[WIP] Enable DP+EP for MoE inference in vLLM wrapper#3236
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… and fix combine() shape mismatch (#3595) Stack from [ghstack](https://github.com/ezyang/ghstack/tree/0.15.0) (oldest at bottom): * #3236 * #3142 * __->__ #3595 ## What's the problem - [Currently] combine() wrongly assume the tokens are evenly sharded on each rank https://github.com/pytorch/torchtitan/blob/c0428bb186f5c97e1d1b4ed89febd22916eee302/torchtitan/models/common/token_dispatcher.py#L439-L456 (infer global SPMD in local SPMD region) - If uneven sharded, out_TD will have different shapes across SP ranks. - We should directly ban if input number of tokens in input batch can not be evenly sharded by SP ranks - [Future] Router will use spmd_types soon, and router is per SP rank. Per SP rank should have even sharding - [Future] we want to avoid dispatch/load_balacing the padded token, we should be able to do that by adding metadata field to record the actually local tokens for each sp rank ## What does this PR do? This PR is doing "virtual padding" , and passing metadata around - Calculate num_local_tokens_after_padding = (T + pad_tokens) // sp_size in MoE module - Pass num_local_tokens_after_padding to GroupedExperts module, then to combine() - combine() returns a tensor with shape (num_local_tokens_after_padding * sp_rank, .... ) - slice the combined tensor to (T, ...) in MoE
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Stack from ghstack (oldest at bottom):