mirror of
https://github.com/turboderp-org/exllamav3.git
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107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from aqlm import QuantizedLinear
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from awq.modules.linear import WQLinear_GEMM
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from vptq import VQuantLinear
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from bitsandbytes.nn import Linear4bit
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def get_tensors_size(tensors):
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return 8 * sum(t.element_size() * t.numel() for t in tensors.values() if t is not None)
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def get_tensor_size(tensor):
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return 8 * tensor.element_size() * tensor.numel()
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def scan_gpu_tensors(obj, seen = None):
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if seen is None:
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seen = set()
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obj_id = id(obj)
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if obj_id in seen:
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return 0
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seen.add(obj_id)
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total_size = 0
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# If it's a GPU tensor, add its memory usage.
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if isinstance(obj, torch.Tensor) and obj.is_cuda:
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total_size += obj.element_size() * obj.nelement()
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else:
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if isinstance(obj, dict):
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for key, value in obj.items():
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total_size += scan_gpu_tensors(key, seen)
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total_size += scan_gpu_tensors(value, seen)
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return total_size
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if isinstance(obj, (list, tuple, set)):
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for item in obj:
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total_size += scan_gpu_tensors(item, seen)
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return total_size
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if hasattr(obj, '__dict__'):
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total_size += scan_gpu_tensors(vars(obj), seen)
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if hasattr(obj, '__slots__'):
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for slot in obj.__slots__:
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try:
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attr = getattr(obj, slot)
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total_size += scan_gpu_tensors(attr, seen)
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except AttributeError:
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continue
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return total_size
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def get_storage_info(model):
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sum_bits = 0
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sum_numel = 0
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head_bpw = 0
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head_numel = 0
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for name, module in model.named_modules():
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if any(isinstance(module, x) for x in [Linear4bit]):
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if module.out_features >= model.vocab_size * 0.9: # this is foolproof
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head_numel = module.in_features * module.out_features
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head_bpw = module.weight.numel() * 8
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head_bpw = (head_bpw + scan_gpu_tensors(module.quant_state) * 8) / head_numel
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else:
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sum_bits += module.weight.numel() * 8
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sum_bits += scan_gpu_tensors(module.quant_state) * 8
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sum_numel += module.in_features * module.out_features
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elif any(isinstance(module, x) for x in [torch.nn.Linear]):
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if module.out_features >= model.vocab_size * 0.9:
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head_bpw = module.weight.element_size() * 8
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head_numel = module.weight.numel()
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else:
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sum_bits += get_tensor_size(module.weight)
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sum_numel += module.weight.numel()
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elif any(isinstance(module, x) for x in [QuantizedLinear, VQuantLinear]):
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sum_bits += get_tensors_size(dict(module.named_parameters()))
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sum_numel += module.in_features * module.out_features
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elif any(isinstance(module, x) for x in [WQLinear_GEMM]):
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sum_bits += get_tensors_size({
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"qweight": module.qweight,
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"qzeros": module.qzeros,
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"scales": module.scales,
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})
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sum_numel += module.in_features * module.out_features
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vram_bits = head_numel * head_bpw + sum_bits
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return sum_bits / sum_numel, head_bpw, vram_bits
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@torch.inference_mode
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def load_transformers(model_dir: str, auto = False):
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model = AutoModelForCausalLM.from_pretrained(
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model_dir,
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device_map = "auto" if auto else "cuda:0",
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torch_dtype = torch.half
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)
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bpw_layer, bpw_head, vram_bits = get_storage_info(model)
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return model, bpw_layer, bpw_head, vram_bits
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@torch.inference_mode
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def load_transformers_auto(model_dir: str):
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return load_transformers(model_dir, auto = True)
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@torch.inference_mode
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def fwd_transformers(model_instance, input_ids: torch.Tensor):
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input_ids = input_ids.to("cuda:0")
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output = model_instance(input_ids)
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return output.logits
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@torch.inference_mode
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def tokenize_transformers(tokenizer_dir: str, text: str):
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir)
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output = tokenizer(text, return_tensors="pt")
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return output.input_ids
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