testing dynamic register

vllm-v0.6.2/vllm/model_executor/models/transformers/legacy.py

@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright 2024 The vLLM team.
+"""Transformers modeling backend mixin for legacy models.
+
+This module provides LegacyMixin for BERT-like encoder models that have
+different weight naming conventions and special position handling.
+
+Following latest vLLM architecture patterns adapted for v0.6.2.
+"""
+
+from typing import TYPE_CHECKING, List, Optional
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.model_executor.models.utils import WeightsMapper
+from vllm.sequence import IntermediateTensors
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+
+logger = init_logger(__name__)
+
+
+class LegacyMixin:
+    """
+    Mixin class for legacy/encoder models like BERT, RoBERTa.
+    
+    This mixin provides:
+    - Weight name mapping for legacy suffix conventions (.gamma/.beta)
+    - Prefix mapping for BERT-like model structures
+    - RoBERTa-specific position handling
+    - Skip prefixes for unsupported output layers
+    
+    Should be used with Base class:
+        class TransformersForLegacy(LegacyMixin, Base): ...
+    """
+    
+    # Weight name mapping for legacy models
+    hf_to_vllm_mapper = WeightsMapper(
+        # These are applied in order, so the order matters!
+        orig_to_new_prefix={
+            # Handle BERT-like models
+            "roberta": "model",
+            "bert": "model",
+        },
+        orig_to_new_suffix={
+            # Replace legacy suffixes used for norms
+            ".gamma": ".weight",
+            ".beta": ".bias",
+        },
+    )
+    
+    def __init__(self, *, vllm_config: "VllmConfig", prefix: str = "") -> None:
+        # Call next class in MRO (should be Base)
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        
+        # Skip unsupported/unwanted output embeddings layers
+        self.skip_prefixes.extend([
+            "model.lm_head.",
+            "model.predictions.",
+            "model.qa_outputs.",
+            "model.embeddings_project.",
+            "model.discriminator_predictions.",
+        ])
+        
+        # v0.6.2 doesn't have skip_substrs, so we handle it differently
+        # Store patterns to skip during weight loading
+        self._legacy_skip_patterns: List[str] = [
+            "position_ids",  # Some encoder models have position_ids buffer
+            "score.bias",    # Final classifier bias not used by vLLM
+        ]
+        
+        # RoBERTa-like models have extra padding in positions
+        model_type = getattr(self.text_config, "model_type", "").lower()
+        self.is_roberta = "roberta" in model_type
+        self.padding_idx = getattr(self.text_config, "pad_token_id", 1)
+        
+        if self.is_roberta:
+            logger.info("LegacyMixin detected RoBERTa model, enabling position padding")
+        
+        logger.info("LegacyMixin initialized for legacy/encoder model")
+    
+    def _should_skip_weight(self, name: str) -> bool:
+        """Check if a weight should be skipped during loading."""
+        for pattern in self._legacy_skip_patterns:
+            if pattern in name:
+                return True
+        return False
+    
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        Forward pass with RoBERTa position handling.
+        
+        RoBERTa models require positions to be offset by padding_idx + 1.
+        """
+        if self.is_roberta and positions is not None:
+            # RoBERTa-specific positions padding
+            positions = positions + self.padding_idx + 1
+        
+        return super().forward(
+            input_ids=input_ids,
+            positions=positions,
+            kv_caches=kv_caches,
+            attn_metadata=attn_metadata,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+            **kwargs,
+        )