chore: import upstream snapshot with attribution

tests/backend/tensorflow/__init__.py

@@ -0,0 +1,110 @@
+from __future__ import absolute_import
+
+import numpy as np
+import tensorflow as tf
+from scipy.sparse import coo_matrix
+
+
+def cuda():
+    return "/gpu:0"
+
+
+def is_cuda_available():
+    return tf.test.is_gpu_available(cuda_only=True)
+
+
+def array_equal(a, b):
+    return np.array_equal(a.numpy(), b.numpy())
+
+
+def allclose(a, b, rtol=1e-4, atol=1e-4):
+    return np.allclose(
+        tf.convert_to_tensor(a).numpy(),
+        tf.convert_to_tensor(b).numpy(),
+        rtol=rtol,
+        atol=atol,
+    )
+
+
+def randn(shape):
+    return tf.random.normal(shape)
+
+
+def full(shape, fill_value, dtype, ctx):
+    with tf.device(ctx):
+        t = tf.constant(fill_value, shape=shape, dtype=dtype)
+    return t
+
+
+def narrow_row_set(x, start, stop, new):
+    # x[start:stop] = new
+    raise NotImplementedError("TF doesn't support inplace update")
+
+
+def sparse_to_numpy(x):
+    # tf.sparse.to_dense assume sorted indices, need to turn off validate_indices in our cases
+    return tf.sparse.to_dense(x, validate_indices=False).numpy()
+
+
+def clone(x):
+    return tf.identity(x)
+
+
+def reduce_sum(x):
+    return tf.reduce_sum(x)
+
+
+def softmax(x, dim):
+    return tf.math.softmax(x, axis=dim)
+
+
+def spmm(x, y):
+    return tf.sparse.sparse_dense_matmul(x, y)
+
+
+def add(a, b):
+    return a + b
+
+
+def sub(a, b):
+    return a - b
+
+
+def mul(a, b):
+    return a * b
+
+
+def div(a, b):
+    return a / b
+
+
+def sum(x, dim, keepdims=False):
+    return tf.reduce_sum(x, axis=dim, keepdims=keepdims)
+
+
+def max(x, dim):
+    return tf.reduce_max(x, axis=dim)
+
+
+def min(x, dim):
+    return tf.reduce_min(x, axis=dim)
+
+
+def prod(x, dim):
+    return tf.reduce_prod(x, axis=dim)
+
+
+def matmul(a, b):
+    return tf.linalg.matmul(a, b)
+
+
+def dot(a, b):
+    return sum(mul(a, b), dim=-1)
+
+
+def abs(a):
+    return tf.abs(a)
+
+
+def seed(a):
+    return tf.random.set_seed(a)