Merge branch 'huggingface:main' into pipeline

optimum/intel/generation/modeling.py

@@ -21,26 +21,20 @@
 
 import torch
 from huggingface_hub import hf_hub_download
-from transformers import AutoConfig, AutoModelForCausalLM, PretrainedConfig, PreTrainedModel
+from transformers import AutoConfig, AutoModelForCausalLM, GenerationConfig, PretrainedConfig, PreTrainedModel
+from transformers.generation import GenerationMixin
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from transformers.utils import WEIGHTS_NAME
 
 from optimum.exporters import TasksManager
-from optimum.exporters.onnx import MODEL_TYPES_REQUIRING_POSITION_IDS
 from optimum.modeling_base import OptimizedModel
 from optimum.utils import NormalizedConfigManager
 
 from ..utils.constant import _TASK_ALIASES
-from ..utils.import_utils import is_torch_version, is_transformers_version
+from ..utils.import_utils import is_torch_version
 from ..utils.modeling_utils import MULTI_QUERY_ATTN_MODELS, patch_decoder_attention_mask
 
 
-if is_transformers_version("<", "4.25.0"):
-    from transformers.generation_utils import GenerationMixin
-else:
-    from transformers.generation import GenerationMixin
-
-
 logger = logging.getLogger(__name__)
 
 
@@ -112,12 +106,14 @@ def __init__(
         self.normalized_config = NormalizedConfigManager.get_normalized_config_class(config.model_type)(config)
         self.model_dtype = kwargs.get("model_dtype", None)
 
-        if is_transformers_version("<=", "4.25.1"):
-            self.generation_config = None
+        if isinstance(model, torch.jit.ScriptModule):
+            self.input_names = {
+                inputs.debugName().split(".")[0] for inputs in model.graph.inputs() if inputs.debugName() != "self"
+            }
         else:
-            from transformers import GenerationConfig
+            self.input_names = set()
 
-            self.generation_config = GenerationConfig.from_model_config(config)
+        self.generation_config = GenerationConfig.from_model_config(config)
 
         # Avoid warnings when creating a transformers pipeline
         AutoConfig.register(self.base_model_prefix, AutoConfig)
@@ -267,6 +263,7 @@ def forward(
         position_ids: Optional[torch.FloatTensor] = None,
         **kwargs,
     ) -> CausalLMOutputWithPast:
+        # 1. Prepare model inputs
         if attention_mask is None:
             attention_mask = torch.ones_like(input_ids)
 
@@ -275,6 +272,15 @@ def forward(
             "attention_mask": attention_mask,
         }
 
+        if "position_ids" in self.input_names and position_ids is None:
+            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)
+
+        if "position_ids" in self.input_names or not self.input_names:
+            inputs["position_ids"] = position_ids
+
         model_type = self.config.model_type.replace("_", "-")
 
         if self.use_cache:
@@ -308,17 +314,17 @@ def forward(
 
             inputs["past_key_values"] = past_key_values
 
-        if position_ids is not None and model_type in MODEL_TYPES_REQUIRING_POSITION_IDS:
-            inputs["position_ids"] = position_ids
-
+        # 2. Model forward
         outputs = self.model(**inputs)
 
+        # 3. Process model outputs
         if isinstance(outputs, (list, tuple)):
             logits = outputs[0]
             past_key_values = outputs[1] if self.use_cache else None
         else:
             logits = outputs["logits"]
             past_key_values = outputs["past_key_values"] if self.use_cache else None
+
         return CausalLMOutputWithPast(logits=logits, past_key_values=past_key_values)
 
 

tests/generation/test_modeling.py

@@ -160,3 +160,15 @@ def test_compare_with_and_without_past_key_values(self):
             f"With pkv latency: {with_pkv_timer.elapsed:.3f} ms, without pkv latency: {without_pkv_timer.elapsed:.3f} ms,"
             f" speedup: {without_pkv_timer.elapsed / with_pkv_timer.elapsed:.3f}",
         )
+
+    @parameterized.expand(SUPPORTED_ARCHITECTURES)
+    def test_input_names(self, model_arch):
+        model_id = MODEL_NAMES[model_arch]
+        model = TSModelForCausalLM.from_pretrained(model_id, export=True)
+        self.assertTrue(isinstance(model.input_names, set))
+        self.assertTrue("input_ids" in model.input_names)
+        self.assertTrue("attention_mask" in model.input_names)
+        if model.use_cache:
+            self.assertTrue("past_key_values" in model.input_names)
+        else:
+            self.assertTrue("past_key_values" not in model.input_names)

tests/ipex/test_inference.py

@@ -41,6 +41,7 @@
     "gpt_neo": "hf-internal-testing/tiny-random-GPTNeoModel",
     "gpt_neox": "hf-internal-testing/tiny-random-GPTNeoXForCausalLM",
     "gpt_bigcode": "hf-internal-testing/tiny-random-GPTBigCodeModel",
+    "llama": "fxmarty/tiny-llama-fast-tokenizer",
 }
 
 _CLASSIFICATION_TASK_TO_AUTOMODELS = {
@@ -56,7 +57,7 @@ class IPEXIntegrationTest(unittest.TestCase):
         "roberta",
     )
 
-    TEXT_GENERATION_SUPPORTED_ARCHITECTURES = ("gptj", "gpt2", "gpt_neo", "gpt_bigcode")
+    TEXT_GENERATION_SUPPORTED_ARCHITECTURES = ("gptj", "gpt2", "gpt_neo", "gpt_bigcode", "llama")
 
     QA_SUPPORTED_ARCHITECTURES = (
         "bert",

tests/openvino/test_modeling.py

@@ -480,7 +480,7 @@ class OVModelForCausalLMIntegrationTest(unittest.TestCase):
         "gpt_neo",
         "gpt_neox",
         "llama",
-        "marian",
+        # "marian", # TODO : enable it back with openvino 2023.3.0
         # "mistral",
         "mpt",
         "opt",
@@ -767,7 +767,7 @@ class OVModelForSeq2SeqLMIntegrationTest(unittest.TestCase):
         "blenderbot-small",
         # "longt5",
         "m2m_100",
-        "marian",
+        # "marian", # TODO : enable it back with openvino 2023.3.0
         "mbart",
         "mt5",
         "pegasus",