torch/onnx/symbolic_opset14.py

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

# This file exports ONNX ops for opset 14
import torch
import torch.onnx.symbolic_helper as sym_help
from torch.onnx._globals import GLOBALS
from torch.onnx.symbolic_helper import parse_args

# Note [ONNX operators that are added/updated in opset 14]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# New operators:
#   HardSwish, Trilu
#
# Updated operators:
#   Reshape
#   Add, Sub, Mul, Div
#   GRU, LSTM, RNN
#   BatchNorm, Cumsum, Relu


@parse_args("v")
def hardswish(g, self):
    return g.op("HardSwish", self)


@parse_args("v", "i")
def tril(g, self, diagonal, out=None):
    k = g.op("Constant", value_t=torch.tensor(diagonal, dtype=torch.int64))
    return g.op("Trilu", self, k, upper_i=0)


@parse_args("v", "i")
def triu(g, self, diagonal, out=None):
    k = g.op("Constant", value_t=torch.tensor(diagonal, dtype=torch.int64))
    return g.op("Trilu", self, k, upper_i=1)


@parse_args("v", "v")
def reshape(g, self, shape):
    # NOTE: Due to bug in ORT https://github.com/microsoft/onnxruntime/issues/10664
    #       Reshape export cannot utilize the new allowzero attribute introduced in opset 14.
    return sym_help._reshape_helper(g, self, shape, allowzero=0)


@parse_args("v", "v", "v", "v", "v", "i", "f", "f", "i")
def batch_norm(
    g,
    input,
    weight,
    bias,
    running_mean,
    running_var,
    training,
    momentum,
    eps,
    cudnn_enabled,
):

    if (
        torch.is_autocast_enabled()
        and not sym_help.args_have_same_dtype(
            [input, weight, bias, running_mean, running_var]
        )
        and GLOBALS.export_onnx_opset_version < 15
    ):
        return sym_help._onnx_opset_unsupported_detailed(
            "BatchNormalization",
            14,
            15,
            "All input tensors must have the same `dtype`."
            " Turn off Autocast or export using opset version 15.",
        )

    sym_help.check_training_mode(training, "batch_norm")
    weight, bias, running_mean, running_var = sym_help._batchnorm_helper(
        g, input, weight, bias, running_mean, running_var
    )
    out = g.op(
        "BatchNormalization",
        input,
        weight,
        bias,
        running_mean,
        running_var,
        epsilon_f=eps,
        momentum_f=1 - momentum,
        training_mode_i=0 if not training else 1,
        outputs=1 if not training else 3,
    )
    if not training:
        return out
    else:
        res, new_running_mean, new_running_var = out
        new_running_mean.setType(running_mean.type())
        new_running_var.setType(running_var.type())
        return res


class Quantized:
    """
    https://github.com/pytorch/pytorch/wiki/PyTorch-ONNX-exporter#quantized-model-export
    """

    domain = "quantized"

    @staticmethod
    def hardswish(g, x, op_scale, op_zero_point):
        x, _, _, _ = sym_help.dequantize_helper(g, x)

        output = hardswish(g, x)

        return sym_help.quantize_helper(g, output, op_scale, op_zero_point)