278 lines
12 KiB
Python
278 lines
12 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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# This file is adapted from
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# https://huggingface.co/THUDM/chatglm-6b/blob/63ce1bac4a7a7da57c67448bab39ddbe0e115a19/configuration_chatglm.py
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#
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import math
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import torch
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import torch.utils.checkpoint
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import torch.nn.functional as F
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from typing import Optional, Tuple
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from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
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from ipex_llm.transformers.models.utils import use_sdp
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def rotate_half(x):
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x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
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return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions
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@torch.jit.script
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def apply_rotary_pos_emb_index(q, k, cos, sin, position_id):
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# position_id: [sq, b], q, k: [sq, b, np, hn], cos: [sq, 1, hn] -> [sq, b, 1, hn]
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cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(2), \
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F.embedding(position_id, sin.squeeze(1)).unsqueeze(2)
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q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
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return q, k
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def glm_sdpa(query, key, value, attention_mask=None, is_causal=False):
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if query.device.type == 'cpu':
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context_layer = F.scaled_dot_product_attention(query.to(key.dtype),
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key,
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value,
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attention_mask,
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is_causal=is_causal).to(key.dtype)
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else:
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# attention_mask is not None only when past_key_value is not None and q_len > 1
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if attention_mask is not None:
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attn_bias = torch.zeros(attention_mask.shape, dtype=query.dtype,
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device=query.device)
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attention_mask = ~attention_mask
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if attention_mask.dtype == torch.bool:
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attn_bias.masked_fill_(attention_mask.logical_not(), float("-inf"))
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else:
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attn_bias += attention_mask
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elif is_causal:
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L, S = query.size(-2), key.size(-2)
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attn_bias = torch.zeros(L, S, dtype=query.dtype, device=query.device)
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temp_mask = torch.ones(L, S, dtype=torch.bool, device=query.device).tril(diagonal=0)
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attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
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attn_bias.to(key.dtype)
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else:
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attn_bias = None
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if use_sdp(query.shape[2], key.shape[2],
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query.shape[-1], query):
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import xe_addons
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attn_output = xe_addons.sdp(query, key, value, attn_bias)
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context_layer = attn_output.view(query.shape)
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else:
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head_dim = query.size(-1)
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attn = torch.matmul(query.to(key.dtype) / math.sqrt(head_dim),
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key.transpose(2, 3))
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if attn_bias is not None:
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attn += attn_bias
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attn = F.softmax(attn, dim=-1,
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dtype=torch.float32).to(value.dtype)
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context_layer = torch.matmul(attn, value)
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return context_layer
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import os
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KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
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KV_CACHE_ALLOC_MIN_LENGTH = 512
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def attention_fn(
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self,
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query_layer,
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key_layer,
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value_layer,
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attention_mask,
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hidden_size_per_partition,
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layer_id,
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layer_past=None,
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scaling_attention_score=True,
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use_cache=False,
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):
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key_layer = key_layer.permute(1, 2, 0, 3).contiguous()
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value_layer = value_layer.permute(1, 2, 0, 3).contiguous()
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# query_layer = query_layer.permute(1, 2, 0, 3)
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cur_length, batch_size = query_layer.shape[0], query_layer.shape[1]
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device = query_layer.device
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if layer_past is not None:
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cache_k, cache_v = layer_past[0], layer_past[1]
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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 cache_k.stride()[1] < (past_length + cur_length) * 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 = extend_kv_cache(batch_size,
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self.num_attention_heads_per_partition,
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self.hidden_size_per_attention_head,
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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, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
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elif use_cache:
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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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key_cache, value_cache = init_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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# seqlen, batch, num_attention_heads, hidden_size_per_attention_head
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b, nh, seq_len, hidden_size = key_layer.shape
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if use_cache:
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present = (key_layer.permute(2, 0, 1, 3), value_layer.permute(2, 0, 1, 3))
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else:
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present = None
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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 = glm_sdpa(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=True)
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else:
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# attention_mask is not None only when past_key_value is not None and q_len > 1
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if attention_mask is not None:
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attn_bias = torch.zeros(attention_mask.shape, dtype=query_layer.dtype,
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device=query_layer.device)
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attention_mask = ~attention_mask
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if attention_mask.dtype == torch.bool:
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attn_bias.masked_fill_(attention_mask.logical_not(), float("-inf"))
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else:
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attn_bias += attention_mask
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else:
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attn_bias = None
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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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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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else:
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head_dim = query_layer.size(-1)
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attn = torch.matmul(query_layer.to(key_layer.dtype),
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key_layer.transpose(2, 3)) / math.sqrt(head_dim)
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if attn_bias is not None:
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attn += attn_bias
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attn = F.softmax(attn, dim=-1,
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dtype=torch.float32).to(value_layer.dtype)
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context_layer = torch.matmul(attn, value_layer)
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context_layer = context_layer.permute(2, 0, 1, 3)
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new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
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context_layer = context_layer.reshape(*new_context_layer_shape)
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attention_probs = None
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outputs = (context_layer, present, attention_probs)
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return outputs
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def chatglm_attention_forward(
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self,
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hidden_states: torch.Tensor,
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position_ids,
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attention_mask: torch.Tensor,
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layer_id,
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layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
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use_cache: bool = False,
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output_attentions: bool = False,
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):
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"""
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hidden_states: [seq_len, batch, hidden_size]
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attention_mask: [(1, 1), seq_len, seq_len]
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"""
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# [seq_len, batch, 3 * hidden_size]
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mixed_raw_layer = self.query_key_value(hidden_states)
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# [seq_len, batch, 3 * hidden_size] -->
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# [seq_len, batch, num_attention_heads, 3 * hidden_size_per_attention_head]
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new_tensor_shape = mixed_raw_layer.size()[:-1] + (
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self.num_attention_heads_per_partition,
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3 * self.hidden_size_per_attention_head,
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)
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mixed_raw_layer = mixed_raw_layer.view(*new_tensor_shape)
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# [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
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(query_layer, key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_raw_layer, 3)
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if self.position_encoding_2d:
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q1, q2 = query_layer.chunk(2, dim=(query_layer.ndim - 1))
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k1, k2 = key_layer.chunk(2, dim=(key_layer.ndim - 1))
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cos, sin = self.rotary_emb(q1, seq_len=position_ids.max() + 1)
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position_ids, block_position_ids = position_ids[:, 0, :].transpose(0, 1).contiguous(), \
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position_ids[:, 1, :].transpose(0, 1).contiguous()
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q1, k1 = apply_rotary_pos_emb_index(q1, k1, cos, sin, position_ids)
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q2, k2 = apply_rotary_pos_emb_index(q2, k2, cos, sin, block_position_ids)
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query_layer = torch.concat([q1, q2], dim=(q1.ndim - 1))
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key_layer = torch.concat([k1, k2], dim=(k1.ndim - 1))
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else:
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position_ids = position_ids.transpose(0, 1)
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cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1)
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# [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
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query_layer, key_layer = apply_rotary_pos_emb_index(query_layer, key_layer,
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cos, sin, position_ids)
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# [seq_len, batch, hidden_size]
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context_layer, present, attention_probs = attention_fn(
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self=self,
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query_layer=query_layer,
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key_layer=key_layer,
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value_layer=value_layer,
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attention_mask=attention_mask,
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hidden_size_per_partition=self.hidden_size_per_partition,
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layer_id=layer_id,
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layer_past=layer_past,
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use_cache=use_cache
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)
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output = self.dense(context_layer)
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outputs = (output, present)
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if output_attentions:
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outputs += (attention_probs,)
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return outputs # output, present, attention_probs
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