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glm-4v-9b support (#11327)

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* chatglm4v support

* fix style check

* update glm4v
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Xin Qiu 2024-06-17 13:52:37 +08:00 committed by GitHub
parent bca5cbd96c
commit 183e0c6cf5
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2 changed files with 379 additions and 15 deletions
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30
python/llm/src/ipex_llm/transformers/convert.py
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@ -1055,11 +1055,35 @@ 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
elif model.config.num_layers == 40 and hasattr(model.config, 'rope_ratio'):
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
if hasattr(model.transformer, "vision"):
# glm-4v-9b
modeling_module_name = model.transformer.vision.__class__.__module__
vision_module = importlib.import_module(modeling_module_name)
from ipex_llm.transformers.models.chatglm4v import chatglm4v_attention_forward
from ipex_llm.transformers.models.chatglm4v import chatglm4v_model_forward
from ipex_llm.transformers.models.chatglm4v import visual_attention_forward
from ipex_llm.transformers.models.chatglm4v import patch_embedding_forward
from ipex_llm.transformers.models.chatglm2 import chatglm_rms_norm_forward
convert_forward(model,
module.SelfAttention,
chatglm4v_attention_forward)
convert_forward(model,
module.ChatGLMModel,
chatglm4v_model_forward)
convert_forward(model,
module.RMSNorm,
chatglm_rms_norm_forward)
convert_forward(model,
vision_module.Attention,
visual_attention_forward)
convert_forward(model,
vision_module.PatchEmbedding,
patch_embedding_forward)
else:
# glm-4-9b-chat
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

340
python/llm/src/ipex_llm/transformers/models/chatglm4v.py Normal file
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@ -0,0 +1,340 @@
#
# 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/glm-4v-9b/blob/main/modeling_chatglm.py
#
import torch
from typing import Optional, Tuple, Union
from ipex_llm.transformers.models.utils import restore_fp8_kv_cache, update_past_key_value
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, use_sdp, use_sdp_causal
from ipex_llm.transformers.models.utils import should_use_fuse_rope, apply_rotary_pos_emb
from ipex_llm.transformers.models.chatglm2 import repeat_kv
from ipex_llm.utils.common import invalidInputError
from transformers.modeling_outputs import BaseModelOutputWithPast
import math
from typing import List
# copied from https://huggingface.co/THUDM/glm-4v-9b/blob/main/modeling_chatglm.py#L753
def is_empty(images_list: Optional[List[List[torch.Tensor]]]):
if images_list is None or len(images_list) == 0:
return True
for image_list in images_list:
if image_list is not None:
return False
return True
def chatglm4v_model_forward(
self,
input_ids: torch.LongTensor = None,
images: torch.Tensor = None,
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]:
# generate mode with past_key_values. the image features are already mapped
if past_key_values is None:
# not allow for inputs_embeds, because we want to process image feature
invalidInputError(input_ids is not None and inputs_embeds is None,
f"{input_ids} should not be None, {inputs_embeds} should be None.")
if not is_empty(images): # multi-modality
image_size: int = self.config.vision_config['image_size']
patch_size: int = self.config.vision_config['patch_size']
num_patches = (image_size // patch_size // 2) ** 2
invalidInputError(len(input_ids) == len(images),
f"{len(input_ids)} should equal to {len(images)}")
inputs_embeds = self.embedding(input_ids)
images = images.to(dtype=inputs_embeds.dtype)
images_features = self.vision(images)
if position_ids is None:
position_ids = self.get_position_ids(input_ids, device=inputs_embeds.device)
new_input_embeds, new_position_ids = [], []
for i in range(len(input_ids)):
input_id = input_ids[i].tolist()
boi_token_pos = input_id.index(self.config.boi_token_id)
eoi_token_pos = input_id.index(self.config.eoi_token_id)
invalidInputError(eoi_token_pos - boi_token_pos == 2,
"eoi_token_pos - boi_token_pos should equal to 2, but got"
f"{eoi_token_pos} - {boi_token_pos} = "
f"{eoi_token_pos - boi_token_pos}")
new_input_embeds.append(torch.cat(
(inputs_embeds[i, :boi_token_pos],
images_features[i].to(inputs_embeds.device),
inputs_embeds[i, eoi_token_pos + 1:])))
new_position_ids.append(torch.cat(
(position_ids[i, :boi_token_pos + 1],
position_ids[i, boi_token_pos + 1].repeat(num_patches),
position_ids[i, eoi_token_pos:])
))
inputs_embeds = torch.stack(new_input_embeds, dim=0)
position_ids = torch.stack(new_position_ids, dim=0)
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)
# ipex-llm changes begin
# 1. replace `rotary_pos_emb` with `inv_freq` and `position_ids`
# 2. generate `causal_mask` and replace `full_attention_mask` with it
if position_ids is None:
if past_key_values is None:
position_ids = torch.arange(seq_length, dtype=torch.int64, device=inputs_embeds.device)
else:
kv_length = past_key_values[0][0].size(2)
position_ids = torch.arange(kv_length, kv_length + seq_length,
dtype=torch.int64, device=inputs_embeds.device)
position_ids = position_ids.repeat(batch_size, 1)
if not getattr(self.rotary_pos_emb, "cached", False):
rot_dim = self.rotary_pos_emb.dim
base = 10000 * getattr(self.rotary_pos_emb, "rope_ratio", 1)
# We should generate float inv_freq to avoid overflow, as base is too large.
inv_freq = 1.0 / (base ** (torch.arange(0, rot_dim, 2,
dtype=torch.float,
device=inputs_embeds.device) / rot_dim))
inv_freq = inv_freq.to(inputs_embeds.dtype)
self.rotary_pos_emb.register_buffer("inv_freq", inv_freq, persistent=False)
self.rotary_pos_emb.cached = True
# `full_attention_mask` is not None only when
# `past_key_values` is not None and `seq_length` > 1
if full_attention_mask is not None:
causal_mask = torch.zeros([batch_size, 1, seq_length, full_attention_mask.size(-1)],
dtype=inputs_embeds.dtype, device=inputs_embeds.device)
mask_value = torch.finfo(inputs_embeds.dtype).min
causal_mask.masked_fill_(full_attention_mask, mask_value)
elif self.training or (inputs_embeds.device.type != "xpu" and past_key_values is None):
full_attention_mask = self.get_masks(input_ids,
past_key_values,
padding_mask=attention_mask)
causal_mask = torch.zeros([batch_size, 1, seq_length, full_attention_mask.size(-1)],
dtype=inputs_embeds.dtype, device=inputs_embeds.device)
mask_value = torch.finfo(inputs_embeds.dtype).min
causal_mask.masked_fill_(full_attention_mask, mask_value)
else:
causal_mask = None
hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
inputs_embeds, causal_mask,
rotary_pos_emb=(self.rotary_pos_emb.inv_freq, position_ids),
kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
)
# ipex-llm changes end
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,
)
def chatglm4v_attention_forward(
self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
):
# hidden_states: [b, sq, h]
bsz, q_len, _ = hidden_states.size()
# past_key_value: [bsz, n_kv_head, seq_len, head_dim]
past_key_value = None
if kv_cache is not None:
if isinstance(kv_cache, tuple):
past_key_value = kv_cache
else:
past_key_value = (kv_cache[0][0], kv_cache[0][1])
n_head = self.num_attention_heads_per_partition
n_kv_head = self.num_multi_query_groups_per_partition if self.multi_query_attention else n_head
head_dim = self.hidden_size_per_attention_head
qkv = self.query_key_value(hidden_states)
# [bs, q_len, np * 3 * hn] -> [bsz, n_head, seq_len, head_dim]
qkv = qkv.view(bsz, q_len, n_head + 2 * n_kv_head, head_dim)
qkv = qkv.transpose(1, 2)
query_states, key_states, value_states = qkv.split([n_head,
n_kv_head,
n_kv_head], dim=1)
kv_seq_len = key_states.shape[2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[2]
# IPEX-LLM OPT: fuse rope
inv_freq, position_ids = rotary_pos_emb
rot_dim = inv_freq.size(-1) * 2
if should_use_fuse_rope(hidden_states, rotary_pos_emb[1], self.training):
import xe_addons
xe_addons.rotary_two_inplaced(inv_freq, position_ids,
query_states[..., :rot_dim], key_states[..., :rot_dim])
else:
idx_theta = torch.outer(position_ids[0].float(),
inv_freq.float()).to(hidden_states.dtype)
idx_theta = idx_theta.unsqueeze(0).unsqueeze(0)
cos = torch.cos(idx_theta).repeat_interleave(2, -1)
sin = torch.sin(idx_theta).repeat_interleave(2, -1)
q_rot, k_rot = apply_rotary_pos_emb(query_states[..., :rot_dim], key_states[..., :rot_dim],
cos, sin, position_ids, "chatglm")
query_states[..., :rot_dim] = q_rot[...]
key_states[..., :rot_dim] = k_rot[...]
# IPEX-LLM OPT: kv cache and quantize kv
use_quantize_kv = use_quantize_kv_cache(self.query_key_value, query_states)
key_states, value_states = update_past_key_value(
past_key_value, key_states, value_states,
kv_seq_len, use_quantize_kv, hidden_states.device
)
if use_cache:
if past_key_value is None:
past_key_value = torch.cat((key_states.unsqueeze(0).unsqueeze(0),
value_states.unsqueeze(0).unsqueeze(0)), dim=1)
else:
past_key_value = (key_states, value_states)
else:
past_key_value = None
# IPEX-LLM OPT: sdp
attn_weights = None
if use_sdp(q_len, kv_seq_len, head_dim, query_states):
import xe_addons
if use_quantize_kv:
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, head_dim, query_states, self.training):
import xe_addons
if use_quantize_kv:
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)
elif query_states.device.type == "cpu":
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, n_head // n_kv_head)
value_states = repeat_kv(value_states, n_head // n_kv_head)
if q_len == kv_seq_len:
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states, key_states, value_states, is_causal=True
)
else:
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states, key_states, value_states, attention_mask
)
else:
if use_quantize_kv:
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, n_head // n_kv_head)
value_states = repeat_kv(value_states, n_head // n_kv_head)
attn_weights = torch.matmul(query_states / math.sqrt(head_dim),
key_states.transpose(2, 3)).to(value_states.dtype)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
if kv_seq_len >= 2048 or bsz >= 64:
# for memory considerations, do not upcast attention to fp32
# for long sequences or large batches
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
else:
# upcast attention to fp32
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
dtype=torch.float32).to(value_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
# context_layer's shape: [bsz, n_head, seq_len, head_dim] -> [seq_len, bsz, n_head * head_dim]
attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, n_head * head_dim)
output = self.dense(attn_output)
return output, past_key_value
def visual_attention_forward(self, x: "tensor(B, L, D)") -> "tensor(B, L, D)":
B, L, _ = x.shape
qkv = self.query_key_value(x)
qkv = qkv.reshape(B, L, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, H, L, D
q, k, v = qkv[0], qkv[1], qkv[2]
bsz, q_len, kv_seq_len, head_dim = q.shape
if use_sdp(q_len, kv_seq_len, head_dim, q):
import xe_addons
out = xe_addons.sdp(q, k, v, None)
elif q.device.type == "cpu":
out = torch.nn.functional.scaled_dot_product_attention(q, k, v,
attn_mask=None,
dropout_p=0.,
is_causal=False)
else:
attn_weights = torch.matmul(q / math.sqrt(head_dim),
k.transpose(2, 3)).to(v.dtype)
if kv_seq_len >= 2048 or bsz >= 64:
# for memory considerations, do not upcast attention to fp32
# for long sequences or large batches
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
else:
# upcast attention to fp32
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
dtype=torch.float32).to(v.dtype)
out = torch.matmul(attn_weights, v)
output = self.dense(out.transpose(1, 2).reshape(B, L, -1))
output = self.output_dropout(output)
return output
def patch_embedding_forward(self, images: "tensor(B, C, H, W)") -> "tensor(B, L, D)":
x = self.proj(images)
x = x.flatten(2).transpose(1, 2)
cls_token = self.cls_embedding.expand(x.shape[0], -1, -1)
x = torch.cat((cls_token, x), dim=1)
x += self.position_embedding.weight.unsqueeze(0).to(images.device)
return x