vvchernov commented on code in PR #13802: URL: https://github.com/apache/tvm/pull/13802#discussion_r1088612244
########## 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( + k=np.sum(tvm_flat, dtype="int32"), n=len(tvm_flat), p=ideal_mean + ) + assert bnm_test_res.pvalue >= 1e-6 + + # Test input sequence of 0 and 1 + inputs = np.random.randint(2, size=[10000]).astype("float32") + verify_bernoulli(inputs, in_out_equal=True) + + # Binomial test input with 0.5 values + val_num = 10000 + arr = [0.5] * val_num + inputs = np.array(arr).astype("float32") + verify_bernoulli(inputs) + + # Binomial test input with 0.1 values + arr = [0.1] * val_num + inputs = np.array(arr).astype("float32") + verify_bernoulli(inputs) + + # Simple test + verify_bernoulli(shape=[1000]) + + # Floating output type + verify_bernoulli(shape=[1000], out_dtype="float32") + + # Double input type + verify_bernoulli(shape=[1000], in_dtype="float64") + + # Test N-D tensor generation + verify_bernoulli(shape=[2, 4, 100, 100]) + + # Test with seed + verify_bernoulli(shape=[1000], seed=np.random.randint(1e6)) Review Comment: done -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@tvm.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org