876 lines
35 KiB
Python
876 lines
35 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Team Inc.
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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 clone 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 importlib.metadata
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import tempfile
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import unittest
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from packaging import version
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from transformers import (
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AutoConfig,
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AutoModel,
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AutoModelForCausalLM,
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AutoModelForSeq2SeqLM,
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AutoModelForSequenceClassification,
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AutoTokenizer,
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BitsAndBytesConfig,
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pipeline,
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)
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from transformers.testing_utils import (
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is_accelerate_available,
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is_torch_available,
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require_accelerate,
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require_bitsandbytes,
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require_torch,
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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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def get_some_linear_layer(model):
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if model.config.model_type == "gpt2":
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return model.transformer.h[0].mlp.c_fc
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return model.transformer.h[0].mlp.dense_4h_to_h
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if is_accelerate_available():
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from accelerate import PartialState
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from accelerate.logging import get_logger
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logger = get_logger(__name__)
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_ = PartialState()
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if is_torch_available():
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import torch
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import torch.nn as nn
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class LoRALayer(nn.Module):
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"""Wraps a linear layer with LoRA-like adapter - Used for testing purposes only"""
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def __init__(self, module: nn.Module, rank: int):
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super().__init__()
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self.module = module
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self.adapter = nn.Sequential(
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nn.Linear(module.in_features, rank, bias=False),
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nn.Linear(rank, module.out_features, bias=False),
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)
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small_std = (2.0 / (5 * min(module.in_features, module.out_features))) ** 0.5
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nn.init.normal_(self.adapter[0].weight, std=small_std)
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nn.init.zeros_(self.adapter[1].weight)
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self.adapter.to(module.weight.device)
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def forward(self, input, *args, **kwargs):
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return self.module(input, *args, **kwargs) + self.adapter(input)
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@require_bitsandbytes
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@require_accelerate
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@require_torch
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@require_torch_gpu
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@slow
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class BaseMixedInt8Test(unittest.TestCase):
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# We keep the constants inside the init function and model loading inside setUp function
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# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
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# Therefore here we use only bloom-1b3 to test our module
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model_name = "bigscience/bloom-1b7"
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# Constant values
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EXPECTED_RELATIVE_DIFFERENCE = (
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1.540025 # This was obtained on a Quadro RTX 8000 so the number might slightly change
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)
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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.\nI am a friend of the family.\n")
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# Expected values on a A10
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EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n")
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MAX_NEW_TOKENS = 10
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# Expected values with offload
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EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer based in")
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def setUp(self):
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# Models and tokenizer
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
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class MixedInt8Test(BaseMixedInt8Test):
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def setUp(self):
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super().setUp()
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# Models and tokenizer
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self.model_fp16 = AutoModelForCausalLM.from_pretrained(
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self.model_name, torch_dtype=torch.float16, device_map="auto"
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)
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self.model_8bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
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def tearDown(self):
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r"""
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TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
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avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
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"""
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del self.model_fp16
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del self.model_8bit
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gc.collect()
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torch.cuda.empty_cache()
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def test_get_keys_to_not_convert_trust_remote_code(self):
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r"""
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Test the `get_keys_to_not_convert` function with `trust_remote_code` models.
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"""
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from accelerate import init_empty_weights
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from transformers.integrations.bitsandbytes import get_keys_to_not_convert
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model_id = "mosaicml/mpt-7b"
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config = AutoConfig.from_pretrained(
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model_id, trust_remote_code=True, revision="ada218f9a93b5f1c6dce48a4cc9ff01fcba431e7"
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)
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with init_empty_weights():
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model = AutoModelForCausalLM.from_config(
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config, trust_remote_code=True, code_revision="ada218f9a93b5f1c6dce48a4cc9ff01fcba431e7"
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)
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self.assertEqual(get_keys_to_not_convert(model), ["transformer.wte"])
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def test_get_keys_to_not_convert(self):
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r"""
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Test the `get_keys_to_not_convert` function.
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"""
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from accelerate import init_empty_weights
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from transformers import AutoModelForMaskedLM, Blip2ForConditionalGeneration, MptForCausalLM, OPTForCausalLM
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from transformers.integrations.bitsandbytes import get_keys_to_not_convert
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model_id = "mosaicml/mpt-7b"
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config = AutoConfig.from_pretrained(model_id, revision="72e5f594ce36f9cabfa2a9fd8f58b491eb467ee7")
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with init_empty_weights():
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model = MptForCausalLM(config)
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# The order of the keys does not matter, so we sort them before comparing, same for the other tests.
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self.assertEqual(get_keys_to_not_convert(model).sort(), ["lm_head", "transformer.wte"].sort())
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model_id = "Salesforce/blip2-opt-2.7b"
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config = AutoConfig.from_pretrained(model_id, revision="1ef7f63a8f0a144c13fdca8103eb7b4691c74cec")
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with init_empty_weights():
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model = Blip2ForConditionalGeneration(config)
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self.assertEqual(
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get_keys_to_not_convert(model).sort(),
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["language_model.lm_head", "language_model.model.decoder.embed_tokens"].sort(),
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)
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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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with init_empty_weights():
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model = OPTForCausalLM(config)
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self.assertEqual(get_keys_to_not_convert(model).sort(), ["lm_head", "model.decoder.embed_tokens"].sort())
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model_id = "FacebookAI/roberta-large"
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config = AutoConfig.from_pretrained(model_id, revision="716877d372b884cad6d419d828bac6c85b3b18d9")
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with init_empty_weights():
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model = AutoModelForMaskedLM.from_config(config)
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self.assertEqual(
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get_keys_to_not_convert(model).sort(),
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["'roberta.embeddings.word_embeddings', 'lm_head', 'lm_head.decoder"].sort(),
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)
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def test_quantization_config_json_serialization(self):
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r"""
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A simple test to check if the quantization config is correctly serialized and deserialized
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"""
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config = self.model_8bit.config
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self.assertTrue(hasattr(config, "quantization_config"))
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_ = config.to_dict()
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_ = config.to_diff_dict()
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_ = config.to_json_string()
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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.model_8bit.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.model_8bit.config._pre_quantization_dtype == torch.float16)
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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 and the class type of the linear layers of the converted models
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"""
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from bitsandbytes.nn import Int8Params
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mem_fp16 = self.model_fp16.get_memory_footprint()
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mem_8bit = self.model_8bit.get_memory_footprint()
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self.assertAlmostEqual(mem_fp16 / mem_8bit, self.EXPECTED_RELATIVE_DIFFERENCE)
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self.assertTrue(get_some_linear_layer(self.model_8bit).weight.__class__ == Int8Params)
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def test_linear_are_8bit(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 and the class type of the linear layers of the converted models
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"""
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from transformers import T5PreTrainedModel
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self.model_fp16.get_memory_footprint()
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self.model_8bit.get_memory_footprint()
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for name, module in self.model_8bit.named_modules():
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if isinstance(module, torch.nn.Linear):
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if name not in ["lm_head"] + T5PreTrainedModel._keep_in_fp32_modules:
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self.assertTrue(module.weight.dtype == torch.int8)
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def test_llm_skip(self):
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r"""
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A simple test to check if `llm_int8_skip_modules` works as expected
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"""
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import bitsandbytes as bnb
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quantization_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_skip_modules=["classifier"])
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seq_classification_model = AutoModelForSequenceClassification.from_pretrained(
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"FacebookAI/roberta-large-mnli", quantization_config=quantization_config
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)
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self.assertTrue(seq_classification_model.roberta.encoder.layer[0].output.dense.weight.dtype == torch.int8)
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self.assertTrue(
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isinstance(seq_classification_model.roberta.encoder.layer[0].output.dense, bnb.nn.Linear8bitLt)
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)
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self.assertTrue(isinstance(seq_classification_model.classifier.dense, nn.Linear))
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self.assertTrue(seq_classification_model.classifier.dense.weight.dtype != torch.int8)
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self.assertTrue(isinstance(seq_classification_model.classifier.out_proj, nn.Linear))
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self.assertTrue(seq_classification_model.classifier.out_proj != torch.int8)
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def test_generate_quality(self):
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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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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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output_sequences = self.model_8bit.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
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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_config(self):
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r"""
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Test that loading the model with the config is equivalent
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"""
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bnb_config = BitsAndBytesConfig()
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bnb_config.load_in_8bit = True
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model_8bit_from_config = AutoModelForCausalLM.from_pretrained(
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self.model_name, quantization_config=bnb_config, device_map="auto"
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)
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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output_sequences = model_8bit_from_config.generate(
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input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10
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)
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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_raise_if_config_and_load_in_8bit(self):
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r"""
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Test that loading the model with the config and `load_in_8bit` raises an error
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"""
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bnb_config = BitsAndBytesConfig()
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with self.assertRaises(ValueError):
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_ = AutoModelForCausalLM.from_pretrained(
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self.model_name,
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quantization_config=bnb_config,
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load_in_8bit=True,
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device_map="auto",
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llm_int8_enable_fp32_cpu_offload=True,
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)
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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 converting it in 8-bit will throw an error.
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Checks also if other models are casted correctly.
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"""
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with self.assertRaises(ValueError):
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# Tries with `str`
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self.model_8bit.to("cpu")
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with self.assertRaises(ValueError):
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# Tries with a `dtype``
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self.model_8bit.to(torch.float16)
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with self.assertRaises(ValueError):
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# Tries with a `device`
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self.model_8bit.to(torch.device("cuda:0"))
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with self.assertRaises(ValueError):
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# Tries with a `device`
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self.model_8bit.float()
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with self.assertRaises(ValueError):
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# Tries with a `device`
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self.model_8bit.half()
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# Test if we did not break anything
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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self.model_fp16 = self.model_fp16.to(torch.float32)
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_ = self.model_fp16.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
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# Check this does not throw an error
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_ = self.model_fp16.to("cpu")
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# Check this does not throw an error
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_ = self.model_fp16.half()
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# Check this does not throw an error
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_ = self.model_fp16.float()
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def test_fp32_int8_conversion(self):
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r"""
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Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
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"""
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model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small", load_in_8bit=True, device_map="auto")
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self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
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def test_int8_serialization(self):
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r"""
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Test whether it is possible to serialize a model in 8-bit.
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"""
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from bitsandbytes.nn import Int8Params
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with tempfile.TemporaryDirectory() as tmpdirname:
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self.model_8bit.save_pretrained(tmpdirname)
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# check that the file `quantization_config` is present
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config = AutoConfig.from_pretrained(tmpdirname)
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self.assertTrue(hasattr(config, "quantization_config"))
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model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, load_in_8bit=True, device_map="auto")
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linear = get_some_linear_layer(model_from_saved)
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self.assertTrue(linear.weight.__class__ == Int8Params)
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self.assertTrue(hasattr(linear.weight, "SCB"))
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# generate
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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output_sequences = model_from_saved.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
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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_int8_serialization_regression(self):
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r"""
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Test whether it is possible to serialize a model in 8-bit - using not safetensors
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"""
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from bitsandbytes.nn import Int8Params
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with tempfile.TemporaryDirectory() as tmpdirname:
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self.model_8bit.save_pretrained(tmpdirname, safe_serialization=False)
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# check that the file `quantization_config` is present
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config = AutoConfig.from_pretrained(tmpdirname)
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self.assertTrue(hasattr(config, "quantization_config"))
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model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, load_in_8bit=True, device_map="auto")
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linear = get_some_linear_layer(model_from_saved)
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self.assertTrue(linear.weight.__class__ == Int8Params)
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self.assertTrue(hasattr(linear.weight, "SCB"))
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# generate
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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output_sequences = model_from_saved.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
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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_int8_serialization_sharded(self):
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r"""
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Test whether it is possible to serialize a model in 8-bit - sharded version.
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"""
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from bitsandbytes.nn import Int8Params
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with tempfile.TemporaryDirectory() as tmpdirname:
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self.model_8bit.save_pretrained(tmpdirname, max_shard_size="200MB")
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# check that the file `quantization_config` is present
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config = AutoConfig.from_pretrained(tmpdirname)
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self.assertTrue(hasattr(config, "quantization_config"))
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model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname)
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linear = get_some_linear_layer(model_from_saved)
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self.assertTrue(linear.weight.__class__ == Int8Params)
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self.assertTrue(hasattr(linear.weight, "SCB"))
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# generate
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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output_sequences = model_from_saved.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
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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_int8_from_pretrained(self):
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r"""
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Test whether loading a 8bit model from the Hub works as expected
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"""
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from bitsandbytes.nn import Int8Params
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model_id = "ybelkada/bloom-1b7-8bit"
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model = AutoModelForCausalLM.from_pretrained(model_id)
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linear = get_some_linear_layer(model)
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self.assertTrue(linear.weight.__class__ == Int8Params)
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self.assertTrue(hasattr(linear.weight, "SCB"))
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# generate
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encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
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output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
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self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
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@require_bitsandbytes
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@require_accelerate
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@require_torch
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|
@require_torch_gpu
|
|
@slow
|
|
class MixedInt8T5Test(unittest.TestCase):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls.model_name = "google-t5/t5-small"
|
|
cls.dense_act_model_name = "google/flan-t5-small" # flan-t5 uses dense-act instead of dense-relu-dense
|
|
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
|
|
cls.input_text = "Translate in German: Hello, my dog is cute"
|
|
|
|
def tearDown(self):
|
|
r"""
|
|
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
|
|
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
|
|
"""
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def test_inference_without_keep_in_fp32(self):
|
|
r"""
|
|
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
|
|
`flan-t5-small` uses `T5DenseGatedActDense` whereas `google-t5/t5-small` uses `T5DenseReluDense`. We need to test
|
|
both cases.
|
|
"""
|
|
from transformers import T5ForConditionalGeneration
|
|
|
|
modules = T5ForConditionalGeneration._keep_in_fp32_modules
|
|
T5ForConditionalGeneration._keep_in_fp32_modules = None
|
|
|
|
# test with `google-t5/t5-small`
|
|
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
|
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
|
_ = model.generate(**encoded_input)
|
|
|
|
# test with `flan-t5-small`
|
|
model = T5ForConditionalGeneration.from_pretrained(
|
|
self.dense_act_model_name, load_in_8bit=True, device_map="auto"
|
|
)
|
|
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
|
_ = model.generate(**encoded_input)
|
|
T5ForConditionalGeneration._keep_in_fp32_modules = modules
|
|
|
|
def test_inference_with_keep_in_fp32(self):
|
|
r"""
|
|
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
|
|
`flan-t5-small` uses `T5DenseGatedActDense` whereas `google-t5/t5-small` uses `T5DenseReluDense`. We need to test
|
|
both cases.
|
|
"""
|
|
import bitsandbytes as bnb
|
|
|
|
from transformers import T5ForConditionalGeneration
|
|
|
|
# test with `google-t5/t5-small`
|
|
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
|
|
|
# there was a bug with decoders - this test checks that it is fixed
|
|
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q, bnb.nn.Linear8bitLt))
|
|
|
|
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
|
_ = model.generate(**encoded_input)
|
|
|
|
# test with `flan-t5-small`
|
|
model = T5ForConditionalGeneration.from_pretrained(
|
|
self.dense_act_model_name, load_in_8bit=True, device_map="auto"
|
|
)
|
|
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
|
_ = model.generate(**encoded_input)
|
|
|
|
def test_inference_with_keep_in_fp32_serialized(self):
|
|
r"""
|
|
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly on
|
|
a serialized model.
|
|
`flan-t5-small` uses `T5DenseGatedActDense` whereas `google-t5/t5-small` uses `T5DenseReluDense`. We need to test
|
|
both cases.
|
|
"""
|
|
import bitsandbytes as bnb
|
|
|
|
from transformers import T5ForConditionalGeneration
|
|
|
|
# test with `google-t5/t5-small`
|
|
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(tmp_dir)
|
|
|
|
model = T5ForConditionalGeneration.from_pretrained(tmp_dir)
|
|
|
|
# there was a bug with decoders - this test checks that it is fixed
|
|
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q, bnb.nn.Linear8bitLt))
|
|
|
|
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
|
_ = model.generate(**encoded_input)
|
|
|
|
# test with `flan-t5-small`
|
|
model = T5ForConditionalGeneration.from_pretrained(
|
|
self.dense_act_model_name, load_in_8bit=True, device_map="auto"
|
|
)
|
|
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
|
|
_ = model.generate(**encoded_input)
|
|
|
|
|
|
class MixedInt8ModelClassesTest(BaseMixedInt8Test):
|
|
def setUp(self):
|
|
super().setUp()
|
|
# model_name
|
|
self.model_name = "bigscience/bloom-560m"
|
|
self.seq_to_seq_name = "google-t5/t5-small"
|
|
|
|
# Different types of model
|
|
|
|
self.base_model = AutoModel.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
|
# Sequence classification model
|
|
self.sequence_model = AutoModelForSequenceClassification.from_pretrained(
|
|
self.model_name, load_in_8bit=True, device_map="auto"
|
|
)
|
|
# CausalLM model
|
|
self.model_8bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
|
|
# Seq2seq model
|
|
self.seq_to_seq_model = AutoModelForSeq2SeqLM.from_pretrained(
|
|
self.seq_to_seq_name, load_in_8bit=True, device_map="auto"
|
|
)
|
|
|
|
def tearDown(self):
|
|
r"""
|
|
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
|
|
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
|
|
"""
|
|
del self.base_model
|
|
del self.sequence_model
|
|
del self.model_8bit
|
|
del self.seq_to_seq_model
|
|
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def test_correct_head_class(self):
|
|
r"""
|
|
A simple test to check if the last modules for some classes (AutoModelForCausalLM or SequenceClassification)
|
|
are kept in their native class.
|
|
"""
|
|
from bitsandbytes.nn import Int8Params
|
|
|
|
# last param of a base model should be a linear8bit module
|
|
self.assertTrue(self.base_model.h[-1].mlp.dense_4h_to_h.weight.__class__ == Int8Params)
|
|
|
|
# Other heads should be nn.Parameter
|
|
self.assertTrue(self.model_8bit.lm_head.weight.__class__ == torch.nn.Parameter)
|
|
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
|
|
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)
|
|
|
|
|
|
class MixedInt8TestPipeline(BaseMixedInt8Test):
|
|
def setUp(self):
|
|
super().setUp()
|
|
|
|
def tearDown(self):
|
|
r"""
|
|
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
|
|
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
|
|
"""
|
|
del self.pipe
|
|
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def test_pipeline(self):
|
|
r"""
|
|
The aim of this test is to verify that the mixed int8 is compatible with `pipeline` from transformers. Since
|
|
we used pipline for inference speed benchmarking we want to make sure that this feature does not break anything
|
|
on pipline.
|
|
"""
|
|
# self._clear_cuda_cache()
|
|
self.pipe = pipeline(
|
|
"text-generation",
|
|
model=self.model_name,
|
|
model_kwargs={"device_map": "auto", "load_in_8bit": True},
|
|
max_new_tokens=self.MAX_NEW_TOKENS,
|
|
)
|
|
|
|
# Real second forward pass
|
|
pipeline_output = self.pipe(self.input_text)
|
|
self.assertIn(pipeline_output[0]["generated_text"], self.EXPECTED_OUTPUTS)
|
|
|
|
|
|
@require_torch_multi_gpu
|
|
class MixedInt8TestMultiGpu(BaseMixedInt8Test):
|
|
def setUp(self):
|
|
super().setUp()
|
|
|
|
def test_multi_gpu_loading(self):
|
|
r"""
|
|
This tests that the model has been loaded and can be used correctly on a multi-GPU setup.
|
|
Let's just try to load a model on 2 GPUs and see if it works. The model we test has ~2GB of total, 3GB should suffice
|
|
"""
|
|
|
|
model_parallel = AutoModelForCausalLM.from_pretrained(
|
|
self.model_name, load_in_8bit=True, device_map="balanced"
|
|
)
|
|
|
|
# Check correct device map
|
|
self.assertEqual(set(model_parallel.hf_device_map.values()), {0, 1})
|
|
|
|
# Check that inference pass works on the model
|
|
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
|
|
|
# Second real batch
|
|
output_parallel = model_parallel.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
|
self.assertIn(self.tokenizer.decode(output_parallel[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
|
|
|
|
|
|
@require_torch_multi_gpu
|
|
class MixedInt8TestCpuGpu(BaseMixedInt8Test):
|
|
def setUp(self):
|
|
super().setUp()
|
|
|
|
def check_inference_correctness(self, model):
|
|
# Check that inference pass works on the model
|
|
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
|
|
|
# Check the exactness of the results
|
|
output_parallel = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
|
|
|
# Get the generation
|
|
output_text = self.tokenizer.decode(output_parallel[0], skip_special_tokens=True)
|
|
self.assertIn(output_text, self.EXPECTED_OUTPUTS)
|
|
|
|
def test_cpu_gpu_loading_random_device_map(self):
|
|
r"""
|
|
A test to check is dispatching a model on cpu & gpu works correctly using a random `device_map`.
|
|
"""
|
|
device_map = {
|
|
"transformer.word_embeddings": 0,
|
|
"transformer.word_embeddings_layernorm": 0,
|
|
"lm_head": 0,
|
|
"transformer.h.0": "cpu",
|
|
"transformer.h.1": "cpu",
|
|
"transformer.h.2": 0,
|
|
"transformer.h.3": 0,
|
|
"transformer.h.4": 0,
|
|
"transformer.h.5": 0,
|
|
"transformer.h.6": 0,
|
|
"transformer.h.7": 0,
|
|
"transformer.h.8": 0,
|
|
"transformer.h.9": 1,
|
|
"transformer.h.10": 0,
|
|
"transformer.h.11": 1,
|
|
"transformer.h.12": 0,
|
|
"transformer.h.13": 0,
|
|
"transformer.h.14": 1,
|
|
"transformer.h.15": 0,
|
|
"transformer.h.16": 0,
|
|
"transformer.h.17": 1,
|
|
"transformer.h.18": 1,
|
|
"transformer.h.19": 0,
|
|
"transformer.h.20": 1,
|
|
"transformer.h.21": 1,
|
|
"transformer.h.22": 0,
|
|
"transformer.h.23": 0,
|
|
"transformer.ln_f": 1,
|
|
}
|
|
|
|
bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True, load_in_8bit=True)
|
|
|
|
model_8bit = AutoModelForCausalLM.from_pretrained(
|
|
self.model_name,
|
|
device_map=device_map,
|
|
quantization_config=bnb_config,
|
|
)
|
|
|
|
# Check that the model has been correctly set on device 0, 1, and `cpu`.
|
|
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu"})
|
|
|
|
self.check_inference_correctness(model_8bit)
|
|
|
|
def test_cpu_gpu_loading_custom_device_map(self):
|
|
r"""
|
|
A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
|
|
This time the device map is more organized than the test above and uses the abstraction
|
|
`transformer.h` to encapsulate all the decoder layers.
|
|
"""
|
|
device_map = {
|
|
"transformer.word_embeddings": "cpu",
|
|
"transformer.word_embeddings_layernorm": "cpu",
|
|
"lm_head": "cpu",
|
|
"transformer.h": 0,
|
|
"transformer.ln_f": 1,
|
|
}
|
|
bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True, load_in_8bit=True)
|
|
|
|
# Load model
|
|
model_8bit = AutoModelForCausalLM.from_pretrained(
|
|
self.model_name,
|
|
device_map=device_map,
|
|
quantization_config=bnb_config,
|
|
)
|
|
|
|
# Check that the model has been correctly set on device 0, 1, and `cpu`.
|
|
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu"})
|
|
|
|
self.check_inference_correctness(model_8bit)
|
|
|
|
def test_cpu_gpu_disk_loading_custom_device_map(self):
|
|
r"""
|
|
A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
|
|
This time we also add `disk` on the device_map.
|
|
"""
|
|
device_map = {
|
|
"transformer.word_embeddings": 0,
|
|
"transformer.word_embeddings_layernorm": "cpu",
|
|
"lm_head": 0,
|
|
"transformer.h": 1,
|
|
"transformer.ln_f": "disk",
|
|
}
|
|
bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True, load_in_8bit=True)
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
# Load model
|
|
model_8bit = AutoModelForCausalLM.from_pretrained(
|
|
self.model_name,
|
|
device_map=device_map,
|
|
quantization_config=bnb_config,
|
|
offload_folder=tmpdirname,
|
|
)
|
|
|
|
# Check that the model has been correctly set on device 0, 1, and `cpu`.
|
|
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu", "disk"})
|
|
|
|
self.check_inference_correctness(model_8bit)
|
|
|
|
def test_cpu_gpu_disk_loading_custom_device_map_kwargs(self):
|
|
r"""
|
|
A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
|
|
This time we also add `disk` on the device_map - using the kwargs directly instead of the quantization config
|
|
"""
|
|
device_map = {
|
|
"transformer.word_embeddings": 0,
|
|
"transformer.word_embeddings_layernorm": "cpu",
|
|
"lm_head": 0,
|
|
"transformer.h": 1,
|
|
"transformer.ln_f": "disk",
|
|
}
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
# Load model
|
|
model_8bit = AutoModelForCausalLM.from_pretrained(
|
|
self.model_name,
|
|
device_map=device_map,
|
|
load_in_8bit=True,
|
|
llm_int8_enable_fp32_cpu_offload=True,
|
|
offload_folder=tmpdirname,
|
|
)
|
|
|
|
# Check that the model has been correctly set on device 0, 1, and `cpu`.
|
|
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu", "disk"})
|
|
|
|
self.check_inference_correctness(model_8bit)
|
|
|
|
|
|
class MixedInt8TestTraining(BaseMixedInt8Test):
|
|
def setUp(self):
|
|
self.model_name = "facebook/opt-350m"
|
|
super().setUp()
|
|
|
|
def test_training(self):
|
|
if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.37.0"):
|
|
return
|
|
|
|
# Step 1: freeze all parameters
|
|
model = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True)
|
|
|
|
self.assertEqual(set(model.hf_device_map.values()), {torch.cuda.current_device()})
|
|
|
|
for param in model.parameters():
|
|
param.requires_grad = False # freeze the model - train adapters later
|
|
if param.ndim == 1:
|
|
# cast the small parameters (e.g. layernorm) to fp32 for stability
|
|
param.data = param.data.to(torch.float32)
|
|
|
|
# Step 2: add adapters
|
|
for _, module in model.named_modules():
|
|
if "OPTAttention" in repr(type(module)):
|
|
module.q_proj = LoRALayer(module.q_proj, rank=16)
|
|
module.k_proj = LoRALayer(module.k_proj, rank=16)
|
|
module.v_proj = LoRALayer(module.v_proj, rank=16)
|
|
|
|
# Step 3: dummy batch
|
|
batch = self.tokenizer("Test batch ", return_tensors="pt").to(0)
|
|
|
|
# Step 4: Check if the gradient is not None
|
|
with torch.cuda.amp.autocast():
|
|
out = model.forward(**batch)
|
|
out.logits.norm().backward()
|
|
|
|
for module in model.modules():
|
|
if isinstance(module, LoRALayer):
|
|
self.assertTrue(module.adapter[1].weight.grad is not None)
|
|
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
|
|
elif isinstance(module, nn.Embedding):
|
|
self.assertTrue(module.weight.grad is None)
|
|
|
|
|
|
class MixedInt8GPT2Test(MixedInt8Test):
|
|
model_name = "openai-community/gpt2-xl"
|
|
EXPECTED_RELATIVE_DIFFERENCE = 1.8720077507258357
|
|
EXPECTED_OUTPUTS = set()
|
|
EXPECTED_OUTPUTS.add("Hello my name is John Doe, and I'm a big fan of")
|
|
EXPECTED_OUTPUTS.add("Hello my name is John Doe, and I'm a fan of the")
|
|
# Expected values on a A10
|
|
EXPECTED_OUTPUTS.add("Hello my name is John Doe, and I am a member of the")
|
|
|
|
def test_int8_from_pretrained(self):
|
|
r"""
|
|
Test whether loading a 8bit model from the Hub works as expected
|
|
"""
|
|
from bitsandbytes.nn import Int8Params
|
|
|
|
model_id = "ybelkada/gpt2-xl-8bit"
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
linear = get_some_linear_layer(model)
|
|
self.assertTrue(linear.weight.__class__ == Int8Params)
|
|
self.assertTrue(hasattr(linear.weight, "SCB"))
|
|
|
|
# generate
|
|
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
|
output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
|
|
|
self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
|