#
# 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_mtp_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, mtp would not break the
correctess for the target model outputs.
"""

import pytest

from .conftest import run_equality_correctness_test

# main model
# NOTE vLLM use fp8 model, vllm-ascend use bf16 model
MAIN_MODEL = "wemaster/deepseek_mtp_main_random_bf16"

# max. number of speculative tokens: this corresponds to
# num_nextn_predict_layers in the config.json of the speculator model.
MAX_SPEC_TOKENS = 1

# precision
PRECISION = "bfloat16"


@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,

        # GPU memory utilization
        "gpu_memory_utilization": 0.85
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "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_mtp_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,

        # GPU memory utilization
        "gpu_memory_utilization": 0.85
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "num_speculative_tokens": MAX_SPEC_TOKENS,
        "disable_logprobs_during_spec_decoding": False,
    },
    {
        "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_mtp_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,
        "gpu_memory_utilization": 0.85
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "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_mtp_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.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,

        # GPU memory utilization
        "gpu_memory_utilization": 0.9
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "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_mtp_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,

        # GPU memory utilization
        "gpu_memory_utilization": 0.9
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize(
    "test_llm_kwargs",
    [
        {
            "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_mtp_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 mtp 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,

        # GPU memory utilization
        "gpu_memory_utilization": 0.9
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs",
                         [{
                             "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_mtp_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 mtp 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)


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