Flops Profiler


The flops profiler in DeepSpeed profiles the forward pass of a model and measures its parameters, latency, and floating point operations. 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 without user code changes. To use the flops profiler outside of the DeepSpeed runtime, one can simply install DeepSpeed and import the flops_profiler package to use the APIs directly.

Please see the Flops Profiler tutorial for usage details.

Flops Profiler

class deepspeed.profiling.flops_profiler.profiler.FlopsProfiler(model, ds_engine=None)[source]

Bases: 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:

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.

Parameters:object (torch.nn.Module) – The PyTorch model to profile.
start_profile(ignore_list=None)[source]

Starts profiling.

Extra attributes are added recursively to all the modules and the profiled torch.nn.functionals are monkey patched.

Parameters:ignore_list (list, optional) – the list of modules to ignore while profiling. Defaults to None.
stop_profile()[source]

Stop profiling.

All torch.nn.functionals are restored to their originals.

reset_profile()[source]

Resets the profiling.

Adds or resets the extra attributes.

end_profile()[source]

Ends profiling.

The added attributes and handles are removed recursively on all the modules.

get_total_flops(as_string=False)[source]

Returns the total flops of the model.

Parameters: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.
get_total_duration(as_string=False)[source]

Returns the total duration of the model forward pass.

Parameters:as_string (bool, optional) – whether to output the duration as string. Defaults to False.
Returns:The latency of the model forward pass.
get_total_params(as_string=False)[source]

Returns the total parameters of the model.

Parameters:as_string (bool, optional) – whether to output the parameters as string. Defaults to False.
Returns:The number of parameters in the model.
print_model_profile(profile_step=1, module_depth=-1, top_modules=1, detailed=True, output_file=None)[source]

Prints the model graph with the measured profile attached to each module.

Parameters:
  • 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.
print_model_aggregated_profile(module_depth=-1, top_modules=1)[source]

Prints the names of the top top_modules modules in terms of aggregated time, flops, and parameters at depth module_depth.

Parameters:
  • 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.
deepspeed.profiling.flops_profiler.profiler.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)[source]

Returns the total MACs and parameters of a model.

Example:

model = torchvision.models.alexnet()
batch_size = 256
macs, params = get_model_profile(model=model, input_res= (batch_size, 3, 224, 224)))
Parameters:
  • 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 multiply-accumulate operations (MACs) and parameters in the model.