[feature] support for roberta embedding models (#5730)

python/sglang/srt/layers/pooler.py

@@ -12,6 +12,7 @@ from sglang.srt.model_executor.model_runner import ForwardBatch
 
 class PoolingType(IntEnum):
     LAST = 0
+    CLS = 1
 
 
 @dataclass
@@ -41,6 +42,11 @@ class Pooler(nn.Module):
         if self.pooling_type == PoolingType.LAST:
             last_token_indices = torch.cumsum(forward_batch.extend_seq_lens, dim=0) - 1
             pooled_data = hidden_states[last_token_indices]
+        elif self.pooling_type == PoolingType.CLS:
+            prompt_lens = forward_batch.extend_seq_lens
+            first_token_flat_indices = torch.zeros_like(prompt_lens)
+            first_token_flat_indices[1:] += torch.cumsum(prompt_lens, dim=0)[:-1]
+            pooled_data = hidden_states[first_token_flat_indices]
         else:
             raise ValueError(f"Invalid pooling type: {self.pooling_type}")
 

python/sglang/srt/models/roberta.py

@@ -0,0 +1,178 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import itertools
+from typing import Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.bert import BertEncoder
+
+RobertaConfig = None
+
+
+class RobertaEmbedding(nn.Module):
+
+    def __init__(self, config: RobertaConfig):
+        super().__init__()
+        self.size = config.hidden_size
+        self.word_embeddings = VocabParallelEmbedding(
+            config.vocab_size, config.hidden_size
+        )
+        self.padding_idx = config.pad_token_id
+        self.position_embeddings = nn.Embedding(
+            config.max_position_embeddings,
+            config.hidden_size,
+            padding_idx=self.padding_idx,
+        )
+
+        self.token_type_embeddings = nn.Embedding(
+            config.type_vocab_size, config.hidden_size
+        )
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.position_ids = nn.Parameter(
+            torch.empty((1, config.max_position_embeddings)),
+        )
+
+        self.position_embedding_type = config.position_embedding_type
+        if self.position_embedding_type != "absolute":
+            raise ValueError(
+                "Only 'absolute' position_embedding_type" + " is supported"
+            )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        seq_lens: torch.Tensor,
+        position_ids: torch.Tensor,
+        inputs_embeds=None,
+        token_type_ids: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        input_shape = input_ids.size()
+        inputs_embeds = self.word_embeddings(input_ids)
+
+        # adpated from vllm: https://github.com/vllm-project/vllm/commit/4a18fd14ba4a349291c798a16bf62fa8a9af0b6b/vllm/model_executor/models/roberta.py
+
+        pos_list = []
+        token_list = []
+        offset = 0
+        for seq_len in seq_lens:
+            pos_list.append(position_ids[offset : offset + seq_len])
+            token_list.append(input_ids[offset : offset + seq_len])
+            offset += seq_len
+
+        new_pos_list = []
+        for positions, tokens in zip(pos_list, token_list):
+            # Verify assumption that incoming position are
+            # always a sequence from 0 to N.
+            expected_pos = torch.arange(
+                positions.size()[0], dtype=torch.long, device=inputs_embeds.device
+            )
+            assert torch.equal(positions, expected_pos)
+            new_pos_list.append(
+                create_position_ids_from_input_ids(tokens, self.padding_idx)
+            )
+        position_ids = torch.cat(new_pos_list)
+
+        # Position embeddings.
+        position_embeddings = self.position_embeddings(position_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(
+                input_shape, dtype=torch.long, device=inputs_embeds.device
+            )
+
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+        embeddings = inputs_embeds + token_type_embeddings + position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        return embeddings
+
+
+class XLMRobertaModel(nn.Module):
+    def __init__(
+        self,
+        *,
+        config: RobertaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.config = config
+        self.embeddings = RobertaEmbedding(config)
+        self.encoder = BertEncoder(config=config, quant_config=quant_config, prefix="")
+        self.pooler = Pooler(pooling_type=PoolingType.CLS, normalize=True)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        get_embedding: bool = False,
+    ) -> torch.Tensor:
+        assert get_embedding == True
+        # Your tokenized IDs
+
+        hidden_states = self.embeddings(
+            input_ids=input_ids,
+            position_ids=positions,
+            seq_lens=forward_batch.seq_lens,
+        )
+
+        hidden_states = self.encoder(hidden_states, forward_batch=forward_batch)
+        pooler_out = self.pooler(hidden_states, forward_batch)
+        return pooler_out
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "query", "q"),
+            ("qkv_proj", "key", "k"),
+            ("qkv_proj", "value", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            name = name.replace("self", "self_attn")
+            if "pooler" in name:
+                continue
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+# Adapted from transformers
+def create_position_ids_from_input_ids(
+    input_ids, padding_idx, past_key_values_length=0
+):
+    mask = input_ids.ne(padding_idx).int()
+    incremental_indices = (
+        torch.cumsum(mask, dim=0).type_as(mask) + past_key_values_length
+    ) * mask
+    return incremental_indices.long() + padding_idx
+
+
+EntryClass = [XLMRobertaModel]