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Feat: Support Phi-3.5-MoE in SGLang (#7907)

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Binyao Jiang
2025-07-09 23:51:33 -07:00
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2
docs/supported_models/generative_models.md
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@@ -30,7 +30,7 @@ in the GitHub search bar.
| **Llama** (2, 3.x, 4 series) | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | Meta’s open LLM series, spanning 7B to 400B parameters (Llama 2, 3, and new Llama 4) with well-recognized performance. [SGLang provides Llama-4 model-specific optimizations](https://docs.sglang.ai/references/llama4) |
| **Mistral** (Mixtral, NeMo, Small3) | `mistralai/Mistral-7B-Instruct-v0.2` | Open 7B LLM by Mistral AI with strong performance; extended into MoE (“Mixtral”) and NeMo Megatron variants for larger scale. |
| **Gemma** (v1, v2, v3) | `google/gemma-3-1b-it` | Google’s family of efficient multilingual models (1B–27B); Gemma 3 offers a 128K context window, and its larger (4B+) variants support vision input. |
| **Phi** (Phi-3, Phi-4 series) | `microsoft/Phi-4-multimodal-instruct` | Microsoft’s Phi family of small models (1.3B–5.6B); Phi-4-mini is a high-accuracy text model and Phi-4-multimodal (5.6B) processes text, images, and speech in one compact model. |
| **Phi** (Phi-3, Phi-4, Phi-MoE series) | `microsoft/Phi-4-multimodal-instruct`, `microsoft/Phi-3.5-MoE-instruct` | Microsoft’s Phi family of small models (1.3B–5.6B); Phi-4-mini is a high-accuracy text model, Phi-3.5-MoE is a mixture-of-experts model, and Phi-4-multimodal (5.6B) processes text, images, and speech. |
| **MiniCPM** (v3, 4B) | `openbmb/MiniCPM3-4B` | OpenBMB’s series of compact LLMs for edge devices; MiniCPM 3 (4B) achieves GPT-3.5-level results in text tasks. |
| **OLMoE** (Open MoE) | `allenai/OLMoE-1B-7B-0924` | Allen AI’s open Mixture-of-Experts model (7B total, 1B active parameters) delivering state-of-the-art results with sparse expert activation. |
| **StableLM** (3B, 7B) | `stabilityai/stablelm-tuned-alpha-7b` | StabilityAI’s early open-source LLM (3B & 7B) for general text generation; a demonstration model with basic instruction-following ability. |

553
python/sglang/srt/models/phimoe.py Normal file
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@@ -0,0 +1,553 @@
from typing import Iterable, Optional, Tuple, Union
import torch
from torch import nn
from transformers.configuration_utils import PretrainedConfig
from sglang.srt.distributed import get_tensor_model_parallel_world_size
from sglang.srt.layers.dp_attention import get_attention_tp_rank, get_attention_tp_size
from sglang.srt.layers.linear import (
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
from sglang.srt.layers.pooler import Pooler, PoolingType
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.rotary_embedding import get_rope
from sglang.srt.layers.utils import PPMissingLayer
from sglang.srt.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE,
ParallelLMHead,
VocabParallelEmbedding,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.weight_utils import (
default_weight_loader,
maybe_remap_kv_scale_name,
)
from sglang.srt.utils import add_prefix, make_layers
class PhiMoEConfig(PretrainedConfig):
model_type = "phimoe"
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=14336,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
head_dim=None,
hidden_act="silu",
max_position_embeddings=4096 * 32,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_theta=1e6,
sliding_window=None,
attention_dropout=0.0,
num_experts_per_tok=2,
num_local_experts=16,
output_router_logits=False,
router_aux_loss_coef=0.001,
router_jitter_noise=0.0,
attention_bias=False,
lm_head_bias=False,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.sliding_window = sliding_window
self.attention_bias = attention_bias
self.lm_head_bias = lm_head_bias
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
if head_dim is None:
head_dim = hidden_size // num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
self.num_experts_per_tok = num_experts_per_tok
self.num_local_experts = num_local_experts
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
self.router_jitter_noise = router_jitter_noise
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def sparsemixer(scores, jitter_eps=0.01):
################ Select first expert (topk=2) ################
# compute mask for sparsity
mask_logits_threshold, max_ind = scores.max(dim=-1, keepdim=True)
factor = scores.abs().clamp(min=mask_logits_threshold)
mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (
2 * jitter_eps
)
# apply mask
masked_gates = scores.masked_fill(mask_logits_threshold, float("-inf"))
selected_experts = max_ind
# compute scores for gradients
masked_gates = torch.softmax(masked_gates, dim=-1)
multiplier_o = masked_gates.gather(dim=-1, index=selected_experts)
multiplier = multiplier_o
# masked out first expert
masked_scores = torch.scatter(
scores,
-1,
selected_experts,
float("-inf"),
)
################ Select second expert (topk=2) ################
# compute mask for sparsity
mask_logits_threshold, max_ind = masked_scores.max(dim=-1, keepdim=True)
factor = scores.abs().clamp(min=mask_logits_threshold)
mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (
2 * jitter_eps
)
# apply mask
masked_gates_top2 = masked_scores.masked_fill(mask_logits_threshold, float("-inf"))
selected_experts_top2 = max_ind
# compute scores for gradients
masked_gates_top2 = torch.softmax(masked_gates_top2, dim=-1)
multiplier_top2 = masked_gates_top2.gather(dim=-1, index=selected_experts_top2)
multiplier = torch.concat((multiplier, multiplier_top2), dim=-1)
selected_experts = torch.concat((selected_experts, selected_experts_top2), dim=-1)
return (
multiplier,
selected_experts,
)
def phimoe_routing_function(
hidden_states: torch.Tensor,
gating_output: torch.Tensor,
topk: int,
renormalize: bool,
):
assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
assert topk == 2, "Only top-2 routing is supported"
assert renormalize is False, "Renormalization is not supported"
topk_weights, topk_ids = sparsemixer(gating_output)
return topk_weights, topk_ids
class PhiMoE(nn.Module):
"""A tensor-parallel MoE implementation for PhiMoE that shards each expert
across all ranks.
Each expert's weights are sharded across all ranks and a fused MoE
kernel is used for the forward pass, and finally we reduce the outputs
across ranks.
"""
def __init__(
self,
num_experts: int,
top_k: int,
hidden_size: int,
intermediate_size: int,
layer_id: int,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.hidden_size = hidden_size
self.tp_size = get_tensor_model_parallel_world_size()
# Gate always runs at half / full precision for now.
self.gate = ReplicatedLinear(
hidden_size,
num_experts,
bias=False,
quant_config=None,
)
self.experts = FusedMoE(
num_experts=num_experts,
top_k=top_k,
hidden_size=hidden_size,
intermediate_size=intermediate_size,
reduce_results=True,
renormalize=False,
quant_config=quant_config,
custom_routing_function=phimoe_routing_function,
prefix=add_prefix("experts", prefix),
)
def forward(
self, hidden_states: torch.Tensor, forward_batch: Optional[ForwardBatch] = None
) -> torch.Tensor:
# NOTE: hidden_states can have either 1D or 2D shape.
orig_shape = hidden_states.shape
hidden_states = hidden_states.view(-1, self.hidden_size)
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(hidden_states, router_logits)
return final_hidden_states.view(orig_shape)
class PhiMoEAttention(nn.Module):
def __init__(
self,
hidden_size: int,
num_heads: int,
num_kv_heads: int,
head_dim: Optional[int] = None,
max_position: int = 4096 * 32,
rope_theta: float = 10000,
layer_id: int = 0,
attention_bias: bool = False,
quant_config: Optional[QuantizationConfig] = None,
rope_scaling: Optional[dict] = None,
prefix: str = "",
) -> None:
super().__init__()
self.hidden_size = hidden_size
attn_tp_rank = get_attention_tp_rank()
attn_tp_size = get_attention_tp_size()
self.total_num_heads = num_heads
assert self.total_num_heads % attn_tp_size == 0
self.num_heads = self.total_num_heads // attn_tp_size
self.total_num_kv_heads = num_kv_heads
if self.total_num_kv_heads >= attn_tp_size:
# Number of KV heads is greater than TP size, so we partition
# the KV heads across multiple tensor parallel GPUs.
assert self.total_num_kv_heads % attn_tp_size == 0
else:
# Number of KV heads is less than TP size, so we replicate
# the KV heads across multiple tensor parallel GPUs.
assert attn_tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
if head_dim is None:
head_dim = hidden_size // num_heads
self.head_dim = head_dim
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim**-0.5
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.qkv_proj = QKVParallelLinear(
hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_kv_heads,
bias=attention_bias,
quant_config=quant_config,
tp_rank=attn_tp_rank,
tp_size=attn_tp_size,
prefix=add_prefix("qkv_proj", prefix),
)
self.o_proj = RowParallelLinear(
self.total_num_heads * self.head_dim,
hidden_size,
bias=attention_bias,
quant_config=quant_config,
tp_rank=attn_tp_rank,
tp_size=attn_tp_size,
prefix=add_prefix("o_proj", prefix),
)
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=max_position,
base=int(self.rope_theta),
rope_scaling=self.rope_scaling,
)
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(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
forward_batch: ForwardBatch,
) -> torch.Tensor:
qkv, _ = self.qkv_proj(hidden_states)
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = self.rotary_emb(positions, q, k)
attn_output = self.attn(q, k, v, forward_batch)
output, _ = self.o_proj(attn_output)
return output
class PhiMoEDecoderLayer(nn.Module):
def __init__(
self,
config: PhiMoEConfig,
layer_id: int,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.hidden_size = config.hidden_size
rope_theta = getattr(config, "rope_theta", 10000)
self.self_attn = PhiMoEAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
max_position=config.max_position_embeddings,
num_kv_heads=config.num_key_value_heads,
head_dim=getattr(
config, "head_dim", self.hidden_size // config.num_attention_heads
),
rope_theta=rope_theta,
layer_id=layer_id,
attention_bias=config.attention_bias,
quant_config=quant_config,
rope_scaling=config.rope_scaling,
prefix=add_prefix("self_attn", prefix),
)
self.block_sparse_moe = PhiMoE(
num_experts=config.num_local_experts,
top_k=config.num_experts_per_tok,
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
layer_id=layer_id,
quant_config=quant_config,
prefix=add_prefix("block_sparse_moe", prefix),
)
self.input_layernorm = nn.LayerNorm(
config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True
)
self.post_attention_layernorm = nn.LayerNorm(
config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True
)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
residual: Optional[torch.Tensor],
forward_batch: ForwardBatch,
) -> Tuple[torch.Tensor, torch.Tensor]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = self.self_attn(
positions=positions,
hidden_states=hidden_states,
forward_batch=forward_batch,
)
hidden_states = hidden_states + residual
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.block_sparse_moe(
hidden_states, forward_batch=forward_batch
)
hidden_states = hidden_states + residual
return hidden_states, residual
class PhiMoEModel(nn.Module):
def __init__(
self,
config: PhiMoEConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.config = config
self.quant_config = quant_config
self.vocab_size = config.vocab_size
self.embed_tokens = VocabParallelEmbedding(
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=add_prefix("embed_tokens", prefix),
)
self.layers = make_layers(
config.num_hidden_layers,
lambda idx, prefix: PhiMoEDecoderLayer(
config, int(prefix.split(".")[-1]), quant_config, prefix=prefix
),
prefix=add_prefix("layers", prefix),
)
self.norm = nn.LayerNorm(
config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True
)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
forward_batch: ForwardBatch,
input_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor]:
if input_embeds is None:
hidden_states = self.embed_tokens(input_ids)
else:
hidden_states = input_embeds
residual = None
for layer in self.layers:
hidden_states, residual = layer(
positions, hidden_states, residual, forward_batch=forward_batch
)
hidden_states = self.norm(hidden_states)
return hidden_states
class PhiMoEForCausalLM(nn.Module):
def __init__(
self,
config: PhiMoEConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.config = config
self.quant_config = quant_config
self.model = PhiMoEModel(
config=config, quant_config=quant_config, prefix=add_prefix("model", prefix)
)
self.lm_head = ParallelLMHead(
config.vocab_size,
config.hidden_size,
org_num_embeddings=config.vocab_size,
padding_size=DEFAULT_VOCAB_PADDING_SIZE,
quant_config=quant_config,
bias=True,
prefix=add_prefix("lm_head", prefix),
)
if self.config.tie_word_embeddings:
self.lm_head.weight = self.model.embed_tokens.weight
self.logits_processor = LogitsProcessor(config)
self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
@torch.no_grad()
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
forward_batch: ForwardBatch,
inputs_embeds: Optional[torch.Tensor] = None,
get_embedding: bool = False,
) -> LogitsProcessorOutput:
hidden_states = self.model(input_ids, positions, forward_batch, inputs_embeds)
if not get_embedding:
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
)
else:
return self.pooler(hidden_states, forward_batch)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("qkv_proj", "q_proj", "q"),
("qkv_proj", "k_proj", "k"),
("qkv_proj", "v_proj", "v"),
]
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())
for name, loaded_weight in weights:
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
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:
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)
EntryClass = PhiMoEForCausalLM

6
test/srt/models/test_generation_models.py
View File

@@ -68,6 +68,12 @@ ALL_MODELS = [
ModelCase("microsoft/Phi-3-small-8k-instruct", trust_remote_code=True),
ModelCase("allenai/OLMo-2-1124-7B-Instruct", skip_long_prompt=True),
ModelCase("ibm-granite/granite-3.0-2b-instruct", skip_long_prompt=True),
ModelCase(
"microsoft/Phi-3.5-MoE-instruct",
tp_size=2,
trust_remote_code=True,
skip_long_prompt=True,
),
]
TORCH_DTYPES = [torch.float16]