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prune for qwen2.5vl(divprune,holitom,sparsevlm,vispruner) #441

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Merged
llmc-reviewer merged 1 commit into from
Oct 30, 2025
Merged

prune for qwen2.5vl(divprune,holitom,sparsevlm,vispruner) #441

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57 changes: 57 additions & 0 deletions llmc/compression/token_reduction/divprune.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import functools
from functools import wraps
from types import MethodType

Expand All @@ -6,6 +7,7 @@
from llmc.utils.registry_factory import TOKEN_REDUCTION_REGISTRY

from .token_reduction_module import TokenReductionModule
from .utils import prefill_wrapper


def pairwise_cosine_similarity(matrix):
Expand Down Expand Up @@ -84,6 +86,41 @@ def divprune_post_hook(*args, pruning_paras=None):
return tuple(args)


def prune_qwenv25vl_hook(module, args, kwargs, pruning_paras):
if kwargs['position_ids'].shape[-1] == 1:
return args, kwargs
inputs_embeds = kwargs['inputs_embeds']
attention_mask = kwargs['attention_mask']
rate = pruning_paras['reduction_ratio']
SYS_TOKEN_LEN = pruning_paras['vision_token_start_index']
img_feature_len = pruning_paras['vision_token_length']
device = inputs_embeds.device

visual_tokens = inputs_embeds[0][SYS_TOKEN_LEN: SYS_TOKEN_LEN + img_feature_len]
selected_visual_tokens, cosine_matrix = divprune(
visual_tokens, img_feature_len, None, threshold_ratio=1 - rate
)
selected_visual_tokens += SYS_TOKEN_LEN
keep_indexs = torch.cat(
(
torch.arange(SYS_TOKEN_LEN, device=device),
selected_visual_tokens,
torch.arange(
SYS_TOKEN_LEN + img_feature_len, inputs_embeds.shape[1], device=device
),
)
)
keep_indexs = keep_indexs.sort().values

kwargs['inputs_embeds'] = inputs_embeds[:, keep_indexs, :]
kwargs['position_ids'] = kwargs['position_ids'][:, :, keep_indexs]
if attention_mask is not None:
kwargs['attention_mask'] = attention_mask[:, keep_indexs]
kwargs['cache_position'] = keep_indexs

return args, kwargs


@TOKEN_REDUCTION_REGISTRY.register('DivPrune')
class DivPrune(TokenReductionModule):
def __init__(self, config, model, blocks):
Expand Down Expand Up @@ -114,6 +151,14 @@ def wrapper(self, *args, **kwargs):
return divprune_post_hook(*outs, pruning_paras=pruning_paras)
return wrapper

@prefill_wrapper
def vtoken_length_hook(module, args, pruning_paras):
input_ids = args[0]
token_indices = torch.where(
input_ids[0] == pruning_paras['vision_token_index']
)[0]
pruning_paras['vision_token_length'] = token_indices.shape[0]

if self.model.__class__.__name__ == 'Llava':

self.model.vlm_model.prepare_inputs_labels_for_multimodal = MethodType(
Expand All @@ -123,3 +168,15 @@ def wrapper(self, *args, **kwargs):
llava_next=self.special_config['vision_token_length'] is None
), self.model.vlm_model
)
elif self.model.__class__.__name__ == 'Qwen2_5VL':

self.model.embed_tokens.register_forward_pre_hook(
functools.partial(vtoken_length_hook, pruning_paras=self.pruning_paras)
)
self.model.language_model.register_forward_pre_hook(
functools.partial(
prune_qwenv25vl_hook,
pruning_paras=self.pruning_paras,
),
with_kwargs=True
)
12 changes: 12 additions & 0 deletions llmc/compression/token_reduction/random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -228,6 +228,14 @@ def store_attention_hook(m, x, layer_outputs, pruning_paras):
layer_attention = layer_outputs[1]
pruning_paras['attn_scores'] = layer_attention

@prefill_wrapper
def vtoken_length_hook(module, args, pruning_paras):
input_ids = args[0]
token_indices = torch.where(
input_ids[0] == pruning_paras['vision_token_index']
)[0]
pruning_paras['vision_token_length'] = token_indices.shape[0]

if self.special_config['vision_token_length'] is None:
if self.model.__class__.__name__ == 'Llava':
self.model.vlm_model.prepare_inputs_labels_for_multimodal = MethodType(
Expand All @@ -236,6 +244,10 @@ def store_attention_hook(m, x, layer_outputs, pruning_paras):
self.pruning_paras
), self.model.vlm_model
)
elif self.model.__class__.__name__ == 'Qwen2_5VL':
self.model.embed_tokens.register_forward_pre_hook(
functools.partial(vtoken_length_hook, pruning_paras=self.pruning_paras)
)

if self.special_config['metric'] == 'random':
self.blocks[self.pruning_loc].register_forward_pre_hook(
Expand Down
103 changes: 100 additions & 3 deletions llmc/compression/token_reduction/sparsevlm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
sparse_token_list_192 = []
sparse_token_list_128 = []
sparse_token_list_64 = []
sparse_token_list_960 = []
sparse_token_list_640 = []
sparse_token_list_320 = []
sparse_token_list_160 = []
Expand Down Expand Up @@ -329,8 +330,9 @@ def decoder_attn_hook(module, inputs, kwargs, layer_outputs, pruning_paras, laye

if attention_mask is not None:
attention_mask = attention_mask[:, :, keep_indexs, keep_indexs]
new_pe0 = position_embeddings[0][:, keep_indexs, :].clone()
new_pe1 = position_embeddings[1][:, keep_indexs, :].clone()
index_dim = 1 if position_embeddings[0].dim() == 3 else 2
new_pe0 = position_embeddings[0].index_select(index_dim, keep_indexs).clone()
new_pe1 = position_embeddings[1].index_select(index_dim, keep_indexs).clone()
position_embeddings = (new_pe0, new_pe1)

pruning_paras['v_token_num'] = v_token_num
Expand All @@ -352,6 +354,75 @@ def read_parameter_hook(module, args, kwargs, pruning_paras):

return args, kwargs

@prefill_wrapper
def vtoken_length_hook(module, args, pruning_paras):
input_ids = args[0]
token_indices = torch.where(
input_ids[0] == pruning_paras['vision_token_index']
)[0]
pruning_paras['vision_token_length'] = token_indices.shape[0]
pruning_paras['pre_prompt_length_list'] = [token_indices[0].item()]

def get_attn_logits_for_qwen25vl(
module,
args, kwargs, layer_outs,
pruning_paras, layer_idx
):
if kwargs['position_ids'].shape[-1] == 1:
return layer_outs

from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
apply_multimodal_rotary_pos_emb, repeat_kv)

hidden_states = kwargs['hidden_states']
position_embeddings = kwargs['position_embeddings']
past_key_value = layer_outs[2]
attention_mask = kwargs['attention_mask']

t_token_idx = pruning_paras['t_token_idx']
v_token_start = pruning_paras['v_token_start']
v_token_num = pruning_paras['v_token_num']

bsz, q_len, _ = hidden_states.size()

query_states = module.q_proj(hidden_states)
key_states = module.k_proj(hidden_states)
value_states = module.v_proj(hidden_states)

query_states = query_states.view(bsz, q_len, -1, module.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, -1, module.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, -1, module.head_dim).transpose(1, 2)

cos, sin = position_embeddings
query_states, key_states = apply_multimodal_rotary_pos_emb(
query_states, key_states, cos, sin, module.rope_scaling['mrope_section']
)

if past_key_value is not None:
key_states = past_key_value.key_cache[layer_idx]
value_states = past_key_value.value_cache[layer_idx]

key_states = repeat_kv(key_states, module.num_key_value_groups)
value_states = repeat_kv(value_states, module.num_key_value_groups)

t_token_idx = t_token_idx[1] + v_token_start + v_token_num
L, S = query_states.size(-2), key_states.size(-2)
scale_factor = 1 / math.sqrt(query_states.size(-1))
attn_bias = torch.zeros(L, S, dtype=query_states.dtype)
if module.is_causal:
assert attention_mask is None
temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
attn_bias.masked_fill_(temp_mask.logical_not(), float('-inf'))
attn_bias.to(query_states.dtype)

attn_logits = query_states @ key_states.transpose(2, 3) * scale_factor
attn_logits += attn_bias.to(query_states.device)
attn_logits = torch.softmax(attn_logits, dim=-1)

pruning_paras['attn_logits'] = attn_logits

return layer_outs

if self.model.__class__.__name__ == 'LlavaHf':
self.model.embed_tokens.register_forward_pre_hook(
functools.partial(input_hook, pruning_paras=self.pruning_paras)
Expand All @@ -364,11 +435,17 @@ def read_parameter_hook(module, args, kwargs, pruning_paras):
llava_next=self.special_config['vision_token_length'] is None
), self.model.vlm_model
)
elif self.model.__class__.__name__ == 'Qwen2_5VL':
self.model.embed_tokens.register_forward_pre_hook(
functools.partial(vtoken_length_hook, pruning_paras=self.pruning_paras)
)

if self.model.__class__.__name__ == 'LlavaHf':
llama_model = self.model.model
elif self.model.__class__.__name__ == 'Llava':
llama_model = self.model.model.model
elif self.model.__class__.__name__ == 'Qwen2_5VL':
llama_model = self.model.language_model
llama_model.register_forward_pre_hook(
functools.partial(register_module_paras, pruning_paras=self.pruning_paras),
with_kwargs=True
Expand Down Expand Up @@ -405,6 +482,23 @@ def read_parameter_hook(module, args, kwargs, pruning_paras):
),
with_kwargs=True
)
elif self.model.__class__.__name__ == 'Qwen2_5VL':
self.blocks[block_idx].register_forward_pre_hook(
functools.partial(
update_kwargs_hook,
pruning_paras=self.pruning_paras,
layer_idx=block_idx,
),
with_kwargs=True
)
self.blocks[block_idx].self_attn.register_forward_hook(
functools.partial(
get_attn_logits_for_qwen25vl,
pruning_paras=self.pruning_paras,
layer_idx=block_idx,
),
with_kwargs=True
)
self.blocks[block_idx].register_forward_hook(
functools.partial(
decoder_attn_hook,
Expand All @@ -425,7 +519,7 @@ def read_parameter_hook(module, args, kwargs, pruning_paras):

def update_list():
global sparse_token_list_192, sparse_token_list_128, sparse_token_list_64
global sparse_token_list_640, sparse_token_list_320, sparse_token_list_160
global sparse_token_list_960, sparse_token_list_640, sparse_token_list_320, sparse_token_list_160 # noqa
global prune_flag, merge_flag, sparse_token_dict

if layer_dict == {2: 0, 6: 1, 15: 2}: # 2*576 4*300 10*200 16*110
Expand All @@ -437,13 +531,15 @@ def update_list():
sparse_token_list_192 = [180]
sparse_token_list_128 = [114]
sparse_token_list_64 = [48]
sparse_token_list_960 = [0.3125]
sparse_token_list_640 = [0.1979]
sparse_token_list_320 = [0.0833]
sparse_token_list_160 = [0.0261]
elif prune_flag:
sparse_token_list_192 = [192]
sparse_token_list_128 = [128]
sparse_token_list_64 = [64]
sparse_token_list_960 = [0.3333]
sparse_token_list_640 = [0.2222]
sparse_token_list_320 = [0.1111]
sparse_token_list_160 = [0.0555]
Expand All @@ -460,6 +556,7 @@ def update_list():
192: sparse_token_list_192,
128: sparse_token_list_128,
64: sparse_token_list_64,
960: sparse_token_list_960,
640: sparse_token_list_640,
320: sparse_token_list_320,
160: sparse_token_list_160
Expand Down
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