[Model][Bugfix] Add FATReLU activation and support for openbmb/MiniCPM-S-1B-sft (#9396)

docs/source/models/supported_models.rst

@@ -159,7 +159,7 @@ Text Generation
     -
   * - :code:`MiniCPMForCausalLM`
     - MiniCPM
-    - :code:`openbmb/MiniCPM-2B-sft-bf16`, :code:`openbmb/MiniCPM-2B-dpo-bf16`, etc.
+    - :code:`openbmb/MiniCPM-2B-sft-bf16`, :code:`openbmb/MiniCPM-2B-dpo-bf16`, :code:`openbmb/MiniCPM-S-1B-sft`, etc.
     - ✅︎
     - ✅︎
   * - :code:`MiniCPM3ForCausalLM`

vllm/model_executor/layers/activation.py

@@ -13,6 +13,33 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.utils import set_weight_attrs
 
 
+class FatreluAndMul(CustomOp):
+    """An activation function for FATReLU.
+    
+    The function computes x -> FATReLU(x[:d]) * x[d:] where
+    d = x.shape[-1] // 2.
+    This is used in openbmb/MiniCPM-S-1B-sft.
+
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+
+    def __init__(self, threshold: float = 0.):
+        super().__init__()
+        self.threshold = threshold
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        x1 = x[..., :d]
+        x2 = x[..., d:]
+        x1 = F.threshold(x1, self.threshold, 0.0)
+        return x1 * x2
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        return self.forward_native(x)
+
+
 class SiluAndMul(CustomOp):
     """An activation function for SwiGLU.
 

vllm/model_executor/models/minicpm.py

@@ -33,7 +33,7 @@ from vllm.config import CacheConfig, LoRAConfig
 from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
                               tensor_model_parallel_all_reduce)
-from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.activation import FatreluAndMul, SiluAndMul
 from vllm.model_executor.layers.fused_moe import fused_moe
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
@@ -152,6 +152,7 @@ class MiniCPMMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
+        hidden_act_param: float,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -163,10 +164,13 @@ class MiniCPMMLP(nn.Module):
                                            hidden_size,
                                            bias=False,
                                            quant_config=quant_config)
-        if hidden_act != "silu":
-            raise ValueError(f"Unsupported activation: {hidden_act}. "
-                             "Only silu is supported for now.")
+        if hidden_act == "silu":
             self.act_fn = SiluAndMul()
+        elif hidden_act == "fatrelu":
+            self.act_fn = FatreluAndMul(threshold=hidden_act_param)
+        else:
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu and fatrelu are supported for now.")
 
     def forward(self, x):
         gate_up, _ = self.gate_up_proj(x)
@@ -304,6 +308,7 @@ class MiniCPMDecoderLayer(nn.Module):
                 hidden_size=self.hidden_size,
                 intermediate_size=self.config.intermediate_size,
                 hidden_act=self.config.hidden_act,
+                hidden_act_param=getattr(self.config, "hidden_act_param", 0.),
                 quant_config=self.quant_config,
             )
         else: