#!/usr/bin/env python3
import os, sys
import accelerate, safetensors.torch, transformers, torch, tqdm

model_id, revision = sys.argv[1:]
user, model = model_id.split('/')

fn = f'{user}_{model}_{revision}.logits.safetensors'

config = transformers.AutoConfig.from_pretrained(model_id, revision=revision, trust_remote_code=True)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id, revision=revision, trust_remote_code=True)
Model = transformers.AutoModelForCausalLM
if config.model_type == 'deepseek_v3':
    #Model = transformers.DeepseekV3ForCausalLM
    pass
model = Model.from_pretrained(model_id, revision=revision, trust_remote_code=True, torch_dtype='auto', device_map='cpu')
if config.model_type == 'deepseek_v3':
    model._supports_cache_class = False
model = accelerate.cpu_offload(model, 'cuda:0', offload_buffers=True)

pipe = transformers.pipeline('text-generation', model=model, config=config, tokenizer=tokenizer)

tensors = {}
def store_tensor(descr, tensor):
    tensors[descr] = tensor.cpu().detach().contiguous()

IDX = 0
module_names = {mod:name for name, mod in pipe.model.named_modules()}
tensors = {}
def hook(module, inputs, outputs):
    global IDX
    name = module_names[module]
    for idx, input in enumerate(inputs):
        if isinstance(input, torch.Tensor):
            store_tensor(f'{name}.input.{idx}', input);
    if isinstance(outputs, torch.Tensor):
        store_tensor(f'{name}.output', outputs);
    else:
        for idx, output in enumerate(outputs):
            if isinstance(output, torch.Tensor):
                store_tensor(f'{name}.output.{idx}', output);
    IDX += 1

for module in pipe.model.modules():
    module.register_forward_hook(hook)

prompt = 'Once upon a time,'
output = pipe(prompt, do_sample=False, max_new_tokens=1, temperature=1.0, top_p=1.0)
safetensors.torch.save_file(tensors, fn, dict(prompt=prompt))
print()
print(output)