529 lines
18 KiB
Python
529 lines
18 KiB
Python
# Copyright 2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import math
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from typing import Callable, Optional
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import torch
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import torch.nn.functional as F
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from sglang.srt.managers import expert_location_dispatch
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from sglang.srt.managers.expert_distribution import (
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ExpertDistributionRecorder,
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get_global_expert_distribution_recorder,
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)
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from sglang.srt.managers.expert_location_dispatch import (
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ExpertLocationDispatchInfo,
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topk_ids_logical_to_physical,
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)
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from sglang.srt.managers.schedule_batch import global_server_args_dict
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from sglang.srt.utils import (
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cpu_has_amx_support,
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get_bool_env_var,
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get_compiler_backend,
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is_cpu,
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is_cuda,
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is_hip,
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)
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_is_cuda = is_cuda()
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_is_hip = is_hip()
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_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
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_is_cpu_amx_available = cpu_has_amx_support()
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_is_cpu = is_cpu()
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if _is_cuda:
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from sgl_kernel import moe_fused_gate
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if _is_cuda or _is_hip:
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from sgl_kernel import topk_softmax
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if _use_aiter:
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try:
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from aiter import biased_grouped_topk as aiter_biased_grouped_topk
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except ImportError:
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raise ImportError("aiter is required when SGLANG_USE_AITER is set to True")
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def fused_topk_torch_native(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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topk: int,
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renormalize: bool,
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):
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assert (
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hidden_states.shape[0] == gating_output.shape[0]
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), f"Number of tokens mismatch, {hidden_states.shape=} vs {gating_output.shape=}"
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M, _ = hidden_states.shape
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topk_weights = torch.empty(
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M, topk, dtype=torch.float32, device=hidden_states.device
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)
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topk_ids = torch.empty(M, topk, dtype=torch.int32, device=hidden_states.device)
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topk_weights = F.softmax(gating_output.float(), dim=-1)
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topk_weights, topk_ids = torch.topk(topk_weights, topk, dim=-1)
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if renormalize:
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topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
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return topk_weights, topk_ids
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def fused_topk_cpu(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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topk: int,
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renormalize: bool,
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):
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return torch.ops.sgl_kernel.topk_softmax_cpu(
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hidden_states=hidden_states,
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gating_output=gating_output,
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topk=topk,
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renormalize=renormalize,
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)
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def fused_topk(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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topk: int,
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renormalize: bool,
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num_token_non_padded: Optional[torch.Tensor] = None,
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expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
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):
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assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
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M, _ = hidden_states.shape
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topk_weights = torch.empty(
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M, topk, dtype=torch.float32, device=hidden_states.device
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)
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topk_ids = torch.empty(M, topk, dtype=torch.int32, device=hidden_states.device)
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token_expert_indicies = torch.empty(
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M, topk, dtype=torch.int32, device=hidden_states.device
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)
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topk_softmax(
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topk_weights,
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topk_ids,
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token_expert_indicies,
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gating_output.float(),
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)
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del token_expert_indicies
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return _fused_topk_postprocess(
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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renormalize=renormalize,
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expert_location_dispatch_info=expert_location_dispatch_info,
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num_token_non_padded=num_token_non_padded,
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)
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@torch.compile(dynamic=True, backend=get_compiler_backend())
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def _fused_topk_postprocess(
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topk_weights,
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topk_ids,
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renormalize,
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expert_location_dispatch_info,
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num_token_non_padded,
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):
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if renormalize:
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topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
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topk_ids = topk_ids_logical_to_physical(topk_ids, expert_location_dispatch_info)
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_mask_topk_ids_padded_region(topk_ids, num_token_non_padded)
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return topk_weights, topk_ids
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# This is used by the Deepseek V2/V3/R1 series models
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@torch.compile(dynamic=True, backend=get_compiler_backend())
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def grouped_topk_gpu(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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topk: int,
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renormalize: bool,
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num_expert_group: int = 0,
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topk_group: int = 0,
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num_fused_shared_experts: int = 0,
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routed_scaling_factor: Optional[float] = None,
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num_token_non_padded: Optional[torch.Tensor] = None,
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expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
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):
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assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
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scores = torch.softmax(gating_output, dim=-1)
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num_token = scores.shape[0]
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num_experts = scores.shape[1]
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group_scores = (
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scores.view(num_token, num_expert_group, -1).max(dim=-1).values
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) # [n, n_group]
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group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=False)[
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1
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] # [n, top_k_group]
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group_mask = torch.zeros_like(group_scores) # [n, n_group]
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group_mask.scatter_(1, group_idx, 1) # [n, n_group]
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score_mask = (
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group_mask.unsqueeze(-1)
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.expand(num_token, num_expert_group, scores.shape[-1] // num_expert_group)
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.reshape(num_token, -1)
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) # [n, e]
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tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0) # [n, e]
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topk_weights, topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)
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if num_fused_shared_experts:
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topk_ids[:, -1] = torch.randint(
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low=num_experts,
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high=num_experts + num_fused_shared_experts,
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size=(topk_ids.size(0),),
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dtype=topk_ids.dtype,
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device=topk_ids.device,
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)
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topk_weights[:, -1] = topk_weights[:, :-1].sum(dim=-1) / routed_scaling_factor
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if renormalize:
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topk_weights_sum = (
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topk_weights.sum(dim=-1, keepdim=True)
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if num_fused_shared_experts == 0
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else topk_weights[:, :-1].sum(dim=-1, keepdim=True)
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)
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topk_weights = topk_weights / topk_weights_sum
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topk_weights, topk_ids = topk_weights.to(torch.float32), topk_ids.to(torch.int32)
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topk_ids = topk_ids_logical_to_physical(topk_ids, expert_location_dispatch_info)
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_mask_topk_ids_padded_region(topk_ids, num_token_non_padded)
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return topk_weights, topk_ids
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def grouped_topk_cpu(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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topk: int,
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renormalize: bool,
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num_expert_group: int = 0,
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topk_group: int = 0,
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num_fused_shared_experts: int = 0,
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routed_scaling_factor: Optional[float] = None,
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num_token_non_padded: Optional[torch.Tensor] = None,
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expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
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):
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assert expert_location_dispatch_info is None
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return torch.ops.sgl_kernel.grouped_topk_cpu(
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hidden_states,
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gating_output,
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topk,
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renormalize,
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num_expert_group,
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topk_group,
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num_fused_shared_experts,
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routed_scaling_factor,
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num_token_non_padded,
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)
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def biased_grouped_topk_impl(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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correction_bias: torch.Tensor,
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topk: int,
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renormalize: bool,
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num_expert_group: int = 0,
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topk_group: int = 0,
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num_fused_shared_experts: int = 0,
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routed_scaling_factor: Optional[float] = None,
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num_token_non_padded: Optional[torch.Tensor] = None,
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expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
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):
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assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
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scores = gating_output.sigmoid()
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num_token = scores.shape[0]
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num_experts = scores.shape[1]
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scores_for_choice = scores.view(num_token, -1) + correction_bias.unsqueeze(0)
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group_scores = (
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scores_for_choice.view(num_token, num_expert_group, -1)
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.topk(2, dim=-1)[0]
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.sum(dim=-1)
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) # [n, n_group]
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group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=False)[
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1
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] # [n, top_k_group]
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group_mask = torch.zeros_like(group_scores) # [n, n_group]
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group_mask.scatter_(1, group_idx, 1) # [n, n_group]
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score_mask = (
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group_mask.unsqueeze(-1)
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.expand(num_token, num_expert_group, scores.shape[-1] // num_expert_group)
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.reshape(num_token, -1)
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) # [n, e]
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tmp_scores = scores_for_choice.masked_fill(
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~score_mask.bool(), float("-inf")
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) # [n, e]
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_, topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)
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topk_weights = scores.gather(1, topk_ids)
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if num_fused_shared_experts:
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topk_ids[:, -1] = torch.randint(
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low=num_experts,
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high=num_experts + num_fused_shared_experts,
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size=(topk_ids.size(0),),
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dtype=topk_ids.dtype,
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device=topk_ids.device,
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)
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topk_weights[:, -1] = topk_weights[:, :-1].sum(dim=-1) / routed_scaling_factor
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if renormalize:
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topk_weights_sum = (
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topk_weights.sum(dim=-1, keepdim=True)
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if num_fused_shared_experts == 0
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else topk_weights[:, :-1].sum(dim=-1, keepdim=True)
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)
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topk_weights = topk_weights / topk_weights_sum
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topk_weights, topk_ids = topk_weights.to(torch.float32), topk_ids.to(torch.int32)
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topk_ids = topk_ids_logical_to_physical(topk_ids, expert_location_dispatch_info)
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_mask_topk_ids_padded_region(topk_ids, num_token_non_padded)
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return topk_weights, topk_ids
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def is_power_of_two(n):
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return n > 0 and math.log2(n).is_integer()
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def _mask_topk_ids_padded_region(
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topk_ids: torch.Tensor,
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num_token_non_padded: Optional[torch.Tensor] = None,
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):
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if num_token_non_padded is None:
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return
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indices = torch.arange(0, topk_ids.shape[0], device=topk_ids.device)
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topk_ids[indices >= num_token_non_padded, :] = -1
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@torch.compile(dynamic=True, backend=get_compiler_backend())
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def _biased_grouped_topk_postprocess(
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topk_ids, expert_location_dispatch_info, num_token_non_padded
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):
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topk_ids = topk_ids_logical_to_physical(topk_ids, expert_location_dispatch_info)
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_mask_topk_ids_padded_region(topk_ids, num_token_non_padded)
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return topk_ids
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def biased_grouped_topk_gpu(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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correction_bias: torch.Tensor,
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topk: int,
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renormalize: bool,
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num_expert_group: int = 0,
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topk_group: int = 0,
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compiled: bool = True,
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num_fused_shared_experts: int = 0,
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routed_scaling_factor: Optional[float] = None,
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num_token_non_padded: Optional[torch.Tensor] = None,
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expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
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):
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assert (
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routed_scaling_factor is not None
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), "routed_scaling_factor is required for biased_grouped_topk"
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# TODO: moe_fused_gate kernel is not supported for num_fused_shared_experts > 0 now.
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if (
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_is_cuda
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and gating_output.shape[1] // num_expert_group
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<= 32 # moe_fused_gate kernel ensure that num_experts/num_expert_group does not exceed MAX_VPT=32 now. And when kernel can handle MAX_VPT > 32, we can remove this assertion.
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and is_power_of_two(correction_bias.shape[0])
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):
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topk_weights, topk_ids = moe_fused_gate(
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gating_output,
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correction_bias,
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num_expert_group,
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topk_group,
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topk,
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num_fused_shared_experts,
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routed_scaling_factor,
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)
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# TODO merge into kernel
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if (expert_location_dispatch_info is not None) or (
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num_token_non_padded is not None
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):
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topk_ids = _biased_grouped_topk_postprocess(
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topk_ids, expert_location_dispatch_info, num_token_non_padded
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)
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return topk_weights, topk_ids
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elif _use_aiter:
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token = gating_output.shape[0]
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device = gating_output.device
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assert (
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hidden_states.shape[0] == gating_output.shape[0]
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), f"Number of tokens mismatch: hidden_states.shape[0] = {hidden_states.shape[0]}, gating_output.shape[0] = {gating_output.shape[0]}"
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topk_weights = torch.empty((token, topk), dtype=torch.float32, device=device)
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topk_ids = torch.empty((token, topk), dtype=torch.int32, device=device)
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aiter_biased_grouped_topk(
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gating_output,
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correction_bias,
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topk_weights,
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topk_ids,
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num_expert_group,
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topk_group,
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renormalize,
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routed_scaling_factor,
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)
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return topk_weights, topk_ids
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else:
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biased_grouped_topk_fn = (
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torch.compile(
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biased_grouped_topk_impl, dynamic=True, backend=get_compiler_backend()
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)
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if compiled
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else biased_grouped_topk_impl
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)
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return biased_grouped_topk_fn(
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hidden_states,
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gating_output,
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correction_bias,
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topk,
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renormalize,
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num_expert_group,
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topk_group,
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num_fused_shared_experts=num_fused_shared_experts,
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routed_scaling_factor=routed_scaling_factor,
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num_token_non_padded=num_token_non_padded,
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expert_location_dispatch_info=expert_location_dispatch_info,
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)
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def biased_grouped_topk_cpu(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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correction_bias: torch.Tensor,
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topk: int,
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renormalize: bool,
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num_expert_group: int = 0,
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topk_group: int = 0,
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compiled: bool = True,
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num_fused_shared_experts: int = 0,
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routed_scaling_factor: Optional[float] = None,
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num_token_non_padded: Optional[torch.Tensor] = None,
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expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
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):
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assert expert_location_dispatch_info is None
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return torch.ops.sgl_kernel.biased_grouped_topk_cpu(
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hidden_states,
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gating_output,
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correction_bias,
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topk,
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renormalize,
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num_expert_group,
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topk_group,
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num_fused_shared_experts,
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routed_scaling_factor,
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num_token_non_padded,
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)
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if _is_cpu and _is_cpu_amx_available:
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biased_grouped_topk = biased_grouped_topk_cpu
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grouped_topk = grouped_topk_cpu
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fused_topk_native = fused_topk_cpu
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else:
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biased_grouped_topk = biased_grouped_topk_gpu
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grouped_topk = grouped_topk_gpu
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fused_topk_native = fused_topk_torch_native
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def select_experts(
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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top_k: int,
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use_grouped_topk: bool,
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renormalize: bool,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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num_fused_shared_experts: int = 0,
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custom_routing_function: Optional[Callable] = None,
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correction_bias: Optional[torch.Tensor] = None,
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torch_native: bool = False,
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routed_scaling_factor: Optional[float] = None,
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num_token_non_padded: Optional[torch.Tensor] = None,
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expert_location_dispatch_info: Optional[ExpertLocationDispatchInfo] = None,
|
|
):
|
|
router_logits, correction_bias = (
|
|
expert_location_dispatch.transform_select_experts_inputs(
|
|
router_logits=router_logits,
|
|
correction_bias=correction_bias,
|
|
info=expert_location_dispatch_info,
|
|
)
|
|
)
|
|
|
|
# DeepSeek V2/V3/R1 series models use grouped_top_k
|
|
if use_grouped_topk:
|
|
assert topk_group is not None
|
|
assert num_expert_group is not None
|
|
if correction_bias is None:
|
|
topk_weights, topk_ids = grouped_topk(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize,
|
|
num_expert_group=num_expert_group,
|
|
topk_group=topk_group,
|
|
num_fused_shared_experts=num_fused_shared_experts,
|
|
routed_scaling_factor=routed_scaling_factor,
|
|
num_token_non_padded=num_token_non_padded,
|
|
expert_location_dispatch_info=expert_location_dispatch_info,
|
|
)
|
|
else:
|
|
topk_weights, topk_ids = biased_grouped_topk(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
correction_bias=correction_bias,
|
|
topk=top_k,
|
|
renormalize=renormalize,
|
|
num_expert_group=num_expert_group,
|
|
topk_group=topk_group,
|
|
num_fused_shared_experts=num_fused_shared_experts,
|
|
routed_scaling_factor=routed_scaling_factor,
|
|
num_token_non_padded=num_token_non_padded,
|
|
expert_location_dispatch_info=expert_location_dispatch_info,
|
|
)
|
|
elif torch_native and custom_routing_function is None:
|
|
assert (
|
|
num_token_non_padded is None
|
|
), "num_token_non_padded is not yet supported in fused_topk_native"
|
|
assert expert_location_dispatch_info is None
|
|
topk_weights, topk_ids = fused_topk_native(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize,
|
|
)
|
|
elif custom_routing_function is None:
|
|
# Qwen3MOE uses fused_topk
|
|
topk_weights, topk_ids = fused_topk(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize,
|
|
num_token_non_padded=num_token_non_padded,
|
|
expert_location_dispatch_info=expert_location_dispatch_info,
|
|
)
|
|
else:
|
|
assert (
|
|
num_token_non_padded is None
|
|
), "num_token_non_padded is not yet supported in custom_routing_function"
|
|
assert expert_location_dispatch_info is None
|
|
topk_weights, topk_ids = custom_routing_function(
|
|
hidden_states=hidden_states,
|
|
gating_output=router_logits,
|
|
topk=top_k,
|
|
renormalize=renormalize,
|
|
)
|
|
|
|
get_global_expert_distribution_recorder().on_select_experts(topk_ids=topk_ids)
|
|
|
|
return topk_weights, topk_ids
|