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

import pytest
import regex as re

from vllm import LLM, SamplingParams
from vllm.config import CompilationConfig, CompilationMode, CUDAGraphMode

from .common import FUSION_LOG_PATTERNS, AttentionBackendCase, Matches


def run_model(compile_config: int | CompilationConfig, model: str, **model_kwargs):
    """Run a model with the given compilation config for E2E fusion tests."""
    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}")

    # Get the compile ranges split points after vllm config post init
    # in order to compute compile ranges correctly
    compilation_config.compile_ranges_split_points = (
        llm.llm_engine.vllm_config.compilation_config.compile_ranges_split_points
    )


@pytest.fixture
def run_e2e_fusion_test(monkeypatch, caplog_mp_spawn):
    def run(
        model_name: str,
        matches: Matches,
        model_kwargs: dict,
        attn_backend: AttentionBackendCase,
        compilation_config: dict,
        matches_check: list[str],
        use_deepgemm: bool = False,
        use_aiter: bool = False,
        tp_size: int = 1,
    ):
        monkeypatch.setenv("VLLM_USE_DEEP_GEMM", "1" if use_deepgemm else "0")
        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1" if use_aiter else "0")
        from vllm._aiter_ops import rocm_aiter_ops

        rocm_aiter_ops.refresh_env_variables()

        # Disable, compile cache to make sure custom passes run.
        # Otherwise, we can't verify fusion happened through the logs.
        monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", "1")

        # To capture subprocess logs, we need to know whether spawn or fork is used.
        # Force spawn as it is more general.
        monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")

        model_kwargs = {**attn_backend.model_kwargs, **model_kwargs}
        model_kwargs["attention_config"] = {"backend": attn_backend.backend.name}
        model_kwargs["tensor_parallel_size"] = tp_size

        # Always compile the full graph instead of piecewise
        if not compilation_config["use_inductor_graph_partition"]:
            compilation_config["splitting_ops"] = []

        full_compilation_config = CompilationConfig(
            cudagraph_mode=CUDAGraphMode.NONE,
            mode=CompilationMode.VLLM_COMPILE,
            inductor_compile_config={"force_disable_caches": True},
            **compilation_config,
        )

        with caplog_mp_spawn(logging.DEBUG) as log_holder:
            run_model(full_compilation_config, model_name, **model_kwargs)

        num_compile_ranges = len(full_compilation_config.get_compile_ranges())
        assert num_compile_ranges in [1, 2, 3]

        print(f"Compile ranges: {full_compilation_config.get_compile_ranges()}")
        print("Fusion results:")

        # Iterate through all so printing happens before asserting
        log_matches_dict = {}
        for match_name, pattern in FUSION_LOG_PATTERNS.items():
            log_matches_dict[match_name] = list(pattern.findall(log_holder.text))
            print(f"- {match_name}={','.join(log_matches_dict[match_name])}")

        # Now check the matches
        for match_name in matches_check:
            log_matches = list(int(ms) for ms in log_matches_dict[match_name])

            # AR+RMS skips the largest range; SP skips the smallest.
            # When both are enabled, AR+RMS activation count is
            # model-dependent (hidden_size affects threshold), so derive
            # from log data.
            if (
                match_name == "ar_rms_fusion"
                and "sequence_parallel" in matches_check
                and num_compile_ranges >= 2
            ):
                assert (
                    len(log_matches) >= tp_size and len(log_matches) % tp_size == 0
                ), (
                    f"Expected multiple of {tp_size} ar_rms log entries, "
                    f"found {len(log_matches)}"
                )
                num_ranges_activated = len(log_matches) // tp_size
            elif (
                match_name in ("ar_rms_fusion", "sequence_parallel")
                and num_compile_ranges >= 2
            ):
                num_ranges_activated = num_compile_ranges - 1
            else:
                num_ranges_activated = num_compile_ranges

            n_expected = tp_size * num_ranges_activated
            assert len(log_matches) == n_expected, (
                f"Could not find {n_expected} {match_name} "
                f"(found {len(log_matches)}) in:\n {log_holder.text}"
            )

            expected_matches = getattr(matches, match_name)

            if match_name == "rms_quant_fusion" and "ar_rms_fusion" in matches_check:
                # AR+rms+quant takes precedence over rms+quant if activated.
                # That means we get full matching where ar+rms+quant was not
                # activated, and less where it was (only the smallest range).
                assert sum(m == expected_matches for m in log_matches) == tp_size * (
                    num_ranges_activated - 1
                ), "Expecting full rms+quant fusion where ar+rms+quant not activated"

                assert all(
                    expected_matches - matches.ar_rms_fusion <= m <= expected_matches
                    for m in log_matches
                ), (
                    f"Expecting at least {expected_matches - matches.ar_rms_fusion} "
                    f"where ar+rms+quant was activated"
                )
            elif (
                match_name == "async_tp"
                and "sequence_parallel" in matches_check
                and num_compile_ranges >= 2
            ):
                # AsyncTP only finds patterns on ranges where SP ran.
                n_sp_ranges = num_compile_ranges - 1
                assert (
                    sum(m == expected_matches for m in log_matches)
                    == tp_size * n_sp_ranges
                ), (
                    f"Expecting {expected_matches} async_tp on "
                    f"{tp_size * n_sp_ranges} SP-range entries, "
                    f"found: {log_matches}"
                )
                assert sum(m == 0 for m in log_matches) == tp_size, (
                    f"Expecting 0 async_tp on {tp_size} small-range entries "
                    f"(no SP), found: {log_matches}"
                )
            elif (
                match_name == "ar_rms_fusion"
                and "sequence_parallel" in matches_check
                and num_compile_ranges >= 2
            ):
                # SP consumes allreduce patterns first, so AR+RMS finds
                # full matches only on the smallest range (no SP).
                assert sum(m == expected_matches for m in log_matches) == tp_size, (
                    f"Expecting {expected_matches} ar_rms on "
                    f"{tp_size} small-range entries, found: {log_matches}"
                )
                assert sum(m == 0 for m in log_matches) == tp_size * (
                    num_ranges_activated - 1
                ), (
                    f"Expecting 0 ar_rms on "
                    f"{tp_size * (num_ranges_activated - 1)} large-range "
                    f"entries (SP took precedence), found: {log_matches}"
                )
            else:
                expected_matches_list = [expected_matches] * n_expected
                assert sorted(log_matches) == expected_matches_list, (
                    f"{match_name} expected: {expected_matches_list}, "
                    f"found: {sorted(log_matches)}"
                )

            if match_name == "ar_rms_fusion" and num_compile_ranges >= 2:
                log_matches = re.findall(
                    r"pass_manager.py:\d+] Skipping "
                    r".*AllReduceFusionPass.* with compile range",
                    log_holder.text,
                )

                n_expected = tp_size * (num_compile_ranges - num_ranges_activated)
                assert len(log_matches) == n_expected, (
                    f'Could not find {n_expected} "Skipping AllReduceFusionPass" '
                    f"(found {len(log_matches)}) in:\n {log_holder.text}"
                )

            if match_name == "sequence_parallel" and num_compile_ranges >= 2:
                log_matches = re.findall(
                    r"pass_manager.py:\d+] Skipping "
                    r".*SequenceParallelismPass.* with compile range",
                    log_holder.text,
                )

                n_expected = tp_size * (num_compile_ranges - num_ranges_activated)
                assert len(log_matches) == n_expected, (
                    f'Could not find {n_expected} "Skipping SequenceParallelismPass" '
                    f"(found {len(log_matches)}) in:\n {log_holder.text}"
                )

    return run
