252 lines
10 KiB
Python
252 lines
10 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://huggingface.co/IEITYuan/Yuan2-2B-hf/blob/7ab7b3c18eb8e5232ce2a3f720d4e6f4b53a2806/yuan_hf_model.py
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# which is licensed under Apache License 2.0:
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#
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# https://huggingface.co/IEITYuan/Yuan2-2B-hf/blob/7ab7b3c18eb8e5232ce2a3f720d4e6f4b53a2806/README.md#%E5%A3%B0%E6%98%8E%E4%B8%8E%E5%8D%8F%E8%AE%AEterms-and-conditions
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#
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import math
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from typing import Optional, Tuple
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import torch
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, \
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mlp_fusion_check, fp16_fusion_check
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
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from ipex_llm.transformers.models.utils import SILU, update_past_key_value
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from ipex_llm.transformers.models.utils import should_use_fuse_rope, use_sdp, use_sdp_causal
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def merge_qk(module: torch.nn.Module):
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if "YuanAttention" in module.__class__.__name__:
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q_weight = module.q_proj.weight.data
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k_weight = module.k_proj.weight.data
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num_heads = module.num_heads
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head_dim = module.head_dim
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hidden_size = module.hidden_size
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merged_qk_proj = torch.nn.Linear(0, 0, False)
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weight = torch.cat([
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q_weight.view(num_heads, head_dim, hidden_size)[0::2, :, :],
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k_weight.view(num_heads, head_dim, hidden_size)[0::2, :, :],
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q_weight.view(num_heads, head_dim, hidden_size)[1::2, :, :],
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k_weight.view(num_heads, head_dim, hidden_size)[1::2, :, :],
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], dim=0).view(num_heads * head_dim * 2, hidden_size)
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merged_qk_proj.weight = torch.nn.Parameter(weight, requires_grad=False)
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merged_qk_proj.in_features = hidden_size
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merged_qk_proj.out_features = num_heads * head_dim * 2
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module.qk_proj = merged_qk_proj
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del module.q_proj
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del module.k_proj
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def yuan_localized_filtering_forward(
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self,
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inputs: torch.Tensor,
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before_hidden_states: torch.Tensor,
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dtype: torch.dtype,
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):
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if self.conv1.weight.dtype != torch.half:
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self.half()
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invalidInputError(self.lf_conv2d_num_pad == 1, "padding must be 1")
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invalidInputError(not self.training, ("training is not supported for now, "
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"please call model.eval() before inference"))
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if before_hidden_states is None:
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inputs = inputs.half()
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lf_output = self._inference_forward(inputs, None)
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else:
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# only change next token logic
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bsz, seq_len, embed_dim = inputs.size()
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seq_len_before, _, _ = before_hidden_states.size()
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invalidInputError(seq_len == 1 and seq_len_before == 3,
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f"wrong sequence length: {seq_len} {seq_len_before}")
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residual = before_hidden_states[-1:, :, :]
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inputs = before_hidden_states.view(3, 1, bsz, embed_dim).permute(2, 3, 0, 1)
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output1 = self.conv1(inputs)
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output2 = self.conv2(output1[:, :, 1:-1, :])
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output2 = output2[:, :, 1:-1, :]
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output2 = output2.view(1, bsz, embed_dim)
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invalidInputError(output2.shape == residual.shape,
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f"wrong shape: {output2.shape} {residual.shape}")
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lf_output = self.output_layernorm(output2 + residual)
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lf_output = lf_output.transpose(0, 1)
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lf_output = lf_output.to(dtype)
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return lf_output
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def yuan_mlp_forward(
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self,
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x: torch.Tensor,
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residual=None
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) -> torch.Tensor:
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x_2d = x.view(-1, x.shape[-1])
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bsz, hidden_size = x_2d.shape
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qtype = getattr(self.up_proj, "qtype", None)
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if mlp_fusion_check(x_2d, qtype, self.training):
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import xe_linear
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if not x_2d.is_contiguous():
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x_2d = x_2d.contiguous()
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out = self.down_proj(xe_linear.mlp_forward_xpu(
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x_2d, self.up_proj.weight.data, self.gate_proj.weight.data,
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x_2d.shape[0], x_2d.shape[1], self.up_proj.out_len,
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SILU, qtype
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))
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if residual is not None:
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return out + residual
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else:
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return out
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elif fp16_fusion_check(self.up_proj, x, self.training) and \
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hidden_size == 4096 and bsz == 1:
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hidden_states1 = torch.ops.torch_ipex.mm_silu(x, self.up_proj.weight)
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hidden_states = torch.ops.torch_ipex.mm_resmul(
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x, self.gate_proj.weight, hidden_states1
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)
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if residual is None:
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hidden_states = torch.matmul(hidden_states, self.down_proj.weight)
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else:
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attn_output = torch.addmm(
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residual.flatten(0, -2),
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hidden_states.flatten(0, -2),
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self.down_proj.weight,
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beta=1,
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)
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hidden_states = attn_output.view(x.shape)
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return hidden_states
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else:
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out = self.down_proj(self.act_fn(self.up_proj(x)) * self.gate_proj(x))
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if residual is not None:
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return out + residual
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else:
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return out
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def yuan_attention_forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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invalidInputError(use_cache, "use_cache=True is needed")
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invalidInputError(not self.use_shareqk, "use_shareqk is not supported for now")
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if past_key_value is None:
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if q_len >= 2:
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before_hidden_states = hidden_states[:, -2:, :].transpose(0, 1).half()
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else:
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before_hidden_states = torch.zeros(2, bsz, self.hidden_size,
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dtype=torch.half, device=hidden_states.device)
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before_hidden_states[-1:, :, :] = hidden_states[:, -1:, :].transpose(0, 1)
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else:
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before_hidden_states = past_key_value[2]
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this_hidden_states = torch.cat([
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before_hidden_states,
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hidden_states.transpose(0, 1).half(),
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], dim=0)
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before_hidden_states = this_hidden_states[-2:, :, ]
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value_states = self.v_proj(hidden_states)
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value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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if past_key_value is None:
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hidden_states = yuan_localized_filtering_forward(self.lf_gate, hidden_states,
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None, hidden_states.dtype)
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else:
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hidden_states = yuan_localized_filtering_forward(self.lf_gate, hidden_states,
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this_hidden_states, hidden_states.dtype)
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qk_states = self.qk_proj(hidden_states)
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qk_states = qk_states.view(bsz, q_len, self.num_heads * 2, self.head_dim)
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qk_states = qk_states.transpose(1, 2)
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query_states, key_states = torch.chunk(qk_states, 2, dim=1)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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if should_use_fuse_rope(hidden_states, position_ids, self.training):
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import xe_addons
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xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids,
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query_states, key_states)
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else:
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states,
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key_states,
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cos, sin,
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position_ids,
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"yuan")
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# IPEX-LLM OPT: kv cache and quantzie kv cache
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use_quantize_kv = use_quantize_kv_cache(self.qk_proj, hidden_states)
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key_states, value_states = update_past_key_value(
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None if past_key_value is None else (past_key_value[0], past_key_value[1]),
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key_states, value_states,
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kv_seq_len, use_quantize_kv, device
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)
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past_key_value = (key_states, value_states, before_hidden_states) if use_cache else None
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# IPEX-LLM OPT: sdp
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if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
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import xe_addons
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if use_quantize_kv:
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attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
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attention_mask)
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else:
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attn_output = xe_addons.sdp(query_states, key_states, value_states,
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attention_mask)
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elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
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import xe_addons
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if use_quantize_kv:
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attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
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value_states, attention_mask)
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else:
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attn_output = xe_addons.sdp_causal(query_states, key_states,
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value_states, attention_mask)
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else:
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if use_quantize_kv:
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key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
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query_states.dtype)
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attn_weights = torch.matmul(query_states,
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key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attention_mask is not None:
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(value_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2)
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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