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[Misc] Upgrade to pytorch 2.5 (#9588)

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Signed-off-by: Bill Nell <bill@neuralmagic.com>
Signed-off-by: youkaichao <youkaichao@gmail.com>
Co-authored-by: youkaichao <youkaichao@gmail.com>
This commit is contained in:
bnellnm
2024-10-27 05:44:24 -04:00
committed by GitHub
parent 8549c82660
commit 3cb07a36a2
8 changed files with 47 additions and 24 deletions
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44
tests/models/decoder_only/language/test_big_models.py
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@@ -8,7 +8,7 @@ import pytest
from vllm.platforms import current_platform
from ...utils import check_outputs_equal
from ...utils import check_logprobs_close, check_outputs_equal
MODELS = [
"meta-llama/Llama-2-7b-hf",
@@ -43,18 +43,40 @@ def test_models(
dtype: str,
max_tokens: int,
) -> None:
with hf_runner(model, dtype=dtype) as hf_model:
hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
if model == "openbmb/MiniCPM3-4B":
# the output becomes slightly different when upgrading to
# pytorch 2.5 . Changing to logprobs checks instead of exact
# output checks.
NUM_LOG_PROBS = 8
with hf_runner(model, dtype=dtype) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
example_prompts, max_tokens, NUM_LOG_PROBS)
check_outputs_equal(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, NUM_LOG_PROBS)
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
else:
with hf_runner(model, dtype=dtype) as hf_model:
hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model:
vllm_outputs = vllm_model.generate_greedy(example_prompts,
max_tokens)
check_outputs_equal(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
@pytest.mark.parametrize("model", MODELS)