65bb3580e5cdd9adee17b5f80fba949550931271,test/test_tensorflow.py,MPITests,test_horovod_broadcast_grad,#MPITests#,591

Before Change

            dtypes = [tf.float32, tf.float64]
            dims = [1, 2, 3]
            root_ranks = list(range(size))
            for dtype, dim, root_rank in itertools.product(
                    dtypes, dims, root_ranks):
                tensor = tf.ones([5] * dim) * rank
                if dtype == tf.bool:
                    tensor = tensor % 2
                tensor = tf.cast(tensor, dtype=dtype)
                broadcasted_tensor = hvd.broadcast(tensor, root_rank)

                grad_ys = tf.ones([5] * dim)
                grad = tf.gradients(broadcasted_tensor, tensor, grad_ys)[0]
                grad_out = session.run(grad)

                c = size if rank == root_rank else 0
                expected = np.ones([5] * dim) * c
                err = np.linalg.norm(expected - grad_out)
                self.assertLess(err, 0.00000001,
                                "gradient %s differs from expected %s, "
                                "error: %s" % (grad_out, expected, str(err)))

    def test_compression_fp16(self):
        valid_dtypes = [tf.float16, tf.float32, tf.float64]
        invalid_dtypes = [tf.uint8, tf.int8, tf.uint16, tf.int16,
                          tf.int32, tf.int64, tf.bool]

After Change

        for dtype, dim, root_rank in itertools.product(
                dtypes, dims, root_ranks):
            if _executing_eagerly():
                tensor = self.tfe.Variable(tf.ones([5] * dim) * rank)
            else:
                tensor = tf.ones([5] * dim) * rank
            if dtype == tf.bool:
                tensor = tensor % 2
            if _executing_eagerly():
                with tf.GradientTape() as tape:
                    tensor = tf.cast(tensor, dtype=dtype)
                    broadcasted_tensor = hvd.broadcast(tensor, root_rank)
                grad_out = tape.gradient(broadcasted_tensor, tensor)
            else:
                tensor = tf.cast(tensor, dtype=dtype)
                broadcasted_tensor = hvd.broadcast(tensor, root_rank)

                grad_ys = tf.ones([5] * dim)
                grad = tf.gradients(broadcasted_tensor, tensor, grad_ys)[0]
                grad_out = self.evaluate(grad)

            c = size if rank == root_rank else 0
            expected = np.ones([5] * dim) * c
            err = np.linalg.norm(expected - grad_out)

Instances