[NVIDIA] Fix cutedsl backend of MoE (#12353)
This commit is contained in:
Kaixi Hou
@@ -821,7 +821,7 @@ jobs:
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python3 run_suite.py --suite per-commit-4-gpu-b200 --auto-partition-id 0 --auto-partition-size 1 --timeout-per-file 3600
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unit-test-backend-4-gpu-gb200:
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needs: [check-changes, unit-test-backend-2-gpu, sgl-kernel-build-wheels-arm]
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needs: [check-changes, sgl-kernel-build-wheels-arm]
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if: always() && !failure() && !cancelled() &&
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((needs.check-changes.outputs.main_package == 'true') || (needs.check-changes.outputs.sgl_kernel == 'true'))
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runs-on: 4-gpu-gb200
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@@ -841,7 +841,7 @@ jobs:
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- name: Install dependencies
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run: |
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CUSTOM_BUILD_SGL_KERNEL=${{needs.check-changes.outputs.sgl_kernel}} IS_BLACKWELL=1 bash scripts/ci/ci_install_dependency.sh
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CUSTOM_BUILD_SGL_KERNEL=${{needs.check-changes.outputs.sgl_kernel}} IS_BLACKWELL=1 GRACE_BLACKWELL=1 bash scripts/ci/ci_install_deepep.sh
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- name: Run test
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timeout-minutes: 45
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@@ -1,4 +1,4 @@
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from typing import Optional, Union
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from typing import Optional
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import torch
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from flashinfer.cute_dsl.blockscaled_gemm import grouped_gemm_nt_masked
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@@ -20,7 +20,7 @@ def get_cute_dtype(input: torch.Tensor) -> str:
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def flashinfer_cutedsl_moe_masked(
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hidden_states: Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]],
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hidden_states: tuple[torch.Tensor, Optional[torch.Tensor]],
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input_global_scale: torch.Tensor,
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w1: torch.Tensor,
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w1_blockscale: torch.Tensor,
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@@ -40,7 +40,7 @@ def flashinfer_cutedsl_moe_masked(
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Args:
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hidden_states: Either of the following case
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* torch.Tensor: [num_experts, m, k], bf16
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* tuple[torch.Tensor, None]: [num_experts, m, k], bf16, None means no quant
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* tuple[torch.Tensor, torch.Tensor]: [num_experts, m, k // 2], uint8, [num_experts, m, k // 16], float8_e4m3fn
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input_global_scale (torch.Tensor): (l,)
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w1 (torch.Tensor): fp4 weights, [l, 2 * n, k // 2], uint8
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@@ -74,21 +74,21 @@ def flashinfer_cutedsl_moe_masked(
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assert (
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w2_alpha.dtype == torch.float32
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), f"w2_alpha must be float32, got {w2_alpha.dtype}"
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assert (
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len(hidden_states) == 2
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), f"hidden_states must be a tuple of length 2, got {len(hidden_states)}"
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# === Assertions on shapes ===
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n = w2.shape[-1] * 2 # intermediate dimension
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if isinstance(hidden_states, tuple):
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assert (
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input_global_scale is None
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), "input_global_scale is needed when input needs quant"
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if hidden_states[1] is not None:
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a_q = hidden_states[0].view(torch.uint8)
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a_q_sf = hidden_states[1].view(torch.float8_e4m3fn)
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m, k_by_2, num_experts = a_q.shape
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k = k_by_2 * 2
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else:
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num_experts, m, k = hidden_states.shape
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num_experts, m, k = hidden_states[0].shape
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assert (
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input_global_scale.dtype == torch.float32
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@@ -98,7 +98,7 @@ def flashinfer_cutedsl_moe_masked(
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), f"input_global_scale must be (l,), got {input_global_scale.shape}"
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a_q, a_q_sf = scaled_fp4_grouped_quant(
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hidden_states,
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hidden_states[0],
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input_global_scale,
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masked_m,
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)
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@@ -1451,7 +1451,11 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
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)
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layer.dispatcher.set_quant_config(
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{"input_global_scale": layer.w13_input_scale_quant}
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{
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"input_global_scale": (
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layer.w13_input_scale_quant if CUTEDSL_MOE_NVFP4_DISPATCH else None
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)
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}
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)
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# Validate weight scales
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@@ -1688,7 +1692,7 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
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def apply_without_routing_weights(
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self,
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layer: FusedMoE,
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x: torch.Tensor,
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x: tuple[torch.Tensor, Optional[torch.Tensor]],
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masked_m: torch.Tensor,
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moe_runner_config: MoeRunnerConfig,
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down_gemm_overlap_args: Optional["DownGemmOverlapArgs"],
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@@ -57,7 +57,7 @@ DEFAULT_MODEL_NAME_FOR_TEST_MLA = "lmsys/sglang-ci-dsv3-test"
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DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN = "lmsys/sglang-ci-dsv3-test-NextN"
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# NVFP4 models
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DEFAULT_DEEPSEEK_NVFP4_MODEL_FOR_TEST = "nvidia/DeepSeek-R1-0528-FP4"
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DEFAULT_DEEPSEEK_NVFP4_MODEL_FOR_TEST = "nvidia/DeepSeek-V3-0324-FP4"
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# FP8 models
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DEFAULT_MODEL_NAME_FOR_TEST_FP8 = "neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8"
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@@ -10,9 +10,20 @@ export LD_LIBRARY_PATH="${NVSHMEM_DIR}/lib:$LD_LIBRARY_PATH"
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export PATH="${NVSHMEM_DIR}/bin:$PATH"
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export CUDA_HOME=/usr/local/cuda
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if python3 -c "import deep_ep" >/dev/null 2>&1; then
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echo "deep_ep is already installed or importable. Skipping installation."
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exit 0
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GRACE_BLACKWELL=${GRACE_BLACKWELL:-0}
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# Detect architecture
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ARCH=$(uname -m)
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if [ "$ARCH" != "x86_64" ] && [ "$ARCH" != "aarch64" ]; then
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echo "Unsupported architecture: $ARCH"
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exit 1
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fi
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# It seems GB200 ci runner preinstalls some wrong version of deep_ep, so we cannot rely on it.
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if [ "$GRACE_BLACKWELL" != "1" ]; then
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if python3 -c "import deep_ep" >/dev/null 2>&1; then
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echo "deep_ep is already installed or importable. Skipping installation."
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exit 0
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fi
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fi
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# Install system dependencies
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@@ -35,8 +46,10 @@ dpkg -i libgdrapi_*.deb
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dpkg -i gdrcopy-tests_*.deb
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dpkg -i gdrcopy_*.deb
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if [ ! -e "/usr/lib/x86_64-linux-gnu/libmlx5.so" ]; then
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ln -s /usr/lib/x86_64-linux-gnu/libmlx5.so.1 /usr/lib/x86_64-linux-gnu/libmlx5.so
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# Set up library paths based on architecture
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LIB_PATH="/usr/lib/$ARCH-linux-gnu"
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if [ ! -e "$LIB_PATH/libmlx5.so" ]; then
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ln -s $LIB_PATH/libmlx5.so.1 $LIB_PATH/libmlx5.so
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fi
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apt-get update && apt-get install -y libfabric-dev
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@@ -45,6 +58,11 @@ cd /opt/nvshmem
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wget https://developer.download.nvidia.com/compute/redist/nvshmem/3.4.5/source/nvshmem_src_cuda12-all-all-3.4.5.tar.gz
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tar -xf nvshmem_src_cuda12-all-all-3.4.5.tar.gz
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mv nvshmem_src nvshmem && cd nvshmem
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if [ "$GRACE_BLACKWELL" = "1" ]; then
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CUDA_ARCH="100;120"
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else
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CUDA_ARCH="90"
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fi
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NVSHMEM_SHMEM_SUPPORT=0 \
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NVSHMEM_UCX_SUPPORT=0 \
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NVSHMEM_USE_NCCL=0 \
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@@ -53,13 +71,45 @@ NVSHMEM_IBGDA_SUPPORT=1 \
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NVSHMEM_PMIX_SUPPORT=0 \
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NVSHMEM_TIMEOUT_DEVICE_POLLING=0 \
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NVSHMEM_USE_GDRCOPY=1 \
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cmake -S . -B build/ -DCMAKE_INSTALL_PREFIX=/opt/nvshmem/install -DCMAKE_CUDA_ARCHITECTURES=90
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cmake -S . -B build/ -DCMAKE_INSTALL_PREFIX=/opt/nvshmem/install -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH}
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cd build
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make -j$(nproc) install
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# Install DeepEP
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rm -rf /root/.cache/deepep && git clone https://github.com/deepseek-ai/DeepEP.git /root/.cache/deepep && cd /root/.cache/deepep && git checkout 9af0e0d0e74f3577af1979c9b9e1ac2cad0104ee
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cd /root/.cache/deepep && python3 setup.py install
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DEEPEP_DIR=/root/.cache/deepep
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rm -rf ${DEEPEP_DIR}
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if [ "$GRACE_BLACKWELL" = "1" ]; then
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# We use Tom's DeepEP fork for GB200 for now, which supports fp4 dispatch.
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GRACE_BLACKWELL_DEEPEP_BRANCH=gb200_blog_part_2
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git clone https://github.com/fzyzcjy/DeepEP.git ${DEEPEP_DIR} && \
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pushd ${DEEPEP_DIR} && \
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git checkout ${GRACE_BLACKWELL_DEEPEP_BRANCH} && \
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sed -i 's/#define NUM_CPU_TIMEOUT_SECS 100/#define NUM_CPU_TIMEOUT_SECS 1000/' csrc/kernels/configs.cuh && \
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popd
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else
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git clone https://github.com/deepseek-ai/DeepEP.git ${DEEPEP_DIR} && \
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pushd ${DEEPEP_DIR} && \
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git checkout 9af0e0d0e74f3577af1979c9b9e1ac2cad0104ee && \
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popd
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fi
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cd ${DEEPEP_DIR}
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if [ "$GRACE_BLACKWELL" = "1" ]; then
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CUDA_VERSION=$(nvidia-smi | grep "CUDA Version" | head -n1 | awk '{print $9}')
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if [ "$CUDA_VERSION" = "12.8" ]; then
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CHOSEN_TORCH_CUDA_ARCH_LIST='10.0'
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elif awk -v ver="$CUDA_VERSION" 'BEGIN {exit !(ver > 12.8)}'; then
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CHOSEN_TORCH_CUDA_ARCH_LIST='10.0;10.3'
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else
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echo "Unsupported CUDA version for Grace Blackwell: $CUDA_VERSION" && exit 1
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fi && \
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if [ "${CUDA_VERSION%%.*}" = "13" ]; then \
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sed -i "/^ include_dirs = \['csrc\/'\]/a\ include_dirs.append('${CUDA_HOME}/include/cccl')" setup.py; \
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fi
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NVSHMEM_DIR=/opt/nvshmem/install TORCH_CUDA_ARCH_LIST="${CHOSEN_TORCH_CUDA_ARCH_LIST}" pip install --no-build-isolation .
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else
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python3 setup.py install
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fi
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# Verify configuration
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echo "=== Verify NVSHMEM ==="
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@@ -179,7 +179,10 @@ suites = {
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TestFile("test_llama31_fp4.py", 300),
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],
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"per-commit-4-gpu-gb200": [
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TestFile("test_cutedsl_moe.py", 300),
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TestFile("test_deepseek_v3_fp4_4gpu.py", 3600),
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# Disabled temporarily, see https://github.com/sgl-project/sglang/issues/12533
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# TestFile("test_deepseek_v3_cutedsl_4gpu.py", 3600),
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],
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"per-commit-4-gpu-deepep": [
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TestFile("ep/test_deepep_small.py", 531),
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@@ -0,0 +1,482 @@
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# SPDX-License-Identifier: Apache-2.0
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import unittest
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from typing import Callable
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import torch
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from flashinfer import fp4_quantize
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from sgl_kernel import scaled_fp4_grouped_quant, scaled_fp4_quant
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from torch.nn import functional as F
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from sglang.srt.layers.activation import SiluAndMul
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from sglang.srt.layers.moe.flashinfer_cutedsl_moe import flashinfer_cutedsl_moe_masked
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from sglang.srt.layers.moe.topk import TopKConfig, select_experts
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SKIP_TEST = torch.cuda.get_device_capability() < (10, 0)
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SKIP_REASON = "Nvfp4 Requires compute capability of 10 or above."
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kE2M1ToFloat = torch.tensor(
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[0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0], dtype=torch.float32
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)
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FLOAT8_E4M3_MAX = 448.0
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FLOAT4_E2M1_MAX = 6.0
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def convert_swizzled_to_linear(a_sf_swizzled: torch.Tensor, m, k, block_size):
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m_tiles = (m + 128 - 1) // 128
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f = block_size * 4
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k_tiles = (k + f - 1) // f
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tmp = torch.reshape(a_sf_swizzled, (1, m_tiles, k_tiles, 32, 4, 4))
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tmp = torch.permute(tmp, (0, 1, 4, 3, 2, 5))
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out = tmp.reshape(m_tiles * 128, k_tiles * f // block_size)
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return out[0:m, 0:k]
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def dequantize_nvfp4_to_dtype(
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tensor_fp4, tensor_sf, global_scale, dtype, device, block_size=16
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):
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"""Dequantize the fp4 tensor back to high precision."""
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# Two fp4 values are packed into one uint8.
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assert tensor_fp4.dtype == torch.uint8
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m, packed_k = tensor_fp4.shape
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k = packed_k * 2
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tensor_f32 = break_fp4_bytes(tensor_fp4, dtype)
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tensor_f32 = tensor_f32.reshape(m, k // block_size, block_size)
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tensor_sf = tensor_sf.view(torch.float8_e4m3fn)
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tensor_sf = convert_swizzled_to_linear(tensor_sf, m, k, block_size)
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tensor_sf_dtype = tensor_sf.to(torch.float32) / global_scale
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# scale the tensor
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out = (tensor_f32 * tensor_sf_dtype.unsqueeze(-1)).reshape(m, k)
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return out.to(dtype=dtype)
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def break_fp4_bytes(a, dtype):
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assert a.dtype == torch.uint8
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m, n = a.shape
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# Vectorized nibble processing
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a_flat = a.flatten()
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high = (a_flat & 0xF0) >> 4 # Upper nibbles
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low = a_flat & 0x0F # Lower nibbles
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# Combine nibbles for batch processing
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combined = torch.stack((low, high), dim=1).flatten()
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# Vectorized sign and magnitude extraction
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signs = (combined & 0x08).to(torch.bool) # Sign bits
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abs_vals = (combined & 0x07).to(torch.long) # Magnitude indices
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# Device-aware lookup and sign application
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kE2M1 = kE2M1ToFloat.to(device=a.device)
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values = kE2M1[abs_vals] * torch.where(signs, -1.0, 1.0)
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# Reshape to final form
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return values.reshape(m, n * 2).to(dtype=dtype)
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def compute_routing(router_logits: torch.Tensor, top_k: int):
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routing_weights = torch.softmax(router_logits, dim=1, dtype=torch.float)
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routing_weights, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
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routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
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routing_weights = routing_weights.float()
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return routing_weights, selected_experts
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def prepare_inputs(
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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num_experts: int,
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topk: int,
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):
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routing_weights, topk_idx = compute_routing(router_logits, topk)
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masked_m = []
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for i in range(num_experts):
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mask = topk_idx.view(-1) == i
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masked_m.append(mask.sum())
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masked_m = torch.tensor(masked_m, dtype=torch.int32)
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hidden_states_3d = torch.empty(
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(num_experts, max(masked_m), hidden_states.shape[1]), dtype=hidden_states.dtype
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)
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for i in range(num_experts):
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hidden_states_3d[i, : masked_m[i], :] = hidden_states[topk_idx.view(-1) == i]
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return hidden_states_3d, masked_m, topk_idx, routing_weights
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MNK_FACTORS = [
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(2, 1024, 1024),
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(2, 1024, 1536),
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(2, 3072, 1024),
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(2, 3072, 1536),
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(64, 1024, 1024),
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(64, 1024, 1536),
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(64, 3072, 1024),
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(64, 2048, 1024),
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(224, 1024, 1024),
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(224, 1024, 1536),
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]
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# Reference implementation of torch_moe
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def torch_moe(a, w1, w2, score, topk, expert_map):
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B, D = a.shape
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a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
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out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
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score = torch.softmax(score, dim=-1, dtype=torch.float32)
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topk_weight, topk_ids = torch.topk(score, topk)
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topk_weight = topk_weight.view(-1)
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topk_ids = topk_ids.view(-1)
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if expert_map is not None:
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topk_ids = expert_map[topk_ids]
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for i in range(w1.shape[0]):
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mask = topk_ids == i
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if mask.sum():
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out[mask] = SiluAndMul()(a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(
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0, 1
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)
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return (
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out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
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).sum(dim=1)
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def torch_moe_nvfp4(a, w1, w2, topk, topk_weight, topk_ids):
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B, D = a.shape
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a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
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out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
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topk_weight = topk_weight.view(-1)
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topk_ids = topk_ids.view(-1)
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for i in range(w1.shape[0]):
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mask = topk_ids == i
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if mask.sum():
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m = w1[i].shape[0]
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assert m % 2 == 0
|
||||
# Note: w1 and w3 are swapped!
|
||||
w3_expert, w1_expert = w1[i][m // 2 :, :], w1[i][: m // 2, :]
|
||||
inter = F.silu(a[mask] @ w1_expert.t()) * (a[mask] @ w3_expert.t())
|
||||
inter_gs = torch.tensor(1.0).cuda()
|
||||
inter_q, inter_blockscale = fp4_quantize(inter, inter_gs)
|
||||
inter = dequantize_nvfp4_to_dtype(
|
||||
inter_q,
|
||||
inter_blockscale,
|
||||
inter_gs,
|
||||
dtype=inter.dtype,
|
||||
device=inter.device,
|
||||
block_size=16,
|
||||
).cuda()
|
||||
out[mask] = inter @ w2[i].transpose(0, 1)
|
||||
return (
|
||||
out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
|
||||
).sum(dim=1)
|
||||
|
||||
|
||||
def check_moe(
|
||||
m: int,
|
||||
n: int,
|
||||
k: int,
|
||||
e: int,
|
||||
topk: int,
|
||||
dtype: torch.dtype,
|
||||
moe_impl: Callable,
|
||||
flip_w13: bool,
|
||||
):
|
||||
torch.manual_seed(7)
|
||||
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
|
||||
w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
|
||||
quant_blocksize = 16
|
||||
round_up = lambda x, y: (x + y - 1) // y * y
|
||||
sf_w1_2n = round_up(2 * n, 128)
|
||||
sf_w1_k = round_up(k // quant_blocksize, 4)
|
||||
w1_blockscale = torch.empty(
|
||||
(e, sf_w1_2n, sf_w1_k), device="cuda", dtype=torch.float8_e4m3fn
|
||||
)
|
||||
|
||||
w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
|
||||
sf_w2_k = round_up(k, 128)
|
||||
sf_w2_n = round_up(n // quant_blocksize, 4)
|
||||
w2_blockscale = torch.empty(
|
||||
(e, sf_w2_k, sf_w2_n), device="cuda", dtype=torch.float8_e4m3fn
|
||||
)
|
||||
|
||||
w1_q = torch.empty((e, 2 * n, k // 2), device="cuda", dtype=torch.uint8)
|
||||
w2_q = torch.empty((e, k, n // 2), device="cuda", dtype=torch.uint8)
|
||||
w1_gs = torch.empty((e,), device="cuda", dtype=torch.float32)
|
||||
w2_gs = torch.empty((e,), device="cuda", dtype=torch.float32)
|
||||
|
||||
for expert in range(e):
|
||||
w1_amax = torch.abs(w1).max().to(torch.float32)
|
||||
w2_amax = torch.abs(w2).max().to(torch.float32)
|
||||
w1_gs[expert] = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w1_amax
|
||||
w2_gs[expert] = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w2_amax
|
||||
|
||||
w1_q[expert], w1_blockscale[expert] = scaled_fp4_quant(
|
||||
w1[expert], w1_gs[expert]
|
||||
)
|
||||
|
||||
w2_q[expert], w2_blockscale[expert] = scaled_fp4_quant(
|
||||
w2[expert], w2_gs[expert]
|
||||
)
|
||||
|
||||
score = torch.randn((m, e), device="cuda", dtype=dtype)
|
||||
|
||||
topk_output = select_experts(
|
||||
hidden_states=a,
|
||||
router_logits=score,
|
||||
topk_config=TopKConfig(top_k=topk, renormalize=False),
|
||||
)
|
||||
topk_weights, topk_ids, _ = topk_output
|
||||
|
||||
a1_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
|
||||
a2_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
|
||||
test_output = moe_impl(
|
||||
a=a,
|
||||
topk_weights=topk_weights,
|
||||
topk_ids=topk_ids,
|
||||
w1_q=w1_q,
|
||||
w2_q=w2_q,
|
||||
a1_gs=a1_gs,
|
||||
w1_blockscale=w1_blockscale,
|
||||
w1_alphas=(1 / w1_gs),
|
||||
a2_gs=a2_gs,
|
||||
w2_blockscale=w2_blockscale,
|
||||
w2_alphas=(1 / w2_gs),
|
||||
)
|
||||
|
||||
# Reference check:
|
||||
a_global_scale = (
|
||||
(FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(a.flatten(), dim=-1)
|
||||
).to(torch.float32)
|
||||
a_fp4, a_scale_interleaved = scaled_fp4_quant(a, a_global_scale)
|
||||
_, m_k = a_fp4.shape
|
||||
a_in_dtype = dequantize_nvfp4_to_dtype(
|
||||
a_fp4,
|
||||
a_scale_interleaved,
|
||||
a_global_scale,
|
||||
dtype=a.dtype,
|
||||
device=a.device,
|
||||
block_size=quant_blocksize,
|
||||
)
|
||||
|
||||
w1_d = torch.empty((e, 2 * n, k), device="cuda", dtype=dtype)
|
||||
w2_d = torch.empty((e, k, n), device="cuda", dtype=dtype)
|
||||
|
||||
for idx in range(0, e):
|
||||
w1_d[idx] = dequantize_nvfp4_to_dtype(
|
||||
w1_q[idx],
|
||||
w1_blockscale[idx],
|
||||
w1_gs[idx],
|
||||
dtype=w1.dtype,
|
||||
device=w1.device,
|
||||
block_size=quant_blocksize,
|
||||
)
|
||||
w2_d[idx] = dequantize_nvfp4_to_dtype(
|
||||
w2_q[idx],
|
||||
w2_blockscale[idx],
|
||||
w2_gs[idx],
|
||||
dtype=w2.dtype,
|
||||
device=w2.device,
|
||||
block_size=quant_blocksize,
|
||||
)
|
||||
|
||||
if flip_w13:
|
||||
dim = -2
|
||||
size = w1_d.size(dim)
|
||||
assert size % 2 == 0, f"Expected even size in dim {dim}, got {size}"
|
||||
half = size // 2
|
||||
# Reorder weight
|
||||
w1, w3 = w1_d.split(half, dim=dim)
|
||||
w1_d = torch.cat([w3, w1], dim=dim).contiguous()
|
||||
|
||||
torch_output = torch_moe(a_in_dtype, w1_d, w2_d, score, topk, None)
|
||||
|
||||
torch.testing.assert_close(torch_output, test_output, atol=1e-1, rtol=1e-1)
|
||||
|
||||
|
||||
class TestFlashinferCutedslMoe(unittest.TestCase):
|
||||
@unittest.skipIf(SKIP_TEST, SKIP_REASON)
|
||||
def test_flashinfer_cutedsl_moe_masked(self):
|
||||
# Test parameters
|
||||
test_cases = [
|
||||
(2, 128, 256, 1),
|
||||
(2, 128, 256, 2),
|
||||
(2, 128, 256, 4),
|
||||
(16, 128, 512, 1),
|
||||
(16, 128, 512, 2),
|
||||
(16, 128, 512, 4),
|
||||
]
|
||||
|
||||
for bs, hidden_dim, inter_dim, topk in test_cases:
|
||||
with self.subTest(
|
||||
bs=bs, hidden_dim=hidden_dim, inter_dim=inter_dim, topk=topk
|
||||
):
|
||||
print(
|
||||
f"Testing with bs={bs}, hidden_dim={hidden_dim}, inter_dim={inter_dim}, topk={topk}"
|
||||
)
|
||||
with torch.inference_mode():
|
||||
torch.manual_seed(42)
|
||||
device = "cuda"
|
||||
dtype = torch.bfloat16
|
||||
num_experts = 8
|
||||
hidden_states = (
|
||||
torch.randn(bs, hidden_dim, dtype=torch.bfloat16, device=device)
|
||||
/ 5.0
|
||||
)
|
||||
w1 = (
|
||||
torch.randn(
|
||||
num_experts,
|
||||
2 * inter_dim,
|
||||
hidden_dim,
|
||||
dtype=torch.bfloat16,
|
||||
device=device,
|
||||
)
|
||||
/ 10.0
|
||||
)
|
||||
w2 = (
|
||||
torch.randn(
|
||||
num_experts,
|
||||
hidden_dim,
|
||||
inter_dim,
|
||||
dtype=torch.bfloat16,
|
||||
device=device,
|
||||
)
|
||||
/ 10.0
|
||||
)
|
||||
router_logits = torch.randn(bs, num_experts, dtype=torch.float32)
|
||||
|
||||
hidden_states_expanded = (
|
||||
hidden_states.view(bs, -1, hidden_dim)
|
||||
.repeat(1, topk, 1)
|
||||
.reshape(-1, hidden_dim)
|
||||
)
|
||||
hidden_states_3d, masked_m, topk_idx, routing_weights = (
|
||||
prepare_inputs(
|
||||
hidden_states_expanded, router_logits, num_experts, topk
|
||||
)
|
||||
)
|
||||
|
||||
w1_amax = w1.abs().amax(dim=(1, 2)).to(torch.float32).to(w1.device)
|
||||
w2_amax = w2.abs().amax(dim=(1, 2)).to(torch.float32).to(w2.device)
|
||||
input_global_scale = torch.ones(
|
||||
(num_experts,), dtype=torch.float32, device=hidden_states.device
|
||||
)
|
||||
|
||||
w1_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w1_amax
|
||||
w2_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w2_amax
|
||||
a2_global_scale = torch.ones(
|
||||
(num_experts,), dtype=torch.float32, device=hidden_states.device
|
||||
) # assume intermediate scale is 1.0
|
||||
|
||||
w1_fp4, w1_blockscale = scaled_fp4_grouped_quant(
|
||||
w1,
|
||||
w1_global_scale,
|
||||
torch.ones(num_experts, dtype=torch.int32, device=w1.device)
|
||||
* 2
|
||||
* inter_dim,
|
||||
)
|
||||
w2_fp4, w2_blockscale = scaled_fp4_grouped_quant(
|
||||
w2,
|
||||
w2_global_scale,
|
||||
torch.ones(num_experts, dtype=torch.int32, device=w2.device)
|
||||
* hidden_dim,
|
||||
)
|
||||
|
||||
w1_alpha = 1.0 / (input_global_scale * w1_global_scale)
|
||||
w2_alpha = 1.0 / (a2_global_scale * w2_global_scale)
|
||||
|
||||
out = flashinfer_cutedsl_moe_masked(
|
||||
(hidden_states_3d.to(hidden_states.device), None),
|
||||
input_global_scale,
|
||||
w1_fp4.permute(2, 0, 1),
|
||||
w1_blockscale,
|
||||
w1_alpha,
|
||||
w2_fp4.permute(2, 0, 1),
|
||||
a2_global_scale,
|
||||
w2_blockscale,
|
||||
w2_alpha,
|
||||
masked_m.to(hidden_states.device),
|
||||
)
|
||||
|
||||
# reference
|
||||
a_fp4, a_scale_interleaved = fp4_quantize(
|
||||
hidden_states, input_global_scale
|
||||
)
|
||||
a_in_dtype = dequantize_nvfp4_to_dtype(
|
||||
a_fp4,
|
||||
a_scale_interleaved,
|
||||
input_global_scale,
|
||||
dtype=hidden_states.dtype,
|
||||
device=hidden_states.device,
|
||||
block_size=16,
|
||||
)
|
||||
w1_d = torch.empty(
|
||||
(num_experts, 2 * inter_dim, hidden_dim),
|
||||
device=w1.device,
|
||||
dtype=w1.dtype,
|
||||
)
|
||||
w2_d = torch.empty(
|
||||
(num_experts, hidden_dim, inter_dim),
|
||||
device=w2.device,
|
||||
dtype=w2.dtype,
|
||||
)
|
||||
|
||||
for idx in range(0, num_experts):
|
||||
w1_fp4_sliced, w1_blockscale_sliced = fp4_quantize(
|
||||
w1[idx], w1_global_scale[idx]
|
||||
)
|
||||
w2_fp4_sliced, w2_blockscale_sliced = fp4_quantize(
|
||||
w2[idx], w2_global_scale[idx]
|
||||
)
|
||||
w1_d[idx] = dequantize_nvfp4_to_dtype(
|
||||
w1_fp4_sliced,
|
||||
w1_blockscale_sliced,
|
||||
w1_global_scale[idx],
|
||||
dtype=w1.dtype,
|
||||
device=w1.device,
|
||||
block_size=16,
|
||||
)
|
||||
w2_d[idx] = dequantize_nvfp4_to_dtype(
|
||||
w2_fp4_sliced,
|
||||
w2_blockscale_sliced,
|
||||
w2_global_scale[idx],
|
||||
dtype=w2.dtype,
|
||||
device=w2.device,
|
||||
block_size=16,
|
||||
)
|
||||
|
||||
ref_output = torch_moe_nvfp4(
|
||||
a_in_dtype,
|
||||
w1_d,
|
||||
w2_d,
|
||||
topk,
|
||||
routing_weights.to(a_in_dtype.device),
|
||||
topk_idx.to(a_in_dtype.device),
|
||||
)
|
||||
out_weighted = torch.zeros_like(
|
||||
ref_output, device=out.device, dtype=out.dtype
|
||||
)
|
||||
|
||||
positions = torch.nonzero(masked_m[topk_idx], as_tuple=False)
|
||||
rows, cols = positions[:, 0], positions[:, 1]
|
||||
experts = topk_idx[rows, cols]
|
||||
for i in range(num_experts):
|
||||
mask = experts == i
|
||||
if mask.any():
|
||||
idx = torch.nonzero(mask, as_tuple=False).squeeze(-1)
|
||||
r, c = rows[idx], cols[idx]
|
||||
out_weighted[r] += out[i, : len(r), :] * routing_weights[
|
||||
r, c
|
||||
].to(out.device).unsqueeze(-1)
|
||||
torch.testing.assert_close(
|
||||
out_weighted.cpu(), ref_output.cpu(), atol=5e-2, rtol=5e-2
|
||||
)
|
||||
print(
|
||||
f"Test passed with bs={bs}, hidden_dim={hidden_dim}, inter_dim={inter_dim}, topk={topk}"
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
+18
-6
@@ -24,20 +24,31 @@ class TestDeepseekR1Nvfp4CuteDSLDeepEP(CustomTestCase):
|
||||
other_args = [
|
||||
"--trust-remote-code",
|
||||
"--disable-radix-cache",
|
||||
"--mem-fraction-static",
|
||||
"0.89",
|
||||
"--max-prefill-tokens",
|
||||
"16384",
|
||||
"--max-running-requests",
|
||||
"256",
|
||||
"--chunked-prefill-size",
|
||||
"2048",
|
||||
"1024",
|
||||
"--tp",
|
||||
"8",
|
||||
"4",
|
||||
"--dp",
|
||||
"8",
|
||||
"4",
|
||||
"--ep",
|
||||
"4",
|
||||
"--moe-dense-tp-size",
|
||||
"1",
|
||||
"--enable-dp-attention",
|
||||
"--enable-ep-moe",
|
||||
"--quantization",
|
||||
"modelopt_fp4",
|
||||
"--enable-flashinfer-cutedsl-moe",
|
||||
"--enable-deepep-moe",
|
||||
"--attention-backend",
|
||||
"trtllm_mla",
|
||||
"--moe-a2a-backend",
|
||||
"deepep",
|
||||
"--moe-runner-backend",
|
||||
"flashinfer_cutedsl",
|
||||
"--deepep-mode",
|
||||
"low_latency",
|
||||
]
|
||||
@@ -50,6 +61,7 @@ class TestDeepseekR1Nvfp4CuteDSLDeepEP(CustomTestCase):
|
||||
**os.environ,
|
||||
"SGLANG_DEEPEP_BF16_DISPATCH": "1",
|
||||
"SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256",
|
||||
"SGLANG_CUTEDSL_MOE_NVFP4_DISPATCH": "0",
|
||||
},
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user