diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -1658,12 +1658,33 @@
     return success();
   }
 };
+
+/// Fold linalg.fill -> linalg.tensor_reshape chain.
+///
+/// For such op chains, we can create new linalg.fill ops with the result
+/// type of the linalg.tensor_reshape op.
+struct FoldFillWithTensorReshape : OpRewritePattern<TensorReshapeOp> {
+  using OpRewritePattern<TensorReshapeOp>::OpRewritePattern;
+  LogicalResult matchAndRewrite(TensorReshapeOp reshapeOp,
+                                PatternRewriter &rewriter) const override {
+    auto oldFill = reshapeOp.src().getDefiningOp<FillOp>();
+    if (!oldFill)
+      return failure();
+
+    auto newInit = rewriter.create<InitTensorOp>(
+        oldFill.getLoc(), reshapeOp.getResultType().getShape(),
+        reshapeOp.getResultType().getElementType());
+    rewriter.replaceOpWithNewOp<FillOp>(reshapeOp, newInit, oldFill.value());
+
+    return success();
+  }
+};
 } // namespace
 
 void TensorReshapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                                   MLIRContext *context) {
-  results.add<CollapseReshapeOps<TensorReshapeOp>, FoldReshapeWithConstant,
-              ReplaceDimOfReshapeOpResult>(context);
+  results.add<CollapseReshapeOps<TensorReshapeOp>, FoldFillWithTensorReshape,
+              FoldReshapeWithConstant, ReplaceDimOfReshapeOpResult>(context);
 }
 
 //===----------------------------------------------------------------------===//
diff --git a/mlir/test/Dialect/Linalg/canonicalize.mlir b/mlir/test/Dialect/Linalg/canonicalize.mlir
--- a/mlir/test/Dialect/Linalg/canonicalize.mlir
+++ b/mlir/test/Dialect/Linalg/canonicalize.mlir
@@ -802,3 +802,19 @@
 //   CHECK: return
   return
 }
+
+// -----
+
+// CHECK-LABEL: func @fold_fill_reshape()
+func @fold_fill_reshape() -> tensor<6x4xf32> {
+  %zero = constant 0.0 : f32
+  // CHECK: %[[INIT:.+]] = linalg.init_tensor [6, 4] : tensor<6x4xf32>
+  %init = linalg.init_tensor [1, 2, 3, 4] : tensor<1x2x3x4xf32>
+  // CHECK: %[[FILL:.+]] = linalg.fill(%[[INIT]], %cst) : tensor<6x4xf32>, f32 -> tensor<6x4xf32>
+  %fill = linalg.fill(%init, %zero) : tensor<1x2x3x4xf32>, f32 -> tensor<1x2x3x4xf32>
+  %reshape = linalg.tensor_reshape %fill [
+    affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>,
+    affine_map<(d0, d1, d2, d3) -> (d3)>] : tensor<1x2x3x4xf32> into tensor<6x4xf32>
+  // CHECK: return %[[FILL]] : tensor<6x4xf32>
+  return %reshape : tensor<6x4xf32>
+}
diff --git a/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir b/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
--- a/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
+++ b/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
@@ -379,12 +379,14 @@
 
 //       CHECK: func @fold_unit_dim_for_init_tensor
 
+
 //       CHECK: %[[INPUT_RESHAPE:.+]] = linalg.tensor_reshape %{{.+}} [#[[MAP0]]] : tensor<1x1000xf32> into tensor<1000xf32>
-//       CHECK: %[[INIT_RESHAPE:.+]] = linalg.tensor_reshape %{{.+}} [] : tensor<1xf32> into tensor<f32>
+//       CHECK: %[[INIT:.+]] = linalg.init_tensor [] : tensor<f32>
+//       CHECK: %[[FILL:.+]] = linalg.fill(%[[INIT]], %cst) : tensor<f32>, f32 -> tensor<f32>
 //       CHECK: %[[GENERIC:.+]] = linalg.generic
 //  CHECK-SAME:     indexing_maps = [#[[MAP1]], #[[MAP2]]]
 //  CHECK-SAME:     iterator_types = ["reduction"]
 //  CHECK-SAME:   ins(%[[INPUT_RESHAPE]] : tensor<1000xf32>)
-//  CHECK-SAME:   outs(%[[INIT_RESHAPE]] : tensor<f32>)
+//  CHECK-SAME:   outs(%[[FILL]] : tensor<f32>)
 //       CHECK: %[[GENERIC_RESHAPE:.+]] = linalg.tensor_reshape %[[GENERIC]] [] : tensor<f32> into tensor<1xf32>
 //       CHECK: return %[[GENERIC_RESHAPE:.+]] : tensor<1xf32>