Fix chatglm2 attention and kv cache (#8924)
* fix chatglm2 attention * fix bf16 bug * make model stateless * add utils * cleanup * fix style
This commit is contained in:
Yang Wang
GitHub
parent
b209b8f7b6
commit
25428b22b4
2 changed files with 82 additions and 56 deletions
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@ -20,6 +20,7 @@
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import torch
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from typing import Optional, Tuple, Union, List, Callable, Dict, Any
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import torch.nn.functional as F
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from bigdl.llm.transformers.models.utils import create_kv_cache, append_kv_cache
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KV_CACHE_ALLOC_BLOCK_LENGTH = 256
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@ -145,39 +146,38 @@ def chatglm2_attention_forward_8eb45c(
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# adjust key and value for inference
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if kv_cache is not None:
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cache_k, cache_v = kv_cache
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past_length = cache_k.size(0)
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cache_k = cache_k.permute(1, 2, 0, 3)
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cache_v = cache_v.permute(1, 2, 0, 3)
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past_length = cache_k.size(2)
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if past_length + cur_length > self.max_cache_length:
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self.max_cache_length = past_length + cur_length + KV_CACHE_ALLOC_BLOCK_LENGTH
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self.kv_cache = (torch.empty(batch_size,
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if cache_k.stride()[1] <= cache_k.size(2) * cache_k.size(3):
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max_cache_length = past_length + cur_length + KV_CACHE_ALLOC_BLOCK_LENGTH
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new_cache_k, new_cache_v = create_kv_cache(batch_size,
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self.num_attention_heads_per_partition,
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self.max_cache_length,
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self.hidden_size_per_attention_head,
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device=device),
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torch.empty(batch_size,
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self.num_attention_heads_per_partition,
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self.max_cache_length,
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self.hidden_size_per_attention_head,
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device=device))
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self.kv_cache[0][:, :, :past_length, :] = cache_k.permute(1, 2, 0, 3)
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self.kv_cache[1][:, :, :past_length, :] = cache_v.permute(1, 2, 0, 3)
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self.kv_cache[0][:, :, past_length:past_length + cur_length, :] = key_layer
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self.kv_cache[1][:, :, past_length:past_length + cur_length, :] = value_layer
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past_length,
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max_cache_length,
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dtype=query_layer.dtype,
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device=device)
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new_cache_k[:] = cache_k
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new_cache_v[:] = cache_v
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cache_k = new_cache_k
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cache_v = new_cache_v
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key_layer = self.kv_cache[0][:, :, :past_length + cur_length, :]
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value_layer = self.kv_cache[1][:, :, :past_length + cur_length, :]
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key_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
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elif use_cache:
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self.max_cache_length = max(KV_CACHE_ALLOC_MIN_LENGTH, cur_length) \
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max_cache_length = max(KV_CACHE_ALLOC_MIN_LENGTH, cur_length) \
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+ KV_CACHE_ALLOC_BLOCK_LENGTH
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self.kv_cache = (torch.empty(batch_size, self.num_attention_heads_per_partition,
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self.max_cache_length, self.hidden_size_per_attention_head,
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device=device),
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torch.empty(batch_size, self.num_attention_heads_per_partition,
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self.max_cache_length, self.hidden_size_per_attention_head,
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device=device))
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self.kv_cache[0][:, :, :cur_length, :] = key_layer
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self.kv_cache[1][:, :, :cur_length, :] = value_layer
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key_cache, value_cache = create_kv_cache(batch_size, self.num_attention_heads_per_partition,
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self.hidden_size_per_attention_head, cur_length,
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max_cache_length,
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dtype=query_layer.dtype, device=device)
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key_cache[:] = key_layer
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value_cache[:] = value_layer
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key_layer = key_cache
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value_layer = value_cache
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if use_cache:
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kv_cache = (key_layer, value_layer)
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@ -204,22 +204,6 @@ def core_attn_forward_8eb45c(self, query_layer, key_layer, value_layer, attentio
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if pytorch_major_version >= 2 and (query_layer.device.type == 'xpu' or query_layer.size(0) > 1):
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query_layer = query_layer.permute(1, 2, 0, 3)
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if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
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if torch.is_autocast_cpu_enabled():
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attention_mask = torch.ones(query_layer.shape[2],
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key_layer.shape[2],
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dtype=torch.bool).tril(diagonal=0)
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attention_mask = attention_mask.masked_fill(~attention_mask, -float('inf'), )
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attention_mask = attention_mask.to(torch.get_autocast_cpu_dtype())
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query_layer = query_layer.to(torch.get_autocast_cpu_dtype())
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key_layer = key_layer.to(torch.get_autocast_cpu_dtype())
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value_layer = value_layer.to(torch.get_autocast_cpu_dtype())
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context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer,
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key_layer,
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value_layer,
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attention_mask,
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is_causal=False)
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else:
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context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer,
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key_layer,
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value_layer,
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@ -227,13 +211,7 @@ def core_attn_forward_8eb45c(self, query_layer, key_layer, value_layer, attentio
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is_causal=True)
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else:
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if attention_mask is not None:
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attention_mask = attention_mask.masked_fill(~attention_mask, -float('inf'), )
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if torch.is_autocast_cpu_enabled():
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query_layer = query_layer.to(torch.get_autocast_cpu_dtype())
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key_layer = key_layer.to(torch.get_autocast_cpu_dtype())
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value_layer = value_layer.to(torch.get_autocast_cpu_dtype())
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attention_mask = attention_mask.to(torch.get_autocast_cpu_dtype())
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attention_mask = ~attention_mask
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context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer,
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key_layer,
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value_layer,
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48
python/llm/src/bigdl/llm/transformers/models/utils.py
Normal file
48
python/llm/src/bigdl/llm/transformers/models/utils.py
Normal file
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@ -0,0 +1,48 @@
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#
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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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def create_kv_cache(batch_size, num_heads, head_dim, current_length, max_length, dtype, device):
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key_cache_storage = torch.empty(batch_size, num_heads,
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max_length, head_dim,
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dtype=dtype, device=device)
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value_cache_storage = torch.empty(batch_size, num_heads,
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max_length, head_dim,
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dtype=dtype, device=device)
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key_cache = key_cache_storage.as_strided((batch_size, num_heads,
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current_length, head_dim),
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key_cache_storage.stride(),
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storage_offset=0)
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value_cache = value_cache_storage.as_strided((batch_size, num_heads,
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current_length, head_dim),
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value_cache_storage.stride(),
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storage_offset=0)
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return key_cache, value_cache
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def append_kv_cache(cache_k, cache_v, key_states, value_states):
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new_size = (cache_k.size(0),
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cache_k.size(1),
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cache_k.size(2) + key_states.size(2),
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cache_k.size(3))
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new_cache_k = cache_k.as_strided(new_size, cache_k.stride(), storage_offset=0)
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new_cache_k[:, :, cache_k.size(2):cache_k.size(2) + key_states.size(2), :] = key_states
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new_cache_v = cache_v.as_strided(new_size, cache_v.stride(), storage_offset=0)
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new_cache_v[:, :, cache_v.size(2):cache_k.size(2) + key_states.size(2), :] = value_states
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return new_cache_k, new_cache_v
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