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| # SPDX-License-Identifier: Apache-2.0 | |
| # Adapted from | |
| # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py | |
| # Copyright 2024 The Qwen team. | |
| # 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. | |
| """Inference-only Qwen2 model compatible with HuggingFace weights.""" | |
| from vllm.model_executor.models.qwen2 import * | |
| class CosyVoice2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP): | |
| packed_modules_mapping = { | |
| "qkv_proj": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| ], | |
| "gate_up_proj": [ | |
| "gate_proj", | |
| "up_proj", | |
| ], | |
| } | |
| def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): | |
| super().__init__() | |
| config = vllm_config.model_config.hf_config | |
| quant_config = vllm_config.quant_config | |
| lora_config = vllm_config.lora_config | |
| self.config = config | |
| self.lora_config = lora_config | |
| self.quant_config = quant_config | |
| self.model = Qwen2Model(vllm_config=vllm_config, | |
| prefix=maybe_prefix(prefix, "model")) | |
| if get_pp_group().is_last_rank: | |
| if config.tie_word_embeddings: | |
| self.lm_head = self.model.embed_tokens | |
| else: | |
| self.lm_head = ParallelLMHead(config.vocab_size, | |
| config.hidden_size, | |
| True, | |
| quant_config=quant_config, | |
| prefix=maybe_prefix( | |
| prefix, "lm_head")) | |
| else: | |
| self.lm_head = PPMissingLayer() | |
| self.logits_processor = LogitsProcessor(config.vocab_size) | |
| self.make_empty_intermediate_tensors = ( | |
| self.model.make_empty_intermediate_tensors) | |
| def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: | |
| return self.model.get_input_embeddings(input_ids) | |
| def forward( | |
| self, | |
| input_ids: torch.Tensor, | |
| positions: torch.Tensor, | |
| intermediate_tensors: Optional[IntermediateTensors] = None, | |
| inputs_embeds: Optional[torch.Tensor] = None, | |
| ) -> Union[torch.Tensor, IntermediateTensors]: | |
| hidden_states = self.model(input_ids, positions, intermediate_tensors, | |
| inputs_embeds) | |
| return hidden_states | |
| def compute_logits( | |
| self, | |
| hidden_states: torch.Tensor, | |
| sampling_metadata: SamplingMetadata, | |
| ) -> Optional[torch.Tensor]: | |
| logits = self.logits_processor(self.lm_head, hidden_states, | |
| sampling_metadata, self.lm_head.bias) | |
| return logits | |
| def load_weights(self, weights: Iterable[tuple[str, | |
| torch.Tensor]]) -> set[str]: | |
| loader = AutoWeightsLoader( | |
| self, | |
| skip_prefixes=(["lm_head."] | |
| if self.config.tie_word_embeddings else None), | |
| ) | |
| return loader.load_weights(weights) | |