# # Copyright 2016 The BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Some parts of this file is adapted from # https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/modeling_phi3.py # which is licensed under Apache License 2.0: # # Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import torch import warnings from torch import nn from ipex_llm.transformers.models.utils import ( rotate_half, should_use_fuse_rope, ) from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU from ipex_llm.transformers.models.utils import use_sdp, use_sdp_causal from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache from typing import Optional, Tuple, List from transformers.models.phi.modeling_phi import repeat_kv from transformers.cache_utils import Cache def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def pre_compute_inv_freq(module: torch.nn.Module): if module.__class__.__name__ == "Phi3RotaryEmbedding": module.inv_freq = 1.0 / ( module.base ** (torch.arange(0, module.dim, 2, dtype=torch.int64).float() / module.dim) ) elif module.__class__.__name__ == "Phi3SuScaledRotaryEmbedding": inv_freq_shape = torch.arange(0, module.dim, 2, dtype=torch.int64).float() / module.dim short_ext_factors = torch.tensor(module.short_factor, dtype=torch.float32) module.inv_freq = 1.0 / (short_ext_factors * module.base ** inv_freq_shape) long_ext_factors = torch.tensor(module.long_factor, dtype=torch.float32) module.register_buffer("long_inv_freq", None, persistent=False) module.long_inv_freq = 1.0 / (long_ext_factors * module.base ** inv_freq_shape) if module.max_position_embeddings <= module.original_max_position_embeddings: module.scaling_factor = 1.0 else: scale = module.max_position_embeddings / module.original_max_position_embeddings module.scaling_factor = math.sqrt( 1 + math.log(scale) / math.log(module.original_max_position_embeddings) ) def attention_forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: warnings.warn("You are not running the flash-attention implementation, " "expect numerical differences.") bsz, q_len, _ = hidden_states.size() qkv = self.qkv_proj(hidden_states) qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim) qkv = qkv.transpose(1, 2) query_states, key_states, value_states = qkv.split([self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=1) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) # IPEX-LLM OPT: fuse rope if should_use_fuse_rope(hidden_states, position_ids, self.training): import xe_addons if self.rotary_emb.__class__.__name__ == "Phi3RotaryEmbedding": # 4k xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids, query_states, key_states) else: # 128k if kv_seq_len > self.rotary_emb.original_max_position_embeddings: xe_addons.rotary_half_inplaced(self.rotary_emb.long_inv_freq, position_ids, query_states, key_states) else: xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids, query_states, key_states) # todo: fuse scaling_factor query_states *= self.rotary_emb.scaling_factor key_states *= self.rotary_emb.scaling_factor else: cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) if past_key_value is not None: key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, None) if use_sdp(q_len, kv_seq_len, self.head_dim, query_states): import xe_addons if isinstance(past_key_value, DynamicFp8Cache): attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states, attention_mask) else: attn_output = xe_addons.sdp(query_states, key_states, value_states, attention_mask) # disable sdp_causal to avoid overflow for now # elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training): # import xe_addons # if isinstance(past_key_value, DynamicFp8Cache): # attn_output = xe_addons.sdp_fp8_causal(query_states, key_states, # value_states, attention_mask) # else: # attn_output = xe_addons.sdp_causal(query_states, key_states, # value_states, attention_mask) else: if isinstance(past_key_value, DynamicFp8Cache): key_states, value_states = restore_fp8_kv_cache(key_states, value_states, query_states.dtype) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attention_mask is not None: attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype) attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value def split_mlp(module: torch.nn.Module): if module.__class__.__name__ == "Phi3MLP": gate_weight, up_weight = module.gate_up_proj.weight.data.chunk(2, dim=0) gate_proj = torch.nn.Linear(0, 0, bias=False) gate_proj.weight = torch.nn.Parameter(gate_weight, requires_grad=False) gate_proj.in_features = gate_weight.size(1) gate_proj.out_features = gate_weight.size(0) up_proj = torch.nn.Linear(0, 0, bias=False) up_proj.weight = torch.nn.Parameter(up_weight, requires_grad=False) up_proj.in_features = up_weight.size(1) up_proj.out_features = up_weight.size(0) module.gate_proj = gate_proj module.up_proj = up_proj del module.gate_up_proj def mlp_forward( self, hidden_states: torch.FloatTensor ) -> torch.FloatTensor: x_2d = hidden_states.view(-1, hidden_states.shape[-1]) qtype = getattr(self.gate_proj, "qtype", None) if mlp_fusion_check(x_2d, qtype, self.training): x_2d = x_2d.contiguous() import xe_linear return self.down_proj(xe_linear.mlp_forward_xpu( x_2d, self.gate_proj.weight.data, self.up_proj.weight.data, x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_features, SILU, qtype )) return self.down_proj( self.activation_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states) ) def phi3_model_forward_wrapper(origin_model_forward): def model_forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): # IPEX-LLM OPT: kv cache and quantize kv cache and sdp use_cache = use_cache if use_cache is not None else self.config.use_cache input = input_ids if input_ids is not None else inputs_embeds use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.down_proj, input) if use_cache: if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache): past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values) if not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache): past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values) return origin_model_forward( self=self, input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) return model_forward def phi3v_model_forward_wrapper(origin_model_forward): def model_forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, image_sizes: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): # IPEX-LLM OPT: kv cache and quantize kv cache and sdp use_cache = use_cache if use_cache is not None else self.config.use_cache use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.down_proj, input_ids) if use_cache: if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache): past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values) if not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache): past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values) return origin_model_forward( self=self, input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, pixel_values=pixel_values, image_sizes=image_sizes, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) return model_forward def phi3_rms_norm_forward(self, hidden_states): if hidden_states.device.type == "xpu" and not (self.training and hidden_states.requires_grad): import xe_addons x_2d = hidden_states.reshape(-1, hidden_states.size(-1)).contiguous() output = xe_addons.rms_norm(self.weight, x_2d, self.variance_epsilon) return output.reshape(hidden_states.shape) input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype)