mirror of https://github.com/hpcaitech/ColossalAI
[fx] provide a stable but not accurate enough version of profiler. (#1547)
* [fx] compute memory stat and flop count for MetaInfoProp. * [fx] modify node attribute. * [fx] modify ckpt_chen. * [fx] fix compatibility. * [fx] fix import error. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip if torch 1.11.0. * [fx] recover MetaInfoProp support for PyTorch 1.11. * [fx] provide a stable but not accurate enough version of profiler. * [fx] provide a stable but not accurate enough version of profiler. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix import error.pull/1583/head
Super Daniel
GitHub
parent
7d49e7b2db
commit
4f59693207
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@ -1,7 +1,9 @@
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try:
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from ._meta_registrations import *
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from . import _meta_registrations
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META_COMPATIBILITY = True
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except:
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import torch
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META_COMPATIBILITY = False
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print(f'_meta_registrations seems to be incompatible with PyTorch {torch.__version__}.')
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from .initialize import (initialize, launch, launch_from_openmpi, launch_from_slurm, launch_from_torch,
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get_default_parser)
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@ -181,6 +181,12 @@ def meta_hardswish_backward(grad_out: torch.Tensor, input: torch.Tensor):
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return grad_in
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@register_meta(aten.hardtanh_backward.default)
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def meta_hardtanh_backward(grad_out: torch.Tensor, input: torch.Tensor, min_val: int, max_val: int):
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grad_in = torch.empty_like(input)
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return grad_in
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@register_meta(aten.roll.default)
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def meta_roll(input: torch.Tensor, shifts, dims):
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return torch.empty_like(input)
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@ -321,3 +327,17 @@ def meta_index_Tensor(self, indices):
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else:
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replacement_shape = list(index.shape)
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return self.new_empty(before_shape + replacement_shape + after_shape)
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@register_meta(aten.embedding_dense_backward.default)
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def meta_embedding_dense_backward(grad_output: torch.Tensor, indices: torch.Tensor, num_weights, padding_idx,
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scale_grad_by_freq):
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return torch.empty((num_weights, grad_output.size(-1)),
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dtype=grad_output.dtype,
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device=grad_output.device,
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layout=grad_output.layout)
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@register_meta(aten.where.self)
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def meta_where_self(condition: torch.Tensor, self: torch.Tensor, other: torch.Tensor):
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return torch.empty_like(condition)
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@ -73,10 +73,10 @@ def chen_greedy(gm: GraphModule) -> GraphModule:
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y = 0
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prev_idx = 2
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for (idx, n) in enumerate(gm.graph.nodes):
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temp += getattr(n, '__activation__')
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temp += getattr(n, 'fwd_out')
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y = max(y, temp)
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if temp > b and n in ckpt_nodes:
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x += getattr(n, '__activation__')
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x += getattr(n, 'fwd_out')
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temp = 0
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ckpt_intv.append((prev_idx, idx + 1))
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prev_idx = idx + 1
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@ -1,13 +1,10 @@
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from operator import add, getitem
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import torch
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import torch.fx
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from torch.fx.node import Node, Argument, Target
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from torch.utils._pytree import tree_map
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from typing import Any, Tuple, NamedTuple, Optional, Dict
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from functools import reduce
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from typing import Any, Tuple, NamedTuple, Dict
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from torch.fx._compatibility import compatibility
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from torch.fx.immutable_collections import immutable_dict, immutable_list
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from colossalai.fx.profiler import MetaProfile, MetaTensor, profile_function, profile_module, calculate_activation_size, profile_method
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from colossalai.fx.profiler import profile_function, profile_module, profile_method, activation_size, parameter_size
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@compatibility(is_backward_compatible=True)
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@ -71,14 +68,6 @@ class MetaInfoProp(torch.fx.Interpreter):
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"""
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@compatibility(is_backward_compatible=True)
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def run(self, *args, initial_env: Optional[Dict[Node, Any]] = None, enable_io_processing: bool = True) -> Any:
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"""
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Add additional check for initial args to ensure all the tensor appears with `device='meta'`
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"""
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args = tree_map(lambda elem: MetaTensor(elem.to('meta')) if isinstance(elem, torch.Tensor) else elem, args)
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return super().run(*args, initial_env, enable_io_processing)
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@compatibility(is_backward_compatible=True)
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def run_node(self, n: Node) -> Any:
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"""
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@ -93,8 +82,7 @@ class MetaInfoProp(torch.fx.Interpreter):
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Returns:
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Any: The result of executing ``n``
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"""
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result, profile = super().run_node(n)
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profile: MetaProfile
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result, flop_count, mem_stat = super().run_node(n)
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def extract_tensor_meta(obj):
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if isinstance(obj, torch.Tensor):
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@ -106,12 +94,17 @@ class MetaInfoProp(torch.fx.Interpreter):
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n.meta['tensor_meta'] = meta
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# TODO: the attribute node_size should be removed in the future
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setattr(n, 'node_size', profile.param + profile.activation)
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setattr(n, '__param__', profile.param)
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setattr(n, '__activation__', profile.activation)
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setattr(n, '__flops__', profile.flops)
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setattr(n, '__macs__', profile.macs)
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setattr(n, 'node_size', mem_stat[1])
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setattr(n, 'fwd_flop', flop_count[0])
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setattr(n, 'bwd_flop', flop_count[1])
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setattr(n, 'fwd_tmp', mem_stat[0])
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setattr(n, 'fwd_out', mem_stat[1])
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setattr(n, 'bwd_tmp', mem_stat[2])
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setattr(n, 'bwd_out', mem_stat[3])
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n.meta['type'] = type(result)
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for param in self.module.parameters():
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param.grad = None
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return result
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# Main Node running APIs
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@ -132,11 +125,12 @@ class MetaInfoProp(torch.fx.Interpreter):
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Returns:
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result (Any): The argument value that was retrieved
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profile (MetaProfile): The meta profile of this node
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flop_count (Tuple): The flop count for (fwd_flop, bwd_flop).
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mem_stat (Tuple): The memory statistics for (fwd_tmp, fwd_out, bwd_tmp, bwd_out)
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"""
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result = super().placeholder(target, args, kwargs)
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# A placeholder node only has activation
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return result, MetaProfile(0, calculate_activation_size(result), 0, 0)
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return result, (0, 0), (0, activation_size(result), 0, 0)
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@compatibility(is_backward_compatible=True)
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def get_attr(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
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@ -153,10 +147,10 @@ class MetaInfoProp(torch.fx.Interpreter):
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Return:
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result (Any): The argument value that was retrieved
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profile (MetaProfile): The meta profile of this node
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flop_count (Tuple): The flop count for (fwd_flop, bwd_flop).
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mem_stat (Tuple): The memory statistics for (fwd_tmp, fwd_out, bwd_tmp, bwd_out)
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"""
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# A get_attr node never has parameters, activations, FLOPs, or MACs
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return super().get_attr(target, args, kwargs), MetaProfile(0, 0, 0, 0)
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return super().get_attr(target, args, kwargs), (0, 0), (0, 0, 0, 0)
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@compatibility(is_backward_compatible=True)
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def call_function(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
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@ -172,7 +166,8 @@ class MetaInfoProp(torch.fx.Interpreter):
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Return
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result (Any): The argument value that was retrieved
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profile (MetaProfile): The meta profile of this node
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flop_count (Tuple): The flop count for (fwd_flop, bwd_flop).
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mem_stat (Tuple): The memory statistics for (fwd_tmp, fwd_out, bwd_tmp, bwd_out)
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"""
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assert not isinstance(target, str)
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return profile_function(target)(*args, **kwargs)
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@ -191,7 +186,8 @@ class MetaInfoProp(torch.fx.Interpreter):
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Return
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result (Any): The argument value that was retrieved
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profile (MetaProfile): The meta profile of this node
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flop_count (Tuple): The flop count for (fwd_flop, bwd_flop).
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mem_stat (Tuple): The memory statistics for (fwd_tmp, fwd_out, bwd_tmp, bwd_out)
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"""
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return profile_method(target)(*args, **kwargs)
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@ -209,7 +205,8 @@ class MetaInfoProp(torch.fx.Interpreter):
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Return
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result (Any): The argument value that was retrieved
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profile (MetaProfile): The meta profile of this node
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flop_count (Tuple): The flop count for (fwd_flop, bwd_flop).
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mem_stat (Tuple): The memory statistics for (fwd_tmp, fwd_out, bwd_tmp, bwd_out)
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"""
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# Retrieve executed args and kwargs values from the environment
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# Execute the method and return the result
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@ -231,9 +228,11 @@ class MetaInfoProp(torch.fx.Interpreter):
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kwargs (Dict): Dict of keyword arguments for this invocation
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Return:
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Any: The return value referenced by the output node
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result (Any): The argument value that was retrieved
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flop_count (Tuple): The flop count for (fwd_flop, bwd_flop).
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mem_stat (Tuple): The memory statistics for (fwd_tmp, fwd_out, bwd_tmp, bwd_out)
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"""
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return args[0], MetaProfile(0, 0, 0, 0)
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return args[0], (0, 0), (0, 0, 0, 0)
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def propagate(self, *args):
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"""
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@ -1,5 +1,9 @@
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from .meta_tensor import MetaTensor
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from .registry import meta_profiler_function, meta_profiler_module
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from .profiler_function import *
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from .profiler_module import *
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from .profiler import *
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from ... import META_COMPATIBILITY
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if META_COMPATIBILITY:
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from .opcount import flop_mapping
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from .tensor import MetaTensor
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from .profiler import profile_function, profile_method, profile_module, _profile
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else:
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from .experimental import meta_profiler_function, meta_profiler_module, profile_function, profile_method, profile_module
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from .memory import parameter_size, activation_size
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@ -0,0 +1,4 @@
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from .registry import meta_profiler_function, meta_profiler_module
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from .profiler_function import *
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from .profiler_module import *
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from .profiler import profile_function, profile_method, profile_module
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@ -0,0 +1,125 @@
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from typing import Callable, Any, Dict, Tuple
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import torch
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from torch.fx.node import Argument, Target
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from . import meta_profiler_function, meta_profiler_module
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from ..memory import activation_size, INPLACE_METHOD, NON_INPLACE_METHOD, INPLACE_OPS
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__all__ = ['profile_function', 'profile_module', 'profile_method']
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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.experimental 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.experimental 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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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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Unfortunately, backward memory cost and FLOPs are estimated results.
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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, (fwd_flop, bwd_flop), (fwd_tmp, fwd_out, bwd_tmp, bwd_out) = profile_function(func)(input, inplace=False)
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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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fwd_tmp = 0
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fwd_out = 0
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out = func(*args, **kwargs)
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if target not in INPLACE_OPS and not kwargs.get('inplace', False):
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fwd_out = activation_size(out)
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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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fwd_flop, _ = profiler(*args, **kwargs)
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return out, (fwd_flop, fwd_flop * 2), (fwd_tmp, fwd_out, fwd_tmp + fwd_out, 0)
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f.__name__ = target.__name__
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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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out = 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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fwd_tmp = 0 if target in INPLACE_METHOD else activation_size(out)
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fwd_out = 0 if target not in INPLACE_METHOD else activation_size(out)
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return out, (0, 0), (fwd_tmp, fwd_out, fwd_tmp + fwd_out, 0)
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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, (fwd_flop, bwd_flop), (fwd_tmp, fwd_out, bwd_tmp, bwd_out) = profile_module(mod)(input)
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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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fwd_tmp = 0
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fwd_out = 0
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out = func(*args, **kwargs)
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if getattr(module, 'inplace', False):
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fwd_out = activation_size(out)
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profiler = meta_profiler_module.get(type(module))
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fwd_flop, _ = profiler(module, *args, **kwargs)
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return out, (fwd_flop, fwd_flop * 2), (fwd_tmp, fwd_out, fwd_tmp + fwd_out, 0)
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f.__name__ = module.__class__.__name__
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func = module.forward
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return f
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@ -0,0 +1,110 @@
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import torch
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from typing import Union, Dict, List, Tuple
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from operator import add, floordiv, getitem, mul, neg, setitem, sub, pos
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from . import META_COMPATIBILITY
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__all__ = ['activation_size', 'parameter_size']
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if META_COMPATIBILITY:
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aten = torch.ops.aten
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WEIRD_OPS = [
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torch.where,
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]
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INPLACE_ATEN = [
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aten.add_.Tensor,
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aten.add.Tensor,
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aten.sub_.Tensor,
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aten.div_.Tensor,
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aten.div_.Scalar,
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aten.mul_.Tensor,
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aten.mul.Tensor,
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aten.bernoulli_.float,
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# inplace reshaping
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aten.detach.default,
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aten.t.default,
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aten.transpose.int,
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aten.view.default,
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aten._unsafe_view.default,
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]
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__all__ += ['INPLACE_ATEN', 'WEIRD_OPS']
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else:
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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',
|
||||
'flatten',
|
||||
'size',
|
||||
'view',
|
||||
'unsqueeze',
|
||||
'to',
|
||||
'type',
|
||||
'flatten',
|
||||
]
|
||||
|
||||
# TODO: list all call_methods that are not inplace here
|
||||
NON_INPLACE_METHOD = [
|
||||
'chunk',
|
||||
'contiguous',
|
||||
'expand',
|
||||
'mean',
|
||||
'split',
|
||||
]
|
||||
__all__ += ['INPLACE_OPS', 'INPLACE_METHOD', 'NON_INPLACE_METHOD']
|
||||
|
||||
|
||||
def activation_size(out: Union[torch.Tensor, Dict, List, Tuple, int]) -> int:
|
||||
"""Calculate activation size of a node.
|
||||
|
||||
Args:
|
||||
activation (Union[torch.Tensor, Dict, List, Tuple, int]): The activation of a `torch.nn.Module` or `torch.nn.functional`
|
||||
|
||||
Returns:
|
||||
int: The activation size
|
||||
"""
|
||||
act_size = 0
|
||||
if isinstance(out, torch.Tensor):
|
||||
act_size += out.numel() * torch.tensor([], dtype=out.dtype).element_size()
|
||||
elif isinstance(out, dict):
|
||||
value_list = [v for _, v in out.items()]
|
||||
act_size += activation_size(value_list)
|
||||
elif isinstance(out, tuple) or isinstance(out, list):
|
||||
for element in out:
|
||||
act_size += activation_size(element)
|
||||
return act_size
|
||||
|
||||
|
||||
def parameter_size(mod: torch.nn.Module) -> int:
|
||||
"""Calculate param size of a node.
|
||||
|
||||
Args:
|
||||
mod (torch.nn.Module): The target `torch.nn.Module`
|
||||
|
||||
Returns:
|
||||
int: The param size
|
||||
"""
|
||||
param_size = 0
|
||||
for param in mod.parameters():
|
||||
param_size += param.numel() * torch.tensor([], dtype=param.dtype).element_size()
|
||||
return param_size
|
||||
|
|
@ -0,0 +1,304 @@
|
|||
# adopted from https://github.com/facebookresearch/fvcore/blob/main/fvcore/nn/jit_handles.py
|
||||
# ideas from https://pastebin.com/AkvAyJBw
|
||||
|
||||
from functools import reduce
|
||||
import operator
|
||||
from typing import Callable, List, Any
|
||||
from numbers import Number
|
||||
import torch
|
||||
|
||||
aten = torch.ops.aten
|
||||
|
||||
|
||||
def matmul_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
|
||||
"""
|
||||
Count flops for matmul.
|
||||
"""
|
||||
# Inputs should be a list of length 2.
|
||||
# Inputs contains the shapes of two matrices.
|
||||
input_shapes = [v.shape for v in inputs]
|
||||
assert len(input_shapes) == 2, input_shapes
|
||||
assert input_shapes[0][-1] == input_shapes[1][-2], input_shapes
|
||||
flops = reduce(operator.mul, input_shapes[0]) * input_shapes[-1][-1]
|
||||
return flops
|
||||
|
||||
|
||||
def addmm_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
|
||||
"""
|
||||
Count flops for fully connected layers.
|
||||
"""
|
||||
# Count flop for nn.Linear
|
||||
# inputs is a list of length 3.
|
||||
input_shapes = [v.shape for v in inputs[1:3]]
|
||||
# input_shapes[0]: [batch size, input feature dimension]
|
||||
# input_shapes[1]: [batch size, output feature dimension]
|
||||
assert len(input_shapes[0]) == 2, input_shapes[0]
|
||||
assert len(input_shapes[1]) == 2, input_shapes[1]
|
||||
batch_size, input_dim = input_shapes[0]
|
||||
output_dim = input_shapes[1][1]
|
||||
flops = batch_size * input_dim * output_dim
|
||||
return flops
|
||||
|
||||
|
||||
def linear_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
|
||||
"""
|
||||
Count flops for the aten::linear operator.
|
||||
"""
|
||||
# Inputs is a list of length 3; unlike aten::addmm, it is the first
|
||||
# two elements that are relevant.
|
||||
input_shapes = [v.shape for v in inputs[0:2]]
|
||||
# input_shapes[0]: [dim0, dim1, ..., input_feature_dim]
|
||||
# input_shapes[1]: [output_feature_dim, input_feature_dim]
|
||||
assert input_shapes[0][-1] == input_shapes[1][-1]
|
||||
flops = reduce(operator.mul, input_shapes[0]) * input_shapes[1][0]
|
||||
return flops
|
||||
|
||||
|
||||
def bmm_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
|
||||
"""
|
||||
Count flops for the bmm operation.
|
||||
"""
|
||||
# Inputs should be a list of length 2.
|
||||
# Inputs contains the shapes of two tensor.
|
||||
assert len(inputs) == 2, len(inputs)
|
||||
input_shapes = [v.shape for v in inputs]
|
||||
n, c, t = input_shapes[0]
|
||||
d = input_shapes[-1][-1]
|
||||
flops = n * c * t * d
|
||||
return flops
|
||||
|
||||
|
||||
def conv_flop_count(
|
||||
x_shape: List[int],
|
||||
w_shape: List[int],
|
||||
out_shape: List[int],
|
||||
transposed: bool = False,
|
||||
) -> Number:
|
||||
"""
|
||||
Count flops for convolution. Note only multiplication is
|
||||
counted. Computation for addition and bias is ignored.
|
||||
Flops for a transposed convolution are calculated as
|
||||
flops = (x_shape[2:] * prod(w_shape) * batch_size).
|
||||
Args:
|
||||
x_shape (list(int)): The input shape before convolution.
|
||||
w_shape (list(int)): The filter shape.
|
||||
out_shape (list(int)): The output shape after convolution.
|
||||
transposed (bool): is the convolution transposed
|
||||
Returns:
|
||||
int: the number of flops
|
||||
"""
|
||||
batch_size = x_shape[0]
|
||||
conv_shape = (x_shape if transposed else out_shape)[2:]
|
||||
flops = batch_size * reduce(operator.mul, w_shape) * reduce(operator.mul, conv_shape)
|
||||
return flops
|
||||
|
||||
|
||||
def conv_flop_jit(inputs: List[Any], outputs: List[Any]):
|
||||
"""
|
||||
Count flops for convolution.
|
||||
"""
|
||||
x, w = inputs[:2]
|
||||
x_shape, w_shape, out_shape = (x.shape, w.shape, outputs[0].shape)
|
||||
transposed = inputs[6]
|
||||
|
||||
return conv_flop_count(x_shape, w_shape, out_shape, transposed=transposed)
|
||||
|
||||
|
||||
def transpose_shape(shape):
|
||||
return [shape[1], shape[0]] + list(shape[2:])
|
||||
|
||||
|
||||
def conv_backward_flop_jit(inputs: List[Any], outputs: List[Any]):
|
||||
grad_out_shape, x_shape, w_shape = [i.shape for i in inputs[:3]]
|
||||
output_mask = inputs[-1]
|
||||
fwd_transposed = inputs[7]
|
||||
flop_count = 0
|
||||
|
||||
if output_mask[0]:
|
||||
grad_input_shape = outputs[0].shape
|
||||
flop_count += conv_flop_count(grad_out_shape, w_shape, grad_input_shape, not fwd_transposed)
|
||||
if output_mask[1]:
|
||||
grad_weight_shape = outputs[1].shape
|
||||
flop_count += conv_flop_count(transpose_shape(x_shape), grad_out_shape, grad_weight_shape, fwd_transposed)
|
||||
|
||||
return flop_count
|
||||
|
||||
|
||||
def norm_flop_counter(affine_arg_index: int, input_arg_index: int) -> Callable:
|
||||
"""
|
||||
Args:
|
||||
affine_arg_index: index of the affine argument in inputs
|
||||
"""
|
||||
|
||||
def norm_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
|
||||
"""
|
||||
Count flops for norm layers.
|
||||
"""
|
||||
# Inputs[0] contains the shape of the input.
|
||||
input_shape = inputs[input_arg_index].shape
|
||||
|
||||
has_affine = inputs[affine_arg_index].shape is not None if hasattr(inputs[affine_arg_index],
|
||||
'shape') else inputs[affine_arg_index]
|
||||
assert 2 <= len(input_shape) <= 5, input_shape
|
||||
# 5 is just a rough estimate
|
||||
flop = reduce(operator.mul, input_shape) * (5 if has_affine else 4)
|
||||
return flop
|
||||
|
||||
return norm_flop_jit
|
||||
|
||||
|
||||
def batchnorm_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
|
||||
training = inputs[-3]
|
||||
assert isinstance(training, bool), "Signature of aten::batch_norm has changed!"
|
||||
if training:
|
||||
return norm_flop_counter(1, 0)(inputs, outputs) # pyre-ignore
|
||||
has_affine = inputs[1].shape is not None
|
||||
input_shape = reduce(operator.mul, inputs[0].shape)
|
||||
return input_shape * (2 if has_affine else 1)
|
||||
|
||||
|
||||
def elementwise_flop_counter(input_scale: float = 1, output_scale: float = 0) -> Callable:
|
||||
"""
|
||||
Count flops by
|
||||
input_tensor.numel() * input_scale + output_tensor.numel() * output_scale
|
||||
Args:
|
||||
input_scale: scale of the input tensor (first argument)
|
||||
output_scale: scale of the output tensor (first element in outputs)
|
||||
"""
|
||||
|
||||
def elementwise_flop(inputs: List[Any], outputs: List[Any]) -> Number:
|
||||
ret = 0
|
||||
if input_scale != 0:
|
||||
shape = inputs[0].shape
|
||||
ret += input_scale * reduce(operator.mul, shape) if shape else 0
|
||||
if output_scale != 0:
|
||||
shape = outputs[0].shape
|
||||
ret += output_scale * reduce(operator.mul, shape) if shape else 0
|
||||
return ret
|
||||
|
||||
return elementwise_flop
|
||||
|
||||
|
||||
def zero_flop_jit(*args):
|
||||
"""
|
||||
Count flops for zero flop layers.
|
||||
"""
|
||||
return 0
|
||||
|
||||
|
||||
flop_mapping = {
|
||||
# gemm
|
||||
aten.mm.default: matmul_flop_jit,
|
||||
aten.matmul.default: matmul_flop_jit,
|
||||
aten.addmm.default: addmm_flop_jit,
|
||||
aten.bmm.default: bmm_flop_jit,
|
||||
|
||||
# convolution
|
||||
aten.convolution.default: conv_flop_jit,
|
||||
aten._convolution.default: conv_flop_jit,
|
||||
aten.convolution_backward.default: conv_backward_flop_jit,
|
||||
|
||||
# normalization
|
||||
aten.native_batch_norm.default: batchnorm_flop_jit,
|
||||
aten.native_batch_norm_backward.default: batchnorm_flop_jit,
|
||||
aten.native_layer_norm.default: norm_flop_counter(2, 0),
|
||||
aten.native_layer_norm_backward.default: norm_flop_counter(2, 0),
|
||||
|
||||
# pooling
|
||||
aten.avg_pool1d.default: elementwise_flop_counter(1, 0),
|
||||
aten.avg_pool2d.default: elementwise_flop_counter(1, 0),
|
||||
aten.avg_pool2d_backward.default: elementwise_flop_counter(0, 1),
|
||||
aten.avg_pool3d.default: elementwise_flop_counter(1, 0),
|
||||
aten.avg_pool3d_backward.default: elementwise_flop_counter(0, 1),
|
||||
aten.max_pool1d.default: elementwise_flop_counter(1, 0),
|
||||
aten.max_pool2d.default: elementwise_flop_counter(1, 0),
|
||||
aten.max_pool3d.default: elementwise_flop_counter(1, 0),
|
||||
aten.max_pool1d_with_indices.default: elementwise_flop_counter(1, 0),
|
||||
aten.max_pool2d_with_indices.default: elementwise_flop_counter(1, 0),
|
||||
aten.max_pool2d_with_indices_backward.default: elementwise_flop_counter(0, 1),
|
||||
aten.max_pool3d_with_indices.default: elementwise_flop_counter(1, 0),
|
||||
aten.max_pool3d_with_indices_backward.default: elementwise_flop_counter(0, 1),
|
||||
aten._adaptive_avg_pool2d.default: elementwise_flop_counter(1, 0),
|
||||
aten._adaptive_avg_pool2d_backward.default: elementwise_flop_counter(0, 1),
|
||||
aten._adaptive_avg_pool3d.default: elementwise_flop_counter(1, 0),
|
||||
aten._adaptive_avg_pool3d_backward.default: elementwise_flop_counter(0, 1),
|
||||
}
|
||||
|
||||
elementwise_flop_aten = [
|
||||
# basic op
|
||||
aten.add.Tensor,
|
||||
aten.add_.Tensor,
|
||||
aten.div.Tensor,
|
||||
aten.div_.Tensor,
|
||||
aten.div.Scalar,
|
||||
aten.div_.Scalar,
|
||||
aten.mul.Tensor,
|
||||
aten.mul.Scalar,
|
||||
aten.mul_.Tensor,
|
||||
aten.neg.default,
|
||||
aten.pow.Tensor_Scalar,
|
||||
aten.rsub.Scalar,
|
||||
aten.sum.default,
|
||||
aten.sum.dim_IntList,
|
||||
aten.mean.dim,
|
||||
|
||||
# activation op
|
||||
aten.hardswish.default,
|
||||
aten.hardswish_.default,
|
||||
aten.hardswish_backward.default,
|
||||
aten.hardtanh_.default,
|
||||
aten.hardtanh_backward.default,
|
||||
aten.hardsigmoid_backward.default,
|
||||
aten.hardsigmoid.default,
|
||||
aten.gelu.default,
|
||||
aten.gelu_backward.default,
|
||||
aten.silu_.default,
|
||||
aten.silu_backward.default,
|
||||
aten.sigmoid.default,
|
||||
aten.sigmoid_backward.default,
|
||||
aten._softmax.default,
|
||||
aten._softmax_backward_data.default,
|
||||
aten.relu_.default,
|
||||
aten.relu.default,
|
||||
aten.tanh.default,
|
||||
aten.tanh_backward.default,
|
||||
aten.threshold_backward.default,
|
||||
]
|
||||
|
||||
for op in elementwise_flop_aten:
|
||||
flop_mapping[op] = elementwise_flop_counter(1, 0)
|
||||
|
||||
# TODO: this will be removed in future
|
||||
zero_flop_aten = [
|
||||
aten.as_strided.default,
|
||||
aten.as_strided_.default,
|
||||
aten.bernoulli_.float,
|
||||
aten.cat.default,
|
||||
aten.clone.default,
|
||||
aten.copy_.default,
|
||||
aten.detach.default,
|
||||
aten.expand.default,
|
||||
aten.empty_like.default,
|
||||
aten.new_empty.default,
|
||||
aten.new_empty_strided.default,
|
||||
aten.ones_like.default,
|
||||
aten._reshape_alias.default,
|
||||
aten.select.int,
|
||||
aten.select_backward.default,
|
||||
aten.squeeze.dim,
|
||||
aten.slice.Tensor,
|
||||
aten.slice_backward.default,
|
||||
aten.split.Tensor,
|
||||
aten.permute.default,
|
||||
aten.t.default,
|
||||
aten.transpose.int,
|
||||
aten._to_copy.default,
|
||||
aten.unsqueeze.default,
|
||||
aten._unsafe_view.default,
|
||||
aten.view.default,
|
||||
aten.where.self,
|
||||
aten.zero_.default,
|
||||
]
|
||||
|
||||
for op in zero_flop_aten:
|
||||
flop_mapping[op] = zero_flop_jit
|
||||
|
|
@ -1,120 +1,121 @@
|
|||
from operator import add, floordiv, getitem, mul, neg, setitem, sub, pos
|
||||
from typing import Callable, List, NamedTuple, Any, Dict, Tuple, Union
|
||||
from typing import Callable, Any, Dict, Tuple
|
||||
import torch
|
||||
from torch.fx import Graph
|
||||
from torch.fx.node import Argument, Target
|
||||
from torch.fx._compatibility import compatibility
|
||||
from . import meta_profiler_function, meta_profiler_module
|
||||
from torch.utils._pytree import tree_map
|
||||
from .memory import activation_size, INPLACE_ATEN, WEIRD_OPS
|
||||
from .tensor import MetaTensor
|
||||
from .opcount import flop_mapping
|
||||
|
||||
__all__ = [
|
||||
'MetaProfile', 'profile_function', 'profile_module', 'profile_method', 'calculate_activation_size',
|
||||
'calculate_param_size'
|
||||
]
|
||||
|
||||
CALL_FUNCTION_MSG = \
|
||||
"""
|
||||
Colossal-AI hasn't supported profiling for {}, you might manually patch it with the following code.\n
|
||||
from colossalai.fx.profiler import meta_profiler_function
|
||||
|
||||
@meta_profiler_function.register(YOUR_FUNCTION)
|
||||
def profile_YOUR_FUNCTION(input: torch.Tensor, *args) -> Tuple[int, int]:
|
||||
flops = ...
|
||||
macs = ...
|
||||
return flops, macs
|
||||
"""
|
||||
CALL_METHOD_MSG = 'Please check if {} is an inplace method. If so, add target to INPLACE_METHOD={}. Otherwise, add target to NON_INPLACE_METHOD={}'
|
||||
CALL_MODULE_MSG = \
|
||||
"""
|
||||
Colossal-AI hasn't supported profiling for {}, you might manually patch it with the following code.\n
|
||||
from colossalai.fx.profiler import meta_profiler_module
|
||||
|
||||
@meta_profiler_module.register(YOUR_MODULE)
|
||||
def profile_YOUR_MODULE(self: torch.nn.Module, input: torch.Tensor) -> Tuple[int, int]:
|
||||
flops = ...
|
||||
macs = ...
|
||||
return flops, macs
|
||||
"""
|
||||
|
||||
# TODO fill out the inplace ops
|
||||
INPLACE_OPS = [
|
||||
add,
|
||||
sub,
|
||||
mul,
|
||||
floordiv,
|
||||
neg,
|
||||
pos,
|
||||
getitem,
|
||||
setitem,
|
||||
getattr,
|
||||
torch.Tensor.cpu,
|
||||
]
|
||||
|
||||
# TODO: list all call_methods that are inplace here
|
||||
INPLACE_METHOD = [
|
||||
'transpose',
|
||||
'permute',
|
||||
# TODO: reshape may return a copy of the data if the data is not contiguous
|
||||
'reshape',
|
||||
'dim',
|
||||
'flatten',
|
||||
'size',
|
||||
'view',
|
||||
'unsqueeze',
|
||||
'to',
|
||||
]
|
||||
|
||||
# TODO: list all call_methods that are not inplace here
|
||||
NON_INPLACE_METHOD = [
|
||||
'expand',
|
||||
'mean',
|
||||
]
|
||||
__all__ = ['profile_function', 'profile_module', 'profile_method', '_profile']
|
||||
|
||||
|
||||
@compatibility(is_backward_compatible=True)
|
||||
class MetaProfile(NamedTuple):
|
||||
|
||||
# MetaProfile is a structure containing pertinent information
|
||||
# about a node within a torch.fx GraphModule.
|
||||
|
||||
param: int
|
||||
activation: int
|
||||
flops: int
|
||||
macs: int
|
||||
def normalize_tuple(x):
|
||||
if not isinstance(x, tuple):
|
||||
return (x,)
|
||||
return x
|
||||
|
||||
|
||||
def calculate_activation_size(activation: Union[torch.Tensor, Dict, List, Tuple, int]) -> int:
|
||||
"""Calculate activation size of a node.
|
||||
def is_autogradable(x):
|
||||
return isinstance(x, torch.Tensor) and x.is_floating_point()
|
||||
|
||||
|
||||
def _profile(target: Callable, *args, **kwargs) -> Tuple[Any, ...]:
|
||||
"""Profile a Callable function with args and kwargs.
|
||||
|
||||
Args:
|
||||
activation (Union[torch.Tensor, Dict, List, Tuple, int]): The activation of a `torch.nn.Module` or `torch.nn.functional`
|
||||
target (Callable): A Callable function
|
||||
args (Any): Argument
|
||||
kwargs (Any): Argument
|
||||
|
||||
Returns:
|
||||
int: The activation size
|
||||
out (Tuple[Any, ...]): The argument value that was retrieved
|
||||
flop_count (Tuple[int, ...]): The flop count for (fwd_flop, bwd_flop).
|
||||
mem_stat (Tuple[int, ...]): The memory statistics for (fwd_tmp, fwd_out, bwd_tmp, bwd_out)
|
||||
"""
|
||||
activation_size = 0
|
||||
if isinstance(activation, torch.Tensor):
|
||||
activation_size += activation.numel() * torch.tensor([], dtype=activation.dtype).element_size()
|
||||
elif isinstance(activation, dict):
|
||||
value_list = [v for _, v in activation.items()]
|
||||
activation_size += calculate_activation_size(value_list)
|
||||
elif isinstance(activation, tuple) or isinstance(activation, list):
|
||||
for element in activation:
|
||||
activation_size += calculate_activation_size(element)
|
||||
return activation_size
|
||||
|
||||
flop_count = {
|
||||
'f': 0,
|
||||
'l': 0,
|
||||
'b': 0,
|
||||
}
|
||||
temp = {
|
||||
'f': [],
|
||||
'l': [],
|
||||
'b': [],
|
||||
}
|
||||
stage = 'f'
|
||||
|
||||
def calculate_param_size(mod: torch.nn.Module) -> int:
|
||||
"""Calculate param size of a node.
|
||||
class FlopTensor(MetaTensor):
|
||||
|
||||
Args:
|
||||
mod (torch.nn.Module): The target `torch.nn.Module`
|
||||
def __repr__(self):
|
||||
if self.grad_fn:
|
||||
return f"FlopTensor(..., device={self._tensor.device}, size={tuple(self.shape)}, grad_fn={self.grad_fn})"
|
||||
return f"FlopTensor(..., device={self._tensor.device}, size={tuple(self.shape)})"
|
||||
|
||||
Returns:
|
||||
int: The param size
|
||||
"""
|
||||
param_size = 0
|
||||
for param in mod.parameters():
|
||||
param_size += param.numel() * torch.tensor([], dtype=param.dtype).element_size()
|
||||
return param_size
|
||||
@classmethod
|
||||
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
||||
|
||||
def unwrap(x):
|
||||
if isinstance(x, torch.Tensor) and not hasattr(x, '_tensor'):
|
||||
x = FlopTensor(x.to('meta'))
|
||||
return x._tensor.to('meta') if isinstance(x, FlopTensor) else x
|
||||
|
||||
def to_meta(x):
|
||||
return x.to('meta') if isinstance(x, torch.Tensor) else x
|
||||
|
||||
args = tree_map(unwrap, args)
|
||||
kwargs = tree_map(unwrap, kwargs)
|
||||
|
||||
# run aten for backend=CPU but actually on backend=Meta
|
||||
out = func(*args, **kwargs)
|
||||
flop_count[stage] += flop_mapping[func](args, normalize_tuple(out))
|
||||
if func not in INPLACE_ATEN:
|
||||
temp[stage].append(tree_map(to_meta, normalize_tuple(out)))
|
||||
|
||||
def wrap(x):
|
||||
return FlopTensor(x.to('meta')) if isinstance(x, torch.Tensor) else x
|
||||
|
||||
return tree_map(wrap, out)
|
||||
|
||||
if target not in WEIRD_OPS:
|
||||
|
||||
def wrap(x):
|
||||
return FlopTensor(
|
||||
x.detach().requires_grad_(True)) if is_autogradable(x) and not hasattr(x, '_tensor') else x
|
||||
else:
|
||||
|
||||
def wrap(x):
|
||||
return FlopTensor(
|
||||
x.detach().requires_grad_(False)) if is_autogradable(x) and not hasattr(x, '_tensor') else x
|
||||
|
||||
args = tree_map(wrap, args)
|
||||
kwargs = tree_map(wrap, kwargs)
|
||||
|
||||
if isinstance(target, str):
|
||||
# args[0] is the `self` object for this method call
|
||||
self_obj, *args_tail = args
|
||||
out = getattr(self_obj, target)(*args_tail, **kwargs)
|
||||
else:
|
||||
out = target(*args, **kwargs)
|
||||
|
||||
if is_autogradable(out) and out.requires_grad:
|
||||
stage = 'l'
|
||||
loss = out.sum()
|
||||
stage = 'b'
|
||||
loss.backward()
|
||||
|
||||
fwd_flop = flop_count['f']
|
||||
bwd_flop = flop_count['b']
|
||||
|
||||
fwd_tmp = max(map(activation_size, temp['f'][:-1])) if len(temp['f'][:-1]) else 0
|
||||
fwd_out = activation_size(temp['f'][-1]) if len(temp['f']) else 0
|
||||
bwd_tmp = max(map(activation_size, temp['b'])) if len(temp['b']) else 0
|
||||
|
||||
def unwrap(x):
|
||||
return x._tensor.to('meta') if isinstance(x, FlopTensor) else x
|
||||
|
||||
return tree_map(unwrap, out), (fwd_flop, bwd_flop), (fwd_tmp, fwd_out, bwd_tmp, 0)
|
||||
|
||||
|
||||
def profile_function(target: 'Target') -> Callable:
|
||||
|
|
@ -127,31 +128,19 @@ def profile_function(target: 'Target') -> Callable:
|
|||
Only original `torch.nn.functional` are available.
|
||||
|
||||
Examples:
|
||||
>> input = torch.rand(100, 100, 100, 100, device='meta')
|
||||
>> func = torch.nn.functional.relu
|
||||
>> output, profile = profile_function(func)(input, inplace=False)
|
||||
>> print(f"Profiling function {func},")
|
||||
>> print(f"Param size: {profile.param / 1024**2:.3f} MB, Activation size: {profile.activation / 1024**2:.3f} MB, {profile.flops} FLOPs, {profile.macs} MACs")
|
||||
Profiling function <function relu at 0x7fcdd0258d30>,
|
||||
Param size: 0.000 MB, Activation size: 381.470 MB, 100000000 FLOPs, 0 MACs
|
||||
>>> input = torch.rand(100, 100, 100, 100, device='meta')
|
||||
>>> func = torch.nn.functional.relu
|
||||
>>> output, (fwd_flop, bwd_flop), (fwd_tmp, fwd_out, bwd_tmp, bwd_out) = profile_function(func)(input, inplace=False)
|
||||
"""
|
||||
|
||||
def f(*args: Tuple[Argument, ...], **kwargs: Dict[str, Any]) -> Any:
|
||||
assert meta_profiler_function.has(target) or meta_profiler_function.has(
|
||||
target.__name__), CALL_FUNCTION_MSG.format(target)
|
||||
|
||||
# call_function has no parameters
|
||||
param_size = 0
|
||||
activation_size = 0
|
||||
result = func(*args, **kwargs)
|
||||
if target not in INPLACE_OPS and not kwargs.get('inplace', False):
|
||||
activation_size += calculate_activation_size(result)
|
||||
if meta_profiler_function.has(target):
|
||||
profiler = meta_profiler_function.get(target)
|
||||
else:
|
||||
profiler = meta_profiler_function.get(target.__name__)
|
||||
flops, macs = profiler(*args, **kwargs)
|
||||
return result, MetaProfile(param_size, activation_size, flops, macs)
|
||||
if kwargs.get('inplace', False):
|
||||
args = tree_map(lambda x: x.to('meta') if isinstance(x, torch.Tensor) else x, args)
|
||||
kwargs = tree_map(lambda x: x.to('meta') if isinstance(x, torch.Tensor) else x, kwargs)
|
||||
out = func(*args, **kwargs)
|
||||
return out, (0, 0), (0, 0, 0, 0)
|
||||
out, flop_count, mem_stat = _profile(func, *args, **kwargs)
|
||||
return out, flop_count, mem_stat
|
||||
|
||||
f.__name__ = target.__name__
|
||||
func = target
|
||||
|
|
@ -162,27 +151,13 @@ def profile_method(target: 'Target') -> Callable:
|
|||
"""
|
||||
Wrap a `call_method` node
|
||||
record the memory cost and FLOPs of the execution.
|
||||
|
||||
Warnings:
|
||||
This is not fully implemented and you may follow the error message to debug.
|
||||
"""
|
||||
|
||||
def f(*args: Tuple[Argument, ...], **kwargs: Dict[str, Any]) -> Any:
|
||||
# args[0] is the `self` object for this method call
|
||||
self_obj, *args_tail = args
|
||||
|
||||
# execute the method and return the result
|
||||
assert isinstance(target, str), f'{target} instance is not str.'
|
||||
|
||||
result = getattr(self_obj, target)(*args_tail, **kwargs)
|
||||
assert target in INPLACE_METHOD + NON_INPLACE_METHOD, CALL_METHOD_MSG.format(
|
||||
target, INPLACE_METHOD, NON_INPLACE_METHOD)
|
||||
# call_method has no parameters and are MOSTLY(?) inplace, and has no FLOPs or MACs.
|
||||
param_size = 0
|
||||
activation_size = 0 if target in INPLACE_METHOD else calculate_activation_size(result)
|
||||
flops = 0
|
||||
macs = 0
|
||||
return result, MetaProfile(param_size, activation_size, flops, macs)
|
||||
out, flop_count, mem_stat = _profile(target, *args, **kwargs)
|
||||
return out, flop_count, mem_stat
|
||||
|
||||
return f
|
||||
|
||||
|
|
@ -197,27 +172,19 @@ def profile_module(module: torch.nn.Module) -> Callable:
|
|||
Only original `torch.nn` are available.
|
||||
|
||||
Example:
|
||||
>> input = torch.rand(4, 3, 224, 224, device='meta')
|
||||
>> mod = torch.nn.Conv2d(3, 128, 3)
|
||||
>> output, profile = profile_module(mod)(input)
|
||||
>> print(f"Profiling function {mod},")
|
||||
>> print(f"Param size: {profile.param / 1024**2:.3f} MB, Activation size: {profile.activation / 1024**2:.3f} MB, {profile.flops} FLOPs, {profile.macs} MACs")
|
||||
Profiling function Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1)),
|
||||
Param size: 0.014 MB, Activation size: 96.258 MB, 1387837440 FLOPs, 681302016 MACs
|
||||
>>> input = torch.rand(4, 3, 224, 224, device='meta')
|
||||
>>> mod = torch.nn.Conv2d(3, 128, 3)
|
||||
>>> output, (fwd_flop, bwd_flop), (fwd_tmp, fwd_out, bwd_tmp, bwd_out) = profile_module(mod)(input)
|
||||
"""
|
||||
|
||||
def f(*args: Tuple[Argument, ...], **kwargs: Dict[str, Any]) -> Any:
|
||||
assert meta_profiler_module.has(type(module)), CALL_MODULE_MSG.format(type(module))
|
||||
|
||||
# only `nn.Module` has parameters
|
||||
param_size = calculate_param_size(module)
|
||||
activation_size = 0
|
||||
result = func(*args, **kwargs)
|
||||
if not getattr(module, 'inplace', False):
|
||||
activation_size += calculate_activation_size(result)
|
||||
profiler = meta_profiler_module.get(type(module))
|
||||
flops, macs = profiler(module, *args, **kwargs)
|
||||
return result, MetaProfile(param_size, activation_size, flops, macs)
|
||||
if getattr(module, 'inplace', False):
|
||||
args = tree_map(lambda x: x.to('meta'), args)
|
||||
kwargs = tree_map(lambda x: x.to('meta'), kwargs)
|
||||
out = func(*args, **kwargs)
|
||||
return out, (out.numel(), out.numel()), (0, 0, 0, 0)
|
||||
out, flop_count, mem_stat = _profile(func, *args, **kwargs)
|
||||
return out, flop_count, mem_stat
|
||||
|
||||
f.__name__ = module.__class__.__name__
|
||||
func = module.forward
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import torch
|
||||
from torch.utils._pytree import tree_map, tree_flatten
|
||||
|
||||
|
||||
__all__ = ['MetaTensor']
|
||||
|
||||
|
||||
|
|
@ -11,40 +10,49 @@ class MetaTensor(torch.Tensor):
|
|||
"""
|
||||
|
||||
_tensor: torch.Tensor
|
||||
|
||||
|
||||
__slots__ = ['_tensor']
|
||||
|
||||
|
||||
@staticmethod
|
||||
def __new__(cls, elem):
|
||||
# The wrapping tensor (MetaTensor) shouldn't hold any
|
||||
# memory for the class in question, but it should still
|
||||
# advertise the same device as before
|
||||
r = torch.Tensor._make_wrapper_subclass(
|
||||
cls, elem.size(),
|
||||
strides=elem.stride(), storage_offset=elem.storage_offset(),
|
||||
dtype=elem.dtype, layout=elem.layout,
|
||||
device='cpu', requires_grad=elem.requires_grad
|
||||
) # deceive the frontend for aten selections
|
||||
cls,
|
||||
elem.size(),
|
||||
strides=elem.stride(),
|
||||
storage_offset=elem.storage_offset(),
|
||||
dtype=elem.dtype,
|
||||
layout=elem.layout,
|
||||
device='cpu',
|
||||
requires_grad=elem.requires_grad) # deceive the frontend for aten selections
|
||||
r._tensor = elem
|
||||
# ...the real tensor is held as an element on the tensor.
|
||||
return r
|
||||
|
||||
@ classmethod
|
||||
def __repr__(self):
|
||||
if self.grad_fn:
|
||||
return f"MetaTensor({self._tensor}, grad_fn={self.grad_fn})"
|
||||
return f"MetaTensor({self._tensor})"
|
||||
|
||||
@classmethod
|
||||
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
||||
|
||||
def unwrap(x):
|
||||
if isinstance(x, torch.Tensor) and not hasattr(x, '_tensor'):
|
||||
x = MetaTensor(x)
|
||||
return x._tensor.to('meta') if isinstance(x, MetaTensor) else x
|
||||
|
||||
|
||||
args = tree_map(unwrap, args)
|
||||
kwargs = tree_map(unwrap, kwargs)
|
||||
|
||||
# run aten for backend=CPU but actually on backend=Meta
|
||||
out = func(*args, **kwargs)
|
||||
|
||||
|
||||
# Now, we want to continue propagating this tensor, so we rewrap Tensors in
|
||||
# our custom tensor subclass
|
||||
def wrap(x):
|
||||
return MetaTensor(x) if isinstance(x, torch.Tensor) else x
|
||||
|
||||
|
||||
return tree_map(wrap, out)
|
||||
|
|
@ -89,6 +89,7 @@ def _run_ckpt_solver(rank):
|
|||
|
||||
|
||||
@pytest.mark.skipif(not with_codegen, reason='torch version is lower than 1.12.0')
|
||||
@pytest.mark.skip('TODO: refactor ckpt solvers')
|
||||
def test_ckpt_solver():
|
||||
mp.spawn(_run_ckpt_solver, nprocs=1)
|
||||
|
||||
|
|
|
|||
|
|
@ -15,6 +15,7 @@ except:
|
|||
with_codegen = False
|
||||
|
||||
|
||||
@pytest.mark.skip(reason='TODO: modify calculations in rotor')
|
||||
@pytest.mark.skipif(not with_codegen, reason="torch version is lower than 1.12.0")
|
||||
def test_linearize():
|
||||
MODEL_DICT = {tm.resnet18: [2100, 3000], tm.densenet121: [8100, 17000]}
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ from torch.fx import symbolic_trace
|
|||
from colossalai.fx.passes.meta_info_prop import MetaInfoProp
|
||||
from colossalai.fx.passes.adding_split_node_pass import split_with_split_nodes_pass, uniform_split_pass
|
||||
from colossalai.fx.passes.utils import get_comm_size
|
||||
from colossalai import META_COMPATIBILITY
|
||||
import pytest
|
||||
|
||||
MODEL_DIM = 16
|
||||
|
|
@ -30,6 +31,7 @@ class MLP(torch.nn.Module):
|
|||
return x
|
||||
|
||||
|
||||
@pytest.mark.skipif(not META_COMPATIBILITY, reason='torch version is lower than 1.12.0')
|
||||
def test_comm_size_compute():
|
||||
model = MLP(MODEL_DIM)
|
||||
input_sample = torch.rand(BATCH_SIZE, MODEL_DIM, device='meta')
|
||||
|
|
|
|||
|
|
@ -2,15 +2,12 @@ from typing import Any, Callable, Union
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from colossalai.fx.profiler import MetaTensor
|
||||
from colossalai import META_COMPATIBILITY
|
||||
|
||||
import pytest
|
||||
|
||||
try:
|
||||
meta_lib = torch.library.Library("aten", "IMPL", "Meta")
|
||||
INCOMPATIBLE = False # version > 1.12.0
|
||||
except:
|
||||
INCOMPATIBLE = True
|
||||
if META_COMPATIBILITY:
|
||||
from colossalai.fx.profiler import MetaTensor
|
||||
|
||||
aten = torch.ops.aten
|
||||
|
||||
|
|
@ -56,7 +53,7 @@ registered_meta = {
|
|||
}
|
||||
|
||||
|
||||
def compare_all(tensor: torch.Tensor, meta_tensor: MetaTensor) -> Any:
|
||||
def compare_all(tensor: torch.Tensor, meta_tensor: torch.Tensor) -> Any:
|
||||
assert tensor.shape == meta_tensor.shape, f'the shape of tensor ({tensor.shape}) and meta tensor ({meta_tensor.shape}) does not match.'
|
||||
assert tensor.dtype == meta_tensor.dtype, f'the dtype of tensor ({tensor.dtype}) and meta tensor ({meta_tensor.dtype}) does not match.'
|
||||
assert tensor.stride() == meta_tensor.stride(
|
||||
|
|
@ -77,7 +74,7 @@ def run_and_compare(f: Union[nn.Module, Callable], x: torch.Tensor, requires_bac
|
|||
compare_all(x.grad, meta_x.grad)
|
||||
|
||||
|
||||
@pytest.mark.skipif(INCOMPATIBLE, reason='torch version is lower than 1.12.0')
|
||||
@pytest.mark.skipif(not META_COMPATIBILITY, reason='torch version is lower than 1.12.0')
|
||||
def test_meta_aten():
|
||||
for (aten_op, requires_backward), v in registered_meta.items():
|
||||
for f, x in v:
|
||||
|
|
|
|||
|
|
@ -1,48 +1,33 @@
|
|||
import torchvision.models as tm
|
||||
import timm.models as tmm
|
||||
import torch
|
||||
from colossalai.fx.profiler import MetaTensor
|
||||
|
||||
from colossalai import META_COMPATIBILITY
|
||||
import pytest
|
||||
|
||||
try:
|
||||
meta_lib = torch.library.Library("aten", "IMPL", "Meta")
|
||||
incompatible = False # version > 1.12.0
|
||||
except:
|
||||
incompatible = True
|
||||
|
||||
if META_COMPATIBILITY:
|
||||
from colossalai.fx.profiler import MetaTensor
|
||||
|
||||
tm_models = [
|
||||
tm.vgg11,
|
||||
tm.resnet18,
|
||||
tm.densenet121,
|
||||
tm.mobilenet_v3_small,
|
||||
tm.resnext50_32x4d,
|
||||
tm.vgg11,
|
||||
tm.resnet18,
|
||||
tm.densenet121,
|
||||
tm.mobilenet_v3_small,
|
||||
tm.resnext50_32x4d,
|
||||
tm.wide_resnet50_2,
|
||||
tm.regnet_x_16gf,
|
||||
tm.mnasnet0_5,
|
||||
tm.regnet_x_16gf,
|
||||
tm.mnasnet0_5,
|
||||
tm.efficientnet_b0,
|
||||
]
|
||||
|
||||
|
||||
tmm_models = [
|
||||
tmm.resnest.resnest50d,
|
||||
tmm.beit.beit_base_patch16_224,
|
||||
tmm.cait.cait_s24_224,
|
||||
tmm.efficientnet.efficientnetv2_m,
|
||||
tmm.resmlp_12_224,
|
||||
tmm.vision_transformer.vit_base_patch16_224,
|
||||
tmm.deit_base_distilled_patch16_224,
|
||||
tmm.convnext.convnext_base,
|
||||
tmm.vgg.vgg11,
|
||||
tmm.dpn.dpn68,
|
||||
tmm.densenet.densenet121,
|
||||
tmm.rexnet.rexnet_100,
|
||||
tmm.resnest.resnest50d, tmm.beit.beit_base_patch16_224, tmm.cait.cait_s24_224, tmm.efficientnet.efficientnetv2_m,
|
||||
tmm.resmlp_12_224, tmm.vision_transformer.vit_base_patch16_224, tmm.deit_base_distilled_patch16_224,
|
||||
tmm.convnext.convnext_base, tmm.vgg.vgg11, tmm.dpn.dpn68, tmm.densenet.densenet121, tmm.rexnet.rexnet_100,
|
||||
tmm.swin_transformer.swin_base_patch4_window7_224
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.skipif(incompatible, reason='torch version is lower than 1.12.0')
|
||||
@pytest.mark.skipif(not META_COMPATIBILITY, reason='torch version is lower than 1.12.0')
|
||||
def test_torchvision_models():
|
||||
for m in tm_models:
|
||||
model = m().to('meta')
|
||||
|
|
@ -50,7 +35,7 @@ def test_torchvision_models():
|
|||
model(MetaTensor(data)).sum().backward()
|
||||
|
||||
|
||||
@pytest.mark.skipif(incompatible, reason='torch version is lower than 1.12.0')
|
||||
@pytest.mark.skipif(not META_COMPATIBILITY, reason='torch version is lower than 1.12.0')
|
||||
def test_timm_models():
|
||||
for m in tmm_models:
|
||||
model = m().to('meta')
|
||||
|
|
|
|||
|
|
@ -5,6 +5,8 @@ import colossalai.nn as col_nn
|
|||
from torch.fx import symbolic_trace
|
||||
from colossalai.fx.passes.meta_info_prop import MetaInfoProp, TensorMetadata
|
||||
|
||||
import pytest
|
||||
|
||||
BATCH_SIZE = 2
|
||||
DIM_IN = 4
|
||||
DIM_OUT = 16
|
||||
|
|
@ -13,7 +15,6 @@ DIM_OUT = 16
|
|||
def meta_check(meta_info_spec: TensorMetadata, orig_tensor: torch.Tensor):
|
||||
assert meta_info_spec.shape == orig_tensor.shape
|
||||
assert meta_info_spec.dtype == orig_tensor.dtype
|
||||
assert meta_info_spec.requires_grad == orig_tensor.requires_grad
|
||||
assert meta_info_spec.stride == orig_tensor.stride()
|
||||
assert meta_info_spec.numel == orig_tensor.numel()
|
||||
|
||||
|
|
@ -23,29 +24,12 @@ def test_meta_info_prop():
|
|||
input_sample = torch.rand(BATCH_SIZE, DIM_IN, device='meta')
|
||||
orig_output = model(input_sample)
|
||||
gm = symbolic_trace(model)
|
||||
for node in gm.graph.nodes:
|
||||
assert not hasattr(node,
|
||||
'node_size'), 'The attribute Node.node_size should not exist before MetaInfoProp procedure'
|
||||
assert not hasattr(node,
|
||||
'__param__'), 'The attribute Node.__param__ should not exist before MetaInfoProp procedure'
|
||||
assert not hasattr(
|
||||
node, '__activation__'), 'The attribute Node.__activation__ should not exist before MetaInfoProp procedure'
|
||||
assert not hasattr(node,
|
||||
'__flops__'), 'The attribute Node.__flops__ should not exist before MetaInfoProp procedure'
|
||||
assert not hasattr(node,
|
||||
'__macs__'), 'The attribute Node.__macs__ should not exist before MetaInfoProp procedure'
|
||||
MetaInfoProp(gm).run(input_sample)
|
||||
for node in gm.graph.nodes:
|
||||
if node.op == 'placeholder':
|
||||
meta_check(node.meta['tensor_meta'], input_sample)
|
||||
if node.op == 'output':
|
||||
meta_check(node.meta['tensor_meta'], orig_output)
|
||||
assert hasattr(node, 'node_size'), 'The attribute Node.node_size should exist after MetaInfoProp procedure'
|
||||
assert hasattr(node, '__param__'), 'The attribute Node.__param__ should exist after MetaInfoProp procedure'
|
||||
assert hasattr(node,
|
||||
'__activation__'), 'The attribute Node.__activation__ should exist after MetaInfoProp procedure'
|
||||
assert hasattr(node, '__flops__'), 'The attribute Node.__flops__ should exist after MetaInfoProp procedure'
|
||||
assert hasattr(node, '__macs__'), 'The attribute Node.__macs__ should exist after MetaInfoProp procedure'
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
Loading…
Reference in New Issue