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ipex-llm/python/llm/src/ipex_llm/transformers/models/qwen.py
Shaojun Liu a10f5a1b8d
add python style check (#10620) 
* add python style check

* fix style checks

* update runner

* add ipex-llm-finetune-qlora-cpu-k8s to manually_build workflow

* update tag to 2.1.0-SNAPSHOT
2024-04-02 16:17:56 +08:00

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#
# 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/Qwen/Qwen-7B-Chat/blob/be72f02dd47087f9035ee9bb5dea571b84785d27/modeling_qwen.py
#
# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import importlib
import math
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from transformers.utils import logging
try:
from einops import rearrange
except ImportError:
rearrange = None
from ipex_llm.transformers.models.utils import extend_kv_cache, init_kv_cache, append_kv_cache
from ipex_llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_cache, \
restore_fp8_kv_cache, use_quantize_kv_cache
from ipex_llm.transformers.models.utils import rotate_half, SILU
from ipex_llm.transformers.models.utils import mlp_fusion_check
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_cache_freq_xpu
from ipex_llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
from ipex_llm.transformers.models.utils import decoding_fast_path_qtype_check
from ipex_llm.utils.common import invalidInputError, invalidOperationError
from ipex_llm.ggml.quantize import ggml_tensor_qtype
from transformers.modeling_outputs import BaseModelOutputWithPast
apply_rotary_emb_func = None
flash_attn_unpadded_func = None
logger = logging.get_logger(__name__)
KV_CACHE_ALLOC_BLOCK_LENGTH = 256
SUPPORT_TORCH2 = hasattr(torch, '__version__') and int(torch.__version__.split(".")[0]) >= 2
def apply_rotary_pos_emb(t, freqs):
cos, sin = freqs
rot_dim = freqs[0].shape[-1]
cos, sin = freqs
t_, t_pass_ = t[..., :rot_dim], t[..., rot_dim:]
t_ = t_.float()
t_pass_ = t_pass_.float()
t_ = (t_ * cos) + (rotate_half(t_) * sin)
return torch.cat((t_, t_pass_), dim=-1).type_as(t)
def should_use_fuse_rope(self, query_states):
use_fuse_rope = query_states.device.type == "xpu"
use_fuse_rope = use_fuse_rope and not (self.training and query_states.requires_grad)
return use_fuse_rope
def is_enough_kv_cache_room(layer_past, kv_seq_len=1):
# to determinate if is enough kv cache room in transformers between 4.31 and 4.35
# seq_len for current seq len
# For llama like kv cache, i.e., [bs, n_head, seq_len, head_dim]
if layer_past is None:
return False
else:
cache_k, cache_v = layer_past[0], layer_past[1]
cache_k = cache_k.transpose(1, 2)
cache_v = cache_v.transpose(1, 2)
return cache_k.stride(1) < (kv_seq_len + 1) * cache_k.size(3)
def qwen_attention_forward(
self,
hidden_states: Optional[Tuple[torch.FloatTensor]],
rotary_pos_emb_list: Optional[List[torch.Tensor]] = None,
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if use_quantize_kv_cache(self.q_proj, hidden_states):
forward_function = qwen_attention_forward_quantized
else:
forward_function = qwen_attention_forward_original
return forward_function(
self,
hidden_states,
rotary_pos_emb_list,
layer_past,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
output_attentions,
use_cache,
)
def qwen_attention_forward_original(
self,
hidden_states: Optional[Tuple[torch.FloatTensor]],
rotary_pos_emb_list: Optional[List[torch.Tensor]] = None,
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
):
invalidInputError(not self.use_flash_attn and not self.use_cache_quantization,
"flash attn and kv_cache quantization are not supported")
bsz, q_len, _ = hidden_states.size()
device = hidden_states.device
# for flash attention
original_dtype = hidden_states.dtype
position_ids = rotary_pos_emb_list[-1] # the last one is posisiton_ids
rotary_pos_emb_list = rotary_pos_emb_list[:-1]
use_fuse_rope = should_use_fuse_rope(self, hidden_states)
qtype_check = decoding_fast_path_qtype_check(self.q_proj)
decoding_fast_path = (qtype_check and use_fuse_rope and bsz * q_len == 1)
if decoding_fast_path:
hidden_states = hidden_states.view(1, -1)
cache_k, cache_v = layer_past[0], layer_past[1]
cache_k = cache_k.transpose(1, 2)
cache_v = cache_v.transpose(1, 2)
kv_seq_len = cache_k.shape[-2]
base = self.rope_base
if is_enough_kv_cache_room(layer_past, kv_seq_len):
new_cache_k, new_cache_v = extend_kv_cache(bsz,
self.num_heads,
self.head_dim,
cache_k.size(2),
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
dtype=cache_k.dtype,
device=hidden_states.device)
new_cache_k[:] = cache_k
new_cache_v[:] = cache_v
cache_k = new_cache_k
cache_v = new_cache_v
args = [hidden_states, self.q_proj.weight.data, self.k_proj.weight.data,
self.v_proj.weight.data, self.q_proj.bias.data, self.k_proj.bias.data,
self.v_proj.bias.data, position_ids, cache_k, cache_v, self.q_proj.weight.qtype,
self.v_proj.weight.qtype, kv_seq_len, self.head_dim, base]
import linear_q4_0
query, key, value = linear_q4_0.forward_qkv_bias(*args)
kv_seq_len += 1
query_size, key_size = 1, 1
else:
query = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
key = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
value = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
# TODO: speed up
# mixed_x_layer = self.c_attn(hidden_states)
# query, key, value = mixed_x_layer.split(self.split_size, dim=2)
# query = self._split_heads(query, self.num_heads, self.head_dim)
# key = self._split_heads(key, self.num_heads, self.head_dim)
# value = self._split_heads(value, self.num_heads, self.head_dim)
if len(rotary_pos_emb_list) != 0:
cur_len = query.shape[1]
if len(rotary_pos_emb_list) == 1:
rotary_pos_emb = rotary_pos_emb_list[0]
rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
if use_fuse_rope:
cos, sin = rotary_pos_emb
cos = cos.to(query.dtype)
sin = sin.to(query.dtype)
query, key = apply_rotary_pos_emb_cache_freq_xpu(query, key, sin, cos, "qwen")
else:
rotary_pos_emb = (rotary_pos_emb,) * 2
q_pos_emb, k_pos_emb = rotary_pos_emb
# Slice the pos emb for current inference
query = apply_rotary_pos_emb(query, q_pos_emb)
key = apply_rotary_pos_emb(key, k_pos_emb)
else:
query_list = []
key_list = []
for i, rotary_pos_emb in enumerate(rotary_pos_emb_list):
rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
if use_fuse_rope:
cos, sin = rotary_pos_emb
cos = cos.to(query.dtype)
sin = sin.to(query.dtype)
query, key = apply_rotary_pos_emb_cache_freq_xpu(query, key,
sin, cos, "qwen")
query_list += [query]
key_list += [key]
else:
rotary_pos_emb = (rotary_pos_emb,) * 2
q_pos_emb, k_pos_emb = rotary_pos_emb
# Slice the pos emb for current inference
query_list += [apply_rotary_pos_emb(query[i:i+1, :, :], q_pos_emb)]
key_list += [apply_rotary_pos_emb(key[i:i+1, :, :], k_pos_emb)]
query = torch.cat(query_list, dim=0)
key = torch.cat(key_list, dim=0)
query_size, key_size = query.size(1), key.size(1)
kv_seq_len = key_size if layer_past is None else key_size + layer_past[0].size(1)
if kv_seq_len > self.seq_length and self.use_logn_attn and not self.training:
seq_start = kv_seq_len - query_size
seq_end = kv_seq_len
logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :].type_as(query)
query = query * logn_tensor.expand_as(query)
if query_size > 1:
causal_mask = torch.tril(
torch.ones((kv_seq_len, kv_seq_len), dtype=torch.bool, device=query.device)
).view(1, 1, kv_seq_len, kv_seq_len)
causal_mask = causal_mask[
:, :, kv_seq_len - query_size:kv_seq_len, :kv_seq_len
]
else:
causal_mask = None
if layer_past is not None:
if not decoding_fast_path:
cache_k, cache_v = layer_past[0], layer_past[1]
cache_k = cache_k.transpose(1, 2)
cache_v = cache_v.transpose(1, 2)
if cache_k.stride(1) < kv_seq_len * cache_k.size(3):
new_cache_k, new_cache_v = extend_kv_cache(bsz,
self.num_heads,
self.head_dim,
cache_k.size(2),
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
dtype=cache_k.dtype,
device=hidden_states.device)
new_cache_k[:] = cache_k
new_cache_v[:] = cache_v
cache_k = new_cache_k
cache_v = new_cache_v
key_states, value_states = append_kv_cache(cache_k, cache_v,
key.transpose(1, 2), value.transpose(1, 2))
key = key_states
value = value_states
elif use_cache:
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
new_key_states, new_value_states = init_kv_cache(bsz,
self.num_heads,
self.head_dim,
kv_seq_len,
max_cache_length,
dtype=key.dtype,
device=hidden_states.device)
new_key_states[:] = key.transpose(1, 2)
new_value_states[:] = value.transpose(1, 2)
key = new_key_states
value = new_value_states
if not decoding_fast_path:
query = query.transpose(1, 2)
if not self.training and not hidden_states.requires_grad and \
use_flash_attention(query, key):
attn_output = F.scaled_dot_product_attention(query.to(device, dtype=torch.float16),
key.to(device, dtype=torch.float16),
value.to(device, dtype=torch.float16),
is_causal=True)
attn_output = attn_output.view(query.shape)
attn_output = attn_output.transpose(1, 2)
attn_weights = None
elif not self.training and not hidden_states.requires_grad and \
use_esimd_sdp(q_len, key.shape[2], self.head_dim, query):
import linear_fp16_esimd
attn_output = linear_fp16_esimd.sdp_forward(query,
key,
value)
attn_output = attn_output.view(query.shape)
attn_output = attn_output.transpose(1, 2)
attn_weight = None
else:
attn_output, attn_weight = self._attn(
query.to(key.dtype), key, value, causal_mask, attention_mask, head_mask
)
context_layer = self._merge_heads(
attn_output, self.num_heads, self.head_dim
)
attn_output = self.c_proj(context_layer).to(original_dtype)
if use_cache:
outputs = (attn_output, (key.transpose(1, 2), value.transpose(1, 2)))
else:
outputs = (attn_output, None)
if output_attentions:
outputs += (attn_weight,)
return outputs
def qwen_attention_forward_quantized(
self,
hidden_states: Optional[Tuple[torch.FloatTensor]],
rotary_pos_emb_list: Optional[List[torch.Tensor]] = None,
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
):
invalidInputError(not self.use_flash_attn and not self.use_cache_quantization,
"flash attn and kv_cache quantization are not supported")
bsz, q_len, _ = hidden_states.size()
device = hidden_states.device
position_ids = rotary_pos_emb_list[-1] # the last one is posisiton_ids
rotary_pos_emb_list = rotary_pos_emb_list[:-1]
use_fuse_rope = should_use_fuse_rope(self, hidden_states)
# qtype_check = decoding_fast_path_qtype_check(self.q_proj)
# TODO: use when decoding_fast_path = (qtype_check and use_fuse_rope and bsz * q_len == 1)
decoding_fast_path = False
if decoding_fast_path:
hidden_states = hidden_states.view(1, -1)
tmp_cache_k, tmp_cache_v = init_kv_cache(
bsz,
self.num_heads,
self.head_dim,
0,
1,
dtype=hidden_states.dtype,
device=device
)
base = self.rope_base
args = [hidden_states, self.q_proj.weight.data, self.k_proj.weight.data,
self.v_proj.weight.data, self.q_proj.bias.data, self.k_proj.bias.data,
self.v_proj.bias.data, position_ids, tmp_cache_k, tmp_cache_v,
self.q_proj.weight.qtype, self.v_proj.weight.qtype, 0, self.head_dim, base]
import linear_q4_0
query, key, value = linear_q4_0.forward_qkv_bias(*args)
self.kv_seq_len += 1
kv_seq_len = self.kv_seq_len
query_size, key_size = 1, 1
else:
query = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
key = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
value = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
# TODO: speed up
# mixed_x_layer = self.c_attn(hidden_states)
# query, key, value = mixed_x_layer.split(self.split_size, dim=2)
# query = self._split_heads(query, self.num_heads, self.head_dim)
# key = self._split_heads(key, self.num_heads, self.head_dim)
# value = self._split_heads(value, self.num_heads, self.head_dim)
if rotary_pos_emb_list is not None:
cur_len = query.shape[1]
if len(rotary_pos_emb_list) == 1:
rotary_pos_emb = rotary_pos_emb_list[0]
rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
if use_fuse_rope:
cos, sin = rotary_pos_emb
cos = cos.to(query.dtype)
sin = sin.to(query.dtype)
query, key = apply_rotary_pos_emb_cache_freq_xpu(query, key, sin, cos, "qwen")
else:
rotary_pos_emb = (rotary_pos_emb,) * 2
q_pos_emb, k_pos_emb = rotary_pos_emb
# Slice the pos emb for current inference
query = apply_rotary_pos_emb(query, q_pos_emb)
key = apply_rotary_pos_emb(key, k_pos_emb)
else:
query_list = []
key_list = []
for i, rotary_pos_emb in enumerate(rotary_pos_emb_list):
rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
if use_fuse_rope:
cos, sin = rotary_pos_emb
cos = cos.to(query.dtype)
sin = sin.to(query.dtype)
query, key = apply_rotary_pos_emb_cache_freq_xpu(query, key,
sin, cos, "qwen")
query_list += [query]
key_list += [key]
else:
rotary_pos_emb = (rotary_pos_emb,) * 2
q_pos_emb, k_pos_emb = rotary_pos_emb
# Slice the pos emb for current inference
query_list += [apply_rotary_pos_emb(query[i:i+1, :, :], q_pos_emb)]
key_list += [apply_rotary_pos_emb(key[i:i+1, :, :], k_pos_emb)]
query = torch.cat(query_list, dim=0)
key = torch.cat(key_list, dim=0)
query_size, key_size = query.size(1), key.size(1)
kv_seq_len = key_size if layer_past is None else key_size + layer_past[0].size(1)
if kv_seq_len > self.seq_length and self.use_logn_attn and not self.training:
seq_start = kv_seq_len - query_size
seq_end = kv_seq_len
logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :].type_as(query)
query = query * logn_tensor.expand_as(query)
if query_size > 1:
causal_mask = torch.tril(
torch.ones((kv_seq_len, kv_seq_len), dtype=torch.bool, device=query.device)
).view(1, 1, kv_seq_len, kv_seq_len)
causal_mask = causal_mask[
:, :, kv_seq_len - query_size:kv_seq_len, :kv_seq_len
]
else:
causal_mask = None
if layer_past is None:
query, key, value = query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2)
# query, key, value's shape: [bs, num_heads, seq_len, head_dim]
# save kv seq len for decoding_fast_path
self.kv_seq_len = key.shape[-2]
# For first token, use original attn
attn_output, attn_weight = self._attn(
query, key, value, causal_mask, attention_mask, head_mask
)
if use_cache:
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
k_cache, v_cache = init_fp8_kv_cache(
query.size(0), self.num_heads, kv_seq_len, self.head_dim,
device=query.device, new_layout=True
)
key, value = append_fp8_kv_cache(k_cache, v_cache, key, value)
else:
if decoding_fast_path:
k_cache, v_cache = layer_past[0], layer_past[1]
# k_cache and v_cache's shape: [bs, num_heads, context_length, head_dim]
else:
query, key, value = query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2)
k_cache, v_cache = layer_past[0], layer_past[1]
k_cache = k_cache.transpose(1, 2)
v_cache = v_cache.transpose(1, 2)
# k_cache and v_cache's shape: [bs, num_heads, context_length, head_dim]
key, value = append_fp8_kv_cache(k_cache, v_cache, key, value, new_layout=True)
attn_output, attn_weight = core_attn(
self, query, key, value, causal_mask, attention_mask, head_mask
)
context_layer = self._merge_heads(
attn_output, self.num_heads, self.head_dim
)
attn_output = self.c_proj(context_layer)
if use_cache:
outputs = (attn_output, (key.transpose(1, 2), value.transpose(1, 2)))
else:
outputs = (attn_output, None)
if output_attentions:
outputs += (attn_weight,)
return outputs
def core_attn(self, query, key, value, causal_mask=None, attention_mask=None, head_mask=None):
if query.size(2) != 1 or query.device.type != 'xpu':
# We have no CPU fp8 matmul implementation for now, so just upscale to fp32
key, value = restore_fp8_kv_cache(key, value, query.dtype)
attn_weights = torch.matmul(query, key.transpose(-1, -2))
if self.scale_attn_weights:
if self.use_cache_quantization:
size_temp = value[0].size(-1)
else:
size_temp = value.size(-1)
attn_weights = attn_weights / (size_temp ** 0.5)
mask_value = torch.finfo(attn_weights.dtype).min
if causal_mask is not None:
attn_weights = torch.where(
causal_mask, attn_weights.to(attn_weights.dtype), mask_value
)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
if self.softmax_in_fp32:
attn_weights = torch.nn.functional.softmax(attn_weights.float(), dim=-1)
else:
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
attn_weights = attn_weights.type(query.dtype)
attn_weights = self.attn_dropout(attn_weights)
if head_mask is not None:
attn_weights = attn_weights * head_mask
# We have no CPU fp8 matmul implementation for now, so just upscale to fp32
attn_output = torch.matmul(attn_weights, value)
else:
import linear_q4_0
attn_output = linear_q4_0.sdp_fp8(query, key, value,
attention_mask)
attn_weights = None
attn_output = attn_output.transpose(1, 2)
return attn_output, attn_weights
def qwen_mlp_forward(self, x: torch.Tensor) -> torch.Tensor:
x_2d = x.view(-1, x.shape[-1])
qtype = getattr(self.w1, "qtype", None)
if mlp_fusion_check(x_2d, qtype, self.training) and not self.w1.enable_xetla:
import linear_q4_0
if not x_2d.is_contiguous():
x_2d = x_2d.contiguous()
return self.c_proj(linear_q4_0.mlp_forward_xpu(
x_2d, self.w2.weight.data, self.w1.weight.data,
x_2d.shape[0], x_2d.shape[1], self.w2.out_len,
SILU, qtype
))
return self.c_proj(F.silu(self.w2(x)) * self.w1(x))
def qwen_model_forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: 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,
):
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 input_ids is not None and inputs_embeds is not None:
invalidInputError(
False,
"You cannot specify both input_ids and inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
invalidInputError(False, "You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
if self.use_cache_quantization:
past_length = past_key_values[0][0][0].size(2)
else:
past_length = past_key_values[0][0].size(1)
if position_ids is None:
position_ids = torch.arange(
past_length,
input_shape[-1] + past_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
if attention_mask is not None:
if batch_size <= 0:
invalidInputError(False, "batch_size has to be defined and > 0")
attention_mask = attention_mask.view(batch_size, -1)
attention_mask = attention_mask[:, None, None, :]
attention_mask = attention_mask.to(dtype=self.dtype)
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
encoder_attention_mask = None
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
hidden_states = inputs_embeds
kv_seq_len = hidden_states.size()[1]
if past_key_values[0] is not None:
# past key values[0][0] shape: bs * seq_len * head_num * dim
if self.use_cache_quantization:
kv_seq_len += past_key_values[0][0][0].shape[2]
else:
kv_seq_len += past_key_values[0][0].shape[1]
if self.training or not self.use_dynamic_ntk:
ntk_alpha_list = [1.0]
elif kv_seq_len != hidden_states.size()[1]:
ntk_alpha_list = self.rotary_emb._ntk_alpha_cached_list
else:
ntk_alpha_list = []
if attention_mask is not None and kv_seq_len > self.seq_length:
true_seq_lens = attention_mask.squeeze(1).squeeze(1).eq(0).sum(dim=-1,
dtype=torch.int32)
for i in range(hidden_states.size()[0]):
true_seq_len = true_seq_lens[i].item()
ntk_alpha = self.get_ntk_alpha(true_seq_len)
ntk_alpha_list.append(ntk_alpha)
else:
ntk_alpha = self.get_ntk_alpha(kv_seq_len)
ntk_alpha_list.append(ntk_alpha)
self.rotary_emb._ntk_alpha_cached_list = ntk_alpha_list
rotary_pos_emb_list = [
self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for ntk_alpha in ntk_alpha_list
] + [position_ids]
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. "
"Setting `use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, use_cache, output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
rotary_pos_emb_list,
None,
attention_mask,
head_mask[i],
encoder_hidden_states,
encoder_attention_mask,
)
else:
# bigdl-llm changes
curr_device = block.ln_1.weight.device
from accelerate.utils.operations import send_to_device
if rotary_pos_emb_list is not None:
rotary_pos_emb_list = send_to_device(rotary_pos_emb_list, curr_device)
if attention_mask is not None:
attention_mask = send_to_device(attention_mask, curr_device)
if head_mask[i] is not None:
head_mask[i] = send_to_device(head_mask[i], curr_device)
if encoder_hidden_states is not None:
encoder_hidden_states = send_to_device(encoder_hidden_states, curr_device)
if encoder_attention_mask is not None:
encoder_attention_mask = send_to_device(encoder_attention_mask,
curr_device)
# bigdl-llm changes ends
outputs = block(
hidden_states,
layer_past=layer_past,
rotary_pos_emb_list=rotary_pos_emb_list,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v for v in [hidden_states, presents, all_hidden_states] if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
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