[inference] add smoothquant llama (hpcaitech#4861)

* add smoothquant llama

* fix attention accuracy

* fix accuracy

* add kv cache and save pretrained

* refactor example

* delete smooth

* refactor code

colossalai/inference/quant/smoothquant/calibration.py

@@ -0,0 +1,53 @@
+# Adapted from AutoGPTQ: https://github.com/PanQiWei/AutoGPTQ
+
+import functools
+
+import torch
+import torch.nn as nn
+from datasets import load_dataset
+from tqdm import tqdm
+
+
+def get_act_scales(model, tokenizer, dataset_path, num_samples=512, seq_len=512):
+    model.eval()
+    device = next(model.parameters()).device
+    act_scales = {}
+
+    def stat_tensor(name, tensor):
+        hidden_dim = tensor.shape[-1]
+        tensor = tensor.view(-1, hidden_dim).abs().detach()
+        comming_max = torch.max(tensor, dim=0)[0].float().cpu()
+        if name in act_scales:
+            act_scales[name] = torch.max(act_scales[name], comming_max)
+        else:
+            act_scales[name] = comming_max
+
+    def stat_input_hook(m, x, y, name):
+        if isinstance(x, tuple):
+            x = x[0]
+        stat_tensor(name, x)
+
+    hooks = []
+    for name, m in model.named_modules():
+        if isinstance(m, nn.Linear):
+            hooks.append(m.register_forward_hook(functools.partial(stat_input_hook, name=name)))
+
+    dataset = load_dataset("json", data_files=dataset_path)
+
+    print("text", dataset["train"]["rows"][0][1]["row"]["text"])
+
+    dataset = dataset.shuffle(seed=42)
+
+    for i in tqdm(range(num_samples)):
+        input_ids = tokenizer(
+            dataset["train"]["rows"][0][i]["row"]["text"],
+            return_tensors="pt",
+            max_length=seq_len,
+            truncation=True,
+        ).input_ids.to(device)
+        model(input_ids)
+
+    for h in hooks:
+        h.remove()
+
+    return act_scales