chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,279 @@
|
||||
# Copyright (c) 2023 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.
|
||||
|
||||
import copy
|
||||
import logging
|
||||
|
||||
import paddle
|
||||
from paddle.base.log_helper import get_logger
|
||||
|
||||
_logger = get_logger(
|
||||
__name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s'
|
||||
)
|
||||
|
||||
|
||||
class OperatorStatsUnit:
|
||||
def __init__(self):
|
||||
self.op_type = None
|
||||
self.fp32_calls = 0
|
||||
self.fp16_calls = 0
|
||||
self.bf16_calls = 0
|
||||
self.other_calls = 0
|
||||
|
||||
def update(self, dtype):
|
||||
if dtype is None:
|
||||
self.other_calls = self.other_calls + 1
|
||||
else:
|
||||
if dtype == paddle.float32:
|
||||
self.fp32_calls = self.fp32_calls + 1
|
||||
elif dtype == paddle.float16:
|
||||
self.fp16_calls = self.fp16_calls + 1
|
||||
elif dtype == paddle.bfloat16:
|
||||
self.bf16_calls = self.bf16_calls + 1
|
||||
else:
|
||||
self.other_calls = self.other_calls + 1
|
||||
|
||||
def addto(self, another):
|
||||
self.fp32_calls += another.fp32_calls
|
||||
self.fp16_calls += another.fp16_calls
|
||||
self.bf16_calls += another.bf16_calls
|
||||
self.other_calls += another.other_calls
|
||||
|
||||
def convert_to_list(self):
|
||||
return [
|
||||
self.fp16_calls,
|
||||
self.bf16_calls,
|
||||
self.fp32_calls,
|
||||
self.other_calls,
|
||||
]
|
||||
|
||||
|
||||
def _is_floating_point(dtype):
|
||||
if dtype in [
|
||||
paddle.base.core.VarDesc.VarType.FP64,
|
||||
paddle.base.core.VarDesc.VarType.FP32,
|
||||
paddle.base.core.VarDesc.VarType.FP16,
|
||||
paddle.base.core.VarDesc.VarType.BF16,
|
||||
]:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
|
||||
def _get_var_dtype_from_block(block, op, arg_name, is_input):
|
||||
var_names = op.input(arg_name) if is_input else op.output(arg_name)
|
||||
assert isinstance(var_names, list)
|
||||
if len(var_names) == 0:
|
||||
return None
|
||||
|
||||
var_name = var_names[0]
|
||||
try:
|
||||
var = block._var_recursive(var_name)
|
||||
return var.dtype
|
||||
except:
|
||||
_logger.warning(
|
||||
"Operator < {} > gets {} < {} : {} > error!".format(
|
||||
op.type, "input" if is_input else "output", arg_name, var_name
|
||||
)
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
def _extract_compute_dtype(op, block):
|
||||
var_name = None
|
||||
compute_dtype = None
|
||||
for in_name in op.input_names:
|
||||
var_dtype = _get_var_dtype_from_block(block, op, in_name, True)
|
||||
if var_dtype is None:
|
||||
continue
|
||||
|
||||
if compute_dtype is None:
|
||||
compute_dtype = var_dtype
|
||||
else:
|
||||
if compute_dtype != var_dtype:
|
||||
if _is_floating_point(compute_dtype) and _is_floating_point(
|
||||
var_dtype
|
||||
):
|
||||
_logger.warning(
|
||||
f"Operator < {op.type} > has different input data types, input_names = {op.input_names}, output_names = {op.output_names}."
|
||||
)
|
||||
elif _is_floating_point(var_dtype):
|
||||
# When there are multiple inputs, such as embedding
|
||||
# (ids is integer, w is floating-point), the kernel
|
||||
# dtype is normally decided by the input of floating-point.
|
||||
compute_dtype = var_dtype
|
||||
|
||||
for out_name in op.output_names:
|
||||
var_dtype = _get_var_dtype_from_block(block, op, out_name, False)
|
||||
if var_dtype is None:
|
||||
continue
|
||||
|
||||
if compute_dtype is None:
|
||||
# Kernel dtype is mostly decided by the input's dtype.
|
||||
# When the operator has no input, it might have an attr
|
||||
# such as dtype to specify the output's dtype.
|
||||
compute_dtype = var_dtype
|
||||
else:
|
||||
if compute_dtype != var_dtype:
|
||||
if _is_floating_point(compute_dtype) and _is_floating_point(
|
||||
var_dtype
|
||||
):
|
||||
_logger.warning(
|
||||
f"Operator < {op.type} > has different input / output data types, input_names = {op.input_names}, output_names = {op.output_names}."
|
||||
)
|
||||
return compute_dtype
|
||||
|
||||
|
||||
def _merge_op_stats(op_stats_list):
|
||||
merged_op_stats_dict = {}
|
||||
for each_op_stats_dict in op_stats_list:
|
||||
for op_type, unit in each_op_stats_dict.items():
|
||||
if merged_op_stats_dict.get(op_type, None) is None:
|
||||
merged_op_stats_dict[op_type] = copy.copy(unit)
|
||||
else:
|
||||
merged_op_stats_dict[op_type].addto(unit)
|
||||
return merged_op_stats_dict
|
||||
|
||||
|
||||
def _get_op_stats_list(program):
|
||||
def _is_special_ops_with_input_x(op_type):
|
||||
# operators have input X and have inputs different dtypes.
|
||||
special_op_list = ['cast', 'batch_norm', 'instance_norm', 'layer_norm']
|
||||
if op_type in special_op_list:
|
||||
return True
|
||||
if op_type.replace("_grad", "") in special_op_list:
|
||||
return True
|
||||
return False
|
||||
|
||||
op_stats_list = []
|
||||
for block in program.blocks:
|
||||
block_op_stats_dict = {}
|
||||
for op in block.ops:
|
||||
if block_op_stats_dict.get(op.type, None) is None:
|
||||
unit = OperatorStatsUnit()
|
||||
block_op_stats_dict[op.type] = unit
|
||||
else:
|
||||
unit = block_op_stats_dict[op.type]
|
||||
|
||||
if op.type in [
|
||||
'create_py_reader',
|
||||
'read',
|
||||
'create_double_buffer_reader',
|
||||
]:
|
||||
compute_dtype = None
|
||||
elif _is_special_ops_with_input_x(op.type):
|
||||
# Not check the input and output dtype difference for this operators.
|
||||
compute_dtype = _get_var_dtype_from_block(block, op, 'X', True)
|
||||
elif "Param" in op.input_names:
|
||||
# Specify compute_dtype for optimizers.
|
||||
compute_dtype = _get_var_dtype_from_block(
|
||||
block, op, 'Param', True
|
||||
)
|
||||
else:
|
||||
compute_dtype = _extract_compute_dtype(op, block)
|
||||
unit.update(dtype=compute_dtype)
|
||||
op_stats_list.append(block_op_stats_dict)
|
||||
return op_stats_list
|
||||
|
||||
|
||||
def collect_operator_stats(program=None, print_subblocks=False):
|
||||
"""
|
||||
Collect the number of operators for different data types through parsing
|
||||
the program. The statistical data are categorized according to four data
|
||||
types, namely float32, float16, bfloat16 and others.
|
||||
|
||||
Args:
|
||||
program(Program, optional): The program to parse. Default None, and the default main_program will be parsed.
|
||||
print_subblocks(bool, optional): Whether to print the operator stats for each subblock. Default False.
|
||||
|
||||
Examples:
|
||||
|
||||
.. code-block:: pycon
|
||||
|
||||
>>> import paddle
|
||||
>>> paddle.enable_static()
|
||||
|
||||
>>> class SimpleConvNet(paddle.nn.Layer):
|
||||
... def __init__(self):
|
||||
... super().__init__()
|
||||
... self.conv = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=3)
|
||||
... self.linear = paddle.nn.Linear(in_features=26, out_features=10)
|
||||
...
|
||||
... def forward(self, x):
|
||||
... out = self.conv(x)
|
||||
... out = paddle.nn.functional.relu(out)
|
||||
... out = self.linear(out)
|
||||
... out = paddle.nn.functional.softmax(out)
|
||||
... return out
|
||||
|
||||
>>> main_program = paddle.static.Program()
|
||||
>>> startup_program = paddle.static.Program()
|
||||
>>> with paddle.utils.unique_name.guard():
|
||||
... with paddle.static.program_guard(main_program, startup_program):
|
||||
... model = SimpleConvNet()
|
||||
... x = paddle.static.data(name='input', shape=[None, 1, 28, 28], dtype='float32')
|
||||
... out = model(x)
|
||||
... loss = paddle.mean(out)
|
||||
... optimizer = paddle.optimizer.AdamW()
|
||||
... optimizer = paddle.static.amp.decorate(optimizer)
|
||||
... optimizer.minimize(loss)
|
||||
>>> paddle.static.amp.debugging.collect_operator_stats(main_program)
|
||||
<------------------------------------------------ op list of all blocks ------------------------------------------------->
|
||||
<------------------------------------------------------- op list -------------------------------------------------------->
|
||||
<--------------- Op Name ---------------- | -- FP16 Calls --- | -- BF16 Calls --- | --- FP32 Calls--- | -- Other Calls -->
|
||||
adamw | 0 | 0 | 4 | 0
|
||||
cast | 5 | 0 | 6 | 0
|
||||
check_finite_and_unscale | 0 | 0 | 1 | 0
|
||||
conv2d | 1 | 0 | 0 | 0
|
||||
conv2d_grad | 1 | 0 | 0 | 0
|
||||
elementwise_add | 2 | 0 | 0 | 0
|
||||
elementwise_add_grad | 2 | 0 | 0 | 0
|
||||
elementwise_mul | 0 | 0 | 1 | 0
|
||||
elementwise_mul_grad | 0 | 0 | 1 | 0
|
||||
fill_constant | 0 | 0 | 1 | 0
|
||||
matmul_v2 | 1 | 0 | 0 | 0
|
||||
matmul_v2_grad | 1 | 0 | 0 | 0
|
||||
memcpy | 0 | 0 | 0 | 1
|
||||
reduce_mean | 0 | 0 | 1 | 0
|
||||
reduce_mean_grad | 0 | 0 | 1 | 0
|
||||
relu | 1 | 0 | 0 | 0
|
||||
relu_grad | 1 | 0 | 0 | 0
|
||||
reshape2 | 0 | 0 | 1 | 0
|
||||
reshape2_grad | 0 | 0 | 1 | 0
|
||||
softmax | 0 | 0 | 1 | 0
|
||||
softmax_grad | 0 | 0 | 1 | 0
|
||||
update_loss_scaling | 0 | 0 | 1 | 0
|
||||
<----------------------------------------------------- op count: 22 ----------------------------------------------------->
|
||||
"""
|
||||
|
||||
def _convert_to_list(op_stats_unit_dict):
|
||||
for key, value in op_stats_unit_dict.items():
|
||||
op_stats_unit_dict[key] = value.convert_to_list()
|
||||
return op_stats_unit_dict
|
||||
|
||||
if program is None:
|
||||
program = paddle.static.default_main_program()
|
||||
|
||||
op_stats_list = _get_op_stats_list(program)
|
||||
merged_op_stats = _merge_op_stats(op_stats_list)
|
||||
if print_subblocks and len(op_stats_list) > 1:
|
||||
for i in range(len(op_stats_list)):
|
||||
print("<{:-^120}>".format(" op list of block " + str(i) + " "))
|
||||
paddle.amp.debugging._print_operator_stats(
|
||||
_convert_to_list(op_stats_list[i])
|
||||
)
|
||||
print("<{:-^120}>".format(" op list of all blocks "))
|
||||
paddle.amp.debugging._print_operator_stats(
|
||||
_convert_to_list(merged_op_stats)
|
||||
)
|
||||
Reference in New Issue
Block a user