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vllm-ascend/vllm_ascend/models/deepseek_v2.py
anon189Ty 07e39620ea [Feat] Unquantized Linear to nz and control all nz-cast (#3356) 
### What this PR does / why we need it?
Currently, when executing to the Linear layer of models in vLLM-Ascend,
the weights format is ND in unquantized case and skipped ascend case.
This PR supplements the execution logic for Linear layer. We use a new
global variable: VLLM_ASCEND_ENABLE_NZ. When VLLM_ASCEND_ENABLE_NZ=1 and
CANN version is 8.3, the weights of the Linear layer will be converted
to FRACTAL_NZ, in both unquantized case and skipped ascend case. We also
use VLLM_ASCEND_ENABLE_NZ to control the existing NZ conversion, such as
w8a8-quantized case.

### Does this PR introduce _any_ user-facing change?
Add a new global variable VLLM_ASCEND_ENABLE_NZ. If you want to use NZ
format, you should set VLLM_ASCEND_ENABLE_NZ=1.

### How was this patch tested?

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
2025-10-14 17:39:26 +08:00

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# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
# Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
# # Adapted from
# # vllm-project/vllm/blob/main/vllm/model_executor/models/deepseek_v2.py
# # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
# # vllm-project/vllm/vllm/model_executor/models/deepseek_v2.py
# """Inference-only DeepseekV2/DeepseekV3 model."""
from typing import Any, Dict, Iterable, Optional, Union
import torch
from torch import nn
from transformers import PretrainedConfig
from vllm.attention import AttentionMetadata
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (divide, get_pp_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
get_tp_group, split_tensor_along_last_dim,
tensor_model_parallel_all_reduce)
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (WEIGHT_LOADER_V2_SUPPORTED,
ColumnParallelLinear,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mla import MultiHeadLatentAttention
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader, maybe_remap_kv_scale_name)
from vllm.model_executor.models.deepseek_v2 import \
yarn_get_mscale # noqa: E501
from vllm.model_executor.models.deepseek_v2 import (
DeepseekV2Attention, DeepseekV2DecoderLayer, DeepseekV2ForCausalLM,
DeepseekV2MLAAttention, DeepseekV2MLP, DeepseekV2Model, DeepseekV2MoE,
get_spec_layer_idx_from_weight_name)
from vllm.model_executor.models.utils import (PPMissingLayer,
is_pp_missing_parameter,
maybe_prefix)
from vllm.model_executor.utils import set_weight_attrs
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.models.layers.mla import AscendMLAModules
from vllm_ascend.models.layers.sfa import (AscendSFAModules,
AscendSparseFlashAttention, Indexer)
from vllm_ascend.ops.common_fused_moe import AscendFusedMoE
from vllm_ascend.ops.linear import AscendLinearBase
class CustomDeepseekV2RowParallelLinear(RowParallelLinear):
def __init__(
self,
input_size: int,
output_size: int,
bias: bool = True,
input_is_parallel: bool = True,
skip_bias_add: bool = False,
params_dtype: Optional[torch.dtype] = None,
reduce_results: bool = True,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
*,
return_bias: bool = True,
disable_tp: bool = False,
):
# Divide the weight matrix along the first dimension.
self.tp_rank = (get_tensor_model_parallel_rank()
if not disable_tp else 0)
self.tp_size = (get_tensor_model_parallel_world_size()
if not disable_tp else 1)
self.input_size_per_partition = divide(input_size, self.tp_size)
self.output_size_per_partition = output_size
self.output_partition_sizes = [output_size]
AscendLinearBase.__init__(self,
input_size,
output_size,
skip_bias_add,
params_dtype,
quant_config,
prefix,
return_bias=return_bias,
disable_tp=disable_tp)
self.input_is_parallel = input_is_parallel
self.reduce_results = reduce_results
assert self.quant_method is not None
self.quant_method.create_weights(
layer=self,
input_size_per_partition=self.input_size_per_partition,
output_partition_sizes=self.output_partition_sizes,
input_size=self.input_size,
output_size=self.output_size,
params_dtype=self.params_dtype,
weight_loader=(
self.weight_loader_v2 if self.quant_method.__class__.__name__
in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader))
if not reduce_results and (bias and not skip_bias_add):
raise ValueError("When not reduce the results, adding bias to the "
"results can lead to incorrect results")
if bias:
self.bias = nn.Parameter(
torch.empty(self.output_size, dtype=params_dtype))
set_weight_attrs(self.bias, {
"output_dim": 0,
"weight_loader": self.weight_loader,
})
else:
self.register_parameter("bias", None)
self.update_param_tp_status()
def forward(
self,
input_,
is_prefill=True,
is_force_scatter=False
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[nn.Parameter]]]:
if self.input_is_parallel:
input_parallel = input_
else:
tp_rank = get_tensor_model_parallel_rank()
splitted_input = split_tensor_along_last_dim(
input_, num_partitions=self.tp_size)
input_parallel = splitted_input[tp_rank].contiguous()
# Matrix multiply.
assert self.quant_method is not None
# Only fuse bias add into GEMM for rank 0 (this ensures that
# bias will not get added more than once in TP>1 case)
bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
output_parallel = self.quant_method.apply(self,
input_parallel,
bias=bias_)
if self.reduce_results and self.tp_size > 1:
output = tensor_model_parallel_all_reduce(output_parallel)
else:
output = output_parallel
output_bias = self.bias if self.skip_bias_add else None
if not self.return_bias:
return output
return output, output_bias
class CustomDeepseekV2MLAAttention(DeepseekV2MLAAttention):
def __init__(
self,
config: PretrainedConfig,
hidden_size: int,
num_heads: int,
qk_nope_head_dim: int,
qk_rope_head_dim: int,
v_head_dim: int,
q_lora_rank: Optional[int],
kv_lora_rank: int,
rope_theta: float = 10000,
rope_scaling: Optional[Dict[str, Any]] = None,
max_position_embeddings: int = 8192,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
nn.Module.__init__(self)
self.hidden_size = hidden_size
self.qk_nope_head_dim = qk_nope_head_dim
self.qk_rope_head_dim = qk_rope_head_dim
self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
self.v_head_dim = v_head_dim
self.q_lora_rank = q_lora_rank
self.kv_lora_rank = kv_lora_rank
self.num_heads = num_heads
self.tp_size = get_tensor_model_parallel_world_size()
assert num_heads % self.tp_size == 0
self.num_local_heads = num_heads // self.tp_size
self.layers = config.num_hidden_layers
self.first_k_dense_replace = config.first_k_dense_replace
self.scaling = self.qk_head_dim**-0.5
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.prefix = prefix
self.debug_layer_idx = int(self.prefix.split(".")[-2])
ascend_config = get_ascend_config()
self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
if self.q_lora_rank is not None:
self.q_a_proj = ReplicatedLinear(self.hidden_size,
self.q_lora_rank,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_a_proj")
self.q_a_layernorm = RMSNorm(self.q_lora_rank,
eps=config.rms_norm_eps)
self.q_b_proj = ColumnParallelLinear(q_lora_rank,
self.num_heads *
self.qk_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_b_proj")
else:
self.q_proj = ColumnParallelLinear(self.hidden_size,
self.num_heads *
self.qk_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_proj")
self.kv_a_proj_with_mqa = ReplicatedLinear(
self.hidden_size,
self.kv_lora_rank + self.qk_rope_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.kv_a_proj_with_mqa")
self.kv_a_layernorm = RMSNorm(self.kv_lora_rank,
eps=config.rms_norm_eps)
self.kv_b_proj = ColumnParallelLinear(
self.kv_lora_rank,
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.kv_b_proj")
self.o_proj = CustomDeepseekV2RowParallelLinear(
self.num_heads * self.v_head_dim,
self.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.o_proj")
if rope_scaling:
rope_scaling["rope_type"] = 'deepseek_yarn'
self.rotary_emb = get_rope(qk_rope_head_dim,
rotary_dim=qk_rope_head_dim,
max_position=max_position_embeddings,
base=rope_theta,
rope_scaling=rope_scaling,
is_neox_style=False)
if rope_scaling:
mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
scaling_factor = rope_scaling["factor"]
mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
self.scaling = self.scaling * mscale * mscale
mla_modules = AscendMLAModules(
q_a_proj=self.q_a_proj if self.q_lora_rank is not None else None,
q_a_layernorm=self.q_a_layernorm
if self.q_lora_rank is not None else None,
q_proj=self.q_proj if self.q_lora_rank is None else self.q_b_proj,
kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
kv_a_layernorm=self.kv_a_layernorm,
kv_b_proj=self.kv_b_proj,
o_proj=self.o_proj,
rotary_emb=self.rotary_emb,
)
self.mla_attn = MultiHeadLatentAttention(
self.hidden_size,
self.enable_shared_expert_dp,
self.debug_layer_idx,
self.first_k_dense_replace,
self.tp_size,
mla_modules,
self.num_local_heads,
self.scaling,
self.layers,
self.kv_lora_rank,
self.qk_rope_head_dim,
self.q_lora_rank,
self.qk_nope_head_dim,
self.qk_head_dim,
self.v_head_dim,
cache_config,
quant_config,
prefix,
)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
kv_cache: Optional[torch.Tensor] = None,
attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
return self.mla_attn(positions, hidden_states, kv_cache, attn_metadata)
class CustomDeepseekV2SFAAttention(DeepseekV2MLAAttention):
def __init__(
self,
config: PretrainedConfig,
hidden_size: int,
num_heads: int,
qk_nope_head_dim: int,
qk_rope_head_dim: int,
v_head_dim: int,
q_lora_rank: Optional[int],
kv_lora_rank: int,
rope_theta: float = 10000,
rope_scaling: Optional[Dict[str, Any]] = None,
max_position_embeddings: int = 8192,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
nn.Module.__init__(self)
self.hidden_size = hidden_size
self.qk_nope_head_dim = qk_nope_head_dim
self.qk_rope_head_dim = qk_rope_head_dim
self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
self.v_head_dim = v_head_dim
self.q_lora_rank = q_lora_rank
self.kv_lora_rank = kv_lora_rank
self.num_heads = num_heads
self.tp_size = get_tensor_model_parallel_world_size()
assert num_heads % self.tp_size == 0
self.num_local_heads = num_heads // self.tp_size
self.layers = config.num_hidden_layers
self.first_k_dense_replace = config.first_k_dense_replace
self.scaling = self.qk_head_dim**-0.5
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.prefix = prefix
self.debug_layer_idx = int(self.prefix.split(".")[-2])
ascend_config = get_ascend_config()
self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
if self.q_lora_rank is not None:
self.q_a_proj = ReplicatedLinear(
self.hidden_size,
self.q_lora_rank,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_a_proj",
return_bias=False,
)
self.q_a_layernorm = RMSNorm(self.q_lora_rank,
eps=config.rms_norm_eps)
self.q_b_proj = ColumnParallelLinear(
q_lora_rank,
self.num_heads * self.qk_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_b_proj",
return_bias=False,
)
else:
self.q_proj = ColumnParallelLinear(
self.hidden_size,
self.num_heads * self.qk_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_proj",
return_bias=False,
)
self.kv_a_proj_with_mqa = ReplicatedLinear(
self.hidden_size,
self.kv_lora_rank + self.qk_rope_head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.kv_a_proj_with_mqa",
return_bias=False,
)
self.kv_a_layernorm = RMSNorm(self.kv_lora_rank,
eps=config.rms_norm_eps)
self.kv_b_proj = ColumnParallelLinear(
self.kv_lora_rank,
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.kv_b_proj",
return_bias=False,
)
self.o_proj = CustomDeepseekV2RowParallelLinear(
self.num_heads * self.v_head_dim,
self.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.o_proj",
return_bias=False,
)
if rope_scaling:
rope_scaling["rope_type"] = 'deepseek_yarn'
self.rotary_emb = get_rope(qk_rope_head_dim,
rotary_dim=qk_rope_head_dim,
max_position=max_position_embeddings,
base=rope_theta,
rope_scaling=rope_scaling,
is_neox_style=False)
if rope_scaling:
mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
scaling_factor = rope_scaling["factor"]
mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
self.scaling = self.scaling * mscale * mscale
self.dim: int = config.hidden_size # 7168
# TODO(zzzzwwjj): wait transformers add these params
self.n_heads: int = 64 # 64
self.head_dim: int = 128 # 128
self.index_topk: int = 2048 # 2048
self.indexer = Indexer(
config,
quant_config=quant_config,
dim=self.dim,
n_heads=self.n_heads,
head_dim=self.head_dim,
index_topk=self.index_topk,
prefix=f"{prefix}.indexer",
)
sfa_modules = AscendSFAModules(
q_a_proj=self.q_a_proj if self.q_lora_rank is not None else None,
q_a_layernorm=self.q_a_layernorm
if self.q_lora_rank is not None else None,
q_proj=self.q_proj if self.q_lora_rank is None else self.q_b_proj,
kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
kv_a_layernorm=self.kv_a_layernorm,
kv_b_proj=self.kv_b_proj,
o_proj=self.o_proj,
rotary_emb=self.rotary_emb,
indexer=self.indexer)
self.sfa_attn = AscendSparseFlashAttention(
self.hidden_size,
self.enable_shared_expert_dp,
self.debug_layer_idx,
self.first_k_dense_replace,
self.tp_size,
sfa_modules,
self.num_local_heads,
self.scaling,
self.layers,
self.kv_lora_rank,
self.qk_rope_head_dim,
self.q_lora_rank,
self.qk_nope_head_dim,
self.qk_head_dim,
self.v_head_dim,
cache_config,
quant_config,
prefix,
)
self.prefix = prefix
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
kv_cache: Optional[torch.Tensor] = None,
attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
return self.sfa_attn(positions, hidden_states, kv_cache, attn_metadata)
class CustomDeepseekV2DecoderLayer(DeepseekV2DecoderLayer):
def __init__(self,
vllm_config: VllmConfig,
prefix: str,
topk_indices_buffer=None) -> None:
nn.Module.__init__(self)
config = vllm_config.model_config.hf_config
model_config = vllm_config.model_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
parallel_config = vllm_config.parallel_config
ascend_config = get_ascend_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)
# DecoderLayers are created with `make_layers` which passes the prefix
# with the layer's index.
layer_idx = int(prefix.split(sep='.')[-1])
self.layer_idx = layer_idx
self.layers = config.num_hidden_layers
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tp_group().rank_in_group
# TODO: enable mla in vllm-ascend
if model_config.use_mla:
if ascend_config.use_sfa:
attn_cls = CustomDeepseekV2SFAAttention
else:
attn_cls = CustomDeepseekV2MLAAttention
else:
attn_cls = DeepseekV2Attention
self.self_attn = attn_cls(
config=config,
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
qk_nope_head_dim=config.qk_nope_head_dim,
qk_rope_head_dim=config.qk_rope_head_dim,
v_head_dim=config.v_head_dim,
q_lora_rank=config.q_lora_rank
if hasattr(config, "q_lora_rank") else None,
kv_lora_rank=config.kv_lora_rank,
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
)
if (config.n_routed_experts is not None
and layer_idx >= config.first_k_dense_replace
and layer_idx % config.moe_layer_freq == 0):
self.mlp = DeepseekV2MoE(
config=config,
parallel_config=parallel_config,
quant_config=quant_config,
prefix=f"{prefix}.mlp",
)
if self.mlp.gate.e_score_correction_bias is not None:
self.mlp.gate.e_score_correction_bias.data = (
self.mlp.gate.e_score_correction_bias.data.to(
dtype=torch.get_default_dtype()))
else:
self.mlp = DeepseekV2MLP(
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
quant_config=quant_config,
prefix=f"{prefix}.mlp",
)
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.routed_scaling_factor = config.routed_scaling_factor
self.first_k_dense_replace = config.first_k_dense_replace
self.tp_group = get_tp_group().device_group
class CustomDeepseekV2ForCausalLM(DeepseekV2ForCausalLM):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
nn.Module.__init__(self)
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
# `packed_modules_mapping` needs to be modified before
# initializing DeepseekV2Model, as it is passed inplace to
# quantization config init and may be used to select the
# quant_method for relevant layers during initialization.
self.fuse_qkv_a_proj = hasattr(
config, "q_lora_rank") and config.q_lora_rank is not None
if self.fuse_qkv_a_proj:
self.packed_modules_mapping["fused_qkv_a_proj"] = [
"q_a_proj",
"kv_a_proj_with_mqa",
]
self.model = DeepseekV2Model(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
if get_pp_group().is_last_rank:
self.lm_head = ParallelLMHead(config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=maybe_prefix(
prefix, "lm_head"))
else:
self.lm_head = PPMissingLayer()
self.logits_processor = LogitsProcessor(config.vocab_size)
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
self.expert_weights: list[Any] = []
# Set MoE hyperparameters
self.num_moe_layers = (config.num_hidden_layers -
config.first_k_dense_replace)
self.num_expert_groups = config.n_group
self.moe_layers: list[FusedMoE] = []
example_moe = None
for layer in self.model.layers:
if isinstance(layer, PPMissingLayer):
continue
assert isinstance(layer, DeepseekV2DecoderLayer)
if isinstance(layer.mlp, DeepseekV2MoE):
# Pick last one layer since the first ones may be dense layers.
example_moe = layer.mlp
self.moe_layers.append(layer.mlp.experts)
if example_moe is None:
raise RuntimeError("No DeepseekV2MoE layer found in model.layers.")
self.num_logical_experts = example_moe.n_logical_experts
self.num_physical_experts = example_moe.n_physical_experts
self.num_local_physical_experts = example_moe.n_local_physical_experts
self.num_routed_experts = example_moe.n_routed_experts
self.num_shared_experts = example_moe.n_shared_experts
self.num_redundant_experts = example_moe.n_redundant_experts
# NOTE: This `load_weights` is mainly copied from
# https://github.com/vllm-project/vllm/commit/07b8fae219b1fff51ef115c38c44b51395be5bb5
# to fix CI, and it is different from the implementation in main
# TODO: support eplb style load_weights
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
""""""
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("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 = AscendFusedMoE.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.n_routed_experts)
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
continue
if "module" in name:
continue
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
if spec_layer is not None:
continue # skip spec decode layers for main model
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) and name not in params_dict):
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 is_pp_missing_parameter(name, self):
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)
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id,
return_success=False)
break
else:
# Skip loading extra bias for GPTQ models.
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
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
class CustomDeepseekV3ForCausalLM(CustomDeepseekV2ForCausalLM):
pass
DeepseekV2DecoderLayer.__init__ = CustomDeepseekV2DecoderLayer.__init__
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