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chore: upgrade nixl to 0.6.0 (#3550)
Signed-off-by: richardhuo-nv <[email protected]>
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Cargo.lock

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container/Dockerfile

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@@ -39,7 +39,7 @@ ARG SCCACHE_REGION=""
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# NIXL configuration
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ARG NIXL_UCX_REF=v1.19.0
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ARG NIXL_REF=0.4.1
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ARG NIXL_REF=0.6.0
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# Python configuration
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ARG PYTHON_VERSION=3.12

container/build.sh

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@@ -115,7 +115,7 @@ NONE_BASE_IMAGE_TAG="25.01-cuda12.8-devel-ubuntu24.04"
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SGLANG_BASE_IMAGE="nvcr.io/nvidia/cuda-dl-base"
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SGLANG_BASE_IMAGE_TAG="25.01-cuda12.8-devel-ubuntu24.04"
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NIXL_REF=0.4.1
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NIXL_REF=0.6.0
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NIXL_UCX_REF=v1.19.0
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NIXL_UCX_EFA_REF=9d2b88a1f67faf9876f267658bd077b379b8bb76
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docs/support_matrix.md

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@@ -58,14 +58,14 @@ If you are using a **GPU**, the following GPU models and architectures are suppo
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| :----------------- | :---------- | :------------------------------------ | :----------- |
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| ai-dynamo | 0.5.1 | >=2.28 | |
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| ai-dynamo-runtime | 0.5.1 | >=2.28 (Python 3.12 has known issues) | |
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| NIXL | 0.4.1 | >=2.27 | >=11.8 |
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| NIXL | 0.6.0 | >=2.27 | >=11.8 |
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### Build Dependency
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| **Build Dependency** | **Version** |
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| :------------------- | :------------------------------------------------------------------------------- |
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| **TensorRT-LLM** | 1.1.0rc5 |
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| **NIXL** | 0.4.1 |
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| **NIXL** | 0.6.0 |
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| **vLLM** | 0.10.1.1 |
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| **SGLang** | 0.5.3rc0 |
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lib/bindings/python/Cargo.lock

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lib/llm/Cargo.toml

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@@ -93,7 +93,7 @@ dialoguer = { version = "0.11", default-features = false, features = [
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] }
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# block_manager
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nixl-sys = { version = "0.4.1", optional = true }
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nixl-sys = { version = "=0.6.0", optional = true }
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cudarc = { version = "0.17.1", features = ["cuda-12020"], optional = true }
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ndarray = { version = "0.16", optional = true }
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nix = { version = "0.26", optional = true }

lib/llm/src/block_manager/block/transfer/nixl.rs

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@@ -4,7 +4,7 @@
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use super::*;
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use anyhow::Result;
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use nixl_sys::{MemoryRegion, NixlDescriptor, XferDescList};
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use nixl_sys::{MemoryRegion, NixlDescriptor, XferDescList, XferStatus};
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use std::future::Future;
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fn append_xfer_request<Source, Destination>(
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.storage_type()
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.nixl_mem_type();
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let mut src_dl = XferDescList::new(src_mem_type, false)?;
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let mut dst_dl = XferDescList::new(dst_mem_type, false)?;
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let mut src_dl = XferDescList::new(src_mem_type)?;
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let mut dst_dl = XferDescList::new(dst_mem_type)?;
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for (src, dst) in src.iter().zip(dst.iter_mut()) {
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append_xfer_request(src, dst, &mut src_dl, &mut dst_dl)?;
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}
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debug_assert!(!src_dl.has_overlaps()? && !dst_dl.has_overlaps()?);
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let xfer_req = nixl_agent.create_xfer_req(
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transfer_type.as_xfer_op(),
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&src_dl,
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loop {
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match nixl_agent.get_xfer_status(&xfer_req) {
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Ok(false) => break, // Transfer is complete.
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Ok(true) => tokio::time::sleep(std::time::Duration::from_millis(5)).await, // Transfer is still in progress.
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Ok(XferStatus::Success) => break, // Transfer is complete.
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Ok(XferStatus::InProgress) => {
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tokio::time::sleep(std::time::Duration::from_millis(5)).await
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} // Transfer is still in progress.
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Err(e) => {
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tracing::error!("Error getting transfer status: {}", e);
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break;

pyproject.toml

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@@ -53,13 +53,13 @@ trtllm =[
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vllm = [
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"uvloop",
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"nixl<=0.4.1",
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"nixl<=0.6.0",
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"vllm[flashinfer]==0.10.2",
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]
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sglang = [
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"uvloop",
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"nixl<=0.4.1",
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"nixl<=0.6.0",
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"sglang[all]==0.5.3",
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]
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