fix(autodiff): proxy_graph_detail::make_backward_graph support multiple opnodes

GitOrigin-RevId: 2c0c8f330da645438f2a5ef17c9acef588f89fb3

imperative/src/impl/proxy_graph.cpp

@@ -11,10 +11,13 @@
 
 #include "./proxy_graph.h"
 #include "./blob_manager_impl.h"
+#include "megbrain/graph.h"
 #include "megbrain/graph/operator_node.h"
 #include "megbrain/graph/static_infer.h"
 #include "megbrain/imperative/ops/backward_graph.h"
 #include "megbrain/imperative/ops/opr_attr.h"
+#include "megbrain/imperative/subgraph_detail.h"
+#include "megbrain/opr/basic_arith.h"
 #include "megbrain/opr/internal/megdnn_opr_wrapper.h"
 #include "megbrain/opr/io.h"
 #include "megbrain/opr/tensor_manip.h"
@@ -486,139 +489,83 @@ EncodedSubgraph ProxyGraph::make_backward_graph(
         const OpDef& opdef, const SmallVector<LogicalTensorDesc>& input_descs,
         const SmallVector<bool>& input_requires_grad,
         const SmallVector<bool>& output_has_grad) {
-    ThinHashMap<VarNode*, size_t> var2idx;
-    auto push = [&var2idx,
-                 cnt = 1](VarNode* var) mutable {  // cnt is always greater non zero
-        auto&& ret = var2idx.emplace(var, cnt++);
-        mgb_assert(ret.second, "var %s has been already inserted", var->cname());
-        return ret.first->second;
-    };
+    using op_t = OperatorNodeBase*;
+    using var_t = VarNode*;
+    using vars_t = VarNodeArray;
     auto inputs = make_input_place_holders(input_descs);
-    auto fwd = OpDef::apply_on_var_node(opdef, inputs)[0]->owner_opr();
-    auto&& outputs = fwd->usable_output();
+    auto outputs = OpDef::apply_on_var_node(opdef, inputs);
     SmallVector<LogicalTensorDesc> output_descs;
     for (auto&& i : outputs) {
         output_descs.push_back({TensorLayout{i->dtype()}, i->comp_node()});
     }
-    auto output_grads = make_input_place_holders(output_descs);
-    mgb_assert(
-            output_grads.size() == output_has_grad.size(), "%d vs %d",
-            output_grads.size(), output_has_grad.size());
-    bool any_input_has_grad = false;
-    for (size_t i = 0; i < output_grads.size(); ++i) {
-        if (!output_has_grad[i]) {
-            output_grads[i] = nullptr;
-        } else {
-            any_input_has_grad = true;
-        }
-    }
-    if (!any_input_has_grad) {
-        return {};
-    }
-    auto* gfunc = cg::lookup_grad_func(fwd->dyn_typeinfo());
-
-    EncodedSubgraph result;
-    auto&& igraph = result.graph;
-
-    size_t nr_backward_graph_inputs = 0;
-    auto gen_expr = [this, &var2idx, &igraph, &push, &fwd,
-                     &nr_backward_graph_inputs](cg::OperatorNodeBase* op) {
-        if (auto t = as_tensor(op)) {
-            mgb_assert(op->output().size() == 1);
-            igraph.constants.emplace_back(push(op->output(0)), std::move(t));
-        } else if (op->same_type<InputPlaceholder>()) {
-            ++nr_backward_graph_inputs;
-            push(op->output(0));
-        } else {
-            SmallVector<size_t> inputs, outputs;
-            for (auto&& i : op->input()) {
-                if (i->owner_opr() == fwd) {
-                    if (var2idx.find(i) == var2idx.end()) {
-                        ++nr_backward_graph_inputs;
-                        push(i);
+    GradContext<op_t, var_t> grad_context{[&](VarNode* lhs, VarNode* rhs) -> VarNode* {
+        auto add = opr::Elemwise::Mode::ADD;
+        return opr::Elemwise::make(VarNodeArray{lhs, rhs}, add).node();
+    }};
+    cg::DepOprIter iter{[&](OperatorNodeBase* op) {
+        grad_context.record_expr(op, op->input(), op->output());
+    }};
+    for (size_t i = 0; i < inputs.size(); ++i) {
+        auto& input = inputs[i];
+        iter.set_visited(input->owner_opr());
+        if (input_requires_grad[i]) {
+            grad_context.mark_require_grad(input);
         }
     }
-                inputs.push_back(var2idx.at(i));
+    for (auto&& output : outputs) {
+        iter.add(output);
     }
-            for (auto&& i : op->usable_output()) {
-                outputs.push_back(push(i));
+    auto output_grads = make_input_place_holders(output_descs);
+    for (size_t i = 0; i < outputs.size(); ++i) {
+        if (!output_has_grad[i]) {
+            output_grads[i] = nullptr;
         }
-            igraph.exprs.push_back({OpDef::make_from_op_node(op), inputs, outputs});
     }
-    };
-
-    // set backward graph outputs
-    cg::DepOprIter iter{gen_expr};
-    iter.set_visited(fwd);
-    result.output_mask.resize(inputs.size());
-
-    VarNodeArray output_grads_with_unused_var;
-    {
-        auto iter = output_grads.begin();
-        for (auto&& i : fwd->output()) {
-            if (i->contain_flag(VarNode::Flag::VOLATILE_CONTENT)) {
-                // the var node with VOLATILE_CONTENT(e.g. workspace
-                // or an empty var) would not be considered as a normal
-                // output, so its grad is always NULL
-                output_grads_with_unused_var.push_back(nullptr);
-            } else {
-                output_grads_with_unused_var.push_back(*iter);
-                ++iter;
+    auto compute_input_grads = [&](op_t op, vars_t inputs, vars_t outputs,
+                                   vars_t output_grads) {
+        auto* gfunc = cg::lookup_grad_func(op->dyn_typeinfo());
+        vars_t input_grads(inputs.size(), nullptr);
+        bool any_grad = false;
+        for (auto&& output_grad : output_grads) {
+            if (output_grad) {
+                any_grad = true;
             }
         }
-        mgb_assert(iter == output_grads.end());
+        if (!gfunc || !any_grad) {
+            return input_grads;
         }
-
         Maybe<VarNodeArray> grad_results;
+        auto&& input_requires_grad = grad_context.get_require_grads(inputs);
         for (size_t i = 0; i < inputs.size(); ++i) {
             VarNode* grad;
             if (grad_results.valid()) {
                 grad = grad_results.val()[i];
             } else {
                 mgb_assert(gfunc, "could not find grad function");
-            auto res = (*gfunc)(fwd, i, output_grads_with_unused_var);
+                auto res = (*gfunc)(op, i, output_grads);
                 if (res.from_single()) {
                     grad = res.single();
                 } else {
-                grad_results.emplace(res.all(fwd));
+                    grad_results.emplace(res.all(op));
                     grad = grad_results.val()[i];
                 }
             }
-        if (grad && !grad->owner_opr()->same_type<opr::InvalidGrad>() &&
-            input_requires_grad[i]) {
-            mgb_assert(
-                    !grad->owner_opr()->same_type<opr::InvalidGrad>(),
-                    "gradient of operator %s w.r.t. input #%lu is "
-                    "either not well defined or not implemented",
-                    fwd->dyn_typeinfo()->name, i);
-            iter.add(grad);
-            igraph.outputs.push_back(var2idx.at(grad));
-            result.output_mask[i] = true;
-        } else {
-            result.output_mask[i] = false;
+            if (grad && !grad->owner_opr()->same_type<opr::InvalidGrad>()) {
+                if (input_requires_grad[i]) {
+                    input_grads[i] = grad;
                 }
             }
-    if (igraph.outputs.empty()) {
-        return {};
-    }
-
-    // set backward graph inputs
-    auto write_inputs = [&igraph, &var2idx, &result](const VarNodeArray& vars) {
-        for (auto&& i : vars) {
-            auto&& iter = var2idx.find(i);
-            if (iter != var2idx.end()) {
-                igraph.inputs.push_back(iter->second);
-                result.input_mask.push_back(true);
-            } else {
-                result.input_mask.push_back(false);
-            }
         }
+        return input_grads;
     };
-    write_inputs(inputs);
-    write_inputs(outputs);
-    write_inputs(output_grads);
-    mgb_assert(igraph.inputs.size() == nr_backward_graph_inputs);
-    return result;
+    grad_context.backward(outputs, output_grads, compute_input_grads);
+    auto input_grads = grad_context.get_grads(inputs);
+    VarNodeArray bgraph_inputs;
+    bgraph_inputs.insert(bgraph_inputs.end(), inputs.begin(), inputs.end());
+    bgraph_inputs.insert(bgraph_inputs.end(), outputs.begin(), outputs.end());
+    bgraph_inputs.insert(bgraph_inputs.end(), output_grads.begin(), output_grads.end());
+    auto graph = subgraph_detail::make_from_computing_graph(bgraph_inputs, input_grads);
+    return graph;
 }
 
 VarNodeArray ProxyGraph::make_input_place_holders(

imperative/src/impl/subgraph_detail.cpp

@@ -107,13 +107,16 @@ EncodedSubgraph make_backward_graph_from_forward(
     Subgraph::Builder<LogicalTensorDesc> builder(
             [](auto&& op, auto&& input_descs, size_t nr_outputs) {
                 auto [descs, _] = OpDef::infer_output_attrs_fallible(*op, input_descs);
+                mgb_assert(
+                        descs.size() == nr_outputs, "nr_outputs mismatch for %s",
+                        op->to_string().c_str());
                 return descs;
             });
     auto accum_grad = [&](var_t lhs, var_t rhs) {
         return builder.write_expr(
                 Elemwise::make(Elemwise::Mode::ADD), {lhs, rhs}, 1)[0];
     };
-    GradContext<var_t> grad_context{accum_grad};
+    GradContext<std::shared_ptr<OpDef>, var_t> grad_context{accum_grad};
     auto input_vars = builder.write_inputs(inputs);
     auto outputs = forward_graph.apply<var_t>(
             input_vars, std::bind(&decltype(builder)::write_expr, &builder, _1, _2, _3),
@@ -143,19 +146,17 @@ EncodedSubgraph make_backward_graph_from_forward(
     grad_context.backward(
             apply_mask(outputs, output_has_grad),
             apply_mask(output_grads, output_has_grad),
-            [&](Subgraph::expr_t expr, vars_t output_grads) {
+            [&](Subgraph::op_t op, vars_t inputs, vars_t outputs, vars_t output_grads) {
                 auto bg = OpDef::make_backward_graph(
-                        *expr.op, builder.get_descs(expr.inputs),
-                        grad_context.get_require_grads(expr.inputs),
-                        grad_context.get_has_grads(expr.outputs));
+                        *op, builder.get_descs(inputs),
+                        grad_context.get_require_grads(inputs),
+                        grad_context.get_has_grads(outputs));
                 if (bg.graph.empty()) {
-                    return vars_t(expr.inputs.size(), 0);
+                    return vars_t(inputs.size(), 0);
                 }
                 vars_t grad_inputs;
-                grad_inputs.insert(
-                        grad_inputs.end(), expr.inputs.begin(), expr.inputs.end());
-                grad_inputs.insert(
-                        grad_inputs.end(), expr.outputs.begin(), expr.outputs.end());
+                grad_inputs.insert(grad_inputs.end(), inputs.begin(), inputs.end());
+                grad_inputs.insert(grad_inputs.end(), outputs.begin(), outputs.end());
                 grad_inputs.insert(
                         grad_inputs.end(), output_grads.begin(), output_grads.end());
                 auto apply_functor =
@@ -183,6 +184,77 @@ EncodedSubgraph make_backward_graph(
             forward_graph, inputs, input_requires_grad, output_has_grad);
 }
 
+EncodedSubgraph make_from_computing_graph(
+        const VarNodeArray& inputs, const VarNodeArray& outputs) {
+    Subgraph subgraph;
+    std::unordered_map<VarNode*, size_t> var2idx;
+    size_t next_idx = 0;
+    var2idx[nullptr] = next_idx++;
+    for (auto&& input : inputs) {
+        if (input) {
+            var2idx[input] = next_idx++;
+        }
+    }
+    auto is_tensor_holder = [](cg::OperatorNodeBase* op) {
+        return op->input().empty();
+    };
+    auto as_tensor = [](VarNode* var) -> TensorPtr {
+        auto* opr = var->owner_opr();
+        if (auto* imm_tensor = opr->try_cast_final<opr::ImmutableTensor>()) {
+            auto&& dv = imm_tensor->value();
+            HostTensorND hv(dv.comp_node(), dv.shape(), dv.dtype());
+            // get host value
+            auto&& cpu_value = imm_tensor->host_value();
+            mgb_assert(cpu_value.comp_node() == CompNode::default_cpu());
+            // default_cpu is synchronous with respect to caller
+            hv.proxy_to_default_cpu().copy_from_fixlayout(cpu_value);
+            return Tensor::make(dv, hv);
+        } else if (
+                auto* shared_tensor = opr->try_cast_final<opr::SharedDeviceTensor>()) {
+            return Tensor::make(shared_tensor->get_dev_tensor());
+        } else {
+            mgb_assert(
+                    false, "unsupported tensor holder opr %s",
+                    opr->dyn_typeinfo()->name);
+        }
+    };
+    cg::DepOprIter iter{[&](cg::OperatorNodeBase* op) {
+        // TODO: implement make_backward_graph for mm ops
+        // mgb_assert(!op->node_prop().contain(cg::OperatorNodeProp::Flag::IMPURE_FUNC));
+        if (is_tensor_holder(op)) {
+            for (auto&& output : op->usable_output()) {
+                subgraph.constants.push_back(
+                        {var2idx[output] = next_idx++, as_tensor(output)});
+            }
+        } else {
+            Subgraph::vars_t inputs;
+            Subgraph::vars_t outputs;
+            for (auto&& input : op->input()) {
+                inputs.push_back(var2idx.at(input));
+            }
+            // NOTE: use usable_output
+            for (auto&& output : op->usable_output()) {
+                outputs.push_back(var2idx[output] = next_idx++);
+            }
+            auto opdef = OpDef::make_from_op_node(op);
+            subgraph.exprs.push_back({opdef, inputs, outputs});
+        }
+    }};
+    for (auto&& input : inputs) {
+        if (input) {
+            iter.set_visited(input->owner_opr());
+        }
+        subgraph.inputs.push_back(var2idx.at(input));
+    }
+    for (auto&& output : outputs) {
+        if (output) {
+            iter.add(output);
+        }
+        subgraph.outputs.push_back(var2idx.at(output));
+    }
+    return EncodedSubgraph::make(subgraph);
+}
+
 }  // namespace subgraph_detail
 }  // namespace imperative
 }  // namespace mgb

imperative/src/include/megbrain/imperative/subgraph.h

@@ -189,12 +189,17 @@ struct EncodedSubgraph {
     size_t hash() const;
 };
 
-template <typename T>
+template <typename TOp, typename TVar>
 class GradContext {
 public:
-    using var_t = T;
+    using op_t = TOp;
+    using var_t = TVar;
     using vars_t = SmallVector<var_t>;
-    using expr_t = Expr<T>;
+    struct expr_t {
+        op_t op;
+        vars_t inputs;
+        vars_t outputs;
+    };
 
 private:
     std::unordered_map<var_t, var_t> m_grads;
@@ -219,6 +224,7 @@ public:
         }
         return mask;
     }
+    void mark_require_grad(var_t dest) { m_vars_require_grad.insert(dest); }
     void mark_require_grads(vars_t dests) {
         for (auto&& dest : dests) {
             m_vars_require_grad.insert(dest);
@@ -231,7 +237,7 @@ public:
             return m_grads[dest] = m_accumulator(m_grads[dest], grad);
         }
     }
-    void record_expr(std::shared_ptr<OpDef> op, vars_t inputs, vars_t outputs) {
+    void record_expr(op_t op, vars_t inputs, vars_t outputs) {
         bool require_grad = false;
         for (auto&& input : inputs) {
             if (m_vars_require_grad.count(input)) {
@@ -254,7 +260,8 @@ public:
         std::reverse(exprs.begin(), exprs.end());
         for (const expr_t& expr : exprs) {
             size_t nr_inputs = expr.inputs.size();
-            vars_t input_grads = functor(expr, get_grads(expr.outputs));
+            vars_t input_grads = functor(
+                    expr.op, expr.inputs, expr.outputs, get_grads(expr.outputs));
             mgb_assert(input_grads.size() == nr_inputs, "input size mismatch");
             for (size_t i = 0; i < nr_inputs; ++i) {
                 if (input_grads[i] && m_vars_require_grad.count(expr.inputs[i])) {

imperative/src/include/megbrain/imperative/subgraph_detail.h

@@ -43,6 +43,8 @@ EncodedSubgraph make_backward_graph_from_forward(
         const EncodedSubgraph& forward, const SmallVector<LogicalTensorDesc>& inputs,
         const SmallVector<bool>& input_requires_grad,
         const SmallVector<bool>& output_has_grad);
+EncodedSubgraph make_from_computing_graph(
+        const VarNodeArray& inputs, const VarNodeArray& outputs);
 
 }  // namespace subgraph_detail
 }  // namespace imperative