| import torch |
| from torch import nn |
| import torch.nn.functional as F |
| import torch.distributed as dist |
|
|
| from jetengine_ext.engine.sequence import RunType |
| from jetengine_ext.utils.context import get_context |
|
|
|
|
| class VocabParallelEmbedding(nn.Module): |
|
|
| def __init__( |
| self, |
| num_embeddings: int, |
| embedding_dim: int, |
| ): |
| super().__init__() |
| self.tp_rank = dist.get_rank() |
| self.tp_size = dist.get_world_size() |
| 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 |
| dist.all_reduce(y) |
| 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) |
|
|
| def forward(self, x: torch.Tensor): |
| context = get_context() |
| if context.run_type == RunType.PREFILL: |
| last_indices = context.cu_seqlens_q[1:] - 1 |
| x = x[last_indices].contiguous() |
| logits = F.linear(x, self.weight, self.bias) |
| if self.tp_size > 1: |
| all_logits = [torch.empty_like(logits) for _ in range(self.tp_size)] if self.tp_rank == 0 else None |
| dist.gather(logits, all_logits, 0) |
| logits = torch.cat(all_logits, -1) if self.tp_rank == 0 else None |
| return logits |
|
|
