OPT - fix docstring and improve tests slighly (#17228)

* correct some stuff

* fix doc tests

* make style

src/transformers/models/opt/modeling_opt.py

@@ -37,12 +37,12 @@ from .configuration_opt import OPTConfig
 
 logger = logging.get_logger(__name__)
 
-_CHECKPOINT_FOR_DOC = ""
+_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
 _CONFIG_FOR_DOC = "OPTConfig"
 _TOKENIZER_FOR_DOC = "GPT2Tokenizer"
 
 # Base model docstring
-_EXPECTED_OUTPUT_SHAPE = [1, 8, 768]
+_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]
 
 
 OPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
@@ -424,25 +424,6 @@ class OPTPreTrainedModel(PreTrainedModel):
             module.gradient_checkpointing = value
 
 
-OPT_GENERATION_EXAMPLE = r"""
-    Generation example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
-
-    >>> model = OPTForCausalLM.from_pretrained("ArthurZ/opt-350m")
-    >>> tokenizer = GPT2Tokenizer.from_pretrained("patrickvonplaten/opt_gpt2_tokenizer")
-
-    >>> TEXTS_TO_GENERATE = "Hey, are you consciours? Can you talk to me?" "Hi there, my name is Barack"
-    >>> inputs = tokenizer([TEXTS_TO_GENERATE], max_length=1024, return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(inputs["input_ids"], num_beams=2, min_length=0, max_length=20)
-    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    'I'm not conscious.<\s>'
-    ```
-"""
-
 OPT_INPUTS_DOCSTRING = r"""
     Args:
         input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
@@ -933,19 +914,18 @@ class OPTForCausalLM(OPTPreTrainedModel):
         Example:
 
         ```python
-        >>> from transformers import OPTTokenizer, OPTForCausalLM
-        # this needs fixing
-
-        >>> tokenizer = OPTTokenizer.from_pretrained("patrickvonplaten/opt_gpt2_tokenizer")
-        >>> model = OPTForCausalLM.from_pretrained("ArthurZ/opt-350m")
-        >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
-        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-        >>> outputs = model(**inputs)
-
-        >>> logits = outputs.logits
-        >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
-        >>> list(logits.shape) == expected_shape
-        True
+        >>> from transformers import GPT2Tokenizer, OPTForCausalLM
+
+        >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
+        >>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m")
+
+        >>> prompt = "Hey, are you consciours? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
         ```"""
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

tests/models/opt/test_modeling_opt.py

@@ -21,7 +21,7 @@ import unittest
 
 import timeout_decorator  # noqa
 
-from transformers import OPTConfig, is_torch_available, pipeline
+from transformers import OPTConfig, is_torch_available
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 from transformers.utils import cached_property
 
@@ -330,33 +330,61 @@ class OPTEmbeddingsTest(unittest.TestCase):
         assert torch.allclose(logits, logits_meta, atol=1e-4)
 
 
-@require_tokenizers
 @slow
 class OPTGenerationTest(unittest.TestCase):
-    def setUp(self):
-        super().setUp()
-        self.all_model_path = ["facebook/opt-125m", "facebook/opt-350m"]
-
-    def test_generation(self):
-        prompts = [
+    @property
+    def prompts(self):
+        return [
             "Today is a beautiful day and I want to",
             "In the city of",
             "Paris is the capital of France and",
             "Computers and mobile phones have taken",
         ]
-        NEXT_TOKENS = [3392, 764, 5, 81]
-        GEN_OUTPUT = []
 
-        tokenizer = GPT2Tokenizer.from_pretrained("patrickvonplaten/opt_gpt2_tokenizer")
-        for model in self.all_model_path:
-            model = OPTForCausalLM.from_pretrained(self.path_model)
-            model = model.eval()
-            model.config.eos_token_id = tokenizer.eos_token_id
+    def test_generation_pre_attn_layer_norm(self):
+        model_id = "facebook/opt-125m"
+
+        EXPECTED_OUTPUTS = [
+            "Today is a beautiful day and I want to thank",
+            "In the city of Rome Canaver Canaver Canaver Canaver",
+            "Paris is the capital of France and Parisdylib",
+            "Computers and mobile phones have taken precedence over",
+        ]
+
+        predicted_outputs = []
+        tokenizer = GPT2Tokenizer.from_pretrained(model_id)
+        model = OPTForCausalLM.from_pretrained(model_id)
+
+        for prompt in self.prompts:
+            input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+
+            generated_ids = model.generate(input_ids, max_length=10)
+
+            generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+            predicted_outputs += generated_string
+
+        self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)
+
+    def test_generation_post_attn_layer_norm(self):
+        model_id = "facebook/opt-350m"
+
+        EXPECTED_OUTPUTS = [
+            "Today is a beautiful day and I want to share",
+            "In the city of San Francisco, the city",
+            "Paris is the capital of France and the capital",
+            "Computers and mobile phones have taken over the",
+        ]
+
+        predicted_outputs = []
+        tokenizer = GPT2Tokenizer.from_pretrained(model_id)
+        model = OPTForCausalLM.from_pretrained(model_id)
+
+        for prompt in self.prompts:
+            input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+
+            generated_ids = model.generate(input_ids, max_length=10)
 
-            gen = pipeline("text-generation", model=model, tokenizer=tokenizer, return_tensors=True)
+            generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+            predicted_outputs += generated_string
 
-            for prompt in prompts:
-                len_input_sentence = len(tokenizer.tokenize(prompt))
-                predicted_next_token = gen(prompt)[0]["generated_token_ids"][len_input_sentence]
-                GEN_OUTPUT.append(predicted_next_token)
-            self.assertListEqual(GEN_OUTPUT, NEXT_TOKENS)
+        self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)

utils/documentation_tests.txt

@@ -35,6 +35,7 @@ src/transformers/models/marian/modeling_marian.py
 src/transformers/models/mbart/modeling_mbart.py
 src/transformers/models/mobilebert/modeling_mobilebert.py
 src/transformers/models/mobilebert/modeling_tf_mobilebert.py
+src/transformers/models/opt/modeling_opt.py
 src/transformers/models/pegasus/modeling_pegasus.py
 src/transformers/models/plbart/modeling_plbart.py
 src/transformers/models/poolformer/modeling_poolformer.py