139 lines
5.5 KiB
Python
139 lines
5.5 KiB
Python
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import torch
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import torch.distributed
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from accelerate import init_empty_weights
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from opentelemetry import trace
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from pathlib import Path
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from transformers import AutoTokenizer, AutoConfig
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from typing import Optional, List
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_santacoder_modeling import (
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FlashSantacoderForCausalLM
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)
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from text_generation_server.utils import (
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weight_files,
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download_weights,
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weight_hub_files,
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LocalEntryNotFoundError,
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)
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tracer = trace.get_tracer(__name__)
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class FlashSantacoder(FlashCausalLM):
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def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
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if torch.cuda.is_available():
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device = torch.device("cuda")
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dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
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else:
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raise NotImplementedError("FlashSantacoder is only available on GPU")
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if quantize:
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raise NotImplementedError("FlashSantacoder does not support quantization")
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tokenizer = AutoTokenizer.from_pretrained(
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model_id, revision=revision, padding_side="left"
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)
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config = AutoConfig.from_pretrained(
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model_id, revision=revision,
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trust_remote_code=True # Needed as the config is not part of Transformers
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)
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# We do not use from_pretrained as we modified the model internal module layout
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try:
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filenames = weight_files(model_id, revision, ".bin")
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# Local files not found
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except LocalEntryNotFoundError:
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hub_files = weight_hub_files(model_id, revision, ".bin")
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filenames = download_weights(hub_files, model_id, revision)
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with init_empty_weights():
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model = FlashSantacoderForCausalLM(config)
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self.load_weights(
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model,
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filenames,
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)
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self.model = model.eval().to(device).to(dtype)
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super(FlashCausalLM, self).__init__(
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tokenizer=tokenizer,
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device=device,
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)
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@staticmethod
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def load_weights(
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model: FlashSantacoderForCausalLM,
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filenames: List[Path],
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):
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for filename in filenames:
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state_dict = torch.load(filename, map_location="cpu")
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for key, value in state_dict.items():
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layer_name = ".".join(key.split(".")[:4])
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# Fused qkv
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if "q_attn.weight" in key or "kv_attn.weight" in key:
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final_key = layer_name + ".attn.weight"
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elif "q_attn.bias" in key or "kv_attn.bias" in key:
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final_key = layer_name + ".attn.bias"
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else:
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final_key = key
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module_name, param_name = final_key.rsplit(".", 1)
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module = model.get_submodule(module_name)
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try:
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current_parameter_tensor = module._parameters[param_name]
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except KeyError:
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current_parameter_tensor = None
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if current_parameter_tensor is not None:
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if "c_fc.weight" in key or "c_proj.weight" in key or "q_attn.weight" in key or "kv_attn.weight" in key:
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# Tranpose as we use nn.Linear instead of Conv1D
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value = value.T
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if current_parameter_tensor.device == torch.device("meta"):
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# Init qkv
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if "attn.weight" in final_key:
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module._parameters[param_name] = value.new_empty(
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(model.transformer.head_size * (model.transformer.num_heads + 2), value.shape[1])
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)
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elif "attn.bias" in final_key:
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module._parameters[param_name] = value.new_empty(
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(model.transformer.head_size * (model.transformer.num_heads + 2))
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)
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# Copy to correct slice
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if "q_attn.weight" in key:
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module._parameters[param_name][: value.shape[0]] = value
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elif "q_attn.bias" in key:
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module._parameters[param_name][: value.shape[0]] = value
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elif "kv_attn.weight" in key:
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module._parameters[param_name][
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model.transformer.head_size * model.transformer.num_heads:
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] = value
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elif "kv_attn.bias" in key:
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module._parameters[param_name][
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model.transformer.head_size * model.transformer.num_heads:
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] = value
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else:
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if current_parameter_tensor.shape != value.shape:
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raise ValueError(
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f"Name {final_key} -- Current {current_parameter_tensor.shape} and got {value.shape}"
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)
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module._parameters[param_name] = value
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else:
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module._buffers[param_name] = value
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torch.cuda.empty_cache()
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model.post_load_weights()
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def decode(self, generated_ids: List[int]) -> str:
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# Do not skip special tokens as they are used for custom parsing rules of the generated text
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return self.tokenizer.decode(
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generated_ids, skip_special_tokens=False, cleanup_tokenization_spaces=False
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)
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