e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
123 lines
4.3 KiB
Python
123 lines
4.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
"""Tests which cover integration of the speculative decoding framework with
|
|
tensor parallelism.
|
|
"""
|
|
|
|
import openai
|
|
import pytest
|
|
import torch
|
|
|
|
from .conftest import run_equality_correctness_test_tp
|
|
|
|
MAIN_MODEL = "JackFram/llama-68m"
|
|
SPEC_MODEL = "JackFram/llama-68m"
|
|
|
|
|
|
@pytest.mark.skipif(torch.cuda.device_count() < 4,
|
|
reason="Need at least 4 GPUs to run the test.")
|
|
@pytest.mark.parametrize(
|
|
"common_llm_kwargs",
|
|
[[
|
|
# Skip cuda graph recording for fast test.
|
|
"--enforce_eager",
|
|
"--tensor-parallel-size",
|
|
"4",
|
|
]])
|
|
@pytest.mark.parametrize("per_test_common_llm_kwargs", [
|
|
[
|
|
"--speculative-model",
|
|
f"{SPEC_MODEL}",
|
|
"--num-speculative-tokens",
|
|
"5",
|
|
],
|
|
])
|
|
@pytest.mark.parametrize("baseline_llm_kwargs", [[]])
|
|
@pytest.mark.parametrize(
|
|
"test_llm_kwargs",
|
|
[
|
|
#TODO(wooyeon): add spec_draft_dp=2 case
|
|
[
|
|
"--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_tp4(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(MAIN_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)
|
|
|
|
|
|
@pytest.mark.skipif(torch.cuda.device_count() < 4,
|
|
reason="Need at least 4 GPUs to run the test.")
|
|
@pytest.mark.parametrize(
|
|
"common_llm_kwargs",
|
|
[[
|
|
|
|
# Skip cuda graph recording for fast test.
|
|
"--enforce-eager",
|
|
"--tensor-parallel-size",
|
|
"4",
|
|
]])
|
|
@pytest.mark.parametrize("per_test_common_llm_kwargs", [[]])
|
|
@pytest.mark.parametrize("baseline_llm_kwargs", [[]])
|
|
@pytest.mark.parametrize(
|
|
"test_llm_kwargs",
|
|
[
|
|
[
|
|
"--speculative-model",
|
|
f"{SPEC_MODEL}",
|
|
"--num-speculative-tokens",
|
|
"5",
|
|
|
|
# Artificially limit the draft model max model len; this forces vLLM
|
|
# to skip speculation once the sequences grow beyond 32-k tokens.
|
|
"--speculative-max-model-len",
|
|
"32",
|
|
],
|
|
])
|
|
@pytest.mark.parametrize("batch_size", [8])
|
|
@pytest.mark.parametrize(
|
|
"output_len",
|
|
[
|
|
# This must be a good bit larger than speculative_max_model_len so that
|
|
# we can test the case where all seqs are skipped, but still small to
|
|
# ensure fast test.
|
|
64,
|
|
])
|
|
@pytest.mark.parametrize("seed", [1])
|
|
def test_skip_speculation(common_llm_kwargs, per_test_common_llm_kwargs,
|
|
baseline_llm_kwargs, test_llm_kwargs,
|
|
batch_size: int, output_len: int, seed: int):
|
|
"""Verify job failure with RuntimeError when all sequences skip speculation.
|
|
We do this by setting the max model len of the draft model to an
|
|
artificially low value, such that when the sequences grow beyond it, they
|
|
are skipped in speculative decoding.
|
|
|
|
TODO: fix it to pass without raising Error. (#5814)
|
|
"""
|
|
with pytest.raises(
|
|
(openai.APIConnectionError, openai.InternalServerError)):
|
|
run_equality_correctness_test_tp(MAIN_MODEL,
|
|
common_llm_kwargs,
|
|
per_test_common_llm_kwargs,
|
|
baseline_llm_kwargs,
|
|
test_llm_kwargs,
|
|
batch_size,
|
|
output_len,
|
|
seed,
|
|
temperature=0.0)
|