Revert "[Release 2.10] Update to Torch 2.10 - final release (#30525)"

This reverts commit f97ca67176.

.buildkite/image_build/image_build.yaml

@@ -3,7 +3,6 @@ steps:
   - label: ":docker: Build image"
     key: image-build
     depends_on: []
-    timeout_in_minutes: 600
     commands:
     - if [[ "$BUILDKITE_BRANCH" != "main" ]]; then .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $VLLM_USE_PRECOMPILED $VLLM_MERGE_BASE_COMMIT $IMAGE_TAG; fi
     - if [[ "$BUILDKITE_BRANCH" == "main" ]]; then .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $VLLM_USE_PRECOMPILED $VLLM_MERGE_BASE_COMMIT $IMAGE_TAG $IMAGE_TAG_LATEST; fi
@@ -42,7 +41,7 @@ steps:
           limit: 2
         - exit_status: -10  # Agent was lost
           limit: 2
-
+  
   - label: ":docker: Build CPU arm64 image"
     key: cpu-arm64-image-build
     depends_on: []

CMakeLists.txt

@@ -56,8 +56,8 @@ endif()
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from docker/Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.10.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.9.1")
-set(TORCH_SUPPORTED_VERSION_ROCM "2.10.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.9.1")
 
 #
 # Try to find python package with an executable that exactly matches
@@ -433,7 +433,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
       list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_BF16_KERNEL_SRC})
     endif()
 
-    if (MARLIN_SM75_ARCHS)
+    if (MARLIN_SM75_ARCHS) 
       file(GLOB MARLIN_TEMPLATE_SM75_KERNEL_SRC "csrc/quantization/marlin/sm75_kernel_*.cu")
       set_gencode_flags_for_srcs(
         SRCS "${MARLIN_TEMPLATE_SM75_KERNEL_SRC}"
@@ -445,7 +445,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
       list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_SM75_KERNEL_SRC})
     endif()
 
-    if (MARLIN_FP8_ARCHS)
+    if (MARLIN_FP8_ARCHS) 
       file(GLOB MARLIN_TEMPLATE_FP8_KERNEL_SRC "csrc/quantization/marlin/sm89_kernel_*.cu")
       set_gencode_flags_for_srcs(
         SRCS "${MARLIN_TEMPLATE_FP8_KERNEL_SRC}"
@@ -1042,7 +1042,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
       list(APPEND VLLM_MOE_EXT_SRC ${MARLIN_MOE_SRC})
     endif()
 
-    if (MARLIN_MOE_SM75_ARCHS)
+    if (MARLIN_MOE_SM75_ARCHS) 
       file(GLOB MARLIN_MOE_SM75_SRC "csrc/moe/marlin_moe_wna16/sm75_kernel_*.cu")
       set_gencode_flags_for_srcs(
         SRCS "${MARLIN_MOE_SM75_SRC}"

cmake/external_projects/triton_kernels.cmake

@@ -1,9 +1,9 @@
 # Install OpenAI triton_kernels from https://github.com/triton-lang/triton/tree/main/python/triton_kernels
 
-set(DEFAULT_TRITON_KERNELS_TAG "v3.6.0")
+set(DEFAULT_TRITON_KERNELS_TAG "v3.5.0")
 
 # Set TRITON_KERNELS_SRC_DIR for use with local development with vLLM. We expect TRITON_KERNELS_SRC_DIR to
-# be directly set to the triton_kernels python directory.
+# be directly set to the triton_kernels python directory. 
 if (DEFINED ENV{TRITON_KERNELS_SRC_DIR})
   message(STATUS "[triton_kernels] Fetch from $ENV{TRITON_KERNELS_SRC_DIR}")
   FetchContent_Declare(
@@ -24,7 +24,7 @@ else()
   )
 endif()
 
-# Fetch content
+# Fetch content 
 FetchContent_MakeAvailable(triton_kernels)
 
 if (NOT triton_kernels_SOURCE_DIR)
@@ -47,7 +47,7 @@ install(CODE "file(MAKE_DIRECTORY \"\${CMAKE_INSTALL_PREFIX}/vllm/third_party/tr
 ## Copy .py files to install directory.
 install(DIRECTORY
         ${TRITON_KERNELS_PYTHON_DIR}
-        DESTINATION
+        DESTINATION 
         vllm/third_party/triton_kernels/
         COMPONENT triton_kernels
         FILES_MATCHING PATTERN "*.py")

pyproject.toml

@@ -6,7 +6,7 @@ requires = [
     "packaging>=24.2",
     "setuptools>=77.0.3,<81.0.0",
     "setuptools-scm>=8.0",
-    "torch == 2.10.0",
+    "torch == 2.9.1",
     "wheel",
     "jinja2",
     "grpcio-tools==1.78.0",

requirements/build.txt

@@ -4,7 +4,7 @@ ninja
 packaging>=24.2
 setuptools>=77.0.3,<81.0.0
 setuptools-scm>=8
-torch==2.10.0
+torch==2.9.1
 wheel
 jinja2>=3.1.6
 regex

requirements/cuda.txt

@@ -5,9 +5,9 @@ numba == 0.61.2 # Required for N-gram speculative decoding
 
 # Dependencies for NVIDIA GPUs
 ray[cgraph]>=2.48.0
-torch==2.10.0
+torch==2.9.1
-torchaudio==2.10.0
+torchaudio==2.9.1
 # These must be updated alongside torch
-torchvision==0.25.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
+torchvision==0.24.1 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 # FlashInfer should be updated together with the Dockerfile
 flashinfer-python==0.6.3

requirements/rocm-build.txt

@@ -1,11 +1,12 @@
 # Common dependencies
 -r common.txt
 
---extra-index-url https://download.pytorch.org/whl/test/rocm7.0
+--extra-index-url https://download.pytorch.org/whl/rocm6.4
-torch==2.10.0
+torch==2.9.1
-torchvision==0.25.0
+torchvision==0.24.1
-torchaudio==2.10.0
+torchaudio==2.9.1
-triton==3.6.0
+
+triton==3.5.1
 cmake>=3.26.1,<4
 packaging>=24.2
 setuptools>=77.0.3,<80.0.0

requirements/test.in

@@ -24,10 +24,10 @@ sentence-transformers>=5.2.0 # required for embedding tests
 soundfile # required for audio tests
 jiwer # required for audio tests
 tblib # for pickling test exceptions
-timm >=1.0.17 # required for internvl and gemma3n-mm test
+timm==1.0.17 # required for internvl and gemma3n-mm test
-torch==2.10.0
+torch==2.9.1
-torchaudio==2.10.0
+torchaudio==2.9.1
-torchvision==0.25.0
+torchvision==0.24.1
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
 mistral_common[image,audio] >= 1.9.0 # required for voxtral test

requirements/test.txt

@@ -155,10 +155,6 @@ coverage==7.10.6
     # via pytest-cov
 cramjam==2.9.0
     # via fastparquet
-cuda-bindings==12.9.4
-    # via torch
-cuda-pathfinder==1.3.3
-    # via cuda-bindings
 cupy-cuda12x==13.6.0
     # via ray
 cycler==0.12.1
@@ -635,7 +631,7 @@ nvidia-nvjitlink-cu12==12.9.86
     #   nvidia-cusolver-cu12
     #   nvidia-cusparse-cu12
     #   torch
-nvidia-nvshmem-cu12==3.4.5
+nvidia-nvshmem-cu12==3.3.20
     # via torch
 nvidia-nvtx-cu12==12.9.79
     # via torch
@@ -1167,7 +1163,7 @@ tomli==2.2.1
     # via schemathesis
 tomli-w==1.2.0
     # via schemathesis
-torch==2.10.0+cu129
+torch==2.9.1+cu129
     # via
     #   -r requirements/test.in
     #   accelerate
@@ -1195,7 +1191,7 @@ torch==2.10.0+cu129
     #   torchvision
     #   vector-quantize-pytorch
     #   vocos
-torchaudio==2.10.0+cu129
+torchaudio==2.9.1+cu129
     # via
     #   -r requirements/test.in
     #   encodec
@@ -1208,7 +1204,7 @@ torchmetrics==1.7.4
     #   pytorch-lightning
     #   terratorch
     #   torchgeo
-torchvision==0.25.0+cu129
+torchvision==0.24.1+cu129
     # via
     #   -r requirements/test.in
     #   lightly
@@ -1250,7 +1246,7 @@ transformers==4.57.5
     #   transformers-stream-generator
 transformers-stream-generator==0.0.5
     # via -r requirements/test.in
-triton==3.6.0
+triton==3.5.1
     # via torch
 tritonclient==2.64.0
     # via -r requirements/test.in

tests/compile/test_aot_compile.py

@@ -90,7 +90,9 @@ def use_vllm_config(vllm_config: VllmConfig):
         yield
 
 
-@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
 def test_no_dynamo_cache_entry(monkeypatch: pytest.MonkeyPatch):
     with monkeypatch.context() as m:
         vllm_config = make_vllm_config()
@@ -114,7 +116,9 @@ def test_no_dynamo_cache_entry(monkeypatch: pytest.MonkeyPatch):
             assert torch.allclose(actual, expected)
 
 
-@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
 def test_force_aot_load(monkeypatch: pytest.MonkeyPatch):
     with tempfile.TemporaryDirectory() as tmpdirname, monkeypatch.context() as m:
         args = (torch.randn(10, 10),)
@@ -128,7 +132,9 @@ def test_force_aot_load(monkeypatch: pytest.MonkeyPatch):
             CompiledMod(vllm_config=vllm_config)(*args)
 
 
-@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
 def test_save_and_load(monkeypatch: pytest.MonkeyPatch):
     with monkeypatch.context() as m:
         args = (torch.randn(10, 10),)
@@ -156,7 +162,9 @@ def test_save_and_load(monkeypatch: pytest.MonkeyPatch):
             assert torch.allclose(ret, expected)
 
 
-@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
 def test_cache_load_returns_tuple_consistency(monkeypatch: pytest.MonkeyPatch):
     """
     Test that cache loading correctly handles the returns_tuple logic.
@@ -215,7 +223,9 @@ def test_cache_load_returns_tuple_consistency(monkeypatch: pytest.MonkeyPatch):
             )
 
 
-@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
 def test_cache_load_returns_tuple_consistency_tuple_output(
     monkeypatch: pytest.MonkeyPatch,
 ):
@@ -284,7 +294,9 @@ def test_cache_load_returns_tuple_consistency_tuple_output(
             )
 
 
-@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
 def test_shape_env(monkeypatch: pytest.MonkeyPatch):
     """
     Test that the shape environment is correctly serialized and preserved
@@ -321,7 +333,9 @@ def test_shape_env(monkeypatch: pytest.MonkeyPatch):
                 assert guards_string == " - s77 <= 42\n - Eq(Mod(s77, 2), 0)"
 
 
-@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
 def test_partition_wrapper_applied_on_aot_load(
     monkeypatch: pytest.MonkeyPatch, vllm_tmp_cache: Path, mocker
 ):
@@ -412,7 +426,9 @@ def test_partition_wrapper_applied_on_aot_load(
         )
 
 
-@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
 @create_new_process_for_each_test("spawn")
 def test_gpt2_cache_hit(monkeypatch: pytest.MonkeyPatch):
     """
@@ -476,7 +492,9 @@ def test_gpt2_cache_hit(monkeypatch: pytest.MonkeyPatch):
         symbolic_shapes_module.make_symbol = original_make_symbol
 
 
-@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
 class TestStandaloneCompiledArtifacts:
     def test_init(self):
         cache = StandaloneCompiledArtifacts()
@@ -650,7 +668,9 @@ class TestStandaloneCompiledArtifacts:
         assert len(restored_cache.loaded_submodule_store) == 0
 
 
-@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
 class TestStandaloneCompiledArtifactsIntegration:
     def test_add_pickle_unpickle(self):
         cache = StandaloneCompiledArtifacts()

tests/compile/test_dynamic_shapes_compilation.py

@@ -39,7 +39,9 @@ def get_test_models():
 @pytest.mark.parametrize("use_aot_compile", ["0", "1"])
 @pytest.mark.parametrize("use_bytecode_hook", [True, False])
 @pytest.mark.parametrize("evaluate_guards", [False, True])
-@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+@pytest.mark.skipif(
+    not is_torch_equal_or_newer("2.10.0.dev"), reason="requires torch 2.10"
+)
 def test_dynamic_shapes_compilation(
     monkeypatch,
     model_name,

tests/kernels/moe/test_shared_fused_moe_routed_transform.py

@@ -14,7 +14,6 @@ import torch.nn as nn
 from vllm.config import VllmConfig, set_current_vllm_config
 from vllm.forward_context import set_forward_context
 from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
-from vllm.utils.torch_utils import is_torch_equal_or_newer
 
 
 class SimpleLinear(nn.Module):
@@ -61,10 +60,6 @@ def setup_cuda():
 @pytest.mark.parametrize("num_tokens", [1, 32])
 @pytest.mark.parametrize("hidden_size,latent_size", [(256, 128), (128, 64)])
 @pytest.mark.parametrize("dtype", [torch.bfloat16])
-@pytest.mark.skipif(
-    is_torch_equal_or_newer("2.10.0"),
-    reason="Test fails with PyTorch 2.10.0 see: https://github.com/vllm-project/vllm/issues/33995",
-)
 def test_routed_input_transform_inside_vs_outside(
     num_tokens: int,
     hidden_size: int,

vllm/compilation/compiler_interface.py

@@ -233,7 +233,7 @@ class InductorStandaloneAdaptor(CompilerInterface):
 
         from torch._inductor import standalone_compile
 
-        supports_aot = is_torch_equal_or_newer("2.10.0")
+        supports_aot = is_torch_equal_or_newer("2.10.0.dev")
 
         if not supports_aot and envs.VLLM_USE_MEGA_AOT_ARTIFACT:
             logger.error(

vllm/compilation/decorators.py

@@ -333,7 +333,7 @@ def _support_torch_compile(
     ) -> None:
         def mark_dynamic(arg: torch.Tensor, dims: list[int]) -> None:
             if ds_type == DynamicShapesType.UNBACKED:
-                if is_torch_equal_or_newer("2.10.0"):
+                if is_torch_equal_or_newer("2.10.0.dev"):
                     for dim in dims:
                         torch._dynamo.decorators.mark_unbacked(
                             arg, dim, hint_override=arg.size()[dim]
@@ -373,7 +373,7 @@ def _support_torch_compile(
                     if isinstance(arg, torch.Tensor):
                         # In case dims is specified with negative indexing
                         dims = [arg.ndim + dim if dim < 0 else dim for dim in dims]
-                        if is_torch_equal_or_newer("2.10.0"):
+                        if is_torch_equal_or_newer("2.10.0.dev"):
                             for dim in dims:
                                 torch._dynamo.decorators.mark_unbacked(
                                     arg, dim, hint_override=arg.size()[dim]
@@ -525,9 +525,9 @@ def _support_torch_compile(
             fx_config_patches["backed_size_oblivious"] = True
 
         # Prepare inductor config patches
-        # assume_32bit_indexing is only available in torch 2.10.0+
+        # assume_32bit_indexing is only available in torch 2.10.0.dev+
         inductor_config_patches = {}
-        if is_torch_equal_or_newer("2.10.0"):
+        if is_torch_equal_or_newer("2.10.0.dev"):
             inductor_config_patches["assume_32bit_indexing"] = (
                 self.compilation_config.dynamic_shapes_config.assume_32_bit_indexing
             )

vllm/envs.py

@@ -271,7 +271,7 @@ def use_aot_compile() -> bool:
 
     default_value = (
         "1"
-        if is_torch_equal_or_newer("2.11.0.dev") and not disable_compile_cache()
+        if is_torch_equal_or_newer("2.10.0.dev") and not disable_compile_cache()
         else "0"
     )
 

vllm/model_executor/layers/batch_invariant.py

@@ -974,7 +974,7 @@ def enable_batch_invariant_mode():
     )
 
     reduced_precision_val = (
-        (False, False) if is_torch_equal_or_newer("2.10.0") else False
+        (False, False) if is_torch_equal_or_newer("2.10.0.dev") else False
     )
     torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = (
         reduced_precision_val

vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py

@@ -27,21 +27,9 @@ logger = init_logger(__name__)
 if has_triton_kernels():
     try:
         import triton_kernels.swiglu
-        from triton_kernels.matmul_ogs import (
+        from triton_kernels.matmul_ogs import FnSpecs, FusedActivation, matmul_ogs
-            FnSpecs,
+        from triton_kernels.routing import RoutingData, routing, routing_from_bitmatrix
-            FusedActivation,
+        from triton_kernels.tensor import Bitmatrix
-            GatherIndx,
-            RoutingData,
-            ScatterIndx,
-            matmul_ogs,
-        )
-        from triton_kernels.tensor import (
-            BIT,
-            Bitmatrix,
-            SparseMatrix,
-            make_ragged_tensor_metadata,
-        )
-        from triton_kernels.topk import topk
     except (AttributeError, ImportError) as e:
         logger.error(
             "Failed to import Triton kernels. Please make sure your triton "
@@ -90,58 +78,6 @@ def pack_bitmatrix(
         tl.store(bitmatrix_ptrs, y, mask=offsets_m[:, None] < n_rows)
 
 
-def legacy_routing_from_bitmatrix(
-    bitmatrix: "Bitmatrix",
-    expt_scal: torch.Tensor,
-    expt_indx: torch.Tensor,
-    n_expts_tot: int,
-    n_expts_act: int,
-) -> tuple["RoutingData", "GatherIndx", "ScatterIndx"]:
-    """
-    Replacement for the removed triton_kernels.routing.routing_from_bitmatrix.
-    Creates routing data from a bitmatrix representation.
-    """
-    sparse_logits = SparseMatrix(indx=expt_indx, vals=expt_scal, mask=bitmatrix)
-    dispatch_indx = sparse_logits.mask_metadata.row_sorted_indx
-    combine_indx = sparse_logits.mask_metadata.col_sorted_indx
-    ragged_batch_metadata = make_ragged_tensor_metadata(
-        sparse_logits.mask_metadata.col_sum,
-        dispatch_indx.shape[0],
-    )
-    gate_scal = sparse_logits.vals.flatten()[combine_indx]
-    routing_data = RoutingData(
-        gate_scal,
-        ragged_batch_metadata.block_sizes,
-        n_expts_tot,
-        n_expts_act,
-        ragged_batch_metadata,
-    )
-    gather_idx = GatherIndx(combine_indx, dispatch_indx)
-    scatter_idx = ScatterIndx(dispatch_indx, combine_indx)
-    return routing_data, gather_idx, scatter_idx
-
-
-def legacy_routing(
-    logits: torch.Tensor,
-    n_expts_act: int,
-    sm_first: bool = False,
-) -> tuple["RoutingData", "GatherIndx", "ScatterIndx"]:
-    """
-    Replacement for the removed triton_kernels.routing.routing function.
-    Computes routing data from gating logits.
-    """
-    if sm_first:
-        logits = torch.softmax(logits, dim=-1)
-    sparse_logits = topk(logits, n_expts_act, apply_softmax=not sm_first)
-    return legacy_routing_from_bitmatrix(
-        sparse_logits.mask,
-        sparse_logits.vals,
-        sparse_logits.indx,
-        logits.shape[-1],
-        n_expts_act,
-    )
-
-
 def triton_kernel_moe_forward(
     hidden_states: torch.Tensor,
     w1,  # Tensor or triton_kernels.Tensor
@@ -155,7 +91,7 @@ def triton_kernel_moe_forward(
     global_num_experts: int = -1,
     expert_map: torch.Tensor | None = None,
 ) -> torch.Tensor:
-    routing_data, gather_idx, scatter_idx = legacy_routing(
+    routing_data, gather_idx, scatter_idx = routing(
         gating_output, topk, sm_first=not renormalize
     )
 
@@ -232,10 +168,9 @@ def triton_kernel_fused_experts(
     output_tensor = _resize_cache(output_tensor, (batch_dim, M, K))
 
     act = FusedActivation(
-        FnSpecs(
+        FnSpecs("swiglu", triton_kernels.swiglu.swiglu_fn, ("alpha", "limit")),
-            "swiglu", triton_kernels.swiglu.swiglu_fn, ("alpha", "limit"), reduction_n=2
-        ),
         (swiglu_alpha, swiglu_limit),
+        2,
     )
     gammas = routing_data.gate_scal if routing_data else None
 
@@ -297,12 +232,12 @@ def make_routing_data(
     bitmatrix_shape = [n_rows, bm_cols * 32]
     bitmatrix_shape_max = [n_rows, None]
     bitmatrix = Bitmatrix(
-        bitmatrix, dtype=BIT, shape=bitmatrix_shape, shape_max=bitmatrix_shape_max
+        bitmatrix, shape=bitmatrix_shape, shape_max=bitmatrix_shape_max, scratchpad=None
     )
 
     # matmul_ogs expects invalid topk_weights to be -1s
     topk_weights = torch.where(topk_ids == -1, -1.0, topk_weights)
-    routing_data, gather_indx, scatter_indx = legacy_routing_from_bitmatrix(
+    routing_data, gather_indx, scatter_indx = routing_from_bitmatrix(
         bitmatrix, topk_weights, topk_ids, num_local_experts, num_topk
     )