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Revert "Add SDAR model support" (#19032)

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This commit is contained in:
Cheng Wan
2026-02-19 16:03:56 -08:00
committed by GitHub
parent db34c1cbfb
commit 73a7f0d049
5 changed files with 6 additions and 1439 deletions
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2
docs/supported_models/text_generation/diffusion_language_models.md
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@@ -107,5 +107,3 @@ Below the supported models are summarized in a table.
| Model Family | Example Model | Description |
| -------------------------- | ---------------------------- | ---------------------------------------------------------------------------------------------------- |
| **LLaDA2.0 (mini, flash)** | `inclusionAI/LLaDA2.0-flash` | LLaDA2.0-flash is a diffusion language model featuring a 100B Mixture-of-Experts (MoE) architecture. |
| **SDAR (JetLM)** | `JetLM/SDAR-8B-Chat` | SDAR series diffusion language model (Chat), dense architecture. |
| **SDAR (JetLM)** | `JetLM/SDAR-30B-A3B-Chat` | SDAR series diffusion language model (Chat), MoE architecture. |

17
python/sglang/srt/dllm/config.py
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@@ -31,19 +31,14 @@ class DllmConfig:
model_path=server_args.model_path,
model_revision=server_args.revision,
)
DLLM_PARAMS = {
"LLaDA2MoeModelLM": {"block_size": 32, "mask_id": 156895},
"SDARForCausalLM": {"block_size": 4, "mask_id": 151669},
"SDARMoeForCausalLM": {"block_size": 4, "mask_id": 151669},
}
arch = model_config.hf_config.architectures[0]
if arch in DLLM_PARAMS:
params = DLLM_PARAMS[arch]
block_size = params["block_size"]
mask_id = params["mask_id"]
if model_config.hf_config.architectures[0] == "LLaDA2MoeModelLM":
block_size = 32
mask_id = 156895
else:
raise RuntimeError(f"Unknown diffusion LLM: {arch}")
raise RuntimeError(
f"Unknown diffusion LLM: {model_config.hf_config.architectures[0]}"
)
max_running_requests = (
1

589
python/sglang/srt/models/sdar.py
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@@ -1,589 +0,0 @@
# coding=utf-8
"""
SGLang SDARModelLM (block diffusion / dLLM-style forward).
"""
import logging
from typing import Iterable, Optional, Tuple, Union
import torch
from torch import nn
from transformers import PretrainedConfig
from sglang.srt.distributed import get_pp_group, get_tensor_model_parallel_world_size
from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
from sglang.srt.layers.dp_attention import (
get_attention_tp_rank,
get_attention_tp_size,
is_dp_attention_enabled,
)
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
MergedColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.radix_attention import AttentionType, RadixAttention
from sglang.srt.layers.rotary_embedding import get_rope
from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
from sglang.srt.layers.vocab_parallel_embedding import (
ParallelLMHead,
VocabParallelEmbedding,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
from sglang.srt.model_loader.weight_utils import (
default_weight_loader,
maybe_remap_kv_scale_name,
)
from sglang.srt.models.utils import (
apply_qk_norm,
create_fused_set_kv_buffer_arg,
enable_fused_set_kv_buffer,
)
from sglang.srt.server_args import get_global_server_args
from sglang.srt.utils import add_prefix, is_cuda, make_layers
logger = logging.getLogger(__name__)
_is_cuda = is_cuda()
class SDARMLP(nn.Module):
def __init__(
self,
config: PretrainedConfig,
quant_config=None,
reduce_results: bool = True,
prefix: str = "",
):
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
config.hidden_size,
[config.intermediate_size] * 2,
bias=False,
quant_config=quant_config,
prefix=add_prefix("gate_up_proj", prefix),
)
self.down_proj = RowParallelLinear(
config.intermediate_size,
config.hidden_size,
bias=False,
reduce_results=reduce_results,
quant_config=quant_config,
prefix=add_prefix("down_proj", prefix),
)
self.act_fn = SiluAndMul()
def forward(self, hidden_states: torch.Tensor, use_reduce_scatter: bool = False):
gate_up, _ = self.gate_up_proj(hidden_states)
hidden_states = self.act_fn(gate_up)
hidden_states, _ = self.down_proj(
hidden_states, skip_all_reduce=use_reduce_scatter
)
return hidden_states
class SDARAttention(nn.Module):
def __init__(
self,
config: PretrainedConfig,
layer_id: int,
quant_config=None,
reduce_results: bool = True,
prefix: str = "",
alt_stream: Optional[torch.cuda.Stream] = None,
):
super().__init__()
self.layer_id = layer_id
self.hidden_size = config.hidden_size
self.total_num_heads = config.num_attention_heads
self.tp_size = get_tensor_model_parallel_world_size()
attn_tp_rank = get_attention_tp_rank()
attn_tp_size = get_attention_tp_size()
assert self.total_num_heads % attn_tp_size == 0
self.num_heads = self.total_num_heads // attn_tp_size
self.total_num_kv_heads = config.num_key_value_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)
self.head_dim = getattr(
config, "head_dim", self.hidden_size // self.total_num_heads
)
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scale = self.head_dim**-0.5
self.qkv_proj = QKVParallelLinear(
self.hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_kv_heads,
bias=getattr(config, "attention_bias", False),
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,
self.hidden_size,
bias=getattr(config, "attention_bias", False),
quant_config=quant_config,
reduce_results=reduce_results,
tp_rank=attn_tp_rank,
tp_size=attn_tp_size,
prefix=add_prefix("o_proj", prefix),
)
self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
rope_theta = getattr(config, "rope_theta", 10000.0)
rope_scaling = getattr(config, "rope_scaling", None)
max_pos = getattr(config, "max_position_embeddings", 32768)
self.rotary_dim = self.head_dim
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.rotary_dim,
max_position=max_pos,
base=rope_theta,
rope_scaling=rope_scaling,
)
# RadixAttention: ENCODER_ONLY lets ForwardBatch provide non-causal / block masks (dLLM)
# NOTE: this is the key change vs AR Llama-style DECODER self-attn.
self.attn = RadixAttention(
self.num_heads,
self.head_dim,
self.scale,
num_kv_heads=self.num_kv_heads,
layer_id=layer_id,
attn_type=AttentionType.ENCODER_ONLY,
prefix=add_prefix("attn", prefix),
)
self.alt_stream = alt_stream
def forward_prepare_native(self, positions, hidden_states, forward_batch):
qkv, _ = self.qkv_proj(hidden_states)
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = apply_qk_norm(
q=q,
k=k,
q_norm=self.q_norm,
k_norm=self.k_norm,
head_dim=self.head_dim,
alt_stream=self.alt_stream,
)
q, k = self.rotary_emb(
positions,
q,
k,
fused_set_kv_buffer_arg=(
create_fused_set_kv_buffer_arg(
value=v,
layer=self.attn,
forward_batch=forward_batch,
)
if enable_fused_set_kv_buffer(forward_batch)
else None
),
)
return q, k, v
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
forward_batch: ForwardBatch,
):
if get_global_server_args().rl_on_policy_target is not None:
hidden_states = hidden_states.bfloat16()
qkv, _ = self.qkv_proj(hidden_states)
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = apply_qk_norm(
q=q,
k=k,
q_norm=self.q_norm,
k_norm=self.k_norm,
head_dim=self.head_dim,
alt_stream=self.alt_stream,
)
q, k = self.rotary_emb(
positions,
q,
k,
fused_set_kv_buffer_arg=(
create_fused_set_kv_buffer_arg(
value=v,
layer=self.attn,
forward_batch=forward_batch,
)
if enable_fused_set_kv_buffer(forward_batch)
else None
),
)
if get_global_server_args().rl_on_policy_target is not None:
q = q.to(torch.bfloat16)
k = k.to(torch.bfloat16)
context_layer = self.attn(
q,
k,
v,
forward_batch,
save_kv_cache=not enable_fused_set_kv_buffer(forward_batch),
)
out, _ = self.o_proj(context_layer)
return out
class SDARBlock(nn.Module):
def __init__(
self,
config: PretrainedConfig,
layer_id: int,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
alt_stream: Optional[torch.cuda.Stream] = None,
):
super().__init__()
self.hidden_size = config.hidden_size
self.layer_id = layer_id
norm_kwargs = (
dict(
weight_dtype=torch.float32,
cast_x_before_out_mul=True,
override_orig_dtype=torch.float32,
fp32_residual=True,
)
if get_global_server_args().rl_on_policy_target is not None
else {}
)
self.input_layernorm = RMSNorm(
self.hidden_size, eps=config.rms_norm_eps, **norm_kwargs
)
self.post_attention_layernorm = RMSNorm(
self.hidden_size, eps=config.rms_norm_eps, **norm_kwargs
)
self.self_attn = SDARAttention(
layer_id=layer_id,
config=config,
quant_config=quant_config,
reduce_results=False,
prefix=add_prefix("self_attn", prefix),
alt_stream=alt_stream,
)
self.mlp = SDARMLP(
config=config,
quant_config=quant_config,
reduce_results=True,
prefix=add_prefix("mlp", prefix),
)
self.layer_scatter_modes = LayerScatterModes.init_new(
layer_id=layer_id,
num_layers=config.num_hidden_layers,
is_layer_sparse=False,
is_previous_layer_sparse=False,
is_next_layer_sparse=False,
)
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],
) -> Tuple[torch.Tensor, torch.Tensor]:
hidden_states, residual = self.layer_communicator.prepare_attn(
hidden_states,
residual,
forward_batch,
)
if hidden_states.shape[0] != 0:
hidden_states = self.self_attn(
positions=positions,
hidden_states=hidden_states,
forward_batch=forward_batch,
)
hidden_states, residual = self.layer_communicator.prepare_mlp(
hidden_states,
residual,
forward_batch,
)
use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
forward_batch
)
hidden_states = self.mlp(hidden_states, use_reduce_scatter=use_reduce_scatter)
hidden_states, residual = self.layer_communicator.postprocess_layer(
hidden_states, residual, forward_batch
)
return hidden_states, residual
class SDARModel(nn.Module):
def __init__(
self,
config: PretrainedConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
alt_stream: Optional[torch.cuda.Stream] = None,
):
super().__init__()
self.config = config
self.vocab_size = config.vocab_size
self.embed_dim = config.hidden_size
self.pp_group = get_pp_group()
if self.pp_group.is_first_rank:
self.embed_tokens = VocabParallelEmbedding(
self.vocab_size,
self.embed_dim,
quant_config=quant_config,
use_attn_tp_group=is_dp_attention_enabled(),
prefix=add_prefix("embed_tokens", prefix),
)
else:
self.embed_tokens = PPMissingLayer()
self.layers, self.start_layer, self.end_layer = make_layers(
config.num_hidden_layers,
lambda idx, prefix: SDARBlock(
layer_id=idx,
config=config,
quant_config=quant_config,
prefix=prefix,
alt_stream=alt_stream,
),
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:
norm_kwargs = (
dict(
weight_dtype=torch.float32,
cast_x_before_out_mul=True,
override_orig_dtype=torch.float32,
fp32_residual=True,
)
if get_global_server_args().rl_on_policy_target is not None
else {}
)
self.norm = RMSNorm(self.embed_dim, eps=config.rms_norm_eps, **norm_kwargs)
else:
self.norm = PPMissingLayer(return_tuple=True)
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:
hidden_states = (
self.embed_tokens(input_ids) if input_embeds is None else input_embeds
)
residual = None
else:
assert pp_proxy_tensors is not None
hidden_states = pp_proxy_tensors["hidden_states"]
residual = pp_proxy_tensors.get("residual", None)
for i in range(self.start_layer, self.end_layer):
layer = self.layers[i]
hidden_states, residual = layer(
positions, hidden_states, forward_batch, residual
)
if not self.pp_group.is_last_rank:
return PPProxyTensors(
{"hidden_states": hidden_states, "residual": residual}
)
else:
if not forward_batch.forward_mode.is_idle():
hidden_states, residual = self.norm(hidden_states, residual)
return hidden_states
class SDARForCausalLM(nn.Module):
def __init__(
self,
config: PretrainedConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.pp_group = get_pp_group()
assert self.pp_group.world_size == 1, (
f"SDARMoeForCausalLM does not support pipeline parallel (pp_size={self.pp_group.world_size}). "
"Please set pp_size=1."
)
self.config = config
self.quant_config = quant_config
alt_stream = torch.cuda.Stream() if _is_cuda else None
self.model = SDARModel(
config,
quant_config=quant_config,
prefix=add_prefix("model", ""),
alt_stream=alt_stream,
)
if self.pp_group.is_last_rank:
tp_size = get_tensor_model_parallel_world_size()
if (
self.pp_group.world_size == 1
and config.tie_word_embeddings
and tp_size == 1
):
self.lm_head = self.model.embed_tokens
else:
self.lm_head = ParallelLMHead(
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
use_attn_tp_group=get_global_server_args().enable_dp_lm_head,
prefix=add_prefix("lm_head", prefix),
)
else:
self.lm_head = PPMissingLayer()
self.logits_processor = LogitsProcessor(config, return_full_logits=True)
@property
def start_layer(self):
return self.model.start_layer
@property
def end_layer(self):
return self.model.end_layer
@torch.no_grad()
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
forward_batch: ForwardBatch,
input_embeds: Optional[torch.Tensor] = None,
pp_proxy_tensors: Optional[PPProxyTensors] = None,
) -> torch.Tensor:
hidden_states = self.model(
input_ids=input_ids,
positions=positions,
forward_batch=forward_batch,
input_embeds=input_embeds,
pp_proxy_tensors=pp_proxy_tensors,
)
if self.pp_group.is_last_rank:
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
)
else:
return hidden_states
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"),
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
]
params_dict = dict(self.named_parameters())
for name, loaded_weight in weights:
if not name.startswith("model.") and (
name.startswith("layers.")
or name.startswith("embed_tokens.")
or name.startswith("norm.")
):
name = add_prefix(name, "model")
if name == "model.embed_tokens.weight":
if self.pp_group.is_last_rank and self.config.tie_word_embeddings:
param = params_dict["lm_head.weight"]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(param, loaded_weight)
layer_id = get_layer_id(name)
if (
layer_id is not None
and hasattr(self.model, "start_layer")
and (
layer_id < self.model.start_layer
or layer_id >= self.model.end_layer
)
):
continue
if "rotary_emb.inv_freq" in name or "projector" in name:
continue
if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
# Models trained using ColossalAI may include these tensors in
# the checkpoint. Skip them.
continue
if name.startswith("model.vision_tower") and name not in params_dict:
continue
if "scale" in name:
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
continue
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)
# 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:
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if name in params_dict.keys():
param = params_dict[name]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(param, loaded_weight)
else:
logger.warning(f"Parameter {name} not found in params_dict")
EntryClass = SDARForCausalLM

746
python/sglang/srt/models/sdar_moe.py
View File

@@ -1,746 +0,0 @@
# coding=utf-8
"""
SGLang SDARMoeModelLM (block diffusion / dLLM-style forward) with MoE MLP.
"""
import logging
from typing import Iterable, Optional, Tuple, Union
import torch
from torch import nn
from transformers import PretrainedConfig
from sglang.srt.distributed import (
get_moe_expert_parallel_world_size,
get_pp_group,
get_tensor_model_parallel_world_size,
tensor_model_parallel_all_reduce,
)
from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
from sglang.srt.layers.dp_attention import (
get_attention_tp_rank,
get_attention_tp_size,
is_dp_attention_enabled,
)
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.moe import (
get_moe_a2a_backend,
should_use_flashinfer_cutlass_moe_fp4_allgather,
)
from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
from sglang.srt.layers.moe.topk import TopK
from sglang.srt.layers.moe.utils import (
RoutingMethodType,
filter_moe_weight_param_global_expert,
)
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.radix_attention import AttentionType, RadixAttention
from sglang.srt.layers.rotary_embedding import get_rope
from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
from sglang.srt.layers.vocab_parallel_embedding import (
ParallelLMHead,
VocabParallelEmbedding,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
from sglang.srt.model_loader.weight_utils import (
default_weight_loader,
maybe_remap_kv_scale_name,
)
from sglang.srt.models.utils import (
apply_qk_norm,
create_fused_set_kv_buffer_arg,
enable_fused_set_kv_buffer,
)
from sglang.srt.server_args import get_global_server_args
from sglang.srt.utils import LazyValue, add_prefix, is_cuda, make_layers
logger = logging.getLogger(__name__)
_is_cuda = is_cuda()
class SDARMoeSparseMoeBlock(nn.Module):
"""
Qwen3MoE-style sparse MoE block:
- gate: ReplicatedLinear(hidden, num_experts)
- topk routing: TopK
- experts: get_moe_impl_class(quant_config)(...)
"""
def __init__(
self,
layer_id: int,
config: PretrainedConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.layer_id = layer_id
self.tp_size = get_tensor_model_parallel_world_size()
if self.tp_size > config.num_experts:
raise ValueError(
f"Tensor parallel size {self.tp_size} > num_experts {config.num_experts}."
)
self.topk = TopK(
top_k=config.num_experts_per_tok,
renormalize=config.norm_topk_prob,
use_grouped_topk=False,
layer_id=layer_id,
)
self.experts = get_moe_impl_class(quant_config)(
num_experts=config.num_experts
+ get_global_server_args().ep_num_redundant_experts,
top_k=config.num_experts_per_tok,
layer_id=layer_id,
hidden_size=config.hidden_size,
intermediate_size=config.moe_intermediate_size,
quant_config=quant_config,
prefix=add_prefix("experts", prefix),
routing_method_type=RoutingMethodType.Renormalize,
)
self.gate = ReplicatedLinear(
config.hidden_size,
config.num_experts,
bias=False,
quant_config=None,
prefix=add_prefix("gate", prefix),
)
# Deepep / FuseEP support
if get_moe_a2a_backend().is_deepep():
self.ep_size = get_moe_expert_parallel_world_size()
self.num_experts = (
config.num_experts + get_global_server_args().ep_num_redundant_experts
)
self.top_k = config.num_experts_per_tok
def forward(
self,
hidden_states: torch.Tensor,
forward_batch: Optional[ForwardBatch] = None,
should_allreduce_fusion: bool = False,
use_reduce_scatter: bool = False,
) -> torch.Tensor:
if (
not get_moe_a2a_backend().is_deepep()
and not get_moe_a2a_backend().is_ascend_fuseep()
):
return self.forward_normal(
hidden_states,
should_allreduce_fusion=should_allreduce_fusion,
use_reduce_scatter=use_reduce_scatter,
)
else:
assert forward_batch is not None, "deepep/fuseep MoE needs forward_batch"
return self.forward_deepep(hidden_states, forward_batch)
def forward_normal(
self,
hidden_states: torch.Tensor,
should_allreduce_fusion: bool = False,
use_reduce_scatter: bool = False,
) -> torch.Tensor:
num_tokens, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)
router_logits, _ = self.gate(hidden_states) # (T, E)
topk_output = self.topk(hidden_states, router_logits)
out = self.experts(hidden_states, topk_output) # (T, H)
# TP all-reduce (unless fused / reduce_scatter / fp4 allgather path)
if (
self.tp_size > 1
and not should_allreduce_fusion
and not use_reduce_scatter
and not should_use_flashinfer_cutlass_moe_fp4_allgather()
):
out = tensor_model_parallel_all_reduce(out)
return out.view(num_tokens, hidden_dim)
def forward_deepep(self, hidden_states: torch.Tensor, forward_batch: ForwardBatch):
if hidden_states.shape[0] > 0:
router_logits, _ = self.gate(hidden_states)
topk_output = self.topk(
hidden_states,
router_logits,
num_token_non_padded=forward_batch.num_token_non_padded,
expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
layer_id=self.layer_id
),
)
else:
topk_output = self.topk.empty_topk_output(hidden_states.device)
out = self.experts(hidden_states=hidden_states, topk_output=topk_output)
return out
def get_moe_weights(self):
return [
p.data
for name, p in self.experts.named_parameters()
if name not in ["correction_bias"]
and filter_moe_weight_param_global_expert(
name, p, self.experts.num_local_experts
)
]
class SDARMoeAttention(nn.Module):
def __init__(
self,
config: PretrainedConfig,
layer_id: int,
quant_config: Optional[QuantizationConfig] = None,
reduce_results: bool = True,
prefix: str = "",
alt_stream: Optional[torch.cuda.Stream] = None,
):
super().__init__()
self.layer_id = layer_id
self.hidden_size = config.hidden_size
self.total_num_heads = config.num_attention_heads
attn_tp_rank = get_attention_tp_rank()
attn_tp_size = get_attention_tp_size()
assert self.total_num_heads % attn_tp_size == 0
self.num_heads = self.total_num_heads // attn_tp_size
self.total_num_kv_heads = config.num_key_value_heads
if self.total_num_kv_heads >= attn_tp_size:
assert self.total_num_kv_heads % attn_tp_size == 0
else:
assert attn_tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
self.head_dim = getattr(
config, "head_dim", self.hidden_size // self.total_num_heads
)
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scale = self.head_dim**-0.5
self.qkv_proj = QKVParallelLinear(
self.hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_kv_heads,
bias=getattr(config, "attention_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=getattr(config, "attention_bias", False),
quant_config=quant_config,
reduce_results=reduce_results,
tp_rank=attn_tp_rank,
tp_size=attn_tp_size,
prefix=add_prefix("o_proj", prefix),
)
self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
rope_theta = getattr(config, "rope_theta", 10000.0)
rope_scaling = getattr(config, "rope_scaling", None)
max_pos = getattr(config, "max_position_embeddings", 32768)
self.rotary_dim = self.head_dim
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.rotary_dim,
max_position=max_pos,
base=rope_theta,
rope_scaling=rope_scaling,
)
self.attn = RadixAttention(
self.num_heads,
self.head_dim,
self.scale,
num_kv_heads=self.num_kv_heads,
layer_id=layer_id,
attn_type=AttentionType.ENCODER_ONLY,
prefix=add_prefix("attn", prefix),
)
self.alt_stream = alt_stream
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
forward_batch: ForwardBatch,
) -> torch.Tensor:
if get_global_server_args().rl_on_policy_target is not None:
hidden_states = hidden_states.bfloat16()
qkv, _ = self.qkv_proj(hidden_states)
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = apply_qk_norm(
q=q,
k=k,
q_norm=self.q_norm,
k_norm=self.k_norm,
head_dim=self.head_dim,
alt_stream=self.alt_stream,
)
q, k = self.rotary_emb(
positions,
q,
k,
fused_set_kv_buffer_arg=(
create_fused_set_kv_buffer_arg(
value=v,
layer=self.attn,
forward_batch=forward_batch,
)
if enable_fused_set_kv_buffer(forward_batch)
else None
),
)
if get_global_server_args().rl_on_policy_target is not None:
q = q.to(torch.bfloat16)
k = k.to(torch.bfloat16)
context = self.attn(
q,
k,
v,
forward_batch,
save_kv_cache=not enable_fused_set_kv_buffer(forward_batch),
)
out, _ = self.o_proj(context)
return out
class SDARMoeBlock(nn.Module):
def __init__(
self,
config: PretrainedConfig,
layer_id: int,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
alt_stream: Optional[torch.cuda.Stream] = None,
):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.layer_id = layer_id
norm_kwargs = (
dict(
weight_dtype=torch.float32,
cast_x_before_out_mul=True,
override_orig_dtype=torch.float32,
fp32_residual=True,
)
if get_global_server_args().rl_on_policy_target is not None
else {}
)
self.input_layernorm = RMSNorm(
self.hidden_size, eps=config.rms_norm_eps, **norm_kwargs
)
self.post_attention_layernorm = RMSNorm(
self.hidden_size, eps=config.rms_norm_eps, **norm_kwargs
)
self.self_attn = SDARMoeAttention(
config=config,
layer_id=layer_id,
quant_config=quant_config,
reduce_results=False,
prefix=add_prefix("self_attn", prefix),
alt_stream=alt_stream,
)
self.mlp = SDARMoeSparseMoeBlock(
layer_id=layer_id,
config=config,
quant_config=quant_config,
prefix=add_prefix("mlp", prefix),
)
self.layer_scatter_modes = LayerScatterModes.init_new(
layer_id=layer_id,
num_layers=config.num_hidden_layers,
is_layer_sparse=True,
is_previous_layer_sparse=True,
is_next_layer_sparse=True,
)
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,
is_last_layer=(layer_id == config.num_hidden_layers - 1),
)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
forward_batch: ForwardBatch,
residual: Optional[torch.Tensor],
) -> Tuple[torch.Tensor, torch.Tensor]:
hidden_states, residual = self.layer_communicator.prepare_attn(
hidden_states, residual, forward_batch
)
if hidden_states.shape[0] != 0:
hidden_states = self.self_attn(
positions=positions,
hidden_states=hidden_states,
forward_batch=forward_batch,
)
hidden_states, residual = self.layer_communicator.prepare_mlp(
hidden_states, residual, forward_batch
)
should_allreduce_fusion = (
self.layer_communicator.should_fuse_mlp_allreduce_with_next_layer(
forward_batch
)
)
use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
forward_batch
)
hidden_states = self.mlp(
hidden_states,
forward_batch=forward_batch,
should_allreduce_fusion=should_allreduce_fusion,
use_reduce_scatter=use_reduce_scatter,
)
if should_allreduce_fusion:
hidden_states._sglang_needs_allreduce_fusion = True
else:
hidden_states, residual = self.layer_communicator.postprocess_layer(
hidden_states, residual, forward_batch
)
return hidden_states, residual
class SDARMoeModel(nn.Module):
def __init__(
self,
config: PretrainedConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
alt_stream: Optional[torch.cuda.Stream] = None,
):
super().__init__()
self.config = config
self.vocab_size = config.vocab_size
self.embed_dim = config.hidden_size
self.pp_group = get_pp_group()
if self.pp_group.is_first_rank:
self.embed_tokens = VocabParallelEmbedding(
self.vocab_size,
self.embed_dim,
quant_config=quant_config,
use_attn_tp_group=is_dp_attention_enabled(),
prefix=add_prefix("embed_tokens", prefix),
)
else:
self.embed_tokens = PPMissingLayer()
self.layers, self.start_layer, self.end_layer = make_layers(
config.num_hidden_layers,
lambda idx, prefix: SDARMoeBlock(
config=config,
layer_id=idx,
quant_config=quant_config,
prefix=prefix,
alt_stream=alt_stream,
),
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:
norm_kwargs = (
dict(
weight_dtype=torch.float32,
cast_x_before_out_mul=True,
override_orig_dtype=torch.float32,
fp32_residual=True,
)
if get_global_server_args().rl_on_policy_target is not None
else {}
)
self.norm = RMSNorm(self.embed_dim, eps=config.rms_norm_eps, **norm_kwargs)
else:
self.norm = PPMissingLayer(return_tuple=True)
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:
hidden_states = (
self.embed_tokens(input_ids) if input_embeds is None else input_embeds
)
residual = None
else:
assert pp_proxy_tensors is not None
hidden_states = pp_proxy_tensors["hidden_states"]
residual = pp_proxy_tensors.get("residual", None)
for i in range(self.start_layer, self.end_layer):
layer = self.layers[i]
with get_global_expert_distribution_recorder().with_current_layer(i):
hidden_states, residual = layer(
positions, hidden_states, forward_batch, residual
)
if not self.pp_group.is_last_rank:
return PPProxyTensors(
{"hidden_states": hidden_states, "residual": residual}
)
if not forward_batch.forward_mode.is_idle():
hidden_states, residual = self.norm(hidden_states, residual)
return hidden_states
class SDARMoeForCausalLM(nn.Module):
fall_back_to_pt_during_load = False
def __init__(
self,
config: PretrainedConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.pp_group = get_pp_group()
assert self.pp_group.world_size == 1, (
f"SDARMoeForCausalLM does not support pipeline parallel (pp_size={self.pp_group.world_size}). "
"Please set pp_size=1."
)
self.pp_group = get_pp_group()
self.config = config
self.quant_config = quant_config
alt_stream = torch.cuda.Stream() if _is_cuda else None
self.model = SDARMoeModel(
config,
quant_config=quant_config,
prefix=add_prefix("model", ""),
alt_stream=alt_stream,
)
if self.pp_group.is_last_rank:
tp_size = get_tensor_model_parallel_world_size()
if (
self.pp_group.world_size == 1
and getattr(config, "tie_word_embeddings", False)
and tp_size == 1
):
self.lm_head = self.model.embed_tokens
else:
self.lm_head = ParallelLMHead(
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
use_attn_tp_group=get_global_server_args().enable_dp_lm_head,
prefix=add_prefix("lm_head", prefix),
)
else:
self.lm_head = PPMissingLayer()
self.logits_processor = LogitsProcessor(config, return_full_logits=True)
@property
def start_layer(self):
return self.model.start_layer
@property
def end_layer(self):
return self.model.end_layer
@torch.no_grad()
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
forward_batch: ForwardBatch,
input_embeds: Optional[torch.Tensor] = None,
pp_proxy_tensors: Optional[PPProxyTensors] = None,
) -> torch.Tensor:
hidden_states = self.model(
input_ids=input_ids,
positions=positions,
forward_batch=forward_batch,
input_embeds=input_embeds,
pp_proxy_tensors=pp_proxy_tensors,
)
if self.pp_group.is_last_rank:
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
)
return hidden_states
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
stacked_params_mapping = [
("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),
]
expert_params_mapping = FusedMoE.make_expert_params_mapping(
ckpt_gate_proj_name="gate_proj",
ckpt_down_proj_name="down_proj",
ckpt_up_proj_name="up_proj",
num_experts=self.config.num_experts,
)
if not hasattr(self, "_cached_params_dict"):
self._cached_params_dict = dict(self.named_parameters())
params_dict = self._cached_params_dict
for name, loaded_weight in weights:
if not name.startswith("model.") and (
name.startswith("layers.")
or name.startswith("embed_tokens.")
or name.startswith("norm.")
):
name = add_prefix(name, "model")
if name == "model.embed_tokens.weight":
if self.pp_group.is_last_rank and getattr(
self.config, "tie_word_embeddings", False
):
if "lm_head.weight" in params_dict:
param = params_dict["lm_head.weight"]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(param, loaded_weight)
layer_id = get_layer_id(name)
if (
layer_id is not None
and hasattr(self.model, "start_layer")
and (
layer_id < self.model.start_layer
or layer_id >= self.model.end_layer
)
):
continue
if "rotary_emb.inv_freq" in name or "projector" in name:
continue
if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
continue
if "scale" in name:
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
continue
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
if "mlp.experts" in name:
continue
name2 = name.replace(weight_name, param_name)
if name2.endswith(".bias") and name2 not in params_dict:
continue
if name2 not in params_dict:
continue
param = params_dict[name2]
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, loaded_weight, shard_id)
break
else:
is_expert_weight = False
for mapping in expert_params_mapping:
param_name, weight_name, expert_id, shard_id = mapping
if weight_name not in name:
continue
is_expert_weight = True
name2 = name.replace(weight_name, param_name)
if name2 not in params_dict:
continue
param = params_dict[name2]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(
param,
loaded_weight,
name2,
shard_id=shard_id,
expert_id=expert_id,
)
break
else:
if is_expert_weight:
continue
# 3) regular params
if name.endswith(".bias") and name not in params_dict:
continue
if name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(param, loaded_weight)
if not hasattr(self, "routed_experts_weights_of_layer"):
self.routed_experts_weights_of_layer = LazyValue(
lambda: {
lid: self.model.layers[lid].mlp.get_moe_weights()
for lid in range(self.start_layer, self.end_layer)
if isinstance(self.model.layers[lid].mlp, SDARMoeSparseMoeBlock)
}
)
@classmethod
def get_model_config_for_expert_location(cls, config):
return ModelConfigForExpertLocation(
num_layers=config.num_hidden_layers,
num_logical_experts=config.num_experts,
num_groups=None,
)
EntryClass = SDARMoeForCausalLM

91
test/registered/dllm/test_sdar.py
View File

@@ -1,91 +0,0 @@
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
register_cuda_ci(est_time=181, suite="stage-b-test-large-1-gpu")
register_amd_ci(est_time=330, suite="stage-b-test-small-1-gpu-amd")
import unittest
from types import SimpleNamespace
from sglang.srt.utils import kill_process_tree
from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
from sglang.test.send_one import BenchArgs, send_one_prompt
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTestCase,
is_in_amd_ci,
is_in_ci,
popen_launch_server,
write_github_step_summary,
)
class TestSDARMini(CustomTestCase):
@classmethod
def setUpClass(cls):
cls.model = "JetLM/SDAR-8B-Chat"
cls.base_url = DEFAULT_URL_FOR_TEST
other_args = [
"--trust-remote-code",
"--mem-fraction-static",
"0.8",
"--max-running-requests",
"64",
"--attention-backend",
"flashinfer",
"--dllm-algorithm",
"LowConfidence",
"--tp",
"1",
]
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=other_args,
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_gsm8k(self):
args = SimpleNamespace(
num_shots=5,
data_path=None,
num_questions=200,
max_new_tokens=1024,
parallel=128,
host="http://127.0.0.1",
port=int(self.base_url.split(":")[-1]),
)
metrics = run_eval_few_shot_gsm8k(args)
print(f"{metrics=}")
self.assertGreater(metrics["accuracy"], 0.88)
if is_in_amd_ci():
self.assertGreater(metrics["output_throughput"], 80)
else:
self.assertGreater(metrics["output_throughput"], 250)
def test_bs_1_speed(self):
args = BenchArgs(port=int(self.base_url.split(":")[-1]), max_new_tokens=2048)
acc_length, speed = send_one_prompt(args)
print(f"{speed=:.2f}")
if is_in_ci():
write_github_step_summary(
f"### test_bs_1_speed (SDAR-8B-Chat) with tp1\n"
f"{speed=:.2f} token/s\n"
)
if is_in_amd_ci():
self.assertGreater(speed, 10)
else:
self.assertGreater(speed, 70)
if __name__ == "__main__":
unittest.main()