Source code for aitemplate.frontend.nn.layer_norm

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"""
LayerNorm module.
"""
from aitemplate.compiler import ops
from aitemplate.frontend.nn.module import Module
from aitemplate.frontend.nn.parameter import Parameter

# pylint: disable=C0103


[docs]class LayerNorm(Module): """LayerNorm nn module""" def __init__( self, normalized_shape, eps=1e-5, dtype="float16", **kwargs, ): """Standalone layernorm op. Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization. The mean and standard-deviation are calculated over the last D dimensions, where D is the dimension of normalized_shape. Input shape: [M0, M1, ..., Mp, N1, N2, ..., ND] Normalized_shape: [N1, N2, ..., ND] Gamma/Beta, if not None, have the same shape as normalized_shape. """ super().__init__() self.eps = eps self.dim = ( normalized_shape if isinstance(normalized_shape, (tuple, list)) else (normalized_shape,) ) self.weight = Parameter(shape=self.dim, dtype=dtype) self.bias = Parameter(shape=self.dim, dtype=dtype) self.op = ops.layernorm()
[docs] def forward(self, *args): assert len(args) == 1 x = args[0] y = self.op(x, self.weight.tensor(), self.bias.tensor(), self.dim, self.eps) return y