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add basic glm-edge support (#12531)

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Yishuo Wang 2024-12-12 16:02:22 +08:00 committed by GitHub
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2 changed files with 219 additions and 1 deletions
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18
python/llm/src/ipex_llm/transformers/convert.py
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@ -1070,6 +1070,10 @@ def _optimize_pre(model, qtype=None):
model.apply(split_mlp) model.apply(split_mlp)
elif model.config.num_layers in [40, 28]: elif model.config.num_layers in [40, 28]:
model.apply(split_mlp) model.apply(split_mlp)
elif model.config.model_type == "glm":
from ipex_llm.transformers.models.glm import merge_qkv, split_mlp
model.apply(merge_qkv)
model.apply(split_mlp)
return model return model
@ -1487,7 +1491,19 @@ def _optimize_post(model, lightweight_bmm=False):
# workaround glm4-9b fp16 overflow # workaround glm4-9b fp16 overflow
from ipex_llm.transformers.models.chatglm4 import chatglm4_block_forward from ipex_llm.transformers.models.chatglm4 import chatglm4_block_forward
convert_forward(model, module.GLMBlock, chatglm4_block_forward) convert_forward(model, module.GLMBlock, chatglm4_block_forward)
elif model.config.model_type == "glm":
# glm-edge series
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from ipex_llm.transformers.models.common import rms_norm_forward
from ipex_llm.transformers.models.common import mlp_silu_forward
from ipex_llm.transformers.models.glm import glm_attention_forward
from ipex_llm.transformers.models.glm import glm_model_forward_wrapper
convert_forward(model, module.GlmRMSNorm, rms_norm_forward)
convert_forward(model, module.GlmMLP, mlp_silu_forward)
convert_forward(model, module.GlmAttention, glm_attention_forward)
glm_model_forward = glm_model_forward_wrapper(module.GlmModel.forward)
convert_forward(model, module.GlmModel, glm_model_forward)
elif "mpt" in model.config.model_type: elif "mpt" in model.config.model_type:
if model.config.architectures is not None: if model.config.architectures is not None:
modeling_module_name = model.__class__.__module__ modeling_module_name = model.__class__.__module__

202
python/llm/src/ipex_llm/transformers/models/glm.py Normal file
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@ -0,0 +1,202 @@
#
# 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://github.com/huggingface/transformers/blob/main/src/transformers/models/glm/modeling_glm.py
#
# which is licensed under Apache License 2.0:
#
# Copyright 2024 The GLM & ZhipuAI team and HuggingFace Inc. team. All rights reserved.
#
#
# 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.
import torch
from typing import Optional, Tuple
from transformers.cache_utils import Cache
from transformers.models.glm.modeling_glm import GlmAttention, GlmMLP
from transformers.models.glm.modeling_glm import repeat_kv, apply_rotary_pos_emb
from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache
from ipex_llm.transformers.models.common import merge_qkv_base
from ipex_llm.transformers.models.utils import use_sdp, use_sdp_causal
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
def merge_qkv(module: torch.nn.Module):
merge_qkv_base(module, GlmAttention)
def split_mlp(module: torch.nn.Module):
if isinstance(module, GlmMLP):
gate_weight, up_weight = module.gate_up_proj.weight.data.chunk(2, dim=0)
gate_proj = torch.nn.Linear(0, 0, bias=False)
gate_proj.weight = torch.nn.Parameter(gate_weight, requires_grad=False)
gate_proj.in_features = gate_weight.size(1)
gate_proj.out_features = gate_weight.size(0)
up_proj = torch.nn.Linear(0, 0, bias=False)
up_proj.weight = torch.nn.Parameter(up_weight, requires_grad=False)
up_proj.in_features = up_weight.size(1)
up_proj.out_features = up_weight.size(0)
module.gate_proj = gate_proj
module.up_proj = up_proj
del module.gate_up_proj
# rename activation function
module.act_fn = module.activation_fn
def glm_attention_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
**kwargs,
):
bsz, q_len, _ = hidden_states.size()
qkv = self.qkv_proj(hidden_states)
qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
qkv = qkv.transpose(1, 2)
query_states, key_states, value_states = qkv.split([self.num_heads,
self.num_key_value_heads,
self.num_key_value_heads], dim=1)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
use_quantizekv = isinstance(past_key_value, DynamicFp8Cache)
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states,
self.layer_idx, cache_kwargs)
kv_seq_len = key_states.size(-2)
if attention_mask is not None: # no matter the length, we just slice it
attention_mask = attention_mask[:, :, :, : kv_seq_len]
if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
import xe_addons
if use_quantizekv:
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)
elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
import xe_addons
if use_quantizekv:
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_quantizekv:
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
query_states.dtype)
# 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_weights = torch.matmul(query_states,
key_states.transpose(2, 3)) * self.scaling
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(query_states.dtype)
attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
training=self.training)
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 glm_model_forward_wrapper(origin_forward):
def glm_model_forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = 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,
cache_position: Optional[torch.LongTensor] = None,
**flash_attn_kwargs,
):
# ipex-llm changes start
# IPEX-LLM OPT: kv cache and quantize kv cache
inputs = input_ids if input_ids is not None else inputs_embeds
use_cache = use_cache if use_cache is not None else self.config.use_cache
use_cache = use_cache or inputs.device.type == 'xpu'
use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.down_proj, inputs,
self.config.num_attention_heads //
self.config.num_key_value_heads)
if use_cache:
if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
elif not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
# ipex-llm changes end
return origin_forward(
self=self,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
**flash_attn_kwargs,
)
return glm_model_forward