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feature/sk
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clean-stat
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909d3cc6a8 |
@@ -95,6 +95,8 @@ def create_hnsw_embedding_server(
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passage_sources.append(source_copy)
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passages = PassageManager(passage_sources)
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# Use index dimensions from metadata for shaping fallback responses
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embedding_dim: int = int(meta.get("dimensions", 0))
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logger.info(
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f"Loaded PassageManager with {len(passages.global_offset_map)} passages from metadata"
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)
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@@ -109,6 +111,9 @@ def create_hnsw_embedding_server(
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socket.setsockopt(zmq.RCVTIMEO, 300000)
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socket.setsockopt(zmq.SNDTIMEO, 300000)
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# Track last request type for safe fallback responses on exceptions
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last_request_type = "unknown" # one of: 'text', 'distance', 'embedding', 'unknown'
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last_request_length = 0
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while True:
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try:
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message_bytes = socket.recv()
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@@ -121,6 +126,8 @@ def create_hnsw_embedding_server(
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if isinstance(request_payload, list) and len(request_payload) > 0:
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# Check if this is a direct text request (list of strings)
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if all(isinstance(item, str) for item in request_payload):
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last_request_type = "text"
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last_request_length = len(request_payload)
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logger.info(
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f"Processing direct text embedding request for {len(request_payload)} texts in {embedding_mode} mode"
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)
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@@ -145,43 +152,66 @@ def create_hnsw_embedding_server(
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):
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node_ids = request_payload[0]
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query_vector = np.array(request_payload[1], dtype=np.float32)
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last_request_type = "distance"
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last_request_length = len(node_ids)
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logger.debug("Distance calculation request received")
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logger.debug(f" Node IDs: {node_ids}")
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logger.debug(f" Query vector dim: {len(query_vector)}")
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# Get embeddings for node IDs
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texts = []
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for nid in node_ids:
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# Get embeddings for node IDs, tolerate missing IDs
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texts: list[str] = []
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found_indices: list[int] = []
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for idx, nid in enumerate(node_ids):
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try:
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passage_data = passages.get_passage(str(nid))
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txt = passage_data["text"]
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texts.append(txt)
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txt = passage_data.get("text", "")
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if isinstance(txt, str) and len(txt) > 0:
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texts.append(txt)
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found_indices.append(idx)
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else:
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logger.error(f"Empty text for passage ID {nid}")
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except KeyError:
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logger.error(f"Passage ID {nid} not found")
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raise RuntimeError(f"FATAL: Passage with ID {nid} not found")
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except Exception as e:
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logger.error(f"Exception looking up passage ID {nid}: {e}")
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raise
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# Process embeddings
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embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
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logger.info(
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f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
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# Prepare full-length response distances with safe fallbacks
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large_distance = 1e9
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response_distances = [large_distance] * len(node_ids)
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if texts:
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try:
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# Process embeddings only for found indices
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embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
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logger.info(
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f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
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)
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# Calculate distances for found embeddings only
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if distance_metric == "l2":
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partial_distances = np.sum(
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np.square(embeddings - query_vector.reshape(1, -1)), axis=1
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)
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else: # mips or cosine
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partial_distances = -np.dot(embeddings, query_vector)
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# Place computed distances back into the full response array
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for pos, dval in zip(
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found_indices, partial_distances.flatten().tolist()
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):
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response_distances[pos] = float(dval)
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except Exception as e:
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logger.error(
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f"Distance computation error, falling back to large distances: {e}"
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)
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# Always reply with exactly len(node_ids) distances
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response_bytes = msgpack.packb([response_distances], use_single_float=True)
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logger.debug(
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f"Sending distance response with {len(response_distances)} distances (found={len(found_indices)})"
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)
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# Calculate distances
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if distance_metric == "l2":
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distances = np.sum(
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np.square(embeddings - query_vector.reshape(1, -1)), axis=1
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)
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else: # mips or cosine
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distances = -np.dot(embeddings, query_vector)
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response_payload = distances.flatten().tolist()
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response_bytes = msgpack.packb([response_payload], use_single_float=True)
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logger.debug(f"Sending distance response with {len(distances)} distances")
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socket.send(response_bytes)
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e2e_end = time.time()
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logger.info(f"⏱️ Distance calculation E2E time: {e2e_end - e2e_start:.6f}s")
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@@ -201,40 +231,61 @@ def create_hnsw_embedding_server(
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node_ids = request_payload[0]
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logger.debug(f"Request for {len(node_ids)} node embeddings")
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last_request_type = "embedding"
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last_request_length = len(node_ids)
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# Look up texts by node IDs
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texts = []
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for nid in node_ids:
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# Allocate output buffer (B, D) and fill with zeros for robustness
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if embedding_dim <= 0:
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logger.error("Embedding dimension unknown; cannot serve embedding request")
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dims = [0, 0]
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data = []
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else:
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dims = [len(node_ids), embedding_dim]
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data = [0.0] * (dims[0] * dims[1])
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# Look up texts by node IDs; compute embeddings where available
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texts: list[str] = []
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found_indices: list[int] = []
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for idx, nid in enumerate(node_ids):
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try:
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passage_data = passages.get_passage(str(nid))
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txt = passage_data["text"]
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if not txt:
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raise RuntimeError(f"FATAL: Empty text for passage ID {nid}")
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texts.append(txt)
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txt = passage_data.get("text", "")
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if isinstance(txt, str) and len(txt) > 0:
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texts.append(txt)
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found_indices.append(idx)
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else:
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logger.error(f"Empty text for passage ID {nid}")
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except KeyError:
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raise RuntimeError(f"FATAL: Passage with ID {nid} not found")
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logger.error(f"Passage with ID {nid} not found")
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except Exception as e:
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logger.error(f"Exception looking up passage ID {nid}: {e}")
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raise
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# Process embeddings
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embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
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logger.info(
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f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
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)
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if texts:
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try:
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# Process embeddings for found texts only
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embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
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logger.info(
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f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
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)
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# Serialization and response
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if np.isnan(embeddings).any() or np.isinf(embeddings).any():
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logger.error(
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f"NaN or Inf detected in embeddings! Requested IDs: {node_ids[:5]}..."
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)
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raise AssertionError()
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if np.isnan(embeddings).any() or np.isinf(embeddings).any():
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logger.error(
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f"NaN or Inf detected in embeddings! Requested IDs: {node_ids[:5]}..."
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)
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dims = [0, embedding_dim]
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data = []
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else:
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# Copy computed embeddings into the correct positions
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emb_f32 = np.ascontiguousarray(embeddings, dtype=np.float32)
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flat = emb_f32.flatten().tolist()
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for j, pos in enumerate(found_indices):
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start = pos * embedding_dim
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end = start + embedding_dim
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data[start:end] = flat[j * embedding_dim : (j + 1) * embedding_dim]
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except Exception as e:
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logger.error(f"Embedding computation error, returning zeros: {e}")
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hidden_contiguous_f32 = np.ascontiguousarray(embeddings, dtype=np.float32)
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response_payload = [
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list(hidden_contiguous_f32.shape),
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hidden_contiguous_f32.flatten().tolist(),
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]
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response_payload = [dims, data]
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response_bytes = msgpack.packb(response_payload, use_single_float=True)
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socket.send(response_bytes)
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@@ -249,7 +300,22 @@ def create_hnsw_embedding_server(
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import traceback
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traceback.print_exc()
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socket.send(msgpack.packb([[], []]))
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# Fallback to a safe, minimal-structure response to avoid client crashes
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if last_request_type == "distance":
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# Return a vector of large distances with the expected length
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fallback_len = max(0, int(last_request_length))
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large_distance = 1e9
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safe_response = [[large_distance] * fallback_len]
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elif last_request_type == "embedding":
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# Return an empty embedding block with known dimension if available
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if embedding_dim > 0:
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safe_response = [[0, embedding_dim], []]
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else:
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safe_response = [[0, 0], []]
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else:
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# Unknown request type: default to empty embedding structure
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safe_response = [[0, int(embedding_dim) if embedding_dim > 0 else 0], []]
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socket.send(msgpack.packb(safe_response, use_single_float=True))
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zmq_thread = threading.Thread(target=zmq_server_thread, daemon=True)
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zmq_thread.start()
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