152 lines
5.9 KiB
Python
152 lines
5.9 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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# Some parts of this file is adapted from
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# https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2024 The HuggingFace Team. All rights reserved.
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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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import math
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import torch
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from typing import Optional
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from ipex_llm.transformers.utils import get_xpu_device_type
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from ipex_llm.transformers.models.common import padding_qkv_hd
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from ipex_llm.transformers.models.common import scaled_dot_product_attention
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from diffusers.models.attention_processor import Attention
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class AttnProcessor2_0:
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r"""
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Processor for implementing scaled dot-product attention.
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"""
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def __call__(
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self,
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attn: Attention,
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hidden_states: torch.Tensor,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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temb: Optional[torch.Tensor] = None,
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*args,
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**kwargs,
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) -> torch.Tensor:
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residual = hidden_states
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if attn.spatial_norm is not None:
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hidden_states = attn.spatial_norm(hidden_states, temb)
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input_ndim = hidden_states.ndim
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if input_ndim == 4:
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batch_size, channel, height, width = hidden_states.shape
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hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
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)
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if attention_mask is not None:
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attention_mask = attn.prepare_attention_mask(attention_mask,
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sequence_length, batch_size)
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# scaled_dot_product_attention expects attention_mask shape to be
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# (batch, heads, source_length, target_length)
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attention_mask = attention_mask.view(batch_size, attn.heads,
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-1, attention_mask.shape[-1])
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if attn.group_norm is not None:
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hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
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query = attn.to_q(hidden_states)
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if encoder_hidden_states is None:
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encoder_hidden_states = hidden_states
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elif attn.norm_cross:
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encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
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key = attn.to_k(encoder_hidden_states)
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value = attn.to_v(encoder_hidden_states)
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inner_dim = key.shape[-1]
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head_dim = inner_dim // attn.heads
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query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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if attn.norm_q is not None:
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query = attn.norm_q(query)
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if attn.norm_k is not None:
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key = attn.norm_k(key)
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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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# IPEX-LLM changes start
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if query.device.type == "xpu" and query.dtype in [torch.half, torch.float]:
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# padding head_dim 40 to 64
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query, key, value = padding_qkv_hd(query, key, value, 40, 64)
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hidden_states = scaled_dot_product_attention(
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query, key.contiguous(), value.contiguous(),
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attention_mask, False, 1 / math.sqrt(head_dim)
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)
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hidden_states = hidden_states[:, :, :, :head_dim]
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else:
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hidden_states = torch.nn.functional.scaled_dot_product_attention(
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
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)
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# IPEX-LLM changes end
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hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1,
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attn.heads * head_dim)
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hidden_states = hidden_states.to(query.dtype)
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# linear proj
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hidden_states = attn.to_out[0](hidden_states)
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# dropout
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hidden_states = attn.to_out[1](hidden_states)
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if input_ndim == 4:
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hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel,
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height, width)
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if attn.residual_connection:
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hidden_states = hidden_states + residual
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hidden_states = hidden_states / attn.rescale_output_factor
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return hidden_states
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def upcast_vae(self):
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# workaround overflow and ipex's bugs
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if get_xpu_device_type(self.vae.post_quant_conv.weight) in ["arc", "flex", "pvc"]:
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self.vae.to(torch.bfloat16)
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else:
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self.vae.decoder.up_blocks.to(torch.bfloat16)
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self.vae.decoder.conv_norm_out.to(torch.bfloat16)
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self.vae.decoder.conv_out.to(torch.bfloat16)
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