emijiayw commented on a change in pull request #8426:
URL: https://github.com/apache/tvm/pull/8426#discussion_r668211743
##########
File path: tests/python/frontend/onnx/test_forward.py
##########
@@ -4872,6 +4872,66 @@ def test_qlinearadd():
verify_qlinearadd([5, 1, 7], [2, 7], [5, 2, 7])
+def get_random_uniform(shape, dtype="float32", high=1.0, low=0.0, seed=None,
target="llvm"):
+ ONNX_DTYPE = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)]
+ node = helper.make_node(
+ "RandomUniform", [], ["out"], shape=shape, dtype=ONNX_DTYPE,
high=high, low=low
+ )
+ if seed is not None:
+ seed_attr = helper.make_attribute("seed", seed)
+ node.attribute.append(seed_attr)
+
+ graph = helper.make_graph(
+ [node],
+ "random_uniform_test",
+ inputs=[],
+ outputs=[helper.make_tensor_value_info("out", ONNX_DTYPE, shape)],
+ )
+ model = helper.make_model(graph, producer_name="random_uniform_test")
+ return get_tvm_output_with_vm(model, [], target=target,
device=tvm.device(target, 0))
+
+
+def test_random_uniform():
+ targets = [tgt for (tgt, _) in tvm.testing.enabled_targets()]
+ for target in targets:
+ # Check that function runs and produces proper shape.
+ vals = get_random_uniform([10], dtype="float32", target=target)
+ assert list(vals.shape) == [10]
+ assert vals.dtype == "float32"
+
+ # Test N-D tensor generation.
+ vals = get_random_uniform([1, 3, 100, 100], dtype="float32",
target=target)
+ assert list(vals.shape) == [1, 3, 100, 100]
+
+ # Check that bounds aren't exceeded.
+ vals = get_random_uniform(shape=[100], high=100, low=-100)
+ assert list(vals.shape) == [100]
+ assert all(vals >= -100) and all(vals <= 100)
+
+ # Check that fixed seed produces the same values.
Review comment:
Check that if fixed seeds will produce the same values.
##########
File path: tests/python/frontend/onnx/test_forward.py
##########
@@ -4872,6 +4872,66 @@ def test_qlinearadd():
verify_qlinearadd([5, 1, 7], [2, 7], [5, 2, 7])
+def get_random_uniform(shape, dtype="float32", high=1.0, low=0.0, seed=None,
target="llvm"):
+ ONNX_DTYPE = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)]
+ node = helper.make_node(
+ "RandomUniform", [], ["out"], shape=shape, dtype=ONNX_DTYPE,
high=high, low=low
+ )
+ if seed is not None:
+ seed_attr = helper.make_attribute("seed", seed)
+ node.attribute.append(seed_attr)
+
+ graph = helper.make_graph(
+ [node],
+ "random_uniform_test",
+ inputs=[],
+ outputs=[helper.make_tensor_value_info("out", ONNX_DTYPE, shape)],
+ )
+ model = helper.make_model(graph, producer_name="random_uniform_test")
+ return get_tvm_output_with_vm(model, [], target=target,
device=tvm.device(target, 0))
+
+
+def test_random_uniform():
+ targets = [tgt for (tgt, _) in tvm.testing.enabled_targets()]
+ for target in targets:
+ # Check that function runs and produces proper shape.
+ vals = get_random_uniform([10], dtype="float32", target=target)
+ assert list(vals.shape) == [10]
+ assert vals.dtype == "float32"
+
+ # Test N-D tensor generation.
+ vals = get_random_uniform([1, 3, 100, 100], dtype="float32",
target=target)
+ assert list(vals.shape) == [1, 3, 100, 100]
+
+ # Check that bounds aren't exceeded.
+ vals = get_random_uniform(shape=[100], high=100, low=-100)
+ assert list(vals.shape) == [100]
+ assert all(vals >= -100) and all(vals <= 100)
+
+ # Check that fixed seed produces the same values.
Review comment:
Check that if the fixed seed will produce the same values.
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