Add LlamaForClassification (#559)

python/sglang/srt/models/llama_classification.py

@@ -0,0 +1,104 @@
+from typing import Iterable, Optional, Tuple
+
+import torch
+import tqdm
+from torch import nn
+from transformers import LlamaConfig
+from vllm.config import CacheConfig
+from vllm.distributed import (
+    get_tensor_model_parallel_rank,
+)
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+from sglang.srt.managers.controller.model_runner import InputMetadata
+from sglang.srt.layers.logits_processor import LogitProcessorOutput
+from sglang.srt.models.llama2 import LlamaModel
+
+
+class LlamaForClassification(nn.Module):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = LlamaModel(config, quant_config=quant_config)
+
+        self.classification_head = nn.Linear(config.hidden_size, config.classification_out_size)
+        self.eos_token_id = config.eos_token_id
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        input_metadata: InputMetadata,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
+        is_eos_token = input_ids == self.eos_token_id
+        hidden_states = hidden_states[is_eos_token]
+        scores = self.classification_head(hidden_states)
+
+        if scores.shape[0] != input_metadata.batch_size:
+            print("Warning: the EOS tokens are missing in some sentences.")
+            scores = torch.ones((input_metadata.batch_size, self.config.classification_out_size)).to(input_ids.device)
+
+        return LogitProcessorOutput(
+            next_token_logits=scores,
+            next_token_logprobs=scores,
+            normalized_prompt_logprobs=scores,
+            prefill_token_logprobs=torch.ones_like(input_ids),
+            prefill_top_logprobs=None,
+            decode_top_logprobs=None,
+        )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        if get_tensor_model_parallel_rank() == 0:
+            weights = tqdm.tqdm(weights, total=int(len(params_dict) * 1.5))
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name or "projector" in name:
+                continue
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            if "lm_head" 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
+                if name.startswith("model.vision_tower") 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
+                if name.startswith("model.vision_tower") 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)
+
+EntryClass = LlamaForClassification