Decouple TE compute and schedule lowering in ScheduleBuilder

masahi · masahi · commit 6e68fd9aff9f · 2022-03-11T08:36:52.000+09:00
diff --git a/src/relay/backend/te_compiler_cache.cc b/src/relay/backend/te_compiler_cache.cc
@@ -45,6 +45,7 @@
 #include "../../te/operation/create_primfunc.h"
 #include "../op/memory/memory.h"
 #include "../transforms/pass_utils.h"
+#include "tvm/relay/op_strategy.h"
 #include "utils.h"
 
 namespace tvm {
@@ -115,99 +116,24 @@ Array<IndexExpr> GetShape(const Array<IndexExpr>& shape) {
 }
 
 // Construct a schedule for a given Relay primitive function and target.
-class ScheduleBuilder : public backend::MemoizedExprTranslator<Array<te::Tensor>> {
+class LowerToTECompute : public backend::MemoizedExprTranslator<Array<te::Tensor>> {
  public:
-  explicit ScheduleBuilder(Target target, bool create_schedule = true)
-      : target_(target),
-        device_copy_op_(Op::Get("device_copy")),
-        create_schedule_(create_schedule) {
-    // Whether to use auto_scheduler schedule.
-    use_auto_scheduler_ = backend::IsAutoSchedulerEnabled();
-    use_meta_schedule_ = backend::IsMetaScheduleEnabled();
-  }
+  explicit LowerToTECompute(Target target)
+      : target_(target), device_copy_op_(Op::Get("device_copy")) {}
 
-  CachedFunc Create(const Function& relay_func, std::function<std::string(std::string)> renamer) {
-    Array<tvm::te::Tensor> fn_inputs;
+  Array<te::Tensor> Lower(const Function& relay_func,
+                          std::function<std::string(std::string)> renamer) {
     for (Var param : relay_func->params) {
       Array<tvm::te::Tensor> inputs;
       for (const auto& ttype : FlattenTupleType(param->checked_type())) {
         tvm::te::Tensor tensor = tvm::te::placeholder(GetShape(ttype->shape), ttype->dtype);
-        fn_inputs.push_back(tensor);
         inputs.push_back(tensor);
+        fn_inputs_.push_back(tensor);
       }
       memo_[param] = inputs;
     }
     readable_name_stream_ << "fused";
-    auto outputs = this->VisitExpr(relay_func->body);
-    auto candidate_name = readable_name_stream_.str();
-    constexpr static size_t kMaxFuncNameLength = 80;
-    // WARNING: Please make sure to also update TVM_CRT_MAX_STRLEN_FUNCTION_NAME
-    //          whenever the value of kMaxFuncNameLength changes
-    if (candidate_name.size() > kMaxFuncNameLength) {
-      std::stringstream truncated_name;
-      truncated_name << candidate_name.substr(0, kMaxFuncNameLength);
-      truncated_name << "_" << std::hex << std::hash<std::string>{}(candidate_name) << "_";
-      candidate_name = truncated_name.str();
-    }
-
-    // TODO(mbs): This should be the definitive global by which the PrimFunc is known and
-    // no other GlobalVar ctors should appear inside the lowering machinery.
-    auto prim_fn_var = GlobalVar(renamer(candidate_name));
-    prim_fn_var->checked_type_ = relay_func->checked_type();
-
-    // Fusion over tupled results may leave identity relationships
-    // between inputs and outputs, and those should not be scheduled.
-    // Hence schedule only non PlaceholderOp outputs.
-    tvm::Array<te::Tensor> tensor_outs;
-    for (const auto& tensor : outputs) {
-      if (!tensor->op.as<te::PlaceholderOpNode>()) {
-        tensor_outs.push_back(tensor);
-      }
-    }
-
-    te::Schedule schedule{nullptr};
-    tir::PrimFunc prim_func{nullptr};
-    // No need to register schedule for device copy op.
-    if (anchor_attrs_.as<DeviceCopyAttrs>() == nullptr && create_schedule_) {
-      if (use_auto_scheduler_) {
-        const auto* fauto_schedule =
-            runtime::Registry::Get("auto_scheduler.relay_integration.auto_schedule_topi_compute");
-        ICHECK(fauto_schedule != nullptr)
-            << "auto_scheduler.relay_integration.auto_schedule_topi_compute is not registered";
-        ObjectRef obj = (*fauto_schedule)(prim_fn_var->name_hint, tensor_outs);
-        if (obj.defined()) {
-          schedule = Downcast<te::Schedule>(obj);
-        }
-      }
-      if (use_meta_schedule_) {
-        prim_func = tir::CreatePrimFunc(Concat(fn_inputs, tensor_outs));
-        Optional<ObjectRef> opt_mod_or_base_func =
-            meta_schedule::MetaScheduleContext::QueryInsideWithScope(
-                prim_fn_var->name_hint, IRModule({{prim_fn_var, relay_func}}), target_,
-                Array<IRModule>{IRModule({{prim_fn_var, prim_func}})});
-        if (const auto* result = opt_mod_or_base_func.as<tir::PrimFuncNode>()) {
-          prim_func = GetRef<tir::PrimFunc>(result);
-        } else {
-          prim_func = tir::PrimFunc(nullptr);
-        }
-      }
-
-      // Use TOPI schedule if user specificed, or the function has no auto_scheduler schedule.
-      if (!schedule.defined() && !prim_func.defined()) {
-        ICHECK(anchor_implementation_.defined());
-        schedule = anchor_implementation_.Schedule(anchor_attrs_, tensor_outs, target_);
-      }
-      if (schedule.defined()) {
-        for (const auto& scalar : scalars_) {
-          if (schedule->Contain(scalar)) {
-            schedule[scalar].compute_inline();
-          }
-        }
-      }
-    }
-
-    return CachedFunc(target_, prim_fn_var, fn_inputs, outputs, schedule, prim_func, {},
-                      IRModule(Map<GlobalVar, BaseFunc>({})), constant_tensors_);
+    return this->VisitExpr(relay_func->body);
   }
 
   Array<te::Tensor> VisitExpr_(const VarNode* op) final {
@@ -254,7 +180,6 @@ class ScheduleBuilder : public backend::MemoizedExprTranslator<Array<te::Tensor>
   }
 
   Array<te::Tensor> VisitExpr_(const CallNode* call_node) final {
-    static auto fpattern = Op::GetAttrMap<TOpPattern>("TOpPattern");
     static auto flower_call = tvm::runtime::Registry::Get("relay.backend.lower_call");
     ICHECK(flower_call) << "relay.backend.lower_call is not registered.";
 
@@ -278,28 +203,13 @@ class ScheduleBuilder : public backend::MemoizedExprTranslator<Array<te::Tensor>
     ICHECK(call_node->op.as<OpNode>()) << "Primitive function only allows call into primitive ops";
     Op op = Downcast<Op>(call_node->op);
 
-    Array<te::Tensor> outputs;
-    OpImplementation impl;
     // TODO(mbs): device_copy cleanup
     ICHECK_NE(op, device_copy_op_) << "device_copy cannot be lowered";
+
     LoweredOutput lowered_out = (*flower_call)(GetRef<Call>(call_node), inputs, target_);
-    outputs = lowered_out->outputs;
-    impl = lowered_out->implementation;
+    Array<te::Tensor> outputs = lowered_out->outputs;
+    anchor_implementation_ = lowered_out->implementation;
 
-    if (create_schedule_) {
-      int op_pattern = fpattern[op];
-      if (!use_auto_scheduler_ && op_pattern >= kCommReduce) {
-        ICHECK(!anchor_op_.defined() || anchor_op_pattern_ < kCommReduce)
-            << "Cannot apply TOPI schedule to a primitive function with two complicated ops"
-            << " anchor=" << anchor_op_ << " current=" << op;
-      }
-      if (op_pattern >= anchor_op_pattern_) {
-        anchor_op_ = op;
-        anchor_attrs_ = call_node->attrs;
-        anchor_op_pattern_ = op_pattern;
-        anchor_implementation_ = impl;
-      }
-    }
     if (outputs.size() != 1) {
       const auto* tuple_type = call_node->checked_type().as<TupleTypeNode>();
       ICHECK(tuple_type) << "Expected output to be a tuple type "
@@ -308,8 +218,6 @@ class ScheduleBuilder : public backend::MemoizedExprTranslator<Array<te::Tensor>
       ICHECK_EQ(tuple_type->fields.size(), outputs.size());
     }
 
-    // TODO(mbs): device_copy cleanup
-    ICHECK_NE(op, device_copy_op_) << "device_copy cannot be lowered";
     readable_name_stream_ << '_' << op->name;
     return outputs;
   }
@@ -347,27 +255,146 @@ class ScheduleBuilder : public backend::MemoizedExprTranslator<Array<te::Tensor>
     return {tuple[op->index]};
   }
 
+ public:
+  // Additional outputs
+  Array<tvm::te::Tensor> fn_inputs_;
+  Array<te::Operation> scalars_;
+  std::unordered_map<const ConstantNode*, te::Tensor> constant_tensors_;
+  std::ostringstream readable_name_stream_;
+  OpImplementation anchor_implementation_;
+
+ private:
+  tvm::Target target_;
+  // Index of the global constants
+  static int const_index;
+  // Cache device copy op for equivalence checking to reduce registry lookup
+  // overhead for each invocation of call node when retrieving schedules.
+  const Op& device_copy_op_;
+};
+
+int LowerToTECompute::const_index = 0;
+
+// Construct a schedule for a given Relay primitive function and target.
+class ScheduleBuilder : ExprVisitor {
+ public:
+  explicit ScheduleBuilder(Target target, bool create_schedule = true)
+      : target_(target),
+
+        create_schedule_(create_schedule) {
+    // Whether to use auto_scheduler schedule.
+    use_auto_scheduler_ = backend::IsAutoSchedulerEnabled();
+    use_meta_schedule_ = backend::IsMetaScheduleEnabled();
+  }
+
+  CachedFunc Create(const Function& relay_func, std::function<std::string(std::string)> renamer) {
+    LowerToTECompute lower_te_compute(target_);
+    Array<te::Tensor> outputs = lower_te_compute.Lower(relay_func, renamer);
+    std::string candidate_name = lower_te_compute.readable_name_stream_.str();
+    VisitExpr(relay_func->body);
+
+    constexpr static size_t kMaxFuncNameLength = 80;
+    // WARNING: Please make sure to also update TVM_CRT_MAX_STRLEN_FUNCTION_NAME
+    //          whenever the value of kMaxFuncNameLength changes
+    if (candidate_name.size() > kMaxFuncNameLength) {
+      std::stringstream truncated_name;
+      truncated_name << candidate_name.substr(0, kMaxFuncNameLength);
+      truncated_name << "_" << std::hex << std::hash<std::string>{}(candidate_name) << "_";
+      candidate_name = truncated_name.str();
+    }
+
+    // TODO(mbs): This should be the definitive global by which the PrimFunc is known and
+    // no other GlobalVar ctors should appear inside the lowering machinery.
+    auto prim_fn_var = GlobalVar(renamer(candidate_name));
+    prim_fn_var->checked_type_ = relay_func->checked_type();
+
+    // Fusion over tupled results may leave identity relationships
+    // between inputs and outputs, and those should not be scheduled.
+    // Hence schedule only non PlaceholderOp outputs.
+    tvm::Array<te::Tensor> tensor_outs;
+    for (const auto& tensor : outputs) {
+      if (!tensor->op.as<te::PlaceholderOpNode>()) {
+        tensor_outs.push_back(tensor);
+      }
+    }
+
+    te::Schedule schedule{nullptr};
+    tir::PrimFunc prim_func{nullptr};
+    // No need to register schedule for device copy op.
+    if (anchor_attrs_.as<DeviceCopyAttrs>() == nullptr && create_schedule_) {
+      if (use_auto_scheduler_) {
+        const auto* fauto_schedule =
+            runtime::Registry::Get("auto_scheduler.relay_integration.auto_schedule_topi_compute");
+        ICHECK(fauto_schedule != nullptr)
+            << "auto_scheduler.relay_integration.auto_schedule_topi_compute is not registered";
+        ObjectRef obj = (*fauto_schedule)(prim_fn_var->name_hint, tensor_outs);
+        if (obj.defined()) {
+          schedule = Downcast<te::Schedule>(obj);
+        }
+      }
+      if (use_meta_schedule_) {
+        prim_func = tir::CreatePrimFuncFromOutputs(tensor_outs);
+        Optional<ObjectRef> opt_mod_or_base_func =
+            meta_schedule::MetaScheduleContext::QueryInsideWithScope(
+                prim_fn_var->name_hint, IRModule({{prim_fn_var, relay_func}}), target_,
+                Array<IRModule>{IRModule({{prim_fn_var, prim_func}})});
+        if (const auto* result = opt_mod_or_base_func.as<tir::PrimFuncNode>()) {
+          prim_func = GetRef<tir::PrimFunc>(result);
+        } else {
+          prim_func = tir::PrimFunc(nullptr);
+        }
+      }
+
+      // Use TOPI schedule if user specificed, or the function has no auto_scheduler schedule.
+      if (!schedule.defined() && !prim_func.defined()) {
+        ICHECK(lower_te_compute.anchor_implementation_.defined());
+        schedule =
+            lower_te_compute.anchor_implementation_.Schedule(anchor_attrs_, tensor_outs, target_);
+      }
+      if (schedule.defined()) {
+        for (const auto& scalar : lower_te_compute.scalars_) {
+          if (schedule->Contain(scalar)) {
+            schedule[scalar].compute_inline();
+          }
+        }
+      }
+    }
+
+    return CachedFunc(target_, prim_fn_var, lower_te_compute.fn_inputs_, outputs, schedule,
+                      prim_func, {}, IRModule(Map<GlobalVar, BaseFunc>({})),
+                      lower_te_compute.constant_tensors_);
+  }
+
+  void VisitExpr_(const CallNode* call_node) final {
+    static auto fpattern = Op::GetAttrMap<TOpPattern>("TOpPattern");
+
+    ICHECK(call_node->op.as<OpNode>()) << "Primitive function only allows call into primitive ops";
+    Op op = Downcast<Op>(call_node->op);
+
+    if (create_schedule_) {
+      int op_pattern = fpattern[op];
+      if (!use_auto_scheduler_ && op_pattern >= kCommReduce) {
+        ICHECK(!anchor_op_.defined() || anchor_op_pattern_ < kCommReduce)
+            << "Cannot apply TOPI schedule to a primitive function with two complicated ops"
+            << " anchor=" << anchor_op_ << " current=" << op;
+      }
+      if (op_pattern >= anchor_op_pattern_) {
+        anchor_op_ = op;
+        anchor_attrs_ = call_node->attrs;
+        anchor_op_pattern_ = op_pattern;
+      }
+    }
+  }
+
  private:
   tvm::Target target_;
   Op anchor_op_;
   Attrs anchor_attrs_;
   int anchor_op_pattern_{0};
-  OpImplementation anchor_implementation_;
-  std::ostringstream readable_name_stream_;
-  Array<te::Operation> scalars_;
-  std::unordered_map<const ConstantNode*, te::Tensor> constant_tensors_;
   bool use_auto_scheduler_;
   bool use_meta_schedule_;
-  // Cache device copy op for equivalence checking to reduce registry lookup
-  // overhead for each invocation of call node when retrieving schedules.
-  const Op& device_copy_op_;
   bool create_schedule_;
-  // Index of the global constants
-  static int const_index;
 };
 
-int ScheduleBuilder::const_index = 0;
-
 /*!
  * \brief Create schedule for target.
  * \param source_func The primitive function to be lowered.
