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@ -23,9 +23,7 @@ This API is still under active development and may change drastically.
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from .. import core
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from ..framework import Program, Variable
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__all__ = ['MemoryInfo']
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DEBUG = False
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__all__ = ['memory_usage']
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dtype_to_size = {
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core.VarDesc.VarType.FP16: 2,
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@ -38,62 +36,67 @@ dtype_to_size = {
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core.VarDesc.VarType.UINT8: 1,
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}
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DEBUG = False
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class MemoryInfo(object):
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def __init__(self, program):
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if not isinstance(program, Program):
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raise TypeError(
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"Calculating Memory Usage requires Program as its Parameter."
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"But you passed in %s" % (type(prgram)))
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self._program = program
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def _has_var(self, block, var_name):
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return block.has_var(str(var_name))
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def memory_usage(program, batch_size):
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"""
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Get the estimate memory usage of program with input batch size.
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def _find_var(self, block, var_name):
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return block.var(str(var_name))
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Args:
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program(Program): The current Program.
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batch_size(int): The current input data batch_size.
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Returns:
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min_total_memory(float): the estimate memory usage lower bound.
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max_total_memory(float): the estimate memory usage upper bound.
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unit_str(string): the unit of estimate usage result.
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Examples:
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>>> import paddle.fluid as fluid
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>>> lower_usage, upper_usage, unit = fluid.contrib.memory_usage(
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fluid.default_main_program(), batch_size=10)
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>>> print "memory usage is about %.3f - %.3f %s" % \
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(lower_usage, upper_usage, unit)
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def get_memory_usage(self, batch_size, with_details=False):
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"""
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# get the first block of program
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first_block = self._program.global_block()
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# Parameters check
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if not isinstance(program, Program):
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raise TypeError(
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"Calculating Memory Usage requires Program as its Parameter."
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"But you passed in %s" % (type(prgram)))
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if batch_size <= 0:
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raise ValueError("The batch size need to be positive.")
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# get the var_name list of first block
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# TODO(chenweihang): not find the API get block's var list directly
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total_memory = 0.0
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for var in self._program.list_vars():
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if DEBUG:
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print "All Block's Var: %s" % (var.name)
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# TODO(chenweihang): why not used program.list_vars()
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# calculate all variable's memory directly?
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if self._has_var(first_block, var.name):
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if DEBUG:
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print "First Block's Var: %s" % (var.name)
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print "Var's shape: ", var.shape
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print "Var's dtype: ", var.dtype
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data_count = 1
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for x in var.shape:
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if x == -1:
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data_count *= batch_size
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else:
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data_count *= x
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var_memory = data_count * dtype_to_size[var.dtype]
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if DEBUG:
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print "Var's memory: %d" % (var_memory)
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total_memory += var_memory
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# Get the var_name list of first block and calculate
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total_memory = 0.0
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for var in program.global_block().vars.itervalues():
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data_count = 1
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for x in var.shape:
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if x == -1:
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data_count *= batch_size
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else:
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data_count *= x
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var_memory = data_count * dtype_to_size[var.dtype]
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if DEBUG:
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print "%s memory usage: %d" % (var.name, var_memory)
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total_memory += var_memory
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if DEBUG:
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print "total memory usage: %.2f" % (total_memory)
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# Convert unit and make result string
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result_str = "- With current batch size, memory usage is about "
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unit_str = " B."
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# Convert appropriate unit
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unit_str = "B"
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if total_memory > 1024:
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total_memory /= 1024
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unit_str = "KB"
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if total_memory > 1024:
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total_memory /= 1024
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unit_str = " KB."
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if total_memory > 1024:
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total_memory /= 1024
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unit_str = " MB."
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unit_str = "MB"
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# Append extra memory consumption (5% - 10%)
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result_str += str(round(total_memory * 1.05, 3)) + " - " \
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+ str(round(total_memory * 1.10, 3)) + unit_str
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# Append extra memory consumption (5% - 10%)
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min_total_memory = total_memory * 1.05
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max_total_memory = total_memory * 1.1
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return result_str
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return min_total_memory, max_total_memory, unit_str
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