499 lines
21 KiB
Python
499 lines
21 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.
|
|
|
|
# This file is adapted from
|
|
# https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/cb7fc748b78b7ea99772e4cf76db155729ce774e/modeling_baichuan.py
|
|
# and
|
|
# https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/blob/c6f8592a60b4ad73c210b28dd2ab3cca51abbf93/modeling_baichuan.py
|
|
|
|
import math
|
|
from typing import List, Optional, Tuple, Union
|
|
import torch
|
|
import torch.utils.checkpoint
|
|
from torch.nn import functional as F
|
|
from transformers.modeling_outputs import BaseModelOutputWithPast
|
|
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache, \
|
|
should_use_compresskv, get_compresskv_attn_mask
|
|
from ipex_llm.transformers.models.utils import update_past_key_value
|
|
from ipex_llm.transformers.models.utils import should_use_fuse_rope
|
|
from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_causal
|
|
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, SILU
|
|
from ipex_llm.transformers.models.utils import mlp_fusion_check
|
|
from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
|
|
from ipex_llm.transformers.kv import DynamicCompressFp8Cache, DynamicCompressCache
|
|
from ipex_llm.transformers.models.utils import extend_kv_cache, append_kv_cache
|
|
import warnings
|
|
import os
|
|
|
|
KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
|
|
|
|
def pre_compute_inv_freq(module: torch.nn.Module):
|
|
if module.__class__.__name__ == "RotaryEmbedding":
|
|
inv_freq = module.inv_freq
|
|
del module.inv_freq
|
|
module.register_buffer("inv_freq", inv_freq, persistent=False)
|
|
|
|
|
|
def baichuan_13b_rms_norm_forward(self, hidden_states):
|
|
if hidden_states.device.type == "xpu" and not (self.training or 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.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.epsilon)
|
|
return self.weight * hidden_states.to(input_dtype)
|
|
|
|
|
|
def baichuan_mlp_forward(
|
|
self,
|
|
x: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
x_2d = x.view(-1, x.shape[-1])
|
|
qtype = getattr(self.gate_proj, "qtype", None)
|
|
if mlp_fusion_check(x_2d, qtype, self.training) and not self.down_proj.enable_xetla:
|
|
import xe_linear
|
|
if not x_2d.is_contiguous():
|
|
x_2d = x_2d.contiguous()
|
|
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_len,
|
|
SILU, qtype
|
|
))
|
|
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
|
|
|
def baichuan_model_7b_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,
|
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if use_cache:
|
|
inputs = input_ids if input_ids is not None else inputs_embeds
|
|
use_compress_kv = should_use_compresskv(inputs, inputs.shape[1])
|
|
use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.up_proj, inputs)
|
|
if use_compress_kv and not isinstance(past_key_values,
|
|
DynamicCompressCache):
|
|
if use_quantize_kv:
|
|
past_key_values = DynamicCompressFp8Cache.from_legacy_cache(past_key_values)
|
|
else:
|
|
past_key_values = DynamicCompressCache.from_legacy_cache(past_key_values)
|
|
|
|
# retrieve input_ids and inputs_embeds
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
batch_size, seq_length = input_ids.shape
|
|
elif inputs_embeds is not None:
|
|
batch_size, seq_length, _ = inputs_embeds.shape
|
|
else:
|
|
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
|
|
|
seq_length_with_past = seq_length
|
|
past_key_values_length = 0
|
|
|
|
if past_key_values is not None:
|
|
if isinstance(past_key_values, DynamicCompressCache):
|
|
past_key_values_length = past_key_values.get_seq_length()
|
|
else:
|
|
past_key_values_length = past_key_values[0][0].shape[2]
|
|
seq_length_with_past = seq_length_with_past + past_key_values_length
|
|
|
|
if position_ids is None:
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
position_ids = torch.arange(
|
|
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
|
)
|
|
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
|
else:
|
|
position_ids = position_ids.view(-1, seq_length).long()
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
# embed positions
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(
|
|
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
|
|
)
|
|
attention_mask = self._prepare_decoder_attention_mask(
|
|
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
use_cache = False
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
next_decoder_cache = () if use_cache else None
|
|
|
|
use_compresskv = isinstance(past_key_values, DynamicCompressCache)
|
|
|
|
# if not past_key_values and not use_compresskv:
|
|
# past_key_values = [None for _ in range(self.num_layers)]
|
|
|
|
for idx, decoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
if not use_compresskv:
|
|
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
# None for past_key_value
|
|
return module(*inputs, output_attentions, None)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(decoder_layer),
|
|
hidden_states,
|
|
attention_mask,
|
|
position_ids,
|
|
None,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values if use_compresskv else past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if use_cache:
|
|
if use_compresskv:
|
|
next_decoder_cache = past_key_values
|
|
else:
|
|
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = next_decoder_cache if use_cache else None
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
|
|
def baichuan_attention_forward_7b(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
):
|
|
bsz, q_len, _ = hidden_states.size()
|
|
device = hidden_states.device
|
|
|
|
use_compresskv = isinstance(past_key_value, DynamicCompressCache)
|
|
|
|
qkv = self.W_pack(hidden_states)
|
|
qkv = qkv.view(bsz, q_len, self.num_heads * 3, self.head_dim)
|
|
qkv = qkv.transpose(1, 2)
|
|
query_states, key_states, value_states = qkv.split([self.num_heads,
|
|
self.num_heads,
|
|
self.num_heads], dim=1)
|
|
|
|
kv_seq_len = key_states.shape[2]
|
|
if past_key_value is not None:
|
|
if use_compresskv:
|
|
kv_seq_len += past_key_value.get_usable_length(kv_seq_len,
|
|
self.layer_idx)
|
|
else:
|
|
kv_seq_len += past_key_value[0].shape[2]
|
|
|
|
# IPEX-LLM OPT: fuse rope
|
|
if should_use_fuse_rope(hidden_states, position_ids, self.training):
|
|
import xe_addons
|
|
xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids,
|
|
query_states, key_states)
|
|
else:
|
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
|
|
cos, sin, position_ids, "baichuan")
|
|
query_states = query_states.to(hidden_states.dtype)
|
|
key_states = key_states.to(hidden_states.dtype)
|
|
|
|
# IPEX-LLM OPT: kv cache and quantize kv
|
|
use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_states)
|
|
if use_quantize_kv or (not use_compresskv):
|
|
key_states, value_states = update_past_key_value(
|
|
past_key_value, key_states, value_states,
|
|
kv_seq_len, use_quantize_kv, device
|
|
)
|
|
past_key_value = (key_states, value_states) if use_cache else None
|
|
|
|
else:
|
|
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value,
|
|
self.layer_idx,
|
|
q_len)
|
|
key_states, value_states = past_key_value.update(
|
|
key_states, value_states, self.layer_idx,
|
|
query_states, attention_mask, 1,
|
|
self.config, enough_kv_room, KV_CACHE_ALLOC_BLOCK_LENGTH)
|
|
|
|
if self.training:
|
|
warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
|
|
|
|
# IPEX-LLM OPT: sdp
|
|
attn_weights = None
|
|
if not self.training and not hidden_states.requires_grad and \
|
|
use_flash_attention(query_states, key_states, attention_mask):
|
|
attn_output = F.scaled_dot_product_attention(query_states.to(dtype=torch.float16),
|
|
key_states.to(dtype=torch.float16),
|
|
value_states.to(dtype=torch.float16),
|
|
is_causal=True).to(hidden_states.dtype)
|
|
elif use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
|
|
import xe_addons
|
|
if use_quantize_kv:
|
|
attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
|
|
attention_mask)
|
|
elif use_compresskv:
|
|
attention_mask = get_compresskv_attn_mask(key_states, attention_mask)
|
|
attn_output = xe_addons.sdp(query_states, key_states, value_states,
|
|
attention_mask)
|
|
else:
|
|
attn_output = xe_addons.sdp(query_states, key_states, value_states,
|
|
attention_mask)
|
|
elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
|
|
import xe_addons
|
|
if use_quantize_kv:
|
|
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 use_quantize_kv:
|
|
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
|
query_states.dtype)
|
|
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_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 baichuan_attention_forward_13b(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
bsz, q_len, _ = hidden_states.size()
|
|
device = hidden_states.device
|
|
|
|
qkv = self.W_pack(hidden_states)
|
|
qkv = qkv.view(bsz, q_len, self.num_heads * 3, self.head_dim)
|
|
qkv = qkv.transpose(1, 2)
|
|
query_states, key_states, value_states = qkv.split([self.num_heads,
|
|
self.num_heads,
|
|
self.num_heads], dim=1)
|
|
|
|
kv_seq_len = key_states.shape[2]
|
|
if past_key_value is not None:
|
|
kv_seq_len += past_key_value[0].shape[2]
|
|
|
|
# IPEX-LLM OPT: kv cache and quantize kv
|
|
use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_states)
|
|
key_states, value_states = update_past_key_value(
|
|
past_key_value, key_states, value_states,
|
|
kv_seq_len, use_quantize_kv, device
|
|
)
|
|
past_key_value = (key_states, value_states) if use_cache else None
|
|
|
|
if self.training:
|
|
warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
|
|
|
|
if attention_mask is not None:
|
|
if len(attention_mask.size()) == 4:
|
|
attention_mask = attention_mask[:, :, -q_len:, :]
|
|
else:
|
|
attention_mask = attention_mask[None, :, -q_len:, :]
|
|
|
|
if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
|
|
import xe_addons
|
|
if use_quantize_kv:
|
|
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)
|
|
attn_weights = None
|
|
else:
|
|
if use_quantize_kv:
|
|
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
|
query_states.dtype)
|
|
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
|
|
attn_weights = attn_weights.to(query_states.dtype)
|
|
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
|
|
attn_output = torch.matmul(attn_weights.to(dtype=value_states.dtype), value_states)
|
|
attn_output = attn_output.transpose(1, 2)
|
|
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 _get_interleave(n):
|
|
def _get_interleave_power_of_2(n):
|
|
start = 2 ** (-(2 ** -(math.log2(n) - 3)))
|
|
ratio = start
|
|
return [start * ratio**i for i in range(n)]
|
|
|
|
if math.log2(n).is_integer():
|
|
return _get_interleave_power_of_2(n)
|
|
else:
|
|
closest_power_of_2 = 2 ** math.floor(math.log2(n))
|
|
return (
|
|
_get_interleave_power_of_2(closest_power_of_2)
|
|
+ _get_interleave(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
|
|
)
|
|
|
|
|
|
def _fill_with_neg_inf(t):
|
|
"""FP16-compatible function that fills a tensor with -inf."""
|
|
return t.float().fill_(float("-inf")).type_as(t)
|
|
|
|
|
|
def _buffered_future_mask(tensor, maxpos, alibi, attn_heads):
|
|
_future_mask = torch.triu(_fill_with_neg_inf(torch.zeros([maxpos, maxpos])), 1)
|
|
_future_mask = _future_mask.unsqueeze(0) + alibi
|
|
new_future_mask = _future_mask.to(tensor)
|
|
return new_future_mask[: tensor.shape[0] * attn_heads, :maxpos, :maxpos]
|
|
|
|
|
|
def baichuan_13b_gen_alibi_mask(tensor, n_head, max_pos):
|
|
slopes = torch.Tensor(_get_interleave(n_head)).to(tensor.dtype)
|
|
position_point = torch.arange(max_pos) - max_pos + 1
|
|
position_point = position_point.unsqueeze(0).unsqueeze(0).expand(n_head, -1, -1)
|
|
diag = torch.diag(position_point[0])
|
|
position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(-1, -2)
|
|
alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
|
|
alibi = alibi.view(n_head, 1, max_pos)
|
|
alibi_mask = torch.triu(
|
|
_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1).to(tensor.dtype)
|
|
alibi_mask = alibi_mask.unsqueeze(0) + alibi
|
|
if tensor.device.type == "xpu":
|
|
alibi_mask = alibi_mask.to(tensor.device)
|
|
return alibi_mask
|
|
|
|
|
|
MASK_BLOCK_SIZE = 512
|
|
|
|
|
|
def baichuan_13b_get_alibi_mask(self, tensor, seq_length_with_past):
|
|
if self.training:
|
|
slopes = torch.Tensor(_get_interleave(self.n_head))
|
|
position_point = (
|
|
torch.arange(seq_length_with_past) - seq_length_with_past + 1
|
|
)
|
|
position_point = (
|
|
position_point.unsqueeze(0)
|
|
.unsqueeze(0)
|
|
.expand(self.n_head, seq_length_with_past, -1)
|
|
)
|
|
diag = torch.diag(position_point[0])
|
|
position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(
|
|
-1, -2
|
|
)
|
|
alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
|
|
mask = _buffered_future_mask(
|
|
tensor, seq_length_with_past, alibi, self.n_head
|
|
)
|
|
else:
|
|
if self.first_run:
|
|
# Override the default max_cache_pos=4096 for memory considerations
|
|
self.max_cache_pos = seq_length_with_past + MASK_BLOCK_SIZE
|
|
self.first_run = False
|
|
self.register_buffer(
|
|
"future_mask",
|
|
baichuan_13b_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos),
|
|
persistent=False,
|
|
)
|
|
if seq_length_with_past > self.max_cache_pos:
|
|
# When max_cache_pos is not enough for current sequence length,
|
|
# increase by MASK_BLOCK_SIZE and recalculate future_mask.
|
|
self.max_cache_pos = seq_length_with_past + MASK_BLOCK_SIZE
|
|
self.register_buffer(
|
|
"future_mask",
|
|
baichuan_13b_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos),
|
|
persistent=False,
|
|
)
|
|
mask = self.future_mask[
|
|
: self.n_head, :seq_length_with_past, :seq_length_with_past
|
|
]
|
|
return mask
|