fix repeat interleave 0-size tensor bug (#8414)

Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
Oneflow-Inc · Jun 14, 2022 · 9bf8090 · 9bf8090
1 parent 041f787
commit 9bf8090
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Showing 2 changed files with 26 additions and 16 deletions.
diff --git a/oneflow/core/functional/impl/array_functor.cpp b/oneflow/core/functional/impl/array_functor.cpp
@@ -2998,20 +2998,8 @@ class RepeatInterLeaveTensorFunctor {
     std::shared_ptr<one::Tensor> cumsum = JUST(Cumsum(repeats, 0, DType::Int32()));
     const int64_t& output_size_value =
         std::accumulate(repeats_value.begin(), repeats_value.end(), 0);
-    std::shared_ptr<one::Tensor> res;
-    if (output_size_value > 0) {
-      res = JUST(IndexSelect(input, dim_,
-                             JUST(RepeatInterLeaveIndex(repeats, cumsum, output_size_value))));
-    } else {
-      // Deal with 0-size Tensor.
-      DimVector new_input_shape(input_shape->dim_vec().begin(), input_shape->dim_vec().end());
-      new_input_shape[dim_] = 0;
-      std::shared_ptr<one::Tensor> new_input =
-          JUST(Constant(Shape{new_input_shape}, Scalar(0), input->dtype(), JUST(input->device())));
-      res = JUST(IndexSelect(new_input, dim_,
-                             JUST(RepeatInterLeaveIndex(repeats, cumsum, output_size_value))));
-    }
-    return res;
+    return JUST(
+        IndexSelect(input, dim_, JUST(RepeatInterLeaveIndex(repeats, cumsum, output_size_value))));
   }
 };
 

diff --git a/python/oneflow/test/modules/test_repeat_interleave.py b/python/oneflow/test/modules/test_repeat_interleave.py
@@ -15,8 +15,10 @@
 """
 import unittest
 
+import numpy as np
 import oneflow as flow
 import oneflow.unittest
+import torch as torch_original
 
 from oneflow.test_utils.automated_test_util import *
 
@@ -39,17 +41,37 @@ def test_flow_int_repeat_interleave_with_dim(test_case):
     @autotest(n=5)
     def test_flow_tensor_repeat_interleave_dim(test_case):
         x = random_tensor(ndim=3, dim0=2, dim1=2, dim2=3)
-        y = random_tensor(ndim=1, dim0=2, dtype=int, low=1, high=4)
+        y = random_tensor(ndim=1, dim0=2, dtype=int, low=0, high=4)
         z = torch.repeat_interleave(x, y, 1)
         return z
 
     @autotest(n=5)
     def test_flow_tensor_repeat_interleave_dim_with_output_size(test_case):
         x = random_tensor(ndim=3, dim0=2, dim1=2, dim2=3)
-        y = random_tensor(ndim=1, dim0=2, dtype=int, low=1, high=4)
+        y = random_tensor(ndim=1, dim0=2, dtype=int, low=0, high=4)
         z = torch.repeat_interleave(x, y, 1, output_size=2)
         return z
 
+    def test_flow_tensor_repeat_interleave_0size_tensor(test_case):
+        np_arr = np.array(
+            [
+                [[0.8548, 0.0436, 0.7977], [0.1919, 0.4191, 0.2186]],
+                [[0.4741, 0.8896, 0.6859], [0.5223, 0.7803, 0.1134]],
+            ]
+        )
+        x_torch = torch_original.tensor(np_arr)
+        x_torch.requires_grad = True
+        y_torch = torch_original.tensor([0, 0])
+        z_torch = torch_original.repeat_interleave(x_torch, y_torch, 1)
+        z_torch.sum().backward()
+
+        x_flow = flow.tensor(np_arr)
+        x_flow.requires_grad = True
+        y_flow = flow.tensor([0, 0])
+        z_flow = flow.repeat_interleave(x_flow, y_flow, 1)
+        z_flow.sum().backward()
+        test_case.assertTrue(np.array_equal(x_torch.grad.numpy(), x_flow.grad.numpy()))
+
 
 if __name__ == "__main__":
     unittest.main()