z2: don't pass dtype to report_ipg_memory_usage

[stas00]

This PR is fixing this:

[rank0]:   File "/code/users/stas/github/DeepSpeed/deepspeed/runtime/zero/stage_1_and_2.py", line 985, in grad_handling_hook
[rank0]:     self.process_gradients(param, i)
[rank0]:   File "/code/users/stas/github/DeepSpeed/deepspeed/runtime/zero/stage_1_and_2.py", line 1524, in process_gradients
[rank0]:     self.reduce_ready_partitions_and_remove_grads(param, i)
[rank0]:   File "/code/users/stas/github/DeepSpeed/deepspeed/runtime/zero/stage_1_and_2.py", line 1528, in reduce_ready_partitions_and_remove_grads
[rank0]:     self.reduce_independent_p_g_buckets_and_remove_grads(param, i)
[rank0]:   File "/code/users/stas/github/DeepSpeed/deepspeed/runtime/zero/stage_1_and_2.py", line 1006, in reduce_independent_p_g_buckets_and_remove_grads
[rank0]:     self.report_ipg_memory_usage("In ipg_remove_grads before reduce_ipg_grads", param.numel(), param.dtype)
[rank0]:   File "/code/users/stas/github/DeepSpeed/deepspeed/runtime/base_optimizer.py", line 70, in report_ipg_memory_usage
[rank0]:     bucket = self.ipg_buckets[dt]
[rank0]:              ~~~~~~~~~~~~~~~~^^^^
[rank0]: KeyError: torch.bfloat16

the problem doesn't exist if: seq_parallel_communication_data_type: bf16 is used, but fails with fp32 (or no setting).

In this PR I'm syncing with the z3 implementation which doesn't pass the dtype arg and lets the traversal of existing dtypes do the thing.

DeepSpeed/deepspeed/runtime/base_optimizer.py

Lines 66 to 75 in 407708c

	def report_ipg_memory_usage(self, tag, param_elems, dtype=None):
	dtypes = self.ipg_buckets.keys() if dtype is None else [dtype]
	for dt in dtypes:
	bucket = self.ipg_buckets[dt]
	elem_count = bucket.elements + param_elems
	percent_of_bucket_size = (100.0 * elem_count) // self.reduce_bucket_size
	see_memory_usage(
	f"{tag}: elems in_bucket {dt} {bucket.elements} param {param_elems} max_percent {percent_of_bucket_size}"
	)

Fixes: #7607