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mixstral fused qkv and rope (#9724)

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* mixstral fused qkv and rope

* fix and clean

* fix style

* update

* update

* fix

* update

* fix
This commit is contained in:
Xin Qiu 2023-12-22 09:26:35 +08:00 committed by GitHub
parent e4f6e43675
commit e36111e713
4 changed files with 108 additions and 65 deletions
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9
python/llm/src/bigdl/llm/transformers/models/llama.py
View file

@ -39,7 +39,7 @@ import math
import torch.nn.functional as F
from bigdl.llm.utils.common import invalidInputError
from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
from bigdl.llm.transformers.models.utils import rotate_half, apply_rotary_pos_emb
from bigdl.llm.transformers.models.utils import is_enough_kv_cache_room_4_31, apply_rotary_pos_emb
from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
from bigdl.llm.transformers.low_bit_linear import SYM_INT4
from bigdl.llm.ggml.quantize import ggml_tensor_qtype
@ -111,11 +111,6 @@ def llama_mlp_forward(
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
def is_enough_kv_cache_room(past_key_value):
return past_key_value is not None and \
past_key_value[0].stride()[1] > past_key_value[0].size(2) * past_key_value[0].size(3)
def should_use_fuse_rope(self, query_states, position_ids):
use_fuse_rope = query_states.device.type == "xpu"
use_fuse_rope = use_fuse_rope and not (self.training and query_states.requires_grad)
@ -149,7 +144,7 @@ def llama_attention_forward_4_31(
attention_dtype = original_dtype
use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
enough_kv_room = is_enough_kv_cache_room(past_key_value)
enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value)
is_q4_0 = self.q_proj.qtype == SYM_INT4
no_tp = not self.config.pretraining_tp > 1
decoding_fast_path = (no_tp and is_q4_0 and use_fuse_rope and

4
python/llm/src/bigdl/llm/transformers/models/mistral.py
View file

@ -44,7 +44,7 @@ from bigdl.llm.utils.common import invalidInputError
from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb,\
apply_rotary_pos_emb_no_cache_xpu
from bigdl.llm.transformers.models.llama import is_enough_kv_cache_room
from bigdl.llm.transformers.models.utils import is_enough_kv_cache_room_4_31
from bigdl.llm.transformers.low_bit_linear import SYM_INT4
KV_CACHE_ALLOC_BLOCK_LENGTH = 256
@ -89,7 +89,7 @@ def mistral_attention_forward(
device = hidden_states.device
use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
enough_kv_room = is_enough_kv_cache_room(past_key_value)
enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value)
decoding_fast_path = use_decoding_fast_path(self.q_proj.qtype,
use_fuse_rope,
enough_kv_room,

147
python/llm/src/bigdl/llm/transformers/models/mixtral.py
View file

@ -47,7 +47,8 @@ from bigdl.llm.ggml.quantize import ggml_tensor_qtype
from bigdl.llm.utils.common import invalidInputError
from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb,\
apply_rotary_pos_emb_no_cache_xpu
apply_rotary_pos_emb_no_cache_xpu, is_enough_kv_cache_room_4_36
from bigdl.llm.transformers.models.mistral import should_use_fuse_rope, use_decoding_fast_path
KV_CACHE_ALLOC_BLOCK_LENGTH = 256
@ -142,69 +143,103 @@ def mixtral_attention_forward(
bsz, q_len, _ = hidden_states.size()
device = hidden_states.device
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
decoding_fast_path = use_decoding_fast_path(self.q_proj.qtype,
use_fuse_rope,
enough_kv_room,
bsz * q_len)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len,
self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len,
self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
if self.layer_idx is None:
invalidInputError(False, "The cache structure has changed since version v4.36. "
f"If you are using {self.__class__.__name__} for "
"auto-regressive decodingwith k/v caching, please make sure "
"to initialize the attention class with a layer index.")
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
if query_states.device.type == "xpu" and not (self.training and query_states.requires_grad):
query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
key_states,
position_ids,
"mixtral")
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, "mixtral")
if past_key_value is not None:
# update the number of seen tokens
if decoding_fast_path:
hidden_states = hidden_states.view(1, -1)
cache_k = past_key_value.key_cache[self.layer_idx]
cache_v = past_key_value.value_cache[self.layer_idx]
kv_seq_len = cache_k.shape[-2]
import linear_q4_0
query_states, key_states, value_states = linear_q4_0.forward_qkv(hidden_states,
self.q_proj.weight,
self.k_proj.weight,
self.v_proj.weight,
position_ids,
cache_k, cache_v,
self.q_proj.weight.qtype,
kv_seq_len,
self.head_dim)
kv_seq_len += 1
# update past_key_value's seem_tokens and kv caches.
if self.layer_idx == 0:
past_key_value.seen_tokens += key_states.shape[-2]
past_key_value.seen_tokens = kv_seq_len
past_key_value.key_cache[self.layer_idx] = key_states
past_key_value.value_cache[self.layer_idx] = value_states
# reuse k, v, self_attention
# update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
if len(past_key_value.key_cache) <= self.layer_idx:
past_key_value.key_cache.append(key_states)
past_key_value.value_cache.append(value_states)
else:
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len,
self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len,
self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
if self.layer_idx is None:
invalidInputError(False,
"The cache structure has changed since version v4.36. "
f"If you are using {self.__class__.__name__} for "
"auto-regressive decodingwith k/v caching, please make sure "
"to initialize the attention class with a layer index.")
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
if use_fuse_rope:
query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
key_states,
position_ids,
"mixtral")
else:
cache_k = past_key_value.key_cache[self.layer_idx]
cache_v = past_key_value.value_cache[self.layer_idx]
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, "mixtral")
if cache_k.stride()[1] <= cache_k.size(2) * cache_k.size(3):
# allocate new
new_cache_k, new_cache_v = extend_kv_cache(bsz,
self.num_key_value_heads, # Support GQA
self.head_dim,
cache_k.size(2),
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
dtype=cache_k.dtype,
device=device)
if past_key_value is not None:
# update the number of seen tokens
if self.layer_idx == 0:
past_key_value.seen_tokens += key_states.shape[-2]
new_cache_k[:] = cache_k
new_cache_v[:] = cache_v
cache_k = new_cache_k
cache_v = new_cache_v
# reuse k, v, self_attention
# update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
if len(past_key_value.key_cache) <= self.layer_idx:
past_key_value.key_cache.append(key_states)
past_key_value.value_cache.append(value_states)
else:
cache_k = past_key_value.key_cache[self.layer_idx]
cache_v = past_key_value.value_cache[self.layer_idx]
key_states, value_states = append_kv_cache(cache_k, cache_v, key_states, value_states)
if not enough_kv_room:
# allocate new
new_c_k, new_c_v = extend_kv_cache(bsz,
self.num_key_value_heads, # Support GQA
self.head_dim,
cache_k.size(2),
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
dtype=cache_k.dtype,
device=device)
# update past_key_value
past_key_value.key_cache[self.layer_idx] = key_states
past_key_value.value_cache[self.layer_idx] = value_states
new_c_k[:] = cache_k
new_c_v[:] = cache_v
cache_k = new_c_k
cache_v = new_c_v
key_states, value_states = append_kv_cache(cache_k,
cache_v,
key_states,
value_states)
# update past_key_value
past_key_value.key_cache[self.layer_idx] = key_states
past_key_value.value_cache[self.layer_idx] = value_states
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)

13
python/llm/src/bigdl/llm/transformers/models/utils.py
View file

@ -106,3 +106,16 @@ def apply_rotary_pos_emb_no_cache_xpu(q, k, position_ids, model_family):
else:
invalidInputError(False,
f"{model_family} is not supported.")
def is_enough_kv_cache_room_4_36(past_key_value, idx):
# to determinate if is enough kv cache room in transformers==4.36
return past_key_value is not None and len(past_key_value.key_cache) > idx and \
past_key_value.key_cache[idx].stride()[1] > past_key_value.key_cache[idx].size(2) * \
past_key_value.key_cache[idx].size(3)
def is_enough_kv_cache_room_4_31(past_key_value):
# to determinate if is enough kv cache room in transformers between 4.31 and 4.35
return past_key_value is not None and \
past_key_value[0].stride()[1] > past_key_value[0].size(2) * past_key_value[0].size(3)