fix EP fsdp gradient divide factor #1551
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this PR is a followup of SimpleFSDP+EP [PR](#1529). Here, we add a `gradient_divide_factor` following FSDP2 to ensure modules wrapped by (FSDP+EP) has the correct gradient reduction value. - The original FSDP2 implementation is in this [PR](#1551). - The `gradient_divide_factor` logic is [here](https://github.com/pytorch/pytorch/blob/main/torch/distributed/fsdp/_fully_shard/_fsdp_collectives.py#L688) We have two ways of handling `gradient_divide_factor` in `reduce_scatter`: 1. The first one is to use `ReduceOp.PREMUL_SUM` to handle the `gradient_divide_factor`. However, DTensor's `_reduce_shard_value` only accepts `reduce_op` as a str input ([here](https://github.com/pytorch/pytorch/blob/8f705d019a64b1ca882e043b3eb98559273a9e59/torch/distributed/tensor/placement_types.py#L177-L210)). To make` _reduce_shard_value` work correctly with ReduceOp.PREMUL_SUM, we need to update the DTensor `_reduce_shard_tensor` and `torch.distributed._functional_collectives.reduce_scatter_tensor` so that it can pass the factor associated with ReduceOp.PREMUL_SUM as an input. 2. Another way is to simulate `ReduceOp.PREMUL_SUM` with `ReduceOp.SUM`. The logic is in this [Diff](https://www.internalfb.com/diff/D76546536). It does a `div_` over gradient before performing `ReduceOp.SUM`. Currently I'm following 2 since it is requires less change to `_functional_collectives`. After enabling `reduction_divide_factor`, we will see FSDP(=2) + EP (=4) have identical loss: <img width="1194" height="780" alt="Screenshot 2025-10-08 at 5 27 24 PM" src="https://github.com/user-attachments/assets/aaf83109-8db8-4051-973d-c7b6950513de" />
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this PR is a followup of SimpleFSDP+EP [PR](pytorch#1529). Here, we add a `gradient_divide_factor` following FSDP2 to ensure modules wrapped by (FSDP+EP) has the correct gradient reduction value. - The original FSDP2 implementation is in this [PR](pytorch#1551). - The `gradient_divide_factor` logic is [here](https://github.com/pytorch/pytorch/blob/main/torch/distributed/fsdp/_fully_shard/_fsdp_collectives.py#L688) We have two ways of handling `gradient_divide_factor` in `reduce_scatter`: 1. The first one is to use `ReduceOp.PREMUL_SUM` to handle the `gradient_divide_factor`. However, DTensor's `_reduce_shard_value` only accepts `reduce_op` as a str input ([here](https://github.com/pytorch/pytorch/blob/8f705d019a64b1ca882e043b3eb98559273a9e59/torch/distributed/tensor/placement_types.py#L177-L210)). To make` _reduce_shard_value` work correctly with ReduceOp.PREMUL_SUM, we need to update the DTensor `_reduce_shard_tensor` and `torch.distributed._functional_collectives.reduce_scatter_tensor` so that it can pass the factor associated with ReduceOp.PREMUL_SUM as an input. 2. Another way is to simulate `ReduceOp.PREMUL_SUM` with `ReduceOp.SUM`. The logic is in this [Diff](https://www.internalfb.com/diff/D76546536). It does a `div_` over gradient before performing `ReduceOp.SUM`. Currently I'm following 2 since it is requires less change to `_functional_collectives`. After enabling `reduction_divide_factor`, we will see FSDP(=2) + EP (=4) have identical loss: <img width="1194" height="780" alt="Screenshot 2025-10-08 at 5 27 24 PM" src="https://github.com/user-attachments/assets/aaf83109-8db8-4051-973d-c7b6950513de" />
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issue pointed out in
#1534 (comment)
pytorch/pytorch#160285
solution given by @rakkit in #1534 (comment)