File size: 2,677 Bytes
b50f36e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | 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
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