Feat: Support Granite 3.0 MoE in SGLang (#7959)
This commit is contained in:
Minglei Zhu
379
python/sglang/srt/models/granitemoe.py
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379
python/sglang/srt/models/granitemoe.py
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"""Inference-only GraniteMoe model."""
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from typing import Iterable, Optional
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import torch
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from torch import nn
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from transformers import GraniteConfig
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from sglang.srt.distributed import get_tensor_model_parallel_world_size
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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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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, LogitsProcessorOutput
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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from sglang.srt.layers.pooler import Pooler, PoolingType
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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.models import mixtral
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from sglang.srt.utils import add_prefix
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class GraniteMoeMoE(nn.Module):
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"""A tensor-parallel MoE implementation for GraniteMoe that shards each
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expert across all ranks.
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Each expert's weights are sharded across all ranks and a fused MoE
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kernel is used for the forward pass, and finally we reduce the outputs
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across ranks.
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"""
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def __init__(
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self,
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num_experts: int,
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top_k: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: Optional[torch.dtype] = None,
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quant_config: Optional[QuantizationConfig] = None,
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tp_size: Optional[int] = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = hidden_size
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# Gate always runs at half / full precision for now.
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self.gate = ReplicatedLinear(
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hidden_size,
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num_experts,
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bias=False,
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params_dtype=params_dtype,
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quant_config=None,
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prefix=f"{prefix}.gate",
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)
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self.experts = FusedMoE(
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num_experts=num_experts,
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top_k=top_k,
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hidden_size=hidden_size,
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intermediate_size=intermediate_size,
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params_dtype=params_dtype,
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reduce_results=True,
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renormalize=True,
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quant_config=quant_config,
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tp_size=tp_size,
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prefix=f"{prefix}.experts",
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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# NOTE: hidden_states can have either 1D or 2D shape.
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orig_shape = hidden_states.shape
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hidden_states = hidden_states.view(-1, self.hidden_size)
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = self.experts(hidden_states, router_logits)
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return final_hidden_states.view(orig_shape)
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class GraniteMoeAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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max_position: int = 4096 * 32,
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layer_id: int = 0,
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rope_theta: float = 10000,
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quant_config: Optional[QuantizationConfig] = None,
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attention_multiplier: Optional[float] = 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 = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_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 = num_kv_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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self.head_dim = hidden_size // self.total_num_heads
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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 = (
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attention_multiplier
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if attention_multiplier is not None
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else self.head_dim**-1
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)
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self.rope_theta = rope_theta
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self.qkv_proj = QKVParallelLinear(
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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=False,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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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=f"{prefix}.o_proj",
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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=max_position,
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base=int(self.rope_theta),
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is_neox_style=True,
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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.scaling,
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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=f"{prefix}.attn",
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(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(positions, q, k)
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attn_output = self.attn(q, k, v, forward_batch)
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output, _ = self.o_proj(attn_output)
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return output
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class GraniteMoeDecoderLayer(nn.Module):
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def __init__(
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self,
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config: GraniteConfig,
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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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rope_theta = getattr(config, "rope_theta", 10000)
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self.self_attn = GraniteMoeAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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max_position=config.max_position_embeddings,
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num_kv_heads=config.num_key_value_heads,
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rope_theta=rope_theta,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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attention_multiplier=config.attention_multiplier,
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)
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self.block_sparse_moe = GraniteMoeMoE(
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num_experts=config.num_local_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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quant_config=quant_config,
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prefix=f"{prefix}.block_sparse_moe",
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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.residual_multiplier = config.residual_multiplier
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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# Self Attention
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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forward_batch=forward_batch,
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)
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hidden_states = residual + hidden_states * self.residual_multiplier
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.block_sparse_moe(hidden_states)
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hidden_states = residual + hidden_states * self.residual_multiplier
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return hidden_states
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class GraniteMoeModel(nn.Module):
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def __init__(
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self,
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config: GraniteConfig,
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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.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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)
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self.embedding_multiplier = config.embedding_multiplier
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self.layers = nn.ModuleList(
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[
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GraniteMoeDecoderLayer(
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config,
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i,
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quant_config=quant_config,
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prefix=add_prefix(f"layers.{i}", prefix),
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)
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for i in range(config.num_hidden_layers)
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]
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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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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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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hidden_states *= self.embedding_multiplier
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for i in range(len(self.layers)):
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layer = self.layers[i]
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hidden_states = layer(
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positions,
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hidden_states,
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forward_batch,
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)
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hidden_states = self.norm(hidden_states)
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return hidden_states
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class GraniteMoeForCausalLM(nn.Module):
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def __init__(
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self,
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config: GraniteConfig,
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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.quant_config = quant_config
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self.model = GraniteMoeModel(
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config, quant_config=quant_config, prefix=add_prefix("model", prefix)
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)
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=add_prefix("lm_head", prefix),
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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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# Granite logit scaling factors are applied via division, but
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# LogitsProcessor expects a multiplicative factor.
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if hasattr(config, "logits_scaling"):
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logit_scale = 1.0 / config.logits_scaling
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else:
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logit_scale = None
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self.logits_processor = LogitsProcessor(config, logit_scale=logit_scale)
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self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
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@torch.no_grad()
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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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input_embeds: torch.Tensor = None,
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get_embedding: bool = False,
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) -> LogitsProcessorOutput:
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hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
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if not get_embedding:
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logits_processor_output: LogitsProcessorOutput = self.logits_processor(
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input_ids, hidden_states, self.lm_head, forward_batch
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)
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return logits_processor_output
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else:
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return self.pooler(hidden_states, forward_batch)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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new_weights = {}
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for n, p in weights:
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if n.endswith(".block_sparse_moe.input_linear.weight"):
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for e in range(p.size(0)):
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w1_name = n.replace(
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".block_sparse_moe.input_linear.weight",
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f".block_sparse_moe.experts.{e}.w1.weight",
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)
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w3_name = n.replace(
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".block_sparse_moe.input_linear.weight",
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f".block_sparse_moe.experts.{e}.w3.weight",
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)
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w1_param, w3_param = p[e].chunk(2, dim=0)
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assert w1_name not in new_weights
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assert w3_name not in new_weights
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new_weights[w1_name] = w1_param
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new_weights[w3_name] = w3_param
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elif n.endswith(".block_sparse_moe.output_linear.weight"):
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for e in range(p.size(0)):
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w2_name = n.replace(
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".block_sparse_moe.output_linear.weight",
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f".block_sparse_moe.experts.{e}.w2.weight",
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)
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w2_param = p[e]
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assert w2_name not in new_weights
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new_weights[w2_name] = w2_param
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elif n.endswith(".block_sparse_moe.router.layer.weight"):
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gate_name = n.replace(
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".block_sparse_moe.router.layer.weight",
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".block_sparse_moe.gate.weight",
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)
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assert gate_name not in new_weights
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new_weights[gate_name] = p
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else:
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new_weights[n] = p
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mixtral.MixtralForCausalLM.load_weights(self, new_weights.items())
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EntryClass = [GraniteMoeForCausalLM]
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