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device_type_analysis.cpp
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device_type_analysis.cpp
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#include <ATen/core/interned_strings.h>
#include <ATen/core/jit_type.h>
#include <c10/core/Device.h>
#include <c10/util/ArrayRef.h>
#include <c10/util/Optional.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/jit_log.h>
#include <torch/csrc/jit/passes/device_type_analysis.h>
#include <torch/csrc/jit/passes/shape_analysis.h>
#include <torch/library.h>
#include <memory>
#include <utility>
namespace torch {
namespace jit {
namespace {
using Tensor = at::Tensor;
using Device = at::Device;
using PropRule = std::function<bool(Node*)>;
/*
A Propagation Rule takes the Node, and
applies the relevant properties to the Tensor outputs
of the Node (based on the rule itself)
Returns: Bool indicating if anything was changed
*/
bool setDeviceType(Value* value, c10::optional<Device> device) {
auto tensor_type = value->type()->expect<TensorType>();
bool changed = tensor_type->device() != device;
if (changed) {
value->setType(tensor_type->withDevice(device));
}
return changed;
}
bool setReturnsToDevice(Node* n, c10::optional<Device> device) {
bool changed = false;
for (Value* out : n->outputs()) {
auto tensor_type = out->type()->cast<TensorType>();
if (!tensor_type) {
continue;
}
changed |= setDeviceType(out, device);
}
return changed;
}
PropRule setReturnstoDeviceRule(DeviceType deviceType) {
Device device = Device(deviceType);
return [=](Node* n) { return setReturnsToDevice(n, device); };
}
bool returnFirstArgDeviceRule(Node* n) {
// Custom Rule for when multiple args can have mismatched device types
auto tensor_type = n->inputs()[0]->type()->cast<TensorType>();
TORCH_INTERNAL_ASSERT(tensor_type, "Expecting a tensor type");
return setReturnsToDevice(n, tensor_type->device());
}
bool returnSecondArgDeviceRule(Node* n) {
// Custom Rule for when multiple args can have mismatched device types
auto tensor_type = n->inputs()[1]->type()->cast<TensorType>();
TORCH_INTERNAL_ASSERT(tensor_type, "Expecting a tensor type");
return setReturnsToDevice(n, tensor_type->device());
}
bool isZerodimCPUTensor(std::shared_ptr<TensorType> tensor_type) {
// CPU devices on zerodim tensors are the only device that can be
// overwritten by another device. Therefore, to be conservative
// assume that it is not a zerodim cpu tensor if something is not known.
bool is_zerodim = tensor_type->symbolic_sizes().rank().value_or(-1) == 0;
bool is_cpu = tensor_type->device() && tensor_type->device()->is_cpu();
return is_zerodim && is_cpu;
}
bool propWithNoDevice(Node* n) {
// Propagate if we can verify that all input devices match,
// except CPU zerodim, which any other type can overwrite
int input_num = 0;
for (; input_num < n->inputs().size(); input_num++) {
if (n->inputs()[input_num]->type()->cast<TensorType>()) {
break;
}
}
if (input_num == n->inputs().size()) {
// No tensor found
return setReturnsToDevice(n, c10::nullopt);
}
auto tensor_type = n->inputs()[input_num]->type()->expect<TensorType>();
bool only_seen_cpu_zerodim = isZerodimCPUTensor(tensor_type);
c10::optional<Device> device = tensor_type->device();
// Now see if all inputs have a consistent device type
for (input_num++; input_num < n->inputs().size(); input_num++) {
auto tensor_type = n->inputs()[input_num]->type()->cast<TensorType>();
if (!tensor_type || isZerodimCPUTensor(tensor_type)) {
continue;
}
if (device != tensor_type->device()) {
if (only_seen_cpu_zerodim) {
device = tensor_type->device();
only_seen_cpu_zerodim = false;
} else {
// Bail on the type not match case
return setReturnsToDevice(n, c10::nullopt);
}
}
}
return setReturnsToDevice(n, device);
}
bool defaultDeviceProp(Node* n) {
// Detecting if the op has a device object argument
// as there is implicit string conversion to device
auto schema = n->maybeSchema();
if (!schema) {
return false;
}
auto arguments = schema->arguments();
for (int i = 0; i < arguments.size(); i++) {
Argument& argument = arguments[i];
if (DeviceObjType::get()->isSubtypeOf(argument.type())) {
// Optional args are filled in by torchscript with default val
auto input_val = toIValue(n->inputs().at(i));
if (!input_val.has_value()) {
// Can't propagate if there is a dynamic device type
return false;
}
if (input_val->isNone()) {
continue;
}
if (!input_val->isDevice()) {
// Bail on union types
return false;
}
TORCH_INTERNAL_ASSERT(input_val->isDevice())
Device device = input_val->toDevice();
return setReturnsToDevice(n, device);
}
}
return propWithNoDevice(n);
}
struct DeviceTypePropagationPass : public PropertyPropBase {
explicit DeviceTypePropagationPass(std::shared_ptr<Graph> graph)
: PropertyPropBase(graph) {
buildRuleRegistry();
}
// returns true if at least one node has its scalar type set on a tensor node
bool run() {
propagateBlock(graph_->block(), false);
return changed_;
}
private:
void propagateNode(Node* n, bool _ = false) override {
GRAPH_DEBUG("processNode");
switch (n->kind()) {
case prim::If:
return processIf(n);
case prim::Loop:
return processLoop(n);
case prim::CallMethod:
case prim::CallFunction:
return; // Not handled for now
default:
break;
}
bool has_tensor_output =
std::any_of(n->outputs().begin(), n->outputs().end(), [](Value* v) {
return (bool)v->type()->cast<TensorType>();
});
if (!has_tensor_output) {
// if output contains no tensor, nothing to propagate
return;
}
switch (n->kind()) {
case prim::Constant:
// This is already been propagated by something else
case prim::ListConstruct:
case prim::ListUnpack:
return; // Not handled for now
default:
if (n->kind().is_aten()) {
return processAtenOps(n);
} else {
return; // Not handled for now
}
}
}
void processAtenOps(Node* n) {
GRAPH_DEBUG("processAtenOps");
GRAPH_DEBUG("case = ", n->kind(), " ", *n);
// Custom Rule Matching
auto op = n->maybeOperator();
if (!op) {
return;
}
auto prop_fn = device_prop_registry_->find(*op);
if (prop_fn) {
PropRule rule = *prop_fn;
changed_ |= rule(n);
return;
}
changed_ |= defaultDeviceProp(n);
}
void buildRuleRegistry() {
// building a registry for all of the custom Device Type rules
if (device_prop_registry_)
return;
static OperatorMap<PropRule> temp_registry{
{"aten::cpu(Tensor self) -> Tensor",
setReturnstoDeviceRule(DeviceType::CPU)},
{"aten::cuda(Tensor self) -> Tensor",
setReturnstoDeviceRule(DeviceType::CUDA)},
{"aten::to_mkldnn(Tensor self, ScalarType? dtype) -> Tensor",
setReturnstoDeviceRule(DeviceType::MKLDNN)},
{"aten::reshape_as(Tensor self, Tensor other) -> Tensor",
returnFirstArgDeviceRule},
{"aten::view_as(Tensor self, Tensor other) -> Tensor",
returnFirstArgDeviceRule},
{"aten::expand_as(Tensor self, Tensor other) -> Tensor",
returnFirstArgDeviceRule},
{"aten::type_as(Tensor self, Tensor other) -> Tensor",
returnSecondArgDeviceRule},
};
device_prop_registry_ =
std::make_unique<OperatorMap<PropRule>>(std::move(temp_registry));
}
static std::unique_ptr<OperatorMap<PropRule>> device_prop_registry_;
bool changed_ = false;
};
std::unique_ptr<OperatorMap<PropRule>>
DeviceTypePropagationPass::device_prop_registry_ = nullptr;
} // anonymous namespace
// This analysis propagates input device types (if any) throughout the
// graph.
bool DeviceTypePropagation(std::shared_ptr<Graph>& graph) {
auto tp = std::make_unique<DeviceTypePropagationPass>((graph));
bool changed = tp->run();
if (changed) {
GRAPH_DUMP("After TensorPropertyPropagation pass:", graph);
}
return changed;
}
} // namespace jit
} // namespace torch