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- # 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
- 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
- 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
- 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)
- 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)
- 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")
- 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
- 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
- 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
- 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
- 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()
- 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 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)
- def _patch_tensor_methods():
- torch.matmul = wrapFunc(torch.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 += m.__duration__
- return duration
- 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
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