biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Misc][Refactor] Generalize linear_method to be quant_method (#4373)

Browse Source
This commit is contained in:
Cody Yu
2024-04-26 13:41:14 -07:00
committed by GitHub
parent 603ad84815
commit a62aaf1df5
45 changed files with 759 additions and 713 deletions
Download Patch File Download Diff File Expand all files Collapse all files

43
vllm/model_executor/models/baichuan.py
View File

@@ -31,11 +31,12 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (LinearMethodBase,
MergedColumnParallelLinear,
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -77,17 +78,17 @@ class BaiChuanMLP(nn.Module):
hidden_size: int,
intermediate_size: int,
hidden_act: str,
linear_method: Optional[LinearMethodBase] = None,
quant_config: Optional[QuantizationConfig] = None,
):
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
hidden_size, [intermediate_size] * 2,
bias=False,
linear_method=linear_method)
quant_config=quant_config)
self.down_proj = RowParallelLinear(intermediate_size,
hidden_size,
bias=False,
linear_method=linear_method)
quant_config=quant_config)
if hidden_act != "silu":
raise ValueError(f"Unsupported activation: {hidden_act}. "
"Only silu is supported for now.")
@@ -110,7 +111,7 @@ class BaiChuanAttention(nn.Module):
position_embedding: str,
rope_theta: float = 10000,
max_position_embeddings: int = 8192,
linear_method: Optional[LinearMethodBase] = None,
quant_config: Optional[QuantizationConfig] = None,
):
super().__init__()
self.hidden_size = hidden_size
@@ -132,13 +133,13 @@ class BaiChuanAttention(nn.Module):
self.total_num_heads,
self.total_num_heads,
bias=False,
linear_method=linear_method,
quant_config=quant_config,
)
self.o_proj = RowParallelLinear(
self.total_num_heads * self.head_dim,
hidden_size,
bias=False,
linear_method=linear_method,
quant_config=quant_config,
)
# Create the alibi slopes and slice them.
if self.postion_embedding == "ALIBI":
@@ -184,7 +185,7 @@ class BaiChuanDecoderLayer(nn.Module):
def __init__(self,
config: PretrainedConfig,
position_embedding: str,
linear_method: Optional[LinearMethodBase] = None):
quant_config: Optional[QuantizationConfig] = None):
super().__init__()
self.hidden_size = config.hidden_size
rope_theta = getattr(config, "rope_theta", 10000)
@@ -196,13 +197,13 @@ class BaiChuanDecoderLayer(nn.Module):
position_embedding=position_embedding,
rope_theta=rope_theta,
max_position_embeddings=max_position_embeddings,
linear_method=linear_method,
quant_config=quant_config,
)
self.mlp = BaiChuanMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
linear_method=linear_method,
quant_config=quant_config,
)
self.input_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
@@ -243,7 +244,7 @@ class BaiChuanModel(nn.Module):
def __init__(self,
config: PretrainedConfig,
position_embedding: str,
linear_method: Optional[LinearMethodBase] = None):
quant_config: Optional[QuantizationConfig] = None):
super().__init__()
self.config = config
self.padding_idx = config.pad_token_id
@@ -254,7 +255,7 @@ class BaiChuanModel(nn.Module):
config.hidden_size,
)
self.layers = nn.ModuleList([
BaiChuanDecoderLayer(config, position_embedding, linear_method)
BaiChuanDecoderLayer(config, position_embedding, quant_config)
for _ in range(config.num_hidden_layers)
])
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -303,13 +304,13 @@ class BaiChuanBaseForCausalLM(nn.Module):
self,
config,
position_embedding: str,
linear_method: Optional[LinearMethodBase] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
):
super().__init__()
self.config = config
self.linear_method = linear_method
self.model = BaiChuanModel(config, position_embedding, linear_method)
self.quant_config = quant_config
self.model = BaiChuanModel(config, position_embedding, quant_config)
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.logits_processor = LogitsProcessor(config.vocab_size)
self.sampler = Sampler()
@@ -388,13 +389,13 @@ class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
def __init__(
self,
config,
linear_method: Optional[LinearMethodBase] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
):
if config.hidden_size == 4096: # baichuan2 7b
super().__init__(config, "ROPE", linear_method, lora_config)
super().__init__(config, "ROPE", quant_config, lora_config)
else: # baichuan 13b, baichuan2 13b
super().__init__(config, "ALIBI", linear_method, lora_config)
super().__init__(config, "ALIBI", quant_config, lora_config)
class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
@@ -403,7 +404,7 @@ class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
def __init__(
self,
config,
linear_method: Optional[LinearMethodBase] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
):
super().__init__(config, "ROPE", linear_method, lora_config)
super().__init__(config, "ROPE", quant_config, lora_config)