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refactor hard coded device string in test files under tests/v1 and tests/lora (#37566)

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Signed-off-by: Liao, Wei <wei.liao@intel.com>
This commit is contained in:
wliao2
2026-04-02 20:21:47 -07:00
committed by GitHub
parent 4a06e1246e
commit 32e0c0bfa2
28 changed files with 239 additions and 146 deletions
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34
tests/v1/worker/test_gpu_model_runner.py
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@@ -45,7 +45,7 @@ from vllm.v1.worker.utils import AttentionGroup, select_common_block_size
BLOCK_SIZE = 16
NUM_BLOCKS = 10
DEVICE = current_platform.device_type
DEVICE_TYPE = current_platform.device_type
def initialize_kv_cache(runner: GPUModelRunner):
@@ -121,7 +121,7 @@ def model_runner():
vllm_config.compilation_config.static_forward_context["layer.0"] = Attention(
num_heads, head_size, 0.1
)
runner = GPUModelRunner(vllm_config, DEVICE)
runner = GPUModelRunner(vllm_config, DEVICE_TYPE)
initialize_kv_cache(runner)
yield runner
@@ -340,7 +340,7 @@ def test_get_nans_in_logits(model_runner, dist_init):
[1.0, 2.0, 3.0],
[3.0, 2.0, 1.0],
],
device=DEVICE,
device=DEVICE_TYPE,
)
result = model_runner._get_nans_in_logits(logits)
assert result == {"req_0": 0, "req_1": 0}
@@ -350,7 +350,7 @@ def test_get_nans_in_logits(model_runner, dist_init):
[1.0, float("nan"), 3.0],
[4.0, float("nan"), float("nan")],
],
device=DEVICE,
device=DEVICE_TYPE,
)
result = model_runner._get_nans_in_logits(logits)
assert result == {"req_0": 1, "req_1": 2}
@@ -360,7 +360,7 @@ def test_get_nans_in_logits(model_runner, dist_init):
[1.0, 2.0, 3.0],
[4.0, float("nan"), float("nan")],
],
device=DEVICE,
device=DEVICE_TYPE,
)
result = model_runner._get_nans_in_logits(logits)
assert result == {"req_0": 0, "req_1": 2}
@@ -372,7 +372,7 @@ def test_get_nans_in_logits(model_runner, dist_init):
[
[1.0, float("nan"), 3.0],
],
device=DEVICE,
device=DEVICE_TYPE,
)
result = model_runner._get_nans_in_logits(logits)
assert result == {"req_0": 1, "req_1": 0}
@@ -383,7 +383,7 @@ def test_get_nans_in_logits(model_runner, dist_init):
[1.0, 2.0, 3.0],
[float("nan"), 2.0, 3.0],
],
device=DEVICE,
device=DEVICE_TYPE,
)
result = model_runner._get_nans_in_logits(logits)
assert result == {"req_0": 2, "req_1": 0}
@@ -643,7 +643,7 @@ def test_init_kv_cache_without_kv_sharing(default_vllm_config):
# Set high context length to test max context length estimation
vllm_config.model_config.max_model_len = 3_000_000
vllm_ctx = vllm_config.compilation_config.static_forward_context
runner = GPUModelRunner(vllm_config, DEVICE)
runner = GPUModelRunner(vllm_config, DEVICE_TYPE)
kv_cache_spec = runner.get_kv_cache_spec()
assert len(kv_cache_spec) == 2
assert len(runner.shared_kv_cache_layers) == 0
@@ -711,7 +711,7 @@ def test_init_kv_cache_with_kv_sharing_valid(default_vllm_config):
# Set high context length to test max context length estimation
vllm_config.model_config.max_model_len = 3_000_000
vllm_ctx = vllm_config.compilation_config.static_forward_context
runner = GPUModelRunner(vllm_config, DEVICE)
runner = GPUModelRunner(vllm_config, DEVICE_TYPE)
kv_cache_spec = runner.get_kv_cache_spec()
assert len(kv_cache_spec) == 1
assert layer_0 in kv_cache_spec
@@ -850,7 +850,7 @@ def test_hybrid_attention_mamba_tensor_shapes():
assert fwd_context is not None
vllm_ctx = vllm_config.compilation_config.static_forward_context
runner = GPUModelRunner(vllm_config, DEVICE)
runner = GPUModelRunner(vllm_config, DEVICE_TYPE)
current_platform.update_block_size_for_backend(vllm_config)
kv_cache_spec = runner.get_kv_cache_spec()
@@ -896,13 +896,13 @@ def test_hybrid_attention_mamba_tensor_shapes():
ssm_constant_shape = ssm_shape[1:]
attn_blocks_constant = torch.full(
(test_block_size, *attn_constant_shape), device=DEVICE, fill_value=3.33
(test_block_size, *attn_constant_shape), device=DEVICE_TYPE, fill_value=3.33
)
conv_blocks_constant = torch.full(
(test_block_size, *conv_constant_shape), device=DEVICE, fill_value=6.66
(test_block_size, *conv_constant_shape), device=DEVICE_TYPE, fill_value=6.66
)
ssm_blocks_constant = torch.full(
(test_block_size, *ssm_constant_shape), device=DEVICE, fill_value=9.99
(test_block_size, *ssm_constant_shape), device=DEVICE_TYPE, fill_value=9.99
)
# Fill attention blocks with constants using kv block indices
@@ -997,7 +997,7 @@ def test_hybrid_block_table_initialization():
max_num_blocks_per_req=max_num_blocks_per_req,
max_num_batched_tokens=max_num_batched_tokens,
pin_memory=False,
device=torch.device(DEVICE),
device=torch.device(DEVICE_TYPE),
kernel_block_size=kernel_block_sizes[0],
cp_kv_cache_interleave_size=cp_kv_cache_interleave_size,
)
@@ -1036,7 +1036,7 @@ def test_input_batch_with_kernel_block_sizes():
max_num_reqs = 10
max_model_len = 512
max_num_batched_tokens = 512
device = torch.device(DEVICE)
device = torch.device(DEVICE_TYPE)
pin_memory = False
vocab_size = 50272
@@ -1083,7 +1083,7 @@ def test_hybrid_cache_integration(default_vllm_config, dist_init):
num_heads, head_size, 0.1
)
runner = GPUModelRunner(vllm_config, DEVICE)
runner = GPUModelRunner(vllm_config, DEVICE_TYPE)
# Initialize KV cache with configuration
attn_spec = FullAttentionSpec(
@@ -1306,7 +1306,7 @@ def test_mamba_cache_raises_when_max_num_seqs_exceeds_blocks():
)
assert fwd_context is not None
runner = GPUModelRunner(vllm_config, DEVICE)
runner = GPUModelRunner(vllm_config, DEVICE_TYPE)
current_platform.update_block_size_for_backend(vllm_config)
kv_cache_spec = runner.get_kv_cache_spec()