# SPDX-License-Identifier: Apache-2.0
"""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.

For ngram lookup, its idea comes from https://github.com/apoorvumang/prompt-lookup-decoding,
and is merged into transform code base: https://github.com/huggingface/transformers/pull/27775.
Since there is no model is needed for generate the proposal, we could make
the testcase much simpler than drafter multi-step one.

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 ngram sizes / speculative sizes

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

import pytest

from ..utils import maybe_enable_chunked_prefill
from .conftest import run_equality_correctness_test


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

        # Print spec metrics.
        "disable_log_stats": False,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [
    {
        "model_name": "JackFram/llama-68m",
    },
])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "speculative_model": "[ngram]",
        "num_speculative_tokens": 5,
        "ngram_prompt_lookup_max": 3,
        "speculative_disable_mqa_scorer": False,
    },
    {
        "speculative_model": "[ngram]",
        "num_speculative_tokens": 5,
        "ngram_prompt_lookup_max": 3,
        "speculative_disable_mqa_scorer": True,
    },
])
@pytest.mark.parametrize("output_len", [
    256,
])
@pytest.mark.parametrize("batch_size", [1, 32])
@pytest.mark.parametrize("prefill_chunk_size", [-1, 4])
@pytest.mark.parametrize("seed", [1])
def test_ngram_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,
                                      prefill_chunk_size: int, seed: int):
    """Verify greedy equality on a tiny model with different batch size."""
    maybe_enable_chunked_prefill(prefill_chunk_size, common_llm_kwargs)
    run_equality_correctness_test(vllm_runner,
                                  common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs,
                                  test_llm_kwargs,
                                  batch_size,
                                  max_output_len=output_len,
                                  seed=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,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [
    {
        "model_name": "JackFram/llama-68m",
    },
])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "speculative_model": "[ngram]",
        "num_speculative_tokens": 5,
        "ngram_prompt_lookup_max": 3,
        "disable_logprobs_during_spec_decoding": False,
    },
    {
        "speculative_model": "[ngram]",
        "num_speculative_tokens": 5,
        "ngram_prompt_lookup_max": 3,
        "disable_logprobs_during_spec_decoding": True,
    },
])
@pytest.mark.parametrize("output_len", [
    8,
])
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("seed", [1])
@pytest.mark.parametrize("logprobs", [1, 6])
def test_ngram_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):
    """Verify greedy equality on a tiny model with different batch size."""
    run_equality_correctness_test(vllm_runner,
                                  common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs,
                                  test_llm_kwargs,
                                  batch_size,
                                  max_output_len=output_len,
                                  seed=seed,
                                  temperature=0.0,
                                  logprobs=logprobs,
                                  prompt_logprobs=logprobs,
                                  disable_logprobs=test_llm_kwargs[
                                      'disable_logprobs_during_spec_decoding'])


@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,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [
    {
        "model_name": "JackFram/llama-160m",
    },
])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs", [
    {
        "speculative_model": "[ngram]",
        "num_speculative_tokens": 5,
        "ngram_prompt_lookup_max": 3,
        "enable_chunked_prefill": False,
    },
    {
        "speculative_model": "[ngram]",
        "num_speculative_tokens": 5,
        "ngram_prompt_lookup_max": 3,
        "enable_chunked_prefill": True,
        "speculative_disable_mqa_scorer": True,
        "max_num_batched_tokens": 4,
        "max_num_seqs": 4
    },
])
@pytest.mark.parametrize(
    "output_len",
    [
        # Use small output len for fast test.
        256,
    ])
@pytest.mark.parametrize("batch_size", [4])
@pytest.mark.parametrize("seed", [1])
def test_ngram_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,
                                  max_output_len=output_len,
                                  temperature=0,
                                  seed=seed)


@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        "model_name": "JackFram/llama-68m",

        # Skip cuda graph recording for fast test.
        "enforce_eager": True,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize(
    "test_llm_kwargs",
    [
        {
            "speculative_model": "[ngram]",
            "num_speculative_tokens": k,
            "ngram_prompt_lookup_max": 3,
        }
        # Try a range of common k, as well as large speculation.
        for k in [1, 3, 5]
    ] + [
        {
            "speculative_model": "[ngram]",
            "num_speculative_tokens": k,
            "ngram_prompt_lookup_max": 1,
        }
        # Try a range of common k, as well as large speculation.
        for k in [1, 3, 5]
    ])
@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_ngram_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 ngram speculative decoding produces exact equality
    to without spec decode with many different values of k and
    different ngram_prompt_lookup_max.
    """
    run_equality_correctness_test(vllm_runner,
                                  common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs,
                                  test_llm_kwargs,
                                  batch_size,
                                  max_output_len=output_len,
                                  seed=seed,
                                  temperature=0.0)


@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        "model_name": "JackFram/llama-68m",

        # Skip cuda graph recording for fast test.
        "enforce_eager": True,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs",
                         [{
                             "speculative_model": "[ngram]",
                             "num_speculative_tokens": 5,
                             "ngram_prompt_lookup_max": 3,
                             "speculative_disable_by_batch_size": 4
                         }, {
                             "speculative_model": "[ngram]",
                             "num_speculative_tokens": 5,
                             "ngram_prompt_lookup_max": 3,
                             "speculative_disable_by_batch_size": 4,
                             "enable_chunked_prefill": True,
                             "speculative_disable_mqa_scorer": True,
                             "max_num_batched_tokens": 4,
                             "max_num_seqs": 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_ngram_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 ngram speculative decoding produces exact equality
    to without spec decode with many different values of k and
    different ngram_prompt_lookup_max.
    """
    run_equality_correctness_test(vllm_runner,
                                  common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs,
                                  test_llm_kwargs,
                                  batch_size,
                                  max_output_len=output_len,
                                  seed=seed,
                                  temperature=0.0)


@pytest.mark.parametrize(
    "common_llm_kwargs",
    [{
        "model_name": "JackFram/llama-68m",

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

        # Required for spec decode.
        "speculative_model": "[ngram]",
        "num_speculative_tokens": 5,
        "ngram_prompt_lookup_max": 3,
    }])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
@pytest.mark.parametrize("test_llm_kwargs",
                         [{
                             "speculative_disable_mqa_scorer": True,
                         }])
@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_ngram_scorer(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 ngram speculative decoding generates the same output 
    with batch expansion scorer and mqa scorer.
    """
    run_equality_correctness_test(vllm_runner,
                                  common_llm_kwargs,
                                  per_test_common_llm_kwargs,
                                  baseline_llm_kwargs,
                                  test_llm_kwargs,
                                  batch_size,
                                  max_output_len=output_len,
                                  seed=seed,
                                  temperature=0.0)
