feat: replace get_act_fn for gpt_bigcode (#1231)

python/sglang/srt/layers/activation.py

@@ -13,10 +13,20 @@
 
 """Fused operators for activation layers."""
 
+from typing import Optional
+
 import torch
+import torch.nn as nn
 import torch.nn.functional as F
 from flashinfer.activation import gelu_tanh_and_mul, silu_and_mul
+from vllm.distributed import (
+    divide,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
 from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.utils import set_weight_attrs
 
 
 class SiluAndMul(CustomOp):
@@ -53,3 +63,76 @@ def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
         gelu_tanh_and_mul(x, out)
         return out
+
+
+class ScaledActivation(nn.Module):
+    """An activation function with post-scale parameters.
+
+    This is used for some quantization methods like AWQ.
+    """
+
+    def __init__(
+        self,
+        act_module: nn.Module,
+        intermediate_size: int,
+        input_is_parallel: bool = True,
+        params_dtype: Optional[torch.dtype] = None,
+    ):
+        super().__init__()
+        self.act = act_module
+        self.input_is_parallel = input_is_parallel
+        if input_is_parallel:
+            tp_size = get_tensor_model_parallel_world_size()
+            intermediate_size_per_partition = divide(intermediate_size, tp_size)
+        else:
+            intermediate_size_per_partition = intermediate_size
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        self.scales = nn.Parameter(
+            torch.empty(intermediate_size_per_partition, dtype=params_dtype)
+        )
+        set_weight_attrs(self.scales, {"weight_loader": self.weight_loader})
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.act(x) / self.scales
+
+    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
+        param_data = param.data
+        if self.input_is_parallel:
+            tp_rank = get_tensor_model_parallel_rank()
+            shard_size = param_data.shape[0]
+            start_idx = tp_rank * shard_size
+            loaded_weight = loaded_weight.narrow(0, start_idx, shard_size)
+        assert param_data.shape == loaded_weight.shape
+        param_data.copy_(loaded_weight)
+
+
+_ACTIVATION_REGISTRY = {
+    "gelu": nn.GELU(),
+    "gelu_pytorch_tanh": nn.GELU(approximate="tanh"),
+}
+
+
+def get_act_fn(
+    act_fn_name: str,
+    quant_config: Optional[QuantizationConfig] = None,
+    intermediate_size: Optional[int] = None,
+    input_is_parallel: bool = True,
+    params_dtype: Optional[torch.dtype] = None,
+) -> nn.Module:
+    """Get an activation function by name."""
+    act_fn_name = act_fn_name.lower()
+    if act_fn_name not in _ACTIVATION_REGISTRY:
+        raise ValueError(f"Activation function {act_fn_name!r} is not supported.")
+
+    act_fn = _ACTIVATION_REGISTRY[act_fn_name]
+    if quant_config is not None and act_fn_name in quant_config.get_scaled_act_names():
+        if intermediate_size is None:
+            raise ValueError(
+                "intermediate_size must be specified for scaled "
+                "activation functions."
+            )
+        return ScaledActivation(
+            act_fn, intermediate_size, input_is_parallel, params_dtype
+        )
+    return act_fn

python/sglang/srt/models/gpt_bigcode.py

@@ -23,7 +23,6 @@
 from transformers import GPTBigCodeConfig
 from vllm.config import CacheConfig, LoRAConfig
 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,
     QKVParallelLinear,
@@ -33,6 +32,7 @@
 from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
+from sglang.srt.layers.activation import get_act_fn
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.layers.sampler import Sampler