438 lines
17 KiB
Python
438 lines
17 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import tempfile
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import unittest
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import pytest
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from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
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from transformers.testing_utils import (
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is_torch_available,
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require_accelerate,
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require_auto_gptq,
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require_optimum,
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require_torch_gpu,
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require_torch_multi_gpu,
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slow,
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)
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if is_torch_available():
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import torch
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class GPTQConfigTest(unittest.TestCase):
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def test_bits(self):
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with self.assertRaises(ValueError):
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GPTQConfig(bits="")
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GPTQConfig(bits=1)
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GPTQConfig(bits=2)
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GPTQConfig(bits=4)
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def test_dataset(self):
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with self.assertRaises(ValueError):
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GPTQConfig(bits=2, dataset="auto_gpt")
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GPTQConfig(bits=2, dataset="c4")
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GPTQConfig(bits=2, dataset="ptb-new")
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def test_damp_percent(self):
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with self.assertRaises(ValueError):
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GPTQConfig(bits=2, damp_percent=10)
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GPTQConfig(bits=2, damp_percent=-1)
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GPTQConfig(bits=2, damp_percent="0")
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GPTQConfig(bits=2, damp_percent=0.01)
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def test_to_dict(self):
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quantization_config = GPTQConfig(bits=2)
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quantization_config.to_dict()
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def test_from_dict(self):
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dict = {"bits": 2}
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quantization_config = GPTQConfig.from_dict(dict)
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self.assertEqual(dict["bits"], quantization_config.bits)
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@require_optimum
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def test_optimum_config(self):
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from optimum.gptq import GPTQQuantizer
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config = GPTQConfig(bits=2)
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optimum_config = GPTQQuantizer.from_dict(config.to_dict_optimum())
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self.assertEqual(optimum_config.bits, config.bits)
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new_config = GPTQConfig.from_dict_optimum(optimum_config.to_dict())
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self.assertEqual(optimum_config.bits, new_config.bits)
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@slow
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@require_optimum
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@require_auto_gptq
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@require_torch_gpu
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class GPTQTest(unittest.TestCase):
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model_name = "bigscience/bloom-560m"
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input_text = "Hello my name is"
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EXPECTED_OUTPUTS = set()
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EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I")
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EXPECTED_OUTPUTS.add("Hello my name is John, I am a professional photographer and I")
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EXPECTED_OUTPUTS.add("Hello my name is John, I am a student in the University of")
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EXPECTED_OUTPUTS.add("Hello my name is John and I am a very good looking man.")
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EXPECTED_OUTPUTS.add("Hello my name is Alyson, I am a student in the")
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EXPECTED_OUTPUTS.add("Hello my name is Alyson and I am a very sweet,")
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# this seems a little small considering that we are doing 4bit quant but we have a small model and ww don't quantize the embeddings
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EXPECTED_RELATIVE_DIFFERENCE = 1.664253062
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bits = 4
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group_size = 128
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desc_act = False
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use_exllama = False
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dataset = [
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"auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."
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]
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device_map = None
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# called only once for all test in this class
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@classmethod
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def setUpClass(cls):
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"""
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Setup quantized model
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"""
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cls.model_fp16 = AutoModelForCausalLM.from_pretrained(
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cls.model_name, torch_dtype=torch.float16, device_map=cls.device_map
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)
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cls.mem_fp16 = cls.model_fp16.get_memory_footprint()
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cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name, use_fast=True)
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quantization_config = GPTQConfig(
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bits=cls.bits,
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dataset=cls.dataset,
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tokenizer=cls.tokenizer,
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group_size=cls.group_size,
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desc_act=cls.desc_act,
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use_exllama=cls.use_exllama,
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)
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cls.quantized_model = AutoModelForCausalLM.from_pretrained(
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cls.model_name,
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torch_dtype=torch.float16,
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device_map=cls.device_map,
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quantization_config=quantization_config,
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)
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def test_memory_footprint(self):
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r"""
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A simple test to check if the model conversion has been done correctly by checking on the
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memory footprint of the converted model
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"""
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mem_quantized = self.quantized_model.get_memory_footprint()
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self.assertAlmostEqual(self.mem_fp16 / mem_quantized, self.EXPECTED_RELATIVE_DIFFERENCE)
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def test_device_and_dtype_assignment(self):
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r"""
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Test whether trying to cast (or assigning a device to) a model after quantization will throw an error.
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Checks also if other models are casted correctly.
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"""
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# This should work
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if self.device_map is None:
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_ = self.quantized_model.to(0)
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with self.assertRaises(ValueError):
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# Tries with a `dtype``
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self.quantized_model.to(torch.float16)
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def test_original_dtype(self):
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r"""
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A simple test to check if the model succesfully stores the original dtype
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"""
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self.assertTrue(hasattr(self.quantized_model.config, "_pre_quantization_dtype"))
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self.assertFalse(hasattr(self.model_fp16.config, "_pre_quantization_dtype"))
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self.assertTrue(self.quantized_model.config._pre_quantization_dtype == torch.float16)
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def test_quantized_layers_class(self):
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"""
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Simple test to check if the model conversion has been done correctly by checking on
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the class type of the linear layers of the converted models
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"""
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from auto_gptq.utils.import_utils import dynamically_import_QuantLinear
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QuantLinear = dynamically_import_QuantLinear(
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use_triton=False,
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desc_act=self.desc_act,
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group_size=self.group_size,
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bits=self.bits,
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disable_exllama=not self.use_exllama,
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disable_exllamav2=True,
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)
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self.assertTrue(self.quantized_model.transformer.h[0].mlp.dense_4h_to_h.__class__ == QuantLinear)
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def check_inference_correctness(self, model):
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r"""
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Test the generation quality of the quantized model and see that we are matching the expected output.
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Given that we are operating on small numbers + the testing model is relatively small, we might not get
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the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
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"""
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# Check that inference pass works on the model
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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# Check the exactness of the results
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output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
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# Get the generation
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self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
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def check_quantized_layers_type(self, model, value):
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self.assertTrue(model.transformer.h[0].mlp.dense_4h_to_h.QUANT_TYPE == value)
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def test_generate_quality(self):
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"""
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Simple test to check the quality of the model by comparing the generated tokens with the expected tokens
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"""
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if self.device_map is None:
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self.check_inference_correctness(self.quantized_model.to(0))
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else:
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self.check_inference_correctness(self.quantized_model)
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def test_serialization(self):
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"""
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Test the serialization of the model and the loading of the quantized weights works
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"""
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with tempfile.TemporaryDirectory() as tmpdirname:
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self.quantized_model.save_pretrained(tmpdirname)
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if not self.use_exllama:
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quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
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tmpdirname, quantization_config=GPTQConfig(use_exllama=False, bits=4)
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).to(0)
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self.check_quantized_layers_type(quantized_model_from_saved, "cuda-old")
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else:
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# we need to put it directly to the gpu. Otherwise, we won't be able to initialize the exllama kernel
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quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map={"": 0})
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self.check_quantized_layers_type(quantized_model_from_saved, "exllama")
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self.check_inference_correctness(quantized_model_from_saved)
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@require_accelerate
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def test_serialization_big_model_inference(self):
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"""
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Test the serialization of the model and the loading of the quantized weights with big model inference
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"""
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with tempfile.TemporaryDirectory() as tmpdirname:
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self.quantized_model.save_pretrained(tmpdirname)
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quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map="auto")
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self.check_inference_correctness(quantized_model_from_saved)
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def test_change_loading_attributes(self):
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"""
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Test the serialization of the model and the loading of the quantized weights works with another config file
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"""
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with tempfile.TemporaryDirectory() as tmpdirname:
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self.quantized_model.save_pretrained(tmpdirname)
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if not self.use_exllama:
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self.check_quantized_layers_type(self.quantized_model, "cuda-old")
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# we need to put it directly to the gpu. Otherwise, we won't be able to initialize the exllama kernel
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quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
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tmpdirname, quantization_config=GPTQConfig(use_exllama=True, bits=4), device_map={"": 0}
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)
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self.assertEqual(quantized_model_from_saved.config.quantization_config.bits, self.bits)
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self.check_quantized_layers_type(quantized_model_from_saved, "exllama")
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self.check_inference_correctness(quantized_model_from_saved)
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@require_accelerate
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@require_torch_multi_gpu
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class GPTQTestDeviceMap(GPTQTest):
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device_map = "auto"
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@require_accelerate
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@require_torch_multi_gpu
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class GPTQTestDeviceMapExllama(GPTQTest):
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device_map = "auto"
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use_exllama = True
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@slow
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@require_optimum
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@require_auto_gptq
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@require_torch_gpu
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@require_accelerate
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class GPTQTestActOrderExllama(unittest.TestCase):
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"""
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Test GPTQ model with exllama kernel and desc_act=True (also known as act-order).
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More information on those arguments here:
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https://huggingface.co/docs/transformers/main_classes/quantization#transformers.GPTQConfig
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"""
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EXPECTED_OUTPUTS = set()
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EXPECTED_OUTPUTS.add("Hello, how are you ? I'm doing good, thanks for asking.")
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# 4bit + act_order + 128g
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model_name = "hf-internal-testing/TinyLlama-1.1B-Chat-v0.3-GPTQ"
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input_text = "Hello, how are you ?"
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@classmethod
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def setUpClass(cls):
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"""
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Setup quantized model
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"""
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cls.quantization_config = GPTQConfig(bits=4, max_input_length=4028)
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cls.quantized_model = AutoModelForCausalLM.from_pretrained(
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cls.model_name,
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torch_dtype=torch.float16,
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device_map={"": 0},
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quantization_config=cls.quantization_config,
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)
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cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name, use_fast=True)
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def check_inference_correctness(self, model):
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"""
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Test the generation quality of the quantized model and see that we are matching the expected output.
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Given that we are operating on small numbers + the testing model is relatively small, we might not get
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the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
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"""
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# Check that inference pass works on the model
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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# Check the exactness of the results
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output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
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# Get the generation
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self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
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def test_quantized_layers_type(self):
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self.assertTrue(self.quantized_model.model.layers[0].self_attn.k_proj.QUANT_TYPE == "exllama")
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def test_generate_quality(self):
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"""
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Simple test to check the quality of the model by comparing the generated tokens with the expected tokens
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"""
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self.check_inference_correctness(self.quantized_model)
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def test_max_input_length(self):
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"""
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Test if the max_input_length works. It modifies the maximum input length that of the model that runs with exllama backend.
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"""
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prompt = "I am in Paris and" * 1000
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inp = self.tokenizer(prompt, return_tensors="pt").to(0)
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self.assertTrue(inp["input_ids"].shape[1] > 4028)
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with self.assertRaises(RuntimeError) as cm:
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self.quantized_model.generate(**inp, num_beams=1, min_new_tokens=3, max_new_tokens=3)
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self.assertTrue("temp_state buffer is too small" in str(cm.exception))
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prompt = "I am in Paris and"
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inp = self.tokenizer(prompt, return_tensors="pt").to(0)
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self.assertTrue(inp["input_ids"].shape[1] < 4028)
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self.quantized_model.generate(**inp, num_beams=1, min_new_tokens=3, max_new_tokens=3)
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@slow
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@require_optimum
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@require_auto_gptq
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@require_torch_gpu
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@require_accelerate
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class GPTQTestExllamaV2(unittest.TestCase):
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"""
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Test GPTQ model with exllamav2 kernel and desc_act=True (also known as act-order).
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More information on those arguments here:
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https://huggingface.co/docs/transformers/main_classes/quantization#transformers.GPTQConfig
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"""
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EXPECTED_OUTPUTS = set()
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EXPECTED_OUTPUTS.add("Hello, how are you ? I'm doing good, thanks for asking.")
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# 4bit + act_order + 128g
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model_name = "hf-internal-testing/TinyLlama-1.1B-Chat-v0.3-GPTQ"
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input_text = "Hello, how are you ?"
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@classmethod
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def setUpClass(cls):
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"""
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Setup quantized model
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"""
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cls.quantization_config = GPTQConfig(bits=4, exllama_config={"version": 2})
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cls.quantized_model = AutoModelForCausalLM.from_pretrained(
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cls.model_name,
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torch_dtype=torch.float16,
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device_map={"": 0},
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quantization_config=cls.quantization_config,
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)
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cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name, use_fast=True)
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def test_quantized_layers_type(self):
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self.assertTrue(self.quantized_model.model.layers[0].self_attn.k_proj.QUANT_TYPE == "exllamav2")
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def check_inference_correctness(self, model):
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"""
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Test the generation quality of the quantized model and see that we are matching the expected output.
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Given that we are operating on small numbers + the testing model is relatively small, we might not get
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the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
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"""
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# Check that inference pass works on the model
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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# Check the exactness of the results
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output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
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# Get the generation
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self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
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def test_generate_quality(self):
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"""
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Simple test to check the quality of the model by comapring the the generated tokens with the expected tokens
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"""
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self.check_inference_correctness(self.quantized_model)
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# fail when run all together
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@pytest.mark.skip
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@require_accelerate
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@require_torch_multi_gpu
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class GPTQTestDeviceMapCPUOffload(GPTQTest):
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device_map = {
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"transformer.word_embeddings": 0,
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"transformer.word_embeddings_layernorm": 0,
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"lm_head": 0,
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"transformer.h.0": 0,
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"transformer.h.1": 0,
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"transformer.h.2": 0,
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"transformer.h.3": 0,
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"transformer.h.4": 0,
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"transformer.h.5": 0,
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"transformer.h.6": 0,
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"transformer.h.7": 0,
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"transformer.h.8": 0,
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"transformer.h.9": 0,
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"transformer.h.10": 1,
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"transformer.h.11": 1,
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"transformer.h.12": 1,
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"transformer.h.13": 1,
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"transformer.h.14": 1,
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"transformer.h.15": 1,
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"transformer.h.16": 1,
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"transformer.h.17": 0,
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"transformer.h.18": "cpu",
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"transformer.h.19": "cpu",
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"transformer.h.20": "cpu",
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"transformer.h.21": "cpu",
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"transformer.h.22": "cpu",
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"transformer.h.23": 1,
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"transformer.ln_f": 0,
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}
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