wehub-resource-sync
321 lines
10 KiB
Python
321 lines
10 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Custom normalization layers."""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# Import kernels
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import vllm.kernels # noqa: F401
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from vllm import envs, ir
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from vllm.logger import init_logger
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from vllm.model_executor.custom_op import CustomOp
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from vllm.model_executor.layers.batch_invariant import rms_norm_batch_invariant
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logger = init_logger(__name__)
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def poly_norm(
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x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, variance_epsilon: float
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) -> torch.Tensor:
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from vllm import _custom_ops as ops
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out = torch.empty_like(x)
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ops.poly_norm( # type: ignore[attr-defined]
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out,
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x,
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weight,
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bias,
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variance_epsilon,
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)
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return out
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# --8<-- [start:rms_norm]
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@CustomOp.register("rms_norm")
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class RMSNorm(CustomOp):
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"""Root mean square normalization.
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Computes x -> w * x / sqrt(E[x^2] + eps) where w is the learned weight.
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Refer to https://arxiv.org/abs/1910.07467
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"""
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# --8<-- [end:rms_norm]
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def __init__(
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self,
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hidden_size: int,
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eps: float = 1e-6,
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var_hidden_size: int | None = None,
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has_weight: bool = True,
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dtype: torch.dtype | None = None,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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self.variance_epsilon = eps
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self.variance_size_override = (
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None if var_hidden_size == hidden_size else var_hidden_size
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)
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weight_dtype = dtype or torch.get_default_dtype()
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self.has_weight = has_weight
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self.weight = torch.ones(hidden_size, dtype=weight_dtype)
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if self.has_weight:
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self.weight = nn.Parameter(self.weight)
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# When has_weight=False, pass weight=None so implementations that
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# support a weightless path can skip the per-channel multiply.
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# Implementations that require weight (e.g. oink) fall back via IR
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# op priority when weight=None is unsupported.
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self.pass_weight = self.has_weight
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self.pass_weight_add = self.has_weight
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def forward_native(
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self,
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x: torch.Tensor,
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residual: torch.Tensor | None = None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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"""PyTorch-native implementation equivalent to forward()."""
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if residual is None:
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return ir.ops.rms_norm(
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x,
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self.weight.data if self.pass_weight else None,
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self.variance_epsilon,
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self.variance_size_override,
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)
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else:
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return ir.ops.fused_add_rms_norm.maybe_inplace(
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x,
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residual,
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self.weight.data if self.pass_weight_add else None,
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self.variance_epsilon,
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self.variance_size_override,
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)
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def forward_cuda(
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self,
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x: torch.Tensor,
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residual: torch.Tensor | None = None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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if envs.VLLM_BATCH_INVARIANT:
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assert self.variance_size_override is None, (
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"Batch invariance is not supported for variance_size_override"
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)
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return rms_norm_batch_invariant(
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x,
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self.weight.data,
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self.variance_epsilon,
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residual=residual,
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)
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return self.forward_native(x, residual)
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def forward_xpu(
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self,
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x: torch.Tensor,
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residual: torch.Tensor | None = None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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return self.forward_cuda(x, residual)
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def extra_repr(self) -> str:
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s = f"hidden_size={self.weight.data.size(0)}"
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s += f", eps={self.variance_epsilon}"
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return s
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# --8<-- [start:gemma_rms_norm]
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@CustomOp.register("gemma_rms_norm")
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class GemmaRMSNorm(CustomOp):
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"""RMS normalization for Gemma.
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Two differences from the above RMSNorm:
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1. x * (1 + w) instead of x * w.
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2. (x * w).to(orig_dtype) instead of x.to(orig_dtype) * w.
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"""
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# --8<-- [end:gemma_rms_norm]
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def __init__(
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self,
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hidden_size: int,
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eps: float = 1e-6,
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) -> None:
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super().__init__()
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self.weight = nn.Parameter(torch.zeros(hidden_size))
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self.variance_epsilon = eps
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def forward_native(
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self,
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x: torch.Tensor,
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residual: torch.Tensor | None = None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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"""PyTorch-native implementation equivalent to forward()."""
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weight = self.weight.float() + 1.0
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if residual is None:
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return ir.ops.rms_norm(x, weight, self.variance_epsilon)
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return ir.ops.fused_add_rms_norm(x, residual, weight, self.variance_epsilon)
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def forward_cuda(
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self,
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x: torch.Tensor,
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residual: torch.Tensor | None = None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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return self.forward_native(x, residual)
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# --8<-- [start:rms_norm_gated]
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@CustomOp.register("rms_norm_gated")
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class RMSNormGated(CustomOp):
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"""RMS Normalization with optional gating.
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This is a native PyTorch implementation that supports:
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- Standard RMS normalization
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- Group RMS normalization
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- Optional gating with SiLU activation
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"""
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# --8<-- [end:rms_norm_gated]
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def __init__(
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self,
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hidden_size: int,
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eps: float = 1e-5,
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group_size: int | None = None,
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norm_before_gate: bool = False,
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device: torch.device | None = None,
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dtype: torch.dtype | None = None,
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activation: str = "swish",
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):
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"""Initialize RMSNormGated.
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Args:
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hidden_size: Size of the hidden dimension
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eps: Epsilon for numerical stability
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group_size: If not None, do GroupNorm with each group
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having group_size elements.
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group_size=None is equivalent to group_size=hidden_size
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(i.e. there's only 1 group).
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norm_before_gate: If True and z is provided: out = norm(x) * silu(z)
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If False and z is provided: out = norm(x * silu(z))
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device: Device to create parameters on
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dtype: Data type for parameters
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activation: Activation function name for gating
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"""
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factory_kwargs = {"device": device, "dtype": dtype}
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super().__init__()
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self.eps = eps
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self.activation = activation
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self.weight = nn.Parameter(torch.empty(hidden_size, **factory_kwargs))
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self.register_parameter("bias", None)
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self.group_size = group_size
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self.norm_before_gate = norm_before_gate
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self.reset_parameters()
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def reset_parameters(self):
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torch.nn.init.ones_(self.weight)
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@staticmethod
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def forward_static(
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x: torch.Tensor,
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z: torch.Tensor | None,
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weight: torch.Tensor,
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epsilon: float,
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orig_dtype: torch.dtype,
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group_size: int | None = None,
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norm_before_gate: bool = True,
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activation: str = "swish",
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) -> torch.Tensor:
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"""Pure-PyTorch RMS normalization with optional gating.
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This static method contains the full native logic so that both
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``forward_native`` and ``MatcherRMSNormGated`` (used by the
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compilation pattern matcher) can share the same implementation.
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If *z* is not None and *norm_before_gate* is True:
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``out = rms_norm(x) * act(z)``
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If *z* is not None and *norm_before_gate* is False:
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``out = rms_norm(x * act(z))``
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"""
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x = x.float()
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weight = weight.float()
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if z is not None:
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z = z.float()
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assert activation in ["silu", "sigmoid", "swish"]
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act_fn = F.sigmoid if activation == "sigmoid" else F.silu
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if z is not None and not norm_before_gate:
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x = x * act_fn(z)
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if group_size is None:
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variance = x.pow(2).mean(dim=-1, keepdim=True)
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x_normed = x * torch.rsqrt(variance + epsilon)
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out = x_normed * weight
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else:
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from einops import rearrange
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x_group = rearrange(x, "... (g d) -> ... g d", d=group_size)
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variance = x_group.pow(2).mean(dim=-1, keepdim=True)
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x_normed = x_group * torch.rsqrt(variance + epsilon)
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out = rearrange(x_normed, "... g d -> ... (g d)") * weight
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if z is not None and norm_before_gate:
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out = out * act_fn(z)
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return out.to(orig_dtype)
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def forward_native(
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self, x: torch.Tensor, z: torch.Tensor | None = None
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) -> torch.Tensor:
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"""PyTorch-native implementation equivalent to forward()."""
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return self.forward_static(
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x,
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z,
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self.weight,
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self.eps,
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x.dtype,
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group_size=self.group_size,
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norm_before_gate=self.norm_before_gate,
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activation=self.activation,
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)
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def forward_cuda(
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self, x: torch.Tensor, z: torch.Tensor | None = None
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) -> torch.Tensor:
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from vllm.model_executor.layers.fla.ops.layernorm_guard import rmsnorm_fn
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return rmsnorm_fn(
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x,
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self.weight,
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self.bias,
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z=z,
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eps=self.eps,
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group_size=self.group_size,
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norm_before_gate=self.norm_before_gate,
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activation=self.activation,
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)
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def forward_xpu(
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self, x: torch.Tensor, z: torch.Tensor | None = None
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) -> torch.Tensor:
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return self.forward_cuda(x, z)
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class LayerNorm(nn.Module):
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"""
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Layer Normalization.
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"""
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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self.dim = dim
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
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self.bias = nn.Parameter(torch.zeros(dim, dtype=torch.float32))
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def forward(self, x: torch.Tensor):
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return F.layer_norm(
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x.float(), (self.dim,), self.weight, self.bias, self.eps
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).type_as(x)
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