[Perf] Batch Weight Prefetching via cuMemcpyBatchAsync to Reduce Latency#41474
[Perf] Batch Weight Prefetching via cuMemcpyBatchAsync to Reduce Latency#41474xiaobao520123 wants to merge 1 commit intovllm-project:mainfrom
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Code Review
This pull request introduces a performance optimization for parameter prefetching by implementing a fast path that utilizes batched H2D copies via cuMemcpyBatchAsync (through ops.swap_blocks_batch). It adds infrastructure to track buffer pointers and sizes while maintaining a fallback for CUDA graph capture. Feedback suggests initializing the synchronization event with enable_timing=False to further reduce overhead and refactoring the copy logic to ensure that the per-parameter fallback is used whenever the batched path is unavailable, avoiding a potential bug where prefetching might be skipped.
| if in_capture: | ||
| # cuMemcpyBatchAsync is not capture-safe. | ||
| # Slow path: Fallbacks to per-param copy_() so they can get recorded into the graph. | ||
| for name, offloader in self._param_offloaders.items(): | ||
| cpu_storage = offloader._cpu_storage | ||
| gpu_buffer = offloader._gpu_buffer | ||
| assert cpu_storage is not None, "CPU storage not initialized" | ||
| assert gpu_buffer is not None, "GPU buffer not assigned" | ||
| assert not should_pin_memory() or cpu_storage.is_pinned(), ( | ||
| f"CPU storage for {name} is not pinned! " | ||
| "non_blocking=True H2D copy from non-pinned memory " | ||
| "causes stream synchronization that breaks " | ||
| "event-based fork synchronization." | ||
| ) | ||
| gpu_buffer.copy_(cpu_storage, non_blocking=True) | ||
| elif ( | ||
| self._buffer_src_ptrs is not None | ||
| and self._buffer_dst_ptrs is not None | ||
| and self._buffer_sizes is not None | ||
| ): | ||
| # Fast path: batched copy using custom op (single cuMemcpyBatchAsync call on CUDA 12.8+) | ||
| # cuMemcpyBatchAsync can have less driver-call overhead and better performance. | ||
| # swap_blocks_batch() will fallback to per-param copy_() if cuMemcpyBatchAsync is not available. | ||
| ops.swap_blocks_batch( | ||
| src_ptrs=self._buffer_src_ptrs, | ||
| dst_ptrs=self._buffer_dst_ptrs, | ||
| sizes=self._buffer_sizes | ||
| ) | ||
| gpu_buffer.copy_(cpu_storage, non_blocking=True) | ||
| else: | ||
| # No params to copy (shouldn't normally happen). | ||
| pass |
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The current implementation of the else block (lines 602-604) is a potential bug. If self._buffer_src_ptrs is None (which can happen if parameters were pruned or if a contiguity check fails), the code currently does nothing (pass), meaning weights will never be prefetched for that module.
Additionally, the in_capture path and the fallback path are identical. I suggest refactoring this to use the copy_() loop as a general fallback whenever the fast path is unavailable.
if (
not in_capture
and self._buffer_src_ptrs is not None
and self._buffer_dst_ptrs is not None
and self._buffer_sizes is not None
):
# Fast path: batched copy using custom op (single cuMemcpyBatchAsync call on CUDA 12.8+)
# cuMemcpyBatchAsync can have less driver-call overhead and better performance.
# swap_blocks_batch() will fallback to per-param copy_() if cuMemcpyBatchAsync is not available.
ops.swap_blocks_batch(
src_ptrs=self._buffer_src_ptrs,
dst_ptrs=self._buffer_dst_ptrs,
sizes=self._buffer_sizes
)
else:
# Slow path: Fallback to per-param copy_().
# This is used during CUDA graph capture or when tensors are non-contiguous.
for name, offloader in self._param_offloaders.items():
cpu_storage = offloader._cpu_storage
gpu_buffer = offloader._gpu_buffer
assert cpu_storage is not None, "CPU storage not initialized"
assert gpu_buffer is not None, "GPU buffer not assigned"
assert not should_pin_memory() or cpu_storage.is_pinned(), (
f"CPU storage for {name} is not pinned! "
"non_blocking=True H2D copy from non-pinned memory "
"causes stream synchronization that breaks "
"event-based fork synchronization."
)
gpu_buffer.copy_(cpu_storage, non_blocking=True)
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Thanks for the work and detailed data! |
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@minosfuture, thanks for the question!
Yes, it should. When doing data transfer, current implementation runs in async. Yet, such prefetching on future layers starts at the end after computation on previous layer is complete. # prefetch.py:210-242
def _hook_module_forward(self, index: int, module: nn.Module):
original_forward = module.forward
def forward(*args, **kwargs):
module.forward = original_forward
torch.ops.vllm.wait_prefetch(input_tensor, index)
output = original_forward(*args, **kwargs)
next_index = (index + self.prefetch_step) % len(self.module_offloaders)
# NOTICE: start prefetch task immediately after forward completes.
if isinstance(output, tuple):
torch.ops.vllm.start_prefetch(output[0], next_index)
else:
torch.ops.vllm.start_prefetch(output, next_index)
module.forward = forward
return output
module.forward = forwardThe startup is hooked onto the inference critical path (forward) and because it takes API calls for the CPU to submit tasks to the GPU. This leads to overhead. Keeping this 'hook' scheme, we must reduce such overhead and avoid run bubbles as much as possible.
Agree. That’s why we can see bigger latency decrease on TTFT, even bigger in eager mode. One of the biggest application of offloading is when only commodity hardware is available, or running in single node instance. The work is trying to reduce any other factors regardless it is compute/memory/bandwidth-bound.
The savings are per-prefetch-call of inter-forward-steps, applied to every start_prefetch invocation on the critical path, not just the first layer in a step.
In my benchmark, depending on offload_prefetch_step settings, one prefetch launch call stands
I hope CUDA official will support it in the future. Nonetheless, everything is still compatible with |
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Purpose
Current implementation of weight prefetching requires launching
cuMemcpyAsyncmultiple times, which creates driver-call overhead. This overhead accumulates and hurts inference performance because prefetching is hooked up near the end of each layerforward.Replace per-layer weight prefetching with
swap_blocks_batch.swap_blocks_batchutilizescuMemcpyBatchAsyncsince CUDA 12.8+. According to the implementation,swap_blocks_batchwill fallback tocuMemcpyAsyncloop when the feature is not available.In addition, objects related to CUDA stream synchronization have been optimized to further reduce the overhead.
As a result, it yields >50% latency decrease in triggering prefetching and reduces both TTFT and TPOT/ITL (See the test result below).
Original Trace
Trace after This PR
Test Plan
Hardware
GPU: 1xA100-40GB 40GB, CUDA 12.8/12.9
CPU: 2× Intel Xeon Gold 5318Y @ 2.10 GHz (24 cores/socket, 2 threads/core → 96 logical CPUs, 48 physical cores), max turbo 3.4 GHz, 2 NUMA nodes.
GPU ↔ CPU: PCIe Gen4 x16 (30GB/s R/W theoretical, 25GB/s actual)
Memory: ~690 GiB usable (724 GB raw); DDR4-3200 ECC, running at 2933 MT/s
vLLM
Version:
vllm==0.19.1rc1.dev750+ga3ec4a35f.precompiledVLLM_USE_PRECOMPILED=1 VLLM_PRECOMPILED_WHEEL_COMMIT=3527229517f01a5f2406fa6fbf35ff9223c65ed5 uv pip install --editable . --torch -backend=cu129.a3ec4a35f5943c250974d504706d22297d423468 [Bugfix][Metrics] Fix RayPrometheusMetric.labels() returning shared labeled child (#40840)Configuration:
prefetch50%offload; 18 groups, 2 layers each, 1 layer offloaded, ratio:2:1; Prefetch steps: 275%offload; 16 groups, 4 layers each, 3 layers offloaded, ratio:4:3; Prefetch steps: 2vLLM Instance:
Dataset & Benchmark:
Dataset: LongBench-NarrativeQA (Shuffled): Link
QPS: 1
Prompts: 10
Benchmark & Torch Profiling:
Test Result
Qwen3-8B
(Major Improvement) Per-prefetch-call Host-side Latency
End-to-end Throughput & Latency
CUDA Graph Enabled
Torch Profile (Original): Link
Torch Profile (cuMemcpyBatchAsync): Link
Eager
Torch Profile (Original): Link
Torch Profile (cuMemcpyBatchAsync): Link
Qwen3-32B (Profiling disabled)
End-to-end Throughput & Latency
CUDA Graph Enabled
Eager
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