# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from typing import List, Optional
from collections import OrderedDict
import numpy as np
from deepspeed.accelerator import get_accelerator
from deepspeed.utils import logger
from deepspeed.moe.layer import MoE
from deepspeed.utils.timer import FORWARD_GLOBAL_TIMER, BACKWARD_GLOBAL_TIMER, STEP_GLOBAL_TIMER
Tensor = torch.Tensor
module_flop_count = []
module_mac_count = []
old_functions = {}
DEFAULT_PRECISION = 2
[docs]class FlopsProfiler(object):
"""Measures the latency, number of estimated floating-point operations and parameters of each module in a PyTorch model.
The flops-profiler profiles the forward pass of a PyTorch model and prints the model graph with the measured profile attached to each module. It shows how latency, flops and parameters are spent in the model and which modules or layers could be the bottleneck. It also outputs the names of the top k modules in terms of aggregated latency, flops, and parameters at depth l with k and l specified by the user. The output profile is computed for each batch of input.
The DeepSpeed flops profiler can be used with the DeepSpeed runtime or as a standalone package.
When using DeepSpeed for model training, the flops profiler can be configured in the deepspeed_config file and no user code change is required.
If using the profiler as a standalone package, one imports the flops_profiler package and use the APIs.
Here is an example for usage in a typical training workflow:
.. code-block:: python
model = Model()
prof = FlopsProfiler(model)
for step, batch in enumerate(data_loader):
if step == profile_step:
prof.start_profile()
loss = model(batch)
if step == profile_step:
flops = prof.get_total_flops(as_string=True)
params = prof.get_total_params(as_string=True)
prof.print_model_profile(profile_step=profile_step)
prof.end_profile()
loss.backward()
optimizer.step()
To profile a trained model in inference, use the `get_model_profile` API.
Args:
object (torch.nn.Module): The PyTorch model to profile.
"""
def __init__(self, model, ds_engine=None, recompute_fwd_factor=0.0):
self.model = model
self.ds_engine = ds_engine
self.recompute_fwd_factor = recompute_fwd_factor
self.started = False
self.func_patched = False
[docs] def start_profile(self, ignore_list=None):
"""Starts profiling.
Extra attributes are added recursively to all the modules and the profiled torch.nn.functionals are monkey patched.
Args:
ignore_list (list, optional): the list of modules to ignore while profiling. Defaults to None.
"""
logger.info("Flops profiler started")
self.reset_profile()
_patch_functionals()
_patch_tensor_methods()
def register_module_hooks(module, ignore_list):
if ignore_list and type(module) in ignore_list:
return
# if computing the flops of a module directly
if type(module) in MODULE_HOOK_MAPPING:
if not hasattr(module, "__flops_handle__"):
module.__flops_handle__ = module.register_forward_hook(MODULE_HOOK_MAPPING[type(module)])
return
# if computing the flops of the functionals in a module
def pre_hook(module, input):
module_flop_count.append([])
module_mac_count.append([])
if not hasattr(module, "__pre_hook_handle__"):
module.__pre_hook_handle__ = module.register_forward_pre_hook(pre_hook)
def post_hook(module, input, output):
if module_flop_count:
module.__flops__ += sum([elem[1] for elem in module_flop_count[-1]])
module_flop_count.pop()
module.__macs__ += sum([elem[1] for elem in module_mac_count[-1]])
module_mac_count.pop()
if not hasattr(module, "__post_hook_handle__"):
module.__post_hook_handle__ = module.register_forward_hook(post_hook)
def start_time_hook(module, input):
get_accelerator().synchronize()
module.__start_time__ = time.time()
if not hasattr(module, "__start_time_hook_handle"):
module.__start_time_hook_handle__ = module.register_forward_pre_hook(start_time_hook)
def end_time_hook(module, input, output):
get_accelerator().synchronize()
module.__duration__ += time.time() - module.__start_time__
if not hasattr(module, "__end_time_hook_handle__"):
module.__end_time_hook_handle__ = module.register_forward_hook(end_time_hook)
self.model.apply(partial(register_module_hooks, ignore_list=ignore_list))
self.started = True
self.func_patched = True
[docs] def stop_profile(self):
"""Stop profiling.
All torch.nn.functionals are restored to their originals.
"""
if self.started and self.func_patched:
_reload_functionals()
_reload_tensor_methods()
self.func_patched = False
def remove_profile_attrs(module):
if hasattr(module, "__pre_hook_handle__"):
module.__pre_hook_handle__.remove()
del module.__pre_hook_handle__
if hasattr(module, "__post_hook_handle__"):
module.__post_hook_handle__.remove()
del module.__post_hook_handle__
if hasattr(module, "__flops_handle__"):
module.__flops_handle__.remove()
del module.__flops_handle__
if hasattr(module, "__start_time_hook_handle__"):
module.__start_time_hook_handle__.remove()
del module.__start_time_hook_handle__
if hasattr(module, "__end_time_hook_handle__"):
module.__end_time_hook_handle__.remove()
del module.__end_time_hook_handle__
self.model.apply(remove_profile_attrs)
[docs] def reset_profile(self):
"""Resets the profiling.
Adds or resets the extra attributes.
"""
def get_param_count_and_ep(param):
"""
Return the number of parameters in the layer, whether the layer is an MoE layer,
and its expert parallelism size if so
"""
prefix = 'ep_size_'
offset = len(prefix)
expert_parallelism = 0
if getattr(param, "group_name", "").startswith(prefix):
try:
expert_parallelism = int(param.group_name[offset:])
except ValueError:
pass
return param.numel(), expert_parallelism, param.element_size()
def add_or_reset_attrs(module):
module.__flops__ = 0
module.__macs__ = 0
module.__params__ = module.__expert_params__ = module.__model_expert_params__ = 0
parameters = (get_param_count_and_ep(p) for p in module.parameters())
for num_params, expert_parallelism, per_param_size in parameters:
params = num_params if not expert_parallelism else 0
expert_params = num_params if expert_parallelism else 0
# number of expert parameters taking into account other expert parallel groups
model_expert_params = num_params * expert_parallelism
module.__params__ += params
module.__expert_params__ += expert_params
module.__model_expert_params__ += model_expert_params
module.__start_time__ = 0
module.__duration__ = 0
self.model.apply(add_or_reset_attrs)
[docs] def end_profile(self):
"""Ends profiling.
The added attributes and handles are removed recursively on all the modules.
"""
if not self.started:
return
self.stop_profile()
self.started = False
def remove_profile_attrs(module):
if hasattr(module, "__flops__"):
del module.__flops__
if hasattr(module, "__macs__"):
del module.__macs__
if hasattr(module, "__params__"):
del module.__params__
if hasattr(module, "__expert_params__"):
del module.__expert_params__
if hasattr(module, "__model_expert_params__"):
del module.__model_expert_params__
if hasattr(module, "__start_time__"):
del module.__start_time__
if hasattr(module, "__duration__"):
del module.__duration__
self.model.apply(remove_profile_attrs)
logger.info("Flops profiler finished")
[docs] def get_total_flops(self, as_string=False):
"""Returns the total flops of the model.
Args:
as_string (bool, optional): whether to output the flops as string. Defaults to False.
Returns:
The number of multiply-accumulate operations of the model forward pass.
"""
total_flops = get_module_flops(self.model)
return number_to_string(total_flops) if as_string else total_flops
[docs] def get_total_macs(self, as_string=False):
"""Returns the total MACs of the model.
Args:
as_string (bool, optional): whether to output the flops as string. Defaults to False.
Returns:
The number of multiply-accumulate operations of the model forward pass.
"""
total_macs = get_module_macs(self.model)
return macs_to_string(total_macs) if as_string else total_macs
[docs] def get_total_duration(self, as_string=False):
"""Returns the total duration of the model forward pass.
Args:
as_string (bool, optional): whether to output the duration as string. Defaults to False.
Returns:
The latency of the model forward pass.
"""
total_duration = get_module_duration(self.model)
return duration_to_string(total_duration) if as_string else total_duration
[docs] def get_total_params(self, as_string=False):
"""Returns the total number of parameters stored per rank.
Args:
as_string (bool, optional): whether to output the parameters as string. Defaults to False.
Returns:
The total number of parameters stored per rank.
"""
total_params = self.model.__expert_params__ + self.model.__params__
return params_to_string(total_params) if as_string else total_params
def is_expert_tensor_parallelism_enabled(self):
for _, module in self.model.named_modules():
if isinstance(module, MoE) and hasattr(module, 'enable_expert_tensor_parallelism'):
return module.enable_expert_tensor_parallelism
return False
[docs] def print_model_profile(self, profile_step=1, module_depth=-1, top_modules=1, detailed=True, output_file=None):
"""Prints the model graph with the measured profile attached to each module.
Args:
profile_step (int, optional): The global training step at which to profile. Note that warm up steps are needed for accurate time measurement.
module_depth (int, optional): The depth of the model to which to print the aggregated module information. When set to -1, it prints information from the top to the innermost modules (the maximum depth).
top_modules (int, optional): Limits the aggregated profile output to the number of top modules specified.
detailed (bool, optional): Whether to print the detailed model profile.
output_file (str, optional): Path to the output file. If None, the profiler prints to stdout.
"""
if not self.started:
return
import sys
import os.path
original_stdout = None
f = None
if output_file and output_file != "":
dir_path = os.path.dirname(os.path.abspath(output_file))
if not os.path.exists(dir_path):
os.makedirs(dir_path)
original_stdout = sys.stdout
f = open(output_file, "w")
sys.stdout = f
total_flops = self.get_total_flops()
total_macs = self.get_total_macs()
total_duration = self.get_total_duration()
total_params = self.get_total_params()
expert_tensor_parallelism = None # silence the linters
total_model_expert_params = total_model_nonexpert_params = 0
if self.ds_engine:
total_model_nonexpert_params = self.model.__params__ * self.ds_engine.mp_world_size
if self.ds_engine.has_moe_layers:
expert_tensor_parallelism = self.ds_engine.mp_world_size if self.is_expert_tensor_parallelism_enabled(
) else 1
total_model_expert_params = self.model.__model_expert_params__ * expert_tensor_parallelism
self.flops = total_flops
self.macs = total_macs
self.params = total_params
print("\n-------------------------- DeepSpeed Flops Profiler --------------------------")
print(f'Profile Summary at step {profile_step}:')
print("Notations:\n"
"data parallel size (dp_size), model parallel size(mp_size),\n"
"number of parameters (params), number of multiply-accumulate operations(MACs),\n"
"number of floating-point operations (flops), floating-point operations per second (FLOPS),\n"
"fwd latency (forward propagation latency), bwd latency (backward propagation latency),\n"
"step (weights update latency), iter latency (sum of fwd, bwd and step latency)\n")
line_fmt = '{:<70} {:<8}'
if self.ds_engine:
print(line_fmt.format('world size: ', self.ds_engine.world_size))
print(line_fmt.format('data parallel size: ', self.ds_engine.dp_world_size))
print(line_fmt.format('model parallel size: ', self.ds_engine.mp_world_size))
print(line_fmt.format('batch size per GPU: ', self.ds_engine.train_micro_batch_size_per_gpu()))
if self.ds_engine.has_moe_layers:
print(line_fmt.format('expert tensor parallelism enabled: ', expert_tensor_parallelism > 1))
print(line_fmt.format('params per GPU: ', params_to_string(total_params)))
if total_model_expert_params > 0:
print(
line_fmt.format('params of model: ',
params_to_string(total_model_nonexpert_params + total_model_expert_params)))
print(line_fmt.format(' non-expert params of model: ', params_to_string(total_model_nonexpert_params)))
print(line_fmt.format(' expert params of model: ', params_to_string(total_model_expert_params)))
else:
print(
line_fmt.format('params of model = params per GPU * mp_size: ',
params_to_string(total_model_nonexpert_params)))
print(line_fmt.format('fwd MACs per GPU: ', macs_to_string(total_macs)))
print(line_fmt.format('fwd flops per GPU: ', number_to_string(total_flops)))
print(
line_fmt.format('fwd flops of model = fwd flops per GPU * mp_size: ',
number_to_string(total_flops * (self.ds_engine.mp_world_size if self.ds_engine else 1))))
fwd_latency = self.get_total_duration()
if self.ds_engine and self.ds_engine.wall_clock_breakdown():
fwd_latency = self.ds_engine.timers(FORWARD_GLOBAL_TIMER).elapsed(False) / 1000.0
print(line_fmt.format('fwd latency: ', duration_to_string(fwd_latency)))
print(
line_fmt.format('fwd FLOPS per GPU = fwd flops per GPU / fwd latency: ',
flops_to_string(total_flops / fwd_latency)))
if self.ds_engine and self.ds_engine.wall_clock_breakdown():
bwd_factor = 2 + self.recompute_fwd_factor
bwd_latency = self.ds_engine.timers(BACKWARD_GLOBAL_TIMER).elapsed(False) / 1000.0
step_latency = self.ds_engine.timers(STEP_GLOBAL_TIMER).elapsed(False) / 1000.0
print(line_fmt.format('bwd latency: ', duration_to_string(bwd_latency)))
print(
line_fmt.format(f'bwd FLOPS per GPU = {bwd_factor:g} * fwd flops per GPU / bwd latency: ',
flops_to_string(bwd_factor * total_flops / bwd_latency)))
print(
line_fmt.format(
f'fwd+bwd FLOPS per GPU = {bwd_factor + 1:g} * fwd flops per GPU / (fwd+bwd latency): ',
flops_to_string((bwd_factor + 1) * total_flops / (fwd_latency + bwd_latency))))
print(line_fmt.format('step latency: ', duration_to_string(step_latency)))
iter_latency = fwd_latency + bwd_latency + step_latency
print(line_fmt.format('iter latency: ', duration_to_string(iter_latency)))
print(
line_fmt.format(f'FLOPS per GPU = {bwd_factor + 1:g} * fwd flops per GPU / iter latency: ',
flops_to_string((bwd_factor + 1) * total_flops / iter_latency)))
samples_per_iter = self.ds_engine.train_micro_batch_size_per_gpu() * self.ds_engine.world_size
print(line_fmt.format('samples/second: ', round(samples_per_iter / iter_latency, DEFAULT_PRECISION)))
def flops_repr(module):
params = module.__params__ + module.__expert_params__
flops = get_module_flops(module)
macs = get_module_macs(module)
duration = get_module_duration(module)
items = [
"{} = {:g}% Params".format(
params_to_string(params),
round(100 * params / total_params, DEFAULT_PRECISION) if total_params else 0),
"{} = {:g}% MACs".format(macs_to_string(macs),
round(100 * macs / total_macs, DEFAULT_PRECISION) if total_macs else 0),
"{} = {:g}% latency".format(
duration_to_string(duration),
round(100 * duration / total_duration, DEFAULT_PRECISION) if total_duration else 0),
flops_to_string(round(flops / duration, DEFAULT_PRECISION) if duration else 0),
]
original_extra_repr = module.original_extra_repr()
if original_extra_repr:
items.append(original_extra_repr)
return ", ".join(items)
def add_extra_repr(module):
flops_extra_repr = flops_repr.__get__(module)
if module.extra_repr != flops_extra_repr:
module.original_extra_repr = module.extra_repr
module.extra_repr = flops_extra_repr
assert module.extra_repr != module.original_extra_repr
def del_extra_repr(module):
if hasattr(module, "original_extra_repr"):
module.extra_repr = module.original_extra_repr
del module.original_extra_repr
self.model.apply(add_extra_repr)
print("\n----------------------------- Aggregated Profile per GPU -----------------------------")
self.print_model_aggregated_profile(module_depth=module_depth, top_modules=top_modules)
if detailed:
print("\n------------------------------ Detailed Profile per GPU ------------------------------")
print(
"Each module profile is listed after its name in the following order: \nparams, percentage of total params, MACs, percentage of total MACs, fwd latency, percentage of total fwd latency, fwd FLOPS"
)
print(
"\nNote: 1. A module can have torch.nn.module or torch.nn.functional to compute logits (e.g. CrossEntropyLoss). They are not counted as submodules, thus not to be printed out. However they make up the difference between a parent's MACs (or latency) and the sum of its submodules'.\n2. Number of floating-point operations is a theoretical estimation, thus FLOPS computed using that could be larger than the maximum system throughput.\n3. The fwd latency listed in the top module's profile is directly captured at the module forward function in PyTorch, thus it's less than the fwd latency shown above which is captured in DeepSpeed.\n"
)
print(self.model)
self.model.apply(del_extra_repr)
print("------------------------------------------------------------------------------")
if output_file:
sys.stdout = original_stdout
f.close()
[docs] def print_model_aggregated_profile(self, module_depth=-1, top_modules=1):
"""Prints the names of the top top_modules modules in terms of aggregated time, flops, and parameters at depth module_depth.
Args:
module_depth (int, optional): the depth of the modules to show. Defaults to -1 (the innermost modules).
top_modules (int, optional): the number of top modules to show. Defaults to 1.
"""
info = {}
if not hasattr(self.model, "__flops__"):
print("no __flops__ attribute in the model, call this function after start_profile and before end_profile")
return
def walk_module(module, curr_depth, info):
if curr_depth not in info:
info[curr_depth] = {}
if module.__class__.__name__ not in info[curr_depth]:
info[curr_depth][module.__class__.__name__] = [
0,
0,
0,
] # macs, params, time
info[curr_depth][module.__class__.__name__][0] += get_module_macs(module)
info[curr_depth][module.__class__.__name__][1] += module.__params__ + module.__expert_params__
info[curr_depth][module.__class__.__name__][2] += get_module_duration(module)
has_children = len(module._modules.items()) != 0
if has_children:
for child in module.children():
walk_module(child, curr_depth + 1, info)
walk_module(self.model, 0, info)
depth = module_depth
if module_depth == -1:
depth = len(info) - 1
print(f'Top {top_modules} modules in terms of params, MACs or fwd latency at different model depths:')
for d in range(depth):
num_items = min(top_modules, len(info[d]))
sort_macs = {
k: macs_to_string(v[0])
for k, v in sorted(info[d].items(), key=lambda item: item[1][0], reverse=True)[:num_items]
}
sort_params = {
k: params_to_string(v[1])
for k, v in sorted(info[d].items(), key=lambda item: item[1][1], reverse=True)[:num_items]
}
sort_time = {
k: duration_to_string(v[2])
for k, v in sorted(info[d].items(), key=lambda item: item[1][2], reverse=True)[:num_items]
}
print(f"depth {d}:")
print(f" params - {sort_params}")
print(f" MACs - {sort_macs}")
print(f" fwd latency - {sort_time}")
def _prod(dims):
p = 1
for v in dims:
p *= v
return p
def _linear_flops_compute(input, weight, bias=None):
out_features = weight.shape[0]
macs = input.numel() * out_features
return 2 * macs, macs
def _relu_flops_compute(input, inplace=False):
return input.numel(), 0
def _prelu_flops_compute(input: Tensor, weight: Tensor):
return input.numel(), 0
def _elu_flops_compute(input: Tensor, alpha: float = 1.0, inplace: bool = False):
return input.numel(), 0
def _leaky_relu_flops_compute(input: Tensor, negative_slope: float = 0.01, inplace: bool = False):
return input.numel(), 0
def _relu6_flops_compute(input: Tensor, inplace: bool = False):
return input.numel(), 0
def _silu_flops_compute(input: Tensor, inplace: bool = False):
return input.numel(), 0
def _gelu_flops_compute(input, **kwargs):
return input.numel(), 0
def _pool_flops_compute(input,
kernel_size,
stride=None,
padding=0,
dilation=None,
ceil_mode=False,
count_include_pad=True,
divisor_override=None,
return_indices=None):
return input.numel(), 0
def _conv_flops_compute(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
assert weight.shape[1] * groups == input.shape[1]
batch_size = input.shape[0]
in_channels = input.shape[1]
out_channels = weight.shape[0]
kernel_dims = list(weight.shape[2:])
input_dims = list(input.shape[2:])
length = len(input_dims)
strides = stride if type(stride) is tuple else (stride, ) * length
dilations = dilation if type(dilation) is tuple else (dilation, ) * length
if isinstance(padding, str):
if padding == 'valid':
paddings = (0, ) * length
elif padding == 'same':
paddings = ()
for d, k in zip(dilations, kernel_dims):
total_padding = d * (k - 1)
paddings += (total_padding // 2, )
elif isinstance(padding, tuple):
paddings = padding
else:
paddings = (padding, ) * length
output_dims = []
for idx, input_dim in enumerate(input_dims):
output_dim = (input_dim + 2 * paddings[idx] - (dilations[idx] *
(kernel_dims[idx] - 1) + 1)) // strides[idx] + 1
output_dims.append(output_dim)
filters_per_channel = out_channels // groups
conv_per_position_macs = int(_prod(kernel_dims)) * in_channels * filters_per_channel
active_elements_count = batch_size * int(_prod(output_dims))
overall_conv_macs = conv_per_position_macs * active_elements_count
overall_conv_flops = 2 * overall_conv_macs
bias_flops = 0
if bias is not None:
bias_flops = out_channels * active_elements_count
return int(overall_conv_flops + bias_flops), int(overall_conv_macs)
def _conv_trans_flops_compute(
input,
weight,
bias=None,
stride=1,
padding=0,
output_padding=0,
groups=1,
dilation=1,
):
batch_size = input.shape[0]
in_channels = input.shape[1]
out_channels = weight.shape[1]
kernel_dims = list(weight.shape[2:])
input_dims = list(input.shape[2:])
length = len(input_dims)
paddings = padding if type(padding) is tuple else (padding, ) * length
strides = stride if type(stride) is tuple else (stride, ) * length
dilations = dilation if type(dilation) is tuple else (dilation, ) * length
output_dims = []
for idx, input_dim in enumerate(input_dims):
output_dim = (input_dim + 2 * paddings[idx] - (dilations[idx] *
(kernel_dims[idx] - 1) + 1)) // strides[idx] + 1
output_dims.append(output_dim)
paddings = padding if type(padding) is tuple else (padding, padding)
strides = stride if type(stride) is tuple else (stride, stride)
dilations = dilation if type(dilation) is tuple else (dilation, dilation)
filters_per_channel = out_channels // groups
conv_per_position_macs = int(_prod(kernel_dims)) * in_channels * filters_per_channel
active_elements_count = batch_size * int(_prod(input_dims))
overall_conv_macs = conv_per_position_macs * active_elements_count
overall_conv_flops = 2 * overall_conv_macs
bias_flops = 0
if bias is not None:
bias_flops = out_channels * batch_size * int(_prod(output_dims))
return int(overall_conv_flops + bias_flops), int(overall_conv_macs)
def _batch_norm_flops_compute(
input,
running_mean,
running_var,
weight=None,
bias=None,
training=False,
momentum=0.1,
eps=1e-05,
):
has_affine = weight is not None
if training:
# estimation
return input.numel() * (5 if has_affine else 4), 0
flops = input.numel() * (2 if has_affine else 1)
return flops, 0
def _layer_norm_flops_compute(
input: Tensor,
normalized_shape: List[int],
weight: Optional[Tensor] = None,
bias: Optional[Tensor] = None,
eps: float = 1e-5,
):
has_affine = weight is not None
# estimation
return input.numel() * (5 if has_affine else 4), 0
def _group_norm_flops_compute(input: Tensor,
num_groups: int,
weight: Optional[Tensor] = None,
bias: Optional[Tensor] = None,
eps: float = 1e-5):
has_affine = weight is not None
# estimation
return input.numel() * (5 if has_affine else 4), 0
def _instance_norm_flops_compute(
input: Tensor,
running_mean: Optional[Tensor] = None,
running_var: Optional[Tensor] = None,
weight: Optional[Tensor] = None,
bias: Optional[Tensor] = None,
use_input_stats: bool = True,
momentum: float = 0.1,
eps: float = 1e-5,
):
has_affine = weight is not None
# estimation
return input.numel() * (5 if has_affine else 4), 0
def _upsample_flops_compute(*args, **kwargs):
input = args[0]
size = kwargs.get('size', None)
if size is None and len(args) > 1:
size = args[1]
if size is not None:
if isinstance(size, tuple) or isinstance(size, list):
return int(_prod(size)), 0
else:
return int(size), 0
scale_factor = kwargs.get('scale_factor', None)
if scale_factor is None and len(args) > 2:
scale_factor = args[2]
assert scale_factor is not None, "either size or scale_factor should be defined"
flops = input.numel()
if isinstance(scale_factor, tuple) and len(scale_factor) == len(input):
flops *= int(_prod(scale_factor))
else:
flops *= scale_factor**len(input)
return flops, 0
def _softmax_flops_compute(input, dim=None, _stacklevel=3, dtype=None):
return input.numel(), 0
def _embedding_flops_compute(
input,
weight,
padding_idx=None,
max_norm=None,
norm_type=2.0,
scale_grad_by_freq=False,
sparse=False,
):
return 0, 0
def _dropout_flops_compute(input, p=0.5, training=True, inplace=False):
return 0, 0
def _matmul_flops_compute(input, other, *, out=None):
"""
Count flops for the matmul operation.
"""
macs = _prod(input.shape) * other.shape[-1]
return 2 * macs, macs
def _addmm_flops_compute(input, mat1, mat2, *, beta=1, alpha=1, out=None):
"""
Count flops for the addmm operation.
"""
macs = _prod(mat1.shape) * mat2.shape[-1]
return 2 * macs + _prod(input.shape), macs
def _einsum_flops_compute(equation, *operands):
"""
Count flops for the einsum operation.
"""
equation = equation.replace(" ", "")
input_shapes = [o.shape for o in operands]
# Re-map equation so that same equation with different alphabet
# representations will look the same.
letter_order = OrderedDict((k, 0) for k in equation if k.isalpha()).keys()
mapping = {ord(x): 97 + i for i, x in enumerate(letter_order)}
equation = equation.translate(mapping)
np_arrs = [np.zeros(s) for s in input_shapes]
optim = np.einsum_path(equation, *np_arrs, optimize="optimal")[1]
for line in optim.split("\n"):
if "optimized flop" in line.lower():
flop = int(float(line.split(":")[-1]))
return flop, 0
raise NotImplementedError("Unsupported einsum operation.")
def _tensor_addmm_flops_compute(self, mat1, mat2, *, beta=1, alpha=1, out=None):
"""
Count flops for the tensor addmm operation.
"""
macs = _prod(mat1.shape) * mat2.shape[-1]
return 2 * macs + _prod(self.shape), macs
def _mul_flops_compute(input, other, *, out=None):
return _elementwise_flops_compute(input, other)
def _add_flops_compute(input, other, *, alpha=1, out=None):
return _elementwise_flops_compute(input, other)
def _elementwise_flops_compute(input, other):
if not torch.is_tensor(input):
if torch.is_tensor(other):
return _prod(other.shape), 0
else:
return 1, 0
elif not torch.is_tensor(other):
return _prod(input.shape), 0
else:
dim_input = len(input.shape)
dim_other = len(other.shape)
max_dim = max(dim_input, dim_other)
final_shape = []
for i in range(max_dim):
in_i = input.shape[i] if i < dim_input else 1
ot_i = other.shape[i] if i < dim_other else 1
if in_i > ot_i:
final_shape.append(in_i)
else:
final_shape.append(ot_i)
flops = _prod(final_shape)
return flops, 0
def _attn_flops_compute(q, k, v, *args, **kwargs):
"""
Count flops for the scaled_dot_product_attention operation.
"""
macs = _prod(q.shape) * k.shape[-2]
macs += _prod(q.shape[:-1]) * k.shape[-2] * v.shape[-1]
return 2 * macs, macs
def wrapFunc(func, funcFlopCompute):
oldFunc = func
name = func.__str__
old_functions[name] = oldFunc
def newFunc(*args, **kwds):
flops, macs = funcFlopCompute(*args, **kwds)
if module_flop_count:
module_flop_count[-1].append((name, flops))
if module_mac_count and macs:
module_mac_count[-1].append((name, macs))
return oldFunc(*args, **kwds)
newFunc.__str__ = func.__str__
return newFunc
def _patch_functionals():
# FC
F.linear = wrapFunc(F.linear, _linear_flops_compute)
# convolutions
F.conv1d = wrapFunc(F.conv1d, _conv_flops_compute)
F.conv2d = wrapFunc(F.conv2d, _conv_flops_compute)
F.conv3d = wrapFunc(F.conv3d, _conv_flops_compute)
# conv transposed
F.conv_transpose1d = wrapFunc(F.conv_transpose1d, _conv_trans_flops_compute)
F.conv_transpose2d = wrapFunc(F.conv_transpose2d, _conv_trans_flops_compute)
F.conv_transpose3d = wrapFunc(F.conv_transpose3d, _conv_trans_flops_compute)
# activations
F.relu = wrapFunc(F.relu, _relu_flops_compute)
F.prelu = wrapFunc(F.prelu, _prelu_flops_compute)
F.elu = wrapFunc(F.elu, _elu_flops_compute)
F.leaky_relu = wrapFunc(F.leaky_relu, _leaky_relu_flops_compute)
F.relu6 = wrapFunc(F.relu6, _relu6_flops_compute)
if hasattr(F, "silu"):
F.silu = wrapFunc(F.silu, _silu_flops_compute)
F.gelu = wrapFunc(F.gelu, _gelu_flops_compute)
# Normalizations
F.batch_norm = wrapFunc(F.batch_norm, _batch_norm_flops_compute)
F.layer_norm = wrapFunc(F.layer_norm, _layer_norm_flops_compute)
F.instance_norm = wrapFunc(F.instance_norm, _instance_norm_flops_compute)
F.group_norm = wrapFunc(F.group_norm, _group_norm_flops_compute)
# poolings
F.avg_pool1d = wrapFunc(F.avg_pool1d, _pool_flops_compute)
F.avg_pool2d = wrapFunc(F.avg_pool2d, _pool_flops_compute)
F.avg_pool3d = wrapFunc(F.avg_pool3d, _pool_flops_compute)
F.max_pool1d = wrapFunc(F.max_pool1d, _pool_flops_compute)
F.max_pool2d = wrapFunc(F.max_pool2d, _pool_flops_compute)
F.max_pool3d = wrapFunc(F.max_pool3d, _pool_flops_compute)
F.adaptive_avg_pool1d = wrapFunc(F.adaptive_avg_pool1d, _pool_flops_compute)
F.adaptive_avg_pool2d = wrapFunc(F.adaptive_avg_pool2d, _pool_flops_compute)
F.adaptive_avg_pool3d = wrapFunc(F.adaptive_avg_pool3d, _pool_flops_compute)
F.adaptive_max_pool1d = wrapFunc(F.adaptive_max_pool1d, _pool_flops_compute)
F.adaptive_max_pool2d = wrapFunc(F.adaptive_max_pool2d, _pool_flops_compute)
F.adaptive_max_pool3d = wrapFunc(F.adaptive_max_pool3d, _pool_flops_compute)
# upsample
F.upsample = wrapFunc(F.upsample, _upsample_flops_compute)
F.interpolate = wrapFunc(F.interpolate, _upsample_flops_compute)
# softmax
F.softmax = wrapFunc(F.softmax, _softmax_flops_compute)
# embedding
F.embedding = wrapFunc(F.embedding, _embedding_flops_compute)
# attn
F.scaled_dot_product_attention = wrapFunc(F.scaled_dot_product_attention, _attn_flops_compute)
def _patch_tensor_methods():
torch.matmul = wrapFunc(torch.matmul, _matmul_flops_compute)
torch.Tensor.matmul = wrapFunc(torch.Tensor.matmul, _matmul_flops_compute)
torch.Tensor.__matmul__ = wrapFunc(torch.Tensor.__matmul__, _matmul_flops_compute)
torch.mm = wrapFunc(torch.mm, _matmul_flops_compute)
torch.Tensor.mm = wrapFunc(torch.Tensor.mm, _matmul_flops_compute)
torch.bmm = wrapFunc(torch.bmm, _matmul_flops_compute)
torch.Tensor.bmm = wrapFunc(torch.Tensor.bmm, _matmul_flops_compute)
torch.addmm = wrapFunc(torch.addmm, _addmm_flops_compute)
torch.Tensor.addmm = wrapFunc(torch.Tensor.addmm, _tensor_addmm_flops_compute)
torch.mul = wrapFunc(torch.mul, _mul_flops_compute)
torch.Tensor.mul = wrapFunc(torch.Tensor.mul, _mul_flops_compute)
torch.add = wrapFunc(torch.add, _add_flops_compute)
torch.Tensor.add = wrapFunc(torch.Tensor.add, _add_flops_compute)
torch.einsum = wrapFunc(torch.einsum, _einsum_flops_compute)
torch.baddbmm = wrapFunc(torch.baddbmm, _tensor_addmm_flops_compute)
def _reload_functionals():
# torch.nn.functional does not support importlib.reload()
F.linear = old_functions[F.linear.__str__]
F.conv1d = old_functions[F.conv1d.__str__]
F.conv2d = old_functions[F.conv2d.__str__]
F.conv3d = old_functions[F.conv3d.__str__]
F.conv_transpose1d = old_functions[F.conv_transpose1d.__str__]
F.conv_transpose2d = old_functions[F.conv_transpose2d.__str__]
F.conv_transpose3d = old_functions[F.conv_transpose3d.__str__]
F.relu = old_functions[F.relu.__str__]
F.prelu = old_functions[F.prelu.__str__]
F.elu = old_functions[F.elu.__str__]
F.leaky_relu = old_functions[F.leaky_relu.__str__]
F.relu6 = old_functions[F.relu6.__str__]
if hasattr(F, "silu"):
F.silu = old_functions[F.silu.__str__]
F.gelu = old_functions[F.gelu.__str__]
F.batch_norm = old_functions[F.batch_norm.__str__]
F.layer_norm = old_functions[F.layer_norm.__str__]
F.instance_norm = old_functions[F.instance_norm.__str__]
F.group_norm = old_functions[F.group_norm.__str__]
F.avg_pool1d = old_functions[F.avg_pool1d.__str__]
F.avg_pool2d = old_functions[F.avg_pool2d.__str__]
F.avg_pool3d = old_functions[F.avg_pool3d.__str__]
F.max_pool1d = old_functions[F.max_pool1d.__str__]
F.max_pool2d = old_functions[F.max_pool2d.__str__]
F.max_pool3d = old_functions[F.max_pool3d.__str__]
F.adaptive_avg_pool1d = old_functions[F.adaptive_avg_pool1d.__str__]
F.adaptive_avg_pool2d = old_functions[F.adaptive_avg_pool2d.__str__]
F.adaptive_avg_pool3d = old_functions[F.adaptive_avg_pool3d.__str__]
F.adaptive_max_pool1d = old_functions[F.adaptive_max_pool1d.__str__]
F.adaptive_max_pool2d = old_functions[F.adaptive_max_pool2d.__str__]
F.adaptive_max_pool3d = old_functions[F.adaptive_max_pool3d.__str__]
F.upsample = old_functions[F.upsample.__str__]
F.interpolate = old_functions[F.interpolate.__str__]
F.softmax = old_functions[F.softmax.__str__]
F.embedding = old_functions[F.embedding.__str__]
def _reload_tensor_methods():
torch.matmul = old_functions[torch.matmul.__str__]
torch.Tensor.matmul = old_functions[torch.Tensor.matmul.__str__]
torch.mm = old_functions[torch.mm.__str__]
torch.Tensor.mm = old_functions[torch.Tensor.mm.__str__]
torch.bmm = old_functions[torch.matmul.__str__]
torch.Tensor.bmm = old_functions[torch.Tensor.bmm.__str__]
torch.addmm = old_functions[torch.addmm.__str__]
torch.Tensor.addmm = old_functions[torch.Tensor.addmm.__str__]
torch.mul = old_functions[torch.mul.__str__]
torch.Tensor.mul = old_functions[torch.Tensor.mul.__str__]
torch.add = old_functions[torch.add.__str__]
torch.Tensor.add = old_functions[torch.Tensor.add.__str__]
torch.einsum = old_functions[torch.einsum.__str__]
torch.baddbmm = old_functions[torch.baddbmm.__str__]
def _rnn_flops(flops, rnn_module, w_ih, w_hh, input_size):
gates_size = w_ih.shape[0]
# matrix matrix mult ih state and internal state
flops += 2 * w_ih.shape[0] * w_ih.shape[1] - gates_size
# matrix matrix mult hh state and internal state
flops += 2 * w_hh.shape[0] * w_hh.shape[1] - gates_size
if isinstance(rnn_module, (nn.RNN, nn.RNNCell)):
# add both operations
flops += rnn_module.hidden_size
elif isinstance(rnn_module, (nn.GRU, nn.GRUCell)):
# hadamard of r
flops += rnn_module.hidden_size
# adding operations from both states
flops += rnn_module.hidden_size * 3
# last two hadamard _product and add
flops += rnn_module.hidden_size * 3
elif isinstance(rnn_module, (nn.LSTM, nn.LSTMCell)):
# adding operations from both states
flops += rnn_module.hidden_size * 4
# two hadamard _product and add for C state
flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size
# final hadamard
flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size
return flops
def _rnn_forward_hook(rnn_module, input, output):
flops = 0
# input is a tuple containing a sequence to process and (optionally) hidden state
inp = input[0]
batch_size = inp.shape[0]
seq_length = inp.shape[1]
num_layers = rnn_module.num_layers
for i in range(num_layers):
w_ih = rnn_module.__getattr__("weight_ih_l" + str(i))
w_hh = rnn_module.__getattr__("weight_hh_l" + str(i))
if i == 0:
input_size = rnn_module.input_size
else:
input_size = rnn_module.hidden_size
flops = _rnn_flops(flops, rnn_module, w_ih, w_hh, input_size)
if rnn_module.bias:
b_ih = rnn_module.__getattr__("bias_ih_l" + str(i))
b_hh = rnn_module.__getattr__("bias_hh_l" + str(i))
flops += b_ih.shape[0] + b_hh.shape[0]
flops *= batch_size
flops *= seq_length
if rnn_module.bidirectional:
flops *= 2
rnn_module.__flops__ += int(flops)
def _rnn_cell_forward_hook(rnn_cell_module, input, output):
flops = 0
inp = input[0]
batch_size = inp.shape[0]
w_ih = rnn_cell_module.__getattr__("weight_ih")
w_hh = rnn_cell_module.__getattr__("weight_hh")
input_size = inp.shape[1]
flops = _rnn_flops(flops, rnn_cell_module, w_ih, w_hh, input_size)
if rnn_cell_module.bias:
b_ih = rnn_cell_module.__getattr__("bias_ih")
b_hh = rnn_cell_module.__getattr__("bias_hh")
flops += b_ih.shape[0] + b_hh.shape[0]
flops *= batch_size
rnn_cell_module.__flops__ += int(flops)
MODULE_HOOK_MAPPING = {
# RNN
nn.RNN: _rnn_forward_hook,
nn.GRU: _rnn_forward_hook,
nn.LSTM: _rnn_forward_hook,
nn.RNNCell: _rnn_cell_forward_hook,
nn.LSTMCell: _rnn_cell_forward_hook,
nn.GRUCell: _rnn_cell_forward_hook,
}
def macs_to_string(macs, units=None, precision=DEFAULT_PRECISION):
return f"{number_to_string(macs, units=units, precision=precision)}MACs"
def number_to_string(num, units=None, precision=DEFAULT_PRECISION):
if units is None:
if num >= 1e12:
magnitude, units = 1e12, "T"
elif num >= 1e9:
magnitude, units = 1e9, "G"
elif num >= 1e6:
magnitude, units = 1e6, "M"
elif num >= 1e3:
magnitude, units = 1e3, "K"
elif num >= 1 or num == 0:
magnitude, units = 1, ""
elif num >= 1e-3:
magnitude, units = 1e-3, "m"
else:
magnitude, units = 1e-6, "u"
else:
if units == "T":
magnitude = 1e12
elif units == "G":
magnitude = 1e9
elif units == "M":
magnitude = 1e6
elif units == "K":
magnitude = 1e3
elif units == "m":
magnitude = 1e-3
elif units == "u":
magnitude = 1e-6
else:
magnitude = 1
return f"{round(num / magnitude, precision):g} {units}"
def flops_to_string(flops, units=None, precision=DEFAULT_PRECISION):
return f"{number_to_string(flops, units=units, precision=precision)}FLOPS"
def bytes_to_string(b, units=None, precision=DEFAULT_PRECISION):
return f"{number_to_string(b, units=units, precision=precision)}B"
def params_to_string(params_num, units=None, precision=DEFAULT_PRECISION):
units = units.replace("B", "G") if units else units
return number_to_string(params_num, units=units, precision=precision).replace("G", "B").strip()
def duration_to_string(duration, units=None, precision=DEFAULT_PRECISION):
return f"{number_to_string(duration, units=units, precision=precision)}s"
# can not iterate over all submodules using self.model.modules()
# since modules() returns duplicate modules only once
def get_module_flops(module):
sum = module.__flops__
# iterate over immediate children modules
for child in module.children():
sum += get_module_flops(child)
return sum
def get_module_macs(module):
sum = module.__macs__
# iterate over immediate children modules
for child in module.children():
sum += get_module_macs(child)
return sum
def get_module_duration(module):
duration = module.__duration__
if duration == 0: # e.g. ModuleList
for m in module.children():
duration += get_module_duration(m)
return duration
[docs]def get_model_profile(model,
input_shape=None,
args=[],
kwargs={},
print_profile=True,
detailed=True,
module_depth=-1,
top_modules=1,
warm_up=1,
as_string=True,
output_file=None,
ignore_modules=None,
mode='forward'):
"""Returns the total floating-point operations, MACs, and parameters of a model.
Example:
.. code-block:: python
model = torchvision.models.alexnet()
batch_size = 256
flops, macs, params = get_model_profile(model=model, input_shape=(batch_size, 3, 224, 224)))
Args:
model ([torch.nn.Module]): the PyTorch model to be profiled.
input_shape (tuple): input shape to the model. If specified, the model takes a tensor with this shape as the only positional argument.
args (list): list of positional arguments to the model.
kwargs (dict): dictionary of keyword arguments to the model.
print_profile (bool, optional): whether to print the model profile. Defaults to True.
detailed (bool, optional): whether to print the detailed model profile. Defaults to True.
module_depth (int, optional): the depth into the nested modules. Defaults to -1 (the inner most modules).
top_modules (int, optional): the number of top modules to print in the aggregated profile. Defaults to 3.
warm_up (int, optional): the number of warm-up steps before measuring the latency of each module. Defaults to 1.
as_string (bool, optional): whether to print the output as string. Defaults to True.
output_file (str, optional): path to the output file. If None, the profiler prints to stdout.
ignore_modules ([type], optional): the list of modules to ignore during profiling. Defaults to None.
Returns:
The number of floating-point operations, multiply-accumulate operations (MACs), and parameters in the model.
"""
assert isinstance(model, nn.Module), "model must be a PyTorch module"
prof = FlopsProfiler(model)
model.eval()
if input_shape is not None:
assert type(input_shape) is tuple, "input_shape must be a tuple"
assert len(input_shape) >= 1, "input_shape must have at least one element"
try:
input = torch.ones(()).new_empty(
(*input_shape, ),
dtype=next(model.parameters()).dtype,
device=next(model.parameters()).device,
)
except StopIteration:
input = torch.ones(()).new_empty((*input_shape, ))
args = [input]
assert (len(args) > 0) or (len(kwargs) > 0), "args and/or kwargs must be specified if input_shape is None"
logger.info("Flops profiler warming-up...")
for _ in range(warm_up):
if kwargs:
if mode == 'forward':
_ = model(*args, **kwargs)
if mode == 'generate':
_ = model.generate(*args, **kwargs)
else:
if mode == 'forward':
_ = model(*args)
if mode == 'generate':
_ = model.generate(*args)
prof.start_profile(ignore_list=ignore_modules)
if kwargs:
if mode == 'forward':
_ = model(*args, **kwargs)
if mode == 'generate':
_ = model.generate(*args, **kwargs)
else:
if mode == 'forward':
_ = model(*args)
if mode == 'generate':
_ = model.generate(*args)
flops = prof.get_total_flops()
macs = prof.get_total_macs()
params = prof.get_total_params()
if print_profile:
prof.print_model_profile(profile_step=warm_up,
module_depth=module_depth,
top_modules=top_modules,
detailed=detailed,
output_file=output_file)
prof.end_profile()
if as_string:
return number_to_string(flops), macs_to_string(macs), params_to_string(params)
return flops, macs, params