bert_padding.py

# Adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py

import numpy as np

import torch
import torch.nn.functional as F

from einops import rearrange, repeat


class IndexFirstAxis(torch.autograd.Function):

    @staticmethod
    def forward(ctx, input, indices):
        ctx.save_for_backward(indices)
        assert input.ndim >= 2
        ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:]
        second_dim = np.prod(other_shape)
        # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
        # return input[indices]
        return torch.gather(rearrange(input, 'b ... -> b (...)'), 0,
                            repeat(indices, 'z -> z d', d=second_dim)).reshape(-1, *other_shape)

    @staticmethod
    def backward(ctx, grad_output):
        indices, = ctx.saved_tensors
        assert grad_output.ndim >= 2
        other_shape = grad_output.shape[1:]
        grad_output = rearrange(grad_output, 'b ... -> b (...)')
        grad_input = torch.zeros([ctx.first_axis_dim, grad_output.shape[1]],
                                  device=grad_output.device, dtype=grad_output.dtype)
        # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing.
        # grad_input[indices] = grad_output
        grad_input.scatter_(0, repeat(indices, 'z -> z d', d=grad_output.shape[1]), grad_output)
        return grad_input.reshape(ctx.first_axis_dim, *other_shape), None


index_first_axis = IndexFirstAxis.apply


class IndexPutFirstAxis(torch.autograd.Function):

    @staticmethod
    def forward(ctx, values, indices, first_axis_dim):
        ctx.save_for_backward(indices)
        assert indices.ndim == 1
        assert values.ndim >= 2
        output = torch.zeros(first_axis_dim, *values.shape[1:], device=values.device,
                             dtype=values.dtype)
        # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing.
        output[indices] = values
        # output.scatter_(0, repeat(indices, 'z -> z d', d=values.shape[1]), values)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        indices, = ctx.saved_tensors
        # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
        grad_values = grad_output[indices]
        # grad_values = torch.gather(grad_output, 0, repeat(indices, 'z -> z d', d=grad_output.shape[1]))
        return grad_values, None, None


index_put_first_axis = IndexPutFirstAxis.apply


class IndexFirstAxisResidual(torch.autograd.Function):

    @staticmethod
    def forward(ctx, input, indices):
        ctx.save_for_backward(indices)
        assert input.ndim >= 2
        ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:]
        second_dim = np.prod(other_shape)
        # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
        output = input[indices]
        # We don't want to reshape input (b ... -> b (...)) since it could change the channel_last
        # memory format to channel_first. In other words, input might not be contiguous.
        # If we don't detach, Pytorch complains about output being a view and is being modified inplace
        return output, input.detach()

    @staticmethod
    def backward(ctx, grad_output, grad_residual):
        indices, = ctx.saved_tensors
        assert grad_output.ndim >= 2
        other_shape = grad_output.shape[1:]
        assert grad_residual.shape[1:] == other_shape
        grad_input = grad_residual
        # grad_input[indices] += grad_output
        indices = indices.reshape(indices.shape[0], *((1,) * (grad_output.ndim - 1)))
        indices = indices.expand_as(grad_output)
        grad_input.scatter_add_(0, indices, grad_output)
        return grad_input.reshape(ctx.first_axis_dim, *other_shape), None


index_first_axis_residual = IndexFirstAxisResidual.apply


def unpad_input(hidden_states, attention_mask):
    """
    Arguments:
        hidden_states: (batch, seqlen, ...)
        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
    Return:
        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
        cu_seqlens: (batch + 1), the cumulative sequence lengths, used to index into hidden_states.
        max_seqlen_in_batch: int
    """
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
    # TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the
    # bool mask, then call nonzero to get the indices, then index with those. The indices is @dim
    # times larger than it needs to be, wasting memory. It's faster and more memory-efficient to
    # index with integer indices. Moreover, torch's index is a bit slower than it needs to be,
    # so we write custom forward and backward to make it a bit faster.
    return (index_first_axis(rearrange(hidden_states, 'b s ... -> (b s) ...'), indices), indices,
            cu_seqlens, max_seqlen_in_batch)


def pad_input(hidden_states, indices, batch, seqlen):
    """
    Arguments:
        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
        indices: (total_nnz)
    Return:
        hidden_states: (batch, seqlen, ...)
    """
    dim = hidden_states.shape[-1]
    # output = torch.zeros((batch * seqlen), dim, device=hidden_states.device, dtype=hidden_states.dtype)
    # output[indices] = hidden_states
    output = index_put_first_axis(hidden_states, indices, batch * seqlen)
    return rearrange(output, '(b s) ... -> b s ...', b=batch)