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164 lines
6.3 KiB
Python
164 lines
6.3 KiB
Python
# coding=utf-8
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# Copyright 2024 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 Gemma2 model."""
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import unittest
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from transformers import AutoModelForCausalLM, AutoTokenizer, Gemma2Config, is_torch_available, pipeline
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from transformers.testing_utils import (
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require_read_token,
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require_torch,
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require_torch_gpu,
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slow,
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torch_device,
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)
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from ...models.gemma.test_modeling_gemma import GemmaModelTest, GemmaModelTester
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from ...test_configuration_common import ConfigTester
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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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Gemma2ForCausalLM,
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Gemma2ForSequenceClassification,
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Gemma2ForTokenClassification,
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Gemma2Model,
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)
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class Gemma2ModelTester(GemmaModelTester):
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if is_torch_available():
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config_class = Gemma2Config
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model_class = Gemma2Model
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for_causal_lm_class = Gemma2ForCausalLM
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for_sequence_class = Gemma2ForSequenceClassification
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for_token_class = Gemma2ForTokenClassification
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@require_torch
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class Gemma2ModelTest(GemmaModelTest, unittest.TestCase):
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all_model_classes = (
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(Gemma2Model, Gemma2ForCausalLM, Gemma2ForSequenceClassification, Gemma2ForTokenClassification)
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if is_torch_available()
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else ()
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)
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all_generative_model_classes = ()
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pipeline_model_mapping = (
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{
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"feature-extraction": Gemma2Model,
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"text-classification": Gemma2ForSequenceClassification,
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"token-classification": Gemma2ForTokenClassification,
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"text-generation": Gemma2ForCausalLM,
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"zero-shot": Gemma2ForSequenceClassification,
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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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test_headmasking = False
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test_pruning = False
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_is_stateful = True
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model_split_percents = [0.5, 0.6]
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_torch_compile_test_ckpt = "google/gemma-2-9b"
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def setUp(self):
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self.model_tester = Gemma2ModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Gemma2Config, hidden_size=37)
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@unittest.skip("Eager and SDPA do not produce the same outputs, thus this test fails")
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def test_model_outputs_equivalence(self, **kwargs):
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pass
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@unittest.skip("Gemma2's outputs are expected to be different")
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def test_eager_matches_sdpa_inference(self):
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pass
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@slow
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@require_torch_gpu
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class Gemma2IntegrationTest(unittest.TestCase):
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input_text = ["Hello I am doing", "Hi today"]
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# This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
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# Depending on the hardware we get different logits / generations
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cuda_compute_capability_major_version = None
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@classmethod
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def setUpClass(cls):
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if is_torch_available() and torch.cuda.is_available():
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# 8 is for A100 / A10 and 7 for T4
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cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
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@require_read_token
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def test_model_9b_bf16(self):
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model_id = "google/gemma-2-9b"
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EXPECTED_TEXTS = [
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"<bos>Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
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"<pad><pad><bos>Hi today I'm going to be talking about the history of the United States. The United States of America",
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]
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model = AutoModelForCausalLM.from_pretrained(
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model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="eager"
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).to(torch_device)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
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output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
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output_text = tokenizer.batch_decode(output, skip_special_tokens=False)
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self.assertEqual(output_text, EXPECTED_TEXTS)
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@require_read_token
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def test_model_9b_fp16(self):
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model_id = "google/gemma-2-9b"
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EXPECTED_TEXTS = [
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"<bos>Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
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"<pad><pad><bos>Hi today I'm going to be talking about the history of the United States. The United States of America",
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]
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model = AutoModelForCausalLM.from_pretrained(
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model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16, attn_implementation="eager"
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).to(torch_device)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
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output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
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output_text = tokenizer.batch_decode(output, skip_special_tokens=False)
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self.assertEqual(output_text, EXPECTED_TEXTS)
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@require_read_token
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def test_model_9b_pipeline_bf16(self):
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# See https://github.com/huggingface/transformers/pull/31747 -- pipeline was broken for Gemma2 before this PR
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model_id = "google/gemma-2-9b"
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# EXPECTED_TEXTS should match the same non-pipeline test, minus the special tokens
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EXPECTED_TEXTS = [
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"Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
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"Hi today I'm going to be talking about the history of the United States. The United States of America",
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]
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model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to(
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torch_device
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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output = pipe(self.input_text, max_new_tokens=20, do_sample=False, padding=True)
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self.assertEqual(output[0][0]["generated_text"], EXPECTED_TEXTS[0])
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self.assertEqual(output[1][0]["generated_text"], EXPECTED_TEXTS[1])
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