# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
WARNING: This test runs in both single-node (4 GPUs) and multi-node
 (2 node with 2 GPUs each) modes. If the test only uses 2 GPUs, it is
 important to set the distributed backend to "mp" to avoid Ray scheduling
 all workers in a node other than the head node, which can cause the test
 to fail.
"""

import json
import os
from dataclasses import dataclass
from typing import Literal, NamedTuple

import pytest

from vllm.config.model import _FLOAT16_NOT_SUPPORTED_MODELS, RunnerOption
from vllm.logger import init_logger
from vllm.transformers_utils.config import get_config

from ..models.registry import HF_EXAMPLE_MODELS
from ..utils import compare_two_settings, create_new_process_for_each_test

logger = init_logger("test_pipeline_parallel")

VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1"


class ParallelSetup(NamedTuple):
    tp_size: int
    pp_size: int
    eager_mode: bool


class PPTestOptions(NamedTuple):
    multi_node_only: bool
    load_format: str | None = None


@dataclass
class PPTestSettings:
    parallel_setups: list[ParallelSetup]
    distributed_backends: list[str]
    runner: RunnerOption
    test_options: PPTestOptions

    @staticmethod
    def detailed(
        *,
        tp_base: int = 1,
        pp_base: int = 2,
        multi_node_only: bool = False,
        runner: RunnerOption = "auto",
        load_format: str | None = None,
    ):
        return PPTestSettings(
            parallel_setups=[
                ParallelSetup(tp_size=tp_base, pp_size=pp_base, eager_mode=False),
                ParallelSetup(tp_size=tp_base, pp_size=2 * pp_base, eager_mode=False),
                ParallelSetup(tp_size=tp_base, pp_size=2 * pp_base, eager_mode=True),
                ParallelSetup(tp_size=2 * tp_base, pp_size=pp_base, eager_mode=False),
                ParallelSetup(tp_size=2 * tp_base, pp_size=pp_base, eager_mode=True),
            ],
            distributed_backends=["mp", "ray"],
            runner=runner,
            test_options=PPTestOptions(
                multi_node_only=multi_node_only, load_format=load_format
            ),
        )

    @staticmethod
    def fast(
        *,
        tp_base: int = 1,
        pp_base: int = 2,
        runner: RunnerOption = "auto",
        multi_node_only: bool = False,
        load_format: str | None = None,
    ):
        return PPTestSettings(
            parallel_setups=[
                ParallelSetup(tp_size=tp_base, pp_size=pp_base, eager_mode=True),
            ],
            distributed_backends=["mp"],
            runner=runner,
            test_options=PPTestOptions(
                multi_node_only=multi_node_only, load_format=load_format
            ),
        )

    def iter_params(self, model_id: str):
        opts = self.test_options

        for parallel_setup in self.parallel_setups:
            for backend in self.distributed_backends:
                yield (model_id, parallel_setup, backend, self.runner, opts)


# NOTE: You can adjust tp_base and/or pp_base locally to fit the model in GPU
# The values displayed here are only a rough indicator of the size of the model

TEXT_GENERATION_MODELS = {
    # [Decoder-only]
    "Snowflake/snowflake-arctic-instruct": PPTestSettings.fast(load_format="dummy"),
    "bigscience/bloomz-1b1": PPTestSettings.fast(),
    "zai-org/chatglm3-6b": PPTestSettings.fast(),
    "CohereLabs/c4ai-command-r-v01": PPTestSettings.fast(load_format="dummy"),
    "databricks/dbrx-instruct": PPTestSettings.fast(load_format="dummy"),
    "Deci/DeciLM-7B-instruct": PPTestSettings.fast(),
    "deepseek-ai/deepseek-llm-7b-chat": PPTestSettings.fast(),
    "deepseek-ai/DeepSeek-V2-Lite-Chat": PPTestSettings.fast(tp_base=2),
    "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct": PPTestSettings.fast(),
    "tiiuae/falcon-7b": PPTestSettings.fast(),
    "google/gemma-1.1-2b-it": PPTestSettings.fast(),
    "google/gemma-2-9b": PPTestSettings.fast(),
    "gpt2": PPTestSettings.fast(),
    "bigcode/starcoder": PPTestSettings.fast(),
    "EleutherAI/gpt-j-6b": PPTestSettings.fast(),
    "EleutherAI/pythia-1.4b": PPTestSettings.fast(),
    "ibm/PowerLM-3b": PPTestSettings.fast(),
    "ibm/PowerMoE-3b": PPTestSettings.fast(),
    "internlm/internlm2-chat-7b": PPTestSettings.fast(),
    "ai21labs/Jamba-tiny-dev": PPTestSettings.fast(),
    "pfnet/plamo-2-1b": PPTestSettings.fast(),
    "pfnet/plamo-3-nict-2b-base": PPTestSettings.fast(),
    "meta-llama/Llama-3.2-1B-Instruct": PPTestSettings.detailed(),
    # Tests TransformersForCausalLM
    "hmellor/Ilama-3.2-1B": PPTestSettings.fast(),
    "openbmb/MiniCPM-2B-sft-bf16": PPTestSettings.fast(),
    "openbmb/MiniCPM3-4B": PPTestSettings.fast(),
    # Uses Llama
    # "mistralai/Mistral-7B-Instruct-v0.1": PPTestSettings.fast(),
    "state-spaces/mamba-130m-hf": PPTestSettings.fast(),
    "mistralai/Mixtral-8x7B-Instruct-v0.1": PPTestSettings.fast(load_format="dummy"),
    "mosaicml/mpt-7b": PPTestSettings.fast(),
    "nvidia/Minitron-8B-Base": PPTestSettings.fast(),
    "allenai/OLMo-1B-hf": PPTestSettings.fast(),
    "allenai/OLMo-2-0425-1B": PPTestSettings.fast(),
    "allenai/OLMoE-1B-7B-0924-Instruct": PPTestSettings.fast(),
    "facebook/opt-iml-max-1.3b": PPTestSettings.fast(),
    "OrionStarAI/Orion-14B-Chat": PPTestSettings.fast(),
    "adept/persimmon-8b-chat": PPTestSettings.fast(),
    "microsoft/phi-2": PPTestSettings.fast(),
    "microsoft/Phi-3-small-8k-instruct": PPTestSettings.fast(),
    "microsoft/Phi-3.5-MoE-instruct": PPTestSettings.detailed(
        multi_node_only=True, load_format="dummy"
    ),
    "Qwen/Qwen2.5-0.5B-Instruct": PPTestSettings.fast(),
    "Qwen/Qwen1.5-MoE-A2.7B-Chat": PPTestSettings.fast(),
    "stabilityai/stablelm-3b-4e1t": PPTestSettings.fast(),
    "bigcode/starcoder2-3b": PPTestSettings.fast(),
    "upstage/solar-pro-preview-instruct": PPTestSettings.fast(load_format="dummy"),
    # [Encoder-only]
    # TODO: Implement PP
    # "facebook/bart-base": PPTestSettings.fast(),
}

EMBEDDING_MODELS = {  # type: ignore[var-annotated]
    # [Text-only]
    "intfloat/e5-mistral-7b-instruct": PPTestSettings.fast(runner="pooling"),
    "BAAI/bge-multilingual-gemma2": PPTestSettings.fast(runner="pooling"),
    "Qwen/Qwen2.5-Math-RM-72B": PPTestSettings.fast(
        load_format="dummy", runner="pooling"
    ),
}

MULTIMODAL_MODELS = {
    # [Decoder-only]
    "Salesforce/blip2-opt-6.7b": PPTestSettings.fast(),
    "facebook/chameleon-7b": PPTestSettings.fast(),
    "adept/fuyu-8b": PPTestSettings.fast(),
    "zai-org/glm-4v-9b": PPTestSettings.fast(),
    "OpenGVLab/InternVL3-1B": PPTestSettings.fast(),
    "llava-hf/llava-1.5-7b-hf": PPTestSettings.fast(),
    "llava-hf/llava-v1.6-mistral-7b-hf": PPTestSettings.fast(),
    "llava-hf/LLaVA-NeXT-Video-7B-hf": PPTestSettings.fast(),
    "llava-hf/llava-onevision-qwen2-0.5b-ov-hf": PPTestSettings.fast(),
    "openbmb/MiniCPM-Llama3-V-2_5": PPTestSettings.fast(),
    "allenai/Molmo-7B-D-0924": PPTestSettings.fast(),
    "AIDC-AI/Ovis2-1B": PPTestSettings.fast(),
    "AIDC-AI/Ovis2.5-2B": PPTestSettings.fast(),
    "microsoft/Phi-3.5-vision-instruct": PPTestSettings.fast(),
    "mistralai/Pixtral-12B-2409": PPTestSettings.fast(load_format="dummy"),
    "Qwen/Qwen2-Audio-7B-Instruct": PPTestSettings.fast(),
    "Qwen/Qwen2-VL-2B-Instruct": PPTestSettings.fast(),
    "fixie-ai/ultravox-v0_5-llama-3_2-1b": PPTestSettings.fast(),
}

# NOTE: You can update this on your local machine to run specific tests
TEST_MODELS = [
    # [LANGUAGE GENERATION]
    "microsoft/Phi-3.5-MoE-instruct",
    "meta-llama/Llama-3.2-1B-Instruct",
    "hmellor/Ilama-3.2-1B",
    "ibm/PowerLM-3b",
    "deepseek-ai/DeepSeek-V2-Lite-Chat",
    # [LANGUAGE EMBEDDING]
    "intfloat/e5-mistral-7b-instruct",
    "BAAI/bge-multilingual-gemma2",
    # [MULTIMODAL GENERATION]
    "OpenGVLab/InternVL3-1B",
    "microsoft/Phi-3.5-vision-instruct",
    "fixie-ai/ultravox-v0_5-llama-3_2-1b",
    # [LANGUAGE GENERATION - HYBRID ARCH]
    "ai21labs/Jamba-tiny-dev",
]


def _compare_tp(
    model_id: str,
    parallel_setup: ParallelSetup,
    distributed_backend: str,
    runner: RunnerOption,
    test_options: PPTestOptions,
    num_gpus_available: int,
    *,
    method: Literal["generate", "encode"],
    is_multimodal: bool,
):
    (
        tp_size,
        pp_size,
        eager_mode,
    ) = parallel_setup

    multi_node_only, load_format = test_options

    model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
    model_info.check_transformers_version(on_fail="skip")

    trust_remote_code = model_info.trust_remote_code
    tokenizer_mode = model_info.tokenizer_mode
    hf_overrides = model_info.hf_overrides
    hf_config = get_config(model_id, trust_remote_code)
    require_embed_inputs = model_info.require_embed_inputs
    max_num_seqs = model_info.max_num_seqs
    enable_prefix_caching = model_info.enable_prefix_caching

    dtype = "float16"
    if hf_config.model_type in _FLOAT16_NOT_SUPPORTED_MODELS:
        dtype = "bfloat16"

    if load_format == "dummy":
        # Avoid OOM
        text_overrides = {
            "num_hidden_layers": 4,
            "hidden_size": 512,
            "intermediate_size": 800,
            "num_attention_heads": 4,
            "num_key_value_heads": 1,
        }

        if is_multimodal:
            hf_overrides.update({"text_config": text_overrides})
        else:
            hf_overrides.update(text_overrides)
    else:
        model_info.check_available_online(on_fail="skip")

    if num_gpus_available < tp_size * pp_size:
        pytest.skip(f"Need at least {tp_size} x {pp_size} GPUs")
    if VLLM_MULTI_NODE and distributed_backend == "mp":
        pytest.skip(
            "Skipping multi-node pipeline parallel test for "
            "multiprocessing distributed backend"
        )
    if multi_node_only and not VLLM_MULTI_NODE:
        pytest.skip("Not in multi-node setting")

    common_args = [
        # use half precision for speed and memory savings in CI environment
        "--dtype",
        dtype,
        "--max-model-len",
        "2048",
        "--max-num-seqs",
        "8",
    ]
    if eager_mode:
        common_args.append("--enforce-eager")
    if runner != "auto":
        common_args.extend(["--runner", runner])
    if trust_remote_code:
        common_args.append("--trust-remote-code")
    if tokenizer_mode:
        common_args.extend(["--tokenizer-mode", tokenizer_mode])
    if load_format:
        common_args.extend(["--load-format", load_format])
    if hf_overrides:
        common_args.extend(["--hf-overrides", json.dumps(hf_overrides)])
    if not enable_prefix_caching:
        common_args.append("--no-enable-prefix-caching")
    if require_embed_inputs:
        common_args.extend(
            [
                "--skip-tokenizer-init",
                "--enable-prompt-embeds",
                "--enable-mm-embeds",
            ]
        )
    if max_num_seqs:
        common_args.extend(["--max-num-seqs", f"{max_num_seqs}"])

    if distributed_backend == "ray":
        # Test Ray Compiled Graph for all the tests
        pp_env = {
            "VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL": "1",
        }
    elif distributed_backend == "mp":
        pp_env = None
    else:
        pp_env = None

    tp_env = None

    pp_args = [
        *common_args,
        "--pipeline-parallel-size",
        str(pp_size),
        "--tensor-parallel-size",
        str(tp_size),
        "--distributed-executor-backend",
        distributed_backend,
    ]

    # compare without pipeline parallelism
    # NOTE: use mp backend for TP
    # PP tests might involve multiple nodes, and ray might
    #  schedule all workers in a node other than the head node,
    #  which can cause the test to fail.
    tp_args = [
        *common_args,
        "--tensor-parallel-size",
        str(tp_size),
        "--distributed-executor-backend",
        "mp",
    ]

    compare_two_settings(
        model_id,
        pp_args,
        tp_args,
        pp_env,
        tp_env,
        method=method,
    )


@pytest.mark.parametrize(
    ("model_id", "parallel_setup", "distributed_backend", "runner", "test_options"),
    [
        params
        for model_id, settings in TEXT_GENERATION_MODELS.items()
        for params in settings.iter_params(model_id)
        if model_id in TEST_MODELS
    ],
)
@create_new_process_for_each_test()
def test_tp_language_generation(
    model_id: str,
    parallel_setup: ParallelSetup,
    distributed_backend: str,
    runner: RunnerOption,
    test_options: PPTestOptions,
    num_gpus_available,
):
    _compare_tp(
        model_id,
        parallel_setup,
        distributed_backend,
        runner,
        test_options,
        num_gpus_available,
        method="generate",
        is_multimodal=False,
    )


@pytest.mark.parametrize(
    ("model_id", "parallel_setup", "distributed_backend", "runner", "test_options"),
    [
        params
        for model_id, settings in EMBEDDING_MODELS.items()
        for params in settings.iter_params(model_id)
        if model_id in TEST_MODELS
    ],
)
@create_new_process_for_each_test()
def test_tp_language_embedding(
    model_id: str,
    parallel_setup: ParallelSetup,
    distributed_backend: str,
    runner: RunnerOption,
    test_options: PPTestOptions,
    num_gpus_available,
):
    _compare_tp(
        model_id,
        parallel_setup,
        distributed_backend,
        runner,
        test_options,
        num_gpus_available,
        method="encode",
        is_multimodal=False,
    )


@pytest.mark.parametrize(
    ("model_id", "parallel_setup", "distributed_backend", "runner", "test_options"),
    [
        params
        for model_id, settings in MULTIMODAL_MODELS.items()
        for params in settings.iter_params(model_id)
        if model_id in TEST_MODELS
    ],
)
@create_new_process_for_each_test()
def test_tp_multimodal_generation(
    model_id: str,
    parallel_setup: ParallelSetup,
    distributed_backend: str,
    runner: RunnerOption,
    test_options: PPTestOptions,
    num_gpus_available,
):
    _compare_tp(
        model_id,
        parallel_setup,
        distributed_backend,
        runner,
        test_options,
        num_gpus_available,
        method="generate",
        is_multimodal=True,
    )
