[iree-import-onnx] improve handling of large models (#19217)

This pr adds a few options:

1. `--large-model` allows disabling the onnx model checker if a user
knows ahead of time that the model is too large. It will also not load
the external weights in memory unless saving the parameters.
2. `--num-initializers-threshold` allows storing initializers to the
irpa file in batches with a specified number of entries. This can reduce
the memory overhead of first gathering all of the initializers, then
saving them to the irpa at once.
3. `--externalize-inputs-threshold` allows converting inputs to
externalized weights. This is useful for the following workflow:
exporting a HF pytorch model with safetensors, saving a `.irpa` from the
safetensor weights directly, and exporting to onnx with
`export_params=False` and `do_constant_folding=False` (which converts
weights to inputs and avoids folding weights with things like
transposes). When importing to mlir, you can set
`externalize-inputs-threshold=<num_original_inputs>` and it will convert
the inputs from and beyond that threshold to `util.global` ops.
4. `--save-params`/`--no-save-params` factors saving parameters out of
`import_initializer`, and one can avoid saving parameters with
`--no-save-params`. Useful for debugging compilation failures.

## TODO:

Figure out what to do about loading the onnx model and updating opset
version. It's possible to do opset version updating without weights in a
somewhat hacky way, since models > 2GB fail on opset version updating.

Add documentation

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Signed-off-by: zjgarvey <zjgarvey@gmail.com>