# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. #pylint: disable=invalid-name """QNN dialect operators.""" from __future__ import absolute_import as _abs from tvm.relay.expr import Tuple from tvm.relay.op.nn.util import get_pad_tuple2d from . import _make def requantize(data, input_scale, input_zero_point, output_scale, output_zero_point, axis=-1, rounding="UPWARD", out_dtype="int8"): r"""Requantized operator. The requantize operator converts one quantized tensor representation to another quantized tensor representation. For the output tensor, we are provided with output scale and zero point. The computation is as follows Q_output = zp_output + (scale_input)/(scale_output) * (Q_input - zp_input) Parameters ---------- data : tvm.relay.Expr The input data to the operator. input_scale: tvm.relay.Expr The quantization scale for the input tensor. input_zero_point: tvm.relay.Expr The zero point of the input tensor. output_scale: tvm.relay.Expr The quantization scale for the output tensor. output_zero_point: tvm.relay.Expr The zero point of the output tensor. axis : int The channel axis for quantization. Default value is -1 which corresponds to the last axis. rounding : string, optional Defines the rounding direction when the value is midway between two representable values. out_dtype : str, optional Specifies the output data type. Returns ------- result : tvm.relay.Expr The computed result. """ return _make.requantize(data, input_scale, input_zero_point, output_scale, output_zero_point, axis, rounding, out_dtype) def quantize(data, output_scale, output_zero_point, axis=-1, out_dtype='int8'): r""" Quantize op This operator takes float32 as input and produces quantized int8 or unit8 as output. The input tensor can be of any shape. The output shape is the same as input shape. Q_output = clamp((round(input_tensor/output_scale) + output_zero_point), out_dtype::min, out_dtype::max) Parameters ---------- data : tvm.relay.Expr The input tensor to be quantized. Can be of type float32. output_zero_point : tvm.relay.Expr The output zero_point. output_scale : tvm.relay.Expr The output scale. axis : int The channel axis for quantization. Default value is -1 which corresponds to the last axis. out_dtype : str, optional The data type of the input tensor. Can be [int8, uint8, int32] Returns ------- result : tvm.relay.Expr The computed result. """ return _make.quantize(data, output_scale, output_zero_point, axis, out_dtype) def dequantize(data, input_scale, input_zero_point): r""" Dequantize op This operator takes quantized int8 and unit8 as input and produces dequantized float32 as output. The output shape is the same as input shape. The input tensor can be of any shape. Parameters ---------- data : tvm.relay.Expr The input tensor to be dequantized. Can be of type [int8, uint8]. input_zero_point : tvm.relay.Expr The input zero_point. input_scale : tvm.relay.Expr The input scale. Returns ------- result : tvm.relay.Expr The computed result. """ return _make.dequantize(data, input_scale, input_zero_point) def concatenate(data, input_scales, input_zero_points, output_scale, output_zero_point, axis): """Concatenate the quantized input tensors along the given axis. Parameters ---------- data : Union(List[relay.Expr], Tuple[relay.Expr]) The list of quantized tensors. input_scales : List[relay.Expr] The list of scales of input quantized tensors. input_zero_points : List[relay.Expr] The list of zero points of input quantized tensors. output_scale : relay.Expr The scale of the output quantized tensor. output_zero_point : relay.Expr The zero point of the output quantized tensor. axis : int The axis along which the tensors are concatenated. Returns ------- result: relay.Expr The concatenated quantized tensor. """ data = list(data) if not data: raise ValueError("relay.concatenate requires data to be non-empty.") if not isinstance(axis, int): raise ValueError("For now, we only support integer axis") input_scales = list(input_scales) input_zero_points = list(input_zero_points) return _make.concatenate(Tuple(data), Tuple(input_scales), Tuple(input_zero_points), output_scale, output_zero_point, axis) def conv2d(data, kernel, input_zero_point, kernel_zero_point, input_scale, kernel_scale, kernel_size, channels, strides=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, data_layout="NCHW", kernel_layout="OIHW", out_layout="", out_dtype="int32"): r"""Quantized 2D convolution. This operator convolves quantized data with quantized kernel. The scale of the output quantized tensor is the product of the kernel_scale and input_scale of the input quantized tensors. The zero point of the output quantized tensor is 0. By default, the dtype of output is int32. Please also refer to Requantize operator to understand how to scale back the int32 output to (u)int8. Parameters ---------- data : tvm.relay.Expr The input data to the operator. kernel : tvm.relay.Expr The kernel expressions. input_zero_point: tvm.relay.Expr The zero point of the data distribution. kernel_zero_point: tvm.relay.Expr The zero point of the quantized_kernel distribution. input_scale: tvm.relay.Expr The scale for the input tensor. The scale for the input tensor is stored purely for convenience here. See more commentary below. kernel_scale: tvm.relay.Expr The scale for the weight tensor. The scale for the weight tensor is stored for access to this during relay. This information is not needed in the pass pipeline after qnn.conv2d is lowered to the sequence of steps as in nn.conv2d. See also input_scale in Requantize. kernel_size : tuple of int The spatial width and height of the convolution kernel. channels : int Number of output channels of this convolution. strides : tuple of int, optional The strides of convolution. padding : tuple of int, optional The padding of convolution on both sides of inputs before convolution. dilation : tuple of int, optional Specifies the dilation rate to be used for dilated convolution. groups : int, optional Number of groups for grouped convolution. data_layout : str, optional Layout of the input. kernel_layout : str, optional Layout of the kernel. out_layout : str, optional Layout of the output, by default, out_layout is the same as data_layout out_dtype : str, optional Specifies the output data type for mixed precision conv2d. Returns ------- result : tvm.relay.Expr The computed result. """ # TODO enforce 4-way padding in topi/nn/conv2d after #4644 merged # convert 2-way padding to 4-way padding padding = get_pad_tuple2d(padding) return _make.conv2d(data, kernel, input_zero_point, kernel_zero_point, input_scale, kernel_scale, strides, padding, dilation, groups, channels, kernel_size, data_layout, kernel_layout, out_layout, out_dtype) def add(lhs, rhs, lhs_scale, lhs_zero_point, rhs_scale, rhs_zero_point, output_scale, output_zero_point): """Quantized addition with numpy-style broadcasting. Parameters ---------- lhs : relay.Expr The left hand side quantized input data. rhs : relay.Expr The right hand side quantized input data. lhs_scale: float The scale of the lhs quantized expr. lhs_scale: relay.Expr The scale of the lhs quantized expr. lhs_zero_point: relay.Expr The zero point of lhs quantized expr. rhs_scale: relay.Expr The scale of the rhs quantized expr. rhs_zero_point: relay.Expr The zero point of rhs quantized expr. output_scale: relay.Expr The scale of the output quantized expr. output_zero_point: relay.Expr The zero point of output quantized expr. Returns ------- result : relay.Expr The computed result. """ return _make.add(lhs, rhs, lhs_scale, lhs_zero_point, rhs_scale, rhs_zero_point, output_scale, output_zero_point) def dense(data, weight, input_zero_point, kernel_zero_point, input_scale, kernel_scale, units, out_dtype="int32"): """Qnn Dense operator. Applies a quantized linear transformation .. math:: `Y = X * W` Parameters ---------- data : tvm.relay.Expr The quantized input data to the operator. weight : tvm.relay.Expr The quantized weight expressions. input_zero_point: tvm.relay.Expr The input zero point. kernel_zero_point: tvm.relay.Expr The kernel zero point. input_scale: tvm.relay.Expr The scale for the input tensor. kernel_scale: tvm.relay.Expr The scale for the weight tensor. The scale for the weight tensor is stored for access to this during relay. This information is not needed in the pass pipeline after qnn.conv2d is lowered to the sequence of steps as in nn.conv2d. See also input_scale in Requantize. units : int Number of hidden units of the dense transformation. out_dtype : str, optional Specifies the output data type for mixed precision dense can be int32 or int16. Returns ------- result : tvm.relay.Expr The computed result. """ return _make.dense(data, weight, input_zero_point, kernel_zero_point, input_scale, kernel_scale, units, out_dtype) def mul(lhs, rhs, lhs_scale, lhs_zero_point, rhs_scale, rhs_zero_point, output_scale, output_zero_point): """Quantized multiplication with numpy-style broadcasting. Parameters ---------- lhs : relay.Expr The left hand side quantized input data. rhs : relay.Expr The right hand side quantized input data. lhs_scale: relay.Expr The scale of the lhs quantized expr. lhs_zero_point: relay.Expr The zero point of lhs quantized expr. rhs_scale: relay.Expr The scale of the rhs quantized expr. rhs_zero_point: relay.Expr The zero point of rhs quantized expr. output_scale: relay.Expr The scale of the output quantized expr. output_zero_point: relay.Expr The zero point of output quantized expr. Returns ------- result : relay.Expr The computed result. """ return _make.mul(lhs, rhs, lhs_scale, lhs_zero_point, rhs_scale, rhs_zero_point, output_scale, output_zero_point)