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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 14, 05:30 UTC

[CI] fix mamba kernel test (#26250)

Signed-off-by: zjy0516 <riverclouds.zhu@qq.com>

Jiangyun Zhu committed Oct 6, 2025 at 02:26 UTC 9c3c21c519fc0dc7b7b49c86058749c9af202ad9
2 files changed +12 -1
.buildkite/test-pipeline.yaml
+1
@@ -477,6 +477,7 @@ steps:
477 source_file_dependencies:
478 - csrc/mamba/
479 - tests/kernels/mamba
480 + - vllm/model_executor/layers/mamba/ops
481 commands:
482 - pytest -v -s kernels/mamba
483
tests/kernels/mamba/test_causal_conv1d.py
+11 -1
@@ -165,7 +165,17 @@ def test_causal_conv1d_update(dim, width, seqlen, has_bias, silu_activation, ity
165 bias = torch.randn(dim, device=device, dtype=itype) if has_bias else None
166 conv_state_ref = conv_state.detach().clone()
167 activation = None if not silu_activation else "silu"
168 - out = causal_conv1d_update(x, conv_state, weight, bias, activation=activation)
168 +
169 + conv_state_indices = torch.arange(batch, dtype=torch.int32, device=device)
170 +
171 + out = causal_conv1d_update(
172 + x,
173 + conv_state,
174 + weight,
175 + bias,
176 + activation=activation,
177 + conv_state_indices=conv_state_indices,
178 + )
179 out_ref = causal_conv1d_update_ref(
180 x_ref, conv_state_ref, weight, bias, activation=activation
181 )