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@ -76,6 +76,7 @@ def average_gradients(
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group: Optional[mx.distributed.Group] = None,
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group: Optional[mx.distributed.Group] = None,
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all_reduce_size: int = 32 * 1024**2,
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all_reduce_size: int = 32 * 1024**2,
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communication_type: Optional[mx.Dtype] = None,
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communication_type: Optional[mx.Dtype] = None,
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communication_stream: Optional[mx.Stream] = None,
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):
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):
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"""Average the gradients across the distributed processes in the passed group.
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"""Average the gradients across the distributed processes in the passed group.
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@ -94,6 +95,9 @@ def average_gradients(
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communication_type (Optional[mlx.core.Dtype]): If provided cast to this
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communication_type (Optional[mlx.core.Dtype]): If provided cast to this
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type before performing the communication. Typically cast to a
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type before performing the communication. Typically cast to a
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smaller float to reduce the communication size. Default: ``None``.
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smaller float to reduce the communication size. Default: ``None``.
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communication_stream (Optional[mlx.core.Stream]): The stream to usse
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for the communication. If unspecified the default communication
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stream is used which can vary by back-end. Default: ``None``.
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"""
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"""
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group = group or mx.distributed.init()
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group = group or mx.distributed.init()
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N = group.size()
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N = group.size()
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@ -104,7 +108,7 @@ def average_gradients(
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def _average(x):
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def _average(x):
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dt = x.dtype
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dt = x.dtype
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x = x.astype(communication_type) if communication_type is not None else x
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x = x.astype(communication_type) if communication_type is not None else x
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return mx.distributed.all_sum(x).astype(dt) / N
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return mx.distributed.all_sum(x, stream=communication_stream).astype(dt) / N
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if all_reduce_size <= 0:
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if all_reduce_size <= 0:
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return tree_map(_average, gradients)
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return tree_map(_average, gradients)
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