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optimize Chatglm4 (#11239)

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* chatglm4

* update

* update

* add rms norm

* chatglm4
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Xin Qiu 2024-06-06 18:25:20 +08:00 committed by GitHub
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commit 2f809116e2
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18
python/llm/src/ipex_llm/transformers/convert.py
View file

@ -1033,6 +1033,24 @@ def _optimize_post(model, lightweight_bmm=False):
module.SelfAttention,
chatglm_attention_forward
)
elif (model.config.num_layers == 40 and hasattr(model.config, 'rope_ratio')
and model.config.rope_ratio == 500):
# glm-4-9b-chat
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from ipex_llm.transformers.models.chatglm4 import chatglm4_attention_forward
from ipex_llm.transformers.models.chatglm4 import chatglm4_model_forward
from ipex_llm.transformers.models.chatglm2 import chatglm_rms_norm_forward
convert_forward(model,
module.SelfAttention,
chatglm4_attention_forward)
convert_forward(model,
module.ChatGLMModel,
chatglm4_model_forward)
convert_forward(model,
module.RMSNorm,
chatglm_rms_norm_forward)
elif "mpt" in model.config.model_type:
if model.config.architectures is not None:
modeling_module_name = model.__class__.__module__

321
python/llm/src/ipex_llm/transformers/models/chatglm4.py Normal file
View file

@ -0,0 +1,321 @@
#
# 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/THUDM/chatglm2-6b-32k/blob/main/configuration_chatglm.py
#
import torch
from typing import Optional, Tuple, Union, List, Callable, Dict, Any
import torch.nn.functional as F
from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, apply_ipex_rotate_every_two
from transformers.modeling_outputs import BaseModelOutputWithPast
import os
KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
KV_CACHE_ALLOC_MIN_LENGTH = 512
def split_tensor_along_last_dim(
tensor: torch.Tensor,
num_partitions: int,
contiguous_split_chunks: bool = False,
) -> List[torch.Tensor]:
"""Split a tensor along its last dimension.
Arguments:
tensor: input tensor.
num_partitions: number of partitions to split the tensor
contiguous_split_chunks: If True, make each chunk contiguous
in memory.
Returns:
A list of Tensors
"""
# Get the size and dimension.
last_dim = tensor.dim() - 1
last_dim_size = tensor.size()[last_dim] // num_partitions
# Split.
tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
# Note: torch.split does not create contiguous tensors by default.
if contiguous_split_chunks:
return tuple(chunk.contiguous() for chunk in tensor_list)
return tensor_list
def chatglm4_model_forward(
self,
input_ids,
position_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.BoolTensor] = None,
full_attention_mask: Optional[torch.BoolTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]=None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
from ipex_llm.transformers.kv import DynamicFp8Cache
use_cache = use_cache if use_cache is not None else self.config.use_cache
# if use_cache and use_quantize_kv_cache(
# self.encoder.layers[0].self_attention.query_key_value, input_ids):
# if not isinstance(past_key_values, DynamicFp8Cache):
# past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
return chatglm4_model_forward_internal(
self=self,
input_ids=input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
full_attention_mask=full_attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
def chatglm4_model_forward_internal(
self,
input_ids,
position_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.BoolTensor] = None,
full_attention_mask: Optional[torch.BoolTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]=None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
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
batch_size, seq_length = input_ids.shape
if inputs_embeds is None:
inputs_embeds = self.embedding(input_ids)
if full_attention_mask is None:
if (attention_mask is not None and not attention_mask.all()) or\
(past_key_values and seq_length != 1):
full_attention_mask = self.get_masks(input_ids,
past_key_values,
padding_mask=attention_mask)
use_fuse_rope = input_ids.device.type == "xpu"
use_fuse_rope = use_fuse_rope and not self.training
# Rotary positional embeddings
rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
if position_ids is not None:
rotary_pos_emb = rotary_pos_emb[position_ids]
else:
rotary_pos_emb = rotary_pos_emb[None, :seq_length]
if use_fuse_rope:
# Repeat cos sin here, call only once for each token.
# Chatglm2's rotary embedding is similar to gptj's, is rotate_every_two.
# If put this to attension forward, it will generate too many times.
cos, sin = rotary_pos_emb.split(rotary_pos_emb.shape[-1] // 2, dim=-1)
cos = cos.squeeze(-1)
sin = sin.squeeze(-1)
cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
rotary_pos_emb = (cos, sin)
# Run encoder.
hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb,
kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
)
if presents is not None and type(presents) is torch.Tensor:
presents = presents.split(1, dim=0)
presents = list(presents)
presents = [list(x.squeeze(0).split(1, dim=0)) for x in presents]
presents = [tuple([x.squeeze(0) for x in y]) for y in presents]
presents = tuple(presents)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions]
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,
)
@torch.jit.script
def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
# x: [b, np, sq, hn]
b, np, sq, hn = x.size(0), x.size(1), x.size(2), x.size(3)
rot_dim = rope_cache.shape[-2] * 2
x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
# truncate to support variable sizes
rope_cache = rope_cache[:, :sq]
xshaped = x.reshape(b, np, sq, rot_dim // 2, 2)
rope_cache = rope_cache.view(-1, 1, sq, xshaped.size(3), 2)
x_out2 = torch.stack(
[
xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
],
-1,
)
x_out2 = x_out2.flatten(3)
return torch.cat((x_out2, x_pass), dim=-1)
def chatglm4_attention_forward(
self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
):
# hidden_states: [sq, b, h]
# =================================================
# Pre-allocate memory for key-values for inference.
# =================================================
# =====================
# Query, Key, and Value
# =====================
# Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
device = hidden_states.device
mixed_x_layer = self.query_key_value(hidden_states)
if self.multi_query_attention:
(query_layer, key_layer, value_layer) = mixed_x_layer.split(
[
self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
],
dim=-1,
)
query_layer = query_layer.view(
query_layer.size()[:-1] + (self.num_attention_heads_per_partition,
self.hidden_size_per_attention_head)
)
key_layer = key_layer.view(
key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition,
self.hidden_size_per_attention_head)
)
value_layer = value_layer.view(
value_layer.size()[:-1]
+ (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
)
else:
new_tensor_shape = mixed_x_layer.size()[:-1] + (self.num_attention_heads_per_partition,
3 * self.hidden_size_per_attention_head)
mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
# [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
(query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
# [b, sq, np, hn] -> [b, np, sq, hn]
query_layer, key_layer, value_layer = [k.transpose(1, 2)
for k in [query_layer, key_layer, value_layer]]
# apply relative positional encoding (rotary embedding)
if isinstance(rotary_pos_emb, tuple) and len(rotary_pos_emb) == 2:
# use_fuse_rope, see chatglm2_model_forward
cos, sin = rotary_pos_emb
rot_dim = cos.shape[-1]
query_layer = query_layer.transpose(1, 2)
key_layer = key_layer.transpose(1, 2)
query_layer_cur = query_layer[..., :rot_dim]
key_layer_cur = key_layer[..., :rot_dim]
# ipex_llm's apply_rotary_embedding can change the origin storage,
# so query_layer will get the result directly.
torch.ops.torch_ipex.apply_rotary_embedding(query_layer_cur, sin, cos, query_layer_cur)
torch.ops.torch_ipex.apply_rotary_embedding(key_layer_cur, sin, cos, key_layer_cur)
query_layer = query_layer.transpose(1, 2)
key_layer = key_layer.transpose(1, 2)
elif rotary_pos_emb is not None:
query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb)
key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)
cur_length, batch_size = query_layer.shape[2], query_layer.shape[0]
# adjust key and value for inference
if kv_cache is not None and use_cache:
cache_k, cache_v = kv_cache
past_length = cache_k.size(2)
if cache_k.stride()[1] < (past_length + cur_length) * cache_k.size(3):
max_cache_length = past_length + cur_length + KV_CACHE_ALLOC_BLOCK_LENGTH
new_cache_k, new_cache_v = extend_kv_cache(batch_size,
key_layer.size(1),
self.hidden_size_per_attention_head,
past_length,
max_cache_length,
dtype=query_layer.dtype,
device=device)
new_cache_k[:] = cache_k
new_cache_v[:] = cache_v
cache_k = new_cache_k
cache_v = new_cache_v
key_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
if use_cache:
if kv_cache is None:
kv_cache = torch.cat((key_layer.unsqueeze(0).unsqueeze(0),
value_layer.unsqueeze(0).unsqueeze(0)), dim=1)
else:
kv_cache = (key_layer, value_layer)
else:
kv_cache = None
if self.multi_query_attention:
key_layer = key_layer.unsqueeze(2)
key_layer = key_layer.expand(
-1, -1,
self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition,
-1, -1
)
key_layer = key_layer.contiguous().view(
key_layer.size()[:1] + (self.num_attention_heads_per_partition,) + key_layer.size()[3:]
)
value_layer = value_layer.unsqueeze(2)
value_layer = value_layer.expand(
-1, -1,
self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition,
-1, -1
)
value_layer = value_layer.contiguous().view(
value_layer.size()[:1] +
(self.num_attention_heads_per_partition,) + value_layer.size()[3:]
)
# ==================================
# core attention computation
# ==================================
context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask)
# =================
# Output. [sq, b, h]
# =================
output = self.dense(context_layer)
return output, kv_cache