初始化项目，由ModelHub XC社区提供模型

Model: BoscoTheDog/bitnet_b1_58-large_q8_0_gguf
Source: Original Platform

eval_ppl.py

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+import math
+import argparse
+import torch
+import random
+
+from eval_utils import get_test_dataset
+from .modeling_bitnet import BitnetForCausalLM
+from .tokenization_bitnet import BitnetTokenizer 
+
+from tqdm import tqdm
+torch.set_grad_enabled(False)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--seed', default=0, type=int)
+parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str)
+parser.add_argument('--seqlen', default=2048, type=int)
+
+
+def calulate_loss(model, input, loss_fct):
+    output = model(input,
+                    use_cache=False,
+                    output_hidden_states=False,
+                    output_attentions=False)[0]
+    shift_logits = output[:, :-1, :].contiguous()
+    shift_labels = input[:, 1:]
+    loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+    return loss
+
+
+def main(args):
+    datasets = ['c4', 'wikitext2']
+    model = BitnetForCausalLM.from_pretrained(
+        args.hf_path,
+        device_map='auto',
+        low_cpu_mem_usage=True, 
+        use_flash_attention_2=True,
+        torch_dtype=torch.float16,
+    ).half()
+    tokenizer = BitnetTokenizer.from_pretrained(args.hf_path, use_fast=False)
+    loss_fct = torch.nn.CrossEntropyLoss(reduction="sum").cuda()
+
+    ppl = []
+    for dataset in datasets:
+        testdata = get_test_dataset(dataset, tokenizer, seqlen=args.seqlen)
+        acc_loss, count = 0.0, 0
+        progress = tqdm(range(len(testdata)))
+        for ii in progress:
+            input = torch.Tensor(testdata[ii]).long().cuda().view(1, -1)
+            loss = calulate_loss(model, input, loss_fct)
+            count += (input.size(-1) - 1)
+            acc_loss += loss.item()
+            progress.set_description(f"avg_loss = {acc_loss/ count / math.log(2)}")
+
+        avg_loss = acc_loss / count / math.log(2)
+        ppl.append(2 ** avg_loss)
+        print("{} PPL: {}".format(dataset, ppl[-1]))
+
+    print(ppl)
+    print("Avg PPL:", sum(ppl) / len(ppl))
+
+
+if __name__ == '__main__':
+    torch.set_grad_enabled(False)
+    args = parser.parse_args()
+    random.seed(args.seed)
+    torch.random.manual_seed(args.seed)
+    main(args)