init

vllm/model_executor/layers/activation.py

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+"""Custom activation functions."""
+import math
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from vllm import _custom_ops as ops
+from vllm.distributed import (divide, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.utils import set_weight_attrs
+
+
+class SiluAndMul(nn.Module):
+    """An activation function for SwiGLU.
+
+    The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[-1] // 2.
+
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+
+    def _forward(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        d = x.shape[-1] // 2
+        return F.silu(x[..., :d]) * x[..., d:]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.silu_and_mul(out, x)
+        return out
+
+
+class GeluAndMul(nn.Module):
+    """An activation function for GeGLU.
+
+    The function computes x -> GELU(x[:d]) * x[d:] where d = x.shape[-1] // 2.
+
+    Shapes:
+        x: (batch_size, seq_len, 2 * d) or (num_tokens, 2 * d)
+        return: (batch_size, seq_len, d) or (num_tokens, d)
+    """
+
+    def __init__(self, approximate: str = "none"):
+        super().__init__()
+        self.approximate = approximate
+        if approximate not in ("none", "tanh"):
+            raise ValueError(f"Unknown approximate mode: {approximate}")
+
+    def _forward(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        d = x.shape[-1] // 2
+        return F.gelu(x[..., :d], approximate=self.approximate) * x[..., d:]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        if self.approximate == "none":
+            ops.gelu_and_mul(out, x)
+        elif self.approximate == "tanh":
+            ops.gelu_tanh_and_mul(out, x)
+        return out
+
+    def extra_repr(self) -> str:
+        return f'approximate={repr(self.approximate)}'
+
+
+class NewGELU(nn.Module):
+
+    def _forward(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        c = math.sqrt(2.0 / math.pi)
+        return 0.5 * x * (1.0 + torch.tanh(c *
+                                           (x + 0.044715 * torch.pow(x, 3.0))))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out = torch.empty_like(x)
+        ops.gelu_new(out, x)
+        return out
+
+
+class FastGELU(nn.Module):
+
+    def _forward(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 *
+                                           (1.0 + 0.044715 * x * x)))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out = torch.empty_like(x)
+        ops.gelu_fast(out, x)
+        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_fast": FastGELU(),
+    "gelu_new": NewGELU(),
+    "gelu_pytorch_tanh": nn.GELU(approximate="tanh"),
+    "relu": nn.ReLU(),
+}
+
+
+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