XGLM: Fix left-padding (PT and TF) (#22828)

src/transformers/models/xglm/modeling_flax_xglm.py

@@ -124,18 +124,6 @@ def create_sinusoidal_positions(n_pos, dim, padding_idx=1):
     return jnp.array(emb)
 
 
-def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray:
-    """
-    Shift input ids one token to the right.
-    """
-    shifted_input_ids = jnp.roll(input_ids, 1, axis=-1)
-    shifted_input_ids = shifted_input_ids.at[(..., 0)].set(decoder_start_token_id)
-    # replace possible -100 values in labels by `pad_token_id`
-    shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
-
-    return shifted_input_ids
-
-
 class FlaxXGLMAttention(nn.Module):
     config: XGLMConfig
     embed_dim: int

src/transformers/models/xglm/modeling_tf_xglm.py

@@ -476,19 +476,8 @@ class TFXGLMMainLayer(tf.keras.layers.Layer):
 
         return combined_attention_mask
 
-    def embed_positions(
-        self,
-        input_ids: Optional[TFModelInputType] = None,
-        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
-        past_key_values_length: Optional[int] = None,
-    ) -> tf.Tensor:
-        if input_ids is not None:
-            position_ids = _create_position_ids_from_input_ids(input_ids, past_key_values_length, self.padding_idx)
-        else:
-            position_ids = _create_position_ids_from_inputs_embeds(
-                inputs_embeds, past_key_values_length, self.padding_idx
-            )
-
+    def embed_positions(self, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None) -> tf.Tensor:
+        position_ids += self.offset
         positions = tf.gather(self._embed_positions_weights, position_ids, axis=0)
         return positions
 
@@ -497,6 +486,7 @@ class TFXGLMMainLayer(tf.keras.layers.Layer):
         self,
         input_ids: Optional[TFModelInputType] = None,
         attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
         encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None,
         encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
         head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
@@ -528,9 +518,14 @@ class TFXGLMMainLayer(tf.keras.layers.Layer):
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
-        # past_key_values_length
         past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
 
+        if position_ids is None:
+            position_ids = tf.expand_dims(
+                tf.range(past_key_values_length, input_shape[-1] + past_key_values_length), axis=0
+            )
+        position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
+
         if inputs_embeds is None:
             # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound
             # indices on GPU, returning zeros instead. This is a dangerous silent behavior.
@@ -552,7 +547,7 @@ class TFXGLMMainLayer(tf.keras.layers.Layer):
             encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1])
 
         # embed positions
-        positions = self.embed_positions(input_ids, inputs_embeds, past_key_values_length)
+        positions = self.embed_positions(position_ids)
 
         hidden_states = tf.cast(inputs_embeds, dtype=tf.float32) + positions
 
@@ -713,6 +708,11 @@ XGLM_INPUTS_DOCSTRING = r"""
             - 0 for tokens that are **masked**.
 
             [What are attention masks?](../glossary#attention-mask)
+        position_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
         encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
             Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
             the decoder.
@@ -796,6 +796,7 @@ class TFXGLMModel(TFXGLMPreTrainedModel):
         self,
         input_ids: Optional[TFModelInputType] = None,
         attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
         encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None,
         encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
         head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
@@ -876,9 +877,6 @@ class TFXGLMForCausalLM(TFXGLMPreTrainedModel, TFCausalLanguageModelingLoss):
             name="lm_head",
         )
 
-        # TODO (Joao): investigate why XGLM has numerical issues in XLA generate
-        self.supports_xla_generation = False
-
     def get_output_embeddings(self):
         return self.lm_head
 
@@ -890,11 +888,18 @@ class TFXGLMForCausalLM(TFXGLMPreTrainedModel, TFCausalLanguageModelingLoss):
         if past_key_values:
             inputs = tf.expand_dims(inputs[:, -1], -1)
 
+        position_ids = kwargs.get("position_ids", None)
         attention_mask = kwargs.get("attention_mask", None)
 
+        if attention_mask is not None and position_ids is None:
+            position_ids = tf.math.cumsum(attention_mask, axis=-1, exclusive=True)
+            if past_key_values:
+                position_ids = tf.expand_dims(position_ids[:, -1], -1)
+
         return {
             "input_ids": inputs,
             "attention_mask": attention_mask,
+            "position_ids": position_ids,
             "past_key_values": past_key_values,
             "use_cache": use_cache,
         }
@@ -911,6 +916,7 @@ class TFXGLMForCausalLM(TFXGLMPreTrainedModel, TFCausalLanguageModelingLoss):
         self,
         input_ids: Optional[TFModelInputType] = None,
         attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
         encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None,
         encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
         head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
@@ -935,6 +941,7 @@ class TFXGLMForCausalLM(TFXGLMPreTrainedModel, TFCausalLanguageModelingLoss):
         outputs = self.model(
             input_ids=input_ids,
             attention_mask=attention_mask,
+            position_ids=position_ids,
             encoder_hidden_states=encoder_hidden_states,
             encoder_attention_mask=encoder_attention_mask,
             head_mask=head_mask,

src/transformers/models/xglm/modeling_xglm.py

@@ -75,11 +75,34 @@ XGLM_INPUTS_DOCSTRING = r"""
             - 0 for tokens that are **masked**.
 
             [What are attention masks?](../glossary#attention-mask)
-        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
+            the decoder.
+        encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+            Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+            selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        head_mask (`torch.Tensor` of shape `(num_layers, attention_heads)`, *optional*):
+            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
 
             - 1 indicates the head is **not masked**,
             - 0 indicates the head is **masked**.
+
+        cross_attn_head_mask (`torch.Tensor` of shape `(num_layers, attention_heads)`, *optional*):
+            Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
         past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
             Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
             `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
@@ -88,20 +111,12 @@ XGLM_INPUTS_DOCSTRING = r"""
             Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
             blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
 
-            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
-            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
-            of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size,
-            sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to
-            directly pass an embedded representation. This is useful if you want more control over how to convert
-            `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. If
-            `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
-            `past_key_values`). This is useful if you want more control over how to convert `input_ids` indices into
-            associated vectors than the model's internal embedding lookup matrix.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape
+            `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you
+            can choose to directly pass an embedded representation. This is useful if you want more control over how to
+            convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
         output_attentions (`bool`, *optional*):
             Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
             tensors for more detail.
@@ -146,18 +161,6 @@ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int]
     return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
 
 
-def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
-    """
-    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
-    are ignored. This is modified from fairseq's `utils.make_positions`.
-    """
-    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
-    mask = input_ids.ne(padding_idx).int()
-    incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
-    return incremental_indices.long() + padding_idx
-
-
-# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding with M2M100->XGLM
 class XGLMSinusoidalPositionalEmbedding(nn.Module):
     """This module produces sinusoidal positional embeddings of any length."""
 
@@ -198,43 +201,17 @@ class XGLMSinusoidalPositionalEmbedding(nn.Module):
         return emb.to(torch.get_default_dtype())
 
     @torch.no_grad()
-    def forward(
-        self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0
-    ):
-        if input_ids is not None:
-            bsz, seq_len = input_ids.size()
-            # Create the position ids from the input token ids. Any padded tokens remain padded.
-            position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to(
-                input_ids.device
-            )
-        else:
-            bsz, seq_len = inputs_embeds.size()[:-1]
-            position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length)
+    def forward(self, position_ids: torch.Tensor = None, past_key_values_length: int = 0):
+        bsz, seq_len = position_ids.size()
+        position_ids += self.offset
 
-        # expand embeddings if needed
-        max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
+        # Expand embeddings if needed. `position_ids.max()` is NOT used to keep torch.fx compatibility.
+        max_pos = 2 + seq_len + past_key_values_length
         if max_pos > self.weights.size(0):
-            self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
+            self.make_weights(max_pos, self.embedding_dim, self.padding_idx)
 
         return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()
 
-    def create_position_ids_from_inputs_embeds(self, inputs_embeds, past_key_values_length):
-        """
-        We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
-
-        Args:
-            inputs_embeds: torch.Tensor
-
-        Returns: torch.Tensor
-        """
-        input_shape = inputs_embeds.size()[:-1]
-        sequence_length = input_shape[1]
-
-        position_ids = torch.arange(
-            self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
-        )
-        return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length
-
 
 class XGLMAttention(nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper"""
@@ -605,6 +582,7 @@ class XGLMModel(XGLMPreTrainedModel):
         self,
         input_ids: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         encoder_attention_mask: Optional[torch.Tensor] = None,
         head_mask: Optional[torch.Tensor] = None,
@@ -616,70 +594,6 @@ class XGLMModel(XGLMPreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
-        r"""
-        Args:
-            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
-                provide it.
-
-                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-                [`PreTrainedTokenizer.__call__`] for details.
-
-                [What are input IDs?](../glossary#input-ids)
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
-                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
-                of the decoder.
-            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
-                Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
-                selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            head_mask (`torch.Tensor` of shape `(num_layers, attention_heads)`, *optional*):
-                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-            cross_attn_head_mask (`torch.Tensor` of shape `(num_layers, attention_heads)`, *optional*):
-                Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
-                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
-                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-
-                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
-                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-
-                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
-                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
-                all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
-                shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
-                `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
-                control over how to convert `input_ids` indices into associated vectors than the model's internal
-                embedding lookup matrix.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -698,9 +612,19 @@ class XGLMModel(XGLMPreTrainedModel):
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
-        # past_key_values_length
         past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
 
+        if position_ids is None:
+            position_ids = torch.arange(
+                past_key_values_length,
+                input_shape[-1] + past_key_values_length,
+                dtype=torch.long,
+                device=input_ids.device if input_ids is not None else inputs_embeds.device,
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
+        else:
+            position_ids = position_ids.view(-1, input_shape[-1])
+
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
 
@@ -713,11 +637,7 @@ class XGLMModel(XGLMPreTrainedModel):
             # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
             encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
 
-        # embed positions
-        positions = self.embed_positions(input_ids, inputs_embeds, past_key_values_length)
-
-        hidden_states = inputs_embeds + positions
-
+        hidden_states = inputs_embeds + self.embed_positions(position_ids, past_key_values_length)
         hidden_states = nn.functional.dropout(hidden_states, p=float(self.dropout), training=self.training)
 
         if self.gradient_checkpointing and self.training:
@@ -866,6 +786,7 @@ class XGLMForCausalLM(XGLMPreTrainedModel):
         self,
         input_ids: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         encoder_attention_mask: Optional[torch.Tensor] = None,
         head_mask: Optional[torch.Tensor] = None,
@@ -895,6 +816,7 @@ class XGLMForCausalLM(XGLMPreTrainedModel):
         outputs = self.model(
             input_ids=input_ids,
             attention_mask=attention_mask,
+            position_ids=position_ids,
             encoder_hidden_states=encoder_hidden_states,
             encoder_attention_mask=encoder_attention_mask,
             head_mask=head_mask,
@@ -935,6 +857,15 @@ class XGLMForCausalLM(XGLMPreTrainedModel):
     def prepare_inputs_for_generation(
         self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs
     ):
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
             # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
             if attention_mask is None:
                 attention_mask = input_ids.new_ones(input_ids.shape)
@@ -945,6 +876,7 @@ class XGLMForCausalLM(XGLMPreTrainedModel):
         return {
             "input_ids": input_ids,  # encoder_outputs is defined. input_ids not needed
             "attention_mask": attention_mask,
+            "position_ids": position_ids,
             "past_key_values": past_key_values,
             "use_cache": use_cache,
         }

tests/models/xglm/test_modeling_tf_xglm.py

@@ -175,44 +175,6 @@ class TFXGLMModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase
                 name = model.get_bias()
                 assert name is None
 
-    @slow
-    def test_batch_generation(self):
-        model = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
-        tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
-
-        tokenizer.padding_side = "left"
-
-        # use different length sentences to test batching
-        sentences = [
-            "Hello, my dog is a little",
-            "Today, I",
-        ]
-
-        inputs = tokenizer(sentences, return_tensors="tf", padding=True)
-
-        outputs = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])
-
-        inputs_non_padded = tokenizer(sentences[0], return_tensors="tf").input_ids
-        output_non_padded = model.generate(input_ids=inputs_non_padded)
-
-        num_paddings = (
-            inputs_non_padded.shape[-1]
-            - tf.math.reduce_sum(tf.cast(inputs["attention_mask"][-1], dtype=tf.int64)).numpy()
-        )
-        inputs_padded = tokenizer(sentences[1], return_tensors="tf").input_ids
-        output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
-
-        batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
-        non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
-        padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
-
-        expected_output_sentence = [
-            "Hello, my dog is a little bit of a shy one, but he is very friendly",
-            "Today, I am going to share with you a few of my favorite things",
-        ]
-        self.assertListEqual(expected_output_sentence, batch_out_sentence)
-        self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
-
     @slow
     def test_model_from_pretrained(self):
         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
@@ -246,6 +208,8 @@ class TFXGLMModelLanguageGenerationTest(unittest.TestCase):
         tf.random.set_seed(0)
         tokenized = tokenizer("Today is a nice day and", return_tensors="tf")
         input_ids = tokenized.input_ids
+        # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
+        with tf.device(":/CPU:0"):
             output_ids = model.generate(input_ids, do_sample=True, seed=[7, 0])
         output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
@@ -255,33 +219,41 @@ class TFXGLMModelLanguageGenerationTest(unittest.TestCase):
         self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
 
     @slow
-    def test_lm_generate_xglm_left_padding(self):
-        """Tests that the generated text is the same, regarless of left padding"""
-        tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
+    def test_batch_generation(self):
         model = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
+        tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
 
         tokenizer.padding_side = "left"
 
-        generation_kwargs = {
-            "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids],
-            "no_repeat_ngram_size": 2,
-            "do_sample": False,
-            "repetition_penalty": 1.3,
-        }
-        expected_output_string = (
-            "Today is a beautiful day and I am so glad that we have the opportunity to spend time with"
-        )
+        # use different length sentences to test batching
+        sentences = [
+            "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
+            "left-padding, such as in batched generation. The output for the sequence below should be the same "
+            "regardless of whether left padding is applied or not. When",
+            "Hello, my dog is a little",
+        ]
+
+        inputs = tokenizer(sentences, return_tensors="tf", padding=True)
+        input_ids = inputs["input_ids"]
+
+        outputs = model.generate(input_ids=input_ids, attention_mask=inputs["attention_mask"], max_new_tokens=12)
 
-        sentences = ["Today is a beautiful day and"]
-        input_ids = tokenizer(sentences, return_tensors="tf", padding=True)
-        # using default length
-        output_ids = model.generate(**input_ids, **generation_kwargs)
-        output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-        self.assertEqual(output_strings[0], expected_output_string)
-
-        sentences = ["Today is a beautiful day and", "This is a very long input that we absolutely don't care about"]
-        input_ids = tokenizer(sentences, return_tensors="tf", padding=True)
-        # longer max length to capture the full length (remember: it is left padded)
-        output_ids = model.generate(**input_ids, **generation_kwargs, max_length=28)
-        output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-        self.assertEqual(output_strings[0], expected_output_string)
+        inputs_non_padded = tokenizer(sentences[0], return_tensors="tf").input_ids
+        output_non_padded = model.generate(input_ids=inputs_non_padded, max_new_tokens=12)
+
+        inputs_padded = tokenizer(sentences[1], return_tensors="tf").input_ids
+        output_padded = model.generate(input_ids=inputs_padded, max_new_tokens=12)
+
+        batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
+        padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
+
+        expected_output_sentence = [
+            "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
+            "left-padding, such as in batched generation. The output for the sequence below should be the same "
+            "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
+            "a single",
+            "Hello, my dog is a little bit of a shy one, but he is very friendly",
+        ]
+        self.assertListEqual(expected_output_sentence, batch_out_sentence)
+        self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])

tests/models/xglm/test_modeling_xglm.py

@@ -340,6 +340,35 @@ class XGLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xglm_weight_initialization(*config_and_inputs)
 
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = XGLMModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+@require_torch
+class XGLMModelLanguageGenerationTest(unittest.TestCase):
+    def _test_lm_generate_xglm_helper(
+        self,
+        gradient_checkpointing=False,
+        verify_outputs=True,
+    ):
+        model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
+        if gradient_checkpointing:
+            model.gradient_checkpointing_enable()
+        else:
+            model.gradient_checkpointing_disable()
+        model.to(torch_device)
+        input_ids = torch.tensor([[2, 268, 9865]], dtype=torch.long, device=torch_device)  # The dog
+        # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
+        # fmt: off
+        expected_output_ids = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
+        # fmt: on
+        output_ids = model.generate(input_ids, do_sample=False, num_beams=1)
+        if verify_outputs:
+            self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
+
     @slow
     def test_batch_generation(self):
         model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
@@ -350,65 +379,39 @@ class XGLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
 
         # use different length sentences to test batching
         sentences = [
+            "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
+            "left-padding, such as in batched generation. The output for the sequence below should be the same "
+            "regardless of whether left padding is applied or not. When",
             "Hello, my dog is a little",
-            "Today, I",
         ]
 
         inputs = tokenizer(sentences, return_tensors="pt", padding=True)
         input_ids = inputs["input_ids"].to(torch_device)
 
         outputs = model.generate(
-            input_ids=input_ids,
-            attention_mask=inputs["attention_mask"].to(torch_device),
+            input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), max_new_tokens=12
         )
 
         inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
-        output_non_padded = model.generate(input_ids=inputs_non_padded)
+        output_non_padded = model.generate(input_ids=inputs_non_padded, max_new_tokens=12)
 
-        num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
         inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
-        output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
+        output_padded = model.generate(input_ids=inputs_padded, max_new_tokens=12)
 
         batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
         non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
         padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
 
         expected_output_sentence = [
+            "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
+            "left-padding, such as in batched generation. The output for the sequence below should be the same "
+            "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
+            "a single",
             "Hello, my dog is a little bit of a shy one, but he is very friendly",
-            "Today, I am going to share with you a few of my favorite things",
         ]
         self.assertListEqual(expected_output_sentence, batch_out_sentence)
         self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
 
-    @slow
-    def test_model_from_pretrained(self):
-        for model_name in XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
-            model = XGLMModel.from_pretrained(model_name)
-            self.assertIsNotNone(model)
-
-
-@require_torch
-class XGLMModelLanguageGenerationTest(unittest.TestCase):
-    def _test_lm_generate_xglm_helper(
-        self,
-        gradient_checkpointing=False,
-        verify_outputs=True,
-    ):
-        model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
-        if gradient_checkpointing:
-            model.gradient_checkpointing_enable()
-        else:
-            model.gradient_checkpointing_disable()
-        model.to(torch_device)
-        input_ids = torch.tensor([[2, 268, 9865]], dtype=torch.long, device=torch_device)  # The dog
-        # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
-        # fmt: off
-        expected_output_ids = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
-        # fmt: on
-        output_ids = model.generate(input_ids, do_sample=False, num_beams=1)
-        if verify_outputs:
-            self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
-
     @slow
     def test_lm_generate_xglm(self):
         self._test_lm_generate_xglm_helper()