261 lines
13 KiB
Python
261 lines
13 KiB
Python
# Copyright (c) 2022, Tri Dao.
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# Adapted from https://github.com/NVIDIA/apex/blob/master/apex/contrib/layer_norm/layer_norm.py
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import torch
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from torch.nn import init
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import dropout_layer_norm
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def _dropout_add_layer_norm_forward(x0, residual, gamma, beta, rowscale, colscale, dropout_p,
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epsilon, residual_in_fp32=False, is_rms_norm=False):
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""" Assume that arguments are contiguous
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"""
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hidden_size = gamma.numel()
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x0mat = x0.view((-1, hidden_size))
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residualmat = residual.view((-1, hidden_size)) if residual is not None else None
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rowscale = rowscale.view(-1) if rowscale is not None else None
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zmat, xmat, dmask, mu, rsigma = dropout_layer_norm.dropout_add_ln_fwd(
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x0mat, residualmat, gamma, beta, rowscale, colscale, None, None, dropout_p, epsilon,
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1.0, 0, None, residual_in_fp32, is_rms_norm
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)
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# dmask is None if dropout_p == 0.0
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# xmat is None if dropout_p == 0.0 and residual is None and residual_dtype != input_dtype
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return zmat, xmat if xmat is not None else x0mat, dmask, mu, rsigma
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def _dropout_add_layer_norm_backward(dz, dx, x, x0, dmask, mu, rsigma, gamma, rowscale, colscale,
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dropout_p, has_residual, is_rms_norm=False):
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""" Assume that arguments are contiguous
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dx == None means that it was a post-norm architecture
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(x = drop(x0) + residual was not returned in the fwd).
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x0 must not be None if we have colscale.
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"""
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hidden_size = gamma.numel()
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xmat = x.view((-1, hidden_size))
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dzmat = dz.view(xmat.shape)
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dxmat = dx.view(xmat.shape) if dx is not None else None
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x0mat = x0.view((-1, hidden_size)) if x0 is not None else None
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rowscale = rowscale.view(-1) if rowscale is not None else None
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if colscale is not None:
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assert x0 is not None, 'x0 is required to compute the gradient of colscale'
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dx0mat, dresidualmat, dgamma, dbeta, _, _, *rest = dropout_layer_norm.dropout_add_ln_bwd(
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dzmat, dxmat, xmat, x0mat, dmask, mu, rsigma, gamma, rowscale, colscale, None, None,
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dropout_p, 1.0, 0, has_residual, is_rms_norm
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)
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# dresidualmat is None if not has_residual
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if colscale is None:
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return dx0mat, dresidualmat, dgamma, dbeta
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else:
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dcolscale = rest[0]
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return dx0mat, dresidualmat, dgamma, dbeta, dcolscale
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def _dropout_add_layer_norm_subset_forward(x0, residual, gamma, beta, colscale, x0_subset,
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out_subset, dropout_p, epsilon, rowscale_const,
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out_numrows, residual_in_fp32=False, is_rms_norm=False):
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""" Assume that arguments are contiguous
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"""
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hidden_size = gamma.numel()
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x0mat = x0.view((-1, hidden_size))
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residualmat = residual.view((-1, hidden_size)) if residual is not None else None
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x0_subset = x0_subset.view(-1) if x0_subset is not None else None
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out_subset = out_subset.view(-1) if out_subset is not None else None
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zmat, xmat, dmask, mu, rsigma = dropout_layer_norm.dropout_add_ln_fwd(
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x0mat, residualmat, gamma, beta, None, colscale, x0_subset, out_subset, dropout_p, epsilon,
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rowscale_const, out_numrows, None, residual_in_fp32, is_rms_norm
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)
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# dmask is None if dropout_p == 0.0
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# xmat is None if dropout_p == 0.0 and residual is None and residual_dtype != input_dtype
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return zmat, xmat if xmat is not None else x0mat, dmask, mu, rsigma
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def _dropout_add_layer_norm_subset_backward(dz, dx, x, x0, dmask, mu, rsigma, gamma, colscale,
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x0_subset, out_subset, dropout_p, rowscale_const,
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x0_numrows, has_residual, is_rms_norm=False):
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""" Assume that arguments are contiguous
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dx == None means that it was a post-norm architecture
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(x = drop(x0) + residual was not returned in the fwd).
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x0 must not be None if we have colscale.
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"""
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hidden_size = gamma.numel()
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xmat = x.view((-1, hidden_size))
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dzmat = dz.view(-1, hidden_size)
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dxmat = dx.view(xmat.shape) if dx is not None else None
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x0mat = x0.view((-1, hidden_size)) if x0 is not None else None
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x0_subset = x0_subset.view(-1) if x0_subset is not None else None
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out_subset = out_subset.view(-1) if out_subset is not None else None
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if colscale is not None:
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assert x0 is not None, 'x0 is required to compute the gradient of colscale'
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dx0mat, dresidualmat, dgamma, dbeta, _, _, *rest = dropout_layer_norm.dropout_add_ln_bwd(
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dzmat, dxmat, xmat, x0mat, dmask, mu, rsigma, gamma, None, colscale, x0_subset, out_subset,
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dropout_p, rowscale_const, x0_numrows, has_residual, is_rms_norm
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)
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# dresidualmat is None if not has_residual
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if colscale is None:
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return dx0mat, dresidualmat, dgamma, dbeta
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else:
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dcolscale = rest[0]
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return dx0mat, dresidualmat, dgamma, dbeta, dcolscale
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class DropoutAddLayerNormFn(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x0, residual, gamma, beta, rowscale, colscale, dropout_p, epsilon,
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residual_in_fp32=False, prenorm=False, is_rms_norm=False, return_dmask=False):
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x0 = x0.contiguous()
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residual = residual.contiguous() if residual is not None else None
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gamma = gamma.contiguous()
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beta = beta.contiguous() if beta is not None else None
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rowscale = rowscale.contiguous() if rowscale is not None else None
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colscale = colscale.contiguous() if colscale is not None else None
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zmat, xmat, dmask, mu, rsigma = _dropout_add_layer_norm_forward(
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x0, residual, gamma, beta, rowscale, colscale, dropout_p, epsilon,
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residual_in_fp32, is_rms_norm
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)
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# Only need to save x0 if we need to compute gradient wrt colscale
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x0_saved = x0 if colscale is not None else None
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ctx.save_for_backward(xmat.view(x0.shape), x0, dmask, gamma, mu, rsigma, rowscale, colscale)
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ctx.prenorm = prenorm
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ctx.dropout_p = dropout_p
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ctx.has_residual = residual is not None
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ctx.is_rms_norm = is_rms_norm
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ctx.has_beta = beta is not None
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if not return_dmask:
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return (zmat.view(x0.shape) if not prenorm
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else (zmat.view(x0.shape), xmat.view(x0.shape)))
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else:
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dmask = (dmask.view(x0.shape) if dropout_p > 0.
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else torch.ones(x0.shape, dtype=torch.uint8, device=x0.device))
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ctx.mark_non_differentiable(dmask)
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return ((zmat.view(x0.shape), dmask) if not prenorm
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else (zmat.view(x0.shape), xmat.view(x0.shape), dmask))
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@staticmethod
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def backward(ctx, dz, *args):
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# assert dz.is_contiguous()
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dz = dz.contiguous() # this happens!
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dx = args[0].contiguous() if ctx.prenorm else None
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x, x0, dmask, gamma, mu, rsigma, rowscale, colscale = ctx.saved_tensors
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# x0 is None if colscale is None
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dropout_p = ctx.dropout_p
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has_residual = ctx.has_residual
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dx0mat, dresidualmat, dgamma, dbeta, *rest = _dropout_add_layer_norm_backward(
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dz, dx, x, x0, dmask, mu, rsigma, gamma, rowscale, colscale, dropout_p, has_residual,
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ctx.is_rms_norm
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)
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dx0 = dx0mat.view(x.shape)
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dresidual = dresidualmat.view(x.shape) if dresidualmat is not None else None
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dcolscale = rest[0] if colscale is not None else None
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return (dx0, dresidual, dgamma, dbeta if ctx.has_beta else None, None, dcolscale, None,
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None, None, None, None, None)
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class DropoutAddLayerNormSubsetFn(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x0, residual, gamma, beta, colscale, x0_subset, out_subset, dropout_p, epsilon,
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rowscale_const, out_numrows, residual_in_fp32=False,
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prenorm=False, is_rms_norm=False, return_dmask=False):
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x0 = x0.contiguous()
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residual = residual.contiguous() if residual is not None else None
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gamma = gamma.contiguous()
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beta = beta.contiguous() if beta is not None else None
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colscale = colscale.contiguous() if colscale is not None else None
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zmat, xmat, dmask, mu, rsigma = _dropout_add_layer_norm_subset_forward(
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x0, residual, gamma, beta, colscale, x0_subset, out_subset, dropout_p, epsilon,
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rowscale_const, out_numrows, residual_in_fp32, is_rms_norm
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)
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# Only need to save x0 if we need to compute gradient wrt colscale
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x0_saved = x0 if colscale is not None else None
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x_shape = (-1, *x0.shape[1:])
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ctx.save_for_backward(xmat.view(x_shape), x0, dmask, gamma, mu, rsigma, colscale,
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x0_subset, out_subset)
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ctx.prenorm = prenorm
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ctx.dropout_p = dropout_p
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ctx.rowscale_const = rowscale_const
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ctx.x0_numrows = x0.shape[:-1].numel()
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ctx.has_residual = residual is not None
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ctx.is_rms_norm = is_rms_norm
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ctx.has_beta = beta is not None
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z_shape = (-1, *x0.shape[1:])
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if not return_dmask:
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return (zmat.view(z_shape) if not prenorm
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else (zmat.view(z_shape), xmat.view(x0.shape)))
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else:
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z = zmat.view(z_shape)
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dmask = (dmask.view(x0.shape) if dropout_p > 0.
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else torch.ones(x0.shape, dtype=torch.uint8, device=x0.device))
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ctx.mark_non_differentiable(dmask)
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return ((z, dmask) if not prenorm else (z, xmat.view(x_shape), dmask))
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@staticmethod
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def backward(ctx, dz, *args):
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# assert dz.is_contiguous()
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dz = dz.contiguous() # this happens!
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dx = args[0].contiguous() if ctx.prenorm else None
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x, x0, dmask, gamma, mu, rsigma, colscale, x0_subset, out_subset = ctx.saved_tensors
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# x0 is None if colscale is None
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dropout_p = ctx.dropout_p
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has_residual = ctx.has_residual
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dx0mat, dresidualmat, dgamma, dbeta, *rest = _dropout_add_layer_norm_subset_backward(
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dz, dx, x, x0, dmask, mu, rsigma, gamma, colscale, x0_subset, out_subset, dropout_p,
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ctx.rowscale_const, ctx.x0_numrows, has_residual, ctx.is_rms_norm
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)
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dx0 = dx0mat.view(-1, *x.shape[1:])
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dresidual = dresidualmat.view(x.shape) if dresidualmat is not None else None
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dcolscale = rest[0] if colscale is not None else None
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return (dx0, dresidual, dgamma, dbeta if ctx.has_beta else None, dcolscale, None, None,
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None, None, None, None, None, None, None, None)
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def layer_norm(x, weight, bias, epsilon):
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return DropoutAddLayerNormFn.apply(x, None, weight, bias, None, None, 0.0, epsilon, False)
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def dropout_add_layer_norm(x0, residual, weight, bias, dropout_p, epsilon, rowscale=None,
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layerscale=None, prenorm=False, residual_in_fp32=False,
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return_dropout_mask=False):
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"""residual_in_fp32 only has an effect if residual is None.
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Otherwise residual dtype is residual.dtype.
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"""
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return DropoutAddLayerNormFn.apply(
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x0, residual, weight, bias, rowscale, layerscale, dropout_p, epsilon, residual_in_fp32, prenorm,
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False, return_dropout_mask
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)
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def dropout_add_layer_norm_subset(x0, residual, weight, bias, dropout_p, epsilon, layerscale=None,
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x0_subset=None, out_subset=None, rowscale_const=1.0,
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out_numrows=0, prenorm=False, residual_in_fp32=False,
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return_dropout_mask=False):
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"""residual_in_fp32 only has an effect if residual is None.
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Otherwise residual dtype is residual.dtype.
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"""
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return DropoutAddLayerNormSubsetFn.apply(
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x0, residual, weight, bias, layerscale, x0_subset, out_subset, dropout_p, epsilon,
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rowscale_const, out_numrows, residual_in_fp32, prenorm, False, return_dropout_mask
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)
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class DropoutAddLayerNorm(torch.nn.Module):
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def __init__(self, hidden_size, prenorm=False, p=0.0, eps=1e-5, residual_in_fp32=False,
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device=None, dtype=None):
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factory_kwargs = {'device': device, 'dtype': dtype}
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super().__init__()
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self.prenorm = prenorm
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self.p = p
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self.epsilon = eps
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self.residual_in_fp32 = residual_in_fp32
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self.weight = torch.nn.Parameter(torch.empty(hidden_size, **factory_kwargs))
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self.bias = torch.nn.Parameter(torch.empty(hidden_size, **factory_kwargs))
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self.reset_parameters()
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def reset_parameters(self):
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init.ones_(self.weight)
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init.zeros_(self.bias)
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def forward(self, x0, residual=None):
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return dropout_add_layer_norm(x0, residual, self.weight, self.bias,
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self.p if self.training else 0.0, self.epsilon,
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prenorm=self.prenorm, residual_in_fp32=self.residual_in_fp32)
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