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Update model_loader deps and qqq quantization deps (#2220) (#2318)

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Co-authored-by: HandH1998 <1335248067@qq.com>
This commit is contained in:
Yineng Zhang
2024-12-02 23:22:13 +08:00
committed by GitHub
parent 33deca81b5
commit 85e1a6f3aa
58 changed files with 2363 additions and 366 deletions
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17
python/sglang/srt/models/minicpm3.py
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@@ -27,7 +27,6 @@ from vllm.model_executor.layers.linear import (
RowParallelLinear,
)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.layernorm import RMSNorm
@@ -40,6 +39,7 @@ from sglang.srt.layers.vocab_parallel_embedding import (
)
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.utils import is_flashinfer_available
if is_flashinfer_available():
@@ -105,7 +105,6 @@ class MiniCPM3Attention(nn.Module):
rope_theta: float = 10000,
rope_scaling: Optional[Dict[str, Any]] = None,
max_position_embeddings: int = 8192,
cache_config=None,
quant_config: Optional[QuantizationConfig] = None,
layer_id=None,
) -> None:
@@ -249,7 +248,6 @@ class MiniCPM3AttentionMLA(nn.Module):
rope_theta: float = 10000,
rope_scaling: Optional[Dict[str, Any]] = None,
max_position_embeddings: int = 8192,
cache_config=None,
quant_config: Optional[QuantizationConfig] = None,
layer_id=None,
) -> None:
@@ -406,7 +404,6 @@ class MiniCPM3DecoderLayer(nn.Module):
self,
config: PretrainedConfig,
layer_id: int,
cache_config=None,
quant_config: Optional[QuantizationConfig] = None,
) -> None:
super().__init__()
@@ -430,7 +427,6 @@ class MiniCPM3DecoderLayer(nn.Module):
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
cache_config=cache_config,
quant_config=quant_config,
layer_id=layer_id,
)
@@ -449,7 +445,6 @@ class MiniCPM3DecoderLayer(nn.Module):
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
cache_config=cache_config,
quant_config=quant_config,
layer_id=layer_id,
)
@@ -498,7 +493,6 @@ class MiniCPM3Model(nn.Module):
def __init__(
self,
config: PretrainedConfig,
cache_config=None,
quant_config: Optional[QuantizationConfig] = None,
) -> None:
super().__init__()
@@ -512,9 +506,7 @@ class MiniCPM3Model(nn.Module):
)
self.layers = nn.ModuleList(
[
MiniCPM3DecoderLayer(
config, i, cache_config=cache_config, quant_config=quant_config
)
MiniCPM3DecoderLayer(config, i, quant_config=quant_config)
for i in range(config.num_hidden_layers)
]
)
@@ -549,7 +541,6 @@ class MiniCPM3ForCausalLM(nn.Module):
def __init__(
self,
config: PretrainedConfig,
cache_config=None,
quant_config: Optional[QuantizationConfig] = None,
) -> None:
super().__init__()
@@ -557,9 +548,7 @@ class MiniCPM3ForCausalLM(nn.Module):
self.num_experts = getattr(self.config, "num_experts", 0)
self.quant_config = quant_config
self.model = MiniCPM3Model(
config, cache_config=cache_config, quant_config=quant_config
)
self.model = MiniCPM3Model(config, quant_config=quant_config)
# self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
if not self.config.tie_word_embeddings:
self.lm_head = ParallelLMHead(