Source code for deepspeed.profiling.flops_profiler.profiler

import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from typing import Callable, List, Optional, Tuple
from collections import OrderedDict
import numpy as np

Tensor = torch.Tensor

module_flop_count = []
module_mac_count = []
old_functions = {}


[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): self.model = model self.ds_engine = ds_engine 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. """ 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: 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([]) 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() module.__post_hook_handle__ = module.register_forward_hook(post_hook) def start_time_hook(module, input): module.__start_time__ = time.time() module.__start_time_hook_handle__ = module.register_forward_pre_hook( start_time_hook) def end_time_hook(module, input, output): module.__duration__ += time.time() - module.__start_time__ 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 add_or_reset_attrs(module): module.__flops__ = 0 module.__macs__ = 0 module.__params__ = sum(p.numel() for p in module.parameters() if p.requires_grad) 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, "__start_time__"): del module.__start_time__ if hasattr(module, "__duration__"): del module.__duration__ self.model.apply(remove_profile_attrs)
[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 num_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 parameters of the model. Args: as_string (bool, optional): whether to output the parameters as string. Defaults to False. Returns: The number of parameters in the model. """ return params_to_string( self.model.__params__) if as_string else self.model.__params__
[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 from os import path original_stdout = None f = None if output_file and output_file != "": dir_path = os.path.dirname(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() 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:\ndata parallel size (dp_size), model parallel size(mp_size),\nnumber of parameters (params), number of multiply-accumulate operations(MACs),\nnumber of floating-point operations (flops), floating-point operations per second (FLOPS),\nfwd latency (forward propagation latency), bwd latency (backward propagation latency),\nstep (weights update latency), iter latency (sum of fwd, bwd and step latency)\n" ) if self.ds_engine: print('{:<60} {:<8}'.format('world size: ', self.ds_engine.world_size)) print('{:<60} {:<8}'.format('data parallel size: ', self.ds_engine.dp_world_size)) print('{:<60} {:<8}'.format('model parallel size: ', self.ds_engine.mp_world_size)) print('{:<60} {:<8}'.format( 'batch size per GPU: ', self.ds_engine.train_micro_batch_size_per_gpu())) print('{:<60} {:<8}'.format('params per gpu: ', params_to_string(total_params))) print('{:<60} {:<8}'.format( 'params of model = params per GPU * mp_size: ', params_to_string(total_params * (self.ds_engine.mp_world_size) if self.ds_engine else 1))) print('{:<60} {:<8}'.format('fwd MACs per GPU: ', macs_to_string(total_macs))) print('{:<60} {:<8}'.format('fwd flops per GPU: ', num_to_string(total_flops))) print('{:<60} {:<8}'.format( 'fwd flops of model = fwd flops per GPU * mp_size: ', num_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').elapsed(False) print('{:<60} {:<8}'.format('fwd latency: ', duration_to_string(fwd_latency))) print('{:<60} {:<8}'.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_latency = self.ds_engine.timers('backward').elapsed(False) step_latency = self.ds_engine.timers('step').elapsed(False) print('{:<60} {:<8}'.format('bwd latency: ', duration_to_string(bwd_latency))) print('{:<60} {:<8}'.format( 'bwd FLOPS per GPU = 2 * fwd flops per GPU / bwd latency: ', flops_to_string(2 * total_flops / bwd_latency))) print('{:<60} {:<8}'.format( 'fwd+bwd FLOPS per GPU = 3 * fwd flops per GPU / (fwd+bwd latency): ', flops_to_string(3 * total_flops / (fwd_latency + bwd_latency)))) print('{:<60} {:<8}'.format('step latency: ', duration_to_string(step_latency))) iter_latency = fwd_latency + bwd_latency + step_latency print('{:<60} {:<8}'.format('iter latency: ', duration_to_string(iter_latency))) print('{:<60} {:<8}'.format( 'FLOPS per GPU = 3 * fwd flops per GPU / iter latency: ', flops_to_string(3 * total_flops / iter_latency))) samples_per_iter = self.ds_engine.train_micro_batch_size_per_gpu( ) * self.ds_engine.world_size print('{:<60} {:<8.2f}'.format('samples/second: ', samples_per_iter / iter_latency)) def flops_repr(module): params = module.__params__ flops = get_module_flops(module) macs = get_module_macs(module) items = [ params_to_string(params), "{:.2%} Params".format(params / total_params), macs_to_string(macs), "{:.2%} MACs".format(0.0 if total_macs == 0 else macs / total_macs), ] duration = get_module_duration(module) items.append(duration_to_string(duration)) items.append( "{:.2%} latency".format(0.0 if total_duration == 0 else duration / total_duration)) items.append(flops_to_string(0.0 if duration == 0 else flops / duration)) items.append(module.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__ 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 = torch.numel(input) * out_features return 2 * macs, macs def _relu_flops_compute(input, inplace=False): return torch.numel(input), 0 def _prelu_flops_compute(input: Tensor, weight: Tensor): return torch.numel(input), 0 def _elu_flops_compute(input: Tensor, alpha: float = 1.0, inplace: bool = False): return torch.numel(input), 0 def _leaky_relu_flops_compute(input: Tensor, negative_slope: float = 0.01, inplace: bool = False): return torch.numel(input), 0 def _relu6_flops_compute(input: Tensor, inplace: bool = False): return torch.numel(input), 0 def _silu_flops_compute(input: Tensor, inplace: bool = False): return torch.numel(input), 0 def _gelu_flops_compute(input): return torch.numel(input), 0 def _pool_flops_compute( input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None, ): return torch.numel(input), 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) 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) 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[0] 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 torch.numel(input) * (5 if has_affine else 4), 0 flops = torch.numel(input) * (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 torch.numel(input) * (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 torch.numel(input) * (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 torch.numel(input) * (5 if has_affine else 4), 0 def _upsample_flops_compute(input, size=None, scale_factor=None, mode="nearest", align_corners=None): if size is not None: if isinstance(size, tuple): return int(_prod(size)), 0 else: return int(size), 0 assert scale_factor is not None, "either size or scale_factor should be defined" flops = torch.numel(input) 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 torch.numel(input), 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"): print(line.lower()) 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 _elementwise_flops_compute(input, other, *, out=None): 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.__name__ 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.__name__ = func.__name__ 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.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, _elementwise_flops_compute) torch.Tensor.mul = wrapFunc(torch.Tensor.mul, _elementwise_flops_compute) torch.add = wrapFunc(torch.add, _elementwise_flops_compute) torch.Tensor.add = wrapFunc(torch.Tensor.add, _elementwise_flops_compute) torch.einsum = wrapFunc(torch.einsum, _einsum_flops_compute) def _reload_functionals(): # torch.nn.functional does not support importlib.reload() F.linear = old_functions[F.linear.__name__] F.conv1d = old_functions[F.conv1d.__name__] F.conv2d = old_functions[F.conv2d.__name__] F.conv3d = old_functions[F.conv3d.__name__] F.conv_transpose1d = old_functions[F.conv_transpose1d.__name__] F.conv_transpose2d = old_functions[F.conv_transpose2d.__name__] F.conv_transpose3d = old_functions[F.conv_transpose3d.__name__] F.relu = old_functions[F.relu.__name__] F.prelu = old_functions[F.prelu.__name__] F.elu = old_functions[F.elu.__name__] F.leaky_relu = old_functions[F.leaky_relu.__name__] F.relu6 = old_functions[F.relu6.__name__] F.batch_norm = old_functions[F.batch_norm.__name__] F.avg_pool1d = old_functions[F.avg_pool1d.__name__] F.avg_pool2d = old_functions[F.avg_pool2d.__name__] F.avg_pool3d = old_functions[F.avg_pool3d.__name__] F.max_pool1d = old_functions[F.max_pool1d.__name__] F.max_pool2d = old_functions[F.max_pool2d.__name__] F.max_pool3d = old_functions[F.max_pool3d.__name__] F.adaptive_avg_pool1d = old_functions[F.adaptive_avg_pool1d.__name__] F.adaptive_avg_pool2d = old_functions[F.adaptive_avg_pool2d.__name__] F.adaptive_avg_pool3d = old_functions[F.adaptive_avg_pool3d.__name__] F.adaptive_max_pool1d = old_functions[F.adaptive_max_pool1d.__name__] F.adaptive_max_pool2d = old_functions[F.adaptive_max_pool2d.__name__] F.adaptive_max_pool3d = old_functions[F.adaptive_max_pool3d.__name__] F.upsample = old_functions[F.upsample.__name__] F.interpolate = old_functions[F.interpolate.__name__] F.softmax = old_functions[F.softmax.__name__] F.embedding = old_functions[F.embedding.__name__] def _reload_tensor_methods(): torch.matmul = old_functions[torch.matmul.__name__] def _rnn_flops(flops, rnn_module, w_ih, w_hh, input_size): # matrix matrix mult ih state and internal state flops += w_ih.shape[0] * w_ih.shape[1] # matrix matrix mult hh state and internal state flops += w_hh.shape[0] * w_hh.shape[1] 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 num_to_string(num, precision=2): if num // 10**9 > 0: return str(round(num / 10.0**9, precision)) + " G" elif num // 10**6 > 0: return str(round(num / 10.0**6, precision)) + " M" elif num // 10**3 > 0: return str(round(num / 10.0**3, precision)) + " K" else: return str(num) def macs_to_string(macs, units=None, precision=2): if units is None: if macs // 10**9 > 0: return str(round(macs / 10.0**9, precision)) + " GMACs" elif macs // 10**6 > 0: return str(round(macs / 10.0**6, precision)) + " MMACs" elif macs // 10**3 > 0: return str(round(macs / 10.0**3, precision)) + " KMACs" else: return str(macs) + " MACs" else: if units == "GMACs": return str(round(macs / 10.0**9, precision)) + " " + units elif units == "MMACs": return str(round(macs / 10.0**6, precision)) + " " + units elif units == "KMACs": return str(round(macs / 10.0**3, precision)) + " " + units else: return str(macs) + " MACs" def number_to_string(num, units=None, precision=2): if units is None: if num // 10**9 > 0: return str(round(num / 10.0**9, precision)) + " G" elif num // 10**6 > 0: return str(round(num / 10.0**6, precision)) + " M" elif num // 10**3 > 0: return str(round(num / 10.0**3, precision)) + " K" else: return str(num) + " " else: if units == "G": return str(round(num / 10.0**9, precision)) + " " + units elif units == "M": return str(round(num / 10.0**6, precision)) + " " + units elif units == "K": return str(round(num / 10.0**3, precision)) + " " + units else: return str(num) + " " def flops_to_string(flops, units=None, precision=2): if units is None: if flops // 10**12 > 0: return str(round(flops / 10.0**12, precision)) + " TFLOPS" if flops // 10**9 > 0: return str(round(flops / 10.0**9, precision)) + " GFLOPS" elif flops // 10**6 > 0: return str(round(flops / 10.0**6, precision)) + " MFLOPS" elif flops // 10**3 > 0: return str(round(flops / 10.0**3, precision)) + " KFLOPS" else: return str(flops) + " FLOPS" else: if units == "TFLOPS": return str(round(flops / 10.0**12, precision)) + " " + units if units == "GFLOPS": return str(round(flops / 10.0**9, precision)) + " " + units elif units == "MFLOPS": return str(round(flops / 10.0**6, precision)) + " " + units elif units == "KFLOPS": return str(round(flops / 10.0**3, precision)) + " " + units else: return str(flops) + " FLOPS" def params_to_string(params_num, units=None, precision=2): if units is None: if params_num // 10**6 > 0: return str(round(params_num / 10**6, 2)) + " M" elif params_num // 10**3: return str(round(params_num / 10**3, 2)) + " k" else: return str(params_num) else: if units == "M": return str(round(params_num / 10.0**6, precision)) + " " + units elif units == "K": return str(round(params_num / 10.0**3, precision)) + " " + units else: return str(params_num) def duration_to_string(duration, units=None, precision=2): if units is None: if duration > 1: return str(round(duration, precision)) + " s" elif duration * 10**3 > 1: return str(round(duration * 10**3, precision)) + " ms" elif duration * 10**6 > 1: return str(round(duration * 10**6, precision)) + " us" else: return str(duration) else: if units == "us": return str(round(duration * 10.0**6, precision)) + " " + units elif units == "ms": return str(round(duration * 10.0**3, precision)) + " " + units else: return str(round(duration, 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
[docs]def get_model_profile( model, input_res, input_constructor=None, print_profile=True, detailed=True, module_depth=-1, top_modules=1, warm_up=1, as_string=True, output_file=None, ignore_modules=None, ): """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_res= (batch_size, 3, 224, 224))) Args: model ([torch.nn.Module]): the PyTorch model to be profiled. input_res (list): input shape or input to the input_constructor input_constructor (func, optional): input constructor. If specified, the constructor is applied to input_res and the constructor output is used as the input to the model. Defaults to None. 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 type(input_res) is tuple assert len(input_res) >= 1 assert isinstance(model, nn.Module) prof = FlopsProfiler(model) model.eval() for _ in range(warm_up): if input_constructor: input = input_constructor(input_res) _ = model(**input) else: try: batch = torch.ones(()).new_empty( (*input_res, ), dtype=next(model.parameters()).dtype, device=next(model.parameters()).device, ) except StopIteration: batch = torch.ones(()).new_empty((*input_res, )) _ = model(batch) prof.start_profile(ignore_list=ignore_modules) if input_constructor: input = input_constructor(input_res) _ = model(**input) else: try: batch = torch.ones(()).new_empty( (*input_res, ), dtype=next(model.parameters()).dtype, device=next(model.parameters()).device, ) except StopIteration: batch = torch.ones(()).new_empty((*input_res, )) _ = model(batch) 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