Support bailing moe (#8680)
This commit is contained in:
Praneth Paruchuri
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GitHub
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425
python/sglang/srt/models/bailing_moe.py
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425
python/sglang/srt/models/bailing_moe.py
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# Copyright 2023-2024 SGLang Team
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# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/bailing_moe.py
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from collections.abc import Iterable
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from typing import Optional, Tuple
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import torch
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import torch.nn.functional as F
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from torch import nn
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from transformers.configuration_utils import PretrainedConfig
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from sglang.srt.distributed import (
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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.layers.activation import SiluAndMul
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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.fused_moe_triton import FusedMoE
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from sglang.srt.layers.moe.topk import TopK
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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.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
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.utils import add_prefix, make_layers
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class BailingAttention(nn.Module):
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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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):
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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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self.total_num_heads = config.num_attention_heads
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self.total_num_kv_heads = config.num_key_value_heads
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assert self.total_num_heads % tp_size == 0
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assert self.total_num_kv_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.head_dim = config.head_dim or (self.hidden_size // self.total_num_heads)
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self.q_size = self.num_heads * self.head_dim
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self.num_kv_heads = self.total_num_kv_heads // tp_size
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scale = self.head_dim**-0.5
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self.query_key_value = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=(config.use_bias or config.use_qkv_bias),
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quant_config=quant_config,
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prefix=add_prefix("query_key_value", prefix),
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)
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self.dense = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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self.hidden_size,
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bias=config.use_bias,
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quant_config=quant_config,
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prefix=add_prefix("dense", prefix),
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)
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self.attn = RadixAttention(
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self.num_heads,
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self.head_dim,
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self.scale,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=config.max_position_embeddings,
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base=config.rope_theta,
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is_neox_style=True,
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rope_scaling=config.rope_scaling,
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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qkv, _ = self.query_key_value(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(position_ids, q, k)
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context_layer = self.attn(q, k, v, forward_batch)
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attn_output, _ = self.dense(context_layer)
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return attn_output
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class BailingMLP(nn.Module):
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def __init__(
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self,
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intermediate_size: int,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: Optional[bool] = True,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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config.hidden_size,
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[intermediate_size] * 2,
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bias=config.use_bias,
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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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config.hidden_size,
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bias=config.use_bias,
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quant_config=quant_config,
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reduce_results=reduce_results,
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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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def forward(self, x):
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x, _ = self.gate_up_proj(x)
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x = self.act_fn(x)
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x, _ = self.down_proj(x)
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return x
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class BailingMoE(nn.Module):
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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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self.num_experts = config.num_experts
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self.top_k = config.num_experts_per_tok
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self.hidden_size = config.hidden_size
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self.num_shared_experts = config.num_shared_experts
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self.norm_expert_prob = config.norm_topk_prob
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self.moe_intermediate_size = config.moe_intermediate_size
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self.gate = ReplicatedLinear(
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self.hidden_size, self.num_experts, bias=False, quant_config=None
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)
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self.topk = TopK(top_k=self.top_k, renormalize=self.norm_expert_prob)
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self.experts = FusedMoE(
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num_experts=self.num_experts,
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top_k=self.top_k,
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layer_id=layer_id,
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hidden_size=self.hidden_size,
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intermediate_size=self.moe_intermediate_size,
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reduce_results=False,
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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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if self.num_shared_experts > 0:
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shared_intermediate_size = (
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self.moe_intermediate_size * self.num_shared_experts
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)
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self.shared_experts = BailingMLP(
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intermediate_size=shared_intermediate_size,
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config=config,
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quant_config=quant_config,
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reduce_results=False,
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prefix=add_prefix("shared_experts", prefix),
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)
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else:
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self.shared_experts = None
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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orig_shape = hidden_states.shape
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hidden_states_flat = hidden_states.view(-1, self.hidden_size)
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shared_output = None
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if self.shared_experts is not None:
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shared_output = self.shared_experts(hidden_states_flat)
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router_logits, _ = self.gate(hidden_states_flat)
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topk_output = self.topk(hidden_states_flat, router_logits)
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final_hidden_states = self.experts(hidden_states_flat, topk_output)
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if shared_output is not None:
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final_hidden_states = final_hidden_states + shared_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(orig_shape)
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class BailingMoeBlock(nn.Module):
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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.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.attention = BailingAttention(
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config, layer_id, quant_config, prefix=add_prefix("attention", prefix)
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)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.mlp = BailingMoE(
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config=config,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=add_prefix("mlp", prefix),
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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residual: Optional[torch.Tensor],
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forward_batch: ForwardBatch,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Pre-normalization and residual connection for the attention block
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if residual is None:
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residual = hidden_states
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normed_hidden_states = self.input_layernorm(hidden_states)
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else:
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normed_hidden_states, residual = self.input_layernorm(
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hidden_states, residual
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)
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attn_output = self.attention(
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hidden_states=normed_hidden_states,
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position_ids=position_ids,
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forward_batch=forward_batch,
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)
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# Pre-normalization and residual connection for the MLP block
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normed_hidden_states, residual = self.post_attention_layernorm(
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attn_output, residual
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)
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mlp_output = self.mlp(normed_hidden_states)
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return mlp_output, residual
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class BailingMoeModel(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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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.config = config
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.embed_dim = config.hidden_size
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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prefix=add_prefix("embed_tokens", prefix),
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)
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self.embedding_dropout = torch.nn.Dropout(config.embedding_dropout)
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self.layers = make_layers(
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config.num_hidden_layers,
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lambda idx, prefix: BailingMoeBlock(
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config=config,
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layer_id=idx,
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quant_config=quant_config,
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prefix=prefix,
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),
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prefix=add_prefix("layers", prefix),
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)
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self.norm = RMSNorm(self.embed_dim, eps=config.rms_norm_eps)
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def forward(
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self,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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forward_batch: ForwardBatch,
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input_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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if input_embeds is None:
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hidden_states = self.embed_tokens(input_ids)
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else:
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hidden_states = input_embeds
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residual = None
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for layer in self.layers:
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hidden_states, residual = layer(
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hidden_states,
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position_ids,
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residual,
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forward_batch,
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class BailingMoeForCausalLM(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.config = config
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self.model = BailingMoeModel(config=config, quant_config=quant_config)
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self.lm_head = ParallelLMHead(
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num_embeddings=config.vocab_size,
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embedding_dim=config.hidden_size,
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quant_config=quant_config,
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)
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if config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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self.logits_processor = LogitsProcessor(config)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, forward_batch, inputs_embeds)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head, forward_batch
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)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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expert_params_mapping = FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.num_experts,
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)
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if (
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hasattr(self.config, "norm_head")
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and self.config.norm_head
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and "lm_head.weight" in name
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):
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loaded_weight = F.normalize(loaded_weight, dim=0, p=2, eps=1e-7)
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if "model.word_embeddings.weight" == name:
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name = "model.embed_tokens.weight"
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name in name and "mlp.experts" not in name:
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full_param_name = name.replace(weight_name, param_name)
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param = params_dict[full_param_name]
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param.weight_loader(param, loaded_weight, shard_id)
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break
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else:
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for p_name, w_name, e_id, s_id in expert_params_mapping:
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if w_name in name and "mlp.experts" in name:
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full_param_name = name.replace(w_name, p_name)
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param = params_dict[full_param_name]
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param.weight_loader(
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param,
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loaded_weight,
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full_param_name,
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shard_id=s_id,
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expert_id=e_id,
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)
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break
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else:
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(
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param, "weight_loader", default_weight_loader
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)
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weight_loader(param, loaded_weight)
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EntryClass = BailingMoeForCausalLM
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