wide_and_deep gpu host_device

model_zoo/official/recommend/wide_and_deep/script/run_multigpu_train_host_device.sh

@@ -0,0 +1,34 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+# bash run_multigpu_train.sh RANK_SIZE EPOCH_SIZE DATASET
+script_self=$(readlink -f "$0")
+self_path=$(dirname "${script_self}")
+RANK_SIZE=$1
+EPOCH_SIZE=$2
+DATASET=$3
+VOCAB_SIZE=$4
+EMB_DIM=$5
+
+mpirun --allow-run-as-root -n $RANK_SIZE                       \
+    python -s ${self_path}/../train_and_eval_auto_parallel.py  \
+        --device_target="GPU"                                  \
+        --data_path=$DATASET                                   \
+        --epochs=$EPOCH_SIZE                                   \
+        --vocab_size=$VOCAB_SIZE                               \
+        --emb_dim=$EMB_DIM                                     \
+        --dropout_flag=1                                       \
+        --host_device_mix=1 > log.txt 2>&1 &

model_zoo/official/recommend/wide_and_deep/src/callbacks.py

@@ -18,6 +18,7 @@ import time
 from mindspore.train.callback import Callback
 from mindspore import context
 from mindspore.train import ParallelMode
+from mindspore.communication.management import get_rank
 
 def add_write(file_path, out_str):
     """
@@ -52,7 +53,14 @@ class LossCallBack(Callback):
         wide_loss, deep_loss = cb_params.net_outputs[0].asnumpy(), cb_params.net_outputs[1].asnumpy()
         cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
         cur_num = cb_params.cur_step_num
-        print("===loss===", cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss, flush=True)
+        rank_id = 0
+        parallel_mode = context.get_auto_parallel_context("parallel_mode")
+        if parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL,
+                             ParallelMode.DATA_PARALLEL):
+            rank_id = get_rank()
+
+        print("===loss===", rank_id, cb_params.cur_epoch_num, cur_step_in_epoch,
+              wide_loss, deep_loss, flush=True)
 
         # raise ValueError
         if self._per_print_times != 0 and cur_num % self._per_print_times == 0 and self.config is not None:
@@ -99,13 +107,18 @@ class EvalCallBack(Callback):
         if parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL):
             context.set_auto_parallel_context(strategy_ckpt_save_file="",
                                               strategy_ckpt_load_file="./strategy_train.ckpt")
+        rank_id = 0
+        if parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL,
+                             ParallelMode.DATA_PARALLEL):
+            rank_id = get_rank()
         start_time = time.time()
         out = self.model.eval(self.eval_dataset, dataset_sink_mode=(not self.host_device_mix))
         end_time = time.time()
         eval_time = int(end_time - start_time)
 
         time_str = time.strftime("%Y-%m-%d %H:%M%S", time.localtime())
-        out_str = "{}==== EvalCallBack model.eval(): {}; eval_time: {}s".format(time_str, out.values(), eval_time)
+        out_str = "{} == Rank: {} == EvalCallBack model.eval(): {}; eval_time: {}s".\
+            format(time_str, rank_id, out.values(), eval_time)
         print(out_str)
         self.eval_values = out.values()
         add_write(self.eval_file_name, out_str)

model_zoo/official/recommend/wide_and_deep/src/wide_and_deep.py

@@ -201,6 +201,7 @@ class WideDeepModel(nn.Cell):
         self.cast = P.Cast()
         if is_auto_parallel and host_device_mix:
             self.dense_layer_1.dropout.dropout_do_mask.set_strategy(((1, get_group_size()),))
+            self.dense_layer_1.dropout.dropout.set_strategy(((1, get_group_size()),))
             self.dense_layer_1.matmul.set_strategy(((1, get_group_size()), (get_group_size(), 1)))
             self.deep_embeddinglookup = nn.EmbeddingLookup(self.vocab_size, self.emb_dim,
                                                            slice_mode=nn.EmbeddingLookUpSplitMode.TABLE_COLUMN_SLICE)

model_zoo/official/recommend/wide_and_deep/train_and_eval_auto_parallel.py

@@ -32,13 +32,6 @@ from src.metrics import AUCMetric
 from src.config import WideDeepConfig
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True)
-context.set_context(variable_memory_max_size="24GB")
-context.set_context(enable_sparse=True)
-cost_model_context.set_cost_model_context(multi_subgraphs=True)
-init()
-
-
 
 def get_WideDeep_net(config):
     """
@@ -131,6 +124,14 @@ def train_and_eval(config):
 if __name__ == "__main__":
     wide_deep_config = WideDeepConfig()
     wide_deep_config.argparse_init()
+    context.set_context(mode=context.GRAPH_MODE, device_target=wide_deep_config.device_target, save_graphs=True)
+    context.set_context(variable_memory_max_size="24GB")
+    context.set_context(enable_sparse=True)
+    cost_model_context.set_cost_model_context(multi_subgraphs=True)
+    if wide_deep_config.device_target == "Ascend":
+        init("hccl")
+    elif wide_deep_config.device_target == "GPU":
+        init("nccl")
     if wide_deep_config.host_device_mix == 1:
         context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, mirror_mean=True)
     else:

model_zoo/official/recommend/wide_and_deep_multitable/train_and_eval_distribute.py

@@ -16,6 +16,7 @@
 
 import os
 import sys
+import numpy as np
 from mindspore import Model, context
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
 from mindspore.train.callback import TimeMonitor
@@ -68,6 +69,7 @@ def train_and_eval(config):
     """
     train_and_eval
     """
+    np.random.seed(1000)
     data_path = config.data_path
     epochs = config.epochs
     print("epochs is {}".format(epochs))