170 lines
7.4 KiB
Python
170 lines
7.4 KiB
Python
# Copyright 2024 BigCode and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch Starcoder2 model."""
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import unittest
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import pytest
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from transformers import Starcoder2Config, is_torch_available
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from transformers.testing_utils import (
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Expectations,
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require_bitsandbytes,
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require_flash_attn,
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require_torch,
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require_torch_accelerator,
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slow,
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torch_device,
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)
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if is_torch_available():
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import torch
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from transformers import (
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AutoTokenizer,
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Starcoder2ForCausalLM,
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Starcoder2ForSequenceClassification,
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Starcoder2ForTokenClassification,
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Starcoder2Model,
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)
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from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
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class Starcoder2ModelTester(CausalLMModelTester):
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config_class = Starcoder2Config
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if is_torch_available():
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base_model_class = Starcoder2Model
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causal_lm_class = Starcoder2ForCausalLM
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sequence_class = Starcoder2ForSequenceClassification
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token_class = Starcoder2ForTokenClassification
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@require_torch
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class Starcoder2ModelTest(CausalLMModelTest, unittest.TestCase):
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all_model_classes = (
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(Starcoder2Model, Starcoder2ForCausalLM, Starcoder2ForSequenceClassification, Starcoder2ForTokenClassification)
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if is_torch_available()
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else ()
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)
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test_headmasking = False
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test_pruning = False
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model_tester_class = Starcoder2ModelTester
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pipeline_model_mapping = (
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{
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"feature-extraction": Starcoder2Model,
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"text-classification": Starcoder2ForSequenceClassification,
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"token-classification": Starcoder2ForTokenClassification,
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"text-generation": Starcoder2ForCausalLM,
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}
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if is_torch_available()
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else {}
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)
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@slow
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@require_torch_accelerator
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class Starcoder2IntegrationTest(unittest.TestCase):
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def test_starcoder2_batched_generation_sdpa(self):
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EXPECTED_TEXT = [
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"Hello my name is Younes and I am a student at the University of Liverpool. I am currently studying for my MSc in Computer Science. I am interested in the field of Machine Learning and I am currently working on",
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"def hello_world():\n\treturn 'Hello World!'\n\n@app.route('/hello/<name>')\ndef hello_name(name):\n\treturn 'Hello %s!' % name\n\n@app",
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]
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model_id = "bigcode/starcoder2-7b"
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model = Starcoder2ForCausalLM.from_pretrained(
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model_id, dtype=torch.float16, device_map="auto", attn_implementation="sdpa"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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tokenizer.pad_token = tokenizer.eos_token
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text = ["Hello my name is Younes and", "def hello_world():"]
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inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
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output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
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output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT, output_text)
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def test_starcoder2_batched_generation_eager(self):
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EXPECTED_TEXT = [
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"Hello my name is Younes and I am a student at the University of Liverpool. I am currently studying for my MSc in Computer Science. I am interested in the field of Machine Learning and I am currently working on",
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"def hello_world():\n\treturn 'Hello World!'\n\n@app.route('/hello/<name>')\ndef hello_name(name):\n\treturn 'Hello %s!' % name\n\n@app",
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]
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model_id = "bigcode/starcoder2-7b"
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model = Starcoder2ForCausalLM.from_pretrained(
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model_id, dtype=torch.float16, device_map="auto", attn_implementation="eager"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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tokenizer.pad_token = tokenizer.eos_token
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text = ["Hello my name is Younes and", "def hello_world():"]
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inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
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output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
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output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT, output_text)
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@require_flash_attn
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@pytest.mark.flash_attn_test
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def test_starcoder2_batched_generation_fa2(self):
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EXPECTED_TEXT = [
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"Hello my name is Younes and I am a student at the University of Liverpool. I am currently studying for my MSc in Computer Science. I am interested in the field of Machine Learning and I am currently working on",
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"def hello_world():\n\treturn 'Hello World!'\n\n@app.route('/hello/<name>')\ndef hello_name(name):\n\treturn 'Hello %s!' % name\n\n@app",
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]
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model_id = "bigcode/starcoder2-7b"
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model = Starcoder2ForCausalLM.from_pretrained(
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model_id, dtype=torch.float16, device_map="auto", attn_implementation="flash_attention_2"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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tokenizer.pad_token = tokenizer.eos_token
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text = ["Hello my name is Younes and", "def hello_world():"]
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inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
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output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
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output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT, output_text)
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@require_bitsandbytes
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def test_starcoder2_batched_generation_4bit(self):
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expectations = Expectations(
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{
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(None, None): [
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'Hello my name is Younes and I am a student at the University of Maryland. I am currently working on a project that is related to the topic of "How to make a game". I am currently working on a project',
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'def hello_world():\n\treturn "Hello World"\n\n@app.route(\'/hello/<name>\')\ndef hello_name(name):\n\treturn "Hello " + name\n\n@app.route',
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],
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("cuda", 8): [
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"Hello my name is Younes and I am a student at the University of Maryland. I am currently working on a project that is aimed at creating a new way of learning. I am hoping to create a new way of",
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'def hello_world():\n\treturn "Hello World"\n\n@app.route(\'/hello/<name>\')\ndef hello_name(name):\n\treturn "Hello " + name\n\n@app.route',
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],
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}
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)
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EXPECTED_TEXT = expectations.get_expectation()
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model_id = "bigcode/starcoder2-7b"
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model = Starcoder2ForCausalLM.from_pretrained(model_id, load_in_4bit=True)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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tokenizer.pad_token = tokenizer.eos_token
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text = ["Hello my name is Younes and", "def hello_world():"]
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inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
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output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
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output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT, output_text)
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