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[Model] Add Granite model (vllm-project#7436)
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Co-authored-by: Nick Hill <nickhill@us.ibm.com>
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2 people authored and triple-Mu committed Sep 4, 2024
1 parent 44784e3 commit cbcda47
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49 changes: 49 additions & 0 deletions tests/models/test_granite.py
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"""Compare the outputs of HF and vLLM for Granite models using greedy sampling.
Run `pytest tests/models/test_granite.py`.
"""
import importlib.metadata

import pytest

from .utils import check_logprobs_close

TRANSFORMERS_VERSION = tuple(
map(int,
importlib.metadata.version("transformers").split(".")))

MODELS = [
"ibm/PowerLM-3b",
]


# GraniteForCausalLM will be in transformers >= 4.45
@pytest.mark.skipif(TRANSFORMERS_VERSION < (4, 45),
reason="granite model test requires transformers >= 4.45")
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
# TODO(sang): Sliding window should be tested separately.
with hf_runner(model, dtype=dtype) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
example_prompts, max_tokens, num_logprobs)

with vllm_runner(model, dtype=dtype) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs)
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
1 change: 1 addition & 0 deletions vllm/model_executor/models/__init__.py
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"EAGLEModel": ("eagle", "EAGLE"),
"MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"),
"JambaForCausalLM": ("jamba", "JambaForCausalLM"),
"GraniteForCausalLM": ("granite", "GraniteForCausalLM")
}

_EMBEDDING_MODELS = {
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