Dev postgresql 10 30,adding neurstore example

CMakeLists.txt

@@ -20,6 +20,10 @@ CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 
 PROJECT(singa)
 
+IF(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
+    SET(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
+ENDIF()
+
 LIST(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Thirdparty)
 
 #include(GetGitRevisionDescription)
@@ -47,10 +51,10 @@ message(STATUS "SINGA Version ${SINGA_VERSION}")
 
 # Flags
 IF(UNIX)
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -g -O2 -fPIC -Wall -pthread")
+    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -fPIC -Wall -pthread")
 ENDIF()
 IF(APPLE)
-    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -stdlib=libc++ -g -O2 -fPIC -Wall -pthread")
+    SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -stdlib=libc++ -fPIC -Wall -pthread")
     SET(CMAKE_MACOSX_RPATH 1)
 ENDIF()
 
@@ -61,7 +65,7 @@ IF(WIN32)
         #MESSAGE(STATUS "Using msvc compiler ${CMAKE_CXX_FLAGS}")
     ENDIF()
 ENDIF()
-IF(CMAKE_BUILD_TYPE=Debug)
+IF(CMAKE_BUILD_TYPE STREQUAL "Debug")
     SET(NVCC_FLAG "${NVCC_FLAG} -g -G ")
 ENDIF()
 #message(STATUS "${CMAKE_CXX_FLAGS}")

cmake/Dependencies.cmake

@@ -52,6 +52,7 @@ IF(USE_MODULES)
     #ENDIF()
 ELSE()
     FIND_PACKAGE( Protobuf 3.0 REQUIRED )
+    INCLUDE_DIRECTORIES(${Protobuf_INCLUDE_DIRS})
     #MESSAGE(STATUS "proto libs " ${PROTOBUF_LIBRARY})
     LIST(APPEND SINGA_LINKER_LIBS ${PROTOBUF_LIBRARY})
     #IF(USE_CBLAS)

examples/neurostore/prepare.py

@@ -0,0 +1,67 @@
+"""
+prepare.py
+
+This script downloads models from Hugging Face and exports them to ONNX format.
+In our example, we use three DistilBERT models:
+1. google-bert/bert-base-uncased
+2. lorenzozan/ME2-BERT
+3. prithivMLmods/Spam-Bert-Uncased
+
+To download and export other models, modify: 1). the `models` list in the main function. 2). the
+dummy_inputs and torch.onnx.export function parameters.
+"""
+
+import os
+import torch
+from transformers import AutoModel
+
+DUMMY_SEQ_LENGTH = 32
+
+
+def export_model_to_onnx(model_name: str, save_dir: str):
+    """
+    Downloads and exports a Hugging Face model to ONNX format.
+    """
+    print(f"Processing {model_name}...")
+    try:
+        model = AutoModel.from_pretrained(model_name)
+        model.eval()
+
+        dummy_inputs = {
+            "input_ids": torch.randint(0, 30522, (1, DUMMY_SEQ_LENGTH)),
+            "attention_mask": torch.ones((1, DUMMY_SEQ_LENGTH), dtype=torch.long)
+        }
+
+        safe_model_name = model_name.replace("/", "_")
+        export_path = os.path.join(save_dir, f"{safe_model_name}.onnx")
+
+        torch.onnx.export(
+            model,
+            (dummy_inputs["input_ids"], dummy_inputs["attention_mask"]),
+            export_path,
+            input_names=["input_ids", "attention_mask"],
+            output_names=["last_hidden_state"],
+            dynamic_axes={
+                "input_ids": {0: "batch_size", 1: "sequence_length"},
+                "attention_mask": {0: "batch_size", 1: "sequence_length"},
+                "last_hidden_state": {0: "batch_size", 1: "sequence_length"}
+            },
+            opset_version=14,
+        )
+        print(f"Exported ONNX to {export_path}")
+    except Exception as e:
+        print(f"Failed to export {model_name}: {e}")
+
+
+if __name__ == "__main__":
+    model_dir = "./models"
+    os.makedirs(model_dir, exist_ok=True)
+
+    models = [
+        "distilbert/distilbert-base-uncased",
+        "winegarj/distilbert-base-uncased-finetuned-sst2",
+        "eliasalbouzidi/distilbert-nsfw-text-classifier"
+    ]
+
+    for model_name in models:
+        export_model_to_onnx(model_name, model_dir)

java/CMakeLists.txt

@@ -104,7 +104,7 @@ PROTOBUF_GENERATE_JAVA(proto_javas ${proto_files})
 # MESSAGE(STATUS "proto javas: ${proto_javas}")
 
 execute_process(
-    COMMAND swig -c++ -java -I${CMAKE_SOURCE_DIR}/include
+    COMMAND ${SWIG_EXECUTABLE} -c++ -java -I${CMAKE_BINARY_DIR}/src/api -I${CMAKE_SOURCE_DIR}/include
     -I${JAVA_INCLUDE_PATH} -I${JAVA_INCLUDE_PATH2}
     -package org.apache.singa.swig
     -outdir ${CMAKE_BINARY_DIR}/java/src/main/java/org/apache/singa/swig

python/CMakeLists.txt

@@ -94,17 +94,22 @@ file(MAKE_DIRECTORY ${CMAKE_BINARY_DIR}/python/singa/proto)
 file(MAKE_DIRECTORY ${CMAKE_BINARY_DIR}/python/rafiki)
 file(MAKE_DIRECTORY ${CMAKE_BINARY_DIR}/src/api)
 
-IF(USE_PYTHON3)
-	SET(SWIG_PYTHON3 "-py3")
+IF(USE_PYTHON3 AND SWIG_VERSION VERSION_LESS "4.1")
+    SET(SWIG_PYTHON3 "-py3")
 ELSE()
-	SET(SWIG_PYTHON3 "")
+    SET(SWIG_PYTHON3 "")
 ENDIF()
 
 execute_process(
-    COMMAND swig -c++ -python ${SWIG_PYTHON3} -I${CMAKE_SOURCE_DIR}/include
+    COMMAND ${SWIG_EXECUTABLE} -c++ -python ${SWIG_PYTHON3}
+    -I${CMAKE_BINARY_DIR}/src/api -I${CMAKE_SOURCE_DIR}/include
     -outdir ${CMAKE_BINARY_DIR}/python/singa
     -o ${CMAKE_BINARY_DIR}/src/api/singa_wrap.cxx
-    ${CMAKE_SOURCE_DIR}/src/api/singa.i)
+    ${CMAKE_SOURCE_DIR}/src/api/singa.i
+    RESULT_VARIABLE SWIG_RESULT)
+IF(NOT SWIG_RESULT EQUAL 0)
+    MESSAGE(FATAL_ERROR "SINGA SWIG generation failed (${SWIG_RESULT})")
+ENDIF()
 
 set(python_srcs "${CMAKE_BINARY_DIR}/src/api/singa_wrap.cxx")
 
@@ -115,7 +120,12 @@ create_symlinks(${python_source_files})
 
 execute_process(
     COMMAND ${PYTHON_EXECUTABLE} -c "from __future__ import print_function; import numpy; print(numpy.get_include())"
-    OUTPUT_VARIABLE NUMPY_INCLUDE_DIR)
+    OUTPUT_VARIABLE NUMPY_INCLUDE_DIR
+    OUTPUT_STRIP_TRAILING_WHITESPACE
+    RESULT_VARIABLE NUMPY_RESULT)
+IF(NOT NUMPY_RESULT EQUAL 0)
+    MESSAGE(FATAL_ERROR "NumPy is unavailable in ${PYTHON_EXECUTABLE}")
+ENDIF()
 
 #message(status "numpy path ${NUMPY_INCLUDE_DIR}")
 

python/singa/model.py

@@ -27,7 +27,7 @@
 import zipfile
 import numpy as np
 from functools import wraps
-from collections import Iterable
+from collections.abc import Iterable
 
 from singa import tensor
 from singa import autograd

python/singa/sonnx.py

@@ -23,9 +23,10 @@
 
 import onnx
 import onnx.utils
+import onnxoptimizer as optimizer
 from onnx.backend.base import Backend, BackendRep
 from onnx import (checker, helper, numpy_helper, GraphProto, NodeProto,
-                  TensorProto, OperatorSetIdProto, optimizer, mapping,
+                  TensorProto, OperatorSetIdProto, mapping,
                   shape_inference)
 import warnings
 
@@ -38,6 +39,7 @@
 from . import singa_wrap as singa
 
 import collections
+from collections.abc import Iterable
 OrderedDict = collections.OrderedDict
 namedtuple = collections.namedtuple
 
@@ -57,7 +59,7 @@
     np.dtype('complex128'): None,
     np.dtype('uint32'): None,
     np.dtype('uint64'): None,
-    np.dtype(np.object): None
+    np.dtype(object): None
 }
 
 
@@ -900,7 +902,7 @@ def singa_op_to_onnx_node(cls, op, op_t):
         else:
             translator = cls._common_singa_tensor_to_onnx_node
         nodes = translator(op, op_t)
-        if not isinstance(nodes, collections.Iterable):
+        if not isinstance(nodes, Iterable):
             nodes = [nodes]
         nodes = [node for node in nodes if node is not None]
         return nodes
@@ -1826,7 +1828,7 @@ def _run_node(cls, operator, inputs):
             list, the output
         """
         outputs = operator(*inputs)
-        if not isinstance(outputs, collections.Iterable):
+        if not isinstance(outputs, Iterable):
             outputs = [outputs]
         return outputs
 
@@ -1919,10 +1921,10 @@ def prepare(cls, model, device='CPU', **kwargs):
         """
         super(SingaBackend, cls).prepare(model, device, **kwargs)
         # optimize and infer the shape of the model
-        try:
-            model = onnx.utils.polish_model(model)
-        except IndexError as err:
-            model = shape_inference.infer_shapes(model)
+        checker.check_model(model)
+        model = shape_inference.infer_shapes(model)
+        model = optimizer.optimize(model)
+        checker.check_model(model)
 
         # check the opset version and ir version
         # SINGA supports opset version(11), ir version(1.6.0 -> 6)
@@ -1939,8 +1941,8 @@ def prepare(cls, model, device='CPU', **kwargs):
 
         if model.ir_version > cls._ir_version:
             warnings.warn(
-                "The imported ir verion {} is larger than the supported version {}."
-                .format(cls._ir_version, imp.version))
+                "The imported IR version {} is larger than the supported version {}."
+                .format(model.ir_version, cls._ir_version))
 
         graph = model.graph
         params, inputs, outputs, layers = cls._onnx_model_to_singa_ops(

python/singa/tensor.py

@@ -372,7 +372,7 @@ def copy_from_numpy(self, np_array, offset=0):
             self.data.CopyFloatDataFromHostPtr(np_array)
         elif dt == np.float16:
             self.data.CopyHalfFloatDataFromHostPtr(np_array)
-        elif dt == np.int or dt == np.int32:
+        elif dt == int or dt == np.int32:
             self.data.CopyIntDataFromHostPtr(np_array)
         else:
             raise NotImplementedError('Not implemented yet for ', dt)
@@ -886,10 +886,10 @@ def from_numpy(np_array, dev=None):
     '''
     assert type(np_array) is np.ndarray, 'Must input numpy array'
     # convert to float32 array
-    if np_array.dtype == np.float64 or np_array.dtype == np.float:
+    if np_array.dtype == np.float64 or np_array.dtype == float:
         np_array = np_array.astype(np.float32)
 
-    if np_array.dtype == np.int64 or np_array.dtype == np.int:
+    if np_array.dtype == np.int64 or np_array.dtype == int:
         np_array = np_array.astype(np.int32)
 
     if np_array.dtype == np.float32:
@@ -1791,7 +1791,7 @@ def copy_from_numpy(data, np_array):
         data.CopyFloatDataFromHostPtr(np_array)
     elif dt == np.float16:
         data.CopyHalfFloatDataFromHostPtr(np_array)
-    elif dt == np.int or dt == np.int32:
+    elif dt == int or dt == np.int32:
         data.CopyIntDataFromHostPtr(np_array)
     else:
         raise NotImplementedError('Not implemented yet for ', dt)

src/CMakeLists.txt

@@ -111,7 +111,7 @@ IF(CODE_COVERAGE)
 ENDIF(CODE_COVERAGE)
 
 #pass configure infor to swig
-FILE(REMOVE "${CMAKE_CURRENT_SOURCE_DIR}/api/config.i")
-CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/api/config.i.in" "${CMAKE_CURRENT_SOURCE_DIR}/api/config.i")
+FILE(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/src/api")
+CONFIGURE_FILE("${CMAKE_CURRENT_SOURCE_DIR}/api/config.i.in" "${CMAKE_BINARY_DIR}/src/api/config.i")
 
 

test/python/test_tensor.py

@@ -208,7 +208,7 @@ def test_tensor_inplace_api(self):
         self.assertTrue(y is x)
 
     def test_numpy_convert(self):
-        a = np.asarray([[1, 0, 0], [0, 1, 0]], dtype=np.int)
+        a = np.asarray([[1, 0, 0], [0, 1, 0]], dtype=int)
         t = tensor.from_numpy(a)
         b = tensor.to_numpy(t)
         self.assertEqual(np.sum(a - b), 0)