Fix 29807 sinusoidal positional encodings in Flaubert, Informer and XLM (#29904)

* Fix sinusoidal_embeddings in FlaubertModel

* Fix for Informer

* Fix for XLM

* Move sinusoidal emb for XLM

* Move sinusoidal emb for Flaubert

* Small cleanup

* Add comments on tests code copied from

* Add with Distilbert->

src/transformers/models/flaubert/modeling_flaubert.py

@@ -58,10 +58,10 @@ from ..deprecated._archive_maps import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST  #
 # Copied from transformers.models.xlm.modeling_xlm.create_sinusoidal_embeddings
 def create_sinusoidal_embeddings(n_pos, dim, out):
     position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
+    out.requires_grad = False
     out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
     out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
     out.detach_()
-    out.requires_grad = False
 
 
 # Copied from transformers.models.xlm.modeling_xlm.get_masks
@@ -370,6 +370,10 @@ class FlaubertPreTrainedModel(PreTrainedModel):
         if isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
+        if isinstance(module, FlaubertModel) and self.config.sinusoidal_embeddings:
+            create_sinusoidal_embeddings(
+                self.config.max_position_embeddings, self.config.emb_dim, out=module.position_embeddings.weight
+            )
 
 
 class FlaubertModel(FlaubertPreTrainedModel):
@@ -407,8 +411,6 @@ class FlaubertModel(FlaubertPreTrainedModel):
 
         # embeddings
         self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.dim)
-        if config.sinusoidal_embeddings:
-            create_sinusoidal_embeddings(config.max_position_embeddings, self.dim, out=self.position_embeddings.weight)
         if config.n_langs > 1 and config.use_lang_emb:
             self.lang_embeddings = nn.Embedding(self.n_langs, self.dim)
         self.embeddings = nn.Embedding(self.n_words, self.dim, padding_idx=self.pad_index)

src/transformers/models/informer/modeling_informer.py

@@ -890,7 +890,7 @@ class InformerPreTrainedModel(PreTrainedModel):
             module.weight.data.normal_(mean=0.0, std=std)
             if module.bias is not None:
                 module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
+        elif isinstance(module, nn.Embedding) and not isinstance(module, InformerSinusoidalPositionalEmbedding):
             module.weight.data.normal_(mean=0.0, std=std)
             if module.padding_idx is not None:
                 module.weight.data[module.padding_idx].zero_()

src/transformers/models/xlm/modeling_xlm.py

@@ -59,10 +59,10 @@ from ..deprecated._archive_maps import XLM_PRETRAINED_MODEL_ARCHIVE_LIST  # noqa
 
 def create_sinusoidal_embeddings(n_pos, dim, out):
     position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
+    out.requires_grad = False
     out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
     out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
     out.detach_()
-    out.requires_grad = False
 
 
 def get_masks(slen, lengths, causal, padding_mask=None):
@@ -245,6 +245,10 @@ class XLMPreTrainedModel(PreTrainedModel):
         if isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
+        if isinstance(module, XLMModel) and self.config.sinusoidal_embeddings:
+            create_sinusoidal_embeddings(
+                self.config.max_position_embeddings, self.config.emb_dim, out=module.position_embeddings.weight
+            )
 
 
 @dataclass
@@ -414,8 +418,6 @@ class XLMModel(XLMPreTrainedModel):
 
         # embeddings
         self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.dim)
-        if config.sinusoidal_embeddings:
-            create_sinusoidal_embeddings(config.max_position_embeddings, self.dim, out=self.position_embeddings.weight)
         if config.n_langs > 1 and config.use_lang_emb:
             self.lang_embeddings = nn.Embedding(self.n_langs, self.dim)
         self.embeddings = nn.Embedding(self.n_words, self.dim, padding_idx=self.pad_index)

tests/models/flaubert/test_modeling_flaubert.py

@@ -36,6 +36,7 @@ if is_torch_available():
         FlaubertModel,
         FlaubertWithLMHeadModel,
     )
+    from transformers.models.flaubert.modeling_flaubert import create_sinusoidal_embeddings
 
 
 class FlaubertModelTester(object):
@@ -431,6 +432,14 @@ class FlaubertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_model(*config_and_inputs)
 
+    # Copied from tests/models/distilbert/test_modeling_distilbert.py with Distilbert->Flaubert
+    def test_flaubert_model_with_sinusoidal_encodings(self):
+        config = FlaubertConfig(sinusoidal_embeddings=True)
+        model = FlaubertModel(config=config)
+        sinusoidal_pos_embds = torch.empty((config.max_position_embeddings, config.emb_dim), dtype=torch.float32)
+        create_sinusoidal_embeddings(config.max_position_embeddings, config.emb_dim, sinusoidal_pos_embds)
+        self.model_tester.parent.assertTrue(torch.equal(model.position_embeddings.weight, sinusoidal_pos_embds))
+
     def test_flaubert_lm_head(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_lm_head(*config_and_inputs)

tests/models/informer/test_modeling_informer.py

@@ -35,7 +35,11 @@ if is_torch_available():
     import torch
 
     from transformers import InformerConfig, InformerForPrediction, InformerModel
-    from transformers.models.informer.modeling_informer import InformerDecoder, InformerEncoder
+    from transformers.models.informer.modeling_informer import (
+        InformerDecoder,
+        InformerEncoder,
+        InformerSinusoidalPositionalEmbedding,
+    )
 
 
 @require_torch
@@ -164,6 +168,12 @@ class InformerModelTester:
 
         self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
 
+        embed_positions = InformerSinusoidalPositionalEmbedding(
+            config.context_length + config.prediction_length, config.d_model
+        )
+        self.parent.assertTrue(torch.equal(model.encoder.embed_positions.weight, embed_positions.weight))
+        self.parent.assertTrue(torch.equal(model.decoder.embed_positions.weight, embed_positions.weight))
+
         with tempfile.TemporaryDirectory() as tmpdirname:
             decoder = model.get_decoder()
             decoder.save_pretrained(tmpdirname)

tests/models/xlm/test_modeling_xlm.py

@@ -36,6 +36,7 @@ if is_torch_available():
         XLMModel,
         XLMWithLMHeadModel,
     )
+    from transformers.models.xlm.modeling_xlm import create_sinusoidal_embeddings
 
 
 class XLMModelTester:
@@ -432,6 +433,14 @@ class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_model(*config_and_inputs)
 
+    # Copied from tests/models/distilbert/test_modeling_distilbert.py with Distilbert->XLM
+    def test_xlm_model_with_sinusoidal_encodings(self):
+        config = XLMConfig(sinusoidal_embeddings=True)
+        model = XLMModel(config=config)
+        sinusoidal_pos_embds = torch.empty((config.max_position_embeddings, config.emb_dim), dtype=torch.float32)
+        create_sinusoidal_embeddings(config.max_position_embeddings, config.emb_dim, sinusoidal_pos_embds)
+        self.model_tester.parent.assertTrue(torch.equal(model.position_embeddings.weight, sinusoidal_pos_embds))
+
     def test_xlm_lm_head(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs)