init commit

nanovllm/layers/embed_head.py

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+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.distributed as dist
+
+from nanovllm.utils.context import get_context
+
+
+class VocabParallelEmbedding(nn.Module):
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+    ):
+        super().__init__()
+        self.tp_rank = 0  # get_tensor_model_parallel_rank()
+        self.tp_size = 1  # get_tensor_model_parallel_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.embedding_dim = embedding_dim
+        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 * 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.is_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)]
+        #     dist.gather(logits, all_logits, 0)
+        #     logits = torch.cat(all_logits, -1)
+        return logits if self.tp_rank == 0 else None