diff --git a/tests/kernels/moe/test_ocp_mx_moe.py b/tests/kernels/moe/test_ocp_mx_moe.py index 8fe471d124f43..c9b2b85f004ac 100644 --- a/tests/kernels/moe/test_ocp_mx_moe.py +++ b/tests/kernels/moe/test_ocp_mx_moe.py @@ -30,7 +30,6 @@ if TRTLLM_GEN_MXFP4_AVAILABLE: from flashinfer import ( fp4_quantize, mxfp8_quantize, - next_positive_power_of_2, reorder_rows_for_gated_act_gemm, shuffle_matrix_a, shuffle_matrix_sf_a, @@ -188,30 +187,6 @@ def reference_moe( return t.to(torch.bfloat16) -def get_tile_tokens_dim(x: torch.Tensor, top_k: int, num_experts: int): - # Number of tokens in the input tensor. - num_tokens = x.shape[0] - # Factor to account for the imbalance of the experts. - # factor equals to the - # max_real_num_tokens_per_expert / perfect_num_tokens_per_expert - # - 1.0 means perfect expert distribution. - # - > 1.0 means some experts have more - # tokens than the perfect distribution. - # - < 1.0 does not make sense. - imbalance_factor = 1.3 - # Calculate the number of tokens per expert - # assuming perfect distribution. - num_tokens_per_expert = (num_tokens * top_k) // num_experts - # Apply the imbalance factor. - num_tokens_per_expert = int(num_tokens_per_expert * imbalance_factor) - # And pad the number to the next power of 2. - tile_tokens_dim = next_positive_power_of_2(num_tokens_per_expert) - # Cap to 8-64 tokens per CTA tile - # as it's the range supported by the kernel. - tile_tokens_dim = min(max(tile_tokens_dim, 8), 64) - return tile_tokens_dim - - def tg_mxfp4_moe( router_logits, topk, @@ -460,7 +435,6 @@ def tg_mxfp4_moe( local_expert_offset=0, local_num_experts=num_experts, routed_scaling_factor=None, - tile_tokens_dim=get_tile_tokens_dim(hidden_states, topk, num_experts), routing_method_type=1, # renormalize do_finalize=True, )[0] diff --git a/vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py b/vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py index 51e06ac54f497..d14300443c814 100644 --- a/vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py +++ b/vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py @@ -5,9 +5,6 @@ import torch from vllm.model_executor.layers.fused_moe.config import RoutingMethodType from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input -from vllm.model_executor.layers.quantization.utils.flashinfer_utils import ( - calculate_tile_tokens_dim, -) from vllm.model_executor.layers.quantization.utils.fp8_utils import ( per_token_group_quant_fp8, ) @@ -63,7 +60,6 @@ def flashinfer_fused_moe_blockscale_fp8( local_expert_offset=expert_offset, local_num_experts=local_num_experts, routed_scaling_factor=routed_scaling, - tile_tokens_dim=None, routing_method_type=routing_method_type, use_shuffled_weight=False, ) @@ -151,9 +147,6 @@ def flashinfer_fused_moe_per_tensor_scale_fp8( local_num_experts=local_num_experts, routed_scaling_factor=routed_scaling_factor, use_routing_scales_on_input=use_routing_scales_on_input, - tile_tokens_dim=calculate_tile_tokens_dim( - hidden_states.shape[0], top_k, num_experts - ), routing_method_type=routing_method_type, ) diff --git a/vllm/model_executor/layers/fused_moe/trtllm_moe.py b/vllm/model_executor/layers/fused_moe/trtllm_moe.py index 132d35e65aba8..4923d96af8dee 100644 --- a/vllm/model_executor/layers/fused_moe/trtllm_moe.py +++ b/vllm/model_executor/layers/fused_moe/trtllm_moe.py @@ -123,7 +123,6 @@ class TrtLlmGenExperts(mk.FusedMoEPermuteExpertsUnpermute): "local_expert_offset": local_expert_offset, "local_num_experts": local_num_experts, "routed_scaling_factor": None, - "tile_tokens_dim": None, "routing_method_type": 1, "do_finalize": True, "output": output, diff --git a/vllm/model_executor/layers/quantization/mxfp4.py b/vllm/model_executor/layers/quantization/mxfp4.py index 832925825c453..c50753270b86e 100644 --- a/vllm/model_executor/layers/quantization/mxfp4.py +++ b/vllm/model_executor/layers/quantization/mxfp4.py @@ -977,8 +977,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase): self.intermediate_size, # padded to multiple of 256 layer.ep_rank * layer.local_num_experts, # local_expert_offset self.num_experts, # local num experts - None, - None, + None, # routed_scaling_factor 1 if layer.renormalize else 0, # routing_method_type, renormalize True, # do finalize tune_max_num_tokens=max(self.max_capture_size, 1), diff --git a/vllm/model_executor/layers/quantization/utils/flashinfer_fp4_moe.py b/vllm/model_executor/layers/quantization/utils/flashinfer_fp4_moe.py index 1d410316d6299..4611b83757a69 100644 --- a/vllm/model_executor/layers/quantization/utils/flashinfer_fp4_moe.py +++ b/vllm/model_executor/layers/quantization/utils/flashinfer_fp4_moe.py @@ -325,7 +325,6 @@ def flashinfer_trtllm_fp4_moe( local_expert_offset=layer.ep_rank * layer.local_num_experts, local_num_experts=layer.local_num_experts, routed_scaling_factor=None, - tile_tokens_dim=None, routing_method_type=routing_method_type, do_finalize=True, )[0] @@ -404,7 +403,6 @@ def flashinfer_trtllm_fp4_routed_moe( local_expert_offset=layer.ep_rank * layer.local_num_experts, local_num_experts=layer.local_num_experts, routed_scaling_factor=None, - tile_tokens_dim=None, routing_method_type=1, do_finalize=True, )[0] diff --git a/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py b/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py index 3d6e9cda87667..e87f87c3656a7 100644 --- a/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py +++ b/vllm/model_executor/layers/quantization/utils/flashinfer_utils.py @@ -28,30 +28,6 @@ class FlashinferMoeBackend(Enum): CUTEDSL = "CUTEDSL" -def calculate_tile_tokens_dim(num_tokens, top_k, num_experts): - from flashinfer import next_positive_power_of_2 - - # FlashInfer 0.2.10 has issues with larger tile sizes. Set to 8 for now. - # TODO: Revert this to dynamic calculation once a new version of FlashInfer - # with the necessary kernels is released. - tile_tokens_dim = 8 - - # A factor considering tokens are not perfectly balanced among experts. - imbalance_factor = 1.3 - # Calculate the number of tokens per expert - # assuming perfect distribution. - num_tokens_per_expert = (num_tokens * top_k) // num_experts - # Apply the imbalance factor. - num_tokens_per_expert = int(num_tokens_per_expert * imbalance_factor) - # And pad the number to the next power of 2. - tile_tokens_dim = next_positive_power_of_2(num_tokens_per_expert) - # Cap to 8-max_tile_tokens_dim tokens per CTA tile - # as it's the range supported by the kernel. - tile_tokens_dim = min(max(tile_tokens_dim, 8), 64) - - return tile_tokens_dim - - def swap_w13_to_w31(x: torch.Tensor) -> torch.Tensor: return ( x.reshape(-1, 2, x.shape[-2] // 2, x.shape[-1]).flip(dims=[1]).reshape(x.shape)