Update deepspeed requirement from <0.6.0 to <0.7.0

[carmocca]

🚀 Feature

Re-land #13048
Blocked by a failing Lite test:

pytorch_lightning/lite/lite.py:407: in _run_impl
    return run_method(*args, **kwargs)
pytorch_lightning/lite/lite.py:412: in _run_with_strategy_setup
    return run_method(*args, **kwargs)
tests/lite/test_lite.py:402: in run
    model, optimizer = self.setup(model, optimizer)
pytorch_lightning/lite/lite.py:173: in setup
    model, optimizers = self._strategy._setup_model_and_optimizers(model, list(optimizers))
pytorch_lightning/strategies/deepspeed.py:414: in _setup_model_and_optimizers
    self.model, optimizer = self._setup_model_and_optimizer(model, optimizers[0])
pytorch_lightning/strategies/deepspeed.py:426: in _setup_model_and_optimizer
    deepspeed_engine, deepspeed_optimizer, _, _ = deepspeed.initialize(
/usr/local/lib/python3.9/dist-packages/deepspeed/__init__.py:120: in initialize
    engine = DeepSpeedEngine(args=args,
/usr/local/lib/python3.9/dist-packages/deepspeed/runtime/zero/partition_parameters.py:377: in wrapper
    if not hasattr(module, "_ds_child_entered"):
/usr/local/lib/python3.9/dist-packages/deepspeed/runtime/engine.py:432: in __getattr__
    if name in dir(self):
/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1847: in __dir__
    parameters = list(self._parameters.keys())
/usr/local/lib/python3.9/dist-packages/deepspeed/runtime/engine.py:432: in __getattr__
    if name in dir(self):
/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1847: in __dir__
    parameters = list(self._parameters.keys())
E   RecursionError: maximum recursion depth exceeded while calling a Python object
!!! Recursion detected (same locals & position)
=========================== short test summary info ============================
FAILED tests/lite/test_lite.py::test_deepspeed_multiple_models - RecursionErr...

---

If you enjoy Lightning, check out our other projects! ⚡

• Metrics: Machine learning metrics for distributed, scalable PyTorch applications.

• Lite: enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.

• Flash: The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.

• Bolts: Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.

• Lightning Transformers: Flexible interface for high-performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra.

cc @carmocca @akihironitta @Borda @SeanNaren @awaelchli @rohitgr7

[carmocca]

The test fails in the 0.6.0 to 0.6.4 releases with the following error:

        for mw_b, mw_a in zip(state_dict.values(), model.state_dict().values()):
>           assert not torch.allclose(mw_b, mw_a)
E           RuntimeError: The size of tensor a (0) must match the size of tensor b (32) at non-singleton dimension 1

lite/test_lite.py:420: RuntimeError

And in 0.6.5+ with the error reported in the top post. I will try git bisecting deepspeed to see if I can find the patch that changed the error

[carmocca]

Git bisect points to microsoft/DeepSpeed#1915 for the RecursionError and microsoft/DeepSpeed#1453 for the RuntimeError

[awaelchli]

I opened microsoft/DeepSpeed#2139 for the recursion error in 0.6.5. It can be reproduced with pure PyTorch+DeepSpeed and I don't know if the change was intentional, but suspect not.

[carmocca]

#13863 uncovered a new error:

../../src/pytorch_lightning/plugins/precision/deepspeed.py:115: in optimizer_step
    return deepspeed_engine.step(**kwargs)
DeepSpeed/deepspeed/runtime/engine.py:1887: in step
    self._take_model_step(lr_kwargs)
DeepSpeed/deepspeed/runtime/engine.py:1788: in _take_model_step
    self.optimizer.step()
DeepSpeed/deepspeed/runtime/zero/stage_1_and_2.py:1640: in step
    self.check_overflow()
DeepSpeed/deepspeed/runtime/zero/stage_1_and_2.py:1895: in check_overflow
    self._check_overflow(partition_gradients)
DeepSpeed/deepspeed/runtime/zero/stage_1_and_2.py:1800: in _check_overflow
    self.overflow = self.has_overflow(partition_gradients)
DeepSpeed/deepspeed/runtime/zero/stage_1_and_2.py:1819: in has_overflow
    overflow = self.local_overflow if self.cpu_offload else self.has_overflow_partitioned_grads_serial(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <deepspeed.runtime.zero.stage_1_and_2.DeepSpeedZeroOptimizer object at 0x7fe5d0f230a0>

    def has_overflow_partitioned_grads_serial(self):
        for i in range(len(self.bit16_groups)):
>           for j, grad in enumerate(self.averaged_gradients[i]):
E           KeyError: 0

DeepSpeed/deepspeed/runtime/zero/stage_1_and_2.py:1812: KeyError

Running
PL_RUN_STANDALONE_TESTS=1 pytest -v models/test_hooks.py::test_trainer_model_hook_system_fit[False-kwargs3]

git bisect points to microsoft/DeepSpeed#1801