| import torch |
|
|
| import torch.nn as nn |
| import torch.nn.functional as F |
| import torch.distributed as dist |
| import os |
|
|
| from diffulex.distributed.parallel_state import fetch_parallel_state |
| from diffulex.vllm_compat import get_vllm_tp_group |
|
|
|
|
| LM_HEAD_FP32 = os.environ.get("DIFFULEX_LM_HEAD_FP32", "0") == "1" |
| LM_HEAD_FP32_GATHER = LM_HEAD_FP32 or os.environ.get("DIFFULEX_LM_HEAD_FP32_GATHER", "0") == "1" |
|
|
|
|
| def _tp_all_reduce(x: torch.Tensor, group) -> torch.Tensor: |
| vllm_tp_group = get_vllm_tp_group() |
| if vllm_tp_group is not None: |
| try: |
| return vllm_tp_group.all_reduce(x) |
| except Exception: |
| pass |
| dist.all_reduce(x, group=group) |
| return x |
|
|
|
|
| def _tp_gather_to_rank0(x: torch.Tensor, group, tp_size: int, tp_rank: int) -> torch.Tensor | None: |
| vllm_tp_group = get_vllm_tp_group() |
| if vllm_tp_group is not None: |
| try: |
| return vllm_tp_group.gather(x, dst=0, dim=-1) |
| except Exception: |
| pass |
|
|
| gathered = [torch.empty_like(x) for _ in range(tp_size)] |
| dist.all_gather(gathered, x, group=group) |
| return torch.cat(gathered, -1) if tp_rank == 0 else None |
|
|
|
|
| class VocabParallelEmbedding(nn.Module): |
| def __init__( |
| self, |
| num_embeddings: int, |
| embedding_dim: int, |
| ): |
| super().__init__() |
| parallel_state = fetch_parallel_state() |
| self.tp_rank = parallel_state.get_tp_rank() |
| self.tp_size = parallel_state.get_tp_world_size() |
| self.tp_group = parallel_state.get_tp_group() |
| assert num_embeddings % self.tp_size == 0 |
| self.num_embeddings = num_embeddings |
| self.num_embeddings_per_partition = self.num_embeddings // self.tp_size |
| self.vocab_start_idx = self.num_embeddings_per_partition * self.tp_rank |
| self.vocab_end_idx = self.vocab_start_idx + self.num_embeddings_per_partition |
| self.weight = nn.Parameter(torch.empty(self.num_embeddings_per_partition, embedding_dim)) |
| self.weight.weight_loader = self.weight_loader |
|
|
| def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor): |
| param_data = param.data |
| shard_size = param_data.size(0) |
| start_idx = self.tp_rank * shard_size |
| loaded_weight = loaded_weight.narrow(0, start_idx, shard_size) |
| assert param_data.size() == loaded_weight.size() |
| param_data.copy_(loaded_weight) |
|
|
| def forward(self, x: torch.Tensor): |
| if self.tp_size > 1: |
| mask = (x >= self.vocab_start_idx) & (x < self.vocab_end_idx) |
| x = mask * (x - self.vocab_start_idx) |
| y = F.embedding(x, self.weight) |
| if self.tp_size > 1: |
| y = mask.unsqueeze(1) * y |
| y = _tp_all_reduce(y, self.tp_group) |
| return y |
|
|
|
|
| class ParallelLMHead(VocabParallelEmbedding): |
| def __init__( |
| self, |
| num_embeddings: int, |
| embedding_dim: int, |
| bias: bool = False, |
| ): |
| super().__init__(num_embeddings, embedding_dim) |
| if bias: |
| self.bias = nn.Parameter(torch.empty(self.num_embeddings_per_partition)) |
| self.bias.weight_loader = self.weight_loader |
| else: |
| self.register_parameter("bias", None) |
| self._local_logits_workspace: torch.Tensor | None = None |
|
|
| def _get_local_logits_workspace(self, x: torch.Tensor) -> torch.Tensor: |
| shape = (*x.shape[:-1], self.num_embeddings_per_partition) |
| workspace = self._local_logits_workspace |
| if ( |
| workspace is None |
| or tuple(workspace.shape) != tuple(shape) |
| or workspace.device != x.device |
| or workspace.dtype != x.dtype |
| ): |
| workspace = torch.empty(shape, device=x.device, dtype=x.dtype) |
| self._local_logits_workspace = workspace |
| return workspace |
|
|
| def _linear_into_workspace(self, x: torch.Tensor) -> torch.Tensor: |
| if LM_HEAD_FP32: |
| return F.linear( |
| x.to(torch.float32), |
| self.weight.to(torch.float32), |
| self.bias.to(torch.float32) if self.bias is not None else None, |
| ).to(x.dtype) |
|
|
| if x.dim() != 2: |
| return F.linear(x, self.weight, self.bias) |
|
|
| logits = self._get_local_logits_workspace(x) |
| torch.mm(x, self.weight.t(), out=logits) |
| if self.bias is not None: |
| logits.add_(self.bias) |
| return logits |
|
|
| def forward(self, x: torch.Tensor): |
| logits = self._linear_into_workspace(x) |
| if self.tp_size > 1: |
| if LM_HEAD_FP32_GATHER: |
| logits_dtype = logits.dtype |
| logits = _tp_gather_to_rank0(logits.to(torch.float32), self.tp_group, self.tp_size, self.tp_rank) |
| logits = logits.to(logits_dtype) if logits is not None else None |
| else: |
| logits = _tp_gather_to_rank0(logits, self.tp_group, self.tp_size, self.tp_rank) |
| return logits |
|
|
