torch/onnx/symbolic_opset10.py

from __future__ import absolute_import, division, print_function, unicode_literals

import torch
from torch.nn.modules.utils import _single, _pair, _triple
import torch.onnx
# This import monkey-patches graph manipulation methods on Graph, used for the
# ONNX symbolics
import torch.onnx.utils
from sys import maxsize

import torch.onnx.symbolic_helper as sym_help
from torch.onnx.symbolic_helper import parse_args, _unimplemented
import torch.onnx.symbolic_opset9


# EDITING THIS FILE? READ THIS FIRST!
# see Note [Edit Symbolic Files] in symbolic_helper.py

# This file exports ONNX ops for opset 10
# Opset 10 is supported by ONNX release 1.5.0
# release on 04/24/19


@parse_args('v', 'i', 'i', 'none')
def sort(g, self, dim, decending, out=None):
    return sym_help._sort_helper(g, self, dim, decending=decending, out=out)


@parse_args('v', 'v', 'i', 'i', 'i', 'none')
def topk(g, self, k, dim, largest, sorted, out=None):
    return sym_help._topk_helper(g, self, k, dim, largest=largest, sorted=sorted, out=out)


def _max_pool(name, tuple_fn, ndims, return_indices):
    @parse_args('v', 'is', 'is', 'is', 'is', 'i')
    def symbolic_fn(g, input, kernel_size, stride, padding, dilation, ceil_mode):
        if not stride:
            stride = kernel_size
        kwargs = {
            'kernel_shape_i': tuple_fn(kernel_size),
            'pads_i': tuple_fn(padding) * 2,
            'strides_i': tuple_fn(stride),
            'ceil_mode_i': ceil_mode,
        }
        if set(tuple_fn(dilation)) != {1}:
            kwargs['dilations_i'] = tuple_fn(dilation)
        # easy but hacky way to get flattened indices values
        # to be used to convert the indices values to non-flattened.
        # In ONNX the indices are computed as a flatten 1-D tensor,
        # so the values in indices are in [0, N x C x D1 x ... x Dn).
        # To convert the indices to the same format used by Pytorch,
        # we first execute a maxpool with a kernel and stride of 1 on the same input.
        # This will result in a tensor of indices in which each index will have it's own value.
        # Using this tensor as a reference, we extract the first index of each axis and subtract
        # it from each index of this axis in the indices to convert.
        # This step will result in a tensor were each dimension has values of indices within
        # the dimension it is in.
        # For more information :
        # https://github.com/pytorch/pytorch/pull/16455#issuecomment-460776407
        if return_indices:
            r, indices = g.op("MaxPool", input, outputs=2, **kwargs)
            _, flattened_indices = g.op("MaxPool", input, outputs=2,
                                        kernel_shape_i=[1 for _ in range(ndims)],
                                        strides_i=[1 for _ in range(ndims)])
            # convert indices to have non-flattened indices values
            from torch.onnx.symbolic_opset9 import sub
            s = sym_help._slice_helper(g, flattened_indices, axes=[2 + i for i in range(ndims)],
                                       starts=tuple_fn(0), ends=tuple_fn(1))
            indices = sub(g, indices, s)
            return r, indices
        else:
            r = g.op("MaxPool", input, outputs=1, **kwargs)
            return r

    return symbolic_fn


max_pool1d = _max_pool("max_pool1d", _single, 1, return_indices=False)
max_pool2d = _max_pool("max_pool2d", _pair, 2, return_indices=False)
max_pool3d = _max_pool("max_pool3d", _triple, 3, return_indices=False)
max_pool1d_with_indices = _max_pool("max_pool1d_with_indices", _single, 1, return_indices=True)
max_pool2d_with_indices = _max_pool("max_pool2d_with_indices", _pair, 2, return_indices=True)
max_pool3d_with_indices = _max_pool("max_pool3d_with_indices", _triple, 3, return_indices=True)


def _avg_pool(name, tuple_fn):
    @parse_args('v', 'is', 'is', 'is', 'i', 'i', 'none')
    def symbolic_fn(g, input, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override=None):
        if not stride:
            stride = kernel_size
        padding = sym_help._avgpool_helper(tuple_fn, padding, kernel_size, stride, divisor_override, name)
        if count_include_pad:
            input = g.op("Pad", input,
                         pads_i=((0,) * 2 + padding) * 2,
                         mode_s='constant',
                         value_f=0.)
            padding = (0,) * len(padding)
        output = g.op("AveragePool", input,
                      kernel_shape_i=tuple_fn(kernel_size),
                      strides_i=tuple_fn(stride),
                      pads_i=padding * 2,
                      ceil_mode_i=ceil_mode)
        return output
    return symbolic_fn


avg_pool1d = _avg_pool('avg_pool1d', _single)
avg_pool2d = _avg_pool('avg_pool2d', _pair)
avg_pool3d = _avg_pool('avg_pool3d', _triple)


def _interpolate(name, dim, interpolate_mode):
    def symbolic_fn(g, input, output_size, *args):
        scales, align_corners = sym_help._get_interpolate_attributes(g, interpolate_mode, args)
        sym_help._interpolate_warning(interpolate_mode)
        align_corners = sym_help._maybe_get_scalar(align_corners)
        if align_corners:
            return _unimplemented(name, "align_corners == True")
        if scales is None:
            scales = sym_help._interpolate_size_to_scales(g, input, output_size, dim)
        return g.op("Resize", input, scales, mode_s=interpolate_mode)
    return symbolic_fn


upsample_nearest1d = _interpolate('upsample_nearest1d', 3, "nearest")
upsample_nearest2d = _interpolate('upsample_nearest2d', 4, "nearest")
upsample_nearest3d = _interpolate('upsample_nearest3d', 5, "nearest")
upsample_linear1d = _interpolate('upsample_linear1d', 3, "linear")
upsample_bilinear2d = _interpolate('upsample_bilinear2d', 4, "linear")
upsample_trilinear3d = _interpolate('upsample_trilinear3d', 5, "linear")

def __interpolate(g, input, size, scale_factor, mode , align_corners, recompute_scale_factor):
    scales, mode = sym_help._interpolate_get_scales_and_mode(g, input, size, scale_factor,
                                                             mode , align_corners)
    return g.op("Resize", input, scales, mode_s=mode)


def _slice(g, input, axes, starts, ends, steps=None, dynamic_slice=False):
    if dynamic_slice:
        starts = g.op("Unsqueeze", starts, axes_i=[0])
        ends = g.op("Unsqueeze", ends, axes_i=[0])
        axes = g.op("Unsqueeze", axes, axes_i=[0])
    else:
        assert len(starts) == len(ends)
        assert len(starts) == len(axes)
        assert steps is None or len(starts) == len(steps)
        if len(starts) == 1 and starts[0] == 0 and ends[0] == 9223372036854775807 \
           and (steps is None or (len(steps) == 1 and steps[0] == 1)):
            return input
        axes = g.op("Constant", value_t=torch.tensor(axes))
        starts = g.op("Constant", value_t=torch.tensor(starts))
        ends = g.op("Constant", value_t=torch.tensor(ends))
    if steps is None:
        return g.op("Slice", input, starts, ends, axes)
    steps = g.op("Constant", value_t=torch.tensor(steps))
    return g.op("Slice", input, starts, ends, axes, steps)


@parse_args('v', 'v', 'v', 'v', 'i')
def slice(g, self, dim, start, end, step):
    if (start.node().kind() != 'onnx::Constant' or
       end.node().kind() != 'onnx::Constant' or dim.node().kind() != 'onnx::Constant'):
        dynamic_slice = True
    else:
        start = [sym_help._parse_arg(start, 'i')]
        end = [sym_help._parse_arg(end, 'i')]
        dim = [sym_help._parse_arg(dim, 'i')]
        dynamic_slice = False
    return sym_help._slice_helper(g, self, axes=dim, starts=start, ends=end, steps=[step], dynamic_slice=dynamic_slice)


@parse_args('v', 'is')
def flip(g, input, dims):
    return sym_help._slice_helper(g, input, axes=dims,
                                  starts=[-1] * len(dims),
                                  ends=[-9223372036854775807] * len(dims),
                                  steps=[-1] * len(dims))


def fmod(g, input, other):
    return g.op("Mod", input, other, fmod_i=1)


@parse_args('v', 'v', 'v', 'i', 'i', 'i', 'v', 'i')
def embedding_bag(g,
                  embedding_matrix,
                  indices,
                  offsets,
                  scale_grad_by_freq,
                  mode,
                  sparse,
                  per_sample_weights,
                  include_last_offset):
    if scale_grad_by_freq and sym_help._training_mode:
        return sym_help._onnx_unsupported('embedding_bag with scale_grad_by_freq for training mode')

    from torch.onnx.symbolic_opset9 import size, div, select

    # Check if initial indices was 2D. In functional.py:
    # offsets is set to torch.arange(0, indices.numel(), indices.size(1))
    # Then indices is reshaped to 1D: indices.reshape(-1)
    if len(list(indices.node().inputs())) > 0 and indices.node().inputs().__next__().type().sizes() is not None \
            and len(indices.node().inputs().__next__().type().sizes()) == 2:
        # Assert include_last_offset is False
        assert not include_last_offset
        embeddings = g.op("Gather", embedding_matrix, indices)
        dim_0 = size(g, offsets, g.op("Constant", value_t=torch.LongTensor([0])))
        dim_1 = div(g, size(g, indices, g.op("Constant", value_t=torch.LongTensor([0]))), dim_0)
        dim_2 = g.op("Constant", value_t=torch.LongTensor([-1]))

        shape = [dim_0, dim_1, dim_2]
        shape = g.op("Concat", *shape, axis_i=0)

        if not sym_help._is_none(per_sample_weights):
            per_sample_weights = g.op("Unsqueeze", per_sample_weights, axes_i=[1])
            embeddings = g.op("Mul", embeddings, per_sample_weights)

        embeddings = g.op("Reshape", embeddings, shape)
        if mode == 0:
            embeddings = g.op("ReduceSum", embeddings, axes_i=[1], keepdims_i=0)
        elif mode == 1:
            embeddings = g.op("ReduceMean", embeddings, axes_i=[1], keepdims_i=0)
        else:
            embeddings = g.op("ReduceMax", embeddings, axes_i=[1], keepdims_i=0)
        # aten::embedding_bag returns a tuple of 4 elements: output, offset2bag, bag_size, max_indices.
        # But the last three outputs are not used in torch.nn.EmbeddingBag or torch.nn.functional.embedding_bag.          
        return embeddings, None, None, None
    elif offsets.type().sizes() is not None:
        if include_last_offset:
            offset_len = offsets.type().sizes()[0] - 1
            offsets_extended = offsets
        else:
            offset_len = offsets.type().sizes()[0]
            offsets_extended = [offsets, g.op("Constant", value_t=torch.tensor([maxsize]))]
            offsets_extended = g.op("Concat", *offsets_extended, axis_i=0)
        list_ = []
        for i in range(offset_len):
            start_ = g.op("Unsqueeze", select(g, offsets_extended, torch.tensor(0), torch.tensor(i)), axes_i=[0])
            end_ = g.op("Unsqueeze", select(g, offsets_extended, torch.tensor(0), torch.tensor(i + 1)), axes_i=[0])
            axes_ = g.op("Constant", value_t=torch.tensor([0]))
            indices_row = g.op("Slice", indices, start_, end_, axes_)

            embeddings = g.op("Gather", embedding_matrix, indices_row)
            if not sym_help._is_none(per_sample_weights):
                per_sample_weights_row = g.op("Slice", per_sample_weights, start_, end_, axes_)
                per_sample_weights_row = g.op("Unsqueeze", per_sample_weights_row, axes_i=[1])
                embeddings = g.op("Mul", embeddings, per_sample_weights_row)
            if mode == 0:
                embeddings = g.op("ReduceSum", embeddings, axes_i=[0], keepdims_i=0)
            elif mode == 1:
                embeddings = g.op("ReduceMean", embeddings, axes_i=[0], keepdims_i=0)
            else:
                embeddings = g.op("ReduceMax", embeddings, axes_i=[0], keepdims_i=0)

            embeddings = g.op("Unsqueeze", embeddings, axes_i=[0])
            list_.append(embeddings)

        output = g.op("Concat", *list_, axis_i=0)
        # aten::embedding_bag returns a tuple of 4 elements: output, offset2bag, bag_size, max_indices.
        # But the last three outputs are not used in torch.nn.EmbeddingBag or torch.nn.functional.embedding_bag.
        return output, None, None, None
    else:
        return sym_help._onnx_unsupported('embedding_bag with unknown shape of indices')


@parse_args('v', 't', 'i', 'i', 'i')
def fake_quantize_per_tensor_affine(g, inputs, scale, zero_point, quant_min=-128, quant_max=127):
    if quant_min not in [0, -128] or quant_max not in [127, 255]:
        raise RuntimeError(
            "ONNX defines [0, 255] for quint8 and [-128, 127] for qint8, got [{}, {}]".format(quant_min, quant_max))
    scale = scale.float().data  # Avoid exporter generating double type
    zero_point_dtype = torch.int8 if quant_min == -128 else torch.uint8
    zero_point = torch.tensor(zero_point, dtype=zero_point_dtype)  # ONNX requires zero_point to be tensor
    return g.op("DequantizeLinear", g.op("QuantizeLinear", inputs, scale, zero_point), scale, zero_point)