[OV]: load and convert llms in original precision

optimum/exporters/openvino/__main__.py

@@ -21,12 +21,17 @@
 from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
 from requests.exceptions import ConnectionError as RequestsConnectionError
 from transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizerBase
+from transformers.utils import is_torch_available
 
 from optimum.exporters import TasksManager
 from optimum.exporters.onnx.base import OnnxConfig
 from optimum.exporters.onnx.constants import SDPA_ARCHS_ONNX_EXPORT_NOT_SUPPORTED
 from optimum.exporters.openvino.convert import export_from_model
-from optimum.intel.utils.import_utils import is_openvino_tokenizers_available, is_transformers_version
+from optimum.intel.utils.import_utils import (
+    is_openvino_tokenizers_available,
+    is_openvino_version,
+    is_transformers_version,
+)
 from optimum.utils.save_utils import maybe_load_preprocessors
 
 from .utils import clear_class_registry
@@ -35,6 +40,11 @@
 if TYPE_CHECKING:
     from optimum.intel.openvino.configuration import OVConfig
 
+
+if is_torch_available():
+    import torch
+
+
 _COMPRESSION_OPTIONS = {
     "int8": {"bits": 8},
     "int4_sym_g128": {"bits": 4, "sym": True, "group_size": 128},
@@ -100,6 +110,7 @@ def main_export(
     stateful: bool = True,
     convert_tokenizer: bool = False,
     library_name: Optional[str] = None,
+    model_loading_kwargs: Optional[Dict[str, Any]] = None,
     **kwargs_shapes,
 ):
     """
@@ -230,7 +241,8 @@ def main_export(
 
     do_gptq_patching = False
     custom_architecture = False
-    loading_kwargs = {}
+    patch_16bit = False
+    loading_kwargs = model_loading_kwargs or {}
     if library_name == "transformers":
         config = AutoConfig.from_pretrained(
             model_name_or_path,
@@ -281,11 +293,32 @@ def main_export(
                 "Please provide custom export config if you want load model with remote code."
             )
             trust_remote_code = False
+    dtype = loading_kwargs.get("torch_dtype")
+    if isinstance(dtype, str):
+        dtype = config.torch_dtype if dtype == "auto" else getattr(torch, dtype)
 
+    if (
+        dtype is None
+        and framework == "pt"
+        and not do_gptq_patching
+        and task.startswith("text-generation")
+        and getattr(config, "torch_dtype", torch.float32) in [torch.float16, torch.bfloat16]
+    ):
+        if ov_config is not None and ov_config.dtype in {"fp16", "fp32"}:
+            dtype = torch.float16 if ov_config.dtype == "fp16" else torch.float32
+        elif is_openvino_version(">=", "2024.2") and config.torch_dtype == torch.float16:
+            dtype = torch.float16
+        elif is_openvino_version(">=", "2024.3") and config.torch_dtype == torch.bfloat16:
+            dtype = torch.bfloat16
+
+    if dtype is not None:
+        if dtype in [torch.float16, torch.bfloat16]:
+            patch_16bit = True
+        loading_kwargs["torch_dtype"] = dtype
+
+    logger.warning(loading_kwargs)
     # Patch the modules to export of GPTQ models w/o GPU
     if do_gptq_patching:
-        import torch
-
         torch.set_default_dtype(torch.float32)
         orig_cuda_check = torch.cuda.is_available
         torch.cuda.is_available = lambda: True
@@ -383,6 +416,7 @@ class StoreAttr(object):
         preprocessors=preprocessors,
         device=device,
         trust_remote_code=trust_remote_code,
+        patch_16bit_model=patch_16bit,
         **kwargs_shapes,
     )
 

optimum/exporters/openvino/convert.py

@@ -104,6 +104,7 @@ def export(
     model_kwargs: Optional[Dict[str, Any]] = None,
     ov_config: Optional["OVConfig"] = None,
     stateful: bool = True,
+    patch_16bit_model: bool = False,
 ) -> Tuple[List[str], List[str]]:
     """
     Exports a Pytorch or TensorFlow model to an OpenVINO Intermediate Representation.
@@ -155,6 +156,7 @@ def export(
             ov_config=ov_config,
             model_kwargs=model_kwargs,
             stateful=stateful,
+            patch_16bit_model=patch_16bit_model,
         )
 
     elif is_tf_available() and issubclass(type(model), TFPreTrainedModel):
@@ -288,6 +290,7 @@ def export_pytorch(
     model_kwargs: Optional[Dict[str, Any]] = None,
     ov_config: Optional["OVConfig"] = None,
     stateful: bool = False,
+    patch_16bit_model: bool = False,
 ) -> Tuple[List[str], List[str]]:
     """
     Exports a PyTorch model to an OpenVINO Intermediate Representation.
@@ -380,6 +383,10 @@ def ts_patched_forward(*args, **kwargs):
             patcher.patched_forward = ts_patched_forward
 
             with patcher:
+                if patch_16bit_model:
+                    from openvino.frontend.pytorch.patch_model import __make_16bit_traceable
+
+                    __make_16bit_traceable(model)
                 check_dummy_inputs_are_allowed(model, dummy_inputs)
                 sig = inspect.signature(model.forward) if hasattr(model, "forward") else inspect.signature(model.call)
                 inputs = config.ordered_inputs(model)
@@ -400,6 +407,13 @@ def ts_patched_forward(*args, **kwargs):
                     "A stateless model will be exported instead. It may result in sub-optimal inference performance."
                     "Provide a model that can be converted to OpenVINO without fallback to ONNX conversion path."
                 )
+
+            if patch_16bit_model:
+                from openvino.frontend.pytorch.patch_model import unpatch_model
+
+                unpatch_model(model, "_openvino_module_extension_patch_orig_forward")
+                model.to(torch.float32)
+
             return export_pytorch_via_onnx(
                 model,
                 config,
@@ -466,6 +480,7 @@ def export_models(
     model_kwargs: Optional[Dict[str, Any]] = None,
     ov_config: Optional["OVConfig"] = None,
     stateful: bool = True,
+    patch_16bit_model: bool = False,
 ) -> Tuple[List[List[str]], List[List[str]]]:
     """
     Export the models to OpenVINO IR format
@@ -517,6 +532,7 @@ def export_models(
                 model_kwargs=model_kwargs,
                 ov_config=ov_config,
                 stateful=stateful,
+                patch_16bit_model=patch_16bit_model,
             )
         )
 
@@ -537,6 +553,7 @@ def export_from_model(
     preprocessors: List = None,
     device: str = "cpu",
     trust_remote_code: bool = False,
+    patch_16bit_model: bool = False,
     **kwargs_shapes,
 ):
     model_kwargs = model_kwargs or {}
@@ -699,6 +716,7 @@ def export_from_model(
         stateful=stateful,
         opset=opset,
         model_kwargs=model_kwargs,
+        patch_16bit_model=patch_16bit_model,
     )
 
 

optimum/exporters/openvino/model_configs.py

@@ -16,13 +16,15 @@
 from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
 
 from packaging import version
+from transformers import PreTrainedModel, TFPreTrainedModel
 from transformers.utils import is_tf_available
 
 from optimum.exporters.onnx.config import TextDecoderOnnxConfig, TextDecoderWithPositionIdsOnnxConfig
 from optimum.exporters.onnx.model_configs import (
     CodeGenOnnxConfig,
     FalconOnnxConfig,
     GemmaOnnxConfig,
+    GPTNeoXOnnxConfig,
     LlamaOnnxConfig,
     MistralOnnxConfig,
     MPTOnnxConfig,
@@ -31,6 +33,7 @@
     VaeDecoderOnnxConfig,
     VaeEncoderOnnxConfig,
 )
+from optimum.exporters.onnx.model_patcher import ModelPatcher
 from optimum.exporters.tasks import TasksManager
 from optimum.utils import DEFAULT_DUMMY_SHAPES
 from optimum.utils.input_generators import (
@@ -50,6 +53,9 @@
     ChatGLMModelPatcher,
     CodeGenModelPatcher,
     DBRXModelPatcher,
+    FalconModelPatcher,
+    GptNeoxJapaneseModelPatcher,
+    GptNeoxModelPatcher,
     InternLM2Patcher,
     InternLMModelPatcher,
     JaisModelPatcher,
@@ -60,6 +66,7 @@
     PersimmonModelPatcher,
     Phi3ModelPatcher,
     QwenModelPatcher,
+    RotaryEmbPatcher,
     UpdateCausalMaskModelPatcher,
     XverseModelPatcher,
 )
@@ -490,6 +497,12 @@ def patch_model_for_export(
         return UpdateCausalMaskModelPatcher(self, model, model_kwargs=model_kwargs)
 
 
+def patch_model_for_export(
+    self, model: Union["PreTrainedModel", "TFPreTrainedModel"], model_kwargs: Optional[Dict[str, Any]] = None
+) -> "ModelPatcher":
+    return RotaryEmbPatcher(self, model, model_kwargs=model_kwargs)
+
+
 @register_in_tasks_manager("internlm2", *["text-generation", "text-generation-with-past"], library_name="transformers")
 class InternLM2OpenVINOConfig(TextDecoderWithPositionIdsOnnxConfig):
     DEFAULT_ONNX_OPSET = 14
@@ -617,6 +630,11 @@ class FalconOpenVINOConfig(FalconOnnxConfig):
     ) + TextDecoderOnnxConfig.DUMMY_INPUT_GENERATOR_CLASSES
     DUMMY_PKV_GENERATOR_CLASS = OVFalconDummyPastKeyValuesGenerator
 
+    def patch_model_for_export(
+        self, model: Union["PreTrainedModel", "TFPreTrainedModel"], model_kwargs: Optional[Dict[str, Any]] = None
+    ) -> "ModelPatcher":
+        return FalconModelPatcher(self, model, model_kwargs=model_kwargs)
+
 
 @register_in_tasks_manager("unet", *["semantic-segmentation"], library_name="diffusers")
 class UNetOpenVINOConfig(UNetOnnxConfig):
@@ -710,6 +728,11 @@ class GPTNeoxJapaneseOpenVINOConfig(TextDecoderOnnxConfig):
     DEFAULT_ONNX_OPSET = 13
     NORMALIZED_CONFIG_CLASS = NormalizedTextConfig
 
+    def patch_model_for_export(
+        self, model: Union["PreTrainedModel", "TFPreTrainedModel"], model_kwargs: Optional[Dict[str, Any]] = None
+    ) -> "ModelPatcher":
+        return GptNeoxJapaneseModelPatcher(self, model, model_kwargs=model_kwargs)
+
 
 @register_in_tasks_manager(
     "cohere",
@@ -898,3 +921,21 @@ def patch_model_for_export(
         self, model: Union["PreTrainedModel", "TFPreTrainedModel"], model_kwargs: Optional[Dict[str, Any]] = None
     ) -> "ModelPatcher":
         return MistralModelPatcher(self, model, model_kwargs=model_kwargs)
+
+
+@register_in_tasks_manager(
+    "gpt-neox",
+    *[
+        "feature-extraction",
+        "feature-extraction-with-past",
+        "text-generation",
+        "text-generation-with-past",
+        "text-classification",
+    ],
+    library_name="transformers",
+)
+class GPTNeoxOpenVINOConfig(GPTNeoXOnnxConfig):
+    def patch_model_for_export(
+        self, model: Union["PreTrainedModel", "TFPreTrainedModel"], model_kwargs: Optional[Dict[str, Any]] = None
+    ) -> "ModelPatcher":
+        return GptNeoxModelPatcher(self, model, model_kwargs=model_kwargs)

optimum/exporters/openvino/model_patcher.py

@@ -106,6 +106,15 @@ def patch_update_causal_mask(model, transformers_version):
         model.model._update_causal_mask = types.MethodType(_llama_gemma_update_causal_mask, model.model)
 
 
+# initialization of sin/cos cached in bf16/fp16 leads to accuracy loss
+# reinitialize them to save in float32 before export
+def _reinitialize_cos_sin_cached_fp32(rotary_emb):
+    if rotary_emb.cos_cached.dtype != torch.float32:
+        rotary_emb._set_cos_sin_cache(
+            seq_len=rotary_emb.max_position_embeddings, device=rotary_emb.inv_freq.device, dtype=torch.float32
+        )
+
+
 def _mixtral_sparse_moe_block_forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
     """ """
     batch_size, sequence_length, hidden_dim = hidden_states.shape
@@ -156,6 +165,7 @@ def __enter__(self):
             layer.block_sparse_moe.forward = types.MethodType(
                 _mixtral_sparse_moe_block_forward, layer.block_sparse_moe
             )
+            _reinitialize_cos_sin_cached_fp32(layer.self_attn.rotary_emb)
 
     def __exit__(self, exc_type, exc_value, traceback):
         super().__exit__(exc_type, exc_value, traceback)
@@ -686,6 +696,10 @@ def __enter__(self):
             self._model.model._orig_update_causal_mask = self._model.model._update_causal_mask
             self._model.model._update_causal_mask = types.MethodType(_mistral_update_causal_mask, self._model.model)
 
+        else:
+            for layer in self._model.model.layers:
+                _reinitialize_cos_sin_cached_fp32(layer.self_attn.rotary_emb)
+
     def __exit__(self, exc_type, exc_value, traceback):
         super().__exit__(exc_type, exc_value, traceback)
 
@@ -2221,6 +2235,7 @@ def __enter__(self):
                 orig_self_attn_fwd = layer.self_attn.forward
                 layer.self_attn.forward = types.MethodType(_persimmon_self_attn_sdpa_forward, layer.self_attn)
                 layer.self_attn._orig_forward = orig_self_attn_fwd
+            _reinitialize_cos_sin_cached_fp32(layer.self_attn.rotary_emb)
 
     def __exit__(self, exc_type, exc_value, traceback):
         super().__exit__(exc_type, exc_value, traceback)
@@ -2356,8 +2371,39 @@ class UpdateCausalMaskModelPatcher(DecoderModelPatcher):
     def __enter__(self):
         super().__enter__()
         patch_update_causal_mask(self._model, "4.42.0")
+        if hasattr(self._model.model.layers[0].self_attn.rotary_emb, "_set_cos_sin_cache"):
+            for layer in self._model.model.layers:
+                _reinitialize_cos_sin_cached_fp32(layer.self_attn.rotary_emb)
 
     def __exit__(self, exc_type, exc_value, traceback):
         super().__exit__(exc_type, exc_value, traceback)
         if hasattr(self._model.model, "_orig_update_causal_mask"):
             self._model.model._update_causal_mask = self._model.model._orig_update_causal_mask
+
+
+class RotaryEmbPatcher(DecoderModelPatcher):
+    def __enter__(self):
+        super().__enter__()
+        for layer in self._model.model.layers:
+            _reinitialize_cos_sin_cached_fp32(layer.self_attn.rotary_emb)
+
+
+class FalconModelPatcher(DecoderModelPatcher):
+    def __enter__(self):
+        super().__enter__()
+        for layer in self._model.transformer.h:
+            _reinitialize_cos_sin_cached_fp32(layer.self_attention.rotary_emb)
+
+
+class GptNeoxModelPatcher(DecoderModelPatcher):
+    def __enter__(self):
+        super().__enter__()
+        for layer in self._model.gpt_neox.layers:
+            _reinitialize_cos_sin_cached_fp32(layer.attention.rotary_emb)
+
+
+class GptNeoxJapaneseModelPatcher(DecoderModelPatcher):
+    def __enter__(self):
+        super().__enter__()
+        for layer in self._model.gpt_neox_japanese.layers:
+            _reinitialize_cos_sin_cached_fp32(layer.attention.rotary_emb)

optimum/intel/openvino/modeling_decoder.py

@@ -281,10 +281,17 @@ def _from_transformers(
         if load_in_8bit is None and not quantization_config:
             ov_export_config = None
         else:
-            ov_export_config = OVConfig(dtype="fp32")
+            ov_export_config = OVConfig(dtype="auto")
 
         stateful = kwargs.pop("stateful", ensure_stateful_is_available(warn=False) and use_cache)
 
+        torch_dtype = kwargs.pop("torch_dtype", None)
+
+        model_loading_kwargs = {}
+
+        if torch_dtype is not None:
+            model_loading_kwargs["torch_dtype"] = torch_dtype
+
         main_export(
             model_name_or_path=model_id,
             output=save_dir_path,
@@ -298,6 +305,7 @@ def _from_transformers(
             trust_remote_code=trust_remote_code,
             ov_config=ov_export_config,
             stateful=stateful,
+            model_loading_kwargs=model_loading_kwargs,
         )
 
         config.is_decoder = True