Add FP16 and FP64 Supports to PureNcclComunicator #187
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shu65
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shu65
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chainermn/communicators/__init__.py
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| @@ -41,6 +45,10 @@ def create_communicator( | |||
| import mpi4py.MPI | |||
| mpi_comm = mpi4py.MPI.COMM_WORLD | |||
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| if communicator_name != 'pure_nccl' and allreduce_grad_dtype is not None: | |||
| raise ValueError( | |||
| 'allreduce_grad_dtype is not supported except for \'pure_nccl\'.') | |||
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'allreduce_grad_dtype is only available at 'pure_nccl' communicator.'
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Thank you. I will fix this error message according to your comment.
chainermn/communicators/__init__.py
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| @@ -31,6 +34,7 @@ def create_communicator( | |||
| ``hierarchical``, ``two_dimensional``, ``pure_nccl``, or | |||
| ``single_node``) | |||
| mpi_comm: MPI4py communicator | |||
| allreduce_grad_dtype: Data type of gradient used in All-Reduce | |||
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I think allowed value here is only np.float32, np.float16 or None. Maybe we can add value check somewhere around here, before actually creating communicator?
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Sorry there are already value check in PureNcclCommunicator, so may be we just can add documentation here, like "Allowed types are numpy.float16, 32, 64 or None....".
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So the behaviour when None is passed should be described here, which is to use float type of models.
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OK. I will add the description.
| @@ -70,8 +70,8 @@ def ptr(self): | |||
| def buffer(self, size): | |||
| return self.ffi.buffer(self.ffi.cast('void *', self.memory.ptr), size) | |||
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| def array(self, shape, offset=0): | |||
| return cp.ndarray(shape, memptr=self.memory + offset, dtype=cp.float32) | |||
| def array(self, shape, offset=0, dtype=np.float32): | |||
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So if we don't want cupy's default behaviour setting dtype as float64 when None is passed, we might better add non-None assertion here.
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I I also think so, I will add the assertion.
This PR adds FP16 and FP64 support to allreduce_grad of PureNcclCommunicator.