310 lines
14 KiB
Python
310 lines
14 KiB
Python
#
|
|
# 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)
|