128 lines
4.8 KiB
Python
128 lines
4.8 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 vllm.platforms import current_platform
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from .conftest import run_equality_correctness_test_tp
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@pytest.mark.skipif(torch.cuda.device_count() < 2,
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reason="Need at least 2 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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# Skip cuda graph recording for fast test.
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"--enforce-eager",
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"--tensor-parallel-size",
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"2",
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# Workaround the restriction that cnnlGetTensorElementNum(key_cache_desc) <= INT32_MAX.
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"--num_gpu_blocks_override", "2048",
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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("test_llm_kwargs", [
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[
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"--speculative-model",
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"JackFram/llama-68m",
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"--num-speculative-tokens",
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"3",
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],
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[
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"--speculative-model",
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"[ngram]",
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"--num-speculative-tokens",
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"5",
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"--ngram-prompt-lookup-max",
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"3",
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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(
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"output_len",
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[
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# Use smaller output len for fast test.
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32,
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])
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@pytest.mark.parametrize("seed", [1])
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def test_target_model_tp_gt_1(common_llm_kwargs, per_test_common_llm_kwargs,
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baseline_llm_kwargs, test_llm_kwargs,
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batch_size: int, output_len: int, seed: int):
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"""Verify greedy equality when tensor parallelism is used.
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"""
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if current_platform.is_rocm():
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pytest.skip("hip is not well-supported yet")
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run_equality_correctness_test_tp("JackFram/llama-68m",
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common_llm_kwargs,
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per_test_common_llm_kwargs,
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baseline_llm_kwargs,
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test_llm_kwargs,
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batch_size,
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output_len,
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seed,
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temperature=0.0)
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@pytest.mark.skipif(torch.cuda.device_count() < 2,
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reason="Need at least 2 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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# Skip cuda graph recording for fast test.
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"--enforce-eager",
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"--tensor_parallel_size",
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"2",
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# precision
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"--dtype",
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"bfloat16",
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# Workaround the restriction that cnnlGetTensorElementNum(key_cache_desc) <= INT32_MAX.
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"--num_gpu_blocks_override", "2048",
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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("model, test_llm_kwargs",
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[("JackFram/llama-68m", [
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"--speculative-model",
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"JackFram/llama-68m",
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"--num_speculative-tokens",
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"5",
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"--speculative-draft-tensor-parallel-size",
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"1",
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]),
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# Skip this case since vLLM does not support mlu
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# xformers backend, and mlu flash attention does not
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# support this case with head size 80.
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# ("ibm-granite/granite-3b-code-instruct", [
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# "--speculative-model",
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# "ibm-granite/granite-3b-code-instruct",
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# "--num_speculative-tokens",
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# "5",
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# "--speculative-draft-tensor-parallel-size",
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# "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_tp2(model, common_llm_kwargs,
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per_test_common_llm_kwargs,
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baseline_llm_kwargs,
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test_llm_kwargs, batch_size: int,
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seed: 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_equality_correctness_test_tp(model,
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common_llm_kwargs,
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per_test_common_llm_kwargs,
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baseline_llm_kwargs,
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test_llm_kwargs,
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batch_size,
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max_output_len=32,
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seed=seed,
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temperature=0.0)
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