[NPU] Add NPU Fused MoE kernel by zheliuyu · Pull Request #1183 · linkedin/Liger-Kernel

zheliuyu · 2026-04-03T09:28:02Z

Motivation

This pr ports fused_moe.py and fused_moe_kernels.py to an NPU-affine implementation while preserving the original math. The computational definition is unchanged: forward remains W1 (gate/up) -> SwiGLU -> W2 -> token-weighted gather, and backward still follows dA' = dO @ W2^T to produce d_pre_act / dS / dW2 / dX / dW1.
The main changes are execution-strategy optimizations for NPU.

Note: Use the Skill

For this fused_moe kernel migration, we followed the skill document from #1197.

Testing Done

Hardware Type: Ascend 910B2
run make test to ensure correctness
run make checkstyle to ensure code style
run make test-convergence to ensure convergence

🤖 Generated with: cursor.

zheliuyu · 2026-04-21T06:33:24Z

Test

We also added all shapes from benchmark_fused_moe.py to test_fused_moe.py to validate kernel generalization more broadly.

Extra code

# Benchmark num_tokens sweep in benchmark/scripts/benchmark_fused_moe.py.
BENCHMARK_T_VALUES = [128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]

@pytest.mark.parametrize("T", BENCHMARK_T_VALUES)
def test_benchmark_token_shapes_forward_smoke_bfloat16(T):
    """Smoke-test benchmark token sweep shapes on fused_moe forward path."""
    dtype = torch.bfloat16
    # Keep dimensions modest so CI runtime remains bounded while covering T sweep.
    E, H, intermediate_dim, K = 16, 256, 128, 8
    x, gate_up_proj, down_proj, top_k_index, top_k_weights = _make_inputs(T, E, H, intermediate_dim, K, dtype, device)
    out = LigerFusedMoEFunction.apply(x, gate_up_proj, down_proj, top_k_index, top_k_weights)
    assert out.shape == (T, H)
    assert torch.isfinite(out).all()


@pytest.mark.parametrize("T", BENCHMARK_T_VALUES)
def test_benchmark_token_shapes_forward_backward_correctness_float32(T):
    """Validate forward/backward numerical correctness on benchmark token shapes."""
    dtype = torch.float32
    # Keep dimensions small enough for full-shape gradient checks to finish quickly.
    E, H, intermediate_dim, K = 8, 64, 32, 2

    x, gate_up_proj, down_proj, top_k_index, top_k_weights = _make_inputs(T, E, H, intermediate_dim, K, dtype, device)

    ref = _reference_moe_forward(x, gate_up_proj, down_proj, top_k_index, top_k_weights)
    out = LigerFusedMoEFunction.apply(x, gate_up_proj, down_proj, top_k_index, top_k_weights)
    torch.testing.assert_close(out, ref, atol=1e-3, rtol=1e-4)

    x1 = x.clone().requires_grad_(True)
    gup1 = gate_up_proj.clone().requires_grad_(True)
    dn1 = down_proj.clone().requires_grad_(True)
    wts1 = top_k_weights.clone().requires_grad_(True)
    x2 = x.clone().requires_grad_(True)
    gup2 = gate_up_proj.clone().requires_grad_(True)
    dn2 = down_proj.clone().requires_grad_(True)
    wts2 = top_k_weights.clone().requires_grad_(True)

    out_ref = _reference_moe_forward(x1, gup1, dn1, top_k_index, wts1)
    out_ref.sum().backward()
    out_fused = LigerFusedMoEFunction.apply(x2, gup2, dn2, top_k_index, wts2)
    out_fused.sum().backward()

    b_atol, b_rtol = 3e-3, 1e-2
    torch.testing.assert_close(wts2.grad, wts1.grad, atol=b_atol, rtol=b_rtol)
    torch.testing.assert_close(dn2.grad, dn1.grad, atol=b_atol, rtol=b_rtol)
    torch.testing.assert_close(x2.grad, x1.grad, atol=b_atol, rtol=b_rtol)
    torch.testing.assert_close(gup2.grad, gup1.grad, atol=b_atol, rtol=b_rtol)

Pytest results

==================================================================================== test session starts ====================================================================================
platform linux -- Python 3.10.8, pytest-9.0.2, pluggy-1.6.0
rootdir: /root/Liger-Kernel-dev
configfile: pyproject.toml
plugins: xdist-3.8.0, cov-7.0.0, rerunfailures-16.1, asyncio-1.3.0, anyio-4.9.0
asyncio: mode=auto, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
collected 37 items                                                                                                                                                                          

test/transformers/test_fused_moe.py::test_routing_metadata_invariants[64-8-2] PASSED                                                                                                  [  2%]
test/transformers/test_fused_moe.py::test_routing_metadata_invariants[128-16-4] PASSED                                                                                                [  5%]
test/transformers/test_fused_moe.py::test_routing_metadata_invariants[100-8-2] PASSED                                                                                                 [  8%]
test/transformers/test_fused_moe.py::test_routing_metadata_invariants[256-32-8] PASSED                                                                                                [ 10%]
test/transformers/test_fused_moe.py::test_correctness[dtype0-0.001-0.0001-7-4-64-32-2] PASSED                                                                                         [ 13%]
test/transformers/test_fused_moe.py::test_correctness[dtype0-0.001-0.0001-512-8-256-128-2] PASSED                                                                                     [ 16%]
test/transformers/test_fused_moe.py::test_correctness[dtype0-0.001-0.0001-512-8-97-47-2] PASSED                                                                                       [ 18%]
test/transformers/test_fused_moe.py::test_correctness[dtype0-0.001-0.0001-512-7-128-64-3] PASSED                                                                                      [ 21%]
test/transformers/test_fused_moe.py::test_correctness[dtype0-0.001-0.0001-512-8-256-64-1] PASSED                                                                                      [ 24%]
test/transformers/test_fused_moe.py::test_correctness[dtype0-0.001-0.0001-128-8-256-64-8] PASSED                                                                                      [ 27%]
test/transformers/test_fused_moe.py::test_correctness[dtype1-0.1-0.01-7-4-64-32-2] PASSED                                                                                             [ 29%]
test/transformers/test_fused_moe.py::test_correctness[dtype1-0.1-0.01-512-8-256-128-2] PASSED                                                                                         [ 32%]
test/transformers/test_fused_moe.py::test_correctness[dtype1-0.1-0.01-512-8-97-47-2] PASSED                                                                                           [ 35%]
test/transformers/test_fused_moe.py::test_correctness[dtype1-0.1-0.01-512-7-128-64-3] PASSED                                                                                          [ 37%]
test/transformers/test_fused_moe.py::test_correctness[dtype1-0.1-0.01-512-8-256-64-1] PASSED                                                                                          [ 40%]
test/transformers/test_fused_moe.py::test_correctness[dtype1-0.1-0.01-128-8-256-64-8] PASSED                                                                                          [ 43%]
test/transformers/test_fused_moe.py::test_all_tokens_to_one_expert PASSED                                                                                                             [ 45%]
test/transformers/test_fused_moe.py::test_single_token PASSED                                                                                                                         [ 48%]
test/transformers/test_fused_moe.py::test_K_equals_E PASSED                                                                                                                           [ 51%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_smoke_bfloat16[128] PASSED                                                                                   [ 54%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_smoke_bfloat16[256] PASSED                                                                                   [ 56%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_smoke_bfloat16[512] PASSED                                                                                   [ 59%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_smoke_bfloat16[1024] PASSED                                                                                  [ 62%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_smoke_bfloat16[2048] PASSED                                                                                  [ 64%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_smoke_bfloat16[4096] PASSED                                                                                  [ 67%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_smoke_bfloat16[8192] PASSED                                                                                  [ 70%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_smoke_bfloat16[16384] PASSED                                                                                 [ 72%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_smoke_bfloat16[32768] PASSED                                                                                 [ 75%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_backward_correctness_float32[128] PASSED                                                                     [ 78%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_backward_correctness_float32[256] PASSED                                                                     [ 81%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_backward_correctness_float32[512] PASSED                                                                     [ 83%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_backward_correctness_float32[1024] PASSED                                                                    [ 86%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_backward_correctness_float32[2048] PASSED                                                                    [ 89%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_backward_correctness_float32[4096] PASSED                                                                    [ 91%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_backward_correctness_float32[8192] PASSED                                                                    [ 94%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_backward_correctness_float32[16384] PASSED                                                                   [ 97%]
test/transformers/test_fused_moe.py::test_benchmark_token_shapes_forward_backward_correctness_float32[32768] PASSED                                                                   [100%]

Benchmark results

Benchmark visualization

fused_moe_speed_full_token_length (ms)	fused_moe_speed_forward_token_length (ms)

fused_moe_speed_backward_token_length (ms)	fused_moe_memory_full_token_length (MB)

Raw benchmark data

BENCHMARKING SPEED for FUSED_MOE

Model: qwen3_moe_30b — E=128, H=2048, intermediate_dim=768, K=8, T_base=8192, dtype=torch.bfloat16
Pre-warming Liger autotune (H=2048, intermediate_dim=768)...
Autotune warmup complete.

**************************************
     BENCHMARKING SPEED for FUSED_MOE
**************************************

********** Benchmark Data **********
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BENCHMARKING MEMORY for FUSED_MOE

**************************************
     BENCHMARKING MEMORY for FUSED_MOE
**************************************
********** Benchmark Data **********
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zheliuyu · 2026-04-21T06:59:09Z

@Tcc0403 This PR is ready for review.

Tcc0403

LGTM, just a tiny issue

Tcc0403 · 2026-04-21T14:20:56Z

-    torch.cuda.synchronize()
+    if device == "cuda":
+        torch.cuda.synchronize()
+    elif device == "npu":
+        torch.npu.synchronize()
+


Great catch, we also have CPU support. Could you add it?

zheliuyu · 2026-04-22T03:24:31Z

+    if device == "cuda":
+        torch.cuda.synchronize()
+    elif device == "npu":
+        torch.npu.synchronize()
+    else:
+        torch.cpu.synchronize()


@Tcc0403 Thanks for the suggestion. torch provides a cpu equivalent, so I've added it here.

Sorry typo, meant to be xpu not cpu 😅

Got it. Added torch.xpu.synchronize, please take another look.

zheliuyu · 2026-04-23T10:56:51Z

+def compute_routing_metadata(topk_indices: torch.Tensor, E: int, block_m_token: int = BLOCK_M_TOKEN):
+    """Compute token→expert routing permutation metadata via 3 Triton kernels.
+
+    Also computes GPU tile metadata (tile_row_start, tile_expert) inside
+    Kernel 3 — no CPU loop, one .item() sync for num_m_tiles allocation.
+
+    Args:
+        topk_indices:  (T, K) int32 — pre-computed top-k expert indices per token
+        E:             number of experts
+        block_m_token: BLOCK_M for token-dimension tiling (default BLOCK_M_TOKEN)
+
+    Returns:
+        expert_token_count:     (E,)            int32
+        expert_start_idx:       (E+1,)          int32
+        x_gather_idx:           (TK,)           int32
+        s_scatter_idx:          (TK,)           int32
+        s_reverse_scatter_idx:  (TK,)           int32
+        tile_row_start:         (num_m_tiles,)  int32 — absolute row_start per M-tile
+        tile_expert:            (num_m_tiles,)  int32 — expert index per M-tile
+    """


Supplementary testing

Adding the Fused MoE kernel also tweaked the SwiGLU test, which currently breaks test_swiglu.py on NPU. The NPU Fused MoE kernel will fix this.

Before

After

Tcc0403

Lgtm

Mecoli1219 · 2026-04-29T18:04:15Z

-from liger_kernel.ops.fused_moe import LigerFusedMoEFunction
-from liger_kernel.ops.fused_moe import compute_routing_metadata
+from liger_kernel.ops import LigerFusedMoEFunction
+from liger_kernel.ops import compute_routing_metadata


Hi @zheliuyu . Is this change required for testing on NPU? I tend not to export compute_routing_metadata externally as there is no use case to use this function outside LigerFusedMoEFuction.

Is there any workaround like have a test-level import redirect based on the infer_device?

This is currently breaking the unit test on other devices (gpu) since we didn't export compute_routing_metadata in non-ascend backend.

@Mecoli1219 Fix: #1209

…on device type (linkedin#1209) ## Summary As Title. Fix: linkedin#1183 (comment) - Hardware Type: npu - [x] run `make test` to ensure correctness - [x] run `make checkstyle` to ensure code style - [x] run `make test-convergence` to ensure convergence

zheliuyu closed this Apr 15, 2026

zheliuyu force-pushed the main branch from 941d8ad to 1faa013 Compare April 15, 2026 01:37

zheliuyu added 2 commits April 16, 2026 17:18

support npu fused moe kernel

d70e6ef

optimize fused_moe kernel performance

7ee78a1

zheliuyu changed the title ~~[WIP] Support npu fused moe~~ [NPU] Add NPU Fused MoE kernel Apr 21, 2026

zheliuyu reopened this Apr 21, 2026

lint code

dabd601

zheliuyu force-pushed the main branch from fab9772 to dabd601 Compare April 21, 2026 06:27

Merge branch 'main' into main

494f027

zheliuyu marked this pull request as ready for review April 21, 2026 06:55

Tcc0403 reviewed Apr 21, 2026

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fix device.synchronize

5feadf4

zheliuyu commented Apr 22, 2026

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zheliuyu commented Apr 23, 2026

View reviewed changes

add torch.xpu.synchronize

e22b8b4

Tcc0403 approved these changes Apr 24, 2026

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Tcc0403 added this pull request to the merge queue Apr 24, 2026

Merged via the queue into linkedin:main with commit e4831e4 Apr 24, 2026
5 of 7 checks passed

Mecoli1219 reviewed Apr 29, 2026

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zheliuyu mentioned this pull request Apr 30, 2026

[fix] Distinguish the import path for compute_routing_metadata based on device type #1209

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[NPU] Add NPU Fused MoE kernel#1183

[NPU] Add NPU Fused MoE kernel#1183
Tcc0403 merged 6 commits intolinkedin:mainfrom
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