[inference] Dynamic Batching for Single and Multiple GPUs

colossalai/inference/dynamic_batching/infer_batch.py

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+import collections
+from dataclasses import dataclass
+from typing import Dict, List , Tuple
+
+import numpy as np
+import torch
+
+from colossalai.inference.tensor_parallel import MemoryManager
+
+# make batch infer state an attr of InferBatch
+
+
+class InferSamplingParams:
+    def __init__(
+        self,
+        do_sample: bool = False,
+        presence_penalty: float = 0.0,
+        frequency_penalty: float = 0.0,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = -1,
+        vocab_size: int = -1,
+    ) -> None:
+        self.do_sample = do_sample
+        self.presence_penalty = presence_penalty
+        self.frequency_penalty = frequency_penalty
+        self.temperature = temperature
+        self.top_p = top_p
+        self.top_k = top_k
+        if self.top_k == -1:
+            self.top_k = vocab_size
+        return
+
+
+@dataclass
+class InferBatch:
+    batch_id: int
+    requests: List
+    requests_idx_mapping: Dict[int, int]
+
+    input_ids: torch.Tensor
+
+    all_input_ids: List[List[int]]
+    input_lengths: List[int]
+
+    out_token_id_counts: List
+    sampling_param_list: List[InferSamplingParams]
+
+    nopad_total_token_num: int
+    nopad_max_len_in_batch: int
+    nopad_b_loc: torch.Tensor
+    nopad_b_start_loc: torch.Tensor
+    nopad_b_seq_len: torch.Tensor
+    cache_manager: MemoryManager
+    max_total_len: int
+
+    @classmethod
+    @torch.no_grad()
+    def init_batch(
+        cls,
+        batch_id,
+        requests,
+        dtype: torch.dtype,
+        device: torch.device,
+        cache_manager: MemoryManager,
+        vocab_size: int,
+        max_total_len: int,
+    ) -> 'InferBatch':
+        input_lengths = []
+        all_input_ids = []
+        requests_idx_mapping = {}
+
+        out_token_id_counts = []
+        sampling_param_list = []
+
+        nopad_total_token_num = 0
+        nopad_max_len_in_batch = 0
+        nopad_b_loc = torch.empty((len(requests), max_total_len + 12), dtype=torch.long, device="cuda")
+        # to avoid memory leak , we pre-allocate 12 more space for each batch. 
+        nopad_b_start_loc = torch.zeros(len(requests), dtype=torch.int32, device="cuda")
+        for i, r in enumerate(requests):
+            # request id -> idx in list mapping
+            requests_idx_mapping[r["request_id"]] = i
+
+            tokenized_input = r["input_id"]
+
+            input_length = len(tokenized_input)
+            input_lengths.append(input_length)
+            all_input_ids.append(tokenized_input)
+            out_token_id_counts.append(collections.defaultdict(int))
+
+            # postprocessor
+            sampling_param = r["sampling_param"]
+            sampling_param["vocab_size"] = vocab_size
+            sampling_param_list.append(InferSamplingParams(**sampling_param))
+
+            nopad_total_token_num += input_length
+            nopad_max_len_in_batch = max(nopad_max_len_in_batch, input_length)
+
+        nopad_b_seq_len = torch.tensor(input_lengths, dtype=torch.int32, device="cuda")
+        nopad_b_start_loc[1:] = torch.cumsum(nopad_b_seq_len, dim=0, dtype=torch.int32)[0:-1]
+
+        if len(requests) > 1:
+            input_ids = np.concatenate(all_input_ids, dtype=np.int64)
+        else:
+            input_ids = all_input_ids[0]
+
+        # Create tensors on device
+        input_ids = torch.tensor(input_ids, dtype=torch.int64, device=device)
+
+        return cls(
+            batch_id=batch_id,
+            requests=requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=input_ids,
+            input_lengths=input_lengths,
+            all_input_ids=all_input_ids,
+            nopad_total_token_num=nopad_total_token_num,
+            nopad_max_len_in_batch=nopad_max_len_in_batch,
+            nopad_b_loc=nopad_b_loc,
+            nopad_b_start_loc=nopad_b_start_loc,
+            nopad_b_seq_len=nopad_b_seq_len,
+            out_token_id_counts=out_token_id_counts,
+            sampling_param_list=sampling_param_list,
+            cache_manager=cache_manager,
+            max_total_len=max_total_len,
+        )
+
+    @torch.no_grad()
+    def free_self(self) -> None:
+        """
+        Free the memory of the InferBatch itself
+        """
+        remove_index = []
+        for idx in range(len(self)):
+            remove_index.append(
+                self.nopad_b_loc[
+                    idx,
+                    (self.nopad_max_len_in_batch - 1)
+                    - (self.nopad_b_seq_len[idx] - 1) : (self.nopad_max_len_in_batch - 1),
+                ]
+            )
+        remove_index = torch.cat(remove_index, dim=-1)
+        self.cache_manager.free(remove_index)
+
+
+    @torch.no_grad()
+    def filter(self, request_ids: List[int]) -> 'InferBatch':
+        """
+        Filter finished batch and return a new InferBatch with left ones.
+        """
+        if len(request_ids) == 0:
+            raise ValueError("Batch must have at least one request")
+        if len(request_ids) == len(self):
+            return self
+        requests_idx_mapping = {}
+        indices = []
+        requests = []
+        all_input_ids = []
+        input_lengths = []
+        nopad_total_token_num = 0
+        nopad_max_len_in_batch = 0
+        nopad_b_loc = torch.empty((len(request_ids), self.max_total_len + 12), dtype=torch.long, device="cuda")
+        nopad_b_start_loc = torch.zeros(len(request_ids), dtype=torch.int32, device="cuda")
+        nopad_b_seq_len = torch.zeros(len(request_ids), dtype=torch.int32, device="cuda")
+
+        left_idx = []
+        for i, request_id in enumerate(request_ids):
+            idx = self.requests_idx_mapping[request_id]
+            left_idx.append(idx)
+
+        left_idx_set = set(left_idx)
+        remove_index = []
+        for idx in range(len(self)):
+            if idx not in left_idx_set:
+                remove_index.append(
+                    self.nopad_b_loc[
+                        idx,
+                        (self.nopad_max_len_in_batch - 1)
+                        - (self.nopad_b_seq_len[idx] - 1) : (self.nopad_max_len_in_batch - 1),
+                    ]
+                )
+        remove_index = torch.cat(remove_index, dim=-1)
+        self.cache_manager.free(remove_index)
+
+        nopad_max_len_in_batch = 0
+        for i, request_id in enumerate(request_ids):
+            idx = self.requests_idx_mapping[request_id]
+            indices.append(idx)
+
+        nopad_b_seq_len[:] = self.nopad_b_seq_len[indices]
+        nopad_max_len_in_batch = torch.max(nopad_b_seq_len).item()
+        nopad_b_start_loc[1:] = torch.cumsum(nopad_b_seq_len, dim=0, dtype=torch.int32)[0:-1]
+        nopad_total_token_num = torch.sum(nopad_b_seq_len).item()
+
+        nopad_b_loc[:, 0 : (nopad_max_len_in_batch - 1)] = self.nopad_b_loc[
+            indices,
+            (self.nopad_max_len_in_batch - 1) - (nopad_max_len_in_batch - 1) : (self.nopad_max_len_in_batch - 1),
+        ]
+        for i, request_id in enumerate(request_ids):
+            idx = self.requests_idx_mapping[request_id]
+            requests_idx_mapping[request_id] = i
+            requests.append(self.requests[idx])
+            all_input_ids.append(self.all_input_ids[idx])
+            input_lengths.append(self.input_lengths[idx])
+
+        input_ids = self.input_ids[indices]
+
+        return InferBatch(
+            batch_id=self.batch_id,
+            requests=requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=input_ids,
+            input_lengths=input_lengths,
+            all_input_ids=all_input_ids,
+            nopad_total_token_num=nopad_total_token_num,
+            nopad_max_len_in_batch=nopad_max_len_in_batch,
+            nopad_b_loc=nopad_b_loc,
+            nopad_b_start_loc=nopad_b_start_loc,
+            nopad_b_seq_len=nopad_b_seq_len,
+            out_token_id_counts=[self.out_token_id_counts[_i] for _i in indices],
+            sampling_param_list=[self.sampling_param_list[_i] for _i in indices],
+            cache_manager=self.cache_manager,
+            max_total_len=self.max_total_len,
+        )
+
+    @classmethod
+    @torch.no_grad()
+    def merge(cls, batch1, batch2) -> 'InferBatch':
+        """
+        Return megerd new InferBatch
+        """
+        requests = batch1.requests + batch2.requests
+        requests_idx_mapping = {}
+        new_batch_size = len(batch1) + len(batch2)
+
+        input_ids = batch1.input_ids.new_empty(new_batch_size)
+        all_input_ids = []
+        input_lengths = []
+        out_token_id_counts = []
+        sampling_param_list = []
+
+        cumulative_batch_size = 0
+        nopad_total_token_num = batch1.nopad_total_token_num + batch2.nopad_total_token_num
+        nopad_max_len_in_batch = max(batch1.nopad_max_len_in_batch, batch2.nopad_max_len_in_batch)
+        max_total_len = max(batch1.max_total_len, batch2.max_total_len)
+        nopad_b_loc = torch.empty((new_batch_size, batch1.max_total_len + 12), dtype=torch.long, device="cuda")
+        nopad_b_start_loc = torch.zeros(new_batch_size, dtype=torch.int32, device="cuda")
+        nopad_b_seq_len = torch.zeros(new_batch_size, dtype=torch.int32, device="cuda")
+        nopad_start_loc_len_temp = 0
+        batches = [batch1, batch2]
+        for i, batch in enumerate(batches):
+            if i == 0:
+                requests_idx_mapping = batch.requests_idx_mapping
+            else:
+                for k, v in batch.requests_idx_mapping.items():
+                    requests_idx_mapping[k] = v + cumulative_batch_size
+            start_index = cumulative_batch_size
+            end_index = cumulative_batch_size + len(batch)
+            input_ids[start_index:end_index] = batch.input_ids
+            nopad_b_seq_len[start_index:end_index] = batch.nopad_b_seq_len
+            nopad_b_start_loc[start_index:end_index] = batch.nopad_b_start_loc + nopad_start_loc_len_temp
+            nopad_start_loc_len_temp = nopad_b_start_loc[end_index - 1] + nopad_b_seq_len[end_index - 1]
+            nopad_b_loc[
+                start_index:end_index,
+                nopad_max_len_in_batch - batch.nopad_max_len_in_batch : nopad_max_len_in_batch - 1,
+            ] = batch.nopad_b_loc[:, : batch.nopad_max_len_in_batch - 1]
+
+            all_input_ids.extend(batch.all_input_ids)
+
+            input_lengths.extend(batch.input_lengths)
+            out_token_id_counts.extend(batch.out_token_id_counts)
+            sampling_param_list.extend(batch.sampling_param_list)
+            # Update
+            cumulative_batch_size += len(batch)
+
+        nopad_b_loc[:, nopad_max_len_in_batch - 1] = (
+            nopad_total_token_num - new_batch_size + torch.arange(0, new_batch_size, dtype=torch.int32, device="cuda")
+        )
+        return InferBatch(
+            batch_id=batches[0].batch_id,
+            requests=requests,
+            requests_idx_mapping=requests_idx_mapping,
+            input_ids=input_ids,
+            input_lengths=input_lengths,
+            all_input_ids=all_input_ids,
+            nopad_total_token_num=nopad_total_token_num,
+            nopad_max_len_in_batch=nopad_max_len_in_batch,
+            nopad_b_loc=nopad_b_loc,
+            nopad_b_start_loc=nopad_b_start_loc,
+            nopad_b_seq_len=nopad_b_seq_len,
+            out_token_id_counts=out_token_id_counts,
+            sampling_param_list=sampling_param_list,
+            cache_manager=batches[0].cache_manager,
+            max_total_len=max_total_len,
+        )
+
+    def __len__(self):
+        return len(self.requests)
+
+    def get_post_sample_tensors(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        presence_penalties: List[float] = []
+        frequency_penalties: List[float] = []
+        temperatures: List[float] = []
+        top_ps: List[float] = []
+        top_ks: List[int] = []
+        p_token_ids: List[int] = []
+        p_token_counts: List[int] = []
+        p_seq_len: List[int] = [
+            0,
+        ]
+        p_max_len_in_batch: int = 0
+        for i, id_to_count in enumerate(self.out_token_id_counts):
+            sample_param = self.sampling_param_list[i]
+            presence_penalties.append(sample_param.presence_penalty)
+            frequency_penalties.append(sample_param.frequency_penalty)
+            temperatures.append(sample_param.temperature)
+            top_ps.append(sample_param.top_p)
+            top_ks.append(sample_param.top_k)
+
+            for token_id, count in id_to_count.items():
+                p_token_ids.append(token_id)
+                p_token_counts.append(count)
+            p_seq_len.append(len(id_to_count))
+            p_max_len_in_batch = max(p_max_len_in_batch, len(id_to_count))
+
+        presence_penalties = torch.tensor(presence_penalties, dtype=torch.float, device="cuda")
+        frequency_penalties = torch.tensor(frequency_penalties, dtype=torch.float, device="cuda")
+        temperatures = torch.tensor(temperatures, dtype=torch.float, device="cuda")
+        top_ps = torch.tensor(top_ps, dtype=torch.float, device="cuda")
+        top_ks = torch.tensor(top_ks, dtype=torch.int32, device="cuda")
+        p_token_ids = torch.tensor(p_token_ids, dtype=torch.int32, device="cuda")
+        p_token_counts = torch.tensor(p_token_counts, dtype=torch.int32, device="cuda")
+        p_seq_len = torch.tensor(p_seq_len, dtype=torch.int32, device="cuda")
+        p_cumsum_seq_len = torch.cumsum(p_seq_len, dim=0, dtype=torch.int32)
+        return (
+            presence_penalties,
+            frequency_penalties,
+            temperatures,
+            top_ps,
+            top_ks,
+            p_token_ids,
+            p_token_counts,
+            p_cumsum_seq_len,
+            p_max_len_in_batch,
+        )