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[torch.compile] support all attention backends (#10558)

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Signed-off-by: youkaichao <youkaichao@gmail.com>
This commit is contained in:
youkaichao
2024-11-22 14:04:42 -08:00
committed by GitHub
parent db100c5cde
commit eebad39f26
77 changed files with 876 additions and 648 deletions
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21
vllm/model_executor/models/dbrx.py
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@@ -154,6 +154,7 @@ class DbrxAttention(nn.Module):
config: DbrxConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.d_model = config.d_model
@@ -208,7 +209,8 @@ class DbrxAttention(nn.Module):
self.scaling,
num_kv_heads=self.num_kv_heads,
cache_config=cache_config,
quant_config=quant_config)
quant_config=quant_config,
prefix=f"{prefix}.attn")
def forward(
self,
@@ -234,10 +236,14 @@ class DbrxFusedNormAttention(nn.Module):
config: DbrxConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.d_model = config.d_model
self.attn = DbrxAttention(config, cache_config, quant_config)
self.attn = DbrxAttention(config,
cache_config,
quant_config,
prefix=f"{prefix}.attn")
self.norm_1 = nn.LayerNorm(self.d_model)
self.norm_2 = nn.LayerNorm(self.d_model)
@@ -269,10 +275,14 @@ class DbrxBlock(nn.Module):
config: DbrxConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.norm_attn_norm = DbrxFusedNormAttention(config, cache_config,
quant_config)
self.norm_attn_norm = DbrxFusedNormAttention(
config,
cache_config,
quant_config,
prefix=f"{prefix}.norm_attn_norm")
self.ffn = DbrxMoE(config, quant_config)
def forward(
@@ -308,7 +318,8 @@ class DbrxModel(nn.Module):
)
self.start_layer, self.end_layer, self.blocks = make_layers(
config.n_layers,
lambda prefix: DbrxBlock(config, cache_config, quant_config),
lambda prefix: DbrxBlock(
config, cache_config, quant_config, prefix=prefix),
prefix=f"{prefix}.blocks",
)
self.norm_f = nn.LayerNorm(config.d_model, eps=1e-5)