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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, 03:00 UTC

[ROCm][CI] add warmup to mem_util test before measurement (#43236)

Signed-off-by: Divakar Verma <divakar.verma@amd.com>

Divakar Verma committed May 21, 2026 at 23:17 UTC 35d0141a0b68a188777e277e372f211098419f58
1 file changed +7
tests/utils_/test_mem_utils.py
+7
@@ -20,6 +20,13 @@ def test_memory_profiling():
20 # 512 MiB allocation outside of this instance
21 handle1 = lib.cudaMalloc(512 * 1024 * 1024)
22
23 + # Warm up PyTorch's CUDA/ROCm context so that its internal initialization
24 + # overhead (streams, cuBLAS handles, etc.) is included in the baseline and
25 + # does not inflate non-torch increase which is larger on ROCm than on CUDA
26 + _warmup = torch.zeros(1, device="cuda")
27 + del _warmup
28 + torch.accelerator.empty_cache()
29 +
30 baseline_snapshot = MemorySnapshot()
31
32 # load weights