[Deepspeed] Allow HF optimizer and scheduler to be passed to deepspeed 
# Use HF optimizer and/or scheduler unless specified in deepspeed config

If HF is already creating an optimizer and LR scheduler, we should not try to match that config/implementation in a ds_config.json instead we pass it to deepspeed.initialize(..., lr_scheduler=hf_lr_scheduler)

* [x] This PR checks if ds_config has an optimizer or scheduler, if it does not, calls  `create_optimizer` or `create_cheduler` (after splitting it) to create an optimizer or scheduler.  Then HF optimizer and scheduler are passed it to deepspeed.initialize().
DeepSpeed can handle any optimizer and scheduler if these are passed directly to deepspeed.initialize() as an object.

Due to the chicken-n-egg init problem, the valid combinations are:


| Combos       | HF Scheduler | DS Scheduler |
|--------------|--------------|--------------|
| HF Optimizer | Yes          | Yes          |
| DS Optimizer | No           | Yes          |


but if `cpu_offload` is used all bets are off - we can only use DS optim/sched.

----------
added by @stas00 below:

Added:
* [x] make init_deepspeed support config dict, besides the config file - this makes the testing much easier
* [x] add tests for this PR using this new feature of passing the dict
* [x] various small clean ups
* [x] update the docs
* [x] check for `cpu_offload` - add test
* [x] recode the config overrides to have one true source of values
* [x] tweak one not working test 

**blocking event: waiting for a new release 0.3.13 from DeepSpeed.**

@sgugger 

OK, 2 tests added and no, this doesn't work w/o neither the default optimizer nor the default scheduler. e.g. if you comment out the `del ` lines in the tests then we are using DS optim/sched and things are back to normal.

I didn't have time to investigate as it's late, so just sharing the outputs at the moment - will look closer tomorrow. I think both are issues on the DeepSpeed side, but I could be wrong.

Also note that the normal CI doesn't run these tests, so green doesn't say anything about those. 

```
pytest -sv examples/tests/deepspeed/test_deepspeed.py -k test_hf_native_scheduler

examples/tests/deepspeed/test_deepspeed.py:103:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
src/transformers/trainer.py:917: in train
    model, optimizer, lr_scheduler = init_deepspeed(self, num_training_steps=max_steps)
src/transformers/integrations.py:351: in init_deepspeed
    trainer.create_scheduler(num_training_steps=num_training_steps)
src/transformers/trainer.py:685: in create_scheduler
    self.lr_scheduler = get_scheduler(
src/transformers/optimization.py:266: in get_scheduler
    return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)
src/transformers/optimization.py:98: in get_linear_schedule_with_warmup
    return LambdaLR(optimizer, lr_lambda, last_epoch)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <torch.optim.lr_scheduler.LambdaLR object at 0x7fd86fb0a160>, optimizer = None
lr_lambda = <function get_linear_schedule_with_warmup.<locals>.lr_lambda at 0x7fd86fafc160>, last_epoch = -1, verbose = False

    def __init__(self, optimizer, lr_lambda, last_epoch=-1, verbose=False):
        self.optimizer = optimizer

        if not isinstance(lr_lambda, list) and not isinstance(lr_lambda, tuple):
>           self.lr_lambdas = [lr_lambda] * len(optimizer.param_groups)
E           AttributeError: 'NoneType' object has no attribute 'param_groups'

/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:197: AttributeError
````


```
pytest -sv examples/tests/deepspeed/test_deepspeed.py -k test_hf_native_optimizer

examples/tests/deepspeed/test_deepspeed.py:91: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
src/transformers/trainer.py:917: in train
    model, optimizer, lr_scheduler = init_deepspeed(self, num_training_steps=max_steps)
src/transformers/integrations.py:384: in init_deepspeed
    model, optimizer, _, lr_scheduler = deepspeed.initialize(
../../github/00optimize/DeepSpeed/deepspeed/__init__.py:110: in initialize
    engine = DeepSpeedEngine(args=args,
../../github/00optimize/DeepSpeed/deepspeed/runtime/engine.py:174: in __init__
    self._configure_optimizer(optimizer, model_parameters)
../../github/00optimize/DeepSpeed/deepspeed/runtime/engine.py:570: in _configure_optimizer
    self.optimizer = self._configure_zero_optimizer(basic_optimizer)
../../github/00optimize/DeepSpeed/deepspeed/runtime/engine.py:691: in _configure_zero_optimizer
    optimizer = FP16_DeepSpeedZeroOptimizer(
../../github/00optimize/DeepSpeed/deepspeed/runtime/zero/stage2.py:239: in __init__
    flatten_dense_tensors_aligned(
../../github/00optimize/DeepSpeed/deepspeed/runtime/zero/stage2.py:74: in flatten_dense_tensors_aligned
    return _flatten_dense_tensors(padded_tensor_list)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

tensors = []

    def _flatten_dense_tensors(tensors):
        """Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
        same dense type.
    
        Since inputs are dense, the resulting tensor will be a concatenated 1D
        buffer. Element-wise operation on this buffer will be equivalent to
        operating individually.
    
        Args:
            tensors (Iterable[Tensor]): dense tensors to flatten.

        Returns:
            A contiguous 1D buffer containing input tensors.
        """
        if len(tensors) == 1:
            return tensors[0].contiguous().view(-1)
>       flat = torch.cat([t.contiguous().view(-1) for t in tensors], dim=0)
E       RuntimeError: There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema aten::_cat.  This usually means that this function requires a non-empty list of Tensors.  Available functions are [CPU, CUDA, QuantizedCPU, BackendSelect, Named, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradNestedTensor, UNKNOWN_TENSOR_TYPE_ID, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, Autocast, Batched, VmapMode].
E
E       CPU: registered at /pytorch/build/aten/src/ATen/RegisterCPU.cpp:5925 [kernel]
E       CUDA: registered at /pytorch/build/aten/src/ATen/RegisterCUDA.cpp:7100 [kernel]
E       QuantizedCPU: registered at /pytorch/build/aten/src/ATen/RegisterQuantizedCPU.cpp:641 [kernel]
E       BackendSelect: fallthrough registered at /pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
E       Named: registered at /pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
E       AutogradOther: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:9161 [autograd kernel]
E       AutogradCPU: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:9161 [autograd kernel]
E       AutogradCUDA: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:9161 [autograd kernel]
E       AutogradXLA: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:9161 [autograd kernel]
E       AutogradNestedTensor: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:9161 [autograd kernel]
E       UNKNOWN_TENSOR_TYPE_ID: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:9161 [autograd kernel]
E       AutogradPrivateUse1: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:9161 [autograd kernel]
E       AutogradPrivateUse2: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:9161 [autograd kernel]
E       AutogradPrivateUse3: registered at /pytorch/torch/csrc/autograd/generated/VariableType_2.cpp:9161 [autograd kernel]
E       Tracer: registered at /pytorch/torch/csrc/autograd/generated/TraceType_2.cpp:10551 [kernel]
E       Autocast: registered at /pytorch/aten/src/ATen/autocast_mode.cpp:254 [kernel]
E       Batched: registered at /pytorch/aten/src/ATen/BatchingRegistrations.cpp:1016 [backend fallback]
E       VmapMode: fallthrough registered at /pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]

/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/torch/_utils.py:259: RuntimeError
```<|||||>OK, so I had a look at the first failing test. 

These 2 can't be separated the way it was done, since  the optimizer is needed to init the scheduler. But we don't have it yet if it's Deepspeed that creates the optimizer. So we have a chicken-n-egg problem here. Unless deepspeed provides a new API to handle that.

So probably at the moment we can only support one of: 1, 2, 3 and not 4.

1. DS scheduler + DS optimizer
2. HF scheduler + HF optimizer
3. DS scheduler + HF optimizer
4. HF scheduler + DS optimizer

Note I added a new test for the combo 2 and renamed all tests to match, so now we have:
```
pytest -sv examples/tests/deepspeed/test_deepspeed.py -k test_hf_scheduler_hf_optimizer
pytest -sv examples/tests/deepspeed/test_deepspeed.py -k test_ds_scheduler_hf_optimizer
pytest -sv examples/tests/deepspeed/test_deepspeed.py -k test_hf_scheduler_ds_optimizer
```<|||||>This deepspeed PR https://github.com/microsoft/DeepSpeed/pull/827 fixes the issues. The following tests would pass.
DS scheduler + DS optimizer
HF scheduler + HF optimizer
DS scheduler + HF optimizer 

Shall we put a check in HF to disallow the case HF scheduler + DS optimizer?<|||||>I tested with your https://github.com/microsoft/DeepSpeed/pull/827 PR tree and indeed 

```
pytest -sv examples/tests/deepspeed/test_deepspeed.py -k test_hf_scheduler_hf_optimizer
pytest -sv examples/tests/deepspeed/test_deepspeed.py -k test_ds_scheduler_hf_optimizer
```

now pass. awesome!

> Shall we put a check in HF to disallow the case HF scheduler + DS optimizer?

Correct! Please let me know if you will be taking care of it or you'd rather me finish this up. Either way works.

Also will need to update the docs to reflect this new more flexible reality. I can take care of that.

We will need to wait for a new release from your side to commit this PR and add a requirement for that new version. I already have this check setup in another PR waiting for this new release: https://github.com/huggingface/transformers/pull/9624

There is another PR by @jeffra today that also needs a new release first before we can update our tree.<|||||>Great. Can you please add the check for the case HF scheduler + DS optimizer? Since you are updating the docs, I think it makes more sense for you to do it. I will work with @jeffra to push the deepspeed PRs into the new release. Thanks.<|||||>@cli99, I made further changes to your original code

1. as @jeffra suggested we can't use HF optimizer with offload enabled - so coded to defend against that
2. I realized my original design was flawed and that the user could end up with a mismatch between cl args and the ds config, so I recoded the optimizer/scheduler config sections to override ds config with cl args where needed.

Please let me know if I broke anything in your original plan. I have also updated the docs extensively. They look a bit scary at the moment and will need a rework down the road.

My main goal here is to prevent from user getting subtle errors, so setting command line arguments to override DS config. Hope it makes sense.

<|||||>@sgugger, I made more doc updates - if you get a chance please kindly skim over them? Thank you!

I think we will merge this on Monday when deepspeed==0.3.13 is planned to be released.<|||||>@stas00 I'll let you merge when you are ready (since you followed this more closely than me). It looks good to merge to me :-)
Thanks for your contribution @cli99!<|||||>I'm on top of this - we are waiting for a new DeepSpeed release required by this PR. Thank you, @sgugger 