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finalize.cpp
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finalize.cpp
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#include <torch/csrc/jit/passes/quantization/finalize.h>
#include <torch/csrc/jit/jit_log.h>
#include <torch/csrc/jit/passes/clear_profiling.h>
#include <torch/csrc/jit/passes/common_subexpression_elimination.h>
#include <torch/csrc/jit/passes/constant_pooling.h>
#include <torch/csrc/jit/passes/constant_propagation.h>
#include <torch/csrc/jit/passes/dead_code_elimination.h>
#include <torch/csrc/jit/passes/freeze_module.h>
#include <torch/csrc/jit/passes/loop_unrolling.h>
#include <torch/csrc/jit/passes/peephole.h>
#include <torch/csrc/jit/passes/prepack_folding.h>
#include <torch/csrc/jit/passes/quantization/quantization_patterns.h>
#include <torch/csrc/jit/passes/quantization/register_packed_params.h>
#include <torch/csrc/jit/runtime/graph_iterator.h>
#include <utility>
namespace torch {
namespace jit {
namespace {
void insertPrepackUnpackForLinear(std::shared_ptr<Graph>& graph) {
std::vector<QuantFusionInfo> patterns_and_replacements =
linear_prepack_unpack_patterns();
for (const auto& entry : patterns_and_replacements) {
SubgraphRewriter rewriter;
rewriter.RegisterRewritePattern(entry.pattern, entry.replacement);
rewriter.runOnGraph(graph, entry.filters);
}
}
void insertPrepackUnpackForConv(std::shared_ptr<Graph>& graph) {
std::vector<QuantFusionInfo> patterns_and_replacements =
conv_prepack_unpack_patterns();
for (const auto& entry : patterns_and_replacements) {
SubgraphRewriter rewriter;
rewriter.RegisterRewritePattern(entry.pattern, entry.replacement);
rewriter.runOnGraph(graph, entry.filters);
}
}
void removePackedParamInsertionAndFPWeightsSetAttr(
std::shared_ptr<Graph>& g,
const std::unordered_set<std::string>& packed_param_attr_names) {
DepthFirstGraphNodeIterator it(g);
Node* n = nullptr;
std::vector<Node*> nodes_to_delete;
while ((n = it.next()) != nullptr) {
if (n->kind() == prim::SetAttr) {
const std::string& attr_name = n->s(attr::name);
if (packed_param_attr_names.count(attr_name)) {
nodes_to_delete.push_back(n);
} else {
Value* v = n->input(0);
Value* self = g->inputs()[0];
std::vector<std::string> paths = getModuleAccessPath(v, self);
std::string path = joinPaths(paths);
if (packed_param_attr_names.count(path)) {
nodes_to_delete.push_back(n);
}
}
}
}
for (auto node : nodes_to_delete) {
node->removeAllInputs();
}
for (auto node : nodes_to_delete) {
node->destroy();
}
ConstantPooling(g);
EliminateDeadCode(g);
}
void removeObserverCallMethods(std::shared_ptr<Graph>& g) {
DepthFirstGraphNodeIterator it(g);
Node* n = nullptr;
std::vector<Node*> nodes_to_delete;
while ((n = it.next()) != nullptr) {
if (n->kind() == prim::CallMethod) {
const std::string& attr_name = n->s(attr::name);
if (attr_name == "calculate_qparams") {
auto observer_node = n->input(0)->node();
if (observer_node->kind() == prim::GetAttr &&
observer_node->s(attr::name).find("_observer_") !=
std::string::npos) {
nodes_to_delete.push_back(n);
}
}
}
}
for (auto node : nodes_to_delete) {
node->removeAllInputs();
}
for (auto node : nodes_to_delete) {
node->destroy();
}
EliminateDeadCode(g);
}
void keepOnlyPackedParamsGeneration(Module& m, const std::string& method_name) {
auto g = m.get_method(method_name).graph();
Function& function = m.get_method(method_name).function();
const auto& schema = function.getSchema();
auto new_schema = schema.cloneWithReturns({Argument("", NoneType::get())});
for (size_t i = 0, output_size = g->outputs().size(); i < output_size; i++) {
g->eraseOutput(i);
}
Node* none_node = g->createNone();
g->registerOutput(none_node->output());
none_node->insertBefore(g->return_node());
function.setSchema(std::move(new_schema));
EliminateDeadCode(g);
}
} // namespace
void QuantFusion(std::shared_ptr<Graph>& graph, QuantType quant_type) {
std::vector<QuantFusionInfo> patterns;
if (quant_type == QuantType::DYNAMIC) {
patterns = dynamic_quant_fusion_pattern_and_replacements();
std::vector<QuantFusionInfo> patterns_wo_dynamic_activation_quant =
dynamic_quantized_linear_pattern_and_replacements();
patterns.insert(
patterns.end(),
patterns_wo_dynamic_activation_quant.begin(),
patterns_wo_dynamic_activation_quant.end());
} else {
patterns = quant_fusion_pattern_and_replacements();
}
for (const auto& info : patterns) {
SubgraphRewriter rewriter;
rewriter.RegisterRewritePattern(info.pattern, info.replacement);
rewriter.runOnGraph(graph, info.filters);
}
}
void InsertPrepackUnpack(std::shared_ptr<Graph>& graph) {
insertPrepackUnpackForLinear(graph);
insertPrepackUnpackForConv(graph);
}
void InsertPrepackUnpack(Module& module) {
for (auto& method : module.get_methods()) {
auto graph = method.graph();
InsertPrepackUnpack(graph);
}
for (Module m : module.children()) {
InsertPrepackUnpack(m);
}
}
void FoldQuantizedPrepackingOps(Module& module) {
auto filter_fn = [](const Node* n) -> bool {
return (
n->kind() == Symbol::fromQualString("quantized::linear_prepack") ||
n->kind() == Symbol::fromQualString("quantized::conv1d_prepack") ||
n->kind() == Symbol::fromQualString("quantized::conv2d_prepack") ||
n->kind() == Symbol::fromQualString("quantized::conv3d_prepack") ||
n->kind() ==
Symbol::fromQualString("quantized::conv_transpose1d_prepack") ||
n->kind() ==
Symbol::fromQualString("quantized::conv_transpose2d_prepack"));
};
PrePackingOpsFolder(module, filter_fn, "quantized");
}
static std::unordered_set<std::string> RegisterPrePackingParams(
Module& module,
const std::string& method_name) {
auto filter_fn = [](const Node* n) -> bool {
return (
n->kind() == Symbol::fromQualString("quantized::linear_prepack") ||
n->kind() == Symbol::fromQualString("quantized::conv1d_prepack") ||
n->kind() == Symbol::fromQualString("quantized::conv2d_prepack") ||
n->kind() == Symbol::fromQualString("quantized::conv3d_prepack") ||
n->kind() ==
Symbol::fromQualString("quantized::conv_transpose1d_prepack") ||
n->kind() ==
Symbol::fromQualString("quantized::conv_transpose2d_prepack"));
};
return RegisterPrePackParams(module, method_name, filter_fn, "");
}
Module Finalize(
Module& module,
QuantType quant_type,
const std::vector<std::string>& preserved_attrs) {
// Tracing annotates the resulting graph with shape information. In many case,
// user applies different input shapes to traced graph. It is on the user to
// know it is correct to do so. The quantized module needs to be clean up and
// To prevent the JIT optimizations from leveraging the annotated shape info,
// clear shape information in the graph.
for (auto func : module.type()->methods()) {
ClearProfilingInformation(toGraphFunction(*func).graph());
}
auto graph = module.get_method("forward").graph();
InsertPrepackUnpack(graph);
GRAPH_DUMP("Before QuantFusion:", graph);
QuantFusion(graph, quant_type);
auto frozen = freeze_module(module, preserved_attrs);
FoldQuantizedPrepackingOps(frozen);
return frozen;
}
Module FinalizeOnDevicePTQ(
Module& module,
QuantType quant_type,
const std::string& method_name) {
// Tracing annotates the resulting graph with shape information. In many case,
// user applies different input shapes to traced graph. It is on the user to
// know it is correct to do so. The quantized module needs to be clean up and
// To prevent the JIT optimizations from leveraging the annotated shape info,
// clear shape information in the graph.
for (auto func : module.type()->methods()) {
ClearProfilingInformation(toGraphFunction(*func).graph());
}
const std::string kQuantizeString = "quantize_";
const auto matched_pos = method_name.find(kQuantizeString);
const auto end_pos = matched_pos + kQuantizeString.length();
const std::string orig_method_name = method_name.substr(end_pos);
TORCH_CHECK(
matched_pos == 0,
"Quantized ops can only be added to quantize_",
orig_method_name,
". Please make sure to run quant/dequant nodes insertion step for on-device PTQ.");
const std::string quantized_method_name = "quantized_" + orig_method_name;
auto graph = module.get_method(method_name).graph();
// Doing some AOT optimizations here
// Of all CSE seems to be required otherwise in some experiments
// serialized model is incorrect. As in it cannot be deserialized
// Rest are included as canonical optimizations that are not for inference
EliminateCommonSubexpression(graph);
EliminateDeadCode(graph);
PeepholeOptimize(graph);
ConstantPropagation(graph);
UnrollConstantLoops(graph);
ConstantPooling(graph);
InsertPrepackUnpack(graph);
GRAPH_DUMP("Before QuantFusion:", graph);
QuantFusion(graph, quant_type);
auto packed_param_attr_names = RegisterPrePackingParams(module, method_name);
GRAPH_DUMP("After QuantFusion + packed param registration:", graph);
// Now we have:
// 1. Inserted quantized weights packed params
// 2. Inserted packed params to module
// 3. Inserted quantized op
// The next thing we need is:
// 1. Replicate this method in quantize_forward
// 2. Remove SetAttr for fp weights that are reset by quantize_forward
// 3. Remove SetAttr node which will subsequently optimize away the nodes
// producing packed_params
// 4. Modify quantized_forward to remove all the nodes except for SetAttrs
cloneMethod(module, method_name, quantized_method_name);
// removeWeightSetAttrs(module, quantized_method_name);
auto quantized_graph = module.get_method(quantized_method_name).graph();
removePackedParamInsertionAndFPWeightsSetAttr(
quantized_graph, packed_param_attr_names);
// Removing packed params is not sufficient since that does not do DCE
// for observer node's getatts and callmethods because callmethods have side
// effects
removeObserverCallMethods(quantized_graph);
// This step removed the return output from the graph and subsequent
// DCE removes all the ops. After that only remaining things should be
// packed_params
keepOnlyPackedParamsGeneration(module, method_name);
return module;
}
} // namespace jit
} // namespace torch