octoJon commented on code in PR #13802:
URL: https://github.com/apache/tvm/pull/13802#discussion_r1085998987
##########
tests/python/frontend/onnx/test_forward.py:
##########
@@ -6663,6 +6663,105 @@ def verify_qlinearsigmoid(a_shape):
verify_qlinearsigmoid([])
[email protected]_targets("llvm")
+def test_random_bernoulli(target, dev):
+ """test_random_bernoulli"""
+
+ def verify_bernoulli_with_ort(
+ shape,
+ in_dtype="float32",
+ out_dtype="int32",
+ seed=None,
+ out_shape=None,
+ target=target,
+ dev=dev,
+ use_vm=False,
+ opset=None,
+ freeze_params=False,
+ rtol=0.1,
+ atol=0.1,
+ opt_level=1,
+ convert_config=None,
+ ):
+ 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", 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")
+
+ inputs = np.random.uniform(size=shape).astype(in_dtype)
+ if seed is None:
+ ort_seed = None
+ else:
+ ort_seed = float(seed)
+ model = get_bernoulli_model(shape, in_dtype, out_dtype, ort_seed)
+ if opset is not None:
+ model.opset_import[0].version = opset
+
+ ort_out = get_onnxruntime_output(model, inputs)
+ if use_vm:
+ tvm_out = get_tvm_output_with_vm(
+ model,
+ inputs,
+ target,
+ dev,
+ opset=opset,
+ freeze_params=freeze_params,
+ convert_config=convert_config,
+ )
+ else:
+ tvm_out = get_tvm_output(
+ model,
+ inputs,
+ target,
+ dev,
+ out_shape,
+ opset=opset,
+ opt_level=opt_level,
+ convert_config=convert_config,
+ )
+
+ if not isinstance(tvm_out, list):
+ tvm_out = [tvm_out]
+ if not isinstance(ort_out, list):
+ ort_out = [ort_out]
+ for tvm_val, ort_val in zip(tvm_out, ort_out):
+ tvm.testing.assert_allclose(ort_val.mean(), tvm_val.mean(),
rtol=rtol, atol=atol)
+ tvm.testing.assert_allclose(np.std(ort_val), np.std(tvm_val),
rtol=rtol, atol=atol)
Review Comment:
I agree that there's no need to check standard deviations. Standard
deviation isn't meaningless here, it's just redundant in the sense that if you
know the sample mean then you also know the sample standard deviation.
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