2023-04-14 11:20:43 +08:00
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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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2023-04-24 15:12:20 +08:00
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import logging
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2023-04-14 11:20:43 +08:00
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import paddle
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2023-09-07 17:26:19 +08:00
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from paddle.base.log_helper import get_logger
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_logger = get_logger(
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__name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s'
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)
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2023-04-14 11:20:43 +08:00
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class OperatorStatsUnit:
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def __init__(self):
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self.op_type = None
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self.fp32_calls = 0
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self.fp16_calls = 0
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self.bf16_calls = 0
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self.other_calls = 0
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def update(self, dtype):
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if dtype is None:
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self.other_calls = self.other_calls + 1
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else:
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if dtype == paddle.float32:
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self.fp32_calls = self.fp32_calls + 1
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elif dtype == paddle.float16:
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self.fp16_calls = self.fp16_calls + 1
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elif dtype == paddle.bfloat16:
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self.bf16_calls = self.bf16_calls + 1
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else:
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self.other_calls = self.other_calls + 1
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def addto(self, another):
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self.fp32_calls += another.fp32_calls
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self.fp16_calls += another.fp16_calls
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self.bf16_calls += another.bf16_calls
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self.other_calls += another.other_calls
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def convert_to_list(self):
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return [
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self.fp16_calls,
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self.bf16_calls,
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self.fp32_calls,
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self.other_calls,
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]
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def _is_floating_point(dtype):
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if dtype in [
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paddle.base.core.VarDesc.VarType.FP64,
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paddle.base.core.VarDesc.VarType.FP32,
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paddle.base.core.VarDesc.VarType.FP16,
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paddle.base.core.VarDesc.VarType.BF16,
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]:
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return True
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else:
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return False
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def _get_var_dtype_from_block(block, op, arg_name, is_input):
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var_names = op.input(arg_name) if is_input else op.output(arg_name)
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assert isinstance(var_names, list)
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if len(var_names) == 0:
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return None
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var_name = var_names[0]
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try:
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var = block._var_recursive(var_name)
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return var.dtype
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except:
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_logger.warning(
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"Operator < {} > gets {} < {} : {} > error!".format(
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op.type, "input" if is_input else "output", arg_name, var_name
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)
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)
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return None
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def _extract_compute_dtype(op, block):
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var_name = None
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compute_dtype = None
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for in_name in op.input_names:
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var_dtype = _get_var_dtype_from_block(block, op, in_name, True)
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if var_dtype is None:
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continue
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if compute_dtype is None:
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compute_dtype = var_dtype
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else:
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if compute_dtype != var_dtype:
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if _is_floating_point(compute_dtype) and _is_floating_point(
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var_dtype
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):
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_logger.warning(
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f"Operator < {op.type} > has different input data types, input_names = {op.input_names}, output_names = {op.output_names}."
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)
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elif _is_floating_point(var_dtype):
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# When there are multiple inputs, such as embedding
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# (ids is integer, w is floating-point), the kernel
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# dtype is normally decided by the input of floating-point.
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compute_dtype = var_dtype
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for out_name in op.output_names:
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var_dtype = _get_var_dtype_from_block(block, op, out_name, False)
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if var_dtype is None:
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continue
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if compute_dtype is None:
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# Kernel dtype is mostly decided by the input's dtype.
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# When the operator has no input, it might have an attr
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# such as dtype to specify the output's dtype.
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compute_dtype = var_dtype
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else:
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if compute_dtype != var_dtype:
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if _is_floating_point(compute_dtype) and _is_floating_point(
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var_dtype
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):
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_logger.warning(
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f"Operator < {op.type} > has different input / output data types, input_names = {op.input_names}, output_names = {op.output_names}."
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)
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return compute_dtype
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def _merge_op_stats(op_stats_list):
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merged_op_stats_dict = {}
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for each_op_stats_dict in op_stats_list:
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for op_type, unit in each_op_stats_dict.items():
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if merged_op_stats_dict.get(op_type, None) is None:
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merged_op_stats_dict[op_type] = copy.copy(unit)
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else:
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merged_op_stats_dict[op_type].addto(unit)
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return merged_op_stats_dict
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def _get_op_stats_list(program):
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def _is_special_ops_with_input_x(op_type):
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# operators have input X and have inputs different dtypes.
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special_op_list = ['cast', 'batch_norm', 'instance_norm', 'layer_norm']
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if op_type in special_op_list:
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return True
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if op_type.replace("_grad", "") in special_op_list:
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return True
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return False
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op_stats_list = []
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for block in program.blocks:
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block_op_stats_dict = {}
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for op in block.ops:
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if block_op_stats_dict.get(op.type, None) is None:
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unit = OperatorStatsUnit()
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block_op_stats_dict[op.type] = unit
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else:
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unit = block_op_stats_dict[op.type]
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if op.type in [
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'create_py_reader',
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'read',
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'create_double_buffer_reader',
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]:
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compute_dtype = None
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elif _is_special_ops_with_input_x(op.type):
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# Not check the input and output dtype difference for this operators.
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compute_dtype = _get_var_dtype_from_block(block, op, 'X', True)
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elif "Param" in op.input_names:
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# Specify compute_dtype for optimizers.
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compute_dtype = _get_var_dtype_from_block(
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block, op, 'Param', True
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)
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else:
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compute_dtype = _extract_compute_dtype(op, block)
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unit.update(dtype=compute_dtype)
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op_stats_list.append(block_op_stats_dict)
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return op_stats_list
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def collect_operator_stats(program=None, print_subblocks=False):
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"""
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Collect the number of operators for different data types through parsing
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the program. The statistical data are categorized according to four data
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types, namely float32, float16, bfloat16 and others.
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Args:
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program(Program, optional): The program to parse. Default None, and the default main_program will be parsed.
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print_subblocks(bool, optional): Whether to print the operator stats for each subblock. Default False.
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Examples:
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2026-02-20 13:42:49 +08:00
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.. code-block:: pycon
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>>> import paddle
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>>> paddle.enable_static()
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>>> class SimpleConvNet(paddle.nn.Layer):
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... def __init__(self):
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... super().__init__()
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... self.conv = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=3)
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... self.linear = paddle.nn.Linear(in_features=26, out_features=10)
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...
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... def forward(self, x):
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... out = self.conv(x)
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... out = paddle.nn.functional.relu(out)
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... out = self.linear(out)
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... out = paddle.nn.functional.softmax(out)
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... return out
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>>> main_program = paddle.static.Program()
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>>> startup_program = paddle.static.Program()
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>>> with paddle.utils.unique_name.guard():
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... with paddle.static.program_guard(main_program, startup_program):
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... model = SimpleConvNet()
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... x = paddle.static.data(name='input', shape=[None, 1, 28, 28], dtype='float32')
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... out = model(x)
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... loss = paddle.mean(out)
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... optimizer = paddle.optimizer.AdamW()
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... optimizer = paddle.static.amp.decorate(optimizer)
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... optimizer.minimize(loss)
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>>> paddle.static.amp.debugging.collect_operator_stats(main_program)
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<------------------------------------------------ op list of all blocks ------------------------------------------------->
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<------------------------------------------------------- op list -------------------------------------------------------->
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<--------------- Op Name ---------------- | -- FP16 Calls --- | -- BF16 Calls --- | --- FP32 Calls--- | -- Other Calls -->
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adamw | 0 | 0 | 4 | 0
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cast | 5 | 0 | 6 | 0
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check_finite_and_unscale | 0 | 0 | 1 | 0
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conv2d | 1 | 0 | 0 | 0
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conv2d_grad | 1 | 0 | 0 | 0
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elementwise_add | 2 | 0 | 0 | 0
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elementwise_add_grad | 2 | 0 | 0 | 0
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elementwise_mul | 0 | 0 | 1 | 0
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elementwise_mul_grad | 0 | 0 | 1 | 0
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fill_constant | 0 | 0 | 1 | 0
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matmul_v2 | 1 | 0 | 0 | 0
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matmul_v2_grad | 1 | 0 | 0 | 0
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memcpy | 0 | 0 | 0 | 1
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reduce_mean | 0 | 0 | 1 | 0
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reduce_mean_grad | 0 | 0 | 1 | 0
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relu | 1 | 0 | 0 | 0
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relu_grad | 1 | 0 | 0 | 0
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reshape2 | 0 | 0 | 1 | 0
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reshape2_grad | 0 | 0 | 1 | 0
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softmax | 0 | 0 | 1 | 0
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softmax_grad | 0 | 0 | 1 | 0
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update_loss_scaling | 0 | 0 | 1 | 0
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<----------------------------------------------------- op count: 22 ----------------------------------------------------->
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"""
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def _convert_to_list(op_stats_unit_dict):
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for key, value in op_stats_unit_dict.items():
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op_stats_unit_dict[key] = value.convert_to_list()
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return op_stats_unit_dict
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if program is None:
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program = paddle.static.default_main_program()
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op_stats_list = _get_op_stats_list(program)
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merged_op_stats = _merge_op_stats(op_stats_list)
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if print_subblocks and len(op_stats_list) > 1:
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for i in range(len(op_stats_list)):
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print("<{:-^120}>".format(" op list of block " + str(i) + " "))
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paddle.amp.debugging._print_operator_stats(
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_convert_to_list(op_stats_list[i])
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)
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print("<{:-^120}>".format(" op list of all blocks "))
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paddle.amp.debugging._print_operator_stats(
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_convert_to_list(merged_op_stats)
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)
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