#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
# Adapted from vllm-project/vllm/tests/spec_decode/e2e/test_eagle_correctness.py
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""This docstring details important information on the testing methodology.

Most of the tests rely on "greedy equality", where we expect the output of
speculative decoding on a sequence to exactly match the output of normal non-
speculative decoding.

Since speculative decoding with rejection sampling guarantees that the output
distribution matches the target model's output distribution (up to hardware
numerics, see https://arxiv.org/pdf/2302.01318.pdf), we can expect greedy
equality.

However, we still need to verify below scenario could be passed:
    * Batch size 1 greedy equality
    * Batch size >1 greedy equality
    * Test greedy equality under preemption
    * Test greedy equality under various number of speculative tokens.

With those tests, we can say at least, EAGLE would not break the
correctess for the target model outputs.
"""

import pytest

from .conftest import run_equality_correctness_test

# main model
MAIN_MODEL = "JackFram/llama-68m"

# speculative model
SPEC_MODEL = "abhigoyal/vllm-eagle-llama-68m-random"

# max. number of speculative tokens: this corresponds to
# num_heads in the config.json of the speculator model.
MAX_SPEC_TOKENS = 4

# precision
# TODO The vLLM here uses float32, but some op on the vllm-ascend
# do not support float32, such as ROPE, When it is fixed, it is
# recommended to change this to float32 to keep it consistent
# with vLLM.
PRECISION = "float16"


@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        # Skip cuda graph recording for fast test.
        "enforce_eager": True,

        # Print spec metrics.
        "disable_log_stats": False,

        # Precision
        "dtype": PRECISION,

        # Main model
        "model_name": MAIN_MODEL,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "speculative_model": SPEC_MODEL,
        "num_speculative_tokens": MAX_SPEC_TOKENS,
    },
])
@pytest.mark.parametrize("output_len", [
    128,
])
@pytest.mark.parametrize("batch_size", [1, 32])
@pytest.mark.parametrize("seed", [1])
def test_eagle_e2e_greedy_correctness(vllm_runner, common_llm_kwargs,
                                      per_test_common_llm_kwargs,
                                      baseline_llm_kwargs, test_llm_kwargs,
                                      batch_size: int, output_len: int,
                                      seed: int):

    run_equality_correctness_test(vllm_runner, common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs, test_llm_kwargs,
                                  batch_size, output_len, seed)


@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        # Skip cuda graph recording for fast test.
        "enforce_eager": True,

        # Print spec metrics.
        "disable_log_stats": False,

        # Precision
        "dtype": PRECISION,

        # Main model
        "model_name": MAIN_MODEL,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "speculative_model": SPEC_MODEL,
        "num_speculative_tokens": MAX_SPEC_TOKENS,
        "disable_logprobs_during_spec_decoding": False,
    },
    {
        "speculative_model": SPEC_MODEL,
        "num_speculative_tokens": MAX_SPEC_TOKENS,
        "disable_logprobs_during_spec_decoding": True,
    },
])
@pytest.mark.parametrize("output_len", [
    128,
])
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("seed", [1])
@pytest.mark.parametrize("logprobs", [1, 6])
def test_eagle_e2e_greedy_logprobs(vllm_runner, common_llm_kwargs,
                                   per_test_common_llm_kwargs,
                                   baseline_llm_kwargs, test_llm_kwargs,
                                   batch_size: int, output_len: int, seed: int,
                                   logprobs: int):

    run_equality_correctness_test(vllm_runner,
                                  common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs,
                                  test_llm_kwargs,
                                  batch_size,
                                  output_len,
                                  seed,
                                  logprobs=logprobs,
                                  prompt_logprobs=logprobs,
                                  disable_logprobs=test_llm_kwargs[
                                      'disable_logprobs_during_spec_decoding'])


@pytest.mark.skipif(
    True,
    reason=
    "Open it when vllm-ascend support graph mode and support enforce_eager status is False to run model in graph mode"
)
@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        "enforce_eager": False,

        # Print spec metrics.
        "disable_log_stats": False,

        # Precision
        "dtype": PRECISION,

        # Main model
        "model_name": MAIN_MODEL,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "speculative_model": SPEC_MODEL,
        "num_speculative_tokens": MAX_SPEC_TOKENS,
    },
])
@pytest.mark.parametrize("output_len", [
    128,
])
@pytest.mark.parametrize("batch_size", [1, 32])
@pytest.mark.parametrize("seed", [1])
def test_eagle_e2e_greedy_correctness_cuda_graph(
        vllm_runner, common_llm_kwargs, per_test_common_llm_kwargs,
        baseline_llm_kwargs, test_llm_kwargs, batch_size: int, output_len: int,
        seed: int):
    """Verify greedy equality with cuda graph enabled and different
    batch sizes."""
    run_equality_correctness_test(vllm_runner, common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs, test_llm_kwargs,
                                  batch_size, output_len, seed)


@pytest.mark.skipif(
    True,
    reason=
    "Please release this case after the preemptive scheduling preblem is solved."
)
@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        "block_size": 8,
        # 2 for small prompt, 256//8 for generated.
        "num_gpu_blocks_override": 2 + 256 // 8,
        "max_model_len": (2 + 256 // 8) * 8,

        # Skip cuda graph recording for fast test.
        "enforce_eager": True,

        # Precision
        "dtype": PRECISION,

        # Main model
        "model_name": MAIN_MODEL,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "speculative_model": SPEC_MODEL,
        "num_speculative_tokens": MAX_SPEC_TOKENS,
    },
])
@pytest.mark.parametrize(
    "output_len",
    [
        # Use small output len for fast test.
        128,
    ])
@pytest.mark.parametrize("batch_size", [4])
@pytest.mark.parametrize("seed", [1])
def test_eagle_e2e_greedy_correctness_with_preemption(
        vllm_runner, common_llm_kwargs, per_test_common_llm_kwargs,
        baseline_llm_kwargs, test_llm_kwargs, batch_size: int, output_len: int,
        seed: int):
    """Verify greedy equality, even when some sequences are preempted mid-
    generation.
    """
    run_equality_correctness_test(vllm_runner, common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs, test_llm_kwargs,
                                  batch_size, output_len, seed)


@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        # Skip cuda graph recording for fast test.
        "enforce_eager": True,

        # Precision
        "dtype": PRECISION,

        # Main model
        "model_name": MAIN_MODEL,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize(
    "test_llm_kwargs",
    [
        {
            "speculative_model": SPEC_MODEL,
            "num_speculative_tokens": k,
        }
        # Try a range of num. speculative tokens
        for k in range(1, 1 + MAX_SPEC_TOKENS)
    ])
@pytest.mark.parametrize("batch_size", [2])
@pytest.mark.parametrize(
    "output_len",
    [
        # Use smaller output len for fast test.
        32,
    ])
@pytest.mark.parametrize("seed", [1])
def test_eagle_different_k(vllm_runner, common_llm_kwargs,
                           per_test_common_llm_kwargs, baseline_llm_kwargs,
                           test_llm_kwargs, batch_size: int, output_len: int,
                           seed: int):
    """Verify that eagle speculative decoding produces exact equality
    to without spec decode with different values of num_speculative_tokens.
    """
    run_equality_correctness_test(vllm_runner, common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs, test_llm_kwargs,
                                  batch_size, output_len, seed)


@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        # Skip cuda graph recording for fast test.
        "enforce_eager": True,

        # Precision
        "dtype": PRECISION,

        # Main model
        "model_name": MAIN_MODEL,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs",
                         [{
                             "speculative_model": SPEC_MODEL,
                             "num_speculative_tokens": MAX_SPEC_TOKENS,
                             "speculative_disable_by_batch_size": 4
                         }])
@pytest.mark.parametrize("batch_size", [1, 5])
@pytest.mark.parametrize(
    "output_len",
    [
        # Use smaller output len for fast test.
        32,
    ])
@pytest.mark.parametrize("seed", [1])
def test_eagle_disable_queue(vllm_runner, common_llm_kwargs,
                             per_test_common_llm_kwargs, baseline_llm_kwargs,
                             test_llm_kwargs, batch_size: int, output_len: int,
                             seed: int):
    """Verify that eagle speculative decoding produces exact equality
    to without spec decode when speculation is disabled for large
    batch sizes.
    """
    run_equality_correctness_test(vllm_runner, common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs, test_llm_kwargs,
                                  batch_size, output_len, seed)


@pytest.mark.skipif(True, reason="Open it when CI could use modelscope")
@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        # Skip cuda graph recording for fast test.
        "enforce_eager": True,

        # Print spec metrics.
        "disable_log_stats": False,

        # Precision
        "dtype": "float16",

        # Main model
        "model_name": "vllm-ascend/Llama-2-7b-chat-hf"
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "speculative_model": "vllm-ascend/EAGLE-llama2-chat-7B",
        "num_speculative_tokens": MAX_SPEC_TOKENS,
    },
])
@pytest.mark.parametrize(
    "output_len",
    [
        # Use smaller output len for fast test.
        32,
    ])
@pytest.mark.parametrize("batch_size", [1, 5])
@pytest.mark.parametrize("seed", [1])
def test_llama2_eagle_e2e_greedy_correctness(monkeypatch: pytest.MonkeyPatch,
                                             vllm_runner, common_llm_kwargs,
                                             per_test_common_llm_kwargs,
                                             baseline_llm_kwargs,
                                             test_llm_kwargs, batch_size: int,
                                             output_len: int, seed: int):

    # TODO: it is a wrong way to use modelscope.
    with monkeypatch.context() as m:
        m.setenv("VLLM_USE_MODELSCOPE", "True")
        run_equality_correctness_test(vllm_runner,
                                      common_llm_kwargs,
                                      per_test_common_llm_kwargs,
                                      baseline_llm_kwargs,
                                      test_llm_kwargs,
                                      batch_size,
                                      output_len,
                                      seed,
                                      temperature=0.0)


@pytest.mark.skipif(True, reason="Open it when CI could use modelscope")
@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        # Skip cuda graph recording for fast test.
        "enforce_eager": True,

        # Print spec metrics.
        "disable_log_stats": False,

        # Precision
        "dtype": "float16",

        # Main model
        "model_name": "vllm-ascend/Meta-Llama-3-8B-Instruct"
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "speculative_model": "vllm-ascend/EAGLE-LLaMA3-Instruct-8B",
        "num_speculative_tokens": MAX_SPEC_TOKENS,
    },
])
@pytest.mark.parametrize(
    "output_len",
    [
        # Use smaller output len for fast test.
        32,
    ])
@pytest.mark.parametrize("batch_size", [1, 5])
@pytest.mark.parametrize("seed", [1])
def test_llama3_eagle_e2e_greedy_correctness(monkeypatch: pytest.MonkeyPatch,
                                             vllm_runner, common_llm_kwargs,
                                             per_test_common_llm_kwargs,
                                             baseline_llm_kwargs,
                                             test_llm_kwargs, batch_size: int,
                                             output_len: int, seed: int):

    # TODO: it is a wrong way to use modelscope.
    with monkeypatch.context() as m:
        m.setenv("VLLM_USE_MODELSCOPE", "True")
        run_equality_correctness_test(vllm_runner,
                                      common_llm_kwargs,
                                      per_test_common_llm_kwargs,
                                      baseline_llm_kwargs,
                                      test_llm_kwargs,
                                      batch_size,
                                      output_len,
                                      seed,
                                      temperature=0.0)


@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        # Skip cuda graph recording for fast test.
        "enforce_eager": True,

        # Print spec metrics.
        "disable_log_stats": False,

        # Precision
        "dtype": "float16",

        # Main model
        "model_name": "Qwen/Qwen2-7B-Instruct",
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "speculative_model": "yuhuili/EAGLE-Qwen2-7B-Instruct",
        "num_speculative_tokens": MAX_SPEC_TOKENS,
    },
])
@pytest.mark.parametrize(
    "output_len",
    [
        # Use smaller output len for fast test.
        32,
    ])
@pytest.mark.parametrize("batch_size", [1, 5])
@pytest.mark.parametrize("seed", [1])
def test_qwen2_eagle_e2e_greedy_correctness(vllm_runner, common_llm_kwargs,
                                            per_test_common_llm_kwargs,
                                            baseline_llm_kwargs,
                                            test_llm_kwargs, batch_size: int,
                                            output_len: int, seed: int):

    run_equality_correctness_test(vllm_runner,
                                  common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs,
                                  test_llm_kwargs,
                                  batch_size,
                                  output_len,
                                  seed,
                                  temperature=0.0)


if __name__ == "__main__":
    import pytest
    pytest.main([__file__])
