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292 lines
11 KiB
Python
292 lines
11 KiB
Python
# Copyright 2024 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 gc
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import tempfile
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import unittest
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from typing import Any
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, FbgemmFp8Config, OPTForCausalLM
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from transformers.testing_utils import (
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backend_empty_cache,
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require_accelerate,
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require_deterministic_for_xpu,
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require_torch_accelerator,
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require_torch_multi_accelerator,
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slow,
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torch_device,
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)
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from transformers.utils import (
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is_fbgemm_gpu_available,
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is_kernels_available,
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is_torch_available,
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is_torch_xpu_available,
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)
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if is_torch_available():
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import torch
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@require_torch_accelerator
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class FbgemmFp8ConfigTest(unittest.TestCase):
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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 = FbgemmFp8Config()
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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 = {"modules_to_not_convert": ["lm_head.weight"], "quant_method": "fbgemm_fp8"}
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quantization_config = FbgemmFp8Config.from_dict(dict)
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self.assertEqual(dict["modules_to_not_convert"], quantization_config.modules_to_not_convert)
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self.assertEqual(dict["quant_method"], quantization_config.quant_method)
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@slow
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@require_torch_accelerator
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@unittest.skipIf(
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not (is_torch_xpu_available() and is_kernels_available()) and not is_fbgemm_gpu_available(),
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"test requires fbgemm-gpu or (xpu and kernels)",
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)
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@require_accelerate
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class FbgemmFp8Test(unittest.TestCase):
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model_name = "meta-llama/Meta-Llama-3-8B"
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input_text = "What are we having for dinner?"
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max_new_tokens = 9
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EXPECTED_OUTPUT = set[Any](
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[
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"What are we having for dinner?\nI'm having a steak and a salad",
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"What are we having for dinner? I don’t know. What are we having",
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"What are we having for dinner? I don’t know, what are you having",
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]
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)
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device_map = "xpu" if is_torch_xpu_available() else "cuda"
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offload_device_map = {
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"model.embed_tokens": 0,
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"model.layers.0": 0,
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"model.layers.1": 0,
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"model.layers.2": 0,
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"model.layers.3": 0,
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"model.layers.4": 0,
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"model.layers.5": 0,
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"model.layers.6": 0,
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"model.layers.7": 0,
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"model.layers.8": 0,
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"model.layers.9": 0,
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"model.layers.10": 0,
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"model.layers.11": 0,
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"model.layers.12": 0,
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"model.layers.13": 0,
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"model.layers.14": 0,
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"model.layers.15": 0,
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"model.layers.16": "cpu",
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"model.layers.17": "cpu",
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"model.layers.18": "cpu",
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"model.layers.19": "cpu",
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"model.layers.20": "disk",
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"model.layers.21": "disk",
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"model.layers.22": "disk",
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"model.layers.23": "disk",
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"model.layers.24": "disk",
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"model.layers.25": "disk",
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"model.layers.26": "disk",
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"model.layers.27": "disk",
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"model.layers.28": "disk",
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"model.layers.29": "disk",
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"model.layers.30": "disk",
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"model.layers.31": "disk",
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"model.norm": "disk",
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"lm_head": "disk",
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}
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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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quantization_config = FbgemmFp8Config()
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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, device_map=cls.device_map, quantization_config=quantization_config
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)
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def tearDown(self):
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gc.collect()
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backend_empty_cache(torch_device)
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gc.collect()
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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 transformers.integrations import FbgemmFp8Linear, replace_with_fbgemm_fp8_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 = FbgemmFp8Config()
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with torch.device("meta"):
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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_fbgemm_fp8_linear(model, quantization_config=quantization_config)
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nb_fbgemm_linear = 0
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for module in model.modules():
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if isinstance(module, FbgemmFp8Linear):
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nb_fbgemm_linear += 1
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self.assertEqual(nb_linears, nb_fbgemm_linear)
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with torch.device("meta"):
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model = OPTForCausalLM(config)
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quantization_config = FbgemmFp8Config(modules_to_not_convert=["fc1"])
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model = replace_with_fbgemm_fp8_linear(
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model, modules_to_not_convert=["fc1"], quantization_config=quantization_config
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)
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nb_fbgemm_linear = 0
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for module in model.modules():
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if isinstance(module, FbgemmFp8Linear):
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nb_fbgemm_linear += 1
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self.assertEqual(nb_linears - 24, nb_fbgemm_linear)
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@require_deterministic_for_xpu
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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=self.max_new_tokens, do_sample=False)
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self.assertTrue(self.tokenizer.decode(output[0], skip_special_tokens=True) in self.EXPECTED_OUTPUT)
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@require_deterministic_for_xpu
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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=self.max_new_tokens, do_sample=False)
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self.assertTrue(self.tokenizer.decode(output[0], skip_special_tokens=True) in self.EXPECTED_OUTPUT)
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def test_change_loading_attributes(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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quantization_config = FbgemmFp8Config(activation_scale_ub=1000.0)
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model = AutoModelForCausalLM.from_pretrained(
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tmpdirname, device_map=self.device_map, quantization_config=quantization_config
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)
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self.assertEqual(model.model.layers[1].mlp.down_proj.input_scale_ub.item(), 1000.0)
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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=self.max_new_tokens, do_sample=False)
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self.assertTrue(self.tokenizer.decode(output[0], skip_special_tokens=True) in self.EXPECTED_OUTPUT)
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@require_torch_multi_accelerator
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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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set CUDA_VISIBLE_DEVICES=0,1 if you have more than 2 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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quantization_config = FbgemmFp8Config()
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quantized_model = AutoModelForCausalLM.from_pretrained(
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self.model_name,
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device_map="auto",
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quantization_config=quantization_config,
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max_memory={0: "6GB", 1: "6GB"},
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)
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self.assertTrue(set(quantized_model.hf_device_map.values()) == {0, 1})
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output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens, do_sample=False)
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self.assertTrue(self.tokenizer.decode(output[0], skip_special_tokens=True) in self.EXPECTED_OUTPUT)
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def test_quantized_model_offload(self):
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"""
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Simple test that checks if the quantized model returns an error when loading with cpu/disk offloaded
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"""
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quantization_config = FbgemmFp8Config()
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with self.assertRaisesRegex(
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ValueError, "You are attempting to load an FP8 model with a device_map that contains a CPU or disk device."
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):
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AutoModelForCausalLM.from_pretrained(
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self.model_name, device_map=self.offload_device_map, quantization_config=quantization_config
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)
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@require_deterministic_for_xpu
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def test_save_pretrained_offload(self):
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"""
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Simple test that checks if the saved quantized model is working properly cpu/disk offload
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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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input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
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quantized_model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=self.offload_device_map)
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output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens, do_sample=False)
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self.assertTrue(self.tokenizer.decode(output[0], skip_special_tokens=True) in self.EXPECTED_OUTPUT)
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@require_torch_multi_accelerator
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@require_deterministic_for_xpu
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def test_save_pretrained_multi_gpu(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(
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tmpdirname, device_map="auto", max_memory={0: "6GB", 1: "6GB"}
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)
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self.assertTrue(set(model.hf_device_map.values()) == {0, 1})
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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=self.max_new_tokens, do_sample=False)
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self.assertTrue(self.tokenizer.decode(output[0], skip_special_tokens=True) in self.EXPECTED_OUTPUT)
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