mirror of https://github.com/llvm/torch-mlir
100 lines
2.9 KiB
C++
100 lines
2.9 KiB
C++
//===- dynamic_ir.h -------------------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// Also available under a BSD-style license. See LICENSE.
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//
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//===----------------------------------------------------------------------===//
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// This file is adapted from pytorch/pytorch
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// https://github.com/pytorch/pytorch/blob/master/torch/csrc/lazy/ts_backend/dynamic_ir.h
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//===----------------------------------------------------------------------===//
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#pragma once
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#include <ATen/core/symbol.h>
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#include <functional>
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#include <memory>
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#include <set>
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#include <string>
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#include <unordered_map>
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#include <unordered_set>
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#include <utility>
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#include <vector>
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#include "mlir_node.h"
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#include <c10/core/ScalarType.h>
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#include <c10/util/Flags.h>
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#include <torch/csrc/lazy/core/hash.h>
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#include <torch/csrc/lazy/core/ir.h>
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#include <torch/csrc/lazy/core/ir_metadata.h>
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C10_DECLARE_bool(ltc_enable_dynamic_shapes);
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namespace torch {
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namespace lazy {
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/**
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* The goal of "dynamic" Nodes is to patch a hole in our tracing.
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* Previously, if a user called `sizes` on a Tensor, it would leak out
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* of our tracing system, as `sizes` returns a torch.Size or an int. To
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* prevent this from happening, we introduce DimensionNode, a new type
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* of Node that abstracts the operation of getting the dimensions of a
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* Tensor.
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*
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* Consider the following example:
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* ```
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* numel = x.shape()[0] * x.shape()[1]
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* ```
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*
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* Here, `x.shape()[i]` will be a SizeNode (subclass of DimensionNode),
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* and the multiplication of the two SizeNodes will be represented by
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* a SizeMul (also a subclass of DimensionNode). Through this, we can
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* prevent `numel` from being represented as a Python int and thus
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* burned into the Graph.
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*/
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class TORCH_API DimensionNode : public lazy::TorchMlirNode {
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public:
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DimensionNode(OpKind op, OpList operands, hash_t hash_seed = kHashSeed);
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bool isDynamic() { return false; }
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std::string ToString() const override;
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virtual int64_t getStaticValue() const = 0;
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};
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// Represents the result of calling `size` on a Tensor
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class TORCH_API SizeNode : public DimensionNode {
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public:
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SizeNode(Value input, size_t dim);
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int64_t getStaticValue() const override;
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std::string ToString() const override;
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size_t dim_ = 0;
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};
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class TORCH_API SizeAdd : public DimensionNode {
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public:
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SizeAdd(Value a, Value b);
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int64_t getStaticValue() const override;
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std::string ToString() const override;
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};
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class TORCH_API SizeMul : public DimensionNode {
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public:
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SizeMul(Value a, Value b);
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int64_t getStaticValue() const override;
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std::string ToString() const override;
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};
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class TORCH_API SizeDiv : public DimensionNode {
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public:
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SizeDiv(Value a, Value b);
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int64_t getStaticValue() const override;
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std::string ToString() const override;
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};
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} // namespace lazy
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} // namespace torch
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