vllm/tests/models/quantization/test_fp8.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# flake8: noqa
"""Tests fp8 models against ground truth generation
Note: these tests will only pass on L4 GPU.
"""

import pytest

from tests.quantization.utils import is_quant_method_supported
from vllm.platforms import current_platform
from vllm.v1.attention.backends.fa_utils import get_flash_attn_version
from ..utils import check_logprobs_close


@pytest.mark.skipif(
    not is_quant_method_supported("fp8"),
    reason="fp8 is not supported on this GPU type.",
)
@pytest.mark.parametrize(
    "kv_cache_dtype,base_model,test_model",
    [
        # Test FP8 checkpoint w. fp8_e4m3 kv-cache scaling factors.
        (
            "fp8_e4m3",
            "meta-llama/Llama-3.2-1B-Instruct",
            "nm-testing/Llama-3.2-1B-Instruct-FP8-KV",
        ),
        # Test BF16 checkpoint w. fp8_e5m2 kv-cache.
        (
            "fp8_e5m2",
            "meta-llama/Llama-3.2-1B-Instruct",
            "meta-llama/Llama-3.2-1B-Instruct",
        ),
        # Test BF16 checkpoint w. fp8_e4m3 kv-cache scaling factors in json.
        (
            "fp8_e4m3",
            "meta-llama/Llama-3.2-1B-Instruct",
            "meta-llama/Llama-3.2-1B-Instruct",
        ),
    ],
)
# Due to low-precision numerical divergence, we only test logprob of 4 tokens
@pytest.mark.parametrize("max_tokens", [4])
@pytest.mark.parametrize("enforce_eager", [True])
@pytest.mark.parametrize("backend", ["FLASH_ATTN"])
# NOTE: Increasing this in this suite will fail CI because we currently cannot
# reset distributed env properly. Use a value > 1 just when you test.
@pytest.mark.parametrize("tensor_parallel_size", [1])
def test_models(
    vllm_runner,
    example_prompts,
    kv_cache_dtype: str,
    base_model: str,
    test_model: str,
    max_tokens: int,
    enforce_eager: bool,
    backend: str,
    tensor_parallel_size: int,
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    """
    Only checks log probs match to cover the discrepancy in
    numerical sensitive kernels.
    """

    if kv_cache_dtype == "fp8_e5m2" and current_platform.is_rocm():
        pytest.skip(f"{kv_cache_dtype} is currently not supported on ROCm/HIP.")

    if not (
        current_platform.is_xpu()
        or (
            get_flash_attn_version() == 3
            and current_platform.is_device_capability_family(90)
        )
    ):
        pytest.skip(
            f"{kv_cache_dtype} is not supported on this GPU type with {backend} attention."
        )

    with monkeypatch.context() as m:
        m.setenv("TOKENIZERS_PARALLELISM", "true")

        MAX_MODEL_LEN = 1024
        NUM_LOG_PROBS = 8

        with vllm_runner(
            base_model,
            max_model_len=MAX_MODEL_LEN,
            tensor_parallel_size=tensor_parallel_size,
            enforce_eager=enforce_eager,
            kv_cache_dtype="auto",
            attention_config={"backend": backend},
        ) as vllm_model:
            baseline_outputs = vllm_model.generate_greedy_logprobs(
                example_prompts, max_tokens, NUM_LOG_PROBS
            )

        with vllm_runner(
            test_model,
            max_model_len=MAX_MODEL_LEN,
            tensor_parallel_size=tensor_parallel_size,
            enforce_eager=enforce_eager,
            kv_cache_dtype=kv_cache_dtype,
            attention_config={"backend": backend},
        ) as vllm_model:
            test_outputs = vllm_model.generate_greedy_logprobs(
                example_prompts, max_tokens, NUM_LOG_PROBS
            )

        check_logprobs_close(
            outputs_0_lst=baseline_outputs,
            outputs_1_lst=test_outputs,
            name_0="fp16_kv_cache",
            name_1="fp8_kv_cache",
        )


@pytest.mark.cpu_model
@pytest.mark.skipif(not current_platform.is_cpu(), reason="test for the CPU backend.")
@pytest.mark.parametrize(
    "kv_cache_dtype,base_model,test_model",
    [
        # Test BF16 checkpoint w. fp8_e5m2 kv-cache.
        (
            "fp8_e5m2",
            "meta-llama/Llama-3.2-1B-Instruct",
            "meta-llama/Llama-3.2-1B-Instruct",
        ),
    ],
)
# Due to low-precision numerical divergence, we only test logprob of 4 tokens
@pytest.mark.parametrize("max_tokens", [4])
def test_cpu_models(
    vllm_runner,
    example_prompts,
    kv_cache_dtype: str,
    base_model: str,
    test_model: str,
    max_tokens: int,
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    """
    Only checks log probs match to cover the discrepancy in
    numerical sensitive kernels.
    """
    with monkeypatch.context() as m:
        m.setenv("TOKENIZERS_PARALLELISM", "true")

        MAX_MODEL_LEN = 1024
        NUM_LOG_PROBS = 8

        with vllm_runner(
            base_model,
            max_model_len=MAX_MODEL_LEN,
            dtype="bfloat16",
            kv_cache_dtype="auto",
        ) as vllm_model:
            baseline_outputs = vllm_model.generate_greedy_logprobs(
                example_prompts, max_tokens, NUM_LOG_PROBS
            )

        with vllm_runner(
            test_model,
            max_model_len=MAX_MODEL_LEN,
            dtype="bfloat16",
            kv_cache_dtype=kv_cache_dtype,
        ) as vllm_model:
            test_outputs = vllm_model.generate_greedy_logprobs(
                example_prompts, max_tokens, NUM_LOG_PROBS
            )

        check_logprobs_close(
            outputs_0_lst=baseline_outputs,
            outputs_1_lst=test_outputs,
            name_0="bf16_kv_cache",
            name_1="fp8_kv_cache",
        )