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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 3, 09:15 UTC

[Bugfix] Use platform-agnostic device in example_connector load (#42926)

Signed-off-by: Revital Sur <eres@il.ibm.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

Revital Sur committed May 19, 2026 at 06:12 UTC afd7b1dce94fed484351fafd5bf5ea6601ac621e
1 file changed +3 -1
vllm/distributed/kv_transfer/kv_connector/v1/example_connector.py
+3 -1
@@ -188,7 +188,9 @@ class ExampleConnector(KVConnectorBase_V1):
188 filename = self._generate_filename_debug(
189 layer_name, request.token_ids, request.mm_hashes
190 )
191 - kv_cache = safetensors.torch.load_file(filename)["kv_cache"].cuda()
191 + kv_cache = safetensors.torch.load_file(
192 + filename, device=str(kv_cache_layer.device)
193 + )["kv_cache"]
194 if isinstance(attn_metadata, dict):
195 inject_kv_into_layer(
196 kv_cache_layer,