NicolaLancellotti commented on a change in pull request #9442: URL: https://github.com/apache/tvm/pull/9442#discussion_r745465992
########## 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". + ofm_layout : str, optional + The layout of the Output Feature Map tensor. Can be "NHWC" or "NHCWB16". + + Returns + ------- + te.Tensor + The OFM tensor. Review comment: Done ########## File path: src/relay/op/contrib/ethosu/binary_elementwise.cc ########## @@ -0,0 +1,299 @@ +/* + * 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. + */ + +/*! + * \file src/relay/op/contrib/ethosu/binary_elementwise.cc + * \brief Binary elementwise operators definitions for the Arm(R) Ethos(TM)-U NPU. + */ +#include <tvm/relay/op.h> + +#include "common.h" + +namespace tvm { +namespace relay { +namespace op { +namespace contrib { +namespace ethosu { + +/*! \brief Attributes used by the Ethos(TM)-U NPU binary elementwise operators */ +struct EthosuBinaryElementwiseAttrs : public tvm::AttrsNode<EthosuBinaryElementwiseAttrs> { + String operator_type; + double ifm_scale; + int ifm_zero_point; + double ifm2_scale; + int ifm2_zero_point; + double ofm_scale; + int ofm_zero_point; + IndexExpr ofm_channels; + bool reversed_operands; + String activation; + int clip_min; + int clip_max; + String ifm_layout; + String ifm2_layout; + String ofm_layout; + String ofm_dtype; + + TVM_DECLARE_ATTRS(EthosuBinaryElementwiseAttrs, "relay.attrs.EthosuBinaryElementwiseAttrs") { + TVM_ATTR_FIELD(operator_type) + .describe( + "The type of the binary elementwise operator." + "'ADD'" + "'SUB'" + "'MUL'" + "'MIN'" + "'MAX'" + "'SHR'" + "'SHL'"); + TVM_ATTR_FIELD(ifm_scale).describe("The quantization scale for the Input Feature Map tensor."); + TVM_ATTR_FIELD(ifm_zero_point) + .describe("The quantization zero point for the Input Feature Map tensor."); + TVM_ATTR_FIELD(ifm2_scale) + .describe("The quantization scale for the Input Feature Map tensor 2."); + TVM_ATTR_FIELD(ifm2_zero_point) + .describe("The quantization zero point for the Input Feature Map tensor 2."); + TVM_ATTR_FIELD(ofm_scale).describe("The quantization scale for the Output Feature Map tensor."); + TVM_ATTR_FIELD(ofm_zero_point) + .describe("The quantization zero point for the Output Feature Map tensor."); + TVM_ATTR_FIELD(ofm_channels).describe("The number of the Output Feature Map channels."); + TVM_ATTR_FIELD(reversed_operands) + .describe("True if IFM2 is the first operand and IFM is the second operand.") + .set_default(false); + TVM_ATTR_FIELD(activation) + .describe( + "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'") + .set_default("NONE"); + TVM_ATTR_FIELD(clip_min) + .describe("The minimum clipping value if activation = 'CLIP'.") + .set_default(0); + TVM_ATTR_FIELD(clip_max) + .describe("The maximum clipping value if activation = 'CLIP'.") + .set_default(0); + TVM_ATTR_FIELD(ifm_layout) + .describe("The layout of the Input Feature Map tensor. Can be 'NHWC' or 'NHCWB16'.") + .set_default("NHWC"); + TVM_ATTR_FIELD(ifm2_layout) + .describe("The layout of the Input Feature Map tensor 2. Can be 'NHWC' or 'NHCWB16'.") + .set_default("NHWC"); + TVM_ATTR_FIELD(ofm_layout) + .describe("The layout of the Output Feature Map tensor. Can be 'NHWC' or 'NHCWB16'.") + .set_default("NHWC"); + TVM_ATTR_FIELD(ofm_dtype) + .describe( + "The Output Feature Map tensor type." + "MUL, ADD, SUB {IFM}->{OFM}:" + " {uint8, int8 int32} -> {uint8, int8, int32}, any pairing" + "MAX, MIN:" + " IFM and OFM must be of the same type, one of:" + " {int8, uint8}" + "SHR {IFM}->{OFM}:" + " {int32}->{int8, uint8, int32}, any pairing" + "SHL:" + " {int32}->{int32} only") + .set_default("NHWC"); + } +}; + +TVM_REGISTER_NODE_TYPE(EthosuBinaryElementwiseAttrs); + +bool EthosuBinaryElementwiseRel(const Array<Type>& types, int num_inputs, const Attrs& attrs, + const TypeReporter& reporter) { + int ifm_index = 0; + int ifm2_index = 1; + int result_index = 3; Review comment: Done ########## File path: src/relay/op/contrib/ethosu/binary_elementwise.cc ########## @@ -0,0 +1,299 @@ +/* + * 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. + */ + +/*! + * \file src/relay/op/contrib/ethosu/binary_elementwise.cc + * \brief Binary elementwise operators definitions for the Arm(R) Ethos(TM)-U NPU. + */ +#include <tvm/relay/op.h> + +#include "common.h" + +namespace tvm { +namespace relay { +namespace op { +namespace contrib { +namespace ethosu { + +/*! \brief Attributes used by the Ethos(TM)-U NPU binary elementwise operators */ +struct EthosuBinaryElementwiseAttrs : public tvm::AttrsNode<EthosuBinaryElementwiseAttrs> { + String operator_type; + double ifm_scale; + int ifm_zero_point; + double ifm2_scale; + int ifm2_zero_point; + double ofm_scale; + int ofm_zero_point; + IndexExpr ofm_channels; + bool reversed_operands; + String activation; + int clip_min; + int clip_max; + String ifm_layout; + String ifm2_layout; + String ofm_layout; + String ofm_dtype; + + TVM_DECLARE_ATTRS(EthosuBinaryElementwiseAttrs, "relay.attrs.EthosuBinaryElementwiseAttrs") { + TVM_ATTR_FIELD(operator_type) + .describe( + "The type of the binary elementwise operator." + "'ADD'" + "'SUB'" + "'MUL'" + "'MIN'" + "'MAX'" + "'SHR'" + "'SHL'"); + TVM_ATTR_FIELD(ifm_scale).describe("The quantization scale for the Input Feature Map tensor."); + TVM_ATTR_FIELD(ifm_zero_point) + .describe("The quantization zero point for the Input Feature Map tensor."); + TVM_ATTR_FIELD(ifm2_scale) + .describe("The quantization scale for the Input Feature Map tensor 2."); + TVM_ATTR_FIELD(ifm2_zero_point) + .describe("The quantization zero point for the Input Feature Map tensor 2."); + TVM_ATTR_FIELD(ofm_scale).describe("The quantization scale for the Output Feature Map tensor."); + TVM_ATTR_FIELD(ofm_zero_point) + .describe("The quantization zero point for the Output Feature Map tensor."); + TVM_ATTR_FIELD(ofm_channels).describe("The number of the Output Feature Map channels."); + TVM_ATTR_FIELD(reversed_operands) + .describe("True if IFM2 is the first operand and IFM is the second operand.") + .set_default(false); + TVM_ATTR_FIELD(activation) + .describe( + "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'") + .set_default("NONE"); + TVM_ATTR_FIELD(clip_min) + .describe("The minimum clipping value if activation = 'CLIP'.") + .set_default(0); + TVM_ATTR_FIELD(clip_max) + .describe("The maximum clipping value if activation = 'CLIP'.") + .set_default(0); + TVM_ATTR_FIELD(ifm_layout) + .describe("The layout of the Input Feature Map tensor. Can be 'NHWC' or 'NHCWB16'.") + .set_default("NHWC"); + TVM_ATTR_FIELD(ifm2_layout) + .describe("The layout of the Input Feature Map tensor 2. Can be 'NHWC' or 'NHCWB16'.") + .set_default("NHWC"); + TVM_ATTR_FIELD(ofm_layout) + .describe("The layout of the Output Feature Map tensor. Can be 'NHWC' or 'NHCWB16'.") + .set_default("NHWC"); + TVM_ATTR_FIELD(ofm_dtype) + .describe( + "The Output Feature Map tensor type." + "MUL, ADD, SUB {IFM}->{OFM}:" + " {uint8, int8 int32} -> {uint8, int8, int32}, any pairing" + "MAX, MIN:" + " IFM and OFM must be of the same type, one of:" + " {int8, uint8}" + "SHR {IFM}->{OFM}:" + " {int32}->{int8, uint8, int32}, any pairing" + "SHL:" + " {int32}->{int32} only") + .set_default("NHWC"); + } +}; + +TVM_REGISTER_NODE_TYPE(EthosuBinaryElementwiseAttrs); + +bool EthosuBinaryElementwiseRel(const Array<Type>& types, int num_inputs, const Attrs& attrs, + const TypeReporter& reporter) { + int ifm_index = 0; + int ifm2_index = 1; + int result_index = 3; + ICHECK_EQ(types.size(), result_index + 1); + + const auto* ifm = types[ifm_index].as<TensorTypeNode>(); + const auto* ifm2 = types[ifm2_index].as<TensorTypeNode>(); + if (ifm == nullptr) return false; + if (ifm2 == nullptr) return false; + + const auto* param = attrs.as<EthosuBinaryElementwiseAttrs>(); + ICHECK(param != nullptr) << "EthosuBinaryElementwiseAttrs cannot be nullptr."; + + String operator_type = param->operator_type; + auto ifm_dtype = ifm->dtype; + auto ifm2_dtype = ifm2->dtype; + DataType ofm_dtype; + + if (param->ofm_dtype == "int8") { + ofm_dtype = DataType::Int(8); + } else if (param->ofm_dtype == "uint8") { + ofm_dtype = DataType::UInt(8); + } else if (param->ofm_dtype == "int32") { + ofm_dtype = DataType::Int(32); + } + + if (ifm_dtype != ifm2_dtype) { + reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan()) + << "Invalid operator: expected ethosu_binary_elementwise " + << "type for ifm2 be the same of ifm but was " << ifm2_dtype + << " instead of " << ifm_dtype); + return false; + } + + if (operator_type == "ADD" || operator_type == "SUB" || operator_type == "MUL") { + if (ifm_dtype != DataType::UInt(8) && ifm_dtype != DataType::Int(8) && + ifm_dtype != DataType::Int(32)) { + reporter->GetDiagCtx().EmitFatal( + Diagnostic::Error(reporter->GetSpan()) + << "Invalid operator: expected ethosu_binary_elementwise " << operator_type + << " type(uint8) or type(int8) or type(int32) for ifm but was " << ifm_dtype); + return false; + } + if (ofm_dtype != DataType::UInt(8) && ofm_dtype != DataType::Int(8) && + ofm_dtype != DataType::Int(32)) { + reporter->GetDiagCtx().EmitFatal( + Diagnostic::Error(reporter->GetSpan()) + << "Invalid operator: expected ethosu_binary_elementwise " << operator_type + << " type(uint8) or type(int8) or type(int32) for ofm but was " << ofm_dtype); + return false; + } + } else if (operator_type == "MIN" || operator_type == "MAX") { + if (ifm_dtype != DataType::UInt(8) && ifm_dtype != DataType::Int(8)) { + reporter->GetDiagCtx().EmitFatal( + Diagnostic::Error(reporter->GetSpan()) + << "Invalid operator: expected ethosu_binary_elementwise " << operator_type + << " type(uint8) or type(int8) for ifm but was " << ifm_dtype); + return false; + } + if (ifm_dtype != ofm_dtype) { + reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan()) + << "Invalid operator: expected ethosu_binary_elementwise " + << operator_type + << " type for ofm be the same of ifm but was " << ofm_dtype + << " instead of " << ifm_dtype); + return false; + } + } else if (operator_type == "SHR") { + if (ifm_dtype != DataType::Int(32)) { + reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan()) + << "Invalid operator: expected ethosu_binary_elementwise " + << operator_type << " type(int32) for ifm but was " + << ifm_dtype); + return false; + } + if (ofm_dtype != DataType::UInt(8) && ofm_dtype != DataType::Int(8) && + ofm_dtype != DataType::Int(32)) { + reporter->GetDiagCtx().EmitFatal( + Diagnostic::Error(reporter->GetSpan()) + << "Invalid operator: expected ethosu_binary_elementwise " << operator_type + << " type(uint8) or type(int8) or type(int32) for ofm but was " << ofm_dtype); + return false; + } + } else if (operator_type == "SHL") { + if (ifm_dtype != DataType::Int(32)) { + reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan()) + << "Invalid operator: expected ethosu_binary_elementwise " + << operator_type << " type(int32) for ifm but was " + << ifm_dtype); + + return false; + } + if (ofm_dtype != DataType::Int(32)) { + reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan()) + << "Invalid operator: expected ethosu_binary_elementwise " + << operator_type << " type(int32) for ofm but was " + << ofm_dtype); + return false; + } + } else { + reporter->GetDiagCtx().EmitFatal( + Diagnostic::Error(reporter->GetSpan()) + << "Invalid operator: expected ethosu_binary_elementwise 'ADD' or 'SUB' or 'MUL' or " + << "'MIN' or 'MAX' or 'SHR' or 'SHL' for operator_type but was " << param->operator_type); + return false; + } + + // Assign ofm type + auto ofm_shape = EthosuInferBinaryElementwiseOutputShape(ifm->shape, param->ifm_layout, + param->ofm_layout, param->ofm_channels); + reporter->Assign(types[result_index], TensorType(ofm_shape, ofm_dtype)); + return true; +} + +Expr MakeEthosuBinaryElementwise(Expr ifm, Expr ifm2, Expr lut, String operator_type, + double ifm_scale, int ifm_zero_point, double ifm2_scale, + int ifm2_zero_point, double ofm_scale, int ofm_zero_point, + IndexExpr ofm_channels, bool reversed_operands, String activation, + int clip_min, int clip_max, String ifm_layout, String ifm2_layout, + String ofm_layout, String ofm_dtype) { + auto attrs = make_object<EthosuBinaryElementwiseAttrs>(); + + attrs->operator_type = std::move(operator_type); + attrs->ifm_scale = ifm_scale; + attrs->ifm_zero_point = ifm_zero_point; + attrs->ifm2_scale = ifm2_scale; + attrs->ifm2_zero_point = ifm2_zero_point; + attrs->ofm_scale = ofm_scale; + attrs->ofm_zero_point = ofm_zero_point; + attrs->ofm_channels = ofm_channels; Review comment: Done -- This is an automated message from the Apache Git Service. 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