fix(L4T13 backends): switch vllm/sglang/vllm-omni to PyPI aarch64+cu130 wheels

backend/python/sglang/install.sh

@@ -36,15 +36,11 @@ fi
 # flash-attn-4 4.0 stable lands.
 EXTRA_PIP_INSTALL_FLAGS+=" --prerelease=allow"
 
-# JetPack 7 / L4T arm64 wheels are built for cp312 and shipped via
-# pypi.jetson-ai-lab.io. Bump the venv Python so the prebuilt sglang
-# wheel resolves cleanly. The actual install on l4t13 goes through
-# pyproject.toml (see the elif branch below) so [tool.uv.sources] can
-# pin only torch/torchvision/torchaudio/sglang to the jetson-ai-lab
-# index — leaving PyPI as the path for transitive deps like
-# markdown-it-py / anthropic / propcache that the L4T mirror's proxy
-# 503s on. No --index-strategy flag here: the explicit index keeps the
-# scoping clean.
+# JetPack 7 / L4T arm64 sglang + torch wheels come straight from PyPI now
+# (torch 2.11+ ships aarch64 + cu130 manylinux wheels and sglang 0.5.11+
+# ships a cp312 aarch64 wheel pinned to that torch). They're cp312-only,
+# so bump the venv Python accordingly.
+# https://pytorch.org/blog/vllm-and-pytorch-work-together-to-improve-the-developer-experience-on-aarch64/
 if [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
     PYTHON_VERSION="3.12"
     PYTHON_PATCH="12"
@@ -110,27 +106,6 @@ if [ "x${BUILD_TYPE}" == "x" ] || [ "x${FROM_SOURCE:-}" == "xtrue" ]; then
         fi
         uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} .
     popd
-# L4T arm64 (JetPack 7): drive the install through pyproject.toml so that
-# [tool.uv.sources] can pin torch/torchvision/torchaudio/sglang to the
-# jetson-ai-lab index, while everything else (transitive deps and
-# PyPI-resolvable packages like transformers / accelerate) comes from
-# PyPI. Bypasses installRequirements because uv pip install -r
-# requirements.txt does not honor sources — see
-# backend/python/sglang/pyproject.toml for the rationale. Mirrors the
-# equivalent path in backend/python/vllm/install.sh.
-elif [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
-    ensureVenv
-    if [ "x${PORTABLE_PYTHON}" == "xtrue" ]; then
-        export C_INCLUDE_PATH="${C_INCLUDE_PATH:-}:$(_portable_dir)/include/python${PYTHON_VERSION}"
-    fi
-    pushd "${backend_dir}"
-        # Build deps first (matches installRequirements' requirements-install.txt
-        # pass — sglang/sgl-kernel sdists need packaging/setuptools-scm in the
-        # venv before they can build under --no-build-isolation).
-        uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} -r requirements-install.txt
-        uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} --requirement pyproject.toml
-    popd
-    runProtogen
 else
     installRequirements
 fi

backend/python/sglang/requirements-l4t13-after.txt

@@ -0,0 +1,15 @@
+# sglang 0.5.11+ ships an aarch64 manylinux wheel on PyPI whose Requires-Dist
+# pins torch==2.11.0 / torchaudio==2.11.0, locking an ABI-consistent set with
+# the cu130 torch wheel installed above. 0.5.11 is the floor for Gemma 4
+# support (sgl-project/sglang#21952).
+#
+# The [all] extra is deliberately NOT used on aarch64: it pulls the
+# [diffusion] sub-extra which requires `xatlas`, and xatlas ships no
+# aarch64 wheel and its sdist depends on scikit_build_core without
+# declaring it in build-system.requires — so under --no-build-isolation
+# uv can't build it. Upstream sglang gates st_attn and vsa on
+# platform_machine != aarch64 in the diffusion extra but forgot xatlas.
+# Plain `sglang` carries everything backend.py uses (Engine, ServerArgs,
+# FunctionCallParser, ReasoningParser); the [all] extras are optional
+# accelerators not required at import time.
+sglang>=0.5.11

backend/python/sglang/requirements-l4t13.txt

@@ -0,0 +1,9 @@
+# JetPack 7 / L4T arm64 + CUDA 13. Since PyTorch 2.11 (April 2026), PyPI ships
+# aarch64 + cu130 manylinux wheels for torch/torchvision/torchaudio directly,
+# so we no longer need a custom --extra-index-url for the L4T mirror.
+# https://pytorch.org/blog/vllm-and-pytorch-work-together-to-improve-the-developer-experience-on-aarch64/
+accelerate
+torch
+torchvision
+torchaudio
+transformers

backend/python/vllm-omni/install.sh

@@ -13,14 +13,14 @@ else
 fi
 
 # Handle l4t build profiles (Python 3.12, pip fallback) if needed.
-# unsafe-best-match is required on l4t13 because the jetson-ai-lab index
-# lists transitive deps at limited versions — without it uv pins to the
-# first matching index and fails to resolve a compatible wheel from PyPI.
+# Since PyTorch 2.11 (April 2026) PyPI ships aarch64 + cu130 manylinux wheels
+# directly for torch/torchvision/torchaudio and an aarch64 vllm wheel pinned
+# to that torch, so the jetson-ai-lab mirror is no longer needed.
+# https://pytorch.org/blog/vllm-and-pytorch-work-together-to-improve-the-developer-experience-on-aarch64/
 if [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
   PYTHON_VERSION="3.12"
   PYTHON_PATCH="12"
   PY_STANDALONE_TAG="20251120"
-  EXTRA_PIP_INSTALL_FLAGS="${EXTRA_PIP_INSTALL_FLAGS:-} --index-strategy=unsafe-best-match"
 fi
 
 if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
@@ -42,18 +42,11 @@ if [ "x${BUILD_TYPE}" == "xhipblas" ]; then
     else
         uv pip install vllm==0.14.0 --extra-index-url https://wheels.vllm.ai/rocm/0.14.0/rocm700
     fi
-elif [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
-    # JetPack 7 / L4T arm64 cu130 — vllm comes from the prebuilt SBSA wheel
-    # at jetson-ai-lab. Version is unpinned: the index ships whatever build
-    # matches the cu130/cp312 ABI. unsafe-best-match lets uv fall through
-    # to PyPI for transitive deps not present on the jetson-ai-lab index.
-    if [ "x${USE_PIP}" == "xtrue" ]; then
-        pip install vllm --extra-index-url https://pypi.jetson-ai-lab.io/sbsa/cu130
-    else
-        uv pip install --index-strategy=unsafe-best-match vllm --extra-index-url https://pypi.jetson-ai-lab.io/sbsa/cu130
-    fi
-elif [ "x${BUILD_PROFILE}" == "xcublas13" ]; then
-    # vllm 0.19+ defaults to cu130 wheels on PyPI, no extra index needed.
+elif [ "x${BUILD_PROFILE}" == "xcublas13" ] || [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
+    # cublas13 (x86_64) and l4t13 (aarch64) both pull vllm from PyPI now:
+    # vllm 0.19+ defaults to cu130 wheels on x86_64 and vllm 0.20+ ships an
+    # aarch64 manylinux wheel pinned to torch==2.11.0. No extra index needed
+    # in either case.
     if [ "x${USE_PIP}" == "xtrue" ]; then
         pip install vllm --torch-backend=auto
     else

backend/python/vllm-omni/requirements-l4t13.txt

@@ -1,11 +1,15 @@
---extra-index-url https://pypi.jetson-ai-lab.io/sbsa/cu130
+# JetPack 7 / L4T arm64 + CUDA 13. PyPI ships aarch64 + cu130 manylinux wheels
+# for torch/torchvision/torchaudio directly since PyTorch 2.11 (April 2026),
+# so no custom index is needed. flash-attn is dropped here: PyPI has no
+# aarch64 wheel for it, but vLLM 0.20+ bundles its own vllm_flash_attn
+# (fa2 + fa3) inside the main wheel, so it is not required at runtime.
+# https://pytorch.org/blog/vllm-and-pytorch-work-together-to-improve-the-developer-experience-on-aarch64/
 accelerate
 torch
 torchvision
 torchaudio
 transformers
 bitsandbytes
-flash-attn
 diffusers
 librosa
 soundfile

backend/python/vllm/install.sh

@@ -43,14 +43,11 @@ if [ "x${BUILD_PROFILE}" == "xcublas13" ]; then
     EXTRA_PIP_INSTALL_FLAGS+=" --index-strategy=unsafe-best-match"
 fi
 
-# JetPack 7 / L4T arm64 wheels (torch, vllm, flash-attn) live on
-# pypi.jetson-ai-lab.io and are built for cp312, so bump the venv Python
-# accordingly. JetPack 6 keeps cp310 + USE_PIP=true.
-#
-# l4t13 uses pyproject.toml (see the elif branch below) to pin only the
-# L4T-specific wheels to the jetson-ai-lab index via [tool.uv.sources].
-# That keeps PyPI as the resolution path for transitive deps like
-# anthropic/openai/propcache, which the L4T mirror's proxy 503s on.
+# JetPack 7 / L4T arm64 vllm + torch wheels come straight from PyPI now
+# (torch 2.11+ ships aarch64 + cu130 manylinux wheels and vllm 0.20+ ships
+# an aarch64 wheel pinned to that torch). They're cp312-only, so bump the
+# venv Python accordingly. JetPack 6 keeps cp310 + USE_PIP=true.
+# https://pytorch.org/blog/vllm-and-pytorch-work-together-to-improve-the-developer-experience-on-aarch64/
 if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
     USE_PIP=true
 fi
@@ -103,25 +100,6 @@ if [ "x${BUILD_TYPE}" == "xintel" ]; then
         export CMAKE_PREFIX_PATH="$(python -c 'import site; print(site.getsitepackages()[0])'):${CMAKE_PREFIX_PATH:-}"
         VLLM_TARGET_DEVICE=xpu uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} --no-deps .
     popd
-# L4T arm64 (JetPack 7): drive the install through pyproject.toml so that
-# [tool.uv.sources] can pin torch/vllm/flash-attn/torchvision/torchaudio
-# to the jetson-ai-lab index, while everything else (transitive deps and
-# PyPI-resolvable packages like transformers) comes from PyPI. Bypasses
-# installRequirements because uv pip install -r requirements.txt does not
-# honor sources — see backend/python/vllm/pyproject.toml for the rationale.
-elif [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
-    ensureVenv
-    if [ "x${PORTABLE_PYTHON}" == "xtrue" ]; then
-        export C_INCLUDE_PATH="${C_INCLUDE_PATH:-}:$(_portable_dir)/include/python${PYTHON_VERSION}"
-    fi
-    pushd "${backend_dir}"
-        # Build deps first (matches installRequirements' requirements-install.txt
-        # pass — fastsafetensors and friends need pybind11 in the venv before
-        # their sdists can build under --no-build-isolation).
-        uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} -r requirements-install.txt
-        uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} --requirement pyproject.toml
-    popd
-    runProtogen
 # FROM_SOURCE=true on a CPU build skips the prebuilt vllm wheel in
 # requirements-cpu-after.txt and compiles vllm locally against the host's
 # actual CPU. Not used by default because it takes ~30-40 minutes, but

backend/python/vllm/requirements-l4t13-after.txt

@@ -0,0 +1,4 @@
+# vLLM 0.20+ ships an aarch64 manylinux wheel on PyPI whose Requires-Dist pins
+# torch==2.11.0 / torchvision==0.26.0 / torchaudio==2.11.0, locking an ABI-
+# consistent set with the cu130 torch wheel installed above.
+vllm

backend/python/vllm/requirements-l4t13.txt

@@ -0,0 +1,8 @@
+# JetPack 7 / L4T arm64 + CUDA 13. Since PyTorch 2.11 (April 2026), PyPI ships
+# aarch64 + cu130 manylinux wheels for torch/torchvision/torchaudio directly,
+# so we no longer need a custom --extra-index-url for the L4T mirror.
+# https://pytorch.org/blog/vllm-and-pytorch-work-together-to-improve-the-developer-experience-on-aarch64/
+accelerate
+torch
+transformers
+bitsandbytes