301 lines
13 KiB
Python
301 lines
13 KiB
Python
################################################################################
|
|
# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
|
|
# 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.
|
|
#
|
|
################################################################################
|
|
|
|
from collections.abc import Iterable
|
|
from typing import Optional, Union
|
|
|
|
import torch
|
|
import torch_br
|
|
from fastcore.basics import patch_to
|
|
|
|
import vllm
|
|
from vllm.distributed import get_pp_group, tensor_model_parallel_all_reduce
|
|
from vllm.forward_context import ForwardContext, get_forward_context
|
|
from vllm.logger import init_logger
|
|
from vllm.model_executor.layers.fused_moe import FusedMoE
|
|
from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
|
|
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
|
from vllm.model_executor.models.qwen3_moe import Qwen3MoeModel
|
|
from vllm.model_executor.models.utils import is_pp_missing_parameter
|
|
from vllm.sequence import IntermediateTensors
|
|
from vllm_br.v1.attention.backends.attention_v1 import (
|
|
SUPAFlashAttentionMetadata)
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
@patch_to(vllm.model_executor.models.qwen3_moe.Qwen3MoeSparseMoeBlock)
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# NOTE: hidden_states can have either 1D or 2D shape.
|
|
orig_shape = hidden_states.shape
|
|
if len(hidden_states.shape) == 3:
|
|
hidden_states = hidden_states.squeeze(0)
|
|
final_hidden_states = self.experts(hidden_states=hidden_states,
|
|
router_logits=(self.gate.weight, None,
|
|
None))
|
|
if hasattr(final_hidden_states, 'all_reduced'):
|
|
# NOTE: this flag indicates that the final_hidden_states has been reduced in fused_moe
|
|
delattr(final_hidden_states, 'all_reduced')
|
|
elif self.tp_size > 1:
|
|
final_hidden_states = tensor_model_parallel_all_reduce(
|
|
final_hidden_states)
|
|
return final_hidden_states.view(orig_shape)
|
|
|
|
|
|
@patch_to(vllm.model_executor.models.qwen3_moe.Qwen3MoeAttention)
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
forward_context: ForwardContext = get_forward_context()
|
|
attn_metadata: SUPAFlashAttentionMetadata = forward_context.attn_metadata
|
|
if attn_metadata is None:
|
|
## for dummy run
|
|
return hidden_states
|
|
|
|
seq_len = hidden_states.shape[-2]
|
|
decode_seql = 512
|
|
|
|
if isinstance(attn_metadata, dict):
|
|
attn_metadata = attn_metadata[self.attn.layer_name]
|
|
kv_cache = self.attn.kv_cache[forward_context.virtual_engine]
|
|
if kv_cache is not None:
|
|
if seq_len <= decode_seql:
|
|
if hasattr(self.qkv_proj, "qweight"):
|
|
qkv_weight = self.qkv_proj.qweight.data
|
|
qkv_scales = self.qkv_proj.scales.data
|
|
elif hasattr(self.qkv_proj, "weight_packed"):
|
|
qkv_weight = self.qkv_proj.weight_packed.data
|
|
qkv_scales = self.qkv_proj.weight_scale.data
|
|
else:
|
|
qkv_weight = self.qkv_proj.weight
|
|
qkv_scales = None
|
|
if isinstance(self.rotary_emb, MRotaryEmbedding):
|
|
assert len(
|
|
self.rotary_emb.mrope_section
|
|
) == 3 and self.rotary_emb.mrope_section[
|
|
1] == self.rotary_emb.mrope_section[
|
|
2], "current only support mrope_section width and height are equal!"
|
|
q, k, v = torch_br.br_qwen3_vl_prefix_attn_infer(
|
|
hidden_states,
|
|
qkv_weight, [self.q_size, self.kv_size, self.kv_size],
|
|
self.head_dim,
|
|
self.q_norm.variance_epsilon,
|
|
self.q_norm.weight,
|
|
self.k_norm.weight,
|
|
self.rotary_emb.cos_sin_cache,
|
|
kv_cache,
|
|
positions,
|
|
attn_metadata.slot_mapping,
|
|
self.rotary_emb.mrope_section[1],
|
|
bias=self.qkv_proj.bias,
|
|
scales=qkv_scales)
|
|
else:
|
|
q, k, v = torch_br.br_qwen3_prefix_attn_infer(
|
|
hidden_states,
|
|
qkv_weight, [self.q_size, self.kv_size, self.kv_size],
|
|
self.head_dim,
|
|
self.q_norm.variance_epsilon,
|
|
self.q_norm.weight,
|
|
self.k_norm.weight,
|
|
self.rotary_emb.sin_cache,
|
|
self.rotary_emb.cos_cache,
|
|
kv_cache,
|
|
positions,
|
|
attn_metadata.slot_mapping,
|
|
bias=self.qkv_proj.bias,
|
|
scales=qkv_scales)
|
|
else:
|
|
qkv, _ = self.qkv_proj(hidden_states)
|
|
if isinstance(self.rotary_emb, MRotaryEmbedding):
|
|
assert len(
|
|
self.rotary_emb.mrope_section
|
|
) == 3 and self.rotary_emb.mrope_section[
|
|
1] == self.rotary_emb.mrope_section[
|
|
2], "current only support mrope_section width and height are equal!"
|
|
q, k, v = torch_br.br_fused_rms_mrope_kvstore_infer(
|
|
qkv, [self.q_size, self.kv_size, self.kv_size],
|
|
self.head_dim, self.q_norm.variance_epsilon,
|
|
self.q_norm.weight, self.k_norm.weight,
|
|
self.rotary_emb.cos_sin_cache, kv_cache, positions,
|
|
attn_metadata.slot_mapping, attn_metadata.block_table,
|
|
attn_metadata.query_start_loc, attn_metadata.context_lens,
|
|
self.rotary_emb.mrope_section[1])
|
|
else:
|
|
q, k, v = torch_br.br_fused_rms_rope_kvstore_infer(
|
|
qkv, [self.q_size, self.kv_size, self.kv_size],
|
|
self.head_dim, self.q_norm.variance_epsilon,
|
|
self.q_norm.weight, self.k_norm.weight,
|
|
self.rotary_emb.sin_cache, self.rotary_emb.cos_cache,
|
|
kv_cache, positions, attn_metadata.slot_mapping,
|
|
attn_metadata.block_table, attn_metadata.query_start_loc,
|
|
attn_metadata.context_lens)
|
|
if hasattr(attn_metadata, 'do_cache'):
|
|
attn_metadata.do_cache = False
|
|
attn_output = self.attn(q, k, v)
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
else:
|
|
return hidden_states
|
|
|
|
|
|
def model_forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
if get_pp_group().is_first_rank:
|
|
if inputs_embeds is not None:
|
|
hidden_states = inputs_embeds
|
|
else:
|
|
hidden_states = self.get_input_embeddings(input_ids)
|
|
residual = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
residual = intermediate_tensors["residual"]
|
|
|
|
if len(hidden_states.shape) == 2:
|
|
hidden_states = hidden_states.unsqueeze(0)
|
|
for i in range(self.start_layer, self.end_layer):
|
|
layer = self.layers[i]
|
|
hidden_states, residual = layer(positions, hidden_states, residual)
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors({
|
|
"hidden_states":
|
|
hidden_states.squeeze(0) if hidden_states is not None else None,
|
|
"residual":
|
|
residual.squeeze(0) if residual is not None else None
|
|
})
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
|
|
# NOTE: convert back to 2D
|
|
hidden_states = hidden_states.squeeze()
|
|
if hidden_states.dim() == 1:
|
|
hidden_states = hidden_states.unsqueeze(0)
|
|
|
|
return hidden_states
|
|
|
|
|
|
Qwen3MoeModel.forward = model_forward
|
|
|
|
|
|
def Qwen3MoeModel_load_weights(
|
|
self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
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 for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
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)
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
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") or name.endswith("_bias"))
|
|
and name not in params_dict):
|
|
continue
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
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:
|
|
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)
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
# Skip loading extra bias for GPTQ models.
|
|
if ((name.endswith(".bias") or name.endswith("_bias"))
|
|
and name not in params_dict):
|
|
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:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if ((name.endswith(".bias") or name.endswith("_bias"))
|
|
and name not in params_dict):
|
|
continue
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
# Remapping the name of FP8 kv-scale.
|
|
if name.endswith("kv_scale"):
|
|
remapped_kv_scale_name = name.replace(
|
|
".kv_scale", ".attn.kv_scale")
|
|
if remapped_kv_scale_name not in params_dict:
|
|
logger.warning_once(
|
|
"Found kv scale in the checkpoint (e.g. %s), but not found the expected name in the model (e.g. %s). kv-scale is not loaded.", # noqa: E501
|
|
name,
|
|
remapped_kv_scale_name,
|
|
)
|
|
continue
|
|
else:
|
|
name = remapped_kv_scale_name
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
if name.find("norm.weight") != -1:
|
|
param.data = param.data.to(torch.float32)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
Qwen3MoeModel.load_weights = Qwen3MoeModel_load_weights
|