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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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14
vllm/model_executor/models/gpt_bigcode.py
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@@ -25,6 +25,7 @@ from torch import nn
from transformers import GPTBigCodeConfig
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 get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@@ -46,6 +47,7 @@ class GPTBigCodeAttention(nn.Module):
def __init__(
self,
config: GPTBigCodeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
):
super().__init__()
@@ -85,7 +87,8 @@ class GPTBigCodeAttention(nn.Module):
self.attn = Attention(self.num_heads,
self.head_dim,
scale=self.scale,
num_kv_heads=self.num_kv_heads)
num_kv_heads=self.num_kv_heads,
cache_config=cache_config)
def forward(
self,
@@ -143,6 +146,7 @@ class GPTBigCodeBlock(nn.Module):
def __init__(
self,
config: GPTBigCodeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
):
super().__init__()
@@ -151,7 +155,7 @@ class GPTBigCodeBlock(nn.Module):
hidden_size)
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = GPTBigCodeAttention(config, quant_config)
self.attn = GPTBigCodeAttention(config, cache_config, quant_config)
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = GPTBigMLP(inner_dim, config, quant_config)
@@ -184,6 +188,7 @@ class GPTBigCodeModel(nn.Module):
def __init__(
self,
config: GPTBigCodeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
):
super().__init__()
@@ -195,7 +200,7 @@ class GPTBigCodeModel(nn.Module):
self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
self.h = nn.ModuleList([
GPTBigCodeBlock(config, quant_config)
GPTBigCodeBlock(config, cache_config, quant_config)
for _ in range(config.num_hidden_layers)
])
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
@@ -224,12 +229,13 @@ class GPTBigCodeForCausalLM(nn.Module):
def __init__(
self,
config: GPTBigCodeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
):
super().__init__()
self.config = config
self.quant_config = quant_config
self.transformer = GPTBigCodeModel(config, quant_config)
self.transformer = GPTBigCodeModel(config, cache_config, quant_config)
self.lm_head_weight = self.transformer.wte.weight
self.logits_processor = LogitsProcessor(config.vocab_size)
self.sampler = Sampler()