[TPU] Enable gemma3-27b with TP>1 on multi-chips. (#17335)

Signed-off-by: Xiongfei Wei <isaacwxf23@gmail.com>
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XiongfeiWei 2025-05-05 14:19:58 -07:00 committed by GitHub
parent 5ea5c514da
commit 9765940824
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2 changed files with 44 additions and 0 deletions

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@ -8,6 +8,7 @@ from __future__ import annotations
from typing import TYPE_CHECKING
import pytest
from torch_xla._internal import tpu
from vllm.platforms import current_platform
@ -63,3 +64,45 @@ def test_basic(
output = vllm_outputs[0][1]
assert "1024" in output or "0, 1" in output
TP_SIZE_8 = 8
@pytest.mark.skipif(not current_platform.is_tpu(),
reason="This is a test for TPU only")
@pytest.mark.skipif(tpu.num_available_chips() < TP_SIZE_8,
reason=f"This test requires {TP_SIZE_8} TPU chips.")
def test_gemma3_27b_with_text_input_and_tp(
vllm_runner: type[VllmRunner],
monkeypatch: pytest.MonkeyPatch,
) -> None:
model = "google/gemma-3-27b-it"
max_tokens = 16
tensor_parallel_size = TP_SIZE_8
max_num_seqs = 4
prompts = [
"A robot may not injure a human being",
"It is only with the heart that one can see rightly;",
"The greatest glory in living lies not in never falling,",
]
answers = [
" or, through inaction, allow a human being to come to harm.",
" what is essential is invisible to the eye.",
" but in rising every time we fall.",
]
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
with vllm_runner(
model,
max_num_batched_tokens=256,
max_num_seqs=max_num_seqs,
tensor_parallel_size=tensor_parallel_size) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(prompts, max_tokens)
# vllm_outputs is a list of tuples whose first element is the token id
# and the second element is the output (including the prompt).
for output, answer in zip(vllm_outputs, answers):
generated_text = output[1]
assert answer in generated_text

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@ -30,6 +30,7 @@ class TpuPlatform(Platform):
dispatch_key: str = "XLA"
ray_device_key: str = "TPU"
device_control_env_var: str = "TPU_VISIBLE_CHIPS"
simple_compile_backend: str = "openxla"
supported_quantization: list[str] = ["tpu_int8", "compressed-tensors"]