# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Compare the short outputs of HF and vLLM when using greedy sampling.

Run `pytest tests/basic_correctness/test_basic_correctness.py`.
"""

import os
import weakref
from unittest.mock import Mock

import pytest
import torch

from vllm import LLM
from vllm.v1.engine.llm_engine import LLMEngine

from ..conftest import HfRunner, VllmRunner
from ..models.utils import check_outputs_equal
from ..utils import multi_gpu_test

MODELS = [
    "hmellor/tiny-random-Gemma2ForCausalLM",
    "meta-llama/Llama-3.2-1B-Instruct",
]

TARGET_TEST_SUITE = os.environ.get("TARGET_TEST_SUITE", "L4")


def test_vllm_gc_ed():
    """Verify vllm instance is GC'ed when it is deleted"""
    llm = LLM("hmellor/tiny-random-LlamaForCausalLM")
    weak_llm = weakref.ref(llm)
    del llm
    # If there's any circular reference to vllm, this fails
    # because llm instance is not GC'ed.
    assert weak_llm() is None


def _fix_prompt_embed_outputs(
    vllm_outputs: list[tuple[list[int], str]],
    hf_model: HfRunner,
    example_prompts: list[str],
) -> list[tuple[list[int], str]]:
    fixed_vllm_outputs = []
    for vllm_output, hf_input, prompt in zip(
        vllm_outputs, hf_model.get_inputs(example_prompts), example_prompts
    ):
        hf_input_ids = hf_input["input_ids"].tolist()[0]
        fixed_vllm_outputs.append(
            (
                hf_input_ids + vllm_output[0][len(hf_input_ids) :],
                prompt + vllm_output[1],
            )
        )
    return fixed_vllm_outputs


@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("backend", ["FLASH_ATTN"])
@pytest.mark.parametrize("max_tokens", [5])
@pytest.mark.parametrize("enforce_eager", [False])
@pytest.mark.parametrize("async_scheduling", [True, False])
@pytest.mark.parametrize("model_executor", ["uni", "mp"])
@pytest.mark.parametrize("enable_prompt_embeds", [True, False])
def test_models(
    monkeypatch: pytest.MonkeyPatch,
    hf_runner,
    model: str,
    backend: str,
    max_tokens: int,
    enforce_eager: bool,
    async_scheduling: bool,
    model_executor: str,
    enable_prompt_embeds: bool,
) -> None:
    if backend == "XFORMERS" and model == "google/gemma-2-2b-it":
        pytest.skip(f"{backend} does not support gemma2 with full context length.")

    with monkeypatch.context() as m:
        m.setenv("VLLM_ATTENTION_BACKEND", backend)

        # 5042 tokens for gemma2
        # gemma2 has alternating sliding window size of 4096
        # we need a prompt with more than 4096 tokens to test the sliding window
        prompt = (
            "The following numbers of the sequence "
            + ", ".join(str(i) for i in range(1024))
            + " are:"
        )
        example_prompts = [prompt]

        with hf_runner(model) as hf_model:
            hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
            if enable_prompt_embeds:
                with torch.no_grad():
                    prompt_embeds = hf_model.get_prompt_embeddings(example_prompts)

        with VllmRunner(
            model,
            max_model_len=8192,
            enforce_eager=enforce_eager,
            enable_prompt_embeds=enable_prompt_embeds,
            gpu_memory_utilization=0.7,
            async_scheduling=async_scheduling,
            distributed_executor_backend=model_executor,
        ) as vllm_model:
            if enable_prompt_embeds:
                vllm_outputs = vllm_model.generate_greedy(prompt_embeds, max_tokens)
                vllm_outputs = _fix_prompt_embed_outputs(
                    vllm_outputs, hf_model, example_prompts
                )
            else:
                vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)

        check_outputs_equal(
            outputs_0_lst=hf_outputs,
            outputs_1_lst=vllm_outputs,
            name_0="hf",
            name_1="vllm",
        )


@multi_gpu_test(num_gpus=2)
@pytest.mark.parametrize(
    "model, distributed_executor_backend, attention_backend, test_suite, extra_env",
    [
        ("facebook/opt-125m", "ray", "", "L4", {}),
        ("facebook/opt-125m", "mp", "", "L4", {}),
        ("facebook/opt-125m", "ray", "", "L4", {"VLLM_SLEEP_WHEN_IDLE": "1"}),
        ("facebook/opt-125m", "mp", "", "L4", {"VLLM_SLEEP_WHEN_IDLE": "1"}),
        ("meta-llama/Llama-3.2-1B-Instruct", "ray", "", "L4", {}),
        ("meta-llama/Llama-3.2-1B-Instruct", "mp", "", "L4", {}),
        ("facebook/opt-125m", "ray", "", "A100", {}),
        ("facebook/opt-125m", "mp", "", "A100", {}),
    ],
)
@pytest.mark.parametrize("enable_prompt_embeds", [True, False])
def test_models_distributed(
    monkeypatch: pytest.MonkeyPatch,
    hf_runner,
    vllm_runner,
    example_prompts,
    model: str,
    distributed_executor_backend: str,
    attention_backend: str,
    test_suite: str,
    extra_env: dict[str, str],
    enable_prompt_embeds: bool,
) -> None:
    if test_suite != TARGET_TEST_SUITE:
        pytest.skip(f"Skip test for {test_suite}")

    with monkeypatch.context() as monkeypatch_context:
        if (
            model == "meta-llama/Llama-3.2-1B-Instruct"
            and distributed_executor_backend == "ray"
            and attention_backend == ""
            and test_suite == "L4"
        ):  # noqa
            if enable_prompt_embeds:
                pytest.skip("enable_prompt_embeds does not work with ray compiled dag.")
            monkeypatch_context.setenv("VLLM_USE_RAY_SPMD_WORKER", "1")
            monkeypatch_context.setenv("VLLM_USE_RAY_COMPILED_DAG", "1")

        if attention_backend:
            monkeypatch_context.setenv(
                "VLLM_ATTENTION_BACKEND",
                attention_backend,
            )

        for k, v in extra_env.items():
            monkeypatch_context.setenv(k, v)

        dtype = "half"
        max_tokens = 5

        # NOTE: take care of the order. run vLLM first, and then run HF.
        # vLLM needs a fresh new process without cuda initialization.
        # if we run HF first, the cuda initialization will be done and it
        # will hurt multiprocessing backend with fork method
        # (the default method).
        with vllm_runner(
            model,
            dtype=dtype,
            tensor_parallel_size=2,
            distributed_executor_backend=distributed_executor_backend,
            enable_prompt_embeds=enable_prompt_embeds,
            gpu_memory_utilization=0.7,
        ) as vllm_model:
            if enable_prompt_embeds:
                with hf_runner(model, dtype=dtype) as hf_model:
                    with torch.no_grad():
                        prompt_embeds = hf_model.get_prompt_embeddings(example_prompts)
                    vllm_outputs = vllm_model.generate_greedy(prompt_embeds, max_tokens)
                    vllm_outputs = _fix_prompt_embed_outputs(
                        vllm_outputs, hf_model, example_prompts
                    )
                    hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
            else:
                vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
                with hf_runner(model, dtype=dtype) as hf_model:
                    hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)

    check_outputs_equal(
        outputs_0_lst=hf_outputs,
        outputs_1_lst=vllm_outputs,
        name_0="hf",
        name_1="vllm",
    )


def test_failed_model_execution(vllm_runner, monkeypatch) -> None:
    # Needed to mock an error in the same process
    monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")

    with vllm_runner("facebook/opt-125m", enforce_eager=True) as vllm_model:
        if isinstance(vllm_model.llm.llm_engine, LLMEngine):
            v1_test_failed_model_execution(vllm_model)


def v1_test_failed_model_execution(vllm_model):
    engine = vllm_model.llm.llm_engine
    mocked_execute_model = Mock(side_effect=RuntimeError("Mocked Critical Error"))
    engine.engine_core.engine_core.model_executor.execute_model = mocked_execute_model

    with pytest.raises(RuntimeError) as exc_info:
        prompts = [
            "Hello, my name is",
            "The president of the United States is",
            "The capital of France is",
            "The future of AI is",
        ]
        vllm_model.generate_greedy(prompts, 200, use_tqdm=False)
    assert isinstance(exc_info.value, RuntimeError)
    assert "Mocked Critical Error" in str(exc_info.value)
