c6d4f91777
* Add uts for transformers api load_low_bit generation * Small fixes * Remove replit-code for CPU tests due to current load_low_bit issue on MPT * Small change * Small reorganization to llm unit tests on CPU * Small fixes
261 lines
12 KiB
Python
261 lines
12 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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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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#
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import os, time
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import pytest
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import tempfile
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import torch
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from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel, AutoModelForSpeechSeq2Seq
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from transformers import LlamaTokenizer, AutoTokenizer
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device = os.environ['DEVICE']
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print(f'Running on {device}')
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@pytest.mark.parametrize('prompt, answer', [
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('What is the capital of France?\n\n', 'Paris')
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])
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@pytest.mark.parametrize('Model, Tokenizer, model_path',[
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(AutoModelForCausalLM, LlamaTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH')),
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(AutoModel, AutoTokenizer, os.environ.get('CHATGLM2_6B_ORIGIN_PATH')),
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(AutoModelForCausalLM, AutoTokenizer, os.environ.get('FALCON_7B_ORIGIN_PATH')),
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(AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
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])
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def test_completion(Model, Tokenizer, model_path, prompt, answer):
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with torch.inference_mode():
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tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
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model = Model.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=True,
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trust_remote_code=True)
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model = model.to(device)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
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output = model.generate(input_ids, max_new_tokens=32)
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model.to('cpu') # deallocate gpu memory
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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assert answer in output_str
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@pytest.mark.parametrize('prompt, answer', [
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('What is the capital of France?\n\n', 'Paris')
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])
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@pytest.mark.parametrize('Model, Tokenizer, model_path',[
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(AutoModelForCausalLM, LlamaTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH')),
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(AutoModel, AutoTokenizer, os.environ.get('CHATGLM2_6B_ORIGIN_PATH')),
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])
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def test_load_low_bit_completion(Model, Tokenizer, model_path, prompt, answer):
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tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
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model = Model.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=True,
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trust_remote_code=True)
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with tempfile.TemporaryDirectory() as tempdir:
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model.save_low_bit(tempdir)
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loaded_model = Model.load_low_bit(tempdir,
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optimize_model=True,
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trust_remote_code=True)
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with torch.inference_mode():
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loaded_model = loaded_model.to(device)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
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output = loaded_model.generate(input_ids, max_new_tokens=32)
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loaded_model.to('cpu') # deallocate gpu memory
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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assert answer in output_str
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def test_transformers_auto_model_for_speech_seq2seq_int4():
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with torch.inference_mode():
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from transformers import WhisperProcessor
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from datasets import load_from_disk
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model_path = os.environ.get('WHISPER_TINY_ORIGIN_PATH')
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dataset_path = os.environ.get('SPEECH_DATASET_PATH')
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processor = WhisperProcessor.from_pretrained(model_path)
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ds = load_from_disk(dataset_path)
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sample = ds[0]["audio"]
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input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
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input_features = input_features.to(device)
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model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True, optimize_model=True)
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model = model.to(device)
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predicted_ids = model.generate(input_features)
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# decode token ids to text
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
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model.to('cpu')
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print('Output:', transcription)
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assert 'Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.' in transcription[0]
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prompt = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
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# @pytest.mark.parametrize('Model, Tokenizer, model_path',[
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# (AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
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# (AutoModelForCausalLM, AutoTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH'))
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# ])
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# def test_optimize_model(Model, Tokenizer, model_path):
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# with torch.inference_mode():
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# tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
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# input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
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# model = Model.from_pretrained(model_path,
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# load_in_4bit=True,
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# optimize_model=False,
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# trust_remote_code=True)
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# model = model.to(device)
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# logits_base_model = (model(input_ids)).logits
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# model.to('cpu') # deallocate gpu memory
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# model = Model.from_pretrained(model_path,
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# load_in_4bit=True,
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# optimize_model=True,
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# trust_remote_code=True)
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# model = model.to(device)
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# logits_optimized_model = (model(input_ids)).logits
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# model.to('cpu')
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# tol = 1e-02
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# num_false = torch.isclose(logits_optimized_model, logits_base_model, rtol=tol, atol=tol)\
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# .flatten().tolist().count(False)
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# percent_false = num_false / logits_optimized_model.numel()
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# assert percent_false < 1e-02
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class Test_Optimize_Gpu_Model:
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def setup(self):
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self.layer_outputs = []
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self.pre_layer_outputs = []
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def run_optimize_gpu_model(self, Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound):
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with torch.inference_mode():
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def forward_hook(module, input, output, layer_name):
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self.layer_outputs.append(output)
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def pre_forward_hook(module, input, output, layer_name):
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self.pre_layer_outputs.append(output)
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tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
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model = Model.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=False,
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trust_remote_code=True)
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model = model.to(device)
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for layer_name, layer_module in model.named_modules():
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if layer_name == layer_norm:
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layer_module.register_forward_hook(
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lambda module, input, output, layer_name=layer_name: pre_forward_hook(module, input,
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output, layer_name))
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if layer_name == self_attn:
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layer_module.register_forward_hook(
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lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
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output, layer_name))
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logits_base_model = (model(input_ids)).logits
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# the list `layer_output` has only one element.
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layer_tensor = self.layer_outputs.pop()
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model.to('cpu')
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opt_model = Model.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=True,
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trust_remote_code=True)
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opt_model = opt_model.to(device)
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def replace_forward_hook(module, input, output, layer_name):
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output = self.pre_layer_outputs[0]
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return output
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for layer_name, layer_module in opt_model.named_modules():
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if layer_name == layer_norm:
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layer_module.register_forward_hook(
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lambda module, input, output, layer_name=layer_name: replace_forward_hook(module, input,
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output, layer_name))
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if layer_name == self_attn:
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layer_module.register_forward_hook(
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lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
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output, layer_name))
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logits_optimized_model = (opt_model(input_ids)).logits
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# the list `layer_output` has only one element.
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opt_layer_tensor = self.layer_outputs[0]
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opt_model.to('cpu')
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attn_output_diff = []
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for i, (t1, t2) in enumerate(zip(layer_tensor, opt_layer_tensor)):
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if t1 is not None and t2 is not None:
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if isinstance(t1, torch.Tensor) and isinstance(t2, torch.Tensor):
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# 'attn_output' is of type torch.Tensor.
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attn_output_diff.append(t1 - t2)
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else:
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# 'past_key_value'is of type tuple as default.
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for i, (t3, t4) in enumerate(zip(t1, t2)):
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if model.config.architectures[0] == "ChatGLMModel" and \
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hasattr(model.config, 'padded_vocab_size') and \
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model.config.padded_vocab_size == 65024:
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# chatglm2's past_key_value is expanded 16x for some speedup.
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# We need to narrow it here.
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t4 = t4[:, :, 15:17, :]
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attn_output_diff.append(t3 - t4)
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max_diff_tensor = [torch.max(item).item() for item in attn_output_diff]
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print(max_diff_tensor)
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assert all(max_diff <= lower_bound for max_diff in max_diff_tensor)
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# def test_falcon_gpu_model(self):
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# Model = AutoModelForCausalLM
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# Tokenizer = AutoTokenizer
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# model_path = os.environ.get('FALCON_7B_ORIGIN_PATH')
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# # currently only compare the output of the last self-attention layer.
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# layer_norm = "transformer.h.31.input_layernorm"
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# self_attn = "transformer.h.31.self_attention"
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# lower_bound = 0
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# self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
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# def test_llama_gpu_model(self):
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# Model = AutoModelForCausalLM
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# Tokenizer = AutoTokenizer
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# model_path = os.environ.get('LLAMA2_7B_ORIGIN_PATH')
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# # currently only compare the output of the last self-attention layer.
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# layer_norm = "model.layers.31.input_layernorm"
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# self_attn = "model.layers.31.self_attn"
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# lower_bound = 5e-2
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# self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
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# def test_chatglm2_gpu_model(self):
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# Model = AutoModel
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# Tokenizer = AutoTokenizer
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# model_path = os.environ.get('CHATGLM2_6B_ORIGIN_PATH')
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# # currently only need to compare the output of one self-attention layer.
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# layer_norm = "transformer.encoder.layers.27.input_layernorm"
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# self_attn = "transformer.encoder.layers.27.self_attention"
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# lower_bound = 1e-3
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# self.run_optimize_gpu_model(Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
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if __name__ == '__main__':
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pytest.main([__file__])
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