1150 lines
42 KiB
Python
1150 lines
42 KiB
Python
# Adapted from qwen2_moe.py
|
|
|
|
# Copyright 2023-2024 SGLang Team
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
|
|
|
|
"""Inference-only Qwen3MoE model compatible with HuggingFace weights."""
|
|
|
|
import logging
|
|
import math
|
|
from typing import Any, Dict, Iterable, List, Optional, Tuple, TypeVar
|
|
|
|
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_rank,
|
|
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
|
|
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
|
|
from sglang.srt.layers.quantization.base_config import QuantizationConfig
|
|
from sglang.srt.layers.radix_attention import RadixAttention
|
|
from sglang.srt.layers.rotary_embedding import MRotaryEmbedding, get_rope
|
|
from sglang.srt.layers.utils import get_layer_id
|
|
from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
|
|
from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
|
|
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
|
|
from sglang.srt.model_loader.weight_utils import default_weight_loader
|
|
from sglang.srt.models.qwen2_moe import Qwen2MoeMLP as Qwen3MoeMLP
|
|
from sglang.srt.models.qwen2_moe import Qwen2MoeModel
|
|
from sglang.srt.models.utils import (
|
|
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,
|
|
is_flashinfer_available,
|
|
is_non_idle_and_non_empty,
|
|
is_npu,
|
|
)
|
|
|
|
_is_cuda = is_cuda()
|
|
|
|
if _is_cuda:
|
|
from sgl_kernel import fused_qk_norm_rope
|
|
|
|
TConfig = TypeVar("TConfig", bound=PretrainedConfig)
|
|
|
|
Qwen3MoeConfig = None
|
|
|
|
_is_flashinfer_available = is_flashinfer_available()
|
|
|
|
logger = logging.getLogger(__name__)
|
|
_is_cuda = is_cuda()
|
|
_is_npu = is_npu()
|
|
|
|
if _is_npu:
|
|
from sgl_kernel_npu.norm.split_qkv_rmsnorm_rope import split_qkv_rmsnorm_rope
|
|
|
|
|
|
def compute_yarn_parameters(
|
|
config: PretrainedConfig,
|
|
) -> tuple[float, float, float, float]:
|
|
"""
|
|
Refer to https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_rope_utils.py#L197C1-L288C1
|
|
Computes the inverse frequencies with NTK scaling. Please refer to the
|
|
[original paper](https://huggingface.co/papers/2309.00071)
|
|
Args:
|
|
config ([`~transformers.PretrainedConfig`]):
|
|
The model configuration.
|
|
Returns:
|
|
factor: float, the scaling factor for the RoPE embeddings
|
|
low: float, the lower bound of the dimension range
|
|
high: float, the upper bound of the dimension range
|
|
attention_factor: float, the post-processing scaling factor applied to the computed cos/sin
|
|
"""
|
|
|
|
# The config does not contain rope_scaling, which means the model is not using yarn
|
|
rope_scaling = getattr(config, "rope_scaling", None)
|
|
if rope_scaling is None:
|
|
return 1.0, 0, 0, 1.0
|
|
|
|
base = config.rope_theta
|
|
partial_rotary_factor = (
|
|
config.partial_rotary_factor
|
|
if hasattr(config, "partial_rotary_factor")
|
|
else 1.0
|
|
)
|
|
head_dim = getattr(
|
|
config, "head_dim", config.hidden_size // config.num_attention_heads
|
|
)
|
|
dim = int(head_dim * partial_rotary_factor)
|
|
factor = getattr(rope_scaling, "factor", 1.0)
|
|
attention_factor = rope_scaling.get("attention_factor")
|
|
mscale = rope_scaling.get("mscale")
|
|
mscale_all_dim = rope_scaling.get("mscale_all_dim")
|
|
|
|
if "original_max_position_embeddings" in rope_scaling:
|
|
original_max_position_embeddings = rope_scaling[
|
|
"original_max_position_embeddings"
|
|
]
|
|
factor = config.max_position_embeddings / original_max_position_embeddings
|
|
else:
|
|
original_max_position_embeddings = config.max_position_embeddings
|
|
|
|
def get_mscale(scale, mscale=1):
|
|
if scale <= 1:
|
|
return 1.0
|
|
return 0.1 * mscale * math.log(scale) + 1.0
|
|
|
|
# Sets the attention factor as suggested in the paper
|
|
if attention_factor is None:
|
|
if mscale and mscale_all_dim:
|
|
attention_factor = float(
|
|
get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dim)
|
|
)
|
|
else:
|
|
attention_factor = get_mscale(factor)
|
|
|
|
# Optional config options
|
|
# beta_fast/beta_slow: as suggested in the paper, default to 32/1 (correspondingly)
|
|
beta_fast = rope_scaling.get("beta_fast") or 32
|
|
beta_slow = rope_scaling.get("beta_slow") or 1
|
|
|
|
# Compute the inverse frequencies
|
|
def find_correction_dim(num_rotations, dim, base, max_position_embeddings):
|
|
"""Inverse dimension formula to find the dimension based on the number of rotations"""
|
|
return (
|
|
dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))
|
|
) / (2 * math.log(base))
|
|
|
|
def find_correction_range(
|
|
low_rot, high_rot, dim, base, max_position_embeddings, truncate
|
|
):
|
|
"""Find dimension range bounds based on rotations"""
|
|
low = find_correction_dim(low_rot, dim, base, max_position_embeddings)
|
|
high = find_correction_dim(high_rot, dim, base, max_position_embeddings)
|
|
if truncate:
|
|
low = math.floor(low)
|
|
high = math.ceil(high)
|
|
return max(low, 0), min(high, dim - 1)
|
|
|
|
truncate = rope_scaling.get("truncate", True)
|
|
low, high = find_correction_range(
|
|
beta_fast, beta_slow, dim, base, original_max_position_embeddings, truncate
|
|
)
|
|
|
|
# These parts are implemented in the fusedQKNormRopeKernel.cu
|
|
# # def linear_ramp_factor(min, max, dim):
|
|
# # if min == max:
|
|
# # max += 0.001 # Prevent singularity
|
|
|
|
# # linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
|
|
# # ramp_func = torch.clamp(linear_func, 0, 1)
|
|
# # return ramp_func
|
|
|
|
# # Note on variable naming: "interpolation" comes from the original technique, where we interpolate the position IDs
|
|
# # to expand the possible context length. In other words, interpolation = apply scaling factor.
|
|
# # pos_freqs = base ** (torch.arange(0, dim, 2).to(device=device, dtype=torch.float) / dim)
|
|
# # inv_freq_extrapolation = 1.0 / pos_freqs
|
|
# # inv_freq_interpolation = 1.0 / (factor * pos_freqs)
|
|
|
|
# # # Get n-dimensional rotational scaling corrected for extrapolation
|
|
# # inv_freq_extrapolation_factor = 1 - linear_ramp_factor(low, high, dim // 2).to(device=device, dtype=torch.float)
|
|
# # inv_freq = (
|
|
# # inv_freq_interpolation * (1 - inv_freq_extrapolation_factor)
|
|
# # + inv_freq_extrapolation * inv_freq_extrapolation_factor
|
|
# # )
|
|
# # return inv_freq, attention_factor
|
|
return factor, low, high, attention_factor
|
|
|
|
|
|
class Qwen3MoeSparseMoeBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
layer_id: int,
|
|
config: Qwen3MoeConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__()
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
self.layer_id = layer_id
|
|
if self.tp_size > config.num_experts:
|
|
raise ValueError(
|
|
f"Tensor parallel size {self.tp_size} is greater than "
|
|
f"the number of experts {config.num_experts}."
|
|
)
|
|
|
|
self.topk = TopK(
|
|
top_k=config.num_experts_per_tok,
|
|
renormalize=config.norm_topk_prob,
|
|
use_grouped_topk=False,
|
|
)
|
|
|
|
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),
|
|
)
|
|
|
|
if get_moe_a2a_backend().is_deepep():
|
|
# TODO: we will support tp < ep in the future
|
|
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, use_reduce_scatter
|
|
)
|
|
else:
|
|
return self.forward_deepep(hidden_states, forward_batch)
|
|
|
|
def get_moe_weights(self):
|
|
return [
|
|
x.data
|
|
for name, x in self.experts.named_parameters()
|
|
if name not in ["correction_bias"]
|
|
]
|
|
|
|
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: (num_tokens, n_experts)
|
|
router_logits, _ = self.gate(hidden_states)
|
|
topk_output = self.topk(hidden_states, router_logits)
|
|
final_hidden_states = self.experts(hidden_states, topk_output)
|
|
if (
|
|
self.tp_size > 1
|
|
and not should_allreduce_fusion
|
|
and not use_reduce_scatter
|
|
and not should_use_flashinfer_cutlass_moe_fp4_allgather()
|
|
):
|
|
final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
|
|
|
|
return final_hidden_states.view(num_tokens, hidden_dim)
|
|
|
|
def forward_deepep(
|
|
self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
|
|
) -> torch.Tensor:
|
|
if hidden_states.shape[0] > 0:
|
|
# router_logits: (num_tokens, n_experts)
|
|
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)
|
|
final_hidden_states = self.experts(
|
|
hidden_states=hidden_states,
|
|
topk_output=topk_output,
|
|
)
|
|
return final_hidden_states
|
|
|
|
def op_gate(self, state):
|
|
if is_non_idle_and_non_empty(
|
|
state.forward_batch.forward_mode, state.hidden_states_mlp_input
|
|
):
|
|
# router_logits: (num_tokens, n_experts)
|
|
state.router_logits, _ = self.gate(state.hidden_states_mlp_input)
|
|
else:
|
|
state.router_logits = None
|
|
|
|
def op_select_experts(self, state):
|
|
router_logits = state.pop("router_logits")
|
|
hidden_states = state.hidden_states_mlp_input
|
|
if router_logits is not None:
|
|
with get_global_expert_distribution_recorder().with_current_layer(
|
|
self.layer_id
|
|
):
|
|
state.topk_output = self.topk(
|
|
hidden_states=hidden_states,
|
|
router_logits=router_logits,
|
|
num_token_non_padded=state.forward_batch.num_token_non_padded,
|
|
expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
|
|
layer_id=self.layer_id,
|
|
),
|
|
)
|
|
else:
|
|
state.topk_output = self.topk.empty_topk_output(hidden_states.device)
|
|
|
|
def op_dispatch_a(self, state):
|
|
if self.ep_size > 1:
|
|
self.experts.dispatcher.dispatch_a(
|
|
hidden_states=state.pop("hidden_states_mlp_input"),
|
|
topk_output=state.pop("topk_output"),
|
|
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
|
)
|
|
|
|
def op_dispatch_b(self, state):
|
|
if self.ep_size > 1:
|
|
with get_global_expert_distribution_recorder().with_current_layer(
|
|
self.layer_id
|
|
):
|
|
state.dispatch_output = self.experts.dispatcher.dispatch_b(
|
|
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
|
)
|
|
|
|
def op_experts(self, state):
|
|
state.combine_input = self.experts.run_moe_core(
|
|
dispatch_output=state.dispatch_output,
|
|
)
|
|
|
|
def op_combine_a(self, state):
|
|
if self.ep_size > 1:
|
|
self.experts.dispatcher.combine_a(
|
|
combine_input=state.pop("combine_input"),
|
|
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
|
)
|
|
state.pop("dispatch_output")
|
|
|
|
def op_combine_b(self, state):
|
|
if self.ep_size > 1:
|
|
state.hidden_states_after_combine = self.experts.dispatcher.combine_b(
|
|
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
|
)
|
|
|
|
def op_output(self, state):
|
|
state.hidden_states_mlp_output = state.pop("hidden_states_after_combine")
|
|
|
|
|
|
class Qwen3MoeAttention(nn.Module):
|
|
def __init__(
|
|
self,
|
|
hidden_size: int,
|
|
num_heads: int,
|
|
num_kv_heads: int,
|
|
layer_id: int = 0,
|
|
rope_theta: float = 10000,
|
|
rope_scaling: Optional[Dict[str, Any]] = None,
|
|
max_position_embeddings: int = 8192,
|
|
head_dim: Optional[int] = None,
|
|
rms_norm_eps: float = 1e-06,
|
|
attention_bias: bool = False,
|
|
config: Optional[TConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
dual_chunk_attention_config: Optional[dict[str, Any]] = None,
|
|
alt_stream: Optional[torch.cuda.Stream] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.hidden_size = hidden_size
|
|
|
|
attn_tp_rank = get_attention_tp_rank()
|
|
attn_tp_size = get_attention_tp_size()
|
|
|
|
self.config = config
|
|
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)
|
|
self.head_dim = head_dim or 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.scaling = self.head_dim**-0.5
|
|
self.rope_theta = rope_theta
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.tp_rank = get_tensor_model_parallel_rank()
|
|
|
|
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,
|
|
reduce_results=False,
|
|
prefix=add_prefix("o_proj", prefix),
|
|
)
|
|
|
|
self.rotary_emb = get_rope(
|
|
self.head_dim,
|
|
rotary_dim=self.head_dim,
|
|
max_position=max_position_embeddings,
|
|
base=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
dual_chunk_attention_config=dual_chunk_attention_config,
|
|
)
|
|
self.compatible_with_fused_kv_buffer = (
|
|
False if isinstance(self.rotary_emb, MRotaryEmbedding) else True
|
|
)
|
|
self.compatible_with_fused_qk_norm_rope = (
|
|
not isinstance(self.rotary_emb, MRotaryEmbedding)
|
|
) and self.head_dim in (64, 128, 256)
|
|
self.use_fused_qk_norm_rope = (
|
|
get_global_server_args().enable_fused_qk_norm_rope
|
|
and self.compatible_with_fused_qk_norm_rope
|
|
)
|
|
self._used_fused_qk_norm_rope_last_call = False
|
|
|
|
self.attn = RadixAttention(
|
|
self.num_heads,
|
|
self.head_dim,
|
|
self.scaling,
|
|
num_kv_heads=self.num_kv_heads,
|
|
layer_id=layer_id,
|
|
prefix=add_prefix("attn", prefix),
|
|
)
|
|
|
|
self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
|
|
self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
|
|
self.alt_stream = alt_stream
|
|
|
|
def _apply_qk_norm(
|
|
self, q: torch.Tensor, k: torch.Tensor
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
# overlap qk norm
|
|
if self.alt_stream is not None and get_is_capture_mode():
|
|
current_stream = torch.cuda.current_stream()
|
|
self.alt_stream.wait_stream(current_stream)
|
|
q_by_head = q.reshape(-1, self.head_dim)
|
|
q_by_head = self.q_norm(q_by_head)
|
|
with torch.cuda.stream(self.alt_stream):
|
|
k_by_head = k.reshape(-1, self.head_dim)
|
|
k_by_head = self.k_norm(k_by_head)
|
|
current_stream.wait_stream(self.alt_stream)
|
|
q = q_by_head.view(q.shape)
|
|
k = k_by_head.view(k.shape)
|
|
return q, k
|
|
else:
|
|
q_by_head = q.reshape(-1, self.head_dim)
|
|
q_by_head = self.q_norm(q_by_head)
|
|
k_by_head = k.reshape(-1, self.head_dim)
|
|
k_by_head = self.k_norm(k_by_head)
|
|
q = q_by_head.view(q.shape)
|
|
k = k_by_head.view(k.shape)
|
|
return q, k
|
|
|
|
def op_prepare(self, state):
|
|
state.attn_intermediate_state = self.forward_prepare(
|
|
positions=state.positions,
|
|
hidden_states=state.pop("hidden_states_after_comm_pre_attn"),
|
|
forward_batch=state.forward_batch,
|
|
)
|
|
|
|
def op_core(self, state):
|
|
state.hidden_states_after_attn = self.forward_core(
|
|
state.pop("attn_intermediate_state")
|
|
)
|
|
|
|
def forward_prepare_npu(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
):
|
|
qkv, _ = self.qkv_proj(hidden_states)
|
|
if self.attn.layer_id == 0:
|
|
self.rotary_emb.get_cos_sin_with_position(positions)
|
|
q, k, v = split_qkv_rmsnorm_rope(
|
|
qkv,
|
|
self.rotary_emb.position_sin,
|
|
self.rotary_emb.position_cos,
|
|
self.q_norm.weight,
|
|
self.k_norm.weight,
|
|
self.q_size,
|
|
self.kv_size,
|
|
self.head_dim,
|
|
self.q_norm.variance_epsilon,
|
|
q_bias=getattr(self.q_norm, "bias", None),
|
|
k_bias=getattr(self.k_norm, "bias", None),
|
|
)
|
|
|
|
inner_state = q, k, v, forward_batch
|
|
return None, forward_batch, inner_state
|
|
|
|
def forward_prepare_native(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
):
|
|
qkv, _ = self.qkv_proj(hidden_states)
|
|
|
|
q, k, v = self.apply_qk_norm_rope(qkv, positions, forward_batch)
|
|
|
|
inner_state = q, k, v, forward_batch
|
|
return None, forward_batch, inner_state
|
|
|
|
def apply_qk_norm_rope(self, qkv, positions, forward_batch):
|
|
use_fused = self.use_fused_qk_norm_rope and qkv.dtype == torch.bfloat16
|
|
if use_fused:
|
|
theta = getattr(self.config, "rope_theta", 10000.0)
|
|
positions = (
|
|
positions.view(-1).to(dtype=torch.int32, device=qkv.device).contiguous()
|
|
)
|
|
factor, low, high, attention_factor = compute_yarn_parameters(self.config)
|
|
fused_qk_norm_rope(
|
|
qkv,
|
|
self.num_heads,
|
|
self.num_kv_heads,
|
|
self.num_kv_heads,
|
|
self.head_dim,
|
|
self.q_norm.variance_epsilon,
|
|
self.q_norm.weight,
|
|
self.k_norm.weight,
|
|
theta,
|
|
self.rotary_emb.is_neox_style,
|
|
positions,
|
|
factor,
|
|
low,
|
|
high,
|
|
attention_factor,
|
|
)
|
|
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
|
self._used_fused_qk_norm_rope_last_call = True
|
|
else:
|
|
# Fallback to non-fused QK Norm & RoPE implementation
|
|
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
|
q, k = self._apply_qk_norm(q, k)
|
|
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)
|
|
and self.compatible_with_fused_kv_buffer
|
|
else None
|
|
),
|
|
)
|
|
self._used_fused_qk_norm_rope_last_call = False
|
|
return q, k, v
|
|
|
|
def forward_prepare(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
):
|
|
if hidden_states.shape[0] == 0:
|
|
return hidden_states, forward_batch, None
|
|
if not _is_npu:
|
|
return self.forward_prepare_native(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
forward_batch=forward_batch,
|
|
)
|
|
else:
|
|
return self.forward_prepare_npu(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
forward_batch=forward_batch,
|
|
)
|
|
|
|
def forward_core(self, intermediate_state):
|
|
hidden_states, forward_batch, inner_state = intermediate_state
|
|
if inner_state is None:
|
|
return hidden_states
|
|
|
|
q, k, v, fb = inner_state
|
|
|
|
must_save_kv = self._used_fused_qk_norm_rope_last_call
|
|
save_kv_cache = must_save_kv or not (
|
|
enable_fused_set_kv_buffer(forward_batch)
|
|
and self.compatible_with_fused_kv_buffer
|
|
)
|
|
attn_output = self.attn(
|
|
q,
|
|
k,
|
|
v,
|
|
fb,
|
|
save_kv_cache=save_kv_cache,
|
|
)
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
s = self.forward_prepare(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
forward_batch=forward_batch,
|
|
)
|
|
return self.forward_core(s)
|
|
|
|
|
|
class Qwen3MoeDecoderLayer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: Qwen3MoeConfig,
|
|
layer_id: int,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
alt_stream: Optional[torch.cuda.Stream] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
rope_theta = getattr(config, "rope_theta", 10000)
|
|
rope_scaling = getattr(config, "rope_scaling", None)
|
|
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
|
|
head_dim = getattr(
|
|
config, "head_dim", config.hidden_size // config.num_attention_heads
|
|
)
|
|
rms_norm_eps = config.rms_norm_eps
|
|
attention_bias = config.attention_bias
|
|
dual_chunk_attention_config = getattr(
|
|
config, "dual_chunk_attention_config", None
|
|
)
|
|
self.self_attn = Qwen3MoeAttention(
|
|
hidden_size=self.hidden_size,
|
|
num_heads=config.num_attention_heads,
|
|
num_kv_heads=config.num_key_value_heads,
|
|
layer_id=layer_id,
|
|
rope_theta=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
max_position_embeddings=max_position_embeddings,
|
|
head_dim=head_dim,
|
|
rms_norm_eps=rms_norm_eps,
|
|
attention_bias=attention_bias,
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("self_attn", prefix),
|
|
dual_chunk_attention_config=dual_chunk_attention_config,
|
|
alt_stream=alt_stream,
|
|
)
|
|
|
|
self.layer_id = layer_id
|
|
|
|
self.attn_tp_size = get_attention_tp_size()
|
|
self.attn_tp_rank = get_attention_tp_rank()
|
|
|
|
# Qwen3MoE all layers are sparse and have no nextn now
|
|
self.is_layer_sparse = True
|
|
is_previous_layer_sparse = True
|
|
is_next_layer_sparse = True
|
|
|
|
self.layer_scatter_modes = LayerScatterModes.init_new(
|
|
layer_id=layer_id,
|
|
num_layers=config.num_hidden_layers,
|
|
is_layer_sparse=self.is_layer_sparse,
|
|
is_previous_layer_sparse=is_previous_layer_sparse,
|
|
is_next_layer_sparse=is_next_layer_sparse,
|
|
)
|
|
|
|
if self.is_layer_sparse:
|
|
self.mlp = Qwen3MoeSparseMoeBlock(
|
|
layer_id=self.layer_id,
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("mlp", prefix),
|
|
)
|
|
else:
|
|
self.mlp = Qwen3MoeMLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("mlp", prefix),
|
|
)
|
|
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = RMSNorm(
|
|
config.hidden_size, eps=config.rms_norm_eps
|
|
)
|
|
|
|
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=(self.layer_id == self.config.num_hidden_layers - 1),
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
residual: Optional[torch.Tensor],
|
|
captured_last_layer_outputs: Optional[List[torch.Tensor]] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
|
|
hidden_states, residual = (
|
|
self.layer_communicator.prepare_attn_and_capture_last_layer_outputs(
|
|
hidden_states,
|
|
residual,
|
|
forward_batch,
|
|
captured_last_layer_outputs=captured_last_layer_outputs,
|
|
)
|
|
)
|
|
|
|
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
|
|
)
|
|
)
|
|
|
|
# For DP with padding, reduce scatter can be used instead of all-reduce.
|
|
use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
|
|
forward_batch
|
|
)
|
|
|
|
hidden_states = self.mlp(
|
|
hidden_states, forward_batch, should_allreduce_fusion, 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
|
|
|
|
def op_comm_prepare_attn(
|
|
self,
|
|
state,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
residual: Optional[torch.Tensor],
|
|
tbo_subbatch_index: Optional[int] = None,
|
|
):
|
|
state.hidden_states_after_comm_pre_attn, state.residual_after_input_ln = (
|
|
self.layer_communicator.prepare_attn(hidden_states, residual, forward_batch)
|
|
)
|
|
state.update(
|
|
dict(
|
|
forward_batch=forward_batch,
|
|
positions=positions,
|
|
tbo_subbatch_index=tbo_subbatch_index,
|
|
)
|
|
)
|
|
|
|
def op_comm_prepare_mlp(self, state):
|
|
state.hidden_states_mlp_input, state.residual_after_comm_pre_mlp = (
|
|
self.layer_communicator.prepare_mlp(
|
|
state.pop("hidden_states_after_attn"),
|
|
state.pop("residual_after_input_ln"),
|
|
state.forward_batch,
|
|
)
|
|
)
|
|
|
|
def op_mlp(self, state):
|
|
hidden_states = state.pop("hidden_states_mlp_input")
|
|
state.hidden_states_mlp_output = self.mlp(hidden_states, state.forward_batch)
|
|
|
|
def op_comm_postprocess_layer(self, state):
|
|
hidden_states, residual = self.layer_communicator.postprocess_layer(
|
|
state.pop("hidden_states_mlp_output"),
|
|
state.pop("residual_after_comm_pre_mlp"),
|
|
state.forward_batch,
|
|
)
|
|
|
|
output = dict(
|
|
positions=state.positions,
|
|
hidden_states=hidden_states,
|
|
residual=residual,
|
|
forward_batch=state.forward_batch,
|
|
tbo_subbatch_index=state.tbo_subbatch_index,
|
|
)
|
|
|
|
state.clear(
|
|
expect_keys={
|
|
"positions",
|
|
"forward_batch",
|
|
"tbo_subbatch_index",
|
|
}
|
|
)
|
|
return output
|
|
|
|
|
|
class Qwen3MoeModel(Qwen2MoeModel):
|
|
def __init__(
|
|
self,
|
|
config: Qwen3MoeConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
decoder_layer_type=Qwen3MoeDecoderLayer,
|
|
) -> None:
|
|
alt_stream = torch.cuda.Stream() if _is_cuda else None
|
|
super().__init__(
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=prefix,
|
|
decoder_layer_type=decoder_layer_type,
|
|
alt_stream=alt_stream,
|
|
)
|
|
|
|
|
|
class Qwen3MoeForCausalLM(nn.Module):
|
|
fall_back_to_pt_during_load = False
|
|
|
|
def __init__(
|
|
self,
|
|
config: Qwen3MoeConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
self.pp_group = get_pp_group()
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.model = Qwen3MoeModel(
|
|
config, quant_config, prefix=add_prefix("model", prefix)
|
|
)
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("lm_head", prefix),
|
|
use_attn_tp_group=get_global_server_args().enable_dp_lm_head,
|
|
)
|
|
self.logits_processor = LogitsProcessor(config)
|
|
self.capture_aux_hidden_states = False
|
|
|
|
def get_input_embeddings(self) -> nn.Embedding:
|
|
return self.model.embed_tokens
|
|
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.model(
|
|
input_ids,
|
|
positions,
|
|
forward_batch,
|
|
input_embeds,
|
|
pp_proxy_tensors=pp_proxy_tensors,
|
|
)
|
|
|
|
aux_hidden_states = None
|
|
if self.capture_aux_hidden_states:
|
|
hidden_states, aux_hidden_states = hidden_states
|
|
|
|
if self.pp_group.is_last_rank:
|
|
return self.logits_processor(
|
|
input_ids, hidden_states, self.lm_head, forward_batch, aux_hidden_states
|
|
)
|
|
else:
|
|
return hidden_states
|
|
|
|
@torch.no_grad()
|
|
def forward_split_prefill(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
split_interval: Tuple[int, int], # [start, end) 0-based
|
|
input_embeds: torch.Tensor = None,
|
|
):
|
|
start, end = split_interval
|
|
# embed
|
|
if start == 0:
|
|
if input_embeds is None:
|
|
forward_batch.hidden_states = self.model.embed_tokens(input_ids)
|
|
else:
|
|
forward_batch.hidden_states = input_embeds
|
|
|
|
# decoder layer
|
|
for i in range(start, end):
|
|
with get_global_expert_distribution_recorder().with_current_layer(i):
|
|
layer = self.model.layers[i]
|
|
forward_batch.hidden_states, forward_batch.residual = layer(
|
|
positions,
|
|
forward_batch.hidden_states,
|
|
forward_batch,
|
|
forward_batch.residual,
|
|
)
|
|
|
|
if end == self.model.config.num_hidden_layers:
|
|
# norm
|
|
hidden_states, _ = self.model.norm(
|
|
forward_batch.hidden_states, forward_batch.residual
|
|
)
|
|
forward_batch.hidden_states = hidden_states
|
|
# logits process
|
|
result = self.logits_processor(
|
|
input_ids, forward_batch.hidden_states, self.lm_head, forward_batch
|
|
)
|
|
else:
|
|
result = None
|
|
|
|
return result
|
|
|
|
@property
|
|
def start_layer(self):
|
|
return self.model.start_layer
|
|
|
|
@property
|
|
def end_layer(self):
|
|
return self.model.end_layer
|
|
|
|
def get_embed_and_head(self):
|
|
return self.model.embed_tokens.weight, self.lm_head.weight
|
|
|
|
def set_eagle3_layers_to_capture(self, layer_ids: Optional[List[int]] = None):
|
|
if not self.pp_group.is_last_rank:
|
|
return
|
|
|
|
self.capture_aux_hidden_states = True
|
|
if layer_ids is None:
|
|
num_layers = self.config.num_hidden_layers
|
|
self.model.set_eagle3_layers_to_capture(
|
|
[
|
|
2,
|
|
num_layers // 2,
|
|
num_layers - 3,
|
|
]
|
|
) # Specific layers for EAGLE3 support
|
|
else:
|
|
self.model.set_eagle3_layers_to_capture([val + 1 for val in layer_ids])
|
|
|
|
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),
|
|
]
|
|
|
|
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,
|
|
)
|
|
|
|
# Cache params_dict to avoid repeated expensive traversal of model parameters
|
|
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:
|
|
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:
|
|
continue
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
# Skip non-stacked layers and experts (experts handled below).
|
|
if weight_name not in name:
|
|
continue
|
|
# We have mlp.experts[0].gate_proj in the checkpoint.
|
|
# Since we handle the experts below in expert_params_mapping,
|
|
# we need to skip here BEFORE we update the name, otherwise
|
|
# name will be updated to mlp.experts[0].gate_up_proj, which
|
|
# will then be updated below in expert_params_mapping
|
|
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
|
if "mlp.experts" in name:
|
|
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
|
|
if name not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
# Track if this is an expert weight to enable early skipping
|
|
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
|
|
|
|
# Mark as expert weight regardless of whether we can process it
|
|
is_expert_weight = True
|
|
|
|
name = name.replace(weight_name, param_name)
|
|
if name not in params_dict:
|
|
# Expert weight not on this rank, will be skipped below
|
|
continue
|
|
|
|
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 is_expert_weight:
|
|
# This is an expert weight but not mapped to this rank, skip all remaining processing
|
|
continue
|
|
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if 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")
|
|
|
|
# TODO mimic deepseek
|
|
# Lazy initialization of expert weights cache to avoid slowing down load_weights
|
|
if not hasattr(self, "routed_experts_weights_of_layer"):
|
|
self.routed_experts_weights_of_layer = {
|
|
layer_id: self.model.layers[layer_id].mlp.get_moe_weights()
|
|
for layer_id in range(self.start_layer, self.end_layer)
|
|
if isinstance(self.model.layers[layer_id].mlp, Qwen3MoeSparseMoeBlock)
|
|
}
|
|
|
|
@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 = Qwen3MoeForCausalLM
|