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dump model before compress cli #723

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dump model before compress cli #723

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25 changes: 20 additions & 5 deletions optimum/exporters/openvino/convert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,11 +19,12 @@
import os
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
import tempfile

import onnx
from transformers.utils import is_tf_available, is_torch_available

from openvino.runtime import Model, PartialShape, save_model
from openvino.runtime import Model, PartialShape, save_model, Core
from openvino.runtime.exceptions import OVTypeError
from openvino.runtime.utils.types import get_element_type
from openvino.tools.ovc import convert_model
Expand Down Expand Up @@ -58,6 +59,7 @@


logger = logging.getLogger(__name__)
core = Core()

if is_torch_available():
import torch.nn as nn
Expand Down Expand Up @@ -412,11 +414,24 @@ def ts_patched_forward(*args, **kwargs):

if stateful:
patch_stateful(model.config, ov_model)
if ov_config.quantization_config:
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there is another PR where we probably solve this problem: https://github.com/huggingface/optimum-intel/pull/721/files

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@nikita-savelyevv PR, as I understand it works only for quantization with dataset for specific cases, when you need to infer model. The goal of my changes remove pytorch model before any weights compression process started for free memory (IR before saving on disk shares weights with pytorch model and additionally may requires own memory on top of that. When we use API for exporting model, weights compression happens after conversion step finished and we already removed pyotrch model from RAM, but when optimum-cli used conversion and compression combined in one step and pytorch model is still alive at compression step)

I open it for experiment only, both changes can be useful, I think we can combine them

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Yes, seems like the changes are independent.

@eaidova Just out of curiosity, do you have numbers for how much memory can be saved with this approach?

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thanks, @eaidova. @nikita-savelyevv, can you please adopt changes from this PR?

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@AlexKoff88 I don't think this applies to my case because I don't have access to the PyTorch or OpenVINO model objects after the main_export call. So I can't delete them.

Or do you mean to copy changes from this PR to my PR? If so, in my opinion these should be added separately.

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sorry, I didn't notice that this is for torch models only. Then, it makes sense to keep these changes separately.

with tempfile.TemporaryDirectory() as temp_dir:
tmp_output = Path(temp_dir) / output.name
_save_model(ov_model, tmp_output, ov_config=None)
clear_class_registry()
del ov_model
del model
gc.collect()

ov_model = core.read_model(tmp_output)
_save_model(ov_model, output, ov_config)
else:
_save_model(ov_model, output, ov_config)
clear_class_registry()
del ov_model
del model
gc.collect()

_save_model(ov_model, output, ov_config=ov_config)
clear_class_registry()
del model
gc.collect()
return input_names, output_names, False


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