wehub-resource-sync
309 lines
10 KiB
Python
309 lines
10 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
from __future__ import annotations
|
|
|
|
import os
|
|
import random
|
|
import unittest
|
|
from enum import IntEnum
|
|
|
|
import numpy as np
|
|
|
|
import paddle
|
|
import paddle.static
|
|
|
|
map_np_dtype_to_base_dtype = {
|
|
'bool': "bool",
|
|
'int8': "int8",
|
|
'uint8': "uint8",
|
|
"int32": "int32",
|
|
"int64": "int64",
|
|
"float16": "float16",
|
|
"float32": "float32",
|
|
"float64": "float64",
|
|
}
|
|
|
|
|
|
def np_dtype_to_base_str(dtype: np.dtype) -> str:
|
|
return map_np_dtype_to_base_dtype[dtype.name]
|
|
|
|
|
|
class ExecutionModeFull(IntEnum):
|
|
# Run fp32 model on cpu
|
|
CPU_FP32 = 1
|
|
# Run fp32 model on ipu
|
|
IPU_FP32 = 2
|
|
# Convert model to fp16 using mixed-precision approach
|
|
# All parameters will be converted to fp16
|
|
IPU_FP16 = 3
|
|
|
|
|
|
class ExecutionMode(IntEnum):
|
|
CPU_FP32 = ExecutionModeFull.CPU_FP32
|
|
IPU_FP32 = ExecutionModeFull.IPU_FP32
|
|
IPU_FP16 = ExecutionModeFull.IPU_FP16
|
|
|
|
|
|
class IPUTest(unittest.TestCase):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
# Get random seeds
|
|
cls._np_rand_state = np.random.get_state()
|
|
cls._py_rand_state = random.getstate()
|
|
|
|
cls.SEED = 2021
|
|
np.random.seed(cls.SEED)
|
|
random.seed(cls.SEED)
|
|
paddle.seed(cls.SEED)
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
"""Restore random seeds"""
|
|
np.random.set_state(cls._np_rand_state)
|
|
random.setstate(cls._py_rand_state)
|
|
|
|
# Check if ipumodel mode is enabled
|
|
@classmethod
|
|
def use_ipumodel(cls):
|
|
if 'POPLAR_IPUMODEL' not in os.environ:
|
|
return False
|
|
else:
|
|
flag = os.environ['POPLAR_IPUMODEL']
|
|
if flag.upper() in ['1', "TRUE"]:
|
|
return True
|
|
|
|
|
|
@unittest.skipIf(
|
|
not paddle.is_compiled_with_ipu(), "core is not compiled with IPU"
|
|
)
|
|
class IPUD2STest(IPUTest):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
super().setUpClass()
|
|
|
|
# Disable paddle static graph mode
|
|
paddle.disable_static()
|
|
|
|
def tearDown(self):
|
|
# Manual reset when using ipumodel
|
|
if self.use_ipumodel():
|
|
paddle.framework.core.IpuBackend.get_instance().reset()
|
|
|
|
|
|
@unittest.skipIf(
|
|
not paddle.is_compiled_with_ipu(), "core is not compiled with IPU"
|
|
)
|
|
class IPUOpTest(IPUTest):
|
|
"""Base Class for single op unit tests using static graph on IPU."""
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
super().setUpClass()
|
|
|
|
# Enable paddle static graph mode
|
|
paddle.enable_static()
|
|
|
|
# Items that a op_tester needs
|
|
cls.main_prog: paddle.static.Program = None
|
|
cls.startup_prog: paddle.static.Program = None
|
|
cls.scope: paddle.static.Scope = None
|
|
cls.feed_list: list[str] = None
|
|
cls.fetch_list: list[str] = None
|
|
cls.output_dict: dict | None = {}
|
|
|
|
def tearDown(self):
|
|
# Manual reset when using ipumodel
|
|
if self.use_ipumodel():
|
|
paddle.framework.core.IpuBackend.get_instance().reset()
|
|
|
|
@property
|
|
def fp16_enabled(self):
|
|
return True
|
|
|
|
def skip_mode(self, exec_mode):
|
|
if exec_mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
def is_ipu_mode(self, exec_mode):
|
|
if exec_mode == ExecutionMode.CPU_FP32:
|
|
return False
|
|
return True
|
|
|
|
def is_fp16_mode(self, exec_mode):
|
|
if exec_mode != ExecutionMode.IPU_FP16:
|
|
return False
|
|
return True
|
|
|
|
def set_atol(self):
|
|
self.atol = 1e-10
|
|
self.rtol = 1e-6
|
|
self.atol_fp16 = 1e-3
|
|
self.rtol_fp16 = 1e-3
|
|
|
|
def set_training(self):
|
|
self.is_training = False
|
|
self.epoch = 1
|
|
|
|
# Decorator for static graph building
|
|
def static_graph(builder):
|
|
def wrapper(self, *args, **kwargs):
|
|
self.scope = paddle.static.Scope()
|
|
self.main_prog = paddle.static.Program()
|
|
self.startup_prog = paddle.static.Program()
|
|
paddle.seed(self.SEED)
|
|
with (
|
|
paddle.static.scope_guard(self.scope),
|
|
paddle.utils.unique_name.guard(
|
|
paddle.utils.unique_name.generate('')
|
|
),
|
|
paddle.static.program_guard(self.main_prog, self.startup_prog),
|
|
):
|
|
builder(self, *args, **kwargs)
|
|
|
|
return wrapper
|
|
|
|
# Cast a fp32 model to a full-fp16 model
|
|
@classmethod
|
|
def cast_model_to_fp16(cls, main_program):
|
|
amp_list = paddle.static.amp.CustomOpLists()
|
|
amp_list.unsupported_list = {'scale'}
|
|
to_fp16_var_names = paddle.static.amp.cast_model_to_fp16(
|
|
main_program, amp_list, use_fp16_guard=False
|
|
)
|
|
paddle.static.amp.cast_parameters_to_fp16(
|
|
paddle.CPUPlace(), main_program, to_fp16_var_names=to_fp16_var_names
|
|
)
|
|
|
|
def run_op_test(self, exec_mode, ipu_strategy=None):
|
|
# NOTE: some op has no inputs
|
|
# if len(self.feed_list) == 0 or len(self.fetch_list) == 0:
|
|
# raise ValueError('feed_list or fetch_list is empty')
|
|
if self.is_ipu_mode(exec_mode):
|
|
place = paddle.IPUPlace()
|
|
else:
|
|
place = paddle.CPUPlace()
|
|
exe = paddle.static.Executor(place)
|
|
exe.run(self.startup_prog)
|
|
if self.is_ipu_mode(exec_mode):
|
|
if ipu_strategy is None:
|
|
ipu_strategy = paddle.static.IpuStrategy()
|
|
ipu_strategy.set_graph_config(is_training=self.is_training)
|
|
if self.is_fp16_mode(exec_mode):
|
|
ipu_strategy.set_precision_config(enable_fp16=True)
|
|
IPUOpTest.cast_model_to_fp16(self.main_prog)
|
|
|
|
# TODO(ipu) remove in the future version of popart
|
|
# keep the log clean, no side effects for tests without profiling
|
|
ipu_strategy.set_options(
|
|
{'engine_options': {'debug.retainDebugInformation': 'false'}}
|
|
)
|
|
|
|
program = paddle.static.IpuCompiledProgram(
|
|
self.main_prog, ipu_strategy=ipu_strategy
|
|
).compile(self.feed_list, self.fetch_list)
|
|
else:
|
|
program = self.main_prog
|
|
|
|
feed = self.feed_fp32
|
|
if self.is_fp16_mode(exec_mode):
|
|
feed = self.feed_fp16
|
|
|
|
if self.is_training:
|
|
result = []
|
|
for _ in range(self.epoch):
|
|
loss_res = exe.run(
|
|
program, feed=feed, fetch_list=self.fetch_list
|
|
)
|
|
result.append(loss_res)
|
|
else:
|
|
result = exe.run(program, feed=feed, fetch_list=self.fetch_list)
|
|
|
|
if isinstance(result, list) and len(result) == 1:
|
|
self.output_dict[exec_mode] = result[0]
|
|
else:
|
|
self.output_dict[exec_mode] = result
|
|
|
|
def check(self, check_shape=False, output_dict=None):
|
|
if output_dict is None:
|
|
output_dict = self.output_dict
|
|
if len(output_dict) == 0:
|
|
raise ValueError("output_dict is empty")
|
|
cpu_fp32 = output_dict[ExecutionMode.CPU_FP32]
|
|
ipu_fp32 = output_dict[ExecutionMode.IPU_FP32]
|
|
# Convert 0-dim tensor
|
|
if isinstance(cpu_fp32, np.ndarray) and cpu_fp32.shape == ():
|
|
cpu_fp32 = cpu_fp32.reshape(1)
|
|
if len(cpu_fp32) != len(ipu_fp32):
|
|
raise ValueError("different outputs number between ipu and cpu.")
|
|
for cpu_fp32_res, ipu_fp32_res in zip(cpu_fp32, ipu_fp32):
|
|
cpu_fp32_res = np.asarray(cpu_fp32_res).astype(np.float32).flatten()
|
|
ipu_fp32_res = np.asarray(ipu_fp32_res).astype(np.float32).flatten()
|
|
pass_check = np.allclose(
|
|
ipu_fp32_res, cpu_fp32_res, rtol=self.rtol, atol=self.atol
|
|
)
|
|
if not pass_check:
|
|
max_atol = np.abs(ipu_fp32_res - cpu_fp32_res).max()
|
|
cpu_fp32_abs = np.abs(cpu_fp32_res)
|
|
cpu_fp32_abs[cpu_fp32_abs == 0.0] = 1e-20
|
|
max_rtol = (
|
|
np.abs(ipu_fp32_res - cpu_fp32_res) / cpu_fp32_abs
|
|
).max()
|
|
raise AssertionError(
|
|
f"ipu_fp32 check failed. max_atol is {max_atol}, max_rtol is {max_rtol}"
|
|
)
|
|
|
|
if check_shape:
|
|
self.assertTrue(cpu_fp32_res.shape == ipu_fp32_res.shape)
|
|
|
|
if ExecutionMode.IPU_FP16 in output_dict.keys():
|
|
ipu_fp16 = output_dict[ExecutionMode.IPU_FP16]
|
|
if len(cpu_fp32) != len(ipu_fp16):
|
|
raise ValueError(
|
|
"different outputs number between ipu and cpu."
|
|
)
|
|
for cpu_fp32_res, ipu_fp16_res in zip(cpu_fp32, ipu_fp16):
|
|
cpu_fp32_res = (
|
|
np.asarray(cpu_fp32_res).astype(np.float32).flatten()
|
|
)
|
|
ipu_fp16_res = (
|
|
np.asarray(ipu_fp16_res).astype(np.float32).flatten()
|
|
)
|
|
pass_check = np.allclose(
|
|
ipu_fp16_res,
|
|
cpu_fp32_res,
|
|
rtol=self.rtol_fp16,
|
|
atol=self.atol_fp16,
|
|
)
|
|
if not pass_check:
|
|
max_atol = np.abs(ipu_fp16_res - cpu_fp32_res).max()
|
|
cpu_fp32_abs = np.abs(cpu_fp32_res)
|
|
cpu_fp32_abs[cpu_fp32_abs == 0.0] = 1e-20
|
|
max_rtol = (
|
|
np.abs(ipu_fp16_res - cpu_fp32_res) / cpu_fp32_abs
|
|
).max()
|
|
raise AssertionError(
|
|
f"ipu_fp16 check failed. max_atol is {max_atol}, max_rtol is {max_rtol}"
|
|
)
|
|
|
|
if check_shape:
|
|
self.assertTrue(ipu_fp16_res.shape == cpu_fp32_res.shape)
|
|
|
|
# Execution Mode
|
|
class ExecutionMode(IntEnum):
|
|
CPU_FP32 = ExecutionModeFull.CPU_FP32
|
|
IPU_FP32 = ExecutionModeFull.IPU_FP32
|
|
IPU_FP16 = ExecutionModeFull.IPU_FP16
|