Bump Flashinfer to v0.4.0

docker/Dockerfile

@@ -15,7 +15,7 @@ ARG PYTHON_VERSION=3.12
 # Example:
 # docker build --build-arg BUILD_BASE_IMAGE=registry.acme.org/mirror/nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04
 
-# Important: We build with an old version of Ubuntu to maintain broad 
+# Important: We build with an old version of Ubuntu to maintain broad
 # compatibility with other Linux OSes. The main reason for this is that the
 # glibc version is baked into the distro, and binaries built with one glibc
 # version are not backwards compatible with OSes that use an earlier version.
@@ -371,7 +371,7 @@ RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist
 # Install FlashInfer from source
 ARG FLASHINFER_GIT_REPO="https://github.com/flashinfer-ai/flashinfer.git"
 # Keep this in sync with "flashinfer" extra in setup.py
-ARG FLASHINFER_GIT_REF="v0.3.1"
+ARG FLASHINFER_GIT_REF="v0.4.0"
 # Flag to control whether to compile FlashInfer AOT kernels
 # Set to "true" to enable AOT compilation:
 # docker build --build-arg FLASHINFER_AOT_COMPILE=true ...
@@ -392,7 +392,7 @@ RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH'
         FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a 10.0a 12.0"
     fi
     pushd flashinfer
-        if [[ "${CUDA_VERSION}" == 12.8.* ]] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then
+        if [[ "${CUDA_VERSION}" == 12.8.* ]] && [ "$TARGETPLATFORM" = "linux/amd64" ] && [ "${FLASHINFER_GIT_REF}" = "v0.3.1" ]; then
             # NOTE: To make new precompiled wheels, see tools/flashinfer-build.sh
             echo "🏗️  Installing FlashInfer from pre-compiled wheel"
             uv pip install --system https://wheels.vllm.ai/flashinfer-python/flashinfer_python-0.3.1-cp39-abi3-manylinux1_x86_64.whl \

docker/Dockerfile.nightly_torch

@@ -246,15 +246,15 @@ RUN pip install setuptools==75.6.0 packaging==23.2 ninja==1.11.1.3 build==1.2.2.
 
 
 # build flashinfer for torch nightly from source around 10 mins
-# release version: v0.3.1
+# release version: v0.4.0
 # todo(elainewy): cache flashinfer build result for faster build
 ENV CCACHE_DIR=/root/.cache/ccache
 RUN --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/root/.cache/uv \
     echo "git clone flashinfer..." \
     && git clone --recursive https://github.com/flashinfer-ai/flashinfer.git \
     && cd flashinfer \
-    && git checkout v0.3.1 \
+    && git checkout v0.4.0 \
     && git submodule update --init --recursive \
     && echo "finish git clone flashinfer..." \
     && rm -rf build \

setup.py

@@ -715,7 +715,7 @@ def _read_requirements(filename: str) -> list[str]:
         ],  # Required for audio processing
         "video": [],  # Kept for backwards compatibility
         # FlashInfer should be updated together with the Dockerfile
-        "flashinfer": ["flashinfer-python==0.3.1"],
+        "flashinfer": ["flashinfer-python==0.4.0"],
         # Optional deps for AMD FP4 quantization support
         "petit-kernel": ["petit-kernel"],
     },

tests/kernels/attention/test_flashinfer_trtllm_attention.py

@@ -7,9 +7,8 @@
 import torch
 
 from tests.kernels.quantization.nvfp4_utils import (
-    FLOAT4_E2M1_MAX,
-    FLOAT8_E4M3_MAX,
     dequantize_nvfp4_to_dtype,
+    get_nvfp4_global_scale,
 )
 from vllm.platforms import current_platform
 from vllm.utils import round_up
@@ -171,13 +170,12 @@ def test_flashinfer_trtllm_decode_with_baseline(
     output = torch.empty(ref_query.shape, dtype=dtype)
     wrapper.run(ref_query, ref_kv_cache, out=output)
     o_scale = 1.0
-    o_sf_scale = None
+    o_sf_scale_float = None
     if o_quant_dtype == FP8_DTYPE:
         _, o_scale = to_float8(output)
     elif o_quant_dtype == FP4_DTYPE:
-        o_sf_scale = (
-            (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(output.flatten(), dim=-1)
-        ).to(torch.float32)
+        o_sf_scale = get_nvfp4_global_scale(output)
+        o_sf_scale_float = o_sf_scale.item()
 
     # TRTLLM Decode
     if o_quant_dtype == FP4_DTYPE:
@@ -204,7 +202,7 @@ def test_flashinfer_trtllm_decode_with_baseline(
         bmm1_scale=q_scale * k_scale * sm_scale,
         bmm2_scale=v_scale / o_scale,
         window_left=window_left,
-        o_sf_scale=o_sf_scale,
+        o_sf_scale=o_sf_scale_float,
         out=output_trtllm,
     )
     if o_quant_dtype == FP8_DTYPE:
@@ -361,13 +359,12 @@ def test_flashinfer_trtllm_prefill_with_baseline(
     output = torch.empty(ref_query.shape, dtype=dtype)
     wrapper.run(ref_query, ref_kv_cache, out=output)
     o_scale = 1.0
-    o_sf_scale = None
+    o_sf_scale_float = None
     if o_quant_dtype == FP8_DTYPE:
         _, o_scale = to_float8(output)
     elif o_quant_dtype == FP4_DTYPE:
-        o_sf_scale = (
-            (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(output.flatten(), dim=-1)
-        ).to(torch.float32)
+        o_sf_scale = get_nvfp4_global_scale(output)
+        o_sf_scale_float = o_sf_scale.item()
 
     # TRTLLM Prefill
     if o_quant_dtype == FP4_DTYPE:
@@ -398,7 +395,7 @@ def test_flashinfer_trtllm_prefill_with_baseline(
         cum_seq_lens_q=q_indptr,
         cum_seq_lens_kv=kv_indptr,
         window_left=window_left,
-        o_sf_scale=o_sf_scale,
+        o_sf_scale=o_sf_scale_float,
         out=output_trtllm,
     )
     if o_quant_dtype == FP8_DTYPE:

tests/kernels/quantization/nvfp4_utils.py

@@ -66,9 +66,11 @@ def break_fp4_bytes(a, dtype):
     return values.reshape(m, n * 2).to(dtype=dtype)
 
 
+def get_nvfp4_global_scale(a: torch.Tensor):
+    return (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.abs(a).max().to(torch.float32)
+
+
 def quant_nvfp4_tensor(a: torch.Tensor):
-    a_global_scale = (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.abs(a).max().to(
-        torch.float32
-    )
+    a_global_scale = get_nvfp4_global_scale(a)
     a_quant, a_block_scale = scaled_fp4_quant(a, a_global_scale)
     return a_quant, a_block_scale, a_global_scale

tests/quantization/test_blackwell_moe.py

@@ -50,7 +50,7 @@ def can_initialize(model: str, extra_args: Optional[list[str]] = None):
     with RemoteOpenAIServer(
         model,
         server_args,
-        max_wait_seconds=1000,  # Due to FlashInfer compile
+        max_wait_seconds=1500,  # Due to FlashInfer compile
         override_hf_configs=dummy_hf_overrides,
     ) as server:
         client = server.get_client()

vllm/v1/attention/backends/flashinfer.py

@@ -1191,7 +1191,7 @@ def fast_plan_decode(
     qo_indptr_host = _get_range_buf(batch_size + 1, "cpu")
 
     try:
-        # Make sure we pass exactly 15 arguments for tensor core version
+        # Make sure we pass exactly 18 arguments for tensor core version
         self._plan_info = self._cached_module.plan(
             self._float_workspace_buffer,
             self._int_workspace_buffer,
@@ -1208,6 +1208,9 @@ def fast_plan_decode(
             head_dim,
             head_dim,
             False,  # causal
+            window_left,
+            -1,  # fixed_split_size
+            False,  # disable_split_kv
         )
     except Exception as e:
         raise RuntimeError(f"Error in tensor core plan: {e}") from e