    python ./infer.py --conf $config
-fi

examples/erniesage/learner.py

@@ -26,6 +26,17 @@ from paddle.fluid.incubate.fleet.collective import fleet as cfleet
 from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet as tfleet
 import paddle.fluid.incubate.fleet.base.role_maker as role_maker
 from tensorboardX import SummaryWriter
+from paddle.fluid.transpiler.distribute_transpiler import DistributedMode
+from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import TrainerRuntimeConfig
+
+# hack it!
+base_get_communicator_flags = TrainerRuntimeConfig.get_communicator_flags
+def get_communicator_flags(self):
+    flag_dict = base_get_communicator_flags(self)
+    flag_dict['communicator_max_merge_var_num'] = str(1)
+    flag_dict['communicator_send_queue_size'] = str(1)
+    return flag_dict
+TrainerRuntimeConfig.get_communicator_flags = get_communicator_flags
 
 
 class Learner(object):
@@ -132,8 +143,6 @@ class TranspilerLearner(Learner):
         self.model = model
 
     def optimize(self, loss, optimizer_type, lr):
-        strategy = DistributeTranspilerConfig()
-        strategy.sync_mode = False
         log.info('learning rate:%f' % lr)
         if optimizer_type == "sgd":
             optimizer = F.optimizer.SGD(learning_rate=lr)
@@ -143,7 +152,8 @@ class TranspilerLearner(Learner):
         else:
             raise ValueError("Unknown Optimizer %s" % optimizer_type)
         #create the DistributeTranspiler configure
-        optimizer = tfleet.distributed_optimizer(optimizer, strategy)
+        self.strategy = StrategyFactory.create_sync_strategy()
+        optimizer = tfleet.distributed_optimizer(optimizer, self.strategy)
         optimizer.minimize(loss)
 
     def init_and_run_ps_worker(self, ckpt_path):

examples/erniesage/local_run.sh

@@ -36,7 +36,7 @@ transpiler_local_train(){
     for((i=0;i<${PADDLE_PSERVERS_NUM};i++))
     do
         echo "start ps server: ${i}"
-        TRAINING_ROLE="PSERVER" PADDLE_TRAINER_ID=${i} sh job.sh local $config \
+        TRAINING_ROLE="PSERVER" PADDLE_TRAINER_ID=${i} python ./train.py --conf $config \
             &> $BASE/pserver.$i.log &
         echo $! >> job_id
     done
@@ -44,8 +44,8 @@ transpiler_local_train(){
     for((j=0;j<${PADDLE_TRAINERS_NUM};j++))
     do
         echo "start ps work: ${j}"
-        TRAINING_ROLE="TRAINER" PADDLE_TRAINER_ID=${j} sh job.sh local $config \
-        echo $! >> job_id
+        TRAINING_ROLE="TRAINER" PADDLE_TRAINER_ID=${j} python ./train.py --conf $config 
+        TRAINING_ROLE="TRAINER" PADDLE_TRAINER_ID=${j} python ./infer.py --conf $config 
     done
 }
 

examples/erniesage/models/ernie_model/transformer_encoder.py

@@ -19,8 +19,6 @@ from contextlib import contextmanager
 import paddle.fluid as fluid
 import paddle.fluid.layers as L
 import paddle.fluid.layers as layers
-#import propeller.paddle as propeller
-#from propeller import log
 
 #determin this at the begining
 to_3d = lambda a: a  # will change later
@@ -262,7 +260,6 @@ def encoder_layer(enc_input,
     with the post_process_layer to add residual connection, layer normalization
     and droput.
     """
-    #L.Print(L.reduce_mean(enc_input), message='1')
     attn_output, ctx_multiheads_attn = multi_head_attention(
         pre_process_layer(
             enc_input,
@@ -279,7 +276,6 @@ def encoder_layer(enc_input,
         attention_dropout,
         param_initializer=param_initializer,
         name=name + '_multi_head_att')
-    #L.Print(L.reduce_mean(attn_output), message='1')
     attn_output = post_process_layer(
         enc_input,
         attn_output,
@@ -287,7 +283,6 @@ def encoder_layer(enc_input,
         prepostprocess_dropout,
         name=name + '_post_att')
 
-    #L.Print(L.reduce_mean(attn_output), message='2')
     ffd_output = positionwise_feed_forward(
         pre_process_layer(
             attn_output,
@@ -300,14 +295,12 @@ def encoder_layer(enc_input,
         hidden_act,
         param_initializer=param_initializer,
         name=name + '_ffn')
-    #L.Print(L.reduce_mean(ffd_output), message='3')
     ret = post_process_layer(
         attn_output,
         ffd_output,
         postprocess_cmd,
         prepostprocess_dropout,
         name=name + '_post_ffn')
-    #L.Print(L.reduce_mean(ret), message='4')
     return ret, ctx_multiheads_attn, ffd_output
 
 
@@ -374,7 +367,7 @@ def encoder(enc_input,
     encoder_layer.
     """
 
-    #global to_2d, to_3d  #, batch, seqlen, dynamic_dim
+    global to_2d, to_3d  #, batch, seqlen, dynamic_dim
     d_shape = L.shape(input_mask)
     pad_idx = build_pad_idx(input_mask)
     attn_bias = build_attn_bias(input_mask, n_head, enc_input.dtype)
@@ -391,14 +384,14 @@ def encoder(enc_input,
     # if attn_bias.dtype != enc_input.dtype:
     # attn_bias = L.cast(attn_bias, enc_input.dtype)
 
-    # def to_2d(t_3d):
-        # t_2d = L.gather_nd(t_3d, pad_idx)
-        # return t_2d
+    def to_2d(t_3d):
+        t_2d = L.gather_nd(t_3d, pad_idx)
+        return t_2d
 
-    # def to_3d(t_2d):
-        # t_3d = L.scatter_nd(
-        # pad_idx, t_2d, shape=[d_shape[0], d_shape[1], d_model])
-        # return t_3d
+    def to_3d(t_2d):
+        t_3d = L.scatter_nd(
+        pad_idx, t_2d, shape=[d_shape[0], d_shape[1], d_model])
+        return t_3d
 
     enc_input = to_2d(enc_input)
     all_hidden = []
@@ -456,7 +449,7 @@ def graph_encoder(enc_input,
     encoder_layer.
     """
 
-    #global to_2d, to_3d  #, batch, seqlen, dynamic_dim
+    global to_2d, to_3d  #, batch, seqlen, dynamic_dim
     d_shape = L.shape(input_mask)
     pad_idx = build_pad_idx(input_mask)
     attn_bias = build_graph_attn_bias(input_mask, n_head, enc_input.dtype, slot_seqlen)
@@ -474,14 +467,14 @@ def graph_encoder(enc_input,
     # if attn_bias.dtype != enc_input.dtype:
     # attn_bias = L.cast(attn_bias, enc_input.dtype)
 
-    # def to_2d(t_3d):
-        # t_2d = L.gather_nd(t_3d, pad_idx)
-        # return t_2d
+    def to_2d(t_3d):
+        t_2d = L.gather_nd(t_3d, pad_idx)
+        return t_2d
 
-    # def to_3d(t_2d):
-        # t_3d = L.scatter_nd(
-        # pad_idx, t_2d, shape=[d_shape[0], d_shape[1], d_model])
-        # return t_3d
+    def to_3d(t_2d):
+        t_3d = L.scatter_nd(
+        pad_idx, t_2d, shape=[d_shape[0], d_shape[1], d_model])
+        return t_3d
 
     enc_input = to_2d(enc_input)
     all_hidden = []