Rampup batch size settings are not validated in the Pydantic config

[Atishyy27]

The legacy config validates the rampup batch size settings, the Pydantic config does not. Invalid combinations are accepted and either switch rampup off with no diagnostic, or ramp to a batch size that was never requested.

pyconfig_deprecated.validate_rampup_batch_size (src/maxtext/configs/pyconfig_deprecated.py:203) asserts five things: per_device_batch_size_start > 0, per_device_batch_size_increment > 0, global_rampup_samples > 0, per_device_batch_size - per_device_batch_size_start > 0, and that the difference divides by the increment.

DatasetGeneral in src/maxtext/configs/types.py declares the same four fields around line 1365 with no validator. The schedule derivation in MaxTextConfig around line 3067 is written defensively, if self.global_batch_size_to_load_increment > 0: then if num_increments > 0:, so when either guard fails the block falls through and leaves rampup_end_step = 0.

Replicating that arithmetic for 8 devices, expansion_factor_real_data=1, gradient_accumulation_steps=1:

settings	rampup_end_step	result
per_device_batch_size=8, start=4, increment=2, samples=500	14	correct
increment=0	0	rampup silently off
global_rampup_samples=0	0	rampup silently off
start=8, per_device_batch_size=4	0	rampup silently off
per_device_batch_size=9, start=4, increment=2	14	ramps to global batch size 64, 72 was requested

The first three mean a run configured with enable_rampup_batch_size=True trains at a constant batch size with nothing in the logs saying rampup never engaged. The last one is worse, num_increments = diff // increment truncates, so ramp-up finishes below the configured per_device_batch_size and stays there.

The Pydantic config should reject these at parse time the way the legacy path does. I can send a PR adding a model_validator(mode="after") on DatasetGeneral with unit tests in tests/unit/configs_value_test.py.