Simplify full broadcast

include/tvm/relay/dataflow_pattern.h

@@ -524,6 +524,8 @@ class DominatorPattern : public DFPattern {
 DFPattern IsVar(const String& name);
 /*! \brief Syntatic Sugar for creating a ConstantPattern */
 DFPattern IsConstant();
+/*! \brief Syntatic Sugar for creating a WildcardPattern */
+DFPattern IsWildcard();
 /*! \brief Syntatic Sugar for creating a ExprPattern */
 DFPattern IsExpr(const Expr& expr);
 /*! \brief Syntatic Sugar for creating a ExprPattern base on an Op*/

src/relay/ir/dataflow_matcher.cc

@@ -162,8 +162,12 @@ bool DFPatternMatcher::VisitDFPattern_(const AttrPatternNode* attr_pattern, cons
       if (Op::HasAttrMap(attr_name)) {
         auto op_map = Op::GetAttrMap<TVMRetValue>(attr_name);
         if (op_map.count(op)) {
-          matches = MatchRetValue(attr_value, op_map[op]);
+          matches &= MatchRetValue(attr_value, op_map[op]);
+        } else {
+          matches = false;
         }
+      } else {
+        matches = false;
       }
     }
   } else if (auto* op = expr.as<CallNode>()) {
@@ -196,6 +200,8 @@ bool DFPatternMatcher::VisitDFPattern_(const AttrPatternNode* attr_pattern, cons
         break;
       }
     }
+  } else {
+    matches = false;
   }
   return matches;
 }

src/relay/ir/dataflow_pattern.cc

@@ -357,6 +357,7 @@ DFPattern DFPattern::HasShape(const Array<PrimExpr> shape) {
 }
 DFPattern IsVar(const String& name) { return VarPattern(name); }
 DFPattern IsConstant() { return ConstantPattern(make_object<ConstantPatternNode>()); }
+DFPattern IsWildcard() { return WildcardPattern(make_object<WildcardPatternNode>()); }
 DFPattern IsExpr(const Expr& expr) { return ExprPattern(expr); }
 DFPattern IsOp(const String& op_name) { return IsExpr(Op::Get(op_name)); }
 DFPattern IsTuple(const Array<DFPattern>& fields) { return TuplePattern(fields); }

src/relay/op/make_op.h

@@ -100,6 +100,12 @@ Expr MakeResize(Expr data, Array<IndexExpr> size, String layout, String method,
 
 Expr MakeSparseToDense(Expr indices, Array<Integer> output_shape, Expr values, Expr default_value);
 
+Expr MakeArange(Expr start, Expr stop, Expr step, DataType dtype);
+
+Expr MakeShapeOf(Expr data, DataType dtype);
+
+Expr MakeTake(Expr data, Expr indices, Integer axis, String mode);
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_OP_MAKE_OP_H_

src/relay/op/tensor/unary.cc

@@ -430,12 +430,14 @@ Array<te::Tensor> ShapeOfCompute(const Attrs& attrs, const Array<te::Tensor>& in
   return {topi::shape(inputs[0], param->dtype)};
 }
 
-TVM_REGISTER_GLOBAL("relay.op._make.shape_of").set_body_typed([](Expr data, DataType dtype) {
+Expr MakeShapeOf(Expr data, DataType dtype) {
   auto attrs = make_object<ShapeOfAttrs>();
   attrs->dtype = dtype;
   static const Op& op = Op::Get("shape_of");
   return Call(op, {data}, Attrs(attrs), {});
-});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.shape_of").set_body_typed(MakeShapeOf);
 
 RELAY_REGISTER_OP("shape_of")
     .describe(R"code(Returns a tensor representing the shape of a tensor.

src/relay/transforms/fold_constant.cc

@@ -148,7 +148,7 @@ class ConstantFolder : public MixedModeMutator {
     }
     static auto op_stateful = Op::GetAttrMap<TOpIsStateful>("TOpIsStateful");
 
-    std::unordered_set<std::string> skip_list{"zeros_like", "ones_like", "full_like", "full"};
+    std::unordered_set<std::string> skip_list{};
 
     auto origin_args = call->args;
     call = post.as<CallNode>();

src/relay/transforms/simplify_expr.cc

@@ -29,24 +29,38 @@
 #include <tvm/support/logging.h>
 
 #include "../op/tensor/transform.h"
+#include "pattern_utils.h"
 
 namespace tvm {
 namespace relay {
 
+class SimplifyPattern {
+ public:
+  virtual Expr callback(const Expr& pre, const Expr& post,
+                        const Map<DFPattern, Array<Expr>>& node_map) const = 0;
+
+  DFPattern pattern() const { return pattern_; }
+
+ protected:
+  /*! \brief Pattern for rewriting */
+  DFPattern pattern_;
+};
+
 /*!
  * \brief SimplifyReshape matches the pattern of consecutive reshape or reverse_reshape ops,
  *   and merges into one reshape op.
  */
-class SimplifyReshape {
+class SimplifyReshape : public SimplifyPattern {
  public:
   SimplifyReshape() {
-    x_ = WildcardPattern(make_object<WildcardPatternNode>());
+    x_ = IsWildcard();
     auto reshape1 = IsOp("reshape") || IsOp("contrib_reverse_reshape");
     auto reshape2 = IsOp("reshape") || IsOp("contrib_reverse_reshape");
     pattern_ = reshape1({reshape2({x_})});
   }
 
-  Expr callback(const Expr& pre, const Expr& post, const Map<DFPattern, Array<Expr>>& node_map) {
+  Expr callback(const Expr& pre, const Expr& post,
+                const Map<DFPattern, Array<Expr>>& node_map) const override {
     auto x = node_map[x_][0];
     bool const_shape = true;
     Array<Integer> newshape;
@@ -63,13 +77,82 @@ class SimplifyReshape {
     return post;
   }
 
-  DFPattern pattern() const { return pattern_; }
-
  private:
   /*! \brief Pattern input */
   DFPattern x_;
-  /*! \brief Pattern for consecutive reshape or reverse_reshape ops */
-  DFPattern pattern_;
+};
+
+/*!
+ * \brief FullArgwhere finds full followed by argwhere and turns it into an Arange op
+ */
+class FullElementwise : public SimplifyPattern {
+ public:
+  FullElementwise() {
+    x_ = IsWildcard();
+    data_ = IsWildcard();
+    value_ = IsConstant();
+
+    full_ = IsOp("full")({value_}) || IsOp("full_like")({data_, value_});
+    ones_ = IsOp("ones")({}) || IsOp("ones_like")({data_});
+    zeros_ = IsOp("zeros")({}) || IsOp("zeros_like")({data_});
+
+    Map<String, ObjectRef> attrs;
+    attrs.Set("TOpPattern", Integer(static_cast<int>(kBroadcast)));
+    DFPattern op = IsWildcard().HasAttr(attrs);
+    DFPattern full = full_ || ones_ || zeros_;
+    pattern_ = op({full, x_}) || op({x_, full});
+  }
+
+  Expr callback(const Expr& pre, const Expr& post,
+                const Map<DFPattern, Array<Expr>>& node_map) const override {
+    const CallNode* call = pre.as<CallNode>();
+    ICHECK(call);
+    Type pre_type = pre->checked_type_;
+    ICHECK(pre_type.as<TensorTypeNode>());
+    auto dtype = pre_type.as<TensorTypeNode>()->dtype;
+    auto x = node_map[x_][0];
+    bool is_left = post.as<CallNode>()->args[1] == x;
+    Type x_type;
+    if (is_left) {
+      x_type = call->args[1]->checked_type_;
+    } else {
+      x_type = call->args[0]->checked_type_;
+    }
+
+    if (StructuralEqual()(x_type, pre_type)) {
+      Expr value;
+      if (node_map.count(full_)) {
+        value = node_map[value_][0];
+        ICHECK(IsConstScalar(value));
+      } else if (node_map.count(ones_)) {
+        value = MakeConstantScalar(dtype, 1);
+      } else if (node_map.count(zeros_)) {
+        value = MakeConstantScalar(dtype, 0);
+      } else {
+        ICHECK(false) << "Didn't find a full op while matching full + elementwise";
+      }
+      if (is_left) {
+        return Call(call->op, {value, x}, call->attrs, call->type_args, call->span);
+      } else {
+        return Call(call->op, {x, value}, call->attrs, call->type_args, call->span);
+      }
+    }
+    return post;
+  }
+
+ private:
+  /*! \brief binary argument */
+  DFPattern x_;
+  /*! \brief data ops get shape from */
+  DFPattern data_;
+  /*! \brief constant input */
+  DFPattern value_;
+  /*! \brief full op */
+  DFPattern full_;
+  /*! \brief ones op */
+  DFPattern ones_;
+  /*! \brief zeros op */
+  DFPattern zeros_;
 };
 
 /*!
@@ -78,22 +161,24 @@ class SimplifyReshape {
 class ExprSimplifier {
  public:
   explicit ExprSimplifier(IRModule mod) : mod_(mod) {
-    auto reshape_func = [this](TVMArgs args, TVMRetValue* rv) {
+    CreateCallback(SimplifyReshape());
+    CreateCallback(FullElementwise());
+  }
+  template <typename T>
+  void CreateCallback(const T& pattern) {
+    auto func = [pattern](TVMArgs args, TVMRetValue* rv) {
       Expr pre = args[0];
       Expr post = args[1];
       Map<DFPattern, Array<Expr>> node_map = args[2];
-      *rv = simplify_reshape_.callback(pre, post, node_map);
+      *rv = pattern.callback(pre, post, node_map);
     };
-    callbacks_.push_back(
-        DFPatternCallback(simplify_reshape_.pattern(), PackedFunc(reshape_func), true));
+    callbacks_.push_back(DFPatternCallback(pattern.pattern(), PackedFunc(func), true));
   }
 
   Expr Simplify(const Expr& expr) { return RewritePatterns(callbacks_, expr, mod_); }
 
  private:
   IRModule mod_;
-  /*! \brief Simplify reshape pattern */
-  SimplifyReshape simplify_reshape_;
   /*! \brief Callbacks for expr simplification */
   Array<DFPatternCallback> callbacks_;
 };

tests/python/relay/test_dataflow_pattern.py

@@ -437,6 +437,8 @@ def test_no_match_op_attr():
     x = relay.var("x")
     y = relay.var("y")
     assert not op_pat.match(x - y)
+    z = relay.var("z")
+    assert not op_pat.match(relay.Let(z, x + y, z))
 
 
 def test_match_func_attr():

tests/python/relay/test_pass_fold_constant.py

@@ -231,22 +231,6 @@ def expected(dtype):
         assert tvm.ir.structural_equal(zz, zexpected)
 
 
-def test_fold_full():
-    c_shape = (8, 9, 10)
-
-    def before():
-        dtype = "float32"
-        return relay.full(relay.const(1.0, dtype), c_shape, dtype=dtype)
-
-    def expected():
-        # expect no changes
-        return before()
-
-    zz = run_opt_pass(before(), transform.FoldConstant())
-    zexpected = run_opt_pass(expected(), transform.InferType())
-    assert tvm.ir.structural_equal(zz, zexpected)
-
-
 def test_fold_batch_norm():
     def expected():
         data = relay.var("data", relay.TensorType((1, 3, 224, 224), "float32"))

tests/python/relay/test_pass_simplify_expr.py

@@ -58,5 +58,70 @@ def symbolic():
     assert tvm.ir.structural_equal(zz, after)
 
 
+def test_simplify_full_elementwise():
+    def validate(shape, value, dtype):
+        def before_left(x, elem_op, full):
+            return elem_op(full, x)
+
+        def after_left(x, elem_op, value):
+            return elem_op(relay.const(value, dtype), x)
+
+        def before_right(x, elem_op, full):
+            return elem_op(x, full)
+
+        def after_right(x, elem_op, value):
+            return elem_op(x, relay.const(value, dtype))
+
+        x = relay.var("x", shape=shape, dtype=dtype)
+        elem_ops = [relay.add, relay.multiply, relay.subtract, relay.divide]
+        full_ops = []
+        if value == 0:
+            full_ops.append(relay.zeros(shape, dtype))
+            full_ops.append(relay.zeros_like(x))
+        if value == 1:
+            full_ops.append(relay.ones(shape, dtype))
+            full_ops.append(relay.ones_like(x))
+        else:
+            full_ops.append(relay.full(relay.const(value, dtype), shape))
+            full_ops.append(relay.full_like(x, relay.const(value, dtype)))
+        for op in elem_ops:
+            for full in full_ops:
+                z = before_left(x, op, full)
+                zz = run_opt_pass(z, transform.SimplifyExpr())
+                after = run_opt_pass(after_left(x, op, value), transform.InferType())
+                assert tvm.ir.structural_equal(zz, after)
+
+                z = before_right(x, op, full)
+                zz = run_opt_pass(z, transform.SimplifyExpr())
+                after = run_opt_pass(after_right(x, op, value), transform.InferType())
+                assert tvm.ir.structural_equal(zz, after)
+
+        # Test the case in which x is broadcast to full's shape
+        full_ops = []
+        if value == 0:
+            full_ops.append(relay.zeros(shape * 2, dtype))
+        if value == 1:
+            full_ops.append(relay.ones(shape * 2, dtype))
+        else:
+            full_ops.append(relay.full(relay.const(value, dtype), shape * 2))
+        for op in elem_ops:
+            for full in full_ops:
+                z = before_left(x, op, full)
+                zz = run_opt_pass(z, transform.SimplifyExpr())
+                after = run_opt_pass(before_left(x, op, full), transform.InferType())
+                assert tvm.ir.structural_equal(zz, after)
+
+                z = before_right(x, op, full)
+                zz = run_opt_pass(z, transform.SimplifyExpr())
+                after = run_opt_pass(before_right(x, op, full), transform.InferType())
+                assert tvm.ir.structural_equal(zz, after)
+
+    for shape in [[10], [10, 10], [10, 10, 10]]:
+        for dtype in ["float32", "int32"]:
+            for value in [0, 1, 2]:
+                validate(shape, value, dtype)
+
+
 if __name__ == "__main__":
     test_simplify_reshape()
+    test_simplify_full_elementwise()