Support float16/bfloat16/sparse vector for bulkwriter

examples/example_bulkwriter.py

@@ -17,6 +17,7 @@
 import time
 import pandas as pd
 import numpy as np
+import tensorflow as tf
 
 import logging
 logging.basicConfig(level=logging.INFO)
@@ -51,13 +52,51 @@
 ALL_TYPES_COLLECTION_NAME = "all_types_for_bulkwriter"
 DIM = 512
 
-def gen_binary_vector():
+# optional input for binary vector:
+# 1. list of int such as [1, 0, 1, 1, 0, 0, 1, 0]
+# 2. numpy array of uint8
+def gen_binary_vector(to_numpy_arr):
     raw_vector = [random.randint(0, 1) for i in range(DIM)]
-    binary_vectors = np.packbits(raw_vector, axis=-1).tolist()
-    return binary_vectors
-
-def gen_float_vector():
-    return [random.random() for _ in range(DIM)]
+    if to_numpy_arr:
+        return np.packbits(raw_vector, axis=-1)
+    return raw_vector
+
+# optional input for float vector:
+# 1. list of float such as [0.56, 1.859, 6.55, 9.45]
+# 2. numpy array of float32
+def gen_float_vector(to_numpy_arr):
+    raw_vector = [random.random() for _ in range(DIM)]
+    if to_numpy_arr:
+        return np.array(raw_vector, dtype="float32")
+    return raw_vector
+
+# optional input for bfloat16 vector:
+# 1. list of float such as [0.56, 1.859, 6.55, 9.45]
+# 2. numpy array of bfloat16
+def gen_bf16_vector(to_numpy_arr):
+    raw_vector = [random.random() for _ in range(DIM)]
+    if to_numpy_arr:
+        return tf.cast(raw_vector, dtype=tf.bfloat16).numpy()
+    return raw_vector
+
+# optional input for float16 vector:
+# 1. list of float such as [0.56, 1.859, 6.55, 9.45]
+# 2. numpy array of float16
+def gen_fp16_vector(to_numpy_arr):
+    raw_vector = [random.random() for _ in range(DIM)]
+    if to_numpy_arr:
+        return np.array(raw_vector, dtype=np.float16)
+    return raw_vector
+
+# optional input for sparse vector:
+# only accepts dict of integer key and float value such as {1: 2.32, 43: 4.56, 65: 35.43}
+# note: no need to sort the keys
+def gen_sparse_vector():
+    raw_vector = {}
+    dim = random.randint(2, 20)
+    while len(raw_vector) < dim:
+        raw_vector[random.randint(0, 10000)] = random.random()
+    return raw_vector
 
 def create_connection():
     print(f"\nCreate connection...")
@@ -81,7 +120,7 @@ def build_simple_collection():
     print(f"Collection '{collection.name}' created")
     return collection.schema
 
-def build_all_type_schema(bin_vec: bool, has_array: bool):
+def build_all_type_schema(is_numpy: bool):
     print(f"\n===================== build all types schema ====================")
     fields = [
         FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=False),
@@ -94,12 +133,18 @@ def build_all_type_schema(bin_vec: bool, has_array: bool):
         FieldSchema(name="double", dtype=DataType.DOUBLE),
         FieldSchema(name="varchar", dtype=DataType.VARCHAR, max_length=512),
         FieldSchema(name="json", dtype=DataType.JSON),
-        FieldSchema(name="vector", dtype=DataType.BINARY_VECTOR, dim=DIM) if bin_vec else FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=DIM),
+        # from 2.4.0, milvus supports multiple vector fields in one collection
+        # FieldSchema(name="float_vector", dtype=DataType.FLOAT_VECTOR, dim=DIM),
+        FieldSchema(name="binary_vector", dtype=DataType.BINARY_VECTOR, dim=DIM),
+        FieldSchema(name="float16_vector", dtype=DataType.FLOAT16_VECTOR, dim=DIM),
+        FieldSchema(name="bfloat16_vector", dtype=DataType.BFLOAT16_VECTOR, dim=DIM),
     ]
 
-    if has_array:
+    # milvus doesn't support parsing array/sparse_vector from numpy file
+    if not is_numpy:
         fields.append(FieldSchema(name="array_str", dtype=DataType.ARRAY, max_capacity=100, element_type=DataType.VARCHAR, max_length=128))
         fields.append(FieldSchema(name="array_int", dtype=DataType.ARRAY, max_capacity=100, element_type=DataType.INT64))
+        fields.append(FieldSchema(name="sparse_vector", dtype=DataType.SPARSE_FLOAT_VECTOR))
 
     schema = CollectionSchema(fields=fields, enable_dynamic_field=True)
     return schema
@@ -118,7 +163,7 @@ def read_sample_data(file_path: str, writer: [LocalBulkWriter, RemoteBulkWriter]
 
         writer.append_row(row)
 
-def local_writer(schema: CollectionSchema, file_type: BulkFileType):
+def local_writer_simple(schema: CollectionSchema, file_type: BulkFileType):
     print(f"\n===================== local writer ({file_type.name}) ====================")
     with LocalBulkWriter(
             schema=schema,
@@ -131,7 +176,7 @@ def local_writer(schema: CollectionSchema, file_type: BulkFileType):
 
         # append rows
         for i in range(100000):
-            local_writer.append_row({"path": f"path_{i}", "vector": gen_float_vector(), "label": f"label_{i}"})
+            local_writer.append_row({"path": f"path_{i}", "vector": gen_float_vector(i%2==0), "label": f"label_{i}"})
 
         print(f"{local_writer.total_row_count} rows appends")
         print(f"{local_writer.buffer_row_count} rows in buffer not flushed")
@@ -141,7 +186,7 @@ def local_writer(schema: CollectionSchema, file_type: BulkFileType):
     print(f"Local writer done! output local files: {batch_files}")
 
 
-def remote_writer(schema: CollectionSchema, file_type: BulkFileType):
+def remote_writer_simple(schema: CollectionSchema, file_type: BulkFileType):
     print(f"\n===================== remote writer ({file_type.name}) ====================")
     with RemoteBulkWriter(
             schema=schema,
@@ -160,7 +205,7 @@ def remote_writer(schema: CollectionSchema, file_type: BulkFileType):
 
         # append rows
         for i in range(10000):
-            remote_writer.append_row({"path": f"path_{i}", "vector": gen_float_vector(), "label": f"label_{i}"})
+            remote_writer.append_row({"path": f"path_{i}", "vector": gen_float_vector(i%2==0), "label": f"label_{i}"})
 
         print(f"{remote_writer.total_row_count} rows appends")
         print(f"{remote_writer.buffer_row_count} rows in buffer not flushed")
@@ -174,7 +219,7 @@ def parallel_append(schema: CollectionSchema):
     def _append_row(writer: LocalBulkWriter, begin: int, end: int):
         try:
             for i in range(begin, end):
-                writer.append_row({"path": f"path_{i}", "vector": gen_float_vector(), "label": f"label_{i}"})
+                writer.append_row({"path": f"path_{i}", "vector": gen_float_vector(False), "label": f"label_{i}"})
                 if i%100 == 0:
                     print(f"{threading.current_thread().name} inserted {i-begin} items")
         except Exception as e:
@@ -224,8 +269,8 @@ def _append_row(writer: LocalBulkWriter, begin: int, end: int):
     print("Data is correct")
 
 
-def all_types_writer(bin_vec: bool, schema: CollectionSchema, file_type: BulkFileType)->list:
-    print(f"\n===================== all field types ({file_type.name}) binary_vector={bin_vec} ====================")
+def all_types_writer(schema: CollectionSchema, file_type: BulkFileType)->list:
+    print(f"\n===================== all field types ({file_type.name}) ====================")
     with RemoteBulkWriter(
             schema=schema,
             remote_path="bulk_data",
@@ -251,11 +296,16 @@ def all_types_writer(bin_vec: bool, schema: CollectionSchema, file_type: BulkFil
                 "double": i/7,
                 "varchar": f"varchar_{i}",
                 "json": {"dummy": i, "ok": f"name_{i}"},
-                "vector": gen_binary_vector() if bin_vec else gen_float_vector(),
+                # "float_vector": gen_float_vector(False),
+                "binary_vector": gen_binary_vector(False),
+                "float16_vector": gen_fp16_vector(False),
+                "bfloat16_vector": gen_bf16_vector(False),
                 f"dynamic_{i}": i,
-                # bulkinsert doesn't support import npy with array field, the below values will be stored into dynamic field
+                # bulkinsert doesn't support import npy with array field and sparse vector,
+                # if file_type is numpy, the below values will be stored into dynamic field
                 "array_str": [f"str_{k}" for k in range(5)],
                 "array_int": [k for k in range(10)],
+                "sparse_vector": gen_sparse_vector(),
             }
             remote_writer.append_row(row)
 
@@ -272,11 +322,16 @@ def all_types_writer(bin_vec: bool, schema: CollectionSchema, file_type: BulkFil
                 "double": np.float64(i/7),
                 "varchar": f"varchar_{i}",
                 "json": json.dumps({"dummy": i, "ok": f"name_{i}"}),
-                "vector": np.array(gen_binary_vector()).astype(np.dtype("int8")) if bin_vec else np.array(gen_float_vector()).astype(np.dtype("float32")),
+                # "float_vector": gen_float_vector(True),
+                "binary_vector": gen_binary_vector(True),
+                "float16_vector": gen_fp16_vector(True),
+                "bfloat16_vector": gen_bf16_vector(True),
                 f"dynamic_{i}": i,
-                # bulkinsert doesn't support import npy with array field, the below values will be stored into dynamic field
+                # bulkinsert doesn't support import npy with array field and sparse vector,
+                # if file_type is numpy, the below values will be stored into dynamic field
                 "array_str": np.array([f"str_{k}" for k in range(5)], np.dtype("str")),
                 "array_int": np.array([k for k in range(10)], np.dtype("int64")),
+                "sparse_vector": gen_sparse_vector(),
             })
 
         print(f"{remote_writer.total_row_count} rows appends")
@@ -316,19 +371,29 @@ def call_bulkinsert(schema: CollectionSchema, batch_files: list):
     print(f"Collection row number: {collection.num_entities}")
 
 
-def retrieve_imported_data(bin_vec: bool):
+def retrieve_imported_data():
     collection = Collection(name=ALL_TYPES_COLLECTION_NAME)
     print("Create index...")
-    index_param = {
-        "index_type": "BIN_FLAT",
-        "params": {},
-        "metric_type": "HAMMING"
-    } if bin_vec else {
-        "index_type": "FLAT",
-        "params": {},
-        "metric_type": "L2"
-    }
-    collection.create_index(field_name="vector", index_params=index_param)
+    for field in collection.schema.fields:
+        if (field.dtype == DataType.FLOAT_VECTOR or field.dtype == DataType.FLOAT16_VECTOR
+                or field.dtype == DataType.BFLOAT16_VECTOR):
+            collection.create_index(field_name=field.name, index_params={
+                "index_type": "FLAT",
+                "params": {},
+                "metric_type": "L2"
+            })
+        elif field.dtype == DataType.BINARY_VECTOR:
+            collection.create_index(field_name=field.name, index_params={
+                "index_type": "BIN_FLAT",
+                "params": {},
+                "metric_type": "HAMMING"
+            })
+        elif field.dtype == DataType.SPARSE_FLOAT_VECTOR:
+            collection.create_index(field_name=field.name, index_params={
+                "index_type": "SPARSE_INVERTED_INDEX",
+                "metric_type": "IP",
+                "params": {"drop_ratio_build": 0.2}
+            })
 
     ids = [100, 5000]
     print(f"Load collection and query items {ids}")
@@ -393,28 +458,21 @@ def cloud_bulkinsert():
 
     schema = build_simple_collection()
     for file_type in file_types:
-        local_writer(schema=schema, file_type=file_type)
+        local_writer_simple(schema=schema, file_type=file_type)
 
     for file_type in file_types:
-        remote_writer(schema=schema, file_type=file_type)
+        remote_writer_simple(schema=schema, file_type=file_type)
 
     parallel_append(schema)
 
-    # float vectors + all scalar types
+    # all vector types + all scalar types
     for file_type in file_types:
-        # Note: bulkinsert doesn't support import npy with array field
-        schema = build_all_type_schema(bin_vec=False, has_array=False if file_type==BulkFileType.NUMPY else True)
-        batch_files = all_types_writer(bin_vec=False, schema=schema, file_type=file_type)
+        # Note: bulkinsert doesn't support import npy with array field and sparse vector field
+        schema = build_all_type_schema(is_numpy= file_type==BulkFileType.NUMPY)
+        batch_files = all_types_writer(schema=schema, file_type=file_type)
         call_bulkinsert(schema, batch_files)
-        retrieve_imported_data(bin_vec=False)
+        retrieve_imported_data()
 
-    # binary vectors + all scalar types
-    for file_type in file_types:
-        # Note: bulkinsert doesn't support import npy with array field
-        schema = build_all_type_schema(bin_vec=True, has_array=False if file_type == BulkFileType.NUMPY else True)
-        batch_files = all_types_writer(bin_vec=True, schema=schema, file_type=file_type)
-        call_bulkinsert(schema, batch_files)
-        retrieve_imported_data(bin_vec=True)
 
     # # to call cloud bulkinsert api, you need to apply a cloud service from Zilliz Cloud(https://zilliz.com/cloud)
     # cloud_bulkinsert()

pymilvus/bulk_writer/buffer.py

@@ -149,7 +149,22 @@ def _persist_npy(self, local_path: str, **kwargs):
                         str_arr.append(json.dumps(val))
                     self._buffer[k] = str_arr
 
-                arr = np.array(self._buffer[k], dtype=dt)
+                # currently, milvus server doesn't support numpy for sparse vector
+                if field_schema.dtype == DataType.SPARSE_FLOAT_VECTOR:
+                    self._throw(
+                        f"Failed to persist file {full_file_name},"
+                        f" error: milvus doesn't support parsing sparse vectors from numpy file"
+                    )
+
+                # special process for float16 vector, the self._buffer stores bytes for
+                # float16 vector, convert the bytes to uint8 array
+                if field_schema.dtype in {DataType.FLOAT16_VECTOR, DataType.BFLOAT16_VECTOR}:
+                    a = []
+                    for b in self._buffer[k]:
+                        a.append(np.frombuffer(b, dtype=dt).tolist())
+                    arr = np.array(a, dtype=dt)
+                else:
+                    arr = np.array(self._buffer[k], dtype=dt)
                 np.save(str(full_file_name), arr)
             except Exception as e:
                 self._throw(f"Failed to persist file {full_file_name}, error: {e}")
@@ -173,7 +188,18 @@ def _persist_json_rows(self, local_path: str, **kwargs):
         while row_index < row_count:
             row = {}
             for k, v in self._buffer.items():
-                row[k] = v[row_index]
+                # special process for float16 vector, the self._buffer stores bytes for
+                # float16 vector, convert the bytes to float list
+                field_schema = self._fields[k]
+                if field_schema.dtype in {DataType.FLOAT16_VECTOR, DataType.BFLOAT16_VECTOR}:
+                    dt = (
+                        np.dtype("bfloat16")
+                        if (field_schema.dtype == DataType.BFLOAT16_VECTOR)
+                        else np.float16
+                    )
+                    row[k] = np.frombuffer(v[row_index], dtype=dt).tolist()
+                else:
+                    row[k] = v[row_index]
             rows.append(row)
             row_index = row_index + 1
 
@@ -196,21 +222,25 @@ def _persist_parquet(self, local_path: str, **kwargs):
         data = {}
         for k in self._buffer:
             field_schema = self._fields[k]
-            if field_schema.dtype == DataType.JSON:
-                # for JSON field, store as string array
+            if field_schema.dtype in {DataType.JSON, DataType.SPARSE_FLOAT_VECTOR}:
+                # for JSON and SPARSE_VECTOR field, store as string array
                 str_arr = []
                 for val in self._buffer[k]:
                     str_arr.append(json.dumps(val))
                 data[k] = pd.Series(str_arr, dtype=None)
-            elif field_schema.dtype == DataType.FLOAT_VECTOR:
+            elif field_schema.dtype in {DataType.BINARY_VECTOR, DataType.FLOAT_VECTOR}:
                 arr = []
                 for val in self._buffer[k]:
-                    arr.append(np.array(val, dtype=np.dtype("float32")))
+                    arr.append(np.array(val, dtype=NUMPY_TYPE_CREATOR[field_schema.dtype.name]))
                 data[k] = pd.Series(arr)
-            elif field_schema.dtype == DataType.BINARY_VECTOR:
+            elif field_schema.dtype in {DataType.FLOAT16_VECTOR, DataType.BFLOAT16_VECTOR}:
+                # special process for float16 vector, the self._buffer stores bytes for
+                # float16 vector, convert the bytes to uint8 array
                 arr = []
                 for val in self._buffer[k]:
-                    arr.append(np.array(val, dtype=np.dtype("uint8")))
+                    arr.append(
+                        np.frombuffer(val, dtype=NUMPY_TYPE_CREATOR[field_schema.dtype.name])
+                    )
                 data[k] = pd.Series(arr)
             elif field_schema.dtype == DataType.ARRAY:
                 dt = NUMPY_TYPE_CREATOR[field_schema.element_type.name]

pymilvus/bulk_writer/bulk_writer.py

@@ -116,14 +116,23 @@ def _throw(self, msg: str):
     def _verify_vector(self, x: object, field: FieldSchema):
         dtype = DataType(field.dtype)
         validator = TYPE_VALIDATOR[dtype.name]
-        dim = field.params["dim"]
-        if not validator(x, dim):
-            self._throw(
-                f"Illegal vector data for vector field: '{field.name}',"
-                f" dim is not {dim} or type mismatch"
-            )
-
-        return len(x) * 4 if dtype == DataType.FLOAT_VECTOR else len(x)
+        if dtype != DataType.SPARSE_FLOAT_VECTOR:
+            dim = field.params["dim"]
+            try:
+                origin_list = validator(x, dim)
+                if dtype == DataType.FLOAT_VECTOR:
+                    return origin_list, dim * 4  # for float vector, each dim occupies 4 bytes
+                if dtype == DataType.BINARY_VECTOR:
+                    return origin_list, dim / 8  # for binary vector, 8 dim occupies 1 byte
+                return origin_list, dim * 2  # for float16 vector, each dim occupies 2 bytes
+            except MilvusException as e:
+                self._throw(f"Illegal vector data for vector field: '{field.name}': {e.message}")
+        else:
+            try:
+                validator(x)
+                return x, len(x) * 12  # for sparse vector, each key-value is int-float, 12 bytes
+            except MilvusException as e:
+                self._throw(f"Illegal vector data for vector field: '{field.name}': {e.message}")
 
     def _verify_json(self, x: object, field: FieldSchema):
         size = 0
@@ -187,11 +196,16 @@ def _verify_row(self, row: dict):
                 self._throw(f"The field '{field.name}' is missed in the row")
 
             dtype = DataType(field.dtype)
-            if dtype in {DataType.BINARY_VECTOR, DataType.FLOAT_VECTOR}:
-                if isinstance(row[field.name], np.ndarray):
-                    row[field.name] = row[field.name].tolist()
-
-                row_size = row_size + self._verify_vector(row[field.name], field)
+            if dtype in {
+                DataType.BINARY_VECTOR,
+                DataType.FLOAT_VECTOR,
+                DataType.FLOAT16_VECTOR,
+                DataType.BFLOAT16_VECTOR,
+                DataType.SPARSE_FLOAT_VECTOR,
+            }:
+                origin_list, byte_len = self._verify_vector(row[field.name], field)
+                row[field.name] = origin_list
+                row_size = row_size + byte_len
             elif dtype == DataType.VARCHAR:
                 row_size = row_size + self._verify_varchar(row[field.name], field)
             elif dtype == DataType.JSON: