fix

Signed-off-by: Roger Wang <hey@rogerw.io>

.buildkite/hardware_tests/amd.yaml

@@ -10,7 +10,7 @@ steps:
       docker build
       --build-arg max_jobs=16
       --build-arg REMOTE_VLLM=1
-      --build-arg ARG_PYTORCH_ROCM_ARCH='gfx942;gfx950'
+      --build-arg ARG_PYTORCH_ROCM_ARCH='gfx90a;gfx942'
       --build-arg VLLM_BRANCH=$BUILDKITE_COMMIT
       --tag "rocm/vllm-ci:${BUILDKITE_COMMIT}"
       -f docker/Dockerfile.rocm

.buildkite/image_build/image_build_cpu_arm64.sh

@@ -14,7 +14,7 @@ BUILDKITE_COMMIT=$3
 aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
 
 # skip build if image already exists
-if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu) ]]; then
+if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu) ]]; then
   echo "Image not found, proceeding with build..."
 else
   echo "Image found"
@@ -25,9 +25,9 @@ fi
 docker build --file docker/Dockerfile.cpu \
   --build-arg max_jobs=16 \
   --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu \
   --target vllm-test \
   --progress plain .
 
 # push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu

.buildkite/performance-benchmarks/tests/latency-tests-hpu.json

@@ -51,56 +51,5 @@
             "max-model-len": 256,
             "async-scheduling": ""
         }
-    },
-    {
-        "test_name": "latency_deepseek_r1",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "deepseek-ai/DeepSeek-R1",
-            "tensor_parallel_size": 8,
-            "load_format": "dummy",
-            "max-model-len": 2048,
-            "dtype": "bfloat16"
-        }
-    },
-    {
-        "test_name": "latency_llama4_maverick_17b128e_instruct_fp8",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
-            "tensor_parallel_size": 8,
-            "max-model-len": 512,
-            "max-num-seqs": 128,
-            "async-scheduling": "",
-            "gpu-memory-utilization": 0.95,
-            "enable_expert_parallel": ""
-        }
-    },
-    {
-        "test_name": "latency_qwen3_8b",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "Qwen/Qwen3-8B",
-            "tensor_parallel_size": 1,
-            "max-model-len": 2048,
-            "max-num-seqs": 128,
-            "dtype": "bfloat16",
-            "async-scheduling": ""
-        }
     }
 ]

.buildkite/performance-benchmarks/tests/serving-tests-hpu.json

@@ -78,84 +78,5 @@
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
             "num_prompts": 200
         }
-    },
-    {
-        "test_name": "serving_deepseek_r1",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "server_parameters": {
-            "model": "deepseek-ai/DeepSeek-R1",
-            "tensor_parallel_size": 8,
-            "swap_space": 16,
-            "disable_log_stats": "",
-            "load_format": "dummy",
-            "max-model-len": 2048,
-            "max-num-seqs": 200,
-            "async-scheduling": "",
-            "dtype": "bfloat16"
-        },
-        "client_parameters": {
-            "model": "deepseek-ai/DeepSeek-R1",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama4_maverick_17b128e_instruct_fp8",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
-            "tensor_parallel_size": 8,
-            "disable_log_stats": "",
-            "max-model-len": 2048,
-            "max-num-seqs": 128,
-            "async-scheduling": "",
-            "enable_expert_parallel": "",
-            "max-num-batched-tokens": 4096
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_qwen3_8b",
-        "qps_list": [1, 4, 10, "inf"],
-        "server_environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "server_parameters": {
-            "model": "Qwen/Qwen-3-8B",
-            "tensor_parallel_size": 1,
-            "dtype": "bfloat16",
-            "disable_log_stats": "",
-            "async-scheduling": ""
-        },
-        "client_parameters": {
-            "model": "Qwen/Qwen-3-8B",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
     }
 ]

.buildkite/performance-benchmarks/tests/throughput-tests-hpu.json

@@ -57,67 +57,5 @@
             "max-num-seqs": 512,
             "async-scheduling": ""
         }
-    },
-    {
-        "test_name": "throughput_deepseek_r1",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "deepseek-ai/DeepSeek-R1",
-            "tensor_parallel_size": 8,
-            "load_format": "dummy",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "dataset_name": "sharegpt",
-            "num_prompts": 1000,
-            "backend": "vllm",
-            "max-model-len": 2048,
-            "max-num-seqs": 384,
-            "async-scheduling": ""
-        }
-    },
-    {
-        "test_name": "throughput_llama4_maverick_17b128e_instruct_fp8",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
-            "tensor_parallel_size": 8,
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "dataset_name": "sharegpt",
-            "num_prompts": 1000,
-            "backend": "vllm",
-            "max-model-len": 2048,
-            "max-num-seqs": 512,
-            "async-scheduling": "",
-            "enable_expert_parallel": ""
-        }
-    },
-    {
-        "test_name": "throughput_qwen3_8b",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "Qwen/Qwen-3-8B",
-            "tensor_parallel_size": 1,
-            "load_format": "dummy",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "dataset_name": "sharegpt",
-            "num_prompts": 1000,
-            "max-num-seqs": 512,
-            "backend": "vllm",
-            "async-scheduling": ""
-        }
     }
 ]

.buildkite/scripts/annotate-rocm-release.sh

@@ -68,7 +68,7 @@ aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/triton
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torchvision-*.whl .
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torchaudio-*.whl .
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/amdsmi-*.whl .
-aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/amd_aiter-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/aiter-*.whl .
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/flash-attn-*.whl .
 \`\`\`
 
@@ -80,7 +80,7 @@ aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/flash-
 - **torchvision**: TorchVision for ROCm PyTorch
 - **torchaudio**: Torchaudio for ROCm PyTorch
 - **amdsmi**: AMD SMI Python bindings
-- **amd_aiter**: Aiter for ROCm
+- **aiter**: Aiter for ROCm
 - **flash-attn**: Flash Attention for ROCm
 
 ### :warning: Notes

.buildkite/scripts/check-ray-compatibility.sh

@@ -1,205 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Check if Ray LLM can generate lock files that are compatible with this
-# version of vllm. Downloads Ray's requirement files and runs a full
-# dependency resolution with the installed vllm's constraints to see if
-# a valid lock file can be produced.
-#
-# See: https://github.com/vllm-project/vllm/issues/33599
-
-set -eo pipefail
-
-RAY_BASE_URL="https://raw.githubusercontent.com/ray-project/ray/master/python"
-
-WORK_DIR=$(mktemp -d)
-trap 'rm -rf "$WORK_DIR"' EXIT
-
-# Fetch all Ray requirement files used in the LLM depset pipeline
-echo ">>> Fetching Ray requirement files"
-RAY_FILES=(
-    "requirements.txt"
-    "requirements/cloud-requirements.txt"
-    "requirements/base-test-requirements.txt"
-    "requirements/llm/llm-requirements.txt"
-    "requirements/llm/llm-test-requirements.txt"
-)
-for FILE in "${RAY_FILES[@]}"; do
-    LOCAL_PATH="${WORK_DIR}/$(basename "$FILE")"
-    echo "    ${FILE}"
-    curl -fsSL -o "$LOCAL_PATH" "${RAY_BASE_URL}/${FILE}"
-done
-
-# Extract installed vllm deps
-echo ">>> Extracting installed vllm dependency constraints"
-python3 - "${WORK_DIR}/vllm-constraints.txt" <<'PYEOF'
-"""Write out the installed vllm's dependencies as pip constraint lines.
-
-Ray uses vllm[audio], so audio-extra deps are included with their extra
-markers stripped. The resolver cannot evaluate extra markers for a
-package that is not itself being resolved from an index, so we activate
-them manually here.
-"""
-import importlib.metadata
-import re
-import sys
-
-out_path = sys.argv[1]
-raw_reqs = importlib.metadata.requires("vllm") or []
-
-# Ray uses vllm[audio] – activate that extra.
-ACTIVE_EXTRAS = {"audio"}
-EXTRA_RE = re.compile(r"""extra\s*==\s*['"]([^'"]+)['"]""")
-
-lines = []
-for r in raw_reqs:
-    if ";" not in r:
-        # Unconditional dep — always include.
-        lines.append(r.strip())
-        continue
-
-    req_part, _, marker_part = r.partition(";")
-    marker_part = marker_part.strip()
-
-    extra_matches = EXTRA_RE.findall(marker_part)
-    if not extra_matches:
-        # Non-extra marker (python_version, etc.) — keep as-is.
-        lines.append(r.strip())
-        continue
-
-    if not ACTIVE_EXTRAS.intersection(extra_matches):
-        continue  # Skip inactive extras (tensorizer, bench, …).
-
-    # Strip the extra== conditions but keep any remaining markers
-    # (e.g. python_version).
-    cleaned = EXTRA_RE.sub("", marker_part)
-    cleaned = re.sub(r"\band\b\s*\band\b", "and", cleaned)
-    cleaned = re.sub(r"^\s*and\s+|\s+and\s*$", "", cleaned).strip()
-
-    if cleaned:
-        lines.append(f"{req_part.strip()} ; {cleaned}")
-    else:
-        lines.append(req_part.strip())
-
-with open(out_path, "w") as f:
-    for line in lines:
-        f.write(line + "\n")
-
-print(f"Wrote {len(lines)} constraints to {out_path}")
-PYEOF
-
-echo ">>> Installed vllm deps (first 20 lines):"
-head -20 "${WORK_DIR}/vllm-constraints.txt"
-
-# Remove Ray's vllm pin — the installed vllm's transitive deps
-# (written above) replace it in the resolution. vllm itself cannot
-# be resolved from PyPI for in-development versions, so we test
-# whether Ray's requirements can coexist with vllm's dependency
-# constraints instead.
-sed -i '/^vllm/d' "${WORK_DIR}/llm-requirements.txt"
-
-# Install uv if needed
-if ! command -v uv &>/dev/null; then
-    echo ">>> Installing uv"
-    pip install uv -q
-fi
-
-# Resolve: given vllm's constraints, can Ray compile a lock file?
-#
-# vllm's dependency constraints are the fixed side — Ray is flexible and
-# can regenerate its lock files. We pass vllm's constraints via -c so
-# the resolver treats them as non-negotiable bounds, then check whether
-# Ray's own requirements can still be satisfied within those bounds.
-echo ""
-echo "============================================================"
-echo ">>> Resolving: Can Ray generate compatible lock files?"
-echo "============================================================"
-
-set +e
-uv pip compile \
-    "${WORK_DIR}/requirements.txt" \
-    "${WORK_DIR}/cloud-requirements.txt" \
-    "${WORK_DIR}/base-test-requirements.txt" \
-    "${WORK_DIR}/llm-requirements.txt" \
-    "${WORK_DIR}/llm-test-requirements.txt" \
-    -c "${WORK_DIR}/vllm-constraints.txt" \
-    --python-version 3.12 \
-    --python-platform x86_64-manylinux_2_31 \
-    --extra-index-url https://download.pytorch.org/whl/cu129 \
-    --index-strategy unsafe-best-match \
-    --unsafe-package setuptools \
-    --unsafe-package ray \
-    --no-header \
-    -o "${WORK_DIR}/resolved.txt" \
-    2>&1
-EXIT_CODE=$?
-set -e
-
-echo ""
-echo "=========================================="
-if [ $EXIT_CODE -eq 0 ]; then
-    echo "SUCCESS: Ray can generate lock files compatible with this vllm."
-    echo ""
-    echo "Key resolved versions:"
-    grep -E '^(protobuf|torch|numpy|transformers)==' \
-        "${WORK_DIR}/resolved.txt" | sort || true
-    echo "=========================================="
-    exit 0
-fi
-
-echo "FAILURE: Ray cannot generate lock files compatible with this vllm."
-echo "This means a fundamental dependency conflict exists that Ray"
-echo "cannot resolve by regenerating its lock files."
-echo "See: https://github.com/vllm-project/vllm/issues/33599"
-echo "=========================================="
-
-# Buildkite annotation
-if [ -f /usr/bin/buildkite-agent ]; then
-    buildkite-agent annotate --style 'warning' --context 'ray-compat' << EOF
-### :warning: Ray Dependency Compatibility Warning
-This PR introduces dependencies that **cannot** be resolved with Ray's requirements.
-Ray would not be able to regenerate its lock files to accommodate this vllm version.
-
-Please check the **Ray Dependency Compatibility Check** step logs for details.
-See [issue #33599](https://github.com/vllm-project/vllm/issues/33599) for context.
-EOF
-fi
-
-# Notify Slack if webhook is configured.
-if [ -n "$RAY_COMPAT_SLACK_WEBHOOK_URL" ]; then
-    echo ">>> Sending Slack notification"
-    # Single quotes are intentional: the f-string expressions are Python, not shell.
-    # shellcheck disable=SC2016
-    PAYLOAD=$(python3 -c '
-import json, os, sys
-pr = os.getenv("BUILDKITE_PULL_REQUEST", "N/A")
-branch = os.getenv("BUILDKITE_BRANCH", "unknown")
-url = os.getenv("BUILDKITE_BUILD_URL", "#")
-data = {
-    "text": ":warning: Ray Dependency Compatibility Check Failed",
-    "blocks": [{
-        "type": "section",
-        "text": {
-            "type": "mrkdwn",
-            "text": (
-                "*:warning: Ray Dependency Compatibility Check Failed*\n"
-                f"PR #{pr} on branch `{branch}` introduces dependencies "
-                f"that cannot be resolved with Ray'\''s requirements.\n"
-                f"<{url}|View Build>"
-            ),
-        },
-    }],
-}
-print(json.dumps(data))
-')
-
-    HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -X POST "$RAY_COMPAT_SLACK_WEBHOOK_URL" \
-        -H 'Content-type: application/json' \
-        -d "$PAYLOAD")
-    echo "    Slack webhook response: $HTTP_CODE"
-else
-    echo ">>> Skipping Slack notification (RAY_COMPAT_SLACK_WEBHOOK_URL not set)"
-fi
-
-exit 1

.buildkite/scripts/hardware_ci/run-amd-test.sh

@@ -1,57 +1,25 @@
 #!/bin/bash
 
-# This script runs tests inside the corresponding ROCm docker container.
-# It handles both single-node and multi-node test configurations.
-#
-# Multi-node detection: Instead of matching on fragile group names, we detect
-# multi-node jobs structurally by looking for the bracket command syntax
-# "[node0_cmds] && [node1_cmds]" or via the NUM_NODES environment variable.
-#
-###############################################################################
-# QUOTING / COMMAND PASSING
-#
-# Passing commands as positional arguments ($*) is fragile when the command
-# string itself contains double quotes, e.g.:
-#
-#   bash run-amd-test.sh "export FLAGS="value" && pytest -m "not slow""
-#
-# The outer shell resolves the nested quotes *before* this script runs, so
-# the script receives mangled input it cannot fully recover.
-#
-# Preferred: pass commands via the VLLM_TEST_COMMANDS environment variable:
-#
-#   export VLLM_TEST_COMMANDS='export FLAGS="value" && pytest -m "not slow"'
-#   bash run-amd-test.sh
-#
-# Single-quoted assignment preserves all inner double quotes verbatim.
-# The $* path is kept for backward compatibility but callers should migrate.
-###############################################################################
+# This script runs test inside the corresponding ROCm docker container.
 set -o pipefail
 
 # Export Python path
 export PYTHONPATH=".."
 
-###############################################################################
-# Helper Functions
-###############################################################################
+# Print ROCm version
+echo "--- Confirming Clean Initial State"
+while true; do
+        sleep 3
+        if grep -q clean /opt/amdgpu/etc/gpu_state; then
+                echo "GPUs state is \"clean\""
+                break
+        fi
+done
 
-wait_for_clean_gpus() {
-  local timeout=${1:-300}
-  local start=$SECONDS
-  echo "--- Waiting for clean GPU state (timeout: ${timeout}s)"
-  while true; do
-    if grep -q clean /opt/amdgpu/etc/gpu_state; then
-      echo "GPUs state is \"clean\""
-      return
-    fi
-    if (( SECONDS - start >= timeout )); then
-      echo "Error: GPUs did not reach clean state within ${timeout}s" >&2
-      exit 1
-    fi
-    sleep 3
-  done
-}
+echo "--- ROCm info"
+rocminfo
 
+# cleanup older docker images
 cleanup_docker() {
   # Get Docker's root directory
   docker_root=$(docker info -f '{{.DockerRootDir}}')
@@ -60,12 +28,15 @@ cleanup_docker() {
     exit 1
   fi
   echo "Docker root directory: $docker_root"
-
+  # Check disk usage of the filesystem where Docker's root directory is located
   disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
+  # Define the threshold
   threshold=70
   if [ "$disk_usage" -gt "$threshold" ]; then
     echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
+    # Remove dangling images (those that are not tagged and not used by any container)
     docker image prune -f
+    # Remove unused volumes / force the system prune for old images as well.
     docker volume prune -f && docker system prune --force --filter "until=72h" --all
     echo "Docker images and volumes cleanup completed."
   else
@@ -74,431 +45,193 @@ cleanup_docker() {
 }
 
 cleanup_network() {
-  local max_nodes=${NUM_NODES:-2}
-  for node in $(seq 0 $((max_nodes - 1))); do
-    if docker ps -a -q -f name="node${node}" | grep -q .; then
-      docker stop "node${node}" || true
+  for node in $(seq 0 $((NUM_NODES-1))); do
+    if docker pr -a -q -f name="node${node}" | grep -q .; then
+      docker stop "node${node}"
     fi
   done
-  if docker network ls | grep -q docker-net; then
-    docker network rm docker-net || true
+  if docker network ls | grep docker-net; then
+    docker network rm docker-net
   fi
 }
 
-is_multi_node() {
-  local cmds="$1"
-  # Primary signal: NUM_NODES environment variable set by the pipeline
-  if [[ "${NUM_NODES:-1}" -gt 1 ]]; then
-    return 0
-  fi
-  # Fallback: detect the bracket syntax structurally
-  # Pattern: [...] && [...] (per-node command arrays)
-  if [[ "$cmds" =~ \[.*\].*\&\&.*\[.*\] ]]; then
-    return 0
-  fi
-  return 1
-}
-
-handle_pytest_exit() {
-  local exit_code=$1
-  if [ "$exit_code" -eq 5 ]; then
-    echo "Pytest exit code 5 (no tests collected) - treating as success."
-    exit 0
-  fi
-  exit "$exit_code"
-}
-
-###############################################################################
-# Pytest marker/keyword re-quoting
-#
-# When commands are passed through Buildkite -> shell -> $* -> bash -c,
-# quotes around multi-word pytest -m/-k expressions get stripped:
-#   pytest -v -s -m 'not cpu_test' v1/core
-# becomes:
-#   pytest -v -s -m not cpu_test v1/core
-#
-# pytest then interprets "cpu_test" as a file path, not part of the marker.
-#
-# This function detects unquoted expressions after -m/-k and re-quotes them
-# by collecting tokens until a recognizable boundary is reached:
-#   - test path (contains '/')
-#   - test file (ends with '.py')
-#   - another pytest flag (--xxx or -x single-char flags)
-#   - command separator (&& || ; |)
-#   - environment variable assignment (FOO=bar)
-#
-# Single-word markers (e.g. -m cpu_test, -m hybrid_model) pass through
-# unquoted since they have no spaces and work fine.
-#
-# Already-quoted expressions (containing literal single quotes) are passed
-# through untouched to avoid double-quoting values injected by
-# apply_rocm_test_overrides.
-#
-# NOTE: This ONLY fixes -m/-k flags. It cannot recover arbitrary inner
-# double-quotes stripped by the calling shell (see header comment).
-# Use VLLM_TEST_COMMANDS to avoid the problem entirely.
-###############################################################################
-re_quote_pytest_markers() {
-  local input="$1"
-  local output=""
-  local collecting=false
-  local marker_buf=""
-
-  # Strip backslash-newline continuations, then flatten remaining newlines
-  local flat="${input//$'\\\n'/ }"
-  flat="${flat//$'\n'/ }"
-
-  # Disable globbing to prevent *.py etc. from expanding during read -ra
-  local restore_glob
-  restore_glob="$(shopt -p -o noglob 2>/dev/null || true)"
-  set -o noglob
-  local -a words
-  read -ra words <<< "$flat"
-  eval "$restore_glob"
-
-  for word in "${words[@]}"; do
-    if $collecting; then
-      # If the token we're about to collect already contains a literal
-      # single quote, the expression was already quoted upstream.
-      # Flush and stop collecting.
-      if [[ "$word" == *"'"* ]]; then
-        if [[ -n "$marker_buf" ]]; then
-          # Should not normally happen (partial buf + quote), flush raw
-          output+="${marker_buf} "
-          marker_buf=""
-        fi
-        output+="${word} "
-        collecting=false
-        continue
-      fi
-
-      local is_boundary=false
-      case "$word" in
-        # Line-continuation artifact
-        "\\")
-          is_boundary=true ;;
-        # Command separators
-        "&&"|"||"|";"|"|")
-          is_boundary=true ;;
-        # Long flags (--ignore, --shard-id, etc.)
-        --*)
-          is_boundary=true ;;
-        # Short flags (-v, -s, -x, etc.) but NOT negative marker tokens
-        # like "not" which don't start with "-". Also skip -k/-m which
-        # would start a new marker (handled below).
-        -[a-zA-Z])
-          is_boundary=true ;;
-        # Test path (contains /)
-        */*)
-          is_boundary=true ;;
-        # Test file (ends with .py, possibly with ::method)
-        *.py|*.py::*)
-          is_boundary=true ;;
-        # Environment variable assignment preceding a command (FOO=bar)
-        *=*)
-          # Only treat as boundary if it looks like VAR=value, not
-          # pytest filter expressions like num_gpus=2 inside markers
-          if [[ "$word" =~ ^[A-Z_][A-Z0-9_]*= ]]; then
-            is_boundary=true
-          fi
-          ;;
-      esac
-
-      if $is_boundary; then
-        # Flush the collected marker expression
-        if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
-          output+="'${marker_buf}' "
-        else
-          output+="${marker_buf} "
-        fi
-        collecting=false
-        marker_buf=""
-        # Check if this boundary word itself starts a new -m/-k
-        if [[ "$word" == "-m" || "$word" == "-k" ]]; then
-          output+="${word} "
-          collecting=true
-        # Drop stray backslash tokens silently
-        elif [[ "$word" == "\\" ]]; then
-          :
-        else
-          output+="${word} "
-        fi
-      else
-        # Accumulate into marker buffer
-        if [[ -n "$marker_buf" ]]; then
-          marker_buf+=" ${word}"
-        else
-          marker_buf="${word}"
-        fi
-      fi
-    elif [[ "$word" == "-m" || "$word" == "-k" ]]; then
-      output+="${word} "
-      collecting=true
-      marker_buf=""
-    else
-      output+="${word} "
-    fi
-  done
-
-  # Flush any trailing marker expression (marker at end of command)
-  if $collecting && [[ -n "$marker_buf" ]]; then
-    if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
-      output+="'${marker_buf}'"
-    else
-      output+="${marker_buf}"
-    fi
-  fi
-
-  echo "${output% }"
-}
-
-###############################################################################
-# ROCm-specific pytest command rewrites
-#
-# These apply ignore flags and environment overrides for tests that are not
-# yet supported or behave differently on ROCm hardware. Kept as a single
-# function so new exclusions are easy to add in one place.
-###############################################################################
-
-apply_rocm_test_overrides() {
-  local cmds="$1"
-
-  # --- Model registry filter ---
-  if [[ $cmds == *"pytest -v -s models/test_registry.py"* ]]; then
-    cmds=${cmds//"pytest -v -s models/test_registry.py"/"pytest -v -s models/test_registry.py -k 'not BambaForCausalLM and not GritLM and not Mamba2ForCausalLM and not Zamba2ForCausalLM'"}
-  fi
-
-  # --- LoRA: disable custom paged attention ---
-  if [[ $cmds == *"pytest -v -s lora"* ]]; then
-    cmds=${cmds//"pytest -v -s lora"/"VLLM_ROCM_CUSTOM_PAGED_ATTN=0 pytest -v -s lora"}
-  fi
-
-  # --- Kernel ignores ---
-  if [[ $cmds == *" kernels/core"* ]]; then
-    cmds="${cmds} \
-    --ignore=kernels/core/test_fused_quant_layernorm.py \
-    --ignore=kernels/core/test_permute_cols.py"
-  fi
-
-  if [[ $cmds == *" kernels/attention"* ]]; then
-    cmds="${cmds} \
-    --ignore=kernels/attention/test_attention_selector.py \
-    --ignore=kernels/attention/test_encoder_decoder_attn.py \
-    --ignore=kernels/attention/test_flash_attn.py \
-    --ignore=kernels/attention/test_flashinfer.py \
-    --ignore=kernels/attention/test_prefix_prefill.py \
-    --ignore=kernels/attention/test_cascade_flash_attn.py \
-    --ignore=kernels/attention/test_mha_attn.py \
-    --ignore=kernels/attention/test_lightning_attn.py \
-    --ignore=kernels/attention/test_attention.py"
-  fi
-
-  if [[ $cmds == *" kernels/quantization"* ]]; then
-    cmds="${cmds} \
-    --ignore=kernels/quantization/test_int8_quant.py \
-    --ignore=kernels/quantization/test_machete_mm.py \
-    --ignore=kernels/quantization/test_block_fp8.py \
-    --ignore=kernels/quantization/test_block_int8.py \
-    --ignore=kernels/quantization/test_marlin_gemm.py \
-    --ignore=kernels/quantization/test_cutlass_scaled_mm.py \
-    --ignore=kernels/quantization/test_int8_kernel.py"
-  fi
-
-  if [[ $cmds == *" kernels/mamba"* ]]; then
-    cmds="${cmds} \
-    --ignore=kernels/mamba/test_mamba_mixer2.py \
-    --ignore=kernels/mamba/test_causal_conv1d.py \
-    --ignore=kernels/mamba/test_mamba_ssm_ssd.py"
-  fi
-
-  if [[ $cmds == *" kernels/moe"* ]]; then
-    cmds="${cmds} \
-    --ignore=kernels/moe/test_moe.py \
-    --ignore=kernels/moe/test_cutlass_moe.py \
-    --ignore=kernels/moe/test_triton_moe_ptpc_fp8.py"
-  fi
-
-  # --- Entrypoint ignores ---
-  if [[ $cmds == *" entrypoints/openai "* ]]; then
-    cmds=${cmds//" entrypoints/openai "/" entrypoints/openai \
-    --ignore=entrypoints/openai/test_audio.py \
-    --ignore=entrypoints/openai/test_shutdown.py \
-    --ignore=entrypoints/openai/test_completion.py \
-    --ignore=entrypoints/openai/test_models.py \
-    --ignore=entrypoints/openai/test_lora_adapters.py \
-    --ignore=entrypoints/openai/test_return_tokens_as_ids.py \
-    --ignore=entrypoints/openai/test_root_path.py \
-    --ignore=entrypoints/openai/test_tokenization.py \
-    --ignore=entrypoints/openai/test_prompt_validation.py "}
-  fi
-
-  if [[ $cmds == *" entrypoints/llm "* ]]; then
-    cmds=${cmds//" entrypoints/llm "/" entrypoints/llm \
-    --ignore=entrypoints/llm/test_chat.py \
-    --ignore=entrypoints/llm/test_accuracy.py \
-    --ignore=entrypoints/llm/test_init.py \
-    --ignore=entrypoints/llm/test_prompt_validation.py "}
-  fi
-
-  # Clean up escaped newlines from --ignore appends
-  cmds=$(echo "$cmds" | sed 's/ \\ / /g')
-
-  echo "$cmds"
-}
-
-###############################################################################
-# Main
-###############################################################################
-
-# --- GPU initialization ---
-echo "--- Confirming Clean Initial State"
-wait_for_clean_gpus
-
-echo "--- ROCm info"
-rocminfo
-
-# --- Docker housekeeping ---
+# Call the cleanup docker function
 cleanup_docker
 
 echo "--- Resetting GPUs"
-echo "reset" > /opt/amdgpu/etc/gpu_state
-wait_for_clean_gpus
 
-# --- Pull test image ---
+echo "reset" > /opt/amdgpu/etc/gpu_state
+
+while true; do
+        sleep 3
+        if grep -q clean /opt/amdgpu/etc/gpu_state; then
+                echo "GPUs state is \"clean\""
+                break
+        fi
+done
+
 echo "--- Pulling container"
 image_name="rocm/vllm-ci:${BUILDKITE_COMMIT}"
 container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
 docker pull "${image_name}"
 
 remove_docker_container() {
-  docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
+   docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
 }
 trap remove_docker_container EXIT
 
-# --- Prepare commands ---
 echo "--- Running container"
 
 HF_CACHE="$(realpath ~)/huggingface"
 mkdir -p "${HF_CACHE}"
 HF_MOUNT="/root/.cache/huggingface"
 
-# ---- Command source selection ----
-# Prefer VLLM_TEST_COMMANDS (preserves all inner quoting intact).
-# Fall back to $* for backward compatibility, but warn that inner
-# double-quotes will have been stripped by the calling shell.
-if [[ -n "${VLLM_TEST_COMMANDS:-}" ]]; then
-  commands="${VLLM_TEST_COMMANDS}"
-  echo "Commands sourced from VLLM_TEST_COMMANDS (quoting preserved)"
-else
-  commands="$*"
-  if [[ -z "$commands" ]]; then
-    echo "Error: No test commands provided." >&2
-    echo "Usage:" >&2
-    echo "  Preferred:  VLLM_TEST_COMMANDS='...' bash $0" >&2
-    echo "  Legacy:     bash $0 \"commands here\"" >&2
-    exit 1
-  fi
-  echo "Commands sourced from positional args (legacy mode)"
-  echo "WARNING: Inner double-quotes in the command string may have been"
-  echo "  stripped by the calling shell. If you see syntax errors, switch to:"
-  echo "  export VLLM_TEST_COMMANDS='your commands here'"
-  echo "  bash $0"
-fi
-
+commands=$@
 echo "Raw commands: $commands"
 
-# Fix quoting before ROCm overrides (so overrides see correct structure)
-commands=$(re_quote_pytest_markers "$commands")
-echo "After re-quoting: $commands"
+commands=${commands//"pytest -v -s basic_correctness/test_basic_correctness.py"/"pytest -v -s basic_correctness/test_basic_correctness.py"}
 
-commands=$(apply_rocm_test_overrides "$commands")
+if [[ $commands == *"pytest -v -s models/test_registry.py"* ]]; then
+  commands=${commands//"pytest -v -s models/test_registry.py"/"pytest -v -s models/test_registry.py -k 'not BambaForCausalLM and not GritLM and not Mamba2ForCausalLM and not Zamba2ForCausalLM'"}
+fi
+
+commands=${commands//"pytest -v -s compile/test_basic_correctness.py"/"pytest -v -s compile/test_basic_correctness.py"}
+
+if [[ $commands == *"pytest -v -s lora"* ]]; then
+  commands=${commands//"pytest -v -s lora"/"VLLM_ROCM_CUSTOM_PAGED_ATTN=0 pytest -v -s lora"}
+fi
+
+#ignore certain kernels tests
+if [[ $commands == *" kernels/core"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/core/test_fused_quant_layernorm.py \
+  --ignore=kernels/core/test_permute_cols.py"
+fi
+
+if [[ $commands == *" kernels/attention"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/attention/test_attention_selector.py \
+  --ignore=kernels/attention/test_encoder_decoder_attn.py \
+  --ignore=kernels/attention/test_flash_attn.py \
+  --ignore=kernels/attention/test_flashinfer.py \
+  --ignore=kernels/attention/test_prefix_prefill.py \
+  --ignore=kernels/attention/test_cascade_flash_attn.py \
+  --ignore=kernels/attention/test_mha_attn.py \
+  --ignore=kernels/attention/test_lightning_attn.py \
+  --ignore=kernels/attention/test_attention.py"
+fi
+
+if [[ $commands == *" kernels/quantization"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/quantization/test_int8_quant.py \
+  --ignore=kernels/quantization/test_machete_mm.py \
+  --ignore=kernels/quantization/test_block_fp8.py \
+  --ignore=kernels/quantization/test_block_int8.py \
+  --ignore=kernels/quantization/test_marlin_gemm.py \
+  --ignore=kernels/quantization/test_cutlass_scaled_mm.py \
+  --ignore=kernels/quantization/test_int8_kernel.py"
+fi
+
+if [[ $commands == *" kernels/mamba"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/mamba/test_mamba_mixer2.py \
+  --ignore=kernels/mamba/test_causal_conv1d.py \
+  --ignore=kernels/mamba/test_mamba_ssm_ssd.py"
+fi
+
+if [[ $commands == *" kernels/moe"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/moe/test_moe.py \
+  --ignore=kernels/moe/test_cutlass_moe.py \
+  --ignore=kernels/moe/test_triton_moe_ptpc_fp8.py"
+fi
+
+#ignore certain Entrypoints/openai tests
+if [[ $commands == *" entrypoints/openai "* ]]; then
+  commands=${commands//" entrypoints/openai "/" entrypoints/openai \
+  --ignore=entrypoints/openai/test_audio.py \
+  --ignore=entrypoints/openai/test_shutdown.py \
+  --ignore=entrypoints/openai/test_completion.py \
+  --ignore=entrypoints/openai/test_models.py \
+  --ignore=entrypoints/openai/test_lora_adapters.py \
+  --ignore=entrypoints/openai/test_return_tokens_as_ids.py \
+  --ignore=entrypoints/openai/test_root_path.py \
+  --ignore=entrypoints/openai/test_tokenization.py \
+  --ignore=entrypoints/openai/test_prompt_validation.py "}
+fi
+
+#ignore certain Entrypoints/llm tests
+if [[ $commands == *" entrypoints/llm "* ]]; then
+  commands=${commands//" entrypoints/llm "/" entrypoints/llm \
+  --ignore=entrypoints/llm/test_chat.py \
+  --ignore=entrypoints/llm/test_accuracy.py \
+  --ignore=entrypoints/llm/test_init.py \
+  --ignore=entrypoints/llm/test_prompt_validation.py "}
+fi
+
+commands=$(echo "$commands" | sed 's/ \\ / /g')
 echo "Final commands: $commands"
 
+# --ignore=entrypoints/openai/test_encoder_decoder.py \
+# --ignore=entrypoints/openai/test_embedding.py \
+# --ignore=entrypoints/openai/test_oot_registration.py
+# --ignore=entrypoints/openai/test_accuracy.py \
+# --ignore=entrypoints/openai/test_models.py <= Fails on MI250 but passes on MI300 as of 2025-03-13
+
+
 MYPYTHONPATH=".."
 
-# Verify GPU access
+# Test that we're launching on the machine that has
+# proper access to GPUs
 render_gid=$(getent group render | cut -d: -f3)
 if [[ -z "$render_gid" ]]; then
   echo "Error: 'render' group not found. This is required for GPU access." >&2
   exit 1
 fi
 
-# --- RDMA device passthrough (conditional) ---
-# If the host has RDMA devices, pass them through so tests like
-# test_moriio_connector can access ibverbs. On hosts without RDMA
-# hardware the tests will gracefully skip via _rdma_available().
-RDMA_FLAGS=""
-if [ -d /dev/infiniband ]; then
-  echo "RDMA devices detected on host, enabling passthrough"
-  RDMA_FLAGS="--device /dev/infiniband --cap-add=IPC_LOCK"
-else
-  echo "No RDMA devices found on host, RDMA tests will be skipped"
-fi
+if [[ $commands == *"VLLM_TEST_GROUP_NAME=mi325_4-2-node-tests-4-gpus-in-total"* ]]; then
 
-# --- Route: multi-node vs single-node ---
-if is_multi_node "$commands"; then
-  echo "--- Multi-node job detected"
   export DCKR_VER=$(docker --version | sed 's/Docker version \(.*\), build .*/\1/')
 
-  # Parse the bracket syntax:  prefix ; [node0_cmds] && [node1_cmds]
-  #   BASH_REMATCH[1] = prefix (everything before first bracket)
-  #   BASH_REMATCH[2] = comma-separated node0 commands
-  #   BASH_REMATCH[3] = comma-separated node1 commands
-  if [[ "$commands" =~ ^(.*)\[(.*)"] && ["(.*)\]$ ]]; then
-    prefix=$(echo "${BASH_REMATCH[1]}" | sed 's/;//g')
-    echo "PREFIX: ${prefix}"
-
-    export composite_command="(command rocm-smi || true)"
-    saved_IFS=$IFS
-    IFS=','
-    read -ra node0 <<< "${BASH_REMATCH[2]}"
-    read -ra node1 <<< "${BASH_REMATCH[3]}"
-    IFS=$saved_IFS
-
-    if [[ ${#node0[@]} -ne ${#node1[@]} ]]; then
-      echo "Warning: node0 has ${#node0[@]} commands, node1 has ${#node1[@]}. They will be paired by index."
-    fi
-
-    for i in "${!node0[@]}"; do
-      command_node_0=$(echo "${node0[i]}" | sed 's/\"//g')
-      command_node_1=$(echo "${node1[i]}" | sed 's/\"//g')
-
-      step_cmd="./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 ${image_name} '${command_node_0}' '${command_node_1}'"
-      echo "COMMANDS: ${step_cmd}"
-      composite_command="${composite_command} && ${step_cmd}"
-    done
-
-    /bin/bash -c "${composite_command}"
-    exit_code=$?
-    cleanup_network
-    handle_pytest_exit "$exit_code"
+  if [[ "$commands" =~ ^(.*)"["(.*)"] && ["(.*)"]"$ ]]; then
+      prefix=$( echo "${BASH_REMATCH[1]}" | sed 's/;//g')
+      echo "PREFIX: ${prefix}"
+      export composite_command="(command rocm-smi || true)"
+      myIFS=$IFS
+      IFS=','
+      read -ra node0 <<< ${BASH_REMATCH[2]}
+      read -ra node1 <<< ${BASH_REMATCH[3]}
+      IFS=$myIFS
+      for i in "${!node0[@]}";do 
+        command_node_0=$(echo ${node0[i]} | sed 's/\"//g')
+        command_node_1=$(echo ${node1[i]} | sed 's/\"//g')
+        
+        export commands="./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 ${image_name} '${command_node_0}' '${command_node_1}'"
+        echo "COMMANDS: ${commands}"
+        composite_command=$(echo "${composite_command} && ${commands}")
+      done
+      /bin/bash -c "${composite_command}"
+      cleanup_network
   else
-    echo "Multi-node job detected but failed to parse bracket command syntax."
-    echo "Expected format: prefix ; [node0_cmd1, node0_cmd2] && [node1_cmd1, node1_cmd2]"
-    echo "Got: $commands"
-    cleanup_network
-    exit 111
+      echo "Failed to parse node commands! Exiting."
+      cleanup_network
+      exit 111
   fi
 else
-  echo "--- Single-node job"
   echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
   docker run \
-    --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
-    $RDMA_FLAGS \
-    --network=host \
-    --shm-size=16gb \
-    --group-add "$render_gid" \
-    --rm \
-    -e HF_TOKEN \
-    -e AWS_ACCESS_KEY_ID \
-    -e AWS_SECRET_ACCESS_KEY \
-    -v "${HF_CACHE}:${HF_MOUNT}" \
-    -e "HF_HOME=${HF_MOUNT}" \
-    -e "PYTHONPATH=${MYPYTHONPATH}" \
-    --name "${container_name}" \
-    "${image_name}" \
-    /bin/bash -c "${commands}"
-
-  exit_code=$?
-  handle_pytest_exit "$exit_code"
+          --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
+          --network=host \
+          --shm-size=16gb \
+          --group-add "$render_gid" \
+          --rm \
+          -e HF_TOKEN \
+          -e AWS_ACCESS_KEY_ID \
+          -e AWS_SECRET_ACCESS_KEY \
+          -v "${HF_CACHE}:${HF_MOUNT}" \
+          -e "HF_HOME=${HF_MOUNT}" \
+          -e "PYTHONPATH=${MYPYTHONPATH}" \
+          --name "${container_name}" \
+          "${image_name}" \
+          /bin/bash -c "${commands}"
 fi

.buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh

@@ -1,43 +1,26 @@
 #!/bin/bash
 set -euox pipefail
-export VLLM_CPU_CI_ENV=0
 
 echo "--- PP+TP"
 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 &
 server_pid=$!
-timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
+timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
 vllm bench serve \
     --backend vllm \
     --dataset-name random \
     --model meta-llama/Llama-3.2-3B-Instruct \
     --num-prompts 20 \
-    --result-dir ./test_results \
-    --result-filename tp_pp.json \
-    --save-result \
     --endpoint /v1/completions
-kill -s SIGTERM $server_pid; wait $server_pid || true
-failed_req=$(jq '.failed' ./test_results/tp_pp.json)
-if [ "$failed_req" -ne 0 ]; then
-  echo "Some requests were failed!"
-  exit 1
-fi
+kill -s SIGTERM $server_pid &
 
 echo "--- DP+TP"
 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 &
 server_pid=$!
-timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
+timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
 vllm bench serve \
     --backend vllm \
     --dataset-name random \
     --model meta-llama/Llama-3.2-3B-Instruct \
     --num-prompts 20 \
-    --result-dir ./test_results \
-    --result-filename dp_pp.json \
-    --save-result \
     --endpoint /v1/completions
-kill -s SIGTERM $server_pid; wait $server_pid || true
-failed_req=$(jq '.failed' ./test_results/dp_pp.json)
-if [ "$failed_req" -ne 0 ]; then
-  echo "Some requests were failed!"
-  exit 1
-fi
+kill -s SIGTERM $server_pid &

.buildkite/scripts/hardware_ci/run-hpu-test.sh

@@ -1,27 +1,9 @@
 #!/bin/bash
 
-# This script builds the HPU docker image and runs the offline inference inside the container.
+# This script build the CPU docker image and run the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
-#
-# vllm-gaudi compatibility pinning:
-#   The vllm-gaudi plugin is installed on top of the vllm upstream checkout used by this CI job.
-#   When upstream vllm changes its API, the plugin may break before it has been updated.
-#   To handle this, the vllm-gaudi repository maintains a file:
-#     vllm/last-good-commit-for-vllm-gaudi/VLLM_COMMUNITY_COMMIT
-#   The first line of that file controls what version of vllm is used inside the Docker image:
-#     - "latest"        : no checkout override; the current Buildkite CI commit is used as-is.
-#     - "<commit SHA>"  : vllm is checked out to that specific commit before building, pinning
-#                         the test to a known-compatible baseline.
-#   To unpin (resume testing against the live vllm tip), set the file content back to "latest".
 set -exuo pipefail
 
-# Fetch the vllm community commit reference from vllm-gaudi (first line only).
-VLLM_COMMUNITY_COMMIT=$(curl -s \
-  https://raw.githubusercontent.com/vllm-project/vllm-gaudi/vllm/last-good-commit-for-vllm-gaudi/VLLM_COMMUNITY_COMMIT \
-  | head -1 | tr -d '\n')
-
-echo "Using vllm community commit: ${VLLM_COMMUNITY_COMMIT}"
-
 # Try building the docker image
 image_name="hpu/upstream-vllm-ci:${BUILDKITE_COMMIT}"
 container_name="hpu-upstream-vllm-ci-${BUILDKITE_COMMIT}-container"
@@ -30,13 +12,6 @@ FROM gaudi-base-image:latest
 
 COPY ./ /workspace/vllm
 
-# If VLLM_COMMUNITY_COMMIT is a specific commit (not "latest"), check it out to pin vllm
-# to the version known to be compatible with vllm-gaudi. When the value is "latest",
-# the current checkout (the Buildkite CI commit) is used unchanged.
-RUN if [ "${VLLM_COMMUNITY_COMMIT}" != "latest" ]; then \
-      cd /workspace/vllm && git fetch --unshallow 2>/dev/null || true && git checkout ${VLLM_COMMUNITY_COMMIT}; \
-    fi
-
 WORKDIR /workspace/vllm
 
 ENV no_proxy=localhost,127.0.0.1

.buildkite/scripts/run-prime-rl-test.sh

@@ -0,0 +1,64 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Setup script for Prime-RL integration tests
+# This script prepares the environment for running Prime-RL tests with nightly vLLM
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
+PRIME_RL_REPO="https://github.com/PrimeIntellect-ai/prime-rl.git"
+PRIME_RL_DIR="${REPO_ROOT}/prime-rl"
+
+if command -v rocm-smi &> /dev/null || command -v rocminfo &> /dev/null; then
+    echo "AMD GPU detected. Prime-RL currently only supports NVIDIA. Skipping..."
+    exit 0
+fi
+
+echo "Setting up Prime-RL integration test environment..."
+
+# Clean up any existing Prime-RL directory
+if [ -d "${PRIME_RL_DIR}" ]; then
+    echo "Removing existing Prime-RL directory..."
+    rm -rf "${PRIME_RL_DIR}"
+fi
+
+# Install UV if not available
+if ! command -v uv &> /dev/null; then
+    echo "Installing UV package manager..."
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+    source "$HOME"/.local/bin/env
+fi
+
+# Clone Prime-RL repository at specific branch for reproducible tests
+PRIME_RL_BRANCH="integ-vllm-main"
+echo "Cloning Prime-RL repository at branch: ${PRIME_RL_BRANCH}..."
+git clone --branch "${PRIME_RL_BRANCH}" --single-branch "${PRIME_RL_REPO}" "${PRIME_RL_DIR}"
+cd "${PRIME_RL_DIR}"
+
+echo "Setting up UV project environment..."
+export UV_PROJECT_ENVIRONMENT=/usr/local
+ln -s /usr/bin/python3 /usr/local/bin/python
+
+# Remove vllm pin from pyproject.toml
+echo "Removing vllm pin from pyproject.toml..."
+sed -i '/vllm==/d' pyproject.toml
+
+# Sync Prime-RL dependencies
+echo "Installing Prime-RL dependencies..."
+uv sync --inexact && uv sync --inexact --all-extras
+
+# Verify installation
+echo "Verifying installations..."
+uv run python -c "import vllm; print(f'vLLM version: {vllm.__version__}')"
+uv run python -c "import prime_rl; print('Prime-RL imported successfully')"
+
+echo "Prime-RL integration test environment setup complete!"
+
+echo "Running Prime-RL integration tests..."
+export WANDB_MODE=offline # this makes this test not require a WANDB_API_KEY
+uv run pytest -vs tests/integration/test_rl.py -m gpu
+
+echo "Prime-RL integration tests completed!"

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh

@@ -1,57 +0,0 @@
-#!/usr/bin/env bash
-set -euxo pipefail
-
-# Nightly e2e test for prefetch offloading with a MoE model.
-# Runs DeepSeek-V2-Lite with prefetch offloading of MoE expert weights
-# and validates GSM8K accuracy matches baseline (no offloading).
-#
-# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
-THRESHOLD=${1:-0.25}
-NUM_Q=${2:-1319}
-PORT=${3:-8030}
-OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
-mkdir -p "${OUT_DIR}"
-
-wait_for_server() {
-  local port=$1
-  timeout 600 bash -c '
-    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
-      sleep 1
-    done'
-}
-
-MODEL="deepseek-ai/DeepSeek-V2-Lite"
-
-cleanup() {
-  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
-    kill "${SERVER_PID}" 2>/dev/null || true
-    for _ in {1..20}; do
-      kill -0 "${SERVER_PID}" 2>/dev/null || break
-      sleep 0.5
-    done
-    kill -9 "${SERVER_PID}" 2>/dev/null || true
-  fi
-}
-trap cleanup EXIT
-
-vllm serve "$MODEL" \
-  --max-model-len 2048 \
-  --offload-group-size 8 \
-  --offload-num-in-group 2 \
-  --offload-prefetch-step 1 \
-  --offload-params w13_weight w2_weight \
-  --port "$PORT" &
-SERVER_PID=$!
-wait_for_server "$PORT"
-
-TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
-OUT="${OUT_DIR}/${TAG}_prefetch_offload.json"
-python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
-python3 - <<PY
-import json; acc=json.load(open('${OUT}'))['accuracy']
-print(f"${MODEL} prefetch_offload: accuracy {acc:.3f}")
-assert acc >= ${THRESHOLD}, f"${MODEL} prefetch_offload accuracy {acc}"
-PY
-
-cleanup
-SERVER_PID=

.buildkite/test_areas/distributed.yaml

@@ -103,8 +103,8 @@ steps:
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
   # NEW rlhf examples
   - cd new_weight_syncing
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf_nccl.py
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf_ipc.py
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf.py
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf_async_new_apis.py
 
 - label: Distributed Tests (8 GPUs)(H100)
   timeout_in_minutes: 10
@@ -146,7 +146,6 @@ steps:
   num_devices: 2
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
-    # - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/offline_inference/new_weight_syncing/rlhf_async_new_apis.py --- failing, need to re-enable
     - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
     - pytest -v -s tests/v1/distributed/test_dbo.py
 
@@ -210,7 +209,7 @@ steps:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
-- label: Pipeline + Context Parallelism (4 GPUs)
+- label: Pipeline + Context Parallelism (4 GPUs))
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4

.buildkite/test_areas/e2e_integration.yaml

@@ -29,11 +29,15 @@ steps:
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
 
-- label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100)
-  timeout_in_minutes: 60
-  device: h100
+- label: Prime-RL Integration (2 GPUs)
+  timeout_in_minutes: 30
   optional: true
-  num_devices: 1
+  soft_fail: true
+  num_devices: 2
   working_dir: "/vllm-workspace"
+  source_file_dependencies:
+  - vllm/
+  - .buildkite/scripts/run-prime-rl-test.sh
   commands:
-  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh 0.25 200 8030
+    - nvidia-smi
+    - bash .buildkite/scripts/run-prime-rl-test.sh

.buildkite/test_areas/engine.yaml

@@ -14,7 +14,7 @@ steps:
   commands:
   - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
 
-- label: V1 e2e + engine (1 GPU)
+- label: V1 e2e + engine
   timeout_in_minutes: 45
   source_file_dependencies:
     - vllm/
@@ -28,43 +28,3 @@ steps:
     - pytest -v -s v1/engine/test_preprocess_error_handling.py
     # Run the rest of v1/engine tests
     - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
-      commands:
-      - pytest -v -s v1/e2e
-      - pytest -v -s v1/engine
-
-- label: V1 e2e (2 GPUs)
-  timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
-  optional: true
-  num_devices: 2
-  source_file_dependencies:
-    - vllm/
-    - tests/v1/e2e
-  commands:
-    # Only run tests that need exactly 2 GPUs
-    - pytest -v -s v1/e2e/test_spec_decode.py -k "tensor_parallelism"
-  mirror:
-    amd:
-      device: mi325_2
-      depends_on:
-      - image-build-amd
-
-- label: V1 e2e (4 GPUs)
-  timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
-  optional: true
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/
-    - tests/v1/e2e
-  commands:
-    # Only run tests that need 4 GPUs
-    - pytest -v -s v1/e2e/test_spec_decode.py -k "eagle_correctness_heavy"
-  mirror:
-    amd:
-      device: mi325_4
-      depends_on:
-      - image-build-amd

.buildkite/test_areas/entrypoints.yaml

@@ -65,11 +65,6 @@ steps:
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s entrypoints/pooling
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
 
 - label: Entrypoints Integration (Responses API)
   timeout_in_minutes: 50

.buildkite/test_areas/expert_parallelism.yaml

@@ -20,19 +20,4 @@ steps:
   - tests/distributed/test_eplb_execute.py
   commands:
   - pytest -v -s distributed/test_eplb_execute.py
-  - pytest -v -s distributed/test_eplb_spec_decode.py
-
-- label: Elastic EP Scaling Test
-  timeout_in_minutes: 20
-  device: b200
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/compilation/
-  - tests/distributed/
-  commands:
-  - pytest -v -s distributed/test_elastic_ep.py
+  - pytest -v -s distributed/test_eplb_spec_decode.py

.buildkite/test_areas/kernels.yaml

@@ -44,8 +44,7 @@ steps:
   - vllm/envs.py
   - vllm/config
   commands:
-    - pytest -v -s kernels/moe --ignore=kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-    - pytest -v -s kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+    - pytest -v -s kernels/moe --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   parallelism: 2
 
 - label: Kernels Mamba Test
@@ -71,7 +70,7 @@ steps:
   - tests/kernels/moe/test_batched_deepgemm.py
   - tests/kernels/attention/test_deepgemm_attention.py
   commands:
-    - pytest -v -s kernels/quantization/test_block_fp8.py
+    - pytest -v -s kernels/quantization/test_block_fp8.py -k deep_gemm
     - pytest -v -s kernels/moe/test_deepgemm.py
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
@@ -116,7 +115,6 @@ steps:
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
     - pytest -v -s tests/kernels/moe/test_flashinfer.py
-    - pytest -v -s tests/kernels/moe/test_flashinfer_moe.py
     - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py
     # e2e
     - pytest -v -s tests/models/quantization/test_nvfp4.py
@@ -156,7 +154,9 @@ steps:
   commands:
     - pytest -v -s kernels/moe/test_deepep_deepgemm_moe.py
     - pytest -v -s kernels/moe/test_deepep_moe.py
-
+    - pytest -v -s kernels/moe/test_pplx_cutlass_moe.py
+    # - pytest -v -s kernels/moe/test_pplx_moe.py - failing on main
+  
 - label: Kernels Fp4 MoE Test (B200)
   timeout_in_minutes: 60
   device: b200

.buildkite/test_areas/lm_eval.yaml

@@ -11,17 +11,17 @@ steps:
   commands:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt
 
-# - label: LM Eval Large Models (4 GPUs)(A100)
-#   device: a100
-#   optional: true
-#   num_devices: 4
-#   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-#   source_file_dependencies:
-#   - csrc/
-#   - vllm/model_executor/layers/quantization
-#   commands:
-#   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
+- label: LM Eval Large Models (4 GPUs)(A100)
+  device: a100
+  optional: true
+  num_devices: 4
+  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
+  source_file_dependencies:
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
 - label: LM Eval Large Models (4 GPUs)(H100)
   device: h100
@@ -73,29 +73,3 @@ steps:
   num_devices: 2
   commands:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor-dp-ep/config-b200.txt
-
-- label: GPQA Eval (GPT-OSS) (H100)
-  timeout_in_minutes: 120
-  device: h100
-  optional: true
-  num_devices: 2
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - tests/evals/gpt_oss/
-  commands:
-    - uv pip install --system 'gpt-oss[eval]==0.0.5'
-    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-h100.txt
-
-- label: GPQA Eval (GPT-OSS) (B200)
-  timeout_in_minutes: 120
-  device: b200
-  optional: true
-  num_devices: 2
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - tests/evals/gpt_oss/
-  commands:
-    - uv pip install --system 'gpt-oss[eval]==0.0.5'
-    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-b200.txt

.buildkite/test_areas/misc.yaml

@@ -9,7 +9,6 @@ steps:
     - tests/v1
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     # split the test to avoid interference
     - pytest -v -s -m 'not cpu_test' v1/core
     - pytest -v -s v1/executor
@@ -17,7 +16,6 @@ steps:
     - pytest -v -s v1/sample
     - pytest -v -s v1/logits_processors
     - pytest -v -s v1/worker
-    # TODO: create another `optional` test group for slow tests
     - pytest -v -s -m 'not slow_test' v1/spec_decode
     - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
     - pytest -v -s -m 'not cpu_test' v1/metrics
@@ -27,11 +25,6 @@ steps:
     # Integration test for streaming correctness (requires special branch).
     - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
 
 - label: V1 Others (CPU)
   depends_on:
@@ -154,6 +147,20 @@ steps:
   - pytest -v -s transformers_utils
   - pytest -v -s config
 
+- label: GPT-OSS Eval (B200)
+  timeout_in_minutes: 60
+  working_dir: "/vllm-workspace/"
+  device: b200
+  optional: true
+  source_file_dependencies:
+  - tests/evals/gpt_oss
+  - vllm/model_executor/models/gpt_oss.py
+  - vllm/model_executor/layers/quantization/mxfp4.py
+  - vllm/v1/attention/backends/flashinfer.py
+  commands:
+    - uv pip install --system 'gpt-oss[eval]==0.0.5'
+    - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58
+
 - label: Batch Invariance (H100)
   timeout_in_minutes: 25
   device: h100

.buildkite/test_areas/models_language.yaml

@@ -55,15 +55,6 @@ steps:
     - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.3.0'
     - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
     - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
-      commands:
-      - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
-      - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
-      - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
 - label: Language Models Test (PPL)
   timeout_in_minutes: 110
@@ -82,11 +73,6 @@ steps:
   - tests/models/language/pooling
   commands:
     - pytest -v -s models/language/pooling -m 'not core_model'
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
 
 - label: Language Models Test (MTEB)
   timeout_in_minutes: 110

.buildkite/test_areas/models_multimodal.yaml

@@ -20,7 +20,6 @@ steps:
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal
-  - tests/models/registry.py
   device: cpu
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
@@ -31,7 +30,6 @@ steps:
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal
-  - tests/models/registry.py
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/processing/test_tensor_schema.py
@@ -72,3 +70,12 @@ steps:
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
+
+# This test is used only in PR development phase to test individual models and should never run on main
+- label: Custom Models
+  optional: true
+  commands:
+    - echo 'Testing custom models...'
+    # PR authors can temporarily add commands below to test individual models
+    # e.g. pytest -v -s models/encoder_decoder/vision_language/test_mllama.py
+    # *To avoid merge conflicts, remember to REMOVE (not just comment out) them before merging the PR*

.buildkite/test_areas/plugins.yaml

@@ -19,10 +19,6 @@ steps:
   - pip install -e ./plugins/prithvi_io_processor_plugin
   - pytest -v -s plugins_tests/test_io_processor_plugins.py
   - pip uninstall prithvi_io_processor_plugin -y
-  # test bge_m3_sparse io_processor plugin
-  - pip install -e ./plugins/bge_m3_sparse_plugin
-  - pytest -v -s plugins_tests/test_bge_m3_sparse_io_processor_plugins.py
-  - pip uninstall bge_m3_sparse_plugin -y
   # end io_processor plugins test
   # begin stat_logger plugins test
   - pip install -e ./plugins/vllm_add_dummy_stat_logger

.buildkite/test_areas/ray_compat.yaml

@@ -1,16 +0,0 @@
-group: Ray Compatibility
-depends_on:
-  - image-build
-steps:
-- label: Ray Dependency Compatibility Check
-  # Informational only — does not block the pipeline.
-  # If this fails, it means the PR introduces a dependency that
-  # conflicts with Ray's dependency constraints.
-  # See https://github.com/vllm-project/vllm/issues/33599
-  soft_fail: true
-  timeout_in_minutes: 10
-  source_file_dependencies:
-  - requirements/
-  - setup.py
-  commands:
-  - bash /vllm-workspace/.buildkite/scripts/check-ray-compatibility.sh

.buildkite/test_areas/samplers.yaml

@@ -18,4 +18,4 @@ steps:
       depends_on:
       - image-build-amd
       commands:
-      - pytest -v -s samplers
+      - pytest -v -s -m samplers

.buildkite/test_areas/weight_loading.yaml

@@ -13,13 +13,13 @@ steps:
   commands:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models.txt
 
-# - label: Weight Loading Multiple GPU - Large Models # optional
-#   working_dir: "/vllm-workspace/tests"
-#   num_devices: 2
-#   device: a100
-#   optional: true
-#   source_file_dependencies:
-#   - vllm/
-#   - tests/weight_loading
-#   commands:
-#     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt
+- label: Weight Loading Multiple GPU - Large Models # optional
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  device: a100
+  optional: true
+  source_file_dependencies:
+  - vllm/
+  - tests/weight_loading
+  commands:
+    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt

.github/.bc-linter.yml

@@ -0,0 +1,24 @@
+# doc: https://github.com/pytorch/test-infra/blob/main/tools/stronghold/docs/bc_linter_config.md
+version: 1
+paths:
+# We temporarily disable globally, and will only enable with `annotations.include`
+# include:
+#   - "vllm/v1/attetion/*.py"
+#   - "vllm/v1/core/*.py"
+exclude:
+  - "**/*.py"
+
+scan:
+  functions: true        # check free functions and methods
+  classes: true          # check classes/dataclasses
+  public_only: true      # ignore names starting with "_" at any level
+
+annotations:
+  include:               # decorators that force‑include a symbol
+    - name: "bc_linter_include"  # matched by simple name or dotted suffix
+      propagate_to_members: false # for classes, include methods/inner classes
+  exclude:               # decorators that force‑exclude a symbol
+    - name: "bc_linter_skip"     # matched by simple name or dotted suffix
+      propagate_to_members: true  # for classes, exclude methods/inner classes
+
+excluded_violations: []  # e.g. ["ParameterRenamed", "FieldTypeChanged"]

.github/CODEOWNERS

@@ -2,17 +2,17 @@
 # for more info about CODEOWNERS file
 
 # This lists cover the "core" components of vLLM that require careful review
-/vllm/compilation @zou3519 @youkaichao @ProExpertProg @BoyuanFeng
+/vllm/compilation @zou3519 @youkaichao @ProExpertProg
 /vllm/distributed/kv_transfer @NickLucche @ApostaC @orozery
 /vllm/lora @jeejeelee
-/vllm/model_executor/layers/attention @LucasWilkinson @MatthewBonanni
+/vllm/model_executor/layers/attention @LucasWilkinson
 /vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
 /vllm/model_executor/layers/mamba @tdoublep
 /vllm/model_executor/model_loader @22quinn
 /vllm/model_executor/layers/batch_invariant.py @yewentao256 
 /vllm/multimodal @DarkLight1337 @ywang96 @NickLucche @tjtanaa
-/vllm/vllm_flash_attn @LucasWilkinson @MatthewBonanni
+/vllm/vllm_flash_attn @LucasWilkinson
 CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 
 # Any change to the VllmConfig changes can have a large user-facing impact,
@@ -43,25 +43,22 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /vllm/tool_parsers @aarnphm @chaunceyjiang
 
 # vLLM V1
-/vllm/v1/attention @LucasWilkinson @MatthewBonanni
+/vllm/v1/attention @LucasWilkinson
 /vllm/v1/attention/backend.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @njhill
 /vllm/v1/attention/backends/mla @pavanimajety
 /vllm/v1/attention/backends/flashinfer.py @mgoin @pavanimajety
 /vllm/v1/attention/backends/triton_attn.py @tdoublep
 /vllm/v1/core @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @alexm-redhat @heheda12345 @ApostaC @orozery
 /vllm/v1/sample @22quinn @houseroad @njhill
-/vllm/v1/spec_decode @benchislett @luccafong @MatthewBonanni
+/vllm/v1/spec_decode @benchislett @luccafong
 /vllm/v1/structured_output @mgoin @russellb @aarnphm @benchislett
 /vllm/v1/kv_cache_interface.py @heheda12345
 /vllm/v1/kv_offload @ApostaC @orozery
-/vllm/v1/engine @njhill
-/vllm/v1/executor @njhill
-/vllm/v1/worker @njhill
-/vllm/v1/worker/kv_connector_model_runner_mixin.py @orozery @NickLucche
+/vllm/v1/worker/gpu/kv_connector.py @orozery
+/vllm/v1/worker/kv_connector_model_runner_mixin.py @orozery
 
 # Model runner V2
-/vllm/v1/worker/gpu @WoosukKwon @njhill
-/vllm/v1/worker/gpu/kv_connector.py @orozery
+/vllm/v1/worker/gpu @WoosukKwon
 
 # Test ownership
 /.buildkite/lm-eval-harness @mgoin 

.github/mergify.yml

@@ -259,7 +259,8 @@ pull_request_rules:
       - files=benchmarks/run_structured_output_benchmark.sh
       - files=docs/features/structured_outputs.md
       - files=examples/offline_inference/structured_outputs.py
-      - files=examples/online_serving/structured_outputs/structured_outputs.py
+      - files=examples/online_serving/openai_chat_completion_structured_outputs.py
+      - files=examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py
       - files~=^tests/v1/structured_output/
       - files=tests/v1/entrypoints/llm/test_struct_output_generate.py
       - files~=^vllm/v1/structured_output/

.github/workflows/bc-lint.yml

@@ -0,0 +1,29 @@
+name: BC Lint
+
+on:
+  pull_request:
+    types:
+      - opened
+      - synchronize
+      - reopened
+      - labeled
+      - unlabeled
+
+jobs:
+  bc_lint:
+    if: github.repository_owner == 'vllm-project'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Run BC Lint Action
+        uses: pytorch/test-infra/.github/actions/bc-lint@main
+        with:
+          repo: ${{ github.event.pull_request.head.repo.full_name }}
+          base_sha: ${{ github.event.pull_request.base.sha }}
+          head_sha: ${{ github.event.pull_request.head.sha }}
+          suppression: ${{ contains(github.event.pull_request.labels.*.name, 'suppress-bc-linter') }}
+          docs_link: 'https://github.com/pytorch/test-infra/wiki/BC-Linter'
+          config_dir: .github
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}
+  cancel-in-progress: true

.gitignore

@@ -3,8 +3,6 @@
 
 # vllm-flash-attn built from source
 vllm/vllm_flash_attn/*
-!vllm/vllm_flash_attn/__init__.py
-!vllm/vllm_flash_attn/flash_attn_interface.py
 
 # OpenAI triton kernels copied from source
 vllm/third_party/triton_kernels/*

CMakeLists.txt

@@ -725,7 +725,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # CUTLASS MoE kernels
 
   # The MoE kernel cutlass_moe_mm requires CUDA 12.3 or later (and ONLY works
-  # on Hopper). get_cutlass_(batched_)moe_mm_data should only be compiled
+  # on Hopper). get_cutlass_(pplx_)moe_mm_data should only be compiled
   # if it's possible to compile MoE kernels that use its output.
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
@@ -771,33 +771,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
   endif()
 
-  # Expert-specialization MXFP8 blockscaled grouped kernels (SM100+).
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(ES_MXFP8_GROUPED_MM_ARCHS "10.0f;11.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(ES_MXFP8_GROUPED_MM_ARCHS "10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
-  endif()
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND ES_MXFP8_GROUPED_MM_ARCHS)
-    set(SRCS
-      "csrc/moe/mxfp8_moe/cutlass_mxfp8_grouped_mm.cu"
-      "csrc/moe/mxfp8_moe/mxfp8_experts_quant.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${ES_MXFP8_GROUPED_MM_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_ES_MXFP8_GROUPED_MM_SM100=1")
-    message(STATUS "Building ES MXFP8 grouped kernels for archs: ${ES_MXFP8_GROUPED_MM_ARCHS}")
-  else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8
-        AND ES_MXFP8_GROUPED_MM_ARCHS)
-      message(STATUS "Not building ES MXFP8 grouped kernels as CUDA Compiler version is "
-                     "not >= 12.8.")
-    else()
-      message(STATUS "Not building ES MXFP8 grouped kernels as no compatible archs found "
-                     "in CUDA target architectures.")
-    endif()
-  endif()
-
   # DeepSeek V3 fused A GEMM kernel (requires SM 9.0+, Hopper and later)
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
     cuda_archs_loose_intersection(DSV3_FUSED_A_GEMM_ARCHS "9.0a;10.0f;11.0f" "${CUDA_ARCHS}")
@@ -810,6 +783,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
       SRCS "${DSV3_FUSED_A_GEMM_SRC}"
       CUDA_ARCHS "${DSV3_FUSED_A_GEMM_ARCHS}")
     list(APPEND VLLM_EXT_SRC ${DSV3_FUSED_A_GEMM_SRC})
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_DSV3_FUSED_A_GEMM=1")
     message(STATUS "Building dsv3_fused_a_gemm for archs: ${DSV3_FUSED_A_GEMM_ARCHS}")
   else()
     message(STATUS "Not building dsv3_fused_a_gemm as no compatible archs found "
@@ -998,8 +972,7 @@ set(VLLM_MOE_EXT_SRC
 if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_MOE_EXT_SRC
     "csrc/moe/moe_wna16.cu"
-    "csrc/moe/grouped_topk_kernels.cu"
-    "csrc/moe/router_gemm.cu")
+    "csrc/moe/grouped_topk_kernels.cu")
 endif()
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
@@ -1128,27 +1101,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     message(STATUS "Not building Marlin MOE kernels as no compatible archs found"
                    " in CUDA target architectures")
   endif()
-
-  # DeepSeek V3 router GEMM kernel - requires SM90+
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(DSV3_ROUTER_GEMM_ARCHS "9.0a;10.0f;11.0f" "${CUDA_ARCHS}")
-  else()
-    cuda_archs_loose_intersection(DSV3_ROUTER_GEMM_ARCHS "9.0a;10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
-  endif()
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND DSV3_ROUTER_GEMM_ARCHS)
-    set(DSV3_ROUTER_GEMM_SRC
-      "csrc/moe/dsv3_router_gemm_entry.cu"
-      "csrc/moe/dsv3_router_gemm_float_out.cu"
-      "csrc/moe/dsv3_router_gemm_bf16_out.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${DSV3_ROUTER_GEMM_SRC}"
-      CUDA_ARCHS "${DSV3_ROUTER_GEMM_ARCHS}")
-    list(APPEND VLLM_MOE_EXT_SRC "${DSV3_ROUTER_GEMM_SRC}")
-    message(STATUS "Building DSV3 router GEMM kernel for archs: ${DSV3_ROUTER_GEMM_ARCHS}")
-  else()
-    message(STATUS "Not building DSV3 router GEMM kernel as no compatible archs found"
-                   " (requires SM90+ and CUDA >= 12.0)")
-  endif()
 endif()
 
 message(STATUS "Enabling moe extension.")

benchmarks/attention_benchmarks/__init__.py

@@ -15,6 +15,7 @@ from .common import (
     BenchmarkConfig,
     BenchmarkResult,
     MockLayer,
+    MockModelConfig,
     ResultsFormatter,
     get_attention_scale,
     is_mla_backend,
@@ -35,6 +36,7 @@ __all__ = [
     "ResultsFormatter",
     # Mock objects
     "MockLayer",
+    "MockModelConfig",
     # Utilities
     "setup_mla_dims",
     "get_attention_scale",

benchmarks/attention_benchmarks/common.py

@@ -10,6 +10,7 @@ from dataclasses import asdict, dataclass
 from pathlib import Path
 from typing import Any
 
+import numpy as np
 import torch
 from batch_spec import get_batch_type, parse_batch_spec
 from rich.console import Console
@@ -61,7 +62,10 @@ class MockHfConfig:
 # Import AttentionLayerBase at module level to avoid circular dependencies
 try:
     from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+
+    _HAS_ATTENTION_LAYER_BASE = True
 except ImportError:
+    _HAS_ATTENTION_LAYER_BASE = False
     AttentionLayerBase = object  # Fallback
 
 
@@ -163,6 +167,95 @@ class MockLayer(AttentionLayerBase):
         return self._kv_cache_spec
 
 
+class MockModelConfig:
+    """Mock model configuration."""
+
+    def __init__(
+        self,
+        num_q_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        dtype: torch.dtype = torch.float16,
+        max_model_len: int = 32768,
+    ):
+        self._n_q = num_q_heads
+        self._n_kv = num_kv_heads
+        self._d = head_dim
+        self.dtype = dtype
+        self.max_model_len = max_model_len
+
+    def get_num_attention_heads(self, _=None) -> int:
+        return self._n_q
+
+    def get_num_kv_heads(self, _=None) -> int:
+        return self._n_kv
+
+    def get_head_size(self) -> int:
+        return self._d
+
+    def get_num_layers(self) -> int:
+        """Mock method for layer count queries."""
+        return 1
+
+    def get_sliding_window_for_layer(self, _layer_idx: int):
+        """Mock method for sliding window queries."""
+        return None
+
+    def get_logits_soft_cap_for_layer(self, _layer_idx: int):
+        """Mock method for logits soft cap queries."""
+        return None
+
+    def get_sm_scale_for_layer(self, _layer_idx: int) -> float:
+        """Mock method for SM scale queries."""
+        return 1.0 / (self.get_head_size() ** 0.5)
+
+
+class MockParallelConfig:
+    """Mock parallel configuration."""
+
+    pass
+
+
+class MockCompilationConfig:
+    """Mock compilation configuration."""
+
+    def __init__(self):
+        self.full_cuda_graph = False
+        self.static_forward_context = {}
+
+
+class MockVLLMConfig:
+    """Mock VLLM configuration."""
+
+    def __init__(self):
+        self.compilation_config = MockCompilationConfig()
+
+
+class MockRunner:
+    """Mock GPU runner for metadata builders."""
+
+    def __init__(
+        self,
+        seq_lens: np.ndarray,
+        query_start_locs: np.ndarray,
+        device: torch.device,
+        num_q_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        dtype: torch.dtype,
+    ):
+        self.model_config = MockModelConfig(num_q_heads, num_kv_heads, head_dim, dtype)
+        self.parallel_config = MockParallelConfig()
+        self.vllm_config = MockVLLMConfig()
+        self.seq_lens_np = seq_lens
+        self.query_start_loc_np = query_start_locs
+        self.device = device
+        self.attention_chunk_size = None
+        self.num_query_heads = num_q_heads
+        self.num_kv_heads = num_kv_heads
+        self.dtype = dtype
+
+
 @dataclass
 class ParameterSweep:
     """Configuration for sweeping a backend parameter."""

benchmarks/auto_tune/auto_tune.sh

@@ -85,6 +85,7 @@ start_server() {
     # Each argument and its value are separate elements.
     local common_args_array=(
         "$MODEL"
+        "--disable-log-requests"
         "--port" "8004"
         "--host" "$HOSTNAME"
         "--gpu-memory-utilization" "$gpu_memory_utilization"

benchmarks/backend_request_func.py

@@ -649,3 +649,9 @@ ASYNC_REQUEST_FUNCS = {
     "sglang": async_request_openai_completions,
     "llama.cpp": async_request_openai_completions,
 }
+
+OPENAI_COMPATIBLE_BACKENDS = [
+    k
+    for k, v in ASYNC_REQUEST_FUNCS.items()
+    if v in (async_request_openai_completions, async_request_openai_chat_completions)
+]

benchmarks/benchmark_utils.py

@@ -1,7 +1,78 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import argparse
+import json
+import math
+import os
 import time
 from types import TracebackType
+from typing import Any
+
+
+def convert_to_pytorch_benchmark_format(
+    args: argparse.Namespace, metrics: dict[str, list], extra_info: dict[str, Any]
+) -> list:
+    """
+    Save the benchmark results in the format used by PyTorch OSS benchmark with
+    on metric per record
+    https://github.com/pytorch/pytorch/wiki/How-to-integrate-with-PyTorch-OSS-benchmark-database
+    """
+    records = []
+    if not os.environ.get("SAVE_TO_PYTORCH_BENCHMARK_FORMAT", False):
+        return records
+
+    for name, benchmark_values in metrics.items():
+        record = {
+            "benchmark": {
+                "name": "vLLM benchmark",
+                "extra_info": {
+                    "args": vars(args),
+                },
+            },
+            "model": {
+                "name": args.model,
+            },
+            "metric": {
+                "name": name,
+                "benchmark_values": benchmark_values,
+                "extra_info": extra_info,
+            },
+        }
+
+        tp = record["benchmark"]["extra_info"]["args"].get("tensor_parallel_size")
+        # Save tensor_parallel_size parameter if it's part of the metadata
+        if not tp and "tensor_parallel_size" in extra_info:
+            record["benchmark"]["extra_info"]["args"]["tensor_parallel_size"] = (
+                extra_info["tensor_parallel_size"]
+            )
+
+        records.append(record)
+
+    return records
+
+
+class InfEncoder(json.JSONEncoder):
+    def clear_inf(self, o: Any):
+        if isinstance(o, dict):
+            return {k: self.clear_inf(v) for k, v in o.items()}
+        elif isinstance(o, list):
+            return [self.clear_inf(v) for v in o]
+        elif isinstance(o, float) and math.isinf(o):
+            return "inf"
+        return o
+
+    def iterencode(self, o: Any, *args, **kwargs) -> Any:
+        return super().iterencode(self.clear_inf(o), *args, **kwargs)
+
+
+def write_to_json(filename: str, records: list) -> None:
+    with open(filename, "w") as f:
+        json.dump(
+            records,
+            f,
+            cls=InfEncoder,
+            default=lambda o: f"<{type(o).__name__} object is not JSON serializable>",
+        )
 
 
 # Collect time and generate time metrics

benchmarks/cutlass_benchmarks/utils.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 # Cutlass bench utils
+from collections.abc import Iterable
 
 import torch
 
@@ -85,3 +86,15 @@ def make_rand_sparse_tensors(
 
     # Compressed B, Metadata, Original A, B
     return b_compressed, e, a, b
+
+
+def make_n_rand_sparse_tensors(
+    num_tensors: int, dtype: torch.dtype, m: int, n: int, k: int
+) -> tuple[Iterable[torch.Tensor], Iterable[torch.Tensor]]:
+    ABs = []
+    for _ in range(num_tensors):
+        b_comp, e, a, b = make_rand_sparse_tensors(dtype, m, n, k)
+        if b_comp is not None:
+            ABs.append(make_rand_sparse_tensors(dtype, m, n, k))
+    BComps, Es, As, Bs = zip(*ABs)
+    return list(BComps), list(Es), list(As), list(Bs)

benchmarks/disagg_benchmarks/rate_limiter.py

@@ -0,0 +1,45 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+import time
+
+
+class RateLimiter:
+    """Token bucket rate limiter implementation"""
+
+    def __init__(self, rate_limit):
+        self.rate_limit = rate_limit  # Requests per second
+        self.num_available_tokens = rate_limit  # Available tokens
+        self.last_refill = time.monotonic()  # Last token refill time
+        self.lock = asyncio.Lock()  # Synchronization lock
+
+    async def acquire(self):
+        """Acquire a token from the rate limiter"""
+        while True:
+            async with self.lock:
+                current_time = time.monotonic()
+                elapsed = current_time - self.last_refill
+
+                # Refill num_available_tokens if more than 1 second has passed
+                if elapsed > 1.0:
+                    self.num_available_tokens = self.rate_limit
+                    self.last_refill = current_time
+
+                # Check if num_available_tokens are available
+                if self.num_available_tokens > 0:
+                    self.num_available_tokens -= 1
+                    return True
+
+                # Calculate wait time if no num_available_tokens available
+                wait_time = 1.0 - elapsed
+            await asyncio.sleep(wait_time)
+
+    async def __aenter__(self):
+        """Enter async context manager - acquire token"""
+        await self.acquire()
+        return self
+
+    async def __aexit__(self, exc_type, exc_value, traceback):
+        """Exit async context manager - no cleanup needed"""
+        pass

benchmarks/disagg_benchmarks/request_queue.py

@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+from collections import deque
+
+
+class RequestQueue:
+    """Request queue manager with concurrency control"""
+
+    def __init__(self, max_concurrent, max_queue_size):
+        # Maximum concurrent requests
+        self.max_concurrent = max_concurrent
+        self.max_queue_size = max_queue_size  # Maximum queue size
+        # Concurrency control
+        self.semaphore = asyncio.Semaphore(max_concurrent)
+        self.queue = deque()  # Request queue
+        self.queue_size = 0  # Current queue size
+        self.lock = asyncio.Lock()  # Sync queue Lock
+
+    async def enqueue(self, task):
+        """Add a request task to the queue"""
+        async with self.lock:
+            if self.queue_size >= self.max_queue_size:
+                return False
+
+            self.queue.append(task)
+            self.queue_size += 1
+            return True
+
+    async def process(self):
+        """Process queued requests using semaphore for concurrency control"""
+        while True:
+            if self.queue:
+                async with self.semaphore, self.lock:
+                    task = self.queue.popleft()
+                    self.queue_size -= 1
+                    await task
+            await asyncio.sleep(0.01)  # Yield control to event loop

benchmarks/fused_kernels/layernorm_rms_benchmarks.py

@@ -13,7 +13,6 @@ from torch.utils.benchmark import Measurement as TMeasurement
 from tqdm import tqdm
 
 import vllm._custom_ops as ops
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     per_token_group_quant_fp8,
@@ -292,7 +291,6 @@ def print_timers(timers: Iterable[TMeasurement]):
     compare.print()
 
 
-@default_vllm_config()
 def main():
     torch.set_default_device("cuda")
     bench_params = get_bench_params()

benchmarks/kernels/bench_block_fp8_gemm.py

@@ -8,7 +8,6 @@ os.environ["VLLM_USE_DEEP_GEMM"] = "0"
 
 import torch
 
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     W8A8BlockFp8LinearOp,
 )
@@ -41,7 +40,6 @@ DEEPSEEK_V3_SHAPES = [
 ]
 
 
-@default_vllm_config()
 def build_w8a8_block_fp8_runner(M, N, K, block_size, device, use_cutlass):
     """Build runner function for w8a8 block fp8 matmul."""
     factor_for_scale = 1e-2

benchmarks/kernels/bench_per_token_quant_fp8.py

@@ -7,7 +7,6 @@ from unittest.mock import patch
 import pandas as pd
 import torch
 
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 from vllm.triton_utils import triton
@@ -85,7 +84,6 @@ def calculate_diff(
 configs = []
 
 
-@default_vllm_config()
 def benchmark_quantization(
     batch_size,
     hidden_size,

benchmarks/kernels/benchmark_activation.py

@@ -7,7 +7,6 @@ import itertools
 import torch
 
 import vllm.model_executor.layers.activation  # noqa F401
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.custom_op import op_registry
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
@@ -19,7 +18,6 @@ intermediate_size = [3072, 9728, 12288]
 configs = list(itertools.product(batch_size_range, seq_len_range, intermediate_size))
 
 
-@default_vllm_config()
 def benchmark_activation(
     batch_size: int,
     seq_len: int,

benchmarks/kernels/benchmark_cutlass_moe_fp8.py

@@ -12,12 +12,12 @@ import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from tests.kernels.moe.utils import make_dummy_moe_config
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
-from vllm.model_executor.layers.fused_moe.all2all_utils import (
-    maybe_make_prepare_finalize,
-)
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
 from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP,
+)
 from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.v1.worker.workspace import init_workspace_manager
@@ -137,21 +137,15 @@ def bench_run(
         per_out_ch_quant=per_out_ch,
     )
 
-    moe_config = make_dummy_moe_config(
-        num_experts=num_experts,
-        hidden_dim=k,
-        intermediate_size_per_partition=n,
-        in_dtype=a.dtype,
-    )
-    fn = mk.FusedMoEKernel(
-        maybe_make_prepare_finalize(
-            moe=moe_config,
-            quant_config=quant_config,
-            allow_new_interface=True,
-            use_monolithic=False,
-        ),
+    fn = mk.FusedMoEModularKernel(
+        MoEPrepareAndFinalizeNoEP(),
         CutlassExpertsFp8(
-            moe_config=moe_config,
+            moe_config=make_dummy_moe_config(
+                num_experts=num_experts,
+                hidden_dim=k,
+                intermediate_size_per_partition=n,
+                in_dtype=a.dtype,
+            ),
             quant_config=quant_config,
         ),
     )

benchmarks/kernels/benchmark_cutlass_moe_nvfp4.py

@@ -15,9 +15,6 @@ import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from tests.kernels.moe.utils import make_dummy_moe_config
 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
-from vllm.model_executor.layers.fused_moe.all2all_utils import (
-    maybe_make_prepare_finalize,
-)
 from vllm.model_executor.layers.fused_moe.config import (
     fp8_w8a8_moe_quant_config,
     nvfp4_moe_quant_config,
@@ -26,6 +23,9 @@ from vllm.model_executor.layers.fused_moe.cutlass_moe import (
     CutlassExpertsFp4,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP,
+)
 from vllm.scalar_type import scalar_types
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.v1.worker.workspace import init_workspace_manager
@@ -196,21 +196,10 @@ def bench_run(
             g2_alphas=w2_gs,
         )
 
-        moe_config = make_dummy_moe_config(
-            num_experts=num_experts,
-            hidden_dim=k,
-            intermediate_size_per_partition=n,
-            in_dtype=a.dtype,
-        )
-        kernel = mk.FusedMoEKernel(
-            maybe_make_prepare_finalize(
-                moe=moe_config,
-                quant_config=quant_config,
-                allow_new_interface=True,
-                use_monolithic=False,
-            ),
+        kernel = mk.FusedMoEModularKernel(
+            MoEPrepareAndFinalizeNoEP(),
             CutlassExpertsFp4(
-                moe_config=moe_config,
+                make_dummy_moe_config(),
                 quant_config=quant_config,
             ),
         )
@@ -251,17 +240,11 @@ def bench_run(
             g1_alphas=w1_gs,
             g2_alphas=w2_gs,
         )
-        moe_config = make_dummy_moe_config()
 
-        kernel = mk.FusedMoEKernel(
-            maybe_make_prepare_finalize(
-                moe=moe_config,
-                quant_config=quant_config,
-                allow_new_interface=True,
-                use_monolithic=False,
-            ),
+        kernel = mk.FusedMoEModularKernel(
+            MoEPrepareAndFinalizeNoEP(),
             CutlassExpertsFp4(
-                moe_config=moe_config,
+                make_dummy_moe_config(),
                 quant_config=quant_config,
             ),
         )

benchmarks/kernels/benchmark_device_communicators.py

@@ -30,9 +30,6 @@ import torch.distributed as dist
 from torch.distributed import ProcessGroup
 
 from vllm.distributed.device_communicators.custom_all_reduce import CustomAllreduce
-from vllm.distributed.device_communicators.flashinfer_all_reduce import (
-    FlashInferAllReduce,
-)
 from vllm.distributed.device_communicators.pynccl import (
     PyNcclCommunicator,
     register_nccl_symmetric_ops,
@@ -47,7 +44,7 @@ from vllm.utils.argparse_utils import FlexibleArgumentParser
 logger = init_logger(__name__)
 
 # Default sequence lengths to benchmark
-DEFAULT_SEQUENCE_LENGTHS = [16, 64, 128, 512, 1024, 2048, 4096, 8192]
+DEFAULT_SEQUENCE_LENGTHS = [128, 512, 1024, 2048, 4096, 8192]
 
 # Fixed hidden size and dtype for all benchmarks
 HIDDEN_SIZE = 8192
@@ -84,7 +81,6 @@ class CommunicatorBenchmark:
         self.symm_mem_comm = None
         self.symm_mem_comm_multimem = None
         self.symm_mem_comm_two_shot = None
-        self.fi_ar_comm = None
 
         self._init_communicators()
 
@@ -165,22 +161,6 @@ class CommunicatorBenchmark:
             )
             self.symm_mem_comm_two_shot = None
 
-        try:
-            self.fi_ar_comm = FlashInferAllReduce(
-                group=self.cpu_group,
-                device=self.device,
-            )
-            if not self.fi_ar_comm.disabled:
-                logger.info("Rank %s: FlashInferAllReduce initialized", self.rank)
-            else:
-                logger.info("Rank %s: FlashInferAllReduce disabled", self.rank)
-                self.fi_ar_comm = None
-        except Exception as e:
-            logger.warning(
-                "Rank %s: Failed to initialize FlashInferAllReduce: %s", self.rank, e
-            )
-            self.fi_ar_comm = None
-
     def benchmark_allreduce(
         self, sequence_length: int, num_warmup: int, num_trials: int
     ) -> dict[str, float]:
@@ -200,8 +180,7 @@ class CommunicatorBenchmark:
                     lambda t, c=comm: c.custom_all_reduce(t),
                     lambda t, c=comm: c.should_custom_ar(t),
                     comm.capture(),
-                    {"VLLM_CUSTOM_ALLREDUCE_ALGO": "1stage"},
-                    None,  # no destroy function
+                    "1stage",  # env variable value
                 )
             )
             # CustomAllreduce two-shot
@@ -211,8 +190,7 @@ class CommunicatorBenchmark:
                     lambda t, c=comm: c.custom_all_reduce(t),
                     lambda t, c=comm: c.should_custom_ar(t),
                     comm.capture(),
-                    {"VLLM_CUSTOM_ALLREDUCE_ALGO": "2stage"},
-                    None,  # no destroy function
+                    "2stage",  # env variable value
                 )
             )
 
@@ -224,8 +202,7 @@ class CommunicatorBenchmark:
                     lambda t, c=comm: c.all_reduce(t),
                     lambda t: True,  # Always available if initialized
                     nullcontext(),
-                    {},  # no env variable needed
-                    None,  # no destroy function
+                    None,  # no env variable needed
                 )
             )
             communicators.append(
@@ -234,8 +211,7 @@ class CommunicatorBenchmark:
                     lambda t: torch.ops.vllm.all_reduce_symmetric_with_copy(t),
                     lambda t: True,  # Always available if initialized
                     nullcontext(),
-                    {},  # no env variable needed
-                    None,  # no destroy function
+                    None,  # no env variable needed
                 )
             )
 
@@ -247,8 +223,7 @@ class CommunicatorBenchmark:
                     lambda t, c=comm: c.all_reduce(t),
                     lambda t, c=comm: c.should_use_symm_mem(t),
                     nullcontext(),
-                    {},  # no env variable needed
-                    None,  # no destroy function
+                    None,  # no env variable needed
                 )
             )
 
@@ -260,67 +235,29 @@ class CommunicatorBenchmark:
                     lambda t, c=comm: c.all_reduce(t),
                     lambda t, c=comm: c.should_use_symm_mem(t),
                     nullcontext(),
-                    {},  # no env variable needed
-                    None,  # no destroy function needed
-                )
-            )
-
-        if self.fi_ar_comm is not None:
-            comm = self.fi_ar_comm
-            communicators.append(
-                (
-                    "flashinfer_trtllm",
-                    lambda t, c=comm: c.all_reduce(t),
-                    lambda t, c=comm: c.should_use_fi_ar(t),
-                    nullcontext(),
-                    {"VLLM_FLASHINFER_ALLREDUCE_BACKEND": "trtllm"},
-                    lambda c=comm: c.destroy(),
-                )
-            )
-            communicators.append(
-                (
-                    "flashinfer_mnnvl",
-                    lambda t, c=comm: c.all_reduce(t),
-                    lambda t, c=comm: c.should_use_fi_ar(t),
-                    nullcontext(),
-                    {"VLLM_FLASHINFER_ALLREDUCE_BACKEND": "mnnvl"},
-                    lambda c=comm: c.destroy(),
+                    None,  # no env variable needed
                 )
             )
 
         # Benchmark each communicator
-        for (
-            name,
-            allreduce_fn,
-            should_use_fn,
-            context,
-            env_dict,
-            destroy_fn,
-        ) in communicators:
-            # Save original values and apply new environment variables
-            saved_env = {key: os.environ.get(key) for key in env_dict}
-            for key, value in env_dict.items():
-                os.environ[key] = value
-            try:
-                latency = self.benchmark_allreduce_single(
-                    sequence_length,
-                    allreduce_fn,
-                    should_use_fn,
-                    context,
-                    num_warmup,
-                    num_trials,
-                )
-                if latency is not None:
-                    results[name] = latency
-            finally:
-                if destroy_fn is not None:
-                    destroy_fn()
-                # Restore environment variables to their original state
-                for key, original_value in saved_env.items():
-                    if original_value is None:
-                        os.environ.pop(key, None)
-                    else:
-                        os.environ[key] = original_value
+        for name, allreduce_fn, should_use_fn, context, env_var in communicators:
+            # Set environment variable if needed
+            if env_var is not None:
+                os.environ["VLLM_CUSTOM_ALLREDUCE_ALGO"] = env_var
+            else:
+                # Clear the environment variable to avoid interference
+                os.environ.pop("VLLM_CUSTOM_ALLREDUCE_ALGO", None)
+
+            latency = self.benchmark_allreduce_single(
+                sequence_length,
+                allreduce_fn,
+                should_use_fn,
+                context,
+                num_warmup,
+                num_trials,
+            )
+            if latency is not None:
+                results[name] = latency
 
         return results
 

benchmarks/kernels/benchmark_fused_collective.py

@@ -5,11 +5,8 @@
 Benchmark for FlashInfer fused collective operations vs standard operations.
 
 This benchmark compares:
-1. FlashInfer's allreduce_fusion with trtllm backend
-   (fused allreduce + rmsnorm + optional FP8/FP4 quant)
-2. FlashInfer's allreduce_fusion with mnnvl backend
-   (fused allreduce + rmsnorm only, no quantization support)
-3. Standard tensor_model_parallel_all_reduce + separate rmsnorm/quant operations
+1. FlashInfer's allreduce_fusion (fused allreduce + rmsnorm + optional quant)
+2. Standard tensor_model_parallel_all_reduce + separate rmsnorm/quant operations
 
 Usage with torchrun:
     torchrun --nproc_per_node=2 benchmark_fused_collective.py
@@ -51,12 +48,8 @@ SCALED_FP4_QUANT_OP = torch.ops._C.scaled_fp4_quant
 logger = init_logger(__name__)
 
 # Try to import FlashInfer
-TorchDistBackend = None
 try:
     import flashinfer.comm as flashinfer_comm  # type: ignore
-    from flashinfer.comm.mnnvl import (  # type: ignore
-        TorchDistBackend,
-    )
 
     if not (
         hasattr(flashinfer_comm, "allreduce_fusion")
@@ -81,15 +74,11 @@ _FI_MAX_SIZES = {
     8: 64 * MiB,  # 64MB
 }
 
-# Global workspace tensors for FlashInfer (keyed by backend name)
-_FI_WORKSPACES: dict = {}
-
-# Backends to benchmark
-FLASHINFER_BACKENDS = ["trtllm", "mnnvl"]
+# Global workspace tensor for FlashInfer
+_FI_WORKSPACE = None
 
 
 def setup_flashinfer_workspace(
-    backend: str,
     world_size: int,
     rank: int,
     hidden_dim: int,
@@ -97,54 +86,41 @@ def setup_flashinfer_workspace(
     dtype: torch.dtype,
 ):
     """Setup FlashInfer workspace for fused allreduce operations."""
-    global FI_WORKSPACES
+    global _FI_WORKSPACE
 
     if flashinfer_comm is None:
-        return None
+        return None, None
 
     if world_size not in _FI_MAX_SIZES:
         logger.warning("FlashInfer not supported for world size %s", world_size)
-        return None
+        return None, None
 
     try:
-        kwargs = {}
-        if TorchDistBackend is not None:
-            kwargs["comm_backend"] = TorchDistBackend(group=dist.group.WORLD)
-
         workspace = flashinfer_comm.create_allreduce_fusion_workspace(
-            backend=backend,
+            backend="trtllm",
             world_size=world_size,
             rank=rank,
             max_token_num=max_token_num,
             hidden_dim=hidden_dim,
             dtype=dtype,
-            **kwargs,
         )
 
-        _FI_WORKSPACES[backend] = workspace
+        _FI_WORKSPACE = workspace
         return workspace
     except Exception as e:
-        logger.error(
-            "Failed to setup FlashInfer workspace (backend=%s): %s", backend, e
-        )
+        logger.error("Failed to setup FlashInfer workspace: %s", e)
         return None
 
 
-def cleanup_flashinfer_workspaces():
-    """Cleanup all FlashInfer workspaces."""
-    if flashinfer_comm is None:
+def cleanup_flashinfer_workspace(workspace):
+    """Cleanup FlashInfer workspace."""
+    if flashinfer_comm is None or workspace is None:
         return
 
-    for backend, workspace in _FI_WORKSPACES.items():
-        try:
-            workspace.destroy()
-        except Exception as e:
-            logger.error(
-                "Failed to cleanup FlashInfer workspace (backend=%s): %s",
-                backend,
-                e,
-            )
-    _FI_WORKSPACES.clear()
+    try:
+        workspace.destroy()
+    except Exception as e:
+        logger.error("Failed to cleanup FlashInfer workspace: %s", e)
 
 
 class FlashInferFusedAllReduceParams:
@@ -158,7 +134,7 @@ class FlashInferFusedAllReduceParams:
         self.fp32_acc = True
         self.max_token_num = max_token_num
 
-    def get_flashinfer_fused_allreduce_kwargs(self):
+    def get_trtllm_fused_allreduce_kwargs(self):
         return {
             "launch_with_pdl": self.launch_with_pdl,
             "fp32_acc": self.fp32_acc,
@@ -171,12 +147,11 @@ def flashinfer_fused_allreduce_rmsnorm(
     rms_gamma: torch.Tensor,
     rms_eps: float,
     allreduce_params: "FlashInferFusedAllReduceParams",
-    workspace: object,
     use_oneshot: bool,
     norm_out: torch.Tensor | None = None,
 ):
     """FlashInfer fused allreduce + rmsnorm operation."""
-    if flashinfer_comm is None or workspace is None:
+    if flashinfer_comm is None or _FI_WORKSPACE is None:
         raise RuntimeError("FlashInfer not available or workspace not initialized")
 
     if norm_out is None:
@@ -185,13 +160,9 @@ def flashinfer_fused_allreduce_rmsnorm(
     else:
         residual_out = input_tensor
 
-    layout_code = None
-    if workspace.backend == "trtllm":
-        layout_code = flashinfer_comm.QuantizationSFLayout.SWIZZLED_128x4
-
     flashinfer_comm.allreduce_fusion(
         input=input_tensor,
-        workspace=workspace,
+        workspace=_FI_WORKSPACE,
         pattern=flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNorm,
         residual_in=residual,
         residual_out=residual_out,
@@ -200,10 +171,10 @@ def flashinfer_fused_allreduce_rmsnorm(
         rms_eps=rms_eps,
         quant_out=None,
         scale_out=None,
-        layout_code=layout_code,
+        layout_code=flashinfer_comm.QuantizationSFLayout.SWIZZLED_128x4,
         scale_factor=None,
         use_oneshot=use_oneshot,
-        **allreduce_params.get_flashinfer_fused_allreduce_kwargs(),
+        **allreduce_params.get_trtllm_fused_allreduce_kwargs(),
     )
 
 
@@ -214,16 +185,12 @@ def flashinfer_fused_allreduce_rmsnorm_fp8_quant(
     rms_eps: float,
     scale_factor: torch.Tensor,
     allreduce_params: FlashInferFusedAllReduceParams,
-    workspace: object,
     use_oneshot: bool = True,
     norm_out: torch.Tensor | None = None,
     quant_out: torch.Tensor | None = None,
 ):
-    """FlashInfer fused allreduce + rmsnorm + FP8 quantization.
-
-    Note: Only supported by the trtllm backend.
-    """
-    if flashinfer_comm is None or workspace is None:
+    """FlashInfer fused allreduce + rmsnorm + FP8 quantization."""
+    if flashinfer_comm is None or _FI_WORKSPACE is None:
         raise RuntimeError("FlashInfer not available or workspace not initialized")
 
     if norm_out is None:
@@ -234,7 +201,7 @@ def flashinfer_fused_allreduce_rmsnorm_fp8_quant(
 
     flashinfer_comm.allreduce_fusion(
         input=input_tensor,
-        workspace=workspace,
+        workspace=_FI_WORKSPACE,
         pattern=flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNormFP8Quant,
         residual_in=residual,
         residual_out=residual_out,
@@ -246,7 +213,7 @@ def flashinfer_fused_allreduce_rmsnorm_fp8_quant(
         layout_code=flashinfer_comm.QuantizationSFLayout.SWIZZLED_128x4,
         scale_factor=scale_factor,
         use_oneshot=use_oneshot,
-        **allreduce_params.get_flashinfer_fused_allreduce_kwargs(),
+        **allreduce_params.get_trtllm_fused_allreduce_kwargs(),
     )
 
 
@@ -257,17 +224,13 @@ def flashinfer_fused_allreduce_rmsnorm_fp4_quant(
     rms_eps: float,
     input_global_scale: torch.Tensor,
     allreduce_params: FlashInferFusedAllReduceParams,
-    workspace: object,
     quant_out: torch.Tensor,
     use_oneshot: bool,
     output_scale: torch.Tensor,
     norm_out: torch.Tensor | None = None,
 ):
-    """FlashInfer fused allreduce + rmsnorm + FP4 quantization.
-
-    Note: Only supported by the trtllm backend.
-    """
-    if flashinfer_comm is None or workspace is None:
+    """FlashInfer fused allreduce + rmsnorm + FP4 quantization."""
+    if flashinfer_comm is None or _FI_WORKSPACE is None:
         raise RuntimeError("FlashInfer not available or workspace not initialized")
 
     if norm_out is None:
@@ -278,7 +241,7 @@ def flashinfer_fused_allreduce_rmsnorm_fp4_quant(
 
     flashinfer_comm.allreduce_fusion(
         input=input_tensor,
-        workspace=workspace,
+        workspace=_FI_WORKSPACE,
         pattern=flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNormFP4Quant,
         residual_in=residual,
         residual_out=residual_out,
@@ -290,7 +253,7 @@ def flashinfer_fused_allreduce_rmsnorm_fp4_quant(
         layout_code=flashinfer_comm.QuantizationSFLayout.SWIZZLED_128x4,
         scale_factor=input_global_scale,
         use_oneshot=use_oneshot,
-        **allreduce_params.get_flashinfer_fused_allreduce_kwargs(),
+        **allreduce_params.get_trtllm_fused_allreduce_kwargs(),
     )
 
 
@@ -423,16 +386,13 @@ def run_benchmarks(
     dtype: torch.dtype,
     use_residual: bool,
     allreduce_params: FlashInferFusedAllReduceParams | None,
-    workspaces: dict,
     quant_modes: set[str],
     no_oneshot: bool,
 ):
     """Run all benchmarks for given configuration.
 
     Args:
-        allreduce_params: Shared parameters for FlashInfer fused allreduce.
-        workspaces: Dict mapping backend name ("trtllm", "mnnvl") to workspace.
-        quant_modes: Set of quantization modes: "none", "fp8", "fp4".
+        quant_mode: "none", "fp8_only", "fp4_only", or "all"
     """
     (
         input_tensor,
@@ -448,18 +408,18 @@ def run_benchmarks(
 
     rms_eps = 1e-6
     results = {}
+    vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
     use_oneshot_options = [False] if no_oneshot else [True, False]
 
+    # Create RMSNorm and QuantFP8 layers once for native benchmarks
+
     if "none" in quant_modes:
         # Standard AllReduce + RMSNorm
-        # Re-create VllmFusedAllreduce per config so CustomOp binds the
-        # correct forward method (native vs custom kernel).
         for custom_op in ["-rms_norm", "+rms_norm"]:
             with set_current_vllm_config(
                 VllmConfig(compilation_config=CompilationConfig(custom_ops=[custom_op]))
             ):
                 try:
-                    vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                     suffix = (
                         "_custom_rms_norm" if "+" in custom_op else "_native_rms_norm"
                     )
@@ -478,7 +438,6 @@ def run_benchmarks(
             VllmConfig(compilation_config=CompilationConfig(custom_ops=["-rms_norm"]))
         ):
             try:
-                vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                 standard_allreduce_rmsnorm_native_compiled = torch.compile(
                     vllm_fused_allreduce.allreduce_rmsnorm,
                     fullgraph=True,
@@ -494,11 +453,10 @@ def run_benchmarks(
                 logger.error("Standard AllReduce+RMSNorm Native Compiled failed: %s", e)
                 results["standard_allreduce_rmsnorm_native_compiled"] = float("inf")
 
-        # FlashInfer Fused AllReduce + RMSNorm (all backends)
-        for backend, workspace in workspaces.items():
+        # FlashInfer Fused AllReduce + RMSNorm Oneshot/Twoshot
+        if flashinfer_comm is not None and allreduce_params is not None:
             for use_oneshot in use_oneshot_options:
                 suffix = "_oneshot" if use_oneshot else "_twoshot"
-                key = f"flashinfer_{backend}_fused_allreduce_rmsnorm{suffix}"
                 try:
                     time_ms = benchmark_operation(
                         flashinfer_fused_allreduce_rmsnorm,
@@ -508,17 +466,14 @@ def run_benchmarks(
                         rms_gamma=rms_gamma,
                         rms_eps=rms_eps,
                         allreduce_params=allreduce_params,
-                        workspace=workspace,
                         use_oneshot=use_oneshot,
                     )
-                    results[key] = time_ms
+                    results[f"flashinfer_fused_allreduce_rmsnorm{suffix}"] = time_ms
                 except Exception as e:
-                    logger.error(
-                        "FlashInfer (%s) Fused AllReduce+RMSNorm failed: %s",
-                        backend,
-                        e,
+                    logger.error("FlashInfer Fused AllReduce+RMSNorm failed: %s", e)
+                    results[f"flashinfer_fused_allreduce_rmsnorm{suffix}"] = float(
+                        "inf"
                     )
-                    results[key] = float("inf")
 
     if "fp8" in quant_modes:
         # Standard AllReduce + RMSNorm + FP8 Quant
@@ -527,7 +482,7 @@ def run_benchmarks(
                 "_custom_rms_norm" if "+" in rms_norm_custom_op else "_native_rms_norm"
             )
             for quant_fp8_custom_op in ["-quant_fp8", "+quant_fp8"]:
-                op_suffix = suffix + (
+                suffix += (
                     "_custom_quant_fp8"
                     if "+" in quant_fp8_custom_op
                     else "_native_quant_fp8"
@@ -540,17 +495,16 @@ def run_benchmarks(
                     )
                 ):
                     try:
-                        vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                         time_ms = benchmark_operation(
                             vllm_fused_allreduce.allreduce_rmsnorm_fp8_quant,
                             input_tensor,
                             residual=residual,
                             scale_factor=scale_fp8,
                         )
-                        results[f"standard_allreduce{op_suffix}"] = time_ms
+                        results[f"standard_allreduce{suffix}"] = time_ms
                     except Exception as e:
                         logger.error("Standard AllReduce+RMSNorm+FP8 failed: %s", e)
-                        results[f"standard_allreduce{op_suffix}"] = float("inf")
+                        results[f"standard_allreduce{suffix}"] = float("inf")
 
         # Standard AllReduce + RMSNorm + FP8 Quant Native Compiled
         with set_current_vllm_config(
@@ -561,7 +515,6 @@ def run_benchmarks(
             )
         ):
             try:
-                vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                 standard_allreduce_rmsnorm_fp8_quant_native_compiled = torch.compile(
                     vllm_fused_allreduce.allreduce_rmsnorm_fp8_quant,
                     fullgraph=True,
@@ -584,12 +537,10 @@ def run_benchmarks(
                     "inf"
                 )
 
-        # FlashInfer Fused AllReduce + RMSNorm + FP8 Quant (trtllm only)
-        if "trtllm" in workspaces:
-            trtllm_ws = workspaces["trtllm"]
+        # FlashInfer Fused AllReduce + RMSNorm + FP8 Quant Oneshot
+        if flashinfer_comm is not None and allreduce_params is not None:
             for use_oneshot in use_oneshot_options:
                 suffix = "_oneshot" if use_oneshot else "_twoshot"
-                key = f"flashinfer_trtllm_fused_allreduce_rmsnorm_fp8_quant{suffix}"
                 try:
                     time_ms = benchmark_operation(
                         flashinfer_fused_allreduce_rmsnorm_fp8_quant,
@@ -601,16 +552,19 @@ def run_benchmarks(
                         scale_factor=scale_fp8,
                         quant_out=quant_out_fp8,
                         allreduce_params=allreduce_params,
-                        workspace=trtllm_ws,
                         use_oneshot=use_oneshot,
                     )
-                    results[key] = time_ms
+                    results[f"flashinfer_fused_allreduce_rmsnorm_fp8_quant{suffix}"] = (
+                        time_ms
+                    )
                 except Exception as e:
                     logger.error(
-                        "FlashInfer (trtllm) Fused AllReduce+RMSNorm+FP8 failed: %s",
+                        "FlashInfer Fused AllReduce+RMSNorm+FP8 Oneshot failed: %s",
                         e,
                     )
-                    results[key] = float("inf")
+                    results[f"flashinfer_fused_allreduce_rmsnorm_fp8_quant{suffix}"] = (
+                        float("inf")
+                    )
 
     if "fp4" in quant_modes and current_platform.has_device_capability(100):
         # Standard AllReduce + RMSNorm + FP4 Quant
@@ -626,7 +580,6 @@ def run_benchmarks(
                 )
             ):
                 try:
-                    vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                     time_ms = benchmark_operation(
                         vllm_fused_allreduce.allreduce_rmsnorm_fp4_quant,
                         input_tensor,
@@ -645,7 +598,6 @@ def run_benchmarks(
             VllmConfig(compilation_config=CompilationConfig(custom_ops=["-rms_norm"]))
         ):
             try:
-                vllm_fused_allreduce = VllmFusedAllreduce(hidden_dim, dtype)
                 standard_allreduce_rmsnorm_fp4_quant_native_compiled = torch.compile(
                     vllm_fused_allreduce.allreduce_rmsnorm_fp4_quant,
                     fullgraph=True,
@@ -670,12 +622,10 @@ def run_benchmarks(
                     "inf"
                 )
 
-        # FlashInfer Fused AllReduce + RMSNorm + FP4 Quant (trtllm only)
-        if "trtllm" in workspaces:
-            trtllm_ws = workspaces["trtllm"]
+        # FlashInfer Fused AllReduce + RMSNorm + FP4 Quant Oneshot
+        if flashinfer_comm is not None and allreduce_params is not None:
             for use_oneshot in use_oneshot_options:
                 suffix = "_oneshot" if use_oneshot else "_twoshot"
-                key = f"flashinfer_trtllm_fused_allreduce_rmsnorm_fp4_quant{suffix}"
                 try:
                     time_ms = benchmark_operation(
                         flashinfer_fused_allreduce_rmsnorm_fp4_quant,
@@ -686,18 +636,49 @@ def run_benchmarks(
                         rms_eps=rms_eps,
                         input_global_scale=scale_fp4,
                         allreduce_params=allreduce_params,
-                        workspace=trtllm_ws,
                         quant_out=fp4_quant_out,
                         output_scale=fp4_output_scale,
                         use_oneshot=use_oneshot,
                     )
-                    results[key] = time_ms
+                    results[f"flashinfer_fused_allreduce_rmsnorm_fp4_quant{suffix}"] = (
+                        time_ms
+                    )
                 except Exception as e:
                     logger.error(
-                        "FlashInfer (trtllm) Fused AllReduce+RMSNorm+FP4 failed: %s",
+                        "FlashInfer Fused AllReduce+RMSNorm+FP4 Oneshot failed: %s",
                         e,
                     )
-                    results[key] = float("inf")
+                    results[f"flashinfer_fused_allreduce_rmsnorm_fp4_quant{suffix}"] = (
+                        float("inf")
+                    )
+
+        # FlashInfer Fused AllReduce + RMSNorm + FP4 Quant Two-shot
+        if flashinfer_comm is not None and allreduce_params is not None:
+            try:
+                time_ms = benchmark_operation(
+                    flashinfer_fused_allreduce_rmsnorm_fp4_quant,
+                    input_tensor,
+                    residual=residual,
+                    norm_out=norm_out,
+                    rms_gamma=rms_gamma,
+                    rms_eps=rms_eps,
+                    input_global_scale=scale_fp4,
+                    allreduce_params=allreduce_params,
+                    quant_out=fp4_quant_out,
+                    output_scale=fp4_output_scale,
+                    use_oneshot=False,
+                )
+                results["flashinfer_fused_allreduce_rmsnorm_fp4_quant_twoshot"] = (
+                    time_ms
+                )
+            except Exception as e:
+                logger.error(
+                    "FlashInfer Fused AllReduce+RMSNorm+FP4 Two-shot failed: %s",
+                    e,
+                )
+                results["flashinfer_fused_allreduce_rmsnorm_fp4_quant_twoshot"] = float(
+                    "inf"
+                )
 
     return results
 
@@ -1035,7 +1016,8 @@ def main():
 
     configs = list(itertools.product(args.num_tokens, dtypes, residual_options))
 
-    # Setup FlashInfer workspaces for all backends
+    # Setup FlashInfer workspace if available
+    workspace = None
     allreduce_params = None
 
     if flashinfer_comm is not None:
@@ -1050,17 +1032,15 @@ def main():
             args.hidden_dim * max_element_size
         )
 
-        for backend in FLASHINFER_BACKENDS:
-            setup_flashinfer_workspace(
-                backend=backend,
-                world_size=world_size,
-                rank=rank,
-                hidden_dim=args.hidden_dim,
-                max_token_num=max_num_token,
-                dtype=workspace_dtype,
-            )
+        workspace = setup_flashinfer_workspace(
+            world_size,
+            rank,
+            args.hidden_dim,
+            max_num_token,
+            dtype=workspace_dtype,
+        )
 
-        if _FI_WORKSPACES:
+        if workspace is not None:
             allreduce_params = FlashInferFusedAllReduceParams(
                 max_token_num=max_num_token,
             )
@@ -1086,7 +1066,6 @@ def main():
                 dtype,
                 use_residual,
                 allreduce_params,
-                workspaces=_FI_WORKSPACES,
                 quant_modes=quant_modes,
                 no_oneshot=args.no_oneshot,
             )
@@ -1125,13 +1104,11 @@ def main():
 
     finally:
         # Cleanup
-        cleanup_flashinfer_workspaces()
+        if workspace is not None:
+            cleanup_flashinfer_workspace(workspace)
 
         dist.barrier()
 
 
 if __name__ == "__main__":
-    from vllm.config import VllmConfig, set_current_vllm_config
-
-    with set_current_vllm_config(VllmConfig()):
-        main()
+    main()

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -9,15 +9,15 @@ import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from tests.kernels.moe.utils import make_dummy_moe_config
 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
-from vllm.model_executor.layers.fused_moe.all2all_utils import (
-    maybe_make_prepare_finalize,
-)
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
 from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassExpertsFp8
 from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_experts,
     fused_topk,
 )
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP,
+)
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.v1.worker.workspace import init_workspace_manager
 
@@ -131,22 +131,16 @@ def bench_run(
             w2_scale=w2_scale,
             per_act_token_quant=per_act_token,
         )
-        moe_config = make_dummy_moe_config(
-            num_experts=w2.shape[0],
-            hidden_dim=w2.shape[1],
-            intermediate_size_per_partition=w2.shape[2],
-            in_dtype=a.dtype,
-        )
 
-        fn = mk.FusedMoEKernel(
-            maybe_make_prepare_finalize(
-                moe=moe_config,
-                quant_config=quant_config,
-                allow_new_interface=True,
-                use_monolithic=False,
-            ),
+        fn = mk.FusedMoEModularKernel(
+            MoEPrepareAndFinalizeNoEP(),
             CutlassExpertsFp8(
-                moe_config=moe_config,
+                moe_config=make_dummy_moe_config(
+                    num_experts=w2.shape[0],
+                    hidden_dim=w2.shape[1],
+                    intermediate_size_per_partition=w2.shape[2],
+                    in_dtype=a.dtype,
+                ),
                 quant_config=quant_config,
             ),
         )
@@ -169,22 +163,16 @@ def bench_run(
             w2_scale=w2_scale,
             per_act_token_quant=per_act_token,
         )
-        moe_config = make_dummy_moe_config(
-            num_experts=w2.shape[0],
-            hidden_dim=w2.shape[1],
-            intermediate_size_per_partition=w2.shape[2],
-            in_dtype=a.dtype,
-        )
 
-        fn = mk.FusedMoEKernel(
-            maybe_make_prepare_finalize(
-                moe=moe_config,
-                quant_config=quant_config,
-                allow_new_interface=True,
-                use_monolithic=False,
-            ),
+        fn = mk.FusedMoEModularKernel(
+            MoEPrepareAndFinalizeNoEP(),
             CutlassExpertsFp8(
-                moe_config=moe_config,
+                moe_config=make_dummy_moe_config(
+                    num_experts=w2.shape[0],
+                    hidden_dim=w2.shape[1],
+                    intermediate_size_per_partition=w2.shape[2],
+                    in_dtype=a.dtype,
+                ),
                 quant_config=quant_config,
             ),
         )

benchmarks/kernels/benchmark_layernorm.py

@@ -5,14 +5,12 @@ import time
 
 import torch
 
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
 
 
 @torch.inference_mode()
-@default_vllm_config()
 def main(
     num_tokens: int,
     hidden_size: int,

benchmarks/kernels/benchmark_moe.py

@@ -17,9 +17,6 @@ from ray.experimental.tqdm_ray import tqdm
 
 from vllm.model_executor.layers.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
-from vllm.model_executor.layers.fused_moe.all2all_utils import (
-    maybe_make_prepare_finalize,
-)
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEParallelConfig,
@@ -245,33 +242,24 @@ def benchmark_config(
 
         deep_gemm_experts = None
         if use_deep_gemm:
-            moe_config = (
-                FusedMoEConfig(
-                    num_experts=num_experts,
-                    experts_per_token=topk,
-                    hidden_dim=hidden_size,
-                    intermediate_size_per_partition=shard_intermediate_size,
-                    num_local_experts=num_experts,
-                    num_logical_experts=num_experts,
-                    activation=MoEActivation.SILU,
-                    moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
-                    in_dtype=init_dtype,
-                    routing_method=RoutingMethodType.TopK,
-                    device="cuda",
-                ),
-            )
-            deep_gemm_experts = mk.FusedMoEKernel(
-                prepare_finalize=maybe_make_prepare_finalize(
-                    moe=moe_config,
-                    quant_config=quant_config,
-                    allow_new_interface=True,
-                    use_monolithic=False,
-                ),
+            deep_gemm_experts = mk.FusedMoEModularKernel(
+                prepare_finalize=MoEPrepareAndFinalizeNoEP(),
                 fused_experts=TritonOrDeepGemmExperts(
-                    moe_config=moe_config,
+                    moe_config=FusedMoEConfig(
+                        num_experts=num_experts,
+                        experts_per_token=topk,
+                        hidden_dim=hidden_size,
+                        intermediate_size_per_partition=shard_intermediate_size,
+                        num_local_experts=num_experts,
+                        num_logical_experts=num_experts,
+                        activation=MoEActivation.SILU,
+                        moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
+                        in_dtype=init_dtype,
+                        routing_method=RoutingMethodType.TopK,
+                        device="cuda",
+                    ),
                     quant_config=quant_config,
                 ),
-                inplace=not disable_inplace(),
             )
 
         with override_config(config):
@@ -281,16 +269,8 @@ def benchmark_config(
 
             inplace = not disable_inplace()
             if use_deep_gemm:
-                return deep_gemm_experts.apply(
-                    x,
-                    w1,
-                    w2,
-                    topk_weights,
-                    topk_ids,
-                    activation=MoEActivation.SILU,
-                    global_num_experts=num_experts,
-                    apply_router_weight_on_input=False,
-                    expert_map=False,
+                return deep_gemm_experts(
+                    x, w1, w2, topk_weights, topk_ids, inplace=inplace
                 )
             return fused_experts(
                 x,

benchmarks/kernels/benchmark_moe_defaults.py

@@ -1,278 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-Benchmark comparing old vs new default fused MoE configs.
-
-Runs the triton fused_moe kernel with three configurations for each scenario:
-  1. Tuned config (from JSON file, if available) — the target to match
-  2. Old default (the hardcoded defaults before this change)
-  3. New default (the improved defaults)
-
-Usage:
-    python benchmarks/kernels/benchmark_moe_defaults.py
-
-Produces a table showing kernel time (us) and speedup of new vs old defaults.
-"""
-
-import torch
-
-from vllm.model_executor.layers.fused_moe import fused_topk, override_config
-from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
-from vllm.model_executor.layers.fused_moe.fused_moe import (
-    fused_experts,
-    get_default_config,
-    get_moe_configs,
-)
-from vllm.platforms import current_platform
-from vllm.triton_utils import triton
-from vllm.utils.torch_utils import set_random_seed
-
-FP8_DTYPE = current_platform.fp8_dtype()
-
-
-def old_default_config(M, E, N, K, topk, dtype=None, block_shape=None):
-    """The original defaults before https://github.com/vllm-project/vllm/pull/34846,
-    for comparison."""
-    if dtype == "fp8_w8a8" and block_shape is not None:
-        return {
-            "BLOCK_SIZE_M": 64,
-            "BLOCK_SIZE_N": block_shape[0],
-            "BLOCK_SIZE_K": block_shape[1],
-            "GROUP_SIZE_M": 32,
-            "SPLIT_K": 1,
-            "num_warps": 4,
-            "num_stages": 3 if not current_platform.is_rocm() else 2,
-        }
-    elif M <= E:
-        return {
-            "BLOCK_SIZE_M": 16,
-            "BLOCK_SIZE_N": 32,
-            "BLOCK_SIZE_K": 64,
-            "GROUP_SIZE_M": 1,
-            "SPLIT_K": 1,
-        }
-    else:
-        return {
-            "BLOCK_SIZE_M": 64,
-            "BLOCK_SIZE_N": 64,
-            "BLOCK_SIZE_K": 32,
-            "GROUP_SIZE_M": 8,
-            "SPLIT_K": 1,
-        }
-
-
-def benchmark_config(
-    config,
-    M,
-    E,
-    N,
-    K,
-    topk,
-    dtype,
-    use_fp8=False,
-    block_shape=None,
-    num_iters=100,
-):
-    """Time a single kernel config. Returns kernel time in microseconds."""
-    init_dtype = torch.float16 if use_fp8 else dtype
-
-    a = torch.randn(M, K, device="cuda", dtype=init_dtype) / 10
-    w1 = torch.randn(E, 2 * N, K, device="cuda", dtype=init_dtype) / 10
-    w2 = torch.randn(E, K, N, device="cuda", dtype=init_dtype) / 10
-
-    w1_scale = None
-    w2_scale = None
-    a1_scale = None
-    a2_scale = None
-    if use_fp8:
-        if block_shape is not None:
-            bsn, bsk = block_shape
-            n_tiles_w1 = triton.cdiv(2 * N, bsn)
-            k_tiles_w1 = triton.cdiv(K, bsk)
-            n_tiles_w2 = triton.cdiv(K, bsn)
-            k_tiles_w2 = triton.cdiv(N, bsk)
-            w1_scale = torch.rand(
-                E, n_tiles_w1, k_tiles_w1, device="cuda", dtype=torch.float32
-            )
-            w2_scale = torch.rand(
-                E, n_tiles_w2, k_tiles_w2, device="cuda", dtype=torch.float32
-            )
-        else:
-            w1_scale = torch.rand(E, device="cuda", dtype=torch.float32)
-            w2_scale = torch.rand(E, device="cuda", dtype=torch.float32)
-        a1_scale = torch.rand(1, device="cuda", dtype=torch.float32)
-        a2_scale = torch.rand(1, device="cuda", dtype=torch.float32)
-        # Only weights are stored in fp8; activations stay in bf16/fp16
-        # and get dynamically quantized inside the kernel.
-        w1 = w1.to(FP8_DTYPE)
-        w2 = w2.to(FP8_DTYPE)
-
-    quant_config = FusedMoEQuantConfig.make(
-        quant_dtype=torch.float8_e4m3fn if use_fp8 else None,
-        w1_scale=w1_scale,
-        w2_scale=w2_scale,
-        a1_scale=a1_scale,
-        a2_scale=a2_scale,
-        block_shape=block_shape,
-    )
-
-    gating = torch.randn(M, E, device="cuda", dtype=torch.float32)
-
-    # Warmup
-    for _ in range(20):
-        with override_config(config):
-            topk_weights, topk_ids, _ = fused_topk(a, gating, topk, renormalize=True)
-            fused_experts(
-                a,
-                w1,
-                w2,
-                topk_weights,
-                topk_ids,
-                quant_config=quant_config,
-            )
-    torch.cuda.synchronize()
-
-    # Benchmark
-    start = torch.cuda.Event(enable_timing=True)
-    end = torch.cuda.Event(enable_timing=True)
-    start.record()
-    for _ in range(num_iters):
-        with override_config(config):
-            topk_weights, topk_ids, _ = fused_topk(a, gating, topk, renormalize=True)
-            fused_experts(
-                a,
-                w1,
-                w2,
-                topk_weights,
-                topk_ids,
-                quant_config=quant_config,
-            )
-    end.record()
-    torch.cuda.synchronize()
-    return start.elapsed_time(end) / num_iters * 1000  # ms -> us
-
-
-# Model configurations: (name, E, N, K, topk, dtype_str, use_fp8, block_shape)
-# N = moe_intermediate_size // tp_size (the value used in config file lookup)
-MODELS = [
-    # --- Few experts ---
-    ("Mixtral bf16", 8, 7168, 4096, 2, None, False, None),
-    ("Mixtral fp8", 8, 7168, 4096, 2, "fp8_w8a8", True, None),
-    # --- Many experts: real model shapes at tp=1 ---
-    # Qwen2-MoE-57B: E=60, topk=4, N=1408, K=2048
-    ("Qwen2-MoE bf16", 60, 1408, 2048, 4, None, False, None),
-    # DeepSeek-V2: E=64, topk=6, N=1407, K=4096
-    # (use 1408 to avoid odd alignment; real model is 1407)
-    ("DeepSeek-V2 bf16", 64, 1408, 4096, 6, None, False, None),
-    # OLMoE-7B: E=64, topk=8, N=2048, K=2048
-    ("OLMoE bf16", 64, 2048, 2048, 8, None, False, None),
-    # GLM-4-100B-A10B: E=128, topk=8, N=1408, K=4096
-    ("GLM-4-MoE bf16", 128, 1408, 4096, 8, None, False, None),
-    # Qwen3-30B-A3B: E=128, topk=8, N=768, K=2048
-    ("Qwen3-MoE bf16", 128, 768, 2048, 8, None, False, None),
-    # DeepSeek-V3 / MiMo-V2-Flash: E=256, topk=8, N=2048, K=7168
-    ("DeepSeek-V3 bf16", 256, 2048, 7168, 8, None, False, None),
-    # Qwen3.5-70B-A22B (Qwen3-Next): E=512, topk=10, N=512, K=2048
-    ("Qwen3-Next bf16", 512, 512, 2048, 10, None, False, None),
-    # E=128 N=1856 bf16
-    ("E128 N1856 bf16", 128, 1856, 4096, 8, None, False, None),
-    # E=256 N=512 bf16 (DS-V3 tp=4)
-    ("DS-V3 tp4 bf16", 256, 512, 7168, 8, None, False, None),
-    # E=512 N=512 bf16 (Qwen3-Next tp=1)
-    ("Qwen3-Next bf16", 512, 512, 2048, 10, None, False, None),
-    # E=512 N=256 bf16 (Qwen3-Next tp=2)
-    ("Qwen3-Next tp2", 512, 256, 2048, 10, None, False, None),
-    # --- FP8 block quant (many experts) ---
-    # DS-V3 tp=4: E=256, N=512, fp8 block
-    ("DS-V3 tp4 fp8blk", 256, 512, 7168, 8, "fp8_w8a8", True, [128, 128]),
-    # DS-V3 tp=8: E=256, N=256, fp8 block
-    ("DS-V3 tp8 fp8blk", 256, 256, 7168, 8, "fp8_w8a8", True, [128, 128]),
-    # Qwen3-Next tp=2 fp8 block
-    ("Qwen3-Next tp2 fp8blk", 512, 256, 2048, 10, "fp8_w8a8", True, [128, 128]),
-]
-
-BATCH_SIZES = [1, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]
-
-
-def main():
-    set_random_seed(0)
-    torch.set_default_device("cuda")
-    dtype = torch.bfloat16
-
-    for name, E, N, K, topk, dtype_str, use_fp8, block_shape in MODELS:
-        print(f"\n{'=' * 90}")
-        print(f"  {name}  (E={E}, N={N}, K={K}, topk={topk})")
-        print(f"{'=' * 90}")
-
-        # Try to load tuned config
-        block_n = block_shape[0] if block_shape else None
-        block_k = block_shape[1] if block_shape else None
-        tuned = get_moe_configs(E, N, dtype_str, block_n, block_k)
-        has_tuned = tuned is not None
-        print(f"  Tuned config available: {has_tuned}")
-
-        hdr = (
-            f"{'Batch':>6} | {'Tuned (us)':>11} | {'Old (us)':>11} | "
-            f"{'New (us)':>11} | {'New/Old':>8} | {'New/Tuned':>10}"
-        )
-        print(f"  {hdr}")
-        print(f"  {'-' * len(hdr)}")
-
-        for M in BATCH_SIZES:
-            old_cfg = old_default_config(M, E, N, K, topk, dtype_str, block_shape)
-            new_cfg = get_default_config(M, E, N, K, topk, dtype_str, block_shape)
-
-            if has_tuned:
-                tuned_cfg = tuned[min(tuned.keys(), key=lambda x: abs(x - M))]
-                t_tuned = benchmark_config(
-                    tuned_cfg,
-                    M,
-                    E,
-                    N,
-                    K,
-                    topk,
-                    dtype,
-                    use_fp8=use_fp8,
-                    block_shape=block_shape,
-                )
-            else:
-                t_tuned = None
-
-            t_old = benchmark_config(
-                old_cfg,
-                M,
-                E,
-                N,
-                K,
-                topk,
-                dtype,
-                use_fp8=use_fp8,
-                block_shape=block_shape,
-            )
-            t_new = benchmark_config(
-                new_cfg,
-                M,
-                E,
-                N,
-                K,
-                topk,
-                dtype,
-                use_fp8=use_fp8,
-                block_shape=block_shape,
-            )
-
-            ratio_new_old = t_new / t_old
-            tuned_str = f"{t_tuned:11.2f}" if t_tuned else f"{'N/A':>11}"
-            ratio_tuned = f"{t_new / t_tuned:10.2f}x" if t_tuned else f"{'N/A':>10}"
-            # flag regressions where new default is >5% slower than old
-            marker = " <--" if ratio_new_old > 1.05 else ""
-
-            print(
-                f"  {M:>6} | {tuned_str} | {t_old:11.2f} | {t_new:11.2f} "
-                f"| {ratio_new_old:7.2f}x | {ratio_tuned}{marker}"
-            )
-
-
-if __name__ == "__main__":
-    main()

benchmarks/kernels/benchmark_mrope.py

@@ -36,7 +36,6 @@ from typing import Any
 import numpy as np
 import torch
 
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.transformers_utils.config import get_config
 from vllm.utils.argparse_utils import FlexibleArgumentParser
@@ -79,7 +78,6 @@ def calculate_stats(times: list[float]) -> dict[str, float]:
     }
 
 
-@default_vllm_config()
 def benchmark_mrope(
     model_name: str,
     num_tokens: int,

benchmarks/kernels/benchmark_rope.py

@@ -5,7 +5,6 @@ import itertools
 
 import torch
 
-from vllm.benchmarks.lib.utils import default_vllm_config
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
@@ -30,7 +29,6 @@ def get_benchmark(head_size, rotary_dim, is_neox_style, device):
             args={},
         )
     )
-    @default_vllm_config()
     def benchmark(batch_size, seq_len, num_heads, provider):
         dtype = torch.bfloat16
         max_position = 8192

benchmarks/multi_turn/README.md

@@ -7,7 +7,7 @@ First start serving your model
 ```bash
 export MODEL_PATH=/models/meta-llama/Meta-Llama-3.1-8B-Instruct/
 
-vllm serve $MODEL_PATH --served-model-name Llama
+vllm serve $MODEL_PATH --served-model-name Llama --disable-log-requests
 ```
 
 The variable `MODEL_PATH` should be a path to the model files (e.g. downloaded from huggingface).

cmake/cpu_extension.cmake

@@ -13,16 +13,28 @@ endif()
 #
 # Define environment variables for special configurations
 #
-set(ENABLE_X86_ISA $ENV{VLLM_CPU_X86})
+set(ENABLE_AVX2 $ENV{VLLM_CPU_AVX2})
+set(ENABLE_AVX512 $ENV{VLLM_CPU_AVX512})
+set(ENABLE_AVX512BF16 $ENV{VLLM_CPU_AVX512BF16})
+set(ENABLE_AVX512VNNI $ENV{VLLM_CPU_AVX512VNNI})
+set(ENABLE_AMXBF16 $ENV{VLLM_CPU_AMXBF16})
 set(ENABLE_ARM_BF16 $ENV{VLLM_CPU_ARM_BF16})
 
 include_directories("${CMAKE_SOURCE_DIR}/csrc")
 
+
 set (ENABLE_NUMA TRUE)
 
 #
 # Check the compile flags
 #
+
+if (CMAKE_SYSTEM_PROCESSOR MATCHES "x86_64")
+    list(APPEND CXX_COMPILE_FLAGS
+        "-mf16c"
+    )
+endif()
+
 if(MACOSX_FOUND)
     list(APPEND CXX_COMPILE_FLAGS
         "-DVLLM_CPU_EXTENSION")
@@ -66,6 +78,18 @@ function(check_sysctl TARGET OUT)
     endif()
 endfunction()
 
+
+function (is_avx512_disabled OUT)
+    set(DISABLE_AVX512 $ENV{VLLM_CPU_DISABLE_AVX512})
+    if(DISABLE_AVX512 AND DISABLE_AVX512 STREQUAL "true")
+        set(${OUT} ON PARENT_SCOPE)
+    else()
+        set(${OUT} OFF PARENT_SCOPE)
+    endif()
+endfunction()
+
+is_avx512_disabled(AVX512_DISABLED)
+
 if (MACOSX_FOUND AND CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64")
     message(STATUS "Apple Silicon Detected")
     set(APPLE_SILICON_FOUND TRUE)
@@ -73,6 +97,8 @@ if (MACOSX_FOUND AND CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64")
     check_sysctl(hw.optional.neon ASIMD_FOUND)
     check_sysctl(hw.optional.arm.FEAT_BF16 ARM_BF16_FOUND)
 else()
+    find_isa(${CPUINFO} "avx2" AVX2_FOUND)
+    find_isa(${CPUINFO} "avx512f" AVX512_FOUND)
     find_isa(${CPUINFO} "Power11" POWER11_FOUND)
     find_isa(${CPUINFO} "POWER10" POWER10_FOUND)
     find_isa(${CPUINFO} "POWER9" POWER9_FOUND)
@@ -82,32 +108,77 @@ else()
     find_isa(${CPUINFO} "v" RVV_FOUND) # Check for RISC-V RVV support
 
     # Support cross-compilation by allowing override via environment variables
+    if (ENABLE_AVX2)
+        set(AVX2_FOUND ON)
+        message(STATUS "AVX2 support enabled via VLLM_CPU_AVX2 environment variable")
+    endif()
+    if (ENABLE_AVX512)
+        set(AVX512_FOUND ON)
+        message(STATUS "AVX512 support enabled via VLLM_CPU_AVX512 environment variable")
+    endif()
     if (ENABLE_ARM_BF16)
         set(ARM_BF16_FOUND ON)
         message(STATUS "ARM BF16 support enabled via VLLM_CPU_ARM_BF16 environment variable")
     endif()
 endif()
 
-if (CMAKE_SYSTEM_PROCESSOR MATCHES "x86_64|amd64" OR ENABLE_X86_ISA)
-    set(ENABLE_X86_ISA ON)
-    if (NOT (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND
-            CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 12.3))
-        message(FATAL_ERROR "X86 backend requires gcc/g++ >= 12.3")
-    endif()
-    list(APPEND CXX_COMPILE_FLAGS "-mf16c")
-    list(APPEND CXX_COMPILE_FLAGS_AVX512 ${CXX_COMPILE_FLAGS})
-    list(APPEND CXX_COMPILE_FLAGS_AVX2 ${CXX_COMPILE_FLAGS})
-    list(APPEND CXX_COMPILE_FLAGS_AVX512
+if (AVX512_FOUND AND NOT AVX512_DISABLED)
+    list(APPEND CXX_COMPILE_FLAGS
         "-mavx512f"
         "-mavx512vl"
         "-mavx512bw"
-        "-mavx512dq"
-        "-mavx512bf16"
-        "-mavx512vnni"
-        "-mamx-bf16"
-        "-mamx-tile")
-    list(APPEND CXX_COMPILE_FLAGS_AVX2
-        "-mavx2")
+        "-mavx512dq")
+
+    find_isa(${CPUINFO} "avx512_bf16" AVX512BF16_FOUND)
+    if (AVX512BF16_FOUND OR ENABLE_AVX512BF16)
+        if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND
+            CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 12.3)
+            list(APPEND CXX_COMPILE_FLAGS "-mavx512bf16")
+            set(ENABLE_AVX512BF16 ON)
+        else()
+            set(ENABLE_AVX512BF16 OFF)
+            message(WARNING "Disable AVX512-BF16 ISA support, requires gcc/g++ >= 12.3")
+        endif()
+    else()
+        set(ENABLE_AVX512BF16 OFF)
+        message(WARNING "Disable AVX512-BF16 ISA support, no avx512_bf16 found in local CPU flags." " If cross-compilation is required, please set env VLLM_CPU_AVX512BF16=1.")
+    endif()
+
+    find_isa(${CPUINFO} "avx512_vnni" AVX512VNNI_FOUND)
+    if (AVX512VNNI_FOUND OR ENABLE_AVX512VNNI)
+        if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND
+            CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 12.3)
+            list(APPEND CXX_COMPILE_FLAGS "-mavx512vnni")
+            set(ENABLE_AVX512VNNI ON)
+        else()
+            set(ENABLE_AVX512VNNI OFF)
+            message(WARNING "Disable AVX512-VNNI ISA support, requires gcc/g++ >= 12.3")
+        endif()
+    else()
+        set(ENABLE_AVX512VNNI OFF)
+        message(WARNING "Disable AVX512-VNNI ISA support, no avx512_vnni found in local CPU flags." " If cross-compilation is required, please set env VLLM_CPU_AVX512VNNI=1.")
+    endif()
+
+    find_isa(${CPUINFO} "amx_bf16" AMXBF16_FOUND)
+    if (AMXBF16_FOUND OR ENABLE_AMXBF16)
+        if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND
+            CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 12.3)
+            list(APPEND CXX_COMPILE_FLAGS "-mamx-bf16" "-mamx-tile")
+            set(ENABLE_AMXBF16 ON)
+            add_compile_definitions(-DCPU_CAPABILITY_AMXBF16)
+        else()
+            set(ENABLE_AMXBF16 OFF)
+            message(WARNING "Disable AMX_BF16 ISA support, requires gcc/g++ >= 12.3")
+        endif()
+    else()
+        set(ENABLE_AMXBF16 OFF)
+        message(WARNING "Disable AMX_BF16 ISA support, no amx_bf16 found in local CPU flags." " If cross-compilation is required, please set env VLLM_CPU_AMXBF16=1.")
+    endif()
+    
+elseif (AVX2_FOUND)
+    list(APPEND CXX_COMPILE_FLAGS "-mavx2")
+    message(WARNING "vLLM CPU backend using AVX2 ISA")
+    
 elseif (POWER9_FOUND OR POWER10_FOUND OR POWER11_FOUND)
     message(STATUS "PowerPC detected")
     if (POWER9_FOUND)
@@ -148,12 +219,12 @@ elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "riscv64")
         list(APPEND CXX_COMPILE_FLAGS "-march=rv64gc")
     endif()
 else()
-    message(FATAL_ERROR "vLLM CPU backend requires X86, Power9+ ISA, S390X ISA, ARMv8 or RISC-V support.")
+    message(FATAL_ERROR "vLLM CPU backend requires AVX512, AVX2, Power9+ ISA, S390X ISA, ARMv8 or RISC-V support.")
 endif()
 
 
-# Build oneDNN for GEMM kernels
-if (ENABLE_X86_ISA OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND OR POWER10_FOUND OR POWER11_FOUND)
+# Build oneDNN for GEMM kernels (only for x86-AVX512 /ARM platforms)
+if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND OR POWER10_FOUND OR POWER11_FOUND)
     # Fetch and build Arm Compute Library (ACL) as oneDNN's backend for AArch64
     # TODO [fadara01]: remove this once ACL can be fetched and built automatically as a dependency of oneDNN
     set(ONEDNN_AARCH64_USE_ACL OFF CACHE BOOL "")
@@ -258,21 +329,13 @@ if (ENABLE_X86_ISA OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND
     set(ONEDNN_ENABLE_WORKLOAD "INFERENCE")
     set(ONEDNN_ENABLE_PRIMITIVE "MATMUL;REORDER")
     set(ONEDNN_BUILD_GRAPH "OFF")
-    set(ONEDNN_ENABLE_JIT_PROFILING "ON")
+    set(ONEDNN_ENABLE_JIT_PROFILING "OFF")
     set(ONEDNN_ENABLE_ITT_TASKS "OFF")
-    set(ONEDNN_ENABLE_MAX_CPU_ISA "ON")
-    set(ONEDNN_ENABLE_CPU_ISA_HINTS "ON")
-    set(ONEDNN_VERBOSE "ON")
+    set(ONEDNN_ENABLE_MAX_CPU_ISA "OFF")
+    set(ONEDNN_ENABLE_CPU_ISA_HINTS "OFF")
+    set(ONEDNN_VERBOSE "OFF")
     set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
 
-    # TODO: Refactor this
-    if (ENABLE_X86_ISA)
-        # Note: only enable oneDNN for AVX512
-        list(APPEND DNNL_COMPILE_FLAGS ${CXX_COMPILE_FLAGS_AVX512})
-    else()
-        list(APPEND DNNL_COMPILE_FLAGS ${CXX_COMPILE_FLAGS})
-    endif()
-
     set(VLLM_BUILD_TYPE ${CMAKE_BUILD_TYPE})
     set(CMAKE_BUILD_TYPE "Release") # remove oneDNN debug symbols to reduce size
     FetchContent_MakeAvailable(oneDNN)
@@ -285,20 +348,14 @@ if (ENABLE_X86_ISA OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND
         PRIVATE ${oneDNN_SOURCE_DIR}/src
     )
     target_link_libraries(dnnl_ext dnnl torch)
-    target_compile_options(dnnl_ext PRIVATE ${DNNL_COMPILE_FLAGS} -fPIC)
+    target_compile_options(dnnl_ext PRIVATE ${CXX_COMPILE_FLAGS} -fPIC)
     list(APPEND LIBS dnnl_ext)
     set(USE_ONEDNN ON)
 else()
     set(USE_ONEDNN OFF)
 endif()
 
-# TODO: Refactor this
-if (ENABLE_X86_ISA)
-    message(STATUS "CPU extension (AVX512) compile flags: ${CXX_COMPILE_FLAGS_AVX512}")
-    message(STATUS "CPU extension (AVX2) compile flags: ${CXX_COMPILE_FLAGS_AVX2}")
-else()
-    message(STATUS "CPU extension compile flags: ${CXX_COMPILE_FLAGS}")
-endif()
+message(STATUS "CPU extension compile flags: ${CXX_COMPILE_FLAGS}")
 
 if(ENABLE_NUMA)
     list(APPEND LIBS numa)
@@ -333,6 +390,25 @@ set(VLLM_EXT_SRC
     "csrc/cpu/cpu_attn.cpp"
     "csrc/cpu/torch_bindings.cpp")
 
+if (AVX512_FOUND AND NOT AVX512_DISABLED)
+    set(VLLM_EXT_SRC
+        "csrc/cpu/shm.cpp"
+        "csrc/cpu/cpu_wna16.cpp"
+        "csrc/cpu/cpu_fused_moe.cpp"
+        ${VLLM_EXT_SRC})
+    if (ENABLE_AVX512BF16 AND ENABLE_AVX512VNNI)
+        set(VLLM_EXT_SRC
+            "csrc/cpu/sgl-kernels/gemm.cpp"
+            "csrc/cpu/sgl-kernels/gemm_int8.cpp"
+            "csrc/cpu/sgl-kernels/gemm_fp8.cpp"
+            "csrc/cpu/sgl-kernels/moe.cpp"
+            "csrc/cpu/sgl-kernels/moe_int8.cpp"
+            "csrc/cpu/sgl-kernels/moe_fp8.cpp"
+            ${VLLM_EXT_SRC})
+        add_compile_definitions(-DCPU_CAPABILITY_AVX512)
+    endif()
+endif()
+
 if (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND)
     set(VLLM_EXT_SRC
         "csrc/cpu/shm.cpp"
@@ -345,83 +421,21 @@ if(USE_ONEDNN)
         ${VLLM_EXT_SRC})
 endif()
 
-if (ENABLE_X86_ISA)
-    set(VLLM_EXT_SRC_AVX512
-        "csrc/cpu/sgl-kernels/gemm.cpp"
-        "csrc/cpu/sgl-kernels/gemm_int8.cpp"
-        "csrc/cpu/sgl-kernels/gemm_fp8.cpp"
-        "csrc/cpu/sgl-kernels/moe.cpp"
-        "csrc/cpu/sgl-kernels/moe_int8.cpp"
-        "csrc/cpu/sgl-kernels/moe_fp8.cpp"
-        "csrc/cpu/shm.cpp"
-        "csrc/cpu/cpu_wna16.cpp"
-        "csrc/cpu/cpu_fused_moe.cpp"
-        "csrc/cpu/utils.cpp"
-        "csrc/cpu/cpu_attn.cpp"
-        "csrc/cpu/dnnl_kernels.cpp"
-        "csrc/cpu/torch_bindings.cpp"
-        # TODO: Remove these files
-        "csrc/cpu/activation.cpp"
-        "csrc/cpu/layernorm.cpp"
-        "csrc/cpu/mla_decode.cpp"
-        "csrc/cpu/pos_encoding.cpp"
-        "csrc/moe/dynamic_4bit_int_moe_cpu.cpp") 
+message(STATUS "CPU extension source files: ${VLLM_EXT_SRC}")
 
-    set(VLLM_EXT_SRC_AVX2 
-        "csrc/cpu/utils.cpp"
-        "csrc/cpu/cpu_attn.cpp"
-        "csrc/cpu/torch_bindings.cpp"
-        # TODO: Remove these files
-        "csrc/cpu/activation.cpp"
-        "csrc/cpu/layernorm.cpp"
-        "csrc/cpu/mla_decode.cpp"
-        "csrc/cpu/pos_encoding.cpp"
-        "csrc/moe/dynamic_4bit_int_moe_cpu.cpp") 
+#
+# Define extension targets
+#
 
-    message(STATUS "CPU extension (AVX512) source files: ${VLLM_EXT_SRC_AVX512}")
-    message(STATUS "CPU extension (AVX2) source files: ${VLLM_EXT_SRC_AVX2}")
-
-    define_extension_target(
-        _C
-        DESTINATION vllm
-        LANGUAGE CXX
-        SOURCES ${VLLM_EXT_SRC_AVX512}
-        LIBRARIES ${LIBS}
-        COMPILE_FLAGS ${CXX_COMPILE_FLAGS_AVX512}
-        USE_SABI 3
-        WITH_SOABI
-    )
-
-    # For SGL kernels
-    target_compile_definitions(_C PRIVATE "-DCPU_CAPABILITY_AVX512")
-    # For AMX kernels
-    target_compile_definitions(_C PRIVATE "-DCPU_CAPABILITY_AMXBF16")
-
-    define_extension_target(
-        _C_AVX2
-        DESTINATION vllm
-        LANGUAGE CXX
-        SOURCES ${VLLM_EXT_SRC_AVX2}
-        LIBRARIES ${LIBS}
-        COMPILE_FLAGS ${CXX_COMPILE_FLAGS_AVX2}
-        USE_SABI 3
-        WITH_SOABI
-    )
-else()
-    message(STATUS "CPU extension source files: ${VLLM_EXT_SRC}")
-    #
-    # Define extension targets
-    #
-    define_extension_target(
-        _C
-        DESTINATION vllm
-        LANGUAGE CXX
-        SOURCES ${VLLM_EXT_SRC}
-        LIBRARIES ${LIBS}
-        COMPILE_FLAGS ${CXX_COMPILE_FLAGS}
-        USE_SABI 3
-        WITH_SOABI
-    )
-endif()
+define_extension_target(
+    _C
+    DESTINATION vllm
+    LANGUAGE CXX
+    SOURCES ${VLLM_EXT_SRC}
+    LIBRARIES ${LIBS}
+    COMPILE_FLAGS ${CXX_COMPILE_FLAGS}
+    USE_SABI 3
+    WITH_SOABI
+)
 
 message(STATUS "Enabling C extension.")

cmake/external_projects/vllm_flash_attn.cmake

@@ -17,8 +17,7 @@ endif()
 # They should be identical but if they aren't, this is a massive footgun.
 #
 # The vllm-flash-attn install rules are nested under vllm to make sure the library gets installed in the correct place.
-# To only install vllm-flash-attn, use --component _vllm_fa2_C (for FA2), --component _vllm_fa3_C (for FA3),
-# or --component _vllm_fa4_cutedsl_C (for FA4 CuteDSL Python files).
+# To only install vllm-flash-attn, use --component _vllm_fa2_C (for FA2) or --component _vllm_fa3_C (for FA3).
 # If no component is specified, vllm-flash-attn is still installed.
 
 # If VLLM_FLASH_ATTN_SRC_DIR is set, vllm-flash-attn is installed from that directory instead of downloading.
@@ -39,16 +38,22 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 140c00c0241bb60cc6e44e7c1be9998d4b20d8d2
+          GIT_TAG 5824e6e2008271063c3229ab3e7032bd74abbbc6
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn
   )
 endif()
 
+
+# Ensure the vllm/vllm_flash_attn directory exists before installation
+install(CODE "file(MAKE_DIRECTORY \"\${CMAKE_INSTALL_PREFIX}/vllm/vllm_flash_attn\")" ALL_COMPONENTS)
+
 # Make sure vllm-flash-attn install rules are nested under vllm/
-# ALL_COMPONENTS ensures the save/modify/restore runs exactly once regardless
-# of how many components are being installed, avoiding double-append of /vllm/.
+# This is here to support installing all components under the same prefix with cmake --install.
+# setup.py installs every component separately but uses the same prefix for all.
+# ALL_COMPONENTS is used to avoid duplication for FA2 and FA3,
+# and these statements don't hurt when installing neither component.
 install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY FALSE)" ALL_COMPONENTS)
 install(CODE "set(OLD_CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}\")" ALL_COMPONENTS)
 install(CODE "set(CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}/vllm/\")" ALL_COMPONENTS)
@@ -57,48 +62,22 @@ install(CODE "set(CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}/vllm/\")" ALL_
 FetchContent_MakeAvailable(vllm-flash-attn)
 message(STATUS "vllm-flash-attn is available at ${vllm-flash-attn_SOURCE_DIR}")
 
-# Restore the install prefix after FA's install rules
+# Restore the install prefix
 install(CODE "set(CMAKE_INSTALL_PREFIX \"\${OLD_CMAKE_INSTALL_PREFIX}\")" ALL_COMPONENTS)
 install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
 
-# Install shared Python files for both FA2 and FA3 components
-foreach(_FA_COMPONENT _vllm_fa2_C _vllm_fa3_C)
-  # Ensure the vllm/vllm_flash_attn directory exists before installation
-  install(CODE "file(MAKE_DIRECTORY \"\${CMAKE_INSTALL_PREFIX}/vllm/vllm_flash_attn\")"
-    COMPONENT ${_FA_COMPONENT})
+# Copy over the vllm-flash-attn python files (duplicated for fa2 and fa3, in
+# case only one is built, in the case both are built redundant work is done)
+install(
+  DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
+  DESTINATION vllm/vllm_flash_attn
+  COMPONENT _vllm_fa2_C
+  FILES_MATCHING PATTERN "*.py"
+)
 
-  # Copy vllm_flash_attn python files (except __init__.py and flash_attn_interface.py
-  # which are source-controlled in vllm)
-  install(
-    DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
-    DESTINATION vllm/vllm_flash_attn
-    COMPONENT ${_FA_COMPONENT}
-    FILES_MATCHING PATTERN "*.py"
-    PATTERN "__init__.py" EXCLUDE
-    PATTERN "flash_attn_interface.py" EXCLUDE
-  )
-
-endforeach()
-
-#
-# FA4 CuteDSL component
-# This is a Python-only component that copies the flash_attn/cute directory
-# and transforms imports to match our package structure.
-#
-add_custom_target(_vllm_fa4_cutedsl_C)
-
-# Copy flash_attn/cute directory (needed for FA4) and transform imports
-# The cute directory uses flash_attn.cute imports internally, which we replace
-# with vllm.vllm_flash_attn.cute to match our package structure.
-install(CODE "
-  file(GLOB_RECURSE CUTE_PY_FILES \"${vllm-flash-attn_SOURCE_DIR}/flash_attn/cute/*.py\")
-  foreach(SRC_FILE \${CUTE_PY_FILES})
-    file(RELATIVE_PATH REL_PATH \"${vllm-flash-attn_SOURCE_DIR}/flash_attn/cute\" \${SRC_FILE})
-    set(DST_FILE \"\${CMAKE_INSTALL_PREFIX}/vllm/vllm_flash_attn/cute/\${REL_PATH}\")
-    get_filename_component(DST_DIR \${DST_FILE} DIRECTORY)
-    file(MAKE_DIRECTORY \${DST_DIR})
-    file(READ \${SRC_FILE} FILE_CONTENTS)
-    string(REPLACE \"flash_attn.cute\" \"vllm.vllm_flash_attn.cute\" FILE_CONTENTS \"\${FILE_CONTENTS}\")
-    file(WRITE \${DST_FILE} \"\${FILE_CONTENTS}\")
-  endforeach()
-" COMPONENT _vllm_fa4_cutedsl_C)
+install(
+  DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
+  DESTINATION vllm/vllm_flash_attn
+  COMPONENT _vllm_fa3_C
+  FILES_MATCHING PATTERN "*.py"
+)

csrc/activation_kernels.cu

@@ -5,11 +5,115 @@
 #include <cmath>
 
 #include "cuda_compat.h"
-#include "cuda_vec_utils.cuh"
 #include "dispatch_utils.h"
 
 namespace vllm {
 
+struct alignas(32) u32x8_t {
+  uint32_t u0, u1, u2, u3, u4, u5, u6, u7;
+};
+
+__device__ __forceinline__ void ld256(u32x8_t& val, const u32x8_t* ptr) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 1000
+  asm volatile("ld.global.nc.v8.u32 {%0,%1,%2,%3,%4,%5,%6,%7}, [%8];\n"
+               : "=r"(val.u0), "=r"(val.u1), "=r"(val.u2), "=r"(val.u3),
+                 "=r"(val.u4), "=r"(val.u5), "=r"(val.u6), "=r"(val.u7)
+               : "l"(ptr));
+#else
+  const uint4* uint_ptr = reinterpret_cast<const uint4*>(ptr);
+  uint4 top_half = __ldg(&uint_ptr[0]);
+  uint4 bottom_half = __ldg(&uint_ptr[1]);
+  val.u0 = top_half.x;
+  val.u1 = top_half.y;
+  val.u2 = top_half.z;
+  val.u3 = top_half.w;
+  val.u4 = bottom_half.x;
+  val.u5 = bottom_half.y;
+  val.u6 = bottom_half.z;
+  val.u7 = bottom_half.w;
+#endif
+}
+
+__device__ __forceinline__ void st256(u32x8_t& val, u32x8_t* ptr) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 1000
+  asm volatile("st.global.v8.u32 [%0], {%1,%2,%3,%4,%5,%6,%7,%8};\n"
+               :
+               : "l"(ptr), "r"(val.u0), "r"(val.u1), "r"(val.u2), "r"(val.u3),
+                 "r"(val.u4), "r"(val.u5), "r"(val.u6), "r"(val.u7)
+               : "memory");
+#else
+  uint4* uint_ptr = reinterpret_cast<uint4*>(ptr);
+  uint_ptr[0] = make_uint4(val.u0, val.u1, val.u2, val.u3);
+  uint_ptr[1] = make_uint4(val.u4, val.u5, val.u6, val.u7);
+#endif
+}
+
+template <bool support_256>
+struct VecTraits;
+
+template <>
+struct VecTraits<true> {
+  static constexpr int ARCH_MAX_VEC_SIZE = 32;
+  using vec_t = u32x8_t;
+};
+
+template <>
+struct VecTraits<false> {
+  static constexpr int ARCH_MAX_VEC_SIZE = 16;
+  using vec_t = int4;
+};
+
+template <typename T>
+struct PackedTraits;
+
+template <>
+struct PackedTraits<c10::BFloat16> {
+  using packed_t = __nv_bfloat162;
+};
+
+template <>
+struct PackedTraits<c10::Half> {
+  using packed_t = __half2;
+};
+
+template <>
+struct PackedTraits<float> {
+  using packed_t = float2;
+};
+
+template <typename packed_t>
+__device__ __forceinline__ float2 cast_to_float2(const packed_t& val) {
+  if constexpr (std::is_same_v<packed_t, __nv_bfloat162>) {
+    return __bfloat1622float2(val);
+  } else if constexpr (std::is_same_v<packed_t, __half2>) {
+    return __half22float2(val);
+  } else if constexpr (std::is_same_v<packed_t, float2>) {
+    return float2(val);
+  }
+}
+
+template <typename packed_t>
+__device__ __forceinline__ packed_t cast_to_packed(const float2& val) {
+  if constexpr (std::is_same_v<packed_t, __nv_bfloat162>) {
+    return __float22bfloat162_rn(val);
+  } else if constexpr (std::is_same_v<packed_t, __half2>) {
+    return __float22half2_rn(val);
+  } else if constexpr (std::is_same_v<packed_t, float2>) {
+    return float2(val);
+  }
+}
+
+template <typename packed_t>
+__device__ __forceinline__ packed_t packed_mul(const packed_t& x,
+                                               const packed_t& y) {
+  if constexpr (std::is_same_v<packed_t, __nv_bfloat162> ||
+                std::is_same_v<packed_t, __half2>) {
+    return __hmul2(x, y);
+  } else if constexpr (std::is_same_v<packed_t, float2>) {
+    return make_float2(x.x * y.x, x.y * y.y);
+  }
+}
+
 template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&),
           bool act_first>
 __device__ __forceinline__ scalar_t compute(const scalar_t& x,
@@ -25,6 +129,16 @@ __device__ __forceinline__ packed_t packed_compute(const packed_t& x,
                    : packed_mul(x, PACKED_ACT_FN(y));
 }
 
+// Check if all pointers are 16-byte aligned for int4 vectorized access
+__host__ __device__ __forceinline__ bool is_16byte_aligned(const void* ptr) {
+  return (reinterpret_cast<uintptr_t>(ptr) & 15) == 0;
+}
+
+// Check if all pointers are 16-byte aligned for longlong4_32a vectorized access
+__host__ __device__ __forceinline__ bool is_32byte_aligned(const void* ptr) {
+  return (reinterpret_cast<uintptr_t>(ptr) & 31) == 0;
+}
+
 // Activation and gating kernel template.
 template <typename scalar_t, typename packed_t,
           scalar_t (*ACT_FN)(const scalar_t&),
@@ -39,32 +153,36 @@ __global__ void act_and_mul_kernel(
   scalar_t* out_ptr = out + blockIdx.x * d;
 
   if constexpr (use_vec) {
-    using cuda_t = typename CUDATypeConverter<scalar_t>::Type;
-    using pvec_t = PackedVec<cuda_t, use_256b>;
+    // Fast path: 128-bit/256-bit vectorized loop
+    using vec_t = typename VecTraits<use_256b>::vec_t;
+    constexpr int ARCH_MAX_VEC_SIZE = VecTraits<use_256b>::ARCH_MAX_VEC_SIZE;
+    constexpr int VEC_SIZE = ARCH_MAX_VEC_SIZE / sizeof(packed_t);
 
-    const pvec_t* x_vec = reinterpret_cast<const pvec_t*>(x_ptr);
-    const pvec_t* y_vec = reinterpret_cast<const pvec_t*>(y_ptr);
-    pvec_t* out_vec = reinterpret_cast<pvec_t*>(out_ptr);
-    const int num_vecs = d / 2 / pvec_t::NUM_ELTS;
+    const vec_t* x_vec = reinterpret_cast<const vec_t*>(x_ptr);
+    const vec_t* y_vec = reinterpret_cast<const vec_t*>(y_ptr);
+    vec_t* out_vec = reinterpret_cast<vec_t*>(out_ptr);
+    const int num_vecs = d / 2 / VEC_SIZE;
 
     for (int i = threadIdx.x; i < num_vecs; i += blockDim.x) {
-      pvec_t x, y;
+      vec_t x, y;
       if constexpr (use_256b) {
         ld256(x, &x_vec[i]);
         ld256(y, &y_vec[i]);
       } else {
-        ld128(x, &x_vec[i]);
-        ld128(y, &y_vec[i]);
+        x = VLLM_LDG(&x_vec[i]);
+        y = VLLM_LDG(&y_vec[i]);
       }
+      auto* xp = reinterpret_cast<packed_t*>(&x);
+      auto* yp = reinterpret_cast<packed_t*>(&y);
 #pragma unroll
-      for (int j = 0; j < pvec_t::NUM_ELTS; j++) {
-        x.elts[j] = packed_compute<packed_t, PACKED_ACT_FN, act_first>(
-            x.elts[j], y.elts[j]);
+      for (int j = 0; j < VEC_SIZE; j++) {
+        xp[j] =
+            packed_compute<packed_t, PACKED_ACT_FN, act_first>(xp[j], yp[j]);
       }
       if constexpr (use_256b) {
         st256(x, &out_vec[i]);
       } else {
-        st128(x, &out_vec[i]);
+        out_vec[i] = x;
       }
     }
   } else {
@@ -152,54 +270,51 @@ packed_gelu_tanh_kernel(const packed_t& val) {
 // Launch activation and gating kernel.
 // Use ACT_FIRST (bool) indicating whether to apply the activation function
 // first.
-#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL, PACKED_KERNEL, ACT_FIRST)        \
-  auto dtype = input.scalar_type();                                            \
-  int d = input.size(-1) / 2;                                                  \
-  int64_t num_tokens = input.numel() / input.size(-1);                         \
-  if (num_tokens == 0) {                                                       \
-    return;                                                                    \
-  }                                                                            \
-  dim3 grid(num_tokens);                                                       \
-  int cc_major = at::cuda::getCurrentDeviceProperties()->major;                \
-  int support_vec =                                                            \
-      (CUDA_VERSION >= 12090 && cc_major >= 10 && num_tokens > 128)            \
-          ? vllm::VecTraits<true>::ARCH_MAX_VEC_SIZE                           \
-          : vllm::VecTraits<false>::ARCH_MAX_VEC_SIZE;                         \
-  int vec_size = support_vec / at::elementSize(dtype);                         \
-  const bool use_vec = (d % vec_size == 0);                                    \
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));            \
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();                \
-  if (use_vec) {                                                               \
-    dim3 block(std::min(d / vec_size, 1024));                                  \
-    if (CUDA_VERSION >= 12090 && cc_major >= 10 && num_tokens > 128) {         \
-      VLLM_DISPATCH_FLOATING_TYPES(dtype, "act_and_mul_kernel", [&] {          \
-        vllm::act_and_mul_kernel<                                              \
-            scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,      \
-            KERNEL<scalar_t>,                                                  \
-            PACKED_KERNEL<typename vllm::PackedTypeConverter<scalar_t>::Type>, \
-            ACT_FIRST, true, true><<<grid, block, 0, stream>>>(                \
-            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);          \
-      });                                                                      \
-    } else {                                                                   \
-      VLLM_DISPATCH_FLOATING_TYPES(dtype, "act_and_mul_kernel", [&] {          \
-        vllm::act_and_mul_kernel<                                              \
-            scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,      \
-            KERNEL<scalar_t>,                                                  \
-            PACKED_KERNEL<typename vllm::PackedTypeConverter<scalar_t>::Type>, \
-            ACT_FIRST, true, false><<<grid, block, 0, stream>>>(               \
-            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);          \
-      });                                                                      \
-    }                                                                          \
-  } else {                                                                     \
-    dim3 block(std::min(d, 1024));                                             \
-    VLLM_DISPATCH_FLOATING_TYPES(dtype, "act_and_mul_kernel", [&] {            \
-      vllm::act_and_mul_kernel<                                                \
-          scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,        \
-          KERNEL<scalar_t>,                                                    \
-          PACKED_KERNEL<typename vllm::PackedTypeConverter<scalar_t>::Type>,   \
-          ACT_FIRST, false><<<grid, block, 0, stream>>>(                       \
-          out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);            \
-    });                                                                        \
+#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL, PACKED_KERNEL, ACT_FIRST)     \
+  auto dtype = input.scalar_type();                                         \
+  int d = input.size(-1) / 2;                                               \
+  int64_t num_tokens = input.numel() / input.size(-1);                      \
+  if (num_tokens == 0) {                                                    \
+    return;                                                                 \
+  }                                                                         \
+  dim3 grid(num_tokens);                                                    \
+  int cc_major = at::cuda::getCurrentDeviceProperties()->major;             \
+  int support_vec = (cc_major >= 10 && num_tokens > 128) ? 32 : 16;         \
+  int vec_size = support_vec / at::elementSize(dtype);                      \
+  const bool use_vec = (d % vec_size == 0);                                 \
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));         \
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();             \
+  if (use_vec) {                                                            \
+    dim3 block(std::min(d / vec_size, 1024));                               \
+    if (cc_major >= 10 && num_tokens > 128) {                               \
+      VLLM_DISPATCH_FLOATING_TYPES(dtype, "act_and_mul_kernel", [&] {       \
+        vllm::act_and_mul_kernel<                                           \
+            scalar_t, typename vllm::PackedTraits<scalar_t>::packed_t,      \
+            KERNEL<scalar_t>,                                               \
+            PACKED_KERNEL<typename vllm::PackedTraits<scalar_t>::packed_t>, \
+            ACT_FIRST, true, true><<<grid, block, 0, stream>>>(             \
+            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);       \
+      });                                                                   \
+    } else {                                                                \
+      VLLM_DISPATCH_FLOATING_TYPES(dtype, "act_and_mul_kernel", [&] {       \
+        vllm::act_and_mul_kernel<                                           \
+            scalar_t, typename vllm::PackedTraits<scalar_t>::packed_t,      \
+            KERNEL<scalar_t>,                                               \
+            PACKED_KERNEL<typename vllm::PackedTraits<scalar_t>::packed_t>, \
+            ACT_FIRST, true, false><<<grid, block, 0, stream>>>(            \
+            out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);       \
+      });                                                                   \
+    }                                                                       \
+  } else {                                                                  \
+    dim3 block(std::min(d, 1024));                                          \
+    VLLM_DISPATCH_FLOATING_TYPES(dtype, "act_and_mul_kernel", [&] {         \
+      vllm::act_and_mul_kernel<                                             \
+          scalar_t, typename vllm::PackedTraits<scalar_t>::packed_t,        \
+          KERNEL<scalar_t>,                                                 \
+          PACKED_KERNEL<typename vllm::PackedTraits<scalar_t>::packed_t>,   \
+          ACT_FIRST, false><<<grid, block, 0, stream>>>(                    \
+          out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d);         \
+    });                                                                     \
   }
 
 void silu_and_mul(torch::Tensor& out,    // [..., d]
@@ -261,31 +376,35 @@ __global__ void act_and_mul_kernel_with_param(
   scalar_t* out_ptr = out + blockIdx.x * d;
 
   if constexpr (use_vec) {
-    using cuda_t = typename CUDATypeConverter<scalar_t>::Type;
-    using pvec_t = PackedVec<cuda_t, use_256b>;
+    // Fast path: 128-bit/256-bit vectorized loop
+    using vec_t = typename VecTraits<use_256b>::vec_t;
+    constexpr int ARCH_MAX_VEC_SIZE = VecTraits<use_256b>::ARCH_MAX_VEC_SIZE;
+    constexpr int VEC_SIZE = ARCH_MAX_VEC_SIZE / sizeof(packed_t);
 
-    const pvec_t* x_vec = reinterpret_cast<const pvec_t*>(x_ptr);
-    const pvec_t* y_vec = reinterpret_cast<const pvec_t*>(y_ptr);
-    pvec_t* out_vec = reinterpret_cast<pvec_t*>(out_ptr);
-    const int num_vecs = d / 2 / pvec_t::NUM_ELTS;
+    const vec_t* x_vec = reinterpret_cast<const vec_t*>(x_ptr);
+    const vec_t* y_vec = reinterpret_cast<const vec_t*>(y_ptr);
+    vec_t* out_vec = reinterpret_cast<vec_t*>(out_ptr);
+    const int num_vecs = d / 2 / VEC_SIZE;
 
     for (int i = threadIdx.x; i < num_vecs; i += blockDim.x) {
-      pvec_t x, y;
+      vec_t x, y;
       if constexpr (use_256b) {
         ld256(x, &x_vec[i]);
         ld256(y, &y_vec[i]);
       } else {
-        ld128(x, &x_vec[i]);
-        ld128(y, &y_vec[i]);
+        x = VLLM_LDG(&x_vec[i]);
+        y = VLLM_LDG(&y_vec[i]);
       }
+      auto* xp = reinterpret_cast<packed_t*>(&x);
+      auto* yp = reinterpret_cast<packed_t*>(&y);
 #pragma unroll
-      for (int j = 0; j < pvec_t::NUM_ELTS; j++) {
-        x.elts[j] = packed_mul(PACKED_ACT_FN(x.elts[j], param), y.elts[j]);
+      for (int j = 0; j < VEC_SIZE; j++) {
+        xp[j] = packed_mul(PACKED_ACT_FN(xp[j], param), yp[j]);
       }
       if constexpr (use_256b) {
         st256(x, &out_vec[i]);
       } else {
-        st128(x, &out_vec[i]);
+        out_vec[i] = x;
       }
     }
   } else {
@@ -378,24 +497,21 @@ __global__ void swigluoai_and_mul_kernel(
   }                                                                            \
   dim3 grid(num_tokens);                                                       \
   int cc_major = at::cuda::getCurrentDeviceProperties()->major;                \
-  int support_vec =                                                            \
-      (CUDA_VERSION >= 12090 && cc_major >= 10 && num_tokens > 128)            \
-          ? vllm::VecTraits<true>::ARCH_MAX_VEC_SIZE                           \
-          : vllm::VecTraits<false>::ARCH_MAX_VEC_SIZE;                         \
+  int support_vec = (cc_major >= 10 && num_tokens > 128) ? 32 : 16;            \
   int vec_size = support_vec / at::elementSize(dtype);                         \
   const bool use_vec = (d % vec_size == 0);                                    \
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));            \
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();                \
   if (use_vec) {                                                               \
     dim3 block(std::min(d / vec_size, 1024));                                  \
-    if (CUDA_VERSION >= 12090 && cc_major >= 10 && num_tokens > 128) {         \
+    if (cc_major >= 10 && num_tokens > 128) {                                  \
       VLLM_DISPATCH_FLOATING_TYPES(                                            \
           dtype, "act_and_mul_kernel_with_param", [&] {                        \
             vllm::act_and_mul_kernel_with_param<                               \
-                scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,  \
+                scalar_t, typename vllm::PackedTraits<scalar_t>::packed_t,     \
                 KERNEL<scalar_t>,                                              \
                 PACKED_KERNEL<                                                 \
-                    typename vllm::PackedTypeConverter<scalar_t>::Type>,       \
+                    typename vllm::PackedTraits<scalar_t>::packed_t>,          \
                 true, true><<<grid, block, 0, stream>>>(                       \
                 out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d,       \
                 PARAM);                                                        \
@@ -404,10 +520,10 @@ __global__ void swigluoai_and_mul_kernel(
       VLLM_DISPATCH_FLOATING_TYPES(                                            \
           dtype, "act_and_mul_kernel_with_param", [&] {                        \
             vllm::act_and_mul_kernel_with_param<                               \
-                scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,  \
+                scalar_t, typename vllm::PackedTraits<scalar_t>::packed_t,     \
                 KERNEL<scalar_t>,                                              \
                 PACKED_KERNEL<                                                 \
-                    typename vllm::PackedTypeConverter<scalar_t>::Type>,       \
+                    typename vllm::PackedTraits<scalar_t>::packed_t>,          \
                 true, false><<<grid, block, 0, stream>>>(                      \
                 out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d,       \
                 PARAM);                                                        \
@@ -417,9 +533,9 @@ __global__ void swigluoai_and_mul_kernel(
     dim3 block(std::min(d, 1024));                                             \
     VLLM_DISPATCH_FLOATING_TYPES(dtype, "act_and_mul_kernel_with_param", [&] { \
       vllm::act_and_mul_kernel_with_param<                                     \
-          scalar_t, typename vllm::PackedTypeConverter<scalar_t>::Type,        \
+          scalar_t, typename vllm::PackedTraits<scalar_t>::packed_t,           \
           KERNEL<scalar_t>,                                                    \
-          PACKED_KERNEL<typename vllm::PackedTypeConverter<scalar_t>::Type>,   \
+          PACKED_KERNEL<typename vllm::PackedTraits<scalar_t>::packed_t>,      \
           false><<<grid, block, 0, stream>>>(                                  \
           out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), d, PARAM);     \
     });                                                                        \
@@ -511,17 +627,14 @@ __global__ void activation_kernel(
   }                                                                      \
   dim3 grid(num_tokens);                                                 \
   int cc_major = at::cuda::getCurrentDeviceProperties()->major;          \
-  int support_vec =                                                      \
-      (CUDA_VERSION >= 12090 && cc_major >= 10 && num_tokens > 128)      \
-          ? vllm::VecTraits<true>::ARCH_MAX_VEC_SIZE                     \
-          : vllm::VecTraits<false>::ARCH_MAX_VEC_SIZE;                   \
+  int support_vec = (cc_major >= 10 && num_tokens > 128) ? 32 : 16;      \
   int vec_size = support_vec / at::elementSize(dtype);                   \
   const bool use_vec = (d % vec_size == 0);                              \
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));      \
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();          \
   if (use_vec) {                                                         \
     dim3 block(std::min(d / vec_size, 1024));                            \
-    if (CUDA_VERSION >= 12090 && cc_major >= 10 && num_tokens > 128) {   \
+    if (cc_major >= 10 && num_tokens > 128) {                            \
       VLLM_DISPATCH_FLOATING_TYPES(dtype, "activation_kernel", [&] {     \
         vllm::activation_kernel<scalar_t, KERNEL<scalar_t>, true, true>  \
             <<<grid, block, 0, stream>>>(out.data_ptr<scalar_t>(),       \

csrc/cpu/cpu_attn.cpp

@@ -16,8 +16,6 @@ torch::Tensor get_scheduler_metadata(
     isa = cpu_attention::ISA::VEC16;
   } else if (isa_hint == "neon") {
     isa = cpu_attention::ISA::NEON;
-  } else if (isa_hint == "vxe") {
-    isa = cpu_attention::ISA::VXE;
   } else {
     TORCH_CHECK(false, "Unsupported CPU attention ISA hint: " + isa_hint);
   }
@@ -102,8 +100,6 @@ void cpu_attn_reshape_and_cache(
       return cpu_attention::ISA::VEC16;
     } else if (isa == "neon") {
       return cpu_attention::ISA::NEON;
-    } else if (isa == "vxe") {
-      return cpu_attention::ISA::VXE;
     } else {
       TORCH_CHECK(false, "Invalid ISA type: " + isa);
     }

csrc/cpu/cpu_attn_impl.hpp

@@ -12,7 +12,7 @@
 #include "cpu/utils.hpp"
 
 namespace cpu_attention {
-enum class ISA { AMX, VEC, VEC16, NEON, VXE };
+enum class ISA { AMX, VEC, VEC16, NEON };
 
 template <ISA isa, typename scalar_t, int64_t head_dim>
 class AttentionImpl {};

csrc/cpu/cpu_attn_vxe.hpp

@@ -1,386 +0,0 @@
-#ifndef CPU_ATTN_VXE_HPP
-#define CPU_ATTN_VXE_HPP
-
-#include "cpu_attn_impl.hpp"
-#include <vecintrin.h>
-#include <type_traits>
-
-namespace cpu_attention {
-
-namespace {
-
-// s390x Vector = 16 bytes (128 bits)
-#define BLOCK_SIZE_ALIGNMENT 32
-#define HEAD_SIZE_ALIGNMENT 32
-#define MAX_Q_HEAD_NUM_PER_ITER 16
-
-template <typename kv_cache_t>
-FORCE_INLINE void load_row8_B_as_f32(const kv_cache_t* p, __vector float& b0,
-                                     __vector float& b1);
-
-// [1] Float Specialization
-template <>
-FORCE_INLINE void load_row8_B_as_f32<float>(const float* p, __vector float& b0,
-                                            __vector float& b1) {
-  // Explicitly cast to long long for offset, and float* for pointer
-  b0 = vec_xl((long long)0, const_cast<float*>(p));
-  b1 = vec_xl((long long)0, const_cast<float*>(p + 4));
-}
-
-// [2] BFloat16 Specialization (Big Endian Fix)
-template <>
-FORCE_INLINE void load_row8_B_as_f32<c10::BFloat16>(const c10::BFloat16* p,
-                                                    __vector float& b0,
-                                                    __vector float& b1) {
-  // 1. Load 8 BF16s (16 bytes) into one vector
-  // Explicit cast to unsigned short* for vec_xl to return vector unsigned short
-  __vector unsigned short raw = vec_xl((long long)0, (unsigned short*)p);
-
-  // 2. Prepare Zero vector
-  __vector unsigned short zeros = vec_splat_u16(0);
-
-  // 3. Merge High/Low to expand BF16 -> Float32
-  // On Big Endian, a float is [BF16_bits | 16_zero_bits]
-  b0 = (__vector float)vec_mergeh(raw, zeros);
-  b1 = (__vector float)vec_mergel(raw, zeros);
-}
-
-template <>
-FORCE_INLINE void load_row8_B_as_f32<c10::Half>(const c10::Half* p,
-                                                __vector float& b0,
-                                                __vector float& b1) {
-  alignas(16) float tmp[8];
-
-  // Manual unroll / conversion
-  tmp[0] = static_cast<float>(p[0]);
-  tmp[1] = static_cast<float>(p[1]);
-  tmp[2] = static_cast<float>(p[2]);
-  tmp[3] = static_cast<float>(p[3]);
-  tmp[4] = static_cast<float>(p[4]);
-  tmp[5] = static_cast<float>(p[5]);
-  tmp[6] = static_cast<float>(p[6]);
-  tmp[7] = static_cast<float>(p[7]);
-
-  // Explicit arguments for intrinsic: (long long offset, float* ptr)
-  b0 = vec_xl((long long)0, (float*)tmp);
-  b1 = vec_xl((long long)0, (float*)(tmp + 4));
-}
-
-template <int32_t M, typename kv_cache_t>
-FORCE_INLINE void gemm_micro_s390x_Mx8_Ku4(
-    const float* __restrict A,       // [M x K]
-    const kv_cache_t* __restrict B,  // [K x 8]
-    float* __restrict C,             // [M x 8]
-    int64_t lda, int64_t ldb, int64_t ldc, int32_t K, bool accumulate) {
-  static_assert(1 <= M && M <= 8, "M must be in [1,8]");
-
-// Helper macros to unroll codegen for M rows
-#define ROWS_APPLY(OP) OP(0) OP(1) OP(2) OP(3) OP(4) OP(5) OP(6) OP(7)
-#define IF_M(i) if constexpr (M > (i))
-
-  // 1. Define A pointers
-#define DECL_A(i) const float* a##i = A + (i) * lda;
-  ROWS_APPLY(DECL_A)
-#undef DECL_A
-
-  // 2. Define Accumulators (2 vectors covers 8 columns)
-#define DECL_ACC(i) __vector float acc##i##_0, acc##i##_1;
-  ROWS_APPLY(DECL_ACC)
-#undef DECL_ACC
-
-  // 3. Initialize Accumulators (Load C or Zero)
-#define INIT_ACC(i)                                                    \
-  IF_M(i) {                                                            \
-    if (accumulate) {                                                  \
-      acc##i##_0 =                                                     \
-          vec_xl((long long)0, const_cast<float*>(C + (i) * ldc + 0)); \
-      acc##i##_1 =                                                     \
-          vec_xl((long long)0, const_cast<float*>(C + (i) * ldc + 4)); \
-    } else {                                                           \
-      acc##i##_0 = vec_splats(0.0f);                                   \
-      acc##i##_1 = vec_splats(0.0f);                                   \
-    }                                                                  \
-  }
-  ROWS_APPLY(INIT_ACC)
-#undef INIT_ACC
-
-  int32_t k = 0;
-
-  for (; k + 3 < K; k += 4) {
-    // Load 4 values of A for each Row M: A[k...k+3]
-#define LOAD_A4(i)        \
-  __vector float a##i##v; \
-  IF_M(i) a##i##v = vec_xl((long long)0, const_cast<float*>(a##i + k));
-    ROWS_APPLY(LOAD_A4)
-#undef LOAD_A4
-
-    // Helper: FMA for specific lane L of A
-    // s390x: vec_madd(b, vec_splat(a, lane), acc)
-#define FMAS_LANE(i, aiv, L)                        \
-  IF_M(i) {                                         \
-    __vector float a_broad = vec_splat(aiv, L);     \
-    acc##i##_0 = vec_madd(b0, a_broad, acc##i##_0); \
-    acc##i##_1 = vec_madd(b1, a_broad, acc##i##_1); \
-  }
-
-    // Unroll K=0..3
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 0) * ldb, b0, b1);
-#define STEP_K0(i) FMAS_LANE(i, a##i##v, 0)
-      ROWS_APPLY(STEP_K0)
-#undef STEP_K0
-    }
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 1) * ldb, b0, b1);
-#define STEP_K1(i) FMAS_LANE(i, a##i##v, 1)
-      ROWS_APPLY(STEP_K1)
-#undef STEP_K1
-    }
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 2) * ldb, b0, b1);
-#define STEP_K2(i) FMAS_LANE(i, a##i##v, 2)
-      ROWS_APPLY(STEP_K2)
-#undef STEP_K2
-    }
-
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 3) * ldb, b0, b1);
-#define STEP_K3(i) FMAS_LANE(i, a##i##v, 3)
-      ROWS_APPLY(STEP_K3)
-#undef STEP_K3
-    }
-#undef FMAS_LANE
-  }
-
-  for (; k < K; ++k) {
-    __vector float b0, b1;
-    load_row8_B_as_f32<kv_cache_t>(B + (int64_t)k * ldb, b0, b1);
-#define TAIL_ROW(i)                              \
-  IF_M(i) {                                      \
-    __vector float ai = vec_splats(*(a##i + k)); \
-    acc##i##_0 = vec_madd(b0, ai, acc##i##_0);   \
-    acc##i##_1 = vec_madd(b1, ai, acc##i##_1);   \
-  }
-    ROWS_APPLY(TAIL_ROW)
-#undef TAIL_ROW
-  }
-
-#define STORE_ROW(i)                           \
-  IF_M(i) {                                    \
-    vec_xst(acc##i##_0, 0, C + (i) * ldc + 0); \
-    vec_xst(acc##i##_1, 0, C + (i) * ldc + 4); \
-  }
-  ROWS_APPLY(STORE_ROW)
-#undef STORE_ROW
-
-#undef ROWS_APPLY
-#undef IF_M
-}
-
-template <int32_t N, typename kv_cache_t>
-FORCE_INLINE void gemm_macro_s390x_Mx8_Ku4(const float* __restrict A,
-                                           const kv_cache_t* __restrict B,
-                                           float* __restrict C, int32_t M,
-                                           int32_t K, int64_t lda, int64_t ldb,
-                                           int64_t ldc, bool accumulate) {
-  static_assert(N % 8 == 0, "N must be a multiple of 8");
-  for (int32_t m = 0; m < M;) {
-    int32_t mb = (M - m >= 8) ? 8 : (M - m >= 4) ? 4 : (M - m >= 2) ? 2 : 1;
-    const float* Ab = A + m * lda;
-    float* Cb = C + m * ldc;
-
-    for (int32_t n = 0; n < N; n += 8) {
-      const kv_cache_t* Bn = B + n;
-      float* Cn = Cb + n;
-      switch (mb) {
-        case 8:
-          gemm_micro_s390x_Mx8_Ku4<8, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc, K,
-                                                  accumulate);
-          break;
-        case 4:
-          gemm_micro_s390x_Mx8_Ku4<4, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc, K,
-                                                  accumulate);
-          break;
-        case 2:
-          gemm_micro_s390x_Mx8_Ku4<2, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc, K,
-                                                  accumulate);
-          break;
-        default:
-          gemm_micro_s390x_Mx8_Ku4<1, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc, K,
-                                                  accumulate);
-          break;
-      }
-    }
-    m += mb;
-  }
-}
-
-template <typename kv_cache_t>
-class TileGemmS390X {
- public:
-  template <AttentionGemmPhase phase, int32_t k_size>
-  FORCE_INLINE static void gemm(const int32_t m_size,
-                                float* __restrict__ a_tile,
-                                kv_cache_t* __restrict__ b_tile,
-                                float* __restrict__ c_tile, const int64_t lda,
-                                const int64_t ldb, const int64_t ldc,
-                                const int32_t block_size,
-                                const int32_t dynamic_k_size,
-                                const bool accum_c) {
-    if constexpr (phase == AttentionGemmPhase::QK) {
-      gemm_macro_s390x_Mx8_Ku4<BLOCK_SIZE_ALIGNMENT, kv_cache_t>(
-          a_tile, b_tile, c_tile, m_size, k_size, lda, ldb, ldc, accum_c);
-    } else {
-      gemm_macro_s390x_Mx8_Ku4<HEAD_SIZE_ALIGNMENT, kv_cache_t>(
-          a_tile, b_tile, c_tile, m_size, dynamic_k_size, lda, ldb, ldc,
-          accum_c);
-    }
-  }
-};
-
-}  // namespace
-
-template <typename scalar_t, int64_t head_dim>
-class AttentionImpl<ISA::VXE, scalar_t, head_dim> {
- public:
-  using query_t = scalar_t;
-  using q_buffer_t = float;
-  using kv_cache_t = scalar_t;
-  using logits_buffer_t = float;
-  using partial_output_buffer_t = float;
-  using prob_buffer_t = float;
-
-  constexpr static int64_t BlockSizeAlignment = BLOCK_SIZE_ALIGNMENT;
-  constexpr static int64_t HeadDimAlignment = HEAD_SIZE_ALIGNMENT;
-  constexpr static int64_t MaxQHeadNumPerIteration = MAX_Q_HEAD_NUM_PER_ITER;
-  constexpr static int64_t HeadDim = head_dim;
-  constexpr static ISA ISAType = ISA::VXE;
-  constexpr static bool scale_on_logits =
-      false;  // Scale is applied to Q during copy
-
- public:
-  AttentionImpl() {}
-
-  template <template <typename tile_gemm_t> typename attention>
-  FORCE_INLINE void execute_attention(DEFINE_CPU_ATTENTION_PARAMS) {
-    attention<TileGemmS390X<kv_cache_t>> attention_iteration;
-    attention_iteration(CPU_ATTENTION_PARAMS);
-  }
-
-  // Strides for Memory Layout
-  constexpr static int64_t k_cache_token_group_stride(
-      const int32_t block_size) {
-    return BlockSizeAlignment;  // [head_dim, block_size] layout
-  }
-
-  constexpr static int64_t v_cache_token_group_stride(
-      const int32_t block_size) {
-    return head_dim * BlockSizeAlignment;
-  }
-
-  constexpr static int64_t v_cache_head_group_stride(const int32_t block_size) {
-    return HeadDimAlignment;
-  }
-
-  static void copy_q_heads_tile(scalar_t* __restrict__ src,
-                                float* __restrict__ q_buffer,
-                                const int32_t q_num,
-                                const int32_t q_heads_per_kv,
-                                const int64_t q_num_stride,
-                                const int64_t q_head_stride, float scale) {
-    __vector float scale_vec = vec_splats(scale);
-    constexpr bool is_bf16 = std::is_same<scalar_t, c10::BFloat16>::value;
-
-    // Process 8 elements at a time (32 bytes of float output)
-    for (int32_t i = 0; i < q_num; ++i) {
-      for (int32_t h = 0; h < q_heads_per_kv; ++h) {
-        scalar_t* curr_src = src + i * q_num_stride + h * q_head_stride;
-        float* curr_dst =
-            q_buffer + i * q_heads_per_kv * head_dim + h * head_dim;
-
-        int32_t d = 0;
-        for (; d <= head_dim - 8; d += 8) {
-          if constexpr (is_bf16) {
-            __vector float v0, v1;
-            // Reuse our Big-Endian-Safe loader
-            load_row8_B_as_f32<scalar_t>(curr_src + d, v0, v1);
-
-            v0 = vec_mul(v0, scale_vec);
-            v1 = vec_mul(v1, scale_vec);
-
-            vec_xst(v0, 0, curr_dst + d);
-            vec_xst(v1, 0, curr_dst + d + 4);
-          } else {
-            __vector float v0 = vec_xl((long long)0, (float*)curr_src + d);
-            __vector float v1 = vec_xl((long long)0, (float*)curr_src + d + 4);
-
-            v0 = vec_mul(v0, scale_vec);
-            v1 = vec_mul(v1, scale_vec);
-
-            vec_xst(v0, 0, curr_dst + d);
-            vec_xst(v1, 0, curr_dst + d + 4);
-          }
-        }
-
-        for (; d < head_dim; ++d) {
-          float val = static_cast<float>(curr_src[d]);
-          curr_dst[d] = val * scale;
-        }
-      }
-    }
-  }
-
-  static void reshape_and_cache(
-      const scalar_t* __restrict__ key, const scalar_t* __restrict__ value,
-      scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
-      const int64_t* __restrict__ slot_mapping, const int64_t token_num,
-      const int64_t key_token_num_stride, const int64_t value_token_num_stride,
-      const int64_t head_num, const int64_t key_head_num_stride,
-      const int64_t value_head_num_stride, const int64_t num_blocks,
-      const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride) {
-#pragma omp parallel for collapse(2)
-    for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
-      for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
-        const int64_t pos = slot_mapping[token_idx];
-        if (pos < 0) continue;
-
-        const int64_t block_idx = pos / block_size;
-        const int64_t block_offset = pos % block_size;
-
-        {
-          const scalar_t* key_src = key + token_idx * key_token_num_stride +
-                                    head_idx * key_head_num_stride;
-          scalar_t* key_dst = key_cache + block_idx * num_blocks_stride +
-                              head_idx * cache_head_num_stride + block_offset;
-
-          for (int64_t i = 0, j = 0; i < head_dim; ++i, j += block_size) {
-            key_dst[j] = key_src[i];
-          }
-        }
-
-        {
-          const scalar_t* val_src = value + token_idx * value_token_num_stride +
-                                    head_idx * value_head_num_stride;
-          scalar_t* val_dst = value_cache + block_idx * num_blocks_stride +
-                              head_idx * cache_head_num_stride +
-                              block_offset * head_dim;
-
-          std::memcpy(val_dst, val_src, sizeof(scalar_t) * head_dim);
-        }
-      }
-    }
-  }
-};
-
-}  // namespace cpu_attention
-
-#undef BLOCK_SIZE_ALIGNMENT
-#undef HEAD_SIZE_ALIGNMENT
-#undef MAX_Q_HEAD_NUM_PER_ITER
-
-#endif

csrc/cpu/generate_cpu_attn_dispatch.py

@@ -19,11 +19,10 @@ ISA_TYPES = {
     "VEC": 1,
     "VEC16": 2,
     "NEON": 3,
-    "VXE": 4,
 }
 
 # ISAs supported for head_dims divisible by 32
-ISA_FOR_32 = ["AMX", "NEON", "VEC", "VEC16", "VXE"]
+ISA_FOR_32 = ["AMX", "NEON", "VEC", "VEC16"]
 
 # ISAs supported for head_dims divisible by 16 only
 ISA_FOR_16 = ["VEC16"]
@@ -119,10 +118,6 @@ def generate_header_file() -> str:
   #include "cpu_attn_neon.hpp"
 #endif
 
-#ifdef __s390x__
-  #include "cpu_attn_vxe.hpp"
-#endif
-
 """
 
     header += generate_helper_function()
@@ -168,25 +163,6 @@ def generate_header_file() -> str:
     } \\
   }()
 
-"""
-
-    # s390x with VXE
-    header += """#elif defined(__s390x__)
-#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
-  [&] { \\
-    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
-    switch (encoded_params) { \\
-"""
-    header += generate_cases_for_isa_group(["VXE", "VEC", "VEC16"])
-    header += """
-      default: { \\
-        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
-                    std::to_string(HEAD_DIM) + " isa=" + \\
-                    std::to_string(static_cast<int>(ISA_TYPE))); \\
-      } \\
-    } \\
-  }()
-
 """
 
     # Fallback: VEC and VEC16 only
@@ -206,7 +182,7 @@ def generate_header_file() -> str:
     } \\
   }()
 
-#endif  /* CPU_CAPABILITY_AMXBF16 / __aarch64__ / __s390x__ */
+#endif  /* CPU_CAPABILITY_AMXBF16 / __aarch64__ */
 
 #endif  // CPU_ATTN_DISPATCH_GENERATED_H
 """

csrc/cpu/torch_bindings.cpp

@@ -4,10 +4,6 @@
 
 #include <torch/library.h>
 
-// Note: overwrite the external defination for sharing same name between
-// libraries use different ISAs.
-#define TORCH_EXTENSION_NAME _C
-
 std::string init_cpu_threads_env(const std::string& cpu_ids);
 
 void release_dnnl_matmul_handler(int64_t handler);
@@ -328,12 +324,19 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "str act, str isa) -> ()");
   ops.impl("cpu_fused_moe", torch::kCPU, &cpu_fused_moe);
 #endif
-  ops.def("init_cpu_threads_env(str cpu_ids) -> str", &init_cpu_threads_env);
-  ops.def(
+}
+
+TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _utils), utils) {
+  // CPU utils
+  utils.def("init_cpu_threads_env(str cpu_ids) -> str", &init_cpu_threads_env);
+}
+
+TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cpu), cpu_ops) {
+  cpu_ops.def(
       "mla_decode_kvcache("
       "   Tensor! out, Tensor query, Tensor kv_cache,"
       "   float scale, Tensor block_tables, Tensor seq_lens) -> ()");
-  ops.impl("mla_decode_kvcache", torch::kCPU, &mla_decode_kvcache);
+  cpu_ops.impl("mla_decode_kvcache", torch::kCPU, &mla_decode_kvcache);
 }
 
 REGISTER_EXTENSION(TORCH_EXTENSION_NAME)

csrc/cuda_vec_utils.cuh

@@ -1,334 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-#pragma once
-
-#include <c10/util/BFloat16.h>
-#include <c10/util/Half.h>
-#include <cassert>
-
-#ifdef USE_ROCM
-  #include <hip/hip_runtime.h>
-#else
-  #include <cuda_bf16.h>
-  #include <cuda_fp16.h>
-  #include <cuda_runtime.h>
-#endif
-
-// Device-side: SM100+ architecture with CUDA 12.9+ toolkit, which
-// together enable 256-bit (v8.u32) PTX load/store instructions.
-// Use for PTX instruction selection with architecture fallback paths.
-#if !defined(USE_ROCM) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 1000 && \
-    defined(CUDA_VERSION) && CUDA_VERSION >= 12090
-  #define VLLM_256B_PTX_ENABLED 1
-#else
-  #define VLLM_256B_PTX_ENABLED 0
-#endif
-
-namespace vllm {
-
-// ============================================================
-// Types and traits
-// ============================================================
-
-// 256-bit (32-byte) aligned vector type: 8 x uint32_t
-struct alignas(32) u32x8_t {
-  uint32_t d[8];
-};
-
-// VecTraits — select between 128-bit (int4) and 256-bit
-// (u32x8_t) vector types at compile time.
-template <bool support_256>
-struct VecTraits;
-
-template <>
-struct VecTraits<true> {
-  static constexpr int ARCH_MAX_VEC_SIZE = 32;
-  using vec_t = u32x8_t;
-};
-
-template <>
-struct VecTraits<false> {
-  static constexpr int ARCH_MAX_VEC_SIZE = 16;
-  using vec_t = int4;
-};
-
-// PackedTypeConverter — map between CUDA scalar and packed types
-//   half  <-> half2,  __nv_bfloat16 <-> __nv_bfloat162, etc.
-template <typename T>
-struct PackedTypeConverter {
-  static_assert(sizeof(T) == 0,
-                "PackedTypeConverter is not specialized for this type.");
-};
-
-template <>
-struct PackedTypeConverter<half2> {
-  using Type = half;
-};
-
-template <>
-struct PackedTypeConverter<half> {
-  using Type = half2;
-};
-
-template <>
-struct PackedTypeConverter<__nv_bfloat162> {
-  using Type = __nv_bfloat16;
-};
-
-template <>
-struct PackedTypeConverter<__nv_bfloat16> {
-  using Type = __nv_bfloat162;
-};
-
-template <>
-struct PackedTypeConverter<float> {
-  using Type = float2;
-};
-
-template <>
-struct PackedTypeConverter<float2> {
-  using Type = float;
-};
-
-template <>
-struct PackedTypeConverter<c10::Half> {
-  using Type = half2;
-};
-
-template <>
-struct PackedTypeConverter<c10::BFloat16> {
-  using Type = __nv_bfloat162;
-};
-
-// CUDATypeConverter — map PyTorch scalar types to CUDA scalar
-//   c10::Half -> half,  c10::BFloat16 -> __nv_bfloat16
-template <typename T>
-struct CUDATypeConverter {
-  using Type = T;
-};
-
-template <>
-struct CUDATypeConverter<c10::Half> {
-  using Type = half;
-};
-
-template <>
-struct CUDATypeConverter<c10::BFloat16> {
-  using Type = __nv_bfloat16;
-};
-
-// PackedVec — typed vector container for packed element access.
-//   Derives alignment and element count from VecTraits.
-//   Type is the CUDA scalar type (e.g. half, __nv_bfloat16).
-template <class Type, bool use_256b>
-struct alignas(VecTraits<use_256b>::ARCH_MAX_VEC_SIZE) PackedVec {
-  static constexpr int NUM_ELTS =
-      VecTraits<use_256b>::ARCH_MAX_VEC_SIZE /
-      sizeof(typename PackedTypeConverter<Type>::Type);
-  typename PackedTypeConverter<Type>::Type elts[NUM_ELTS];
-};
-
-// ============================================================
-// Load / store primitives
-// ============================================================
-
-// 256-bit load / store — SM100+ only (PTX v8 instructions).
-__device__ __forceinline__ void ld256(u32x8_t& val, const u32x8_t* ptr) {
-#if VLLM_256B_PTX_ENABLED
-  asm volatile("ld.global.nc.v8.u32 {%0,%1,%2,%3,%4,%5,%6,%7}, [%8];\n"
-               : "=r"(val.d[0]), "=r"(val.d[1]), "=r"(val.d[2]), "=r"(val.d[3]),
-                 "=r"(val.d[4]), "=r"(val.d[5]), "=r"(val.d[6]), "=r"(val.d[7])
-               : "l"(ptr));
-#else
-  assert(false && "ld256 requires SM100+ with CUDA 12.9+");
-#endif
-}
-
-__device__ __forceinline__ void st256(u32x8_t& val, u32x8_t* ptr) {
-#if VLLM_256B_PTX_ENABLED
-  asm volatile("st.global.v8.u32 [%0], {%1,%2,%3,%4,%5,%6,%7,%8};\n"
-               :
-               : "l"(ptr), "r"(val.d[0]), "r"(val.d[1]), "r"(val.d[2]),
-                 "r"(val.d[3]), "r"(val.d[4]), "r"(val.d[5]), "r"(val.d[6]),
-                 "r"(val.d[7])
-               : "memory");
-#else
-  assert(false && "st256 requires SM100+ with CUDA 12.9+");
-#endif
-}
-
-// Generic ld256 / st256 for any 32-byte aligned type (e.g. PackedVec).
-// Non-template overloads above are preferred for u32x8_t.
-template <typename T>
-__device__ __forceinline__ void ld256(T& val, const T* ptr) {
-  static_assert(sizeof(T) == 32, "ld256 requires a 32-byte type");
-  ld256(reinterpret_cast<u32x8_t&>(val), reinterpret_cast<const u32x8_t*>(ptr));
-}
-
-template <typename T>
-__device__ __forceinline__ void st256(T& val, T* ptr) {
-  static_assert(sizeof(T) == 32, "st256 requires a 32-byte type");
-  st256(reinterpret_cast<u32x8_t&>(val), reinterpret_cast<u32x8_t*>(ptr));
-}
-
-// 128-bit load / store via __ldg (read-only cache hint).
-template <typename T>
-__device__ __forceinline__ void ld128(T& val, const T* ptr) {
-  static_assert(sizeof(T) == 16, "ld128 requires a 16-byte type");
-  *reinterpret_cast<int4*>(&val) = __ldg(reinterpret_cast<const int4*>(ptr));
-}
-
-template <typename T>
-__device__ __forceinline__ void st128(T& val, T* ptr) {
-  static_assert(sizeof(T) == 16, "st128 requires a 16-byte type");
-  *reinterpret_cast<int4*>(ptr) = *reinterpret_cast<int4*>(&val);
-}
-
-// 256-bit cache-streaming (.cs) load / store  — SM100+ only.
-__forceinline__ __device__ u32x8_t ld256_cs(const u32x8_t* addr) {
-#if VLLM_256B_PTX_ENABLED
-  u32x8_t val;
-  asm volatile("ld.global.cs.v8.u32 {%0,%1,%2,%3,%4,%5,%6,%7}, [%8];"
-               : "=r"(val.d[0]), "=r"(val.d[1]), "=r"(val.d[2]), "=r"(val.d[3]),
-                 "=r"(val.d[4]), "=r"(val.d[5]), "=r"(val.d[6]), "=r"(val.d[7])
-               : "l"(addr));
-  return val;
-#else
-  assert(false && "ld256_cs requires SM100+ with CUDA 12.9+");
-  return {};
-#endif
-}
-
-__forceinline__ __device__ void st256_cs(u32x8_t* addr, u32x8_t val) {
-#if VLLM_256B_PTX_ENABLED
-  asm volatile(
-      "st.global.cs.v8.u32 [%0], {%1,%2,%3,%4,%5,%6,%7,%8};" ::"l"(addr),
-      "r"(val.d[0]), "r"(val.d[1]), "r"(val.d[2]), "r"(val.d[3]), "r"(val.d[4]),
-      "r"(val.d[5]), "r"(val.d[6]), "r"(val.d[7]));
-#else
-  assert(false && "st256_cs requires SM100+ with CUDA 12.9+");
-#endif
-}
-
-// 32-bit cache-streaming (.cs) load / store  — SM100+ only.
-__forceinline__ __device__ int ld32_cs(const int* addr) {
-#if VLLM_256B_PTX_ENABLED
-  int val;
-  asm volatile("ld.global.cs.b32 %0, [%1];" : "=r"(val) : "l"(addr));
-  return val;
-#else
-  assert(false && "ld32_cs requires SM100+ with CUDA 12.9+");
-  return 0;
-#endif
-}
-
-__forceinline__ __device__ void st32_cs(int* addr, int val) {
-#if VLLM_256B_PTX_ENABLED
-  asm volatile("st.global.cs.b32 [%0], %1;" ::"l"(addr), "r"(val));
-#else
-  assert(false && "st32_cs requires SM100+ with CUDA 12.9+");
-#endif
-}
-
-// Predicated 256-bit / 128-bit cache-global (.cg) loads.
-// Returns zero if pred is false.  SM100+ only.
-__device__ __forceinline__ void ld256_cg_or_zero(u32x8_t& val, const void* ptr,
-                                                 bool pred) {
-#if VLLM_256B_PTX_ENABLED
-  asm volatile(
-      "{\n"
-      "  .reg .pred pr;\n"
-      "  setp.ne.u32 pr, %8, 0;\n"
-      "  mov.u32 %0, 0;\n"
-      "  mov.u32 %1, 0;\n"
-      "  mov.u32 %2, 0;\n"
-      "  mov.u32 %3, 0;\n"
-      "  mov.u32 %4, 0;\n"
-      "  mov.u32 %5, 0;\n"
-      "  mov.u32 %6, 0;\n"
-      "  mov.u32 %7, 0;\n"
-      "  @pr ld.global.cg.v8.u32 {%0,%1,%2,%3,%4,%5,%6,%7}, [%9];\n"
-      "}\n"
-      : "=r"(val.d[0]), "=r"(val.d[1]), "=r"(val.d[2]), "=r"(val.d[3]),
-        "=r"(val.d[4]), "=r"(val.d[5]), "=r"(val.d[6]), "=r"(val.d[7])
-      : "r"((int)pred), "l"(ptr));
-#else
-  assert(false && "ld256_cg_or_zero requires SM100+ with CUDA 12.9+");
-#endif
-}
-
-__device__ __forceinline__ void ld128_cg_or_zero(uint4& val, const void* ptr,
-                                                 bool pred) {
-#if VLLM_256B_PTX_ENABLED
-  uint32_t r0, r1, r2, r3;
-
-  asm volatile(
-      "{\n"
-      "  .reg .pred pr;\n"
-      "  setp.ne.u32 pr, %4, 0;\n"
-      "  mov.u32 %0, 0;\n"
-      "  mov.u32 %1, 0;\n"
-      "  mov.u32 %2, 0;\n"
-      "  mov.u32 %3, 0;\n"
-      "  @pr ld.global.cg.v4.u32 {%0,%1,%2,%3}, [%5];\n"
-      "}\n"
-      : "=r"(r0), "=r"(r1), "=r"(r2), "=r"(r3)
-      : "r"((int)pred), "l"(ptr));
-
-  val = uint4{r0, r1, r2, r3};
-#else
-  assert(false && "ld128_cg_or_zero requires SM100+ with CUDA 12.9+");
-#endif
-}
-
-// ============================================================
-// Alignment helpers
-// ============================================================
-
-__host__ __device__ __forceinline__ bool is_16byte_aligned(const void* ptr) {
-  return (reinterpret_cast<uintptr_t>(ptr) & 15) == 0;
-}
-
-__host__ __device__ __forceinline__ bool is_32byte_aligned(const void* ptr) {
-  return (reinterpret_cast<uintptr_t>(ptr) & 31) == 0;
-}
-
-// ============================================================
-// Packed type conversion and arithmetic
-// ============================================================
-
-template <typename packed_t>
-__device__ __forceinline__ float2 cast_to_float2(const packed_t& val) {
-  if constexpr (std::is_same_v<packed_t, __nv_bfloat162>) {
-    return __bfloat1622float2(val);
-  } else if constexpr (std::is_same_v<packed_t, __half2>) {
-    return __half22float2(val);
-  } else if constexpr (std::is_same_v<packed_t, float2>) {
-    return float2(val);
-  }
-}
-
-template <typename packed_t>
-__device__ __forceinline__ packed_t cast_to_packed(const float2& val) {
-  if constexpr (std::is_same_v<packed_t, __nv_bfloat162>) {
-    return __float22bfloat162_rn(val);
-  } else if constexpr (std::is_same_v<packed_t, __half2>) {
-    return __float22half2_rn(val);
-  } else if constexpr (std::is_same_v<packed_t, float2>) {
-    return float2(val);
-  }
-}
-
-template <typename packed_t>
-__device__ __forceinline__ packed_t packed_mul(const packed_t& x,
-                                               const packed_t& y) {
-  if constexpr (std::is_same_v<packed_t, __nv_bfloat162> ||
-                std::is_same_v<packed_t, __half2>) {
-    return __hmul2(x, y);
-  } else if constexpr (std::is_same_v<packed_t, float2>) {
-    return make_float2(x.x * y.x, x.y * y.y);
-  }
-}
-
-}  // namespace vllm

csrc/dsv3_fused_a_gemm.cu

@@ -745,7 +745,3 @@ void dsv3_fused_a_gemm(torch::Tensor& output, torch::Tensor const& mat_a,
         stream);
   }
 }
-
-TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
-  m.impl("dsv3_fused_a_gemm", &dsv3_fused_a_gemm);
-}

csrc/mamba/mamba_ssm/selective_scan.h

@@ -15,9 +15,9 @@
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
 struct SSMParamsBase {
-    using index_t = size_t;
+    using index_t = uint32_t;
 
-    int batch, dim, seqlen, dstate, n_groups;
+    int batch, dim, seqlen, dstate, n_groups, n_chunks;
     int dim_ngroups_ratio;
     bool is_variable_B;
     bool is_variable_C;
@@ -72,8 +72,6 @@ struct SSMParamsBase {
     void *__restrict__ block_idx_first_scheduled_token_ptr;  // (batch,) - first block to write
     void *__restrict__ block_idx_last_scheduled_token_ptr;   // (batch,) - last block to write
     void *__restrict__ initial_state_idx_ptr;  // (batch,) - index of the initial state to use
-    void *__restrict__ cu_chunk_seqlen_ptr;      // (nchunks+1,) - cumulative chunk token offsets
-    void *__restrict__ last_chunk_indices_ptr;   // (batch,) - index of last chunk per sequence
 };
 
 

csrc/mamba/mamba_ssm/selective_scan_fwd.cu

@@ -81,6 +81,7 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
     constexpr bool kIsVariableC = Ktraits::kIsVariableC;
     constexpr bool kHasZ = Ktraits::kHasZ;
     constexpr bool kVarlen = Ktraits::kVarlen;
+    constexpr int kNThreads = Ktraits::kNThreads;
     constexpr int kNItems = Ktraits::kNItems;
     constexpr int kNRows = Ktraits::kNRows;
     constexpr bool kDirectIO = Ktraits::kDirectIO;
@@ -160,8 +161,17 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
         }
     }
 
+
+    // for (int state_idx = threadIdx.x; state_idx < params.dstate; state_idx += blockDim.x) {
+    //     smem_a[state_idx] = A[state_idx * params.A_dstate_stride];
+    //     smem_bc[state_idx] = B[state_idx * params.B_dstate_stride] * C[state_idx * params.C_dstate_stride];
+    // }
+
+    constexpr int kChunkSize = kNThreads * kNItems;
+
     // Use block_size for chunking when APC is enabled, otherwise use 2048 for backwards compatibility
-    const int block_size = params.cache_enabled ? params.block_size : 2048;
+    const int iteration_chunk_size = params.cache_enabled ? params.block_size : 2048;
+    const int n_chunks = (seqlen + iteration_chunk_size - 1) / iteration_chunk_size;
 
     const int* batch_cache_indices = cache_indices != nullptr ?
                                      cache_indices + batch_id * params.cache_indices_stride : nullptr;
@@ -171,44 +181,10 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
                                           reinterpret_cast<const int*>(params.block_idx_last_scheduled_token_ptr) : nullptr;
     const int* initial_state_idx = params.initial_state_idx_ptr != nullptr ?
                                    reinterpret_cast<const int*>(params.initial_state_idx_ptr) : nullptr;
-    const int* cu_chunk_seqlen = params.cu_chunk_seqlen_ptr != nullptr ?
-                                 reinterpret_cast<const int*>(params.cu_chunk_seqlen_ptr) : nullptr;
-    const int* last_chunk_indices = params.last_chunk_indices_ptr != nullptr ?
-                                    reinterpret_cast<const int*>(params.last_chunk_indices_ptr) : nullptr;
 
     const size_t load_cache_slot = params.cache_enabled && batch_cache_indices != nullptr ? batch_cache_indices[initial_state_idx[batch_id]] : cache_index;
 
-    const int block_idx_first = (params.cache_enabled && block_idx_first_scheduled != nullptr) ?
-                                 block_idx_first_scheduled[batch_id] : 0;
-
-    // Determine chunk boundaries from pre-computed metadata (APC mode)
-    // or fall back to simple block_size chunking.
-    int first_chunk_idx, n_chunks;
-    int current_position;
-
-    if (cu_chunk_seqlen != nullptr && last_chunk_indices != nullptr) {
-        const int last_chunk_idx = last_chunk_indices[batch_id];
-        first_chunk_idx = (batch_id == 0) ? 0 : last_chunk_indices[batch_id - 1] + 1;
-        n_chunks = last_chunk_idx - first_chunk_idx + 1;
-        // Derive current_position: if the first chunk is partial (fills remainder
-        // of a started block), offset into the block accordingly.
-        const int first_chunk_tokens = cu_chunk_seqlen[first_chunk_idx + 1] - cu_chunk_seqlen[first_chunk_idx];
-        const int chunk_start_offset = (n_chunks > 1 && first_chunk_tokens < block_size)
-                                        ? (block_size - first_chunk_tokens) : 0;
-        current_position = block_idx_first * block_size + chunk_start_offset;
-    } else {
-        first_chunk_idx = 0;
-        n_chunks = (seqlen + block_size - 1) / block_size;
-        current_position = 0;
-    }
-
-    int tokens_processed = 0;
-
     for (int chunk = 0; chunk < n_chunks; ++chunk) {
-        const int chunk_tokens = (cu_chunk_seqlen != nullptr)
-            ? cu_chunk_seqlen[first_chunk_idx + chunk + 1] - cu_chunk_seqlen[first_chunk_idx + chunk]
-            : min(block_size, seqlen - tokens_processed);
-        if (chunk_tokens <= 0) break;
         input_t u_vals[kNRows][kNItems], delta_vals_load[kNRows][kNItems];
 
         __syncthreads();
@@ -217,12 +193,12 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
             if constexpr (!kDirectIO) {
                 if (r > 0) { __syncthreads(); }
             }
-            load_input<Ktraits>(u + r * params.u_d_stride, u_vals[r], smem_load, chunk_tokens);
+            load_input<Ktraits>(u + r * params.u_d_stride, u_vals[r], smem_load, seqlen - chunk * kChunkSize);
             if constexpr (!kDirectIO) { __syncthreads(); }
-            load_input<Ktraits>(delta + r * params.delta_d_stride, delta_vals_load[r], smem_load, chunk_tokens);
+            load_input<Ktraits>(delta + r * params.delta_d_stride, delta_vals_load[r], smem_load, seqlen - chunk * kChunkSize);
         }
-        u += chunk_tokens;
-        delta += chunk_tokens;
+        u += kChunkSize;
+        delta += kChunkSize;
     
         float delta_vals[kNRows][kNItems], delta_u_vals[kNRows][kNItems], out_vals[kNRows][kNItems];
         #pragma unroll
@@ -256,7 +232,7 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
             weight_t B_vals[kNItems], C_vals[kNItems];
             if constexpr (kIsVariableB) {
                 load_weight<Ktraits>(Bvar + state_idx * params.B_dstate_stride, B_vals,
-                    smem_load_weight, chunk_tokens);
+                    smem_load_weight, (seqlen - chunk * kChunkSize) * (1));
                 if constexpr (!kIsVariableC) {
                     #pragma unroll
                     for (int r = 0; r < kNRows; ++r) {
@@ -267,7 +243,7 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
             if constexpr (kIsVariableC) {
                 auto &smem_load_weight_C = !kIsVariableB ? smem_load_weight : smem_load_weight1;
                 load_weight<Ktraits>(Cvar + state_idx * params.C_dstate_stride, C_vals,
-                    smem_load_weight_C, chunk_tokens);
+                    smem_load_weight_C, (seqlen - chunk * kChunkSize) * (1));
                 if constexpr (!kIsVariableB) {
                     #pragma unroll
                     for (int r = 0; r < kNRows; ++r) {
@@ -290,8 +266,10 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
                 for (int i = 0; i < kNItems; ++i) {
                     thread_data[i] = make_float2(exp2f(delta_vals[r][i] * A_val[r]),
                                                  !kIsVariableB ? delta_u_vals[r][i] : B_vals[i] * delta_u_vals[r][i]);
-                    if (threadIdx.x * kNItems + i >= chunk_tokens) {
-                        thread_data[i] = make_float2(1.f, 0.f);
+                    if (seqlen % (kNItems * kNThreads) != 0) {  // So that the last state is correct
+                        if (threadIdx.x * kNItems + i >= seqlen - chunk * kChunkSize) {
+                            thread_data[i] = make_float2(1.f, 0.f);
+                        }
                     }
                 }
                 // Initialize running total
@@ -323,14 +301,14 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
                 if (threadIdx.x == 0) {
                     smem_running_prefix[state_idx + r * MAX_DSTATE] = prefix_op.running_prefix;
 
-                    // Store state at the end of each aligned chunk when cache is enabled
+                    // Store state at the end of each chunk when cache is enabled
                     if (params.cache_enabled && batch_cache_indices != nullptr) {
+
                         size_t cache_slot;
                         if (chunk == n_chunks - 1) {
                             cache_slot = batch_cache_indices[block_idx_last_scheduled[batch_id]];
                         } else {
-                            const int block_idx_completed = (current_position + chunk_tokens - 1) / block_size;
-                            cache_slot = batch_cache_indices[block_idx_completed];
+                            cache_slot = batch_cache_indices[block_idx_first_scheduled[batch_id] + chunk];
                         }
 
                         size_t state_offset = cache_slot * params.ssm_states_batch_stride +
@@ -353,41 +331,38 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
             }
         }
         input_t *out = reinterpret_cast<input_t *>(params.out_ptr) + sequence_start_index * params.out_batch_stride
-            + dim_id * kNRows * params.out_d_stride + tokens_processed;
+            + dim_id * kNRows * params.out_d_stride + chunk * kChunkSize;
         __syncthreads();
         #pragma unroll
         for (int r = 0; r < kNRows; ++r) {
             if constexpr (!kDirectIO) {
                 if (r > 0) { __syncthreads(); }
             }
-            store_output<Ktraits>(out + r * params.out_d_stride, out_vals[r], smem_store, chunk_tokens);
+            store_output<Ktraits>(out + r * params.out_d_stride, out_vals[r], smem_store, seqlen - chunk * kChunkSize);
         }
 
         if constexpr (kHasZ) {
             input_t *z = reinterpret_cast<input_t *>(params.z_ptr) + sequence_start_index * params.z_batch_stride
-                + dim_id * kNRows * params.z_d_stride + tokens_processed;
+                + dim_id * kNRows * params.z_d_stride + chunk * kChunkSize;
             input_t *out_z = reinterpret_cast<input_t *>(params.out_z_ptr) + sequence_start_index * params.out_z_batch_stride
-                + dim_id * kNRows * params.out_z_d_stride + tokens_processed;
+                + dim_id * kNRows * params.out_z_d_stride + chunk * kChunkSize;
             #pragma unroll
             for (int r = 0; r < kNRows; ++r) {
                 input_t z_vals[kNItems];
                 __syncthreads();
-                load_input<Ktraits>(z + r * params.z_d_stride, z_vals, smem_load, chunk_tokens);
+                load_input<Ktraits>(z + r * params.z_d_stride, z_vals, smem_load, seqlen - chunk * kChunkSize);
                 #pragma unroll
                 for (int i = 0; i < kNItems; ++i) {
                     float z_val = z_vals[i];
                     out_vals[r][i] *= z_val / (1 + expf(-z_val));
                 }
                 __syncthreads();
-                store_output<Ktraits>(out_z + r * params.out_z_d_stride, out_vals[r], smem_store, chunk_tokens);
+                store_output<Ktraits>(out_z + r * params.out_z_d_stride, out_vals[r], smem_store, seqlen - chunk * kChunkSize);
             }
         }
 
-        Bvar += chunk_tokens;
-        Cvar += chunk_tokens;
-
-        tokens_processed += chunk_tokens;
-        current_position += chunk_tokens;
+        Bvar += kChunkSize * 1;
+        Cvar += kChunkSize * 1;
     }
 }
 
@@ -531,9 +506,7 @@ void set_ssm_params_fwd(SSMParamsBase &params,
                         int64_t block_size,
                         const std::optional<torch::Tensor> &block_idx_first_scheduled_token,
                         const std::optional<torch::Tensor> &block_idx_last_scheduled_token,
-                        const std::optional<torch::Tensor> &initial_state_idx,
-                        const std::optional<torch::Tensor> &cu_chunk_seqlen,
-                        const std::optional<torch::Tensor> &last_chunk_indices) {
+                        const std::optional<torch::Tensor> &initial_state_idx) {
 
     // Reset the parameters
     memset(&params, 0, sizeof(params));
@@ -575,8 +548,6 @@ void set_ssm_params_fwd(SSMParamsBase &params,
     params.block_idx_first_scheduled_token_ptr = block_idx_first_scheduled_token.has_value() ? block_idx_first_scheduled_token.value().data_ptr() : nullptr;
     params.block_idx_last_scheduled_token_ptr = block_idx_last_scheduled_token.has_value() ? block_idx_last_scheduled_token.value().data_ptr() : nullptr;
     params.initial_state_idx_ptr = initial_state_idx.has_value() ? initial_state_idx.value().data_ptr() : nullptr;
-    params.cu_chunk_seqlen_ptr = cu_chunk_seqlen.has_value() ? cu_chunk_seqlen.value().data_ptr() : nullptr;
-    params.last_chunk_indices_ptr = last_chunk_indices.has_value() ? last_chunk_indices.value().data_ptr() : nullptr;
 
     // All stride are in elements, not bytes.
     params.A_d_stride = A.stride(0);
@@ -662,9 +633,7 @@ void selective_scan_fwd(const torch::Tensor &u, const torch::Tensor &delta,
                   int64_t block_size,
                   const std::optional<torch::Tensor> &block_idx_first_scheduled_token,
                   const std::optional<torch::Tensor> &block_idx_last_scheduled_token,
-                  const std::optional<torch::Tensor> &initial_state_idx,
-                  const std::optional<torch::Tensor> &cu_chunk_seqlen,
-                  const std::optional<torch::Tensor> &last_chunk_indices) {
+                  const std::optional<torch::Tensor> &initial_state_idx) {
     auto input_type = u.scalar_type();
     auto weight_type = A.scalar_type();
     TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
@@ -809,9 +778,7 @@ void selective_scan_fwd(const torch::Tensor &u, const torch::Tensor &delta,
                        block_size,
                        block_idx_first_scheduled_token,
                        block_idx_last_scheduled_token,
-                       initial_state_idx,
-                       cu_chunk_seqlen,
-                       last_chunk_indices
+                       initial_state_idx
                        );
 
     

csrc/moe/dsv3_router_gemm_bf16_out.cu

@@ -1,291 +0,0 @@
-/*
- * Adapted from SGLang's sgl-kernel implementation, which was adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/dsv3MinLatencyKernels/dsv3RouterGemm.cu
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/thop/dsv3RouterGemmOp.cpp
- *
- * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-
-#include <cuda_bf16.h>
-#include <cuda_runtime.h>
-
-#include "dsv3_router_gemm_utils.h"
-
-// Custom FMA implementation using PTX assembly instructions
-__device__ __forceinline__ void fma(float2& d, float2 const& a, float2 const& b,
-                                    float2 const& c) {
-  asm volatile("fma.rn.f32x2 %0, %1, %2, %3;\n"
-               : "=l"(reinterpret_cast<uint64_t&>(d))
-               : "l"(reinterpret_cast<uint64_t const&>(a)),
-                 "l"(reinterpret_cast<uint64_t const&>(b)),
-                 "l"(reinterpret_cast<uint64_t const&>(c)));
-}
-
-// Convert 8 bfloat16 values from a uint4 to float array - optimized conversion
-template <int VPT>
-__device__ __forceinline__ void bf16_uint4_to_float8(uint4 const& vec,
-                                                     float* dst) {
-  __nv_bfloat16* bf16_ptr =
-      reinterpret_cast<__nv_bfloat16*>(const_cast<uint4*>(&vec));
-
-#pragma unroll
-  for (int i = 0; i < VPT; i++) {
-    dst[i] = __bfloat162float(bf16_ptr[i]);
-  }
-}
-
-template <typename T, int kBlockSize, int VPT, int kNumTokens, int kNumExperts,
-          int kHiddenDim>
-__global__ __launch_bounds__(128, 1) void router_gemm_kernel_bf16_output(
-    __nv_bfloat16* out, T const* mat_a, T const* mat_b) {
-  // Each block handles one expert column
-  int const n_idx = blockIdx.x;
-  int const tid = threadIdx.x;
-  constexpr int kWarpSize = 32;
-  constexpr int kNumWarps = kBlockSize / kWarpSize;
-  // Constants for this kernel
-  constexpr int k_elems_per_k_iteration = VPT * kBlockSize;
-  constexpr int k_iterations =
-      kHiddenDim / k_elems_per_k_iteration;  // Total K iterations
-
-  // Initialize accumulators for all M rows
-  float acc[kNumTokens] = {};
-
-  // Shared memory for warp-level reduction
-  __shared__ float sm_reduction[kNumTokens][kNumWarps];  // kNumWarps
-
-  // B matrix is in column-major order, so we can directly load a column for the
-  // n_idx expert
-  T const* b_col = mat_b + n_idx * kHiddenDim;
-
-  // Pre-compute k_base values for each iteration to help compiler optimize
-  int k_bases[k_iterations];
-#pragma unroll
-  for (int ki = 0; ki < k_iterations; ki++) {
-    k_bases[ki] = ki * k_elems_per_k_iteration + tid * VPT;
-  }
-
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  asm volatile("griddepcontrol.wait;");
-#endif
-
-  // Process the GEMM in chunks
-  for (int ki = 0; ki < k_iterations; ki++) {
-    int const k_base = k_bases[ki];
-
-    // Load B matrix values using vector load (8 bf16 values)
-    uint4 b_vec = *reinterpret_cast<uint4 const*>(b_col + k_base);
-
-    // Convert B values to float
-    float b_float[VPT];
-    bf16_uint4_to_float8<VPT>(b_vec, b_float);
-
-// Process each token
-#pragma unroll
-    for (int m_idx = 0; m_idx < kNumTokens; m_idx++) {
-      // Load both rows of A matrix using vector loads
-      uint4 a_vec = *reinterpret_cast<uint4 const*>(
-          mat_a + (m_idx * kHiddenDim) + k_base);
-
-      // Convert A values to float
-      float a_float[VPT];
-      bf16_uint4_to_float8<VPT>(a_vec, a_float);
-
-// Process elements in this chunk
-#pragma unroll
-      for (int k = 0; k < VPT; k++) {
-        float a = a_float[k];
-        float b = b_float[k];
-        acc[m_idx] += a * b;
-      }
-    }
-  }
-
-  // Perform warp-level reduction
-  int const warpSize = 32;
-  int const warpId = tid / warpSize;
-  int const laneId = tid % warpSize;
-
-  // Register for warp-level reduction results
-  float warp_result[kNumTokens];
-
-#pragma unroll
-  for (int m_idx = 0; m_idx < kNumTokens; m_idx++) {
-    warp_result[m_idx] = acc[m_idx];
-  }
-
-// Perform warp-level reduction using optimized butterfly pattern
-#pragma unroll
-  for (int m = 0; m < kNumTokens; m++) {
-    float sum = warp_result[m];
-
-    // Butterfly reduction pattern
-    sum += __shfl_xor_sync(0xffffffff, sum, 16);
-    sum += __shfl_xor_sync(0xffffffff, sum, 8);
-    sum += __shfl_xor_sync(0xffffffff, sum, 4);
-    sum += __shfl_xor_sync(0xffffffff, sum, 2);
-    sum += __shfl_xor_sync(0xffffffff, sum, 1);
-
-    // Only the first thread in each warp stores to shared memory
-    if (laneId == 0) {
-      sm_reduction[m][warpId] = sum;
-    }
-  }
-
-  __syncthreads();
-
-  // Final reduction across warps (only first thread)
-  if (tid == 0) {
-#pragma unroll
-    for (int m = 0; m < kNumTokens; m++) {
-      float final_sum = 0.0f;
-
-// Sum across the kNumWarps
-#pragma unroll
-      for (int w = 0; w < kNumWarps; w++) {
-        final_sum += sm_reduction[m][w];
-      }
-
-      // Write final result
-      out[m * kNumExperts + n_idx] = __float2bfloat16(final_sum);
-    }
-  }
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  asm volatile("griddepcontrol.launch_dependents;");
-#endif
-}
-
-template <typename T, int kNumTokens, int kNumExperts, int kHiddenDim>
-void invokeRouterGemmBf16Output(__nv_bfloat16* output, T const* mat_a,
-                                T const* mat_b, cudaStream_t stream) {
-  constexpr int VPT = 16 / sizeof(T);
-  constexpr int kBlockSize = 128;
-  cudaLaunchConfig_t config;
-  config.gridDim = kNumExperts;
-  config.blockDim = kBlockSize;
-  config.dynamicSmemBytes = 0;
-  config.stream = stream;
-  cudaLaunchAttribute attrs[1];
-  attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
-  attrs[0].val.programmaticStreamSerializationAllowed = getEnvEnablePDL();
-  config.numAttrs = 1;
-  config.attrs = attrs;
-  cudaLaunchKernelEx(
-      &config,
-      router_gemm_kernel_bf16_output<T, kBlockSize, VPT, kNumTokens,
-                                     kNumExperts, kHiddenDim>,
-      output, mat_a, mat_b);
-}
-
-// Template instantiations for DEFAULT_NUM_EXPERTS experts
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 1, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 2, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 3, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 4, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 5, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 6, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 7, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 8, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 9, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 10, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 11, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 12, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 13, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 14, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 15, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 16, 256, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-// Template instantiations for KIMI_K2_NUM_EXPERTS experts
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 1, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 2, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 3, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 4, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 5, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 6, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 7, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 8, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 9, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 10, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 11, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 12, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 13, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 14, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 15, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmBf16Output<__nv_bfloat16, 16, 384, 7168>(
-    __nv_bfloat16*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);

csrc/moe/dsv3_router_gemm_entry.cu

@@ -1,169 +0,0 @@
-/*
- * Adapted from SGLang's sgl-kernel implementation, which was adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/dsv3MinLatencyKernels/dsv3RouterGemm.cu
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/thop/dsv3RouterGemmOp.cpp
- *
- * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <torch/all.h>
-
-#include <cuda_bf16.h>
-#include <cuda_runtime.h>
-
-#include "core/registration.h"
-#include "dsv3_router_gemm_utils.h"
-
-static constexpr int DEFAULT_NUM_EXPERTS = 256;
-static constexpr int KIMI_K2_NUM_EXPERTS = 384;
-static constexpr int DEFAULT_HIDDEN_DIM = 7168;
-
-template <typename T, int kNumTokens, int kNumExperts, int kHiddenDim>
-void invokeRouterGemmFloatOutput(float* output, T const* mat_a, T const* mat_b,
-                                 cudaStream_t stream);
-
-template <typename T, int kNumTokens, int kNumExperts, int kHiddenDim>
-void invokeRouterGemmBf16Output(__nv_bfloat16* output, T const* mat_a,
-                                T const* mat_b, cudaStream_t stream);
-
-template <int kBegin, int kEnd, int kNumExperts, int kHiddenDim>
-struct LoopUnroller {
-  static void unroll_float_output(int num_tokens, float* output,
-                                  __nv_bfloat16 const* input,
-                                  __nv_bfloat16 const* weights,
-                                  cudaStream_t stream) {
-    if (num_tokens == kBegin) {
-      invokeRouterGemmFloatOutput<__nv_bfloat16, kBegin, kNumExperts,
-                                  kHiddenDim>(output, input, weights, stream);
-    } else {
-      LoopUnroller<kBegin + 1, kEnd, kNumExperts,
-                   kHiddenDim>::unroll_float_output(num_tokens, output, input,
-                                                    weights, stream);
-    }
-  }
-
-  static void unroll_bf16_output(int num_tokens, __nv_bfloat16* output,
-                                 __nv_bfloat16 const* input,
-                                 __nv_bfloat16 const* weights,
-                                 cudaStream_t stream) {
-    if (num_tokens == kBegin) {
-      invokeRouterGemmBf16Output<__nv_bfloat16, kBegin, kNumExperts,
-                                 kHiddenDim>(output, input, weights, stream);
-    } else {
-      LoopUnroller<kBegin + 1, kEnd, kNumExperts,
-                   kHiddenDim>::unroll_bf16_output(num_tokens, output, input,
-                                                   weights, stream);
-    }
-  }
-};
-
-template <int kEnd, int kNumExperts, int kHiddenDim>
-struct LoopUnroller<kEnd, kEnd, kNumExperts, kHiddenDim> {
-  static void unroll_float_output(int num_tokens, float* output,
-                                  __nv_bfloat16 const* input,
-                                  __nv_bfloat16 const* weights,
-                                  cudaStream_t stream) {
-    if (num_tokens == kEnd) {
-      invokeRouterGemmFloatOutput<__nv_bfloat16, kEnd, kNumExperts, kHiddenDim>(
-          output, input, weights, stream);
-    } else {
-      throw std::invalid_argument("Invalid num_tokens, only supports 1 to 16");
-    }
-  }
-
-  static void unroll_bf16_output(int num_tokens, __nv_bfloat16* output,
-                                 __nv_bfloat16 const* input,
-                                 __nv_bfloat16 const* weights,
-                                 cudaStream_t stream) {
-    if (num_tokens == kEnd) {
-      invokeRouterGemmBf16Output<__nv_bfloat16, kEnd, kNumExperts, kHiddenDim>(
-          output, input, weights, stream);
-    } else {
-      throw std::invalid_argument("Invalid num_tokens, only supports 1 to 16");
-    }
-  }
-};
-
-void dsv3_router_gemm(at::Tensor& output,       // [num_tokens, num_experts]
-                      const at::Tensor& mat_a,  // [num_tokens, hidden_dim]
-                      const at::Tensor& mat_b   // [num_experts, hidden_dim]
-) {
-  TORCH_CHECK(output.dim() == 2 && mat_a.dim() == 2 && mat_b.dim() == 2);
-
-  const int num_tokens = mat_a.size(0);
-  const int num_experts = mat_b.size(0);
-  const int hidden_dim = mat_a.size(1);
-
-  TORCH_CHECK(mat_a.size(1) == mat_b.size(1),
-              "mat_a and mat_b must have the same hidden_dim");
-  TORCH_CHECK(hidden_dim == DEFAULT_HIDDEN_DIM,
-              "Expected hidden_dim=", DEFAULT_HIDDEN_DIM,
-              ", but got hidden_dim=", hidden_dim);
-  TORCH_CHECK(
-      num_experts == DEFAULT_NUM_EXPERTS || num_experts == KIMI_K2_NUM_EXPERTS,
-      "Expected num_experts=", DEFAULT_NUM_EXPERTS,
-      " or num_experts=", KIMI_K2_NUM_EXPERTS,
-      ", but got num_experts=", num_experts);
-  TORCH_CHECK(num_tokens >= 1 && num_tokens <= 16,
-              "currently num_tokens must be less than or equal to 16 for "
-              "router_gemm");
-  TORCH_CHECK(mat_a.dtype() == at::kBFloat16, "mat_a must be bf16");
-  TORCH_CHECK(mat_b.dtype() == at::kBFloat16, "mat_b must be bf16");
-  TORCH_CHECK(output.dtype() == at::kFloat || output.dtype() == at::kBFloat16,
-              "output must be float32 or bf16");
-
-  auto const sm = getSMVersion();
-  TORCH_CHECK(sm >= 90 && sm <= 103, "required SM_103 >= CUDA ARCH >= SM_90");
-
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  if (output.dtype() == at::kFloat) {
-    if (num_experts == DEFAULT_NUM_EXPERTS) {
-      LoopUnroller<1, 16, DEFAULT_NUM_EXPERTS, DEFAULT_HIDDEN_DIM>::
-          unroll_float_output(
-              num_tokens, reinterpret_cast<float*>(output.mutable_data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_a.data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_b.data_ptr()), stream);
-    } else if (num_experts == KIMI_K2_NUM_EXPERTS) {
-      LoopUnroller<1, 16, KIMI_K2_NUM_EXPERTS, DEFAULT_HIDDEN_DIM>::
-          unroll_float_output(
-              num_tokens, reinterpret_cast<float*>(output.mutable_data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_a.data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_b.data_ptr()), stream);
-    }
-  } else if (output.dtype() == at::kBFloat16) {
-    if (num_experts == DEFAULT_NUM_EXPERTS) {
-      LoopUnroller<1, 16, DEFAULT_NUM_EXPERTS, DEFAULT_HIDDEN_DIM>::
-          unroll_bf16_output(
-              num_tokens,
-              reinterpret_cast<__nv_bfloat16*>(output.mutable_data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_a.data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_b.data_ptr()), stream);
-    } else if (num_experts == KIMI_K2_NUM_EXPERTS) {
-      LoopUnroller<1, 16, KIMI_K2_NUM_EXPERTS, DEFAULT_HIDDEN_DIM>::
-          unroll_bf16_output(
-              num_tokens,
-              reinterpret_cast<__nv_bfloat16*>(output.mutable_data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_a.data_ptr()),
-              reinterpret_cast<__nv_bfloat16 const*>(mat_b.data_ptr()), stream);
-    }
-  }
-}
-
-TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
-  m.impl("dsv3_router_gemm", &dsv3_router_gemm);
-}

csrc/moe/dsv3_router_gemm_float_out.cu

@@ -1,291 +0,0 @@
-/*
- * Adapted from SGLang's sgl-kernel implementation, which was adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/dsv3MinLatencyKernels/dsv3RouterGemm.cu
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/thop/dsv3RouterGemmOp.cpp
- *
- * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <ATen/ATen.h>
-#include <ATen/cuda/CUDAContext.h>
-
-#include <cuda_bf16.h>
-#include <cuda_runtime.h>
-
-#include "dsv3_router_gemm_utils.h"
-
-// Custom FMA implementation using PTX assembly instructions
-__device__ __forceinline__ void fma(float2& d, float2 const& a, float2 const& b,
-                                    float2 const& c) {
-  asm volatile("fma.rn.f32x2 %0, %1, %2, %3;\n"
-               : "=l"(reinterpret_cast<uint64_t&>(d))
-               : "l"(reinterpret_cast<uint64_t const&>(a)),
-                 "l"(reinterpret_cast<uint64_t const&>(b)),
-                 "l"(reinterpret_cast<uint64_t const&>(c)));
-}
-
-// Convert 8 bfloat16 values from a uint4 to float array - optimized conversion
-template <int VPT>
-__device__ __forceinline__ void bf16_uint4_to_float8(uint4 const& vec,
-                                                     float* dst) {
-  __nv_bfloat16* bf16_ptr =
-      reinterpret_cast<__nv_bfloat16*>(const_cast<uint4*>(&vec));
-
-#pragma unroll
-  for (int i = 0; i < VPT; i++) {
-    dst[i] = __bfloat162float(bf16_ptr[i]);
-  }
-}
-
-template <typename T, int kBlockSize, int VPT, int kNumTokens, int kNumExperts,
-          int kHiddenDim>
-__global__ __launch_bounds__(128, 1) void router_gemm_kernel_float_output(
-    float* out, T const* mat_a, T const* mat_b) {
-  // Each block handles one expert column
-  int const n_idx = blockIdx.x;
-  int const tid = threadIdx.x;
-  constexpr int kWarpSize = 32;
-  constexpr int kNumWarps = kBlockSize / kWarpSize;
-  // Constants for this kernel
-  constexpr int k_elems_per_k_iteration = VPT * kBlockSize;
-  constexpr int k_iterations =
-      kHiddenDim / k_elems_per_k_iteration;  // Total K iterations
-
-  // Initialize accumulators for all M rows
-  float acc[kNumTokens] = {};
-
-  // Shared memory for warp-level reduction
-  __shared__ float sm_reduction[kNumTokens][kNumWarps];  // kNumWarps
-
-  // B matrix is in column-major order, so we can directly load a column for the
-  // n_idx expert
-  T const* b_col = mat_b + n_idx * kHiddenDim;
-
-  // Pre-compute k_base values for each iteration to help compiler optimize
-  int k_bases[k_iterations];
-#pragma unroll
-  for (int ki = 0; ki < k_iterations; ki++) {
-    k_bases[ki] = ki * k_elems_per_k_iteration + tid * VPT;
-  }
-
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  asm volatile("griddepcontrol.wait;");
-#endif
-
-  // Process the GEMM in chunks
-  for (int ki = 0; ki < k_iterations; ki++) {
-    int const k_base = k_bases[ki];
-
-    // Load B matrix values using vector load (8 bf16 values)
-    uint4 b_vec = *reinterpret_cast<uint4 const*>(b_col + k_base);
-
-    // Convert B values to float
-    float b_float[VPT];
-    bf16_uint4_to_float8<VPT>(b_vec, b_float);
-
-// Process each token
-#pragma unroll
-    for (int m_idx = 0; m_idx < kNumTokens; m_idx++) {
-      // Load both rows of A matrix using vector loads
-      uint4 a_vec = *reinterpret_cast<uint4 const*>(
-          mat_a + (m_idx * kHiddenDim) + k_base);
-
-      // Convert A values to float
-      float a_float[VPT];
-      bf16_uint4_to_float8<VPT>(a_vec, a_float);
-
-// Process elements in this chunk
-#pragma unroll
-      for (int k = 0; k < VPT; k++) {
-        float a = a_float[k];
-        float b = b_float[k];
-        acc[m_idx] += a * b;
-      }
-    }
-  }
-
-  // Perform warp-level reduction
-  int const warpSize = 32;
-  int const warpId = tid / warpSize;
-  int const laneId = tid % warpSize;
-
-  // Register for warp-level reduction results
-  float warp_result[kNumTokens];
-
-#pragma unroll
-  for (int m_idx = 0; m_idx < kNumTokens; m_idx++) {
-    warp_result[m_idx] = acc[m_idx];
-  }
-
-// Perform warp-level reduction using optimized butterfly pattern
-#pragma unroll
-  for (int m = 0; m < kNumTokens; m++) {
-    float sum = warp_result[m];
-
-    // Butterfly reduction pattern
-    sum += __shfl_xor_sync(0xffffffff, sum, 16);
-    sum += __shfl_xor_sync(0xffffffff, sum, 8);
-    sum += __shfl_xor_sync(0xffffffff, sum, 4);
-    sum += __shfl_xor_sync(0xffffffff, sum, 2);
-    sum += __shfl_xor_sync(0xffffffff, sum, 1);
-
-    // Only the first thread in each warp stores to shared memory
-    if (laneId == 0) {
-      sm_reduction[m][warpId] = sum;
-    }
-  }
-
-  __syncthreads();
-
-  // Final reduction across warps (only first thread)
-  if (tid == 0) {
-#pragma unroll
-    for (int m = 0; m < kNumTokens; m++) {
-      float final_sum = 0.0f;
-
-// Sum across the kNumWarps
-#pragma unroll
-      for (int w = 0; w < kNumWarps; w++) {
-        final_sum += sm_reduction[m][w];
-      }
-
-      // Write final result
-      out[m * kNumExperts + n_idx] = final_sum;
-    }
-  }
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  asm volatile("griddepcontrol.launch_dependents;");
-#endif
-}
-
-template <typename T, int kNumTokens, int kNumExperts, int kHiddenDim>
-void invokeRouterGemmFloatOutput(float* output, T const* mat_a, T const* mat_b,
-                                 cudaStream_t stream) {
-  constexpr int VPT = 16 / sizeof(T);
-  constexpr int kBlockSize = 128;
-  cudaLaunchConfig_t config;
-  config.gridDim = kNumExperts;
-  config.blockDim = kBlockSize;
-  config.dynamicSmemBytes = 0;
-  config.stream = stream;
-  cudaLaunchAttribute attrs[1];
-  attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
-  attrs[0].val.programmaticStreamSerializationAllowed = getEnvEnablePDL();
-  config.numAttrs = 1;
-  config.attrs = attrs;
-  cudaLaunchKernelEx(
-      &config,
-      router_gemm_kernel_float_output<T, kBlockSize, VPT, kNumTokens,
-                                      kNumExperts, kHiddenDim>,
-      output, mat_a, mat_b);
-}
-
-// Template instantiations for DEFAULT_NUM_EXPERTS experts
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 1, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 2, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 3, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 4, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 5, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 6, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 7, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 8, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 9, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 10, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 11, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 12, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 13, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 14, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 15, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 16, 256, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-// Template instantiations for KIMI_K2_NUM_EXPERTS experts
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 1, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 2, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 3, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 4, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 5, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 6, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 7, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 8, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 9, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 10, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 11, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 12, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 13, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 14, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 15, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);
-
-template void invokeRouterGemmFloatOutput<__nv_bfloat16, 16, 384, 7168>(
-    float*, __nv_bfloat16 const*, __nv_bfloat16 const*, cudaStream_t);

csrc/moe/dsv3_router_gemm_utils.h

@@ -1,43 +0,0 @@
-/*
- * Adapted from SGLang's sgl-kernel implementation, which was adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/dsv3MinLatencyKernels/dsv3RouterGemm.cu
- * https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/thop/dsv3RouterGemmOp.cpp
- *
- * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <ATen/cuda/CUDAContext.h>
-
-#include <cstdlib>
-#include <mutex>
-
-inline int getSMVersion() {
-  auto* props = at::cuda::getCurrentDeviceProperties();
-  return props->major * 10 + props->minor;
-}
-
-inline bool getEnvEnablePDL() {
-  static std::once_flag flag;
-  static bool enablePDL = false;
-  std::call_once(flag, [&]() {
-    if (getSMVersion() >= 90) {
-      const char* env = std::getenv("TRTLLM_ENABLE_PDL");
-      enablePDL = env && env[0] == '1' && env[1] == '\0';
-    }
-  });
-  return enablePDL;
-}

csrc/moe/grouped_topk_kernels.cu

@@ -1,6 +1,6 @@
 /*
  * Adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/v1.3.0rc2/cpp/tensorrt_llm/kernels/noAuxTcKernels.cu
+ * https://github.com/NVIDIA/TensorRT-LLM/blob/v0.21.0/cpp/tensorrt_llm/kernels/noAuxTcKernels.cu
  * Copyright (c) 2025, The vLLM team.
  * SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION &
  * AFFILIATES. All rights reserved. SPDX-License-Identifier: Apache-2.0
@@ -17,10 +17,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "moeTopKFuncs.cuh"
 #include <c10/cuda/CUDAStream.h>
 #include <torch/all.h>
-#include <cmath>
 #include <cuda_fp16.h>
 #include <cuda_bf16.h>
 #include <cuda/std/limits>
@@ -32,17 +30,7 @@ namespace vllm {
 namespace moe {
 
 constexpr unsigned FULL_WARP_MASK = 0xffffffff;
-static constexpr int WARP_SIZE = 32;
-static constexpr int NumNemotronExperts = 512;
-static constexpr int NumKimiK2Experts = 384;
-static constexpr int NumDeepseekExperts = 256;
-static constexpr int MaxSupportedExpertCount =
-    std::max({NumNemotronExperts, NumKimiK2Experts, NumDeepseekExperts});
-static constexpr int MaxNumExpertsUnit = 128;
-static constexpr int NumTopGroupScores = 2;
-static constexpr int DefaultMaxNumTopExperts = 8;
-static constexpr int MaxSupportedTopExperts = 22;
-static constexpr int MaxNumTopGroups = 4;
+constexpr int32_t WARP_SIZE = 32;
 
 namespace warp_topk {
 
@@ -669,335 +657,76 @@ __global__ void grouped_topk_fused_kernel(
 #endif
 }
 
-template <typename T, typename BiasT, typename IdxT, ScoringFunc SF,
-          int MaxNumExperts, bool UseGroups,
-          int MaxNumTopExperts = DefaultMaxNumTopExperts>
-__global__ void grouped_topk_fused_small_expert_count_kernel(
-    T* scores, float* topkValues, IdxT* topkIndices, BiasT const* routingBias,
-    int64_t const numTokens, int64_t const numGroup, int64_t const topkGroup,
-    int64_t const topk, int64_t const numExperts,
-    int64_t const numExpertsPerGroup, bool const renormalize,
-    double const routedScalingFactor) {
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  cudaGridDependencySynchronize();
-#endif
-  // declare shared memory structure
-  // number of experts is bounded by number of threads
-  __shared__ float __attribute((aligned(128))) smemScoreSigmoid[MaxNumExperts];
-  __shared__ float __attribute((aligned(128))) smemScoreBias[MaxNumExperts];
-  // number of expert groups is bounded by number of warps
-  int constexpr NumWarps = MaxNumExperts / WARP_SIZE;
-  __shared__ float __attribute((aligned(128))) smemGroupScores[NumWarps];
-
-  // needed for warp reduce
-  auto block = cg::this_thread_block();
-  auto warp = cg::tiled_partition<WARP_SIZE>(block);
-
-  // for the final reduction of weight norm, only some lanes need to participate
-  int32_t laneIdx = threadIdx.x % WARP_SIZE;
-  int32_t warpIdx = __shfl_sync(0xffffffff, threadIdx.x / WARP_SIZE, 0);
-
-  if constexpr (UseGroups) {
-    if (warpIdx >= numGroup) {
-      return;
-    }
-  }
-  // note that for invalid scores, we simply use a negative value:
-  // they work well even with the compacted format used in topK, and
-  // sigmoid / bias activated scores cannot be negative
-  const float invalidScoreFloat = float{-INFINITY};
-
-  // load bias already; each warp represents one expert group
-  auto threadExpert = threadIdx.x;
-  bool expertSelected = threadExpert < numExperts;
-  if constexpr (UseGroups) {
-    threadExpert = warpIdx * numExpertsPerGroup + laneIdx;
-    expertSelected = laneIdx < numExpertsPerGroup;
-  }
-
-  auto scoreIdx = int64_t{blockIdx.x} * int64_t{numExperts} + threadExpert;
-  auto biasVal = expertSelected ? static_cast<float>(routingBias[threadExpert])
-                                : invalidScoreFloat;
-  topkValues += blockIdx.x * topk;
-  topkIndices += blockIdx.x * topk;
-
-  // get our assigned thread score; each warp represents one expert group
-  float score =
-      expertSelected ? static_cast<float>(scores[scoreIdx]) : invalidScoreFloat;
-  auto scoreSigmoid = apply_scoring<SF>(score);
-  // write the sigmoid score to shared for later use
-  if (expertSelected) {
-    smemScoreSigmoid[threadExpert] = scoreSigmoid;
-  }
-
-  // get the score with bias
-  // note that with invalid values, because sigmoid is < 1 and bias is -1,
-  // we must get a negative value, which is smaller than any valid value
-  auto scoreBias = float{scoreSigmoid + float{biasVal}};
-
-  if (expertSelected) {
-    smemScoreBias[threadExpert] = scoreBias;
-  }
-
-  // registers for top group score reduction
-  float topExpGroupScores[NumTopGroupScores];
-  [[maybe_unused]] int32_t topExpGroupIdx[NumTopGroupScores];
-  float topGroups[MaxNumTopGroups];  // bound of numGroup
-  int32_t topGroupIdx[MaxNumTopGroups];
-  float expertScoreGroup[MaxNumTopGroups];
-  int32_t expertIdxGroup[MaxNumTopGroups];
-  float topScores[MaxNumTopExperts];  // bound of topk
-  int32_t topExperts[MaxNumTopExperts];
-
-  if constexpr (UseGroups) {
-    reduce_topk::reduceTopK(warp, topExpGroupScores, topExpGroupIdx, scoreBias,
-                            threadExpert,
-                            /* minValue */ invalidScoreFloat);
-
-    // get the final group score and write it to shared
-    if (warp.thread_rank() == 0) {
-      auto groupScore = topExpGroupScores[0] + topExpGroupScores[1];
-      smemGroupScores[warpIdx] = groupScore;
-    }
-  }
-
-  // make group scores available to all warps
-  __syncthreads();
-
-  if constexpr (UseGroups) {
-    if (warpIdx == 0) {
-      // a single warp performs the selection of top groups, and goes on to
-      // select the final experts
-      float groupScore =
-          laneIdx < numGroup ? smemGroupScores[laneIdx] : invalidScoreFloat;
-
-      reduce_topk::reduceTopK(warp, topGroups, topGroupIdx, groupScore, laneIdx,
-                              /* minValue */ invalidScoreFloat);
-      // final expert selection: get relevant indexes and scores from shared
-#pragma unroll
-      for (int ii = 0; ii < MaxNumTopGroups; ++ii) {  // bound of numGroup
-        auto groupIdx = topGroupIdx[ii];
-        expertIdxGroup[ii] = groupIdx * numExpertsPerGroup + laneIdx;
-
-        expertScoreGroup[ii] = (ii < topkGroup) && expertSelected
-                                   ? smemScoreBias[expertIdxGroup[ii]]
-                                   : invalidScoreFloat;
-      }
-
-      reduce_topk::reduceTopK(warp, topScores, topExperts, expertScoreGroup,
-                              expertIdxGroup, /* minValue */ invalidScoreFloat,
-                              topk);
-    }
-  } else if constexpr (MaxNumExperts > MaxNumExpertsUnit) {
-    // without groups, and the expert number is larger than MaxNumExpertsUnit,
-    // we need to use multiple warps to calculate the intermediate topk results
-
-    int constexpr NumExpertWarps = (MaxNumExperts - 1) / MaxNumExpertsUnit + 1;
-    int constexpr NumInterTopK = NumExpertWarps * MaxNumTopExperts;
-    __shared__ float
-        __attribute((aligned(128))) smemInterTopScores[NumInterTopK];
-    __shared__ int32_t
-        __attribute((aligned(128))) smemInterTopExperts[NumInterTopK];
-    if (warpIdx < NumExpertWarps) {
-      int offset = warpIdx * WARP_SIZE * MaxNumTopGroups;
-#pragma unroll
-      for (int ii = 0; ii < MaxNumTopGroups; ++ii) {
-        auto expertIdx = ii * WARP_SIZE + laneIdx;
-        expertIdxGroup[ii] = offset + expertIdx;
-        expertScoreGroup[ii] = offset + expertIdx < numExperts
-                                   ? smemScoreBias[offset + expertIdx]
-                                   : invalidScoreFloat;
-      }
-      reduce_topk::reduceTopK(warp, topScores, topExperts, expertScoreGroup,
-                              expertIdxGroup,
-                              /* minValue */ invalidScoreFloat, topk);
-
-      if (laneIdx < topk) {
-        smemInterTopScores[warpIdx * MaxNumTopExperts + laneIdx] =
-            topScores[laneIdx];
-        smemInterTopExperts[warpIdx * MaxNumTopExperts + laneIdx] =
-            topExperts[laneIdx];
-      } else if (laneIdx >= topk && laneIdx < MaxNumTopExperts) {
-        smemInterTopScores[warpIdx * MaxNumTopExperts + laneIdx] =
-            invalidScoreFloat;
-        smemInterTopExperts[warpIdx * MaxNumTopExperts + laneIdx] =
-            MaxNumExperts - 1;
-      }
-    }
-    __syncthreads();
-    if (warpIdx == 0) {
-      int constexpr NumInterTopKPerThread = (NumInterTopK - 1) / WARP_SIZE + 1;
-      float intermediateScore[NumInterTopKPerThread];
-      int32_t intermediateExpert[NumInterTopKPerThread];
-      for (int i = laneIdx; i < NumInterTopKPerThread * WARP_SIZE;
-           i += WARP_SIZE) {
-        int ii = i / WARP_SIZE;
-        if (i < NumInterTopK) {
-          intermediateScore[ii] = smemInterTopScores[i];
-          intermediateExpert[ii] = smemInterTopExperts[i];
-        } else {
-          intermediateScore[ii] = invalidScoreFloat;
-          intermediateExpert[ii] = MaxNumExperts - 1;
-        }
-      }
-      reduce_topk::reduceTopK(warp, topScores, topExperts, intermediateScore,
-                              intermediateExpert,
-                              /* minValue */ invalidScoreFloat, topk);
-    }
-  } else {
-    // without groups, and the expert number is smaller than MaxNumExpertsUnit
-    // each thread just takes `MaxNumTopGroups` experts
-    if (warpIdx == 0) {
-#pragma unroll
-      for (int ii = 0; ii < MaxNumTopGroups; ++ii) {
-        auto expertIdx = ii * WARP_SIZE + laneIdx;
-        expertIdxGroup[ii] = expertIdx;
-        expertScoreGroup[ii] = expertIdx < numExperts ? smemScoreBias[expertIdx]
-                                                      : invalidScoreFloat;
-      }
-      reduce_topk::reduceTopK(warp, topScores, topExperts, expertScoreGroup,
-                              expertIdxGroup,
-                              /* minValue */ invalidScoreFloat, topk);
-    }
-  }
-
-  if (warpIdx == 0) {
-    // determine our lane's expert index and write to output
-    int32_t expertIdx =
-        laneIdx < topk ? topExperts[laneIdx] : MaxNumExperts - 1;
-    float scoreNorm = laneIdx < topk ? smemScoreSigmoid[expertIdx] : 0.F;
-    float finalScore = static_cast<float>(scoreNorm * routedScalingFactor);
-    // norm the value
-    if (renormalize) {
-      auto redNorm = cg::reduce(warp, scoreNorm, cg::plus<float>{});
-      finalScore /= (redNorm + 1e-20);
-    }
-    // store the topk scores and experts to output
-    if (laneIdx < topk) {
-      topkValues[laneIdx] = finalScore;
-      topkIndices[laneIdx] = expertIdx;
-    }
-  }
-
-#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
-  cudaTriggerProgrammaticLaunchCompletion();
-#endif
-}
-
-template <typename T, typename BiasT, typename IdxT, ScoringFunc SF>
+template <typename T, typename BiasT, typename IdxT>
 void invokeNoAuxTc(T* scores, float* topk_values, IdxT* topk_indices,
                    BiasT const* bias, int64_t const num_tokens,
                    int64_t const num_experts, int64_t const n_group,
                    int64_t const topk_group, int64_t const topk,
                    bool const renormalize, double const routed_scaling_factor,
-                   bool enable_pdl = false, cudaStream_t const stream = 0) {
+                   int const scoring_func, bool enable_pdl = false,
+                   cudaStream_t const stream = 0) {
   cudaLaunchConfig_t config;
+  // One block per token; one warp per group.
+  config.gridDim = static_cast<uint32_t>(num_tokens);
+  config.blockDim = static_cast<uint32_t>(n_group) * WARP_SIZE;
+  // Dynamic shared memory: WarpSelect staging + per-group topk buffers.
+  int32_t const num_warps = static_cast<int32_t>(n_group);
+  size_t const val_bytes =
+      static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(T);
+  size_t const val_bytes_aligned =
+      warp_topk::round_up_to_multiple_of<256>(val_bytes);
+  size_t const idx_bytes =
+      static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(int32_t);
+  size_t const internal_bytes = val_bytes_aligned + idx_bytes;
+  size_t const extra_bytes = 16 + static_cast<size_t>(n_group) * sizeof(T);
+  config.dynamicSmemBytes = internal_bytes + extra_bytes;
   config.stream = stream;
   cudaLaunchAttribute attrs[1];
   attrs[0].id = cudaLaunchAttributeProgrammaticStreamSerialization;
   attrs[0].val.programmaticStreamSerializationAllowed = enable_pdl;
   config.numAttrs = 1;
   config.attrs = attrs;
-
-  // Check if we can use the optimized
-  // grouped_topk_fused_small_expert_count_kernel
-  bool const is_single_group =
-      (n_group == 1) && (topk_group == 1) &&
-      (num_experts <= MaxSupportedExpertCount) &&
-      (topk <= DefaultMaxNumTopExperts || topk == MaxSupportedTopExperts);
-
-  int64_t const experts_per_group = num_experts / n_group;
-  bool const is_multi_group =
-      (n_group > 1) && (num_experts <= NumDeepseekExperts) &&
-      (experts_per_group <= WARP_SIZE) &&
-      (experts_per_group * topk_group <= MaxNumExpertsUnit) &&
-      (topk <= DefaultMaxNumTopExperts) && (topk_group <= MaxNumTopGroups);
-
-  if (is_single_group || is_multi_group) {
-    auto* kernel_instance =
-        &grouped_topk_fused_small_expert_count_kernel<T, BiasT, IdxT, SF,
-                                                      NumDeepseekExperts, true>;
-    int num_threads = NumDeepseekExperts;
-    if (is_single_group) {
-      // Special case for Nemotron, which selects top 22 from 512 experts, and 1
-      // group only.
-      if (num_experts == NumNemotronExperts && n_group == 1 &&
-          topk == MaxSupportedTopExperts) {
-        kernel_instance = &grouped_topk_fused_small_expert_count_kernel<
-            T, BiasT, IdxT, SF, NumNemotronExperts, false,
-            MaxSupportedTopExperts>;
-        num_threads = NumNemotronExperts;
-      } else if (num_experts > NumKimiK2Experts &&
-                 num_experts <= MaxSupportedExpertCount) {
-        kernel_instance = &grouped_topk_fused_small_expert_count_kernel<
-            T, BiasT, IdxT, SF, MaxSupportedExpertCount, false>;
-        num_threads = MaxSupportedExpertCount;
-      } else if (num_experts > MaxNumExpertsUnit &&
-                 num_experts <= NumKimiK2Experts) {
-        kernel_instance = &grouped_topk_fused_small_expert_count_kernel<
-            T, BiasT, IdxT, SF, NumKimiK2Experts, false>;
-        num_threads = NumKimiK2Experts;
-      } else {
-        kernel_instance = &grouped_topk_fused_small_expert_count_kernel<
-            T, BiasT, IdxT, SF, MaxNumExpertsUnit, false>;
-        num_threads = MaxNumExpertsUnit;
-      }
+  auto const sf = static_cast<ScoringFunc>(scoring_func);
+  switch (sf) {
+    case SCORING_NONE: {
+      auto* kernel_instance =
+          &grouped_topk_fused_kernel<T, BiasT, IdxT, SCORING_NONE>;
+      cudaLaunchKernelEx(&config, kernel_instance, scores, topk_values,
+                         topk_indices, bias, num_tokens, num_experts, n_group,
+                         topk_group, topk, renormalize, routed_scaling_factor);
+      return;
     }
-    config.gridDim = num_tokens;
-    config.blockDim = num_threads;
-    config.dynamicSmemBytes = 0;
-    cudaLaunchKernelEx(&config, kernel_instance, scores, topk_values,
-                       topk_indices, bias, num_tokens, n_group, topk_group,
-                       topk, num_experts, num_experts / n_group, renormalize,
-                       routed_scaling_factor);
-  } else {
-    auto* kernel_instance = &grouped_topk_fused_kernel<T, BiasT, IdxT, SF>;
-    // One block per token; one warp per group.
-    config.gridDim = static_cast<uint32_t>(num_tokens);
-    config.blockDim = static_cast<uint32_t>(n_group) * WARP_SIZE;
-    // Dynamic shared memory: WarpSelect staging + per-group topk buffers.
-    int32_t const num_warps = static_cast<int32_t>(n_group);
-    size_t const val_bytes =
-        static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(T);
-    size_t const val_bytes_aligned =
-        warp_topk::round_up_to_multiple_of<256>(val_bytes);
-    size_t const idx_bytes =
-        static_cast<size_t>(num_warps) * WARP_SIZE * sizeof(int32_t);
-    size_t const internal_bytes = val_bytes_aligned + idx_bytes;
-    size_t const extra_bytes = 16 + static_cast<size_t>(n_group) * sizeof(T);
-    config.dynamicSmemBytes = internal_bytes + extra_bytes;
-    cudaLaunchKernelEx(&config, kernel_instance, scores, topk_values,
-                       topk_indices, bias, num_tokens, num_experts, n_group,
-                       topk_group, topk, renormalize, routed_scaling_factor);
+    case SCORING_SIGMOID: {
+      auto* kernel_instance =
+          &grouped_topk_fused_kernel<T, BiasT, IdxT, SCORING_SIGMOID>;
+      cudaLaunchKernelEx(&config, kernel_instance, scores, topk_values,
+                         topk_indices, bias, num_tokens, num_experts, n_group,
+                         topk_group, topk, renormalize, routed_scaling_factor);
+      return;
+    }
+    default:
+      // should be guarded by higher level checks.
+      TORCH_CHECK(false, "Unsupported scoring_func in invokeNoAuxTc");
   }
 }
 
-#define INSTANTIATE_NOAUX_TC(T, BiasT, IdxT, SF)                             \
-  template void invokeNoAuxTc<T, BiasT, IdxT, SF>(                           \
+#define INSTANTIATE_NOAUX_TC(T, BiasT, IdxT)                                 \
+  template void invokeNoAuxTc<T, BiasT, IdxT>(                               \
       T * scores, float* topk_values, IdxT* topk_indices, BiasT const* bias, \
       int64_t const num_tokens, int64_t const num_experts,                   \
       int64_t const n_group, int64_t const topk_group, int64_t const topk,   \
       bool const renormalize, double const routed_scaling_factor,            \
-      bool enable_pdl, cudaStream_t const stream);
+      int const scoring_func, bool enable_pdl, cudaStream_t const stream);
 
-INSTANTIATE_NOAUX_TC(float, float, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(float, half, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(float, __nv_bfloat16, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(half, float, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(half, half, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(half, __nv_bfloat16, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, float, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, half, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, __nv_bfloat16, int32_t, SCORING_SIGMOID);
-INSTANTIATE_NOAUX_TC(float, float, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(float, half, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(float, __nv_bfloat16, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(half, float, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(half, half, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(half, __nv_bfloat16, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, float, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, half, int32_t, SCORING_NONE);
-INSTANTIATE_NOAUX_TC(__nv_bfloat16, __nv_bfloat16, int32_t, SCORING_NONE);
+INSTANTIATE_NOAUX_TC(float, float, int32_t);
+INSTANTIATE_NOAUX_TC(float, half, int32_t);
+INSTANTIATE_NOAUX_TC(float, __nv_bfloat16, int32_t);
+INSTANTIATE_NOAUX_TC(half, float, int32_t);
+INSTANTIATE_NOAUX_TC(half, half, int32_t);
+INSTANTIATE_NOAUX_TC(half, __nv_bfloat16, int32_t);
+INSTANTIATE_NOAUX_TC(__nv_bfloat16, float, int32_t);
+INSTANTIATE_NOAUX_TC(__nv_bfloat16, half, int32_t);
+INSTANTIATE_NOAUX_TC(__nv_bfloat16, __nv_bfloat16, int32_t);
 }  // end namespace moe
 }  // namespace vllm
 
@@ -1033,53 +762,46 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
       {num_tokens, topk}, torch::dtype(torch::kInt32).device(torch::kCUDA));
 
   auto stream = c10::cuda::getCurrentCUDAStream(scores.get_device());
-  auto const sf = static_cast<vllm::moe::ScoringFunc>(scoring_func);
 
-#define LAUNCH_KERNEL_SF(T, BiasT, IdxT)                                      \
-  do {                                                                        \
-    switch (sf) {                                                             \
-      case vllm::moe::SCORING_NONE:                                           \
-        vllm::moe::invokeNoAuxTc<T, BiasT, IdxT, vllm::moe::SCORING_NONE>(    \
-            reinterpret_cast<T*>(scores.mutable_data_ptr()),                  \
-            reinterpret_cast<float*>(topk_values.mutable_data_ptr()),         \
-            reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),         \
-            reinterpret_cast<BiasT const*>(bias.data_ptr()), num_tokens,      \
-            num_experts, n_group, topk_group, topk, renormalize,              \
-            routed_scaling_factor, false, stream);                            \
-        break;                                                                \
-      case vllm::moe::SCORING_SIGMOID:                                        \
-        vllm::moe::invokeNoAuxTc<T, BiasT, IdxT, vllm::moe::SCORING_SIGMOID>( \
-            reinterpret_cast<T*>(scores.mutable_data_ptr()),                  \
-            reinterpret_cast<float*>(topk_values.mutable_data_ptr()),         \
-            reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),         \
-            reinterpret_cast<BiasT const*>(bias.data_ptr()), num_tokens,      \
-            num_experts, n_group, topk_group, topk, renormalize,              \
-            routed_scaling_factor, false, stream);                            \
-        break;                                                                \
-      default:                                                                \
-        throw std::invalid_argument("Unsupported scoring_func");              \
-        break;                                                                \
-    }                                                                         \
-  } while (0)
-
-#define LAUNCH_KERNEL(T, IdxT)                                         \
-  do {                                                                 \
-    switch (bias_type) {                                               \
-      case torch::kFloat16:                                            \
-        LAUNCH_KERNEL_SF(T, half, IdxT);                               \
-        break;                                                         \
-      case torch::kFloat32:                                            \
-        LAUNCH_KERNEL_SF(T, float, IdxT);                              \
-        break;                                                         \
-      case torch::kBFloat16:                                           \
-        LAUNCH_KERNEL_SF(T, __nv_bfloat16, IdxT);                      \
-        break;                                                         \
-      default:                                                         \
-        throw std::invalid_argument(                                   \
-            "Invalid bias dtype, only supports float16, float32, and " \
-            "bfloat16");                                               \
-        break;                                                         \
-    }                                                                  \
+#define LAUNCH_KERNEL(T, IdxT)                                               \
+  do {                                                                       \
+    switch (bias_type) {                                                     \
+      case torch::kFloat16:                                                  \
+        vllm::moe::invokeNoAuxTc<T, half, IdxT>(                             \
+            reinterpret_cast<T*>(scores.mutable_data_ptr()),                 \
+            reinterpret_cast<float*>(topk_values.mutable_data_ptr()),        \
+            reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),        \
+            reinterpret_cast<half const*>(bias.data_ptr()), num_tokens,      \
+            num_experts, n_group, topk_group, topk, renormalize,             \
+            routed_scaling_factor, static_cast<int>(scoring_func), false,    \
+            stream);                                                         \
+        break;                                                               \
+      case torch::kFloat32:                                                  \
+        vllm::moe::invokeNoAuxTc<T, float, IdxT>(                            \
+            reinterpret_cast<T*>(scores.mutable_data_ptr()),                 \
+            reinterpret_cast<float*>(topk_values.mutable_data_ptr()),        \
+            reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),        \
+            reinterpret_cast<float const*>(bias.data_ptr()), num_tokens,     \
+            num_experts, n_group, topk_group, topk, renormalize,             \
+            routed_scaling_factor, static_cast<int>(scoring_func), false,    \
+            stream);                                                         \
+        break;                                                               \
+      case torch::kBFloat16:                                                 \
+        vllm::moe::invokeNoAuxTc<T, __nv_bfloat16, IdxT>(                    \
+            reinterpret_cast<T*>(scores.mutable_data_ptr()),                 \
+            reinterpret_cast<float*>(topk_values.mutable_data_ptr()),        \
+            reinterpret_cast<IdxT*>(topk_indices.mutable_data_ptr()),        \
+            reinterpret_cast<__nv_bfloat16 const*>(bias.data_ptr()),         \
+            num_tokens, num_experts, n_group, topk_group, topk, renormalize, \
+            routed_scaling_factor, static_cast<int>(scoring_func), false,    \
+            stream);                                                         \
+        break;                                                               \
+      default:                                                               \
+        throw std::invalid_argument(                                         \
+            "Invalid bias dtype, only supports float16, float32, and "       \
+            "bfloat16");                                                     \
+        break;                                                               \
+    }                                                                        \
   } while (0)
 
   switch (data_type) {
@@ -1102,6 +824,5 @@ std::tuple<torch::Tensor, torch::Tensor> grouped_topk(
       break;
   }
 #undef LAUNCH_KERNEL
-#undef LAUNCH_KERNEL_SF
   return {topk_values, topk_indices};
 }

csrc/moe/moeTopKFuncs.cuh

@@ -1,257 +0,0 @@
-/*
- * Adapted from
- * https://github.com/NVIDIA/TensorRT-LLM/blob/v1.3.0rc2/cpp/tensorrt_llm/kernels/moeTopKFuncs.cuh
- * Copyright (c) 2026, The vLLM team.
- * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION. All rights
- * reserved. SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#pragma once
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-#include <cub/cub.cuh>
-
-namespace vllm {
-namespace moe {
-namespace reduce_topk {
-namespace cg = cooperative_groups;
-static constexpr int kWARP_SIZE = 32;
-
-template <typename T_>
-struct TopKRedType {
-  using T = T_;
-  static_assert(
-      std::is_same_v<T, float> || std::is_same_v<T, half> ||
-          std::is_same_v<T, __nv_bfloat16> || std::is_same_v<T, int>,
-      "Top K reduction only implemented for int, float, float16 and bfloat16");
-
-  using TypeCmp = std::conditional_t<sizeof(T) == 4, uint64_t, uint32_t>;
-  using IdxT = std::conditional_t<sizeof(T) == 4, int32_t, int16_t>;
-
-  static constexpr int kMoveBits = (sizeof(T) == 4) ? 32 : 16;
-  static constexpr int kMaxIdx = 65535;
-  TypeCmp compValIdx;
-
-  static __host__ __device__ inline TypeCmp makeCmpVal(T val, int32_t idx = 0) {
-    auto valueBits = cub::Traits<T>::TwiddleIn(
-        reinterpret_cast<typename cub::Traits<T>::UnsignedBits&>(val));
-    TypeCmp compactTmp = valueBits;
-    compactTmp = (compactTmp << kMoveBits) | (0xFFFF & (kMaxIdx - idx));
-    // Use 65535 minus idx to give higher priority to elements with smaller
-    // indices.
-    return compactTmp;
-  }
-
-  static __host__ __device__ void unpack(T& value, int32_t& index,
-                                         TypeCmp cmp) {
-    // Since “65535-idx” is always smaller than 65536 and positive, we can
-    // directly use it as the lower 16 bits
-    index = kMaxIdx - static_cast<int32_t>((cmp & 0xFFFF));
-
-    auto compactTmp = cmp >> kMoveBits;
-    auto valueBits = cub::Traits<T>::TwiddleOut(
-        reinterpret_cast<typename cub::Traits<T>::UnsignedBits&>(compactTmp));
-    value = reinterpret_cast<T&>(valueBits);
-  }
-
-  __host__ __device__ TopKRedType() = default;
-
-  __host__ __device__ TopKRedType(T val, int32_t idx)
-      : compValIdx(makeCmpVal(val, idx)) {}
-
-  __host__ __device__ operator TypeCmp() const noexcept { return compValIdx; }
-
-  __device__ inline TypeCmp reduce(
-      cg::thread_block_tile<kWARP_SIZE> const& warp) {
-    return cg::reduce(warp, compValIdx, cg::greater<TypeCmp>{});
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <int K_, bool Enable_>
-struct TopKIdx {
-  // by default, empty
-};
-
-template <int K_>
-struct TopKIdx<K_, true> {
-  static constexpr int K = K_;
-  int32_t val[K];
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-#define TOPK_SWAP(I, J)                                         \
-  {                                                             \
-    auto pairMin = min(topK[I].compValIdx, topK[J].compValIdx); \
-    auto pairMax = max(topK[I].compValIdx, topK[J].compValIdx); \
-    topK[I].compValIdx = pairMax;                               \
-    topK[J].compValIdx = pairMin;                               \
-  }
-
-template <int N, typename RedType>
-struct Sort;
-
-template <typename RedType>
-struct Sort<1, RedType> {
-  static __device__ void run(RedType* topK) {}
-};
-
-template <typename RedType>
-struct Sort<2, RedType> {
-  static __device__ void run(RedType* topK) { TOPK_SWAP(0, 1); }
-};
-
-template <typename RedType>
-struct Sort<3, RedType> {
-  static __device__ void run(RedType* topK) {
-    TOPK_SWAP(0, 1);
-    TOPK_SWAP(1, 2);
-    TOPK_SWAP(0, 1);
-  }
-};
-
-template <typename RedType>
-struct Sort<4, RedType> {
-  static __device__ void run(RedType* topK) {
-    TOPK_SWAP(0, 2);
-    TOPK_SWAP(1, 3);
-    TOPK_SWAP(0, 1);
-    TOPK_SWAP(2, 3);
-    TOPK_SWAP(1, 2);
-  }
-};
-
-template <int K, typename Type>
-__forceinline__ __device__ void reduceTopK(
-    cg::thread_block_tile<kWARP_SIZE> const& warp, Type (&out)[K],
-    int32_t (&outIdx)[K], Type value, int32_t idx, Type const minValue,
-    int actualK = K) {
-  static_assert(K > 0, "Top K must have K > 0");
-  static_assert(K < kWARP_SIZE, "Top K must have K < kWARP_SIZE");
-  using RedType = TopKRedType<Type>;
-  RedType topK{value, idx};
-  typename RedType::TypeCmp packedMax{};
-#pragma unroll
-  for (int kk = 0; kk < actualK; ++kk) {
-    topK =
-        kk > 0 && packedMax == topK.compValIdx ? RedType{minValue, idx} : topK;
-    // get the next largest value
-    packedMax = topK.reduce(warp);
-    RedType::unpack(out[kk], outIdx[kk], packedMax);
-  }
-};
-
-template <int K, typename Type, int N, bool IsSorted = false>
-__device__ void reduceTopKFunc(cg::thread_block_tile<kWARP_SIZE> const& warp,
-                               Type (&out)[K], int32_t (&outIdx)[K],
-                               Type (&value)[N], int32_t (&idx)[N],
-                               Type minValue, int actualK = K) {
-  static_assert(K > 0, "Top K must have K > 0");
-  static_assert(K < kWARP_SIZE, "Top K must have K < kWARP_SIZE");
-  static_assert(N > 0, "Top K must have N > 0");
-  static_assert(N < 5,
-                "Only support candidates number less than or equal to 128");
-  using RedType = TopKRedType<Type>;
-  RedType topK[N];
-#pragma unroll
-  for (int nn = 0; nn < N; ++nn) {
-    topK[nn] = RedType{value[nn], idx[nn]};
-  }
-
-  if constexpr (!IsSorted) {
-    Sort<N, RedType>::run(topK);
-  }
-  typename RedType::TypeCmp packedMax{};
-#pragma unroll
-  for (int kk = 0; kk < actualK; ++kk) {
-    bool update = kk > 0 && packedMax == topK[0].compValIdx;
-#pragma unroll
-    for (int nn = 0; nn < N; ++nn) {
-      topK[nn] = update && nn == N - 1 ? RedType{minValue, idx[nn]}
-                 : update              ? topK[nn + 1]
-                                       : topK[nn];
-    }
-    // get the next largest value
-    packedMax = topK[0].reduce(warp);
-    RedType::unpack(out[kk], outIdx[kk], packedMax);
-  }
-};
-
-template <int K, typename Type, int N>
-__forceinline__ __device__ void reduceTopK(
-    cg::thread_block_tile<kWARP_SIZE> const& warp, Type (&out)[K],
-    int32_t (&outIdx)[K], Type (&value)[N], int32_t (&idx)[N],
-    Type const minValue, int actualK = K) {
-  static_assert(K > 0, "Top K must have K > 0");
-  static_assert(K < kWARP_SIZE, "Top K must have K < kWARP_SIZE");
-  static_assert(N > 0, "Top K must have N > 0");
-  static_assert(
-      N <= 16,
-      "Only support candidates number less than or equal to 16*32=512");
-  static_assert(N <= 4 || N % 4 == 0,
-                "Only support candidates number is a multiple of 4*32=128 or "
-                "less than or equal to 4");
-  using RedType = TopKRedType<Type>;
-
-  if constexpr (N <= 4) {
-    reduceTopKFunc<K, Type, N>(warp, out, outIdx, value, idx, minValue,
-                               actualK);
-  } else {
-    constexpr int numLoops = N / 4;
-    constexpr int numResults = (numLoops * K - 1) / kWARP_SIZE + 1;
-
-    Type topKBufferValue[numResults];
-    int32_t topKBufferIdx[numResults];
-    int32_t laneIdx = threadIdx.x % kWARP_SIZE;
-
-    for (int ii = 0; ii < numResults; ++ii) {
-      topKBufferValue[ii] = minValue;
-      topKBufferIdx[ii] = ii * kWARP_SIZE - 1;
-    }
-    for (int loop = 0; loop < numLoops; ++loop) {
-      int start = loop * 4;
-      Type topKValue[K];
-      int32_t topKIdx[K];
-      Type inValue[4];
-      int32_t inIdx[4];
-      for (int i = 0; i < 4; ++i) {
-        inValue[i] = value[start + i];
-        inIdx[i] = idx[start + i];
-      }
-      reduceTopKFunc<K, Type, 4>(warp, topKValue, topKIdx, inValue, inIdx,
-                                 minValue, actualK);
-      int inOffset = laneIdx % K;
-      if (laneIdx >= loop * K && laneIdx < (loop + 1) * K) {
-        topKBufferValue[0] = topKValue[inOffset];
-        topKBufferIdx[0] = topKIdx[inOffset];
-      }
-      if (loop == numLoops - 1 && (laneIdx < (numLoops * K - kWARP_SIZE))) {
-        topKBufferValue[1] = topKValue[inOffset];
-        topKBufferIdx[1] = topKIdx[inOffset];
-      }
-    }
-
-    reduceTopKFunc<K, Type, numResults>(warp, out, outIdx, topKBufferValue,
-                                        topKBufferIdx, minValue, actualK);
-  }
-};
-
-#undef TOPK_SWAP
-
-}  // namespace reduce_topk
-}  // namespace moe
-}  // namespace vllm

csrc/moe/moe_align_sum_kernels.cu

@@ -172,7 +172,7 @@ __device__ void _moe_align_block_size(
     }
   }
 
-  // Fill remaining expert_ids with -1
+  // Fill remaining expert_ids with 0
   const size_t fill_start_idx =
       cumsum[cumsum_offset + num_experts] / block_size + threadIdx.x;
   for (size_t i = fill_start_idx; i < max_num_m_blocks; i += blockDim.x) {
@@ -265,7 +265,7 @@ __device__ void _moe_align_block_size_small_batch_expert(
     }
   }
 
-  // Fill remaining expert_ids with -1
+  // Fill remaining expert_ids with 0
   const size_t fill_start_idx = cumsum[num_experts] / block_size + tid;
   for (size_t i = fill_start_idx; i < max_num_m_blocks; i += stride) {
     expert_ids[expert_ids_offset + i] = inactive_expert_id;
@@ -332,7 +332,7 @@ __global__ void moe_align_block_size_kernel(
       topk_ids, sorted_token_ids, expert_ids, total_tokens_post_pad, expert_map,
       num_experts, padded_num_experts, experts_per_warp, block_size, numel,
       cumsum, max_num_tokens_padded, CEILDIV(max_num_tokens_padded, block_size),
-      0, -1, topk_num, nullptr, has_expert_map);
+      0, 0, topk_num, nullptr, has_expert_map);
 }
 
 template <typename scalar_t>
@@ -373,7 +373,7 @@ __global__ void moe_align_block_size_small_batch_expert_kernel(
   _moe_align_block_size_small_batch_expert<scalar_t, fill_threads>(
       topk_ids, sorted_token_ids, expert_ids, total_tokens_post_pad, expert_map,
       num_experts, block_size, numel, max_num_tokens_padded,
-      CEILDIV(max_num_tokens_padded, block_size), -1, 0, topk_num, nullptr,
+      CEILDIV(max_num_tokens_padded, block_size), 0, 0, topk_num, nullptr,
       has_expert_map);
 }
 

csrc/moe/moe_ops.h

@@ -55,19 +55,4 @@ bool moe_permute_unpermute_supported();
 
 void shuffle_rows(const torch::Tensor& input_tensor,
                   const torch::Tensor& dst2src_map,
-                  torch::Tensor& output_tensor);
-
-#ifndef USE_ROCM
-// cuBLAS bf16 x bf16 -> fp32 router GEMM (fallback for non-SM90 / batch > 16)
-torch::Tensor router_gemm_bf16_fp32(torch::Tensor const& input,
-                                    torch::Tensor const& weight);
-
-// DeepSeek V3 optimized router GEMM kernel for SM90+
-// Computes output = mat_a @ mat_b.T where:
-//   mat_a: [num_tokens, hidden_dim] in bf16
-//   mat_b: [num_experts, hidden_dim] in bf16
-//   output: [num_tokens, num_experts] in bf16 or fp32
-// Supports num_tokens in [1, 16], num_experts in {256, 384}, hidden_dim = 7168
-void dsv3_router_gemm(torch::Tensor& output, const torch::Tensor& mat_a,
-                      const torch::Tensor& mat_b);
-#endif
+                  torch::Tensor& output_tensor);

csrc/moe/mxfp8_moe/cutlass_mxfp8_grouped_mm.cu

@@ -1,60 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-// Adapted from SGLang:
-// https://github.com/sgl-project/sglang/blob/ded068a76e00878881d52d5bfb791e0f60d7311b/sgl-kernel/csrc/expert_specialization/es_sm100_mxfp8_blockscaled.cu
-
-#include <torch/all.h>
-
-#include "cutlass_mxfp8_grouped_mm_launcher.cuh"
-
-void cutlass_mxfp8_grouped_mm(const torch::Tensor& a, const torch::Tensor& b,
-                              const torch::Tensor& sfa,
-                              const torch::Tensor& sfb, torch::Tensor& d,
-                              const torch::Tensor& problem_sizes,
-                              const torch::Tensor& expert_offsets,
-                              const torch::Tensor& blockscale_offsets) {
-#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
-  TORCH_CHECK(problem_sizes.dim() == 2, "problem_sizes must be 2D tensor");
-  TORCH_CHECK(problem_sizes.size(1) == 3,
-              "problem_sizes must have shape (num_experts, 3)");
-  TORCH_CHECK(problem_sizes.size(0) == expert_offsets.size(0),
-              "Number of experts in problem_sizes must match expert_offsets");
-  TORCH_CHECK(problem_sizes.dtype() == torch::kInt32,
-              "problem_sizes must be int32");
-  TORCH_CHECK(expert_offsets.dtype() == torch::kInt32,
-              "expert_offsets must be int32");
-  TORCH_CHECK(blockscale_offsets.dtype() == torch::kInt32,
-              "blockscale_offsets must be int32");
-  TORCH_CHECK(a.dim() == 2, "a must be a 2D tensor of shape (num_tokens, k)");
-  TORCH_CHECK(b.dim() == 3,
-              "b must be a 3D tensor of shape (num_experts, k, n)");
-  TORCH_CHECK(a.size(1) == b.size(1) && a.size(1) % 128 == 0,
-              "k should align 128");
-  TORCH_CHECK(b.size(2) % 128 == 0, "n should align 128");
-  TORCH_CHECK(a.strides()[1] == 1, "a must be row major");
-  TORCH_CHECK(b.strides()[1] == 1, "b must be column major");
-
-  auto stream = at::cuda::getCurrentCUDAStream();
-  if (d.dtype() == torch::kBFloat16) {
-    expert_specialization::cutlass_mxfp8_grouped_mm_dispatch_out_dtype<
-        cutlass::bfloat16_t>(a, b, sfa, sfb, d, problem_sizes, expert_offsets,
-                             blockscale_offsets, stream);
-  } else if (d.dtype() == torch::kFloat16) {
-    expert_specialization::cutlass_mxfp8_grouped_mm_dispatch_out_dtype<
-        cutlass::half_t>(a, b, sfa, sfb, d, problem_sizes, expert_offsets,
-                         blockscale_offsets, stream);
-  } else {
-    TORCH_CHECK(false, "dtype must be kFloat16 or kBFloat16");
-  }
-#else
-  TORCH_CHECK(false,
-              "No implemented cutlass_mxfp8_grouped_mm for "
-              "current device");
-#endif
-}
-
-#include "core/registration.h"
-
-TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
-  m.impl("cutlass_mxfp8_grouped_mm", cutlass_mxfp8_grouped_mm);
-}

csrc/moe/mxfp8_moe/cutlass_mxfp8_grouped_mm_functor.cuh

@@ -1,141 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-// Adapted from SGLang:
-// https://github.com/sgl-project/sglang/blob/ded068a76e00878881d52d5bfb791e0f60d7311b/sgl-kernel/csrc/expert_specialization/es_sm100_mxfp8_blockscaled_functor.cuh
-
-#pragma once
-#include <cuda.h>
-
-#include "cute/tensor.hpp"
-#include "cutlass/util/packed_stride.hpp"
-#include "cutlass_mxfp8_grouped_mm_traits.cuh"
-
-namespace expert_specialization {
-
-using namespace cute;
-
-template <typename GemmTraits>
-struct CutlassMxfp8GroupedMmOffsetFunctor {
-  using Gemm = typename GemmTraits::Gemm;
-  using ElementA = typename Gemm::ElementA;
-  using ElementB = typename Gemm::ElementB;
-  using ElementSF = typename GemmTraits::ElementSF;
-  using ElementD = typename GemmTraits::ElementOutput;
-  // Input
-  int* expert_offsets{nullptr};
-  int* blockscale_offsets{nullptr};
-  // Output
-  ElementA* a_base{nullptr};
-  ElementB* b_base{nullptr};
-  ElementSF* sfa_base{nullptr};
-  ElementSF* sfb_base{nullptr};
-  ElementD* d_base{nullptr};
-  ElementA** a_offsets{nullptr};
-  ElementB** b_offsets{nullptr};
-  ElementSF** sfa_offsets{nullptr};
-  ElementSF** sfb_offsets{nullptr};
-  ElementD** d_offsets{nullptr};
-
-  CutlassMxfp8GroupedMmOffsetFunctor() = default;
-  CutlassMxfp8GroupedMmOffsetFunctor(
-      int* _expert_offsets, int* _blockscale_offsets, ElementA* _a_base,
-      ElementB* _b_base, ElementSF* _sfa_base, ElementSF* _sfb_base,
-      ElementD* _d_base, ElementA** _a_offsets, ElementB** _b_offsets,
-      ElementSF** _sfa_offsets, ElementSF** _sfb_offsets, ElementD** _d_offsets)
-      : expert_offsets{_expert_offsets},
-        blockscale_offsets{_blockscale_offsets},
-        a_base(_a_base),
-        b_base(_b_base),
-        sfa_base(_sfa_base),
-        sfb_base(_sfb_base),
-        d_base(_d_base),
-        a_offsets(_a_offsets),
-        b_offsets(_b_offsets),
-        sfa_offsets(_sfa_offsets),
-        sfb_offsets(_sfb_offsets),
-        d_offsets(_d_offsets) {}
-
-  void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
-    int64_t expert_offset = static_cast<int64_t>(expert_offsets[expert_id]);
-    int64_t blockscale_offset =
-        static_cast<int64_t>(blockscale_offsets[expert_id]);
-    int64_t a_stride = expert_offset * k;
-    int64_t b_stride = expert_id * k * n;
-    int64_t d_stride = expert_offset * n;
-    int64_t sfa_stride = blockscale_offset * (k / 32);
-    int64_t sfb_stride = expert_id * n * (k / 32);
-
-    a_offsets[expert_id] = a_base + a_stride;
-    b_offsets[expert_id] = b_base + b_stride;
-    sfa_offsets[expert_id] = sfa_base + sfa_stride;
-    sfb_offsets[expert_id] = sfb_base + sfb_stride;
-    d_offsets[expert_id] = d_base + d_stride;
-  }
-};
-
-template <typename GemmTraits>
-struct CutlassMxfp8GroupedMmLayoutFunctor {
-  using Sm1xxBlkScaledConfig = typename GemmTraits::Sm1xxBlkScaledConfig;
-  using LayoutSFA = typename GemmTraits::LayoutSFA;
-  using LayoutSFB = typename GemmTraits::LayoutSFB;
-  LayoutSFA* layout_sfa_base{nullptr};
-  LayoutSFB* layout_sfb_base{nullptr};
-
-  CutlassMxfp8GroupedMmLayoutFunctor() = default;
-  CutlassMxfp8GroupedMmLayoutFunctor(LayoutSFA* _layout_sfa_base,
-                                     LayoutSFB* _layout_sfb_base)
-      : layout_sfa_base(_layout_sfa_base), layout_sfb_base(_layout_sfb_base) {}
-
-  void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
-    LayoutSFA* layout_sfa_ptr = layout_sfa_base + expert_id;
-    LayoutSFB* layout_sfb_ptr = layout_sfb_base + expert_id;
-    *layout_sfa_ptr = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFA(
-        cute::make_shape(m, n, k, 1));
-    *layout_sfb_ptr = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFB(
-        cute::make_shape(m, n, k, 1));
-  }
-};
-
-template <typename GemmTraits>
-struct CutlassMxfp8GroupedMmStrideFunctor {
-  using StrideA = typename GemmTraits::StrideA;
-  using StrideB = typename GemmTraits::StrideB;
-  using StrideD = typename GemmTraits::StrideD;
-  StrideA* stride_A_base{nullptr};
-  StrideB* stride_B_base{nullptr};
-  StrideD* stride_D_base{nullptr};
-
-  CutlassMxfp8GroupedMmStrideFunctor() = default;
-  CutlassMxfp8GroupedMmStrideFunctor(StrideA* _stride_A_base,
-                                     StrideB* _stride_B_base,
-                                     StrideD* _stride_D_base)
-      : stride_A_base(_stride_A_base),
-        stride_B_base(_stride_B_base),
-        stride_D_base(_stride_D_base) {}
-
-  void CUTE_DEVICE operator()(int64_t expert_id, int m, int n, int k) {
-    StrideA* stride_A = stride_A_base + expert_id;
-    StrideB* stride_B = stride_B_base + expert_id;
-    StrideD* stride_D = stride_D_base + expert_id;
-    *stride_A = cutlass::make_cute_packed_stride(StrideA{}, {m, k, 1});
-    *stride_B = cutlass::make_cute_packed_stride(StrideB{}, {n, k, 1});
-    *stride_D = cutlass::make_cute_packed_stride(StrideD{}, {m, n, 1});
-  }
-};
-
-template <typename OffsetFunctor, typename LayoutFunctor,
-          typename StrideFunctor>
-__global__ void cutlassMxfp8GroupedMmPreComputeKernel(
-    int* problem_sizes, OffsetFunctor offset_functor,
-    LayoutFunctor layout_functor, StrideFunctor stride_functor) {
-  int64_t expert_id = static_cast<int64_t>(threadIdx.x);
-  int m = problem_sizes[expert_id * 3 + 0];
-  int n = problem_sizes[expert_id * 3 + 1];
-  int k = problem_sizes[expert_id * 3 + 2];
-
-  offset_functor(expert_id, m, n, k);
-  layout_functor(expert_id, m, n, k);
-  stride_functor(expert_id, m, n, k);
-}
-
-}  // namespace expert_specialization

csrc/moe/mxfp8_moe/cutlass_mxfp8_grouped_mm_launcher.cuh

@@ -1,179 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-// Adapted from SGLang:
-// https://github.com/sgl-project/sglang/blob/ded068a76e00878881d52d5bfb791e0f60d7311b/sgl-kernel/csrc/expert_specialization/es_sm100_mxfp8_blockscaled_launcher.cuh
-
-#pragma once
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include <torch/all.h>
-
-#include <cassert>
-#include <iostream>
-#include <string>
-
-#include "cute/tensor.hpp"
-#include "cutlass_mxfp8_grouped_mm_functor.cuh"
-#include "cutlass_mxfp8_grouped_mm_traits.cuh"
-
-namespace expert_specialization {
-
-template <typename GemmTraits>
-void cutlass_mxfp8_grouped_mm_pre_compute(
-    torch::Tensor& a_ptrs, torch::Tensor& b_ptrs, torch::Tensor& sfa_ptrs,
-    torch::Tensor& sfb_ptrs, torch::Tensor& d_ptrs, torch::Tensor& stride_a,
-    torch::Tensor& stride_b, torch::Tensor& stride_d, torch::Tensor& layout_sfa,
-    torch::Tensor& layout_sfb, const torch::Tensor& a, const torch::Tensor& b,
-    const torch::Tensor& sfa, const torch::Tensor& sfb, const torch::Tensor& d,
-    const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets,
-    const torch::Tensor& blockscale_offsets, cudaStream_t stream) {
-  using OffsetFunctor = CutlassMxfp8GroupedMmOffsetFunctor<GemmTraits>;
-  using ElementA = typename OffsetFunctor::ElementA;
-  using ElementB = typename OffsetFunctor::ElementB;
-  using ElementSF = typename OffsetFunctor::ElementSF;
-  using ElementD = typename OffsetFunctor::ElementD;
-
-  using LayoutFunctor = CutlassMxfp8GroupedMmLayoutFunctor<GemmTraits>;
-  using LayoutSFA = typename LayoutFunctor::LayoutSFA;
-  using LayoutSFB = typename LayoutFunctor::LayoutSFB;
-
-  using StrideFunctor = CutlassMxfp8GroupedMmStrideFunctor<GemmTraits>;
-  using StrideA = typename StrideFunctor::StrideA;
-  using StrideB = typename StrideFunctor::StrideB;
-  using StrideD = typename StrideFunctor::StrideD;
-
-  int num_experts = (int)expert_offsets.size(0);
-  TORCH_CHECK(num_experts <= 1024,
-              "Number of experts cannot exceed 1024, the maximum number of "
-              "threads per block.");
-
-  OffsetFunctor offset_functor(
-      reinterpret_cast<int*>(expert_offsets.data_ptr()),
-      reinterpret_cast<int*>(blockscale_offsets.data_ptr()),
-      reinterpret_cast<ElementA*>(a.data_ptr()),
-      reinterpret_cast<ElementB*>(b.data_ptr()),
-      reinterpret_cast<ElementSF*>(sfa.data_ptr()),
-      reinterpret_cast<ElementSF*>(sfb.data_ptr()),
-      reinterpret_cast<ElementD*>(d.data_ptr()),
-      reinterpret_cast<ElementA**>(a_ptrs.data_ptr()),
-      reinterpret_cast<ElementB**>(b_ptrs.data_ptr()),
-      reinterpret_cast<ElementSF**>(sfa_ptrs.data_ptr()),
-      reinterpret_cast<ElementSF**>(sfb_ptrs.data_ptr()),
-      reinterpret_cast<ElementD**>(d_ptrs.data_ptr()));
-  LayoutFunctor layout_functor(
-      reinterpret_cast<LayoutSFA*>(layout_sfa.data_ptr()),
-      reinterpret_cast<LayoutSFB*>(layout_sfb.data_ptr()));
-  StrideFunctor stride_functor(reinterpret_cast<StrideA*>(stride_a.data_ptr()),
-                               reinterpret_cast<StrideB*>(stride_b.data_ptr()),
-                               reinterpret_cast<StrideD*>(stride_d.data_ptr()));
-  cutlassMxfp8GroupedMmPreComputeKernel<<<1, num_experts, 0, stream>>>(
-      static_cast<int*>(problem_sizes.data_ptr()), offset_functor,
-      layout_functor, stride_functor);
-}
-
-template <typename GemmTraits>
-void cutlass_mxfp8_grouped_mm(
-    const torch::Tensor& a_ptrs, const torch::Tensor& b_ptrs,
-    const torch::Tensor& sfa_ptrs, const torch::Tensor& sfb_ptrs,
-    const torch::Tensor& d_ptrs, const torch::Tensor& stride_a,
-    const torch::Tensor& stride_b, const torch::Tensor& stride_d,
-    const torch::Tensor& layout_sfa, const torch::Tensor& layout_sfb,
-    const torch::Tensor& problem_sizes, cudaStream_t stream) {
-  using Gemm = typename GemmTraits::Gemm;
-  using ElementA = typename Gemm::ElementA;
-  using ElementB = typename Gemm::ElementB;
-  using ElementSF = typename GemmTraits::ElementSF;
-  using ElementD = typename GemmTraits::ElementOutput;
-  using StrideA = typename GemmTraits::StrideA;
-  using StrideB = typename GemmTraits::StrideB;
-  using StrideD = typename GemmTraits::StrideD;
-  using LayoutSFA = typename GemmTraits::LayoutSFA;
-  using LayoutSFB = typename GemmTraits::LayoutSFB;
-  using UnderlyingProblemShape =
-      typename GemmTraits::ProblemShape::UnderlyingProblemShape;
-
-  cutlass::KernelHardwareInfo hw_info;
-  hw_info.device_id = c10::cuda::current_device();
-  hw_info.sm_count =
-      at::cuda::getCurrentDeviceProperties()->multiProcessorCount;
-  hw_info.cluster_shape = GemmTraits::MMAConfig::preferred_cluster;
-  hw_info.cluster_shape_fallback = GemmTraits::MMAConfig::fallback_cluster;
-
-  int num_experts = (int)problem_sizes.size(0);
-
-  UnderlyingProblemShape* underlying_problem_shape =
-      reinterpret_cast<UnderlyingProblemShape*>(problem_sizes.data_ptr());
-
-  typename Gemm::Arguments arguments = {
-      cutlass::gemm::GemmUniversalMode::kGrouped,
-      {num_experts, underlying_problem_shape, nullptr},
-      {reinterpret_cast<const ElementA**>(a_ptrs.data_ptr()),
-       reinterpret_cast<StrideA*>(stride_a.data_ptr()),
-       reinterpret_cast<const ElementB**>(b_ptrs.data_ptr()),
-       reinterpret_cast<StrideB*>(stride_b.data_ptr()),
-       reinterpret_cast<const ElementSF**>(sfa_ptrs.data_ptr()),
-       reinterpret_cast<LayoutSFA*>(layout_sfa.data_ptr()),
-       reinterpret_cast<const ElementSF**>(sfb_ptrs.data_ptr()),
-       reinterpret_cast<LayoutSFB*>(layout_sfb.data_ptr())},
-      {{},
-       nullptr,
-       nullptr,
-       reinterpret_cast<ElementD**>(d_ptrs.data_ptr()),
-       reinterpret_cast<StrideD*>(stride_d.data_ptr())},
-      hw_info,
-      {}  // Scheduler
-  };
-
-  Gemm gemm;
-
-  auto can_implement_status = gemm.can_implement(arguments);
-  TORCH_CHECK(can_implement_status == cutlass::Status::kSuccess,
-              "Failed to implement GEMM");
-
-  torch::TensorOptions options_uint8 =
-      torch::TensorOptions().dtype(torch::kUInt8).device(d_ptrs.device());
-  size_t workspace_size = gemm.get_workspace_size(arguments);
-  torch::Tensor workspace = torch::empty(workspace_size, options_uint8);
-
-  auto status = gemm.initialize(arguments, workspace.data_ptr(), stream);
-  TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to initialize GEMM");
-
-  status = gemm.run(stream, nullptr, true);  // Enable PDL
-  TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to run GEMM");
-}
-
-template <typename OutType>
-void cutlass_mxfp8_grouped_mm_dispatch_out_dtype(
-    const torch::Tensor& a, const torch::Tensor& b, const torch::Tensor& sfa,
-    const torch::Tensor& sfb, torch::Tensor& d,
-    const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets,
-    const torch::Tensor& blockscale_offsets, cudaStream_t stream) {
-  int num_experts = (int)problem_sizes.size(0);
-  torch::TensorOptions options_int64 =
-      torch::TensorOptions().dtype(torch::kInt64).device(a.device());
-  torch::TensorOptions options_int32 =
-      torch::TensorOptions().dtype(torch::kInt32).device(a.device());
-
-  torch::Tensor a_ptrs = torch::empty(num_experts, options_int64);
-  torch::Tensor b_ptrs = torch::empty(num_experts, options_int64);
-  torch::Tensor sfa_ptrs = torch::empty(num_experts, options_int64);
-  torch::Tensor sfb_ptrs = torch::empty(num_experts, options_int64);
-  torch::Tensor d_ptrs = torch::empty(num_experts, options_int64);
-
-  torch::Tensor stride_a = torch::empty(num_experts, options_int64);
-  torch::Tensor stride_b = torch::empty(num_experts, options_int64);
-  torch::Tensor stride_d = torch::empty(num_experts, options_int64);
-  torch::Tensor layout_sfa = torch::empty({num_experts, 5}, options_int32);
-  torch::Tensor layout_sfb = torch::empty({num_experts, 5}, options_int32);
-
-  using GemmTraits = CutlassMxfp8GroupedMmGemmTraits<MMA1SMConfig, OutType>;
-  cutlass_mxfp8_grouped_mm_pre_compute<GemmTraits>(
-      a_ptrs, b_ptrs, sfa_ptrs, sfb_ptrs, d_ptrs, stride_a, stride_b, stride_d,
-      layout_sfa, layout_sfb, a, b, sfa, sfb, d, problem_sizes, expert_offsets,
-      blockscale_offsets, stream);
-  cutlass_mxfp8_grouped_mm<GemmTraits>(
-      a_ptrs, b_ptrs, sfa_ptrs, sfb_ptrs, d_ptrs, stride_a, stride_b, stride_d,
-      layout_sfa, layout_sfb, problem_sizes, stream);
-}
-
-}  // namespace expert_specialization

csrc/moe/mxfp8_moe/cutlass_mxfp8_grouped_mm_traits.cuh

@@ -1,127 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-// Adapted from SGLang:
-// https://github.com/sgl-project/sglang/blob/ded068a76e00878881d52d5bfb791e0f60d7311b/sgl-kernel/csrc/expert_specialization/es_sm100_mxfp8_blockscaled_traits.cuh
-
-#pragma once
-
-// Misc
-#include "cute/tensor.hpp"
-#include "cutlass/arch/arch.h"
-#include "cutlass/arch/mma.h"
-#include "cutlass/cutlass.h"
-#include "cutlass/detail/sm100_blockscaled_layout.hpp"
-#include "cutlass/epilogue/dispatch_policy.hpp"
-#include "cutlass/gemm/dispatch_policy.hpp"
-#include "cutlass/gemm/group_array_problem_shape.hpp"
-#include "cutlass/layout/layout.h"
-#include "cutlass/numeric_conversion.h"
-#include "cutlass/numeric_size.h"
-
-// Collective Builder
-#include "cutlass/epilogue/collective/collective_builder.hpp"
-#include "cutlass/epilogue/fusion/sm90_callbacks_tma_warpspecialized.hpp"
-#include "cutlass/epilogue/thread/activation.h"
-#include "cutlass/gemm/collective/collective_builder.hpp"
-
-// Integration
-#include "cutlass/gemm/device/gemm_universal_adapter.h"
-#include "cutlass/gemm/kernel/gemm_universal.hpp"
-
-namespace expert_specialization {
-
-using namespace cute;
-
-// Different configs for 1SM and 2SM MMA kernel
-struct MMA1SMConfig {
-  using MmaTileShape = Shape<_128, _128, _128>;
-  using KernelSchedule =
-      cutlass::gemm::KernelPtrArrayTmaWarpSpecialized1SmMxf8f6f4Sm100;
-  using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized1Sm;
-  const static dim3 preferred_cluster;
-  const static dim3 fallback_cluster;
-};
-const dim3 MMA1SMConfig::preferred_cluster(1, 4, 1);
-const dim3 MMA1SMConfig::fallback_cluster(1, 2, 1);
-
-template <typename _MMAConfig, typename OutputDtype>
-struct CutlassMxfp8GroupedMmGemmTraits {
-  using MMAConfig = _MMAConfig;
-  using ElementInput = cutlass::float_e4m3_t;
-  using ElementOutput = OutputDtype;
-  using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int, int, int>>;
-
-  // A matrix configuration
-  using ElementA = cutlass::mx_float8_t<ElementInput>;
-  using LayoutA = cutlass::layout::RowMajor;
-  constexpr static int AlignmentA = 32;
-
-  // B matrix configuration
-  using ElementB = cutlass::mx_float8_t<ElementInput>;
-  using LayoutB = cutlass::layout::ColumnMajor;
-  constexpr static int AlignmentB = 32;
-
-  // C/D matrix configuration
-  using ElementC = void;
-  using ElementD = ElementOutput;
-  using LayoutC = cutlass::layout::RowMajor;
-  using LayoutD = cutlass::layout::RowMajor;
-  constexpr static int AlignmentC = 128 / cutlass::sizeof_bits<ElementD>::value;
-  constexpr static int AlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
-  using ElementAccumulator = float;
-
-  static constexpr auto RoundStyle = cutlass::FloatRoundStyle::round_to_nearest;
-  using CustomEVTIdentity =  // acc
-      cutlass::epilogue::fusion::Sm90EVT<
-          cutlass::epilogue::fusion::Sm90Compute<
-              cutlass::epilogue::thread::Identity, ElementD, ElementAccumulator,
-              RoundStyle>,
-          cutlass::epilogue::fusion::Sm90AccFetch>;
-
-  // Core kernel configurations
-  using ArchTag = cutlass::arch::Sm100;
-  using OperatorClass = cutlass::arch::OpClassBlockScaledTensorOp;
-  using StageCountType = cutlass::gemm::collective::StageCountAuto;
-
-  // Runtime Cluster Shape
-  using ClusterShape = Shape<int32_t, int32_t, _1>;
-
-  // Define Epilogue
-  using CollectiveEpilogue =
-      typename cutlass::epilogue::collective::CollectiveBuilder<
-          ArchTag, OperatorClass, typename MMAConfig::MmaTileShape,
-          ClusterShape, Shape<_64, _64>, ElementAccumulator, ElementAccumulator,
-          ElementC, LayoutC*, AlignmentC, ElementD, LayoutD*, AlignmentD,
-          typename MMAConfig::EpilogueSchedule,
-          CustomEVTIdentity>::CollectiveOp;
-
-  // Define Mainloop
-  using CollectiveMainloop =
-      typename cutlass::gemm::collective::CollectiveBuilder<
-          ArchTag, OperatorClass, ElementA, LayoutA*, AlignmentA, ElementB,
-          LayoutB*, AlignmentB, ElementAccumulator,
-          typename MMAConfig::MmaTileShape, ClusterShape,
-          cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
-              sizeof(typename CollectiveEpilogue::SharedStorage))>,
-          typename MMAConfig::KernelSchedule>::CollectiveOp;
-
-  // Define GemmKernel
-  using GemmKernel =
-      cutlass::gemm::kernel::GemmUniversal<ProblemShape, CollectiveMainloop,
-                                           CollectiveEpilogue>;
-  using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
-
-  using ElementSF = typename Gemm::GemmKernel::ElementSF;
-  using StrideA = typename Gemm::GemmKernel::InternalStrideA;
-  using StrideB = typename Gemm::GemmKernel::InternalStrideB;
-  using StrideC = typename Gemm::GemmKernel::InternalStrideC;
-  using StrideD = typename Gemm::GemmKernel::InternalStrideD;
-  using LayoutSFA =
-      typename Gemm::GemmKernel::CollectiveMainloop::InternalLayoutSFA;
-  using LayoutSFB =
-      typename Gemm::GemmKernel::CollectiveMainloop::InternalLayoutSFB;
-  using Sm1xxBlkScaledConfig =
-      typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig;
-};
-
-}  // namespace expert_specialization

csrc/moe/mxfp8_moe/mxfp8_experts_quant.cu

@@ -1,60 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-// Adapted from SGLang:
-// https://github.com/sgl-project/sglang/blob/ded068a76e00878881d52d5bfb791e0f60d7311b/sgl-kernel/csrc/expert_specialization/es_sm100_mxfp8_blockscaled_group_quant.cu
-
-#include <torch/all.h>
-
-#include "mxfp8_experts_quant.cuh"
-
-void mxfp8_experts_quant(const torch::Tensor& input,
-                         const torch::Tensor& problem_sizes,
-                         const torch::Tensor& expert_offsets,
-                         const torch::Tensor& blockscale_offsets,
-                         torch::Tensor& quant_output,
-                         torch::Tensor& scale_factor) {
-#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
-  TORCH_CHECK(input.dim() == 2, "input must be 2D tensor");
-  TORCH_CHECK(input.size(1) % 128 == 0, "k must align to 128");
-  TORCH_CHECK(input.strides()[1] == 1, "input must be row major");
-  TORCH_CHECK(problem_sizes.dim() == 2, "problem_sizes must be 2D tensor");
-  TORCH_CHECK(problem_sizes.dtype() == torch::kInt32,
-              "problem_sizes must be int32");
-  TORCH_CHECK(expert_offsets.dtype() == torch::kInt32,
-              "expert_offsets must be int32");
-  TORCH_CHECK(blockscale_offsets.dtype() == torch::kInt32,
-              "blockscale_offsets must be int32");
-
-  auto groups = problem_sizes.size(0);
-  TORCH_CHECK(
-      expert_offsets.dim() == 1 && expert_offsets.size(0) == groups,
-      "expert_offsets must be 1D and have size equal to the number of groups");
-  TORCH_CHECK(
-      blockscale_offsets.dim() == 1 && blockscale_offsets.size(0) == groups,
-      "blockscale_offsets must be 1D and have size equal to the number of "
-      "groups");
-
-  auto stream = at::cuda::getCurrentCUDAStream();
-  if (input.dtype() == torch::kBFloat16) {
-    expert_specialization::launch_mxfp8_experts_quant<__nv_bfloat16>(
-        input, problem_sizes, expert_offsets, blockscale_offsets, quant_output,
-        scale_factor);
-  } else if (input.dtype() == torch::kFloat16) {
-    expert_specialization::launch_mxfp8_experts_quant<__half>(
-        input, problem_sizes, expert_offsets, blockscale_offsets, quant_output,
-        scale_factor);
-  } else {
-    TORCH_CHECK(false, "dtype must be kFloat16 or kBFloat16");
-  }
-#else
-  TORCH_CHECK(false,
-              "No implemented mxfp8_experts_quant for "
-              "current device");
-#endif
-}
-
-#include "core/registration.h"
-
-TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
-  m.impl("mxfp8_experts_quant", mxfp8_experts_quant);
-}

csrc/moe/mxfp8_moe/mxfp8_experts_quant.cuh

@@ -1,414 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-// Adapted from SGLang:
-// https://github.com/sgl-project/sglang/blob/ded068a76e00878881d52d5bfb791e0f60d7311b/sgl-kernel/csrc/expert_specialization/es_sm100_mxfp8_blockscaled_group_quant.cuh
-
-#pragma once
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include <cuda.h>
-#include <cuda_bf16.h>
-#include <cuda_fp16.h>
-#include <torch/all.h>
-
-#include <cuda/ptx>
-
-#include "cute/tensor.hpp"
-
-namespace expert_specialization {
-
-using namespace cute;
-
-constexpr uint32_t THREAD_BLOCK_SIZE = 128;
-constexpr uint32_t WARP_SIZE = 32;
-constexpr int BLOCK_M = 128;
-constexpr int BLOCK_K = 128;
-using ThrLayout = Layout<Shape<_16, _8>, Stride<_8, _1>>;
-using ValLayout = Layout<Shape<_1, _16>>;
-using SfR2SThrLayout = Layout<Shape<_16, _4>, Stride<_4, _1>>;
-using SfR2SValLayout = Layout<Shape<_1, _1>>;
-using ScaleFactorTileLayout =
-    Layout<Shape<Shape<_32, _4>, _4>, Stride<Stride<_16, _4>, _1>>;
-
-// Fast reciprocal.
-inline __device__ float reciprocal_approximate_ftz(float a) {
-  float b;
-  asm volatile("rcp.approx.ftz.f32 %0, %1;\n" : "=f"(b) : "f"(a));
-  return b;
-}
-
-// Some code references TRT-LLM:
-// https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/quantization.cuh
-template <typename FragmentS, typename FragmentD>
-__inline__ __device__ uint8_t cvt_warp_fp16_to_mxfp8(FragmentS& fragment_s,
-                                                     FragmentD& fragment_d) {
-  using FragmentSLayout = typename FragmentS::layout_type;
-  using FragmentDLayout = typename FragmentD::layout_type;
-  FragmentSLayout fragment_s_layout;
-  FragmentDLayout fragment_d_layout;
-  static_assert(is_static<FragmentSLayout>::value &&
-                size(fragment_s_layout) == 16);
-  static_assert(is_static<FragmentDLayout>::value &&
-                size(fragment_d_layout) == 16);
-
-  constexpr int eles_per_thr = 16;
-  using ValType = typename FragmentS::element_type;
-  using VecType = std::conditional_t<std::is_same_v<ValType, __nv_bfloat16>,
-                                     __nv_bfloat162, __half2>;
-  VecType vec[8];
-  // Assign vals
-  vec[0].x = fragment_s(Int<0>{});
-  vec[0].y = fragment_s(Int<1>{});
-  vec[1].x = fragment_s(Int<2>{});
-  vec[1].y = fragment_s(Int<3>{});
-  vec[2].x = fragment_s(Int<4>{});
-  vec[2].y = fragment_s(Int<5>{});
-  vec[3].x = fragment_s(Int<6>{});
-  vec[3].y = fragment_s(Int<7>{});
-  vec[4].x = fragment_s(Int<8>{});
-  vec[4].y = fragment_s(Int<9>{});
-  vec[5].x = fragment_s(Int<10>{});
-  vec[5].y = fragment_s(Int<11>{});
-  vec[6].x = fragment_s(Int<12>{});
-  vec[6].y = fragment_s(Int<13>{});
-  vec[7].x = fragment_s(Int<14>{});
-  vec[7].y = fragment_s(Int<15>{});
-
-  auto local_max = __habs2(vec[0]);
-  for (int i = 1; i < eles_per_thr / 2; i++) {
-    local_max = __hmax2(__habs2(vec[i]), local_max);
-  }
-  local_max = __hmax2(__shfl_xor_sync(uint32_t(-1), local_max, 1), local_max);
-
-  // Get the final absolute maximum values.
-  float block_max(0.0f);
-  if constexpr (std::is_same_v<ValType, __nv_bfloat16>) {
-    block_max = __bfloat162float(__hmax(local_max.x, local_max.y));
-  } else {
-    block_max = __half2float(__hmax(local_max.x, local_max.y));
-  }
-  // Get the SF (max value of the vector / max value of mxfp8).
-  float sf_val = block_max * reciprocal_approximate_ftz(448.0f);
-  // 8 bits representation of the SF.
-  uint8_t fp8_sf_val;
-
-  __nv_fp8_e8m0 tmp_sf_val;
-  tmp_sf_val.__x =
-      __nv_cvt_float_to_e8m0(sf_val, __NV_SATFINITE, cudaRoundPosInf);
-  sf_val = static_cast<float>(tmp_sf_val);
-  fp8_sf_val = tmp_sf_val.__x;
-  // Get the output scale (reciprocal of the SFValue).
-  float output_scale =
-      block_max != 0.f ? reciprocal_approximate_ftz(sf_val) : 0.0f;
-
-  // Convert the input to float.
-  float2 fp2_vals[eles_per_thr / 2];
-
-#pragma unroll
-  for (int i = 0; i < eles_per_thr / 2; i++) {
-    if constexpr (std::is_same_v<ValType, __half>) {
-      fp2_vals[i] = __half22float2(vec[i]);
-    } else {
-      fp2_vals[i] = __bfloat1622float2(vec[i]);
-    }
-    fp2_vals[i].x *= output_scale;
-    fp2_vals[i].y *= output_scale;
-  }
-  union {
-    uint8_t bytes[16];
-    __nv_fp8x2_e4m3 elts[8];
-  } u;
-  u.elts[0] = __nv_fp8x2_e4m3(fp2_vals[0]);
-  u.elts[1] = __nv_fp8x2_e4m3(fp2_vals[1]);
-  u.elts[2] = __nv_fp8x2_e4m3(fp2_vals[2]);
-  u.elts[3] = __nv_fp8x2_e4m3(fp2_vals[3]);
-  u.elts[4] = __nv_fp8x2_e4m3(fp2_vals[4]);
-  u.elts[5] = __nv_fp8x2_e4m3(fp2_vals[5]);
-  u.elts[6] = __nv_fp8x2_e4m3(fp2_vals[6]);
-  u.elts[7] = __nv_fp8x2_e4m3(fp2_vals[7]);
-  fragment_d(Int<0>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[0]);
-  fragment_d(Int<1>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[1]);
-  fragment_d(Int<2>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[2]);
-  fragment_d(Int<3>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[3]);
-  fragment_d(Int<4>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[4]);
-  fragment_d(Int<5>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[5]);
-  fragment_d(Int<6>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[6]);
-  fragment_d(Int<7>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[7]);
-  fragment_d(Int<8>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[8]);
-  fragment_d(Int<9>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[9]);
-  fragment_d(Int<10>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[10]);
-  fragment_d(Int<11>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[11]);
-  fragment_d(Int<12>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[12]);
-  fragment_d(Int<13>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[13]);
-  fragment_d(Int<14>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[14]);
-  fragment_d(Int<15>{}) = cutlass::float_e4m3_t::bitcast(u.bytes[15]);
-  return fp8_sf_val;
-}
-
-template <typename TensorS, typename TensorP, typename TensorD,
-          typename TensorSharedSF, typename TensorSF, typename TiledCopyG2R,
-          typename TiledCopyR2G, typename TiledCopyR2S>
-__inline__ __device__ void mxfp8_experts_quant_tile(
-    TensorS& tensor_s, TensorP& tensor_p, TensorD& tensor_d,
-    TensorSharedSF& tensor_shared_sf, TensorSF& tensor_sf, int m,
-    TiledCopyG2R& tiled_copy_g2r, TiledCopyR2G& tiled_copy_r2g,
-    TiledCopyR2S& tiled_copy_r2s) {
-  static_assert(size(get<0>(typename TensorS::layout_type{})) == 128 &&
-                size(get<1>(typename TensorS::layout_type{})) == 128 &&
-                stride(get<1>(typename TensorS::layout_type{})) == 1);
-  static_assert(size(get<0>(typename TensorD::layout_type{})) == 128 &&
-                size(get<1>(typename TensorD::layout_type{})) == 128 &&
-                stride(get<1>(typename TensorD::layout_type{})) == 1);
-  static_assert(size(get<0>(typename TensorP::layout_type{})) == 128 &&
-                size(get<1>(typename TensorP::layout_type{})) == 128);
-  static_assert(size(get<0>(typename TensorSharedSF::layout_type{})) == 128 &&
-                size(get<1>(typename TensorSharedSF::layout_type{})) == 4);
-  static_assert(size(get<0>(typename TensorSF::layout_type{})) == 128 &&
-                size(get<1>(typename TensorSF::layout_type{})) == 4);
-
-  using Tiler_MN = typename TiledCopyG2R::Tiler_MN;
-  auto tiler_mn = Tiler_MN{};
-  static_assert(size<0>(tiler_mn) == 16 && size<1>(tiler_mn) == 128);
-
-  auto tiled_tensor_s = tiled_divide(tensor_s, tiler_mn);
-  auto tiled_tensor_p = tiled_divide(tensor_p, tiler_mn);
-  auto tiled_tensor_d = tiled_divide(tensor_d, tiler_mn);
-  static_assert(size<2>(tiled_tensor_s) == 1);
-  static_assert(size<2>(tiled_tensor_p) == 1);
-  static_assert(size<2>(tiled_tensor_d) == 1);
-  auto squeeze_tiled_tensor_s = take<0, 2>(tiled_tensor_s);
-  auto squeeze_tiled_tensor_p = take<0, 2>(tiled_tensor_p);
-  auto squeeze_tiled_tensor_d = take<0, 2>(tiled_tensor_d);
-
-  using SF_Tiler_MN = typename TiledCopyR2S::Tiler_MN;
-  auto sf_tiler_mn = SF_Tiler_MN{};
-  static_assert(size<0>(sf_tiler_mn) == 16 && size<1>(sf_tiler_mn) == 4);
-
-  auto tiled_tensor_sf = tiled_divide(tensor_sf, sf_tiler_mn);
-  auto tiled_tensor_shared_sf = tiled_divide(tensor_shared_sf, sf_tiler_mn);
-  auto squeeze_tiled_tensor_sf = take<0, 2>(tiled_tensor_sf);
-  auto squeeze_tiled_tensor_shared_sf = take<0, 2>(tiled_tensor_shared_sf);
-
-  constexpr int tile_loop_count = size<1>(tiled_tensor_s);
-  constexpr int rows_in_tile = 16;
-  // We don't need to clear shared memory
-  // clear(squeeze_tiled_tensor_shared_sf);
-#pragma unroll 4
-  for (int t = 0; t < tile_loop_count; t++) {
-    if (t * rows_in_tile >= m) {
-      break;
-    }
-    auto current_copy_tile_s = tensor<0>(squeeze_tiled_tensor_s(_, t));
-    auto current_copy_tile_p = tensor<0>(squeeze_tiled_tensor_p(_, t));
-    auto current_copy_tile_d = tensor<0>(squeeze_tiled_tensor_d(_, t));
-    auto current_copy_tile_sf = tensor<0>(squeeze_tiled_tensor_sf(_, t));
-    auto current_copy_tile_shared_sf =
-        tensor<0>(squeeze_tiled_tensor_shared_sf(_, t));
-
-    // Global to Register copy
-    auto thr_copy_g2r = tiled_copy_g2r.get_thread_slice(threadIdx.x);
-    auto thr_tile_g2r_s = thr_copy_g2r.partition_S(current_copy_tile_s);
-    auto thr_tile_g2r_p = thr_copy_g2r.partition_S(current_copy_tile_p);
-    auto input_fragment = make_fragment_like(thr_tile_g2r_s);
-
-    // Register to Global copy
-    auto thr_copy_r2g = tiled_copy_r2g.get_thread_slice(threadIdx.x);
-    auto thr_tile_r2g_d = thr_copy_r2g.partition_D(current_copy_tile_d);
-    auto thr_tile_r2g_p = thr_copy_r2g.partition_D(current_copy_tile_p);
-    auto output_fragment = make_fragment_like(thr_tile_r2g_d);
-
-    // Register to Shared copy
-    auto thr_copy_r2s = tiled_copy_r2s.get_thread_slice(threadIdx.x / 2);
-    auto thr_tile_r2s_shared_sf =
-        thr_copy_r2s.partition_D(current_copy_tile_shared_sf);
-    auto shared_sf_fragment = make_fragment_like(thr_tile_r2s_shared_sf);
-
-    // CopyG2R & convert & CopyR2G
-    copy_if(tiled_copy_g2r, thr_tile_g2r_p, thr_tile_g2r_s, input_fragment);
-    uint8_t fp8_sf_val =
-        cvt_warp_fp16_to_mxfp8(input_fragment, output_fragment);
-    copy_if(tiled_copy_r2g, thr_tile_r2g_p, output_fragment, thr_tile_r2g_d);
-    shared_sf_fragment[0] = fp8_sf_val;
-
-    // Before first copy r2s, clear shared memory and wait previous group
-    if (t == 0 && threadIdx.x == 0) {
-      // Wait for the group to have completed reading from shared memory.
-      cuda::ptx::cp_async_bulk_wait_group_read(cuda::ptx::n32_t<0>());
-    }
-    __syncthreads();
-
-    if (threadIdx.x % 2 == 0) {
-      copy(tiled_copy_r2s, shared_sf_fragment, thr_tile_r2s_shared_sf);
-    }
-    __syncthreads();
-  }
-
-  // Wait for shared memory writes to be visible to TMA engine.
-  cuda::ptx::fence_proxy_async(cuda::ptx::space_shared);  // b)
-  __syncthreads();
-
-  if (threadIdx.x == 0) {
-    cuda::ptx::cp_async_bulk(cuda::ptx::space_global, cuda::ptx::space_shared,
-                             squeeze_tiled_tensor_sf.data().get(),
-                             squeeze_tiled_tensor_shared_sf.data().get(), 512);
-    // Wait for TMA transfer to have finished reading shared memory.
-    // Create a "bulk async-group" out of the previous bulk copy operation.
-    cuda::ptx::cp_async_bulk_commit_group();
-  }
-  __syncthreads();
-}
-
-template <typename T_IN, typename TiledCopyG2R, typename TiledCopyR2G,
-          typename TiledCopyR2S>
-__global__ void mxfp8_experts_quant_kernel(
-    const T_IN* input, const int* problem_sizes, const int* expert_offsets,
-    const int* blockscale_offsets, cutlass::float_e4m3_t* quant_output,
-    uint8_t* scale_factor, int groups, TiledCopyG2R tiled_copy_g2r,
-    TiledCopyR2G tiled_copy_r2g, TiledCopyR2S tiled_copy_r2s) {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 1000
-  __shared__ __align__(512) uint8_t shared_memory[512];
-  ScaleFactorTileLayout scale_factor_tile_layout{};
-  auto scale_factor_shared =
-      make_tensor(make_smem_ptr(shared_memory),
-                  scale_factor_tile_layout);  // ((_32,_4), _4):((_16,_4), _1)
-  // TODO: Transform Groupwise Schedule into a more efficient Schedule
-  for (int g = 0; g < groups; g++) {
-    int m = problem_sizes[g * 3 + 0];
-    int k = problem_sizes[g * 3 + 2];
-    int64_t expert_offset = static_cast<int64_t>(expert_offsets[g]);
-    int64_t blockscale_offset = static_cast<int64_t>(blockscale_offsets[g]);
-
-    auto input_tensor = make_tensor(
-        make_gmem_ptr(input + expert_offset * k),
-        make_layout(make_shape(m, k),
-                    LayoutRight{}));  // (M, K):(K, 1) half_t/bfloat16_t
-
-    auto quant_output_tensor = make_tensor(
-        make_gmem_ptr(quant_output + expert_offset * k),
-        make_layout(make_shape(m, k),
-                    LayoutRight{}));  // (M, K):(K, 1) cutlass::float_e4m3_t
-
-    auto scale_factor_shape = make_shape(ceil_div(m, 128) * 128, k / 32);
-    auto scale_factor_layout = tile_to_shape(scale_factor_tile_layout,
-                                             scale_factor_shape, LayoutRight{});
-    // layout<0>(layout<0>(scale_factor_layout))  (_32,_4):(_16,_4) -- static
-    // layout<1>(layout<0>(scale_factor_layout))  M_align_128 / 128 -- dynamic
-    // shape dynamic stride layout<0>(layout<1>(scale_factor_layout))  _4:_1 --
-    // static layout<1>(layout<1>(scale_factor_layout))  (K / 32) / 4 : _512 --
-    // dynamic shape static stride
-
-    // Reshape to zipped layout for 1D indexing
-    auto zipped_scale_factor_layout = make_layout(
-        make_layout(layout<0>(layout<0>(scale_factor_layout)),
-                    layout<0>(layout<1>(scale_factor_layout))),
-        make_layout(
-            layout<1>(layout<0>(scale_factor_layout)),
-            layout<1>(layout<1>(
-                scale_factor_layout))));  // (((_32,_4),_4),(M_align_128 /
-                                          // 128,(K / 32) /
-                                          // 4)):(((_16,_4),_1),(?,_512))
-
-    auto scale_factor_tensor =
-        make_tensor(make_gmem_ptr(scale_factor + blockscale_offset * (k / 32)),
-                    zipped_scale_factor_layout);
-
-    // Used for cases where M is not divisible by 128 (most scenarios).
-    auto input_shape = shape(input_tensor);  // (M, K):(K, 1)
-    auto identity_tensor = make_identity_tensor(input_shape);
-    auto predict_tensor = cute::lazy::transform(
-        identity_tensor, [&](auto c) { return elem_less(c, input_shape); });
-
-    // (_128, _128)
-    auto tiler = make_shape(Int<BLOCK_M>{}, Int<BLOCK_K>{});
-
-    auto tiled_input_tensor = zipped_divide(
-        input_tensor, tiler);  // ((128, 128), (cdiv(M, 128), cdiv(K, 128)))
-    auto tiled_quant_output_tensor =
-        zipped_divide(quant_output_tensor,
-                      tiler);  // ((128, 128), (cdiv(M, 128), cdiv(K, 128)))
-    auto tiled_predict_tensor = zipped_divide(
-        predict_tensor, tiler);  // ((128, 128), (cdiv(M, 128), cdiv(K, 128)))
-
-    auto total_tiles =
-        size<1>(tiled_input_tensor);  // cdiv(M, 128) * cdiv(K, 128)
-    decltype(total_tiles) blk_offset = blockIdx.x;
-    while (blk_offset < total_tiles) {
-      auto current_input_tile = tensor<0>(tiled_input_tensor(_, blk_offset));
-      auto current_quant_output_tile =
-          tensor<0>(tiled_quant_output_tensor(_, blk_offset));
-      auto current_predict_tile =
-          tensor<0>(tiled_predict_tensor(_, blk_offset));
-      auto current_scale_factor_tile =
-          tensor<0>(scale_factor_tensor(_, blk_offset));
-
-      mxfp8_experts_quant_tile<
-          decltype(current_input_tile), decltype(current_predict_tile),
-          decltype(current_quant_output_tile), decltype(scale_factor_shared),
-          decltype(current_scale_factor_tile), TiledCopyG2R, TiledCopyR2G,
-          TiledCopyR2S>(current_input_tile, current_predict_tile,
-                        current_quant_output_tile, scale_factor_shared,
-                        current_scale_factor_tile, m, tiled_copy_g2r,
-                        tiled_copy_r2g, tiled_copy_r2s);
-      blk_offset += gridDim.x;
-    }
-  }
-#endif
-}
-
-template <typename T_IN>
-void launch_mxfp8_experts_quant(const torch::Tensor& input,
-                                const torch::Tensor& problem_sizes,
-                                const torch::Tensor& expert_offsets,
-                                const torch::Tensor& blockscale_offsets,
-                                torch::Tensor& quant_output,
-                                torch::Tensor& scale_factor) {
-  ThrLayout thr_layout{};
-  ValLayout val_layout{};
-  SfR2SThrLayout r2s_thr_layout{};
-  SfR2SValLayout r2s_val_layout{};
-
-  using CopyOpG2R =
-      UniversalCopy<cutlass::AlignedArray<T_IN, size(val_layout)>>;
-  using CopyAtomG2R = cute::Copy_Atom<CopyOpG2R, T_IN>;
-  auto tiled_copy_g2r = cute::make_tiled_copy(
-      CopyAtomG2R{}, thr_layout, val_layout);  // Tiler_MN: (16, 128)
-
-  using CopyOpR2G = UniversalCopy<
-      cutlass::AlignedArray<cutlass::float_e4m3_t, size(val_layout)>>;
-  using CopyAtomR2G = cute::Copy_Atom<CopyOpR2G, cutlass::float_e4m3_t>;
-  auto tiled_copy_r2g = cute::make_tiled_copy(
-      CopyAtomR2G{}, thr_layout, val_layout);  // Tiler_MN: (16, 128)
-
-  using CopyOpR2S =
-      UniversalCopy<cutlass::AlignedArray<uint8_t, size(r2s_val_layout)>>;
-  using CopyAtomR2S = cute::Copy_Atom<CopyOpR2S, uint8_t>;
-  auto tiled_copy_r2s = cute::make_tiled_copy(
-      CopyAtomR2S{}, r2s_thr_layout, r2s_val_layout);  // Tiler_MN: (16, 4)
-
-  int max_active_blocks_per_sm = -1;
-  AT_CUDA_CHECK(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-      &max_active_blocks_per_sm,
-      mxfp8_experts_quant_kernel<T_IN, decltype(tiled_copy_g2r),
-                                 decltype(tiled_copy_r2g),
-                                 decltype(tiled_copy_r2s)>,
-      THREAD_BLOCK_SIZE, 0));
-
-  dim3 grid(at::cuda::getCurrentDeviceProperties()->multiProcessorCount *
-                max_active_blocks_per_sm,
-            1, 1);
-  dim3 block(THREAD_BLOCK_SIZE, 1, 1);
-  int num_experts = (int)problem_sizes.size(0);
-  auto stream = at::cuda::getCurrentCUDAStream();
-  mxfp8_experts_quant_kernel<T_IN, decltype(tiled_copy_g2r),
-                             decltype(tiled_copy_r2g), decltype(tiled_copy_r2s)>
-      <<<grid, block, 0, stream>>>(
-          reinterpret_cast<const T_IN*>(input.data_ptr()),
-          reinterpret_cast<const int*>(problem_sizes.data_ptr()),
-          reinterpret_cast<const int*>(expert_offsets.data_ptr()),
-          reinterpret_cast<const int*>(blockscale_offsets.data_ptr()),
-          reinterpret_cast<cutlass::float_e4m3_t*>(quant_output.data_ptr()),
-          reinterpret_cast<uint8_t*>(scale_factor.data_ptr()), num_experts,
-          tiled_copy_g2r, tiled_copy_r2g, tiled_copy_r2s);
-}
-
-}  // namespace expert_specialization

csrc/moe/router_gemm.cu

@@ -1,52 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-// bf16 x bf16 -> fp32 router GEMM via cuBLAS.
-// Uses CUBLAS_COMPUTE_32F so bf16 operands accumulate into fp32,
-// matching TRT-LLM's cuBLAS fallback behaviour in dsv3RouterGemmOp.
-
-#include <torch/all.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <cublas_v2.h>
-
-// cuBLAS column-major math for row-major PyTorch tensors:
-//   weight[N,K]_row  lda=K  -> cuBLAS sees (K,N) col-major; CUBLAS_OP_T ->
-//   (N,K) input[M,K]_row   ldb=K  -> cuBLAS sees (K,M) col-major; CUBLAS_OP_N
-//   -> (K,M) out[M,N]_row     ldc=N  -> cuBLAS sees (N,M) col-major (written as
-//   output^T)
-// cuBLAS: C(N,M) = weight(N,K) @ input(K,M)  =>  C^T = output[M,N]
-// params: m=N, n=M, k=K, lda=K (weight), ldb=K (input), ldc=N (output)
-
-torch::Tensor router_gemm_bf16_fp32(torch::Tensor const& input,
-                                    torch::Tensor const& weight) {
-  TORCH_CHECK(input.dtype() == torch::kBFloat16,
-              "router_gemm_bf16_fp32: input must be bfloat16");
-  TORCH_CHECK(weight.dtype() == torch::kBFloat16,
-              "router_gemm_bf16_fp32: weight must be bfloat16");
-  TORCH_CHECK(input.dim() == 2 && weight.dim() == 2,
-              "router_gemm_bf16_fp32: input and weight must be 2-D");
-  TORCH_CHECK(input.size(1) == weight.size(1),
-              "router_gemm_bf16_fp32: inner dimensions must match");
-
-  int64_t const M = input.size(0);
-  int64_t const N = weight.size(0);
-  int64_t const K = input.size(1);
-
-  auto out = torch::empty({M, N}, input.options().dtype(torch::kFloat32));
-
-  cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
-  TORCH_CUDABLAS_CHECK(
-      cublasSetStream(handle, at::cuda::getCurrentCUDAStream()));
-
-  float const alpha = 1.0f;
-  float const beta = 0.0f;
-
-  TORCH_CUDABLAS_CHECK(cublasGemmEx(
-      handle, CUBLAS_OP_T, CUBLAS_OP_N, static_cast<int>(N),
-      static_cast<int>(M), static_cast<int>(K), &alpha, weight.data_ptr(),
-      CUDA_R_16BF, static_cast<int>(K), input.data_ptr(), CUDA_R_16BF,
-      static_cast<int>(K), &beta, out.data_ptr(), CUDA_R_32F,
-      static_cast<int>(N), CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT));
-
-  return out;
-}

csrc/moe/torch_bindings.cpp

@@ -124,14 +124,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "routed_scaling_factor, Tensor bias, int scoring_func) -> (Tensor, "
       "Tensor)");
   m.impl("grouped_topk", torch::kCUDA, &grouped_topk);
-
-  // cuBLAS bf16 x bf16 -> fp32 router GEMM (fallback for non-SM90 / batch > 16)
-  m.def("router_gemm_bf16_fp32(Tensor input, Tensor weight) -> Tensor");
-  m.impl("router_gemm_bf16_fp32", torch::kCUDA, &router_gemm_bf16_fp32);
-
-  // DeepSeek V3 optimized router GEMM for SM90+
-  m.def("dsv3_router_gemm(Tensor! output, Tensor mat_a, Tensor mat_b) -> ()");
-  // conditionally compiled so impl registration is in source file
 #endif
 }
 

csrc/ops.h

@@ -269,13 +269,13 @@ void get_cutlass_moe_mm_problem_sizes_from_expert_offsets(
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
     const int64_t n, const int64_t k, const bool swap_ab);
 
-void get_cutlass_batched_moe_mm_data(torch::Tensor& expert_offsets,
-                                     torch::Tensor& problem_sizes1,
-                                     torch::Tensor& problem_sizes2,
-                                     const torch::Tensor& expert_num_tokens,
-                                     const int64_t num_local_experts,
-                                     const int64_t padded_m, const int64_t n,
-                                     const int64_t k);
+void get_cutlass_pplx_moe_mm_data(torch::Tensor& expert_offsets,
+                                  torch::Tensor& problem_sizes1,
+                                  torch::Tensor& problem_sizes2,
+                                  const torch::Tensor& expert_num_tokens,
+                                  const int64_t num_local_experts,
+                                  const int64_t padded_m, const int64_t n,
+                                  const int64_t k);
 
 void cutlass_scaled_mm_azp(torch::Tensor& out, torch::Tensor const& a,
                            torch::Tensor const& b,
@@ -371,9 +371,7 @@ void selective_scan_fwd(
     const torch::Tensor& ssm_states, int64_t pad_slot_id, int64_t block_size,
     const std::optional<torch::Tensor>& block_idx_first_scheduled_token,
     const std::optional<torch::Tensor>& block_idx_last_scheduled_token,
-    const std::optional<torch::Tensor>& initial_state_idx,
-    const std::optional<torch::Tensor>& cu_chunk_seqlen,
-    const std::optional<torch::Tensor>& last_chunk_indices);
+    const std::optional<torch::Tensor>& initial_state_idx);
 
 torch::Tensor dynamic_4bit_int_moe_cpu(
     torch::Tensor x, torch::Tensor topk_ids, torch::Tensor topk_weights,

csrc/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu

@@ -39,12 +39,12 @@ namespace vllm {
 template <class Type, bool UE8M0_SF = false>
 __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
     silu_mul_cvt_fp16_to_fp4(int32_t numRows, int32_t numCols,
-                             int32_t num_packed_cols,
+                             int32_t num_padded_cols,
                              Type const* __restrict__ in,
                              float const* __restrict__ SFScale,
                              uint32_t* __restrict__ out,
                              uint32_t* __restrict__ SFout) {
-  using PackedVec = vllm::PackedVec<Type, CVT_FP4_PACK16>;
+  using PackedVec = vllm::PackedVec<Type>;
   static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
       (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
   static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
@@ -63,7 +63,7 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
 
   // Input tensor row/col loops.
   for (int rowIdx = blockIdx.x; rowIdx < numRows; rowIdx += gridDim.x) {
-    if (colIdx < num_packed_cols) {
+    if (colIdx < num_padded_cols) {
       PackedVec in_vec;
       PackedVec in_vec2;
       int64_t inOffset =
@@ -73,19 +73,19 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
 
       bool valid = (rowIdx < numRows) && (elem_idx < numCols);
       if constexpr (CVT_FP4_PACK16) {
-        ld256_cg_or_zero(reinterpret_cast<u32x8_t&>(in_vec),
-                         &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
-                         valid);
-        ld256_cg_or_zero(reinterpret_cast<u32x8_t&>(in_vec2),
-                         &reinterpret_cast<const uint32_t*>(in)[inOffset2 * 8],
-                         valid);
+        ld256_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
+            valid);
+        ld256_or_zero_cg_u32<Type>(
+            in_vec2, &reinterpret_cast<const uint32_t*>(in)[inOffset2 * 8],
+            valid);
       } else {
-        ld128_cg_or_zero(reinterpret_cast<uint4&>(in_vec),
-                         &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
-                         valid);
-        ld128_cg_or_zero(reinterpret_cast<uint4&>(in_vec2),
-                         &reinterpret_cast<const uint32_t*>(in)[inOffset2 * 4],
-                         valid);
+        ld128_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
+            valid);
+        ld128_or_zero_cg_u32<Type>(
+            in_vec2, &reinterpret_cast<const uint32_t*>(in)[inOffset2 * 4],
+            valid);
       }
 
       // Compute silu and mul
@@ -107,9 +107,7 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
               (uint64_t(out_val.hi) << 32) | uint64_t(out_val.lo);
           reinterpret_cast<uint64_t*>(out)[outOffset >> 1] = packed64;
         } else {
-          int64_t outOffset =
-              rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
-          out[outOffset] = out_val;
+          out[inOffset] = out_val;
         }
       }
     }
@@ -142,9 +140,9 @@ void silu_and_mul_nvfp4_quant_sm1xxa(torch::Tensor& output,  // [..., d]
   int const numBlocksPerSM =
       vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
 
-  int num_packed_cols = int(n / CVT_FP4_ELTS_PER_THREAD);
+  int sf_n_unpadded = int(n / CVT_FP4_SF_VEC_SIZE);
 
-  int grid_y = vllm::div_round_up(num_packed_cols, static_cast<int>(block.x));
+  int grid_y = vllm::div_round_up(sf_n_unpadded, static_cast<int>(block.x));
   int grid_x = std::min(
       int(m), std::max(1, (multiProcessorCount * numBlocksPerSM) / grid_y));
   dim3 grid(grid_x, grid_y);
@@ -154,7 +152,7 @@ void silu_and_mul_nvfp4_quant_sm1xxa(torch::Tensor& output,  // [..., d]
         using cuda_type = vllm::CUDATypeConverter<scalar_t>::Type;
         auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
         vllm::silu_mul_cvt_fp16_to_fp4<cuda_type><<<grid, block, 0, stream>>>(
-            m, n, num_packed_cols, input_ptr, input_sf_ptr,
+            m, n, sf_n_unpadded, input_ptr, input_sf_ptr,
             reinterpret_cast<uint32_t*>(output_ptr),
             reinterpret_cast<uint32_t*>(sf_out));
       });

csrc/quantization/fp4/nvfp4_experts_quant.cu

@@ -43,7 +43,7 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
                     uint32_t* input_offset_by_experts,
                     uint32_t* output_scale_offset_by_experts, int n_experts,
                     bool low_latency) {
-  using PackedVec = PackedVec<Type, CVT_FP4_PACK16>;
+  using PackedVec = PackedVec<Type>;
   static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
       (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
   static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
@@ -155,7 +155,7 @@ __global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
                     float const* SFScale, uint32_t* out, uint32_t* SFout,
                     uint32_t* input_offset_by_experts,
                     uint32_t* output_scale_offset_by_experts, int n_experts) {
-  using PackedVec = PackedVec<Type, CVT_FP4_PACK16>;
+  using PackedVec = PackedVec<Type>;
   static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
       (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
   static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,

csrc/quantization/fp4/nvfp4_quant_kernels.cu

@@ -42,7 +42,7 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
                     Type const* __restrict__ in,
                     float const* __restrict__ SFScale,
                     uint32_t* __restrict__ out, uint32_t* __restrict__ SFout) {
-  using PackedVec = vllm::PackedVec<Type, CVT_FP4_PACK16>;
+  using PackedVec = vllm::PackedVec<Type>;
 
   static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
       (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
@@ -71,13 +71,13 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
       // If we are outside valid rows OR outside valid columns -> Use Zeros
       bool valid = (rowIdx < numRows) && (elem_idx < numCols);
       if constexpr (CVT_FP4_PACK16) {
-        ld256_cg_or_zero(reinterpret_cast<u32x8_t&>(in_vec),
-                         &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
-                         valid);
+        ld256_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
+            valid);
       } else {
-        ld128_cg_or_zero(reinterpret_cast<uint4&>(in_vec),
-                         &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
-                         valid);
+        ld128_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
+            valid);
       }
 
       auto sf_out =
@@ -109,12 +109,11 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
 template <class Type, bool UE8M0_SF = false>
 __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
     cvt_fp16_to_fp4_sf_major(int32_t numRows, int32_t numCols,
-                             int32_t sf_n_unpadded, int32_t num_packed_cols,
-                             Type const* __restrict__ in,
+                             int32_t sf_n_unpadded, Type const* __restrict__ in,
                              float const* __restrict__ SFScale,
                              uint32_t* __restrict__ out,
                              uint32_t* __restrict__ SFout) {
-  using PackedVec = PackedVec<Type, CVT_FP4_PACK16>;
+  using PackedVec = PackedVec<Type>;
 
   static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
       (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
@@ -132,20 +131,20 @@ __global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
   // Iterate over all rows and cols including padded ones -
   //  ensures we visit every single scale factor address to initialize it.
   for (int rowIdx = blockIdx.x; rowIdx < numRows; rowIdx += gridDim.x) {
-    if (colIdx < num_packed_cols) {
+    if (colIdx < sf_n_unpadded) {
       PackedVec in_vec;
       int64_t inOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
 
       // If we are outside valid rows OR outside valid columns -> Use Zeros
       bool valid = (rowIdx < numRows) && (elem_idx < numCols);
       if constexpr (CVT_FP4_PACK16) {
-        ld256_cg_or_zero(reinterpret_cast<u32x8_t&>(in_vec),
-                         &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
-                         valid);
+        ld256_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 8],
+            valid);
       } else {
-        ld128_cg_or_zero(reinterpret_cast<uint4&>(in_vec),
-                         &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
-                         valid);
+        ld128_or_zero_cg_u32<Type>(
+            in_vec, &reinterpret_cast<const uint32_t*>(in)[inOffset * 4],
+            valid);
       }
 
       auto sf_out =
@@ -223,8 +222,7 @@ void scaled_fp4_quant_sm1xxa(torch::Tensor const& output,
           reinterpret_cast<uint32_t*>(sf_out));
     });
   } else {
-    int num_packed_cols = n / CVT_FP4_ELTS_PER_THREAD;
-    int grid_y = vllm::div_round_up(num_packed_cols, static_cast<int>(block.x));
+    int grid_y = vllm::div_round_up(sf_n_unpadded, static_cast<int>(block.x));
     int grid_x = std::min(
         m, std::max(1, (multiProcessorCount * numBlocksPerSM) / grid_y));
     dim3 grid(grid_x, grid_y);
@@ -234,8 +232,8 @@ void scaled_fp4_quant_sm1xxa(torch::Tensor const& output,
       auto input_ptr = static_cast<cuda_type const*>(input.data_ptr());
       // NOTE: We don't support e8m0 scales at this moment.
       vllm::cvt_fp16_to_fp4_sf_major<cuda_type, false>
-          <<<grid, block, 0, stream>>>(m, n, sf_n_unpadded, num_packed_cols,
-                                       input_ptr, input_sf_ptr,
+          <<<grid, block, 0, stream>>>(m, n, sf_n_unpadded, input_ptr,
+                                       input_sf_ptr,
                                        reinterpret_cast<uint32_t*>(output_ptr),
                                        reinterpret_cast<uint32_t*>(sf_out));
     });

csrc/quantization/fp4/nvfp4_utils.cuh

@@ -19,10 +19,8 @@
 #include <cuda_runtime.h>
 #include <cuda_fp8.h>
 
-#include "../../cuda_vec_utils.cuh"
-
-#if defined(NVFP4_ENABLE_ELTS16) && defined(CUDA_VERSION) && \
-    CUDA_VERSION >= 12090
+#if (defined(NVFP4_ENABLE_ELTS16) && (CUDART_VERSION >= 12090) && \
+     defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100)
   #define ELTS_PER_THREAD 16
 constexpr int CVT_FP4_ELTS_PER_THREAD = 16;
 constexpr bool CVT_FP4_PACK16 = true;
@@ -36,6 +34,68 @@ constexpr int CVT_FP4_SF_VEC_SIZE = 16;
 
 namespace vllm {
 
+// Convert PyTorch cpp type to CUDA type
+template <typename T>
+struct CUDATypeConverter {
+  using Type = T;
+};
+
+template <>
+struct CUDATypeConverter<at::Half> {
+  using Type = half;
+};
+
+template <>
+struct CUDATypeConverter<at::BFloat16> {
+  using Type = __nv_bfloat16;
+};
+
+// Get type2 from type or vice versa (applied to half and bfloat16)
+template <typename T>
+struct TypeConverter {
+  using Type = half2;
+};  // keep for generality
+
+template <>
+struct TypeConverter<half2> {
+  using Type = half;
+};
+
+template <>
+struct TypeConverter<half> {
+  using Type = half2;
+};
+
+template <>
+struct TypeConverter<__nv_bfloat162> {
+  using Type = __nv_bfloat16;
+};
+
+template <>
+struct TypeConverter<__nv_bfloat16> {
+  using Type = __nv_bfloat162;
+};
+
+#if (defined(NVFP4_ENABLE_ELTS16) && (CUDART_VERSION >= 12090) && \
+     defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100)
+// Define a 32 bytes packed data type.
+template <class Type>
+struct alignas(32) PackedVec {
+  typename TypeConverter<Type>::Type elts[8];
+};
+#else
+// Define a 16 bytes packed data type.
+template <class Type>
+struct alignas(16) PackedVec {
+  typename TypeConverter<Type>::Type elts[4];
+};
+#endif
+
+template <>
+struct PackedVec<__nv_fp8_e4m3> {
+  __nv_fp8x2_e4m3 elts[8];
+};
+
 template <typename Int>
 __host__ __device__ inline Int round_up(Int x, Int y) {
   static_assert(std::is_integral_v<Int>,
@@ -148,6 +208,56 @@ __device__ __forceinline__ float reciprocal_approximate_ftz(float a) {
   return b;
 }
 
+template <class Type>
+__device__ __forceinline__ void ld128_or_zero_cg_u32(PackedVec<Type>& out,
+                                                     const void* ptr,
+                                                     bool pred) {
+  uint32_t r0, r1, r2, r3;
+
+  asm volatile(
+      "{\n"
+      "  .reg .pred pr;\n"
+      "  setp.ne.u32 pr, %4, 0;\n"
+      "  mov.u32 %0, 0;\n"
+      "  mov.u32 %1, 0;\n"
+      "  mov.u32 %2, 0;\n"
+      "  mov.u32 %3, 0;\n"
+      "  @pr ld.global.cg.v4.u32 {%0,%1,%2,%3}, [%5];\n"
+      "}\n"
+      : "=r"(r0), "=r"(r1), "=r"(r2), "=r"(r3)
+      : "r"((int)pred), "l"(ptr));
+
+  *reinterpret_cast<uint4*>(&out) = uint4{r0, r1, r2, r3};
+}
+
+template <class Type>
+__device__ __forceinline__ void ld256_or_zero_cg_u32(PackedVec<Type>& out,
+                                                     const void* ptr,
+                                                     bool pred) {
+  uint32_t r0, r1, r2, r3, r4, r5, r6, r7;
+
+  asm volatile(
+      "{\n"
+      "  .reg .pred pr;\n"
+      "  setp.ne.u32 pr, %8, 0;\n"
+      "  mov.u32 %0, 0;\n"
+      "  mov.u32 %1, 0;\n"
+      "  mov.u32 %2, 0;\n"
+      "  mov.u32 %3, 0;\n"
+      "  mov.u32 %4, 0;\n"
+      "  mov.u32 %5, 0;\n"
+      "  mov.u32 %6, 0;\n"
+      "  mov.u32 %7, 0;\n"
+      "  @pr ld.global.cg.v8.u32 {%0,%1,%2,%3,%4,%5,%6,%7}, [%9];\n"
+      "}\n"
+      : "=r"(r0), "=r"(r1), "=r"(r2), "=r"(r3), "=r"(r4), "=r"(r5), "=r"(r6),
+        "=r"(r7)
+      : "r"((int)pred), "l"(ptr));
+
+  reinterpret_cast<uint4*>(&out)[0] = uint4{r0, r1, r2, r3};
+  reinterpret_cast<uint4*>(&out)[1] = uint4{r4, r5, r6, r7};
+}
+
 // Compute SF output offset for swizzled tensor core layout.
 // SF layout: [numMTiles, numKTiles, 32, 4, 4]
 // Caller must precompute: numKTiles = (numCols + 63) / 64
@@ -205,8 +315,8 @@ __device__ __forceinline__ uint8_t* sf_out_rowmajor_u8(int row, int pack,
 
 // Quantizes the provided PackedVec into the uint32_t output
 template <class Type, int CVT_FP4_NUM_THREADS_PER_SF, bool UE8M0_SF = false>
-__device__ __forceinline__ fp4_packed_t cvt_warp_fp16_to_fp4(
-    PackedVec<Type, CVT_FP4_PACK16>& vec, float SFScaleVal, uint8_t* SFout) {
+__device__ __forceinline__ fp4_packed_t
+cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal, uint8_t* SFout) {
   // Get absolute maximum values among the local 8 values.
   auto localMax = __habs2(vec.elts[0]);
 
@@ -262,7 +372,11 @@ __device__ __forceinline__ fp4_packed_t cvt_warp_fp16_to_fp4(
 
 #pragma unroll
   for (int i = 0; i < CVT_FP4_ELTS_PER_THREAD / 2; i++) {
-    fp2Vals[i] = cast_to_float2(vec.elts[i]);
+    if constexpr (std::is_same_v<Type, half>) {
+      fp2Vals[i] = __half22float2(vec.elts[i]);
+    } else {
+      fp2Vals[i] = __bfloat1622float2(vec.elts[i]);
+    }
     fp2Vals[i].x *= outputScale;
     fp2Vals[i].y *= outputScale;
   }
@@ -281,19 +395,22 @@ __device__ __forceinline__ float2 silu2(float2 x) {
 }
 
 template <class Type>
-__inline__ __device__ PackedVec<Type, CVT_FP4_PACK16> compute_silu_mul(
-    const PackedVec<Type, CVT_FP4_PACK16>& x_vec,
-    const PackedVec<Type, CVT_FP4_PACK16>& y_vec) {
-  PackedVec<Type, CVT_FP4_PACK16> result;
+__inline__ __device__ PackedVec<Type> compute_silu_mul(
+    const PackedVec<Type>& x_vec, const PackedVec<Type>& y_vec) {
+  PackedVec<Type> result;
 
 #pragma unroll
   for (int i = 0; i < CVT_FP4_ELTS_PER_THREAD / 2; ++i) {
     // silu_mul in float32
-    using packed_t = typename PackedTypeConverter<Type>::Type;
-    float2 silu_vec = silu2(cast_to_float2(x_vec.elts[i]));
-    float2 y_f2 = cast_to_float2(y_vec.elts[i]);
-    result.elts[i] = cast_to_packed<packed_t>(
-        make_float2(silu_vec.x * y_f2.x, silu_vec.y * y_f2.y));
+    if constexpr (std::is_same_v<Type, half>) {
+      float2 silu_vec = silu2(__half22float2(x_vec.elts[i]));
+      result.elts[i] = __float22half2_rn(
+          __fmul2_rn(silu_vec, __half22float2(y_vec.elts[i])));
+    } else {
+      float2 silu_vec = silu2(__bfloat1622float2(x_vec.elts[i]));
+      result.elts[i] = __float22bfloat162_rn(
+          __fmul2_rn(silu_vec, __bfloat1622float2(y_vec.elts[i])));
+    }
   }
   return result;
 }

csrc/quantization/w8a8/cutlass/c3x/scaled_mm_sm120_fp8_dispatch.cuh

@@ -12,68 +12,6 @@ namespace vllm {
 
 using c3x::cutlass_gemm_caller;
 
-// Custom wrapper to allow specifying EpilogueTile for small M
-template <typename ElementAB_, typename ElementD_,
-          template <typename, typename, typename> typename Epilogue_,
-          typename TileShape, typename ClusterShape, typename KernelSchedule,
-          typename EpilogueSchedule, typename EpilogueTile>
-struct cutlass_3x_gemm_sm120_custom {
-  using ElementAB = ElementAB_;
-  using LayoutA = cutlass::layout::RowMajor;
-  static constexpr int AlignmentA =
-      128 / cutlass::sizeof_bits<ElementAB>::value;
-
-  using LayoutB = cutlass::layout::ColumnMajor;
-  static constexpr int AlignmentB =
-      128 / cutlass::sizeof_bits<ElementAB>::value;
-
-  using ElementC = void;
-  using LayoutC = cutlass::layout::RowMajor;
-  static constexpr int AlignmentC =
-      128 / cutlass::sizeof_bits<ElementD_>::value;
-
-  using ElementD = ElementD_;
-  using LayoutD = cutlass::layout::RowMajor;
-  static constexpr int AlignmentD = AlignmentC;
-
-  using ElementAcc =
-      typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
-                                float>::type;
-  using Epilogue = Epilogue_<ElementAcc, ElementD, TileShape>;
-
-  // MMA type
-  using ElementAccumulator = float;
-
-  // Epilogue types
-  using ElementBias = cutlass::half_t;
-  using ElementCompute = float;
-  using ElementAux = ElementD;
-  using LayoutAux = LayoutD;
-  using ElementAmax = float;
-
-  using EVTCompute = typename Epilogue::EVTCompute;
-
-  using CollectiveEpilogue =
-      typename cutlass::epilogue::collective::CollectiveBuilder<
-          cutlass::arch::Sm120, cutlass::arch::OpClassTensorOp, TileShape,
-          ClusterShape, EpilogueTile,  // Use custom EpilogueTile
-          ElementAccumulator, ElementCompute, ElementC, LayoutC, AlignmentC,
-          ElementD, LayoutD, AlignmentD, EpilogueSchedule,
-          EVTCompute>::CollectiveOp;
-
-  using CollectiveMainloop =
-      typename cutlass::gemm::collective::CollectiveBuilder<
-          cutlass::arch::Sm120, cutlass::arch::OpClassTensorOp, ElementAB,
-          LayoutA, AlignmentA, ElementAB, LayoutB, AlignmentB,
-          ElementAccumulator, TileShape, ClusterShape,
-          cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
-              sizeof(typename CollectiveEpilogue::SharedStorage))>,
-          KernelSchedule, void>::CollectiveOp;
-
-  using GemmKernel = enable_sm120_only<cutlass::gemm::kernel::GemmUniversal<
-      Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue, void>>;
-};
-
 template <typename InType, typename OutType,
           template <typename, typename, typename> typename Epilogue>
 struct sm120_fp8_config_default {
@@ -87,54 +25,6 @@ struct sm120_fp8_config_default {
                             KernelSchedule, EpilogueSchedule>;
 };
 
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
-struct sm120_fp8_config_M64 {
-  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
-  // SM120 Cooperative kernel requires Tile M >= 128.
-  // For M=64 tile, we use Pingpong schedule which is more flexible with small
-  // tiles.
-  using KernelSchedule = cutlass::gemm::KernelTmaWarpSpecializedPingpong;
-  using EpilogueSchedule = cutlass::epilogue::collective::EpilogueScheduleAuto;
-  using TileShape = Shape<_64, _64, _128>;
-  // CUTLASS 3.x on SM120 currently restricts programmatic multicast (Cluster >
-  // 1) for certain schedules/types. Reverting to 1x1x1 to ensure compilation.
-  using ClusterShape = Shape<_1, _1, _1>;
-  using Cutlass3xGemm =
-      cutlass_3x_gemm_sm120<InType, OutType, Epilogue, TileShape, ClusterShape,
-                            KernelSchedule, EpilogueSchedule>;
-};
-
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
-struct sm120_fp8_config_M32 {
-  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
-  using KernelSchedule = cutlass::gemm::KernelTmaWarpSpecializedPingpong;
-  using EpilogueSchedule = cutlass::epilogue::collective::EpilogueScheduleAuto;
-  using TileShape = Shape<_32, _64, _128>;
-  using ClusterShape = Shape<_1, _1, _1>;
-  // Use custom gemm to specify EpilogueTile M=32
-  using Cutlass3xGemm =
-      cutlass_3x_gemm_sm120_custom<InType, OutType, Epilogue, TileShape,
-                                   ClusterShape, KernelSchedule,
-                                   EpilogueSchedule, Shape<_32, _32>>;
-};
-
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
-struct sm120_fp8_config_M16 {
-  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
-  using KernelSchedule = cutlass::gemm::KernelTmaWarpSpecializedPingpong;
-  using EpilogueSchedule = cutlass::epilogue::collective::EpilogueScheduleAuto;
-  using TileShape = Shape<_16, _64, _128>;
-  using ClusterShape = Shape<_1, _1, _1>;
-  // Use custom gemm to specify EpilogueTile M=16
-  using Cutlass3xGemm =
-      cutlass_3x_gemm_sm120_custom<InType, OutType, Epilogue, TileShape,
-                                   ClusterShape, KernelSchedule,
-                                   EpilogueSchedule, Shape<_16, _32>>;
-};
-
 template <typename InType, typename OutType,
           template <typename, typename, typename> typename Epilogue,
           typename... EpilogueArgs>
@@ -146,28 +36,6 @@ inline void cutlass_gemm_sm120_fp8_dispatch(torch::Tensor& out,
   TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
   TORCH_CHECK(b.dtype() == torch::kFloat8_e4m3fn);
 
-  int M = a.size(0);
-
-  if (M <= 16) {
-    using Cutlass3xGemmM16 =
-        typename sm120_fp8_config_M16<InType, OutType, Epilogue>::Cutlass3xGemm;
-    return cutlass_gemm_caller<Cutlass3xGemmM16>(
-        out, a, b, std::forward<EpilogueArgs>(args)...);
-  }
-  if (M <= 32) {
-    using Cutlass3xGemmM32 =
-        typename sm120_fp8_config_M32<InType, OutType, Epilogue>::Cutlass3xGemm;
-    return cutlass_gemm_caller<Cutlass3xGemmM32>(
-        out, a, b, std::forward<EpilogueArgs>(args)...);
-  }
-
-  if (M <= 256) {
-    using Cutlass3xGemmM64 =
-        typename sm120_fp8_config_M64<InType, OutType, Epilogue>::Cutlass3xGemm;
-    return cutlass_gemm_caller<Cutlass3xGemmM64>(
-        out, a, b, std::forward<EpilogueArgs>(args)...);
-  }
-
   using Cutlass3xGemmDefault =
       typename sm120_fp8_config_default<InType, OutType,
                                         Epilogue>::Cutlass3xGemm;
@@ -196,4 +64,4 @@ void cutlass_scaled_mm_sm120_fp8_epilogue(torch::Tensor& out,
   }
 }
 
-}  // namespace vllm
+}  // namespace vllm

csrc/quantization/w8a8/cutlass/moe/moe_data.cu

@@ -263,10 +263,12 @@ void get_cutlass_moe_mm_data_caller(
 }
 
 template <bool SWAP_AB>
-__global__ void compute_batched_moe_data(
-    int32_t* expert_offsets, int32_t* problem_sizes1, int32_t* problem_sizes2,
-    const int32_t* __restrict__ expert_num_tokens, const int padded_m,
-    const int n, const int k) {
+__global__ void compute_pplx_data(int32_t* expert_offsets,
+                                  int32_t* problem_sizes1,
+                                  int32_t* problem_sizes2,
+                                  const int32_t* __restrict__ expert_num_tokens,
+                                  const int padded_m, const int n,
+                                  const int k) {
   int expert_idx = threadIdx.x;
   expert_offsets[expert_idx] = expert_idx * padded_m;
 
@@ -287,22 +289,24 @@ __global__ void compute_batched_moe_data(
   }
 }
 
-void get_cutlass_batched_moe_mm_data_caller(
-    torch::Tensor& expert_offsets, torch::Tensor& problem_sizes1,
-    torch::Tensor& problem_sizes2, const torch::Tensor& expert_num_tokens,
-    const int64_t num_local_experts, const int64_t padded_m, const int64_t n,
-    const int64_t k) {
+void get_cutlass_pplx_moe_mm_data_caller(torch::Tensor& expert_offsets,
+                                         torch::Tensor& problem_sizes1,
+                                         torch::Tensor& problem_sizes2,
+                                         const torch::Tensor& expert_num_tokens,
+                                         const int64_t num_local_experts,
+                                         const int64_t padded_m,
+                                         const int64_t n, const int64_t k) {
   auto stream = at::cuda::getCurrentCUDAStream(expert_offsets.device().index());
 
   if (num_local_experts * padded_m > SWAP_AB_THRESHOLD) {
-    compute_batched_moe_data<false><<<1, num_local_experts, 0, stream>>>(
+    compute_pplx_data<false><<<1, num_local_experts, 0, stream>>>(
         static_cast<int32_t*>(expert_offsets.data_ptr()),
         static_cast<int32_t*>(problem_sizes1.data_ptr()),
         static_cast<int32_t*>(problem_sizes2.data_ptr()),
         static_cast<const int32_t*>(expert_num_tokens.data_ptr()), padded_m, n,
         k);
   } else {
-    compute_batched_moe_data<true><<<1, num_local_experts, 0, stream>>>(
+    compute_pplx_data<true><<<1, num_local_experts, 0, stream>>>(
         static_cast<int32_t*>(expert_offsets.data_ptr()),
         static_cast<int32_t*>(problem_sizes1.data_ptr()),
         static_cast<int32_t*>(problem_sizes2.data_ptr()),

csrc/quantization/w8a8/cutlass/scaled_mm_entry.cu

@@ -82,11 +82,13 @@ void get_cutlass_moe_mm_problem_sizes_from_expert_offsets_caller(
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
     const int64_t n, const int64_t k, const bool swap_ab);
 
-void get_cutlass_batched_moe_mm_data_caller(
-    torch::Tensor& expert_offsets, torch::Tensor& problem_sizes1,
-    torch::Tensor& problem_sizes2, const torch::Tensor& expert_num_tokens,
-    const int64_t num_local_experts, const int64_t padded_m, const int64_t n,
-    const int64_t k);
+void get_cutlass_pplx_moe_mm_data_caller(torch::Tensor& expert_offsets,
+                                         torch::Tensor& problem_sizes1,
+                                         torch::Tensor& problem_sizes2,
+                                         const torch::Tensor& expert_num_tokens,
+                                         const int64_t num_local_experts,
+                                         const int64_t padded_m,
+                                         const int64_t n, const int64_t k);
 #endif
 
 void cutlass_scaled_mm_azp_sm75(torch::Tensor& c, torch::Tensor const& a,
@@ -317,30 +319,29 @@ void get_cutlass_moe_mm_problem_sizes_from_expert_offsets(
       version_num, ". Required capability: 90, 100, or 120");
 }
 
-void get_cutlass_batched_moe_mm_data(torch::Tensor& expert_offsets,
-                                     torch::Tensor& problem_sizes1,
-                                     torch::Tensor& problem_sizes2,
-                                     const torch::Tensor& expert_num_tokens,
-                                     const int64_t num_local_experts,
-                                     const int64_t padded_m, const int64_t n,
-                                     const int64_t k) {
+void get_cutlass_pplx_moe_mm_data(torch::Tensor& expert_offsets,
+                                  torch::Tensor& problem_sizes1,
+                                  torch::Tensor& problem_sizes2,
+                                  const torch::Tensor& expert_num_tokens,
+                                  const int64_t num_local_experts,
+                                  const int64_t padded_m, const int64_t n,
+                                  const int64_t k) {
   // This function currently gets compiled only if we have a valid cutlass moe
   // mm to run it for.
   int32_t version_num = get_sm_version_num();
 #if (defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90) ||   \
     (defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100) || \
     (defined ENABLE_CUTLASS_MOE_SM120 && ENABLE_CUTLASS_MOE_SM120)
-  get_cutlass_batched_moe_mm_data_caller(expert_offsets, problem_sizes1,
-                                         problem_sizes2, expert_num_tokens,
-                                         num_local_experts, padded_m, n, k);
+  get_cutlass_pplx_moe_mm_data_caller(expert_offsets, problem_sizes1,
+                                      problem_sizes2, expert_num_tokens,
+                                      num_local_experts, padded_m, n, k);
   return;
 #endif
-  TORCH_CHECK_NOT_IMPLEMENTED(false,
-                              "No compiled get_cutlass_batched_moe_mm_data: no "
-                              "cutlass_scaled_mm kernel "
-                              "for CUDA device capability: ",
-                              version_num,
-                              ". Required capability: 90, 100, or 120");
+  TORCH_CHECK_NOT_IMPLEMENTED(
+      false,
+      "No compiled get_cutlass_pplx_moe_mm_data: no cutlass_scaled_mm kernel "
+      "for CUDA device capability: ",
+      version_num, ". Required capability: 90, 100, or 120");
 }
 
 void cutlass_scaled_mm_azp(torch::Tensor& c, torch::Tensor const& a,