refactor(opmeth): add OpMethCache struct

GitOrigin-RevId: c1ebe15672

imperative/python/src/common.cpp

@@ -52,7 +52,7 @@ std::string get_default_device() {
 }
 
 void init_common(py::module m) {
-    auto&& PyCompNode = py::class_<CompNode>(m, "CompNode")
+    auto PyCompNode = py::class_<CompNode>(m, "CompNode")
         .def(py::init())
         .def(py::init(py::overload_cast<const std::string&>(&CompNode::load)))
         .def_property_readonly("logical_name", [](const CompNode& cn) {

imperative/python/src/grad.cpp

@@ -34,53 +34,36 @@ struct GradSlotWeakPtr {
     size_t idx;
 };
 
-struct BackwardGraphCache : std::unordered_map<uint64_t, std::shared_ptr<OptimizedBackwardGraphResult>>, CompNodeDepedentObject {
-    std::shared_ptr<void> on_comp_node_finalize() override {
-        clear();
-        return {};
-    }
-} backward_graph_cache;
-
 std::shared_ptr<OptimizedBackwardGraphResult> make_backward_graph(
         ApplyContext& ctx, const apply_result_t& outputs) {
     // hash
-    static_assert(alignof(size_t) % alignof(bool) == 0);
-    size_t buf_size = (1 + ctx.nargs * 2) * sizeof(size_t) + ctx.nargs * sizeof(bool);
-    alignas(alignof(size_t)) std::byte buf[buf_size];
-    size_t* size_t_ptr = reinterpret_cast<size_t*>(buf);
-    bool* bool_ptr = reinterpret_cast<bool*>(size_t_ptr + (1 + ctx.nargs * 2));
-    bool* bool_ptr0 = bool_ptr;
-    *(size_t_ptr++) = ctx.op->hash();
+    using OptimizedBackwardGraphCache = OpMethResultCache<std::shared_ptr<OptimizedBackwardGraphResult>, SmallVector<bool>>;
+    thread_local OptimizedBackwardGraphCache cache;
+    decltype(cache)::key_t cache_key{ctx.op};
+    SmallVector<LogicalTensorDesc>& input_descs = cache_key.inputs;
+    SmallVector<bool>& input_requires_grad = std::get<0>(cache_key.extras);
+    input_descs.resize(ctx.nargs);
+    input_requires_grad.resize(ctx.nargs);
     for (size_t i = 0; i < ctx.nargs; ++i) {
-        *(size_t_ptr++) = mgb::hash(ctx.args[i]->dtype().handle());
-        *(size_t_ptr++) = mgb::hash(ctx.args[i]->comp_node());
-        *(bool_ptr++) = !ctx.args[i]->m_grad_info_dict.empty();
+        input_descs[i].layout.dtype = ctx.args[i]->dtype();
+        input_descs[i].comp_node = ctx.args[i]->comp_node();
+        input_requires_grad[i] = python::input_requires_grad(ctx, i);
     }
-    mgb_assert(bool_ptr0 == reinterpret_cast<bool*>(size_t_ptr) &&
-               bool_ptr == reinterpret_cast<bool*>(buf + buf_size));
-    uint64_t key = XXHash{}.update(buf, buf_size).digest();
 
-    auto&& iter = backward_graph_cache.find(key);
-    if (iter != backward_graph_cache.end()) {
+    auto iter = cache.find(cache_key);
+    if (iter != cache.end()) {
         return iter->second;
     }
 
     // slow path
-    SmallVector<LogicalTensorDesc> inputs(ctx.nargs);
-    SmallVector<bool> input_requires_grad(ctx.nargs, false);
     SmallVector<bool> output_has_grad(outputs.size(), true);
-    for (size_t i = 0; i < ctx.nargs; ++i) {
-        inputs[i].comp_node = ctx.args[i]->comp_node();
-        inputs[i].layout.dtype = ctx.args[i]->dtype();
-        input_requires_grad[i] = python::input_requires_grad(ctx, i);
-    }
     std::shared_ptr<OptimizedBackwardGraphResult> ret;
     auto bg = OpDef::make_backward_graph(
-            *ctx.op, inputs, input_requires_grad, output_has_grad);
+            *ctx.op, input_descs, input_requires_grad, output_has_grad);
     if (!bg.graph.empty()) {
         ret = std::make_shared<OptimizedBackwardGraphResult>(bg);
     }
-    backward_graph_cache.emplace(key, ret);
+    cache.emplace(cache_key, ret);
     return ret;
 }
 

imperative/src/impl/op_def.cpp

@@ -85,7 +85,14 @@ EncodedSubraph OpDef::make_backward_graph(
     const SmallVector<LogicalTensorDesc>& inputs,
     const SmallVector<bool>& input_requires_grad,
     const SmallVector<bool>& output_has_grad) {
-    return def.trait()->make_backward_graph(def, inputs, input_requires_grad, output_has_grad);
+    using BackwardGraphCache = OpMethResultCache<EncodedSubraph, SmallVector<bool>, SmallVector<bool>>;
+    thread_local BackwardGraphCache cache;
+    decltype(cache)::key_t cache_key{const_cast<OpDef&>(def).shared_from_this(), inputs, {input_requires_grad, output_has_grad}};
+    auto iter = cache.find(cache_key);
+    if (iter == cache.end()) {
+        iter = cache.insert({cache_key, def.trait()->make_backward_graph(def, inputs, input_requires_grad, output_has_grad)}).first;
+    }
+    return iter->second;
 }
 
 std::vector<std::pair<const char*, std::string>> OpDef::props(
@@ -94,7 +101,7 @@ std::vector<std::pair<const char*, std::string>> OpDef::props(
 }
 
 std::string OpDef::to_string() const {
-    std::string builder = "{";
+    std::string builder = trait()->make_name(*this) + "{";
     for (auto&& [name, value]: props(*this)) {
         builder += name;
         builder += ": ";
@@ -170,7 +177,7 @@ std::string Subgraph::repr() const {
         if (auto* p = op->try_cast_final<OprAttr>()) {
             buf << p->type;
         } else {
-            buf << op->dyn_typeinfo()->name;
+            buf << op->make_name();
         }
         for (size_t i : ins) {
             buf << " ";
@@ -196,6 +203,26 @@ std::string Subgraph::repr() const {
     return buf.str();
 }
 
+bool Subgraph::is_single() const {
+    if (exprs.size() != 1) {
+        return false;
+    }
+    auto& expr = exprs.at(0);
+    return expr.inputs == inputs && expr.outputs == outputs;
+}
+
+std::shared_ptr<OpDef> Subgraph::as_single() const {
+    if (is_single()) {
+        return exprs.at(0).op;
+    } else {
+        return nullptr;
+    }
+}
+
+bool Subgraph::operator==(const Subgraph& rhs) const {
+    mgb_assert(false, "Not Implemented");
+}
+
 } // namespace imperative
 } // namespace mgb
 

imperative/src/impl/op_trait.h

@@ -12,6 +12,7 @@
 #pragma once
 
 #include "megbrain/imperative/op_def.h"
+#include "megbrain/imperative/graph_cache.h"
 
 namespace mgb {
 namespace imperative {

imperative/src/impl/proxy_graph_detail.cpp

@@ -113,49 +113,12 @@ void execute(const OpDef& def,
 //     return graph->infer_output_attrs_fallible(def, inputs);
 // }
 
-namespace {
-
-size_t get_backward_graph_hash_key(const OpDef& def,
-        const SmallVector<LogicalTensorDesc>& inputs,
-        const SmallVector<bool>& input_requires_grad,
-        const SmallVector<bool>& output_has_grad) {
-    XXHash state;
-    size_t length = 0, data[3 + 2 * inputs.size()];
-    data[length ++] = def.hash();
-    for (auto &&i : inputs) {
-        data[length ++] = mgb::hash(i.layout.dtype.handle());
-        data[length ++] = mgb::hash(i.comp_node);
-    }
-    data[length ++] = mgb::hash(input_requires_grad);
-    data[length ++] = mgb::hash(output_has_grad);
-    mgb_assert(length == 3 + 2 * inputs.size());
-    state.update(data, length * sizeof(size_t));
-    return state.digest();
-}
-
-struct BackwardGraphCache : std::unordered_map<size_t, EncodedSubraph>, CompNodeDepedentObject {
-    std::shared_ptr<void> on_comp_node_finalize() override {
-        clear();
-        return {};
-    }
-} backward_graph_cache;
-
-} // anonymous namespace
-
 EncodedSubraph
 make_backward_graph(const OpDef& def,
         const SmallVector<LogicalTensorDesc>& inputs,
         const SmallVector<bool>& input_requires_grad,
         const SmallVector<bool>& output_has_grad) {
-    auto hash_key = get_backward_graph_hash_key(def, inputs, input_requires_grad, output_has_grad);
-    auto&& iter = backward_graph_cache.find(hash_key);
-    if (iter != backward_graph_cache.end()) {
-        return iter->second;
-    }
-    auto&& graph = ProxyGraph::get_default_graph();
-    auto res = graph->make_backward_graph(def, inputs, input_requires_grad, output_has_grad);
-    backward_graph_cache.emplace(hash_key, res);
-    return res;
+    return ProxyGraph::get_default_graph()->make_backward_graph(def, inputs, input_requires_grad, output_has_grad);
 }
 
 } // namespace proxy_graph_detail

imperative/src/include/megbrain/imperative/graph_cache.h

@@ -0,0 +1,90 @@
+/**
+ * \file imperative/src/include/megbrain/imperative/graph_builder.h
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#pragma once
+
+#include "megbrain/imperative/subgraph.h"
+#include "megbrain/imperative/op_def.h"
+
+namespace mgb {
+namespace imperative {
+
+template <typename... TExtraArgs>
+struct OpMethArgs {
+    std::shared_ptr<OpDef> op;
+    SmallVector<LogicalTensorDesc> inputs;
+    std::tuple<TExtraArgs...> extras;
+
+    size_t hash() const;
+    bool operator==(const OpMethArgs& rhs) const {
+        if (bool(op) ^ bool(rhs.op)) {
+            return false;
+        }
+        if (op && rhs.op && !op->is_same(*rhs.op)) {
+            return false;
+        }
+        if (inputs.size() != rhs.inputs.size()) {
+            return false;
+        }
+        size_t nr_inputs = inputs.size();
+        for (size_t i = 0; i < nr_inputs; ++i) {
+            if (inputs[i].comp_node != rhs.inputs[i].comp_node) {
+                return false;
+            }
+            if (inputs[i].layout.dtype != rhs.inputs[i].layout.dtype) {
+                return false;
+            }
+        }
+        return extras == rhs.extras;
+    }
+
+    struct hash_t {
+        size_t operator()(const OpMethArgs& key) const {
+            return key.hash();
+        }
+    };
+};
+
+template <typename... TExtraArgs>
+inline size_t OpMethArgs<TExtraArgs...>::hash() const {
+    XXHash state;
+    size_t length = 0;
+    size_t data[1 + 2 * inputs.size() + sizeof...(TExtraArgs)];
+    auto append = [&](size_t hash) {
+        data[length++] = hash;
+    };
+    append(op->hash());
+    for (auto &&i : inputs) {
+        append(mgb::hash(i.layout.dtype.handle()));
+        append(mgb::hash(i.comp_node));
+    }
+    std::apply([&](auto&&... extras){
+        (append(mgb::hash(extras)), ...);
+    }, extras);
+    mgb_assert(length == sizeof(data) / sizeof(size_t));
+    state.update(data, sizeof(data));
+    return state.digest();
+}
+
+template <typename TValue, typename... TExtraArgs>
+struct OpMethResultCache : std::unordered_map<OpMethArgs<TExtraArgs...>, TValue, typename OpMethArgs<TExtraArgs...>::hash_t>, CompNodeDepedentObject {
+    std::shared_ptr<void> on_comp_node_finalize() override {
+        static_cast<std::unordered_map<OpMethArgs<TExtraArgs...>, TValue, typename OpMethArgs<TExtraArgs...>::hash_t>*>(this)->clear();
+        // clear();
+        return {};
+    }
+
+    using key_t = OpMethArgs<TExtraArgs...>;
+};
+
+}  // namespace imperative
+}  // namespace mgb