Switch over python calls to RandomPoissonV2.

Part 2 of Support int32/64 in tf.random_poisson().

PiperOrigin-RevId: 174071745

tensorflow/core/ops/random_ops.cc

@@ -265,8 +265,6 @@ output: A tensor with shape `shape + shape(alpha)`. Each slice
   `alpha[i0, i1, ...iN]`. The dtype of the output matches the dtype of alpha.
 )doc");
 
-// TODO(dhananayn): Deprecate RandomPoisson and switch over to RandomPoissonV2
-// after forward compatibility period has passed.
 REGISTER_OP("RandomPoisson")
     .SetIsStateful()
     .Input("shape: S")
@@ -283,32 +281,9 @@ REGISTER_OP("RandomPoisson")
       c->set_output(0, out);
       return Status::OK();
     })
+    .Deprecated(25, "Replaced by RandomPoissonV2")
     .Doc(R"doc(
-Outputs random values from the Poisson distribution(s) described by rate.
+Use RandomPoissonV2 instead.
-
-This op uses two algorithms, depending on rate. If rate >= 10, then
-the algorithm by Hormann is used to acquire samples via
-transformation-rejection.
-See http://www.sciencedirect.com/science/article/pii/0167668793909974.
-
-Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform
-random variables.
-See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer
-Programming, Volume 2. Addison Wesley
-
-shape: 1-D integer tensor. Shape of independent samples to draw from each
-  distribution described by the shape parameters given in rate.
-rate: A tensor in which each scalar is a "rate" parameter describing the
-  associated poisson distribution.
-seed: If either `seed` or `seed2` are set to be non-zero, the random number
-  generator is seeded by the given seed.  Otherwise, it is seeded by a
-  random seed.
-seed2: A second seed to avoid seed collision.
-
-output: A tensor with shape `shape + shape(rate)`. Each slice
-  `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for
-  `rate[i0, i1, ...iN]`. The dtype of the output matches the dtype of
-  rate.
 )doc");
 
 REGISTER_OP("RandomPoissonV2")

tensorflow/core/public/version.h

@@ -90,6 +90,7 @@ limitations under the License.
 // 23. Remove NonMaxSuppression in favor of NonMaxSuppressionV2.
 // 24. Deprecate lookup ops (v1) ops in favor of v2 (30may2017)
 // 25. Deprecate stack (v1) ops in favor of v2 (2017/6/15).
+// 25. Deprecate RandomPoisson (v1) ops in favor of v2 (2017/10/25).
 
 #define TF_GRAPH_DEF_VERSION_MIN_PRODUCER 0
 #define TF_GRAPH_DEF_VERSION_MIN_CONSUMER 0

tensorflow/python/kernel_tests/random/random_poisson_test.py

@@ -24,11 +24,14 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import gen_random_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.platform import test
 from tensorflow.python.platform import tf_logging
 
+# All supported dtypes for random_poisson().
+_SUPPORTED_DTYPES = (dtypes.float16, dtypes.float32, dtypes.float64,
+                     dtypes.int32, dtypes.int64)
+
 
 class RandomPoissonTest(test.TestCase):
   """This is a large test due to the moments computation taking some time."""
@@ -57,7 +60,7 @@ class RandomPoissonTest(test.TestCase):
     # we want to tolerate. Since the z-test approximates a unit normal
     # distribution, it should almost definitely never exceed 6.
     z_limit = 6.0
-    for dt in dtypes.float16, dtypes.float32, dtypes.float64:
+    for dt in _SUPPORTED_DTYPES:
       # Test when lam < 10 and when lam >= 10
       for stride in 0, 4, 10:
         for lam in (3., 20):
@@ -102,7 +105,7 @@ class RandomPoissonTest(test.TestCase):
   # Checks that the CPU and GPU implementation returns the same results,
   # given the same random seed
   def testCPUGPUMatch(self):
-    for dt in dtypes.float16, dtypes.float32, dtypes.float64:
+    for dt in _SUPPORTED_DTYPES:
       results = {}
       for use_gpu in [False, True]:
         sampler = self._Sampler(1000, 1.0, dt, use_gpu=use_gpu, seed=12345)
@@ -183,19 +186,11 @@ class RandomPoissonTest(test.TestCase):
 
   def testDTypeCombinationsV2(self):
     """Tests random_poisson_v2() for all supported dtype combinations."""
-    # All supported dtypes by random_poisson_v2().
-    supported_dtypes = [
-        dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32,
-        dtypes.int64
-    ]
-
     with self.test_session():
-      for lam_dt in supported_dtypes:
+      for lam_dt in _SUPPORTED_DTYPES:
-        for out_dt in supported_dtypes:
+        for out_dt in _SUPPORTED_DTYPES:
-          # TODO(dhananjayn): Change this to use random_poisson() after
+          random_ops.random_poisson(
-          # switching it to RandomPoissonV2.
+              constant_op.constant([1], dtype=lam_dt), [10],
-          gen_random_ops.random_poisson_v2(
-              [10], constant_op.constant([1], dtype=lam_dt),
               dtype=out_dt).eval()
 
 

tensorflow/python/ops/random_ops.py

@@ -438,8 +438,8 @@ def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None):
       distribution(s) to sample.
     shape: A 1-D integer Tensor or Python array. The shape of the output samples
       to be drawn per "rate"-parameterized distribution.
-    dtype: The type of `lam` and the output: `float16`, `float32`, or
+    dtype: The type of the output: `float16`, `float32`, `float64`, `int32` or
-      `float64`.
+      `int64`.
     seed: A Python integer. Used to create a random seed for the distributions.
       See
       @{tf.set_random_seed}
@@ -451,7 +451,7 @@ def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None):
       values of type `dtype`.
   """
   with ops.name_scope(name, "random_poisson", [lam, shape]):
-    lam = ops.convert_to_tensor(lam, name="lam", dtype=dtype)
     shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32)
     seed1, seed2 = random_seed.get_seed(seed)
-    return gen_random_ops._random_poisson(shape, lam, seed=seed1, seed2=seed2)
+    return gen_random_ops.random_poisson_v2(
+        shape, lam, dtype=dtype, seed=seed1, seed2=seed2)