Faster CPU Jit backend

cpu/cpujit.py

@@ -98,6 +98,9 @@ def make_python_function(kernel_function_node, argument_dict={}):
                         returned kernel functor.
     :return: kernel functor
     """
+    from pystencils.cpu.cpujit_module import make_python_function as otherimpl
+    return otherimpl(kernel_function_node, argument_dict)
+
     # build up list of CType arguments
     func = compile_and_load(kernel_function_node)
     func.restype = None
@@ -272,7 +275,7 @@ def compile_object_cache_to_shared_library():
         raise e
 
 
-atexit.register(compile_object_cache_to_shared_library)
+#atexit.register(compile_object_cache_to_shared_library)
 
 
 def generate_code(ast, restrict_qualifier, function_prefix, source_file):
@@ -304,17 +307,20 @@ def run_compile_step(command):
         raise e
 
 
-def compile_linux(ast, code_hash_str, src_file, lib_file):
+def compile_linux(ast, code_hash_str, src_file, lib_file, with_python_include_path=False):
     cache_config = get_cache_config()
     compiler_config = get_compiler_config()
-
+    extra_flags = []
+    if with_python_include_path:
+        from sysconfig import get_paths
+        extra_flags = ['-I' + get_paths()['include']]
     object_file = os.path.join(cache_config['object_cache'], code_hash_str + '.o')
     if not os.path.exists(object_file):
         with file_handle_for_atomic_write(src_file) as f:
             generate_code(ast, compiler_config['restrict_qualifier'], '', f)
         with atomic_file_write(object_file) as file_name:
             compile_cmd = [compiler_config['command'], '-c'] + compiler_config['flags'].split()
-            compile_cmd += ['-o', file_name, src_file]
+            compile_cmd += [*extra_flags, '-o', file_name, src_file]
             run_compile_step(compile_cmd)
 
     # Linking
@@ -323,9 +329,13 @@ def compile_linux(ast, code_hash_str, src_file, lib_file):
                          compiler_config['flags'].split())
 
 
-def compile_windows(ast, code_hash_str, src_file, lib_file):
+def compile_windows(ast, code_hash_str, src_file, lib_file, with_python_include_path=False):
     cache_config = get_cache_config()
     compiler_config = get_compiler_config()
+    extra_flags = []
+    if with_python_include_path:
+        from sysconfig import get_paths
+        extra_flags = ['/I' + get_paths()['include']]
 
     object_file = os.path.join(cache_config['object_cache'], code_hash_str + '.obj')
     # Compilation
@@ -335,7 +345,7 @@ def compile_windows(ast, code_hash_str, src_file, lib_file):
 
         # /c compiles only, /EHsc turns of exception handling in c code
         compile_cmd = ['cl.exe', '/c', '/EHsc'] + compiler_config['flags'].split()
-        compile_cmd += [src_file, '/Fo' + object_file]
+        compile_cmd += [*extra_flags, src_file, '/Fo' + object_file]
         run_compile_step(compile_cmd)
 
     # Linking

cpu/cpujit_module.py

+import os
+import textwrap
+import hashlib
+import numpy as np
+from sysconfig import get_paths
+from pystencils import FieldType
+from pystencils.cpu.cpujit import run_compile_step
+from pystencils.data_types import get_base_type
+from pystencils.backends.cbackend import generate_c, get_headers
+from pystencils.utils import file_handle_for_atomic_write, atomic_file_write
+
+type_mapping = {
+    np.float32: ('PyFloat_AsDouble', 'float'),
+    np.float64: ('PyFloat_AsDouble', 'double'),
+    np.int16: ('PyLong_AsLong', 'int16_t'),
+    np.int32: ('PyLong_AsLong', 'int32_t'),
+    np.int64: ('PyLong_AsLong', 'int64_t'),
+    np.uint16: ('PyLong_AsUnsignedLong', 'uint16_t'),
+    np.uint32: ('PyLong_AsUnsignedLong', 'uint32_t'),
+    np.uint64: ('PyLong_AsUnsignedLong', 'uint64_t'),
+}
+
+
+template_extract_scalar = """
+PyObject * obj_{name} = PyDict_GetItemString(kwargs, "{name}");
+if( obj_{name} == NULL) {{  PyErr_SetString(PyExc_TypeError, "Keyword argument '{name}' missing"); return NULL; }};
+{target_type} {name} = ({target_type}) {extract_function}( obj_{name} );
+if( PyErr_Occurred() ) {{ return NULL; }}
+"""
+
+template_extract_array = """
+PyObject * obj_{name} = PyDict_GetItemString(kwargs, "{name}");
+if( obj_{name} == NULL) {{  PyErr_SetString(PyExc_TypeError, "Keyword argument '{name}' missing"); return NULL; }};
+Py_buffer buffer_{name};
+int buffer_{name}_res = PyObject_GetBuffer(obj_{name}, &buffer_{name}, PyBUF_STRIDES | PyBUF_WRITABLE);
+if (buffer_{name}_res == -1) {{ return NULL; }}
+"""
+
+template_release_buffer = """
+PyBuffer_Release(&buffer_{name});
+"""
+
+template_function_boilerplate = """
+static PyObject * {func_name}(PyObject * self, PyObject * args, PyObject * kwargs)
+{{
+    if( !kwargs || !PyDict_Check(kwargs) ) {{ PyErr_SetString(PyExc_TypeError, "No keyword arguments passed"); return NULL; }}
+    {pre_call_code}
+    kernel_{func_name}({parameters});
+    {post_call_code}
+    Py_RETURN_NONE;
+}}
+"""
+
+template_check_array = """
+if(!({cond})) {{ 
+    PyErr_SetString(PyExc_ValueError, "Wrong {what} of array {name}. Expected {expected}"); 
+    return NULL; 
+}}
+"""
+
+template_size_check = """
+if(!({cond})) {{ 
+    PyErr_SetString(PyExc_TypeError, "Arrays must have same shape"); return NULL; 
+}}"""
+
+template_module_boilerplate = """
+static PyMethodDef method_definitions[] = {{
+    {method_definitions}
+    {{NULL, NULL, 0, NULL}}
+}};
+
+static struct PyModuleDef module_definition = {{
+    PyModuleDef_HEAD_INIT,
+    "{module_name}",   /* name of module */
+    NULL,     /* module documentation, may be NULL */
+    -1,       /* size of per-interpreter state of the module,
+                 or -1 if the module keeps state in global variables. */
+    method_definitions
+}};
+
+PyMODINIT_FUNC
+PyInit_{module_name}(void)
+{{
+    return PyModule_Create(&module_definition);
+}}
+"""
+
+
+def equal_size_check(fields):
+    fields = list(fields)
+    if len(fields) <= 1:
+        return ""
+
+    ref_field = fields[0]
+    cond = ["({field.name}_shape[{i}] == {ref_field.name}_shape[{i}])".format(ref_field=ref_field,
+                                                                              field=field_to_test, i=i)
+            for field_to_test in fields[1:]
+            for i in range(fields[0].spatial_dimensions)]
+    cond = " && ".join(cond)
+    return template_size_check.format(cond=cond)
+
+
+def create_function_boilerplate_code(parameter_info, name, insert_checks=True):
+    pre_call_code = ""
+    parameters = []
+    post_call_code = ""
+    variable_sized_normal_fields = set()
+    variable_sized_index_fields = set()
+
+    for arg in parameter_info:
+        if arg.is_field_argument:
+            if arg.is_field_ptr_argument:
+                pre_call_code += template_extract_array.format(name=arg.field_name)
+                post_call_code += template_release_buffer.format(name=arg.field_name)
+                parameters.append("({dtype} *)buffer_{name}.buf".format(dtype=str(arg.field.dtype),
+                                                                        name=arg.field_name))
+
+                shapes = ", ".join(["buffer_{name}.shape[{i}]".format(name=arg.field_name, i=i)
+                                    for i in range(len(arg.field.strides))])
+                pre_call_code += "Py_ssize_t {name}_shape[] = {{ {elements} }};\n".format(name=arg.field_name,
+                                                                                          elements=shapes)
+
+                item_size = get_base_type(arg.dtype).numpy_dtype.itemsize
+                strides = ["buffer_{name}.strides[{i}] / {bytes}".format(i=i, name=arg.field_name, bytes=item_size)
+                           for i in range(len(arg.field.strides))]
+                strides = ", ".join(strides)
+                pre_call_code += "Py_ssize_t {name}_strides[] = {{ {elements} }};\n".format(name=arg.field_name,
+                                                                                            elements=strides)
+
+                if insert_checks and arg.field.has_fixed_shape:
+                    shape_cond = ["{name}_shape[{i}] == {s}".format(s=s, name=arg.field_name, i=i)
+                                  for i, s in enumerate(arg.field.spatial_shape)]
+                    shape_cond = " && ".join(shape_cond)
+                    pre_call_code += template_check_array.format(cond=shape_cond, what="shape", name=arg.field.name,
+                                                                 expected=str(arg.field.shape))
+
+                    strides_cond = ["({name}_strides[{i}] == {s} || {name}_shape[{i}]<=1)".format(s=s, i=i,
+                                                                                                  name=arg.field_name)
+                                    for i, s in enumerate(arg.field.spatial_strides)]
+                    strides_cond = " && ".join(strides_cond)
+                    expected_strides_str = str([e * item_size for e in arg.field.strides])
+                    pre_call_code += template_check_array.format(cond=strides_cond, what="strides", name=arg.field.name,
+                                                                 expected=expected_strides_str)
+                if insert_checks and not arg.field.has_fixed_shape:
+                    if FieldType.is_generic(arg.field):
+                        variable_sized_normal_fields.add(arg.field)
+                    elif FieldType.is_indexed(arg.field):
+                        variable_sized_index_fields.add(arg.field)
+
+            elif arg.is_field_shape_argument:
+                parameters.append("{name}_shape".format(name=arg.field_name))
+            elif arg.is_field_stride_argument:
+                parameters.append("{name}_strides".format(name=arg.field_name))
+        else:
+            extract_function, target_type = type_mapping[arg.dtype.numpy_dtype.type]
+            pre_call_code += template_extract_scalar.format(extract_function=extract_function, target_type=target_type,
+                                                            name=arg.name)
+            parameters.append(arg.name)
+
+    pre_call_code += equal_size_check(variable_sized_normal_fields)
+    pre_call_code += equal_size_check(variable_sized_index_fields)
+
+    pre_call_code = textwrap.indent(pre_call_code, '    ')
+    post_call_code = textwrap.indent(post_call_code, '    ')
+    return template_function_boilerplate.format(func_name=name, pre_call_code=pre_call_code,
+                                                post_call_code=post_call_code, parameters=", ".join(parameters))
+
+
+def create_module_boilerplate_code(module_name, names):
+    method_definition = '{{"{name}", (PyCFunction){name}, METH_VARARGS | METH_KEYWORDS, ""}},'
+    method_definitions = "\n".join([method_definition.format(name=name) for name in names])
+    return template_module_boilerplate.format(module_name=module_name, method_definitions=method_definitions)
+
+
+def load_kernel_from_file(module_name, function_name, path):
+    from importlib.util import spec_from_file_location, module_from_spec
+    spec = spec_from_file_location(name=module_name, location=path)
+    mod = module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return getattr(mod, function_name)
+
+
+class ExtensionModuleCode:
+    def __init__(self, module_name='generated'):
+        self.module_name = module_name
+
+        self._ast_nodes = []
+        self._function_names = []
+
+    def add_function(self, ast, name=None):
+        self._ast_nodes.append(ast)
+        self._function_names.append(name if name is not None else ast.function_name)
+
+    def write_to_file(self, restrict_qualifier, function_prefix, file):
+        headers = {'<math.h>', '<stdint.h>', '"Python.h"'}
+        for ast in self._ast_nodes:
+            headers.update(get_headers(ast))
+
+        includes = "\n".join(["#include %s" % (include_file,) for include_file in headers])
+        print(includes, file=file)
+        print("\n", file=file)
+        print("#define RESTRICT %s" % (restrict_qualifier,), file=file)
+        print("#define FUNC_PREFIX %s" % (function_prefix,), file=file)
+        print("\n", file=file)
+
+        for ast, name in zip(self._ast_nodes, self._function_names):
+            old_name = ast.function_name
+            ast.function_name = "kernel_" + name
+            print(generate_c(ast), file=file)
+            print(create_function_boilerplate_code(ast.parameters, name), file=file)
+            ast.function_name = old_name
+        print(create_module_boilerplate_code(self.module_name, self._function_names), file=file)
+
+
+class KernelWrapper:
+    def __init__(self, kernel, parameters, ast_node):
+        self.kernel = kernel
+        self.parameters = parameters
+        self.ast = ast_node
+
+    def __call__(self, **kwargs):
+        return self.kernel(**kwargs)
+
+
+def compile_and_load(ast):
+    from pystencils.cpu.cpujit import get_cache_config, get_compiler_config
+
+    cache_config = get_cache_config()
+    code_hash_str = "mod_" + hashlib.sha256(generate_c(ast).encode()).hexdigest()
+    code = ExtensionModuleCode(module_name=code_hash_str)
+    code.add_function(ast, ast.function_name)
+    src_file = os.path.join(cache_config['object_cache'], code_hash_str + ".cpp")
+    lib_file = os.path.join(cache_config['object_cache'], code_hash_str + ".so")
+    if not os.path.exists(lib_file):
+        compiler_config = get_compiler_config()
+        extra_flags = ['-I' + get_paths()['include']]
+        object_file = os.path.join(cache_config['object_cache'], code_hash_str + '.o')
+        if not os.path.exists(object_file):
+            with file_handle_for_atomic_write(src_file) as f:
+                code.write_to_file(compiler_config['restrict_qualifier'], '', f)
+            with atomic_file_write(object_file) as file_name:
+                compile_cmd = [compiler_config['command'], '-c'] + compiler_config['flags'].split()
+                compile_cmd += [*extra_flags, '-o', file_name, src_file]
+                run_compile_step(compile_cmd)
+
+        # Linking
+        with atomic_file_write(lib_file) as file_name:
+            run_compile_step([compiler_config['command'], '-shared', object_file, '-o', file_name] +
+                             compiler_config['flags'].split())
+
+    result = load_kernel_from_file(code_hash_str, ast.function_name, lib_file)
+    return KernelWrapper(result, ast.parameters, ast)
+
+
+def make_python_function(kernel_function_node, argument_dict=None):
+    import functools
+    result = compile_and_load(kernel_function_node)
+    if argument_dict:
+        result = functools.partial(result, **argument_dict)
+    return result

datahandling/parallel_datahandling.py

@@ -246,7 +246,6 @@ class ParallelDataHandling(DataHandling):
             for block in self.blocks:
                 transfer_func(block[self.GPU_DATA_PREFIX + name], block[name])
         else:
-            print("trying to transfer ", self.GPU_DATA_PREFIX + name)
             wlb.cuda.copyFieldToGpu(self.blocks, self.GPU_DATA_PREFIX + name, name)
 
     def is_on_gpu(self, name):

datahandling/serial_datahandling.py

@@ -209,14 +209,9 @@ class SerialDataHandling(DataHandling):
         for name in (self.cpu_arrays.keys() & self.gpu_arrays.keys()) | self._custom_data_transfer_functions.keys():
             self.to_gpu(name)
 
-    def run_kernel(self, kernel_function, *args, **kwargs):
-        data_used_in_kernel = [p.field_name
-                               for p in kernel_function.parameters if
-                               p.is_field_ptr_argument and p.field_name not in kwargs]
+    def run_kernel(self, kernel_function, **kwargs):
         arrays = self.gpu_arrays if kernel_function.ast.backend == 'gpucuda' else self.cpu_arrays
-        array_params = {name: arrays[name] for name in data_used_in_kernel}
-        array_params.update(kwargs)
-        kernel_function(*args, **array_params)
+        kernel_function(**arrays, **kwargs)
 
     def to_cpu(self, name):
         if name in self._custom_data_transfer_functions: