add m3e_base,bge_reranker

pytorch/Embedding/bge_reranker_large/LICENSE.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 staoxiao
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

pytorch/Embedding/bge_reranker_large/READE.md

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+0. Start docker
+启动命令可参考: [README.md](../../README.md)
+
+1. Prerequisites
+```shell
+pip install -r requirements.txt
+
+pip install -U huggingface_hub
+```
+2. export env
+```shell
+export HF_ENDPOINT=https://hf-mirror.com
+```
+
+3. Test
+```shell
+python bge_reranker_large.py
+```

pytorch/Embedding/bge_reranker_large/bge_reranker_large.py

@@ -0,0 +1,107 @@
+from FlagEmbedding import FlagReranker
+import time
+import numpy as np
+import concurrent.futures
+import random
+import string
+
+# 生成长文本（1024 tokens约1500-1800字符）
+def generate_long_text(target_tokens=1024):
+    words = []
+    current_tokens = 0
+    while current_tokens < target_tokens:
+        word_len = random.randint(3, 10)
+        words.append(''.join(random.choices(string.ascii_letters, k=word_len)))
+        current_tokens += 1
+    return " ".join(words)
+
+def process_batch(reranker, batch_pairs):
+    """处理单个批次并返回结果和耗时"""
+    start_time = time.perf_counter()
+    scores = reranker.compute_score(batch_pairs)
+    end_time = time.perf_counter()
+    return scores, end_time - start_time
+
+def main():
+    # 加载模型（FP16精度 + MUSA设备加速）
+    reranker = FlagReranker('BAAI/bge-reranker-large', 
+                           use_fp16=True, 
+                           device="musa")
+
+    # ===== 长文本优化：生成1024 tokens的输入 =====
+    print("=== Generating 1024-token texts ===")
+    long_query = generate_long_text(1024)
+    long_passage = generate_long_text(1024)
+
+    # ===== 关键优化：添加模型预热（使用长文本）===== [1,6](@ref)
+    print("=== Starting model warm-up with long texts ===")
+    warmup_pairs = [[long_query, long_passage]] * 16
+    for _ in range(5):
+        reranker.compute_score(warmup_pairs)
+    print("=== Warm-up completed ===\n")
+
+    # 单次长文本推理测试
+    start_time = time.perf_counter()
+    score = reranker.compute_score([long_query, long_passage])
+    latency = (time.perf_counter() - start_time) * 1000
+    print(f"Long text score: {str(score)} | Latency: {latency:.2f} ms")
+
+    # 准备批量数据（30个并行任务）
+    batch_pairs_list = []
+    for _ in range(30):
+        pairs = []
+        for _ in range(64):  # 每个任务64个样本
+            q = generate_long_text(1024) if random.random() > 0.5 else long_query
+            p = generate_long_text(1024) if random.random() > 0.5 else long_passage
+            pairs.append([q, p])
+        batch_pairs_list.append(pairs)
+
+    # ===== 并行执行30个任务 =====
+    print("\n=== Starting 30 parallel batch processing ===")
+    total_tokens = sum(
+        sum(len(q.split()) + len(p.split()) for q, p in pairs) 
+        for pairs in batch_pairs_list
+    )
+    batch_times = [] 
+    start_time = time.perf_counter()
+    with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:  # 控制并发数
+        futures = [executor.submit(process_batch, reranker, pairs) for pairs in batch_pairs_list]
+
+        batch_results = []
+        for future in concurrent.futures.as_completed(futures):
+            scores, batch_time = future.result()
+            batch_results.append(scores)
+            batch_times.append(batch_time)
+
+    total_time = time.perf_counter() - start_time
+
+    # 性能统计
+    total_pairs = 30 * 64  # 30任务 * 每任务64对
+    throughput_pairs = total_pairs / total_time
+    throughput_tokens = total_tokens / total_time
+
+    avg_batch_time = sum(batch_times) / len(batch_times)
+    max_batch_time = max(batch_times)
+    min_batch_time = min(batch_times)
+
+    print("\n===== Performance Report =====")
+    print(f"Total batches processed: {len(batch_results)}")
+    print(f"Total pairs processed: {total_pairs}")
+    print(f"Total tokens processed: {total_tokens}")
+    print(f"Total processing time: {total_time:.2f} seconds")
+
+    print("\n--- Throughput ---")
+    print(f"Throughput: {throughput_pairs:.2f} pairs/sec")
+    print(f"Token throughput: {throughput_tokens:.2f} tokens/sec")
+
+    print("\n--- Latency ---")
+    print(f"Average batch time: {avg_batch_time:.4f} sec")
+    print(f"Max batch time: {max_batch_time:.4f} sec")
+    print(f"Min batch time: {min_batch_time:.4f} sec")
+
+    print("=============================")
+
+
+
+if __name__ == "__main__":
+    main()

pytorch/Embedding/bge_reranker_large/requirements.txt

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+FlagEmbedding
+accelerate==1.0.1
+transformers==4.44.0
+peft
+

pytorch/Embedding/m3e_base/LICENSE

@@ -0,0 +1,56 @@
+Licensed under the Apache License 2.0
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pytorch/Embedding/m3e_base/README.md

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+0. Start docker
+启动命令可参考: [README.md](../../README.md)
+
+1. Prerequisites
+```shell
+pip install -r requirements.txt
+
+pip install -U huggingface_hub
+```
+2. export env
+```shell
+export HF_ENDPOINT=https://hf-mirror.com
+```
+
+3. Test
+```shell
+python perf_m3e_base.py
+```

pytorch/Embedding/m3e_base/perf_m3e_base.py

@@ -0,0 +1,140 @@
+import time
+import concurrent.futures
+import numpy as np
+import torch
+import torch_musa
+from sentence_transformers import SentenceTransformer
+from tqdm import tqdm
+
+# 生成长文本（1024 tokens约1500-1800字符）
+def generate_long_text(target_tokens=1024):
+    """生成符合目标token长度的随机文本"""
+    import random
+    import string
+
+    words = []
+    current_tokens = 0
+    while current_tokens < target_tokens:
+        word_len = random.randint(3, 10)
+        words.append(''.join(random.choices(string.ascii_letters, k=word_len)))
+        current_tokens += 1
+    return " ".join(words)
+
+def process_batch(model, sentences, batch_size=32):
+    """处理单个批次并返回结果和耗时"""
+    start_time = time.perf_counter()
+    embeddings = model.encode(
+        sentences,
+        batch_size=batch_size,
+        convert_to_numpy=True,
+        show_progress_bar=False
+    )
+    end_time = time.perf_counter()
+    return embeddings, end_time - start_time
+
+def count_tokens(texts):
+    """近似计算token数量（实际应使用tokenizer）"""
+    return sum(len(text.split()) for text in texts)
+
+def main():
+    # 初始化模型（使用MUSA设备加速）
+    model = SentenceTransformer('moka-ai/m3e-base', device='musa')
+
+    # ===== 长文本优化：生成1024 tokens的输入 =====
+    print("=== Generating 1024-token texts ===")
+    long_text = generate_long_text(1024)
+
+    # ===== 关键优化：添加长文本模型预热 =====
+    print("\n=== Starting model warm-up with long texts ===")
+    warmup_sentences = [long_text] * 5  # 使用长文本预热
+    for _ in range(10):
+        model.encode(
+            warmup_sentences,
+            batch_size=32,
+            convert_to_numpy=True,
+            show_progress_bar=False
+        )
+    print("=== Warm-up completed ===\n")
+
+    # ===== 准备30个并行任务 =====
+    num_tasks = 30
+    batch_size = 32
+    batches = []
+
+    # 生成30个批次的输入数据（每个批次包含batch_size个句子）
+    for _ in range(num_tasks):
+        sentences_batch = []
+        for _ in range(batch_size):
+            # 50%概率使用长文本，50%生成新文本
+            if np.random.rand() > 0.5:
+                sentences_batch.append(long_text)
+            else:
+                sentences_batch.append(generate_long_text(1024))
+        batches.append(sentences_batch)
+
+    # 计算总token数
+    total_tokens = sum(count_tokens(batch) for batch in batches)
+    total_sentences = num_tasks * batch_size
+
+    # ===== 并行执行30个任务 =====
+    print(f"=== Starting {num_tasks} parallel batch processing ===")
+    start_time = time.perf_counter()
+
+    # 记录GPU初始状态
+    initial_mem = torch_musa.memory_allocated()
+
+    # 使用线程池并行处理
+    batch_results = []
+    batch_times = []
+
+    with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:
+        futures = [executor.submit(process_batch, model, batch, batch_size) 
+                  for batch in batches]
+
+        # 使用tqdm显示进度条
+        for future in tqdm(concurrent.futures.as_completed(futures), 
+                          total=len(futures), desc="Processing batches"):
+            embeddings, batch_time = future.result()
+            batch_results.append(embeddings)
+            batch_times.append(batch_time)
+
+    end_time = time.perf_counter()
+    total_time = end_time - start_time
+
+    # ===== 性能指标计算 =====
+    # 1. 吞吐量指标
+    throughput_batches = len(batch_results) / total_time
+    throughput_sentences = total_sentences / total_time
+    throughput_tokens = total_tokens / total_time
+
+    # 2. 延迟指标
+    avg_batch_time = sum(batch_times) / len(batch_times)
+    max_batch_time = max(batch_times)
+    min_batch_time = min(batch_times)
+
+
+    # ===== 性能报告 =====
+    print("\n===== Performance Report =====")
+    print(f"Total batches processed: {len(batch_results)}")
+    print(f"Total sentences processed: {total_sentences}")
+    print(f"Total tokens processed: {total_tokens}")
+    print(f"Total processing time: {total_time:.2f} seconds")
+
+    print("\n--- Throughput ---")
+    print(f"Throughput (batches/sec): {throughput_batches:.2f}")
+    print(f"Throughput (sentences/sec): {throughput_sentences:.2f}")
+    print(f"Throughput (tokens/sec): {throughput_tokens:.2f}")
+
+    print("\n--- Latency ---")
+    print(f"Average batch time: {avg_batch_time:.4f} sec")
+    print(f"Max batch time: {max_batch_time:.4f} sec")
+    print(f"Min batch time: {min_batch_time:.4f} sec")
+
+    print("=============================")
+
+    # 打印第一个批次的第一个句子嵌入示例
+    print("\nSample embedding (first sentence of first batch):")
+    print(batch_results[0][0][:10])  # 只打印前10维
+
+if __name__ == "__main__":
+    main()

pytorch/Embedding/m3e_base/requirements.txt

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+accelerate==1.0.1
+transformers==4.44.0
+peft
+