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On top of openvinotoolkit#3049 ### Changes - Added FP8 example. ### Reason for changes - Examples coverage. ### Related tickets - 155923 ### Tests - ubuntu test_examples 627 - passed - windows test-examples 288 - passed - GA Test examples 135 - passed --------- Co-authored-by: Alexander Kozlov <[email protected]>
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examples/llm_compression/openvino/smollm2_360m_fp8/README.md
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# Large Language Models FP8 Compression Example | ||
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This example demonstrates how to apply static FP8 quantization to [HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) model. It can be useful for evaluation and early HW enablement purposes. | ||
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## Prerequisites | ||
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To use this example: | ||
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- Create a separate Python* environment and activate it: `python3 -m venv nncf_env && source nncf_env/bin/activate` | ||
- Install dependencies: | ||
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```bash | ||
pip install -U pip | ||
pip install -r requirements.txt | ||
pip install ../../../../ | ||
``` | ||
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## Run Example | ||
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To run example: | ||
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```bash | ||
python main.py | ||
``` | ||
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It will automatically download the dataset and baseline model and save the resulting model. |
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examples/llm_compression/openvino/smollm2_360m_fp8/main.py
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# Copyright (c) 2024 Intel Corporation | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
from functools import partial | ||
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import datasets | ||
import numpy as np | ||
import openvino as ov | ||
from optimum.intel.openvino import OVModelForCausalLM | ||
from transformers import AutoTokenizer | ||
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import nncf | ||
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def transform_fn(data, model, tokenizer): | ||
tokenized_text = tokenizer(data["text"], return_tensors="np") | ||
input_ids = tokenized_text["input_ids"] | ||
attention_mask = tokenized_text["attention_mask"] | ||
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inputs = {} | ||
inputs["input_ids"] = input_ids | ||
inputs["attention_mask"] = tokenized_text["attention_mask"] | ||
position_ids = np.cumsum(attention_mask, axis=1) - 1 | ||
position_ids[attention_mask == 0] = 1 | ||
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# The magic forms KV cache as model inputs | ||
batch_size = input_ids.shape[0] | ||
for input_name in model.key_value_input_names: | ||
model_inputs = model.model.input(input_name) | ||
shape = model_inputs.get_partial_shape() | ||
shape[0] = batch_size | ||
if shape[2].is_dynamic: | ||
shape[2] = 0 | ||
else: | ||
shape[1] = 0 | ||
inputs[input_name] = ov.Tensor(model_inputs.get_element_type(), shape.get_shape()) | ||
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inputs["position_ids"] = position_ids | ||
return inputs | ||
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def generate_answers(questions, model, tokenizer, max_new_tokens=50): | ||
messages = [ | ||
{"role": "system", "content": "You are a chatbot who always responds as short as possible."}, | ||
{"role": "user", "content": "What is the capital of Spain?"}, | ||
{"role": "assistant", "content": "Madrid."}, | ||
] | ||
answers_by_questions = {} | ||
model.request = None | ||
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for question in questions: | ||
messages.append({"role": "user", "content": question}) | ||
input_ids = tokenizer.apply_chat_template( | ||
messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" | ||
).to(device=model.device) | ||
input_len = len(input_ids[0]) | ||
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output = model.generate(input_ids, max_new_tokens=max_new_tokens, do_sample=False)[0] | ||
answer = tokenizer.decode(output[input_len:], skip_special_tokens=True) | ||
answers_by_questions[question] = answer | ||
messages.append({"role": "assistant", "content": answer}) | ||
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model.request = None | ||
return answers_by_questions | ||
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def main(): | ||
MODEL_ID = "HuggingFaceTB/SmolLM2-360M-Instruct" | ||
OUTPUT_DIR = "smollm2_360m_compressed" | ||
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dataset = datasets.load_dataset("wikitext", "wikitext-2-raw-v1", split="test") | ||
# Filtering to remove empty samples from the dataset | ||
dataset = dataset.filter(lambda example: len(example["text"]) > 1) | ||
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) | ||
model = OVModelForCausalLM.from_pretrained( | ||
MODEL_ID, | ||
export=True, | ||
load_in_8bit=False, | ||
compile=False, | ||
stateful=False, | ||
ov_config={"INFERENCE_PRECISION_HINT": "f32"}, | ||
) | ||
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questions = [ | ||
"What is the capital of France?", | ||
"What is the highest mountain in the Alps?", | ||
"What is the largest city in Canada?", | ||
"What is the most visited city in Japan?", | ||
] | ||
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answers_by_questions = generate_answers(questions, model, tokenizer) | ||
print(f"Non-optimized model outputs:\n{answers_by_questions}\n") | ||
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quantization_dataset = nncf.Dataset(dataset, partial(transform_fn, model=model, tokenizer=tokenizer)) | ||
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model.model = nncf.quantize( | ||
model.model, | ||
calibration_dataset=quantization_dataset, | ||
# Only PERFORMANCE preset supports in combination with FP8 quantization mode | ||
preset=nncf.QuantizationPreset.PERFORMANCE, | ||
mode=nncf.QuantizationMode.FP8_E4M3, | ||
model_type=nncf.ModelType.TRANSFORMER, | ||
# SmoothQuant algorithm is not needed for FP8 quantization | ||
advanced_parameters=nncf.AdvancedQuantizationParameters( | ||
smooth_quant_alphas=nncf.AdvancedSmoothQuantParameters(matmul=-1) | ||
), | ||
) | ||
model.save_pretrained(OUTPUT_DIR) | ||
tokenizer.save_pretrained(OUTPUT_DIR) | ||
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model = OVModelForCausalLM.from_pretrained( | ||
OUTPUT_DIR, ov_config={"DYNAMIC_QUANTIZATION_GROUP_SIZE": "0", "INFERENCE_PRECISION_HINT": "f32"} | ||
) | ||
answers_by_questions = generate_answers(questions, model, tokenizer) | ||
print(f"Optimized model outputs:\n{answers_by_questions}\n") | ||
return answers_by_questions | ||
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if __name__ == "__main__": | ||
main() |
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examples/llm_compression/openvino/smollm2_360m_fp8/requirements.txt
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datasets | ||
openvino==2024.5 | ||
optimum-intel[openvino] | ||
transformers | ||
onnx<1.16.2 |
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