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[fx] add more op patches for profiler and error message for unsupported ops. (#1495)
* [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] merge development into main (#1) * [fx] activation checkpointing using Chen strategies. * [fx] add test for ckpt_solver_chen * [fx] add vanilla activation checkpoint search with test on resnet and densenet * [fx] add a namespace code for solver_chen. * [fx] fix the false interpretation of algorithm 3 in https://arxiv.org/abs/1604.06174. * [fx] fix lowercase naming conventions. * [fx] simplify test for ckpt. * [fx] add rules to linearize computation graphs for searching. (#2) * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] merge development into main (#1) * [fx] activation checkpointing using Chen strategies. * [fx] add test for ckpt_solver_chen * [fx] add vanilla activation checkpoint search with test on resnet and densenet * [fx] add a namespace code for solver_chen. * [fx] fix the false interpretation of algorithm 3 in https://arxiv.org/abs/1604.06174. * [fx] fix lowercase naming conventions. * [fx] simplify test for ckpt. * [fx] fix test and algorithm bugs in activation checkpointing. * [fx] polish ckpt_test. * [fx] add rules to linearize computation graphs for searching. * [fx] remove chen_sqrt for sake of simplicity * [fx] remove chen_sqrt for sake of simplicity * [fx] remove chen_sqrt for sake of simplicity * [fx] remove chen_sqrt for sake of simplicity * [fx] fix inconsistencies. * [fx] fix MetaInfoProp. * [fx] fix MetaInfoProp. * [fx] consider MetaInfoProp for inplace operands. * [fx] consider MetaInfoProp for inplace operands. * [fx] consider MetaInfoProp for inplace operands. * [fx] consider MetaInfoProp for inplace operands. * [fx] consider MetaInfoProp for inplace operands. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] add profiler for fx nodes. * [fx] fix error in tests. * [fx] unfix bug. * [fx] unfix bug. * [fx] patch more modules and functions. * [fx] change name of utils.py to profiler.py * [fx] add profiler for rnn. * [fx] add profiler for rnn. * [fx] polish and add more patch for profiler. * [fx] polish and add more patch for profiler.
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Super Daniel
GitHub
236
colossalai/fx/profiler/profiler.py
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236
colossalai/fx/profiler/profiler.py
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from functools import partial
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from operator import add, floordiv, getitem, mul, neg, setitem, sub, pos
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from typing import Callable, List, NamedTuple, Any, Dict, Tuple, Union
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import torch
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from torch.fx.node import Argument, Target, map_aggregate
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from torch.fx._compatibility import compatibility
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from colossalai.fx.tracer.meta_patch import meta_patched_function, meta_patched_module
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from . import meta_profiler_function, meta_profiler_module
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__all__ = [
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'MetaProfile', 'profile_function', 'profile_module', 'profile_method', 'calculate_activation_size',
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'calculate_param_size'
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]
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CALL_FUNCTION_MSG = \
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"""
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Colossal-AI hasn't supported profiling for {}, you might manually patch it with the following code.\n
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from colossalai.fx.profiler import meta_profiler_function
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@meta_profiler_function.register(YOUR_FUNCTION)
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def profile_YOUR_FUNCTION(input: torch.Tensor, *args) -> Tuple[int, int]:
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flops = ...
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macs = ...
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return flops, macs
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"""
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CALL_METHOD_MSG = 'Please check if {} is an inplace method. If so, add target to INPLACE_METHOD={}. Otherwise, add target to NON_INPLACE_METHOD={}'
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CALL_MODULE_MSG = \
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"""
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Colossal-AI hasn't supported profiling for {}, you might manually patch it with the following code.\n
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from colossalai.fx.profiler import meta_profiler_module
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@meta_profiler_module.register(YOUR_MODULE)
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def profile_YOUR_MODULE(self: torch.nn.Module, input: torch.Tensor) -> Tuple[int, int]:
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flops = ...
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macs = ...
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return flops, macs
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"""
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# TODO fill out the inplace ops
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INPLACE_OPS = [
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add,
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sub,
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mul,
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floordiv,
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neg,
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pos,
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getitem,
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setitem,
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getattr,
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torch.Tensor.cpu,
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]
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# TODO: list all call_methods that are inplace here
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INPLACE_METHOD = [
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'transpose',
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'permute',
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# TODO: reshape may return a copy of the data if the data is not contiguous
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'reshape',
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'dim',
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'flatten',
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]
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# TODO: list all call_methods that are not inplace here
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NON_INPLACE_METHOD = [
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'expand',
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'mean',
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]
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@compatibility(is_backward_compatible=True)
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class MetaProfile(NamedTuple):
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# MetaProfile is a structure containing pertinent information
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# about a node within a torch.fx GraphModule.
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param: int
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activation: int
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flops: int
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macs: int
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def calculate_activation_size(activation: Union[torch.Tensor, Dict, List, Tuple, int]) -> int:
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"""Calculate activation size of a node.
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Args:
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activation (Union[torch.Tensor, Dict, List, Tuple, int]): The activation of a `torch.nn.Module` or `torch.nn.functional`
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Returns:
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int: The activation size
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"""
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activation_size = 0
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if isinstance(activation, torch.Tensor):
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activation_size += activation.numel() * torch.tensor([], dtype=activation.dtype).element_size()
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elif isinstance(activation, dict):
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value_list = [v for _, v in activation.items()]
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activation_size += calculate_activation_size(value_list)
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elif isinstance(activation, tuple) or isinstance(activation, list):
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for element in activation:
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activation_size += calculate_activation_size(element)
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return activation_size
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def calculate_param_size(mod: torch.nn.Module) -> int:
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"""Calculate param size of a node.
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Args:
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mod (torch.nn.Module): The target `torch.nn.Module`
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Returns:
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int: The param size
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"""
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param_size = 0
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for param in mod.parameters():
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param_size += param.numel() * torch.tensor([], dtype=param.dtype).element_size()
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return param_size
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def profile_function(target: 'Target') -> Callable:
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"""
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Wrap a `call_function` node or `torch.nn.functional` in order to
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record the memory cost and FLOPs of the execution.
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Warnings:
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You may only use tensors with `device=meta` for this wrapped function.
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Only original `torch.nn.functional` are available.
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Examples:
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>> input = torch.rand(100, 100, 100, 100, device='meta')
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>> func = torch.nn.functional.relu
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>> output, profile = profile_function(func)(input, inplace=False)
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>> print(f"Profiling function {func},")
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>> print(f"Param size: {profile.param / 1024**2:.3f} MB, Activation size: {profile.activation / 1024**2:.3f} MB, {profile.flops} FLOPs, {profile.macs} MACs")
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Profiling function <function relu at 0x7fcdd0258d30>,
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Param size: 0.000 MB, Activation size: 381.470 MB, 100000000 FLOPs, 0 MACs
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"""
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def f(*args: Tuple[Argument, ...], **kwargs: Dict[str, Any]) -> Any:
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assert meta_profiler_function.has(target) or meta_profiler_function.has(
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target.__name__), CALL_FUNCTION_MSG.format(target)
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# ensure all arguments satisfy `device='meta'`
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args, kwargs = map_aggregate([args, kwargs], lambda a: a.to('meta') if isinstance(a, torch.Tensor) else a)
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# call_function has no parameters
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param_size = 0
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activation_size = 0
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result = func(*args, **kwargs)
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if target not in INPLACE_OPS and not kwargs.get('inplace', False):
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activation_size += calculate_activation_size(result)
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if meta_profiler_function.has(target):
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profiler = meta_profiler_function.get(target)
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else:
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profiler = meta_profiler_function.get(target.__name__)
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flops, macs = profiler(*args, **kwargs)
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return result, MetaProfile(param_size, activation_size, flops, macs)
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f.__name__ = target.__name__
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# fetch patched function
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if meta_patched_function.has(target):
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func = meta_patched_function.get(target)
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elif meta_patched_function.has(target.__name__):
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func = meta_patched_function.get(target.__name__)
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else:
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func = target
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return f
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def profile_method(target: 'Target') -> Callable:
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"""
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Wrap a `call_method` node
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record the memory cost and FLOPs of the execution.
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Warnings:
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This is not fully implemented and you may follow the error message to debug.
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"""
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def f(*args: Tuple[Argument, ...], **kwargs: Dict[str, Any]) -> Any:
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# args[0] is the `self` object for this method call
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self_obj, *args_tail = args
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# execute the method and return the result
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assert isinstance(target, str), f'{target} instance is not str.'
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# ensure all arguments satisfy `device='meta'`
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map_aggregate([args, kwargs], lambda a: a.to('meta') if isinstance(a, torch.Tensor) else a)
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result = getattr(self_obj, target)(*args_tail, **kwargs)
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assert target in INPLACE_METHOD + NON_INPLACE_METHOD, CALL_METHOD_MSG.format(
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target, INPLACE_METHOD, NON_INPLACE_METHOD)
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# call_method has no parameters and are MOSTLY(?) inplace, and has no FLOPs or MACs.
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param_size = 0
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activation_size = 0 if target in INPLACE_METHOD else calculate_activation_size(result)
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flops = 0
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macs = 0
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return result, MetaProfile(param_size, activation_size, flops, macs)
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return f
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def profile_module(module: torch.nn.Module) -> Callable:
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"""
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Wrap a `call_module` node or `torch.nn` in order to
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record the memory cost and FLOPs of the execution.
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Warnings:
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You may only use tensors with `device=meta` for this wrapped function.
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Only original `torch.nn` are available.
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Example:
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>> input = torch.rand(4, 3, 224, 224, device='meta')
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>> mod = torch.nn.Conv2d(3, 128, 3)
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>> output, profile = profile_module(mod)(input)
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>> print(f"Profiling function {mod},")
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>> print(f"Param size: {profile.param / 1024**2:.3f} MB, Activation size: {profile.activation / 1024**2:.3f} MB, {profile.flops} FLOPs, {profile.macs} MACs")
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Profiling function Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1)),
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Param size: 0.014 MB, Activation size: 96.258 MB, 1387837440 FLOPs, 681302016 MACs
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"""
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def f(*args: Tuple[Argument, ...], **kwargs: Dict[str, Any]) -> Any:
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assert meta_profiler_module.has(type(module)), CALL_MODULE_MSG.format(type(module))
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# ensure all arguments satisfy `device='meta'`
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map_aggregate([args, kwargs], lambda a: a.to('meta') if isinstance(a, torch.Tensor) else a)
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param_size = calculate_param_size(module)
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activation_size = 0
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result = func(*args, **kwargs)
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if not getattr(module, 'inplace', False):
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activation_size += calculate_activation_size(result)
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profiler = meta_profiler_module.get(type(module))
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flops, macs = profiler(module, *args, **kwargs)
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return result, MetaProfile(param_size, activation_size, flops, macs)
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f.__name__ = module.__class__.__name__
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# fetch patched module
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if meta_patched_module.has(type(module)):
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func = partial(meta_patched_module.get(type(module)), module)
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else:
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func = module.forward
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return f
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