[WIP] support MiniMax M2 model (#12129)
Signed-off-by: Xinyuan Tong <xinyuantong.cs@gmail.com> Signed-off-by: xuebi <xuebi@minimaxi.com> Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com> Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com> Co-authored-by: Roger Young <42564206+rogeryoungh@users.noreply.github.com> Co-authored-by: xuebi <xuebi@minimaxi.com>
This commit is contained in:
赵晨阳
co-authored by
Xinyuan Tong
Xinyuan Tong
Roger Young
xuebi
Xinyuan Tong
Xinyuan Tong
Roger Young
xuebi
parent
b89111d69b
commit
7ebc28f5d6
@@ -0,0 +1,922 @@
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# Copyright 2023-2024 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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# Adapted from DeepSeek and Mixtral implementation
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"""Inference-only MiniMax M2 model compatible with HuggingFace weights."""
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import logging
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from typing import Iterable, Optional, Set, Tuple, Union
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from sglang.srt.distributed import (
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get_moe_expert_parallel_world_size,
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce,
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)
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from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
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from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
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from sglang.srt.layers.activation import SiluAndMul
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from sglang.srt.layers.communicator import (
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LayerCommunicator,
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LayerScatterModes,
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ScatterMode,
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)
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
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from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
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from sglang.srt.layers.moe.topk import TopK
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from sglang.srt.layers.moe.utils import get_moe_a2a_backend
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.utils import PPMissingLayer
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from sglang.srt.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
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from sglang.srt.model_loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from sglang.srt.server_args import get_global_server_args
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from sglang.srt.two_batch_overlap import model_forward_maybe_tbo
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from sglang.srt.utils import (
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BumpAllocator,
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add_prefix,
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get_compiler_backend,
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is_non_idle_and_non_empty,
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make_layers,
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)
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logger = logging.getLogger(__name__)
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class MiniMaxM2RMSNormTP(nn.Module):
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"""RMSNorm with Tensor Parallel support for QK normalization."""
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def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
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super().__init__()
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self.tp_world = get_tensor_model_parallel_world_size()
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self.tp_rank = get_tensor_model_parallel_rank()
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# Weight parameter is sharded across TP ranks
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self.weight = nn.Parameter(torch.ones(int(hidden_size / self.tp_world)))
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self.weight.weight_loader = self.weight_loader
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self.variance_epsilon = eps
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@staticmethod
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def weight_loader(
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param: nn.Parameter,
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loaded_weight: torch.Tensor,
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) -> None:
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"""Custom weight loader that handles TP sharding."""
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tp_world = get_tensor_model_parallel_world_size()
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tp_rank = get_tensor_model_parallel_rank()
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shard_size = loaded_weight.shape[0] // tp_world
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shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
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param.data.copy_(loaded_weight[shard])
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@torch.compile(dynamic=True, backend=get_compiler_backend())
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def forward(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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"""Forward pass with TP-aware variance computation."""
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assert residual is None, "RMSNormTP does not support residual connection."
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orig_dtype = x.dtype
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x = x.to(torch.float32)
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# Compute variance across the full dimension (not just local shard)
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variance = x.pow(2).mean(dim=-1, keepdim=True, dtype=torch.float32)
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if self.tp_world > 1:
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# All-reduce variance across TP ranks to get global variance
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variance = tensor_model_parallel_all_reduce(variance) / self.tp_world
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# Normalize and apply local weight shard
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x = x * torch.rsqrt(variance + self.variance_epsilon)
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x = x.to(orig_dtype) * self.weight
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return x
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class MiniMaxM2MLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "mlp",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=add_prefix("gate_up_proj", prefix),
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=add_prefix("down_proj", prefix),
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)
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self.act_fn = SiluAndMul()
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return
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class MiniMaxM2MoE(nn.Module):
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"""MiniMax MoE implementation using DeepEP for Expert Parallel support."""
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def __init__(
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self,
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config: PretrainedConfig,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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if self.tp_size > config.num_local_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.num_local_experts}."
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)
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self.use_routing_bias = getattr(config, "use_routing_bias", False)
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if self.use_routing_bias:
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self.e_score_correction_bias = nn.Parameter(
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torch.empty(config.num_local_experts, dtype=torch.float32)
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)
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self.e_score_correction_bias.weight_loader = (
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MiniMaxM2MoE.ebias_weight_loader
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)
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else:
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self.e_score_correction_bias = None
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self.experts = get_moe_impl_class(quant_config)(
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num_experts=config.num_local_experts
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+ get_global_server_args().ep_num_redundant_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=add_prefix("experts", prefix),
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)
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self.topk = TopK(
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top_k=config.num_experts_per_tok,
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renormalize=True,
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scoring_func=config.scoring_func,
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use_grouped_topk=True, # TODO: Use "grouped top-k" flag only for hardcoded sigmoid scoring
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num_expert_group=1,
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topk_group=1,
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correction_bias=self.e_score_correction_bias,
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routed_scaling_factor=1.0,
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)
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self.gate = ReplicatedLinear(
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config.hidden_size,
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config.num_local_experts,
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bias=False,
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params_dtype=torch.float32,
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quant_config=None,
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prefix=add_prefix("gate", prefix),
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)
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self.layer_id = layer_id
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if get_moe_a2a_backend().is_deepep():
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self.ep_size = get_moe_expert_parallel_world_size()
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self.top_k = config.num_experts_per_tok
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@staticmethod
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def ebias_weight_loader(param: nn.Parameter, loaded_weight: torch.Tensor) -> None:
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assert param.size() == loaded_weight.size()
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param.data.copy_(loaded_weight.to(torch.float32))
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def forward(
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self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
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) -> torch.Tensor:
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if get_moe_a2a_backend().is_deepep():
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return self.forward_deepep(hidden_states, forward_batch)
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else:
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return self.forward_normal(hidden_states)
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def forward_normal(self, hidden_states: torch.Tensor) -> torch.Tensor:
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states.to(torch.float32))
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topk_output = self.topk(hidden_states, router_logits)
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final_hidden_states = self.experts(hidden_states, topk_output)
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if self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
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return final_hidden_states.view(num_tokens, hidden_dim)
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def forward_deepep(
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self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
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) -> torch.Tensor:
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if hidden_states.shape[0] > 0:
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states.to(torch.float32))
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topk_weights, topk_idx, _ = self.topk(
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hidden_states,
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router_logits,
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num_token_non_padded=forward_batch.num_token_non_padded,
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expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
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layer_id=self.layer_id,
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),
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)
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else:
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topk_weights, topk_idx, _ = self.topk.empty_topk_output(
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hidden_states.shape[0], self.top_k
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)
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final_hidden_states = self.experts(
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hidden_states=hidden_states,
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topk_idx=topk_idx,
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topk_weights=topk_weights,
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forward_batch=forward_batch,
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)
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return final_hidden_states
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# TBO Operations for MiniMax MoE
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def op_gate(self, state):
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"""Gate operation for TBO - compute router logits"""
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if is_non_idle_and_non_empty(
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state.forward_batch.forward_mode, state.hidden_states_mlp_input
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): # router_logits: (num_tokens, num_experts)
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state.router_logits, _ = self.gate(state.hidden_states_mlp_input)
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else:
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state.router_logits = None
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def op_select_experts(self, state):
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"""Expert selection operation for TBO"""
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router_logits = state.pop("router_logits")
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hidden_states = state.hidden_states_mlp_input
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if router_logits is not None:
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with get_global_expert_distribution_recorder().with_current_layer(
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self.layer_id
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):
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state.topk_weights_local, state.topk_idx_local, _ = self.topk(
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hidden_states=hidden_states,
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router_logits=router_logits,
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num_token_non_padded=state.forward_batch.num_token_non_padded,
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expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
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layer_id=self.layer_id,
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),
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)
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else:
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state.topk_idx_local = torch.full(
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(0, self.top_k), -1, dtype=torch.int, device=hidden_states.device
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)
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state.topk_weights_local = torch.empty(
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(0, self.top_k), dtype=torch.float32, device=hidden_states.device
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)
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def op_dispatch_a(self, state):
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"""Dispatch A operation for TBO - start async dispatch"""
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if self.ep_size > 1:
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self.experts.deepep_dispatcher.dispatch_a(
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hidden_states=state.pop("hidden_states_mlp_input"),
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topk_idx=state.pop("topk_idx_local"),
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topk_weights=state.pop("topk_weights_local"),
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forward_batch=state.forward_batch,
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tbo_subbatch_index=state.get("tbo_subbatch_index"),
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)
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def op_dispatch_b(self, state):
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"""Dispatch B operation for TBO - complete async dispatch"""
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if self.ep_size > 1:
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with get_global_expert_distribution_recorder().with_current_layer(
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self.layer_id
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):
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state.dispatch_output = self.experts.deepep_dispatcher.dispatch_b(
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tbo_subbatch_index=state.get("tbo_subbatch_index"),
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)
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def op_experts(self, state):
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"""Expert computation for TBO"""
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state.hidden_states_experts_output = self.experts.moe_impl(
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dispatch_output=state.dispatch_output,
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)
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def op_combine_a(self, state):
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"""Combine A operation for TBO - start async combine"""
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if self.ep_size > 1:
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self.experts.deepep_dispatcher.combine_a(
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hidden_states=state.pop("hidden_states_experts_output"),
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topk_idx=state.dispatch_output.topk_idx,
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topk_weights=state.dispatch_output.topk_weights,
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forward_batch=state.forward_batch,
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tbo_subbatch_index=state.get("tbo_subbatch_index"),
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)
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state.pop("dispatch_output")
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def op_combine_b(self, state):
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"""Combine B operation for TBO - complete async combine"""
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if self.ep_size > 1:
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state.hidden_states_after_combine = (
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self.experts.deepep_dispatcher.combine_b(
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tbo_subbatch_index=state.get("tbo_subbatch_index"),
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)
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)
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def op_output(self, state):
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"""Output operation for TBO - final MLP output"""
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final_hidden_states = state.pop("hidden_states_after_combine")
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# MiniMax doesn't have shared experts like DeepSeek, so no need to add them
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state.hidden_states_mlp_output = final_hidden_states
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class MiniMaxM2Attention(nn.Module):
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"""MiniMax Attention implementation with QK normalization and partial RoPE."""
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def __init__(
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self,
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config: PretrainedConfig,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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# Get dimensions from config
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self.total_num_heads = config.num_attention_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = config.num_key_value_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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# Use head_dim from config if available, otherwise calculate
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self.head_dim = getattr(
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config, "head_dim", self.hidden_size // self.total_num_heads
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)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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# RoPE settings - support partial RoPE
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self.rope_theta = getattr(config, "rope_theta", 10000)
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self.max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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self.rotary_dim = getattr(
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config, "rotary_dim", self.head_dim
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) # MiniMax uses rotary_dim=64
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# QK Normalization settings
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self.use_qk_norm = getattr(config, "use_qk_norm", False)
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self.qk_norm_type = getattr(config, "qk_norm_type", "per_layer")
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self.qkv_proj = QKVParallelLinear(
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self.hidden_size,
|
||||
self.head_dim,
|
||||
self.total_num_heads,
|
||||
self.total_num_kv_heads,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("qkv_proj", prefix),
|
||||
)
|
||||
|
||||
self.o_proj = RowParallelLinear(
|
||||
self.total_num_heads * self.head_dim,
|
||||
self.hidden_size,
|
||||
bias=False,
|
||||
reduce_results=False,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("o_proj", prefix),
|
||||
)
|
||||
|
||||
# Setup RoPE with partial rotary dimension
|
||||
rope_scaling = getattr(config, "rope_scaling", None)
|
||||
self.rotary_emb = get_rope(
|
||||
self.head_dim,
|
||||
rotary_dim=self.rotary_dim, # Use partial rotary dimension
|
||||
max_position=self.max_position_embeddings,
|
||||
base=self.rope_theta,
|
||||
rope_scaling=rope_scaling,
|
||||
)
|
||||
|
||||
# QK Normalization layers
|
||||
if self.use_qk_norm:
|
||||
if self.qk_norm_type == "per_layer":
|
||||
# Use RMSNormTP for proper tensor parallel support
|
||||
# Use total dimensions (before TP sharding) for correct normalization
|
||||
self.q_norm = MiniMaxM2RMSNormTP(
|
||||
self.total_num_heads * self.head_dim, eps=config.rms_norm_eps
|
||||
)
|
||||
self.k_norm = MiniMaxM2RMSNormTP(
|
||||
self.total_num_kv_heads * self.head_dim, eps=config.rms_norm_eps
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unsupported qk_norm_type: {self.qk_norm_type}")
|
||||
|
||||
self.attn = RadixAttention(
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
self.scaling,
|
||||
num_kv_heads=self.num_kv_heads,
|
||||
layer_id=layer_id,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("attn", prefix),
|
||||
)
|
||||
|
||||
def forward_prepare(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
):
|
||||
qkv, _ = self.qkv_proj(hidden_states)
|
||||
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
||||
if self.use_qk_norm:
|
||||
q = self.q_norm(q.contiguous())
|
||||
k = self.k_norm(k.contiguous())
|
||||
else:
|
||||
q, k = q.contiguous(), k.contiguous()
|
||||
q, k = self.rotary_emb(positions, q, k)
|
||||
inner_state = q, k, v, forward_batch
|
||||
return None, forward_batch, inner_state
|
||||
|
||||
def forward_core(self, intermediate_state):
|
||||
_, _, inner_state = intermediate_state
|
||||
attn_output = self.attn(*inner_state)
|
||||
output, _ = self.o_proj(attn_output)
|
||||
return output
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
) -> torch.Tensor:
|
||||
s = self.forward_prepare(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
forward_batch=forward_batch,
|
||||
)
|
||||
return self.forward_core(s)
|
||||
|
||||
def op_prepare(self, state):
|
||||
state.attn_intermediate_state = self.forward_prepare(
|
||||
positions=state.positions,
|
||||
hidden_states=state.pop("hidden_states_after_comm_pre_attn"),
|
||||
forward_batch=state.forward_batch,
|
||||
)
|
||||
|
||||
def op_core(self, state):
|
||||
state.hidden_states_after_attn = self.forward_core(
|
||||
state.pop("attn_intermediate_state")
|
||||
)
|
||||
|
||||
|
||||
class MiniMaxM2DecoderLayer(nn.Module):
|
||||
"""MiniMax Decoder Layer implementation with MoE support."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
layer_id: int,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
self.layer_id = layer_id
|
||||
|
||||
# TBO support: All MiniMax layers are sparse (MoE)
|
||||
self.is_layer_sparse = True
|
||||
|
||||
self.self_attn = MiniMaxM2Attention(
|
||||
config=config,
|
||||
layer_id=layer_id,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("self_attn", prefix),
|
||||
)
|
||||
|
||||
self.block_sparse_moe = MiniMaxM2MoE(
|
||||
config=config,
|
||||
layer_id=layer_id,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("mlp", prefix),
|
||||
)
|
||||
|
||||
self.input_layernorm = RMSNorm(
|
||||
config.hidden_size, eps=getattr(config, "rms_norm_eps", 1e-6)
|
||||
)
|
||||
self.post_attention_layernorm = RMSNorm(
|
||||
config.hidden_size, eps=getattr(config, "rms_norm_eps", 1e-6)
|
||||
)
|
||||
|
||||
is_previous_layer_sparse = True
|
||||
self.layer_scatter_modes = LayerScatterModes.init_new(
|
||||
layer_id=layer_id,
|
||||
num_layers=config.num_hidden_layers,
|
||||
is_layer_sparse=self.is_layer_sparse,
|
||||
is_previous_layer_sparse=is_previous_layer_sparse,
|
||||
)
|
||||
|
||||
self.layer_communicator = LayerCommunicator(
|
||||
layer_scatter_modes=self.layer_scatter_modes,
|
||||
input_layernorm=self.input_layernorm,
|
||||
post_attention_layernorm=self.post_attention_layernorm,
|
||||
allow_reduce_scatter=True,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
residual: Optional[torch.Tensor],
|
||||
) -> torch.Tensor:
|
||||
# Self Attention
|
||||
hidden_states, residual = self.layer_communicator.prepare_attn(
|
||||
hidden_states, residual, forward_batch
|
||||
)
|
||||
|
||||
hidden_states = self.self_attn(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
forward_batch=forward_batch,
|
||||
)
|
||||
|
||||
# Fully Connected (MLP or MoE)
|
||||
|
||||
hidden_states, residual = self.layer_communicator.prepare_mlp(
|
||||
hidden_states, residual, forward_batch
|
||||
)
|
||||
|
||||
hidden_states = self.block_sparse_moe(hidden_states, forward_batch)
|
||||
|
||||
hidden_states, residual = self.layer_communicator.postprocess_layer(
|
||||
hidden_states, residual, forward_batch
|
||||
)
|
||||
|
||||
return hidden_states, residual
|
||||
|
||||
# TBO Operations for MiniMax Decoder Layer
|
||||
def op_comm_prepare_attn(
|
||||
self,
|
||||
state,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
residual: Optional[torch.Tensor],
|
||||
zero_allocator: BumpAllocator,
|
||||
tbo_subbatch_index: Optional[int] = None,
|
||||
):
|
||||
"""Communication prepare for attention - TBO operation"""
|
||||
state.hidden_states_after_comm_pre_attn, state.residual_after_input_ln = (
|
||||
self.layer_communicator.prepare_attn(hidden_states, residual, forward_batch)
|
||||
)
|
||||
state.update(
|
||||
dict(
|
||||
forward_batch=forward_batch,
|
||||
positions=positions,
|
||||
zero_allocator=zero_allocator,
|
||||
tbo_subbatch_index=tbo_subbatch_index,
|
||||
)
|
||||
)
|
||||
|
||||
def op_comm_prepare_mlp(self, state):
|
||||
"""Communication prepare for MLP - TBO operation"""
|
||||
state.hidden_states_mlp_input, state.residual_after_comm_pre_mlp = (
|
||||
self.layer_communicator.prepare_mlp(
|
||||
state.pop("hidden_states_after_attn"),
|
||||
state.pop("residual_after_input_ln"),
|
||||
state.forward_batch,
|
||||
)
|
||||
)
|
||||
|
||||
def op_mlp(self, state):
|
||||
hidden_states = state.pop("hidden_states_mlp_input")
|
||||
state.hidden_states_mlp_output = self.block_sparse_moe(
|
||||
hidden_states, state.forward_batch
|
||||
)
|
||||
|
||||
def op_comm_postprocess_layer(self, state):
|
||||
"""Communication postprocess for layer - TBO operation"""
|
||||
hidden_states, residual = self.layer_communicator.postprocess_layer(
|
||||
state.pop("hidden_states_mlp_output"),
|
||||
state.pop("residual_after_comm_pre_mlp"),
|
||||
state.forward_batch,
|
||||
)
|
||||
|
||||
output = dict(
|
||||
positions=state.positions,
|
||||
hidden_states=hidden_states,
|
||||
residual=residual,
|
||||
forward_batch=state.forward_batch,
|
||||
zero_allocator=state.zero_allocator,
|
||||
tbo_subbatch_index=state.tbo_subbatch_index,
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
class MiniMaxM2Model(nn.Module):
|
||||
"""MiniMax Model implementation."""
|
||||
|
||||
fall_back_to_pt_during_load = False
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.padding_idx = getattr(config, "pad_token_id", 0)
|
||||
self.vocab_size = config.vocab_size
|
||||
self.pp_group = get_pp_group()
|
||||
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
)
|
||||
|
||||
def layer_fn(idx, prefix: str) -> nn.Module:
|
||||
return MiniMaxM2DecoderLayer(
|
||||
config=config,
|
||||
layer_id=idx,
|
||||
quant_config=quant_config,
|
||||
prefix=prefix,
|
||||
)
|
||||
|
||||
self.layers, self.start_layer, self.end_layer = make_layers(
|
||||
config.num_hidden_layers,
|
||||
layer_fn,
|
||||
pp_rank=self.pp_group.rank_in_group,
|
||||
pp_size=self.pp_group.world_size,
|
||||
prefix=add_prefix("layers", prefix),
|
||||
)
|
||||
if self.pp_group.is_last_rank:
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
else:
|
||||
self.norm = PPMissingLayer(return_tuple=True)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embed_tokens(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
input_embeds: torch.Tensor = None,
|
||||
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
||||
) -> Union[torch.Tensor, PPProxyTensors]:
|
||||
if self.pp_group.is_first_rank:
|
||||
if input_embeds is None:
|
||||
hidden_states = self.get_input_embeddings(input_ids)
|
||||
else:
|
||||
hidden_states = input_embeds
|
||||
residual = None
|
||||
else:
|
||||
assert pp_proxy_tensors is not None
|
||||
hidden_states = pp_proxy_tensors["hidden_states"]
|
||||
residual = pp_proxy_tensors["residual"]
|
||||
|
||||
if forward_batch.can_run_tbo:
|
||||
hidden_states, residual = model_forward_maybe_tbo(
|
||||
layers=self.layers,
|
||||
enable_tbo=True,
|
||||
input_data_scatter_mode=ScatterMode.model_input_output(),
|
||||
positions=positions,
|
||||
forward_batch=forward_batch,
|
||||
hidden_states=hidden_states,
|
||||
residual=residual,
|
||||
)
|
||||
else:
|
||||
for i in range(self.start_layer, self.end_layer):
|
||||
with get_global_expert_distribution_recorder().with_current_layer(i):
|
||||
layer = self.layers[i]
|
||||
hidden_states, residual = layer(
|
||||
positions=positions,
|
||||
forward_batch=forward_batch,
|
||||
hidden_states=hidden_states,
|
||||
residual=residual,
|
||||
)
|
||||
|
||||
if not self.pp_group.is_last_rank:
|
||||
return PPProxyTensors(
|
||||
{"hidden_states": hidden_states, "residual": residual}
|
||||
)
|
||||
|
||||
if residual is not None:
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
else:
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class MiniMaxM2ForCausalLM(nn.Module):
|
||||
"""MiniMax M2 model for causal language modeling."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
|
||||
self.model = MiniMaxM2Model(
|
||||
config, quant_config, prefix=add_prefix("model", prefix)
|
||||
)
|
||||
|
||||
if get_pp_group().is_last_rank:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
quant_config=None,
|
||||
prefix=add_prefix("lm_head", prefix),
|
||||
)
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
|
||||
self.logits_processor = LogitsProcessor(config)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.get_input_embeddings(input_ids)
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
input_embeds: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
# _print_tensor_info(input_ids, "input_ids")
|
||||
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
|
||||
return self.logits_processor(
|
||||
input_ids, hidden_states, self.lm_head, forward_batch
|
||||
)
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
"""Load model weights with proper mapping for MiniMax architecture."""
|
||||
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
|
||||
# Params for weights, fp8 weight scales, fp8 activation scales
|
||||
# (param_name, weight_name, expert_id, shard_id)
|
||||
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
||||
ckpt_gate_proj_name="w1",
|
||||
ckpt_down_proj_name="w2",
|
||||
ckpt_up_proj_name="w3",
|
||||
num_experts=self.config.num_local_experts,
|
||||
)
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: Set[str] = set()
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
|
||||
if spec_layer is not None:
|
||||
continue # skip spec decode layers for main model
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
# Skip non-stacked layers and experts (experts handled below).
|
||||
if weight_name not in name:
|
||||
continue
|
||||
# We have mlp.experts[0].gate_proj in the checkpoint.
|
||||
# Since we handle the experts below in expert_params_mapping,
|
||||
# we need to skip here BEFORE we update the name, otherwise
|
||||
# name will be updated to mlp.experts[0].gate_up_proj, which
|
||||
# will then be updated below in expert_params_mapping
|
||||
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
||||
if ("mlp.experts." in name) and name not in params_dict:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
for mapping in expert_params_mapping:
|
||||
param_name, weight_name, expert_id, shard_id = mapping
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(
|
||||
param,
|
||||
loaded_weight,
|
||||
name,
|
||||
shard_id=shard_id,
|
||||
expert_id=expert_id,
|
||||
)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
|
||||
# Remapping the name of FP8 kv-scale.
|
||||
name = maybe_remap_kv_scale_name(name, params_dict)
|
||||
if name is None:
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
@classmethod
|
||||
def get_model_config_for_expert_location(cls, config):
|
||||
from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
|
||||
|
||||
return ModelConfigForExpertLocation(
|
||||
num_layers=config.num_hidden_layers,
|
||||
num_logical_experts=config.num_local_experts,
|
||||
num_groups=None,
|
||||
)
|
||||
|
||||
|
||||
def get_spec_layer_idx_from_weight_name(
|
||||
config: PretrainedConfig, weight_name: str
|
||||
) -> Optional[int]:
|
||||
if hasattr(config, "num_mtp_modules") and (config.num_mtp_modules > 0):
|
||||
layer_idx = config.num_hidden_layers
|
||||
for i in range(config.num_mtp_modules):
|
||||
if weight_name.startswith(f"model.layers.{layer_idx + i}."):
|
||||
return layer_idx + i
|
||||
return None
|
||||
|
||||
|
||||
# Entry class for model registration
|
||||
EntryClass = MiniMaxM2ForCausalLM
|
||||
Reference in New Issue
Block a user