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[ROCm][CI] Add HY-V4 generation coverage (#55667)

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Signed-off-by: Andreas Karatzas <Andreas.Karatzas@amd.com>
This commit is contained in:
Andreas Karatzas
2026-09-11 17:24:28 +08:00
committed by GitHub
parent e77daef89e
commit eb7e894432
4 changed files with 160 additions and 2 deletions
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.buildkite/test-amd.yaml
+8
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@@ -516,6 +516,11 @@ steps:
optional: true
working_dir: "/vllm-workspace/tests"
source_file_dependencies:
- vllm/models/hy_v4/
- vllm/config/
- vllm/v1/worker/gpu/
- vllm/v1/attention/ops/
- vllm/v1/attention/backend.py
- vllm/model_executor/model_loader/sharded_state_loader.py
- vllm/model_executor/models/
- vllm/model_executor/layers/
@@ -535,6 +540,7 @@ steps:
- pytest models/multimodal/generation/test_phi4siglip.py -v -s -m 'distributed(num_gpus=2)'
- VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)'
- pytest -v -s models/test_vision.py -m 'distributed(num_gpus=2)'
- VLLM_ROCM_USE_AITER=1 pytest -v -s models/test_hyv4_rocm.py -m 'distributed(num_gpus=2)'
#-------------------------------------------------------- mi300 · benchmarks ---------------------------------------------------------#
@@ -1978,9 +1984,11 @@ steps:
- tests/models/test_terratorch.py
- tests/models/transformers/test_backend.py
- tests/models/test_registry.py
- tests/models/test_hyv4_rocm.py
- tests/models/test_vision.py
commands:
- pytest -v -s models/test_terratorch.py models/transformers/test_backend.py models/test_registry.py
- VLLM_ROCM_USE_AITER=1 pytest -v -s models/test_hyv4_rocm.py -m 'not distributed'
- pytest -v -s models/test_vision.py::test_simple_mrope_vision_model_spatial_merge
#----------------------------------------------------- mi300 · models / language -----------------------------------------------------#
.buildkite/test_areas/models_basic.yaml
+5
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@@ -58,6 +58,11 @@ steps:
timeout_in_minutes: 50
depends_on:
- image-build-amd
source_file_dependencies:
- tests/models/test_hyv4_rocm.py
commands:
- pytest -v -s models/test_terratorch.py models/transformers/test_backend.py models/test_registry.py
- VLLM_ROCM_USE_AITER=1 pytest -v -s models/test_hyv4_rocm.py -m 'not distributed'
- label: ":nvidia: (B200) Inkling"
key: inkling-unit-tests-b200
.buildkite/test_areas/models_distributed.yaml
+10 -2
View File
@@ -42,6 +42,13 @@ steps:
depends_on:
- image-build-amd
source_file_dependencies:
- vllm/models/hy_v4/
- vllm/config/
- vllm/v1/worker/
- vllm/v1/attention/ops/
- vllm/v1/attention/backend.py
- tests/conftest.py
- tests/utils.py
- vllm/model_executor/model_loader/sharded_state_loader.py
- vllm/model_executor/models/
- vllm/model_executor/layers/
@@ -60,8 +67,9 @@ steps:
HIP_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
;;
1)
pytest models/transformers/test_backend.py -v -s -m 'distributed(num_gpus=2)'
pytest models/language -v -s -m 'distributed(num_gpus=2)'
pytest models/transformers/test_backend.py -v -s -m 'distributed(num_gpus=2)' &&
pytest models/language -v -s -m 'distributed(num_gpus=2)' &&
VLLM_ROCM_USE_AITER=1 pytest -v -s models/test_hyv4_rocm.py -m 'distributed(num_gpus=2)'
;;
2)
pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/generation/test_phi4siglip.py
tests/models/test_hyv4_rocm.py
+137
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@@ -0,0 +1,137 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import math
import pytest
from tests.utils import multi_gpu_marks
from vllm import SamplingParams
from vllm.config import CompilationConfig
from vllm.config.compilation import CompilationMode, CUDAGraphMode
from vllm.platforms import current_platform
from vllm.v1.metrics.reader import Counter
pytestmark = pytest.mark.skipif(
not current_platform.is_rocm(), reason="ROCm-specific tests"
)
def _small_hyv4_config(config):
# Apply to both target and MTP draft while retaining MLA/indexer dimensions.
config.update(
{
"hidden_size": 512,
"num_hidden_layers": 2,
"num_attention_heads": 64,
"q_lora_rank": 128,
"intermediate_size": 512,
"moe_intermediate_size": 256,
"n_routed_experts": 4,
"num_experts_per_tok": 2,
"n_shared_experts": 1,
"enable_ihc": True,
"index_n_heads": 32,
"index_topk": 2048,
"layer_types": ["deepseek_sparse_attention"] * 2,
"indexer_types": ["full", "shared"],
"num_nextn_predict_layers": 1,
"vocab_size": 256,
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 0,
}
)
return config
def _num_captured_graphs(_worker):
from vllm.compilation.counter import compilation_counter
return compilation_counter.num_cudagraph_captured
@pytest.mark.parametrize(
"tp,enforce_eager,mtp,dtype,kv_cache_dtype",
[
pytest.param(1, True, False, "bfloat16", "auto", id="eager-tp1"),
pytest.param(1, True, False, "float16", "auto", id="eager-fp16"),
pytest.param(1, True, False, "bfloat16", "fp8", id="eager-fp8-kv"),
pytest.param(
2,
False,
False,
"bfloat16",
"auto",
id="graphs-tp2",
marks=multi_gpu_marks(num_gpus=2),
),
pytest.param(1, True, True, "bfloat16", "auto", id="eager-mtp"),
],
)
def test_hyv4_dummy_generation(
vllm_runner,
enable_pickle,
tp: int,
enforce_eager: bool,
mtp: bool,
dtype: str,
kv_cache_dtype: str,
) -> None:
"""Exercise prefill, decode, and batch consistency with dummy HY weights."""
with vllm_runner(
"tencent/Hy4-preview",
trust_remote_code=True,
skip_tokenizer_init=True,
load_format="dummy",
disable_log_stats=not mtp,
dtype=dtype,
kv_cache_dtype=kv_cache_dtype,
hf_overrides=_small_hyv4_config,
max_model_len=256,
max_num_batched_tokens=128,
max_num_seqs=8,
enable_chunked_prefill=True,
enable_prefix_caching=False,
kv_cache_memory_bytes=128 * 1024 * 1024,
enforce_eager=enforce_eager,
tensor_parallel_size=tp,
speculative_config={"method": "mtp", "num_speculative_tokens": 2}
if mtp
else None,
seed=17,
# Exercise supported decode graphs independently of default graph policy.
compilation_config=CompilationConfig(
mode=CompilationMode.NONE,
cudagraph_mode=CUDAGraphMode.NONE
if enforce_eager
else CUDAGraphMode.FULL_DECODE_ONLY,
cudagraph_capture_sizes=[] if enforce_eager else [1, 2, 4],
),
) as model:
if not enforce_eager:
assert model.collective_rpc(_num_captured_graphs) == [3] * tp
prompts = [
{"prompt_token_ids": [11, 12, 13, 14]},
{"prompt_token_ids": list(range(20, 84))},
]
params = SamplingParams(
temperature=0, max_tokens=4, ignore_eos=True, logprobs=1
)
outputs = model.llm.generate(prompts, params)
assert len(outputs) == len(prompts)
for prompt, output in zip(prompts, outputs):
completion = output.outputs[0]
assert len(completion.token_ids) == 4
assert completion.cumulative_logprob is not None
assert math.isfinite(completion.cumulative_logprob)
single = model.llm.generate([prompt], params)[0].outputs[0]
assert single.token_ids == completion.token_ids
if mtp:
assert any(
isinstance(metric, Counter)
and metric.name == "vllm:spec_decode_num_drafts"
and metric.value > 0
for metric in model.llm.get_metrics()
), "MTP drafter did not run"