[Test] Keep the products in test_reduction_empty_dim finite, so the prod case cannot fail at random

[peterdsharpe]

TestTensorDicts::test_reduction_empty_dim[prod-sub_td2-*] failed once on the free-threaded 3.15 job with "Scalars are not close! Expected nan but got 0.0." It passed on rerun. It is a flaky test, not a bug in prod.

What was wrong

The test compares td.prod(dim=()) with torch.prod(leaf) over all the elements of each leaf, on random data. In sub_td2, the leaf c holds 60 values from torch.randint(10, ...) converted to float. The exact product is 0 as soon as one value is 0. But 60 values up to 9 can reach 9**60, about 1e57, which is past float32's maximum of about 3e38. When torch.prod multiplies in an order where one partial product overflows to inf before it meets a 0, the result is inf * 0 = nan. In another order it is 0.0. The tensordict path and the direct torch.prod call on the strided view multiply in different orders, so with some seeds one gives nan and the other 0.0.

With the fixture's data, I counted 1 such seed in 3000 (seed 2123: tensordict 0.0, torch.prod nan).

The fix

For prod only, the test maps every leaf to 1 + 0.01 * x in place before it compares, under torch.no_grad() for td_params. All the products then stay finite and nonzero.

The order still changes the last bits. A lazy stack multiplies per member and then across members, and over hundreds of float32 values the result can differ from torch.prod's by up to about n * eps, above assert_close's float32 default of rtol=1.3e-6. So the prod comparisons use rtol=1e-4, atol=1e-5. The other reductions and the rest of the test are unchanged.

What I ran

• test/tensordict/test_methods.py -k test_reduction_empty_dim with CI's environment variables, -n 2: 171 passed, 9 skipped (td_with_unbatched).
• The sub_td2 setup of the test, with seeds 0 to 5999 and the new rescaling: 0 mismatches. Without it: 1 in 3000.
• The test body for prod, called for each of the 20 CPU td_names with seeds 0 to 199 (4000 calls), on Python 3.12 with torch 2.14.1 and on Python 3.10 with torch 2.11.0: 0 failures. With the rescaling but the default tolerance, on torch 2.11: 16 failures, in stacked_td (5) and nested_stacked_td (11), as in CI's test-oldest-cpu job.