Add llama onnx export & onnxruntime support

docs/source/exporters/onnx/overview.mdx

@@ -53,6 +53,7 @@ Supported architectures:
 - LayoutLM-v3
 - Levit
 - LongT5
+- Llama
 - M2-M100
 - Marian
 - MBart

optimum/exporters/onnx/model_configs.py

@@ -208,6 +208,11 @@ class OPTOnnxConfig(TextDecoderOnnxConfig):
     NORMALIZED_CONFIG_CLASS = NormalizedTextConfig
 
 
+class LlamaOnnxConfig(TextDecoderOnnxConfig):
+    DEFAULT_ONNX_OPSET = 13
+    NORMALIZED_CONFIG_CLASS = NormalizedTextConfig
+
+
 class BloomDummyPastKeyValuesGenerator(DummyPastKeyValuesGenerator):
     def generate(self, input_name: str, framework: str = "pt"):
         past_key_shape = (

optimum/exporters/tasks.py

@@ -659,6 +659,14 @@ class TasksManager:
             "text-classification",
             onnx="OPTOnnxConfig",
         ),
+        "llama": supported_tasks_mapping(
+            "feature-extraction",
+            "feature-extraction-with-past",
+            "text-generation",
+            "text-generation-with-past",
+            "text-classification",
+            onnx="LlamaOnnxConfig",
+        ),
         "pegasus": supported_tasks_mapping(
             "feature-extraction",
             "feature-extraction-with-past",

optimum/onnxruntime/modeling_decoder.py

@@ -682,7 +682,6 @@ def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwarg
             "use_cache": use_cache,
             "position_ids": None,
             "attention_mask": attention_mask,
-            "token_type_ids": None,
         }
 
     # Copied from transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel._reorder_cache

optimum/onnxruntime/utils.py

@@ -114,6 +114,7 @@ class ORTConfigManager:
         "gptj": "gpt2",
         # longt5 with O4 results in segmentation fault
         "longt5": "bert",
+        "llama": "gpt2",
         "marian": "bart",
         "mbart": "bart",
         "mt5": "bart",

optimum/utils/normalized_config.py

@@ -202,6 +202,7 @@ class NormalizedConfigManager:
         "gpt2": GPT2LikeNormalizedTextConfig,
         "gpt_neo": NormalizedTextConfig.with_args(num_attention_heads="num_heads"),
         "gpt_neox": NormalizedTextConfig,
+        "llama": NormalizedTextConfig,
         "gptj": GPT2LikeNormalizedTextConfig,
         "imagegpt": GPT2LikeNormalizedTextConfig,
         "longt5": T5LikeNormalizedTextConfig,

tests/exporters/exporters_utils.py

@@ -63,6 +63,7 @@
     "levit": "hf-internal-testing/tiny-random-LevitModel",
     "layoutlm": "hf-internal-testing/tiny-random-LayoutLMModel",
     "layoutlmv3": "hf-internal-testing/tiny-random-LayoutLMv3Model",
+    "llama": "fxmarty/tiny-llama-fast-tokenizer",
     "longt5": "fxmarty/tiny-random-working-LongT5Model",
     # "longformer": "allenai/longformer-base-4096",
     "m2m-100": "hf-internal-testing/tiny-random-m2m_100",
@@ -171,6 +172,7 @@
     "levit": "facebook/levit-128S",
     "layoutlm": "microsoft/layoutlm-base-uncased",
     "layoutlmv3": "microsoft/layoutlmv3-base",
+    "llama": "decapoda-research/llama-65b-hf",
     "longt5": "fxmarty/tiny-random-working-LongT5Model",  # Not using google/long-t5-local-base because it takes too much time for testing.
     # "longformer": "allenai/longformer-base-4096",
     "m2m-100": "hf-internal-testing/tiny-random-m2m_100",  # Not using facebook/m2m100_418M because it takes too much time for testing.

tests/onnxruntime/test_modeling.py

@@ -1970,6 +1970,7 @@ class ORTModelForCausalLMIntegrationTest(ORTModelTestMixin):
         "gpt_neo",
         "gpt_neox",
         "gptj",
+        "llama",
     ]
 
     FULL_GRID = {
@@ -2021,7 +2022,7 @@ def test_generate_utils(self, test_name: str, model_arch: str, use_cache: str):
         model = ORTModelForCausalLM.from_pretrained(self.onnx_model_dirs[test_name])
         tokenizer = get_preprocessor(model_id)
         text = "This is a sample output"
-        tokens = tokenizer(text, return_tensors="pt")
+        tokens = tokenizer(text, return_tensors="pt", return_token_type_ids=False if model_arch == "llama" else None)
 
         # General case
         outputs = model.generate(**tokens)
@@ -2030,7 +2031,7 @@ def test_generate_utils(self, test_name: str, model_arch: str, use_cache: str):
         self.assertTrue(len(res[0]) > len(text))
 
         # With input ids
-        tokens = tokenizer(text, return_tensors="pt")
+        tokens = tokenizer(text, return_tensors="pt", return_token_type_ids=False if model_arch == "llama" else None)
         outputs = model.generate(input_ids=tokens["input_ids"])
         res = tokenizer.batch_decode(outputs, skip_special_tokens=True)
         self.assertIsInstance(res[0], str)
@@ -2118,7 +2119,11 @@ def test_compare_to_transformers(self, test_name: str, model_arch: str, use_cach
         set_seed(SEED)
         transformers_model = AutoModelForCausalLM.from_pretrained(model_id)
         tokenizer = get_preprocessor(model_id)
-        tokens = tokenizer("This is a sample output", return_tensors="pt")
+        tokens = tokenizer(
+            "This is a sample output",
+            return_tensors="pt",
+            return_token_type_ids=False if model_arch == "llama" else None,
+        )
         onnx_outputs = onnx_model(**tokens)
 
         self.assertTrue("logits" in onnx_outputs)
@@ -2217,12 +2222,16 @@ def test_pipeline_on_trt_execution_provider(self, test_name: str, model_arch: st
 
             # build engine for a short sequence
             text = ["short"]
-            encoded_input = tokenizer(text, return_tensors="pt").to("cuda")
+            encoded_input = tokenizer(
+                text, return_tensors="pt", return_token_type_ids=False if model_arch == "llama" else None
+            ).to("cuda")
             _ = onnx_model(**encoded_input)
 
             # build engine for a long sequence
             text = [" a very long input just for demo purpose, this is very long" * 10]
-            encoded_input = tokenizer(text, return_tensors="pt").to("cuda")
+            encoded_input = tokenizer(
+                text, return_tensors="pt", return_token_type_ids=False if model_arch == "llama" else None
+            ).to("cuda")
             _ = onnx_model(**encoded_input)
 
             pipe = pipeline("text-generation", model=onnx_model, tokenizer=tokenizer, device=0)
@@ -2235,7 +2244,11 @@ def test_pipeline_on_trt_execution_provider(self, test_name: str, model_arch: st
             self.assertTrue(isinstance(outputs[0]["generated_text"], str))
             self.assertTrue(len(outputs[0]["generated_text"]) > len(text))
 
-            encoded_input = tokenizer(["Replace me by any text you'd like."], return_tensors="pt").to("cuda")
+            encoded_input = tokenizer(
+                ["Replace me by any text you'd like."],
+                return_tensors="pt",
+                return_token_type_ids=False if model_arch == "llama" else None,
+            ).to("cuda")
             _ = onnx_model.generate(**encoded_input)
 
             gc.collect()
@@ -2251,7 +2264,7 @@ def test_compare_with_and_without_past_key_values(self, model_arch):
         model_id = MODEL_NAMES[model_arch]
         tokenizer = get_preprocessor(model_id)
         text = "My Name is Philipp and i live"
-        tokens = tokenizer(text, return_tensors="pt")
+        tokens = tokenizer(text, return_tensors="pt", return_token_type_ids=False if model_arch == "llama" else None)
 
         model_with_pkv = ORTModelForCausalLM.from_pretrained(
             self.onnx_model_dirs[model_arch + "_True"], use_cache=True
@@ -2302,7 +2315,7 @@ def test_compare_merged_and_not_merged_models_outputs(self, test_name: str, mode
         model_id = MODEL_NAMES[model_arch]
         tokenizer = get_preprocessor(model_id)
         text = "My Name is Philipp and i live"
-        tokens = tokenizer(text, return_tensors="pt")
+        tokens = tokenizer(text, return_tensors="pt", return_token_type_ids=False if model_arch == "llama" else None)
 
         model_not_merged_dir = self.onnx_model_dirs[test_name + "_False"]
         model_merged_dir = self.onnx_model_dirs[test_name + "_True"]
@@ -2372,7 +2385,11 @@ def test_compare_generation_to_io_binding(self, test_name: str, model_arch: str,
         io_model = ORTModelForCausalLM.from_pretrained(self.onnx_model_dirs[test_name], use_io_binding=True).to("cuda")
 
         tokenizer = get_preprocessor(model_id)
-        tokens = tokenizer("This is a sample output", return_tensors="pt").to("cuda")
+        tokens = tokenizer(
+            "This is a sample output",
+            return_tensors="pt",
+            return_token_type_ids=False if model_arch == "llama" else None,
+        ).to("cuda")
         onnx_outputs = onnx_model.generate(**tokens)
         io_outputs = io_model.generate(**tokens)
 

tests/onnxruntime/utils_onnxruntime_tests.py

@@ -49,6 +49,7 @@
     "layoutlm": "hf-internal-testing/tiny-random-LayoutLMModel",
     "layoutlmv3": "hf-internal-testing/tiny-random-LayoutLMv3Model",
     "longt5": "hf-internal-testing/tiny-random-LongT5Model",
+    "llama": "fxmarty/tiny-llama-fast-tokenizer",
     "m2m_100": "hf-internal-testing/tiny-random-m2m_100",
     "marian": "sshleifer/tiny-marian-en-de",  # hf-internal-testing ones are broken
     "mbart": "hf-internal-testing/tiny-random-mbart",