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[quant][bugfix] fix deepseek quant bug (#478)

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see #465

Signed-off-by: MengqingCao <cmq0113@163.com>
Co-authored-by: zzzzwwjj <1183291235@qq.com>
This commit is contained in:
Mengqing Cao
2025-04-08 09:15:56 +08:00
committed by GitHub
parent 579d858a20
commit f6cf92e7d5
2 changed files with 7 additions and 108 deletions
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111
vllm_ascend/models/deepseek_v2.py
View File

@@ -24,7 +24,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only DeepseekV2/DeepseekV3 model."""
from typing import Iterable, List, Optional, Set, Tuple, Union
from typing import List, Optional, Union
import torch
from torch import nn
@@ -40,14 +40,12 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead, VocabParallelEmbedding)
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 ( # noqa
DeepseekV2Attention, DeepseekV2DecoderLayer, DeepseekV2ForCausalLM,
DeepseekV2MLAAttention, DeepseekV2MLP, DeepseekV2MoE)
from vllm.model_executor.models.utils import (
PPMissingLayer, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
PPMissingLayer, make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
from vllm.sequence import IntermediateTensors
@@ -282,109 +280,6 @@ class CustomDeepseekV2ForCausalLM(DeepseekV2ForCausalLM):
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
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 = 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.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
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
# w8a8 weight from modelslim need flatten before load_weight
if "scale" in name or "offset" in name:
loaded_weight = loaded_weight.flatten()
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)
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
def get_spec_layer_idx_from_weight_name(config: PretrainedConfig,
weight_name: str) -> Optional[int]:
if hasattr(config, "num_nextn_predict_layers") and (
config.num_nextn_predict_layers > 0):
layer_idx = config.num_hidden_layers
for i in range(config.num_nextn_predict_layers):
if weight_name.startswith(f"model.layers.{layer_idx+i}."):
return layer_idx + i
return None

4
vllm_ascend/quantization/quant_config.py
View File

@@ -374,3 +374,7 @@ class AscendFusedMoEMethod(FusedMoEMethodBase):
num_expert_group,
custom_routing_function, scoring_func,
e_score_correction_bias)
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
if hasattr(self.quant_method, "process_weights_after_loading"):
self.quant_method.process_weights_after_loading(layer)