gnmt add hub_config

model_zoo/official/nlp/gnmt_v2/README.md

@@ -135,11 +135,13 @@ The GNMT network script and code result are as follows:
   │      ├──lr_scheduler.py                  // Learning rate scheduler.
   │      ├──optimizer.py                     // Optimizer.
   ├── scripts
-  │   ├──run_distributed_train_ascend.sh     // shell script for distributed train on ascend.
-  │   ├──run_standalone_eval_ascend.sh       // shell script for standalone eval on ascend.
-  │   ├──run_standalone_train_ascend.sh      // shell script for standalone eval on ascend.
-  ├── create_dataset.py                      // dataset preparation.
+  │   ├──run_distributed_train_ascend.sh     // Shell script for distributed train on ascend.
+  │   ├──run_standalone_eval_ascend.sh       // Shell script for standalone eval on ascend.
+  │   ├──run_standalone_train_ascend.sh      // Shell script for standalone eval on ascend.
+  ├── create_dataset.py                      // Dataset preparation.
   ├── eval.py                                // Infer API entry.
+  ├── export.py                              // Export checkpoint file into air models.
+  ├── mindspore_hub_conf.py                  // Hub config.
   ├── requirements.txt                       // Requirements of third party package.
   ├── train.py                               // Train API entry.
 ```

model_zoo/official/nlp/gnmt_v2/mindspore_hub_conf.py

@@ -0,0 +1,40 @@
+# 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.
+# ============================================================================
+"""hub config."""
+import mindspore.common.dtype as mstype
+
+from config import GNMTConfig
+from src.gnmt_model import GNMTNetworkWithLoss, GNMT
+
+
+def get_config(config):
+    config = GNMTConfig.from_json_file(config)
+    config.compute_type = mstype.float16
+    config.dtype = mstype.float32
+    return config
+
+
+def create_network(name, *args, **kwargs):
+    """create gnmt network."""
+    if name == "gnmt":
+        if "config" in kwargs:
+            config = get_config(kwargs["config"])
+        else:
+            raise NotImplementedError(f"Please make sure the configuration file path is correct")
+        is_training = kwargs.get("is_training", False)
+        if is_training:
+            return GNMTNetworkWithLoss(config, is_training=is_training, *args)
+        return GNMT(config, *args)
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/nlp/gnmt_v2/src/gnmt_model/beam_search.py

@@ -172,6 +172,7 @@ class BeamSearchDecoder(nn.Cell):
                  max_decode_length=64,
                  sos_id=2,
                  eos_id=3,
+                 is_using_while=False,
                  compute_type=mstype.float32):
         super(BeamSearchDecoder, self).__init__()
 
@@ -185,6 +186,7 @@ class BeamSearchDecoder(nn.Cell):
         self.cov_penalty_factor = cov_penalty_factor
         self.max_decode_length = max_decode_length
         self.decoder = decoder
+        self.is_using_while = is_using_while
 
         self.add = P.TensorAdd()
         self.expand = P.ExpandDims()
@@ -215,7 +217,12 @@ class BeamSearchDecoder(nn.Cell):
         self.gather_nd = P.GatherNd()
 
         self.start_ids = Tensor(np.full([batch_size * beam_width, 1], sos_id), mstype.int32)
-        self.init_seq = Tensor(np.full([batch_size, beam_width, 1], sos_id), mstype.int32)
+        if self.is_using_while:
+            self.start = Tensor(0, dtype=mstype.int32)
+            self.init_seq = Tensor(np.full([batch_size, beam_width, self.max_decode_length], sos_id),
+                                   mstype.int32)
+        else:
+            self.init_seq = Tensor(np.full([batch_size, beam_width, 1], sos_id), mstype.int32)
 
         init_scores = np.tile(np.array([[0.] + [-INF] * (beam_width - 1)]), [batch_size, 1])
         self.init_scores = Tensor(init_scores, mstype.float32)
@@ -259,7 +266,7 @@ class BeamSearchDecoder(nn.Cell):
         self.sub = P.Sub()
 
     def one_step(self, cur_input_ids, enc_states, enc_attention_mask, state_log_probs,
-                 state_seq, state_length, decoder_hidden_state=None, accu_attn_scores=None,
+                 state_seq, state_length, idx=None, decoder_hidden_state=None, accu_attn_scores=None,
                  state_finished=None):
         """
         Beam search one_step output.
@@ -359,7 +366,13 @@ class BeamSearchDecoder(nn.Cell):
                                           self.hidden_size))
 
         # update state_seq
-        state_seq = self.concat((seq, self.expand(word_indices, -1)))
+        if self.is_using_while:
+            state_seq_new = self.cast(seq, mstype.float32)
+            word_indices_fp32 = self.cast(word_indices, mstype.float32)
+            state_seq_new[:, :, idx] = word_indices_fp32
+            state_seq = self.cast(state_seq_new, mstype.int32)
+        else:
+            state_seq = self.concat((seq, self.expand(word_indices, -1)))
 
         cur_input_ids = self.reshape(word_indices, (-1, 1))
         state_log_probs = topk_scores
@@ -388,11 +401,22 @@ class BeamSearchDecoder(nn.Cell):
         decoder_hidden_state = self.decoder_hidden_state
         accu_attn_scores = self.accu_attn_scores
 
-        for _ in range(self.max_decode_length + 1):
-            cur_input_ids, state_log_probs, state_seq, state_length, decoder_hidden_state, accu_attn_scores, \
-            state_finished = self.one_step(cur_input_ids, enc_states, enc_attention_mask, state_log_probs,
-                                           state_seq, state_length, decoder_hidden_state, accu_attn_scores,
-                                           state_finished)
+        if not self.is_using_while:
+            for _ in range(self.max_decode_length + 1):
+                cur_input_ids, state_log_probs, state_seq, state_length, decoder_hidden_state, accu_attn_scores, \
+                state_finished = self.one_step(cur_input_ids, enc_states, enc_attention_mask, state_log_probs,
+                                               state_seq, state_length, None, decoder_hidden_state, accu_attn_scores,
+                                               state_finished)
+        else:
+            idx = self.start + 1
+            ends = self.start + self.max_decode_length + 1
+            while idx < ends:
+                cur_input_ids, state_log_probs, state_seq, state_length, decoder_hidden_state, accu_attn_scores, \
+                state_finished = self.one_step(cur_input_ids, enc_states, enc_attention_mask, state_log_probs,
+                                               state_seq, state_length, idx, decoder_hidden_state, accu_attn_scores,
+                                               state_finished)
+                idx = idx + 1
+
         # add length penalty scores
         penalty_len = self.length_penalty(state_length)
         # return penalty_len
@@ -408,6 +432,9 @@ class BeamSearchDecoder(nn.Cell):
         gather_indices = self.concat((self.expand(self.batch_ids, -1), self.expand(top_beam_indices, -1)))
         # sort sequence and attention scores
         predicted_ids = self.gather_nd(state_seq, gather_indices)
-        predicted_ids = predicted_ids[:, 0:1, 1:(self.max_decode_length + 1)]
+        if not self.is_using_while:
+            predicted_ids = predicted_ids[:, 0:1, 1:(self.max_decode_length + 1)]
+        else:
+            predicted_ids = predicted_ids[:, 0:1, :self.max_decode_length]
 
         return predicted_ids