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[EXP] Update the benchmark code (#71)

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* chore(hnsw): reorder imports to satisfy ruff I001

* chore: sync changes; fix Ruff import order; update examples, benchmarks, and dependencies

- Fix import order in packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py (Ruff I001)

- Update benchmarks/run_evaluation.py

- Update apps/base_rag_example.py and leann-core API usage

- Add benchmarks/data/README.md

- Update uv.lock

- Misc cleanup

- Note: added paru-bin as an embedded git repo; consider making it a submodule (git rm --cached paru-bin) if unintended

* chore: remove unintended embedded repo paru-bin and ignore it

Fix CI: avoid missing .gitmodules entry by removing gitlink and adding to .gitignore.

* ci: retrigger after removing unintended gitlink (paru-bin)

* feat(benchmarks): add --batch-size option and plumb through to HNSW search (default 0)

* feat(hnsw): add batch_size to LeannSearcher.search and LeannChat.ask; forward only for HNSW backend

* chore(logging): surface recompute and batching params; enable INFO logging in benchmark

* feat(embeddings): add optional manual tokenization path (HF tokenizer+model) with mean pooling; default remains SentenceTransformer.encode

* fix micro bench and fix pre commit

* update readme

---------

Co-authored-by: yichuan-w <yichuan-w@users.noreply.github.com>
This commit is contained in:
Yichuan Wang
2025-08-20 17:31:46 -07:00
committed by GitHub
parent 6d11e86e71
commit dde2221513
11 changed files with 296 additions and 302 deletions
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1
.gitignore vendored
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@@ -93,5 +93,6 @@ packages/leann-backend-diskann/third_party/DiskANN/_deps/
batchtest.py batchtest.py
tests/__pytest_cache__/ tests/__pytest_cache__/
tests/__pycache__/ tests/__pycache__/
paru-bin/
benchmarks/data/ benchmarks/data/

1
.pre-commit-config.yaml
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@@ -13,4 +13,5 @@ repos:
rev: v0.12.7 # Fixed version to match pyproject.toml rev: v0.12.7 # Fixed version to match pyproject.toml
hooks: hooks:
- id: ruff - id: ruff
args: [--fix, --exit-non-zero-on-fix]
- id: ruff-format - id: ruff-format

15
README.md
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@@ -176,8 +176,7 @@ response = chat.ask("How much storage does LEANN save?", top_k=1)
LEANN supports RAG on various data sources including documents (`.pdf`, `.txt`, `.md`), Apple Mail, Google Search History, WeChat, and more. LEANN supports RAG on various data sources including documents (`.pdf`, `.txt`, `.md`), Apple Mail, Google Search History, WeChat, and more.
**AST-Aware Code Chunking** - LEANN also features intelligent code chunking that preserves semantic boundaries (functions, classes, methods) for Python, Java, C#, and TypeScript files, providing improved code understanding compared to traditional text-based approaches.
📖 Read the [AST Chunking Guide →](docs/ast_chunking_guide.md) to learn more.
### Generation Model Setup ### Generation Model Setup
@@ -477,6 +476,15 @@ Once the index is built, you can ask questions like:
### 🚀 Claude Code Integration: Transform Your Development Workflow! ### 🚀 Claude Code Integration: Transform Your Development Workflow!
<details>
<summary><strong>NEW!! AST‑Aware Code Chunking</strong></summary>
LEANN features intelligent code chunking that preserves semantic boundaries (functions, classes, methods) for Python, Java, C#, and TypeScript, improving code understanding compared to text-based chunking.
📖 Read the [AST Chunking Guide →](docs/ast_chunking_guide.md)
</details>
**The future of code assistance is here.** Transform your development workflow with LEANN's native MCP integration for Claude Code. Index your entire codebase and get intelligent code assistance directly in your IDE. **The future of code assistance is here.** Transform your development workflow with LEANN's native MCP integration for Claude Code. Index your entire codebase and get intelligent code assistance directly in your IDE.
**Key features:** **Key features:**
@@ -697,6 +705,9 @@ MIT License - see [LICENSE](LICENSE) for details.
Core Contributors: [Yichuan Wang](https://yichuan-w.github.io/) & [Zhifei Li](https://github.com/andylizf). Core Contributors: [Yichuan Wang](https://yichuan-w.github.io/) & [Zhifei Li](https://github.com/andylizf).
Active Contributors: [Gabriel Dehan](https://github.com/gabriel-dehan)
We welcome more contributors! Feel free to open issues or submit PRs. We welcome more contributors! Feel free to open issues or submit PRs.
This work is done at [**Berkeley Sky Computing Lab**](https://sky.cs.berkeley.edu/). This work is done at [**Berkeley Sky Computing Lab**](https://sky.cs.berkeley.edu/).

1
apps/base_rag_example.py
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@@ -299,7 +299,6 @@ class BaseRAGExample(ABC):
chat = LeannChat( chat = LeannChat(
index_path, index_path,
llm_config=self.get_llm_config(args), llm_config=self.get_llm_config(args),
system_prompt=f"You are a helpful assistant that answers questions about {self.name} data.",
complexity=args.search_complexity, complexity=args.search_complexity,
) )

44
benchmarks/data/README.md Executable file
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@@ -0,0 +1,44 @@
---
license: mit
---
# LEANN-RAG Evaluation Data
This repository contains the necessary data to run the recall evaluation scripts for the [LEANN-RAG](https://huggingface.co/LEANN-RAG) project.
## Dataset Components
This dataset is structured into three main parts:
1. **Pre-built LEANN Indices**:
* `dpr/`: A pre-built index for the DPR dataset.
* `rpj_wiki/`: A pre-built index for the RPJ-Wiki dataset.
These indices were created using the `leann-core` library and are required by the `LeannSearcher`.
2. **Ground Truth Data**:
* `ground_truth/`: Contains the ground truth files (`flat_results_nq_k3.json`) for both the DPR and RPJ-Wiki datasets. These files map queries to the original passage IDs from the Natural Questions benchmark, evaluated using the Contriever model.
3. **Queries**:
* `queries/`: Contains the `nq_open.jsonl` file with the Natural Questions queries used for the evaluation.
## Usage
To use this data, you can download it locally using the `huggingface-hub` library. First, install the library:
```bash
pip install huggingface-hub
```
Then, you can download the entire dataset to a local directory (e.g., `data/`) with the following Python script:
```python
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="LEANN-RAG/leann-rag-evaluation-data",
repo_type="dataset",
local_dir="data"
)
```
This will download all the necessary files into a local `data` folder, preserving the repository structure. The evaluation scripts in the main [LEANN-RAG Space](https://huggingface.co/LEANN-RAG) are configured to work with this data structure.

38
benchmarks/run_evaluation.py
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@@ -12,7 +12,7 @@ import time
from pathlib import Path from pathlib import Path
import numpy as np import numpy as np
from leann.api import LeannBuilder, LeannSearcher from leann.api import LeannBuilder, LeannChat, LeannSearcher
def download_data_if_needed(data_root: Path, download_embeddings: bool = False): def download_data_if_needed(data_root: Path, download_embeddings: bool = False):
@@ -197,6 +197,25 @@ def main():
parser.add_argument( parser.add_argument(
"--ef-search", type=int, default=120, help="The 'efSearch' parameter for HNSW." "--ef-search", type=int, default=120, help="The 'efSearch' parameter for HNSW."
) )
parser.add_argument(
"--batch-size",
type=int,
default=0,
help="Batch size for HNSW batched search (0 disables batching)",
)
parser.add_argument(
"--llm-type",
type=str,
choices=["ollama", "hf", "openai", "gemini", "simulated"],
default="ollama",
help="LLM backend type to optionally query during evaluation (default: ollama)",
)
parser.add_argument(
"--llm-model",
type=str,
default="qwen3:1.7b",
help="LLM model identifier for the chosen backend (default: qwen3:1.7b)",
)
args = parser.parse_args() args = parser.parse_args()
# --- Path Configuration --- # --- Path Configuration ---
@@ -318,9 +337,24 @@ def main():
for i in range(num_eval_queries): for i in range(num_eval_queries):
start_time = time.time() start_time = time.time()
new_results = searcher.search(queries[i], top_k=args.top_k, ef=args.ef_search) new_results = searcher.search(
queries[i],
top_k=args.top_k,
complexity=args.ef_search,
batch_size=args.batch_size,
)
search_times.append(time.time() - start_time) search_times.append(time.time() - start_time)
# Optional: also call the LLM with configurable backend/model (does not affect recall)
llm_config = {"type": args.llm_type, "model": args.llm_model}
chat = LeannChat(args.index_path, llm_config=llm_config, searcher=searcher)
answer = chat.ask(
queries[i],
top_k=args.top_k,
complexity=args.ef_search,
batch_size=args.batch_size,
)
print(f"Answer: {answer}")
# Correct Recall Calculation: Based on TEXT content # Correct Recall Calculation: Based on TEXT content
new_texts = {result.text for result in new_results} new_texts = {result.text for result in new_results}

11
benchmarks/simple_mac_tpt_test.py
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@@ -20,7 +20,7 @@ except ImportError:
@dataclass @dataclass
class BenchmarkConfig: class BenchmarkConfig:
model_path: str = "facebook/contriever" model_path: str = "facebook/contriever-msmarco"
batch_sizes: list[int] = None batch_sizes: list[int] = None
seq_length: int = 256 seq_length: int = 256
num_runs: int = 5 num_runs: int = 5
@@ -34,7 +34,7 @@ class BenchmarkConfig:
def __post_init__(self): def __post_init__(self):
if self.batch_sizes is None: if self.batch_sizes is None:
self.batch_sizes = [1, 2, 4, 8, 16, 32, 64] self.batch_sizes = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
class MLXBenchmark: class MLXBenchmark:
@@ -179,11 +179,14 @@ class Benchmark:
def _run_inference(self, input_ids: torch.Tensor) -> float: def _run_inference(self, input_ids: torch.Tensor) -> float:
attention_mask = torch.ones_like(input_ids) attention_mask = torch.ones_like(input_ids)
# print shape of input_ids and attention_mask
print(f"input_ids shape: {input_ids.shape}")
print(f"attention_mask shape: {attention_mask.shape}")
start_time = time.time() start_time = time.time()
with torch.no_grad(): with torch.no_grad():
self.model(input_ids=input_ids, attention_mask=attention_mask) self.model(input_ids=input_ids, attention_mask=attention_mask)
# mps sync if torch.cuda.is_available():
torch.cuda.synchronize()
if torch.backends.mps.is_available(): if torch.backends.mps.is_available():
torch.mps.synchronize() torch.mps.synchronize()
end_time = time.time() end_time = time.time()

5
packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py
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@@ -1,6 +1,7 @@
import logging import logging
import os import os
import shutil import shutil
import time
from pathlib import Path from pathlib import Path
from typing import Any, Literal, Optional from typing import Any, Literal, Optional
@@ -236,6 +237,7 @@ class HNSWSearcher(BaseSearcher):
distances = np.empty((batch_size_query, top_k), dtype=np.float32) distances = np.empty((batch_size_query, top_k), dtype=np.float32)
labels = np.empty((batch_size_query, top_k), dtype=np.int64) labels = np.empty((batch_size_query, top_k), dtype=np.int64)
search_time = time.time()
self._index.search( self._index.search(
query.shape[0], query.shape[0],
faiss.swig_ptr(query), faiss.swig_ptr(query),
@@ -244,7 +246,8 @@ class HNSWSearcher(BaseSearcher):
faiss.swig_ptr(labels), faiss.swig_ptr(labels),
params, params,
) )
search_time = time.time() - search_time
logger.info(f" Search time in HNSWSearcher.search() backend: {search_time} seconds")
string_labels = [[str(int_label) for int_label in batch_labels] for batch_labels in labels] string_labels = [[str(int_label) for int_label in batch_labels] for batch_labels in labels]
return {"labels": string_labels, "distances": distances} return {"labels": string_labels, "distances": distances}

51
packages/leann-core/src/leann/api.py
View File

@@ -578,6 +578,8 @@ class LeannSearcher:
self.passage_manager = PassageManager( self.passage_manager = PassageManager(
self.meta_data.get("passage_sources", []), metadata_file_path=self.meta_path_str self.meta_data.get("passage_sources", []), metadata_file_path=self.meta_path_str
) )
# Preserve backend name for conditional parameter forwarding
self.backend_name = backend_name
backend_factory = BACKEND_REGISTRY.get(backend_name) backend_factory = BACKEND_REGISTRY.get(backend_name)
if backend_factory is None: if backend_factory is None:
raise ValueError(f"Backend '{backend_name}' not found.") raise ValueError(f"Backend '{backend_name}' not found.")
@@ -597,6 +599,7 @@ class LeannSearcher:
recompute_embeddings: bool = True, recompute_embeddings: bool = True,
pruning_strategy: Literal["global", "local", "proportional"] = "global", pruning_strategy: Literal["global", "local", "proportional"] = "global",
expected_zmq_port: int = 5557, expected_zmq_port: int = 5557,
batch_size: int = 0,
**kwargs, **kwargs,
) -> list[SearchResult]: ) -> list[SearchResult]:
logger.info("🔍 LeannSearcher.search() called:") logger.info("🔍 LeannSearcher.search() called:")
@@ -636,23 +639,33 @@ class LeannSearcher:
use_server_if_available=recompute_embeddings, use_server_if_available=recompute_embeddings,
zmq_port=zmq_port, zmq_port=zmq_port,
) )
# logger.info(f" Generated embedding shape: {query_embedding.shape}") logger.info(f" Generated embedding shape: {query_embedding.shape}")
# time.time() - start_time embedding_time = time.time() - start_time
# logger.info(f" Embedding time: {embedding_time} seconds") logger.info(f" Embedding time: {embedding_time} seconds")
start_time = time.time() start_time = time.time()
backend_search_kwargs: dict[str, Any] = {
"complexity": complexity,
"beam_width": beam_width,
"prune_ratio": prune_ratio,
"recompute_embeddings": recompute_embeddings,
"pruning_strategy": pruning_strategy,
"zmq_port": zmq_port,
}
# Only HNSW supports batching; forward conditionally
if self.backend_name == "hnsw":
backend_search_kwargs["batch_size"] = batch_size
# Merge any extra kwargs last
backend_search_kwargs.update(kwargs)
results = self.backend_impl.search( results = self.backend_impl.search(
query_embedding, query_embedding,
top_k, top_k,
complexity=complexity, **backend_search_kwargs,
beam_width=beam_width,
prune_ratio=prune_ratio,
recompute_embeddings=recompute_embeddings,
pruning_strategy=pruning_strategy,
zmq_port=zmq_port,
**kwargs,
) )
# logger.info(f" Search time: {search_time} seconds") search_time = time.time() - start_time
logger.info(f" Search time in search() LEANN searcher: {search_time} seconds")
logger.info(f" Backend returned: labels={len(results.get('labels', [[]])[0])} results") logger.info(f" Backend returned: labels={len(results.get('labels', [[]])[0])} results")
enriched_results = [] enriched_results = []
@@ -731,9 +744,15 @@ class LeannChat:
index_path: str, index_path: str,
llm_config: Optional[dict[str, Any]] = None, llm_config: Optional[dict[str, Any]] = None,
enable_warmup: bool = False, enable_warmup: bool = False,
searcher: Optional[LeannSearcher] = None,
**kwargs, **kwargs,
): ):
self.searcher = LeannSearcher(index_path, enable_warmup=enable_warmup, **kwargs) if searcher is None:
self.searcher = LeannSearcher(index_path, enable_warmup=enable_warmup, **kwargs)
self._owns_searcher = True
else:
self.searcher = searcher
self._owns_searcher = False
self.llm = get_llm(llm_config) self.llm = get_llm(llm_config)
def ask( def ask(
@@ -747,6 +766,7 @@ class LeannChat:
pruning_strategy: Literal["global", "local", "proportional"] = "global", pruning_strategy: Literal["global", "local", "proportional"] = "global",
llm_kwargs: Optional[dict[str, Any]] = None, llm_kwargs: Optional[dict[str, Any]] = None,
expected_zmq_port: int = 5557, expected_zmq_port: int = 5557,
batch_size: int = 0,
**search_kwargs, **search_kwargs,
): ):
if llm_kwargs is None: if llm_kwargs is None:
@@ -761,10 +781,11 @@ class LeannChat:
recompute_embeddings=recompute_embeddings, recompute_embeddings=recompute_embeddings,
pruning_strategy=pruning_strategy, pruning_strategy=pruning_strategy,
expected_zmq_port=expected_zmq_port, expected_zmq_port=expected_zmq_port,
batch_size=batch_size,
**search_kwargs, **search_kwargs,
) )
search_time = time.time() - search_time search_time = time.time() - search_time
# logger.info(f" Search time: {search_time} seconds") logger.info(f" Search time: {search_time} seconds")
context = "\n\n".join([r.text for r in results]) context = "\n\n".join([r.text for r in results])
prompt = ( prompt = (
"Here is some retrieved context that might help answer your question:\n\n" "Here is some retrieved context that might help answer your question:\n\n"
@@ -800,7 +821,9 @@ class LeannChat:
This method should be called after you're done using the chat interface, This method should be called after you're done using the chat interface,
especially in test environments or batch processing scenarios. especially in test environments or batch processing scenarios.
""" """
if hasattr(self.searcher, "cleanup"): # Only stop the embedding server if this LeannChat instance created the searcher.
# When a shared searcher is passed in, avoid shutting down the server to enable reuse.
if getattr(self, "_owns_searcher", False) and hasattr(self.searcher, "cleanup"):
self.searcher.cleanup() self.searcher.cleanup()
# Enable automatic cleanup patterns # Enable automatic cleanup patterns

139
packages/leann-core/src/leann/embedding_compute.py
View File

@@ -6,6 +6,7 @@ Preserves all optimization parameters to ensure performance
import logging import logging
import os import os
import time
from typing import Any from typing import Any
import numpy as np import numpy as np
@@ -28,6 +29,8 @@ def compute_embeddings(
is_build: bool = False, is_build: bool = False,
batch_size: int = 32, batch_size: int = 32,
adaptive_optimization: bool = True, adaptive_optimization: bool = True,
manual_tokenize: bool = False,
max_length: int = 512,
) -> np.ndarray: ) -> np.ndarray:
""" """
Unified embedding computation entry point Unified embedding computation entry point
@@ -50,6 +53,8 @@ def compute_embeddings(
is_build=is_build, is_build=is_build,
batch_size=batch_size, batch_size=batch_size,
adaptive_optimization=adaptive_optimization, adaptive_optimization=adaptive_optimization,
manual_tokenize=manual_tokenize,
max_length=max_length,
) )
elif mode == "openai": elif mode == "openai":
return compute_embeddings_openai(texts, model_name) return compute_embeddings_openai(texts, model_name)
@@ -71,6 +76,8 @@ def compute_embeddings_sentence_transformers(
batch_size: int = 32, batch_size: int = 32,
is_build: bool = False, is_build: bool = False,
adaptive_optimization: bool = True, adaptive_optimization: bool = True,
manual_tokenize: bool = False,
max_length: int = 512,
) -> np.ndarray: ) -> np.ndarray:
""" """
Compute embeddings using SentenceTransformer with model caching and adaptive optimization Compute embeddings using SentenceTransformer with model caching and adaptive optimization
@@ -214,20 +221,130 @@ def compute_embeddings_sentence_transformers(
logger.info(f"Model cached: {cache_key}") logger.info(f"Model cached: {cache_key}")
# Compute embeddings with optimized inference mode # Compute embeddings with optimized inference mode
logger.info(f"Starting embedding computation... (batch_size: {batch_size})") logger.info(
f"Starting embedding computation... (batch_size: {batch_size}, manual_tokenize={manual_tokenize})"
)
# Use torch.inference_mode for optimal performance start_time = time.time()
with torch.inference_mode(): if not manual_tokenize:
embeddings = model.encode( # Use SentenceTransformer's optimized encode path (default)
texts, with torch.inference_mode():
batch_size=batch_size, embeddings = model.encode(
show_progress_bar=is_build, # Don't show progress bar in server environment texts,
convert_to_numpy=True, batch_size=batch_size,
normalize_embeddings=False, show_progress_bar=is_build, # Don't show progress bar in server environment
device=device, convert_to_numpy=True,
) normalize_embeddings=False,
device=device,
)
# Synchronize if CUDA to measure accurate wall time
try:
if torch.cuda.is_available():
torch.cuda.synchronize()
except Exception:
pass
else:
# Manual tokenization + forward pass using HF AutoTokenizer/AutoModel
try:
from transformers import AutoModel, AutoTokenizer # type: ignore
except Exception as e:
raise ImportError(f"transformers is required for manual_tokenize=True: {e}")
# Cache tokenizer and model
tok_cache_key = f"hf_tokenizer_{model_name}"
mdl_cache_key = f"hf_model_{model_name}_{device}_{use_fp16}"
if tok_cache_key in _model_cache and mdl_cache_key in _model_cache:
hf_tokenizer = _model_cache[tok_cache_key]
hf_model = _model_cache[mdl_cache_key]
logger.info("Using cached HF tokenizer/model for manual path")
else:
logger.info("Loading HF tokenizer/model for manual tokenization path")
hf_tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
torch_dtype = torch.float16 if (use_fp16 and device == "cuda") else torch.float32
hf_model = AutoModel.from_pretrained(model_name, torch_dtype=torch_dtype)
hf_model.to(device)
hf_model.eval()
# Optional compile on supported devices
if device in ["cuda", "mps"]:
try:
hf_model = torch.compile(hf_model, mode="reduce-overhead", dynamic=True) # type: ignore
except Exception:
pass
_model_cache[tok_cache_key] = hf_tokenizer
_model_cache[mdl_cache_key] = hf_model
all_embeddings: list[np.ndarray] = []
# Progress bar when building or for large inputs
show_progress = is_build or len(texts) > 32
try:
if show_progress:
from tqdm import tqdm # type: ignore
batch_iter = tqdm(
range(0, len(texts), batch_size),
desc="Embedding (manual)",
unit="batch",
)
else:
batch_iter = range(0, len(texts), batch_size)
except Exception:
batch_iter = range(0, len(texts), batch_size)
start_time_manual = time.time()
with torch.inference_mode():
for start_index in batch_iter:
end_index = min(start_index + batch_size, len(texts))
batch_texts = texts[start_index:end_index]
tokenize_start_time = time.time()
inputs = hf_tokenizer(
batch_texts,
padding=True,
truncation=True,
max_length=max_length,
return_tensors="pt",
)
tokenize_end_time = time.time()
logger.info(
f"Tokenize time taken: {tokenize_end_time - tokenize_start_time} seconds"
)
# Print shapes of all input tensors for debugging
for k, v in inputs.items():
print(f"inputs[{k!r}] shape: {getattr(v, 'shape', type(v))}")
to_device_start_time = time.time()
inputs = {k: v.to(device) for k, v in inputs.items()}
to_device_end_time = time.time()
logger.info(
f"To device time taken: {to_device_end_time - to_device_start_time} seconds"
)
forward_start_time = time.time()
outputs = hf_model(**inputs)
forward_end_time = time.time()
logger.info(f"Forward time taken: {forward_end_time - forward_start_time} seconds")
last_hidden_state = outputs.last_hidden_state # (B, L, H)
attention_mask = inputs.get("attention_mask")
if attention_mask is None:
# Fallback: assume all tokens are valid
pooled = last_hidden_state.mean(dim=1)
else:
mask = attention_mask.unsqueeze(-1).to(last_hidden_state.dtype)
masked = last_hidden_state * mask
lengths = mask.sum(dim=1).clamp(min=1)
pooled = masked.sum(dim=1) / lengths
# Move to CPU float32
batch_embeddings = pooled.detach().to("cpu").float().numpy()
all_embeddings.append(batch_embeddings)
embeddings = np.vstack(all_embeddings).astype(np.float32, copy=False)
try:
if torch.cuda.is_available():
torch.cuda.synchronize()
except Exception:
pass
end_time = time.time()
logger.info(f"Manual tokenize time taken: {end_time - start_time_manual} seconds")
end_time = time.time()
logger.info(f"Generated {len(embeddings)} embeddings, dimension: {embeddings.shape[1]}") logger.info(f"Generated {len(embeddings)} embeddings, dimension: {embeddings.shape[1]}")
logger.info(f"Time taken: {end_time - start_time} seconds")
# Validate results # Validate results
if np.isnan(embeddings).any() or np.isinf(embeddings).any(): if np.isnan(embeddings).any() or np.isinf(embeddings).any():

292
uv.lock generated
View File

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