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[LLM] Support transformers-v4.36.0 on mistral model (#9744)

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* add support transformers-v4.36.0 on mistral model

* python/llm/src/bigdl/llm/transformers/models/mistral.py

* make the redundant implementation as utils

* fix code style

* fix

* fix style

* update with utils enough_kv_room
This commit is contained in:
SONG Ge 2023-12-22 09:59:27 +08:00 committed by GitHub
parent e36111e713
commit ba0b939579
2 changed files with 205 additions and 48 deletions
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41
python/llm/src/bigdl/llm/transformers/convert.py
View file

@ -652,19 +652,34 @@ def _optimize_post(model, lightweight_bmm=False):
module.MistralRMSNorm,
llama_rms_norm_forward)
else:
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from bigdl.llm.transformers.models.mistral import mistral_attention_forward
convert_forward(model,
module.MistralAttention,
mistral_attention_forward
)
convert_forward(model,
module.MistralRMSNorm,
llama_rms_norm_forward)
convert_forward(model,
module.MistralMLP,
llama_mlp_forward)
if version.parse(trans_version) >= version.parse("4.36.0"):
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from bigdl.llm.transformers.models.mistral import mistral_attention_forward_4_36
convert_forward(model,
module.MistralAttention,
mistral_attention_forward_4_36
)
convert_forward(model,
module.MistralRMSNorm,
llama_rms_norm_forward)
convert_forward(model,
module.MistralMLP,
llama_mlp_forward)
else:
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from bigdl.llm.transformers.models.mistral import mistral_attention_forward
convert_forward(model,
module.MistralAttention,
mistral_attention_forward
)
convert_forward(model,
module.MistralRMSNorm,
llama_rms_norm_forward)
convert_forward(model,
module.MistralMLP,
llama_mlp_forward)
elif model.config.model_type == "Yi":
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)

212
python/llm/src/bigdl/llm/transformers/models/mistral.py
View file

@ -44,7 +44,8 @@ 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.utils import is_enough_kv_cache_room_4_31
from bigdl.llm.transformers.models.utils import is_enough_kv_cache_room_4_31,\
is_enough_kv_cache_room_4_36
from bigdl.llm.transformers.low_bit_linear import SYM_INT4
KV_CACHE_ALLOC_BLOCK_LENGTH = 256
@ -75,6 +76,46 @@ def use_decoding_fast_path(q_type, use_fuse_rope, enough_kv_room, bs):
return q_type == SYM_INT4 and use_fuse_rope and enough_kv_room and bs == 1
def compute_attn_outputs_weights(query_states, key_states, value_states, bsz, q_len, kv_seq_len,
num_heads, head_dim, hidden_size, attention_mask):
attn_weights = torch.matmul(
query_states,
key_states.transpose(2, 3)) / math.sqrt(head_dim)
if attn_weights.size() != (bsz, num_heads, q_len, kv_seq_len):
invalidInputError(
False,
f"Attention weights should be of size {(bsz, num_heads, q_len, kv_seq_len)},"
f" but is {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
invalidInputError(
False,
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
f" but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = nn.functional.\
softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, num_heads, q_len, head_dim):
invalidInputError(
f"`attn_output` should be of size {(bsz, num_heads, q_len, head_dim)},"
f" but is {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, hidden_size)
return attn_output, attn_weights
def mistral_attention_forward(
self,
hidden_states: torch.Tensor,
@ -177,40 +218,141 @@ def mistral_attention_forward(
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 attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
invalidInputError(
False,
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)},"
f" but is {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
invalidInputError(
False,
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
f" but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = nn.functional.\
softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
invalidInputError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)},"
f" but is {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output, attn_weights = compute_attn_outputs_weights(query_states, key_states, value_states,
bsz, q_len, kv_seq_len,
self.num_heads, self.head_dim,
self.hidden_size, attention_mask)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def mistral_attention_forward_4_36(
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,
padding_mask: Optional[torch.Tensor]=None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
device = hidden_states.device
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)
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 = kv_seq_len
past_key_value.key_cache[self.layer_idx] = key_states
past_key_value.value_cache[self.layer_idx] = 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,
"mistral")
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, "mistral")
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]
# 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]
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)
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)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_output, attn_weights = compute_attn_outputs_weights(query_states, key_states, value_states,
bsz, q_len, kv_seq_len,
self.num_heads, self.head_dim,
self.hidden_size, attention_mask)
attn_output = self.o_proj(attn_output)