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#pragma once
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
#include <torch/csrc/jit/tensorexpr/stmt.h>
#include <torch/csrc/jit/tensorexpr/tensor.h>
namespace torch {
namespace jit {
namespace tensorexpr {
class HasRand : public IRVisitor {
public:
HasRand(Stmt* stmt) : stmt_(stmt) {
stmt_->accept(this);
}
bool has_rand() const {
return has_rand_;
}
private:
void visit(const Intrinsics* v) override {
if (v->op_type() == IntrinsicsOp::kRand) {
has_rand_ = true;
} else {
IRVisitor::visit(v);
}
}
Stmt* stmt_;
bool has_rand_ = false;
};
template <typename Node>
class NodeFinder : public IRVisitor {
public:
virtual void visit(const Node* v) override {
nodes.push_back((Node*)v);
IRVisitor::visit(v);
}
static std::vector<Node*> find(Stmt* s) {
NodeFinder<Node> nf;
s->accept(&nf);
return nf.nodes;
}
std::vector<Node*> nodes;
};
class VarFinder : public IRVisitor {
public:
virtual void visit(const Var* v) override {
vars_.insert(v);
IRVisitor::visit(v);
}
static std::unordered_set<const Var*> find(Stmt* s) {
VarFinder nf;
s->accept(&nf);
return nf.vars();
}
static std::unordered_set<const Var*> find(const Expr* e) {
VarFinder nf;
e->accept(&nf);
return nf.vars();
}
const std::unordered_set<const Var*>& vars() {
return vars_;
}
private:
std::unordered_set<const Var*> vars_;
};
// Finds all kinds of write operations to the provided Buf.
class WritesToBuf : public IRVisitor {
public:
WritesToBuf(const Buf* target) : target_(target) {}
std::vector<const Stmt*> writes() {
return writes_;
}
static std::vector<const Stmt*> find(Stmt* s, const Buf* b) {
WritesToBuf finder(b);
s->accept(&finder);
return finder.writes();
}
private:
void visit(const Store* v) override {
if (v->buf() == target_) {
writes_.push_back(v);
}
}
void visit(const AtomicAdd* v) override {
if (v->buf() == target_) {
writes_.push_back(v);
}
}
const Buf* target_;
std::vector<const Stmt*> writes_;
};
// Traverses the IR to determine if a particular Var is modified within it.
class ModifiesVarChecker : public IRVisitor {
public:
ModifiesVarChecker(const Var* v) : var_(v) {}
static bool check(const Stmt* s, const Var* v) {
ModifiesVarChecker checker(v);
s->accept(&checker);
return checker.found();
}
bool found() {
return found_;
}
private:
void visit(const Store* v) override {
if (v->buf()->base_handle() == var_) {
found_ = true;
return;
}
IRVisitor::visit(v);
}
void visit(const AtomicAdd* v) override {
if (v->buf()->base_handle() == var_) {
found_ = true;
return;
}
IRVisitor::visit(v);
}
void visit(const Let* v) override {
if (v->var() == var_) {
found_ = true;
return;
}
IRVisitor::visit(v);
}
void visit(const For* v) override {
if (v->var() == var_) {
found_ = true;
return;
}
IRVisitor::visit(v);
}
const Var* var_;
bool found_{false};
};
// A class that analyzes the given program relevant for Block backend
// It creates a map of multi dim buffers and their flat verions
class CreateBufferMap : public IRVisitor {
public:
const std::unordered_map<std::string, const Buf*>& getBufferMap() const {
return map_input_to_tensor_bufs_;
}
private:
void visit(const Store* v) override {
auto load_node = dynamic_cast<const Load*>(v->value());
auto call_node = dynamic_cast<const FunctionCall*>(v->value());
if (load_node || call_node) {
TORCH_INTERNAL_ASSERT(!(load_node && call_node));
auto t_buf = load_node ? load_node->buf() : call_node->tensor()->buf();
if (load_node) {
map_input_to_tensor_bufs_.emplace(t_buf->name_hint(), v->buf());
} else {
map_input_to_tensor_bufs_.emplace(v->buf()->name_hint(), t_buf);
}
}
v->value()->accept(this);
}
std::unordered_map<std::string, const Buf*> map_input_to_tensor_bufs_;
};
} // namespace tensorexpr
} // namespace jit
} // namespace torch
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