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vllm/model_executor/models/teleflm.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+
+import torch
+import torch.nn as nn
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.models.llama import (
+    LlamaDecoderLayer,
+    LlamaForCausalLM,
+    LlamaModel,
+)
+
+
+class TeleFLMModel(LlamaModel):
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: type[nn.Module] = LlamaDecoderLayer,
+    ):
+        super().__init__(vllm_config=vllm_config, prefix=prefix, layer_type=layer_type)
+        """
+        This implementation is based on the µScaling paper presented at  
+        the ICLR 2025 Workshop:  
+        NanoLM: An Affordable LLM Study Benchmark \
+        via Accurate Loss Prediction across Scales
+        by Yiqun Yao et al.  
+        Available at: https://openreview.net/forum?id=IwaPYg1SCA  
+        arXiv preprint: https://arxiv.org/abs/2304.06875
+        """
+        self.use_mup = self.config.use_mup
+        if self.use_mup:
+            self.input_mult = self.config.input_mult
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        embedding = self.embed_tokens(input_ids)
+        if self.use_mup:
+            embedding = embedding * self.input_mult
+        return embedding
+
+
+class TeleFLMForCausalLM(LlamaForCausalLM):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        # mup
+        self.use_mup = self.config.use_mup
+        if self.use_mup:
+            self.mup_scale_factor = self.config.mup_scale_factor
+            self.output_mult = self.config.output_mult / self.mup_scale_factor
+            logit_scale = self.output_mult
+            self.logits_processor = LogitsProcessor(
+                self.config.vocab_size, scale=logit_scale
+            )