[Relay][Pass] SimplifyCastLike/Cast and ConcretizeFullLikeRewrite rew…

…rites for SimplifyExpr (apache#7827)

src/relay/transforms/simplify_expr.cc

@@ -75,6 +75,33 @@ class SimplifyReshape : public DFPatternRewrite {
   DFPattern x_;
 };
 
+/*!
+ * \brief SimplifyCast matches the pattern of cast data to the same dtype.
+ */
+class SimplifyCast : public DFPatternRewrite {
+ public:
+  SimplifyCast() {
+    data_pat_ = IsWildcard();
+    like_pat_ = IsWildcard();
+    pattern_ = IsOp("cast_like")({data_pat_, like_pat_}) || IsOp("cast")({data_pat_});
+  }
+
+  Expr Callback(const Expr& pre, const Expr& post,
+                const Map<DFPattern, Array<Expr>>& node_map) const override {
+    const CallNode* call = pre.as<CallNode>();
+    const TensorTypeNode* data_ty = call->args[0]->checked_type().as<TensorTypeNode>();
+    const TensorTypeNode* like_ty = pre->checked_type().as<TensorTypeNode>();
+    if (like_ty->dtype == data_ty->dtype) {
+      return node_map[data_pat_][0];
+    }
+    return post;
+  }
+
+ protected:
+  DFPattern data_pat_;
+  DFPattern like_pat_;
+};
+
 /*!
  * \brief SimplifyTranspose matches the pattern of consecutive transpose op,
  *   and merges or cancels them.
@@ -321,6 +348,17 @@ class ConcretizeOnesLikeRewrite : public ConcretizeLikeRewrite {
   }
 };
 
+class ConcretizeFullLikeRewrite : public ConcretizeLikeRewrite {
+ public:
+  ConcretizeFullLikeRewrite() : ConcretizeLikeRewrite(Op::Get("full_like")) {}
+
+  Expr Concretize(const Map<DFPattern, Array<Expr>>& node_map, Array<Integer> shape,
+                  DataType dtype) const override {
+    // `like_pat_` here is `fill_value`
+    return MakeFull(node_map[like_pat_][0], shape, dtype);
+  }
+};
+
 class ConcretizeReshapeLikeRewrite : public ConcretizeLikeRewrite {
  public:
   ConcretizeReshapeLikeRewrite() : ConcretizeLikeRewrite(Op::Get("reshape_like")) {}
@@ -439,12 +477,14 @@ Expr SimplifyExpr(const Expr& expr, const IRModule& mod) {
   DFPatternRewriteComposer composer;
   composer.AddRewrite<ConcretizeZerosLikeRewrite>();
   composer.AddRewrite<ConcretizeOnesLikeRewrite>();
+  composer.AddRewrite<ConcretizeFullLikeRewrite>();
   composer.AddRewrite<ConcretizeReshapeLikeRewrite>();
   composer.AddRewrite<ConcretizeCollapseSumLikeRewrite>();
   composer.AddRewrite<ConcretizeBroadcastToLikeRewrite>();
   composer.AddRewrite<EliminateIdentityRewrite>();
   composer.AddRewrite<SimplifyReshape>();
   composer.AddRewrite<SimplifyTranspose>();
+  composer.AddRewrite<SimplifyCast>();
   composer.AddRewrite<FullElementwise>();
   return RewritePatterns(composer.MakeCallbacks(), expr, mod);
 }

tests/python/relay/test_pass_simplify_expr.py

@@ -236,6 +236,20 @@ def check(x, y=None, do_nothing=False):
         check(id_op(const, x), id_op(op_like(x), x))
 
 
+def test_simplify_cast():
+    dtype = "int32"
+    data = relay.var("data", shape=(3, 4, 5), dtype=dtype)
+    expr1 = relay.cast(data, dtype)
+    dtype_like = relay.var("dtype_like", shape=(2, 2, 2), dtype=dtype)
+    expr2 = relay.cast_like(data, dtype_like)
+
+    expected = run_infer_type(data)
+    actual1 = run_opt_pass(expr1, relay.transform.SimplifyExpr())
+    assert tvm.ir.structural_equal(actual1, expected)
+    actual2 = run_opt_pass(expr2, relay.transform.SimplifyExpr())
+    assert tvm.ir.structural_equal(actual2, expected)
+
+
 def test_concretize_reshape_like():
     data = relay.var("data", shape=(2, 3, 4), dtype="float32")
     shape_like = relay.var("shape_like", shape=(6, 2, 2), dtype="float32")
@@ -276,6 +290,17 @@ def test_concretize_ones_like():
     assert tvm.ir.structural_equal(actual, expected)
 
 
+def test_concretize_full_like():
+    dtype = "int32"
+    shape_like = relay.var("shape_like", shape=(3, 4, 5), dtype=dtype)
+    fill_value = relay.var("fill", relay.TensorType((), "float32"))
+    expr = relay.full_like(shape_like, fill_value)
+
+    expected = run_infer_type(relay.full(fill_value, (3, 4, 5), dtype))
+    actual = run_opt_pass(expr, relay.transform.SimplifyExpr())
+    assert tvm.ir.structural_equal(actual, expected)
+
+
 def test_concretize_collapse_sum_like():
     data = relay.var("data", shape=(3, 3, 3), dtype="float32")
     shape_like = relay.var("shape_like", shape=(3,), dtype="float32")