[tutorial] edited hands-on practices (#1899)

* Add handson to ColossalAI.

* Change names of handsons and edit sequence parallel example.

* Edit wrong folder name

* resolve conflict

* delete readme

examples/tutorial/opt/inference/batch.py

@@ -0,0 +1,59 @@
+import torch
+from typing import List, Deque, Tuple, Hashable, Any
+from energonai import BatchManager, SubmitEntry, TaskEntry
+
+
+class BatchManagerForGeneration(BatchManager):
+    def __init__(self, max_batch_size: int = 1, pad_token_id: int = 0) -> None:
+        super().__init__()
+        self.max_batch_size = max_batch_size
+        self.pad_token_id = pad_token_id
+
+    def _left_padding(self, batch_inputs):
+        max_len = max(len(inputs['input_ids']) for inputs in batch_inputs)
+        outputs = {'input_ids': [], 'attention_mask': []}
+        for inputs in batch_inputs:
+            input_ids, attention_mask = inputs['input_ids'], inputs['attention_mask']
+            padding_len = max_len - len(input_ids)
+            input_ids = [self.pad_token_id] * padding_len + input_ids
+            attention_mask = [0] * padding_len + attention_mask
+            outputs['input_ids'].append(input_ids)
+            outputs['attention_mask'].append(attention_mask)
+        for k in outputs:
+            outputs[k] = torch.tensor(outputs[k])
+        return outputs, max_len
+
+    @staticmethod
+    def _make_batch_key(entry: SubmitEntry) -> tuple:
+        data = entry.data
+        return (data['top_k'], data['top_p'], data['temperature'])
+
+    def make_batch(self, q: Deque[SubmitEntry]) -> Tuple[TaskEntry, dict]:
+        entry = q.popleft()
+        uids = [entry.uid]
+        batch = [entry.data]
+        while len(batch) < self.max_batch_size:
+            if len(q) == 0:
+                break
+            if self._make_batch_key(entry) != self._make_batch_key(q[0]):
+                break
+            if q[0].data['max_tokens'] > entry.data['max_tokens']:
+                break
+            e = q.popleft()
+            batch.append(e.data)
+            uids.append(e.uid)
+        inputs, max_len = self._left_padding(batch)
+        trunc_lens = []
+        for data in batch:
+            trunc_lens.append(max_len + data['max_tokens'])
+        inputs['top_k'] = entry.data['top_k']
+        inputs['top_p'] = entry.data['top_p']
+        inputs['temperature'] = entry.data['temperature']
+        inputs['max_tokens'] = max_len + entry.data['max_tokens']
+        return TaskEntry(tuple(uids), inputs), {'trunc_lens': trunc_lens}
+
+    def split_batch(self, task_entry: TaskEntry, trunc_lens: List[int] = []) -> List[Tuple[Hashable, Any]]:
+        retval = []
+        for uid, output, trunc_len in zip(task_entry.uids, task_entry.batch, trunc_lens):
+            retval.append((uid, output[:trunc_len]))
+        return retval