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from transformers import AutoTokenizer, AutoConfig
import numpy as np
from ml_dtypes import bfloat16
from axengine import InferenceSession
from PIL import Image
from torchvision import transforms
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
import torch
from transformers import AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import onnxruntime
import gc
from glob import glob
from utils import get_rope_index
def post_process(data, topk=1, topp=0.9, temperature=0.6):
def top_p(l: np.ndarray, p: float) -> np.ndarray:
index = np.argsort(l)
res = l.copy()
sum_p = 0
for i in index[::-1]:
if sum_p >= p:
res[i] = 0
sum_p += res[i]
return res / sum_p
def softmax(l: np.ndarray) -> np.ndarray:
l_max = l - l.max()
l_exp = np.exp(l_max)
res = l_exp / np.sum(l_exp)
return res.astype(np.float64)
r = data.astype(np.float32)
r = r.flatten()
# topk
candidate_index = np.argpartition(r, -topk)[-topk:]
candidate_value = r[candidate_index]
# temperature
candidate_value /= temperature
# softmax
candidate_soft = softmax(candidate_value)
# topp
candidate_soft = top_p(candidate_soft, topp)
candidate_soft = candidate_soft.astype(np.float64) / candidate_soft.sum()
pos = np.random.multinomial(1, candidate_soft).argmax()
next_token = candidate_index[pos]
return next_token, candidate_index, candidate_soft
if __name__ == "__main__":
prefill_len = 512
checkpoint_dir=f"../Qwen2.5-VL-3B-Instruct-AX650-video-prefill_512/"
cfg = AutoConfig.from_pretrained(
checkpoint_dir, trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
checkpoint_dir, trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(checkpoint_dir)
messages=[
{
"role": "user",
"content":[
{"type": "text", "text": "你是谁"},
]
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
position_ids,_ = get_rope_index(cfg, inputs["input_ids"])
# pixel_values = inputs['pixel_values_videos']
# # extract img feature by vit
# vit_session = InferenceSession.load_from_model(f'{checkpoint_dir}/Qwen2.5-VL-3B-Instruct_vision.axmodel')
# t = inputs['video_grid_thw'][0,0]
# seq_len, dim = pixel_values.shape
# ht = pixel_values.reshape(t, seq_len//t, dim)
# vit_output = []
# for i in range(t):
# print(i)
# out = vit_session.run({"hidden_states": ht[i].numpy()})[0] # (1, 576, 1176)
# vit_output.append(out.astype(bfloat16))
# del vit_session
# gc.collect()
# vit_output = np.concatenate(vit_output, axis=0)
# # vit_output = np.load("vit_out.npy")
# np.save("vit_out_ax.npy",vit_output)
# vit_output = vit_output[None,:,:]
# print("vit feature extract done!")
token_ids = inputs['input_ids'].squeeze().numpy().tolist()
# image_start_index = np.where(np.array(token_ids) == 151652)[0].tolist()[0]
# image_insert_index = image_start_index + 1
embeds = np.load(f"{checkpoint_dir}/model.embed_tokens.weight.npy")
prefill_data = np.take(embeds, token_ids, axis=0)
prefill_data = prefill_data.astype(bfloat16)
# prefill_data[ image_insert_index : image_insert_index + vit_output.shape[1]] = vit_output[0, :, :]
token_len = len(token_ids)
lastN = 1023
kv_dim = cfg.hidden_size // cfg.num_attention_heads * cfg.num_key_value_heads
k_caches = [
np.zeros((1, lastN, kv_dim), dtype=bfloat16)
for _ in range(cfg.num_hidden_layers)
]
v_caches = [
np.zeros((1, lastN, kv_dim), dtype=bfloat16)
for _ in range(cfg.num_hidden_layers)
]
prefill_decoder_sessins = []
for i in range(cfg.num_hidden_layers):
session = InferenceSession.load_from_model(
f"{checkpoint_dir}/qwen2_5_vl_p{prefill_len}_l{i}_together.axmodel"
)
prefill_decoder_sessins.append(session)
post_process_session = InferenceSession.load_from_model(
f"{checkpoint_dir}/qwen2_5_vl_post.axmodel"
# "../Qwen2.5-VL-3B-Instruct-AX650-video-prefill_512/qwen2_5_vl_post.axmodel"
)
print("model load done!")
"""
prefill
"""
print("position_ids",position_ids)
for i in range(cfg.num_hidden_layers):
prefill_decoder_sessins[i].set_runtime_context(group_id=1)
if prefill_len > 0:
indices = np.zeros((3, prefill_len), dtype=np.uint32)
indices[:, 0:token_len] = position_ids.squeeze(1).numpy().astype(np.uint32)
mask = np.zeros((1, prefill_len, prefill_len)) - 65536
data = np.zeros((1, prefill_len, cfg.hidden_size)).astype(bfloat16)
data[:, 0:token_len] = prefill_data
for i, t in enumerate(token_ids):
mask[:, i, : i + 1] = 0
mask = mask.astype(bfloat16)
for i in range(cfg.num_hidden_layers):
input_feed = {
"K_cache": np.zeros((1, 1, cfg.hidden_size), dtype=bfloat16),
"V_cache": np.zeros((1, 1, cfg.hidden_size), dtype=bfloat16),
"indices": indices,
"input": data,
"mask": mask,
}
outputs = prefill_decoder_sessins[i].run(input_feed)
k_caches[i][:, :token_len, :] = outputs[0][:, :token_len, :]
v_caches[i][:, :token_len, :] = outputs[1][:, :token_len, :]
data = outputs[2][:, :token_len, :]
post_out = post_process_session.run({"input": data[:, token_len - 1, :]})[0]
next_token, posssible_tokens, possible_soft = post_process(post_out, topk=1)
posibles = [tokenizer.decode([t]) for t in posssible_tokens]
posible_soft = [str((t, s)) for t, s in zip(posibles, possible_soft)]
token_ids.append(next_token)
print("prefill done!")
# set to decoder
for i in range(cfg.num_hidden_layers):
prefill_decoder_sessins[i].set_runtime_context(group_id=0)
# lastN = np.max(indices)
start_ids = np.max(indices) + 1
mask = np.zeros((1, 1, lastN + 1), dtype=np.float32).astype(bfloat16)
mask[:, :, :lastN] -= 65536
mask[:, :, :token_len] = 0
for start_indice in range(lastN + 1):
if prefill_len > 0 and start_indice < token_len:
continue
next_token = token_ids[start_indice]
indices = np.array([start_ids], np.uint32).reshape((1, 1))
start_ids += 1
data = embeds[next_token, :].reshape((1, 1, cfg.hidden_size)).astype(bfloat16)
for i in range(cfg.num_hidden_layers):
input_feed = {
"K_cache": k_caches[i],
"V_cache": v_caches[i],
"indices": indices,
"input": data,
"mask": mask,
}
outputs = prefill_decoder_sessins[i].run(input_feed)
k_caches[i][:, start_indice, :] = outputs[0][:, :, :]
v_caches[i][:, start_indice, :] = outputs[1][:, :, :]
data = outputs[2]
mask[..., start_indice] = 0
if start_indice < token_len - 1:
pass
else:
post_out = post_process_session.run({"input": data})[0]
next_token, posssible_tokens, possible_soft = post_process(post_out)
print("next_token",next_token)
token_ids.append(next_token)
if next_token == tokenizer.eos_token_id:
# print("hit eos!")
break
print(tokenizer.decode(token_ids[token_len:]))
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