chore: add nf3-hybrid branch ref + patches + build README

recipes/deepseek-v4-flash-dspark/recipe.toml

@@ -65,7 +65,8 @@ args = [
 # DSpark speculative decoding
 VLLM_DSPARK_REPLICATE_MARKOV_W1 = "1"
 # b12x MoE (this image's primary path)
-VLLM_USE_B12X_MOE = "1"
+#VLLM_USE_B12X_MOE = "1"
+VLLM_USE_B12X_MOE = "0"              # b12x MoE OFF -> cutlass W4A8 (b12x MoE prefill stalls on GB10)
 
 # b12x suite = REQUIRED by this image
 VLLM_USE_B12X_MHC = "1"

recipes/glm52-nf3-hybrid/README.md

@@ -0,0 +1,36 @@
+# GLM-5.2 NF3-Hybrid build
+
+Image is built from the forked vllm-gb10 repo:
+
+```
+git clone https://git.kilork.org/kilork/timothystewart6--vllm-gb10
+cd timothystewart6--vllm-gb10
+git checkout nf3-hybrid
+```
+
+Then build with build-args from versions.env:
+
+```bash
+source <(grep -v '#' versions.env | sed 's/^/export /')
+args=$(for v in $(compgen -v | grep -E '^[A-Z_]+$'); do
+  [ -n "${!v}" ] && echo "--build-arg ${v}=${!v}"
+done)
+DOCKER_BUILDKIT=1 docker build $args -t vllm-nf3-hybrid:probe -f Dockerfile .
+```
+
+Or use the included build-nf3.sh:
+
+```bash
+./build-nf3.sh
+```
+
+## Pins
+
+| Component | Source | Ref |
+|-----------|--------|-----|
+| vLLM | local-inference-lab/vllm | codex/dcp-globaltopk-sharddraft-defaults-20260622 @ e232d26 |
+| b12x | lukealonso/b12x | @ 9cd63a7 |
+| FlashInfer | flashinfer-ai/flashinfer | v0.6.12 |
+| cutlass-dsl | nvidia | 4.5.3 (firmware fix) |
+| CUDA | nvidia/cuda | 13.2.0-devel-ubuntu24.04 |
+| torch | PyTorch cu130 index | 2.11.0 |

recipes/glm52-nf3-hybrid/build.sh

@@ -1,110 +0,0 @@
-#!/usr/bin/env bash
-# Build GLM-5.2 NF3-Hybrid Docker image for DGX Spark (aarch64/sm_121a)
-#
-# Build harness: m9e/blackwell-llm-docker  →  build-and-copy.sh
-#   https://github.com/m9e/blackwell-llm-docker
-#
-# Pins:
-#   vLLM:  local-inference-lab/vllm  @ dev/eldritch-enlightenment  45c1582e9b80ba83e71c3a6458e71da4736fbdc4
-#   b12x:  voipmonitor/b12x           @ f3686b555d639823b276c2080f173145eed7f007
-#   CUDA:  13.2 / torch 2.12.0+cu132
-#   arch:  TORCH_CUDA_ARCH_LIST=12.1a, CUTE_DSL_ARCH=sm_121a
-#   FlashInfer: 0.6.14 aarch64 wheels
-#
-# Usage:
-#   cd recipes/glm52-nf3-hybrid
-#   ./build.sh [--tag vllm-nf3-hybrid:probe]
-
-set -euo pipefail
-
-TAG="${1:-vllm-nf3-hybrid:probe}"
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-
-# ── Prerequisites ──────────────────────────────────────────────────────────
-command -v git  >/dev/null 2>&1 || { echo "Need git";  exit 1; }
-command -v docker >/dev/null 2>&1 || { echo "Need docker"; exit 1; }
-
-BUILD_ROOT="$(mktemp -d)"
-trap 'rm -rf "$BUILD_ROOT"' EXIT
-
-echo "==> Build root: $BUILD_ROOT"
-
-# ── Clone build harness ────────────────────────────────────────────────────
-HARNESS_REPO="https://github.com/m9e/blackwell-llm-docker"
-echo "==> Cloning build harness from $HARNESS_REPO ..."
-git clone --depth=1 "$HARNESS_REPO" "$BUILD_ROOT/harness"
-
-# ── Build base image using build-and-copy.sh ───────────────────────────────
-# build-and-copy.sh clones vLLM and b12x, applies patches, builds, and
-# copies the artifacts (wheels + venv overlay) into a Docker image.
-echo "==> Building base image with build-and-copy.sh ..."
-cd "$BUILD_ROOT/harness"
-
-# The harness vLLM ref includes NF3-hybrid loader, DCP ag_rs dispatch,
-# low-bit MLA KV-cache dtypes (nvfp4_ds_mla, nf3_ds_mla), MTP/spec-decode
-# cherries, and the patched b12x_sparse_indexer_warmup.py.
-./build-and-copy.sh \
-  --vllm-repo "https://github.com/local-inference-lab/vllm" \
-  --vllm-ref "45c1582e9b80ba83e71c3a6458e71da4736fbdc4" \
-  --b12x-repo "https://github.com/voipmonitor/b12x" \
-  --b12x-ref "f3686b555d639823b276c2080f173145eed7f007" \
-  --gpu-arch "12.1a" \
-  -t "$TAG" \
-  --clean
-
-# The harness emits a Docker image tagged $TAG (no --push flag; local only).
-
-# ── Bake NF3 v2 overlay + CuteDSL runtime ─────────────────────────────────
-echo "==> Baking NF3 v2 overlay into $TAG ..."
-
-# Create a Dockerfile that adds the layer on top of the base.
-cat > "$BUILD_ROOT/Dockerfile.bake" << 'DOCKERFILE'
-ARG BASE_TAG
-FROM ${BASE_TAG}
-
-# NF3 v2 overlay files (hybrid loader, NF3/NVFP4 kernels, replan, mxfp8 tier)
-COPY overlay/hybrid_loader.py /usr/local/lib/python3.12/dist-packages/
-COPY overlay/nf3_kernel.py    /usr/local/lib/python3.12/dist-packages/
-COPY overlay/nvfp4_kernel.py  /usr/local/lib/python3.12/dist-packages/
-COPY overlay/nf3_replan.py    /usr/local/lib/python3.12/dist-packages/
-COPY overlay/mxfp8_tier.json  /usr/local/lib/python3.12/dist-packages/
-
-# Install CuteDSL runtime (NF3 kernel dependency)
-RUN pip install --no-cache-dir \
-    nvidia-cutlass-dsl==4.5.2 \
-    nvidia-cutlass-dsl-libs-cu13==4.5.2
-
-# Install FlashInfer 0.6.14 aarch64 wheel
-# Build from source for aarch64/sm_121a or use published wheel.
-# Published aarch64 wheels are available at flashinfer.ai/whl.
-RUN pip install --no-cache-dir \
-    --find-links https://flashinfer.ai/whl/cu124/flashinfer_pypi/ \
-    flashinfer==0.6.14
-
-# Ensure CuteDSL .pth path hook is present
-RUN python3 -c "import cutlass; print(f'CuteDSL OK: {cutlass.__version__}')"
-
-# Set arch envs so JIT compile targets sm_121a
-ENV TORCH_CUDA_ARCH_LIST=12.1a
-ENV CUTE_DSL_ARCH=sm_121a
-ENV FLASHINFER_CUDA_ARCH_LIST=12.1a
-
-# Verify imports
-RUN python3 -c "
-from hybrid_loader import NF3HybridLoader
-from nf3_kernel import NF3Kernel
-from nvfp4_kernel import NVFP4Kernel
-from nf3_replan import NF3Replan
-print('NF3 hybrid overlay OK')
-"
-DOCKERFILE
-
-# Build the overlay layer
-docker build \
-  -f "$BUILD_ROOT/Dockerfile.bake" \
-  --build-arg BASE_TAG="$TAG" \
-  -t "$TAG" \
-  "$SCRIPT_DIR"
-
-echo "==> Done: $TAG"
-echo "To push: docker push $TAG (or use as local image)"