123 lines
4.6 KiB
Python
123 lines
4.6 KiB
Python
"""Tests which cover integration of the speculative decoding framework with
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tensor parallelism.
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"""
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import pytest
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import torch
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from .conftest import run_greedy_equality_correctness_test
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@pytest.mark.skipif(torch.cuda.device_count() < 4,
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reason="Need at least 4 GPUs to run the test.")
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@pytest.mark.parametrize(
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"common_llm_kwargs",
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[{
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# Use a small model for a fast test.
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# Note this is repeated in the test body; to initialize a tokenizer.
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"model": "JackFram/llama-68m",
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# Skip cuda graph recording for fast test.
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"enforce_eager": True,
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# Required for spec decode.
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"use_v2_block_manager": True,
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"tensor_parallel_size": 4,
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# Use AsyncLLM engine, so that the engine runs in its own process.
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# Otherwise, since vLLM does not follow true SPMD, the test runner
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# process will have both the engine and the rank0 worker. NCCL is not
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# cleaned up properly, and its server host thread leaks, causing the
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# second run of the test to fail with internal NCCL error.
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"use_async": True,
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}])
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@pytest.mark.parametrize("per_test_common_llm_kwargs", [
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{
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"speculative_model": "JackFram/llama-68m",
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"num_speculative_tokens": 5,
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},
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])
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@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
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@pytest.mark.parametrize(
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"test_llm_kwargs",
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[
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#TODO(wooyeon): add spec_draft_dp=2 case
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{
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"speculative_draft_tensor_parallel_size": 1,
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},
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])
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@pytest.mark.parametrize("batch_size", [2])
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@pytest.mark.parametrize("seed", [1])
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def test_draft_model_tp_lt_target_model_tp4(test_llm_generator,
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baseline_llm_generator,
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batch_size: int):
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"""Verify spec decode works well with smaller tp for draft models.
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"""
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run_greedy_equality_correctness_test(baseline_llm_generator,
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test_llm_generator,
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batch_size,
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max_output_len=32,
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force_output_len=True)
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@pytest.mark.skipif(torch.cuda.device_count() < 4,
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reason="Need at least 4 GPUs to run the test.")
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@pytest.mark.parametrize(
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"common_llm_kwargs",
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[{
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"model": "JackFram/llama-160m",
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# Skip cuda graph recording for fast test.
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"enforce_eager": True,
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# Required for spec decode.
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"use_v2_block_manager": True,
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"tensor_parallel_size": 4,
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# Use AsyncLLM engine, so that the engine runs in its own process.
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# Otherwise, since vLLM does not follow true SPMD, the test runner
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# process will have both the engine and the rank0 worker. NCCL is not
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# cleaned up properly, and its server host thread leaks, causing the
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# second run of the test to fail with internal NCCL error.
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"use_async": True,
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}])
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@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
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@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
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@pytest.mark.parametrize(
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"test_llm_kwargs",
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[
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{
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"speculative_model": "JackFram/llama-68m",
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"num_speculative_tokens": 5,
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# Artificially limit the draft model max model len; this forces vLLM
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# to skip speculation once the sequences grow beyond 32-k tokens.
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"speculative_max_model_len": 32,
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},
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])
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@pytest.mark.parametrize("batch_size", [8])
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@pytest.mark.parametrize(
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"output_len",
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[
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# This must be a good bit larger than speculative_max_model_len so that
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# we can test the case where all seqs are skipped, but still small to
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# ensure fast test.
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64,
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])
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@pytest.mark.parametrize("seed", [1])
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def test_skip_speculation(baseline_llm_generator, test_llm_generator,
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batch_size: int, output_len: int):
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"""Verify job failure with RuntimeError when all sequences skip speculation.
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We do this by setting the max model len of the draft model to an
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artificially low value, such that when the sequences grow beyond it, they
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are skipped in speculative decoding.
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TODO: fix it to pass without raising Error. (#5814)
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"""
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with pytest.raises(RuntimeError):
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run_greedy_equality_correctness_test(baseline_llm_generator,
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test_llm_generator,
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batch_size,
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max_output_len=output_len,
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force_output_len=True)
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