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