[core / Quantization ] AWQ integration (#27045)
* working v1 * oops * Update src/transformers/modeling_utils.py Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * fixup * oops * push * more changes * add docs * some fixes * fix copies * add v1 doc * added installation guide * relax constraints * revert * attempt llm-awq * oops * oops * fixup * raise error when incorrect cuda compute capability * nit * add instructions for llm-awq * fixup * fix copies * fixup and docs * change * few changes + add demo * add v1 tests * add autoawq in dockerfile * finalize * Update tests/quantization/autoawq/test_awq.py * fix test * fix * fix issue * Update src/transformers/integrations/awq.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update docs/source/en/main_classes/quantization.md Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update docs/source/en/main_classes/quantization.md Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/integrations/awq.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/integrations/awq.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * add link to example script * Update docs/source/en/main_classes/quantization.md Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * add more content * add more details * add link to quantization docs * camel case + change backend class name * change to string * fixup * raise errors if libs not installed * change to `bits` and `group_size` * nit * nit * Apply suggestions from code review Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * disable training * address some comments and fix nits * fix * final nits and fix tests * adapt to our new runners * make fix-copies * Update src/transformers/utils/quantization_config.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/utils/quantization_config.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/integrations/awq.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/integrations/awq.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * move to top * add conversion test * final nit * add more elaborated test --------- Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Younes Belkada
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tests/quantization/autoawq/test_awq.py
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221
tests/quantization/autoawq/test_awq.py
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# 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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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, AwqConfig, OPTForCausalLM
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from transformers.testing_utils import (
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require_accelerate,
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require_auto_awq,
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require_torch_gpu,
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require_torch_multi_gpu,
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slow,
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torch_device,
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)
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from transformers.utils import is_accelerate_available, is_torch_available
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if is_torch_available():
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import torch
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if is_accelerate_available():
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from accelerate import init_empty_weights
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@require_torch_gpu
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class AwqConfigTest(unittest.TestCase):
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def test_wrong_backend(self):
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"""
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Simple test that checks if a user passes a wrong backend an error is raised
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"""
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# This should work fine
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_ = AwqConfig(bits=4)
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with self.assertRaises(ValueError):
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AwqConfig(bits=4, backend="")
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# LLMAWQ does not work on a T4
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with self.assertRaises(ValueError):
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AwqConfig(bits=4, backend="llm-awq")
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def test_to_dict(self):
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"""
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Simple test that checks if one uses a config and converts it to a dict, the dict is the same as the config object
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"""
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quantization_config = AwqConfig(bits=4)
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config_to_dict = quantization_config.to_dict()
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for key in config_to_dict:
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self.assertEqual(getattr(quantization_config, key), config_to_dict[key])
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def test_from_dict(self):
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"""
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Simple test that checks if one uses a dict and converts it to a config object, the config object is the same as the dict
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"""
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dict = {"bits": 2, "zero_point": False, "backend": "autoawq"}
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quantization_config = AwqConfig.from_dict(dict)
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self.assertEqual(dict["bits"], quantization_config.bits)
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self.assertEqual(dict["zero_point"], quantization_config.zero_point)
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self.assertEqual(dict["backend"], quantization_config.backend)
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@slow
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@require_torch_gpu
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@require_auto_awq
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@require_accelerate
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class AwqTest(unittest.TestCase):
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# TODO: @younesbelkada change it to `TheBloke/Mistral-7B-v0.1-AWQ` in the future
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model_name = "ybelkada/test-mistral-7b-v0.1-awq"
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dummy_transformers_model_name = "bigscience/bloom-560m"
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input_text = "Hello my name is"
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EXPECTED_OUTPUT = "Hello my name is Katie and I am a 20 year old student at the University of North Carolina at Chapel Hill. I am a junior and I am majoring in Journalism and minoring in Spanish"
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EXPECTED_OUTPUT_BF16 = "Hello my name is Katie and I am a 20 year old student at the University of North Carolina at Chapel Hill. I am a junior and I am majoring in Exercise and Sport Science with a"
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device_map = "cuda"
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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.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
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cls.quantized_model = AutoModelForCausalLM.from_pretrained(
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cls.model_name,
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device_map=cls.device_map,
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)
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def test_quantized_model_conversion(self):
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"""
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Simple test that checks if the quantized model has been converted properly
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"""
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from awq.modules.linear import WQLinear_GEMM, WQLinear_GEMV
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from transformers.integrations.awq import replace_with_awq_linear
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model_id = "facebook/opt-350m"
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config = AutoConfig.from_pretrained(model_id, revision="cb32f77e905cccbca1d970436fb0f5e6b58ee3c5")
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quantization_config = AwqConfig(bits=4)
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with init_empty_weights():
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model = OPTForCausalLM(config)
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nb_linears = 0
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for module in model.modules():
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if isinstance(module, torch.nn.Linear):
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nb_linears += 1
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model, _ = replace_with_awq_linear(model, quantization_config=quantization_config)
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nb_awq_linear = 0
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for module in model.modules():
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if isinstance(module, (WQLinear_GEMM, WQLinear_GEMV)):
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nb_awq_linear += 1
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self.assertEqual(nb_linears, nb_awq_linear)
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# Try with `modules_not_to_convert`
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with init_empty_weights():
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model = OPTForCausalLM(config)
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model, _ = replace_with_awq_linear(
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model, quantization_config=quantization_config, modules_to_not_convert=["lm_head"]
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)
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nb_awq_linear = 0
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for module in model.modules():
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if isinstance(module, (WQLinear_GEMM, WQLinear_GEMV)):
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nb_awq_linear += 1
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self.assertEqual(nb_linears - 1, nb_awq_linear)
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def test_quantized_model(self):
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"""
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Simple test that checks if the quantized model is working properly
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"""
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input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
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output = self.quantized_model.generate(**input_ids, max_new_tokens=40)
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self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
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def test_quantized_model_bf16(self):
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"""
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Simple test that checks if the quantized model is working properly with bf16
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"""
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input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
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quantized_model = AutoModelForCausalLM.from_pretrained(self.model_name, torch_dtype=torch.bfloat16).to(
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torch_device
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)
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output = quantized_model.generate(**input_ids, max_new_tokens=40)
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self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT_BF16)
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def test_quantized_model_no_device_map(self):
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"""
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Simple test that checks if the quantized model is working properly
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"""
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input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
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quantized_model = AutoModelForCausalLM.from_pretrained(self.model_name).to(torch_device)
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output = quantized_model.generate(**input_ids, max_new_tokens=40)
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self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
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def test_save_pretrained(self):
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"""
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Simple test that checks if the quantized model is working properly after being saved and loaded
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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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model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=self.device_map)
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input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
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output = model.generate(**input_ids, max_new_tokens=40)
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self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
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def test_raise_quantization(self):
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"""
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Simple test that checks if one passes a quantization config to quantize a model, it raises an error
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"""
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quantization_config = AwqConfig(bits=4)
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with self.assertRaises(ValueError) as context:
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_ = AutoModelForCausalLM.from_pretrained(
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self.dummy_transformers_model_name, quantization_config=quantization_config
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)
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self.assertEqual(
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str(context.exception),
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"You cannot pass an `AwqConfig` when loading a model as you can only use AWQ models for inference. To quantize transformers models with AWQ algorithm, please refer to our quantization docs: https://huggingface.co/docs/transformers/main_classes/quantization ",
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)
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@require_torch_multi_gpu
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def test_quantized_model_multi_gpu(self):
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"""
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Simple test that checks if the quantized model is working properly with multiple GPUs
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"""
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input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
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quantized_model = AutoModelForCausalLM.from_pretrained(self.model_name, device_map="auto")
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self.assertTrue(set(quantized_model.hf_device_map.values()) == {0, 1, 2, 3})
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output = quantized_model.generate(**input_ids, max_new_tokens=40)
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self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
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