[Misc/Testing] Use torch.testing.assert_close (#7324)
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@@ -34,7 +34,7 @@ def all_reduce_test_worker(tp_size: int, pp_size: int, rank: int,
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expected = torch.sum(torch.stack(all_tensors, dim=0), dim=0)
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t = all_tensors[rank % tp_size]
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t = tensor_model_parallel_all_reduce(t)
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assert torch.allclose(t, expected)
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torch.testing.assert_close(t, expected)
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@ray.remote(num_gpus=1, max_calls=1)
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@@ -62,7 +62,7 @@ def all_gather_test_worker(tp_size: int, pp_size: int, rank: int,
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expected = torch.cat(all_tensors, dim=all_gather_dimension)
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t = all_tensors[rank % tp_size]
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t = tensor_model_parallel_all_gather(t, all_gather_dimension)
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assert torch.allclose(t, expected)
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torch.testing.assert_close(t, expected)
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@ray.remote(num_gpus=1, max_calls=1)
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@@ -96,12 +96,12 @@ def broadcast_tensor_dict_test_worker(tp_size: int, pp_size: int, rank: int,
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else:
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recv_dict = broadcast_tensor_dict(src=0)
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assert len(recv_dict) == len(test_dict)
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assert torch.allclose(recv_dict["a"], test_dict["a"])
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assert torch.allclose(recv_dict["b"], test_dict["b"])
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torch.testing.assert_close(recv_dict["a"], test_dict["a"])
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torch.testing.assert_close(recv_dict["b"], test_dict["b"])
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assert recv_dict["c"] == test_dict["c"]
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assert recv_dict["d"] == test_dict["d"]
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assert recv_dict["e"] == test_dict["e"]
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assert torch.allclose(recv_dict["f"], test_dict["f"])
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torch.testing.assert_close(recv_dict["f"], test_dict["f"])
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@ray.remote(num_gpus=1, max_calls=1)
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@@ -136,12 +136,12 @@ def send_recv_tensor_dict_test_worker(tp_size: int, pp_size: int, rank: int,
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if not get_pp_group().is_first_rank:
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assert len(recv_dict) == len(test_dict)
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assert torch.allclose(recv_dict["a"], test_dict["a"])
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assert torch.allclose(recv_dict["b"], test_dict["b"])
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torch.testing.assert_close(recv_dict["a"], test_dict["a"])
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torch.testing.assert_close(recv_dict["b"], test_dict["b"])
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assert recv_dict["c"] == test_dict["c"]
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assert recv_dict["d"] == test_dict["d"]
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assert recv_dict["e"] == test_dict["e"]
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assert torch.allclose(recv_dict["f"], test_dict["f"])
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torch.testing.assert_close(recv_dict["f"], test_dict["f"])
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@ray.remote(num_gpus=1, max_calls=1)
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@@ -163,7 +163,7 @@ def send_recv_test_worker(tp_size: int, pp_size: int, rank: int,
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get_pp_group().send(test_tensor)
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if not get_pp_group().is_first_rank:
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assert torch.allclose(test_tensor, recv_tensor)
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torch.testing.assert_close(test_tensor, recv_tensor)
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@pytest.mark.skipif(torch.cuda.device_count() < 2,
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