118 lines
4.1 KiB
Python
118 lines
4.1 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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from .models.utils import (
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init_fp8_kv_cache, append_fp8_kv_cache,
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init_kv_cache, append_kv_cache
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)
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from typing import Optional, Dict, Tuple, Any
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from transformers.cache_utils import DynamicCache
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class DynamicFp8Cache(DynamicCache):
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def update(
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self,
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key_states: torch.Tensor,
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value_states: torch.Tensor,
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layer_idx: int,
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cache_kwargs: Optional[Dict[str, Any]]=None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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batch_size, num_heads, seq_len, head_dim = key_states.shape
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if layer_idx == 0:
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if hasattr(self, "_seen_tokens"):
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# 4.39 uses `_seen_tokens`
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self._seen_tokens += seq_len
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else:
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# 4.37 uses `seen_tokens`
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self.seen_tokens += seq_len
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# Update the cache
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if len(self.key_cache) <= layer_idx:
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k_cache, v_cache = init_fp8_kv_cache(
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batch_size, num_heads, seq_len, head_dim,
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device=key_states.device,
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)
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k_cache, v_cache = append_fp8_kv_cache(k_cache, v_cache, key_states, value_states)
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self.key_cache.append(k_cache)
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self.value_cache.append(v_cache)
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else:
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k_cache = self.key_cache[layer_idx]
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v_cache = self.value_cache[layer_idx]
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k_cache, v_cache = append_fp8_kv_cache(k_cache, v_cache, key_states, value_states)
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self.key_cache[layer_idx] = k_cache
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self.value_cache[layer_idx] = v_cache
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return self.key_cache[layer_idx], self.value_cache[layer_idx]
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class DynamicNormalCache(DynamicCache):
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KV_ALLOC_BLOCK_LENGTH = 256
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def update(
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self,
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key_states: torch.Tensor,
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value_states: torch.Tensor,
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layer_idx: int,
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cache_kwargs: Optional[Dict[str, Any]]=None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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batch_size, num_heads, seq_len, head_dim = key_states.shape
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if layer_idx == 0:
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if hasattr(self, "_seen_tokens"):
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# 4.39 uses `_seen_tokens`
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self._seen_tokens += seq_len
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else:
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# 4.37 uses `seen_tokens`
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self.seen_tokens += seq_len
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# Update the cache
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if len(self.key_cache) <= layer_idx:
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k_cache, v_cache = init_kv_cache(
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batch_size, num_heads, head_dim,
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0, key_states.size(2) + self.KV_ALLOC_BLOCK_LENGTH,
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key_states.dtype, key_states.device
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)
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k_cache, v_cache = append_kv_cache(k_cache, v_cache, key_states, value_states)
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self.key_cache.append(k_cache)
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self.value_cache.append(v_cache)
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else:
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k_cache = self.key_cache[layer_idx]
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v_cache = self.value_cache[layer_idx]
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kv_seq_len = k_cache.size(2) + key_states.size(2)
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if k_cache.stride(1) < kv_seq_len * k_cache.size(3):
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new_k_cache, new_v_cache = init_kv_cache(
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batch_size, num_heads, head_dim,
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k_cache.size(2), kv_seq_len + self.KV_ALLOC_BLOCK_LENGTH,
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key_states.dtype, key_states.device
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)
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new_k_cache[...] = k_cache[...]
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new_v_cache[...] = v_cache[...]
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k_cache = new_k_cache
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v_cache = new_v_cache
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k_cache, v_cache = append_kv_cache(k_cache, v_cache, key_states, value_states)
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self.key_cache[layer_idx] = k_cache
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self.value_cache[layer_idx] = v_cache
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return self.key_cache[layer_idx], self.value_cache[layer_idx]
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