perf(mge): add memory optimization for backward graph

precompute ops in forward to reduce saved tensor size

GitOrigin-RevId: d67043ba82

imperative/python/src/grad.cpp

@@ -11,6 +11,7 @@
 
 #include "./grad.h"
 #include "megbrain/imperative/proxy_graph_detail.h"
+#include "megbrain/imperative/backward_graph_opt.h"
 #include "megbrain/imperative/ops/autogen.h"
 #include "megbrain/imperative/ops/utility.h"
 #include "megbrain/utils/mempool.h"
@@ -32,14 +33,14 @@ struct GradSlotWeakPtr {
     size_t idx;
 };
 
-struct BackwardGraphCache : std::unordered_map<uint64_t, std::shared_ptr<BackwardGraphResult>>, CompNodeDepedentObject {
+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<BackwardGraphResult> make_backward_graph(
+std::shared_ptr<OptimizedBackwardGraphResult> make_backward_graph(
         ApplyContext& ctx, const apply_result_t& outputs) {
     // hash
     static_assert(alignof(size_t) % alignof(bool) == 0);
@@ -72,23 +73,23 @@ std::shared_ptr<BackwardGraphResult> make_backward_graph(
         inputs[i].layout.dtype = ctx.args[i]->dtype();
         input_requires_grad[i] = python::input_requires_grad(ctx, i);
     }
-    auto result = std::make_shared<BackwardGraphResult>(
-        proxy_graph_detail::make_backward_graph(
-            *ctx.op, inputs, input_requires_grad, output_has_grad));
-    if (!result->backward) {
-        result.reset();
+    std::shared_ptr<OptimizedBackwardGraphResult> ret;
+    auto bg = proxy_graph_detail::make_backward_graph(
+            *ctx.op, inputs, input_requires_grad, output_has_grad);
+    if (bg.backward) {
+        ret = std::make_shared<OptimizedBackwardGraphResult>(bg);
     }
-    backward_graph_cache.emplace(key, result);
-    return result;
+    backward_graph_cache.emplace(key, ret);
+    return ret;
 }
 
 struct BackwardGraphWithClosure {
-    std::shared_ptr<BackwardGraphResult> backward_graph;
+    std::shared_ptr<OptimizedBackwardGraphResult> backward_graph;
     SmallVector<std::shared_ptr<Tensor>> closure;
     size_t output_mask_offset;
     size_t grad_mask_offset;
 
-    BackwardGraphWithClosure(std::shared_ptr<BackwardGraphResult> backward_graph_,
+    BackwardGraphWithClosure(std::shared_ptr<OptimizedBackwardGraphResult> backward_graph_,
                              ApplyContext& ctx, const apply_result_t& outputs)
             : backward_graph(backward_graph_),
               output_mask_offset(ctx.nargs),
@@ -107,9 +108,18 @@ struct BackwardGraphWithClosure {
         //     b.requires_grad == False, save_for_backward = [0, 1, 0, 1]
         auto& save_for_backward = backward_graph->save_for_backward;
         mgb_assert(save_for_backward.size() == ctx.nargs + 2 * outputs.size());
-        closure.reserve(std::count_if(save_for_backward.begin(),
-                                      save_for_backward.end(),
-                                      ranges::identity{}));
+        size_t count = std::count_if(save_for_backward.begin(),
+                                     save_for_backward.end(),
+                                     ranges::identity{});
+        if (backward_graph->precomp) {
+            auto&& irng = ranges::span(ctx.args, ctx.nargs);
+            auto&& orng = views::transform(outputs, [](auto&& i){return i.get();});
+            auto precomp = apply(backward_graph->precomp, views::concat(irng, orng));
+            closure.reserve(precomp.size() + count);
+            std::copy(precomp.begin(), precomp.end(), std::back_inserter(closure));
+        } else {
+            closure.reserve(count);
+        }
         for (size_t i = 0; i < ctx.nargs; ++i) {
             if (save_for_backward[i]) {
                 closure.push_back(ctx.args[i]->shared_from_this());

imperative/python/src/tensor.h

@@ -212,7 +212,7 @@ decltype(auto) resolve_arrow(T&& p) {
         if constexpr (std::is_invocable_v<decltype(probe), decltype(p)>) {
             return resolve_arrow(p.operator->());
         } else {
-            return p;
+            return std::forward<T>(p);
         }
     }
 }

imperative/src/impl/backward_graph_opt.cpp

@@ -0,0 +1,114 @@
+/**
+ * \file imperative/src/impl/backward_graph_opt.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2020 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.
+ */
+
+#include "megbrain/imperative/backward_graph_opt.h"
+#include "megbrain/imperative/ops/backward_graph.h"
+#include "megbrain/imperative/ops/autogen.h"
+
+using namespace mgb;
+using namespace imperative;
+
+OptimizedBackwardGraphResult::OptimizedBackwardGraphResult(const BackwardGraphResult& src)
+        : input_has_grad(src.input_has_grad) {
+    if (!src.backward->same_type<BackwardGraph>()) {
+        // backward graph only contains a single op
+        backward = src.backward;
+        save_for_backward = src.save_for_backward;
+        return;
+    }
+    save_for_backward.resize(src.save_for_backward.size(), false);
+    precomp.reset(new BackwardGraph);
+    backward.reset(new BackwardGraph);
+
+    auto&& graph = src.backward->cast_final_safe<BackwardGraph>().graph();
+    auto&& mask = src.save_for_backward;
+    size_t input_size = src.input_has_grad.size();
+    size_t output_size = (mask.size() - input_size) / 2;
+    mgb_assert(input_size + output_size * 2 == mask.size());
+
+    auto& fgraph = precomp->cast_final<BackwardGraph>().graph();
+    auto& bgraph = backward->cast_final<BackwardGraph>().graph();
+
+    // optimization: move ops (e.g. GetVarShape) to forward to
+    // reduce memory footprint
+
+    struct VInfo {
+        bool appears_in_backward = false;
+    };
+    std::unordered_map<size_t, VInfo> vinfo;
+
+    // step 1.1: ops not in whitelist must run in backward.
+    // mark their inputs as always appears in backward
+    for (auto&& [op, iv, ov] : graph.exprs) {
+        if (!op->same_type<GetVarShape>()) {
+            for (auto&& v : iv) {
+                vinfo[v].appears_in_backward = true;
+            }
+        }
+    }
+    // step 1.2: inputs only available in backward (i.e. grads)
+    // should be marked as always appears in backward
+    for (size_t i = 0, j = 0; i < mask.size(); ++i) {
+        if (!mask[i]) continue;
+        if (i > input_size + output_size) {
+            vinfo[graph.inputs[j]].appears_in_backward = true;
+        }
+        ++j;
+    }
+
+    // step 2: try to move ops to forward, if not all their inputs
+    // are marked always appears in backward (otherwise no memory saving)
+    for (auto&& expr : graph.exprs) {
+        auto&& [op, iv, ov] = expr;
+        if (std::all_of(iv.begin(), iv.end(), [&](auto&& v){return vinfo[v].appears_in_backward;})) {
+            bgraph.exprs.push_back(expr);
+            for (auto&& v : ov) {
+                vinfo[v].appears_in_backward = true;
+            }
+            // logically should also mark all inputs as appears in backward
+            // but clearly that's a no-op.
+        } else {
+            fgraph.exprs.push_back(expr);
+            for (auto&& v : ov) {
+                if (vinfo[v].appears_in_backward) {
+                    // appears_in_backward won't change after this point
+                    // so it is safe to set fgraph.outputs based on current value
+                    fgraph.outputs.push_back(v);
+                }
+            }
+        }
+    }
+
+    // initialize remaining parts
+
+    fgraph.constants = graph.constants;
+    fgraph.inputs.reserve(input_size + output_size);
+    for (size_t i = 0, j = 0; i < input_size + output_size; ++i) {
+        if (!mask[i]) {
+            fgraph.inputs.push_back(1000000000 + i);
+            continue;
+        }
+        fgraph.inputs.push_back(graph.inputs[j++]);
+    }
+
+    bgraph.constants = graph.constants;
+    bgraph.outputs = graph.outputs;
+    bgraph.inputs = fgraph.outputs;
+    for (size_t i = 0, j = 0; i < mask.size(); ++i) {
+        if (mask[i]) {
+            auto&& v = graph.inputs[j++];
+            if (vinfo[v].appears_in_backward) {
+                save_for_backward[i] = true;
+                bgraph.inputs.push_back(v);
+            }
+        }
+    }
+}

imperative/src/include/megbrain/imperative/backward_graph_opt.h

@@ -0,0 +1,25 @@
+/**
+ * \file imperative/src/include/megbrain/imperative/backward_graph_opt.h
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2020 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.
+ */
+
+#include "./op_def.h"
+
+namespace mgb::imperative {
+
+struct OptimizedBackwardGraphResult {
+    std::shared_ptr<OpDef> precomp;
+    std::shared_ptr<OpDef> backward;
+    std::vector<bool> save_for_backward;
+    std::vector<bool> input_has_grad;
+
+    OptimizedBackwardGraphResult(const BackwardGraphResult& bgraph);
+};
+
+} // namespace mgb::imperative

imperative/src/test/backward_graph.cpp

@@ -13,11 +13,68 @@
 #include "megbrain/opr/basic_arith.h"
 #include "megbrain/opr/dnn/batch_norm.h"
 #include "megbrain/imperative/ops/opr_attr.h"
+#include "megbrain/imperative/ops/autogen.h"
+#include "megbrain/imperative/backward_graph_opt.h"
 
 using namespace mgb;
 using namespace cg;
 using namespace imperative;
 
+template <typename T>
+T prepare_backward_graph_inputs(const BackwardGraphResult& bg, const T& inputs, const T& outputs, const T& grads) {
+    T ret;
+    size_t i = 0;
+    for (auto&& t : inputs) {
+        if (bg.save_for_backward[i++]) {
+            ret.push_back(t);
+        }
+    }
+    for (auto&& t : outputs) {
+        if (bg.save_for_backward[i++]) {
+            ret.push_back(t);
+        }
+    }
+    for (auto&& t : grads) {
+        if (bg.save_for_backward[i++]) {
+            ret.push_back(t);
+        }
+    }
+    return ret;
+}
+
+template <typename T, typename U>
+T expand_grads(const U& bg, const T& outputs) {
+    T ret(bg.input_has_grad.size());
+    for (size_t i = 0, j = 0; i < bg.input_has_grad.size(); ++i) {
+        if (bg.input_has_grad[i]) {
+            ret[i] = outputs[j++];
+        }
+    }
+    return ret;
+}
+
+template <typename T>
+T prepare_optimized_backward_inputs(const OptimizedBackwardGraphResult& bg, const T& precomp, const T& inputs, const T& outputs, const T& grads) {
+    T ret = precomp;
+    size_t i = 0;
+    for (auto&& t : inputs) {
+        if (bg.save_for_backward[i++]) {
+            ret.push_back(t);
+        }
+    }
+    for (auto&& t : outputs) {
+        if (bg.save_for_backward[i++]) {
+            ret.push_back(t);
+        }
+    }
+    for (auto&& t : grads) {
+        if (bg.save_for_backward[i++]) {
+            ret.push_back(t);
+        }
+    }
+    return ret;
+}
+
 TEST(TestImperative, BackwardGraphBasic) {
     HostTensorGenerator<> gen;
     SmallVector<HostTensorND> hvs;
@@ -121,27 +178,65 @@ TEST(TestImperative, BackwardGraphIdentity) {
 }
 
 TEST(TestImperative, BatchNormGrad) {
-     auto cn = CompNode::load("xpux");
-     using Param = opr::BatchNorm::Param;
-     size_t N=2, C=3, H=5, W=5;
-     LogicalTensorDesc inp{TensorLayout{{N, C, H, W}, dtype::Float32()}, cn};
-     LogicalTensorDesc stat{TensorLayout{{C}, dtype::Float32()}, cn};
-     {
-          auto op = OprAttr::make("BatchNorm");
-          auto&& attr = op->cast_final_safe<OprAttr>();
-          Param param;
-          param.fwd_mode = Param::FwdMode::TRAINING;
-          attr.param.write_pod(param);
-          OpDef::make_backward_graph(attr, {inp, stat, stat, stat, stat},
-               {true, true ,true, false, false}, {false, false, false, false, true});
-     }
-     {
-          auto op = OprAttr::make("BatchNorm");
-          auto&& attr = op->cast_final_safe<OprAttr>();
-          Param param;
-          param.fwd_mode = Param::FwdMode::TRAINING;
-          attr.param.write_pod(param);
-          OpDef::make_backward_graph(attr, {inp, stat, stat},
-               {true, true ,true}, {false, false, true});
-     }
+    auto cn = CompNode::load("xpux");
+    using Param = opr::BatchNorm::Param;
+    size_t N=2, C=3, H=5, W=5;
+    LogicalTensorDesc inp{TensorLayout{{N, C, H, W}, dtype::Float32()}, cn};
+    LogicalTensorDesc stat{TensorLayout{{C}, dtype::Float32()}, cn};
+    {
+        auto op = OprAttr::make("BatchNorm");
+        auto&& attr = op->cast_final_safe<OprAttr>();
+        Param param;
+        param.fwd_mode = Param::FwdMode::TRAINING;
+        attr.param.write_pod(param);
+        OpDef::make_backward_graph(attr, {inp, stat, stat, stat, stat},
+            {true, true ,true, false, false}, {false, false, false, false, true});
+    }
+    {
+        auto op = OprAttr::make("BatchNorm");
+        auto&& attr = op->cast_final_safe<OprAttr>();
+        Param param;
+        param.fwd_mode = Param::FwdMode::TRAINING;
+        attr.param.write_pod(param);
+        OpDef::make_backward_graph(attr, {inp, stat, stat},
+            {true, true ,true}, {false, false, true});
+    }
+}
+
+TEST(TestImperative, OptimizedBackwardGraphBasic) {
+    auto cn = CompNode::load("xpux");
+    LogicalTensorDesc desc = {TensorLayout(dtype::Float32()), cn};
+    HostTensorGenerator<> gen;
+    auto op = std::shared_ptr<OpDef>(Elemwise::make(Elemwise::Mode::ADD));
+    auto bg = OpDef::make_backward_graph(*op, {desc, desc}, {true, true}, {true});
+    auto obg = OptimizedBackwardGraphResult(bg);
+    ASSERT_EQ(obg.save_for_backward.size(), 4);
+    ASSERT_FALSE(obg.save_for_backward[0]);
+    ASSERT_FALSE(obg.save_for_backward[1]);
+    ASSERT_FALSE(obg.save_for_backward[2]);
+
+    auto a_hv = gen({42});
+    auto b_hv = gen({5, 42});
+    auto dc_hv = gen({5, 42});
+    auto a_tn = Tensor::make(*a_hv);
+    auto b_tn = Tensor::make(*b_hv);
+    auto dc_tn = Tensor::make(*dc_hv);
+    auto c_tn = OpDef::apply_on_physical_tensor(*op, {a_tn, b_tn})[0];
+
+    auto backward_graph_inputs = prepare_backward_graph_inputs<SmallVector<TensorPtr>>(bg, {a_tn, b_tn}, {c_tn}, {dc_tn});
+    auto grads = expand_grads(bg, OpDef::apply_on_physical_tensor(*bg.backward, backward_graph_inputs));
+
+    auto precomp = OpDef::apply_on_physical_tensor(*obg.precomp, {a_tn, b_tn, c_tn});
+    ASSERT_EQ(precomp.size(), 2);
+    ASSERT_EQ(precomp[0]->shape().ndim, 1);
+    ASSERT_LE(precomp[0]->shape()[0], 2);
+    ASSERT_EQ(precomp[1]->shape().ndim, 1);
+    ASSERT_LE(precomp[1]->shape()[0], 2);
+
+    auto backward_inputs = prepare_optimized_backward_inputs<SmallVector<TensorPtr>>(obg, precomp, {a_tn, b_tn}, {c_tn}, {dc_tn});
+    auto grads2 = expand_grads(obg, OpDef::apply_on_physical_tensor(*obg.backward, backward_inputs));
+
+    ASSERT_EQ(grads2.size(), 2);
+    MGB_ASSERT_TENSOR_EQ(grads[0]->get_value(), grads2[0]->get_value());
+    MGB_ASSERT_TENSOR_EQ(grads[1]->get_value(), grads2[1]->get_value());
 }