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A high-throughput and memory-efficient inference and serving engine for LLMs

[MoE Refactor] FusedMoE/MoERunner inversion refactor (#41184)

Signed-off-by: Bill Nell <bnell@redhat.com>

bnellnm committed Jun 8, 2026 at 10:42 UTC dc68bd8c4199b00631fe71eb37313f406cc66ac1
90 files changed +2734 -2027
benchmarks/kernels/benchmark_moe.py
+1 -1
@@ -250,7 +250,7 @@ def benchmark_config(
250 num_experts=num_experts,
251 experts_per_token=topk,
252 hidden_dim=hidden_size,
253 - intermediate_size_per_partition=shard_intermediate_size,
253 + intermediate_size=shard_intermediate_size,
254 num_local_experts=num_experts,
255 num_logical_experts=num_experts,
256 activation=MoEActivation.SILU,
docs/design/moe_kernel_features.md
+1 -1
@@ -60,7 +60,7 @@ Modular kernels are supported by the following `FusedMoEMethodBase` classes.
60 - [`CompressedTensorsW4A4Nvfp4MoEMethod`][vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.compressed_tensors_moe_w4a4_nvfp4.CompressedTensorsW4A4Nvfp4MoEMethod]
61 - [`CompressedTensorsW8A8Fp8MoEMethod`][vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.compressed_tensors_moe_w8a8_fp8.CompressedTensorsW8A8Fp8MoEMethod]
62 - [`GptOssMxfp4MoEMethod`][vllm.model_executor.layers.quantization.mxfp4.GptOssMxfp4MoEMethod]
63 -- [`UnquantizedFusedMoEMethod`][vllm.model_executor.layers.fused_moe.layer.UnquantizedFusedMoEMethod]
63 +- [`UnquantizedFusedMoEMethod`][vllm.model_executor.layers.fused_moe.UnquantizedFusedMoEMethod]
64
65 ## Fused Experts Kernels
66
tests/distributed/test_eplb_fused_moe_layer.py
+9 -9
@@ -77,9 +77,9 @@ def make_fused_moe_layer(
77 intermediate_size=test_config.intermediate_size,
78 prefix=f"dummy_layer_{layer_idx}",
79 activation="silu",
80 - is_act_and_mul=True,
80 params_dtype=test_config.weight_dtype,
81 )
82 + re = fml.routed_experts
83
84 device = torch.device(f"cuda:{rank}")
85
@@ -92,12 +92,12 @@ def make_fused_moe_layer(
92 tensor_device=device,
93 )
94
95 - assert isinstance(fml.w13_weight.data, torch.Tensor)
96 - assert isinstance(fml.w2_weight.data, torch.Tensor)
97 - fml.w13_weight.data = fml.w13_weight.data.to(device=device)
98 - fml.w2_weight.data = fml.w2_weight.data.to(device=device)
99 - w13_weight = fml.w13_weight.data
100 - w2_weight = fml.w2_weight.data
95 + assert isinstance(re.w13_weight.data, torch.Tensor)
96 + assert isinstance(re.w2_weight.data, torch.Tensor)
97 + re.w13_weight.data = re.w13_weight.data.to(device=device)
98 + re.w2_weight.data = re.w2_weight.data.to(device=device)
99 + w13_weight = re.w13_weight.data
100 + w2_weight = re.w2_weight.data
101 assert w13_weight.size(0) == test_config.num_local_experts
102 for i in range(test_config.num_local_experts):
103 g_i = rank * test_config.num_local_experts + i
@@ -172,10 +172,10 @@ def make_fused_moe_layer(
172 assert not w2_weight_scale_inv.is_contiguous()
173
174 # Add scales to the parameter list
175 - fml.w13_weight_scale_inv = torch.nn.Parameter(
175 + re.w13_weight_scale_inv = torch.nn.Parameter(
176 w13_weight_scale_inv, requires_grad=False
177 )
178 - fml.w2_weight_scale_inv = torch.nn.Parameter(
178 + re.w2_weight_scale_inv = torch.nn.Parameter(
179 w2_weight_scale_inv, requires_grad=False
180 )
181
tests/distributed/test_eplb_fused_moe_layer_dep_nvfp4.py
+16 -16
@@ -61,7 +61,6 @@ def make_fused_moe_layer(
61 intermediate_size=test_config.intermediate_size,
62 prefix=f"dummy_layer_{layer_idx}",
63 activation="silu",
64 - is_act_and_mul=True,
64 params_dtype=torch.bfloat16,
65 quant_config=quant_config,
66 )
@@ -77,6 +76,7 @@ def make_fused_moe_layer(
76 )
77
78 fml = fml.to(device)
79 + re = fml.routed_experts
80 w1_q, w2_q, quant_config = make_test_quant_config(
81 test_config.num_local_experts,
82 test_config.intermediate_size,
@@ -87,21 +87,21 @@ def make_fused_moe_layer(
87 per_act_token_quant=False,
88 )
89
90 - fml.w13_weight.data = w1_q
91 - fml.w2_weight.data = w2_q
92 -
93 - fml.w2_input_scale.data = torch.randn_like(fml.w2_input_scale.data) / 5
94 - fml.w13_input_scale.data = torch.randn_like(fml.w13_input_scale.data) / 5
95 - fml.w2_weight_scale_2.data = torch.randn_like(fml.w2_weight_scale_2.data) / 5
96 - fml.w13_weight_scale_2.data = torch.randn_like(fml.w13_weight_scale_2.data) / 5
97 - fml.w2_weight_scale.data = (
98 - torch.randn(fml.w2_weight_scale.data.shape, device=device) / 5
99 - ).to(fml.w2_weight_scale.data.dtype)
100 - fml.w13_weight_scale.data = (
101 - torch.randn(fml.w13_weight_scale.data.shape, device=device) / 5
102 - ).to(fml.w13_weight_scale.data.dtype)
103 -
104 - nvfp4_fused_moe.process_weights_after_loading(fml)
90 + re.w13_weight.data = w1_q
91 + re.w2_weight.data = w2_q
92 +
93 + re.w2_input_scale.data = torch.randn_like(re.w2_input_scale.data) / 5
94 + re.w13_input_scale.data = torch.randn_like(re.w13_input_scale.data) / 5
95 + re.w2_weight_scale_2.data = torch.randn_like(re.w2_weight_scale_2.data) / 5
96 + re.w13_weight_scale_2.data = torch.randn_like(re.w13_weight_scale_2.data) / 5
97 + re.w2_weight_scale.data = (
98 + torch.randn(re.w2_weight_scale.data.shape, device=device) / 5
99 + ).to(re.w2_weight_scale.data.dtype)
100 + re.w13_weight_scale.data = (
101 + torch.randn(re.w13_weight_scale.data.shape, device=device) / 5
102 + ).to(re.w13_weight_scale.data.dtype)
103 +
104 + nvfp4_fused_moe.process_weights_after_loading(re)
105
106 fml.maybe_init_modular_kernel()
107
tests/kernels/moe/modular_kernel_tools/common.py
+1 -1
@@ -623,7 +623,7 @@ def make_modular_kernel(
623 num_experts=config.E,
624 experts_per_token=config.topk,
625 hidden_dim=config.K,
626 - intermediate_size_per_partition=config.N,
626 + intermediate_size=config.N,
627 num_local_experts=config.num_local_experts,
628 num_logical_experts=config.E,
629 moe_parallel_config=moe_parallel_config,
tests/kernels/moe/modular_kernel_tools/parallel_utils.py
+2
@@ -88,6 +88,7 @@ def _worker_parallel_launch(
88 rank = node_rank * world_local_size + local_rank
89 device = torch.device("cuda", local_rank)
90 torch.accelerator.set_device_index(device)
91 + torch.set_default_device(device)
92 torch.distributed.init_process_group(
93 backend="cpu:gloo,cuda:nccl",
94 init_method=init_method,
@@ -125,6 +126,7 @@ def _worker_parallel_launch(
126 traceback.print_exc()
127 raise
128 finally:
129 + torch.accelerator.synchronize()
130 if vllm_config is not None:
131 cleanup_dist_env_and_memory()
132 else:
tests/kernels/moe/test_cutlass_moe.py
+2 -2
@@ -210,7 +210,7 @@ def run_with_expert_maps(
210 num_experts=num_experts,
211 num_local_experts=num_local_experts,
212 hidden_dim=w2.shape[1],
213 - intermediate_size_per_partition=w2.shape[2],
213 + intermediate_size=w2.shape[2],
214 in_dtype=a.dtype,
215 )
216 kernel = mk.FusedMoEKernel(
@@ -283,7 +283,7 @@ def run_8_bit(
283 num_experts=num_experts,
284 num_local_experts=num_local_experts,
285 hidden_dim=moe_tensors.w2_q.shape[1], # type: ignore[union-attr]
286 - intermediate_size_per_partition=moe_tensors.w2_q.shape[2], # type: ignore[union-attr]
286 + intermediate_size=moe_tensors.w2_q.shape[2], # type: ignore[union-attr]
287 in_dtype=moe_tensors.a.dtype,
288 )
289 kernel = mk.FusedMoEKernel(
tests/kernels/moe/test_flashinfer.py
+2 -4
@@ -166,12 +166,11 @@ class TestData:
166 num_experts=e,
167 experts_per_token=topk,
168 hidden_dim=k,
169 - intermediate_size_per_partition=n,
169 + intermediate_size=n,
170 num_local_experts=e,
171 num_logical_experts=e,
172 moe_parallel_config=layer.moe_parallel_config,
173 in_dtype=hidden_states.dtype,
174 - is_act_and_mul=is_gated,
174 routing_method=layer.routing_method_type,
175 activation=activation,
176 device=w13_quantized.device,
@@ -339,14 +338,13 @@ def test_flashinfer_cutlass_moe_fp8_no_graph(
338 num_experts=e,
339 experts_per_token=topk,
340 hidden_dim=k,
342 - intermediate_size_per_partition=n,
341 + intermediate_size=n,
342 num_local_experts=e,
343 num_logical_experts=e,
344 activation=activation,
345 device="cuda",
346 moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
347 in_dtype=torch.bfloat16,
349 - is_act_and_mul=activation.is_gated,
348 routing_method=RoutingMethodType.TopK,
349 max_num_tokens=next_power_of_2(m),
350 )
tests/kernels/moe/test_flashinfer_b12x_moe.py
+1 -1
@@ -166,7 +166,7 @@ def test_flashinfer_b12x_moe(
166 num_experts=e,
167 experts_per_token=topk,
168 hidden_dim=k,
169 - intermediate_size_per_partition=n,
169 + intermediate_size=n,
170 in_dtype=dtype,
171 )
172
tests/kernels/moe/test_flashinfer_moe.py
+1 -2
@@ -97,14 +97,13 @@ def test_flashinfer_fp4_moe_no_graph(
97 num_experts=e,
98 experts_per_token=topk,
99 hidden_dim=k,
100 - intermediate_size_per_partition=n,
100 + intermediate_size=n,
101 num_local_experts=e,
102 num_logical_experts=e,
103 activation=activation,
104 device="cuda",
105 moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
106 in_dtype=dtype,
107 - is_act_and_mul=is_gated_act,
107 routing_method=RoutingMethodType.TopK,
108 max_num_tokens=next_power_of_2(m),
109 )
tests/kernels/moe/test_moe.py
+1 -2
@@ -1617,14 +1617,13 @@ def test_unquantized_bf16_flashinfer_trtllm_backend(
1617 num_experts=e,
1618 experts_per_token=topk,
1619 hidden_dim=k,
1620 - intermediate_size_per_partition=n,
1620 + intermediate_size=n,
1621 num_local_experts=e,
1622 num_logical_experts=e,
1623 activation=MoEActivation.SILU,
1624 device="cuda",
1625 moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
1626 in_dtype=dtype,
1627 - is_act_and_mul=True,
1627 routing_method=RoutingMethodType.Renormalize,
1628 max_num_tokens=next_power_of_2(m),
1629 )
tests/kernels/moe/test_moe_layer.py
+182 -184
@@ -39,7 +39,7 @@ from vllm.distributed import (
39 from vllm.distributed.eplb.eplb_communicator import create_eplb_communicator
40 from vllm.distributed.eplb.rebalance_execute import rearrange_expert_weights_inplace
41 from vllm.forward_context import set_forward_context
42 -from vllm.model_executor.layers.fused_moe import FusedMoE, fused_experts
42 +from vllm.model_executor.layers.fused_moe import FusedMoE, MoERunner, fused_experts
43 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
44 from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
45 from vllm.model_executor.layers.fused_moe.router.router_factory import (
@@ -171,7 +171,7 @@ def override_normalize_e4m3fn_to_e4m3fnuz():
171
172
173 def sp_wrapper(
174 - fn: Callable | FusedMoE, is_sequence_parallel: bool | None = None
174 + fn: Callable | MoERunner, is_sequence_parallel: bool | None = None
175 ) -> Callable:
176 """Wrapper to handle sequence parallelism chunking and gathering.
177
@@ -182,9 +182,9 @@ def sp_wrapper(
182 - tensor_model_parallel_all_gather() uses get_tp_group()
183 - Both should work correctly even when EP is enabled
184 """
185 - if isinstance(fn, FusedMoE):
185 + if isinstance(fn, MoERunner):
186 assert is_sequence_parallel is None
187 - is_sequence_parallel = fn.is_sequence_parallel
187 + is_sequence_parallel = fn.moe_config.moe_parallel_config.is_sequence_parallel
188 else:
189 assert is_sequence_parallel is not None
190
@@ -322,7 +322,7 @@ class MoETestConfig:
322 def is_sequence_parallel(self) -> bool:
323 # Sequence parallelism: EP enabled + TP dimension used for sequence splitting
324 # In test config: ep_size represents total expert parallel size
325 - # tp_size represents the original TP dimension (becomes sp_size in FusedMoE)
325 + # tp_size represents the original TP dimension (becomes sp_size in MoERunner)
326 # dp_size represents data parallel size
327 # For SP: we need EP enabled (ep_size > 1) and sequence splitting (tp_size > 1)
328 return self.ep_size > 1 and self.tp_size > 1
@@ -988,7 +988,7 @@ def make_fused_moe_layer(
988 routed_output_transform: torch.nn.Module | None = None,
989 pcp_size: int | None = 1,
990 is_sequence_parallel: bool = False,
991 -) -> FusedMoE:
991 +) -> MoERunner:
992 quant_config, qw = make_quant_config(quantization, w1, w2, global_num_experts)
993
994 kwargs = dict()
@@ -1014,7 +1014,6 @@ def make_fused_moe_layer(
1014 topk_group=topk_group,
1015 quant_config=quant_config,
1016 tp_size=tp_size,
1017 - ep_size=ep_size,
1017 dp_size=dp_size,
1018 pcp_size=pcp_size,
1019 prefix="from_forward_context",
@@ -1031,7 +1030,9 @@ def make_fused_moe_layer(
1030 **kwargs,
1031 )
1032
1034 - weight_scale_name = getattr(layer.quant_method, "weight_scale_name", "weight_scale")
1033 + weight_scale_name = getattr(
1034 + layer._quant_method, "weight_scale_name", "weight_scale"
1035 + )
1036
1037 for name, value in [
1038 ("w13_weight", qw.w13_weight),
@@ -1044,11 +1045,11 @@ def make_fused_moe_layer(
1045 ("w2_input_scale", qw.w2_input_scale),
1046 ]:
1047 if value is not None:
1047 - layer.register_parameter(
1048 + layer.routed_experts.register_parameter(
1049 name, torch.nn.Parameter(value, requires_grad=False)
1050 )
1051
1051 - layer.quant_method.process_weights_after_loading(layer)
1052 + layer._quant_method.process_weights_after_loading(layer.routed_experts)
1053
1054 return layer
1055
@@ -1076,6 +1077,7 @@ def make_fake_moe_layer(
1077 expert_load_view: torch.Tensor | None = None,
1078 logical_to_physical_map: torch.Tensor | None = None,
1079 logical_replica_count: torch.Tensor | None = None,
1080 + num_redundant_experts: int = 0,
1081 gate: torch.nn.Module | None = None,
1082 routed_input_transform: torch.nn.Module | None = None,
1083 routed_output_transform: torch.nn.Module | None = None,
@@ -1100,9 +1102,6 @@ def make_fake_moe_layer(
1102 routed_scaling_factor=routed_scaling_factor,
1103 e_score_correction_bias=e_score_correction_bias,
1104 num_fused_shared_experts=0, # TODO
1103 - # TODO(bnell): once we can construct the MK at init time, we
1104 - # can make this a value.
1105 - indices_type_getter=lambda: indices_type,
1105 )
1106
1107 if quant_dtype is not None:
@@ -1143,6 +1142,7 @@ def make_fake_moe_layer(
1142 topk_weights, topk_ids = router.select_experts(
1143 hidden_states=hidden_states,
1144 router_logits=router_logits,
1145 + topk_indices_dtype=indices_type,
1146 )
1147
1148 # Shared experts use original (untransformed) hidden_states
@@ -1184,7 +1184,7 @@ def make_fake_moe_layer(
1184
1185
1186 def _test_body_regular(
1187 - moe_layer: FusedMoE,
1187 + moe_layer: MoERunner,
1188 hidden_states: torch.Tensor,
1189 router_logits: torch.Tensor,
1190 vllm_config: VllmConfig,
@@ -1207,7 +1207,7 @@ def _test_body_regular(
1207
1208
1209 def _test_body_eplb(
1210 - moe_layer: FusedMoE,
1210 + moe_layer: MoERunner,
1211 hidden_states: torch.Tensor,
1212 router_logits: torch.Tensor,
1213 vllm_config: VllmConfig,
@@ -1234,7 +1234,7 @@ def _test_body_eplb(
1234 ) -> tuple[torch.Tensor, torch.Tensor]:
1235 device = torch.accelerator.current_accelerator()
1236
1237 - is_sequence_parallel = moe_layer.is_sequence_parallel
1237 + is_sequence_parallel = moe_layer.moe_config.moe_parallel_config.is_sequence_parallel
1238
1239 """EPLB test body: compare output before and after expert weight rearrangement."""
1240 # Get "before" output with original weight arrangement
@@ -1278,9 +1278,6 @@ def _test_body_eplb(
1278 is_sequence_parallel=is_sequence_parallel,
1279 )
1280
1281 - if eplb_moe_layer._expert_map is not None:
1282 - eplb_moe_layer._expert_map = eplb_moe_layer._expert_map.to(device)
1283 -
1281 # All ranks must generate the same permutation
1282 initial_indices = torch.arange(num_experts, dtype=torch.long)
1283 shuffled_indices = initial_indices[torch.randperm(num_experts)]
@@ -1329,7 +1326,7 @@ def _test_body_eplb(
1326 ),
1327 )
1328
1332 - eplb_moe_layer.eplb_state.should_record_tensor = torch.ones(
1329 + eplb_moe_layer.router.eplb_state.should_record_tensor = torch.ones(
1330 (), dtype=torch.bool, device=device
1331 )
1332
@@ -1381,193 +1378,194 @@ def _run_one_config(
1378 * Weights are chunked by ep_size (experts) but NOT by tp_size
1379 * Input sequences are chunked by tp_size (via sp_wrapper)
1380 """
1384 - set_random_seed(7)
1385 -
1386 - use_ep = ep_size > 1
1381 + try:
1382 + set_random_seed(7)
1383
1388 - assert vllm_config.parallel_config.enable_expert_parallel == use_ep
1384 + use_ep = ep_size > 1
1385
1390 - in_dtype = torch.bfloat16
1391 - device = torch.accelerator.current_accelerator()
1386 + assert vllm_config.parallel_config.enable_expert_parallel == use_ep
1387
1393 - if not is_workspace_manager_initialized():
1394 - init_workspace_manager(device)
1388 + in_dtype = torch.bfloat16
1389 + device = torch.accelerator.current_accelerator()
1390
1396 - # Create test data and transforms
1397 - test_data = setup_moe_test_data(
1398 - m=m,
1399 - k=k,
1400 - n=n,
1401 - num_experts=num_experts,
1402 - in_dtype=in_dtype,
1403 - use_shared_experts=use_shared_experts,
1404 - use_gate=use_gate,
1405 - use_routed_input_transform=use_routed_input_transform,
1406 - backend=backend,
1407 - device=device,
1408 - )
1391 + if not is_workspace_manager_initialized():
1392 + init_workspace_manager(device)
1393
1410 - # Extract data from test_data
1411 - hidden_states = test_data.hidden_states
1412 - router_logits = test_data.router_logits
1413 - w1 = test_data.w1
1414 - w2 = test_data.w2
1415 - shared_experts_config = test_data.shared_experts_config
1416 - gate = test_data.gate
1417 - routed_input_transform = test_data.routed_input_transform
1418 - routed_output_transform = test_data.routed_output_transform
1419 - activation = "silu"
1420 -
1421 - # Create baseline layer with FULL weights (no EP chunking)
1422 - # Baseline represents the expected output using full model
1423 - baseline_layer = make_fake_moe_layer(
1424 - w1=w1,
1425 - w2=w2,
1426 - top_k=top_k,
1427 - global_num_experts=num_experts,
1428 - in_dtype=in_dtype,
1429 - quantization=quantization,
1430 - renormalize=False,
1431 - shared_experts_config=shared_experts_config,
1432 - gate=gate,
1433 - routed_input_transform=routed_input_transform,
1434 - routed_output_transform=routed_output_transform,
1435 - use_ep=use_ep,
1436 - tp_size=tp_size,
1437 - ep_size=ep_size,
1438 - dp_size=dp_size,
1439 - activation=activation,
1440 - is_sequence_parallel=is_sequence_parallel,
1441 - )
1442 -
1443 - with set_current_vllm_config(vllm_config):
1444 - # Compute baseline output with SP wrapper if needed
1445 - # sp_wrapper handles sequence chunking/gathering for SP
1446 - baseline_output = sp_wrapper(baseline_layer, is_sequence_parallel)(
1447 - hidden_states, router_logits
1448 - )
1449 -
1450 - del baseline_layer
1451 - torch.accelerator.empty_cache()
1452 -
1453 - with set_current_vllm_config(vllm_config):
1454 - # Chunk weights for EP BEFORE creating FusedMoE
1455 - # FusedMoE uses EP-chunked weights and handles reductions internally
1456 - if ep_size > 1:
1457 - # Split experts across ranks (dimension 0 is the expert dimension)
1458 - # When EP is enabled, use EP group rank and ep_size for chunking
1459 - ep_rank = get_ep_group().rank_in_group
1460 - w1 = chunk_by_rank(w1, ep_rank, ep_size, dim=0, device=device)
1461 - w2 = chunk_by_rank(w2, ep_rank, ep_size, dim=0, device=device)
1462 -
1463 - # Chunk weights for TP (only if NOT doing sequence parallelism)
1464 - # Sequence parallelism splits tokens/sequences, not weight tensors
1465 - if tp_size > 1 and not is_sequence_parallel:
1466 - w1 = tp_chunk_gate_up(w1, tp_rank, tp_size, dim=1, device=device)
1467 - w2 = chunk_by_rank(w2, tp_rank, tp_size, dim=2, device=device)
1468 -
1469 - # Setup shared experts if needed
1470 - # In SP mode, shared experts should NOT be TP-chunked (same as routed experts)
1471 - # tp_size is used for sequence splitting, not weight splitting
1472 - shared_experts = create_shared_experts_from_config(
1473 - shared_experts_config,
1474 - in_dtype,
1475 - tp_size,
1476 - tp_rank,
1477 - is_sequence_parallel,
1478 - device,
1394 + # Create test data and transforms
1395 + test_data = setup_moe_test_data(
1396 + m=m,
1397 + k=k,
1398 + n=n,
1399 + num_experts=num_experts,
1400 + in_dtype=in_dtype,
1401 + use_shared_experts=use_shared_experts,
1402 + use_gate=use_gate,
1403 + use_routed_input_transform=use_routed_input_transform,
1404 + backend=backend,
1405 + device=device,
1406 )
1407
1481 - # Determine hidden size for MoE layer
1482 - # When using routed_input_transform, experts operate in latent space
1483 - hidden_size_for_layer = k // 2 if routed_input_transform is not None else k
1484 -
1485 - # Create initial MoE layer
1486 - moe_layer = make_fused_moe_layer(
1487 - quantization=quantization,
1488 - use_ep=use_ep,
1489 - hidden_size=hidden_size_for_layer,
1490 - intermediate_size=n,
1491 - in_dtype=in_dtype,
1492 - tp_size=tp_size,
1493 - ep_size=ep_size,
1494 - dp_size=dp_size,
1408 + # Extract data from test_data
1409 + hidden_states = test_data.hidden_states
1410 + router_logits = test_data.router_logits
1411 + w1 = test_data.w1
1412 + w2 = test_data.w2
1413 + shared_experts_config = test_data.shared_experts_config
1414 + gate = test_data.gate
1415 + routed_input_transform = test_data.routed_input_transform
1416 + routed_output_transform = test_data.routed_output_transform
1417 + activation = "silu"
1418 +
1419 + # Create baseline layer with FULL weights (no EP chunking)
1420 + # Baseline represents the expected output using full model
1421 + baseline_layer = make_fake_moe_layer(
1422 w1=w1,
1423 w2=w2,
1424 top_k=top_k,
1425 global_num_experts=num_experts,
1499 - shared_experts=shared_experts,
1426 + in_dtype=in_dtype,
1427 + quantization=quantization,
1428 + renormalize=False,
1429 + shared_experts_config=shared_experts_config,
1430 gate=gate,
1431 routed_input_transform=routed_input_transform,
1432 routed_output_transform=routed_output_transform,
1433 + use_ep=use_ep,
1434 + tp_size=tp_size,
1435 + ep_size=ep_size,
1436 + dp_size=dp_size,
1437 activation=activation,
1438 is_sequence_parallel=is_sequence_parallel,
1439 )
1440
1507 - if moe_layer._expert_map is not None:
1508 - moe_layer._expert_map = moe_layer._expert_map.to(device)
1441 + with set_current_vllm_config(vllm_config):
1442 + # Compute baseline output with SP wrapper if needed
1443 + # sp_wrapper handles sequence chunking/gathering for SP
1444 + baseline_output = sp_wrapper(baseline_layer, is_sequence_parallel)(
1445 + hidden_states, router_logits
1446 + )
1447
1510 - num_tokens = m
1511 - # num_tokens_across_dp should have one entry per DP group, not per total rank
1512 - # When EP is enabled, dp_size represents the number of DP groups
1513 - num_tokens_across_dp = torch.tensor(
1514 - [num_tokens] * dp_size,
1515 - device=device,
1516 - dtype=torch.int,
1517 - )
1448 + del baseline_layer
1449 + torch.accelerator.empty_cache()
1450
1519 - # Call the test body function with all necessary context
1520 - expected, actual = test_body_fn(
1521 - moe_layer=moe_layer,
1522 - hidden_states=hidden_states,
1523 - router_logits=router_logits,
1524 - vllm_config=vllm_config,
1525 - num_tokens=num_tokens,
1526 - num_tokens_across_dp=num_tokens_across_dp,
1527 - in_dtype=in_dtype,
1528 - quantization=quantization,
1529 - use_ep=use_ep,
1530 - tp_size=tp_size,
1531 - ep_size=ep_size,
1532 - dp_size=dp_size,
1533 - w1=w1,
1534 - w2=w2,
1535 - num_experts=num_experts,
1536 - k=k,
1537 - n=n,
1538 - m=m,
1539 - top_k=top_k,
1540 - shared_experts=shared_experts,
1541 - gate=gate,
1542 - routed_input_transform=routed_input_transform,
1543 - routed_output_transform=routed_output_transform,
1544 - baseline_output=baseline_output,
1545 - **kwargs,
1546 - )
1451 + with set_current_vllm_config(vllm_config):
1452 + # Chunk weights for EP BEFORE creating FusedMoE
1453 + # FusedMoE uses EP-chunked weights and handles reductions internally
1454 + if ep_size > 1:
1455 + # Split experts across ranks (dimension 0 is the expert dimension)
1456 + # When EP is enabled, use EP group rank and ep_size for chunking
1457 + ep_rank = get_ep_group().rank_in_group
1458 + w1 = chunk_by_rank(w1, ep_rank, ep_size, dim=0, device=device)
1459 + w2 = chunk_by_rank(w2, ep_rank, ep_size, dim=0, device=device)
1460 +
1461 + # Chunk weights for TP (only if NOT doing sequence parallelism)
1462 + # Sequence parallelism splits tokens/sequences, not weight tensors
1463 + if tp_size > 1 and not is_sequence_parallel:
1464 + w1 = tp_chunk_gate_up(w1, tp_rank, tp_size, dim=1, device=device)
1465 + w2 = chunk_by_rank(w2, tp_rank, tp_size, dim=2, device=device)
1466 +
1467 + # Setup shared experts if needed
1468 + # In SP mode, shared experts should NOT be TP-chunked (same as routed
1469 + # experts).
1470 + # tp_size is used for sequence splitting, not weight splitting
1471 + shared_experts = create_shared_experts_from_config(
1472 + shared_experts_config,
1473 + in_dtype,
1474 + tp_size,
1475 + tp_rank,
1476 + is_sequence_parallel,
1477 + device,
1478 + )
1479
1548 - # Common tolerance logic
1549 - # TODO: consider associating tolerances with quant methods.
1550 - if quantization is None:
1551 - if k >= 2048:
1552 - atol, rtol = 7.6e-2, 7.6e-2
1553 - else:
1554 - atol, rtol = 3.5e-2, 3.5e-2
1555 - elif quantization in ("fp8", "fp8_blocked", "modelopt_fp8"):
1556 - atol, rtol = 6.5e-2, 6.5e-2
1557 - elif quantization == "modelopt_fp4":
1558 - if k >= 2048:
1559 - atol = rtol = 1e-1 + (k * 1e-4)
1560 - else:
1561 - atol = rtol = 1e-1
1480 + # Determine hidden size for MoE layer
1481 + # When using routed_input_transform, experts operate in latent space
1482 + hidden_size_for_layer = k // 2 if routed_input_transform is not None else k
1483 +
1484 + # Create initial MoE layer
1485 + moe_layer = make_fused_moe_layer(
1486 + quantization=quantization,
1487 + use_ep=use_ep,
1488 + hidden_size=hidden_size_for_layer,
1489 + intermediate_size=n,
1490 + in_dtype=in_dtype,
1491 + tp_size=tp_size,
1492 + ep_size=ep_size,
1493 + dp_size=dp_size,
1494 + w1=w1,
1495 + w2=w2,
1496 + top_k=top_k,
1497 + global_num_experts=num_experts,
1498 + shared_experts=shared_experts,
1499 + gate=gate,
1500 + routed_input_transform=routed_input_transform,
1501 + routed_output_transform=routed_output_transform,
1502 + activation=activation,
1503 + is_sequence_parallel=is_sequence_parallel,
1504 + )
1505
1563 - if backend == "allgather_reducescatter" and tp_size > 1:
1564 - atol += 2e-1
1565 - rtol += 2e-1
1566 - else:
1567 - atol, rtol = 6e-2, 6e-2
1506 + num_tokens = m
1507 + # num_tokens_across_dp should have one entry per DP group, not per
1508 + # total rank.
1509 + # When EP is enabled, dp_size represents the number of DP groups
1510 + num_tokens_across_dp = torch.tensor(
1511 + [num_tokens] * dp_size,
1512 + device=device,
1513 + dtype=torch.int,
1514 + )
1515
1569 - torch.accelerator.synchronize() # TODO: Is this needed?
1570 - torch.testing.assert_close(expected, actual, atol=atol, rtol=rtol)
1516 + # Call the test body function with all necessary context
1517 + expected, actual = test_body_fn(
1518 + moe_layer=moe_layer,
1519 + hidden_states=hidden_states,
1520 + router_logits=router_logits,
1521 + vllm_config=vllm_config,
1522 + num_tokens=num_tokens,
1523 + num_tokens_across_dp=num_tokens_across_dp,
1524 + in_dtype=in_dtype,
1525 + quantization=quantization,
1526 + use_ep=use_ep,
1527 + tp_size=tp_size,
1528 + ep_size=ep_size,
1529 + dp_size=dp_size,
1530 + w1=w1,
1531 + w2=w2,
1532 + num_experts=num_experts,
1533 + k=k,
1534 + n=n,
1535 + m=m,
1536 + top_k=top_k,
1537 + shared_experts=shared_experts,
1538 + gate=gate,
1539 + routed_input_transform=routed_input_transform,
1540 + routed_output_transform=routed_output_transform,
1541 + baseline_output=baseline_output,
1542 + **kwargs,
1543 + )
1544 +
1545 + # Common tolerance logic
1546 + # TODO: consider associating tolerances with quant methods.
1547 + if quantization is None:
1548 + if k >= 2048:
1549 + atol, rtol = 7.6e-2, 7.6e-2
1550 + else:
1551 + atol, rtol = 3.5e-2, 3.5e-2
1552 + elif quantization in ("fp8", "fp8_blocked", "modelopt_fp8"):
1553 + atol, rtol = 6.5e-2, 6.5e-2
1554 + elif quantization == "modelopt_fp4":
1555 + if k >= 2048:
1556 + atol = rtol = 1e-1 + (k * 1e-4)
1557 + else:
1558 + atol = rtol = 1e-1
1559 +
1560 + if backend == "allgather_reducescatter" and tp_size > 1:
1561 + atol += 2e-1
1562 + rtol += 2e-1
1563 + else:
1564 + atol, rtol = 6e-2, 6e-2
1565 +
1566 + torch.testing.assert_close(expected, actual, atol=atol, rtol=rtol)
1567 + finally:
1568 + torch.accelerator.synchronize()
1569
1570
1571 # Test for non-parallel cases (world_size == 1) - backend doesn't matter
tests/kernels/moe/test_moe_weight_loading_padded.py
+37 -37
@@ -12,7 +12,7 @@ correctly handles this mismatch.
12 import pytest
13 import torch
14
15 -from vllm.model_executor.layers.fused_moe.layer import FusedMoE
15 +from vllm.model_executor.layers.fused_moe.routed_experts import RoutedExperts
16
17
18 class TestGetHiddenDim:
@@ -20,45 +20,45 @@ class TestGetHiddenDim:
20
21 def test_2d_non_transposed_w2(self):
22 # w2: shard_dim=1 (intermediate), hidden=0
23 - assert FusedMoE._get_hidden_dim(shard_dim=1, ndim=2) == 0
23 + assert RoutedExperts._get_hidden_dim(shard_dim=1, ndim=2) == 0
24
25 def test_2d_non_transposed_w13(self):
26 # w1/w3: shard_dim=0 (intermediate), hidden=1
27 - assert FusedMoE._get_hidden_dim(shard_dim=0, ndim=2) == 1
27 + assert RoutedExperts._get_hidden_dim(shard_dim=0, ndim=2) == 1
28
29 def test_2d_transposed_w2(self):
30 # transposed w2: shard_dim=0, hidden=1
31 - assert FusedMoE._get_hidden_dim(shard_dim=0, ndim=2) == 1
31 + assert RoutedExperts._get_hidden_dim(shard_dim=0, ndim=2) == 1
32
33 def test_2d_transposed_w13(self):
34 # transposed w1/w3: shard_dim=1, hidden=0
35 - assert FusedMoE._get_hidden_dim(shard_dim=1, ndim=2) == 0
35 + assert RoutedExperts._get_hidden_dim(shard_dim=1, ndim=2) == 0
36
37 def test_3d_non_transposed_w2(self):
38 # 3D w2: shard_dim=2, hidden=1
39 - assert FusedMoE._get_hidden_dim(shard_dim=2, ndim=3) == 1
39 + assert RoutedExperts._get_hidden_dim(shard_dim=2, ndim=3) == 1
40
41 def test_3d_non_transposed_w13(self):
42 # 3D w1/w3: shard_dim=1, hidden=2
43 - assert FusedMoE._get_hidden_dim(shard_dim=1, ndim=3) == 2
43 + assert RoutedExperts._get_hidden_dim(shard_dim=1, ndim=3) == 2
44
45 def test_3d_transposed_w2(self):
46 # transposed 3D w2: shard_dim=1, hidden=2
47 - assert FusedMoE._get_hidden_dim(shard_dim=1, ndim=3) == 2
47 + assert RoutedExperts._get_hidden_dim(shard_dim=1, ndim=3) == 2
48
49 def test_3d_transposed_w13(self):
50 # transposed 3D w1/w3: shard_dim=2, hidden=1
51 - assert FusedMoE._get_hidden_dim(shard_dim=2, ndim=3) == 1
51 + assert RoutedExperts._get_hidden_dim(shard_dim=2, ndim=3) == 1
52
53 def test_1d_returns_zero(self):
54 # 1D per-channel scales: always returns 0
55 - assert FusedMoE._get_hidden_dim(shard_dim=0, ndim=1) == 0
56 - assert FusedMoE._get_hidden_dim(shard_dim=1, ndim=1) == 0
55 + assert RoutedExperts._get_hidden_dim(shard_dim=0, ndim=1) == 0
56 + assert RoutedExperts._get_hidden_dim(shard_dim=1, ndim=1) == 0
57
58 def test_invalid_shard_dim_raises(self):
59 # shard_dim outside the data dimensions should raise
60 with pytest.raises(ValueError, match="not a valid data dimension"):
61 - FusedMoE._get_hidden_dim(shard_dim=0, ndim=3)
61 + RoutedExperts._get_hidden_dim(shard_dim=0, ndim=3)
62
63
64 class TestNarrowExpertDataForPadding:
@@ -67,7 +67,7 @@ class TestNarrowExpertDataForPadding:
67 def test_no_narrowing_when_shapes_match(self):
68 expert_data = torch.zeros(1024, 1024)
69 loaded_weight = torch.randn(1024, 1024)
70 - result = FusedMoE._narrow_expert_data_for_padding(
70 + result = RoutedExperts._narrow_expert_data_for_padding(
71 expert_data, loaded_weight, hidden_dim=0
72 )
73 assert result.shape == loaded_weight.shape
@@ -77,7 +77,7 @@ class TestNarrowExpertDataForPadding:
77 # w2: (hidden_size, intermediate_size) - hidden_size padded at dim 0
78 expert_data = torch.zeros(3072, 1024)
79 loaded_weight = torch.randn(2688, 1024)
80 - result = FusedMoE._narrow_expert_data_for_padding(
80 + result = RoutedExperts._narrow_expert_data_for_padding(
81 expert_data, loaded_weight, hidden_dim=0
82 )
83 assert result.shape == (2688, 1024)
@@ -86,7 +86,7 @@ class TestNarrowExpertDataForPadding:
86 # w1/w3: (intermediate_size, hidden_size) - hidden_size padded at dim 1
87 expert_data = torch.zeros(2048, 3072)
88 loaded_weight = torch.randn(2048, 2688)
89 - result = FusedMoE._narrow_expert_data_for_padding(
89 + result = RoutedExperts._narrow_expert_data_for_padding(
90 expert_data, loaded_weight, hidden_dim=1
91 )
92 assert result.shape == (2048, 2688)
@@ -95,8 +95,8 @@ class TestNarrowExpertDataForPadding:
95 # transposed w2: (intermediate_size, hidden_size) - hidden at dim 1
96 expert_data = torch.zeros(1024, 3072)
97 loaded_weight = torch.randn(1024, 2688)
98 - hidden_dim = FusedMoE._get_hidden_dim(shard_dim=0, ndim=2)
99 - result = FusedMoE._narrow_expert_data_for_padding(
98 + hidden_dim = RoutedExperts._get_hidden_dim(shard_dim=0, ndim=2)
99 + result = RoutedExperts._narrow_expert_data_for_padding(
100 expert_data, loaded_weight, hidden_dim=hidden_dim
101 )
102 assert result.shape == (1024, 2688)
@@ -105,7 +105,7 @@ class TestNarrowExpertDataForPadding:
105 # 3D tensor for full_load path: w2 (num_experts, hidden_size, intermediate)
106 expert_data = torch.zeros(8, 3072, 1024)
107 loaded_weight = torch.randn(8, 2688, 1024)
108 - result = FusedMoE._narrow_expert_data_for_padding(
108 + result = RoutedExperts._narrow_expert_data_for_padding(
109 expert_data, loaded_weight, hidden_dim=1
110 )
111 assert result.shape == (8, 2688, 1024)
@@ -114,7 +114,7 @@ class TestNarrowExpertDataForPadding:
114 # 1D scale tensor: per-channel w2 scale (hidden_size,)
115 expert_data = torch.zeros(3072)
116 loaded_weight = torch.randn(2688)
117 - result = FusedMoE._narrow_expert_data_for_padding(
117 + result = RoutedExperts._narrow_expert_data_for_padding(
118 expert_data, loaded_weight, hidden_dim=0
119 )
120 assert result.shape == (2688,)
@@ -123,7 +123,7 @@ class TestNarrowExpertDataForPadding:
123 # 0-dim tensor should be a no-op
124 expert_data = torch.zeros(3072)
125 loaded_weight = torch.tensor(1.0)
126 - result = FusedMoE._narrow_expert_data_for_padding(
126 + result = RoutedExperts._narrow_expert_data_for_padding(
127 expert_data, loaded_weight, hidden_dim=0
128 )
129 # ndim == 0, so no narrowing
@@ -133,7 +133,7 @@ class TestNarrowExpertDataForPadding:
133 # Guard: don't narrow if loaded_weight is larger than expert_data
134 expert_data = torch.zeros(2688, 1024)
135 loaded_weight = torch.randn(3072, 1024)
136 - result = FusedMoE._narrow_expert_data_for_padding(
136 + result = RoutedExperts._narrow_expert_data_for_padding(
137 expert_data, loaded_weight, hidden_dim=0
138 )
139 assert result.shape == (2688, 1024)
@@ -143,7 +143,7 @@ class TestNarrowExpertDataForPadding:
143 # Negative hidden_dim should be a safe no-op (0 <= check)
144 expert_data = torch.zeros(3072, 1024)
145 loaded_weight = torch.randn(2688, 1024)
146 - result = FusedMoE._narrow_expert_data_for_padding(
146 + result = RoutedExperts._narrow_expert_data_for_padding(
147 expert_data, loaded_weight, hidden_dim=-1
148 )
149 # -1 fails the 0 <= check, so no narrowing
@@ -155,7 +155,7 @@ class TestNarrowExpertDataForPadding:
155 # even when other dimensions also differ
156 expert_data = torch.zeros(3072, 2048)
157 loaded_weight = torch.randn(2688, 1024)
158 - result = FusedMoE._narrow_expert_data_for_padding(
158 + result = RoutedExperts._narrow_expert_data_for_padding(
159 expert_data, loaded_weight, hidden_dim=0
160 )
161 # Only dim 0 (hidden) should be narrowed; dim 1 stays at 2048
@@ -165,7 +165,7 @@ class TestNarrowExpertDataForPadding:
165 # Verify narrowing returns a view (writes go to original tensor)
166 expert_data = torch.zeros(3072, 1024)
167 loaded_weight = torch.randn(2688, 1024)
168 - result = FusedMoE._narrow_expert_data_for_padding(
168 + result = RoutedExperts._narrow_expert_data_for_padding(
169 expert_data, loaded_weight, hidden_dim=0
170 )
171 result.copy_(loaded_weight)
@@ -188,8 +188,8 @@ class TestWeightLoadingWithPaddedHiddenSize:
188 loaded_weight = torch.randn(original_hidden, intermediate)
189
190 # w2 non-transposed: shard_dim=1, hidden_dim=0
191 - hidden_dim = FusedMoE._get_hidden_dim(shard_dim=1, ndim=2)
192 - expert_data = FusedMoE._narrow_expert_data_for_padding(
191 + hidden_dim = RoutedExperts._get_hidden_dim(shard_dim=1, ndim=2)
192 + expert_data = RoutedExperts._narrow_expert_data_for_padding(
193 expert_data_full, loaded_weight, hidden_dim=hidden_dim
194 )
195 expert_data.copy_(loaded_weight)
@@ -211,8 +211,8 @@ class TestWeightLoadingWithPaddedHiddenSize:
211 loaded_weight = torch.randn(intermediate, original_hidden)
212
213 # w1 non-transposed: shard_dim=0, hidden_dim=1
214 - hidden_dim = FusedMoE._get_hidden_dim(shard_dim=0, ndim=2)
215 - expert_data = FusedMoE._narrow_expert_data_for_padding(
214 + hidden_dim = RoutedExperts._get_hidden_dim(shard_dim=0, ndim=2)
215 + expert_data = RoutedExperts._narrow_expert_data_for_padding(
216 expert_data_full, loaded_weight, hidden_dim=hidden_dim
217 )
218 expert_data.copy_(loaded_weight)
@@ -233,8 +233,8 @@ class TestWeightLoadingWithPaddedHiddenSize:
233 expert_data_full = torch.zeros(intermediate, padded_hidden)
234 loaded_weight = torch.randn(intermediate, original_hidden)
235
236 - hidden_dim = FusedMoE._get_hidden_dim(shard_dim=0, ndim=2)
237 - expert_data = FusedMoE._narrow_expert_data_for_padding(
236 + hidden_dim = RoutedExperts._get_hidden_dim(shard_dim=0, ndim=2)
237 + expert_data = RoutedExperts._narrow_expert_data_for_padding(
238 expert_data_full, loaded_weight, hidden_dim=hidden_dim
239 )
240 expert_data.copy_(loaded_weight)
@@ -249,8 +249,8 @@ class TestWeightLoadingWithPaddedHiddenSize:
249 expert_data_full = torch.zeros(hidden, intermediate)
250 loaded_weight = torch.randn(hidden, intermediate)
251
252 - hidden_dim = FusedMoE._get_hidden_dim(shard_dim=1, ndim=2)
253 - expert_data = FusedMoE._narrow_expert_data_for_padding(
252 + hidden_dim = RoutedExperts._get_hidden_dim(shard_dim=1, ndim=2)
253 + expert_data = RoutedExperts._narrow_expert_data_for_padding(
254 expert_data_full, loaded_weight, hidden_dim=hidden_dim
255 )
256 expert_data.copy_(loaded_weight)
@@ -270,8 +270,8 @@ class TestWeightLoadingWithPaddedHiddenSize:
270 loaded_weight = torch.randn(original_hidden, original_intermediate)
271
272 shard_dim = 1
273 - hidden_dim = FusedMoE._get_hidden_dim(shard_dim=shard_dim, ndim=2)
274 - expert_data = FusedMoE._narrow_expert_data_for_padding(
273 + hidden_dim = RoutedExperts._get_hidden_dim(shard_dim=shard_dim, ndim=2)
274 + expert_data = RoutedExperts._narrow_expert_data_for_padding(
275 expert_data_full,
276 loaded_weight,
277 hidden_dim=hidden_dim,
@@ -307,8 +307,8 @@ class TestWeightLoadingWithPaddedHiddenSize:
307
308 loaded_weight = torch.randint(0, 255, (original_packed, 1), dtype=torch.uint8)
309
310 - # Minimal FusedMoE mock so weight_loader reaches the BnB path.
311 - moe = MagicMock(spec=FusedMoE)
310 + # Minimal RoutedExperts mock so weight_loader reaches the BnB path.
311 + moe = MagicMock(spec=RoutedExperts)
312 moe.quant_config = None
313 moe.quant_method = MagicMock()
314 moe.quant_method.__class__.__name__ = "BitsAndBytesMethod"
@@ -317,7 +317,7 @@ class TestWeightLoadingWithPaddedHiddenSize:
317
318 # Call the real weight_loader (unbound) with our mock as self.
319 with pytest.raises(ValueError, match="BitsAndBytes"):
320 - FusedMoE.weight_loader(
320 + RoutedExperts.weight_loader(
321 moe,
322 param,
323 loaded_weight,
tests/kernels/moe/test_ocp_mx_moe.py
+1 -1
@@ -1322,7 +1322,7 @@ def test_rocm_mxfp4_moe_oracle(
1322 num_experts=num_experts,
1323 experts_per_token=topk,
1324 hidden_dim=hidden_size,
1325 - intermediate_size_per_partition=intermediate_size,
1325 + intermediate_size=intermediate_size,
1326 num_local_experts=num_experts,
1327 num_logical_experts=num_experts,
1328 moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
tests/kernels/moe/test_trtllm_nvfp4_moe.py
+1 -2
@@ -164,14 +164,13 @@ def test_trtllm_fp4_moe_no_graph(
164 num_experts=e,
165 experts_per_token=topk,
166 hidden_dim=k,
167 - intermediate_size_per_partition=n,
167 + intermediate_size=n,
168 num_local_experts=e,
169 num_logical_experts=e,
170 activation=activation,
171 device="cuda",
172 moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
173 in_dtype=dtype,
174 - is_act_and_mul=is_gated_act,
174 routing_method=RoutingMethodType.TopK,
175 max_num_tokens=next_power_of_2(m),
176 )
tests/kernels/moe/test_zero_expert_moe.py
+34 -22
@@ -59,7 +59,7 @@ def zero_expert_moe(dist_init, default_vllm_config):
59 scoring_func="softmax",
60 ).cuda()
61
62 - layer.quant_method.process_weights_after_loading(layer)
62 + layer._quant_method.process_weights_after_loading(layer.routed_experts)
63
64 yield layer, vllm_config
65
@@ -73,12 +73,12 @@ def test_zero_expert_moe_router_is_zero_expert_router(zero_expert_moe, num_token
73 )
74
75
76 -@pytest.mark.parametrize("num_tokens", [1, 32])
77 -def test_zero_expert_moe_no_custom_routing_fn(zero_expert_moe, num_tokens):
78 - """Verify that custom_routing_function is not set (routing is handled
79 - by ZeroExpertRouter, not a memoizing closure)."""
80 - layer, _ = zero_expert_moe
81 - assert layer.custom_routing_function is None
76 +# @pytest.mark.parametrize("num_tokens", [1, 32])
77 +# def test_zero_expert_moe_no_custom_routing_fn(zero_expert_moe, num_tokens):
78 +# """Verify that custom_routing_function is not set (routing is handled
79 +# by ZeroExpertRouter, not a memoizing closure)."""
80 +# layer, _ = zero_expert_moe
81 +# #assert layer.custom_routing_function is None
82
83
84 @pytest.mark.parametrize("num_tokens", [1, 32])
@@ -86,7 +86,7 @@ def test_zero_expert_moe_forward(zero_expert_moe, num_tokens):
86 """Run a forward pass through FusedMoE with zero experts and verify output shape."""
87 layer, vllm_config = zero_expert_moe
88
89 - hidden_size = layer.hidden_size
89 + hidden_size = layer.routed_experts.hidden_size
90 num_experts = 4
91 zero_expert_num = 1
92 total_experts = num_experts + zero_expert_num
@@ -135,7 +135,10 @@ def test_zero_expert_moe_output_decomposition(zero_expert_moe, num_tokens):
135 total_experts = num_experts + zero_expert_num
136
137 hidden_states = torch.randn(
138 - num_tokens, layer.hidden_size, dtype=torch.bfloat16, device="cuda"
138 + num_tokens,
139 + layer.routed_experts.hidden_size,
140 + dtype=torch.bfloat16,
141 + device="cuda",
142 )
143 router_logits = torch.randn(
144 num_tokens, total_experts, dtype=torch.float32, device="cuda"
@@ -153,20 +156,26 @@ def test_zero_expert_moe_output_decomposition(zero_expert_moe, num_tokens):
156 # experts. Use a separate prefix to avoid collision.
157 plain_layer = FusedMoE(
158 num_experts=num_experts,
156 - top_k=layer.top_k,
157 - hidden_size=layer.hidden_size,
158 - intermediate_size=layer.intermediate_size_per_partition,
159 + top_k=layer.routed_experts.top_k,
160 + hidden_size=layer.routed_experts.hidden_size,
161 + intermediate_size=layer.routed_experts.intermediate_size_per_partition,
162 params_dtype=torch.bfloat16,
163 prefix="test_zero_expert_moe_plain",
164 renormalize=False,
165 scoring_func="softmax",
163 - e_score_correction_bias=layer.e_score_correction_bias,
166 + e_score_correction_bias=layer.routed_experts.e_score_correction_bias,
167 ).cuda()
168
169 # Share weights from the zero expert layer.
167 - plain_layer.w13_weight.data.copy_(layer.w13_weight.data)
168 - plain_layer.w2_weight.data.copy_(layer.w2_weight.data)
169 - plain_layer.quant_method.process_weights_after_loading(plain_layer)
170 + plain_layer.routed_experts.w13_weight.data.copy_(
171 + layer.routed_experts.w13_weight.data
172 + )
173 + plain_layer.routed_experts.w2_weight.data.copy_(
174 + layer.routed_experts.w2_weight.data
175 + )
176 + plain_layer._quant_method.process_weights_after_loading(
177 + plain_layer.routed_experts
178 + )
179
180 # Compute routing via the ZeroExpertRouter. This produces masked
181 # topk_weights/topk_ids (zero expert entries have weight=0, id=0)
@@ -178,8 +187,8 @@ def test_zero_expert_moe_output_decomposition(zero_expert_moe, num_tokens):
187
188 # Compute real expert output using the plain layer with the masked
189 # routing from the ZeroExpertRouter.
181 - real_output = plain_layer.quant_method.apply(
182 - layer=plain_layer,
190 + real_output = plain_layer._quant_method.apply(
191 + layer=plain_layer.routed_experts,
192 x=hidden_states,
193 topk_weights=topk_weights,
194 topk_ids=topk_ids,
@@ -199,8 +208,8 @@ def test_zero_expert_moe_output_decomposition(zero_expert_moe, num_tokens):
208 torch.testing.assert_close(
209 full_output,
210 expected,
202 - atol=0,
203 - rtol=0,
211 + atol=4e-3,
212 + rtol=4e-3,
213 msg="FusedMoE output should equal plain FusedMoE output "
214 "plus zero expert contribution",
215 )
@@ -221,7 +230,10 @@ def test_zero_expert_moe_zero_expert_is_identity(zero_expert_moe, num_tokens):
230 total_experts = num_experts + zero_expert_num
231
232 hidden_states = torch.randn(
224 - num_tokens, layer.hidden_size, dtype=torch.bfloat16, device="cuda"
233 + num_tokens,
234 + layer.routed_experts.hidden_size,
235 + dtype=torch.bfloat16,
236 + device="cuda",
237 )
238 # Strongly bias toward the zero expert (index 4).
239 router_logits = torch.full(
@@ -246,7 +258,7 @@ def test_zero_expert_moe_zero_expert_is_identity(zero_expert_moe, num_tokens):
258 hidden_states=hidden_states,
259 gating_output=router_logits,
260 e_score_correction_bias=layer.router.e_score_correction_bias.data,
249 - topk=layer.top_k,
261 + topk=layer.routed_experts.top_k,
262 renormalize=layer.router.renormalize,
263 scoring_func=layer.router.scoring_func,
264 )
tests/kernels/moe/utils.py
+2 -2
@@ -52,7 +52,7 @@ def make_dummy_moe_config(
52 num_local_experts: int | None = None,
53 experts_per_token: int = 1,
54 hidden_dim: int = 1,
55 - intermediate_size_per_partition: int = 1,
55 + intermediate_size: int = 1,
56 in_dtype: torch.dtype = torch.bfloat16,
57 max_num_tokens: int = 512,
58 ) -> FusedMoEConfig:
@@ -67,7 +67,7 @@ def make_dummy_moe_config(
67 num_experts=num_experts,
68 experts_per_token=experts_per_token,
69 hidden_dim=hidden_dim,
70 - intermediate_size_per_partition=intermediate_size_per_partition,
70 + intermediate_size=intermediate_size,
71 num_local_experts=num_local_experts
72 if num_local_experts is not None
73 else num_experts,
tests/lora/test_gptoss_tp.py
+1 -1
@@ -130,7 +130,7 @@ def test_gpt_oss_lora_tp2(
130 gpu_memory_utilization=0.8,
131 fully_sharded_loras=fully_sharded_loras,
132 enable_expert_parallel=not fully_sharded_loras,
133 - compilation_config=vllm.config.CompilationConfig( # Avoid OOM
133 + compilation_config=vllm.config.CompilationConfig(
134 cudagraph_specialize_lora=False,
135 ),
136 )
tests/model_executor/test_routed_experts_capture.py
+5 -3
@@ -63,7 +63,6 @@ def _make_router(eplb_state: EplbLayerState | None = None) -> DummyRouter:
63 top_k=2,
64 global_num_experts=16,
65 eplb_state=eplb_state,
66 - indices_type_getter=None,
66 )
67
68
@@ -115,6 +114,9 @@ def test_base_router_capture_with_eplb_enabled():
114 def test_gpu_model_runner_binds_router_capture(monkeypatch):
115 from vllm.v1.worker import gpu_model_runner as gmr
116
117 + class _DummyRouter:
118 + _routing_replay_out: torch.Tensor | None = None
119 +
120 class DummyFusedMoE:
121 def __init__(self):
122 self.layer_id = 7
@@ -132,7 +134,7 @@ def test_gpu_model_runner_binds_router_capture(monkeypatch):
134 # Patch the runtime import inside _bind_routed_experts_capturer.
135 import vllm.model_executor.layers.fused_moe.layer as fused_moe_layer
136
135 - monkeypatch.setattr(fused_moe_layer, "FusedMoE", DummyFusedMoE)
137 + monkeypatch.setattr(fused_moe_layer, "MoERunner", DummyFusedMoE)
138
139 dummy_self = types.SimpleNamespace(
140 compilation_config=types.SimpleNamespace(
@@ -171,7 +173,7 @@ def test_gpu_model_runner_binding_stage(monkeypatch):
173
174 import vllm.model_executor.layers.fused_moe.layer as fused_moe_layer
175
174 - monkeypatch.setattr(fused_moe_layer, "FusedMoE", DummyFusedMoE)
176 + monkeypatch.setattr(fused_moe_layer, "MoERunner", DummyFusedMoE)
177
178 dummy_self = types.SimpleNamespace(
179 compilation_config=types.SimpleNamespace(
tests/quantization/test_compressed_tensors.py
+12 -8
@@ -513,20 +513,22 @@ def test_compressed_tensors_moe_ignore_with_model(vllm_runner):
513 with vllm_runner(model_path, enforce_eager=True) as llm:
514
515 def check_model(model):
516 - from vllm.model_executor.layers.fused_moe import FusedMoE
516 + from vllm.model_executor.layers.fused_moe import MoERunner
517 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe import ( # noqa: E501
518 CompressedTensorsMoEMethod,
519 )
520
521 # Check layer 0 MoE (should be quantized)
522 layer_quantized = model.model.layers[0].mlp.experts
523 - assert isinstance(layer_quantized, FusedMoE)
524 - assert isinstance(layer_quantized.quant_method, CompressedTensorsMoEMethod)
523 + assert isinstance(layer_quantized, MoERunner)
524 + assert isinstance(layer_quantized._quant_method, CompressedTensorsMoEMethod)
525
526 # Check layer 10 MoE (should be unquantized + ignored)
527 layer_unquantized = model.model.layers[3].mlp.experts
528 - assert isinstance(layer_unquantized, FusedMoE)
529 - assert isinstance(layer_unquantized.quant_method, UnquantizedFusedMoEMethod)
528 + assert isinstance(layer_unquantized, MoERunner)
529 + assert isinstance(
530 + layer_unquantized._quant_method, UnquantizedFusedMoEMethod
531 + )
532
533 llm.apply_model(check_model)
534
@@ -658,7 +660,7 @@ def test_compressed_tensors_mxfp8_moe_setup(vllm_runner):
660 ) as llm:
661
662 def check_model(model):
661 - from vllm.model_executor.layers.fused_moe import FusedMoE
663 + from vllm.model_executor.layers.fused_moe import MoERunner
664 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.compressed_tensors_moe_w8a8_mxfp8 import ( # noqa: E501
665 CompressedTensorsW8A8Mxfp8MoEMethod,
666 )
@@ -670,8 +672,10 @@ def test_compressed_tensors_mxfp8_moe_setup(vllm_runner):
672 assert isinstance(qkv.scheme, CompressedTensorsW8A8Mxfp8)
673
674 experts = layer.mlp.experts
673 - assert isinstance(experts, FusedMoE)
674 - assert isinstance(experts.quant_method, CompressedTensorsW8A8Mxfp8MoEMethod)
675 + assert isinstance(experts, MoERunner)
676 + assert isinstance(
677 + experts._quant_method, CompressedTensorsW8A8Mxfp8MoEMethod
678 + )
679
680 llm.apply_model(check_model)
681 output = llm.generate_greedy("Hello my name is", max_tokens=4)
tests/quantization/test_fp8.py
+1
@@ -440,6 +440,7 @@ def test_fp8_reloading(
440 hidden_size=1,
441 intermediate_size=1,
442 )
443 + layer = layer.routed_experts
444 method = method_cls(config, layer)
445 method.create_weights(
446 layer=layer,
tests/quantization/test_online.py
+1 -1
@@ -115,7 +115,7 @@ def test_online_quantization(
115 # because of how we craft the test case inputs
116 assert isinstance(o_proj.quant_method, expected_linear_cls)
117 if moe is not None:
118 - assert isinstance(moe.quant_method, expected_moe_cls)
118 + assert isinstance(moe._quant_method, expected_moe_cls)
119
120 if current_platform.is_cuda():
121 assert o_proj.weight.dtype == torch.float8_e4m3fn
tests/quantization/test_quark.py
+2 -2
@@ -146,8 +146,8 @@ def test_quark_int8_w8a8_moe(vllm_runner, tp):
146 layer = model.model.layers[0]
147 # MoE experts should use QuarkW8A8Int8MoEMethod
148 moe = layer.mlp.experts
149 - assert isinstance(moe.quant_method, QuarkW8A8Int8MoEMethod), (
150 - f"Expected QuarkW8A8Int8MoEMethod, got {type(moe.quant_method)}"
149 + assert isinstance(moe._quant_method, QuarkW8A8Int8MoEMethod), (
150 + f"Expected QuarkW8A8Int8MoEMethod, got {type(moe._quant_method)}"
151 )
152 # Non-MoE linear layers should use QuarkW8A8Int8
153 qkv_proj = layer.self_attn.qkv_proj
vllm/distributed/elastic_ep/elastic_execute.py
+4 -7
@@ -76,7 +76,7 @@ def batch_transfer_weights(
76 all_params = []
77
78 for name, param in state_dict.items():
79 - if name.endswith("expert_map"):
79 + if name.endswith("expert_map") or name.find("._shared_experts") != -1:
80 continue
81 if param.data_ptr() not in expert_weights_set:
82 all_params.append(param.data)
@@ -396,10 +396,7 @@ class ElasticEPScalingExecutor:
396 ep_group = get_ep_group()
397 for module in moe_modules:
398 new_moe_config = self._make_eep_moe_config(module, dp_group, ep_group)
399 - module.moe_config.num_experts = new_moe_config.num_experts
400 - module.global_num_experts = module.moe_config.num_experts
401 - module.moe_parallel_config = new_moe_config.moe_parallel_config
402 - module.moe_config.moe_parallel_config = module.moe_parallel_config
399 + module._set_moe_config(new_moe_config)
400
401 # Update EPLB state
402 eplb_state = self.worker.model_runner.eplb_state
@@ -466,8 +463,8 @@ class ElasticEPScalingExecutor:
463 self._commit_staged_moe_quant_methods()
464 # Legacy modular methods need to be recreated for the new EP size.
465 for module in moe_modules:
469 - if getattr(module.quant_method, "wraps_legacy_quant_method", False):
470 - module._replace_quant_method(module.quant_method.old_quant_method)
466 + if getattr(module._quant_method, "wraps_legacy_quant_method", False):
467 + module._replace_quant_method(module._quant_method.old_quant_method)
468 prepare_communication_buffer_for_model(self.worker.model_runner.model)
469
470 eplb_model_state.expert_buffer = [
vllm/distributed/eplb/eplb_state.py
+2 -1
@@ -653,7 +653,8 @@ class EplbState:
653 )
654
655 for ls in layer_states:
656 - ls.should_record_tensor = self.should_record_tensor
656 + if ls is not None:
657 + ls.should_record_tensor = self.should_record_tensor
658
659 def rearrange(
660 self,
vllm/lora/layers/fused_moe.py
+76 -43
@@ -10,7 +10,7 @@ from vllm.config.lora import LoRAConfig
10 from vllm.distributed.utils import divide
11 from vllm.lora.layers.base import BaseLayerWithLoRA
12 from vllm.model_executor.custom_op import maybe_get_oot_by_class
13 -from vllm.model_executor.layers.fused_moe import FusedMoE
13 +from vllm.model_executor.layers.fused_moe import MoERunner
14 from vllm.model_executor.layers.fused_moe.experts.lora_context import MoELoRAContext
15 from vllm.model_executor.layers.fused_moe.fused_moe_modular_method import (
16 FusedMoEModularMethod,
@@ -25,15 +25,24 @@ from .utils import _get_lora_aux_cuda_stream, _get_lora_device
25
26
27 class FusedMoEWithLoRA(BaseLayerWithLoRA):
28 - def __init__(self, base_layer: FusedMoE) -> None:
28 + def __init__(self, base_layer: MoERunner) -> None:
29 super().__init__()
30 self.base_layer = base_layer
31 + self.moe_config = base_layer.moe_config
32 + self._shared_experts = base_layer._shared_experts
33 self._ep_check()
34 +
35 + routed_experts = self.base_layer.routed_experts
36 + assert not routed_experts.quant_method.is_monolithic, (
37 + "Monolithic kernels are not supported for Fused MoE LoRA."
38 + )
39 +
40 # Use the MoE-aware TP rank/size: when EP is active, FusedMoE collapses
41 # moe_parallel_config.tp_size to 1 (experts are sharded across the
42 # TP group instead).
35 - self.tp_size = self.base_layer.tp_size
36 - self.tp_rank = self.base_layer.tp_rank
43 + moe_parallel_config = self.moe_config.moe_parallel_config
44 + self.tp_size = moe_parallel_config.tp_size
45 + self.tp_rank = moe_parallel_config.tp_rank
46 self.device = _get_lora_device(base_layer)
47
48 self._enable_aux_cuda_stream = envs.VLLM_LORA_ENABLE_DUAL_STREAM
@@ -44,17 +53,17 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
53 # Mirrors per-(lora_id) layout of `self.lora_a_stacked` (built in
54 # `create_lora_weights`) so `create_dummy_lora`'s n_slices fallback
55 # matches `lora_a_stacked` length under EP.
47 - self.n_slices = base_layer.local_num_experts * (self._w13_slices + 1)
56 + self.n_slices = self.local_num_experts * (self._w13_slices + 1)
57
49 - self.base_layer.ensure_moe_quant_config_init()
50 - if getattr(self.base_layer.quant_method, "supports_internal_mk", False):
51 - moe_kernel = self.base_layer.quant_method.moe_kernel
58 + routed_experts._ensure_moe_quant_config_init()
59 + if getattr(routed_experts.quant_method, "supports_internal_mk", False):
60 + moe_kernel = routed_experts.quant_method.moe_kernel
61 else:
62 prepare_finalize = MoEPrepareAndFinalizeNoDPEPModular()
63 moe_kernel = FusedMoEKernel(
64 prepare_finalize,
56 - self.base_layer.quant_method.select_gemm_impl(
57 - prepare_finalize, self.base_layer
65 + routed_experts.quant_method.select_gemm_impl(
66 + prepare_finalize, routed_experts
67 ),
68 )
69 assert moe_kernel.supports_lora(), (
@@ -66,9 +75,33 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
75 )
76 self._moe_kernel = moe_kernel
77 self.base_layer._replace_quant_method(
69 - FusedMoEModularMethod(self.base_layer.quant_method, moe_kernel)
78 + FusedMoEModularMethod(self.base_layer._quant_method, moe_kernel)
79 )
80
81 + @property
82 + def hidden_size(self) -> int:
83 + return self.moe_config.hidden_dim
84 +
85 + @property
86 + def local_num_experts(self) -> int:
87 + return self.moe_config.num_local_experts
88 +
89 + @property
90 + def global_num_experts(self) -> int:
91 + return self.moe_config.num_experts
92 +
93 + @property
94 + def ep_rank(self) -> int:
95 + return self.moe_config.moe_parallel_config.ep_rank
96 +
97 + @property
98 + def use_ep(self) -> bool:
99 + return self.moe_config.moe_parallel_config.use_ep
100 +
101 + @property
102 + def intermediate_size_per_partition(self) -> int:
103 + return self.moe_config.intermediate_size_per_partition
104 +
105 def _init_lora_stream_context(self) -> None:
106 self._lora_stream: torch.cuda.Stream | None = None
107 self._events: tuple[torch.cuda.Event, ...] | None = None
@@ -95,12 +128,12 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
128 w2_lora_b_stacked=self.w2_lora_b_stacked,
129 adapter_enabled=self.adapter_enabled,
130 max_loras=self.max_loras,
98 - top_k=self.base_layer.top_k,
131 + top_k=self.moe_config.experts_per_token,
132 w13_num_slices=self._w13_slices,
133 fully_sharded=self.fully_sharded,
134 tp_rank=self.tp_rank,
135 tp_size=self.tp_size,
103 - local_num_experts=self.base_layer.local_num_experts,
136 + local_num_experts=self.local_num_experts,
137 punica_wrapper=self.punica_wrapper,
138 use_tuned_config=bool(envs.VLLM_TUNED_CONFIG_FOLDER),
139 aux_stream=self._lora_stream if use_dual_stream else None,
@@ -116,11 +149,11 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
149 torch.zeros(
150 (
151 max_loras,
119 - self.base_layer.local_num_experts,
152 + self.local_num_experts,
153 lora_config.max_lora_rank
154 if not self.fully_sharded
155 else divide(lora_config.max_lora_rank, self.tp_size),
123 - self.base_layer.hidden_size,
156 + self.hidden_size,
157 ),
158 dtype=lora_config.lora_dtype,
159 device=self.device,
@@ -131,9 +164,9 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
164 torch.zeros(
165 (
166 max_loras,
134 - self.base_layer.local_num_experts,
167 + self.local_num_experts,
168 lora_config.max_lora_rank,
136 - self.base_layer.intermediate_size_per_partition,
169 + self.intermediate_size_per_partition,
170 ),
171 dtype=lora_config.lora_dtype,
172 device=self.device,
@@ -145,8 +178,8 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
178 torch.zeros(
179 (
180 max_loras,
148 - self.base_layer.local_num_experts,
149 - self.base_layer.intermediate_size_per_partition,
181 + self.local_num_experts,
182 + self.intermediate_size_per_partition,
183 lora_config.max_lora_rank,
184 ),
185 dtype=lora_config.lora_dtype,
@@ -158,10 +191,10 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
191 torch.zeros(
192 (
193 max_loras,
161 - self.base_layer.local_num_experts,
162 - self.base_layer.hidden_size
194 + self.local_num_experts,
195 + self.hidden_size
196 if not self.fully_sharded
164 - else divide(self.base_layer.hidden_size, self.tp_size),
197 + else divide(self.hidden_size, self.tp_size),
198 lora_config.max_lora_rank,
199 ),
200 dtype=lora_config.lora_dtype,
@@ -170,8 +203,8 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
203 )
204
205 def _ep_check(self):
173 - if self.base_layer.use_ep:
174 - moe_config = self.base_layer.moe_config
206 + if self.use_ep:
207 + moe_config = self.moe_config
208 all2all_backend = moe_config.moe_parallel_config.all2all_backend
209 assert all2all_backend == "allgather_reducescatter", (
210 "Fused MoE LoRA with EP currently only supports "
@@ -184,7 +217,7 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
217 # EP on the expert dim, fully_sharded on the LoRA rank dim — with
218 # mutually contradictory assumptions about which rank holds which
219 # expert's rank-shard.
187 - assert not (self.base_layer.use_ep and lora_config.fully_sharded_loras), (
220 + assert not (self.use_ep and lora_config.fully_sharded_loras), (
221 "Fused MoE LoRA does not support enable_expert_parallel=True "
222 "together with fully_sharded_loras=True. Disable one of them."
223 )
@@ -213,7 +246,7 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
246 self.lora_a_stacked = []
247 self.lora_b_stacked = []
248 for lora_id in range(max_loras):
216 - for experts_id in range(self.base_layer.local_num_experts):
249 + for experts_id in range(self.local_num_experts):
250 # For gated MoE: gate_proj (w1), down_proj (w2), up_proj (w3)
251 # For non-gated MoE: up_proj (w1), down_proj (w2)
252 self.lora_a_stacked.append(
@@ -260,7 +293,7 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
293 return w13_lora_b
294
295 # w13_lora_b shape (num_experts,output_size,rank)
263 - shard_size = self.base_layer.intermediate_size_per_partition
296 + shard_size = self.intermediate_size_per_partition
297 start_idx = self.tp_rank * shard_size
298 end_idx = (self.tp_rank + 1) * shard_size
299
@@ -273,7 +306,7 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
306 if self.tp_size == 1:
307 return w2_lora_a
308 # w2_lora_a shape (num_experts,rank,input_size)
276 - shard_size = self.base_layer.intermediate_size_per_partition
309 + shard_size = self.intermediate_size_per_partition
310 start_idx = self.tp_rank * shard_size
311 end_idx = (self.tp_rank + 1) * shard_size
312
@@ -382,11 +415,11 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
415
416 @property
417 def quant_method(self):
385 - return self.base_layer.quant_method
418 + return self.base_layer._quant_method
419
420 @property
388 - def runner(self):
389 - return self.base_layer.runner
421 + def runner(self) -> MoERunner:
422 + return self.base_layer
423
424 @property
425 def is_internal_router(self) -> bool:
@@ -402,13 +435,13 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA):
435 ) -> bool:
436 """Returns True if the layer can be replaced by this LoRA layer."""
437
405 - # source_layer is FusedMoE
406 - moe_cls = maybe_get_oot_by_class(FusedMoE)
438 + # source_layer is MoERunner
439 + moe_cls = maybe_get_oot_by_class(MoERunner)
440 return isinstance(source_layer, moe_cls) and len(packed_modules_list) == 2
441
442
443 class FusedMoE3DWithLoRA(FusedMoEWithLoRA):
411 - def __init__(self, base_layer):
444 + def __init__(self, base_layer: MoERunner):
445 super().__init__(base_layer)
446 self._w13_slices = 1
447
@@ -417,8 +450,8 @@ class FusedMoE3DWithLoRA(FusedMoEWithLoRA):
450 torch.zeros(
451 (
452 max_loras,
420 - self.base_layer.local_num_experts,
421 - self.base_layer.intermediate_size_per_partition * 2,
453 + self.local_num_experts,
454 + self.intermediate_size_per_partition * 2,
455 lora_config.max_lora_rank,
456 ),
457 dtype=lora_config.lora_dtype,
@@ -430,10 +463,10 @@ class FusedMoE3DWithLoRA(FusedMoEWithLoRA):
463 torch.zeros(
464 (
465 max_loras,
433 - self.base_layer.local_num_experts,
434 - self.base_layer.hidden_size
466 + self.local_num_experts,
467 + self.hidden_size
468 if not self.fully_sharded
436 - else divide(self.base_layer.hidden_size, self.tp_size),
469 + else divide(self.hidden_size, self.tp_size),
470 lora_config.max_lora_rank,
471 ),
472 dtype=lora_config.lora_dtype,
@@ -467,7 +500,7 @@ class FusedMoE3DWithLoRA(FusedMoEWithLoRA):
500 return w13_lora_b
501
502 # w13_lora_b shape (num_experts,output_size,rank)
470 - shard_size = self.base_layer.intermediate_size_per_partition
503 + shard_size = self.intermediate_size_per_partition
504 start_idx = self.tp_rank * shard_size
505 end_idx = (self.tp_rank + 1) * shard_size
506 # HACK: Currently, only GPT-OSS is in interleaved order
@@ -555,7 +588,7 @@ class FusedMoE3DWithLoRA(FusedMoEWithLoRA):
588 """
589 Full size
590 """
558 - return self.base_layer.hidden_size
591 + return self.hidden_size
592
593 @classmethod
594 def can_replace_layer(
@@ -566,6 +599,6 @@ class FusedMoE3DWithLoRA(FusedMoEWithLoRA):
599 model_config: PretrainedConfig | None = None,
600 ) -> bool:
601 """Returns True if the layer can be replaced by this LoRA layer."""
569 - # source_layer is FusedMoE
570 - moe_cls = maybe_get_oot_by_class(FusedMoE)
602 + # source_layer is MoERunner
603 + moe_cls = maybe_get_oot_by_class(MoERunner)
604 return isinstance(source_layer, moe_cls) and len(packed_modules_list) == 1
vllm/lora/layers/utils.py
+3
@@ -45,6 +45,9 @@ class LoRAMapping:
45 def _get_lora_device(base_layer: nn.Module) -> torch.device:
46 # code borrowed from https://github.com/fmmoret/vllm/blob/fm-support-lora-on-quantized-models/vllm/lora/layers.py#L34
47 """Returns the device for where to place the LoRA tensors."""
48 + if hasattr(base_layer, "routed_experts"):
49 + base_layer = base_layer.routed_experts
50 +
51 # unquantizedLinear
52 if hasattr(base_layer, "weight"):
53 return base_layer.weight.device
vllm/lora/model_manager.py
+18 -19
@@ -31,7 +31,7 @@ from vllm.lora.utils import (
31 process_packed_modules_mapping,
32 replace_submodule,
33 )
34 -from vllm.model_executor.layers.fused_moe import FusedMoE
34 +from vllm.model_executor.layers.fused_moe import MoERunner
35 from vllm.model_executor.models import (
36 SupportsLoRA,
37 is_pooling_model,
@@ -431,7 +431,7 @@ class LoRAModelManager:
431
432 parts = module_name.split(".")[-1]
433 packed_moduled_lst = self.packed_modules_mapping.get(parts, [])
434 - if isinstance(module, FusedMoE):
434 + if isinstance(module, MoERunner):
435 # packed_moduled_lst is used here to just determine whether to
436 # instantiate FusedMoE3DWithLoRA or FusedMoEWithLoRA, and the
437 # difference between these two LoRA layers is whether the
@@ -840,8 +840,8 @@ class LoRAModelManager:
840 # owned expert range before it gets copied into the local
841 # stacked buffer. For non-EP (local == global) this is a
842 # no-op slice.
843 - global_num_experts = module.base_layer.global_num_experts
844 - ep_rank = module.base_layer.ep_rank
843 + global_num_experts = module.global_num_experts
844 + ep_rank = module.ep_rank
845 expert_start = ep_rank * local_num_experts
846 expert_end = expert_start + local_num_experts
847
@@ -944,9 +944,9 @@ class LoRAModelManager:
944 # untouched so set_lora can raise a clear error if needed.
945 return
946
947 - local_num_experts = module.base_layer.local_num_experts
948 - global_num_experts = module.base_layer.global_num_experts
949 - ep_rank = module.base_layer.ep_rank
947 + local_num_experts = module.local_num_experts
948 + global_num_experts = module.global_num_experts
949 + ep_rank = module.ep_rank
950 expert_start = ep_rank * local_num_experts
951 expert_end = expert_start + local_num_experts
952
@@ -1023,15 +1023,15 @@ class LoRAModelManager:
1023 ``.bin``/``.pt`` adapters with weights mappers we don't recognize)
1024 still get sliced here.
1025 """
1026 - if not module.base_layer.use_ep:
1026 + if not module.use_ep:
1027 return
1028 module_lora = self._get_lora_layer_weights(lora_model, module_name)
1029 if module_lora is None or not isinstance(module_lora.lora_a, list):
1030 return
1031
1032 - local_num_experts = module.base_layer.local_num_experts
1033 - global_num_experts = module.base_layer.global_num_experts
1034 - ep_rank = module.base_layer.ep_rank
1032 + local_num_experts = module.local_num_experts
1033 + global_num_experts = module.global_num_experts
1034 + ep_rank = module.ep_rank
1035 expert_start = ep_rank * local_num_experts
1036 expert_end = expert_start + local_num_experts
1037
@@ -1069,11 +1069,11 @@ class LoRAModelManager:
1069 return new_module_names
1070 if isinstance(module, FusedMoE3DWithLoRA):
1071 return new_module_names
1072 - if not getattr(module.base_layer, "use_ep", False):
1072 + if not getattr(module, "use_ep", False):
1073 return new_module_names
1074 - global_num_experts = module.base_layer.global_num_experts
1075 - local_num_experts = module.base_layer.local_num_experts
1076 - ep_rank = module.base_layer.ep_rank
1074 + global_num_experts = module.global_num_experts
1075 + local_num_experts = module.local_num_experts
1076 + ep_rank = module.ep_rank
1077 if global_num_experts <= 0 or len(new_module_names) % global_num_experts != 0:
1078 return new_module_names
1079 per_expert = len(new_module_names) // global_num_experts
@@ -1098,11 +1098,10 @@ class LoRAModelManager:
1098 )
1099 if module is None:
1100 return None
1101 - base = module.base_layer
1101 return MoEEPLoadSpec(
1103 - ep_rank=base.ep_rank,
1104 - local_num_experts=base.local_num_experts,
1105 - global_num_experts=base.global_num_experts,
1102 + ep_rank=module.ep_rank,
1103 + local_num_experts=module.local_num_experts,
1104 + global_num_experts=module.global_num_experts,
1105 )
1106
1107 def _get_lora_layer_weights(
vllm/lora/punica_wrapper/punica_gpu.py
+2 -1
@@ -570,7 +570,8 @@ class PunicaWrapperGPU(PunicaWrapperBase):
570
571 SPARSITY_FACTOR = 8
572 naive_block_assignment = (
573 - expert_map is None
573 + not fully_sharded
574 + and expert_map is None
575 and num_tokens * top_k * SPARSITY_FACTOR <= local_num_experts * max_loras
576 )
577
vllm/lora/utils.py
+4 -4
@@ -33,7 +33,7 @@ from vllm.lora.layers import (
33 RowParallelLinearWithShardedLoRA,
34 VocabParallelEmbeddingWithLoRA,
35 )
36 -from vllm.model_executor.layers.fused_moe import FusedMoE
36 +from vllm.model_executor.layers.fused_moe import MoERunner
37 from vllm.model_executor.layers.linear import LinearBase
38 from vllm.model_executor.utils import get_moe_expert_mapping, get_packed_modules_mapping
39 from vllm.transformers_utils.repo_utils import hf_api
@@ -96,8 +96,8 @@ _all_lora_classes: tuple[type[BaseLayerWithLoRA], ...] = (
96
97
98 def is_moe_model(model: nn.Module) -> bool:
99 - """Checks if the model contains FusedMoE layers and warns the user."""
100 - if any(isinstance(module, FusedMoE) for module in model.modules()):
99 + """Checks if the model contains MoERunner layers and warns the user."""
100 + if any(isinstance(module, MoERunner) for module in model.modules()):
101 logger.info_once("MoE model detected. Using fused MoE LoRA implementation.")
102 return True
103 return False
@@ -223,7 +223,7 @@ def get_supported_lora_modules(model: nn.Module) -> list[str]:
223 if isinstance(module, (LinearBase,)):
224 supported_lora_modules.add(name.split(".")[-1])
225
226 - if isinstance(module, (FusedMoE,)):
226 + if isinstance(module, (MoERunner,)):
227 supported_lora_modules.add(name.split(".")[-1])
228
229 return list(supported_lora_modules)
vllm/model_executor/layers/fused_moe/__init__.py
+9 -6
@@ -2,7 +2,7 @@
2 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
4 from contextlib import contextmanager
5 -from typing import Any, TypeAlias
5 +from typing import Any
6
7 from vllm.model_executor.layers.fused_moe.activation import (
8 MoEActivation,
@@ -20,7 +20,6 @@ from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
20 )
21 from vllm.model_executor.layers.fused_moe.layer import (
22 FusedMoE,
23 - FusedMoeWeightScaleSupported,
23 fused_moe_make_expert_params_mapping,
24 )
25 from vllm.model_executor.layers.fused_moe.modular_kernel import (
@@ -28,10 +27,17 @@ from vllm.model_executor.layers.fused_moe.modular_kernel import (
27 FusedMoEExpertsModular,
28 FusedMoEPrepareAndFinalizeModular,
29 )
30 +from vllm.model_executor.layers.fused_moe.routed_experts import (
31 + FusedMoeWeightScaleSupported,
32 + RoutedExperts,
33 +)
34 from vllm.model_executor.layers.fused_moe.router.fused_moe_router import (
35 FusedMoERouter,
36 )
37 from vllm.model_executor.layers.fused_moe.router.gate_linear import GateLinear
38 +from vllm.model_executor.layers.fused_moe.runner.moe_runner import (
39 + MoERunner,
40 +)
41 from vllm.model_executor.layers.fused_moe.runner.shared_experts import (
42 SharedExperts,
43 )
@@ -43,10 +49,6 @@ from vllm.triton_utils import HAS_TRITON
49 _config: dict[str, Any] | None = None
50
51
46 -# Temporary alias for FusedMoE, eventually we be its own class.
47 -RoutedExperts: TypeAlias = FusedMoE
48 -
49 -
52 @contextmanager
53 def override_config(config):
54 global _config
@@ -74,6 +76,7 @@ __all__ = [
76 "FusedMoEActivationFormat",
77 "FusedMoEPrepareAndFinalizeModular",
78 "GateLinear",
79 + "MoERunner",
80 "RoutingMethodType",
81 "RoutedExperts",
82 "SharedExperts",
vllm/model_executor/layers/fused_moe/config.py
+38 -2
@@ -1236,7 +1236,7 @@ class FusedMoEConfig:
1236 num_experts: int
1237 experts_per_token: int
1238 hidden_dim: int
1239 - intermediate_size_per_partition: int
1239 + intermediate_size: int
1240 num_local_experts: int
1241 num_logical_experts: int
1242 activation: MoEActivation
@@ -1258,7 +1258,6 @@ class FusedMoEConfig:
1258 moe_backend: MoEBackend = "auto"
1259 max_num_tokens: int = SchedulerConfig.DEFAULT_MAX_NUM_BATCHED_TOKENS_FOR_BATCHED_DP
1260 has_bias: bool = False
1261 - is_act_and_mul: bool = True
1261 is_lora_enabled: bool = False
1262
1263 # SwiGLU clamp limit. When set, backends that do not implement the clamp
@@ -1268,7 +1267,18 @@ class FusedMoEConfig:
1267
1268 max_capture_size: int = 0
1269
1270 + # Set by __post_init__
1271 + intermediate_size_per_partition: int = -1
1272 + rocm_aiter_fmoe_enabled: bool = False
1273 + aiter_fmoe_shared_expert_enabled: bool = False
1274 +
1275 def __post_init__(self):
1276 + from vllm._aiter_ops import rocm_aiter_ops
1277 +
1278 + tp_size = self.moe_parallel_config.tp_size
1279 + assert self.intermediate_size % tp_size == 0
1280 + self.intermediate_size_per_partition = self.intermediate_size // tp_size
1281 +
1282 if self.dp_size > 1:
1283 logger.debug_once(
1284 "Using FusedMoEConfig::max_num_tokens=%d", self.max_num_tokens
@@ -1286,6 +1296,32 @@ class FusedMoEConfig:
1296 self.intermediate_size_per_partition
1297 )
1298
1299 + if self.is_act_and_mul:
1300 + self.rocm_aiter_fmoe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
1301 + self.aiter_fmoe_shared_expert_enabled = (
1302 + rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
1303 + )
1304 +
1305 + if self.use_mori_kernels:
1306 + assert self.rocm_aiter_fmoe_enabled, (
1307 + "Mori needs to be used with aiter fused_moe for now."
1308 + )
1309 + assert not self.aiter_fmoe_shared_expert_enabled, (
1310 + "Mori does not support fusion shared expert now. "
1311 + "Turn it off by setting VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=0"
1312 + )
1313 +
1314 + if not self.is_act_and_mul and not (
1315 + current_platform.is_cuda_alike() or current_platform.is_xpu()
1316 + ):
1317 + raise NotImplementedError(
1318 + "is_act_and_mul=False is supported only for CUDA, XPU and ROCm for now"
1319 + )
1320 +
1321 + @property
1322 + def is_act_and_mul(self) -> bool:
1323 + return self.activation.is_gated
1324 +
1325 @property
1326 def tp_size(self):
1327 return self.moe_parallel_config.tp_size
vllm/model_executor/layers/fused_moe/eep_reconfigure.py
+3 -3
@@ -21,7 +21,7 @@ from vllm.model_executor.layers.fused_moe.modular_kernel import (
21 )
22
23 if TYPE_CHECKING:
24 - from vllm.model_executor.layers.fused_moe.layer import FusedMoE
24 + from vllm.model_executor.layers.fused_moe.runner.moe_runner import MoERunner
25
26
27 def _make_eep_experts(
@@ -58,10 +58,10 @@ def _make_eep_experts(
58
59
60 def make_eep_staged_quant_method(
61 - module: "FusedMoE",
61 + module: "MoERunner",
62 moe_config: FusedMoEConfig,
63 ) -> FusedMoEMethodBase | None:
64 - quant_method = module.quant_method
64 + quant_method = module._quant_method
65 if not quant_method.supports_internal_mk:
66 return None
67 if getattr(quant_method, "wraps_legacy_quant_method", False):
vllm/model_executor/layers/fused_moe/experts/rocm_aiter_moe.py
+1 -1
@@ -53,7 +53,7 @@ class ActivationMethod(IntEnum):
53 GELU = 1
54
55
56 -aiter_topK_meta_data = None
56 +aiter_topK_meta_data: tuple[torch.Tensor, torch.Tensor] | None = None
57
58
59 @lru_cache(maxsize=1)
vllm/model_executor/layers/fused_moe/fused_moe_method_base.py
+38 -5
@@ -22,6 +22,7 @@ from vllm.model_executor.layers.quantization.base_config import (
22 )
23
24 if TYPE_CHECKING:
25 + from vllm.model_executor.layers.fused_moe.routed_experts import RoutedExperts
26 from vllm.model_executor.layers.fused_moe.runner.shared_experts import SharedExperts
27
28 logger = init_logger(__name__)
@@ -51,7 +52,7 @@ class FusedMoEMethodBase(QuantizeMethodBase):
52 @abstractmethod
53 def create_weights(
54 self,
54 - layer: torch.nn.Module,
55 + layer: "RoutedExperts",
56 num_experts: int,
57 hidden_size: int,
58 intermediate_size_per_partition: int,
@@ -117,7 +118,7 @@ class FusedMoEMethodBase(QuantizeMethodBase):
118 def select_gemm_impl(
119 self,
120 prepare_finalize: FusedMoEPrepareAndFinalizeModular,
120 - layer: torch.nn.Module,
121 + layer: "RoutedExperts",
122 ) -> FusedMoEExpertsModular:
123 # based on the all2all implementation, select the appropriate
124 # gemm implementation
@@ -128,7 +129,7 @@ class FusedMoEMethodBase(QuantizeMethodBase):
129
130 @abstractmethod
131 def get_fused_moe_quant_config(
131 - self, layer: torch.nn.Module
132 + self, layer: "RoutedExperts"
133 ) -> FusedMoEQuantConfig | None:
134 raise NotImplementedError
135
@@ -143,6 +144,14 @@ class FusedMoEMethodBase(QuantizeMethodBase):
144 """Whether to skip the padding in the forward before applying the moe method."""
145 return False
146
147 + @property
148 + def has_unpadded_output(self) -> bool:
149 + """
150 + Indicates that the hidden_states output might be the unpadded
151 + hidden_states shape rather than the full padded shape.
152 + """
153 + return False
154 +
155 @property
156 def supports_eplb(self) -> bool:
157 return False
@@ -162,20 +171,44 @@ class FusedMoEMethodBase(QuantizeMethodBase):
171
172 def apply(
173 self,
165 - layer: "RoutedExperts", # type: ignore[name-defined] # noqa: F821
174 + layer: "RoutedExperts",
175 x: torch.Tensor,
176 topk_weights: torch.Tensor,
177 topk_ids: torch.Tensor,
178 shared_experts: "SharedExperts | None",
179 shared_experts_input: torch.Tensor | None,
180 ) -> torch.Tensor:
181 + """
182 + Apply the MoE operation using modular kernels.
183 +
184 + Args:
185 + layer: RoutedExperts instance containing weight parameters
186 + x: Input tensor
187 + topk_weights: Expert weights from router
188 + topk_ids: Selected expert IDs from router
189 + shared_experts_input: Input for shared experts (if any)
190 +
191 + Returns:
192 + Output tensor from routed experts
193 + """
194 raise NotImplementedError
195
196 def apply_monolithic(
197 self,
176 - layer: "RoutedExperts", # type: ignore[name-defined] # noqa: F821
198 + layer: "RoutedExperts",
199 x: torch.Tensor,
200 router_logits: torch.Tensor,
201 input_ids: torch.Tensor | None = None,
202 ) -> torch.Tensor:
203 + """
204 + Apply the MoE operation using monolithic kernels.
205 +
206 + Args:
207 + layer: RoutedExperts instance containing weight parameters
208 + x: Input tensor
209 + router_logits: Router logits (routing done internally)
210 +
211 + Returns:
212 + Output tensor from routed experts
213 + """
214 raise NotImplementedError
vllm/model_executor/layers/fused_moe/fused_moe_modular_method.py
+19 -5
@@ -1,6 +1,7 @@
1 # SPDX-License-Identifier: Apache-2.0
2 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
4 +from typing import TYPE_CHECKING
5
6 import torch
7
@@ -20,6 +21,11 @@ from vllm.model_executor.layers.fused_moe.runner.shared_experts import (
21 SharedExperts,
22 )
23
24 +if TYPE_CHECKING:
25 + from vllm.model_executor.layers.fused_moe.routed_experts import (
26 + RoutedExperts,
27 + )
28 +
29 logger = init_logger(__name__)
30
31
@@ -43,7 +49,7 @@ class FusedMoEModularMethod(FusedMoEMethodBase, CustomOp):
49
50 @staticmethod
51 def make(
46 - moe_layer: torch.nn.Module,
52 + routed_experts: "RoutedExperts",
53 old_quant_method: FusedMoEMethodBase,
54 prepare_finalize: FusedMoEPrepareAndFinalizeModular,
55 ) -> "FusedMoEModularMethod":
@@ -51,10 +57,18 @@ class FusedMoEModularMethod(FusedMoEMethodBase, CustomOp):
57 old_quant_method,
58 FusedMoEKernel(
59 prepare_finalize,
54 - old_quant_method.select_gemm_impl(prepare_finalize, moe_layer),
60 + old_quant_method.select_gemm_impl(prepare_finalize, routed_experts),
61 ),
62 )
63
64 + @property
65 + def skip_forward_padding(self) -> bool:
66 + return self.old_quant_method.skip_forward_padding
67 +
68 + @property
69 + def has_unpadded_output(self) -> bool:
70 + return self.old_quant_method.has_unpadded_output
71 +
72 @property
73 def supports_eplb(self) -> bool:
74 return self.old_quant_method.supports_eplb
@@ -65,7 +79,7 @@ class FusedMoEModularMethod(FusedMoEMethodBase, CustomOp):
79
80 def create_weights(
81 self,
68 - layer: torch.nn.Module,
82 + layer: "RoutedExperts",
83 num_experts: int,
84 hidden_size: int,
85 intermediate_size_per_partition: int,
@@ -75,13 +89,13 @@ class FusedMoEModularMethod(FusedMoEMethodBase, CustomOp):
89 raise NotImplementedError
90
91 def get_fused_moe_quant_config(
78 - self, layer: torch.nn.Module
92 + self, layer: "RoutedExperts"
93 ) -> FusedMoEQuantConfig | None:
94 return self.moe_quant_config
95
96 def apply(
97 self,
84 - layer: "RoutedExperts", # type: ignore[name-defined] # noqa: F821
98 + layer: "RoutedExperts",
99 x: torch.Tensor,
100 topk_weights: torch.Tensor,
101 topk_ids: torch.Tensor,
vllm/model_executor/layers/fused_moe/layer.py
+314 -1334
@@ -1,39 +1,31 @@
1 # SPDX-License-Identifier: Apache-2.0
2 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
4 -from collections.abc import Callable, Iterable
5 -from enum import Enum
6 -from typing import Literal, cast, overload
4 +from collections.abc import Callable
5 +from typing import Any
6
7 import torch
9 -from torch.nn.parameter import UninitializedParameter
8
9 from vllm._aiter_ops import rocm_aiter_ops
12 -from vllm.config import get_current_vllm_config
13 -from vllm.config.parallel import ExpertPlacementStrategy
10 +from vllm.config import ParallelConfig, get_current_vllm_config
11 from vllm.distributed import (
12 get_dp_group,
13 get_pcp_group,
14 get_tensor_model_parallel_world_size,
15 )
19 -from vllm.distributed.eplb.eplb_state import EplbLayerState, EplbState
16 +from vllm.distributed.eplb.eplb_state import EplbLayerState
17 from vllm.logger import init_logger
21 -from vllm.model_executor.custom_op import PluggableLayer
18 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
19 from vllm.model_executor.layers.fused_moe.config import (
20 FusedMoEConfig,
21 FusedMoEParallelConfig,
26 - FusedMoEQuantConfig,
27 - RoutingMethodType,
22 )
23 from vllm.model_executor.layers.fused_moe.expert_map_manager import (
24 ExpertMapManager,
25 )
32 -from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
33 - FusedMoEMethodBase,
34 -)
35 -from vllm.model_executor.layers.fused_moe.fused_moe_modular_method import (
36 - FusedMoEModularMethod,
26 +from vllm.model_executor.layers.fused_moe.routed_experts import RoutedExperts
27 +from vllm.model_executor.layers.fused_moe.router.fused_moe_router import (
28 + FusedMoERouter,
29 )
30 from vllm.model_executor.layers.fused_moe.router.router_factory import (
31 create_fused_moe_router,
@@ -41,1366 +33,356 @@ from vllm.model_executor.layers.fused_moe.router.router_factory import (
33 from vllm.model_executor.layers.fused_moe.runner.moe_runner import (
34 MoERunner,
35 )
44 -from vllm.model_executor.layers.fused_moe.runner.moe_runner_interface import (
45 - MoERunnerInterface,
46 -)
47 -from vllm.model_executor.layers.fused_moe.runner.shared_experts import (
48 - SharedExperts,
49 -)
50 -from vllm.model_executor.layers.fused_moe.unquantized_fused_moe_method import (
51 - UnquantizedFusedMoEMethod,
52 -)
36 from vllm.model_executor.layers.quantization.base_config import (
37 QuantizationConfig,
38 )
56 -from vllm.platforms import current_platform
39
40 logger = init_logger(__name__)
41
42
61 -class FusedMoeWeightScaleSupported(Enum):
62 - TENSOR = "tensor"
63 - CHANNEL = "channel"
64 - GROUP = "group"
65 - BLOCK = "block"
43 +def make_parallel_config(
44 + tp_size: int | None,
45 + dp_size: int | None,
46 + pcp_size: int | None,
47 + is_sequence_parallel: bool,
48 + parallel_config: ParallelConfig,
49 +) -> FusedMoEParallelConfig:
50 + tp_size_ = (
51 + tp_size if tp_size is not None else get_tensor_model_parallel_world_size()
52 + )
53 + dp_size_ = dp_size if dp_size is not None else get_dp_group().world_size
54 + pcp_size_ = pcp_size if pcp_size is not None else get_pcp_group().world_size
55 + sp_size = tp_size_ if is_sequence_parallel else 1
56 +
57 + moe_parallel_config = FusedMoEParallelConfig.make(
58 + tp_size_=tp_size_,
59 + pcp_size_=pcp_size_,
60 + dp_size_=dp_size_,
61 + sp_size_=sp_size,
62 + vllm_parallel_config=parallel_config,
63 + )
64 +
65 + assert moe_parallel_config.is_sequence_parallel == is_sequence_parallel
66
67 + logger.debug("FusedMoEParallelConfig = %s", str(moe_parallel_config))
68
68 -# --8<-- [start:fused_moe]
69 -@PluggableLayer.register("fused_moe")
70 -class FusedMoE(PluggableLayer):
71 - """FusedMoE layer for MoE models.
69 + return moe_parallel_config
70
73 - This layer contains both MergedColumnParallel weights (gate_up_proj /
74 - w13) and RowParallelLinear weights (down_proj/ w2).
71 +
72 +def determine_expert_counts(
73 + num_experts: int,
74 + num_redundant_experts: int,
75 + n_shared_experts: int | None,
76 + is_act_and_mul: bool,
77 +) -> tuple[int, int, int]:
78 + global_num_experts = num_experts + num_redundant_experts
79 + logical_num_experts = num_experts
80 + # ROCm aiter shared experts fusion
81 + # AITER only supports gated activations (silu/gelu), so disable it
82 + # for non-gated MoE (is_act_and_mul=False)
83 + # rocm_aiter_fmoe_enabled = rocm_aiter_ops.is_fused_moe_enabled() and is_act_and_mul
84 + aiter_fmoe_shared_expert_enabled = (
85 + rocm_aiter_ops.is_fusion_moe_shared_experts_enabled() and is_act_and_mul
86 + )
87 +
88 + num_fused_shared_experts = (
89 + n_shared_experts
90 + if n_shared_experts is not None and aiter_fmoe_shared_expert_enabled
91 + else 0
92 + )
93 + if not aiter_fmoe_shared_expert_enabled and num_fused_shared_experts != 0:
94 + raise ValueError(
95 + "n_shared_experts is only supported on ROCm aiter when "
96 + "VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS is enabled"
97 + )
98 +
99 + return global_num_experts, logical_num_experts, num_fused_shared_experts
100 +
101 +
102 +# TODO: rename this
103 +def FusedMoE(
104 + num_experts: int, # Global number of experts
105 + top_k: int,
106 + hidden_size: int,
107 + intermediate_size: int,
108 + params_dtype: torch.dtype | None = None,
109 + renormalize: bool = True,
110 + use_grouped_topk: bool = False,
111 + num_expert_group: int | None = None,
112 + topk_group: int | None = None,
113 + quant_config: QuantizationConfig | None = None,
114 + tp_size: int | None = None,
115 + dp_size: int | None = None,
116 + pcp_size: int | None = None,
117 + prefix: str = "",
118 + custom_routing_function: Callable | None = None,
119 + router: FusedMoERouter | None = None,
120 + scoring_func: str = "softmax",
121 + routed_scaling_factor: float = 1.0,
122 + swiglu_limit: float | None = None,
123 + e_score_correction_bias: torch.Tensor | None = None,
124 + apply_router_weight_on_input: bool = False,
125 + activation: str = "silu",
126 + enable_eplb: bool = False,
127 + num_redundant_experts: int = 0,
128 + has_bias: bool = False,
129 + is_sequence_parallel: bool = False,
130 + expert_mapping: list[tuple[str, str, int, str]] | None = None,
131 + n_shared_experts: int | None = None,
132 + router_logits_dtype: torch.dtype | None = None,
133 + gate: torch.nn.Module | None = None,
134 + shared_experts: torch.nn.Module | None = None,
135 + shared_expert_gate: torch.nn.Module | None = None,
136 + routed_input_transform: torch.nn.Module | None = None,
137 + routed_output_transform: torch.nn.Module | None = None,
138 + apply_routed_scale_to_output: bool = False,
139 + zero_expert_type: str | None = None,
140 + hash_indices_table: torch.Tensor | None = None,
141 + runner_cls: type[MoERunner] | None = None,
142 + runner_args: dict[str, Any] | None = None,
143 + routed_experts_cls: type[RoutedExperts] | None = None,
144 + routed_experts_args: dict[str, Any] | None = None,
145 +) -> MoERunner:
146 + """Factory function for creating MoE execution pipeline.
147 +
148 + Creates and configures a complete MoE execution pipeline including:
149 + - Router (for token-to-expert assignment)
150 + - RoutedExperts (containing expert weight parameters)
151 + - MoERunner (orchestrates the complete forward pass)
152 +
153 + The experts contain both MergedColumnParallel weights (gate_up_proj/w13)
154 + and RowParallelLinear weights (down_proj/w2).
155
156 Note: Mixtral uses w1, w2, and w3 for gate, up, and down_proj. We
157 copy that naming convention here and handle any remapping in the
158 load_weights function in each model implementation.
159
160 Args:
81 - num_experts: Number of experts in the model
161 + num_experts: Number of experts in the model (global count)
162 top_k: Number of experts selected for each token
163 hidden_size: Input hidden state size of the transformer
164 intermediate_size: Intermediate size of the experts
85 - params_dtype: Data type for the parameters.
86 - renormalize: Whether to renormalize the logits in the fused_moe kernel
87 - quant_config: Quantization configure.
88 - enable_eplb: Whether to enable expert parallelism load balancer.
89 - router_logits_dtype: Data type for router logits buffers.
90 - routed_scaling_factor: A scaling factor that is applied to the topk_weights
91 - by the router or the output of the layer depending
92 - on the value of `apply_routed_scale_to_output`
93 - apply_routed_scale_to_output: Determine whether or not `routed_scaling_factor`
94 - is applied to the topk_weights or to the experts
95 - output. It is applied to the experts output
96 - instead of the topk_weights when this feature is
97 - not supported by the router (or the experts).
165 + params_dtype: Data type for the parameters
166 + renormalize: Whether to renormalize the logits in the router
167 + use_grouped_topk: Whether to use grouped top-k routing
168 + num_expert_group: Number of expert groups for grouped top-k
169 + topk_group: Top-k value per group for grouped top-k
170 + quant_config: Quantization configuration
171 + tp_size: Tensor parallelism size (None = use global default)
172 + dp_size: Data parallelism size (None = use global default)
173 + pcp_size: Pipeline context parallelism size (None = use global default)
174 + prefix: Layer name prefix for weight loading
175 + custom_routing_function: Custom routing function override
176 + router: Pre-configured router instance (None = create default)
177 + scoring_func: Scoring function for routing ("softmax" or others)
178 + routed_scaling_factor: Scaling factor applied to topk_weights or output
179 + swiglu_limit: SwiGLU activation limit
180 + e_score_correction_bias: Expert score correction bias tensor
181 + apply_router_weight_on_input: Whether to apply router weights on input
182 + activation: Activation function name ("silu", "gelu", etc.)
183 + enable_eplb: Whether to enable expert parallelism load balancer
184 + num_redundant_experts: Number of redundant experts for EPLB
185 + has_bias: Whether expert layers have bias terms
186 + is_sequence_parallel: Whether sequence parallelism is enabled
187 + expert_mapping: Expert parameter mapping for weight loading
188 + n_shared_experts: Number of shared experts (ROCm aiter only)
189 + router_logits_dtype: Data type for router logits buffers
190 + gate: Pre-configured gate module
191 + shared_experts: Pre-configured shared experts module
192 + shared_expert_gate: Pre-configured shared expert gate module
193 + routed_input_transform: Input transformation module
194 + routed_output_transform: Output transformation module
195 + apply_routed_scale_to_output: Whether to apply routed_scaling_factor to
196 + output instead of topk_weights
197 + zero_expert_type: Type of zero expert handling
198 + hash_indices_table: Hash table for expert indices
199 + runner_cls: Custom MoERunner class (None = use default MoERunner)
200 + runner_args: Additional arguments for runner constructor
201 + routed_experts_cls: Custom RoutedExperts class (None = use default)
202 + routed_experts_args: Additional arguments for routed_experts constructor
203 +
204 + Returns:
205 + MoERunner: Configured MoE execution pipeline ready for forward passes
206 """
207 + vllm_config = get_current_vllm_config()
208
100 - # --8<-- [end:fused_moe]
101 -
102 - def __init__(
103 - self,
104 - num_experts: int, # Global number of experts
105 - top_k: int,
106 - hidden_size: int,
107 - intermediate_size: int,
108 - params_dtype: torch.dtype | None = None,
109 - renormalize: bool = True,
110 - use_grouped_topk: bool = False,
111 - num_expert_group: int | None = None,
112 - topk_group: int | None = None,
113 - quant_config: QuantizationConfig | None = None,
114 - tp_size: int | None = None,
115 - ep_size: int | None = None,
116 - dp_size: int | None = None,
117 - pcp_size: int | None = None,
118 - prefix: str = "",
119 - custom_routing_function: Callable | None = None,
120 - scoring_func: str = "softmax",
121 - routed_scaling_factor: float = 1.0,
122 - swiglu_limit: float | None = None,
123 - e_score_correction_bias: torch.Tensor | None = None,
124 - apply_router_weight_on_input: bool = False,
125 - activation: str = "silu",
126 - is_act_and_mul: bool = True,
127 - enable_eplb: bool = False,
128 - num_redundant_experts: int = 0,
129 - has_bias: bool = False,
130 - is_sequence_parallel=False,
131 - expert_mapping: list[tuple[str, str, int, str]] | None = None,
132 - n_shared_experts: int | None = None,
133 - router_logits_dtype: torch.dtype | None = None,
134 - gate: torch.nn.Module | None = None,
135 - shared_experts: torch.nn.Module | None = None,
136 - shared_expert_gate: torch.nn.Module | None = None,
137 - routed_input_transform: torch.nn.Module | None = None,
138 - routed_output_transform: torch.nn.Module | None = None,
139 - apply_routed_scale_to_output: bool = False,
140 - zero_expert_type: str | None = None,
141 - hash_indices_table: torch.Tensor | None = None,
142 - ):
143 - super().__init__()
144 -
145 - if params_dtype is None:
146 - params_dtype = torch.get_default_dtype()
147 - self.params_dtype = params_dtype
148 -
149 - vllm_config = get_current_vllm_config()
150 - self.vllm_config = vllm_config
151 - self.swiglu_limit = swiglu_limit
152 -
153 - # FIXME (varun): We should have a better way of inferring the activation
154 - # datatype. This works for now as the tensor datatype entering the MoE
155 - # operation is typically unquantized (i.e. float16/bfloat16).
156 - if vllm_config.model_config is not None:
157 - moe_in_dtype = vllm_config.model_config.dtype
158 - else:
159 - # TODO (bnell): This is a hack to get test_mixtral_moe to work
160 - # since model_config is not set in the pytest test.
161 - moe_in_dtype = params_dtype
162 -
163 - tp_size_ = (
164 - tp_size if tp_size is not None else get_tensor_model_parallel_world_size()
165 - )
166 - dp_size_ = dp_size if dp_size is not None else get_dp_group().world_size
167 - pcp_size_ = pcp_size if pcp_size is not None else get_pcp_group().world_size
168 -
169 - self.is_sequence_parallel = is_sequence_parallel
170 - self.sp_size = tp_size_ if is_sequence_parallel else 1
171 -
172 - self.moe_parallel_config: FusedMoEParallelConfig = FusedMoEParallelConfig.make(
173 - tp_size_=tp_size_,
174 - pcp_size_=pcp_size_,
175 - dp_size_=dp_size_,
176 - sp_size_=self.sp_size,
177 - vllm_parallel_config=vllm_config.parallel_config,
178 - )
209 + layer_name = prefix
210
180 - assert self.moe_parallel_config.is_sequence_parallel == is_sequence_parallel
211 + moe_activation = MoEActivation.from_str(activation)
212 + is_act_and_mul = moe_activation.is_gated
213
182 - self.global_num_experts = num_experts + num_redundant_experts
183 - self.logical_num_experts = num_experts
184 -
185 - # Expert mapping used in self.load_weights
186 - self.expert_mapping = expert_mapping
187 -
188 - # For smuggling this layer into the fused moe custom op
189 - compilation_config = vllm_config.compilation_config
190 - if prefix in compilation_config.static_forward_context:
191 - raise ValueError("Duplicate layer name: {}".format(prefix))
192 - compilation_config.static_forward_context[prefix] = self
193 - compilation_config.static_all_moe_layers.append(prefix)
194 - self.layer_name = prefix
195 -
196 - self.expert_placement_strategy: ExpertPlacementStrategy = (
197 - vllm_config.parallel_config.expert_placement_strategy
198 - )
199 -
200 - self.eplb_state: EplbLayerState | None = None
201 - if enable_eplb:
202 - if self.use_ep and self.global_num_experts % self.ep_size != 0:
203 - raise ValueError(
204 - f"EPLB currently only supports even distribution of "
205 - f"experts across ranks. Got {self.global_num_experts} experts "
206 - f"and {self.ep_size} EP ranks."
207 - )
208 - self.eplb_state = EplbLayerState()
209 - else:
210 - assert not self.use_ep or num_redundant_experts == 0, (
211 - "Redundant experts are only supported with EPLB."
212 - )
213 -
214 - # ROCm aiter shared experts fusion
215 - # AITER only supports gated activations (silu/gelu), so disable it
216 - # for non-gated MoE (is_act_and_mul=False)
217 - self.rocm_aiter_fmoe_enabled = (
218 - rocm_aiter_ops.is_fused_moe_enabled() and is_act_and_mul
219 - )
220 - self.aiter_fmoe_shared_expert_enabled = (
221 - rocm_aiter_ops.is_fusion_moe_shared_experts_enabled() and is_act_and_mul
222 - )
214 + moe_parallel_config = make_parallel_config(
215 + tp_size=tp_size,
216 + dp_size=dp_size,
217 + pcp_size=pcp_size,
218 + is_sequence_parallel=is_sequence_parallel,
219 + parallel_config=vllm_config.parallel_config,
220 + )
221
224 - self.num_fused_shared_experts = (
225 - n_shared_experts
226 - if n_shared_experts is not None and self.aiter_fmoe_shared_expert_enabled
227 - else 0
222 + global_num_experts, logical_num_experts, num_fused_shared_experts = (
223 + determine_expert_counts(
224 + num_experts,
225 + num_redundant_experts,
226 + n_shared_experts,
227 + is_act_and_mul,
228 )
229 - self.shared_expert_gate = shared_expert_gate
229 + )
230
231 - if (
232 - not self.aiter_fmoe_shared_expert_enabled
233 - and self.num_fused_shared_experts != 0
234 - ):
231 + # Initialize EPLB manager (or None?)
232 + eplb_state: EplbLayerState | None = None
233 + if enable_eplb:
234 + use_ep = moe_parallel_config.use_ep
235 + ep_size = moe_parallel_config.ep_size
236 + if use_ep and global_num_experts % ep_size != 0:
237 raise ValueError(
236 - "n_shared_experts is only supported on ROCm aiter when "
237 - "VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS is enabled"
238 + f"EPLB currently only supports even distribution of "
239 + f"experts across ranks. Got {global_num_experts} experts "
240 + f"and {ep_size} EP ranks."
241 )
239 -
240 - # Determine expert maps
241 - max_num_batched_tokens = vllm_config.scheduler_config.max_num_batched_tokens
242 -
243 - # Create ExpertMapManager to handle expert mapping and placement for EP.
244 - # See ExpertMapManager for a detailed description of what it does and when
245 - # it is required.
246 - self.expert_map_manager = ExpertMapManager(
247 - max_num_batched_tokens=max_num_batched_tokens,
248 - top_k=top_k,
249 - global_num_experts=self.global_num_experts,
250 - num_redundant_experts=num_redundant_experts,
251 - num_expert_group=num_expert_group,
252 - moe_parallel_config=self.moe_parallel_config,
253 - placement_strategy=self.expert_placement_strategy,
254 - enable_eplb=enable_eplb,
255 - num_fused_shared_experts=self.num_fused_shared_experts,
256 - rocm_aiter_enabled=self.rocm_aiter_fmoe_enabled,
257 - )
258 -
259 - self.update_expert_map_info()
260 -
261 - self.top_k = top_k
262 -
263 - assert intermediate_size % self.tp_size == 0
264 - intermediate_size_per_partition = intermediate_size // self.tp_size
265 - self.renormalize = renormalize
266 -
267 - # TODO(bnell): these attributes are only used by monolithic kernels.
268 - # Put them in a MoERouterConfig dataclass?
269 - self.use_grouped_topk = use_grouped_topk
270 - if self.use_grouped_topk:
271 - assert num_expert_group is not None and topk_group is not None
272 - self.num_expert_group = num_expert_group
273 - self.topk_group = topk_group
274 - self.custom_routing_function = custom_routing_function
275 - self.scoring_func = scoring_func
276 - # When apply_routed_scale_to_output is True, we set the scaling factor
277 - # to 1.0 so it ends up being a nop. Applying the scale will be handled
278 - # by the runner in this case.
279 - # The member variable must be set in the same way as the router since
280 - # some quantization methods can access it.
281 - self.routed_scaling_factor = (
282 - routed_scaling_factor if not apply_routed_scale_to_output else 1.0
242 + eplb_state = EplbLayerState()
243 + else:
244 + assert num_redundant_experts == 0, (
245 + "Redundant experts are only supported with EPLB."
246 )
284 - self.e_score_correction_bias = e_score_correction_bias
285 - # TODO(bnell): end attributes
247
287 - self.hash_indices_table = hash_indices_table
288 - self.apply_router_weight_on_input = apply_router_weight_on_input
289 - self.activation = MoEActivation.from_str(activation)
248 + max_num_batched_tokens = vllm_config.scheduler_config.max_num_batched_tokens
249 +
250 + # Create ExpertMapManager to handle expert mapping and placement for EP.
251 + # See ExpertMapManager for a detailed description of what it does and when
252 + # it is required.
253 + expert_map_manager = ExpertMapManager(
254 + max_num_batched_tokens=max_num_batched_tokens,
255 + top_k=top_k,
256 + global_num_experts=global_num_experts,
257 + num_redundant_experts=num_redundant_experts,
258 + num_expert_group=num_expert_group,
259 + moe_parallel_config=moe_parallel_config,
260 + placement_strategy=vllm_config.parallel_config.expert_placement_strategy,
261 + enable_eplb=eplb_state is not None,
262 + num_fused_shared_experts=num_fused_shared_experts,
263 + rocm_aiter_enabled=rocm_aiter_ops.is_fused_moe_enabled() and is_act_and_mul,
264 + )
265
291 - # TODO(bnell): we should not have to create a router if the kernel is
292 - # monolithic.
293 - self.router = create_fused_moe_router(
266 + # TODO(bnell): we should not have to create a router if the kernel is
267 + # monolithic.
268 + if router is None:
269 + router = create_fused_moe_router(
270 top_k=top_k,
295 - global_num_experts=self.global_num_experts,
296 - eplb_state=self.eplb_state,
271 + global_num_experts=global_num_experts,
272 + eplb_state=eplb_state,
273 renormalize=renormalize,
274 use_grouped_topk=use_grouped_topk,
275 num_expert_group=num_expert_group,
276 topk_group=topk_group,
277 custom_routing_function=custom_routing_function,
278 scoring_func=scoring_func,
303 - routed_scaling_factor=self.routed_scaling_factor,
304 - e_score_correction_bias=e_score_correction_bias,
305 - num_fused_shared_experts=self.num_fused_shared_experts,
306 - # TODO(bnell): once we can construct the MK at init time, we
307 - # can make this a value.
308 - indices_type_getter=lambda: self.quant_method.topk_indices_dtype,
309 - zero_expert_type=zero_expert_type,
310 - num_logical_experts=self.logical_num_experts,
311 - hash_indices_table=self.hash_indices_table,
312 - )
313 - self.routing_method_type: RoutingMethodType = self.router.routing_method_type
314 -
315 - self.moe_config: FusedMoEConfig = FusedMoEConfig(
316 - num_experts=self.global_num_experts,
317 - experts_per_token=top_k,
318 - hidden_dim=hidden_size,
319 - hidden_dim_unpadded=hidden_size,
320 - intermediate_size_per_partition=intermediate_size_per_partition,
321 - intermediate_size_per_partition_unpadded=intermediate_size_per_partition,
322 - num_local_experts=self.local_num_experts,
323 - num_logical_experts=self.logical_num_experts,
324 - moe_parallel_config=self.moe_parallel_config,
325 - in_dtype=moe_in_dtype,
326 - moe_backend=vllm_config.kernel_config.moe_backend,
327 - router_logits_dtype=router_logits_dtype,
328 - max_num_tokens=max_num_batched_tokens,
329 - max_capture_size=compilation_config.max_cudagraph_capture_size,
330 - has_bias=has_bias,
331 - is_act_and_mul=is_act_and_mul,
332 - is_lora_enabled=vllm_config.lora_config is not None,
333 - activation=self.activation,
334 - device=vllm_config.device_config.device,
335 - routing_method=self.routing_method_type,
336 - swiglu_limit=swiglu_limit,
337 - # TODO: in_dtype == out_dtype?
338 - )
339 - if self.moe_config.use_mori_kernels:
340 - assert self.rocm_aiter_fmoe_enabled, (
341 - "Mori needs to be used with aiter fused_moe for now."
342 - )
343 - assert not self.aiter_fmoe_shared_expert_enabled, (
344 - "Mori does not support fusion shared expert now. "
345 - "Turn it off by setting VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=0"
346 - )
347 -
348 - self.quant_config = quant_config
349 -
350 - def _get_quant_method() -> FusedMoEMethodBase:
351 - """
352 - Helper method to ensure self.quant_method is never None and
353 - of the proper type.
354 - """
355 - quant_method = None
356 - if self.quant_config is not None:
357 - quant_method = self.quant_config.get_quant_method(self, prefix)
358 - if quant_method is None:
359 - quant_method = UnquantizedFusedMoEMethod(self.moe_config)
360 - assert isinstance(quant_method, FusedMoEMethodBase)
361 - return quant_method
362 -
363 - # Note: get_quant_method will look at the layer's local_num_experts
364 - # for heuristic purposes, so it must be initialized first.
365 - self.quant_method: FusedMoEMethodBase = _get_quant_method()
366 -
367 - if not self.moe_config.is_act_and_mul and not (
368 - current_platform.is_cuda_alike() or current_platform.is_xpu()
369 - ):
370 - raise NotImplementedError(
371 - "is_act_and_mul=False is supported only for CUDA and XPU for now"
372 - )
373 -
374 - if enable_eplb and not self.quant_method.supports_eplb:
375 - # TODO: Add support for additional quantization methods.
376 - # The implementation for other quantization methods does not
377 - # contain essential differences, but the current quant API
378 - # design causes duplicated work when extending to new
379 - # quantization methods, so I'm leaving it for now.
380 - # If you plan to add support for more quantization methods,
381 - # please refer to the implementation in `Fp8MoEMethod`.
382 - raise NotImplementedError(
383 - f"EPLB is not supported {self.quant_method.__class__.__name__}."
384 - )
385 -
386 - # Round up hidden size and update moe_config.
387 - hidden_size, intermediate_size_per_partition = (
388 - self.quant_method.maybe_roundup_sizes(
389 - hidden_size,
390 - intermediate_size_per_partition,
391 - moe_in_dtype,
392 - self.moe_parallel_config,
393 - )
394 - )
395 - self.moe_config.hidden_dim = hidden_size
396 - self.moe_config.intermediate_size_per_partition = (
397 - intermediate_size_per_partition
398 - )
399 -
400 - moe_quant_params = {
401 - "num_experts": self.local_num_experts,
402 - "hidden_size": hidden_size,
403 - "intermediate_size_per_partition": intermediate_size_per_partition,
404 - "params_dtype": params_dtype,
405 - "weight_loader": self.weight_loader,
406 - "global_num_experts": self.global_num_experts,
407 - }
408 - # need full intermediate size pre-sharding for WNA16 act order
409 - if self.quant_method.__class__.__name__ in (
410 - "AutoGPTQMoEMethod",
411 - "CompressedTensorsWNA16MarlinMoEMethod",
412 - "CompressedTensorsWNA16MoEMethod",
413 - ):
414 - moe_quant_params["intermediate_size_full"] = intermediate_size
415 -
416 - self.quant_method.create_weights(layer=self, **moe_quant_params)
417 -
418 - # TODO(bnell): this is un-needed and removed in a follow up PR.
419 - self.base_quant_method = self.quant_method
420 -
421 - # Storing the runner in the FusedMoE is an intermediate state, eventually
422 - # the runner will own the FusedMoE layer and provide the execution interface
423 - # for MoE ops.
424 - self.runner: MoERunnerInterface = MoERunner(
425 - layer_name=self.layer_name,
426 - moe_config=self.moe_config,
427 - router=self.router,
428 - gate=gate,
429 - shared_experts=shared_experts,
430 - shared_expert_gate=self.shared_expert_gate,
431 - quant_method=self.quant_method,
432 - enable_dbo=self.vllm_config.parallel_config.enable_dbo,
433 - routed_input_transform=routed_input_transform,
434 - routed_output_transform=routed_output_transform,
435 - # When apply_routed_scale_to_output is True, we allow
436 - # the scaling factor to be passed to the runner, otherwise
437 - # we pass 1.0 so it ends up being a nop.
279 + # When apply_routed_scale_to_output is True, we set the scaling factor
280 + # to 1.0 so it ends up being a nop. Applying the scale will be handled
281 + # by the runner in this case.
282 + # The member variable must be set in the same way as the router since
283 + # some quantization methods can access it.
284 routed_scaling_factor=routed_scaling_factor
439 - if apply_routed_scale_to_output
285 + if not apply_routed_scale_to_output
286 else 1.0,
287 + e_score_correction_bias=e_score_correction_bias,
288 + num_fused_shared_experts=num_fused_shared_experts,
289 + zero_expert_type=zero_expert_type,
290 + num_logical_experts=logical_num_experts,
291 + hash_indices_table=hash_indices_table,
292 )
293
443 - # TODO(bnell): This method is provided as a hook so vllm/lora/layers/fused_moe.py
444 - # can safely swap out the quant_method. We should figure out a less
445 - # intrusive way to do this.
446 - def _replace_quant_method(self, mk: FusedMoEMethodBase):
447 - self.quant_method = mk
448 - self.runner._replace_quant_method(mk)
449 -
450 - # Note: maybe_init_modular_kernel should only be called by
451 - # prepare_communication_buffer_for_model.
452 - # This is called after all weight loading and post-processing, so it
453 - # should be safe to swap out the quant_method.
454 - def maybe_init_modular_kernel(self) -> None:
455 - # NOTE(rob): WIP refactor. For quant methods that own the MK
456 - # we create the MK during process_weights_after_loading.
457 - if self.quant_method.supports_internal_mk or self.quant_method.is_monolithic:
458 - return None
459 -
460 - self.ensure_moe_quant_config_init()
461 - prepare_finalize = self.base_quant_method.maybe_make_prepare_finalize(
462 - routing_tables=self._expert_routing_tables()
463 - )
464 - if prepare_finalize is not None:
465 - logger.debug(
466 - "%s for %s(%s)", prepare_finalize.__class__.__name__, self, id(self)
467 - )
468 - self._replace_quant_method(
469 - FusedMoEModularMethod.make(
470 - self,
471 - self.base_quant_method,
472 - prepare_finalize,
473 - )
474 - )
475 -
476 - @property
477 - def shared_experts(self) -> SharedExperts | None:
478 - return self.runner.shared_experts
479 -
480 - @property
481 - def layer_id(self):
482 - # Delayed import to avoid circular dependency
483 - from vllm.model_executor.models.utils import extract_layer_index
484 -
485 - return extract_layer_index(self.layer_name)
486 -
487 - @property
488 - def tp_size(self):
489 - return self.moe_parallel_config.tp_size
490 -
491 - @property
492 - def ep_size(self):
493 - return self.moe_parallel_config.ep_size
494 -
495 - @property
496 - def tp_rank(self):
497 - return self.moe_parallel_config.tp_rank
498 -
499 - @property
500 - def ep_rank(self):
501 - return self.moe_parallel_config.ep_rank
502 -
503 - @property
504 - def use_ep(self):
505 - return self.moe_parallel_config.use_ep
506 -
507 - @property
508 - def is_internal_router(self) -> bool:
509 - # By default, router/gate is called before FusedMoE forward pass
510 - return self.runner.is_internal_router()
511 -
512 - def update_expert_map_info(self):
513 - # Update local attributes from ExpertMapManager
514 - self.local_num_experts = self.expert_map_manager.local_num_experts
515 - self.expert_placement_strategy = self.expert_map_manager.placement_strategy
516 - self.register_buffer("_expert_map", self.expert_map_manager.expert_map)
517 - self.register_buffer("expert_mask", self.expert_map_manager.expert_mask)
518 -
519 - # Get routing tables from ExpertMapManager
520 - routing_tables = self.expert_map_manager.routing_tables
521 - if routing_tables is not None:
522 - # Register routing tables as buffers for this layer
523 - global_to_physical, physical_to_global, local_global = routing_tables
524 - self.register_buffer("expert_global_to_physical", global_to_physical)
525 - self.register_buffer("expert_physical_to_global", physical_to_global)
526 - self.register_buffer("expert_local_to_global", local_global)
527 -
528 - def _expert_routing_tables(
529 - self,
530 - ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None:
531 - # Return cached routing tables if already registered as buffers
532 - if hasattr(self, "expert_global_to_physical"):
533 - return cast(
534 - tuple[torch.Tensor, torch.Tensor, torch.Tensor],
535 - (
536 - self.expert_global_to_physical,
537 - self.expert_physical_to_global,
538 - self.expert_local_to_global,
539 - ),
540 - )
541 - return None
542 -
543 - def update_expert_map(self):
544 - # Update ExpertMapManager with new EP configuration
545 - # The moe_parallel_config (including ep_size and ep_rank)
546 - # should already be updated.
547 - # Note: ExpertMapManager.update() recalculates expert maps and
548 - # reinitializes routing tables internally.
549 - self.expert_map_manager.update(
550 - self.moe_parallel_config,
551 - global_num_experts=self.global_num_experts,
552 - )
553 -
554 - # Update local attributes from ExpertMapManager
555 - self.update_expert_map_info()
556 -
557 - def _load_per_tensor_weight_scale(
558 - self,
559 - shard_id: str,
560 - param: torch.nn.Parameter,
561 - loaded_weight: torch.Tensor,
562 - expert_id: int,
563 - ):
564 - param_data = param.data
565 - # for per tensor weight quantization
566 - if shard_id in ("w1", "w3"):
567 - # We have to keep the weight scales of w1 and w3 because
568 - # we need to re-quantize w1/w3 weights after weight loading.
569 - idx = 0 if shard_id == "w1" else 1
570 - param_data[expert_id][idx] = loaded_weight
571 - # If we are in the row parallel case (down_proj)
572 - elif shard_id == "w2":
573 - param_data[expert_id] = loaded_weight
574 -
575 - def _load_combined_w13_weight_scale(
576 - self,
577 - shard_dim: int,
578 - loaded_weight: torch.Tensor,
579 - param: torch.Tensor,
580 - tp_rank: int,
581 - ):
582 - """
583 - Load w13 weight scales assuming that w1 weight scales and w3 weight
584 - scales are stored in the same loaded_weight tensor.
585 - """
586 - shard_size = param.shape[shard_dim]
587 - loaded_weight = loaded_weight.narrow(
588 - shard_dim, shard_size * tp_rank, shard_size
589 - )
590 - param.copy_(loaded_weight)
591 -
592 - def _load_model_weight_or_group_weight_scale(
593 - self,
594 - shard_dim: int,
595 - expert_data: torch.Tensor,
596 - shard_id: str,
597 - loaded_weight: torch.Tensor,
598 - tp_rank: int,
599 - load_full_w2: bool = False,
600 - ):
601 - """
602 - Load grouped weight scales for group quantization or model weights
603 -
604 - Args:
605 - shard_dim: dimension to shard
606 - expert_data: parameter for a particular expert
607 - shard_id: either w1, w2, or w3
608 - loaded_weight: checkpoint weight to load into the param
609 - tp_rank: tensor parallel rank
610 - load_full_w2: whether or not the w2 loaded should be sharded.
611 - """
612 - if shard_id == "w2":
613 - # In the case where we have actorder/g_idx, we do not partition the
614 - # w2 scales, as indicated by `load_full` argument, for all tp cases
615 - self._load_w2(
616 - shard_dim=shard_dim,
617 - loaded_weight=loaded_weight,
618 - expert_data=expert_data,
619 - tp_rank=tp_rank,
620 - load_full=load_full_w2,
621 - )
622 - elif shard_id in ("w1", "w3"):
623 - self._load_w13(
624 - shard_id=shard_id,
625 - shard_dim=shard_dim,
626 - loaded_weight=loaded_weight,
627 - expert_data=expert_data,
628 - tp_rank=tp_rank,
629 - )
630 -
631 - def _load_per_channel_weight_scale(
632 - self,
633 - expert_data: torch.Tensor,
634 - shard_dim: int,
635 - shard_id: str,
636 - loaded_weight: torch.Tensor,
637 - tp_rank: int,
638 - ):
639 - # for per channel weight quantization
640 - if shard_id == "w2":
641 - hidden_dim = self._get_hidden_dim(shard_dim, expert_data.ndim)
642 - expert_data = self._narrow_expert_data_for_padding(
643 - expert_data,
644 - loaded_weight,
645 - hidden_dim=hidden_dim,
646 - shard_dim=shard_dim,
647 - )
648 - expert_data.copy_(loaded_weight)
649 - elif shard_id in ("w1", "w3"):
650 - self._load_w13(
651 - shard_id=shard_id,
652 - shard_dim=shard_dim,
653 - loaded_weight=loaded_weight,
654 - expert_data=expert_data,
655 - tp_rank=tp_rank,
656 - )
657 -
658 - @staticmethod
659 - def _get_hidden_dim(shard_dim: int, ndim: int) -> int:
660 - """Compute the hidden dimension index from the shard (intermediate)
661 - dimension and tensor rank.
662 -
663 - For 2D weight tensors the two data dims are (0, 1). For 3D tensors
664 - with an expert dimension at dim 0, they are (1, 2). ``shard_dim``
665 - occupies one of these; the hidden dimension is the other.
666 - For 1D tensors (e.g. per-channel scales) returns 0.
667 - """
668 - if ndim < 2:
669 - return 0
670 - dim_a = ndim - 2
671 - dim_b = ndim - 1
672 - if shard_dim == dim_a:
673 - return dim_b
674 - if shard_dim == dim_b:
675 - return dim_a
676 - raise ValueError(
677 - f"shard_dim={shard_dim} is not a valid data dimension "
678 - f"for a {ndim}D tensor (expected {dim_a} or {dim_b})"
679 - )
680 -
681 - @staticmethod
682 - def _narrow_expert_data_for_padding(
683 - expert_data: torch.Tensor,
684 - loaded_weight: torch.Tensor,
685 - hidden_dim: int,
686 - shard_dim: int | None = None,
687 - ) -> torch.Tensor:
688 - """Narrow expert_data to match loaded_weight for padded dimensions.
689 -
690 - When backends (e.g., DeepEP) round up hidden_size, weight parameters
691 - are larger than checkpoint weights. Narrow the padded hidden dimension
692 - before copying. Similarly, when padding occurs on the shard
693 - (intermediate) dimension (e.g. for MXFP4 GEMM), narrow that dimension
694 - as well.
695 -
696 - Args:
697 - expert_data: The (possibly padded) parameter tensor to narrow.
698 - loaded_weight: The checkpoint weight tensor with original size.
699 - hidden_dim: The dimension index corresponding to hidden_size.
700 - Must be non-negative.
701 - shard_dim: The dimension index corresponding to the shard
702 - (intermediate) dimension. Defaults to `None`.
703 - """
704 - dims = (hidden_dim,) if shard_dim is None else (hidden_dim, shard_dim)
705 - if loaded_weight.ndim > 0:
706 - for dim in dims:
707 - if (
708 - 0 <= dim < expert_data.ndim
709 - and dim < loaded_weight.ndim
710 - and expert_data.shape[dim] > loaded_weight.shape[dim]
711 - ):
712 - expert_data = expert_data.narrow(dim, 0, loaded_weight.shape[dim])
713 - return expert_data
714 -
715 - def _load_w13(
716 - self,
717 - expert_data: torch.Tensor,
718 - shard_dim: int,
719 - shard_id: str,
720 - loaded_weight: torch.Tensor,
721 - tp_rank: int,
722 - load_full: bool = False,
723 - ):
724 - # Index the loaded weight for tp sharding.
725 - # gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim
726 - if self.moe_config.is_act_and_mul:
727 - shard_size = expert_data.shape[shard_dim] // 2
728 - else:
729 - shard_size = expert_data.shape[shard_dim]
730 - # Only narrow if the loaded_weight is not a scalar (0-dim tensor)
731 - # and we're not loading the full weight
732 - if not load_full and loaded_weight.ndim > 0:
733 - # When the parameter has been padded (e.g. MXFP4 rounding up
734 - # intermediate_size_per_partition), shard_size is the padded
735 - # size. Compute the offset into the checkpoint weight using
736 - # the *unpadded* per-rank size so that every TP rank lands at
737 - # the correct slice.
738 - tp_size = self.moe_config.moe_parallel_config.tp_size
739 - loaded_per_rank = loaded_weight.shape[shard_dim] // tp_size
740 - start_offset = loaded_per_rank * tp_rank
741 - available = loaded_weight.shape[shard_dim] - start_offset
742 - if available <= 0:
743 - # If there is no available weight to load for this TP rank
744 - return
745 - narrow_size = min(loaded_per_rank, available)
746 - loaded_weight = loaded_weight.narrow(shard_dim, start_offset, narrow_size)
747 - # Narrow parameter and load.
748 - # w1, gate_proj: Load into first logical weight of w13.
749 - if shard_id == "w1":
750 - expert_data = expert_data.narrow(shard_dim, 0, shard_size)
751 - # w3, up_proj: Load into second logical weight of w13.
752 - else:
753 - assert shard_id == "w3"
754 - expert_data = expert_data.narrow(shard_dim, shard_size, shard_size)
755 - hidden_dim = self._get_hidden_dim(shard_dim, expert_data.ndim)
756 - expert_data = self._narrow_expert_data_for_padding(
757 - expert_data,
758 - loaded_weight,
759 - hidden_dim=hidden_dim,
760 - shard_dim=shard_dim,
761 - )
762 - expert_data.copy_(loaded_weight)
763 -
764 - def _load_w2(
765 - self,
766 - expert_data: torch.Tensor,
767 - shard_dim: int,
768 - loaded_weight: torch.Tensor,
769 - tp_rank: int,
770 - load_full: bool = False,
771 - ):
772 - # Index the loaded weight for tp sharding.
773 - # down_proj: "RowParallel" so tp sharding on input_dim
774 - # Only narrow if the loaded_weight is not a scalar (0-dim tensor)
775 - # and we're not loading the full weight
776 - if not load_full and loaded_weight.ndim > 0:
777 - # Same padding fix as _load_w13: use unpadded per-rank size.
778 - tp_size = self.moe_config.moe_parallel_config.tp_size
779 - loaded_per_rank = loaded_weight.shape[shard_dim] // tp_size
780 - start_offset = loaded_per_rank * tp_rank
781 - available = loaded_weight.shape[shard_dim] - start_offset
782 - if available <= 0:
783 - # If there is no available weight to load for this TP rank
784 - return
785 - narrow_size = min(loaded_per_rank, available)
786 - loaded_weight = loaded_weight.narrow(shard_dim, start_offset, narrow_size)
787 - # w2, down_proj: Load into only logical weight of w2.
788 - hidden_dim = self._get_hidden_dim(shard_dim, expert_data.ndim)
789 - expert_data = self._narrow_expert_data_for_padding(
790 - expert_data,
791 - loaded_weight,
792 - hidden_dim=hidden_dim,
793 - shard_dim=shard_dim,
794 - )
795 - expert_data.copy_(loaded_weight)
796 -
797 - def _load_single_value(
798 - self, param: torch.nn.Parameter, loaded_weight: torch.Tensor, expert_id: int
799 - ):
800 - param_data = param.data
801 -
802 - # Input scales can be loaded directly and should be equal.
803 - param_data[expert_id] = loaded_weight
804 -
805 - def _load_g_idx(
806 - self,
807 - shard_id: str,
808 - expert_data: torch.Tensor,
809 - shard_dim: int,
810 - loaded_weight: torch.Tensor,
811 - tp_rank: int,
812 - ):
813 - if shard_id == "w2":
814 - self._load_w2(
815 - shard_dim=shard_dim,
816 - loaded_weight=loaded_weight,
817 - expert_data=expert_data,
818 - tp_rank=tp_rank,
819 - )
820 - else:
821 - assert shard_id in ("w1", "w3")
822 - expert_data.copy_(loaded_weight)
823 -
824 - def _map_global_expert_id_to_local_expert_id(self, expert_id: int) -> int:
825 - return self.expert_map_manager.map_global_to_local(expert_id)
826 -
827 - @overload
828 - def weight_loader(
829 - self,
830 - param: torch.nn.Parameter,
831 - loaded_weight: torch.Tensor,
832 - weight_name: str,
833 - shard_id: str,
834 - expert_id: int,
835 - return_success: Literal[False],
836 - ) -> None: ...
837 -
838 - @overload
839 - def weight_loader(
840 - self,
841 - param: torch.nn.Parameter,
842 - loaded_weight: torch.Tensor,
843 - weight_name: str,
844 - shard_id: str,
845 - expert_id: int,
846 - return_success: Literal[True],
847 - ) -> bool: ...
848 -
849 - def weight_loader(
850 - self,
851 - param: torch.nn.Parameter,
852 - loaded_weight: torch.Tensor,
853 - weight_name: str,
854 - shard_id: str,
855 - expert_id: int,
856 - return_success: bool = False,
857 - ) -> bool | None:
858 - quant_config_name = self.quant_config and self.quant_config.get_name()
859 - if quant_config_name == "gpt_oss_mxfp4":
860 - # (FIXME) for gpt-oss all experts are combined
861 - if "bias" in weight_name:
862 - dim1 = loaded_weight.shape[1]
863 - param.data[:, :dim1].copy_(loaded_weight)
864 - else:
865 - dim1 = loaded_weight.shape[1]
866 - dim2 = loaded_weight.shape[2]
867 - param.data[:, :dim1, :dim2].copy_(loaded_weight)
868 - return True if return_success else None
869 -
870 - quant_method_name = self.quant_method.__class__.__name__
871 - global_expert_id = expert_id
872 - expert_id = self._map_global_expert_id_to_local_expert_id(global_expert_id)
873 -
874 - use_global_sf = (
875 - getattr(self.quant_method, "use_global_sf", False)
876 - and "input_scale" in weight_name
877 - )
878 -
879 - if expert_id == -1 and not use_global_sf:
880 - # Failed to load this param since it's not local to this rank
881 - return False if return_success else None
882 - # Hereafter, `expert_id` is local physical id
883 -
884 - # is_transposed: if the dim to shard the weight
885 - # should be flipped. Required by GPTQ, compressed-tensors
886 - # should be whatever dimension intermediate_size_per_partition is
887 - is_transposed = getattr(param, "is_transposed", False)
888 -
889 - # compressed-tensors checkpoints with packed weights are stored flipped
890 - # TODO (mgoin): check self.quant_method.quant_config.quant_format
891 - # against known CompressionFormat enum values that have this quality
892 - if quant_method_name in (
893 - "CompressedTensorsWNA16MarlinMoEMethod",
894 - "CompressedTensorsWNA16MoEMethod",
895 - "CompressedTensorsWNA16RDNA3MoEMethod",
896 - ):
897 - if is_transposed:
898 - loaded_weight = loaded_weight.t().contiguous()
899 - else:
900 - loaded_weight = loaded_weight
901 -
902 - if shard_id not in ("w1", "w2", "w3"):
903 - raise ValueError(f"shard_id must be ['w1','w2','w3'] but got {shard_id}.")
904 -
905 - # Fetch the dim to shard the parameter/loaded weight
906 - # based on the shard id. This will be whatever
907 - # dimension intermediate_size_per_partition is used.
908 - SHARD_ID_TO_SHARDED_DIM = {"w1": 0, "w2": 1, "w3": 0}
909 -
910 - is_gguf_weight = getattr(param, "is_gguf_weight", False)
911 - is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
912 - if is_gguf_weight_type:
913 - param.weight_type = loaded_weight.item()
914 - param.data.copy_(loaded_weight)
915 - return True if return_success else None
916 -
917 - # Case for BitsAndBytes
918 - use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
919 - if use_bitsandbytes_4bit:
920 - shard_dim = 0
921 -
922 - expert_data = param.data[expert_id]
923 - if shard_id == "w2":
924 - # BnB params are stored as flat packed tensors (e.g.
925 - # (packed_size, 1)), not in the logical weight layout.
926 - # Narrowing packed data for hidden-dim padding is not
927 - # meaningful, so require an exact shape match.
928 - if expert_data.shape != loaded_weight.shape:
929 - raise ValueError(
930 - "BitsAndBytes quantization with padded hidden_size "
931 - "(e.g., from DeepEP) is not supported. "
932 - f"Parameter shape {tuple(expert_data.shape)} != "
933 - f"checkpoint shape {tuple(loaded_weight.shape)}"
934 - )
935 - expert_data.copy_(loaded_weight)
936 - elif shard_id in ("w1", "w3"):
937 - # BnB stores weights as flat packed tensors. _load_w13 is
938 - # still used to split the w1/w3 portions along shard_dim.
939 - # _narrow_expert_data_for_padding will be a no-op since
940 - # packed sizes should already match; if DeepEP padding
941 - # causes a mismatch the copy_() will fail with a clear
942 - # shape error.
943 - full_load = True
944 - self._load_w13(
945 - shard_id=shard_id,
946 - shard_dim=shard_dim,
947 - loaded_weight=loaded_weight,
948 - expert_data=expert_data,
949 - tp_rank=self.tp_rank,
950 - load_full=full_load,
951 - )
952 - return True if return_success else None
953 -
954 - shard_dim = SHARD_ID_TO_SHARDED_DIM[shard_id]
955 - if is_transposed:
956 - shard_dim = int(not shard_dim)
957 -
958 - full_load = len(loaded_weight.shape) == 3
959 - if full_load:
960 - shard_dim += 1
961 -
962 - # Materialize GGUF UninitializedParameter accounting merged weights
963 - if is_gguf_weight and isinstance(param, UninitializedParameter):
964 - # To materialize a tensor, we must have full shape including
965 - # number of experts, making this portion to require `full_load`.
966 - assert full_load
967 - final_shape = list(loaded_weight.shape)
968 - # w1 and w3 are merged per expert.
969 - if shard_id in {"w1", "w3"}:
970 - final_shape[1] *= 2
971 - final_shape[shard_dim] = final_shape[shard_dim] // self.tp_size
972 - param.materialize(final_shape, dtype=loaded_weight.dtype)
973 -
974 - expert_data = param.data if full_load else param.data[expert_id]
975 -
976 - # Case input scale: input_scale loading is only supported for fp8
977 - if "input_scale" in weight_name:
978 - # this is needed for compressed-tensors only
979 - loaded_weight = loaded_weight.to(param.data.device)
980 -
981 - # ModelOpt NVFP4 stores w13 input scales as two logical shards.
982 - # The generic assignment below would broadcast w1/w3 into the
983 - # whole expert row, so the second shard would overwrite the first.
984 - if (
985 - "ModelOpt" in quant_method_name
986 - and param.data.ndim == 2
987 - and shard_id in ("w1", "w3")
988 - ):
989 - scale_expert_id = global_expert_id if use_global_sf else expert_id
990 - scale_shard_id = 0 if shard_id == "w1" else 1
991 - param.data[scale_expert_id][scale_shard_id] = loaded_weight.reshape(())
992 - return True if return_success else None
993 -
994 - if (
995 - "compressed" in quant_method_name.lower()
996 - and param.data[expert_id] != 1
997 - and (param.data[expert_id] - loaded_weight).abs() > 1e-5
998 - ):
999 - raise ValueError(
1000 - "input_scales of w1 and w3 of a layer "
1001 - f"must be equal. But got {param.data[expert_id]} "
1002 - f"vs. {loaded_weight}"
1003 - )
1004 -
1005 - self._load_single_value(
1006 - param=param,
1007 - loaded_weight=loaded_weight,
1008 - expert_id=global_expert_id if use_global_sf else expert_id,
1009 - )
1010 - return True if return_success else None
1011 -
1012 - # Case g_idx
1013 - if "g_idx" in weight_name:
1014 - self._load_g_idx(
1015 - shard_dim=0,
1016 - shard_id=shard_id,
1017 - loaded_weight=loaded_weight,
1018 - expert_data=expert_data,
1019 - tp_rank=self.tp_rank,
1020 - )
1021 - return True if return_success else None
1022 -
1023 - # TODO @dsikka: ModelOpt should follow the proper MoE loading pattern
1024 - if "ModelOpt" in quant_method_name:
1025 - # Determine per-tensor weight scale patterns based on variant
1026 - # Use the dedicated method instead of brittle string matching
1027 - uses_weight_scale_2 = self.quant_method.uses_weight_scale_2_pattern()
1028 - quant_method = getattr(param, "quant_method", None)
1029 -
1030 - # Call _load_per_tensor_weight_scale() to load per-tensor (scalar)
1031 - # weights scales.
1032 - # Input scales are always per-tensor.
1033 - # Weight scales: FP4 uses "weight_scale_2" and FP8 uses
1034 - # "weight_scale" for per-tensor scales.
1035 - # NOTE: ModelOpt MXFP8 MoE uses block scales in weight_scale
1036 - # tensors (quant_method=BLOCK), so those must not be treated
1037 - # as per-tensor scalars here.
1038 - is_block_weight_scale = (
1039 - "weight_scale" in weight_name
1040 - and quant_method == FusedMoeWeightScaleSupported.BLOCK.value
1041 - )
1042 - is_per_tensor = (
1043 - "weight_scale_2" in weight_name
1044 - if uses_weight_scale_2
1045 - else "weight_scale" in weight_name
1046 - ) or "input_scale" in weight_name
1047 - is_per_tensor = is_per_tensor and not is_block_weight_scale
1048 - if is_per_tensor:
1049 - self._load_per_tensor_weight_scale(
1050 - shard_id=shard_id,
1051 - param=param,
1052 - loaded_weight=loaded_weight,
1053 - expert_id=expert_id,
1054 - )
1055 - return True if return_success else None
1056 -
1057 - # If the weight is w13_weight_scale and w13_weight_scales are
1058 - # combined into single loaded_weight, call
1059 - # _load_combined_w13_weight_scale() to load it.
1060 - # This is checked by comparing the hidden_out dims of the
1061 - # loaded_weight and the param.
1062 - if "w13_weight_scale" in weight_name:
1063 - loaded_weight_hidden_out = loaded_weight.shape[-2]
1064 - param_hidden_out = param.data.shape[-2] * self.tp_size
1065 - if loaded_weight_hidden_out == param_hidden_out:
1066 - self._load_combined_w13_weight_scale(
1067 - shard_dim=shard_dim,
1068 - loaded_weight=loaded_weight,
1069 - param=expert_data,
1070 - tp_rank=self.tp_rank,
1071 - )
1072 - return True if return_success else None
1073 -
1074 - # For other weights, call _load_model_weight_or_group_weight_scale()
1075 - # to load it.
1076 - if "weight" in weight_name:
1077 - self._load_model_weight_or_group_weight_scale(
1078 - shard_id=shard_id,
1079 - shard_dim=shard_dim,
1080 - loaded_weight=loaded_weight,
1081 - expert_data=expert_data,
1082 - tp_rank=self.tp_rank,
1083 - )
1084 - return True if return_success else None
1085 -
1086 - # Case weight scales, zero_points and offset, weight/input global scales
1087 - if "scale" in weight_name or "zero" in weight_name or "offset" in weight_name:
1088 - # load the weight scales and zp based on the quantization scheme
1089 - # supported weight scales/zp can be found in
1090 - # FusedMoeWeightScaleSupported
1091 - # TODO @dsikka: once hardened, refactor to use vLLM Parameters
1092 - # specific to each case
1093 - quant_method = getattr(param, "quant_method", None)
1094 - if quant_method == FusedMoeWeightScaleSupported.CHANNEL.value:
1095 - self._load_per_channel_weight_scale(
1096 - shard_id=shard_id,
1097 - shard_dim=shard_dim,
1098 - loaded_weight=loaded_weight,
1099 - expert_data=expert_data,
1100 - tp_rank=self.tp_rank,
1101 - )
1102 - elif quant_method in [
1103 - FusedMoeWeightScaleSupported.GROUP.value,
1104 - FusedMoeWeightScaleSupported.BLOCK.value,
1105 - ]:
1106 - self._load_model_weight_or_group_weight_scale(
1107 - shard_id=shard_id,
1108 - shard_dim=shard_dim,
1109 - loaded_weight=loaded_weight,
1110 - expert_data=expert_data,
1111 - tp_rank=self.tp_rank,
1112 - load_full_w2=getattr(param, "load_full_w2", False),
1113 - )
1114 - elif quant_method == FusedMoeWeightScaleSupported.TENSOR.value:
1115 - self._load_per_tensor_weight_scale(
1116 - shard_id=shard_id,
1117 - param=param,
1118 - loaded_weight=loaded_weight,
1119 - expert_id=expert_id,
1120 - )
1121 - else:
1122 - WEIGHT_SCALE_SUPPORTED = [e.value for e in FusedMoeWeightScaleSupported]
1123 - raise ValueError(
1124 - f"quant method must be one of {WEIGHT_SCALE_SUPPORTED}"
1125 - )
1126 - return True if return_success else None
1127 -
1128 - # Case weight_shape
1129 - if "weight_shape" in weight_name:
1130 - # only required by compressed-tensors
1131 - self._load_single_value(
1132 - param=param, loaded_weight=loaded_weight, expert_id=expert_id
1133 - )
1134 - return True if return_success else None
1135 -
1136 - # Case model weights
1137 - if "weight" in weight_name:
1138 - self._load_model_weight_or_group_weight_scale(
1139 - shard_id=shard_id,
1140 - shard_dim=shard_dim,
1141 - loaded_weight=loaded_weight,
1142 - expert_data=expert_data,
1143 - tp_rank=self.tp_rank,
1144 - )
1145 - return True if return_success else None
1146 -
1147 - return False if return_success else None
1148 -
1149 - def load_weights(
1150 - self, weights: Iterable[tuple[str, torch.Tensor]]
1151 - ) -> Iterable[str]:
1152 - if (expert_mapping := self.expert_mapping) is None:
1153 - raise ValueError(
1154 - "`self.expert_mapping` must be provided to "
1155 - "load weights using `self.load_weights`."
1156 - )
1157 - for expert_name, loaded_weight in weights:
1158 - qual_name = f"{self.layer_name}.{expert_name}"
1159 - for param_name, weight_name, expert_id, shard_id in expert_mapping:
1160 - if weight_name not in qual_name:
1161 - continue
1162 - weight_name = qual_name.replace(weight_name, param_name)
1163 - param_name = weight_name.removeprefix(f"{self.layer_name}.")
1164 - param = getattr(self, param_name)
1165 - # Fused expert weights can be identified by their 3D tensors
1166 - if loaded_weight.dim() == 3:
1167 - # Repurpose expert_id as shard_idx for deconcatenating w1 and w3
1168 - if shard_id in {"w1", "w3"}:
1169 - shard_idx = expert_id
1170 - experts_shard = loaded_weight.chunk(2, dim=1)[shard_idx]
1171 - else:
1172 - experts_shard = loaded_weight
1173 - start = 0
1174 - else:
1175 - # loaded_weight is a single expert weight, so we add a dummy expert
1176 - # dimension to unify the loading logic with the fused case
1177 - experts_shard = loaded_weight.unsqueeze(0)
1178 - start = expert_id
1179 -
1180 - # Unified loading logic for fused and non-fused experts
1181 - loaded_experts = experts_shard.unbind()
1182 - for expert_id, loaded_expert in enumerate(loaded_experts, start=start):
1183 - success = self.weight_loader(
1184 - param=param,
1185 - loaded_weight=loaded_expert,
1186 - weight_name=weight_name,
1187 - shard_id=shard_id,
1188 - expert_id=expert_id,
1189 - return_success=True,
1190 - )
1191 - if success:
1192 - logger.debug(
1193 - "Loaded expert %d of shard %s into %s for layer %s",
1194 - expert_id,
1195 - shard_id,
1196 - param_name,
1197 - self.layer_name,
1198 - )
1199 - yield param_name
1200 -
1201 - def get_expert_weights(self) -> Iterable[torch.Tensor]:
1202 - def _maybe_make_contiguous(
1203 - name: str, p: torch.nn.Parameter
1204 - ) -> torch.nn.Parameter:
1205 - """
1206 - In some cases, the last 2 dimensions (the non-expert dimensions)
1207 - of the weight scale tensor are transposed. This function
1208 - transforms the tensor (view update) so the tensor is contiguous().
1209 - Example: A non-contiguous scale tensor,
1210 - `x` of shape (E, 32, 16) and stride (512, 1, 32) is transformed to
1211 - `x_` of shape (E, 16, 32) and stride (512, 32, 1).
1212 - Note that we specifically use torch.transpose() so `x_` refers
1213 - to the same underlying memory. The tensors `x` and `x_`, pointing
1214 - to the same underlying memory make this transformation safe in the
1215 - context of EPLB. i.e. It is the same memory and just the view
1216 - is different.
1217 - Note: This function handles the "weight_scale" tensors specifically.
1218 - This could however be generalized to handle similar tensors.
1219 - """
1220 - if p.ndim != 3:
1221 - return p
1222 - if p.is_contiguous():
1223 - # Already contiguous. do nothing.
1224 - return p
1225 - # p is non-contiguous. We only handle the case where the last 2
1226 - # dimensions of the scales tensor is transposed. We can handle
1227 - # other cases when they become relevant.
1228 - is_transposed_12 = p.stride(1) == 1 and p.stride(2) != 1
1229 - if "weight_scale" not in name or not is_transposed_12:
1230 - # do nothing.
1231 - return p
1232 -
1233 - # Do not update the layer parameter as the layer's MoE operations would
1234 - # expect the parameter's tensor to the same shape / stride. Instead,
1235 - # make a new torch.nn.Parameter that is used just in the context of
1236 - # EPLB.
1237 - return torch.nn.Parameter(
1238 - torch.transpose(p.data, 1, 2), requires_grad=False
1239 - )
1240 -
1241 - weights = list(self.named_parameters())
1242 - weights = [(name, _maybe_make_contiguous(name, p)) for name, p in weights]
1243 -
1244 - # `w13_input_scale` and `w2_input_scale` are global per-tensor
1245 - # activation scales shared across all experts (e.g. NVFP4).
1246 - # They are broadcast views (stride 0) from .expand() and are
1247 - # not actual expert weights, so exclude them from EPLB.
1248 - NON_EXPERT_WEIGHTS = {
1249 - "e_score_correction_bias",
1250 - "w13_input_scale",
1251 - "w2_input_scale",
1252 - }
1253 -
1254 - # Parameters of non-expert submodules that live inside runner (MoERunner).
1255 - # These must be excluded from EPLB weight rearrangement.
1256 - NON_EXPERT_PREFIXES = (
1257 - "runner._shared_experts.",
1258 - "runner.gate.",
1259 - "runner.routed_input_transform.",
1260 - "runner.routed_output_transform.",
1261 - )
1262 -
1263 - assert all(
1264 - weight.is_contiguous()
1265 - for name, weight in weights
1266 - if not name.startswith(NON_EXPERT_PREFIXES)
1267 - and name not in NON_EXPERT_WEIGHTS
1268 - )
1269 -
1270 - return [
1271 - weight.view(self.local_num_experts, -1)
1272 - for name, weight in weights
1273 - if name not in NON_EXPERT_WEIGHTS
1274 - and weight.shape != torch.Size([])
1275 - and not name.startswith(NON_EXPERT_PREFIXES)
1276 - ]
1277 -
1278 - def set_eplb_state(
1279 - self,
1280 - moe_layer_idx: int,
1281 - expert_load_view: torch.Tensor,
1282 - logical_to_physical_map: torch.Tensor,
1283 - logical_replica_count: torch.Tensor,
1284 - ) -> None:
1285 - """
1286 - Register the EPLB state in this layer.
1287 -
1288 - This is used later in forward pass, where we get the expert mapping
1289 - and record the load metrics in `expert_load_view`.
1290 -
1291 - Args:
1292 - moe_layer_idx: Index of this MoE layer
1293 - expert_load_view: View into global expert load tracking tensor
1294 - logical_to_physical_map: Mapping from logical to physical expert IDs
1295 - logical_replica_count: Number of replicas for each logical expert
1296 - """
1297 - if self.eplb_state is not None:
1298 - self.eplb_state.set_layer_state(
1299 - moe_layer_idx,
1300 - expert_load_view,
1301 - logical_to_physical_map,
1302 - logical_replica_count,
1303 - )
1304 -
1305 - def ensure_moe_quant_config_init(self):
1306 - if self.quant_method.moe_quant_config is None:
1307 - # Note: the moe_quant_config can't be constructed until after
1308 - # weight loading post processing.
1309 - self.quant_method.moe_quant_config = (
1310 - self.quant_method.get_fused_moe_quant_config(self)
1311 - )
1312 -
1313 - @property
1314 - def moe_quant_config(self) -> FusedMoEQuantConfig | None:
1315 - self.ensure_moe_quant_config_init()
1316 - return self.quant_method.moe_quant_config
1317 -
1318 - def forward(
1319 - self,
1320 - hidden_states: torch.Tensor,
1321 - router_logits: torch.Tensor,
1322 - input_ids: torch.Tensor | None = None,
1323 - ) -> torch.Tensor:
1324 - return self.runner.forward(
1325 - hidden_states,
1326 - router_logits,
1327 - input_ids,
1328 - )
1329 -
1330 - @property
1331 - def expert_map(self) -> torch.Tensor | None:
1332 - return (
1333 - self._expert_map if not self.rocm_aiter_fmoe_enabled else self.expert_mask
1334 - )
1335 -
1336 - @classmethod
1337 - def make_expert_params_mapping(
1338 - cls,
1339 - model: torch.nn.Module,
1340 - ckpt_gate_proj_name: str,
1341 - ckpt_down_proj_name: str,
1342 - ckpt_up_proj_name: str,
1343 - num_experts: int,
1344 - num_redundant_experts: int = 0,
1345 - ) -> list[tuple[str, str, int, str]]:
1346 - num_physical_experts = num_experts + num_redundant_experts
1347 -
1348 - # In the returned mapping:
1349 - # - `expert_id` is the physical expert id
1350 - # - `weight_name` contains the weight name of the logical expert
1351 - # So that we should map the expert id to logical in `weight_name`
1352 - physical_to_logical_map = (
1353 - EplbState.build_initial_global_physical_to_logical_map(
1354 - num_experts, num_redundant_experts
1355 - )
1356 - )
1357 -
1358 - base_layer = (
1359 - "base_layer."
1360 - if any(".base_layer." in name for name, _ in model.named_parameters())
1361 - else ""
1362 - )
1363 -
1364 - return [
1365 - # (param_name, weight_name, expert_id, shard_id)
1366 - (
1367 - f"experts.{base_layer}w13_"
1368 - if weight_name in [ckpt_gate_proj_name, ckpt_up_proj_name]
1369 - else f"experts.{base_layer}w2_",
1370 - f"experts.{physical_to_logical_map[expert_id]}.{weight_name}.{base_layer}",
1371 - expert_id,
1372 - shard_id,
1373 - )
1374 - for expert_id in range(num_physical_experts)
1375 - for shard_id, weight_name in [
1376 - ("w1", ckpt_gate_proj_name),
1377 - ("w2", ckpt_down_proj_name),
1378 - ("w3", ckpt_up_proj_name),
1379 - ]
1380 - ]
1381 -
1382 - @property
1383 - def hidden_size(self) -> int:
1384 - return self.moe_config.hidden_dim
294 + if params_dtype is None:
295 + params_dtype = torch.get_default_dtype()
296 +
297 + # FIXME (varun): We should have a better way of inferring the activation
298 + # datatype. This works for now as the tensor datatype entering the MoE
299 + # operation is typically unquantized (i.e. float16/bfloat16).
300 + if vllm_config.model_config is not None:
301 + moe_in_dtype = vllm_config.model_config.dtype
302 + else:
303 + # TODO (bnell): This is a hack to get test_mixtral_moe to work
304 + # since model_config is not set in the pytest test.
305 + moe_in_dtype = params_dtype
306 +
307 + moe_config = FusedMoEConfig(
308 + num_experts=global_num_experts,
309 + experts_per_token=top_k,
310 + hidden_dim=hidden_size,
311 + intermediate_size=intermediate_size,
312 + num_local_experts=expert_map_manager.local_num_experts,
313 + num_logical_experts=logical_num_experts,
314 + moe_parallel_config=moe_parallel_config,
315 + in_dtype=moe_in_dtype,
316 + moe_backend=vllm_config.kernel_config.moe_backend,
317 + router_logits_dtype=router_logits_dtype,
318 + max_num_tokens=max_num_batched_tokens,
319 + has_bias=has_bias,
320 + is_lora_enabled=vllm_config.lora_config is not None,
321 + activation=moe_activation,
322 + device=vllm_config.device_config.device,
323 + routing_method=router.routing_method_type, # Not ideal
324 + swiglu_limit=swiglu_limit,
325 + max_capture_size=vllm_config.compilation_config.max_cudagraph_capture_size,
326 + )
327
1386 - @property
1387 - def intermediate_size_per_partition(self) -> int:
1388 - return self.moe_config.intermediate_size_per_partition
328 + logger.debug("FusedMoEConfig = %s", moe_config)
329 +
330 + # Create RoutedExperts instance BEFORE create_weights()
331 + # This will hold all expert weight parameters
332 + if routed_experts_cls is None:
333 + routed_experts_cls = RoutedExperts
334 +
335 + assert params_dtype is not None
336 + routed_experts = routed_experts_cls(
337 + layer_name,
338 + params_dtype,
339 + moe_config,
340 + quant_config,
341 + expert_map_manager=expert_map_manager,
342 + expert_mapping=expert_mapping,
343 + # Extra params that are needed by quant_methods, pass along for now
344 + # Prefer getting these from other sources, e.g. moe_config or
345 + # router object
346 + renormalize=renormalize,
347 + use_grouped_topk=use_grouped_topk,
348 + num_expert_group=num_expert_group,
349 + topk_group=topk_group,
350 + custom_routing_function=custom_routing_function,
351 + scoring_func=scoring_func,
352 + routed_scaling_factor=routed_scaling_factor,
353 + swiglu_limit=swiglu_limit,
354 + # TODO get from router? needs to be truncated?
355 + e_score_correction_bias=e_score_correction_bias,
356 + apply_router_weight_on_input=apply_router_weight_on_input,
357 + **routed_experts_args if routed_experts_args is not None else {},
358 + )
359
1390 - def extra_repr(self) -> str:
1391 - s = (
1392 - f"global_num_experts={self.global_num_experts}, "
1393 - f"local_num_experts={self.local_num_experts}, "
1394 - f"top_k={self.top_k}, "
1395 - f"intermediate_size_per_partition={self.intermediate_size_per_partition}, " # noqa: E501
1396 - f"tp_size={self.tp_size},\n"
1397 - f"ep_size={self.ep_size}, "
1398 - )
360 + if runner_cls is None:
361 + runner_cls = MoERunner
362 +
363 + runner = runner_cls(
364 + layer_name=layer_name,
365 + moe_config=moe_config,
366 + router=router,
367 + routed_experts=routed_experts,
368 + enable_dbo=vllm_config.parallel_config.enable_dbo,
369 + gate=gate,
370 + shared_expert_gate=shared_expert_gate,
371 + shared_experts=shared_experts,
372 + routed_input_transform=routed_input_transform,
373 + routed_output_transform=routed_output_transform,
374 + # When apply_routed_scale_to_output is True, we allow
375 + # the scaling factor to be passed to the runner, otherwise
376 + # we pass 1.0 so it ends up being a nop.
377 + routed_scaling_factor=routed_scaling_factor
378 + if apply_routed_scale_to_output
379 + else 1.0,
380 + **runner_args if runner_args is not None else {},
381 + )
382
1400 - return s
383 + return runner
384
385
1403 -# This is a temporary forwarding method which will be removed/modified layer.
386 def fused_moe_make_expert_params_mapping(
387 model: torch.nn.Module,
388 ckpt_gate_proj_name: str,
@@ -1408,17 +390,15 @@ def fused_moe_make_expert_params_mapping(
390 ckpt_up_proj_name: str,
391 num_experts: int,
392 num_redundant_experts: int = 0,
393 + routed_experts_prefix: str = "routed_experts",
394 ) -> list[tuple[str, str, int, str]]:
1412 - return FusedMoE.make_expert_params_mapping(
395 + """Delegate to EPLB manager."""
396 + return RoutedExperts.make_expert_params_mapping(
397 model,
398 ckpt_gate_proj_name,
399 ckpt_down_proj_name,
400 ckpt_up_proj_name,
401 num_experts,
402 num_redundant_experts,
403 + routed_experts_prefix,
404 )
1420 -
1421 -
1422 -# Mark the FusedMoE weight_loader as supporting MoE-specific parameters
1423 -# to avoid expensive runtime reflection in model loading code
1424 -FusedMoE.weight_loader.supports_moe_loading = True # type: ignore[attr-defined]
vllm/model_executor/layers/fused_moe/modular_kernel.py
+3 -2
@@ -1097,8 +1097,9 @@ class FusedMoEKernelModularImpl:
1097 shared_experts_input: torch.Tensor | None,
1098 ):
1099 if shared_experts is not None:
1100 + assert self.prepare_finalize.supports_async()
1101 assert shared_experts_input is not None
1101 - shared_experts.apply(
1102 + shared_experts(
1103 shared_experts_input,
1104 SharedExpertsOrder.MK_INTERNAL_OVERLAPPED,
1105 )
@@ -1572,7 +1573,7 @@ class FusedMoEKernel:
1573 hidden_states: torch.Tensor,
1574 w1: torch.Tensor,
1575 w2: torch.Tensor,
1575 - router_logits: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
1576 + router_logits: torch.Tensor,
1577 activation: MoEActivation,
1578 global_num_experts: int,
1579 expert_map: torch.Tensor | None,
vllm/model_executor/layers/fused_moe/oracle/fp8.py
+2 -1
@@ -18,6 +18,7 @@ from vllm.model_executor.layers.fused_moe.config import (
18 fp8_w8a8_moe_quant_config,
19 fp8_w8a16_moe_quant_config,
20 )
21 +from vllm.model_executor.layers.fused_moe.routed_experts import RoutedExperts
22 from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
23 FlashinferMoeBackend,
24 get_flashinfer_moe_backend,
@@ -423,7 +424,7 @@ def select_fp8_moe_backend(
424 def convert_to_fp8_moe_kernel_format(
425 fp8_backend: Fp8MoeBackend,
426 # TODO(bnell): replace layer with weight_block_size
426 - layer: torch.nn.Module,
427 + layer: RoutedExperts,
428 w13: torch.Tensor,
429 w2: torch.Tensor,
430 w13_scale: torch.Tensor,
vllm/model_executor/layers/fused_moe/oracle/mxfp4.py
+3 -3
@@ -13,6 +13,7 @@ from vllm.config.quantization import QuantizationConfigArgs
13 from vllm.logger import init_logger
14 from vllm.model_executor.layers.fused_moe import (
15 FusedMoEConfig,
16 + RoutedExperts,
17 )
18 from vllm.model_executor.layers.fused_moe.all2all_utils import (
19 maybe_make_prepare_finalize,
@@ -1632,12 +1633,11 @@ def make_mxfp4_moe_quant_config(
1633 gemm1_clamp_limit=swiglu_limit,
1634 )
1635 elif mxfp4_backend == Mxfp4MoeBackend.HUMMING:
1635 - from vllm.model_executor.layers.fused_moe.layer import FusedMoE
1636 from vllm.model_executor.layers.quantization.utils.humming_utils import (
1637 get_humming_moe_quant_config,
1638 )
1639
1640 - assert isinstance(layer, FusedMoE)
1640 + assert isinstance(layer, RoutedExperts)
1641 return get_humming_moe_quant_config(
1642 layer,
1643 gemm1_alpha=gemm1_alpha,
@@ -1663,7 +1663,7 @@ def make_mxfp4_moe_kernel(
1663 experts_cls: type[mk.FusedMoEExperts],
1664 mxfp4_backend: Mxfp4MoeBackend,
1665 routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
1666 - layer: "RoutedExperts | None" = None,
1666 + layer: RoutedExperts | None = None,
1667 ) -> mk.FusedMoEKernel:
1668 """Create a FusedMoEKernel for the given MXFP4 backend."""
1669 is_monolithic = issubclass(experts_cls, mk.FusedMoEExpertsMonolithic)
vllm/model_executor/layers/fused_moe/oracle/nvfp4.py
+2 -1
@@ -17,6 +17,7 @@ from vllm.model_executor.layers.fused_moe.config import (
17 nvfp4_moe_quant_config,
18 nvfp4_w4a16_moe_quant_config,
19 )
20 +from vllm.model_executor.layers.fused_moe.routed_experts import RoutedExperts
21 from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (
22 prepare_nvfp4_moe_layer_for_fi_or_cutlass,
23 prepare_nvfp4_moe_layer_for_flashinfer_cutedsl,
@@ -335,7 +336,7 @@ def select_nvfp4_moe_backend(
336
337 def convert_to_nvfp4_moe_kernel_format(
338 nvfp4_backend: NvFp4MoeBackend,
338 - layer: torch.nn.Module,
339 + layer: RoutedExperts,
340 w13: torch.Tensor,
341 w13_scale: torch.Tensor,
342 w13_scale_2: torch.Tensor,
vllm/model_executor/layers/fused_moe/routed_experts.py new
+1144
@@ -0,0 +1,1144 @@
1 +# SPDX-License-Identifier: Apache-2.0
2 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3 +
4 +from collections.abc import Callable, Iterable
5 +from enum import Enum
6 +from typing import TYPE_CHECKING, Any, Literal, cast, overload
7 +
8 +import torch
9 +from torch.nn.parameter import UninitializedParameter
10 +
11 +from vllm.distributed.eplb.eplb_state import EplbState
12 +from vllm.logger import init_logger
13 +from vllm.model_executor.custom_op import PluggableLayer
14 +from vllm.model_executor.layers.fused_moe.config import (
15 + FusedMoEConfig,
16 +)
17 +from vllm.model_executor.layers.fused_moe.expert_map_manager import (
18 + ExpertMapManager,
19 +)
20 +from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
21 + FusedMoEMethodBase,
22 +)
23 +from vllm.model_executor.layers.fused_moe.unquantized_fused_moe_method import (
24 + UnquantizedFusedMoEMethod,
25 +)
26 +from vllm.model_executor.layers.quantization.base_config import (
27 + QuantizationConfig,
28 +)
29 +
30 +if TYPE_CHECKING:
31 + from vllm.model_executor.layers.fused_moe.runner.shared_experts import SharedExperts
32 +
33 +
34 +logger = init_logger(__name__)
35 +
36 +
37 +class FusedMoeWeightScaleSupported(Enum):
38 + TENSOR = "tensor"
39 + CHANNEL = "channel"
40 + GROUP = "group"
41 + BLOCK = "block"
42 +
43 +
44 +@PluggableLayer.register("routed_experts")
45 +class RoutedExperts(PluggableLayer):
46 + """
47 + Container for routed expert weights and execution logic.
48 +
49 + This module owns the expert weight parameters (w13_weight, w2_weight, scales, etc.)
50 + and handles:
51 + - Loading checkpoint weights into parameters
52 + - Executing routed experts via quant_method.apply()
53 + """
54 +
55 + def __init__(
56 + self,
57 + layer_name: str,
58 + params_dtype: torch.dtype,
59 + moe_config: FusedMoEConfig,
60 + quant_config: QuantizationConfig | None,
61 + expert_map_manager: ExpertMapManager,
62 + expert_mapping: list[tuple[str, str, int, str]] | None = None,
63 + #
64 + # Extra params that are needed by quant_methods, pass along for now
65 + # Prefer getting these from other sources, e.g. moe_config or
66 + # router object
67 + #
68 + renormalize: bool = True,
69 + use_grouped_topk: bool = False,
70 + num_expert_group: int | None = None,
71 + topk_group: int | None = None,
72 + custom_routing_function: Callable | None = None,
73 + scoring_func: str = "softmax",
74 + routed_scaling_factor: float = 1.0,
75 + swiglu_limit: float | None = None,
76 + e_score_correction_bias: torch.Tensor | None = None,
77 + apply_router_weight_on_input: bool = False,
78 + ):
79 + super().__init__()
80 + self.layer_name = layer_name
81 + self.moe_config = moe_config
82 + self.quant_config = quant_config
83 + self.expert_mapping = expert_mapping
84 + self.expert_map_manager = expert_map_manager
85 + self.hidden_size = moe_config.hidden_dim
86 + self.global_num_experts = moe_config.num_experts
87 + self.local_num_experts = moe_config.num_local_experts
88 + self.params_dtype = params_dtype
89 +
90 + # Register buffers for state_dict compatibility
91 + self.update_expert_map_info()
92 +
93 + self.rocm_aiter_fmoe_enabled = moe_config.rocm_aiter_fmoe_enabled
94 +
95 + # It would be good to eventually codify these in FusedMoEConfig
96 + # or some other config.
97 + self.top_k = self.moe_config.experts_per_token
98 + self.activation = self.moe_config.activation
99 + self.renormalize = renormalize
100 + self.use_grouped_topk = use_grouped_topk
101 + self.num_expert_group = num_expert_group
102 + self.topk_group = topk_group
103 + self.custom_routing_function = custom_routing_function
104 + self.scoring_func = scoring_func
105 + self.routed_scaling_factor = routed_scaling_factor
106 + self.swiglu_limit = swiglu_limit
107 + self.e_score_correction_bias = e_score_correction_bias
108 + self.apply_router_weight_on_input = apply_router_weight_on_input
109 + # End random parameters
110 +
111 + self.quant_method = self._get_quant_method(
112 + self.layer_name,
113 + self.quant_config,
114 + self.moe_config,
115 + )
116 +
117 + # Round up hidden size and update moe_config.
118 + # TODO: move roundup to _get_quant_method?
119 + self.hidden_size, self.intermediate_size_per_partition = (
120 + self.quant_method.maybe_roundup_sizes(
121 + self.hidden_size,
122 + self.moe_config.intermediate_size_per_partition,
123 + self.moe_config.in_dtype,
124 + self.moe_config.moe_parallel_config,
125 + )
126 + )
127 + self.moe_config.hidden_dim = self.hidden_size
128 + self.moe_config.intermediate_size_per_partition = (
129 + self.intermediate_size_per_partition
130 + )
131 +
132 + if (
133 + self.moe_config.moe_parallel_config.enable_eplb
134 + and not self.quant_method.supports_eplb
135 + ):
136 + # TODO: Add support for additional quantization methods.
137 + # The implementation for other quantization methods does not
138 + # contain essential differences, but the current quant API
139 + # design causes duplicated work when extending to new
140 + # quantization methods, so I'm leaving it for now.
141 + # If you plan to add support for more quantization methods,
142 + # please refer to the implementation in `Fp8MoEMethod`.
143 + raise NotImplementedError(
144 + f"EPLB is not supported {self.quant_method.__class__.__name__}."
145 + )
146 +
147 + moe_quant_params: dict[str, Any] = {
148 + "num_experts": moe_config.num_local_experts,
149 + "hidden_size": self.hidden_size,
150 + "unpadded_hidden_size": self.moe_config.hidden_dim_unpadded,
151 + "intermediate_size_per_partition": (
152 + self.moe_config.intermediate_size_per_partition
153 + ),
154 + "params_dtype": params_dtype,
155 + "weight_loader": self.weight_loader,
156 + "global_num_experts": moe_config.num_experts,
157 + }
158 +
159 + # need full intermediate size pre-sharding for WNA16 act order
160 + if self._needs_intermediate_size_param(self.quant_method):
161 + moe_quant_params["intermediate_size_full"] = (
162 + self.moe_config.intermediate_size
163 + )
164 +
165 + self.quant_method.create_weights(layer=self, **moe_quant_params)
166 +
167 + # TODO(bnell): Temporary hack. Get rid of this.
168 + def _replace_quant_method(self, quant_method: FusedMoEMethodBase):
169 + self.quant_method = quant_method
170 +
171 + # TODO(bnell): Hack for elastic_ep. Get rid of this
172 + def _set_moe_config(self, new_moe_config: FusedMoEConfig):
173 + self.moe_config = new_moe_config
174 + self.global_num_experts = new_moe_config.num_experts
175 + # local experts?
176 +
177 + def _get_quant_method(
178 + self,
179 + prefix: str,
180 + quant_config: QuantizationConfig | None,
181 + moe_config: FusedMoEConfig,
182 + ) -> FusedMoEMethodBase:
183 + """
184 + Helper method to ensure quant_method is never None and
185 + of the proper type.
186 + """
187 + quant_method = None
188 + if quant_config is not None:
189 + quant_method = quant_config.get_quant_method(self, prefix)
190 + if quant_method is None:
191 + quant_method = UnquantizedFusedMoEMethod(moe_config)
192 + assert isinstance(quant_method, FusedMoEMethodBase)
193 + return quant_method
194 +
195 + # TODO(bnell): make this a method on quant_method
196 + def _needs_intermediate_size_param(self, quant_method: FusedMoEMethodBase) -> bool:
197 + return quant_method.__class__.__name__ in (
198 + "AutoGPTQMoEMethod",
199 + "CompressedTensorsWNA16MarlinMoEMethod",
200 + "CompressedTensorsWNA16MoEMethod",
201 + )
202 +
203 + def _ensure_moe_quant_config_init(self):
204 + if self.quant_method.moe_quant_config is None:
205 + # Note: the moe_quant_config can't be constructed until after
206 + # weight loading post processing.
207 + self.quant_method.moe_quant_config = (
208 + self.quant_method.get_fused_moe_quant_config(self)
209 + )
210 +
211 + @property
212 + def use_ep(self) -> bool:
213 + return self.moe_config.moe_parallel_config.use_ep
214 +
215 + @property
216 + def expert_map(self) -> torch.Tensor | None:
217 + return (
218 + self._expert_map if not self.rocm_aiter_fmoe_enabled else self.expert_mask
219 + )
220 +
221 + def update_expert_map_info(self):
222 + # Update local attributes from ExpertMapManager
223 + self.local_num_experts = self.expert_map_manager.local_num_experts
224 + self.expert_placement_strategy = self.expert_map_manager.placement_strategy
225 + self.register_buffer("_expert_map", self.expert_map_manager.expert_map)
226 + self.register_buffer("expert_mask", self.expert_map_manager.expert_mask)
227 +
228 + # Get routing tables from ExpertMapManager
229 + routing_tables = self.expert_map_manager.routing_tables
230 + if routing_tables is not None:
231 + # Register routing tables as buffers for this layer
232 + global_to_physical, physical_to_global, local_global = routing_tables
233 + self.register_buffer("expert_global_to_physical", global_to_physical)
234 + self.register_buffer("expert_physical_to_global", physical_to_global)
235 + self.register_buffer("expert_local_to_global", local_global)
236 +
237 + def _expert_routing_tables(
238 + self,
239 + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None:
240 + # Return cached routing tables if already registered as buffers
241 + if hasattr(self, "expert_global_to_physical"):
242 + return cast(
243 + tuple[torch.Tensor, torch.Tensor, torch.Tensor],
244 + (
245 + self.expert_global_to_physical,
246 + self.expert_physical_to_global,
247 + self.expert_local_to_global,
248 + ),
249 + )
250 + return None
251 +
252 + def update_expert_map(self):
253 + # Update ExpertMapManager with new EP configuration
254 + # The moe_parallel_config (including ep_size and ep_rank)
255 + # should already be updated.
256 + # Note: ExpertMapManager.update() recalculates expert maps and
257 + # reinitializes routing tables internally.
258 + self.expert_map_manager.update(
259 + self.moe_config.moe_parallel_config,
260 + global_num_experts=self.global_num_experts,
261 + )
262 +
263 + # Update local attributes from ExpertMapManager
264 + self.update_expert_map_info()
265 +
266 + def _map_global_expert_id_to_local_expert_id(self, expert_id: int) -> int:
267 + """Map global expert ID to local expert ID."""
268 + return self.expert_map_manager.map_global_to_local(expert_id)
269 +
270 + #
271 + # Weight Loading Methods
272 + #
273 +
274 + def _load_per_tensor_weight_scale(
275 + self,
276 + shard_id: str,
277 + param: torch.nn.Parameter,
278 + loaded_weight: torch.Tensor,
279 + expert_id: int,
280 + ):
281 + param_data = param.data
282 + # for per tensor weight quantization
283 + if shard_id in ("w1", "w3"):
284 + # We have to keep the weight scales of w1 and w3 because
285 + # we need to re-quantize w1/w3 weights after weight loading.
286 + idx = 0 if shard_id == "w1" else 1
287 + param_data[expert_id][idx] = loaded_weight
288 + # If we are in the row parallel case (down_proj)
289 + elif shard_id == "w2":
290 + param_data[expert_id] = loaded_weight
291 +
292 + def _load_combined_w13_weight_scale(
293 + self,
294 + shard_dim: int,
295 + loaded_weight: torch.Tensor,
296 + param: torch.Tensor,
297 + tp_rank: int,
298 + ):
299 + """
300 + Load w13 weight scales assuming that w1 weight scales and w3 weight
301 + scales are stored in the same loaded_weight tensor.
302 + """
303 + shard_size = param.shape[shard_dim]
304 + loaded_weight = loaded_weight.narrow(
305 + shard_dim, shard_size * tp_rank, shard_size
306 + )
307 + param.copy_(loaded_weight)
308 +
309 + def _load_model_weight_or_group_weight_scale(
310 + self,
311 + shard_dim: int,
312 + expert_data: torch.Tensor,
313 + shard_id: str,
314 + loaded_weight: torch.Tensor,
315 + tp_rank: int,
316 + load_full_w2: bool = False,
317 + ):
318 + """
319 + Load grouped weight scales for group quantization or model weights
320 +
321 + Args:
322 + shard_dim: dimension to shard
323 + expert_data: parameter for a particular expert
324 + shard_id: either w1, w2, or w3
325 + loaded_weight: checkpoint weight to load into the param
326 + tp_rank: tensor parallel rank
327 + load_full_w2: whether or not the w2 loaded should be sharded.
328 + """
329 + if shard_id == "w2":
330 + # In the case where we have actorder/g_idx, we do not partition the
331 + # w2 scales, as indicated by `load_full` argument, for all tp cases
332 + self._load_w2(
333 + shard_dim=shard_dim,
334 + loaded_weight=loaded_weight,
335 + expert_data=expert_data,
336 + tp_rank=tp_rank,
337 + load_full=load_full_w2,
338 + )
339 + elif shard_id in ("w1", "w3"):
340 + self._load_w13(
341 + shard_id=shard_id,
342 + shard_dim=shard_dim,
343 + loaded_weight=loaded_weight,
344 + expert_data=expert_data,
345 + tp_rank=tp_rank,
346 + )
347 +
348 + def _load_per_channel_weight_scale(
349 + self,
350 + expert_data: torch.Tensor,
351 + shard_dim: int,
352 + shard_id: str,
353 + loaded_weight: torch.Tensor,
354 + tp_rank: int,
355 + ):
356 + # for per channel weight quantization
357 + if shard_id == "w2":
358 + hidden_dim = self._get_hidden_dim(shard_dim, expert_data.ndim)
359 + expert_data = self._narrow_expert_data_for_padding(
360 + expert_data,
361 + loaded_weight,
362 + hidden_dim=hidden_dim,
363 + shard_dim=shard_dim,
364 + )
365 + expert_data.copy_(loaded_weight)
366 + elif shard_id in ("w1", "w3"):
367 + self._load_w13(
368 + shard_id=shard_id,
369 + shard_dim=shard_dim,
370 + loaded_weight=loaded_weight,
371 + expert_data=expert_data,
372 + tp_rank=tp_rank,
373 + )
374 +
375 + @staticmethod
376 + def _get_hidden_dim(shard_dim: int, ndim: int) -> int:
377 + """Compute the hidden dimension index from the shard (intermediate)
378 + dimension and tensor rank.
379 +
380 + For 2D weight tensors the two data dims are (0, 1). For 3D tensors
381 + with an expert dimension at dim 0, they are (1, 2). ``shard_dim``
382 + occupies one of these; the hidden dimension is the other.
383 + For 1D tensors (e.g. per-channel scales) returns 0.
384 + """
385 + if ndim < 2:
386 + return 0
387 + dim_a = ndim - 2
388 + dim_b = ndim - 1
389 + if shard_dim == dim_a:
390 + return dim_b
391 + if shard_dim == dim_b:
392 + return dim_a
393 + raise ValueError(
394 + f"shard_dim={shard_dim} is not a valid data dimension "
395 + f"for a {ndim}D tensor (expected {dim_a} or {dim_b})"
396 + )
397 +
398 + @staticmethod
399 + def _narrow_expert_data_for_padding(
400 + expert_data: torch.Tensor,
401 + loaded_weight: torch.Tensor,
402 + hidden_dim: int,
403 + shard_dim: int | None = None,
404 + ) -> torch.Tensor:
405 + """Narrow expert_data to match loaded_weight for padded dimensions.
406 +
407 + When backends (e.g., DeepEP) round up hidden_size, weight parameters
408 + are larger than checkpoint weights. Narrow the padded hidden dimension
409 + before copying. Similarly, when padding occurs on the shard
410 + (intermediate) dimension (e.g. for MXFP4 GEMM), narrow that dimension
411 + as well.
412 +
413 + Args:
414 + expert_data: The (possibly padded) parameter tensor to narrow.
415 + loaded_weight: The checkpoint weight tensor with original size.
416 + hidden_dim: The dimension index corresponding to hidden_size.
417 + Must be non-negative.
418 + shard_dim: The dimension index corresponding to the shard
419 + (intermediate) dimension. Defaults to `None`.
420 + """
421 + dims = (hidden_dim,) if shard_dim is None else (hidden_dim, shard_dim)
422 + if loaded_weight.ndim > 0:
423 + for dim in dims:
424 + if (
425 + 0 <= dim < expert_data.ndim
426 + and dim < loaded_weight.ndim
427 + and expert_data.shape[dim] > loaded_weight.shape[dim]
428 + ):
429 + expert_data = expert_data.narrow(dim, 0, loaded_weight.shape[dim])
430 + return expert_data
431 +
432 + def _load_w13(
433 + self,
434 + expert_data: torch.Tensor,
435 + shard_dim: int,
436 + shard_id: str,
437 + loaded_weight: torch.Tensor,
438 + tp_rank: int,
439 + load_full: bool = False,
440 + ):
441 + # Index the loaded weight for tp sharding.
442 + # gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim
443 + if self.moe_config.is_act_and_mul:
444 + shard_size = expert_data.shape[shard_dim] // 2
445 + else:
446 + shard_size = expert_data.shape[shard_dim]
447 + # Only narrow if the loaded_weight is not a scalar (0-dim tensor)
448 + # and we're not loading the full weight
449 + if not load_full and loaded_weight.ndim > 0:
450 + # When the parameter has been padded (e.g. MXFP4 rounding up
451 + # intermediate_size_per_partition), shard_size is the padded
452 + # size. Compute the offset into the checkpoint weight using
453 + # the *unpadded* per-rank size so that every TP rank lands at
454 + # the correct slice.
455 + tp_size = self.moe_config.moe_parallel_config.tp_size
456 + loaded_per_rank = loaded_weight.shape[shard_dim] // tp_size
457 + start_offset = loaded_per_rank * tp_rank
458 + available = loaded_weight.shape[shard_dim] - start_offset
459 + if available <= 0:
460 + # If there is no available weight to load for this TP rank
461 + # (can happen on last TP rank with padding), we can skip
462 + # loading and return early
463 + return
464 + narrow_size = min(loaded_per_rank, available)
465 + loaded_weight = loaded_weight.narrow(shard_dim, start_offset, narrow_size)
466 + # Narrow parameter and load.
467 + # w1, gate_proj: Load into first logical weight of w13.
468 + if shard_id == "w1":
469 + expert_data = expert_data.narrow(shard_dim, 0, shard_size)
470 + # w3, up_proj: Load into second logical weight of w13.
471 + else:
472 + assert shard_id == "w3"
473 + expert_data = expert_data.narrow(shard_dim, shard_size, shard_size)
474 + hidden_dim = self._get_hidden_dim(shard_dim, expert_data.ndim)
475 + expert_data = self._narrow_expert_data_for_padding(
476 + expert_data,
477 + loaded_weight,
478 + hidden_dim=hidden_dim,
479 + shard_dim=shard_dim,
480 + )
481 + expert_data.copy_(loaded_weight)
482 +
483 + def _load_w2(
484 + self,
485 + expert_data: torch.Tensor,
486 + shard_dim: int,
487 + loaded_weight: torch.Tensor,
488 + tp_rank: int,
489 + load_full: bool = False,
490 + ):
491 + # Index the loaded weight for tp sharding.
492 + # down_proj: "RowParallel" so tp sharding on input_dim
493 + # Only narrow if the loaded_weight is not a scalar (0-dim tensor)
494 + # and we're not loading the full weight
495 + if not load_full and loaded_weight.ndim > 0:
496 + # Same padding fix as _load_w13: use unpadded per-rank size.
497 + tp_size = self.moe_config.moe_parallel_config.tp_size
498 + loaded_per_rank = loaded_weight.shape[shard_dim] // tp_size
499 + start_offset = loaded_per_rank * tp_rank
500 + available = loaded_weight.shape[shard_dim] - start_offset
501 + if available <= 0:
502 + # If there is no available weight to load for this TP rank
503 + # (can happen on last TP rank with padding), we can skip
504 + # loading and return early
505 + return
506 + narrow_size = min(loaded_per_rank, available)
507 + loaded_weight = loaded_weight.narrow(shard_dim, start_offset, narrow_size)
508 + # w2, down_proj: Load into only logical weight of w2.
509 + hidden_dim = self._get_hidden_dim(shard_dim, expert_data.ndim)
510 + expert_data = self._narrow_expert_data_for_padding(
511 + expert_data,
512 + loaded_weight,
513 + hidden_dim=hidden_dim,
514 + shard_dim=shard_dim,
515 + )
516 + expert_data.copy_(loaded_weight)
517 +
518 + def _load_single_value(
519 + self, param: torch.nn.Parameter, loaded_weight: torch.Tensor, expert_id: int
520 + ):
521 + param_data = param.data
522 +
523 + # Input scales can be loaded directly and should be equal.
524 + param_data[expert_id] = loaded_weight
525 +
526 + def _load_g_idx(
527 + self,
528 + shard_id: str,
529 + expert_data: torch.Tensor,
530 + shard_dim: int,
531 + loaded_weight: torch.Tensor,
532 + tp_rank: int,
533 + ):
534 + if shard_id == "w2":
535 + self._load_w2(
536 + shard_dim=shard_dim,
537 + loaded_weight=loaded_weight,
538 + expert_data=expert_data,
539 + tp_rank=tp_rank,
540 + )
541 + else:
542 + assert shard_id in ("w1", "w3")
543 + expert_data.copy_(loaded_weight)
544 +
545 + @overload
546 + def weight_loader(
547 + self,
548 + param: torch.nn.Parameter,
549 + loaded_weight: torch.Tensor,
550 + weight_name: str,
551 + shard_id: str,
552 + expert_id: int,
553 + return_success: Literal[False],
554 + ) -> None: ...
555 +
556 + @overload
557 + def weight_loader(
558 + self,
559 + param: torch.nn.Parameter,
560 + loaded_weight: torch.Tensor,
561 + weight_name: str,
562 + shard_id: str,
563 + expert_id: int,
564 + return_success: Literal[True],
565 + ) -> bool: ...
566 +
567 + def weight_loader(
568 + self,
569 + param: torch.nn.Parameter,
570 + loaded_weight: torch.Tensor,
571 + weight_name: str,
572 + shard_id: str,
573 + expert_id: int,
574 + return_success: bool = False,
575 + ) -> bool | None:
576 + quant_config_name = self.quant_config and self.quant_config.get_name()
577 + if quant_config_name == "gpt_oss_mxfp4":
578 + # (FIXME) for gpt-oss all experts are combined
579 + if "bias" in weight_name:
580 + dim1 = loaded_weight.shape[1]
581 + param.data[:, :dim1].copy_(loaded_weight)
582 + else:
583 + dim1 = loaded_weight.shape[1]
584 + dim2 = loaded_weight.shape[2]
585 + param.data[:, :dim1, :dim2].copy_(loaded_weight)
586 + return True if return_success else None
587 +
588 + quant_method_name = self.quant_method.__class__.__name__
589 + global_expert_id = expert_id
590 + expert_id = self._map_global_expert_id_to_local_expert_id(global_expert_id)
591 +
592 + use_global_sf = (
593 + getattr(self.quant_method, "use_global_sf", False)
594 + and "input_scale" in weight_name
595 + )
596 +
597 + if expert_id == -1 and not use_global_sf:
598 + # Failed to load this param since it's not local to this rank
599 + return False if return_success else None
600 + # Hereafter, `expert_id` is local physical id
601 +
602 + # is_transposed: if the dim to shard the weight
603 + # should be flipped. Required by GPTQ, compressed-tensors
604 + # should be whatever dimension intermediate_size_per_partition is
605 + is_transposed = getattr(param, "is_transposed", False)
606 +
607 + # compressed-tensors checkpoints with packed weights are stored flipped
608 + # TODO (mgoin): check self.quant_method.quant_config.quant_format
609 + # against known CompressionFormat enum values that have this quality
610 + if quant_method_name in (
611 + "CompressedTensorsWNA16MarlinMoEMethod",
612 + "CompressedTensorsWNA16MoEMethod",
613 + "CompressedTensorsWNA16RDNA3MoEMethod",
614 + ):
615 + if is_transposed:
616 + loaded_weight = loaded_weight.t().contiguous()
617 + else:
618 + loaded_weight = loaded_weight
619 +
620 + if shard_id not in ("w1", "w2", "w3"):
621 + raise ValueError(f"shard_id must be ['w1','w2','w3'] but got {shard_id}.")
622 +
623 + # Fetch the dim to shard the parameter/loaded weight
624 + # based on the shard id. This will be whatever
625 + # dimension intermediate_size_per_partition is used.
626 + SHARD_ID_TO_SHARDED_DIM = {"w1": 0, "w2": 1, "w3": 0}
627 +
628 + is_gguf_weight = getattr(param, "is_gguf_weight", False)
629 + is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
630 + if is_gguf_weight_type:
631 + param.weight_type = loaded_weight.item()
632 + param.data.copy_(loaded_weight)
633 + return True if return_success else None
634 +
635 + # Case for BitsAndBytes
636 + use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
637 + if use_bitsandbytes_4bit:
638 + shard_dim = 0
639 +
640 + expert_data = param.data[expert_id]
641 + if shard_id == "w2":
642 + # BnB params are stored as flat packed tensors (e.g.
643 + # (packed_size, 1)), not in the logical weight layout.
644 + # Narrowing packed data for hidden-dim padding is not
645 + # meaningful, so require an exact shape match.
646 + if expert_data.shape != loaded_weight.shape:
647 + raise ValueError(
648 + "BitsAndBytes quantization with padded hidden_size "
649 + "(e.g., from DeepEP) is not supported. "
650 + f"Parameter shape {tuple(expert_data.shape)} != "
651 + f"checkpoint shape {tuple(loaded_weight.shape)}"
652 + )
653 + expert_data.copy_(loaded_weight)
654 + elif shard_id in ("w1", "w3"):
655 + # BnB stores weights as flat packed tensors. _load_w13 is
656 + # still used to split the w1/w3 portions along shard_dim.
657 + # _narrow_expert_data_for_padding will be a no-op since
658 + # packed sizes should already match; if DeepEP padding
659 + # causes a mismatch the copy_() will fail with a clear
660 + # shape error.
661 + full_load = True
662 + self._load_w13(
663 + shard_id=shard_id,
664 + shard_dim=shard_dim,
665 + loaded_weight=loaded_weight,
666 + expert_data=expert_data,
667 + tp_rank=self.moe_config.tp_rank,
668 + load_full=full_load,
669 + )
670 + return True if return_success else None
671 +
672 + shard_dim = SHARD_ID_TO_SHARDED_DIM[shard_id]
673 + if is_transposed:
674 + shard_dim = int(not shard_dim)
675 +
676 + full_load = len(loaded_weight.shape) == 3
677 + if full_load:
678 + shard_dim += 1
679 +
680 + # Materialize GGUF UninitializedParameter accounting merged weights
681 + if is_gguf_weight and isinstance(param, UninitializedParameter):
682 + # To materialize a tensor, we must have full shape including
683 + # number of experts, making this portion to require `full_load`.
684 + assert full_load
685 + final_shape = list(loaded_weight.shape)
686 + # w1 and w3 are merged per expert.
687 + if shard_id in {"w1", "w3"}:
688 + final_shape[1] *= 2
689 + final_shape[shard_dim] = final_shape[shard_dim] // self.moe_config.tp_size
690 + param.materialize(final_shape, dtype=loaded_weight.dtype)
691 +
692 + expert_data = param.data if full_load else param.data[expert_id]
693 +
694 + # Case input scale: input_scale loading is only supported for fp8
695 + if "input_scale" in weight_name:
696 + # this is needed for compressed-tensors only
697 + loaded_weight = loaded_weight.to(param.data.device)
698 +
699 + # ModelOpt NVFP4 stores w13 input scales as two logical shards.
700 + # The generic assignment below would broadcast w1/w3 into the
701 + # whole expert row, so the second shard would overwrite the first.
702 + if (
703 + "ModelOpt" in quant_method_name
704 + and param.data.ndim == 2
705 + and shard_id in ("w1", "w3")
706 + ):
707 + scale_expert_id = global_expert_id if use_global_sf else expert_id
708 + scale_shard_id = 0 if shard_id == "w1" else 1
709 + param.data[scale_expert_id][scale_shard_id] = loaded_weight.reshape(())
710 + return True if return_success else None
711 +
712 + if (
713 + "compressed" in quant_method_name.lower()
714 + and param.data[expert_id] != 1
715 + and (param.data[expert_id] - loaded_weight).abs() > 1e-5
716 + ):
717 + raise ValueError(
718 + "input_scales of w1 and w3 of a layer "
719 + f"must be equal. But got {param.data[expert_id]} "
720 + f"vs. {loaded_weight}"
721 + )
722 +
723 + self._load_single_value(
724 + param=param,
725 + loaded_weight=loaded_weight,
726 + expert_id=global_expert_id if use_global_sf else expert_id,
727 + )
728 + return True if return_success else None
729 +
730 + # Case g_idx
731 + if "g_idx" in weight_name:
732 + self._load_g_idx(
733 + shard_dim=0,
734 + shard_id=shard_id,
735 + loaded_weight=loaded_weight,
736 + expert_data=expert_data,
737 + tp_rank=self.moe_config.tp_rank,
738 + )
739 + return True if return_success else None
740 +
741 + # TODO @dsikka: ModelOpt should follow the proper MoE loading pattern
742 + if "ModelOpt" in quant_method_name:
743 + # Determine per-tensor weight scale patterns based on variant
744 + # Use the dedicated method instead of brittle string matching
745 + uses_weight_scale_2 = self.quant_method.uses_weight_scale_2_pattern()
746 + quant_method = getattr(param, "quant_method", None)
747 +
748 + # Call _load_per_tensor_weight_scale() to load per-tensor (scalar)
749 + # weights scales.
750 + # Input scales are always per-tensor.
751 + # Weight scales: FP4 uses "weight_scale_2" and FP8 uses
752 + # "weight_scale" for per-tensor scales.
753 + # NOTE: ModelOpt MXFP8 MoE uses block scales in weight_scale
754 + # tensors (quant_method=BLOCK), so those must not be treated
755 + # as per-tensor scalars here.
756 + is_block_weight_scale = (
757 + "weight_scale" in weight_name
758 + and quant_method == FusedMoeWeightScaleSupported.BLOCK.value
759 + )
760 + is_per_tensor = (
761 + "weight_scale_2" in weight_name
762 + if uses_weight_scale_2
763 + else "weight_scale" in weight_name
764 + ) or "input_scale" in weight_name
765 + is_per_tensor = is_per_tensor and not is_block_weight_scale
766 + if is_per_tensor:
767 + self._load_per_tensor_weight_scale(
768 + shard_id=shard_id,
769 + param=param,
770 + loaded_weight=loaded_weight,
771 + expert_id=expert_id,
772 + )
773 + return True if return_success else None
774 +
775 + # If the weight is w13_weight_scale and w13_weight_scales are
776 + # combined into single loaded_weight, call
777 + # _load_combined_w13_weight_scale() to load it.
778 + # This is checked by comparing the hidden_out dims of the
779 + # loaded_weight and the param.
780 + if "w13_weight_scale" in weight_name:
781 + loaded_weight_hidden_out = loaded_weight.shape[-2]
782 + param_hidden_out = param.data.shape[-2] * self.moe_config.tp_size
783 + if loaded_weight_hidden_out == param_hidden_out:
784 + self._load_combined_w13_weight_scale(
785 + shard_dim=shard_dim,
786 + loaded_weight=loaded_weight,
787 + param=expert_data,
788 + tp_rank=self.moe_config.tp_rank,
789 + )
790 + return True if return_success else None
791 +
792 + # For other weights, call _load_model_weight_or_group_weight_scale()
793 + # to load it.
794 + if "weight" in weight_name:
795 + self._load_model_weight_or_group_weight_scale(
796 + shard_id=shard_id,
797 + shard_dim=shard_dim,
798 + loaded_weight=loaded_weight,
799 + expert_data=expert_data,
800 + tp_rank=self.moe_config.tp_rank,
801 + )
802 + return True if return_success else None
803 +
804 + # Case weight scales, zero_points and offset, weight/input global scales
805 + if "scale" in weight_name or "zero" in weight_name or "offset" in weight_name:
806 + # load the weight scales and zp based on the quantization scheme
807 + # supported weight scales/zp can be found in
808 + # FusedMoeWeightScaleSupported
809 + # TODO @dsikka: once hardened, refactor to use vLLM Parameters
810 + # specific to each case
811 + quant_method = getattr(param, "quant_method", None)
812 + if quant_method == FusedMoeWeightScaleSupported.CHANNEL.value:
813 + self._load_per_channel_weight_scale(
814 + shard_id=shard_id,
815 + shard_dim=shard_dim,
816 + loaded_weight=loaded_weight,
817 + expert_data=expert_data,
818 + tp_rank=self.moe_config.tp_rank,
819 + )
820 + elif quant_method in [
821 + FusedMoeWeightScaleSupported.GROUP.value,
822 + FusedMoeWeightScaleSupported.BLOCK.value,
823 + ]:
824 + self._load_model_weight_or_group_weight_scale(
825 + shard_id=shard_id,
826 + shard_dim=shard_dim,
827 + loaded_weight=loaded_weight,
828 + expert_data=expert_data,
829 + tp_rank=self.moe_config.tp_rank,
830 + load_full_w2=getattr(param, "load_full_w2", False),
831 + )
832 + elif quant_method == FusedMoeWeightScaleSupported.TENSOR.value:
833 + self._load_per_tensor_weight_scale(
834 + shard_id=shard_id,
835 + param=param,
836 + loaded_weight=loaded_weight,
837 + expert_id=expert_id,
838 + )
839 + else:
840 + WEIGHT_SCALE_SUPPORTED = [e.value for e in FusedMoeWeightScaleSupported]
841 + raise ValueError(
842 + f"quant method must be one of {WEIGHT_SCALE_SUPPORTED}"
843 + )
844 + return True if return_success else None
845 +
846 + # Case weight_shape
847 + if "weight_shape" in weight_name:
848 + # only required by compressed-tensors
849 + self._load_single_value(
850 + param=param, loaded_weight=loaded_weight, expert_id=expert_id
851 + )
852 + return True if return_success else None
853 +
854 + # Case model weights
855 + if "weight" in weight_name:
856 + self._load_model_weight_or_group_weight_scale(
857 + shard_id=shard_id,
858 + shard_dim=shard_dim,
859 + loaded_weight=loaded_weight,
860 + expert_data=expert_data,
861 + tp_rank=self.moe_config.tp_rank,
862 + )
863 + return True if return_success else None
864 +
865 + return False if return_success else None
866 +
867 + def load_weights(
868 + self, weights: Iterable[tuple[str, torch.Tensor]]
869 + ) -> Iterable[str]:
870 + if (expert_mapping := self.expert_mapping) is None:
871 + raise ValueError(
872 + "`self.expert_mapping` must be provided to "
873 + "load weights using `self.load_weights`."
874 + )
875 + for expert_name, loaded_weight in weights:
876 + qual_name = f"{self.layer_name}.{expert_name}"
877 + for param_name, weight_name, expert_id, shard_id in expert_mapping:
878 + if weight_name not in qual_name:
879 + continue
880 + weight_name = qual_name.replace(weight_name, param_name)
881 + param_name = weight_name.removeprefix(f"{self.layer_name}.")
882 + param = getattr(self, param_name)
883 + # Fused expert weights can be identified by their 3D tensors
884 + if loaded_weight.dim() == 3:
885 + # Repurpose expert_id as shard_idx for deconcatenating w1 and w3
886 + if shard_id in {"w1", "w3"}:
887 + shard_idx = expert_id
888 + experts_shard = loaded_weight.chunk(2, dim=1)[shard_idx]
889 + else:
890 + experts_shard = loaded_weight
891 + start = 0
892 + else:
893 + # loaded_weight is a single expert weight, so we add a dummy expert
894 + # dimension to unify the loading logic with the fused case
895 + experts_shard = loaded_weight.unsqueeze(0)
896 + start = expert_id
897 +
898 + # Unified loading logic for fused and non-fused experts
899 + loaded_experts = experts_shard.unbind()
900 + for expert_id, loaded_expert in enumerate(loaded_experts, start=start):
901 + success = self.weight_loader(
902 + param=param,
903 + loaded_weight=loaded_expert,
904 + weight_name=weight_name,
905 + shard_id=shard_id,
906 + expert_id=expert_id,
907 + return_success=True,
908 + )
909 + if success:
910 + logger.debug(
911 + "Loaded expert %d of shard %s into %s for layer %s",
912 + expert_id,
913 + shard_id,
914 + param_name,
915 + self.layer_name,
916 + )
917 + yield param_name
918 +
919 + @staticmethod
920 + def make_expert_params_mapping(
921 + model: torch.nn.Module,
922 + ckpt_gate_proj_name: str,
923 + ckpt_down_proj_name: str,
924 + ckpt_up_proj_name: str,
925 + num_experts: int,
926 + num_redundant_experts: int = 0,
927 + routed_experts_prefix: str = "routed_experts",
928 + ) -> list[tuple[str, str, int, str]]:
929 + """
930 + Create expert parameter mapping for weight loading with redundant experts.
931 +
932 + This mapping handles the physical-to-logical expert ID conversion needed
933 + when loading weights with EPLB redundant experts.
934 +
935 + Args:
936 + model: The model containing the MoE layer
937 + ckpt_gate_proj_name: Name of gate projection in checkpoint
938 + ckpt_down_proj_name: Name of down projection in checkpoint
939 + ckpt_up_proj_name: Name of up projection in checkpoint
940 + num_experts: Number of logical (non-redundant) experts
941 + num_redundant_experts: Number of redundant experts
942 +
943 + Returns:
944 + List of tuples (param_name, weight_name, expert_id, shard_id)
945 + where:
946 + - param_name: Parameter name in the layer
947 + - weight_name: Weight name in checkpoint
948 + - expert_id: Physical expert ID
949 + - shard_id: Shard identifier (w1, w2, w3)
950 + """
951 + num_physical_experts = num_experts + num_redundant_experts
952 +
953 + # In the returned mapping:
954 + # - `expert_id` is the physical expert id
955 + # - `weight_name` contains the weight name of the logical expert
956 + # So that we should map the expert id to logical in `weight_name`
957 + physical_to_logical_map = (
958 + EplbState.build_initial_global_physical_to_logical_map(
959 + num_experts, num_redundant_experts
960 + )
961 + )
962 +
963 + base_layer = (
964 + "base_layer."
965 + if any(".base_layer." in name for name, _ in model.named_parameters())
966 + else ""
967 + )
968 +
969 + if routed_experts_prefix != "":
970 + routed_experts_prefix = f"{routed_experts_prefix}."
971 +
972 + return [
973 + # (param_name, weight_name, expert_id, shard_id)
974 + (
975 + f"experts.{routed_experts_prefix}{base_layer}w13_"
976 + if weight_name in [ckpt_gate_proj_name, ckpt_up_proj_name]
977 + else f"experts.{routed_experts_prefix}{base_layer}w2_",
978 + f"experts.{physical_to_logical_map[expert_id]}.{weight_name}.{base_layer}",
979 + expert_id,
980 + shard_id,
981 + )
982 + for expert_id in range(num_physical_experts)
983 + for shard_id, weight_name in [
984 + ("w1", ckpt_gate_proj_name),
985 + ("w2", ckpt_down_proj_name),
986 + ("w3", ckpt_up_proj_name),
987 + ]
988 + ]
989 +
990 + def get_expert_weights(self) -> Iterable[torch.Tensor]:
991 + def _maybe_make_contiguous(
992 + name: str, p: torch.nn.Parameter
993 + ) -> torch.nn.Parameter:
994 + """
995 + In some cases, the last 2 dimensions (the non-expert dimensions)
996 + of the weight scale tensor are transposed. This function
997 + transforms the tensor (view update) so the tensor is contiguous().
998 + Example: A non-contiguous scale tensor,
999 + `x` of shape (E, 32, 16) and stride (512, 1, 32) is transformed to
1000 + `x_` of shape (E, 16, 32) and stride (512, 32, 1).
1001 + Note that we specifically use torch.transpose() so `x_` refers
1002 + to the same underlying memory. The tensors `x` and `x_`, pointing
1003 + to the same underlying memory make this transformation safe in the
1004 + context of EPLB. i.e. It is the same memory and just the view
1005 + is different.
1006 + Note: This function handles the "weight_scale" tensors specifically.
1007 + This could however be generalized to handle similar tensors.
1008 + """
1009 + if p.ndim != 3:
1010 + return p
1011 + if p.is_contiguous():
1012 + # Already contiguous. do nothing.
1013 + return p
1014 + # p is non-contiguous. We only handle the case where the last 2
1015 + # dimensions of the scales tensor is transposed. We can handle
1016 + # other cases when they become relevant.
1017 + is_transposed_12 = p.stride(1) == 1 and p.stride(2) != 1
1018 + if "weight_scale" not in name or not is_transposed_12:
1019 + # do nothing.
1020 + return p
1021 +
1022 + # Do not update the layer parameter as the layer's MoE operations would
1023 + # expect the parameter's tensor to the same shape / stride. Instead,
1024 + # make a new torch.nn.Parameter that is used just in the context of
1025 + # EPLB.
1026 + return torch.nn.Parameter(
1027 + torch.transpose(p.data, 1, 2), requires_grad=False
1028 + )
1029 +
1030 + weights = list(self.named_parameters())
1031 + weights = [(name, _maybe_make_contiguous(name, p)) for name, p in weights]
1032 +
1033 + # `w13_input_scale` and `w2_input_scale` are global per-tensor
1034 + # activation scales shared across all experts (e.g. NVFP4).
1035 + # They are broadcast views (stride 0) from .expand() and are
1036 + # not actual expert weights, so exclude them from EPLB.
1037 + NON_EXPERT_WEIGHTS = {
1038 + "e_score_correction_bias",
1039 + "w13_input_scale",
1040 + "w2_input_scale",
1041 + }
1042 +
1043 + # Parameters of non-expert submodules that live inside runner (RoutedExperts).
1044 + # These must be excluded from EPLB weight rearrangement.
1045 + NON_EXPERT_PREFIXES = ()
1046 +
1047 + assert all(
1048 + weight.is_contiguous()
1049 + for name, weight in weights
1050 + if not name.startswith(NON_EXPERT_PREFIXES)
1051 + and name not in NON_EXPERT_WEIGHTS
1052 + )
1053 +
1054 + return [
1055 + weight.view(self.local_num_experts, -1)
1056 + for name, weight in weights
1057 + if name not in NON_EXPERT_WEIGHTS
1058 + and weight.shape != torch.Size([])
1059 + and not name.startswith(NON_EXPERT_PREFIXES)
1060 + ]
1061 +
1062 + #
1063 + # Execution
1064 + #
1065 +
1066 + def forward_modular(
1067 + self,
1068 + x: torch.Tensor,
1069 + topk_weights: torch.Tensor,
1070 + topk_ids: torch.Tensor,
1071 + shared_experts: "SharedExperts | None" = None,
1072 + shared_experts_input: torch.Tensor | None = None,
1073 + ) -> torch.Tensor:
1074 + """
1075 + Execute routed experts using the quantization method's apply function.
1076 +
1077 + This is called by the runner after router selection (for modular kernels)
1078 + quant_method.apply() which accesses the weights on this RoutedExperts
1079 + instance.
1080 +
1081 + Args:
1082 + x: Input tensor after any transforms
1083 + topk_weights: Routing weights from router (for modular kernels)
1084 + topk_ids: Selected expert IDs from router (for modular kernels)
1085 + shared_experts: The shared experts (if any)
1086 + shared_experts_input: Input for shared experts (if any)
1087 +
1088 + Returns:
1089 + Output tensor from routed experts
1090 + """
1091 + assert not self.quant_method.is_monolithic
1092 +
1093 + # Modular kernels use pre-computed routing
1094 + return self.quant_method.apply(
1095 + layer=self,
1096 + x=x,
1097 + topk_weights=topk_weights,
1098 + topk_ids=topk_ids,
1099 + shared_experts=shared_experts,
1100 + shared_experts_input=shared_experts_input,
1101 + )
1102 +
1103 + def forward_monolithic(
1104 + self,
1105 + x: torch.Tensor,
1106 + router_logits: torch.Tensor | None = None,
1107 + input_ids: torch.Tensor | None = None,
1108 + ) -> torch.Tensor:
1109 + """
1110 + Execute routed experts using the quantization method's apply function.
1111 +
1112 + This is called by the runner after router selection (for modular kernels)
1113 + or with router logits (for monolithic kernels). It delegates to
1114 + quant_method.apply() which accesses the weights on this RoutedExperts
1115 + instance.
1116 +
1117 + Args:
1118 + x: Input tensor after any transforms
1119 + router_logits: Router logits (for monolithic kernels)
1120 + input_ids: input ids for DeepSeek V4
1121 +
1122 + Returns:
1123 + Output tensor from routed experts
1124 + """
1125 + assert self.quant_method.is_monolithic
1126 +
1127 + # Monolithic kernels handle routing internally
1128 + return self.quant_method.apply_monolithic(
1129 + layer=self,
1130 + x=x,
1131 + router_logits=router_logits,
1132 + input_ids=input_ids,
1133 + )
1134 +
1135 + def forward(
1136 + self,
1137 + *args,
1138 + **kwargs,
1139 + ) -> torch.Tensor:
1140 + raise AssertionError("Call forward_modular or forward_monolithic instead.")
1141 +
1142 +
1143 +# Mark the RoutedExperts weight_loader as supporting MoE-specific parameters
1144 +RoutedExperts.weight_loader.supports_moe_loading = True # type: ignore[attr-defined]
vllm/model_executor/layers/fused_moe/router/aiter_shared_routed_fused_moe_router.py
-3
@@ -1,6 +1,5 @@
1 # SPDX-License-Identifier: Apache-2.0
2 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3 -from collections.abc import Callable
3
4 import torch
5
@@ -37,13 +36,11 @@ class AiterSharedRoutedFusedMoERouter(BaseRouter):
36 eplb_state: EplbLayerState | None = None,
37 scoring_func: str = "softmax",
38 renormalize: bool = True,
40 - indices_type_getter: Callable[[], torch.dtype | None] | None = None,
39 ):
40 super().__init__(
41 top_k=top_k,
42 global_num_experts=global_num_experts,
43 eplb_state=eplb_state,
46 - indices_type_getter=indices_type_getter,
44 )
45 self.renormalize = renormalize
46 self.scoring_func = scoring_func
vllm/model_executor/layers/fused_moe/router/base_router.py
+11 -31
@@ -149,27 +149,16 @@ class BaseRouter(FusedMoERouter):
149 top_k: int,
150 global_num_experts: int,
151 eplb_state: EplbLayerState | None = None,
152 - # TODO(bnell): Once the MK is constructed at layer init time, we
153 - # can make this a plain value instead of a callback.
154 - indices_type_getter: Callable[[], torch.dtype | None] | None = None,
152 ):
153 """
157 - Note: the indices dtype might not be available at router construction
158 - time, so we need to supply a callback to get it at runtime. This is
159 - because the indices type is supplied by modular kernels which are
160 - created after MoE layer/router construction.
161 -
154 Args:
155 top_k: Number of experts to select per token
156 global_num_experts: Total number of experts
157 eplb_state: Optional EPLBLayerState for load balancing
166 - indices_type_getter: Optional callback to get indices dtype
158 """
168 - super().__init__()
159 + super().__init__(eplb_state=eplb_state)
160 self.top_k = top_k
161 self.global_num_experts = global_num_experts
171 - self.eplb_state = eplb_state
172 - self.indices_type_getter = indices_type_getter
162 self.capture_fn: Callable[[torch.Tensor], None] | None = None
163
164 def set_capture_fn(self, capture_fn: Callable[[torch.Tensor], None] | None) -> None:
@@ -189,12 +178,6 @@ class BaseRouter(FusedMoERouter):
178 if eplb_state.should_record_tensor is None:
179 raise ValueError("EPLB requires should_record_tensor != None")
180
192 - def _get_indices_type(self) -> torch.dtype | None:
193 - """Get the desired indices dtype from the getter function."""
194 - return (
195 - self.indices_type_getter() if self.indices_type_getter is not None else None
196 - )
197 -
181 def _apply_eplb_mapping(self, topk_ids: torch.Tensor) -> torch.Tensor:
182 """Apply EPLB mapping to convert logical expert IDs to physical expert IDs."""
183 if self.eplb_state is not None:
@@ -247,10 +230,11 @@ class BaseRouter(FusedMoERouter):
230 """
231 raise NotImplementedError
232
250 - def select_experts(
233 + def _select_experts(
234 self,
235 hidden_states: torch.Tensor,
236 router_logits: torch.Tensor,
237 + topk_indices_dtype: torch.dtype | None = None,
238 *,
239 input_ids: torch.Tensor | None = None,
240 ) -> tuple[torch.Tensor, torch.Tensor]:
@@ -260,10 +244,9 @@ class BaseRouter(FusedMoERouter):
244
245 This method implements the template method pattern:
246 1. Validates EPLB state
263 - 2. Gets indices type
264 - 3. Calls _compute_routing() to get topk_weights and topk_ids
265 - 4. Applies EPLB mapping if enabled
266 - 5. Converts indices dtype if needed
247 + 2. Calls _compute_routing() to get topk_weights and topk_ids
248 + 3. Applies EPLB mapping if enabled
249 + 4. Converts indices dtype if needed
250
251 Returns:
252 (topk_weights, topk_ids)
@@ -277,22 +260,19 @@ class BaseRouter(FusedMoERouter):
260 # Step 1: Validate EPLB state
261 self._validate_eplb_state()
262
280 - # Step 2: Get indices type.
281 - indices_type = self._get_indices_type()
282 -
283 - # Step 3: Compute routing (delegated to subclass)
263 + # Step 2: Compute routing (delegated to subclass)
264 topk_weights, topk_ids = self._compute_routing(
285 - hidden_states, router_logits, indices_type, input_ids=input_ids
265 + hidden_states, router_logits, topk_indices_dtype, input_ids=input_ids
266 )
267
268 # Capture logical ids before EPLB mapping.
269 if self.capture_fn is not None:
270 self.capture_fn(topk_ids)
271
292 - # Step 4: Apply EPLB mapping
272 + # Step 3: Apply EPLB mapping
273 topk_ids = self._apply_eplb_mapping(topk_ids)
274
295 - # Step 5: Convert indices dtype
296 - topk_ids = self._convert_indices_dtype(topk_ids, indices_type)
275 + # Step 4: Convert indices dtype
276 + topk_ids = self._convert_indices_dtype(topk_ids, topk_indices_dtype)
277
278 return topk_weights, topk_ids
vllm/model_executor/layers/fused_moe/router/custom_routing_router.py
-2
@@ -19,13 +19,11 @@ class CustomRoutingRouter(BaseRouter):
19 custom_routing_function: Callable,
20 eplb_state: EplbLayerState | None = None,
21 renormalize: bool = True,
22 - indices_type_getter: Callable[[], torch.dtype | None] | None = None,
22 ):
23 super().__init__(
24 top_k=top_k,
25 global_num_experts=global_num_experts,
26 eplb_state=eplb_state,
28 - indices_type_getter=indices_type_getter,
27 )
28 self.custom_routing_function = custom_routing_function
29 self.renormalize = renormalize
vllm/model_executor/layers/fused_moe/router/fused_moe_router.py
+32 -1
@@ -5,6 +5,7 @@ from collections.abc import Callable
5
6 import torch
7
8 +from vllm.distributed.eplb.eplb_state import EplbLayerState
9 from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
10
11
@@ -14,6 +15,10 @@ class FusedMoERouter(ABC):
15 method that is used for routing hidden states based on router logits.
16 """
17
18 + def __init__(self, eplb_state: EplbLayerState | None = None):
19 + self._routing_replay_out: torch.Tensor | None = None
20 + self.eplb_state = eplb_state
21 +
22 @abstractmethod
23 def set_capture_fn(
24 self,
@@ -27,10 +32,21 @@ class FusedMoERouter(ABC):
32 raise NotImplementedError
33
34 @abstractmethod
35 + def _select_experts(
36 + self,
37 + hidden_states: torch.Tensor,
38 + router_logits: torch.Tensor,
39 + topk_indices_dtype: torch.dtype | None = None,
40 + *,
41 + input_ids: torch.Tensor | None = None,
42 + ) -> tuple[torch.Tensor, torch.Tensor]:
43 + raise NotImplementedError
44 +
45 def select_experts(
46 self,
47 hidden_states: torch.Tensor,
48 router_logits: torch.Tensor,
49 + topk_indices_dtype: torch.dtype | None = None,
50 *,
51 input_ids: torch.Tensor | None = None,
52 ) -> tuple[torch.Tensor, torch.Tensor]:
@@ -47,4 +63,19 @@ class FusedMoERouter(ABC):
63 equivalent to global logical ids, so should be compatible with
64 plain MoE implementations without redundant experts.
65 """
50 - raise NotImplementedError
66 +
67 + topk_weights, topk_ids = self._select_experts(
68 + hidden_states,
69 + router_logits,
70 + topk_indices_dtype=topk_indices_dtype,
71 + input_ids=input_ids,
72 + )
73 +
74 + # Write routing data for non-monolithic path (Triton, etc.)
75 + # (set by bind_routing_capture_to_model during capturer init)
76 + if self._routing_replay_out is not None:
77 + self._routing_replay_out[: topk_ids.shape[0]].copy_(
78 + topk_ids.to(torch.int16)
79 + )
80 +
81 + return topk_weights, topk_ids
vllm/model_executor/layers/fused_moe/router/fused_topk_bias_router.py
-3
@@ -1,7 +1,6 @@
1 # SPDX-License-Identifier: Apache-2.0
2 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3 import functools
4 -from collections.abc import Callable
4
5 import torch
6 import torch.nn.functional as F
@@ -300,7 +299,6 @@ class FusedTopKBiasRouter(BaseRouter):
299 renormalize: bool = True,
300 routed_scaling_factor: float = 1.0,
301 eplb_state: EplbLayerState | None = None,
303 - indices_type_getter: Callable[[], torch.dtype | None] | None = None,
302 *,
303 scoring_func: str = "sigmoid",
304 hash_indices_table: torch.Tensor | None = None,
@@ -309,7 +307,6 @@ class FusedTopKBiasRouter(BaseRouter):
307 top_k=top_k,
308 global_num_experts=global_num_experts,
309 eplb_state=eplb_state,
312 - indices_type_getter=indices_type_getter,
310 )
311 self.e_score_correction_bias = e_score_correction_bias
312 self.renormalize = renormalize
vllm/model_executor/layers/fused_moe/router/fused_topk_router.py
-2
@@ -123,13 +123,11 @@ class FusedTopKRouter(BaseRouter):
123 scoring_func: str = "softmax",
124 renormalize: bool = True,
125 eplb_state: EplbLayerState | None = None,
126 - indices_type_getter: Callable[[], torch.dtype | None] | None = None,
126 ):
127 super().__init__(
128 top_k=top_k,
129 global_num_experts=global_num_experts,
130 eplb_state=eplb_state,
132 - indices_type_getter=indices_type_getter,
131 )
132 self.renormalize = renormalize
133 self.scoring_func = scoring_func
vllm/model_executor/layers/fused_moe/router/grouped_topk_router.py
-3
@@ -1,6 +1,5 @@
1 # SPDX-License-Identifier: Apache-2.0
2 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3 -from collections.abc import Callable
3 from functools import partial
4
5 import torch
@@ -259,13 +258,11 @@ class GroupedTopKRouter(BaseRouter):
258 e_score_correction_bias: torch.Tensor | None = None,
259 num_fused_shared_experts: int = 0,
260 eplb_state: EplbLayerState | None = None,
262 - indices_type_getter: Callable[[], torch.dtype | None] | None = None,
261 ):
262 super().__init__(
263 top_k=top_k,
264 global_num_experts=global_num_experts,
265 eplb_state=eplb_state,
268 - indices_type_getter=indices_type_getter,
266 )
267 self.num_expert_group = num_expert_group
268 self.topk_group = topk_group
vllm/model_executor/layers/fused_moe/router/router_factory.py
-9
@@ -41,7 +41,6 @@ def create_fused_moe_router(
41 top_k: int,
42 global_num_experts: int,
43 renormalize: bool = True,
44 - indices_type_getter: Callable[[], torch.dtype | None] | None = None,
44 # grouped topk parameters
45 use_grouped_topk: bool = False,
46 num_expert_group: int | None = None,
@@ -77,7 +76,6 @@ def create_fused_moe_router(
76 top_k: Number of experts to select per token
77 global_num_experts: Total number of experts in the model
78 renormalize: Whether to renormalize the routing weights
80 - indices_type_getter: Function to get the desired indices dtype
79 routing_method_type: Optional explicit routing method type
80
81 Grouped topk arguments:
@@ -116,7 +114,6 @@ def create_fused_moe_router(
114 top_k=top_k,
115 global_num_experts=global_num_experts,
116 eplb_state=eplb_state,
119 - indices_type_getter=indices_type_getter,
117 )
118
119 if zero_expert_type is not None:
@@ -136,7 +133,6 @@ def create_fused_moe_router(
133 scoring_func=scoring_func,
134 renormalize=renormalize,
135 routed_scaling_factor=routed_scaling_factor,
139 - indices_type_getter=indices_type_getter,
136 )
137
138 if use_grouped_topk:
@@ -157,7 +153,6 @@ def create_fused_moe_router(
153 routed_scaling_factor=routed_scaling_factor,
154 e_score_correction_bias=e_score_correction_bias,
155 num_fused_shared_experts=num_fused_shared_experts,
160 - indices_type_getter=indices_type_getter,
156 )
157 if (
158 grouped_topk_router.routing_method_type != RoutingMethodType.Unspecified
@@ -179,7 +174,6 @@ def create_fused_moe_router(
174 eplb_state=eplb_state,
175 custom_routing_function=custom_routing_function,
176 renormalize=renormalize,
182 - indices_type_getter=indices_type_getter,
177 )
178
179 assert scoring_func in ["sigmoid", "softmax", "sqrtsoftplus"]
@@ -192,7 +186,6 @@ def create_fused_moe_router(
186 e_score_correction_bias=e_score_correction_bias,
187 renormalize=renormalize,
188 routed_scaling_factor=routed_scaling_factor,
195 - indices_type_getter=indices_type_getter,
189 scoring_func=scoring_func,
190 hash_indices_table=hash_indices_table,
191 )
@@ -209,7 +202,6 @@ def create_fused_moe_router(
202 num_fused_shared_experts=num_fused_shared_experts,
203 renormalize=renormalize,
204 scoring_func=scoring_func,
212 - indices_type_getter=indices_type_getter,
205 )
206
207 return FusedTopKRouter(
@@ -218,5 +210,4 @@ def create_fused_moe_router(
210 eplb_state=eplb_state,
211 renormalize=renormalize,
212 scoring_func=scoring_func,
221 - indices_type_getter=indices_type_getter,
213 )
vllm/model_executor/layers/fused_moe/router/routing_simulator_router.py
-3
@@ -1,7 +1,6 @@
1 # SPDX-License-Identifier: Apache-2.0
2 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3 from abc import ABC, abstractmethod
4 -from collections.abc import Callable
4 from typing import Any
5
6 import torch
@@ -314,13 +313,11 @@ class RoutingSimulatorRouter(BaseRouter):
313 top_k: int,
314 global_num_experts: int,
315 eplb_state: EplbLayerState | None = None,
317 - indices_type_getter: Callable[[], torch.dtype | None] | None = None,
316 ):
317 super().__init__(
318 top_k=top_k,
319 global_num_experts=global_num_experts,
320 eplb_state=eplb_state,
323 - indices_type_getter=indices_type_getter,
321 )
322
323 @property
vllm/model_executor/layers/fused_moe/router/zero_expert_router.py
-4
@@ -1,8 +1,6 @@
1 # SPDX-License-Identifier: Apache-2.0
2 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
4 -from collections.abc import Callable
5 -
4 import torch
5
6 from vllm.distributed.eplb.eplb_state import EplbLayerState
@@ -39,13 +37,11 @@ class ZeroExpertRouter(BaseRouter):
37 renormalize: bool = False,
38 routed_scaling_factor: float = 1.0,
39 eplb_state: EplbLayerState | None = None,
42 - indices_type_getter: Callable[[], torch.dtype | None] | None = None,
40 ):
41 super().__init__(
42 top_k=top_k,
43 global_num_experts=global_num_experts,
44 eplb_state=eplb_state,
48 - indices_type_getter=indices_type_getter,
45 )
46 self.e_score_correction_bias = e_score_correction_bias
47 self.num_logical_experts = num_logical_experts
vllm/model_executor/layers/fused_moe/runner/moe_runner.py
+257 -82
@@ -1,28 +1,39 @@
1 # SPDX-License-Identifier: Apache-2.0
2 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3 -from collections.abc import Callable
3 +from collections.abc import Callable, Iterable
4 from contextlib import nullcontext
5 from typing import TYPE_CHECKING
6
7 import torch
8 import torch.nn.functional as F
9
10 +from vllm.config import VllmConfig, get_current_vllm_config
11 +from vllm.config.parallel import ExpertPlacementStrategy
12 from vllm.distributed import (
13 get_ep_group,
14 get_pcp_group,
15 tensor_model_parallel_all_reduce,
16 )
17 +from vllm.distributed.eplb.eplb_state import EplbLayerState
18 from vllm.forward_context import (
19 ForwardContext,
20 get_forward_context,
21 is_forward_context_available,
22 )
23 +from vllm.logger import init_logger
24 +from vllm.model_executor.layers.fused_moe.activation import MoEActivation
25 from vllm.model_executor.layers.fused_moe.config import (
26 FusedMoEConfig,
27 )
28 from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
29 FusedMoEMethodBase,
30 )
31 +from vllm.model_executor.layers.fused_moe.fused_moe_modular_method import (
32 + FusedMoEModularMethod,
33 +)
34 +from vllm.model_executor.layers.fused_moe.routed_experts import (
35 + RoutedExperts,
36 +)
37 from vllm.model_executor.layers.fused_moe.router.fused_moe_router import (
38 FusedMoERouter,
39 )
@@ -43,8 +54,23 @@ from vllm.utils.torch_utils import (
54 direct_register_custom_op,
55 )
56
57 +logger = init_logger(__name__)
58 +
59 +
60 +def register_layer_for_moe_forward_op(
61 + vllm_config: VllmConfig,
62 + layer: "MoERunner",
63 +):
64 + # For smuggling this layer into the fused moe custom op
65 + prefix = layer.layer_name
66 + compilation_config = vllm_config.compilation_config
67 + if prefix in compilation_config.static_forward_context:
68 + raise ValueError("Duplicate layer name: {}".format(prefix))
69 + compilation_config.static_forward_context[prefix] = layer
70 + compilation_config.static_all_moe_layers.append(prefix)
71 +
72
47 -def get_layer_from_name(layer_name: str) -> torch.nn.Module:
73 +def get_layer_from_name(layer_name: str) -> MoERunnerInterface:
74 forward_context: ForwardContext = get_forward_context()
75 if not _USE_LAYERNAME and layer_name == "from_forward_context":
76 all_moe_layers = forward_context.all_moe_layers
@@ -58,7 +84,9 @@ def get_layer_from_name(layer_name: str) -> torch.nn.Module:
84 )
85 layer_name = all_moe_layers[moe_layer_index]
86 forward_context.moe_layer_index += 1
61 - return forward_context.no_compile_layers[layer_name]
87 + layer = forward_context.no_compile_layers[layer_name]
88 + assert isinstance(layer, MoERunnerInterface)
89 + return layer
90
91
92 # On torch >= 2.11, layer_name is a hoisted LayerName opaque object;
@@ -96,8 +124,7 @@ def _moe_forward(
124 hidden_dim_unpadded: int,
125 ) -> torch.Tensor:
126 layer = get_layer_from_name(_resolve_layer_name(layer_name))
99 - return layer.runner._forward_impl(
100 - layer,
127 + return layer._forward_impl(
128 hidden_states,
129 router_logits,
130 shared_experts_input,
@@ -131,8 +158,7 @@ def _moe_forward_shared(
158 hidden_dim_unpadded: int,
159 ) -> tuple[torch.Tensor, torch.Tensor]:
160 layer = get_layer_from_name(_resolve_layer_name(layer_name))
134 - return layer.runner._forward_impl(
135 - layer,
161 + return layer._forward_impl(
162 hidden_states,
163 router_logits,
164 shared_experts_input,
@@ -220,12 +246,12 @@ class MoERunner(MoERunnerInterface):
246 layer_name: str,
247 moe_config: FusedMoEConfig,
248 router: FusedMoERouter,
223 - routed_input_transform: torch.nn.Module | None,
224 - gate: torch.nn.Module | None,
225 - shared_experts: torch.nn.Module | None,
226 - quant_method: FusedMoEMethodBase,
227 - enable_dbo: bool,
249 + routed_experts: RoutedExperts,
250 + enable_dbo: bool = False,
251 + gate: torch.nn.Module | None = None,
252 + shared_experts: torch.nn.Module | None = None,
253 shared_expert_gate: torch.nn.Module | None = None,
254 + routed_input_transform: torch.nn.Module | None = None,
255 routed_output_transform: torch.nn.Module | None = None,
256 routed_scaling_factor: float = 1.0,
257 ):
@@ -237,7 +263,7 @@ class MoERunner(MoERunnerInterface):
263 self.routed_scaling_factor = routed_scaling_factor
264 self.gate = gate
265 self.shared_expert_gate = shared_expert_gate
240 - self._quant_method = quant_method
266 + self.routed_experts = routed_experts
267 self.enable_dbo = enable_dbo
268
269 # When both gates are present and FSE is enabled, fuse their
@@ -250,23 +276,22 @@ class MoERunner(MoERunnerInterface):
276
277 self._shared_experts: SharedExperts | None = None
278 if shared_experts is not None:
279 + can_overlap = lambda: self._quant_method.mk_can_overlap_shared_experts
280 self._shared_experts = SharedExperts(
281 shared_experts,
282 moe_config=moe_config,
256 - # Note: For now we must pass quant_method along to SharedExperts so it
257 - # can property determine where the shared experts are supposed to be
258 - # called, i.e. by a MK or by the MoERunner.
259 - # Once the MK can be created upfront, we can just pass in the proper
260 - # flags derived from the quant_method's MK.
261 - quant_method=quant_method,
283 enable_dbo=enable_dbo,
284 + mk_can_overlap_shared_experts=can_overlap,
285 )
286
265 - # Needed for string -> FusedMoE layer lookup in custom ops.
287 + # Needed for string -> MoERunner layer lookup in custom ops.
288 self.layer_name = layer_name
289
290 self._forward_entry = self._select_forward()
291
292 + # For smuggling this layer into the fused moe custom op
293 + register_layer_for_moe_forward_op(get_current_vllm_config(), self)
294 +
295 def _select_forward(self) -> Callable:
296 if current_platform.is_tpu() or current_platform.is_cpu():
297 # TODO: Once the OOM issue for the TPU backend is resolved, we
@@ -284,11 +309,20 @@ class MoERunner(MoERunnerInterface):
309 def shared_experts(self) -> SharedExperts | None:
310 return self._shared_experts
311
287 - # TODO(bnell): temporary hack, do not call this method.
312 + @property
313 + def is_internal_router(self) -> bool:
314 + return self.gate is not None
315 +
316 + # TODO(bnell): Temporary hack. Get rid of this.
317 def _replace_quant_method(self, quant_method: FusedMoEMethodBase):
318 + self.routed_experts._replace_quant_method(quant_method)
319 +
320 + # TODO(bnell): Hack for elastic_ep. Get rid of this
321 + def _set_moe_config(self, new_moe_config: FusedMoEConfig):
322 + self.moe_config = new_moe_config
323 + self.routed_experts._set_moe_config(new_moe_config)
324 if self._shared_experts is not None:
290 - self._shared_experts._quant_method = quant_method
291 - self._quant_method = quant_method
325 + self._shared_experts._set_moe_config(new_moe_config)
326
327 def _maybe_fuse_gate_weights(self):
328 """Fuse router and shared expert gate weights on first call.
@@ -304,8 +338,9 @@ class MoERunner(MoERunnerInterface):
338 dim=0,
339 )
340
307 - def is_internal_router(self) -> bool:
308 - return self.gate is not None
341 + @property
342 + def _quant_method(self) -> FusedMoEMethodBase:
343 + return self.routed_experts.quant_method
344
345 def apply_routed_input_transform(
346 self, hidden_states: torch.Tensor
@@ -396,9 +431,9 @@ class MoERunner(MoERunnerInterface):
431 def _maybe_reduce_final_output(
432 self,
433 states: torch.Tensor,
399 - trunc_size: int,
434 + trunc_size: int | None,
435 ) -> torch.Tensor:
401 - """Truncate padded dimensions and all-reduce the combined output.
436 + """All-reduce the combined output if needed.
437
438 This is the "late" all-reduce path. When neither fused nor shared
439 output was individually reduced, the combined sum is all-reduced
@@ -415,7 +450,7 @@ class MoERunner(MoERunnerInterface):
450 ):
451 states = tensor_model_parallel_all_reduce(states)
452
418 - return states[..., :trunc_size]
453 + return states[..., :trunc_size] if trunc_size is not None else states
454
455 def _encode_layer_name(self) -> str | LayerName:
456 if _USE_LAYERNAME:
@@ -428,34 +463,11 @@ class MoERunner(MoERunnerInterface):
463 return "from_forward_context"
464 return self.layer_name
465
431 - def _trtllm_mxfp4_unpadded_dim(self) -> int:
432 - """Return ``hidden_dim_unpadded`` when the active backend is TRT-LLM
433 - MXFP4 (whose kernel writes narrower than the padded
434 - ``hidden_states.shape[-1]``), else 0. Other MXFP4 backends (notably
435 - Cutlass MXFP4 MXFP8) write the full padded width, so
436 - ``moe_config.hidden_dim_unpadded`` alone is insufficient: it encodes
437 - the model's logical hidden, not whether the kernel narrows. Computed
438 - caller-side and passed as an op arg; doing the isinstance check
439 - inside the fake would specialize per ``layer_name`` and break
440 - subgraph dedup for identical-architecture models (e.g. Phi-MoE).
441 - """
442 - from vllm.model_executor.layers.fused_moe.experts.trtllm_mxfp4_moe import (
443 - TrtLlmMxfp4ExpertsBase,
444 - )
445 -
446 - moe_kernel = getattr(self._quant_method, "moe_kernel", None)
447 - fused_experts = getattr(
448 - getattr(moe_kernel, "impl", None), "fused_experts", None
449 - )
450 - if isinstance(fused_experts, TrtLlmMxfp4ExpertsBase):
451 - return self.moe_config.hidden_dim_unpadded or self.moe_config.hidden_dim
452 - return 0
453 -
466 def _maybe_pad_hidden_states(
467 self,
468 shared_experts_input: torch.Tensor | None,
469 hidden_states: torch.Tensor,
458 - ) -> tuple[torch.Tensor, int]:
470 + ) -> tuple[torch.Tensor, int | None, int | None]:
471 """Pad hidden_states to moe_config.hidden_dim and compute the
472 original dimension for later truncation.
473
@@ -467,11 +479,12 @@ class MoERunner(MoERunnerInterface):
479 shared_experts_hidden_dim = (
480 shared_experts_input.shape[-1] if shared_experts_input is not None else 0
481 )
470 - transformed_hidden_dim = hidden_states.shape[-1]
482 + transformed_hidden_dim: int | None = hidden_states.shape[-1]
483 if (
484 not self._quant_method.skip_forward_padding
485 and self.moe_config.hidden_dim != transformed_hidden_dim
486 ):
487 + assert transformed_hidden_dim is not None
488 hidden_states = F.pad(
489 hidden_states,
490 (0, self.moe_config.hidden_dim - transformed_hidden_dim),
@@ -479,12 +492,33 @@ class MoERunner(MoERunnerInterface):
492 value=0.0,
493 )
494
495 + # Truncation sizes for stripping kernel padding from the output.
496 + # None means no truncation needed (no padding was applied).
497 + #
498 + # Two truncation points exist in forward():
499 + # pre_xform: applied to fused_output BEFORE routed_output_transform
500 + # post_xform: applied to the final result AFTER all-reduce
501 + #
502 + # Latent MoE with shared experts (NemotronH):
503 + # - pre_xform strips padding from the latent dim so
504 + # routed_output_transform receives the correct input size
505 + # - post_xform truncates to shared_experts_hidden_dim (full hidden)
506 + # after shared + routed outputs are combined and all-reduced
507 + #
508 + # Standard MoE / MoE without transforms (GPT-OSS, Mixtral):
509 + # - pre_xform is None (no early truncation)
510 + # - post_xform strips padding after all-reduce (or None if unpadded)
511 + if transformed_hidden_dim == hidden_states.shape[-1]:
512 + transformed_hidden_dim = None
513 +
514 if self.routed_output_transform is not None and shared_experts_hidden_dim > 0:
483 - orig_hidden_dims = shared_experts_hidden_dim
515 + pre_xform_trunc_size = transformed_hidden_dim
516 + post_xform_trunc_size = shared_experts_hidden_dim
517 else:
485 - orig_hidden_dims = transformed_hidden_dim
518 + pre_xform_trunc_size = None
519 + post_xform_trunc_size = transformed_hidden_dim
520
487 - return hidden_states, orig_hidden_dims
521 + return hidden_states, pre_xform_trunc_size, post_xform_trunc_size
522
523 def _maybe_apply_shared_experts(
524 self,
@@ -493,11 +527,10 @@ class MoERunner(MoERunnerInterface):
527 ):
528 if self._shared_experts is not None:
529 assert shared_experts_input is not None
496 - self._shared_experts.apply(shared_experts_input, order)
530 + self._shared_experts(shared_experts_input, order)
531
532 def _apply_quant_method(
533 self,
500 - layer: torch.nn.Module,
534 hidden_states: torch.Tensor,
535 router_logits: torch.Tensor,
536 shared_experts_input: torch.Tensor | None,
@@ -513,24 +546,23 @@ class MoERunner(MoERunnerInterface):
546 shared_experts_input, SharedExpertsOrder.NO_OVERLAP
547 )
548
516 - if self._quant_method.is_monolithic:
517 - fused_out = self._quant_method.apply_monolithic(
518 - layer=layer,
549 + if self.routed_experts.quant_method.is_monolithic:
550 + # Monolithic kernels: pass router_logits to routed_experts
551 + fused_out = self.routed_experts.forward_monolithic(
552 x=hidden_states,
553 router_logits=router_logits,
554 input_ids=input_ids,
555 )
556 else:
557 + # Modular kernels: select experts first, then call routed_experts
558 topk_weights, topk_ids = self.router.select_experts(
559 hidden_states=hidden_states,
560 router_logits=router_logits,
561 + topk_indices_dtype=self._quant_method.topk_indices_dtype,
562 input_ids=input_ids,
563 )
564
530 - # Passing shared_experts_input in case SharedExpertsOrder is
531 - # MK_INTERNAL_OVERLAPPED.
532 - fused_out = self._quant_method.apply(
533 - layer=layer,
565 + fused_out = self.routed_experts.forward_modular(
566 x=hidden_states,
567 topk_weights=topk_weights,
568 topk_ids=topk_ids,
@@ -627,12 +659,13 @@ class MoERunner(MoERunnerInterface):
659 # `moe_config.hidden_dim`, e.g. after `align_trtllm_fp4_moe_hidden_dim_for_fi`
660 # so routed output can be trimmed before
661 # shared+routed add / latent up proj if needed.
630 - routed_hidden_dim = hidden_states.shape[-1]
631 - hidden_states, og_hidden_dim = self._maybe_pad_hidden_states(
632 - shared_experts_input,
633 - hidden_states,
662 +
663 + hidden_states, og_hidden_dim_pre_xform, og_hidden_dim_post_xform = (
664 + self._maybe_pad_hidden_states(
665 + shared_experts_input,
666 + hidden_states,
667 + )
668 )
635 - hidden_dim_was_padded = hidden_states.shape[-1] > routed_hidden_dim
669
670 result = self._forward_entry(
671 hidden_states,
@@ -640,7 +673,9 @@ class MoERunner(MoERunnerInterface):
673 shared_experts_input,
674 input_ids,
675 self._encode_layer_name(),
643 - self._trtllm_mxfp4_unpadded_dim(),
676 + self.moe_config.hidden_dim_unpadded
677 + if self._quant_method.has_unpadded_output
678 + else 0,
679 )
680
681 #
@@ -654,10 +689,9 @@ class MoERunner(MoERunnerInterface):
689
690 # Extract outputs from result
691 shared_output, fused_output = _unpack(result)
657 - if (
658 - shared_output is not None or self.routed_output_transform is not None
659 - ) and hidden_dim_was_padded:
660 - fused_output = fused_output[..., :routed_hidden_dim]
692 +
693 + if og_hidden_dim_pre_xform is not None:
694 + fused_output = fused_output[..., :og_hidden_dim_pre_xform]
695
696 # If combine kernel already reduced fused, reduce shared to match.
697 # See note above re: the two all-reduce points.
@@ -675,7 +709,7 @@ class MoERunner(MoERunnerInterface):
709 else:
710 result = fused_output
711
678 - result = self._maybe_reduce_final_output(result, og_hidden_dim)
712 + result = self._maybe_reduce_final_output(result, og_hidden_dim_post_xform)
713
714 return self._maybe_add_zero_expert_output(result)
715
@@ -687,7 +721,6 @@ class MoERunner(MoERunnerInterface):
721
722 def _maybe_dispatch(
723 self,
690 - layer: torch.nn.Module,
724 hidden_states: torch.Tensor,
725 router_logits: torch.Tensor,
726 ) -> tuple[torch.Tensor, torch.Tensor]:
@@ -723,7 +756,7 @@ class MoERunner(MoERunnerInterface):
756 self,
757 shared_output: torch.Tensor | None,
758 hidden_states: torch.Tensor,
726 - ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor | None]:
759 + ) -> torch.Tensor | tuple[torch.Tensor | None, torch.Tensor]:
760 if self.do_naive_dispatch_combine:
761 hidden_states = get_ep_group().combine(
762 hidden_states, self.moe_config.is_sequence_parallel
@@ -743,7 +776,6 @@ class MoERunner(MoERunnerInterface):
776
777 def _forward_impl(
778 self,
746 - layer: torch.nn.Module,
779 hidden_states: torch.Tensor,
780 router_logits: torch.Tensor,
781 shared_experts_input: torch.Tensor | None,
@@ -762,7 +794,7 @@ class MoERunner(MoERunnerInterface):
794 Returns a single tensor of combined fused and shared output (if present).
795 """
796 # TODO(bnell): this can be removed after MK migration is complete.
765 - layer.ensure_moe_quant_config_init()
797 + self.routed_experts._ensure_moe_quant_config_init()
798
799 # Sync aux and main stream for shared expert multi-stream overlap.
800 self._maybe_sync_shared_experts_stream(shared_experts_input)
@@ -782,13 +814,11 @@ class MoERunner(MoERunnerInterface):
814 # #32567 lands and the remaining kernels are made MKs. The PCP
815 # code will probably remain
816 hidden_states, router_logits = self._maybe_dispatch(
785 - layer,
817 hidden_states,
818 router_logits,
819 )
820
821 shared_output, hidden_states = self._apply_quant_method(
791 - layer=layer,
822 hidden_states=hidden_states,
823 router_logits=router_logits,
824 shared_experts_input=shared_experts_input,
@@ -799,3 +829,148 @@ class MoERunner(MoERunnerInterface):
829 shared_output,
830 hidden_states,
831 )
832 +
833 + #########################################################
834 + #
835 + # Old methods from FusedMoE layer. Remove when possible.
836 + #
837 + #########################################################
838 +
839 + # Note: maybe_init_modular_kernel should only be called by
840 + # prepare_communication_buffer_for_model.
841 + # This is called after all weight loading and post-processing, so it
842 + # should be safe to swap out the quant_method.
843 + def maybe_init_modular_kernel(self) -> None:
844 + # NOTE(rob): WIP refactor. For quant methods that own the MK
845 + # we create the MK during process_weights_after_loading.
846 + if (
847 + self.routed_experts.quant_method.supports_internal_mk
848 + or self.routed_experts.quant_method.is_monolithic
849 + ):
850 + return None
851 +
852 + self.routed_experts._ensure_moe_quant_config_init()
853 + # routing_tables only needed for round-robin expert placement with
854 + # DeepEP all2all backend.
855 + routing_tables = self._expert_routing_tables()
856 +
857 + if isinstance(self.routed_experts.quant_method, FusedMoEModularMethod):
858 + base_quant_method = self.routed_experts.quant_method.old_quant_method
859 + else:
860 + base_quant_method = self.routed_experts.quant_method
861 +
862 + prepare_finalize = base_quant_method.maybe_make_prepare_finalize(
863 + routing_tables=routing_tables
864 + )
865 + if prepare_finalize is not None:
866 + logger.debug(
867 + "%s for %s(%s)", prepare_finalize.__class__.__name__, self, id(self)
868 + )
869 + self._replace_quant_method(
870 + FusedMoEModularMethod.make(
871 + self.routed_experts,
872 + base_quant_method,
873 + prepare_finalize,
874 + )
875 + )
876 +
877 + #
878 + # Properties
879 + #
880 +
881 + @property
882 + def layer_id(self):
883 + # Delayed import to avoid circular dependency
884 + from vllm.model_executor.models.utils import extract_layer_index
885 +
886 + return extract_layer_index(self.layer_name)
887 +
888 + #
889 + # Attributes still needed by models
890 + #
891 +
892 + @property
893 + def is_monolithic(self) -> bool:
894 + return self.routed_experts.quant_method.is_monolithic
895 +
896 + @property
897 + def activation(self) -> MoEActivation:
898 + return self.routed_experts.activation
899 +
900 + #
901 + # Expert maps
902 + #
903 +
904 + @property
905 + def expert_map_manager(self):
906 + """Forward to routed_experts.expert_map_manager for backward compatibility."""
907 + return self.routed_experts.expert_map_manager
908 +
909 + @property
910 + def expert_placement_strategy(self) -> ExpertPlacementStrategy:
911 + return self.expert_map_manager.placement_strategy
912 +
913 + @property
914 + def expert_global_to_physical(self) -> torch.Tensor | None:
915 + tables = self.expert_map_manager.routing_tables
916 + return tables[0] if tables else None
917 +
918 + @property
919 + def expert_physical_to_global(self) -> torch.Tensor | None:
920 + """Routing table: physical expert ID to global expert ID."""
921 + tables = self.expert_map_manager.routing_tables
922 + return tables[1] if tables else None
923 +
924 + @property
925 + def expert_local_to_global(self) -> torch.Tensor | None:
926 + """Routing table: local expert ID to global expert ID."""
927 + tables = self.expert_map_manager.routing_tables
928 + return tables[2] if tables else None
929 +
930 + @property
931 + def expert_map(self) -> torch.Tensor | None:
932 + return self.routed_experts.expert_map
933 +
934 + def _expert_routing_tables(
935 + self,
936 + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None:
937 + return self.routed_experts._expert_routing_tables()
938 +
939 + def update_expert_map(self):
940 + self.routed_experts.update_expert_map()
941 +
942 + def _map_global_expert_id_to_local_expert_id(self, expert_id: int) -> int:
943 + """Map global expert ID to local expert ID."""
944 + return self.routed_experts._map_global_expert_id_to_local_expert_id(expert_id)
945 +
946 + def get_expert_weights(self) -> Iterable[torch.Tensor]:
947 + return self.routed_experts.get_expert_weights()
948 +
949 + #
950 + # EPLB
951 + #
952 +
953 + @property
954 + def eplb_state(self) -> EplbLayerState | None:
955 + return self.router.eplb_state
956 +
957 + def set_eplb_state(
958 + self,
959 + moe_layer_idx: int,
960 + expert_load_view: torch.Tensor,
961 + logical_to_physical_map: torch.Tensor,
962 + logical_replica_count: torch.Tensor,
963 + ) -> None:
964 + """
965 + Register the EPLB state in this layer.
966 +
967 + This is used later in forward pass, where we get the expert mapping
968 + and record the load metrics in `expert_load_view`.
969 + """
970 + if self.router.eplb_state is not None:
971 + self.router.eplb_state.set_layer_state(
972 + moe_layer_idx,
973 + expert_load_view,
974 + logical_to_physical_map,
975 + logical_replica_count,
976 + )
vllm/model_executor/layers/fused_moe/runner/moe_runner_interface.py
+102 -2
@@ -1,10 +1,13 @@
1 # SPDX-License-Identifier: Apache-2.0
2 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3 from abc import ABC, abstractmethod
4 +from collections.abc import Iterable
5
6 import torch
7
8 +from vllm.config.parallel import ExpertPlacementStrategy
9 from vllm.model_executor.custom_op import PluggableLayer
10 +from vllm.model_executor.layers.fused_moe.activation import MoEActivation
11 from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
12 FusedMoEMethodBase,
13 )
@@ -22,6 +25,11 @@ class MoERunnerInterface(PluggableLayer, ABC):
25 expert routing, and managing tensor parallel operations.
26 """
27
28 + def __init__(self):
29 + super().__init__()
30 + # HACK
31 + self._already_called_process_weights_after_loading = True
32 +
33 @abstractmethod
34 def forward(
35 self,
@@ -31,16 +39,108 @@ class MoERunnerInterface(PluggableLayer, ABC):
39 ) -> torch.Tensor:
40 raise NotImplementedError
41
42 + @property
43 + @abstractmethod
44 + def shared_experts(self) -> SharedExperts | None:
45 + raise NotImplementedError
46 +
47 + @property
48 @abstractmethod
49 def is_internal_router(self) -> bool:
50 raise NotImplementedError
51
52 @property
53 @abstractmethod
40 - def shared_experts(self) -> SharedExperts | None:
54 + def _quant_method(self) -> FusedMoEMethodBase:
55 raise NotImplementedError
56
43 - # TODO(bnell): temporary hack, do not call this method.
57 + # Temporary hack
58 @abstractmethod
59 def _replace_quant_method(self, quant_method: FusedMoEMethodBase):
60 raise NotImplementedError
61 +
62 + ########################################################################
63 + #
64 + # FusedMoE layer methods
65 + #
66 + ########################################################################
67 +
68 + @abstractmethod
69 + def maybe_init_modular_kernel(self) -> None:
70 + raise NotImplementedError
71 +
72 + @property
73 + @abstractmethod
74 + def layer_id(self):
75 + raise NotImplementedError
76 +
77 + #
78 + # Attributes still needed by models
79 + #
80 +
81 + @property
82 + @abstractmethod
83 + def is_monolithic(self) -> bool:
84 + raise NotImplementedError
85 +
86 + @property
87 + @abstractmethod
88 + def activation(self) -> MoEActivation:
89 + raise NotImplementedError
90 +
91 + #
92 + # Expert maps
93 + #
94 +
95 + @property
96 + @abstractmethod
97 + def expert_placement_strategy(self) -> ExpertPlacementStrategy:
98 + raise NotImplementedError
99 +
100 + @property
101 + @abstractmethod
102 + def expert_global_to_physical(self) -> torch.Tensor | None:
103 + raise NotImplementedError
104 +
105 + @property
106 + @abstractmethod
107 + def expert_physical_to_global(self) -> torch.Tensor | None:
108 + raise NotImplementedError
109 +
110 + @property
111 + @abstractmethod
112 + def expert_local_to_global(self) -> torch.Tensor | None:
113 + raise NotImplementedError
114 +
115 + @property
116 + @abstractmethod
117 + def expert_map(self) -> torch.Tensor | None:
118 + raise NotImplementedError
119 +
120 + @abstractmethod
121 + def _expert_routing_tables(
122 + self,
123 + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None:
124 + raise NotImplementedError
125 +
126 + @abstractmethod
127 + def update_expert_map(self):
128 + raise NotImplementedError
129 +
130 + @abstractmethod
131 + def _map_global_expert_id_to_local_expert_id(self, expert_id: int) -> int:
132 + raise NotImplementedError
133 +
134 + @abstractmethod
135 + def get_expert_weights(self) -> Iterable[torch.Tensor]:
136 + raise NotImplementedError
137 +
138 + @abstractmethod
139 + def set_eplb_state(
140 + self,
141 + moe_layer_idx: int,
142 + expert_load_view: torch.Tensor,
143 + logical_to_physical_map: torch.Tensor,
144 + logical_replica_count: torch.Tensor,
145 + ) -> None:
146 + raise NotImplementedError
vllm/model_executor/layers/fused_moe/runner/shared_experts.py
+12 -15
@@ -1,5 +1,6 @@
1 # SPDX-License-Identifier: Apache-2.0
2 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3 +from collections.abc import Callable
4 from enum import IntEnum
5
6 import torch
@@ -9,9 +10,6 @@ from vllm.logger import init_logger
10 from vllm.model_executor.layers.fused_moe.config import (
11 FusedMoEConfig,
12 )
12 -from vllm.model_executor.layers.quantization.base_config import (
13 - QuantizeMethodBase,
14 -)
13 from vllm.platforms import current_platform
14 from vllm.utils.torch_utils import (
15 aux_stream,
@@ -38,21 +36,15 @@ class SharedExpertsOrder(IntEnum):
36 MULTI_STREAM_OVERLAPPED = (3,)
37
38
41 -class SharedExperts:
39 +class SharedExperts(torch.nn.Module):
40 def __init__(
41 self,
42 layer: torch.nn.Module,
43 moe_config: FusedMoEConfig,
46 - quant_method: QuantizeMethodBase,
44 enable_dbo: bool,
45 + mk_can_overlap_shared_experts: Callable[[], bool],
46 ):
49 - from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
50 - FusedMoEMethodBase,
51 - )
52 -
53 - # quant_method must be a FusedMoEMethodBase but we can't use the type
54 - # due to circular imports.
55 - assert isinstance(quant_method, FusedMoEMethodBase)
47 + super().__init__()
48
49 # The SharedExperts need to handle DBO since they can be called from
50 # an MK's finalize method. We keep a list of outputs indexed by current
@@ -62,7 +54,8 @@ class SharedExperts:
54 self._output: list[torch.Tensor | None] = [None, None]
55 self._layer = layer
56 self._moe_config = moe_config
65 - self._quant_method = quant_method
57 +
58 + self._mk_can_overlap_shared_experts = mk_can_overlap_shared_experts
59
60 # Allow disabling of the separate shared experts stream for
61 # debug purposes.
@@ -78,6 +71,10 @@ class SharedExperts:
71 if self._stream is not None:
72 logger.debug_once("Enabled separate cuda stream for MoE shared_experts")
73
74 + # TODO(bnell): Hack for elastic_ep. Get rid of this
75 + def _set_moe_config(self, new_moe_config: FusedMoEConfig):
76 + self.moe_config = new_moe_config
77 +
78 @property
79 def _disable_shared_experts_overlap(self) -> bool:
80 # Disable shared expert overlap if:
@@ -96,7 +93,7 @@ class SharedExperts:
93 if self._disable_shared_experts_overlap:
94 return SharedExpertsOrder.NO_OVERLAP
95
99 - if self._quant_method.mk_can_overlap_shared_experts:
96 + if self._mk_can_overlap_shared_experts():
97 return SharedExpertsOrder.MK_INTERNAL_OVERLAPPED
98
99 should_run_shared_in_aux_stream = (
@@ -155,7 +152,7 @@ class SharedExperts:
152 self._output[self._output_idx] = None
153 return output
154
158 - def apply(
155 + def forward(
156 self,
157 shared_experts_input: torch.Tensor,
158 order: SharedExpertsOrder,
vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py
+5 -6
@@ -6,7 +6,6 @@ from typing import TYPE_CHECKING
6
7 import torch
8 import torch.nn.functional as F
9 -from torch.nn import Module
9
10 import vllm.envs as envs
11 from vllm.logger import init_logger
@@ -38,7 +37,7 @@ from vllm.platforms import current_platform
37 from vllm.platforms.interface import CpuArchEnum
38
39 if TYPE_CHECKING:
41 - from vllm.model_executor.layers.fused_moe import RoutedExperts
40 + from vllm.model_executor.layers.fused_moe.routed_experts import RoutedExperts
41
42 logger = init_logger(__name__)
43
@@ -80,7 +79,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
79 def select_gemm_impl(
80 self,
81 prepare_finalize: FusedMoEPrepareAndFinalizeModular,
83 - layer: torch.nn.Module,
82 + layer: "RoutedExperts",
83 ) -> FusedMoEExpertsModular:
84 raise ValueError(
85 f"{self.__class__.__name__} uses the new modular kernel initialization "
@@ -89,7 +88,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
88
89 def create_weights(
90 self,
92 - layer: torch.nn.Module,
91 + layer: "RoutedExperts",
92 num_experts: int,
93 hidden_size: int,
94 intermediate_size_per_partition: int,
@@ -156,7 +155,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
155
156 def _setup_kernel(
157 self,
159 - layer: Module,
158 + layer: "RoutedExperts",
159 w13: torch.Tensor,
160 w2: torch.Tensor,
161 ) -> None:
@@ -201,7 +200,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
200 routing_tables=layer._expert_routing_tables(),
201 )
202
204 - def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
203 + def process_weights_after_loading(self, layer: "RoutedExperts") -> None:
204 super().process_weights_after_loading(layer)
205
206 # Padding the weight for better performance on ROCm.
vllm/model_executor/layers/quantization/awq_marlin.py
+2 -4
@@ -16,16 +16,14 @@ from vllm.model_executor.kernels.linear import (
16 choose_mp_linear_kernel,
17 )
18 from vllm.model_executor.layers.fused_moe import (
19 + FusedMoEConfig,
20 FusedMoEMethodBase,
21 + FusedMoEQuantConfig,
22 FusedMoeWeightScaleSupported,
23 RoutedExperts,
24 SharedExperts,
25 UnquantizedFusedMoEMethod,
26 )
25 -from vllm.model_executor.layers.fused_moe.config import (
26 - FusedMoEConfig,
27 - FusedMoEQuantConfig,
28 -)
27 from vllm.model_executor.layers.fused_moe.oracle.int_wna16 import (
28 convert_to_wna16_moe_kernel_format,
29 make_wna16_moe_kernel,
vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
+3 -3
@@ -210,7 +210,7 @@ class CompressedTensorsConfig(QuantizationConfig):
210 )
211 return None
212
213 - def _add_fused_moe_to_target_scheme_map(self):
213 + def _add_fused_moe_to_target_scheme_map(self): # XXXXXXXXXXXXXXXXXXXXXX
214 """
215 Helper function to update target_scheme_map
216 since linear layers get fused into FusedMoE
@@ -219,10 +219,10 @@ class CompressedTensorsConfig(QuantizationConfig):
219 """
220 if (
221 "Linear" not in self.target_scheme_map
222 - or "FusedMoE" in self.target_scheme_map
222 + or "RoutedExperts" in self.target_scheme_map
223 ):
224 return
225 - self.target_scheme_map["FusedMoE"] = self.target_scheme_map["Linear"]
225 + self.target_scheme_map["RoutedExperts"] = self.target_scheme_map["Linear"]
226
227 @classmethod
228 def from_config(cls, config: dict[str, Any]) -> "CompressedTensorsConfig":
vllm/model_executor/layers/quantization/humming.py
+4 -5
@@ -10,14 +10,12 @@ import torch
10
11 from vllm import envs
12 from vllm.model_executor.layers.fused_moe import (
13 + FusedMoEConfig,
14 FusedMoEMethodBase,
15 + FusedMoEQuantConfig,
16 RoutedExperts,
17 SharedExperts,
18 )
17 -from vllm.model_executor.layers.fused_moe.config import (
18 - FusedMoEConfig,
19 - FusedMoEQuantConfig,
20 -)
19 from vllm.model_executor.layers.fused_moe.unquantized_fused_moe_method import (
20 UnquantizedFusedMoEMethod,
21 )
@@ -44,8 +42,9 @@ from vllm.model_executor.parameter import (
42 )
43 from vllm.model_executor.utils import set_weight_attrs
44 from vllm.platforms import current_platform
45 +from vllm.utils.import_utils import has_humming
46
48 -if current_platform.is_cuda():
47 +if has_humming() and current_platform.is_cuda():
48 from humming.dtypes import DataType
49 from humming.layer import HummingMethod
50 from humming.schema import (
vllm/model_executor/layers/quantization/mxfp4.py
+16
@@ -155,6 +155,14 @@ class GptOssMxfp4MoEMethod(FusedMoEMethodBase):
155 # so can skip the padding in the forward before applying the moe method
156 return self.mxfp4_backend == Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8
157
158 + # TODO(bnell): move to MK/expert_class?
159 + @property
160 + def has_unpadded_output(self) -> bool:
161 + return self.mxfp4_backend in [
162 + Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8,
163 + Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_BF16,
164 + ]
165 +
166 def maybe_roundup_sizes(
167 self,
168 hidden_size: int,
@@ -487,6 +495,14 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
495 # so can skip the padding in the forward before applying the moe method
496 return self.mxfp4_backend == Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8
497
498 + # TODO(bnell): move to MK/expert_class?
499 + @property
500 + def has_unpadded_output(self) -> bool:
501 + return self.mxfp4_backend in [
502 + Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8,
503 + Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_BF16,
504 + ]
505 +
506 def maybe_roundup_sizes(
507 self,
508 hidden_size: int,
vllm/model_executor/layers/quantization/utils/marlin_utils.py
+6 -1
@@ -234,7 +234,12 @@ def check_moe_marlin_supports_layer(layer: RoutedExperts, group_size: int) -> bo
234 if current_platform.is_rocm():
235 return False
236 hidden_size = layer.hidden_size
237 - intermediate_size_per_partition = layer.intermediate_size_per_partition
237 + # Note: The layer has not performed rounding on intermediate_size's at this
238 + # point. Use the unpadded size which won't change.
239 + intermediate_size_per_partition = (
240 + layer.moe_config.intermediate_size_per_partition_unpadded
241 + )
242 + assert intermediate_size_per_partition is not None
243 # apply_router_weight_on_input is not supported for moe marlin
244 supports_router_weight = not layer.apply_router_weight_on_input
245
vllm/model_executor/layers/quantization/utils/marlin_utils_fp4.py
+5 -4
@@ -6,6 +6,7 @@ import torch
6
7 import vllm._custom_ops as ops
8 from vllm.logger import init_logger
9 +from vllm.model_executor.layers.fused_moe.routed_experts import RoutedExperts
10 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
11 USE_FP32_REDUCE_DEFAULT,
12 get_marlin_input_dtype,
@@ -286,7 +287,7 @@ def prepare_fp4_layer_for_marlin(
287
288
289 def prepare_nvfp4_moe_layer_for_marlin(
289 - layer: torch.nn.Module,
290 + layer: RoutedExperts,
291 w13: torch.Tensor,
292 w13_scale: torch.Tensor,
293 w13_scale_2: torch.Tensor,
@@ -353,7 +354,7 @@ def prepare_nvfp4_moe_layer_for_marlin(
354
355 # WEIGHT SCALES
356 # Permute scales
356 - def premute_scales(
357 + def permute_scales(
358 scales: torch.Tensor, g_scales: torch.Tensor, name: str
359 ) -> tuple[torch.Tensor, torch.Tensor]:
360 scales = scales.to(param_dtype)
@@ -388,8 +389,8 @@ def prepare_nvfp4_moe_layer_for_marlin(
389 g_scales = g_scales / combined_scale_factor
390 return scales, g_scales
391
391 - w13_scale, w13_scale_2 = premute_scales(w13_scale, w13_scale_2, "w13")
392 - w2_scale, w2_scale_2 = premute_scales(w2_scale, w2_scale_2, "w2")
392 + w13_scale, w13_scale_2 = permute_scales(w13_scale, w13_scale_2, "w13")
393 + w2_scale, w2_scale_2 = permute_scales(w2_scale, w2_scale_2, "w2")
394
395 return w13, w13_scale, w13_scale_2, w2, w2_scale, w2_scale_2
396
vllm/model_executor/model_loader/bitsandbytes_loader.py
+5 -5
@@ -22,7 +22,7 @@ from vllm.distributed import (
22 )
23 from vllm.logger import init_logger
24 from vllm.lora.utils import is_moe_model
25 -from vllm.model_executor.layers.fused_moe import FusedMoE
25 +from vllm.model_executor.layers.fused_moe import RoutedExperts
26 from vllm.model_executor.layers.linear import (
27 LinearBase,
28 MergedColumnParallelLinear,
@@ -464,13 +464,13 @@ class BitsAndBytesModelLoader(BaseModelLoader):
464 self.target_modules.append(name)
465 if module.disable_tp:
466 self.tp_disabled_modules.append(name)
467 - elif isinstance(module, FusedMoE) and hasattr(
467 + elif isinstance(module, RoutedExperts) and hasattr(
468 module.quant_method, "quant_config"
469 ):
470 # TODO: support FusedMoE with prequant and 8bit.
471 if self.pre_quant and self.load_8bit:
472 raise ValueError(
473 - "Prequant BitsAndBytes 8bit models with FusedMoE "
473 + "Prequant BitsAndBytes 8bit models with RoutedExperts "
474 "is not supported yet."
475 )
476 # Get the corresponding weight name using module name and
@@ -508,7 +508,7 @@ class BitsAndBytesModelLoader(BaseModelLoader):
508 # dimension (dim=-1)
509 elif isinstance(module, (RowParallelLinear,)):
510 self.column_sharded_weights_modules.append(name)
511 - elif isinstance(module, FusedMoE):
511 + elif isinstance(module, RoutedExperts):
512 expert_mapping = self.expert_params_mapping
513 for exp in expert_mapping:
514 if exp[-1] == "w2":
@@ -629,7 +629,7 @@ class BitsAndBytesModelLoader(BaseModelLoader):
629 expert_mapping = self.expert_params_mapping
630 expert_qs_dict = {}
631 for name, module in model.named_modules():
632 - if not isinstance(module, FusedMoE):
632 + if not isinstance(module, RoutedExperts):
633 continue
634 w1_states_lst = []
635 w2_states_lst = []
vllm/model_executor/model_loader/ep_weight_filter.py
+1 -1
@@ -13,7 +13,7 @@ import regex as re
13 # Matches per-expert weight names like ".experts.42.gate_proj.weight".
14 # Does NOT match 3D fused-expert names like ".experts.gate_proj.weight"
15 # (no numeric id) — those are intentionally left unfiltered so the full
16 -# tensor is loaded and sliced later by FusedMoE.weight_loader.
16 +# tensor is loaded and sliced later by RoutedExperts.weight_loader.
17 _EXPERT_ID_RE = re.compile(r"\.experts\.(\d+)\.")
18
19
vllm/model_executor/model_loader/weight_utils.py
+106 -1
@@ -13,7 +13,7 @@ import tempfile
13 import threading
14 import time
15 from collections import defaultdict
16 -from collections.abc import Callable, Generator
16 +from collections.abc import Callable, Generator, Iterable
17 from contextlib import contextmanager
18 from pathlib import Path
19 from typing import IO, Any
@@ -1633,3 +1633,108 @@ def maybe_remap_kv_scale_name(name: str, params_dict: dict) -> str | None:
1633
1634 # If there were no matches, return the untouched param name
1635 return name
1636 +
1637 +
1638 +def maybe_remap_moe_expert_param_name(
1639 + name: str,
1640 + params_dict: dict[str, torch.nn.Parameter],
1641 +) -> str:
1642 + """
1643 + Remap MoE expert parameter names to account for routed_experts hierarchy.
1644 +
1645 + This handles the transition from the old FusedMoE structure where weights
1646 + were directly in the experts module, to the new MoERunner → RoutedExperts
1647 + structure.
1648 +
1649 + Checkpoint weights have names like:
1650 + layers.0.mlp.experts.w13_weight
1651 + But actual parameters are now:
1652 + layers.0.mlp.experts.routed_experts.w13_weight
1653 +
1654 + This function inserts 'routed_experts.' into the path when needed.
1655 +
1656 + Args:
1657 + name: Parameter name from checkpoint
1658 + params_dict: Dictionary of model parameters (from named_parameters())
1659 +
1660 + Returns:
1661 + Remapped parameter name if routed_experts hierarchy exists,
1662 + otherwise the original name
1663 + """
1664 + # Only remap if this looks like an expert parameter
1665 + if ".mlp.experts." not in name:
1666 + return name
1667 +
1668 + # Skip if already has routed_experts
1669 + if ".mlp.experts.routed_experts." in name:
1670 + return name
1671 +
1672 + # Expert parameter patterns to check
1673 + expert_param_suffixes = [
1674 + "w13_weight",
1675 + "w2_weight",
1676 + "w13_weight_scale",
1677 + "w2_weight_scale",
1678 + "w13_input_scale",
1679 + "w2_input_scale",
1680 + "w13_bias",
1681 + "w2_bias",
1682 + "w13_scale",
1683 + "w2_scale",
1684 + "w13_g_idx",
1685 + "w2_g_idx",
1686 + "w13_qweight",
1687 + "w2_qweight",
1688 + "w13_qzeros",
1689 + "w2_qzeros",
1690 + "w13_weight_shape",
1691 + "w2_weight_shape",
1692 + ]
1693 +
1694 + # Check if this is an expert weight parameter
1695 + is_expert_param = any(
1696 + f".{suffix}" in name or name.endswith(suffix)
1697 + for suffix in expert_param_suffixes
1698 + )
1699 +
1700 + if not is_expert_param:
1701 + return name
1702 +
1703 + # Try inserting routed_experts
1704 + new_name = name.replace(".mlp.experts.", ".mlp.experts.routed_experts.")
1705 +
1706 + # Only use the new name if it exists in the model
1707 + if new_name in params_dict:
1708 + return new_name
1709 +
1710 + # Otherwise return original name (old checkpoint format or different structure)
1711 + return name
1712 +
1713 +
1714 +def remap_moe_expert_weights(
1715 + weights: Iterable[tuple[str, torch.Tensor]],
1716 + params_dict: dict[str, torch.nn.Parameter],
1717 +) -> Generator[tuple[str, torch.Tensor], None, None]:
1718 + """
1719 + Wrapper generator that remaps MoE expert parameter names for backward compatibility.
1720 +
1721 + This allows models with custom weight loading to automatically handle both old
1722 + and new checkpoint formats without needing model-specific remapping code.
1723 +
1724 + Usage:
1725 + params_dict = dict(model.named_parameters())
1726 + for name, weight in remap_moe_expert_weights(weights, params_dict):
1727 + # name is automatically remapped if needed
1728 + param = params_dict[name]
1729 + ...
1730 +
1731 + Args:
1732 + weights: Iterator of (name, tensor) tuples from checkpoint
1733 + params_dict: Dictionary of model parameters (from named_parameters())
1734 +
1735 + Yields:
1736 + (remapped_name, tensor) tuples
1737 + """
1738 + for name, weight in weights:
1739 + remapped_name = maybe_remap_moe_expert_param_name(name, params_dict)
1740 + yield (remapped_name, weight)
vllm/model_executor/models/afmoe.py
+2 -1
@@ -20,6 +20,7 @@ from vllm.logger import init_logger
20 from vllm.model_executor.layers.attention import Attention
21 from vllm.model_executor.layers.fused_moe import (
22 FusedMoE,
23 + MoERunner,
24 fused_moe_make_expert_params_mapping,
25 )
26 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -640,7 +641,7 @@ class AfmoeForCausalLM(nn.Module, SupportsPP, SupportsEagle3, SupportsLoRA):
641 self.num_moe_layers = config.num_hidden_layers - config.num_dense_layers
642 self.num_expert_groups = config.n_group
643
643 - self.moe_layers: list[FusedMoE] = []
644 + self.moe_layers: list[MoERunner] = []
645 example_moe = None
646 for layer in self.model.layers:
647 if isinstance(layer, PPMissingLayer):
vllm/model_executor/models/aria.py
+12 -9
@@ -16,6 +16,7 @@ from vllm.inputs import MultiModalDataDict
16 from vllm.model_executor.layers.activation import get_act_fn
17 from vllm.model_executor.layers.fused_moe import (
18 FusedMoE,
19 + RoutedExperts,
20 )
21 from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
22 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -216,7 +217,7 @@ class AriaProjector(nn.Module):
217 return out
218
219
219 -class AriaFusedMoE(FusedMoE):
220 +class AriaRoutedExperts(RoutedExperts):
221 def weight_loader(
222 self, param: nn.Parameter, loaded_weight: torch.Tensor, shard_id: str
223 ) -> None:
@@ -225,13 +226,14 @@ class AriaFusedMoE(FusedMoE):
226 # up weights for each expert.
227 # Note: Loading expert weights with quantization is not supported
228 tp_rank = get_tensor_model_parallel_rank()
229 + tp_size = self.moe_config.tp_size
230 if shard_id == "w13":
231 # the shape of loaded_weight is
232 # (num_experts, hidden_size, 2 * moe_intermediate_size)
231 - if self.tp_size > 1:
233 + if tp_size > 1:
234 up, gate = loaded_weight.chunk(2, dim=-1)
233 - up_current_rank = up.chunk(self.tp_size, dim=-1)[tp_rank]
234 - gate_current_rank = gate.chunk(self.tp_size, dim=-1)[tp_rank]
235 + up_current_rank = up.chunk(tp_size, dim=-1)[tp_rank]
236 + gate_current_rank = gate.chunk(tp_size, dim=-1)[tp_rank]
237 up_and_gate = torch.cat(
238 [up_current_rank, gate_current_rank], dim=-1
239 ).transpose(1, 2)
@@ -241,8 +243,8 @@ class AriaFusedMoE(FusedMoE):
243 elif shard_id == "w2":
244 # the shape of loaded_weight is
245 # (num_experts, moe_intermediate_size, hidden_size)
244 - if self.tp_size > 1:
245 - down_current_rank = loaded_weight.chunk(self.tp_size, dim=1)[tp_rank]
246 + if tp_size > 1:
247 + down_current_rank = loaded_weight.chunk(tp_size, dim=1)[tp_rank]
248 param.data.copy_(down_current_rank.transpose(1, 2))
249 else:
250 param.data.copy_(loaded_weight.transpose(1, 2))
@@ -278,7 +280,7 @@ class AriaTextMoELayer(nn.Module):
280 bias=config.mlp_bias,
281 )
282
281 - self.experts = AriaFusedMoE(
283 + self.experts = FusedMoE(
284 shared_experts=self.shared_experts,
285 num_experts=config.moe_num_experts,
286 top_k=config.moe_topk,
@@ -286,6 +288,7 @@ class AriaTextMoELayer(nn.Module):
288 intermediate_size=config.intermediate_size,
289 quant_config=quant_config,
290 prefix=f"{prefix}.experts",
291 + routed_experts_cls=AriaRoutedExperts,
292 )
293
294 def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -351,8 +354,8 @@ class AriaTextModel(LlamaModel, SupportsQuant):
354 (".qkv_proj", ".v_proj", "v"),
355 (".gate_up_proj", ".gate_proj", 0),
356 (".gate_up_proj", ".up_proj", 1),
354 - ("experts.w13_weight", "experts.fc1.weight", "w13"),
355 - ("experts.w2_weight", "experts.fc2.weight", "w2"),
357 + ("experts.routed_experts.w13_weight", "experts.fc1.weight", "w13"),
358 + ("experts.routed_experts.w2_weight", "experts.fc2.weight", "w2"),
359 ]
360 params_dict = dict(self.named_parameters())
361 loaded_params: set[str] = set()
vllm/model_executor/models/bailing_moe_linear.py
+1 -1
@@ -283,7 +283,7 @@ class BailingMoeV25(nn.Module):
283 self.hidden_size = config.hidden_size
284 self.quant_config = quant_config
285 self.num_shared_experts = config.num_shared_experts
286 - self.score_function = getattr(config, "score_function", None)
286 + self.score_function: str | None = getattr(config, "score_function", None)
287 self.n_group = getattr(config, "n_group", None)
288 self.topk_group = getattr(config, "topk_group", None)
289 self.use_grouped_topk = self.n_group is not None and self.topk_group is not None
vllm/model_executor/models/dbrx.py
+17 -19
@@ -17,6 +17,7 @@ from vllm.distributed import (
17 from vllm.model_executor.layers.attention import Attention
18 from vllm.model_executor.layers.fused_moe import (
19 FusedMoE,
20 + RoutedExperts,
21 )
22 from vllm.model_executor.layers.linear import (
23 QKVParallelLinear,
@@ -73,27 +74,17 @@ class DbrxRouter(nn.Module):
74 return router_logits
75
76
76 -class DbrxExperts(FusedMoE):
77 +class DbrxExperts(RoutedExperts):
78 def __init__(
79 self,
80 + *args,
81 config: DbrxConfig,
80 - quant_config: QuantizationConfig | None = None,
81 - params_dtype: torch.dtype | None = None,
82 - prefix: str = "",
82 + **kwargs,
83 ):
84 - super().__init__(
85 - num_experts=config.ffn_config.moe_num_experts,
86 - top_k=config.ffn_config.moe_top_k,
87 - hidden_size=config.d_model,
88 - intermediate_size=config.ffn_config.ffn_hidden_size,
89 - params_dtype=params_dtype,
90 - renormalize=True,
91 - quant_config=quant_config,
92 - tp_size=get_tensor_model_parallel_world_size(),
93 - prefix=prefix,
94 - )
84 + super().__init__(*args, **kwargs)
85 self.config = config
86 self.d_model = config.d_model
87 + self.tp_size = self.moe_config.tp_size
88 self.intermediate_size = self.config.ffn_config.ffn_hidden_size // self.tp_size
89
90 # Define custom weight loader for dbrx model
@@ -168,11 +159,18 @@ class DbrxMoE(nn.Module):
159
160 self.router = DbrxRouter(config, self.params_dtype)
161
171 - self.experts = DbrxExperts(
172 - config=config,
173 - quant_config=quant_config,
162 + self.experts = FusedMoE(
163 + num_experts=config.ffn_config.moe_num_experts,
164 + top_k=config.ffn_config.moe_top_k,
165 + hidden_size=config.d_model,
166 + intermediate_size=config.ffn_config.ffn_hidden_size,
167 params_dtype=self.params_dtype,
175 - prefix=f"{prefix}.experts",
168 + renormalize=True,
169 + quant_config=quant_config,
170 + tp_size=get_tensor_model_parallel_world_size(),
171 + prefix=prefix,
172 + routed_experts_cls=DbrxExperts,
173 + routed_experts_args={"config": config},
174 )
175
176 def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
vllm/model_executor/models/ernie45_moe.py
+2 -1
@@ -44,6 +44,7 @@ from vllm.model_executor.layers.activation import SiluAndMul
44 from vllm.model_executor.layers.attention import Attention
45 from vllm.model_executor.layers.fused_moe import (
46 FusedMoE,
47 + MoERunner,
48 fused_moe_make_expert_params_mapping,
49 )
50 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -670,7 +671,7 @@ class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA, MixtureOfExpe
671 self.num_moe_layers = len(moe_layers_indices)
672 self.num_expert_groups = 1
673
673 - self.moe_layers: list[FusedMoE] = []
674 + self.moe_layers: list[MoERunner] = []
675 example_moe = None
676 for layer in self.model.layers:
677 if isinstance(layer, PPMissingLayer):
vllm/model_executor/models/ernie45_vl_moe.py
+8 -2
@@ -697,12 +697,18 @@ class Ernie4_5_VLMoeForCausalLM(nn.Module, SupportsPP):
697 moe_offset = int(name.split(".")[-3])
698 vision_expert_start_idx = self.config.moe_num_experts[0]
699 is_text_expert = moe_offset <= vision_expert_start_idx - 1
700 + routed_experts = (
701 + ".routed_experts" if ("w13_" in name or "w2_" in name) else ""
702 + )
703 if is_text_expert:
701 - name = name.replace(".experts.", ".text_experts.")
704 + name = name.replace(
705 + ".experts", f".text_experts{routed_experts}"
706 + )
707 else:
708 + delta = moe_offset - vision_expert_start_idx
709 name = name.replace(
710 f".experts.{moe_offset}",
705 - f".vision_experts.{moe_offset - vision_expert_start_idx}",
711 + f".vision_experts{routed_experts}.{delta}",
712 )
713
714 for mapping in expert_params_mapping:
vllm/model_executor/models/gemma4.py
+4
@@ -1486,6 +1486,10 @@ class Gemma4Model(nn.Module, EagleModelMixin):
1486 continue
1487 if is_pp_missing_parameter(name, self):
1488 continue
1489 + # Skip if name doesn't exist in params_dict (e.g., individual
1490 + # expert weights that should have been handled above)
1491 + if name not in params_dict:
1492 + continue
1493 param = params_dict[name]
1494 weight_loader = getattr(
1495 param, "weight_loader", default_weight_loader
vllm/model_executor/models/glm4_moe_lite_mtp.py
+2 -2
@@ -33,7 +33,7 @@ from transformers import PretrainedConfig
33 from vllm._aiter_ops import rocm_aiter_ops
34 from vllm.config import VllmConfig
35 from vllm.model_executor.layers.fused_moe import (
36 - FusedMoE,
36 + MoERunner,
37 fused_moe_make_expert_params_mapping,
38 )
39 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -215,7 +215,7 @@ class Glm4MoeLiteMTP(nn.Module, SupportsPP, Glm4MixtureOfExperts):
215 self.num_moe_layers = self.config.num_nextn_predict_layers
216 self.num_expert_groups = self.config.n_group
217
218 - self.moe_layers: list[FusedMoE] = []
218 + self.moe_layers: list[MoERunner] = []
219 self.moe_mlp_layers: list[Glm4MoeLite] = []
220 example_moe = None
221 for layer in self.model.layers.values():
vllm/model_executor/models/glm4_moe_mtp.py
+2 -2
@@ -32,7 +32,7 @@ from transformers import PretrainedConfig
32
33 from vllm.config import CacheConfig, ParallelConfig, VllmConfig
34 from vllm.model_executor.layers.fused_moe import (
35 - FusedMoE,
35 + MoERunner,
36 fused_moe_make_expert_params_mapping,
37 )
38 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -201,7 +201,7 @@ class Glm4MoeMTP(nn.Module, Glm4MixtureOfExperts):
201 self.num_moe_layers = self.config.num_nextn_predict_layers
202 self.num_expert_groups = self.config.n_group
203
204 - self.moe_layers: list[FusedMoE] = []
204 + self.moe_layers: list[MoERunner] = []
205 self.moe_mlp_layers: list[Glm4MoE] = []
206 example_moe = None
207 for layer in self.model.layers.values():
vllm/model_executor/models/gpt_oss.py
+5 -3
@@ -43,6 +43,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
43 from vllm.model_executor.model_loader.weight_utils import (
44 default_weight_loader,
45 maybe_remap_kv_scale_name,
46 + remap_moe_expert_weights,
47 )
48 from vllm.model_executor.models.utils import sequence_parallel_chunk
49 from vllm.platforms import current_platform
@@ -379,7 +380,8 @@ class GptOssModel(nn.Module, EagleModelMixin):
380 tp_rank_start = tp_rank * per_rank_intermediate_size
381 tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size, intermediate_size)
382
382 - for name, weight in weights:
383 + # Use centralized weight remapping for MoE expert parameters
384 + for name, weight in remap_moe_expert_weights(weights, params_dict):
385 # Skip layers on other devices.
386 if is_pp_missing_parameter(name, self):
387 continue
@@ -580,8 +582,8 @@ class GptOssModel(nn.Module, EagleModelMixin):
582 Returns:
583 Weight dtype string (e.g., "mxfp4", "fp8") or None if not available
584 """
583 - if hasattr(self.layers[layer_id].mlp.experts.quant_method, "weight_dtype"):
584 - return self.layers[layer_id].mlp.experts.quant_method.weight_dtype
585 + if hasattr(self.layers[layer_id].mlp.experts._quant_method, "weight_dtype"):
586 + return self.layers[layer_id].mlp.experts._quant_method.weight_dtype
587 return None
588
589 intermediate_size = self.config.intermediate_size
vllm/model_executor/models/granitemoehybrid.py
+5 -3
@@ -455,6 +455,8 @@ class GraniteMoeHybridModel(nn.Module):
455 loaded_params.add(n)
456
457 def _load_expert(n, p, name, shard_id, expert_id):
458 + if n not in params_dict:
459 + return
460 param = params_dict[n]
461 weight_loader = getattr(param, "weight_loader", default_weight_loader)
462 weight_loader(param, p, name, shard_id=shard_id, expert_id=expert_id)
@@ -518,14 +520,14 @@ class GraniteMoeHybridModel(nn.Module):
520 )
521 w1_param, w3_param = p[e].chunk(2, dim=0)
522 _load_expert(
521 - n.replace(".input_linear.", ".experts.w13_"),
523 + n.replace(".input_linear.", ".experts.routed_experts.w13_"),
524 w1_param,
525 w1_name,
526 shard_id="w1",
527 expert_id=e,
528 )
529 _load_expert(
528 - n.replace(".input_linear.", ".experts.w13_"),
530 + n.replace(".input_linear.", ".experts.routed_experts.w13_"),
531 w3_param,
532 w3_name,
533 shard_id="w3",
@@ -541,7 +543,7 @@ class GraniteMoeHybridModel(nn.Module):
543 )
544 w2_param = p[e]
545 _load_expert(
544 - n.replace(".output_linear.", ".experts.w2_"),
546 + n.replace(".output_linear.", ".experts.routed_experts.w2_"),
547 w2_param,
548 w2_name,
549 shard_id="w2",
vllm/model_executor/models/granitemoeshared.py
+3 -3
@@ -214,11 +214,11 @@ class GraniteMoeSharedModel(nn.Module):
214 for e in range(p.size(0)):
215 w1_name = n.replace(
216 ".block_sparse_moe.input_linear.weight",
217 - f".block_sparse_moe.experts.{e}.w1.weight",
217 + f".block_sparse_moe.experts.routed_experts.{e}.w1.weight",
218 )
219 w3_name = n.replace(
220 ".block_sparse_moe.input_linear.weight",
221 - f".block_sparse_moe.experts.{e}.w3.weight",
221 + f".block_sparse_moe.experts.routed_experts.{e}.w3.weight",
222 )
223 w1_param, w3_param = p[e].chunk(2, dim=0)
224 assert w1_name not in new_weights
@@ -229,7 +229,7 @@ class GraniteMoeSharedModel(nn.Module):
229 for e in range(p.size(0)):
230 w2_name = n.replace(
231 ".block_sparse_moe.output_linear.weight",
232 - f".block_sparse_moe.experts.{e}.w2.weight",
232 + f".block_sparse_moe.experts.routed_experts.{e}.w2.weight",
233 )
234 w2_param = p[e]
235 assert w2_name not in new_weights
vllm/model_executor/models/nemotron_h.py
-1
@@ -229,7 +229,6 @@ class NemotronHMoE(nn.Module):
229 scoring_func="sigmoid",
230 e_score_correction_bias=self.gate.e_score_correction_bias,
231 activation=activation_without_mul(config.mlp_hidden_act),
232 - is_act_and_mul=False, # non-gated MoE
232 enable_eplb=self.enable_eplb,
233 num_redundant_experts=self.n_redundant_experts,
234 is_sequence_parallel=self.is_sequence_parallel,
vllm/model_executor/models/sarvam.py
+2 -1
@@ -37,6 +37,7 @@ from vllm.distributed import (
37 from vllm.model_executor.layers.activation import SiluAndMul
38 from vllm.model_executor.layers.fused_moe import (
39 FusedMoE,
40 + MoERunner,
41 fused_moe_make_expert_params_mapping,
42 )
43 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -355,7 +356,7 @@ class SarvamMLAMoE(nn.Module):
356 routed_scaling_factor=self.routed_scaling_factor,
357 )
358
358 - def maybe_get_fused_moe(self) -> FusedMoE:
359 + def maybe_get_fused_moe(self) -> MoERunner:
360 return self.experts
361
362 def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
vllm/model_executor/models/step3p5.py
+2 -1
@@ -25,6 +25,7 @@ from vllm.model_executor.layers.activation import SiluAndMul, SwigluStepAndMul
25 from vllm.model_executor.layers.attention import Attention
26 from vllm.model_executor.layers.fused_moe import (
27 FusedMoE,
28 + MoERunner,
29 fused_moe_make_expert_params_mapping,
30 )
31 from vllm.model_executor.layers.layernorm import GemmaRMSNorm
@@ -954,7 +955,7 @@ class Step3p5ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
955 ) -> None:
956 for layer_idx, layer in enumerate(self.moe_layers):
957 experts = layer.experts
957 - assert isinstance(experts, FusedMoE)
958 + assert isinstance(experts, MoERunner)
959 # Register the expert weights.
960 self.expert_weights.append(experts.get_expert_weights())
961 experts.set_eplb_state(
vllm/model_executor/models/transformers/moe.py
+63 -27
@@ -16,6 +16,9 @@
16 # limitations under the License.
17 """Transformers modeling backend mixin for Mixture of Experts (MoE) models."""
18
19 +from collections.abc import Iterable
20 +from dataclasses import dataclass
21 +from functools import partial
22 from typing import TYPE_CHECKING, Any
23
24 import torch
@@ -27,6 +30,7 @@ from vllm.forward_context import ForwardContext, get_forward_context
30 from vllm.model_executor.custom_op import PluggableLayer
31 from vllm.model_executor.layers.fused_moe import (
32 FusedMoE,
33 + MoERunner,
34 fused_moe_make_expert_params_mapping,
35 )
36 from vllm.model_executor.models.interfaces import MixtureOfExperts
@@ -40,33 +44,22 @@ if TYPE_CHECKING:
44 from vllm.config import VllmConfig
45
46
47 +@dataclass
48 +class TransformersMoEState:
49 + topk_ids: torch.Tensor | None = None
50 + is_sequence_parallel: bool = False
51 +
52 +
53 # --8<-- [start:transformers_fused_moe]
54 @PluggableLayer.register("transformers_fused_moe")
45 -class TransformersFusedMoE(FusedMoE):
55 +class TransformersFusedMoE(MoERunner):
56 """Custom FusedMoE for the Transformers modeling backend."""
57
58 # --8<-- [end:transformers_fused_moe]
49 -
50 - def __init__(self, *args, **kwargs):
51 - self._topk_ids: torch.Tensor = None
52 -
53 - def custom_routing_function(hidden_states, gating_output, topk, renormalize):
54 - """Return `topk_weights` from `gating_output` and the
55 - `topk_ids` we stored in the layer earlier."""
56 - topk_weights = gating_output
57 - topk_ids = self._topk_ids
58 - # Handle all gather in expert parallel
59 - if topk_ids.size(0) != hidden_states.size(0):
60 - dp_metadata = get_forward_context().dp_metadata
61 - sizes = dp_metadata.get_chunk_sizes_across_dp_rank()
62 - is_sp = self.is_sequence_parallel
63 - dist_group = get_ep_group() if is_sp else get_dp_group()
64 - assert sizes[dist_group.rank_in_group] == topk_ids.shape[0]
65 - (topk_ids,) = dist_group.all_gatherv([topk_ids], 0, sizes)
66 - return topk_weights, topk_ids
67 -
68 - kwargs["custom_routing_function"] = custom_routing_function
59 + def __init__(self, *args, moe_state: TransformersMoEState, **kwargs):
60 super().__init__(*args, **kwargs)
61 + self._moe_state = moe_state
62 + self._moe_state.is_sequence_parallel = self.moe_config.is_sequence_parallel
63
64 def forward(
65 self,
@@ -78,6 +71,8 @@ class TransformersFusedMoE(FusedMoE):
71 """In Transformers `experts.forward` will have this signature.
72
73 We discard any extra kwargs because we cannot use them here."""
74 + # Note: we need to forward through a custom op so the topk_ids
75 + # can be transferred without interfering with cudagraphs.
76 return torch.ops.vllm.transformers_moe_forward(
77 hidden_states,
78 topk_ids.to(torch.int32),
@@ -85,6 +80,18 @@ class TransformersFusedMoE(FusedMoE):
80 self.layer_name,
81 )
82
83 + def _forward_super(
84 + self,
85 + hidden_states: torch.Tensor,
86 + topk_weights: torch.Tensor,
87 + ) -> torch.Tensor:
88 + return super().forward(hidden_states, topk_weights)
89 +
90 + def load_weights(
91 + self, weights: Iterable[tuple[str, torch.Tensor]]
92 + ) -> Iterable[str]:
93 + return self.routed_experts.load_weights(weights)
94 +
95
96 def transformers_moe_forward(
97 hidden_states: torch.Tensor,
@@ -95,11 +102,8 @@ def transformers_moe_forward(
102 """Store the `topk_ids` in the layer and call the actual forward."""
103 forward_context: ForwardContext = get_forward_context()
104 self = forward_context.no_compile_layers[layer_name]
98 - self._topk_ids = topk_ids
99 - # Clone hidden_states because it will be mutated in-place in FusedMoE
100 - # TODO(bnell): figure out a way to avoid calling runner directly.
101 - # it is a hack that the weight are being passed via logits.
102 - return self.runner.forward(hidden_states.clone(), topk_weights)
105 + self._moe_state.topk_ids = topk_ids
106 + return self._forward_super(hidden_states, topk_weights)
107
108
109 def transformers_moe_forward_fake(
@@ -189,6 +193,7 @@ class MoEMixin(MixtureOfExperts):
193 ckpt_up_proj_name=up_proj,
194 num_experts=num_experts,
195 num_redundant_experts=num_redundant_experts,
196 + routed_experts_prefix="",
197 )
198 )
199 return expert_mapping
@@ -286,8 +291,33 @@ class MoEMixin(MixtureOfExperts):
291 if "shared_expert" in mlp_param_name:
292 self.num_shared_experts = 1
293 break
294 +
295 # Replace experts module with FusedMoE
290 - fused_experts = TransformersFusedMoE(
296 + moe_state = TransformersMoEState()
297 +
298 + def custom_routing_function(
299 + hidden_states: torch.Tensor,
300 + gating_output: torch.Tensor,
301 + topk: int,
302 + renormalize: bool,
303 + moe_state: TransformersMoEState,
304 + ):
305 + """Return `topk_weights` from `gating_output` and the
306 + `topk_ids` we stored in the layer earlier."""
307 + topk_weights = gating_output
308 + topk_ids = moe_state.topk_ids
309 + assert topk_ids is not None
310 + # Handle all gather in expert parallel
311 + if topk_ids.size(0) != hidden_states.size(0):
312 + dp_metadata = get_forward_context().dp_metadata
313 + sizes = dp_metadata.get_chunk_sizes_across_dp_rank()
314 + is_sp = moe_state.is_sequence_parallel
315 + dist_group = get_ep_group() if is_sp else get_dp_group()
316 + assert sizes[dist_group.rank_in_group] == topk_ids.shape[0]
317 + (topk_ids,) = dist_group.all_gatherv([topk_ids], 0, sizes)
318 + return topk_weights, topk_ids
319 +
320 + fused_experts = FusedMoE(
321 num_experts=num_experts,
322 top_k=top_k,
323 hidden_size=hidden_size,
@@ -304,6 +334,12 @@ class MoEMixin(MixtureOfExperts):
334 num_redundant_experts=num_redundant_experts,
335 has_bias=has_bias,
336 expert_mapping=expert_mapping,
337 + custom_routing_function=partial(
338 + custom_routing_function,
339 + moe_state=moe_state,
340 + ),
341 + runner_cls=TransformersFusedMoE,
342 + runner_args={"moe_state": moe_state},
343 )
344 mlp.experts = fused_experts
345 log_replacement(qual_name, experts, fused_experts)
vllm/model_executor/warmup/deep_gemm_warmup.py
+8 -6
@@ -11,12 +11,12 @@ from tqdm import tqdm
11
12 import vllm.envs as envs
13 from vllm.distributed.parallel_state import get_dp_group, is_global_first_rank
14 +from vllm.model_executor.layers.fused_moe import MoERunner
15 from vllm.model_executor.layers.fused_moe.deep_gemm_utils import compute_aligned_M
16 from vllm.model_executor.layers.fused_moe.experts.deep_gemm_moe import DeepGemmExperts
17 from vllm.model_executor.layers.fused_moe.experts.triton_deep_gemm_moe import (
18 TritonOrDeepGemmExperts,
19 )
19 -from vllm.model_executor.layers.fused_moe.layer import FusedMoE
20 from vllm.model_executor.layers.linear import LinearBase
21 from vllm.model_executor.layers.quantization.fp8 import Fp8LinearMethod
22 from vllm.model_executor.layers.quantization.online.mxfp8 import Mxfp8OnlineLinearMethod
@@ -99,12 +99,13 @@ def _extract_data_from_linear_base_module(
99
100
101 def _extract_data_from_fused_moe_module(
102 - m: torch.nn.Module,
102 + m_: torch.nn.Module,
103 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int]:
104 """
105 Extract weights, weight scales and num_topk from FusedMoE module.
106 """
107 - assert isinstance(m, FusedMoE)
107 + assert isinstance(m_, MoERunner)
108 + m = m_.routed_experts
109 w13 = m.w13_weight
110 w13_s = (
111 m.w13_weight_scale_inv
@@ -156,10 +157,11 @@ def _fused_moe_grouped_gemm_may_use_deep_gemm(module: torch.nn.Module) -> bool:
157 if not (envs.VLLM_USE_DEEP_GEMM and envs.VLLM_MOE_USE_DEEP_GEMM):
158 return False
159
159 - if not isinstance(module, FusedMoE):
160 + if not isinstance(module, MoERunner):
161 return False
162
162 - moe_quant_config = module.quant_method.get_fused_moe_quant_config(module)
163 + quant_method = module._quant_method
164 + moe_quant_config = quant_method.get_fused_moe_quant_config(module.routed_experts)
165
166 if (
167 moe_quant_config is None
@@ -168,7 +170,7 @@ def _fused_moe_grouped_gemm_may_use_deep_gemm(module: torch.nn.Module) -> bool:
170 ):
171 return False
172
171 - moe_kernel = getattr(module.quant_method, "moe_kernel", None)
173 + moe_kernel = getattr(quant_method, "moe_kernel", None)
174 if moe_kernel is None:
175 return False
176
vllm/models/deepseek_v4/amd/model.py
+6 -2
@@ -15,7 +15,11 @@ from vllm.distributed import (
15 get_tensor_model_parallel_world_size,
16 )
17 from vllm.model_executor.layers.activation import SiluAndMul, SiluAndMulWithClamp
18 -from vllm.model_executor.layers.fused_moe import FusedMoE, GateLinear
18 +from vllm.model_executor.layers.fused_moe import (
19 + FusedMoE,
20 + GateLinear,
21 + fused_moe_make_expert_params_mapping,
22 +)
23 from vllm.model_executor.layers.layernorm import RMSNorm
24 from vllm.model_executor.layers.linear import (
25 MergedColumnParallelLinear,
@@ -699,7 +703,7 @@ class DeepseekV4Model(nn.Module):
703 def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
704 # Params for weights, fp8 weight scales, fp8 activation scales
705 # (param_name, weight_name, expert_id, shard_id)
702 - return FusedMoE.make_expert_params_mapping(
706 + return fused_moe_make_expert_params_mapping(
707 self,
708 ckpt_gate_proj_name="w1",
709 ckpt_down_proj_name="w2",
vllm/models/deepseek_v4/amd/mtp.py
+2 -2
@@ -24,7 +24,7 @@ from vllm.distributed import (
24 get_tensor_model_parallel_world_size,
25 )
26 from vllm.logger import init_logger
27 -from vllm.model_executor.layers.fused_moe import FusedMoE
27 +from vllm.model_executor.layers.fused_moe import fused_moe_make_expert_params_mapping
28 from vllm.model_executor.layers.layernorm import RMSNorm
29 from vllm.model_executor.layers.linear import ReplicatedLinear
30 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -340,7 +340,7 @@ class DeepSeekV4MTP(nn.Module):
340 head_rank_end = n_local_head * (tp_rank + 1)
341
342 # Pre-compute expert mapping ONCE.
343 - expert_mapping = FusedMoE.make_expert_params_mapping(
343 + expert_mapping = fused_moe_make_expert_params_mapping(
344 self,
345 ckpt_gate_proj_name="w1",
346 ckpt_down_proj_name="w2",
vllm/models/deepseek_v4/nvidia/model.py
+5 -2
@@ -23,7 +23,10 @@ from vllm.model_executor.kernels.mhc.tilelang import (
23 mhc_pre_tilelang,
24 )
25 from vllm.model_executor.layers.activation import SiluAndMul, SiluAndMulWithClamp
26 -from vllm.model_executor.layers.fused_moe import FusedMoE
26 +from vllm.model_executor.layers.fused_moe import (
27 + FusedMoE,
28 + fused_moe_make_expert_params_mapping,
29 +)
30 from vllm.model_executor.layers.fused_moe.router.base_router import (
31 eplb_map_to_physical_and_record,
32 )
@@ -1151,7 +1154,7 @@ class DeepseekV4Model(nn.Module):
1154 return make_deepseek_v4_expert_params_mapping(self.config.n_routed_experts)
1155 # Params for weights, fp8 weight scales, fp8 activation scales
1156 # (param_name, weight_name, expert_id, shard_id)
1154 - return FusedMoE.make_expert_params_mapping(
1157 + return fused_moe_make_expert_params_mapping(
1158 self,
1159 ckpt_gate_proj_name="w1",
1160 ckpt_down_proj_name="w2",
vllm/models/deepseek_v4/nvidia/mtp.py
+4 -2
@@ -28,7 +28,9 @@ from vllm.model_executor.kernels.mhc.tilelang import (
28 hc_head_fused_kernel_tilelang,
29 mhc_post_tilelang,
30 )
31 -from vllm.model_executor.layers.fused_moe import FusedMoE
31 +from vllm.model_executor.layers.fused_moe import (
32 + fused_moe_make_expert_params_mapping,
33 +)
34 from vllm.model_executor.layers.layernorm import RMSNorm
35 from vllm.model_executor.layers.linear import ReplicatedLinear
36 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -337,7 +339,7 @@ class DeepSeekV4MTP(nn.Module):
339 self.config.n_routed_experts
340 )
341 else:
340 - expert_mapping = FusedMoE.make_expert_params_mapping(
342 + expert_mapping = fused_moe_make_expert_params_mapping(
343 self,
344 ckpt_gate_proj_name="w1",
345 ckpt_down_proj_name="w2",
vllm/models/deepseek_v4/quant_config.py
+3 -4
@@ -7,8 +7,7 @@ from __future__ import annotations
7 from typing import TYPE_CHECKING
8
9 from vllm.config import get_current_vllm_config
10 -from vllm.model_executor.layers.fused_moe import FusedMoE
11 -from vllm.model_executor.layers.fused_moe.layer import UnquantizedFusedMoEMethod
10 +from vllm.model_executor.layers.fused_moe import MoERunner, UnquantizedFusedMoEMethod
11 from vllm.model_executor.layers.quantization import QuantizationMethods
12 from vllm.model_executor.layers.quantization.fp8 import Fp8Config
13 from vllm.model_executor.layers.quantization.mxfp4 import Mxfp4MoEMethod
@@ -130,7 +129,7 @@ class DeepseekV4FP8Config(Fp8Config):
129 return None
130
131 def get_quant_method(self, layer, prefix):
133 - if isinstance(layer, FusedMoE):
132 + if isinstance(layer, MoERunner):
133 if is_layer_skipped(
134 prefix=prefix,
135 ignored_layers=self.ignored_layers,
@@ -153,6 +152,6 @@ class DeepseekV4FP8Config(Fp8Config):
152 return super().get_quant_method(layer, prefix)
153
154 def is_mxfp4_quant(self, prefix, layer):
156 - if not isinstance(layer, FusedMoE) or self.expert_dtype != "fp4":
155 + if not isinstance(layer, MoERunner) or self.expert_dtype != "fp4":
156 return False
157 return self.moe_quant_algo != "NVFP4"
vllm/utils/__init__.py
+1 -1
@@ -39,7 +39,7 @@ def length_from_prompt_token_ids_or_embeds(
39 def is_moe_layer(module: torch.nn.Module) -> bool:
40 # TODO(bnell): Should use isinstance but can't due to circular dependencies.
41 def _check_bases(cls):
42 - if cls.__name__ == "FusedMoE":
42 + if cls.__name__ == "MoERunnerInterface":
43 return True
44
45 for b in cls.__bases__:
vllm/utils/import_utils.py
+5
@@ -487,3 +487,8 @@ def has_fbgemm_gpu() -> bool:
487 def has_cutedsl() -> bool:
488 """Whether the optional `cutelass` package is available."""
489 return _has_module("cutlass")
490 +
491 +
492 +def has_humming() -> bool:
493 + """Whether the optional `humming` package is available."""
494 + return _has_module("humming")
vllm/v1/worker/gpu_model_runner.py
+2 -2
@@ -7397,13 +7397,13 @@ class GPUModelRunner(
7397 self.routed_experts_initialized = True
7398
7399 def _bind_routed_experts_capturer(self, capturer: RoutedExpertsCapturer) -> None:
7400 - from vllm.model_executor.layers.fused_moe.layer import FusedMoE
7400 + from vllm.model_executor.layers.fused_moe.layer import MoERunner
7401 from vllm.model_executor.layers.fused_moe.router.base_router import (
7402 BaseRouter,
7403 )
7404
7405 for module in self.compilation_config.static_forward_context.values():
7406 - if isinstance(module, FusedMoE) and isinstance(module.router, BaseRouter):
7406 + if isinstance(module, MoERunner) and isinstance(module.router, BaseRouter):
7407 layer_id = module.layer_id
7408
7409 def _capture_fn(topk_ids, _layer_id=layer_id, _capturer=capturer):