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[Misc] Enhance attention selector (#4751)

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This commit is contained in:
Woosuk Kwon
2024-05-13 10:47:25 -07:00
committed by GitHub
parent e7c46b9527
commit 0fca3cdcf2
49 changed files with 573 additions and 220 deletions
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13
vllm/model_executor/models/orion.py
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@@ -11,6 +11,7 @@ from torch import nn
from transformers import PretrainedConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
@@ -68,6 +69,7 @@ class OrionAttention(nn.Module):
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,
) -> None:
super().__init__()
@@ -118,7 +120,8 @@ class OrionAttention(nn.Module):
self.attn = Attention(self.num_heads,
self.head_dim,
self.scaling,
num_kv_heads=self.num_kv_heads)
num_kv_heads=self.num_kv_heads,
cache_config=cache_config)
def forward(
self,
@@ -140,6 +143,7 @@ class OrionDecoderLayer(nn.Module):
def __init__(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
) -> None:
super().__init__()
@@ -155,6 +159,7 @@ class OrionDecoderLayer(nn.Module):
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
cache_config=cache_config,
quant_config=quant_config,
)
self.mlp = OrionMLP(
@@ -202,6 +207,7 @@ class OrionModel(nn.Module):
def __init__(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
) -> None:
super().__init__()
@@ -213,7 +219,7 @@ class OrionModel(nn.Module):
config.hidden_size,
)
self.layers = nn.ModuleList([
OrionDecoderLayer(config, quant_config)
OrionDecoderLayer(config, cache_config, quant_config)
for _ in range(config.num_hidden_layers)
])
self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -245,12 +251,13 @@ class OrionForCausalLM(nn.Module):
def __init__(
self,
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
) -> None:
super().__init__()
self.config = config
self.quant_config = quant_config
self.model = OrionModel(config, quant_config)
self.model = OrionModel(config, cache_config, quant_config)
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.logits_processor = LogitsProcessor(config.vocab_size)
self.sampler = Sampler()