[GitHub] [tvm] balaram-cadence opened a new issue, #13855: [Bug] [Frontend][Tensorflow] tf.where with broadcast condition fails to import due to Incompatible broadcast type

["balaram-cadence (via GitHub)" <gi...@apache.org>]

balaram-cadence opened a new issue, #13855:
URL: https://github.com/apache/tvm/issues/13855

   The test case below fails to import in tvm:
   
   ```
   def test_forward_where_with_broadcast_cond():
       t1 = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype("float32")
       t2 = np.array([2.0, 4.0, 1.0, 3.0, 5.0]).astype("float32")
       x = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [9.0, 10.0]]).astype("float32")
       y = np.array([[10.0, 9.0], [8.0, 7.0], [6.0, 5.0], [4.0, 3.0], [2.0, 1.0]]).astype("float32")
   
       with tf.Graph().as_default():
           in1 = tf.placeholder(shape=(5), dtype = "float32", name="in1")
           in2 = tf.placeholder(shape=(5), dtype = "float32", name="in2")
           condition = math_ops.less(in1, in2, name="less")
           lhs = tf.placeholder(shape=(5,2), dtype = "float32", name="x")
           rhs = tf.placeholder(shape=(5,2), dtype = "float32", name="y")
           out = tf.where(condition, lhs, rhs)
           compare_tf_with_tvm([t1, t2, x, y], ["in1:0", "in2:0", "x:0", "y:0"], out.name)
   ```
   
   ### Expected behavior
   Should be identical to tensorflow output:
   ```
   [array([[1., 2.],
          [3., 4.],
          [6., 5.],
          [4., 3.],
          [2., 1.]], dtype=float32)]
   ```
   
   ### Actual behavior
   Failed with this error:
   
   `Incompatible broadcast type TensorType([5], bool) and TensorType([5, 2], float32)`
   
   ### Environment
   ```
   Linux
   LSB Version:    :core-4.1-amd64:core-4.1-ia32:core-4.1-noarch:cxx-4.1-amd64:cxx-4.1-ia32:cxx-4.1-noarch:desktop-4.1-amd64:desktop-4.1-ia32:desktop-4.1-noarch:languages-4.1-amd64:languages-4.1-noarch:printing-4.1-amd64:printing-4.1-noarch
   Distributor ID: RedHatEnterpriseWorkstation
   Description:    Red Hat Enterprise Linux Workstation release 7.9 (Maipo)
   Release:        7.9
   Codename:       Maipo
   Name: apache-tvm
   Version: 0.10.0
   Home-page: https://tlcpack.ai
   Author: Apache TVM
   Author-email: None
   License: Apache
   ```
   
   ### Steps to reproduce
   Add above testcase to tests/python/frontend/tensorflow/test_forward.py and run
   `python -m pytest tests/python/frontend/tensorflow/test_forward.py -k test_forward_where_with_broadcast_cond`
   
   ### Triage
   
   * needs-triage
   * frontend:tensorflow 


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[GitHub] [tvm] masahi closed issue #13855: [Bug] [Frontend][Tensorflow] tf.where with broadcast condition fails to import due to Incompatible broadcast type

["masahi (via GitHub)" <gi...@apache.org>]

masahi closed issue #13855: [Bug] [Frontend][Tensorflow] tf.where with broadcast condition fails to import due to Incompatible broadcast type
URL: https://github.com/apache/tvm/issues/13855


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[GitHub] [tvm] balaram-cadence commented on issue #13855: [Bug] [Frontend][Tensorflow] tf.where with broadcast condition fails to import due to Incompatible broadcast type

["balaram-cadence (via GitHub)" <gi...@apache.org>]

balaram-cadence commented on issue #13855:
URL: https://github.com/apache/tvm/issues/13855#issuecomment-1408887189

   This test fails with numpy and tensorflow 2.x but works fine with tensorflow 1.x:
   ```
   import numpy as np
   import tensorflow as tf
   
   try:
       tf_compat_v1 = tf.compat.v1
   except:
       tf_compat_v1 = tf
   
   t1 = np.arange(5, dtype="float32")
   t2 = np.arange(5, dtype="float32")[::-1]
   x = np.arange(10, dtype="float32").reshape(5,2)
   y = np.arange(10, dtype="float32")[::-1].reshape(5,2)
   condition = np.less(t1, t2)
   print("tf1.x output:")
   print(tf_compat_v1.where(condition, x, y))
   print("tf2.x output:")
   print(tf.where(condition, x, y))
   ```
   
   


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