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v1 GPU tests
This commit is contained in:
younesbelkada
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# coding=utf-8
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# Copyright 2023-present the HuggingFace Inc. team.
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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 unittest
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import pytest
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import torch
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from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, WhisperForConditionalGeneration
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from peft import LoraConfig, PeftModel, get_peft_model
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from peft.tuners.lora import Linear8bitLt
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from .testing_utils import require_bitsandbytes, require_torch_gpu, require_torch_multi_gpu
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@require_torch_gpu
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class PeftGPUCommonTests(unittest.TestCase):
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r""" """
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def setUp(self):
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self.seq2seq_model_id = "google/flan-t5-base"
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self.causal_lm_model_id = "facebook/opt-350m"
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self.audio_model_id = "openai/whisper-large"
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self.device = torch.device("cuda:0")
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def tearDown(self):
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r"""
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Efficient mechanism to free GPU memory after each test. Based on
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https://github.com/huggingface/transformers/issues/21094
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"""
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gc.collect()
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torch.cuda.empty_cache()
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gc.collect()
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@require_bitsandbytes
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def test_lora_bnb_quantization(self):
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r"""
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Test that tests if the 8bit quantization using LoRA works as expected
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"""
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whisper_8bit = WhisperForConditionalGeneration.from_pretrained(
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self.audio_model_id,
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device_map="auto",
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load_in_8bit=True,
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)
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opt_8bit = AutoModelForCausalLM.from_pretrained(
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self.causal_lm_model_id,
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device_map="auto",
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load_in_8bit=True,
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)
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flan_8bit = AutoModelForSeq2SeqLM.from_pretrained(
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self.seq2seq_model_id,
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device_map="auto",
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load_in_8bit=True,
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)
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flan_lora_config = LoraConfig(
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r=16, lora_alpha=32, target_modules=["q", "v"], lora_dropout=0.05, bias="none", task_type="SEQ_2_SEQ_LM"
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)
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opt_lora_config = LoraConfig(
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r=16,
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lora_alpha=32,
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target_modules=["q_proj", "v_proj"],
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM",
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)
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config = LoraConfig(r=32, lora_alpha=64, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none")
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flan_8bit = get_peft_model(flan_8bit, flan_lora_config)
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self.assertTrue(isinstance(flan_8bit.base_model.model.encoder.block[0].layer[0].SelfAttention.q, Linear8bitLt))
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opt_8bit = get_peft_model(opt_8bit, opt_lora_config)
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self.assertTrue(isinstance(opt_8bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, Linear8bitLt))
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whisper_8bit = get_peft_model(whisper_8bit, config)
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self.assertTrue(
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isinstance(whisper_8bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, Linear8bitLt)
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)
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@pytest.mark.multi_gpu_tests
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@require_torch_multi_gpu
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def test_lora_causal_lm_mutli_gpu_inference(self):
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r"""
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Test if LORA can be used for inference on multiple GPUs.
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"""
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lora_config = LoraConfig(
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r=16,
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lora_alpha=32,
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target_modules=["q_proj", "v_proj"],
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM",
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)
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model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id, device_map="balanced")
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tokenizer = AutoTokenizer.from_pretrained(self.seq2seq_model_id)
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self.assertEqual(set(model.hf_device_map.values()), {0, 1})
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model = get_peft_model(model, lora_config)
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self.assertTrue(isinstance(model, PeftModel))
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dummy_input = "This is a dummy input:"
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input_ids = tokenizer(dummy_input, return_tensors="pt").input_ids.to(self.device)
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# this should work without any problem
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_ = model.generate(input_ids=input_ids)
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@require_torch_multi_gpu
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@pytest.mark.multi_gpu_tests
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@require_bitsandbytes
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def test_lora_seq2seq_lm_mutli_gpu_inference(self):
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r"""
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Test if LORA can be used for inference on multiple GPUs - 8bit version.
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"""
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lora_config = LoraConfig(
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r=16, lora_alpha=32, target_modules=["q", "v"], lora_dropout=0.05, bias="none", task_type="SEQ_2_SEQ_LM"
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)
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model = AutoModelForSeq2SeqLM.from_pretrained(self.seq2seq_model_id, device_map="balanced", load_in_8bit=True)
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tokenizer = AutoTokenizer.from_pretrained(self.seq2seq_model_id)
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self.assertEqual(set(model.hf_device_map.values()), {0, 1})
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model = get_peft_model(model, lora_config)
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self.assertTrue(isinstance(model, PeftModel))
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self.assertTrue(isinstance(model.base_model.model.encoder.block[0].layer[0].SelfAttention.q, Linear8bitLt))
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dummy_input = "This is a dummy input:"
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input_ids = tokenizer(dummy_input, return_tensors="pt").input_ids.to(self.device)
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# this should work without any problem
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_ = model.generate(input_ids=input_ids)
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@@ -0,0 +1,315 @@
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# coding=utf-8
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# Copyright 2023-present the HuggingFace Inc. team.
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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 os
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import tempfile
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import unittest
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import pytest
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import torch
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from datasets import load_dataset
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from transformers import (
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AutoModelForCausalLM,
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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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DataCollatorForLanguageModeling,
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Trainer,
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TrainingArguments,
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)
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from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training
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from .testing_utils import require_bitsandbytes, require_torch_gpu, require_torch_multi_gpu
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# A full testing suite that tests all the necessary features on GPU. The tests should
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# rely on the example scripts to test the features.
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@require_torch_gpu
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@require_bitsandbytes
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class PeftInt8GPUExampleTests(unittest.TestCase):
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r"""
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A single GPU int8 test suite, this will test if training fits correctly on a single GPU device (1x NVIDIA T4 16GB)
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using bitsandbytes.
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The tests are the following:
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- Seq2Seq model training based on:
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https://github.com/huggingface/peft/blob/main/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb
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- Causal LM model training based on:
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https://github.com/huggingface/peft/blob/main/examples/int8_training/Finetune_opt_bnb_peft.ipynb
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- Audio model training based on:
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https://github.com/huggingface/peft/blob/main/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb
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"""
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def setUp(self):
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self.seq2seq_model_id = "google/flan-t5-base"
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self.causal_lm_model_id = "facebook/opt-6.7b"
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self.audio_model_id = "openai/whisper-large"
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def tearDown(self):
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r"""
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Efficient mechanism to free GPU memory after each test. Based on
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https://github.com/huggingface/transformers/issues/21094
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"""
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gc.collect()
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torch.cuda.empty_cache()
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gc.collect()
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@pytest.mark.single_gpu_tests
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def test_causal_lm_training(self):
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r"""
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Test the CausalLM training on a single GPU device. This test is a converted version of
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https://github.com/huggingface/peft/blob/main/examples/int8_training/Finetune_opt_bnb_peft.ipynb where we train
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`opt-6.7b` on `english_quotes` dataset in few steps. The test would simply fail if the adapters are not set
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correctly.
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"""
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with tempfile.TemporaryDirectory() as tmp_dir:
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model = AutoModelForCausalLM.from_pretrained(
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self.causal_lm_model_id,
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load_in_8bit=True,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained(self.causal_lm_model_id)
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model = prepare_model_for_int8_training(model)
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config = LoraConfig(
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r=16,
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lora_alpha=32,
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target_modules=["q_proj", "v_proj"],
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM",
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)
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model = get_peft_model(model, config)
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data = load_dataset("Abirate/english_quotes")
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data = data.map(lambda samples: tokenizer(samples["quote"]), batched=True)
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trainer = Trainer(
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model=model,
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train_dataset=data["train"],
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args=TrainingArguments(
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per_device_train_batch_size=4,
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gradient_accumulation_steps=4,
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warmup_steps=2,
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max_steps=3,
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learning_rate=2e-4,
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fp16=True,
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logging_steps=1,
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output_dir="outputs",
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),
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data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
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)
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model.config.use_cache = False
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trainer.train()
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model.cpu().save_pretrained(tmp_dir)
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self.assertTrue("adapter_config.json" in os.listdir(tmp_dir))
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self.assertTrue("adapter_model.bin" in os.listdir(tmp_dir))
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# assert loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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@pytest.mark.multi_gpu_tests
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@require_torch_multi_gpu
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def test_causal_lm_training_mutli_gpu(self):
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r"""
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Test the CausalLM training on a multi-GPU device. This test is a converted version of
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https://github.com/huggingface/peft/blob/main/examples/int8_training/Finetune_opt_bnb_peft.ipynb where we train
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`opt-6.7b` on `english_quotes` dataset in few steps. The test would simply fail if the adapters are not set
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correctly.
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"""
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with tempfile.TemporaryDirectory() as tmp_dir:
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model = AutoModelForCausalLM.from_pretrained(
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self.causal_lm_model_id,
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load_in_8bit=True,
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device_map="auto",
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)
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self.assertEqual(set(model.hf_device_map.values()), {0, 1})
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tokenizer = AutoTokenizer.from_pretrained(self.causal_lm_model_id)
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model = prepare_model_for_int8_training(model)
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setattr(model, "model_parallel", True)
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setattr(model, "is_parallelizable", True)
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config = LoraConfig(
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r=16,
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lora_alpha=32,
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target_modules=["q_proj", "v_proj"],
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM",
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)
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model = get_peft_model(model, config)
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data = load_dataset("Abirate/english_quotes")
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data = data.map(lambda samples: tokenizer(samples["quote"]), batched=True)
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trainer = Trainer(
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model=model,
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train_dataset=data["train"],
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args=TrainingArguments(
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per_device_train_batch_size=4,
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gradient_accumulation_steps=4,
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warmup_steps=2,
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max_steps=3,
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learning_rate=2e-4,
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fp16=True,
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logging_steps=1,
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output_dir="outputs",
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),
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data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
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)
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model.config.use_cache = False
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trainer.train()
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model.cpu().save_pretrained(tmp_dir)
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self.assertTrue("adapter_config.json" in os.listdir(tmp_dir))
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self.assertTrue("adapter_model.bin" in os.listdir(tmp_dir))
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# assert loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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@pytest.mark.single_gpu_tests
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@require_torch_gpu
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def test_seq2seq_lm_training_single_gpu(self):
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r"""
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Test the Seq2SeqLM training on a single GPU device. This test is a converted version of
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https://github.com/huggingface/peft/blob/main/examples/int8_training/Finetune_opt_bnb_peft.ipynb where we train
|
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`flan-large` on `english_quotes` dataset in few steps. The test would simply fail if the adapters are not set
|
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correctly.
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"""
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with tempfile.TemporaryDirectory() as tmp_dir:
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model = AutoModelForSeq2SeqLM.from_pretrained(
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self.seq2seq_model_id,
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load_in_8bit=True,
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device_map={"": 0},
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)
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self.assertEqual(set(model.hf_device_map.values()), {0})
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tokenizer = AutoTokenizer.from_pretrained(self.seq2seq_model_id)
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model = prepare_model_for_int8_training(model)
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config = LoraConfig(
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r=16,
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lora_alpha=32,
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target_modules=["q", "v"],
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM",
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)
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model = get_peft_model(model, config)
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data = load_dataset("Abirate/english_quotes")
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data = data.map(lambda samples: tokenizer(samples["quote"]), batched=True)
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trainer = Trainer(
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model=model,
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train_dataset=data["train"],
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args=TrainingArguments(
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per_device_train_batch_size=4,
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gradient_accumulation_steps=4,
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warmup_steps=2,
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max_steps=3,
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learning_rate=2e-4,
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fp16=True,
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logging_steps=1,
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output_dir="outputs",
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),
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data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
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)
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model.config.use_cache = False
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trainer.train()
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model.cpu().save_pretrained(tmp_dir)
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self.assertTrue("adapter_config.json" in os.listdir(tmp_dir))
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self.assertTrue("adapter_model.bin" in os.listdir(tmp_dir))
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# assert loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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@pytest.mark.multi_gpu_tests
|
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@require_torch_multi_gpu
|
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def test_seq2seq_lm_training_mutli_gpu(self):
|
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r"""
|
||||
Test the Seq2SeqLM training on a multi-GPU device. This test is a converted version of
|
||||
https://github.com/huggingface/peft/blob/main/examples/int8_training/Finetune_opt_bnb_peft.ipynb where we train
|
||||
`flan-large` on `english_quotes` dataset in few steps. The test would simply fail if the adapters are not set
|
||||
correctly.
|
||||
"""
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
model = AutoModelForSeq2SeqLM.from_pretrained(
|
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self.seq2seq_model_id,
|
||||
load_in_8bit=True,
|
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device_map="balanced",
|
||||
)
|
||||
|
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self.assertEqual(set(model.hf_device_map.values()), {0, 1})
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(self.seq2seq_model_id)
|
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model = prepare_model_for_int8_training(model)
|
||||
|
||||
config = LoraConfig(
|
||||
r=16,
|
||||
lora_alpha=32,
|
||||
target_modules=["q", "v"],
|
||||
lora_dropout=0.05,
|
||||
bias="none",
|
||||
task_type="CAUSAL_LM",
|
||||
)
|
||||
|
||||
model = get_peft_model(model, config)
|
||||
|
||||
data = load_dataset("Abirate/english_quotes")
|
||||
data = data.map(lambda samples: tokenizer(samples["quote"]), batched=True)
|
||||
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
train_dataset=data["train"],
|
||||
args=TrainingArguments(
|
||||
per_device_train_batch_size=4,
|
||||
gradient_accumulation_steps=4,
|
||||
warmup_steps=2,
|
||||
max_steps=3,
|
||||
learning_rate=2e-4,
|
||||
fp16=True,
|
||||
logging_steps=1,
|
||||
output_dir="outputs",
|
||||
),
|
||||
data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
|
||||
)
|
||||
model.config.use_cache = False
|
||||
trainer.train()
|
||||
|
||||
model.cpu().save_pretrained(tmp_dir)
|
||||
|
||||
self.assertTrue("adapter_config.json" in os.listdir(tmp_dir))
|
||||
self.assertTrue("adapter_model.bin" in os.listdir(tmp_dir))
|
||||
|
||||
# assert loss is not None
|
||||
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
||||
Reference in New Issue
Block a user