Sync from v0.13

vllm/model_executor/models/transformers/legacy.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2024 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Transformers modeling backend mixin for legacy models."""
+
+from typing import TYPE_CHECKING
+
+import torch
+
+from vllm.model_executor.models.utils import WeightsMapper
+from vllm.sequence import IntermediateTensors
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+
+
+class LegacyMixin:
+    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 = ""):
+        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.",
+            ]
+        )
+
+        # Some encoder models have the position_ids buffer in the checkpoint.
+        # vLLM will always pass position_ids as an argument, so we skip loading
+        # the buffer if it exists
+        self.skip_substrs.append("position_ids")
+
+        # Some encoder models have the bias of the final classifier layer
+        # in the checkpoint. vLLM does not use this bias, so we skip loading
+        # it if it exists
+        self.skip_substrs.append("score.bias")
+
+        # roberta-like models an extra padding in positions.
+        # FIXME(Isotr0py): This is quite hacky for roberta edge case,
+        # we should find a better way to handle this.
+        self.is_roberta = "roberta" in self.text_config.model_type
+        self.padding_idx = self.text_config.pad_token_id
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if self.is_roberta:
+            # RoBERTa-specific positions padding
+            positions += self.padding_idx + 1
+        return super().forward(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )