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[circle-mlir/dialect] Enable AddOp IR #14748
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Original file line number | Diff line number | Diff line change |
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/* | ||
* Copyright (c) 2025 Samsung Electronics Co., Ltd. All Rights Reserved | ||
* Copyright 2019 The TensorFlow Authors. All Rights Reserved. | ||
* | ||
* Licensed 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. | ||
*/ | ||
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// from tensorflow/compiler/mlir/lite/ir/tfl_ops.cc | ||
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#ifndef __CIRCLE_MLIR_DIALECT_OPS_ADD_OP_H__ | ||
#define __CIRCLE_MLIR_DIALECT_OPS_ADD_OP_H__ | ||
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#include "circle-mlir/dialect/CircleDialect.h" | ||
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namespace mlir | ||
{ | ||
namespace Circle | ||
{ | ||
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// Return true if the given Add operation has the CPU kernel supported shapes. | ||
bool VerifyAddOpShapeConstraints(AddOp op) | ||
{ | ||
auto element_type = getElementTypeOrSelf(op.getOutput().getType()); | ||
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// Allows F32 and I32 outputs when the operands have valid shapes, | ||
// which are broadcastable shapes up to four dimensions or have same shapes. | ||
// TODO support Quantized Type | ||
if (element_type.isF32() || IsI32Type(element_type) || IsI64Type(element_type)) | ||
{ | ||
return VerifyOperandsHaveSameShapesOrBroadcastableShape( | ||
/*op=*/op.getOperation(), /*indices=*/ArrayRef<unsigned>{0, 1}, | ||
/*max_bcast_rank=*/4); | ||
} | ||
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return false; | ||
} | ||
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//===----------------------------------------------------------------------===// | ||
// AddOp | ||
//===----------------------------------------------------------------------===// | ||
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OpFoldResult AddOp::fold(FoldAdaptor adaptor) | ||
{ | ||
auto operands = adaptor.getOperands(); | ||
// TODO(b/142478136): Handle fused ops. | ||
if (getFusedActivationFunction() != "NONE") | ||
return {}; | ||
return ConstFoldBinaryOp( | ||
getType(), operands, [](APFloat a, APFloat b) { return a + b; }, | ||
[](APInt a, APInt b) { return a + b; }); | ||
} | ||
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} // namespace Circle | ||
} // namespace mlir | ||
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#endif // __CIRCLE_MLIR_DIALECT_OPS_ADD_OP_H__ |
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The reason why there's no Q8 or Q16 dtype here is because current circle-mlir focus on fake-quantized or float models?
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Yes...
But the actual reason is that quantize related codes are linked to TensorFlow that are too huge to import as of now.