[LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 (vllm-project#28971)

Signed-off-by: Xin Yang <[email protected]>
Co-authored-by: Jee Jee Li <[email protected]>

Author: xyang16

tests/kernels/moe/test_modular_oai_triton_moe.py

@@ -0,0 +1,250 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Test modular OAI Triton MoE
+"""
+
+import pytest
+import torch
+
+from vllm.utils.import_utils import has_triton_kernels
+
+if not has_triton_kernels():
+    pytest.skip(
+        "triton_kernels not found, skipping all related tests",
+        allow_module_level=True,
+    )
+
+from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig
+from triton_kernels.numerics import InFlexData
+from triton_kernels.numerics_details.mxfp import downcast_to_mxfp, upcast_from_mxfp
+from triton_kernels.tensor import FP4, convert_layout, wrap_torch_tensor
+from triton_kernels.tensor_details import layout
+from triton_kernels.testing import assert_close
+
+from vllm.config import VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.fused_moe.config import mxfp4_w4a16_moe_quant_config
+from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (
+    OAITritonExperts,
+    UnfusedOAITritonExperts,
+)
+from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEModularKernel
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP,
+)
+from vllm.model_executor.layers.utils import shuffle_weight
+from vllm.platforms import current_platform
+
+MNK = [
+    (1, 512, 384),
+    (1, 2880, 2880),
+    (2, 512, 384),
+    (2, 2880, 2880),
+    (32, 2880, 2880),
+    (64, 2880, 2880),
+]
+
+
+def unshuffle_weight(w: torch.Tensor):
+    first = w[..., ::2]
+    second = w[..., 1::2]
+    return torch.concat((first, second), dim=-1)
+
+
+def make_weights(dtype, k, n, e):
+    w1 = torch.randn((e, k, 2 * n), dtype=dtype, device="cuda")
+    w1_bias = torch.randn((e, 2 * n), dtype=dtype, device="cuda")
+
+    w2 = torch.randn((e, n, k), dtype=dtype, device="cuda")
+    w2_bias = torch.randn((e, k), dtype=dtype, device="cuda")
+
+    w1_tri = w1.clone()
+    w2_tri = w2.clone()
+
+    w1_bias_tri = w1_bias.clone()
+    w2_bias_tri = w2_bias.clone()
+    w1_bias_tri = w1_bias_tri.to(torch.float32)
+    w2_bias_tri = w2_bias_tri.to(torch.float32)
+
+    # shuffle weights
+    w1_tri = shuffle_weight(w1_tri)
+    w1_bias_tri = shuffle_weight(w1_bias_tri)
+
+    # quant triton_weights
+    w1_tri, w1_scale_tri = downcast_to_mxfp(w1_tri, torch.uint8, axis=1)
+    w1 = upcast_from_mxfp(w1_tri, w1_scale_tri, dtype, axis=1)
+    w1 = unshuffle_weight(w1)
+
+    w2_tri, w2_scale_tri = downcast_to_mxfp(w2_tri, torch.uint8, axis=1)
+    w2 = upcast_from_mxfp(w2_tri, w2_scale_tri, dtype, axis=1)
+
+    num_warps = 8
+    w_layout, w_layout_opts = layout.make_default_matmul_mxfp4_w_layout(mx_axis=1)
+    w_scale_layout, w_scale_layout_opts = (
+        layout.make_default_matmul_mxfp4_w_scale_layout(mx_axis=1, num_warps=num_warps)
+    )
+
+    w1_tri = convert_layout(wrap_torch_tensor(w1_tri, FP4), w_layout, **w_layout_opts)
+    w1_scale_tri = convert_layout(
+        wrap_torch_tensor(w1_scale_tri),
+        w_scale_layout,
+        **w_scale_layout_opts,
+    )
+
+    w2_tri = convert_layout(wrap_torch_tensor(w2_tri, FP4), w_layout, **w_layout_opts)
+    w2_scale_tri = convert_layout(
+        wrap_torch_tensor(w2_scale_tri),
+        w_scale_layout,
+        **w_scale_layout_opts,
+    )
+
+    w1_precision_config = PrecisionConfig(
+        weight_scale=w1_scale_tri, flex_ctx=FlexCtx(rhs_data=InFlexData())
+    )
+    w2_precision_config = PrecisionConfig(
+        weight_scale=w2_scale_tri, flex_ctx=FlexCtx(rhs_data=InFlexData())
+    )
+
+    return (
+        w1,
+        w2,
+        w1_bias,
+        w2_bias,
+        w1_tri,
+        w2_tri,
+        w1_bias_tri,
+        w2_bias_tri,
+        w1_precision_config,
+        w2_precision_config,
+    )
+
+
+def swiglu(x, alpha: float = 1.702, limit: float = 1.0):
+    # Note we add an extra bias of 1 to the linear layer
+    x_glu, x_linear = torch.chunk(x, 2, dim=-1)
+    if limit is not None:
+        x_glu = x_glu.clamp(max=limit)
+    out_glu = x_glu * torch.sigmoid(alpha * x_glu)
+    if limit is not None:
+        x_linear = x_linear.clamp(min=-limit, max=limit)
+    return out_glu * (x_linear + 1)
+
+
+def torch_moe_impl(
+    hidden_states: torch.Tensor,  # (M, K)
+    w1: torch.Tensor,  # (E, K, 2N)
+    w2: torch.Tensor,  # (E, N, K)
+    w1_bias: torch.Tensor,  # (E, 2N)
+    w2_bias: torch.Tensor,  # (E, K)
+    topk_weights: torch.Tensor,  # (M, topk)
+    topk_ids: torch.Tensor,  # (M, topk)
+):
+    w1 = w1[topk_ids, ...]
+    w1_bias = w1_bias[topk_ids, ...]
+    hidden_states = torch.einsum("bekc,bk->bec", w1, hidden_states) + w1_bias
+    hidden_states = swiglu(hidden_states, limit=7)
+
+    w2 = w2[topk_ids, ...]
+    w2_bias = w2_bias[topk_ids, ...]
+    hidden_states = torch.einsum("bekc,bek->bec", w2, hidden_states) + w2_bias
+
+    # Weighted sum of experts
+    hidden_states = torch.einsum("bec,be->bc", hidden_states, topk_weights)
+    return hidden_states
+
+
+def oai_triton_moe_impl(
+    x: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    w1_scale: "PrecisionConfig",
+    w2_scale: "PrecisionConfig",
+    w1_bias: torch.Tensor | None,
+    w2_bias: torch.Tensor | None,
+    num_experts: int,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    unfused: bool = False,
+) -> torch.Tensor:
+    quant_config = mxfp4_w4a16_moe_quant_config(
+        w1_bias=w1_bias,
+        w2_bias=w2_bias,
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+    )
+
+    if unfused:
+        fused_experts = UnfusedOAITritonExperts(quant_config)
+    else:
+        fused_experts = OAITritonExperts(quant_config)
+
+    mk = FusedMoEModularKernel(MoEPrepareAndFinalizeNoEP(), fused_experts)
+
+    return mk.forward(
+        hidden_states=x,
+        w1=w1,
+        w2=w2,
+        topk_weights=topk_weights,
+        topk_ids=topk_ids,
+        inplace=True,
+        activation="swigluoai",
+        global_num_experts=num_experts,
+        expert_map=None,
+        apply_router_weight_on_input=False,
+    )
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda(), reason="This test is skipped on non-CUDA platform."
+)
+@pytest.mark.parametrize("dtype", [torch.bfloat16])
+@pytest.mark.parametrize("m,n,k", MNK)
+@pytest.mark.parametrize("num_experts", [32, 128])
+@pytest.mark.parametrize("topk", [4])
+@pytest.mark.parametrize("unfused", [True, False])
+def test_oai_triton_moe(
+    dtype: torch.dtype,
+    m: int,
+    n: int,
+    k: int,
+    num_experts: int,
+    topk: int,
+    unfused: bool,
+):
+    current_platform.seed_everything(0)
+    (
+        w1,
+        w2,
+        w1_bias,
+        w2_bias,
+        w1_tri,
+        w2_tri,
+        w1_bias_tri,
+        w2_bias_tri,
+        w1_precision_config,
+        w2_precision_config,
+    ) = make_weights(dtype, k, n, num_experts)
+
+    x = torch.randn((m, k), dtype=dtype, device="cuda")
+    router_logits = torch.randn(m, num_experts, device="cuda", dtype=dtype)
+    topk_weights, topk_ids = torch.topk(router_logits, k=topk, dim=-1, sorted=True)
+    topk_weights = torch.nn.functional.softmax(topk_weights, dim=-1)
+
+    with set_current_vllm_config(VllmConfig()):
+        out_ref = torch_moe_impl(x, w1, w2, w1_bias, w2_bias, topk_weights, topk_ids)
+
+        out = oai_triton_moe_impl(
+            x,
+            w1_tri,
+            w2_tri,
+            w1_precision_config,
+            w2_precision_config,
+            w1_bias_tri,
+            w2_bias_tri,
+            num_experts,
+            topk_weights,
+            topk_ids,
+            unfused,
+        )
+
+    assert_close(ref=out_ref, tri=out, maxtol=0.025, rmstol=0.005)

vllm/lora/layers/fused_moe.py

@@ -20,15 +20,24 @@
     _get_config_dtype_str,
 )
 from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
-    modular_marlin_fused_moe,
+    MarlinExperts,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import (
-    modular_triton_fused_moe,
+    TritonExperts,
     try_get_optimal_moe_config,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe_modular_method import (
     FusedMoEModularMethod,
 )
+from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (
+    UnfusedOAITritonExperts,
+)
+from vllm.model_executor.layers.fused_moe.modular_kernel import (
+    FusedMoEModularKernel,
+)
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP,
+)
 
 from .utils import _get_lora_device
 
@@ -114,15 +123,23 @@ def _inject_lora_into_fused_moe(self):
         self.base_layer.ensure_moe_quant_config_init()
         quant_config = self.base_layer.quant_method.moe_quant_config
 
-        m_fused_moe_fn = (
-            modular_triton_fused_moe(
-                quant_config, shared_experts=self.base_layer.shared_experts
+        prepare_finalize = MoEPrepareAndFinalizeNoEP()
+        m_fused_moe_fn = FusedMoEModularKernel(
+            prepare_finalize,
+            self.base_layer.quant_method.select_gemm_impl(
+                prepare_finalize, self.base_layer
+            ),
+            self.base_layer.shared_experts,
+            getattr(self.base_layer, "shared_experts_stream", None),
+        )
+        if quant_config.use_mxfp4_w4a16:
+            assert isinstance(
+                m_fused_moe_fn.fused_experts, (MarlinExperts, UnfusedOAITritonExperts)
             )
-            if not quant_config.use_mxfp4_w4a16
-            else modular_marlin_fused_moe(
-                quant_config, shared_experts=self.base_layer.shared_experts
+        else:
+            assert isinstance(
+                m_fused_moe_fn.fused_experts, (MarlinExperts, TritonExperts)
             )
-        )
 
         def fwd_decorator(layer, func):
             def wrapper(*args, **kwargs):