vvchernov commented on code in PR #13802:
URL: https://github.com/apache/tvm/pull/13802#discussion_r1088868698
##########
tests/python/frontend/onnx/test_forward.py:
##########
@@ -6707,6 +6707,117 @@ def verify_qlinearsigmoid(a_shape):
verify_qlinearsigmoid([])
+@tvm.testing.parametrize_targets("llvm")
+def test_random_bernoulli(target, dev):
+ """test_random_bernoulli"""
+
+ def verify_bernoulli(
+ inputs=None,
+ shape=[],
+ in_dtype="float32",
+ out_dtype="int32",
+ seed=None,
+ target=target,
+ dev=dev,
+ use_vm=False,
+ freeze_params=False,
+ rtol=0.1,
+ atol=0.1,
+ in_out_equal=False,
+ ):
+ def get_bernoulli_model(shape, in_dtype="float32", out_dtype="int32",
seed=None):
+ onnx_itype = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(in_dtype)]
+ onnx_otype = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(out_dtype)]
+ node = helper.make_node(
+ "Bernoulli",
+ ["input"],
+ ["output"],
+ )
+ dtype_attr = helper.make_attribute("dtype", onnx_otype)
+ node.attribute.append(dtype_attr)
+ if seed is not None:
+ seed_attr = helper.make_attribute("seed", float(seed))
+ node.attribute.append(seed_attr)
+
+ graph = helper.make_graph(
+ [node],
+ "random_bernoulli_test",
+ inputs=[helper.make_tensor_value_info("input", onnx_itype,
list(shape))],
+ outputs=[helper.make_tensor_value_info("output", onnx_otype,
list(shape))],
+ )
+ return helper.make_model(graph,
producer_name="random_bernoulli_test")
+
+ if inputs is None:
+ assert len(shape) != 0
+ inputs = np.random.uniform(size=shape).astype(in_dtype)
+ else:
+ shape = inputs.shape
+ in_dtype = inputs.dtype
+ model = get_bernoulli_model(shape, in_dtype, out_dtype, seed)
+
+ if use_vm:
+ tvm_out = get_tvm_output_with_vm(
+ model,
+ inputs,
+ target,
+ dev,
+ freeze_params=freeze_params,
+ )
+ else:
+ tvm_out = get_tvm_output(
+ model,
+ inputs,
+ target,
+ dev,
+ )
+
+ if isinstance(tvm_out, list):
+ tvm_out = tvm_out[0]
+ ideal_mean = np.mean(inputs)
+ # check that values are 0 or 1
+ tvm_flat = tvm_out.flatten()
+ for i in range(len(tvm_flat)):
+ assert tvm_flat[i] == 0 or tvm_flat[i] == 1
+ if in_out_equal:
+ tvm.testing.assert_allclose(inputs, tvm_out)
+ else:
+ # check that mean value is close to the theoretical one by
binomial test
+ bnm_test_res = scipy.stats.binomtest(
Review Comment:
Hello @octoJon! I've modified the test due to it was already
"over-conservative" with p-value threshold = 1e-6. I've increased threshold to
0.05 as more classical approach. If test condition failed there are two cases:
something wrong in the operation or we have gotten "bad" output sequence on the
tail of distribution. Due to the last is rare case and should be rechecked I
repeat the test again (and third time if need) with new seed for internal
distribution (input is the same).
P.S. As you know RandomUniform and RandomNormal already were implemented on
TVM side. Possibly their CI tests should be also updated to testing stability
and avoiding of flaky failures.
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