126 lines
3.8 KiB
Python
126 lines
3.8 KiB
Python
"""Memory pool."""
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import logging
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import torch
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logger = logging.getLogger(__name__)
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class ReqToTokenPool:
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"""A memory pool that maps a request to its token locations."""
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def __init__(self, size: int, max_context_len: int):
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self.mem_state = torch.ones((size,), dtype=torch.bool, device="cuda")
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self.req_to_token = torch.empty(
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(size, max_context_len), dtype=torch.int32, device="cuda"
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)
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self.can_use_mem_size = size
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def alloc(self, need_size: int):
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if need_size > self.can_use_mem_size:
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return None
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select_index = (
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torch.nonzero(self.mem_state).squeeze(1)[:need_size].to(torch.int32)
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)
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self.mem_state[select_index] = False
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self.can_use_mem_size -= need_size
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return select_index
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def free(self, free_index: int):
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self.mem_state[free_index] = True
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if isinstance(free_index, (int,)):
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self.can_use_mem_size += 1
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else:
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self.can_use_mem_size += free_index.shape[0]
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def clear(self):
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self.mem_state.fill_(True)
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self.can_use_mem_size = len(self.mem_state)
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class TokenToKVPool:
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"""A memory pool that maps a token to its kv cache locations"""
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def __init__(
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self,
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size: int,
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dtype: torch.dtype,
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head_num: int,
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head_dim: int,
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layer_num: int,
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):
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self.size = size
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# We also add one slot. This slot is used for writing dummy output from padded tokens.
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self.mem_state = torch.ones((self.size + 1,), dtype=torch.bool, device="cuda")
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# [size, head_num, head_dim] for each layer
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self.k_buffer = [
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torch.empty((size + 1, head_num, head_dim), dtype=dtype, device="cuda")
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for _ in range(layer_num)
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]
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self.v_buffer = [
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torch.empty((size + 1, head_num, head_dim), dtype=dtype, device="cuda")
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for _ in range(layer_num)
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]
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# Prefetch buffer
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self.prefetch_buffer = torch.empty(0, device="cuda", dtype=torch.int32)
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self.prefetch_chunk_size = 512
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self.can_use_mem_size = self.size
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self.clear()
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def get_key_buffer(self, layer_id: int):
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return self.k_buffer[layer_id]
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def get_value_buffer(self, layer_id: int):
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return self.v_buffer[layer_id]
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def get_kv_buffer(self, layer_id: int):
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return self.k_buffer[layer_id], self.v_buffer[layer_id]
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def available_size(self):
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return self.can_use_mem_size + len(self.prefetch_buffer)
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def alloc(self, need_size: int):
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buffer_len = len(self.prefetch_buffer)
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if need_size <= buffer_len:
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select_index = self.prefetch_buffer[:need_size]
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self.prefetch_buffer = self.prefetch_buffer[need_size:]
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return select_index
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addition_size = need_size - buffer_len
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alloc_size = max(addition_size, self.prefetch_chunk_size)
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select_index = (
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torch.nonzero(self.mem_state).squeeze(1)[:alloc_size].to(torch.int32)
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)
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if select_index.shape[0] < addition_size:
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return None
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self.mem_state[select_index] = False
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self.can_use_mem_size -= len(select_index)
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self.prefetch_buffer = torch.cat((self.prefetch_buffer, select_index))
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ret_index = self.prefetch_buffer[:need_size]
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self.prefetch_buffer = self.prefetch_buffer[need_size:]
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return ret_index
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def free(self, free_index: torch.Tensor):
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self.mem_state[free_index] = True
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self.can_use_mem_size += len(free_index)
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def clear(self):
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self.prefetch_buffer = torch.empty(0, device="cuda", dtype=torch.int32)
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self.mem_state.fill_(True)
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self.can_use_mem_size = self.size
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# We also add one slot. This slot is used for writing dummy output from padded tokens.
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self.mem_state[0] = False
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