chore: import upstream snapshot with attribution

2026-07-13 12:34:46 +08:00
commit f4e68ed970
84 changed files with 14896 additions and 0 deletions
@@ -0,0 +1,118 @@
+import base64
+from io import BytesIO
+import json
+from datasets import Dataset
+import torch
+from compressed_tensors.offload import dispatch_model
+from datasets import load_dataset
+
+# Note: this is an optional utility for processing vision inputs for qwen.
+# This can be installed via the "qwen" extra
+from qwen_vl_utils import process_vision_info
+from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.modifiers.awq import AWQModifier
+import random
+# Load model.
+model_id = "/mnt/data/ws/project/ms-swift/output/v3_sft_4b/v0-20260421-095015/checkpoint-50349"
+model = Qwen3VLForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+processor = AutoProcessor.from_pretrained(model_id)
+
+# Oneshot arguments
+DATASET_ID = "/mnt/data/ws/project/ms-swift/train_v4.jsonl"
+DATASET_SPLIT = "test[:512]"
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 40000
+
+# Load dataset and preprocess.
+def reservoir_sampling(file_path, k):
+    """
+    从大文件中均匀随机采样 k 个样本
+    
+    时间复杂度: O(n)
+    空间复杂度: O(k)
+    """
+    reservoir = []
+    
+    with open(file_path, 'r', encoding='utf-8') as f:
+        # 先填充前 k 个样本
+        for i, line in enumerate(f):
+            l = line.strip()
+            if len(l) == 0:
+                continue
+            if i < k:
+                reservoir.append(l)
+            else:
+                # 对于第 i 个样本，以 k/i 的概率替换蓄水池中的某个样本
+                j = random.randint(0, i)
+                if j < k:
+                    reservoir[j] = l
+    
+    return reservoir
+
+
+# Apply chat template and tokenize inputs.
+calib_data_raw = reservoir_sampling(DATASET_ID, NUM_CALIBRATION_SAMPLES)
+
+
+def preprocess_and_tokenize(example):
+    # preprocess
+   
+    text = processor.apply_chat_template(
+        example["messages"], tokenize=False, add_generation_prompt=True
+    )
+    if "<image>" not in text:
+        return None
+    text = text.replace("<image>", "<|vision_start|><|image_pad|><|vision_end|>")
+    
+    res = processor(
+        text=[text],
+        images=example["images"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+    )
+    # tokenize
+    return res
+
+calib_data = []
+for i, item in enumerate(calib_data_raw):
+    print("load %d/%d items..." % (i, len(calib_data_raw)))
+    u = preprocess_and_tokenize(json.loads(item))
+    if u is None:
+        continue
+    calib_data.append(preprocess_and_tokenize(json.loads(item)))
+
+calib_dataset = Dataset.from_list(calib_data)
+# Define a oneshot data collator for multimodal inputs.
+def data_collator(batch):
+    assert len(batch) == 1
+    return {key: torch.tensor(value) for key, value in batch[0].items()}
+
+
+# Recipe
+recipe = [
+    AWQModifier(duo_scaling=False),
+    QuantizationModifier(
+        scheme="W8A16",
+        ignore=["re:.*lm_head", "re:.*visual.*"],
+    ),
+]
+# Perform oneshot
+oneshot(
+    model=model,
+    tokenizer=model_id,
+    dataset=calib_dataset,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    data_collator=data_collator,
+    sequential_targets=["Qwen3VLTextDecoderLayer"],
+)
+
+# Save to disk compressed.
+SAVE_DIR = model_id.rstrip("/").split("/")[-1] + "-W8A16"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+processor.save_pretrained(SAVE_DIR)