109 lines
4.3 KiB
Python
109 lines
4.3 KiB
Python
from typing import Iterable, Optional, Tuple
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import torch
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import tqdm
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from torch import nn
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from transformers import LlamaConfig
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from vllm.config import CacheConfig
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from vllm.distributed import get_tensor_model_parallel_rank
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from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from sglang.srt.layers.logits_processor import LogitProcessorOutput
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from sglang.srt.managers.controller.model_runner import InputMetadata
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from sglang.srt.models.llama2 import LlamaModel
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class LlamaForClassification(nn.Module):
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def __init__(
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self,
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config: LlamaConfig,
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quant_config: Optional[QuantizationConfig] = None,
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cache_config: Optional[CacheConfig] = None,
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) -> None:
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super().__init__()
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self.config = config
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self.quant_config = quant_config
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self.model = LlamaModel(config, quant_config=quant_config)
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self.classification_head = nn.Linear(
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config.hidden_size, config.classification_out_size
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)
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self.eos_token_id = config.eos_token_id
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@torch.no_grad()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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input_metadata: InputMetadata,
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input_embeds: torch.Tensor = None,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
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is_eos_token = input_ids == self.eos_token_id
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hidden_states = hidden_states[is_eos_token]
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scores = self.classification_head(hidden_states)
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if scores.shape[0] != input_metadata.batch_size:
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print("Warning: the EOS tokens are missing in some sentences.")
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scores = torch.ones(
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(input_metadata.batch_size, self.config.classification_out_size)
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).to(input_ids.device)
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return LogitProcessorOutput(
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next_token_logits=scores,
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next_token_logprobs=scores,
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normalized_prompt_logprobs=scores,
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input_token_logprobs=torch.ones_like(input_ids),
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input_top_logprobs=None,
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output_top_logprobs=None,
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)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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if get_tensor_model_parallel_rank() == 0:
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weights = tqdm.tqdm(weights, total=int(len(params_dict) * 1.5))
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name or "projector" in name:
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continue
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if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
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# Models trained using ColossalAI may include these tensors in
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# the checkpoint. Skip them.
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continue
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if "lm_head" in name:
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if name.startswith("model.vision_tower") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if name.startswith("model.vision_tower") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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EntryClass = LlamaForClassification
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