@triton.autotune(configs=warps_kernel_configs(), key=['batch_dim', 'feat_dim'])
@triton.heuristics({'BLOCK_SIZE_BATCH': BLOCK_SIZE_BATCH_heuristic,
    'BLOCK_SIZE_FEAT': lambda args: next_power_of_2(args['feat_dim'])})
@triton.jit
def cross_entropy_loss_forward_kernel(input_pointer, target_pointer,
    weight_pointer, sum_weights_pointer, output_pointer, batch_dim,
    feat_dim, input_batch_stride, input_feat_stride, weighted: tl.constexpr,
    BLOCK_SIZE_BATCH: tl.constexpr, BLOCK_SIZE_FEAT: tl.constexpr):
    """
    Measures the mean cross entropy loss between the input and target,
    with optional reweighing of each class.

    Args:
        input_pointer: Pointer to the input.
            The input must be of shape [batch_dim, feat_dim].
        target_pointer: Pointer to the target.
            The target must be of shape [batch_dim].
        weight_pointer: Pointer to an optional class weight vector.
            The class weight vector, if provided, must be of shape [feat_dim].
        sum_weights_pointer: Pointer to a container the sum of the class weights is written to.
            The container must be of shape [batch_dim/BLOCK_SIZE_BATCH].
        output_pointer: Pointer to a container the loss is written to.
            The container must be of shape [batch_dim/BLOCK_SIZE_BATCH].
        batch_dim: Batch dimension.
        feat_dim: Dimensionality of the features.
        input_batch_stride: Stride necessary to jump one element along the
            input's batch dimension.
        input_feat_stride: Stride necessary to jump one element along the
            input's feature dimension.
        weighted: Flag for weighing each class.
        BLOCK_SIZE_BATCH: Block size across the batch dimension.
        BLOCK_SIZE_FEAT: Block size across the feature dimension.
    """
    batch_pid = tl.program_id(axis=0)
    batch_offset = batch_pid * BLOCK_SIZE_BATCH + tl.arange(0, BLOCK_SIZE_BATCH
        )
    feat_offset = tl.arange(0, BLOCK_SIZE_FEAT)
    batch_mask = batch_offset < batch_dim
    feat_mask = feat_offset < feat_dim
    target = tl.load(target_pointer + batch_offset, mask=batch_mask)
    pred_pointer = (input_pointer + input_feat_stride * target + 
        input_batch_stride * batch_offset)
    input_pointer += input_batch_stride * batch_offset[:, None
        ] + input_feat_stride * feat_offset[None, :]
    input = tl.load(input_pointer, mask=batch_mask[:, None] & feat_mask[
        None, :], other=-float('inf')).to(tl.float32)
    pred = tl.load(pred_pointer, mask=batch_mask).to(tl.float32)
    mx = tl.max(input, axis=1)
    input -= mx[:, None]
    loss = tl.log(tl.sum(tl.exp(input), axis=1)) - pred + mx
    if weighted:
        weight = tl.load(weight_pointer + target, mask=batch_mask).to(tl.
            float32)
        loss *= weight
        tl.store(sum_weights_pointer + batch_pid, tl.sum(weight))
    else:
        loss /= batch_dim
    tl.store(output_pointer + batch_pid, tl.sum(loss))
