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Chranos
2026-02-04 17:22:39 +08:00
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vllm-v0.6.2/tests/spec_decode/e2e/test_integration_dist_tp2.py Normal file
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"""Tests which cover integration of the speculative decoding framework with
tensor parallelism.
"""
import pytest
import torch
from vllm.platforms import current_platform
from .conftest import run_equality_correctness_test_tp
@pytest.mark.skipif(torch.cuda.device_count() < 2,
reason="Need at least 2 GPUs to run the test.")
@pytest.mark.parametrize(
"common_llm_kwargs",
[[
# Skip cuda graph recording for fast test.
"--enforce-eager",
"--tensor-parallel-size",
"2",
# Workaround the restriction that cnnlGetTensorElementNum(key_cache_desc) <= INT32_MAX.
"--num_gpu_blocks_override", "2048",
]])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [[]])
@pytest.mark.parametrize("baseline_llm_kwargs", [[]])
@pytest.mark.parametrize("test_llm_kwargs", [
[
"--speculative-model",
"JackFram/llama-68m",
"--num-speculative-tokens",
"3",
],
[
"--speculative-model",
"[ngram]",
"--num-speculative-tokens",
"5",
"--ngram-prompt-lookup-max",
"3",
],
])
@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_target_model_tp_gt_1(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 when tensor parallelism is used.
"""
if current_platform.is_rocm():
pytest.skip("hip is not well-supported yet")
run_equality_correctness_test_tp("JackFram/llama-68m",
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(torch.cuda.device_count() < 2,
reason="Need at least 2 GPUs to run the test.")
@pytest.mark.parametrize(
"common_llm_kwargs",
[[
# Skip cuda graph recording for fast test.
"--enforce-eager",
"--tensor_parallel_size",
"2",
# precision
"--dtype",
"bfloat16",
# Workaround the restriction that cnnlGetTensorElementNum(key_cache_desc) <= INT32_MAX.
"--num_gpu_blocks_override", "2048",
]])
@pytest.mark.parametrize("per_test_common_llm_kwargs", [[]])
@pytest.mark.parametrize("baseline_llm_kwargs", [[]])
@pytest.mark.parametrize("model, test_llm_kwargs",
[("JackFram/llama-68m", [
"--speculative-model",
"JackFram/llama-68m",
"--num_speculative-tokens",
"5",
"--speculative-draft-tensor-parallel-size",
"1",
]),
# Skip this case since vLLM does not support mlu
# xformers backend, and mlu flash attention does not
# support this case with head size 80.
# ("ibm-granite/granite-3b-code-instruct", [
# "--speculative-model",
# "ibm-granite/granite-3b-code-instruct",
# "--num_speculative-tokens",
# "5",
# "--speculative-draft-tensor-parallel-size",
# "1",
# ])
])
@pytest.mark.parametrize("batch_size", [2])
@pytest.mark.parametrize("seed", [1])
def test_draft_model_tp_lt_target_model_tp2(model, common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs,
test_llm_kwargs, batch_size: int,
seed: int):
"""Verify spec decode works well with smaller tp for draft models.
"""
run_equality_correctness_test_tp(model,
common_llm_kwargs,
per_test_common_llm_kwargs,
baseline_llm_kwargs,
test_llm_kwargs,
batch_size,
max_output_len=32,
seed=seed,
temperature=0.0)