test_modeling_gemma2.py

# coding=utf-8
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Testing suite for the PyTorch Gemma2 model."""

import unittest

from pytest import mark

from transformers import AutoModelForCausalLM, AutoTokenizer, Gemma2Config, is_torch_available, pipeline
from transformers.testing_utils import (
    require_flash_attn,
    require_read_token,
    require_torch,
    require_torch_gpu,
    slow,
    torch_device,
)

from ...models.gemma.test_modeling_gemma import GemmaModelTest, GemmaModelTester
from ...test_configuration_common import ConfigTester


if is_torch_available():
    import torch

    from transformers import (
        Gemma2ForCausalLM,
        Gemma2ForSequenceClassification,
        Gemma2ForTokenClassification,
        Gemma2Model,
    )


class Gemma2ModelTester(GemmaModelTester):
    if is_torch_available():
        config_class = Gemma2Config
        model_class = Gemma2Model
        for_causal_lm_class = Gemma2ForCausalLM
        for_sequence_class = Gemma2ForSequenceClassification
        for_token_class = Gemma2ForTokenClassification


@require_torch
class Gemma2ModelTest(GemmaModelTest, unittest.TestCase):
    all_model_classes = (
        (Gemma2Model, Gemma2ForCausalLM, Gemma2ForSequenceClassification, Gemma2ForTokenClassification)
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = ()
    pipeline_model_mapping = (
        {
            "feature-extraction": Gemma2Model,
            "text-classification": Gemma2ForSequenceClassification,
            "token-classification": Gemma2ForTokenClassification,
            "text-generation": Gemma2ForCausalLM,
            "zero-shot": Gemma2ForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    test_headmasking = False
    test_pruning = False
    _is_stateful = True
    model_split_percents = [0.5, 0.6]
    _torch_compile_test_ckpt = "google/gemma-2-9b"

    def setUp(self):
        self.model_tester = Gemma2ModelTester(self)
        self.config_tester = ConfigTester(self, config_class=Gemma2Config, hidden_size=37)

    @unittest.skip("Eager and SDPA do not produce the same outputs, thus this test fails")
    def test_model_outputs_equivalence(self, **kwargs):
        pass

    @unittest.skip("Gemma2's outputs are expected to be different")
    def test_eager_matches_sdpa_inference(self):
        pass


@slow
@require_torch_gpu
class Gemma2IntegrationTest(unittest.TestCase):
    input_text = ["Hello I am doing", "Hi today"]
    # This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
    # Depending on the hardware we get different logits / generations
    cuda_compute_capability_major_version = None

    @classmethod
    def setUpClass(cls):
        if is_torch_available() and torch.cuda.is_available():
            # 8 is for A100 / A10 and 7 for T4
            cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]

    @require_read_token
    def test_model_9b_bf16(self):
        model_id = "google/gemma-2-9b"
        EXPECTED_TEXTS = [
            "<bos>Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
            "<pad><pad><bos>Hi today I'm going to be talking about the history of the United States. The United States of America",
        ]

        model = AutoModelForCausalLM.from_pretrained(
            model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="eager"
        ).to(torch_device)

        tokenizer = AutoTokenizer.from_pretrained(model_id)
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=False)

        self.assertEqual(output_text, EXPECTED_TEXTS)

    @require_read_token
    def test_model_9b_fp16(self):
        model_id = "google/gemma-2-9b"
        EXPECTED_TEXTS = [
            "<bos>Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
            "<pad><pad><bos>Hi today I'm going to be talking about the history of the United States. The United States of America",
        ]

        model = AutoModelForCausalLM.from_pretrained(
            model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16, attn_implementation="eager"
        ).to(torch_device)

        tokenizer = AutoTokenizer.from_pretrained(model_id)
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=False)

        self.assertEqual(output_text, EXPECTED_TEXTS)

    @require_read_token
    def test_model_9b_pipeline_bf16(self):
        # See https://github.com/huggingface/transformers/pull/31747 -- pipeline was broken for Gemma2 before this PR
        model_id = "google/gemma-2-9b"
        # EXPECTED_TEXTS should match the same non-pipeline test, minus the special tokens
        EXPECTED_TEXTS = [
            "Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
            "Hi today I'm going to be talking about the history of the United States. The United States of America",
        ]

        model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to(
            torch_device
        )
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

        output = pipe(self.input_text, max_new_tokens=20, do_sample=False, padding=True)

        self.assertEqual(output[0][0]["generated_text"], EXPECTED_TEXTS[0])
        self.assertEqual(output[1][0]["generated_text"], EXPECTED_TEXTS[1])

    @require_read_token
    @require_flash_attn
    @require_torch_gpu
    @mark.flash_attn_test
    @slow
    def test_model_9b_flash_attn(self):
        # See https://github.com/huggingface/transformers/issues/31953 --- flash attn was generating garbage for gemma2, especially in long context
        model_id = "google/gemma-2-9b"
        EXPECTED_TEXTS = [
            '<bos>Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many people died in the United States. I have found a few sites that say 500,000 but I am not sure if that is correct. I have also found a site that says 675,000 but I am not sure if that is correct either. I am trying to find out how many people died in the United States. I have found a few',
            "<pad><pad><bos>Hi today I'm going to be talking about the history of the United States. The United States of America is a country in North America. It is the third largest country in the world by total area and the third most populous country with over 320 million people. The United States is a federal republic consisting of 50 states and a federal district. The 48 contiguous states and the district of Columbia are in central North America between Canada and Mexico. The state of Alaska is in the"
        ]  # fmt: skip

        model = AutoModelForCausalLM.from_pretrained(
            model_id, attn_implementation="flash_attention_2", torch_dtype="float16"
        ).to(torch_device)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=100, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=False)
        print(output_text)

        self.assertEqual(output_text, EXPECTED_TEXTS)