test_full_graph.py

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

import tempfile
from pathlib import Path
from typing import Any

import pytest
import torch

from tests.quantization.utils import is_quant_method_supported
from vllm import LLM, SamplingParams
from vllm.attention.backends.registry import AttentionBackendEnum
from vllm.config import CompilationConfig, CompilationMode, CUDAGraphMode, PassConfig
from vllm.platforms import current_platform
from vllm.utils.torch_utils import is_torch_equal_or_newer

from ...utils import create_new_process_for_each_test


def models_list(*, all: bool = True, keywords: list[str] | None = None):
    TEST_MODELS: list[tuple[str, dict[str, Any]]] = [
        ("facebook/opt-125m", {}),
        (
            "neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",
            {"dtype": torch.float16},
        ),
        ("meta-llama/Llama-3.2-1B-Instruct", {}),
    ]

    if all:
        TEST_MODELS.extend(
            [
                ("neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8", {}),
                (
                    "nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change",
                    {"dtype": torch.float16},
                ),
            ]
        )

        # TODO: figure out why this fails.
        if False and is_quant_method_supported("gguf"):  # noqa: SIM223
            TEST_MODELS.append(
                ("TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF", {"quantization": "gguf"})
            )

        if is_quant_method_supported("gptq"):
            TEST_MODELS.append(
                ("TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ", {"quantization": "gptq"})
            )

        if is_quant_method_supported("gptq_marlin"):
            TEST_MODELS.append(
                (
                    "TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ",
                    {"quantization": "gptq_marlin"},
                )
            )

        if is_quant_method_supported("gptq_marlin_24"):
            TEST_MODELS.append(
                (
                    "alexm-nm/tinyllama-24-marlin24-4bit-g128",
                    {
                        "quantization": "gptq_marlin_24",
                        "allow_deprecated_quantization": True,
                    },
                )
            )

        if not current_platform.is_rocm() and is_quant_method_supported("awq"):
            TEST_MODELS.append(
                ("TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ", {"quantization": "AWQ"})
            )

    if keywords is None:
        return TEST_MODELS

    # filter by keywords
    pred = lambda model: any(keyword in model[0] for keyword in keywords)
    return list(filter(pred, TEST_MODELS))


@pytest.mark.parametrize(
    "compilation_mode",
    [CompilationMode.DYNAMO_TRACE_ONCE, CompilationMode.VLLM_COMPILE],
)
@pytest.mark.parametrize("model, model_kwargs", models_list(all=True))
@create_new_process_for_each_test()
def test_full_graph(
    monkeypatch: pytest.MonkeyPatch,
    model: str,
    model_kwargs: dict[str, Any],
    compilation_mode: int,
):
    if (
        "w8a8" in model
        or "w8w8" in model
        and current_platform.has_device_capability((10, 0))
    ):
        # int8 removed on Blackwell:
        pytest.skip("int8 support removed on Blackwell")

    with monkeypatch.context():
        print(f"MODEL={model}")

        run_model(compilation_mode, model, **model_kwargs)


# TODO(luka) add other supported compilation config scenarios here
@pytest.mark.parametrize(
    "compilation_config, model, model_kwargs",
    [
        # additional compile sizes, only some of the models
        (
            CompilationConfig(mode=CompilationMode.VLLM_COMPILE, compile_sizes=[1, 2]),
            *model_info,
        )
        for model_info in models_list(all=False)
    ]
    + [
        # RMSNorm + quant fusion, only 8-bit quant models
        (
            CompilationConfig(
                mode=CompilationMode.VLLM_COMPILE,
                custom_ops=["+rms_norm"],
                pass_config=PassConfig(
                    fuse_norm_quant=True, fuse_act_quant=True, eliminate_noops=True
                ),
            ),
            *model_info,
        )
        for model_info in models_list(keywords=["FP8-dynamic", "quantized.w8a8"])
    ]
    + [
        # Test depyf integration works
        (
            CompilationConfig(
                mode=CompilationMode.VLLM_COMPILE,
                debug_dump_path=Path(tempfile.gettempdir()),
            ),
            "facebook/opt-125m",
            {},
        ),
    ]
    + [
        # graph inductor partition
        (
            CompilationConfig(
                mode=CompilationMode.VLLM_COMPILE,
                # inductor graph partition uses
                # torch._C.Tag.cudagraph_unsafe to specify splitting ops
                use_inductor_graph_partition=True,
                cudagraph_mode=CUDAGraphMode.PIECEWISE,
                compile_sizes=[1, 2],
            ),
            *model_info,
        )
        for model_info in models_list(all=False)
        if is_torch_equal_or_newer("2.9.0.dev")
    ]
    + [
        # Test get_raw_stream patch with compile_sizes
        # This tests that TorchInductor autotune works correctly with get_raw_stream
        # patch in torch 2.9 and without patch in torch 2.10+
        (
            CompilationConfig(
                mode=CompilationMode.VLLM_COMPILE,
                compile_sizes=[1, 2],  # Triggers autotune which uses get_raw_stream
                cudagraph_mode=CUDAGraphMode.NONE,
            ),
            "facebook/opt-125m",
            {},
        ),
    ],
)
# only test some of the models
@create_new_process_for_each_test()
def test_custom_compile_config(
    compilation_config: CompilationConfig,
    model: str,
    model_kwargs: dict[str, Any],
):
    if (
        "w8a8" in model
        or "w8w8" in model
        and current_platform.has_device_capability((10, 0))
    ):
        # int8 removed on Blackwell:
        pytest.skip("int8 support removed on Blackwell")

    if compilation_config.use_inductor_graph_partition and not is_torch_equal_or_newer(
        "2.9.0.dev"
    ):
        pytest.skip("inductor graph partition is only available in PyTorch 2.9+")

    print(f"MODEL={model}")
    run_model(compilation_config, model, **model_kwargs)


@pytest.mark.parametrize(
    "compilation_mode",
    [CompilationMode.NONE, CompilationMode.VLLM_COMPILE],
)
@pytest.mark.parametrize(
    "model, backend",
    [
        ("Qwen/Qwen2-0.5B", None),  # Standard attention model
        (
            "deepseek-ai/DeepSeek-V2-Lite",
            AttentionBackendEnum.FLASHINFER_MLA,
        ),  # MLA (Multi-head Latent Attention) model
    ],
)
def test_fp8_kv_scale_compile(
    compilation_mode: int,
    model: str,
    backend: AttentionBackendEnum | None,
):
    model_kwargs = {
        "quantization": "fp8",
        "kv_cache_dtype": "fp8_e4m3",
        "calculate_kv_scales": True,
        "max_model_len": 512,
    }
    if backend:
        model_kwargs["attention_config"] = {"backend": backend.name}

    run_model(compilation_mode, model, **model_kwargs)


def run_model(compile_config: int | CompilationConfig, model: str, **model_kwargs):
    compilation_config = (
        compile_config
        if isinstance(compile_config, CompilationConfig)
        else CompilationConfig(mode=compile_config)
    )

    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    sampling_params = SamplingParams(temperature=0)
    # Allow override from model_kwargs
    model_kwargs = {"tensor_parallel_size": 1, **model_kwargs}
    model_kwargs = {"disable_custom_all_reduce": True, **model_kwargs}

    # No cudagraphs by default
    if compilation_config.cudagraph_mode is None:
        compilation_config.cudagraph_mode = CUDAGraphMode.NONE

    llm = LLM(
        model=model,
        compilation_config=compilation_config,
        **model_kwargs,
    )
    outputs = llm.generate(prompts, sampling_params)

    # Print the outputs.
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")