NicolaLancellotti commented on a change in pull request #9442: URL: https://github.com/apache/tvm/pull/9442#discussion_r745467423
########## File path: python/tvm/relay/backend/contrib/ethosu/te/binary_elementwise.py ########## @@ -0,0 +1,169 @@ +# 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,unused-argument +"""Tensor Expressions for binary_elementwise""" +import operator +from tvm import te +from .dma import dma_ofm_compute, dma_ifm_compute + + +def binary_elementwise_compute( + ifm: te.Tensor, + ifm2: te.Tensor, + lut: te.Tensor, + operator_type: str, + ifm_scale: float, + ifm_zero_point: int, + ifm2_scale: float, + ifm2_zero_point: int, + ofm_scale: float, + ofm_zero_point: int, + ofm_channels: int, + reversed_operands: bool, + activation: str, + clip_min: int, + clip_max: int, + ifm_layout: str, + ifm2_layout: str, + ofm_layout: str, +) -> te.Tensor: + """A compute operator representing the capabilities of binary_elementwise for the NPU. + + Parameters + ---------- + ifm : te.Tensor + The Input Feature Map tensor (IFM). + ifm2 : te.Tensor + The Input Feature Map tensor 1 (IFM2). + lut : te.Tensor + The look-up table values to use if activation = "LUT". + operator_type: str + The type of the binary elementwise operator. + "ADD" + "SUB" + "MUL" + "MIN" + "MAX" + "SHR" + "SHL" + ifm_scale : float + The quantization scale for the Input Feature Map tensor. + ifm_zero_point : int + The quantization zero point for the Input Feature Map tensor. + ifm2_scale : float + The quantization scale for the Input Feature Map tensor 2. + ifm2_zero_point : int + The quantization zero point for the Input Feature Map tensor 1. + ofm_scale : float + The quantization scale for the Output Feature Map tensor. + ofm_zero_point : int + The quantization zero point for the Output Feature Map tensor. + ofm_channels : int + The number of the Output Feature Map channels. + reversed_operands : bool + True if IFM2 is the first operand and IFM is the second operand. + activation : str + The activation function to use. + "NONE" - no activation function. + "CLIP" - clip the output between clip_min and clip_max. + "TANH" - tanh activation function. + "SIGMOID" - sigmoid activation function. + "LUT" - use a look-up table to perform the activation function. + Available activations for activation type: + {int8, uint8}: "NONE", "CLIP", "TANH", "SIGMOID", "LUT" + {int32}: "NONE" + clip_min : int + The minimum clipping value if activation = "CLIP". + clip_max : int + The maximum clipping value if activation = "CLIP". + ifm_layout : str, optional + The layout of the Input Feature Map tensor. Can be "NHWC" or "NHCWB16". + ifm2_layout : str, optional + The layout of the Input Feature Map tensor 1. Can be "NHWC" or "NHCWB16". Review comment: Done -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
