def quantile(column, quant, method, exact):
""" Calculate the `quant` quantile for the column
Returns value with the quantile specified by quant
"""
gdf_context = ffi.new('gdf_context*')
method_api = _join_method_api['sort']
libgdf.gdf_context_view(gdf_context, 0, method_api, 0, 0, 0)
# libgdf.gdf_context_view(gdf_context, 0, method_api, 0)
# px = ffi.new("double *")
res = []
for q in quant:
px = ffi.new("double *")
if exact:
libgdf.gdf_quantile_exact(column.cffi_view,
get_quantile_method(method),
q,
ffi.cast('void *', px),
gdf_context)
else:
libgdf.gdf_quantile_aprrox(column.cffi_view,
q,
ffi.cast('void *', px),
gdf_context)
res.append(px[0])
return res
def apply_join(col_lhs, col_rhs, how, method='hash'):
"""Returns a tuple of the left and right joined indices as gpu arrays.
"""
if (len(col_lhs) != len(col_rhs)):
msg = "Unequal #columns in list 'col_lhs' and list 'col_rhs'"
raise ValueError(msg)
joiner = _join_how_api[how]
method_api = _join_method_api[method]
gdf_context = ffi.new('gdf_context*')
if method == 'hash':
libgdf.gdf_context_view(gdf_context, 0, method_api, 0)
elif method == 'sort':
libgdf.gdf_context_view(gdf_context, 1, method_api, 0)
else:
msg = "method not supported"
raise ValueError(msg)
col_result_l = columnview(0, None, dtype=np.int32)
col_result_r = columnview(0, None, dtype=np.int32)
if (how in ['left', 'inner']):
list_lhs = []
list_rhs = []
for i in range(len(col_lhs)):
list_lhs.append(col_lhs[i].cffi_view)
list_rhs.append(col_rhs[i].cffi_view)
# Call libgdf
joiner(len(col_lhs), list_lhs, list_rhs, col_result_l, col_result_r,
gdf_context)
else:
joiner(col_lhs[0].cffi_view, col_rhs[0].cffi_view, col_result_l,
col_result_r)
# Extract result
# yield ((ary[0], ary[1]) if datasize > 0 else (ary, ary))
left = _as_numba_devarray(intaddr=int(
ffi.cast("uintptr_t", col_result_l.data)),
nelem=col_result_l.size,
dtype=np.int32)
right = _as_numba_devarray(intaddr=int(
ffi.cast("uintptr_t", col_result_r.data)),
nelem=col_result_r.size,
dtype=np.int32)
yield (left, right)
libgdf.gdf_column_free(col_result_l)
libgdf.gdf_column_free(col_result_r)
def _parse_metdata(self):
"Parse the metadata in the IPC handle"
from libgdf_cffi import ffi, libgdf
@contextmanager
def open_parser(schema_ptr, schema_len):
"context to destroy the parser"
_logger.debug('open IPCParser')
ipcparser = libgdf.gdf_ipc_parser_open(schema_ptr, schema_len)
yield ipcparser
_logger.debug('close IPCParser')
libgdf.gdf_ipc_parser_close(ipcparser)
def check_error(ipcparser):
if libgdf.gdf_ipc_parser_failed(ipcparser):
raw_error = libgdf.gdf_ipc_parser_get_error(ipcparser)
error = ffi.string(raw_error).decode()
_logger.error('IPCParser failed: %s', error)
raise MetadataParsingError(error)
def load_json(jsonraw):
jsontext = ffi.string(jsonraw).decode()
return json.loads(jsontext)
# get void* from the gpu array
schema_ptr = ffi.cast("void*", self._schema_data.ctypes.data)
# parse schema
with open_parser(schema_ptr, len(self._schema_data)) as ipcparser:
# check for failure in parseing the schema
check_error(ipcparser)
gpu_addr = self._gpu_data.device_ctypes_pointer.value
gpu_ptr = ffi.cast("void*", gpu_addr)
libgdf.gdf_ipc_parser_open_recordbatches(ipcparser, gpu_ptr,
self._gpu_data.size)
# check for failure in parsing the recordbatches
check_error(ipcparser)
# get schema as json
_logger.debug('IPCParser get metadata as json')
schemadct = load_json(
libgdf.gdf_ipc_parser_get_schema_json(ipcparser))
layoutdct = load_json(
libgdf.gdf_ipc_parser_get_layout_json(ipcparser))
# get data offset
_logger.debug('IPCParser data region offset')
dataoffset = libgdf.gdf_ipc_parser_get_data_offset(ipcparser)
dataoffset = int(ffi.cast('uint64_t', dataoffset))
dataptr = self._gpu_data[dataoffset:]
return schemadct, layoutdct, dataptr
def _parse_metdata(self):
"Parse the metadata in the IPC handle"
from libgdf_cffi import ffi, libgdf
@contextmanager
def open_parser(schema_ptr, schema_len):
"context to destroy the parser"
_logger.debug('open IPCParser')
ipcparser = libgdf.gdf_ipc_parser_open(schema_ptr, schema_len)
yield ipcparser
_logger.debug('close IPCParser')
libgdf.gdf_ipc_parser_close(ipcparser)
def check_error(ipcparser):
if libgdf.gdf_ipc_parser_failed(ipcparser):
raw_error = libgdf.gdf_ipc_parser_get_error(ipcparser)
error = ffi.string(raw_error).decode()
_logger.error('IPCParser failed: %s', error)
raise MetadataParsingError(error)
def load_json(jsonraw):
jsontext = ffi.string(jsonraw).decode()
return json.loads(jsontext)
# get void* from the gpu array
schema_ptr = ffi.cast("void*", self._schema_data.ctypes.data)
# parse schema
with open_parser(schema_ptr, len(self._schema_data)) as ipcparser:
# check for failure in parseing the schema
check_error(ipcparser)
gpu_addr = self._gpu_data.device_ctypes_pointer.value
gpu_ptr = ffi.cast("void*", gpu_addr)
libgdf.gdf_ipc_parser_open_recordbatches(ipcparser, gpu_ptr,
self._gpu_data.size)
# check for failure in parsing the recordbatches
check_error(ipcparser)
# get schema as json
_logger.debug('IPCParser get metadata as json')
schemadct = load_json(
libgdf.gdf_ipc_parser_get_schema_json(ipcparser))
layoutdct = load_json(
libgdf.gdf_ipc_parser_get_layout_json(ipcparser))
# get data offset
_logger.debug('IPCParser data region offset')
dataoffset = libgdf.gdf_ipc_parser_get_data_offset(ipcparser)
dataoffset = int(ffi.cast('uint64_t', dataoffset))
dataptr = self._gpu_data[dataoffset:]
return schemadct, layoutdct, dataptr
def _call_hash_multi(api, ncols, col_input, magic, nrows):
out_ary = np.zeros(nrows, dtype=np.int32)
d_out = cuda.to_device(out_ary)
col_out = new_column()
libgdf.gdf_column_view(col_out, unwrap_devary(d_out), ffi.NULL,
out_ary.size, get_dtype(d_out.dtype))
api(ncols, col_input, magic, col_out)
dataptr = col_out.data
print(dataptr)
datasize = col_out.size
print(datasize)
addr = ctypes.c_uint64(int(ffi.cast("uintptr_t", dataptr)))
print(hex(addr.value))
memptr = cuda.driver.MemoryPointer(context=cuda.current_context(),
pointer=addr,
size=4 * datasize)
print(memptr)
ary = cuda.devicearray.DeviceNDArray(shape=(datasize, ),
strides=(4, ),
dtype=np.dtype(np.int32),
gpu_data=memptr)
hashed_result = ary.copy_to_host()
print(hashed_result)
return hashed_result
def apply_join(col_lhs, col_rhs, how):
"""Returns a tuple of the left and right joined indices as gpu arrays.
"""
if (len(col_lhs) != len(col_rhs)):
msg = "Unequal #columns in list 'col_lhs' and list 'col_rhs'"
raise ValueError(msg)
joiner = _join_how_api[how]
join_result_ptr = ffi.new("gdf_join_result_type**", None)
if (how == 'left'):
list_lhs = []
list_rhs = []
for i in range(len(col_lhs)):
list_lhs.append(col_lhs[i].cffi_view)
list_rhs.append(col_rhs[i].cffi_view)
# Call libgdf
joiner(len(col_lhs), list_lhs, list_rhs, join_result_ptr)
else:
joiner(col_lhs[0].cffi_view, col_rhs[0].cffi_view, join_result_ptr)
# Extract result
join_result = join_result_ptr[0]
dataptr = libgdf.gdf_join_result_data(join_result)
datasize = libgdf.gdf_join_result_size(join_result)
ary = _as_numba_devarray(intaddr=int(ffi.cast("uintptr_t", dataptr)),
nelem=datasize,
dtype=np.int32)
ary = ary.reshape(2, datasize // 2)
yield ((ary[0], ary[1]) if datasize > 0 else (ary, ary))
libgdf.gdf_join_result_free(join_result)
def _call_join_multi(api, ncols, col_left, col_right, ctxt):
join_result_ptr = ffi.new("gdf_join_result_type**", None)
api(ncols, col_left, col_right, join_result_ptr, ctxt)
join_result = join_result_ptr[0]
print('join_result', join_result)
dataptr = libgdf.gdf_join_result_data(join_result)
print(dataptr)
datasize = libgdf.gdf_join_result_size(join_result)
print(datasize)
addr = ctypes.c_uint64(int(ffi.cast("uintptr_t", dataptr)))
print(hex(addr.value))
memptr = cuda.driver.MemoryPointer(context=cuda.current_context(),
pointer=addr,
size=4 * datasize)
print(memptr)
ary = cuda.devicearray.DeviceNDArray(shape=(datasize, ),
strides=(4, ),
dtype=np.dtype(np.int32),
gpu_data=memptr)
joined_idx = ary.reshape(2, datasize // 2).copy_to_host()
print(joined_idx)
libgdf.gdf_join_result_free(join_result)
return joined_idx
def cffi_view_to_column_mem(cffi_view):
data = _as_numba_devarray(intaddr=int(ffi.cast("uintptr_t",
cffi_view.data)),
nelem=cffi_view.size,
dtype=gdf_to_np_dtype(cffi_view.dtype),
cb_dtor=cuda.driver.driver.cuMemFree)
if cffi_view.valid:
mask = _as_numba_devarray(
intaddr=int(ffi.cast("uintptr_t", cffi_view.valid)),
nelem=calc_chunk_size(cffi_view.size, mask_bitsize),
dtype=mask_dtype,
cb_dtor=cuda.driver.driver.cuMemFree)
else:
mask = None
return data, mask
def cffi_view_to_column_mem(cffi_view):
intaddr = int(ffi.cast("uintptr_t", cffi_view.data))
data = rmm.device_array_from_ptr(intaddr,
nelem=cffi_view.size,
dtype=gdf_to_np_dtype(cffi_view.dtype),
finalizer=rmm._make_finalizer(intaddr, 0))
if cffi_view.valid:
intaddr = int(ffi.cast("uintptr_t", cffi_view.valid))
mask = rmm.device_array_from_ptr(
intaddr,
nelem=calc_chunk_size(cffi_view.size, mask_bitsize),
dtype=mask_dtype,
finalizer=rmm._make_finalizer(intaddr, 0))
else:
mask = None
return data, mask
def _copy_int_col_to_arr(col):
dataptr = col.data
datasize = col.size
addr = ctypes.c_uint64(int(ffi.cast("uintptr_t", dataptr)))
memptr = cuda.driver.MemoryPointer(context=cuda.current_context(),
pointer=addr,
size=4 * datasize)
ary = cuda.devicearray.DeviceNDArray(shape=(datasize, ),
strides=(4, ),
dtype=np.dtype(np.int32),
gpu_data=memptr)
return ary.copy_to_host()
def cffi_view_to_column_mem(cffi_view):
gdf_dtype = cffi_view.dtype
if gdf_dtype == libgdf.GDF_STRING_CATEGORY:
data_ptr = int(ffi.cast("uintptr_t", cffi_view.data))
# We need to create this just to make sure the memory is properly freed
data = rmm.device_array_from_ptr(data_ptr,
nelem=cffi_view.size,
dtype='int32',
finalizer=rmm._make_finalizer(
data_ptr, 0))
nvcat_ptr = int(ffi.cast("uintptr_t", cffi_view.dtype_info.category))
nvcat_obj = nvcategory.bind_cpointer(nvcat_ptr)
nvstr_obj = nvcat_obj.to_strings()
mask = None
if cffi_view.valid:
mask_ptr = int(ffi.cast("uintptr_t", cffi_view.valid))
mask = rmm.device_array_from_ptr(
mask_ptr,
nelem=calc_chunk_size(cffi_view.size, mask_bitsize),
dtype=mask_dtype,
finalizer=rmm._make_finalizer(mask_ptr, 0))
return nvstr_obj, mask
else:
intaddr = int(ffi.cast("uintptr_t", cffi_view.data))
data = rmm.device_array_from_ptr(
intaddr,
nelem=cffi_view.size,
dtype=gdf_to_np_dtype(cffi_view.dtype),
finalizer=rmm._make_finalizer(intaddr, 0))
mask = None
if cffi_view.valid:
intaddr = int(ffi.cast("uintptr_t", cffi_view.valid))
mask = rmm.device_array_from_ptr(
intaddr,
nelem=calc_chunk_size(cffi_view.size, mask_bitsize),
dtype=mask_dtype,
finalizer=rmm._make_finalizer(intaddr, 0))
return data, mask
def _parse_metdata(self):
"Parse the metadata in the IPC handle"
from libgdf_cffi import ffi, libgdf
@contextmanager
def open_parser(devptr):
"context to destroy the parser"
_logger.debug('open IPCParser')
ipcparser = libgdf.gdf_ipc_parser_open(devptr)
yield ipcparser
_logger.debug('close IPCParser')
libgdf.gdf_ipc_parser_close(ipcparser)
# get void* from the gpu array
devptr = ffi.cast("void*", self._gpu_data.device_ctypes_pointer.value)
# parse
with open_parser(devptr) as ipcparser:
# check for failure
if libgdf.gdf_ipc_parser_failed(ipcparser):
raw_error = libgdf.gdf_ipc_parser_get_error(ipcparser)
error = ffi.string(raw_error).decode()
_logger.error('IPCParser failed: %s', error)
raise MetadataParsingError(error)
# get schema as json
_logger.debug('IPCParser get metadata as json')
jsonraw = libgdf.gdf_ipc_parser_to_json(ipcparser)
jsontext = ffi.string(jsonraw).decode()
outdct = json.loads(jsontext)
# get data offset
_logger.debug('IPCParser data region offset')
dataoffset = libgdf.gdf_ipc_parser_get_data_offset(ipcparser)
dataoffset = int(ffi.cast('uint64_t', dataoffset))
dataptr = self._gpu_data[dataoffset:]
return outdct, dataptr
def apply_join(col_lhs, col_rhs, how):
"""Returns a tuple of the left and right joined indices as gpu arrays.
"""
joiner = _join_how_api[how]
join_result_ptr = ffi.new("gdf_join_result_type**", None)
# Call libgdf
joiner(col_lhs.cffi_view, col_rhs.cffi_view, join_result_ptr)
# Extract result
join_result = join_result_ptr[0]
dataptr = libgdf.gdf_join_result_data(join_result)
datasize = libgdf.gdf_join_result_size(join_result)
ary = _as_numba_devarray(intaddr=int(ffi.cast("uintptr_t", dataptr)),
nelem=datasize, dtype=np.int32)
ary = ary.reshape(2, datasize // 2)
yield ((ary[0], ary[1]) if datasize > 0 else (ary, ary))
libgdf.gdf_join_result_free(join_result)
def apply_join(col_lhs, col_rhs, how):
"""Returns a tuple of the left and right joined indices as gpu arrays.
"""
joiner = _join_how_api[how]
join_result_ptr = ffi.new("gdf_join_result_type**", None)
# Call libgdf
joiner(col_lhs.cffi_view, col_rhs.cffi_view, join_result_ptr)
# Extract result
join_result = join_result_ptr[0]
dataptr = libgdf.gdf_join_result_data(join_result)
datasize = libgdf.gdf_join_result_size(join_result)
ary = _as_numba_devarray(intaddr=int(ffi.cast("uintptr_t", dataptr)),
nelem=datasize, dtype=np.int32)
ary = ary.reshape(2, datasize // 2)
yield ((ary[0], ary[1]) if datasize > 0 else (ary, ary))
libgdf.gdf_join_result_free(join_result)
def nvtx_range_push(name, color='green'):
"""
Demarcate the beginning of a user-defined NVTX range.
Parameters
----------
name : str
The name of the NVTX range
color : str
The color to use for the range.
Can be named color or hex RGB string.
"""
name_c = ffi.new("char[]", name.encode('ascii'))
try:
color = int(color, 16) # only works if color is a hex string
libgdf.gdf_nvtx_range_push_hex(name_c, ffi.cast('unsigned int', color))
except ValueError:
color = str_to_gdf_color(color)
libgdf.gdf_nvtx_range_push(name_c, color)
def _columnview(size, data, mask, dtype, null_count, nvcat):
colview = ffi.new('gdf_column*')
extra_dtype_info = ffi.new('gdf_dtype_extra_info*')
extra_dtype_info.time_unit = libgdf.TIME_UNIT_NONE
if nvcat is not None:
extra_dtype_info.category = ffi.cast('void*', nvcat.get_cpointer())
else:
extra_dtype_info.category = ffi.NULL
if mask is None:
null_count = 0
mask = ffi.NULL
libgdf.gdf_column_view_augmented(
colview,
data,
mask,
size,
np_to_gdf_dtype(dtype),
null_count,
extra_dtype_info[0],
)
return colview
def test_ipc():
schema_bytes = b'\xa8\x01\x00\x00\x10\x00\x00\x00\x0c\x00\x0e\x00\x06\x00\x05\x00\x08\x00\x00\x00\x0c\x00\x00\x00\x00\x01\x02\x00\x10\x00\x00\x00\x00\x00\n\x00\x08\x00\x00\x00\x04\x00\x00\x00\n\x00\x00\x00\x04\x00\x00\x00\x03\x00\x00\x00\x18\x01\x00\x00p\x00\x00\x00\x04\x00\x00\x00\x08\xff\xff\xff\x00\x00\x01\x03@\x00\x00\x00$\x00\x00\x00\x14\x00\x00\x00\x04\x00\x00\x00\x02\x00\x00\x00$\x00\x00\x00\x18\x00\x00\x00\x00\x00\x00\x00\x00\x00\x06\x00\x08\x00\x06\x00\x06\x00\x00\x00\x00\x00\x02\x00\xe8\xfe\xff\xff@\x00\x01\x00\xf0\xfe\xff\xff\x01\x00\x02\x00\x06\x00\x00\x00weight\x00\x00\x14\x00\x1e\x00\x08\x00\x06\x00\x07\x00\x0c\x00\x10\x00\x14\x00\x18\x00\x00\x00\x14\x00\x00\x00\x00\x00\x01\x05|\x00\x00\x00T\x00\x00\x00\x18\x00\x00\x00D\x00\x00\x000\x00\x00\x00\x00\x00\n\x00\x14\x00\x08\x00\x04\x00\x00\x00\n\x00\x00\x00\x10\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00p\xff\xff\xff\x00\x00\x00\x01 \x00\x00\x00\x03\x00\x00\x000\x00\x00\x00$\x00\x00\x00\x10\x00\x00\x00\x00\x00\x00\x00\x04\x00\x04\x00\x04\x00\x00\x00|\xff\xff\xff\x08\x00\x01\x00\x08\x00\x08\x00\x06\x00\x00\x00\x08\x00\x00\x00\x00\x00 \x00\x94\xff\xff\xff\x01\x00\x02\x00\x04\x00\x00\x00name\x00\x00\x00\x00\x14\x00\x18\x00\x08\x00\x06\x00\x07\x00\x0c\x00\x00\x00\x10\x00\x14\x00\x00\x00\x14\x00\x00\x00\x00\x00\x01\x02L\x00\x00\x00$\x00\x00\x00\x14\x00\x00\x00\x04\x00\x00\x00\x02\x00\x00\x000\x00\x00\x00\x1c\x00\x00\x00\x00\x00\x00\x00\x08\x00\x0c\x00\x08\x00\x07\x00\x08\x00\x00\x00\x00\x00\x00\x01 \x00\x00\x00\xf8\xff\xff\xff \x00\x01\x00\x08\x00\x08\x00\x04\x00\x06\x00\x08\x00\x00\x00\x01\x00\x02\x00\x03\x00\x00\x00idx\x00\xc8\x00\x00\x00\x14\x00\x00\x00\x00\x00\x00\x00\x0c\x00\x14\x00\x06\x00\x05\x00\x08\x00\x0c\x00\x0c\x00\x00\x00\x00\x02\x02\x00\x14\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x08\x00\x12\x00\x08\x00\x04\x00\x08\x00\x00\x00\x18\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\n\x00\x18\x00\x0c\x00\x04\x00\x08\x00\n\x00\x00\x00d\x00\x00\x00\x10\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x06\x00\x00\x00\x0b\x00\x00\x00\x0f\x00\x00\x00\x14\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00orangeapplepeargrape\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
cpu_data = np.ndarray(shape=len(schema_bytes), dtype=np.byte,
buffer=bytearray(schema_bytes))
# Use GDF IPC parser
schema_ptr = ffi.cast("void*", cpu_data.ctypes.data)
ipch = libgdf.gdf_ipc_parser_open(schema_ptr, cpu_data.size)
if libgdf.gdf_ipc_parser_failed(ipch):
print(libgdf.gdf_ipc_parser_get_error(ipch))
jsonraw = libgdf.gdf_ipc_parser_get_schema_json(ipch)
jsontext = ffi.string(jsonraw).decode()
json_schema = json.loads(jsontext)
pprint(json_schema)
recordbatches_bytes = b'\x1c\x01\x00\x00\x14\x00\x00\x00\x00\x00\x00\x00\x0c\x00\x16\x00\x06\x00\x05\x00\x08\x00\x0c\x00\x0c\x00\x00\x00\x00\x03\x02\x00\x18\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x00\n\x00\x18\x00\x0c\x00\x04\x00\x08\x00\n\x00\x00\x00\xac\x00\x00\x00\x10\x00\x00\x00\x1e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x06\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x1e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x1e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x1e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x10\x00\x00\x00\x11\x00\x00\x00\x12\x00\x00\x00\x13\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00\x06\x00\x00\x00\x07\x00\x00\x00\x14\x00\x00\x00\x15\x00\x00\x00\x16\x00\x00\x00\x17\x00\x00\x00\x08\x00\x00\x00\t\x00\x00\x00\n\x00\x00\x00\x0b\x00\x00\x00\x18\x00\x00\x00\x19\x00\x00\x00\x1a\x00\x00\x00\x1b\x00\x00\x00\x0c\x00\x00\x00\r\x00\x00\x00\x0e\x00\x00\x00\x0f\x00\x00\x00\x1c\x00\x00\x00\x1d\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x16\x93\xb7<\xac*\xde?\x00Y\x94@"\x0eo?\xf8+\xee\xac\xf2#\xdc?\xa4\xcauw68\xe2?\xf8\xaa\xc9\x9f*\x9f\xda?\xe0\x1e\x1b-\x8b\xa4\xd7?\xe6y\x8a\x9b\xe4<\xef?\x08\x89\xc4.0W\xc5?h\xa5\x0f\x14\xa2\xe3\xbb?\xc0\xa9/\x8f\xeap\xb8?\x0c7\xed\x99fc\xda?:\tA.\xc6g\xda?\x1c\x1f)\xfd\x03\n\xc1?\xfe\x1e\xf9(/\xf0\xe3?\x08h\x99\x05\x81m\xe7?\xa0\xa8=\xfc\x96\x93\xcd?x\x8b\xf8v\xbe_\xc8?\xa2\xd9Zg\xd9\xb9\xed?;\xdb\xa6\xfas\xdb\xed?\xd8\xc9\xfcA-\xcd\xdd?@\xe27`\x0cQ\x94?d\x11:-\x8e\xcf\xd9?\xc9S\xde\xff\xbbN\xe5?\xe0o(\xf4s?\xba?\x0bq\xb9j%o\xeb?\x10\xe8\xa1t\t\x9b\xcb?\xa5\xf0\x15\t\x1ep\xed?\xc7\xb2~\x02\x82l\xef?0\xe6\xa8g\xec\x82\xc3?\xe0\xc6\xe8\xb1\xc2~\xd6?\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
rb_cpu_data = np.ndarray(shape=len(recordbatches_bytes), dtype=np.byte,
buffer=bytearray(recordbatches_bytes))
rb_gpu_data = cuda.to_device(rb_cpu_data)
del cpu_data
devptr = ffi.cast("void*", rb_gpu_data.device_ctypes_pointer.value)
libgdf.gdf_ipc_parser_open_recordbatches(ipch, devptr, rb_gpu_data.size)
if libgdf.gdf_ipc_parser_failed(ipch):
print(libgdf.gdf_ipc_parser_get_error(ipch))
jsonraw = libgdf.gdf_ipc_parser_get_layout_json(ipch)
jsontext = ffi.string(jsonraw).decode()
json_rb = json.loads(jsontext)
pprint(json_rb)
offset = libgdf.gdf_ipc_parser_get_data_offset(ipch)
libgdf.gdf_ipc_parser_close(ipch)
# Check
dicts = json_schema['dictionaries']
assert len(dicts) == 1
dictdata = dicts[0]['data']['columns'][0]['DATA']
assert set(dictdata) == {'orange', 'apple', 'pear', 'grape'}
gpu_data = rb_gpu_data[offset:]
schema_fields = json_schema['schema']['fields']
assert len(schema_fields) == 3
field_names = [f['name'] for f in schema_fields]
assert field_names == ['idx', 'name', 'weight']
# check the dictionary id in schema
assert schema_fields[1]['dictionary']['id'] == dicts[0]['id']
# Get "idx" column
idx_buf_off = json_rb[0]['data_buffer']['offset']
idx_buf_len = json_rb[0]['data_buffer']['length']
idx_buf = gpu_data[idx_buf_off:][:idx_buf_len]
assert json_rb[0]['dtype']['name'] == 'INT32'
idx_size = json_rb[0]['length']
assert idx_size == 30
idx_data = np.ndarray(shape=idx_size, dtype=np.int32,
buffer=idx_buf.copy_to_host())
print(idx_data)
# Get "name" column
name_buf_off = json_rb[1]['data_buffer']['offset']
name_buf_len = json_rb[1]['data_buffer']['length']
name_buf = gpu_data[name_buf_off:][:name_buf_len]
assert json_rb[1]['dtype']['name'] == 'DICTIONARY'
name_size = json_rb[1]['length']
name_data = np.ndarray(shape=name_size, dtype=np.int32,
buffer=name_buf.copy_to_host())
print(name_data)
# Get "name" column
weight_buf_off = json_rb[2]['data_buffer']['offset']
weight_buf_len = json_rb[2]['data_buffer']['length']
weight_buf = gpu_data[weight_buf_off:][:weight_buf_len]
assert json_rb[2]['dtype']['name'] == 'DOUBLE'
weight_size = json_rb[2]['length']
weight_data = np.ndarray(shape=weight_size, dtype=np.float64,
buffer=weight_buf.copy_to_host())
print(weight_data)
# verify data
sortedidx = np.argsort(idx_data)
idx_data = idx_data[sortedidx]
name_data = name_data[sortedidx]
weight_data = weight_data[sortedidx]
got_iter = zip(idx_data, name_data, weight_data)
for expected, got in zip(get_expected_values(), got_iter):
assert expected[0] == got[0]
assert expected[1] == dictdata[got[1]]
assert expected[2] == got[2]
def unwrap_devary(devary):
ptrval = devary.device_ctypes_pointer.value
ptrval = ptrval or ffi.NULL # replace None with NULL
return ffi.cast('void*', ptrval)
def libgdf_join(col_lhs, col_rhs, on, how, method='sort'):
joiner = _join_how_api[how]
method_api = _join_method_api[method]
gdf_context = ffi.new('gdf_context*')
libgdf.gdf_context_view(gdf_context, 0, method_api, 0, 0, 0)
if how not in ['left', 'inner', 'outer']:
msg = "new join api only supports left or inner"
raise ValueError(msg)
list_lhs = []
list_rhs = []
result_cols = []
result_col_names = []
left_idx = []
right_idx = []
# idx = 0
for name, col in col_lhs.items():
list_lhs.append(col._column.cffi_view)
if name not in on:
result_cols.append(columnview(0, None, dtype=col._column.dtype))
result_col_names.append(name)
for name in on:
result_cols.append(columnview(0, None,
dtype=col_lhs[name]._column.dtype))
result_col_names.append(name)
left_idx.append(list(col_lhs.keys()).index(name))
right_idx.append(list(col_rhs.keys()).index(name))
for name, col in col_rhs.items():
list_rhs.append(col._column.cffi_view)
if name not in on:
result_cols.append(columnview(0, None, dtype=col._column.dtype))
result_col_names.append(name)
num_cols_to_join = len(on)
result_num_cols = len(list_lhs) + len(list_rhs) - num_cols_to_join
joiner(list_lhs,
len(list_lhs),
left_idx,
list_rhs,
len(list_rhs),
right_idx,
num_cols_to_join,
result_num_cols,
result_cols,
ffi.NULL,
ffi.NULL,
gdf_context)
res = []
valids = []
for col in result_cols:
intaddr = int(ffi.cast("uintptr_t", col.data))
res.append(rmm.device_array_from_ptr(ptr=intaddr,
nelem=col.size,
dtype=gdf_to_np_dtype(col.dtype),
finalizer=rmm._make_finalizer(
intaddr, 0)))
intaddr = int(ffi.cast("uintptr_t", col.valid))
valids.append(rmm.device_array_from_ptr(ptr=intaddr,
nelem=calc_chunk_size(
col.size, mask_bitsize),
dtype=mask_dtype,
finalizer=rmm._make_finalizer(
intaddr, 0)))
return res, valids
def unwrap_mask(devary):
ptrval = devary.device_ctypes_pointer.value
ptrval = ptrval or ffi.NULL # replace None with NULL
return ffi.cast('gdf_valid_type*', ptrval), ptrval
def unwrap_devary(devary):
ptrval = devary.device_ctypes_pointer.value
ptrval = ptrval or ffi.NULL # replace None with NULL
return ffi.cast('void*', ptrval)
def read_csv_strings(filepath_or_buffer,
lineterminator='\n',
quotechar='"',
quoting=0,
doublequote=True,
header='infer',
sep=',',
delimiter=None,
delim_whitespace=False,
skipinitialspace=False,
names=None,
dtype=None,
skipfooter=0,
skiprows=0,
dayfirst=False,
compression='infer',
thousands=None,
decimal='.',
true_values=None,
false_values=None,
nrows=None,
byte_range=None,
skip_blank_lines=True,
comment=None,
na_values=None,
keep_default_na=True,
na_filter=True,
prefix=None,
index_col=None):
"""
**Experimental**: This function exists only as a beta way to use
`nvstrings <https://nvstrings.readthedocs.io/en/latest/>`_. with cudf.
Future versions of cuDF will provide cleaner integration.
Uses mostly same arguments as read_csv.
Note: Doesn't currently support auto-column detection, header, usecols
and mangle_dupe_cols args.
Returns
-------
columns : ordered list of cudf.dataframe.Series and nvstrings objects
numeric or date dtyped columns will be Series.
'str' dtyped columns will be
`nvstrings <https://nvstrings.readthedocs.io/en/latest/>`_.
Examples
--------
Create a test csv file
>>> import cudf
>>> filename = 'foo.csv'
>>> lines = [
... "num1,datetime,text",
... "123,2018-11-13T12:00:00,abc",
... "456,2018-11-14T12:35:01,def",
... "789,2018-11-15T18:02:59,ghi"
... ]
>>> with open(filename, 'w') as fp:
... fp.write('\\n'.join(lines)+'\\n')
Read the file with cudf
>>> names = ['num1', 'datetime', 'text']
>>> dtypes = ['int', 'date', 'str']
>>> columns = cudf.io.csv.read_csv_strings(filename, delimiter=',',
... names=names, dtype=dtypes,
... skiprows=1)
Display results
>>> print(columns[0])
0 123
1 456
2 789
>>> print(columns[2])
['abc', 'def', 'ghi']
See Also
--------
.read_csv
"""
import nvstrings
from cudf.dataframe.series import Series
# Alias sep -> delimiter.
if delimiter is None:
delimiter = sep
if dtype is not None:
if isinstance(dtype, collections.abc.Mapping):
dtype_dict = True
elif isinstance(dtype, collections.abc.Iterable):
dtype_dict = False
else:
msg = '''dtype must be 'list like' or 'dict' '''
raise TypeError(msg)
if names is not None and len(dtype) != len(names):
msg = '''All column dtypes must be specified.'''
raise TypeError(msg)
csv_reader = ffi.new('csv_read_arg*')
# Populate csv_reader struct
if is_file_like(filepath_or_buffer):
buffer = filepath_or_buffer.read()
# check if StringIO is used
if hasattr(buffer, 'encode'):
buffer_as_bytes = buffer.encode()
else:
buffer_as_bytes = buffer
buffer_data_holder = ffi.new("char[]", buffer_as_bytes)
csv_reader.input_data_form = libgdf.HOST_BUFFER
csv_reader.filepath_or_buffer = buffer_data_holder
csv_reader.buffer_size = len(buffer_as_bytes)
else:
if (not os.path.isfile(filepath_or_buffer)):
raise (FileNotFoundError)
if (not os.path.exists(filepath_or_buffer)):
raise (FileNotFoundError)
file_path = _wrap_string(filepath_or_buffer)
csv_reader.input_data_form = libgdf.FILE_PATH
csv_reader.filepath_or_buffer = file_path
if header == 'infer':
header = -1
header_infer = header
arr_names = []
arr_dtypes = []
if names is None:
if header is -1:
header_infer = 0
if header is None:
header_infer = -1
csv_reader.names = ffi.NULL
csv_reader.num_cols = 0
else:
if header is None:
header_infer = -1
csv_reader.num_cols = len(names)
for col_name in names:
arr_names.append(_wrap_string(col_name))
if dtype is not None:
if dtype_dict:
arr_dtypes.append(_wrap_string(str(dtype[col_name])))
names_ptr = ffi.new('char*[]', arr_names)
csv_reader.names = names_ptr
if dtype is None:
csv_reader.dtype = ffi.NULL
else:
if not dtype_dict:
for col_dtype in dtype:
arr_dtypes.append(_wrap_string(str(col_dtype)))
dtype_ptr = ffi.new('char*[]', arr_dtypes)
csv_reader.dtype = dtype_ptr
if decimal == delimiter:
raise ValueError("decimal cannot be the same as delimiter")
if thousands == delimiter:
raise ValueError("thousands cannot be the same as delimiter")
if nrows is not None and skipfooter != 0:
raise ValueError("cannot use both nrows and skipfooter parameters")
if byte_range is not None:
if skipfooter != 0 or skiprows != 0 or nrows is not None:
raise ValueError("""cannot manually limit rows to be read when
using the byte range parameter""")
# Start with default values recognized as boolean
arr_true_values = [_wrap_string(str('True')), _wrap_string(str('TRUE'))]
arr_false_values = [_wrap_string(str('False')), _wrap_string(str('FALSE'))]
for value in true_values or []:
arr_true_values.append(_wrap_string(str(value)))
arr_true_values_ptr = ffi.new('char*[]', arr_true_values)
csv_reader.true_values = arr_true_values_ptr
csv_reader.num_true_values = len(arr_true_values)
for value in false_values or []:
arr_false_values.append(_wrap_string(str(value)))
false_values_ptr = ffi.new('char*[]', arr_false_values)
csv_reader.false_values = false_values_ptr
csv_reader.num_false_values = len(arr_false_values)
arr_na_values = []
for value in na_values or []:
arr_na_values.append(_wrap_string(str(value)))
arr_na_values_ptr = ffi.new('char*[]', arr_na_values)
csv_reader.na_values = arr_na_values_ptr
csv_reader.num_na_values = len(arr_na_values)
compression_bytes = _wrap_string(compression)
prefix_bytes = _wrap_string(prefix)
csv_reader.delimiter = delimiter.encode()
csv_reader.lineterminator = lineterminator.encode()
csv_reader.quotechar = quotechar.encode()
csv_reader.quoting = _quoting_enum[quoting]
csv_reader.doublequote = doublequote
csv_reader.delim_whitespace = delim_whitespace
csv_reader.skipinitialspace = skipinitialspace
csv_reader.dayfirst = dayfirst
csv_reader.header = header_infer
csv_reader.skiprows = skiprows
csv_reader.skipfooter = skipfooter
csv_reader.compression = compression_bytes
csv_reader.decimal = decimal.encode()
csv_reader.thousands = thousands.encode() if thousands else b'\0'
csv_reader.nrows = nrows if nrows is not None else -1
if byte_range is not None:
csv_reader.byte_range_offset = byte_range[0]
csv_reader.byte_range_size = byte_range[1]
else:
csv_reader.byte_range_offset = 0
csv_reader.byte_range_size = 0
csv_reader.skip_blank_lines = skip_blank_lines
csv_reader.comment = comment.encode() if comment else b'\0'
csv_reader.keep_default_na = keep_default_na
csv_reader.na_filter = na_filter
csv_reader.prefix = prefix_bytes
# Call read_csv
libgdf.read_csv(csv_reader)
out = csv_reader.data
if out == ffi.NULL:
raise ValueError("Failed to parse CSV")
# Extract parsed columns
outcols = []
for i in range(csv_reader.num_cols_out):
if out[i].dtype == libgdf.GDF_STRING:
ptr = int(ffi.cast("uintptr_t", out[i].data))
outcols.append(nvstrings.bind_cpointer(ptr))
else:
newcol = Column.from_cffi_view(out[i])
if (newcol.dtype == np.dtype('datetime64[ms]')):
col = newcol.view(DatetimeColumn, dtype='datetime64[ms]')
else:
col = newcol.view(NumericalColumn, dtype=newcol.dtype)
outcols.append(Series(col))
return outcols
def read_csv(filepath_or_buffer,
lineterminator='\n',
quotechar='"',
quoting=0,
doublequote=True,
header='infer',
mangle_dupe_cols=True,
usecols=None,
sep=',',
delimiter=None,
delim_whitespace=False,
skipinitialspace=False,
names=None,
dtype=None,
skipfooter=0,
skiprows=0,
dayfirst=False,
compression='infer',
thousands=None,
decimal='.',
true_values=None,
false_values=None,
nrows=None,
byte_range=None,
skip_blank_lines=True,
comment=None,
na_values=None,
keep_default_na=True,
na_filter=True,
prefix=None,
index_col=None):
"""
Load and parse a CSV file into a DataFrame
Parameters
----------
filepath_or_buffer : str
Path of file to be read or a file-like object containing the file.
sep : char, default ','
Delimiter to be used.
delimiter : char, default None
Alternative argument name for sep.
delim_whitespace : bool, default False
Determines whether to use whitespace as delimiter.
lineterminator : char, default '\\n'
Character to indicate end of line.
skipinitialspace : bool, default False
Skip spaces after delimiter.
names : list of str, default None
List of column names to be used.
dtype : list of str or dict of {col: dtype}, default None
List of data types in the same order of the column names
or a dictionary with column_name:dtype (pandas style).
quotechar : char, default '"'
Character to indicate start and end of quote item.
quoting : str or int, default 0
Controls quoting behavior. Set to one of
0 (csv.QUOTE_MINIMAL), 1 (csv.QUOTE_ALL),
2 (csv.QUOTE_NONNUMERIC) or 3 (csv.QUOTE_NONE).
Quoting is enabled with all values except 3.
doublequote : bool, default True
When quoting is enabled, indicates whether to interpret two
consecutive quotechar inside fields as single quotechar
header : int, default 'infer'
Row number to use as the column names. Default behavior is to infer
the column names: if no names are passed, header=0;
if column names are passed explicitly, header=None.
usecols : list of int or str, default None
Returns subset of the columns given in the list. All elements must be
either integer indices (column number) or strings that correspond to
column names
mangle_dupe_cols : boolean, default True
Duplicate columns will be specified as 'X','X.1',...'X.N'.
skiprows : int, default 0
Number of rows to be skipped from the start of file.
skipfooter : int, default 0
Number of rows to be skipped at the bottom of file.
compression : {'infer', 'gzip', 'zip', None}, default 'infer'
For on-the-fly decompression of on-disk data. If ‘infer’, then detect
compression from the following extensions: ‘.gz’,‘.zip’ (otherwise no
decompression). If using ‘zip’, the ZIP file must contain only one
data file to be read in, otherwise the first non-zero-sized file will
be used. Set to None for no decompression.
decimal : char, default '.'
Character used as a decimal point.
thousands : char, default None
Character used as a thousands delimiter.
true_values : list, default None
Values to consider as boolean True
false_values : list, default None
Values to consider as boolean False
nrows : int, default None
If specified, maximum number of rows to read
byte_range : list or tuple, default None
Byte range within the input file to be read. The first number is the
offset in bytes, the second number is the range size in bytes. Set the
size to zero to read all data after the offset location. Reads the row
that starts before or at the end of the range, even if it ends after
the end of the range.
skip_blank_lines : bool, default True
If True, discard and do not parse empty lines
If False, interpret empty lines as NaN values
comment : char, default None
Character used as a comments indicator. If found at the beginning of a
line, the line will be ignored altogether.
na_values : list, default None
Values to consider as invalid
keep_default_na : bool, default True
Whether or not to include the default NA values when parsing the data.
na_filter : bool, default True
Detect missing values (empty strings and the values in na_values).
Passing False can improve performance.
prefix : str, default None
Prefix to add to column numbers when parsing without a header row
index_col : int or string, default None
Column to use as the row labels
Returns
-------
GPU ``DataFrame`` object.
Examples
--------
Create a test csv file
>>> import cudf
>>> filename = 'foo.csv'
>>> lines = [
... "num1,datetime,text",
... "123,2018-11-13T12:00:00,abc",
... "456,2018-11-14T12:35:01,def",
... "789,2018-11-15T18:02:59,ghi"
... ]
>>> with open(filename, 'w') as fp:
... fp.write('\\n'.join(lines)+'\\n')
Read the file with ``cudf.read_csv``
>>> cudf.read_csv(filename)
num1 datetime text
0 123 2018-11-13T12:00:00.000 5451
1 456 2018-11-14T12:35:01.000 5784
2 789 2018-11-15T18:02:59.000 6117
"""
if delim_whitespace:
if delimiter is not None:
raise ValueError("cannot set both delimiter and delim_whitespace")
if sep != ',':
raise ValueError("cannot set both sep and delim_whitespace")
# Alias sep -> delimiter.
if delimiter is None:
delimiter = sep
if dtype is not None:
if isinstance(dtype, collections.abc.Mapping):
dtype_dict = True
elif isinstance(dtype, collections.abc.Iterable):
dtype_dict = False
else:
msg = '''dtype must be 'list like' or 'dict' '''
raise TypeError(msg)
if names is not None and len(dtype) != len(names):
msg = '''All column dtypes must be specified.'''
raise TypeError(msg)
nvtx_range_push("CUDF_READ_CSV", "purple")
csv_reader = ffi.new('csv_read_arg*')
# Populate csv_reader struct
if is_file_like(filepath_or_buffer):
if compression == 'infer':
compression = None
buffer = filepath_or_buffer.read()
# check if StringIO is used
if hasattr(buffer, 'encode'):
buffer_as_bytes = buffer.encode()
else:
buffer_as_bytes = buffer
buffer_data_holder = ffi.new("char[]", buffer_as_bytes)
csv_reader.input_data_form = libgdf.HOST_BUFFER
csv_reader.filepath_or_buffer = buffer_data_holder
csv_reader.buffer_size = len(buffer_as_bytes)
else:
if (not os.path.isfile(filepath_or_buffer)):
raise (FileNotFoundError)
if (not os.path.exists(filepath_or_buffer)):
raise (FileNotFoundError)
file_path = _wrap_string(filepath_or_buffer)
csv_reader.input_data_form = libgdf.FILE_PATH
csv_reader.filepath_or_buffer = file_path
if header == 'infer':
header = -1
header_infer = header
arr_names = []
arr_dtypes = []
if names is None:
if header is -1:
header_infer = 0
if header is None:
header_infer = -1
csv_reader.names = ffi.NULL
csv_reader.num_cols = 0
else:
if header is None:
header_infer = -1
csv_reader.num_cols = len(names)
for col_name in names:
arr_names.append(_wrap_string(col_name))
if dtype is not None:
if dtype_dict:
arr_dtypes.append(_wrap_string(str(dtype[col_name])))
names_ptr = ffi.new('char*[]', arr_names)
csv_reader.names = names_ptr
if dtype is None:
csv_reader.dtype = ffi.NULL
else:
if not dtype_dict:
for col_dtype in dtype:
arr_dtypes.append(_wrap_string(str(col_dtype)))
dtype_ptr = ffi.new('char*[]', arr_dtypes)
csv_reader.dtype = dtype_ptr
csv_reader.use_cols_int = ffi.NULL
csv_reader.use_cols_int_len = 0
csv_reader.use_cols_char = ffi.NULL
csv_reader.use_cols_char_len = 0
if usecols is not None:
arr_col_names = []
if (all(isinstance(x, int) for x in usecols)):
usecols_ptr = ffi.new('int[]', usecols)
csv_reader.use_cols_int = usecols_ptr
csv_reader.use_cols_int_len = len(usecols)
else:
for col_name in usecols:
arr_col_names.append(_wrap_string(col_name))
col_names_ptr = ffi.new('char*[]', arr_col_names)
csv_reader.use_cols_char = col_names_ptr
csv_reader.use_cols_char_len = len(usecols)
if decimal == delimiter:
raise ValueError("decimal cannot be the same as delimiter")
if thousands == delimiter:
raise ValueError("thousands cannot be the same as delimiter")
if nrows is not None and skipfooter != 0:
raise ValueError("cannot use both nrows and skipfooter parameters")
if byte_range is not None:
if skipfooter != 0 or skiprows != 0 or nrows is not None:
raise ValueError("""cannot manually limit rows to be read when
using the byte range parameter""")
arr_true_values = []
for value in true_values or []:
arr_true_values.append(_wrap_string(str(value)))
arr_true_values_ptr = ffi.new('char*[]', arr_true_values)
csv_reader.true_values = arr_true_values_ptr
csv_reader.num_true_values = len(arr_true_values)
arr_false_values = []
for value in false_values or []:
arr_false_values.append(_wrap_string(str(value)))
false_values_ptr = ffi.new('char*[]', arr_false_values)
csv_reader.false_values = false_values_ptr
csv_reader.num_false_values = len(arr_false_values)
arr_na_values = []
for value in na_values or []:
arr_na_values.append(_wrap_string(str(value)))
arr_na_values_ptr = ffi.new('char*[]', arr_na_values)
csv_reader.na_values = arr_na_values_ptr
csv_reader.num_na_values = len(arr_na_values)
compression_bytes = _wrap_string(compression)
prefix_bytes = _wrap_string(prefix)
csv_reader.delimiter = delimiter.encode()
csv_reader.lineterminator = lineterminator.encode()
csv_reader.quotechar = quotechar.encode()
csv_reader.quoting = _quoting_enum[quoting]
csv_reader.doublequote = doublequote
csv_reader.delim_whitespace = delim_whitespace
csv_reader.skipinitialspace = skipinitialspace
csv_reader.dayfirst = dayfirst
csv_reader.header = header_infer
csv_reader.skiprows = skiprows
csv_reader.skipfooter = skipfooter
csv_reader.mangle_dupe_cols = mangle_dupe_cols
csv_reader.windowslinetermination = False
csv_reader.compression = compression_bytes
csv_reader.decimal = decimal.encode()
csv_reader.thousands = thousands.encode() if thousands else b'\0'
csv_reader.nrows = nrows if nrows is not None else -1
if byte_range is not None:
csv_reader.byte_range_offset = byte_range[0]
csv_reader.byte_range_size = byte_range[1]
else:
csv_reader.byte_range_offset = 0
csv_reader.byte_range_size = 0
csv_reader.skip_blank_lines = skip_blank_lines
csv_reader.comment = comment.encode() if comment else b'\0'
csv_reader.keep_default_na = keep_default_na
csv_reader.na_filter = na_filter
csv_reader.prefix = prefix_bytes
# Call read_csv
libgdf.read_csv(csv_reader)
out = csv_reader.data
if out == ffi.NULL:
raise ValueError("Failed to parse CSV")
# Extract parsed columns
outcols = []
new_names = []
for i in range(csv_reader.num_cols_out):
if out[i].dtype == libgdf.GDF_STRING:
ptr = int(ffi.cast("uintptr_t", out[i].data))
new_names.append(ffi.string(out[i].col_name).decode())
outcols.append(nvstrings.bind_cpointer(ptr))
else:
newcol = Column.from_cffi_view(out[i])
new_names.append(ffi.string(out[i].col_name).decode())
if (newcol.dtype.type == np.datetime64):
outcols.append(
newcol.view(DatetimeColumn, dtype='datetime64[ms]'))
else:
outcols.append(newcol.view(NumericalColumn,
dtype=newcol.dtype))
# Build dataframe
df = DataFrame()
# if names is not None and header_infer is -1:
for k, v in zip(new_names, outcols):
df[k] = v
# Set index if the index_col parameter is passed
if index_col is not None and index_col is not False:
if isinstance(index_col, (int)):
df = df.set_index(df.columns[index_col])
else:
df = df.set_index(index_col)
nvtx_range_pop()
return df
def unwrap(buffer):
if buffer is None:
return ffi.NULL
devary = buffer.to_gpu_array()
return ffi.cast('void*', devary.device_ctypes_pointer.value)
def read_csv_strings(filepath,
lineterminator='\n',
quotechar='"',
quoting=True,
doublequote=True,
delimiter=',',
sep=None,
delim_whitespace=False,
skipinitialspace=False,
names=None,
dtype=None,
skipfooter=0,
skiprows=0,
dayfirst=False):
import nvstrings
from cudf.dataframe.series import Series
"""
**Experimental**: This function provided only as an alpha way of providing
a way to use nvstrings alongside cudf.
Future versions of cuDF will provide cleaner integration.
Uses the same arguments as read_csv.
Returns list of Series objects for numeric or date columns and nvstrings
objects for those columns that are strings (dtype='str').
Examples
--------
foo.txt : ::
50,abc|40,def|30,ghi|20,jkl|
.. code-block:: python
import cudf
fn = 'foo.txt'
cols = cudf.io.read_csv_strings(fn, delimiter=',', lineterminator='|',
names=['col1', 'col2'], dtype=['int64', 'str'],
skiprows=1, skipfooter=1)
type(cols[0])
print(cols[0])
type(cols[1])
print(cols[1])
Output:
.. code-block:: python
<class 'cudf.series.Series'>
0 40
1 30
<class 'nvstrings.nvstrings'>
['def', 'ghi']
"""
if names is None or dtype is None:
msg = '''Automatic dtype detection not implemented:
Column names and dtypes must be specified.'''
raise TypeError(msg)
if isinstance(dtype, dict):
dtype_dict = True
elif isinstance(dtype, list):
dtype_dict = False
if len(dtype) != len(names):
msg = '''All column dtypes must be specified.'''
raise TypeError(msg)
else:
msg = '''dtype must be 'list' or 'dict' '''
raise TypeError(msg)
csv_reader = ffi.new('csv_read_arg*')
# Populate csv_reader struct
file_path = _wrap_string(filepath)
csv_reader.file_path = file_path
arr_names = []
arr_dtypes = []
for col_name in names:
arr_names.append(_wrap_string(col_name))
if dtype_dict:
arr_dtypes.append(_wrap_string(str(dtype[col_name])))
names_ptr = ffi.new('char*[]', arr_names)
csv_reader.names = names_ptr
if not dtype_dict:
for col_dtype in dtype:
arr_dtypes.append(_wrap_string(str(col_dtype)))
dtype_ptr = ffi.new('char*[]', arr_dtypes)
csv_reader.dtype = dtype_ptr
csv_reader.delimiter = delimiter.encode()
csv_reader.lineterminator = lineterminator.encode()
csv_reader.quotechar = quotechar.encode()
csv_reader.quoting = quoting
csv_reader.doublequote = doublequote
csv_reader.delim_whitespace = delim_whitespace
csv_reader.skipinitialspace = skipinitialspace
csv_reader.dayfirst = dayfirst
csv_reader.num_cols = len(names)
csv_reader.skiprows = skiprows
csv_reader.skipfooter = skipfooter
# Call read_csv
libgdf.read_csv(csv_reader)
out = csv_reader.data
if out == ffi.NULL:
raise ValueError("Failed to parse CSV")
# Extract parsed columns
outcols = []
for i in range(csv_reader.num_cols_out):
if out[i].dtype == libgdf.GDF_STRING:
ptr = int(ffi.cast("uintptr_t", out[i].data))
outcols.append(nvstrings.bind_cpointer(ptr))
else:
newcol = Column.from_cffi_view(out[i])
if (newcol.dtype == np.dtype('datetime64[ms]')):
col = newcol.view(DatetimeColumn, dtype='datetime64[ms]')
else:
col = newcol.view(NumericalColumn, dtype=newcol.dtype)
outcols.append(Series(col))
return outcols
def test_ipc():
# make gpu array
TESTDATA = b"\x00\x01\x00\x00\x10\x00\x00\x00\x0c\x00\x0e\x00\x06\x00\x05\x00\x08\x00\x00\x00\x0c\x00\x00\x00\x00\x01\x01\x00\x10\x00\x00\x00\x00\x00\n\x00\x08\x00\x00\x00\x04\x00\x00\x00\n\x00\x00\x00\x04\x00\x00\x00\x02\x00\x00\x00l\x00\x00\x00\x04\x00\x00\x00\xb0\xff\xff\xff\x00\x00\x01\x038\x00\x00\x00\x1c\x00\x00\x00\x14\x00\x00\x00\x04\x00\x00\x00\x02\x00\x00\x00\x1c\x00\x00\x00\x10\x00\x00\x00\x00\x00\x00\x00\x9a\xff\xff\xff\x00\x00\x01\x00\x8c\xff\xff\xff \x00\x01\x00\x94\xff\xff\xff\x01\x00\x02\x00\x08\x00\x00\x00dest_lon\x00\x00\x00\x00\x14\x00\x18\x00\x08\x00\x06\x00\x07\x00\x0c\x00\x00\x00\x10\x00\x14\x00\x00\x00\x14\x00\x00\x00\x00\x00\x01\x03H\x00\x00\x00$\x00\x00\x00\x14\x00\x00\x00\x04\x00\x00\x00\x02\x00\x00\x00,\x00\x00\x00\x18\x00\x00\x00\x00\x00\x00\x00\x00\x00\x06\x00\x08\x00\x06\x00\x06\x00\x00\x00\x00\x00\x01\x00\xf8\xff\xff\xff \x00\x01\x00\x08\x00\x08\x00\x04\x00\x06\x00\x08\x00\x00\x00\x01\x00\x02\x00\x08\x00\x00\x00dest_lat\x00\x00\x00\x00\xd8\x00\x00\x00\x14\x00\x00\x00\x00\x00\x00\x00\x0c\x00\x16\x00\x06\x00\x05\x00\x08\x00\x0c\x00\x0c\x00\x00\x00\x00\x03\x01\x00\x18\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\n\x00\x18\x00\x0c\x00\x04\x00\x08\x00\n\x00\x00\x00|\x00\x00\x00\x10\x00\x00\x00\x17\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x04\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x17\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x17\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x91\xa7\x06B\x91\xa7\x06B\x91\xa7\x06B\xc4\xcd\xdfA\x91\xa7\x06B\xc4\xcd\xdfA\xe7\xea\nB\x9c\xb3\x1cB\xe7\xea\nB\x9c\xb3\x1cB\xe7\xea\nB]n\xe3A\xe7\xea\nB\xd9$\'Brc\x03BL\x8a\xffArc\x03B\xd9$\'Brc\x03BL\x8a\xffArc\x03Bt@\x06B\x03o\x1fB\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00C\xa5\xcb\xc2C\xa5\xcb\xc2C\xa5\xcb\xc2\x06\x11\xa5\xc2C\xa5\xcb\xc2\x06\x11\xa5\xc2\xd0r\xb8\xc2\x1eV\x99\xc2\xd0r\xb8\xc2\x1eV\x99\xc2\xd0r\xb8\xc2\xce\xa1\xa2\xc2\xd0r\xb8\xc2>\x81\xaf\xc2\x1b\xb4\xc1\xc2ag\xcc\xc2\x1b\xb4\xc1\xc2>\x81\xaf\xc2\x1b\xb4\xc1\xc2ag\xcc\xc2\x1b\xb4\xc1\xc2\xd1\x81\xad\xc2\x81U\xd1\xc2\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00"
cpu_data = np.ndarray(shape=len(TESTDATA),
dtype=np.byte,
buffer=bytearray(TESTDATA))
gpu_data = cuda.to_device(cpu_data)
del cpu_data
# Use GDF IPC parser
devptr = ffi.cast("void*", gpu_data.device_ctypes_pointer.value)
ipcparser = libgdf.gdf_ipc_parser_open(devptr)
assert not libgdf.gdf_ipc_parser_failed(ipcparser)
assert not ffi.string(libgdf.gdf_ipc_parser_get_error(ipcparser))
# get schema as json
jsonraw = libgdf.gdf_ipc_parser_to_json(ipcparser)
jsontext = ffi.string(jsonraw).decode()
jsonparsed = json.loads(jsontext)
pprint(jsonparsed)
dataptr = libgdf.gdf_ipc_parser_get_data_offset(ipcparser)
dataptr = int(ffi.cast('uint64_t', dataptr))
data_region = gpu_data[dataptr:]
print(data_region.shape)
def get_column(schema):
offset = schema['data_buffer']['offset']
raw_size = schema['data_buffer']['length']
size = schema['length']
assert schema['dtype']['bitwidth'] == 32
assert schema['dtype']['name'] == 'FloatingPoint'
raw_data_col1 = data_region[offset:offset + raw_size]
assert raw_data_col1.size == raw_size
dtype = np.dtype(np.float32)
itemsize = dtype.itemsize
ary = DeviceNDArray(shape=(raw_size // itemsize, ),
strides=(itemsize, ),
dtype=dtype,
gpu_data=raw_data_col1.gpu_data)
hary = ary[:size].copy_to_host()
return hary
# Get first column
schema_col1 = jsonparsed[0]
name_col1 = schema_col1['name']
assert name_col1 == 'dest_lat'
dest_lat = get_column(schema_col1)
# Get second column
schema_col2 = jsonparsed[1]
name_col2 = schema_col2['name']
assert name_col2 == 'dest_lon'
dest_lon = get_column(schema_col2)
libgdf.gdf_ipc_parser_close(ipcparser)
# Check data integrity
np.testing.assert_array_less(dest_lat, 42)
np.testing.assert_array_less(27, dest_lat)
np.testing.assert_array_less(dest_lon, -76)
np.testing.assert_array_less(-105, dest_lon)
def _apply_agg(self, agg_type, result, add_col_values,
ctx, val_columns, val_columns_out, sort_result=True):
"""
Parameters
----------
agg_type : str
The aggregation function to run.
result : DataFrame
The DataFrame to store the result of the aggregation into.
add_col_values : bool
Boolean to indicate whether this is the first aggregation being
run and should add the additional columns' values.
ctx : gdf_context cffi object
Context object to pass information such as if the dataframe
is sorted and/or which method to use for grouping.
val_columns : list of *str*
The list of column names that the aggregation should be performed
on.
val_columns_out : list of *str*
The list of columns names that the aggregation results should be
output into.
"""
if sort_result:
ctx.flag_sort_result = 1
ncols = len(self._by)
cols = [self._df[thisBy]._column.cffi_view for thisBy in self._by]
first_run = add_col_values
need_to_index = self._as_index
col_count = 0
if isinstance(val_columns, (str, Number)):
val_columns = [val_columns]
for val_col in val_columns:
col_agg = self._df[val_col]._column.cffi_view
# assuming here that if there are multiple aggregations that the
# aggregated results will be in the same order for GDF_SORT method
if need_to_index:
out_col_indices_series = Series(
Buffer(
rmm.device_array(
col_agg.size,
dtype=np.int32
)
)
)
out_col_indices = out_col_indices_series._column.cffi_view
else:
out_col_indices = ffi.NULL
out_col_values_series = []
for i in range(0, ncols):
if self._df[self._by[i]].dtype == np.dtype('object'):
# This isn't ideal, but no better way to create an
# nvstrings object of correct size
gather_map = zeros(col_agg.size, dtype='int32')
col = Series([''], dtype='str')[gather_map]\
.reset_index(drop=True)
else:
col = Series(
Buffer(
rmm.device_array(
col_agg.size,
dtype=self._df[self._by[i]]._column.data.dtype
)
)
)
out_col_values_series.append(col)
out_col_values = [
out_col_values_series[i]._column.cffi_view
for i in range(0, ncols)]
if agg_type == "count":
out_col_agg_series = Series(
Buffer(
rmm.device_array(
col_agg.size,
dtype=np.int64
)
)
)
elif agg_type == "mean":
out_col_agg_series = Series(
Buffer(
rmm.device_array(
col_agg.size,
dtype=np.float64
)
)
)
else:
if self._df[val_col].dtype == np.dtype('object'):
# This isn't ideal, but no better way to create an
# nvstrings object of correct size
gather_map = zeros(col_agg.size, dtype='int32')
out_col_agg_series = Series(
[''],
dtype='str'
)[gather_map].reset_index(drop=True)
else:
out_col_agg_series = Series(
Buffer(
rmm.device_array(
col_agg.size,
dtype=self._df[val_col]._column.data.dtype
)
)
)
out_col_agg = out_col_agg_series._column.cffi_view
agg_func = self._NAMED_FUNCTIONS.get(agg_type, None)
if agg_func is None:
raise RuntimeError(
"ERROR: this aggregator has not been implemented yet")
err = agg_func(
ncols,
cols,
col_agg,
out_col_indices,
out_col_values,
out_col_agg,
ctx)
if (err is not None):
raise RuntimeError(err)
num_row_results = out_col_agg.size
# NVStrings columns are not the same going in as coming out but we
# can't create entire CFFI views otherwise multiple objects will
# try to free the memory
for i, col in enumerate(out_col_values_series):
if col.dtype == np.dtype("object") and len(col) > 0:
import nvcategory
nvcat_ptr = int(
ffi.cast(
"uintptr_t",
out_col_values[i].dtype_info.category
)
)
nvcat_obj = None
if nvcat_ptr:
nvcat_obj = nvcategory.bind_cpointer(nvcat_ptr)
nvstr_obj = nvcat_obj.to_strings()
else:
import nvstrings
nvstr_obj = nvstrings.to_device([])
out_col_values_series[i]._column._data = nvstr_obj
out_col_values_series[i]._column._nvcategory = nvcat_obj
if out_col_agg_series.dtype == np.dtype("object") and \
len(out_col_agg_series) > 0:
import nvcategory
nvcat_ptr = int(
ffi.cast(
"uintptr_t",
out_col_agg.dtype_info.category
)
)
nvcat_obj = None
if nvcat_ptr:
nvcat_obj = nvcategory.bind_cpointer(nvcat_ptr)
nvstr_obj = nvcat_obj.to_strings()
else:
import nvstrings
nvstr_obj = nvstrings.to_device([])
out_col_agg_series._column._data = nvstr_obj
out_col_agg_series._column._nvcategory = nvcat_obj
if first_run:
for i, thisBy in enumerate(self._by):
result[thisBy] = out_col_values_series[i][
:num_row_results]
if is_categorical_dtype(self._df[thisBy].dtype):
result[thisBy] = CategoricalColumn(
data=result[thisBy].data,
categories=self._df[thisBy].cat.categories,
ordered=self._df[thisBy].cat.ordered
)
if out_col_agg_series.dtype != np.dtype("object"):
out_col_agg_series.data.size = num_row_results
out_col_agg_series = out_col_agg_series.reset_index(drop=True)
if isinstance(val_columns_out, (str, Number)):
result[val_columns_out] = out_col_agg_series[:num_row_results]
else:
result[val_columns_out[col_count]
] = out_col_agg_series[:num_row_results]
if out_col_agg_series.dtype != np.dtype("object"):
out_col_agg_series.data.size = num_row_results
out_col_agg_series = out_col_agg_series.reset_index(drop=True)
first_run = False
col_count = col_count + 1
return result
def unwrap_devary(devary):
return ffi.cast('void*', devary.device_ctypes_pointer.value)
def read_csv_strings(filepath_or_buffer, lineterminator='\n',
quotechar='"', quoting=True, doublequote=True,
sep=',', delimiter=None, delim_whitespace=False,
skipinitialspace=False, names=None, dtype=None,
skipfooter=0, skiprows=0, dayfirst=False,
compression='infer', thousands=None, decimal='.',
true_values=None, false_values=None, nrows=None):
"""
**Experimental**: This function exists only as a beta way to use
`nvstrings <https://nvstrings.readthedocs.io/en/latest/>`_. with cudf.
Future versions of cuDF will provide cleaner integration.
Uses mostly same arguments as read_csv.
Note: Doesn't currently support auto-column detection, header, usecols
and mangle_dupe_cols args.
Returns
-------
columns : ordered list of cudf.dataframe.Series and nvstrings objects
numeric or date dtyped columns will be Series.
'str' dtyped columns will be
`nvstrings <https://nvstrings.readthedocs.io/en/latest/>`_.
Examples
--------
.. code-block:: python
import cudf
# Create a test csv file
filename = 'foo.csv'
lines = [
"num1,datetime,text",
"123,2018-11-13T12:00:00,abc",
"456,2018-11-14T12:35:01,def",
"789,2018-11-15T18:02:59,ghi"
]
with open(filename, 'w') as fp:
fp.write('\\n'.join(lines)+'\\n')
# Read the file with cudf
names = ['num1', 'datetime', 'text']
dtypes = ['int', 'date', 'str']
columns = cudf.io.csv.read_csv_strings(filename, delimiter=',',
names=names, dtype=dtypes,
skiprows=1)
# Display results
columns[0]
print(columns[0])
columns[2]
print(columns[2])
Output:
.. code-block:: python
<cudf.Series nrows=3 >
0 123
1 456
2 789
<nvstrings count=3>
['abc', 'def', 'ghi']
See Also
--------
.read_csv
"""
import nvstrings
from cudf.dataframe.series import Series
if names is None or dtype is None:
msg = '''Automatic dtype detection not implemented:
Column names and dtypes must be specified.'''
raise TypeError(msg)
# Alias sep -> delimiter.
if delimiter is None:
delimiter = sep
if isinstance(dtype, dict):
dtype_dict = True
elif isinstance(dtype, list):
dtype_dict = False
if len(dtype) != len(names):
msg = '''All column dtypes must be specified.'''
raise TypeError(msg)
else:
msg = '''dtype must be 'list' or 'dict' '''
raise TypeError(msg)
csv_reader = ffi.new('csv_read_arg*')
# Populate csv_reader struct
if is_file_like(filepath_or_buffer):
buffer = filepath_or_buffer.read()
# check if StringIO is used
if hasattr(buffer, 'encode'):
buffer_as_bytes = buffer.encode()
else:
buffer_as_bytes = buffer
buffer_data_holder = ffi.new("char[]", buffer_as_bytes)
csv_reader.input_data_form = libgdf.HOST_BUFFER
csv_reader.filepath_or_buffer = buffer_data_holder
csv_reader.buffer_size = len(buffer_as_bytes)
else:
file_path = _wrap_string(filepath_or_buffer)
csv_reader.input_data_form = libgdf.FILE_PATH
csv_reader.filepath_or_buffer = file_path
arr_names = []
arr_dtypes = []
for col_name in names:
arr_names.append(_wrap_string(col_name))
if dtype_dict:
arr_dtypes.append(_wrap_string(str(dtype[col_name])))
names_ptr = ffi.new('char*[]', arr_names)
csv_reader.names = names_ptr
if not dtype_dict:
for col_dtype in dtype:
arr_dtypes.append(_wrap_string(str(col_dtype)))
dtype_ptr = ffi.new('char*[]', arr_dtypes)
csv_reader.dtype = dtype_ptr
if decimal == delimiter:
raise ValueError("decimal cannot be the same as delimiter")
if thousands == delimiter:
raise ValueError("thousands cannot be the same as delimiter")
if nrows is not None and skipfooter != 0:
raise ValueError("cannot use both nrows and skipfooter parameters")
# Start with default values recognized as boolean
arr_true_values = [_wrap_string(str('True')), _wrap_string(str('TRUE'))]
arr_false_values = [_wrap_string(str('False')), _wrap_string(str('FALSE'))]
for value in true_values or []:
arr_true_values.append(_wrap_string(str(value)))
arr_true_values_ptr = ffi.new('char*[]', arr_true_values)
csv_reader.true_values = arr_true_values_ptr
csv_reader.num_true_values = len(arr_true_values)
for value in false_values or []:
arr_false_values.append(_wrap_string(str(value)))
false_values_ptr = ffi.new('char*[]', arr_false_values)
csv_reader.false_values = false_values_ptr
csv_reader.num_false_values = len(arr_false_values)
compression_bytes = _wrap_string(compression)
csv_reader.delimiter = delimiter.encode()
csv_reader.lineterminator = lineterminator.encode()
csv_reader.quotechar = quotechar.encode()
csv_reader.quoting = quoting
csv_reader.doublequote = doublequote
csv_reader.delim_whitespace = delim_whitespace
csv_reader.skipinitialspace = skipinitialspace
csv_reader.dayfirst = dayfirst
csv_reader.num_cols = len(names)
csv_reader.skiprows = skiprows
csv_reader.skipfooter = skipfooter
csv_reader.compression = compression_bytes
csv_reader.decimal = decimal.encode()
csv_reader.thousands = thousands.encode() if thousands else b'\0'
csv_reader.nrows = nrows if nrows is not None else -1
# Call read_csv
libgdf.read_csv(csv_reader)
out = csv_reader.data
if out == ffi.NULL:
raise ValueError("Failed to parse CSV")
# Extract parsed columns
outcols = []
for i in range(csv_reader.num_cols_out):
if out[i].dtype == libgdf.GDF_STRING:
ptr = int(ffi.cast("uintptr_t", out[i].data))
outcols.append(nvstrings.bind_cpointer(ptr))
else:
newcol = Column.from_cffi_view(out[i])
if(newcol.dtype == np.dtype('datetime64[ms]')):
col = newcol.view(DatetimeColumn, dtype='datetime64[ms]')
else:
col = newcol.view(NumericalColumn, dtype=newcol.dtype)
outcols.append(Series(col))
return outcols
def test_ipc():
batch = make_batch()
schema_bytes = batch.schema.serialize().to_pybytes()
recordbatches_bytes = batch.serialize().to_pybytes()
cpu_data = np.ndarray(shape=len(schema_bytes),
dtype=np.byte,
buffer=bytearray(schema_bytes))
# Use GDF IPC parser
schema_ptr = ffi.cast("void*", cpu_data.ctypes.data)
ipch = libgdf.gdf_ipc_parser_open(schema_ptr, cpu_data.size)
if libgdf.gdf_ipc_parser_failed(ipch):
assert 0, str(ffi.string(libgdf.gdf_ipc_parser_get_error(ipch)))
jsonraw = libgdf.gdf_ipc_parser_get_schema_json(ipch)
jsontext = ffi.string(jsonraw).decode()
json_schema = json.loads(jsontext)
print('json_schema:')
pprint(json_schema)
rb_cpu_data = np.ndarray(shape=len(recordbatches_bytes),
dtype=np.byte,
buffer=bytearray(recordbatches_bytes))
rb_gpu_data = rmm.to_device(rb_cpu_data)
del cpu_data
devptr = ffi.cast("void*", rb_gpu_data.device_ctypes_pointer.value)
libgdf.gdf_ipc_parser_open_recordbatches(ipch, devptr, rb_gpu_data.size)
if libgdf.gdf_ipc_parser_failed(ipch):
assert 0, str(ffi.string(libgdf.gdf_ipc_parser_get_error(ipch)))
jsonraw = libgdf.gdf_ipc_parser_get_layout_json(ipch)
jsontext = ffi.string(jsonraw).decode()
json_rb = json.loads(jsontext)
print('json_rb:')
pprint(json_rb)
offset = libgdf.gdf_ipc_parser_get_data_offset(ipch)
libgdf.gdf_ipc_parser_close(ipch)
# Check
dicts = json_schema['dictionaries']
assert len(dicts) == 1
dictdata = dicts[0]['data']['columns'][0]['DATA']
assert set(dictdata) == {'orange', 'apple', 'pear', 'grape'}
gpu_data = rb_gpu_data[offset:]
schema_fields = json_schema['schema']['fields']
assert len(schema_fields) == 3
field_names = [f['name'] for f in schema_fields]
assert field_names == ['idx', 'name', 'weight']
# check the dictionary id in schema
assert schema_fields[1]['dictionary']['id'] == dicts[0]['id']
# Get "idx" column
idx_buf_off = json_rb[0]['data_buffer']['offset']
idx_buf_len = json_rb[0]['data_buffer']['length']
idx_buf = gpu_data[idx_buf_off:][:idx_buf_len]
assert json_rb[0]['dtype']['name'] == 'INT32'
idx_size = json_rb[0]['length']
assert idx_size == 30
idx_data = np.ndarray(shape=idx_size,
dtype=np.int32,
buffer=idx_buf.copy_to_host())
print('idx_data:')
print(idx_data)
# Get "name" column
name_buf_off = json_rb[1]['data_buffer']['offset']
name_buf_len = json_rb[1]['data_buffer']['length']
name_buf = gpu_data[name_buf_off:][:name_buf_len]
assert json_rb[1]['dtype']['name'] == 'DICTIONARY'
name_size = json_rb[1]['length']
name_data = np.ndarray(shape=name_size,
dtype=np.int32,
buffer=name_buf.copy_to_host())
print('name_data:')
print(name_data)
# Get "weight" column
weight_buf_off = json_rb[2]['data_buffer']['offset']
weight_buf_len = json_rb[2]['data_buffer']['length']
weight_buf = gpu_data[weight_buf_off:][:weight_buf_len]
assert json_rb[2]['dtype']['name'] == 'DOUBLE'
weight_size = json_rb[2]['length']
weight_data = np.ndarray(shape=weight_size,
dtype=np.float64,
buffer=weight_buf.copy_to_host())
print('weight_data:')
print(weight_data)
# verify data
sortedidx = np.argsort(idx_data)
idx_data = idx_data[sortedidx]
name_data = name_data[sortedidx]
weight_data = weight_data[sortedidx]
got_iter = zip(idx_data, name_data, weight_data)
for expected, got in zip(get_expected_values(), got_iter):
assert expected[0] == got[0]
assert expected[1] == dictdata[got[1]]
assert expected[2] == got[2]
