fix(mgb): fix strategy of grad_op and opr_attr

GitOrigin-RevId: bb7ab8fa9d

imperative/python/megengine/core/tensor/array_method.py

@@ -70,15 +70,6 @@ def _matmul(
     maxdim = dim1 if dim1 > dim2 else dim2
     compute_mode = _config._get_actual_op_param(compute_mode, _config.__compute_mode)
 
-    Strategy = builtin.ops.MatrixMul.Strategy
-    strategy = Strategy(0)
-    if _config._benchmark_kernel:
-        strategy |= Strategy.PROFILE
-    else:
-        strategy |= Strategy.HEURISTIC
-    if _config._deterministic_kernel:
-        strategy |= Strategy.REPRODUCIBLE
-
     if dim1 == 1 and dim2 == 1:  # dispatch to Dot
         (result,) = apply(builtin.Dot(), inp1, inp2)
         return result

imperative/python/megengine/functional/nn.py

@@ -621,6 +621,7 @@ def max_pool2d(
         pad_h=padding_h,
         pad_w=padding_w,
         mode="max",
+        strategy=get_execution_strategy(),
         format=conv_format,
     )
     (output,) = apply(op, inp)
@@ -665,6 +666,7 @@ def avg_pool2d(
         pad_h=padding_h,
         pad_w=padding_w,
         mode=mode,
+        strategy=get_execution_strategy(),
         format=conv_format,
     )
     (output,) = apply(op, inp)

imperative/python/src/tensor_utils.cpp

@@ -1493,7 +1493,7 @@ py::object _transpose_cpp(py::handle inp_hdl, py::handle args) {
 py::object _matmul_cpp(
         py::handle inp1, py::handle inp2, py::handle dim1, py::handle dim2,
         py::handle transpose_a, py::handle transpose_b, py::handle compute_mode,
-        py::handle profile, py::handle determistic) {
+        py::handle profile, py::handle deterministic) {
     ::megdnn::param::MatrixMul::ComputeMode mode =
             ::megdnn::param::MatrixMul::ComputeMode::DEFAULT;
     if (compute_mode.cast<std::string>().compare(std::string("float32")) == 0) {
@@ -1506,7 +1506,7 @@ py::object _matmul_cpp(
     } else {
         cstrategy |= ::megdnn::param::ExecutionPolicy::Strategy::HEURISTIC;
     }
-    if (determistic.cast<bool>()) {
+    if (deterministic.cast<bool>()) {
         cstrategy |= ::megdnn::param::ExecutionPolicy::Strategy::REPRODUCIBLE;
     }
     std::shared_ptr<OpDef> op = MatrixMul::make(
@@ -1523,7 +1523,7 @@ py::object _matmul_cpp(
 py::object _batched_matmul_cpp(
         py::handle inp1, py::handle inp2, py::handle dim1, py::handle dim2,
         py::handle transpose_a, py::handle transpose_b, py::handle compute_mode,
-        py::handle profile, py::handle determistic) {
+        py::handle profile, py::handle deterministic) {
     ::megdnn::param::MatrixMul::ComputeMode mode =
             ::megdnn::param::MatrixMul::ComputeMode::DEFAULT;
     if (compute_mode.cast<std::string>().compare(std::string("float32")) == 0) {
@@ -1536,7 +1536,7 @@ py::object _batched_matmul_cpp(
     } else {
         cstrategy |= ::megdnn::param::ExecutionPolicy::Strategy::HEURISTIC;
     }
-    if (determistic.cast<bool>()) {
+    if (deterministic.cast<bool>()) {
         cstrategy |= ::megdnn::param::ExecutionPolicy::Strategy::REPRODUCIBLE;
     }
     std::shared_ptr<OpDef> op = BatchedMatrixMul::make(

imperative/src/impl/ops/opr_attr.cpp

@@ -10,6 +10,10 @@
  */
 
 #include "megbrain/imperative/ops/opr_attr.h"
+#include "megbrain/opr/blas.h"
+#include "megbrain/opr/dnn/convolution.h"
+#include "megbrain/opr/dnn/pooling.h"
+#include "megbrain/rdnn/profiler.h"
 #include "megbrain/serialization/opr_load_dump.h"
 
 #include "../op_trait.h"
@@ -65,6 +69,42 @@ public:
     const serialization::GraphDumpConfig& config() const { mgb_assert(0); }
 };
 
+#define cb(FASTRUN_OPR)                                                           \
+    megdnn::param::ExecutionPolicy get_strategy_##FASTRUN_OPR(                    \
+            cg::OperatorNodeBase* opr) {                                          \
+        auto policy =                                                             \
+                opr->cast_final<opr::FASTRUN_OPR>().execution_policy_transient(); \
+        return policy;                                                            \
+    }                                                                             \
+    void set_strategy_##FASTRUN_OPR(                                              \
+            cg::OperatorNodeBase* opr, megdnn::param::ExecutionPolicy policy) {   \
+        auto&& p = opr->cast_final<opr::FASTRUN_OPR>();                           \
+        p.set_execution_policy(policy);                                           \
+    }
+
+DNN_FOREACH_FASTRUN_OPR(cb)
+#undef cb
+
+typedef thin_function<megdnn::param::ExecutionPolicy(cg::OperatorNodeBase*)> get_func;
+typedef thin_function<void(cg::OperatorNodeBase*, megdnn::param::ExecutionPolicy)>
+        set_func;
+
+static const mgb::thin_hash_table::ThinHashMap<
+        mgb::Typeinfo*, std::pair<get_func, set_func>>&
+get_type2policy() {
+    static mgb::thin_hash_table::ThinHashMap<
+            mgb::Typeinfo*, std::pair<get_func, set_func>>
+            sl_type2policy;
+    static std::once_flag flag;
+    std::call_once(flag, [&]() {
+#define cb(FASTRUN_OPR)                            \
+    sl_type2policy[opr::FASTRUN_OPR::typeinfo()] = \
+            std::make_pair(get_strategy_##FASTRUN_OPR, set_strategy_##FASTRUN_OPR);
+        DNN_FOREACH_FASTRUN_OPR(cb)
+    });
+    return std::as_const(sl_type2policy);
+}
+
 VarNodeArray apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& attr = def.cast_final_safe<OprAttr>();
     auto config = attr.config;
@@ -73,7 +113,12 @@ VarNodeArray apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto registry = serialization::OprRegistry::find_by_name(attr.type);
     mgb_assert(registry, "operator %s not found", attr.type.c_str());
     OprParamsLoadContext ctx{attr.param, inputs[0]->owner_graph()};
-    return registry->loader(ctx, inputs, config).usable_output();
+    auto opr_with_accessor = registry->loader(ctx, inputs, config);
+    auto&& opr = opr_with_accessor.opr();
+    if (get_type2policy().find(opr->dyn_typeinfo()) != get_type2policy().end()) {
+        get_type2policy().at(opr->dyn_typeinfo()).second(opr, attr.policy);
+    }
+    return opr_with_accessor.usable_output();
 }
 
 std::shared_ptr<OpDef> make_from_op_node(cg::OperatorNodeBase* opr) {
@@ -84,7 +129,11 @@ std::shared_ptr<OpDef> make_from_op_node(cg::OperatorNodeBase* opr) {
             registry->dumper, "operator %s cannot be serialized",
             opr->dyn_typeinfo()->name);
     registry->dumper(ctx, *opr);
-    return OprAttr::make(registry->name, std::move(ctx.m_param), opr->config());
+    megdnn::param::ExecutionPolicy policy;
+    if (get_type2policy().find(opr->dyn_typeinfo()) != get_type2policy().end()) {
+        policy = get_type2policy().at(opr->dyn_typeinfo()).first(opr);
+    }
+    return OprAttr::make(registry->name, std::move(ctx.m_param), policy, opr->config());
 }
 
 std::vector<std::pair<const char*, std::string>> props(const OpDef& def) {
@@ -108,6 +157,8 @@ OP_TRAIT_REG(OprAttr, OprAttr)
 bool OprAttr::is_same_st(const Hashable& rhs_) const {
     auto&& rhs = static_cast<const OprAttr&>(rhs_);
     return type == rhs.type && param == rhs.param &&
+           policy.strategy == rhs.policy.strategy &&
+           policy.workspace_limit == rhs.policy.workspace_limit &&
            config.comp_node() == rhs.config.comp_node() &&
            config.output_dtype() == rhs.config.output_dtype();
 }
@@ -115,7 +166,12 @@ bool OprAttr::is_same_st(const Hashable& rhs_) const {
 size_t OprAttr::hash() const {
     return hash_pair_combine(
             hash_pair_combine(
-                    mgb::hash(type), mgb::hash(static_cast<std::vector<char>>(param))),
+                    hash_pair_combine(
+                            mgb::hash(type),
+                            mgb::hash(static_cast<std::vector<char>>(param))),
+                    hash_pair_combine(
+                            static_cast<size_t>(policy.strategy),
+                            policy.workspace_limit)),
             config.hash());
 }
 

imperative/src/include/megbrain/imperative/ops/opr_attr.h

@@ -12,6 +12,7 @@
 #pragma once
 
 #include "megbrain/imperative/op_def.h"
+#include "megbrain/opr/param_defs.h"
 
 namespace mgb {
 namespace imperative {
@@ -38,12 +39,16 @@ public:
 
     Type type;
     Param param;
+    megdnn::param::ExecutionPolicy policy;
     cg::OperatorNodeConfig config;
 
     OprAttr() = default;
     OprAttr(const Type& t) : type(t) {}
     OprAttr(const Type& t, const Param& p, const cg::OperatorNodeConfig& c)
             : type(t), param(p), config(c) {}
+    OprAttr(const Type& t, const Param& p, const megdnn::param::ExecutionPolicy ps,
+            const cg::OperatorNodeConfig& c)
+            : type(t), param(p), policy(ps), config(c) {}
 
     std::string repr() const;
 

imperative/src/test/backward_graph.cpp

@@ -157,6 +157,51 @@ TEST(TestImperative, BackwardGraphBasic) {
     }
 }
 
+TEST(TestImperative, ProfileBackward) {
+    auto cn = CompNode::load("xpux");
+    using Policy = megdnn::param::ExecutionPolicy;
+    using S = Policy::Strategy;
+    Policy policy;
+    policy.strategy = S::PROFILE;
+    {
+        megdnn::param::Convolution param;
+        auto op = std::shared_ptr<OpDef>(Convolution::make(param, policy));
+        LogicalTensorDesc inp_desc = {
+                TensorLayout({16, 3, 16, 16}, dtype::Float32()), cn};
+        LogicalTensorDesc weight_desc = {
+                TensorLayout({16, 3, 5, 5}, dtype::Float32()), cn};
+        auto bg = OpDef::make_backward_graph(
+                *op, {inp_desc, weight_desc}, {true, false}, {true});
+        auto&& bop = (bg.graph.exprs.at(0)).op;
+        auto&& attr = bop->cast_final_safe<OprAttr>();
+        // attr.type = ConvolutionBackwardDataV2
+        mgb_assert(attr.policy.strategy == S::PROFILE);
+    }
+    {
+        megdnn::param::Pooling param;
+        auto op = std::shared_ptr<OpDef>(Pooling::make(param, policy));
+        LogicalTensorDesc inp_desc = {
+                TensorLayout({16, 3, 16, 16}, dtype::Float32()), cn};
+        auto bg = OpDef::make_backward_graph(*op, {inp_desc}, {true}, {true});
+        auto&& bop = (bg.graph.exprs.at(0)).op;
+        auto&& attr = bop->cast_final_safe<OprAttr>();
+        // attr.type = PoolingBackwardV1
+        mgb_assert(attr.policy.strategy == S::PROFILE);
+    }
+    {
+        megdnn::param::MatrixMul param;
+        auto op = std::shared_ptr<OpDef>(MatrixMul::make(param, policy, 2, 2));
+        LogicalTensorDesc inp1_desc = {TensorLayout({12, 16}, dtype::Float32()), cn};
+        LogicalTensorDesc inp2_desc = {TensorLayout({16, 20}, dtype::Float32()), cn};
+        auto bg = OpDef::make_backward_graph(
+                *op, {inp1_desc, inp2_desc}, {true, false}, {true});
+        auto&& bop = (bg.graph.exprs.at(0)).op;
+        auto&& attr = bop->cast_final_safe<OprAttr>();
+        // attr.type = MatrixMulV2
+        mgb_assert(attr.policy.strategy == S::PROFILE);
+    }
+}
+
 TEST(TestImperative, BackwardGraphIdentity) {
     HostTensorGenerator<> gen;
     auto host_a = gen({42}), host_dc = gen({42});

src/opr/impl/blas.cpp

@@ -185,17 +185,21 @@ MGB_IMPL_OPR_GRAD(MatrixMul) {
     if (wrt_idx == 0) {
         // A * B = C, A' = C' * Bt
         if (opr.param().transposeA) {
-            grad = MatrixMul::make(i1, og, {opr.param().transposeB, true});
+            grad = MatrixMul::make(
+                    i1, og, {opr.param().transposeB, true}, opr.execution_policy());
         } else {
-            grad = MatrixMul::make(og, i1, {false, !opr.param().transposeB});
+            grad = MatrixMul::make(
+                    og, i1, {false, !opr.param().transposeB}, opr.execution_policy());
         }
     } else {
         mgb_assert(wrt_idx == 1);
         // A * B = C, B' = At * C'
         if (opr.param().transposeB) {
-            grad = MatrixMul::make(og, i0, {true, opr.param().transposeA});
+            grad = MatrixMul::make(
+                    og, i0, {true, opr.param().transposeA}, opr.execution_policy());
         } else {
-            grad = MatrixMul::make(i0, og, {!opr.param().transposeA, false});
+            grad = MatrixMul::make(
+                    i0, og, {!opr.param().transposeA, false}, opr.execution_policy());
         }
     }
     return grad.node();
@@ -358,17 +362,21 @@ MGB_IMPL_OPR_GRAD(BatchedMatrixMul) {
     if (wrt_idx == 0) {
         // A * B = C, A' = C' * Bt
         if (opr.param().transposeA) {
-            grad = BatchedMatrixMul::make(i1, og, {opr.param().transposeB, true});
+            grad = BatchedMatrixMul::make(
+                    i1, og, {opr.param().transposeB, true}, opr.execution_policy());
         } else {
-            grad = BatchedMatrixMul::make(og, i1, {false, !opr.param().transposeB});
+            grad = BatchedMatrixMul::make(
+                    og, i1, {false, !opr.param().transposeB}, opr.execution_policy());
         }
     } else {
         mgb_assert(wrt_idx == 1);
         // A * B = C, B' = At * C'
         if (opr.param().transposeB) {
-            grad = BatchedMatrixMul::make(og, i0, {true, opr.param().transposeA});
+            grad = BatchedMatrixMul::make(
+                    og, i0, {true, opr.param().transposeA}, opr.execution_policy());
         } else {
-            grad = BatchedMatrixMul::make(i0, og, {!opr.param().transposeA, false});
+            grad = BatchedMatrixMul::make(
+                    i0, og, {!opr.param().transposeA, false}, opr.execution_policy());
         }
     }
     return grad.node();

src/opr/impl/dnn/pooling.cpp

@@ -59,7 +59,8 @@ size_t PoolingForward::get_workspace_size_bytes(
 MGB_IMPL_OPR_GRAD(PoolingForward) {
     mgb_assert(wrt_idx == 0);
     SymbolVar grad = PoolingBackward::make(
-            opr.input(0), opr.output(0), out_grad[0], opr.param());
+            opr.input(0), opr.output(0), out_grad[0], opr.param(),
+            opr.execution_policy());
     return grad.node();
 }
 #endif

src/opr/include/megbrain/opr/blas.h

@@ -26,7 +26,7 @@ namespace opr {
 /*!
  * \brief matrix_mul(trans0(opr0), trans1(opr1))
  */
-MGB_DEFINE_OPR_CLASS(
+MGB_DEFINE_OPR_CLASS_WITH_EXPORT(
         MatrixMul, intl::MegDNNOprWrapperFwd<megdnn::MatrixMul>,
         public mixin::AlgoChooserHelper) // {
 public:
@@ -57,7 +57,7 @@ private:
 /*!
  * \brief batched matrix multiplication on 3D inputs
  */
-MGB_DEFINE_OPR_CLASS(
+MGB_DEFINE_OPR_CLASS_WITH_EXPORT(
         BatchedMatrixMul, intl::MegDNNOprWrapperFwd<megdnn::BatchedMatrixMul>,
         public mixin::AlgoChooserHelper) // {
 public:

src/opr/include/megbrain/opr/dnn/pooling.h

@@ -18,7 +18,7 @@
 namespace mgb {
 namespace opr {
 
-MGB_DEFINE_OPR_CLASS(
+MGB_DEFINE_OPR_CLASS_WITH_EXPORT(
         PoolingForward, intl::MegDNNOprWrapperFwd<megdnn::PoolingForward>,
         public mixin::AlgoChooserHelper) // {
 public:
@@ -37,7 +37,7 @@ public:
 };
 using Pooling = PoolingForward;
 
-MGB_DEFINE_OPR_CLASS(
+MGB_DEFINE_OPR_CLASS_WITH_EXPORT(
         PoolingBackward, intl::MegDNNOprWrapperBwd<megdnn::PoolingBackward>,
         public mixin::AlgoChooserHelper) // {
 public:

src/opr/include/megbrain/opr/search_policy/algo_chooser_helper.h

@@ -51,7 +51,7 @@ public:
      * Exception would be thrown if execution_policy() has been accessed,
      * since it would influence cache and many other decisions.
      */
-    void set_execution_policy(const ExecutionPolicy& policy);
+    MGE_WIN_DECLSPEC_FUC void set_execution_policy(const ExecutionPolicy& policy);
 
     /*!
      * \brief register a hook to implement custom algo chooser