squash latest flops profiling changes (#1) (#664)

Co-authored-by: Cheng Li <pistasable@gmail.com>
Co-authored-by: Jeff Rasley <jerasley@microsoft.com>

deepspeed/profiling/config.py

+"""
+Copyright (c) Microsoft Corporation
+Licensed under the MIT license.
+"""
+
+from deepspeed.runtime.config_utils import get_scalar_param
+from deepspeed.profiling.constants import *
+
+
+class DeepSpeedFlopsProfilerConfig(object):
+    def __init__(self, param_dict):
+        """
+        docstring
+        """
+        super(DeepSpeedFlopsProfilerConfig, self).__init__()
+
+        self.enabled = None
+        self.start_step = None
+        self.end_step = None
+        self.module_depth = None
+        self.top_modules = None
+
+        if FLOPS_PROFILER in param_dict.keys():
+            flops_profiler_dict = param_dict[FLOPS_PROFILER]
+        else:
+            flops_profiler_dict = {}
+
+        self._initialize(flops_profiler_dict)
+
+    def _initialize(self, flops_profiler_dict):
+        """
+        docstring
+        """
+        self.enabled = get_scalar_param(flops_profiler_dict,
+                                        FLOPS_PROFILER_ENABLED,
+                                        FLOPS_PROFILER_ENABLED_DEFAULT)
+
+        self.start_step = get_scalar_param(flops_profiler_dict,
+                                           FLOPS_PROFILER_START_STEP,
+                                           FLOPS_PROFILER_START_STEP_DEFAULT)
+
+        self.end_step = get_scalar_param(flops_profiler_dict,
+                                         FLOPS_PROFILER_END_STEP,
+                                         FLOPS_PROFILER_END_STEP_DEFAULT)
+
+        self.module_depth = get_scalar_param(flops_profiler_dict,
+                                             FLOPS_PROFILER_MODULE_DEPTH,
+                                             FLOPS_PROFILER_MODULE_DEPTH_DEFAULT)
+
+        self.top_modules = get_scalar_param(flops_profiler_dict,
+                                            FLOPS_PROFILER_TOP_MODULES,
+                                            FLOPS_PROFILER_TOP_MODULES_DEFAULT)

deepspeed/profiling/constants.py

+"""
+Copyright (c) Microsoft Corporation
+Licensed under the MIT license.
+"""
+
+#########################################
+# flops profiler
+#########################################
+# Flops profiler. By default, this feature is not enabled.
+# Users can configure in ds_config.json as below example:
+FLOPS_PROFILER_FORMAT = '''
+flops profiler should be enabled as:
+"session_params": {
+  "flops_profiler": {
+    "enalbe": [true|false],
+    "start_step": 5,
+    "end_step": 6,
+    "module_depth": -1,
+    "top_modules": 3,
+    }
+}
+'''
+
+FLOPS_PROFILER = "flops_profiler"
+
+FLOPS_PROFILER_ENABLED = "enabled"
+FLOPS_PROFILER_ENABLED_DEFAULT = False
+
+FLOPS_PROFILER_START_STEP = "start_step"
+FLOPS_PROFILER_START_STEP_DEFAULT = 5
+
+FLOPS_PROFILER_END_STEP = "end_step"
+FLOPS_PROFILER_END_STEP_DEFAULT = FLOPS_PROFILER_START_STEP_DEFAULT + 1
+
+FLOPS_PROFILER_MODULE_DEPTH = "module_depth"
+FLOPS_PROFILER_MODULE_DEPTH_DEFAULT = -1
+
+FLOPS_PROFILER_TOP_MODULES = "top_modules"
+FLOPS_PROFILER_TOP_MODULES_DEFAULT = 3

deepspeed/profiling/flops_profiler/README.md

+# flops-profiler
+
+> Measures the time, number of estimated flops 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 time, 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 time, flops, and parameters at depth l with k and l specified by the user. The output profile is computed for each batch of input. If multiple forward passes are specified by the user to caputre (in the case where the model have different paths or for more accurate timing), the average profile of the multiple batches is taken.
+
+The flops estimation is partly inspired by [ptflops](https://github.com/sovrasov/flops-counter.pytorch) with the major difference being that flops-profiler captures `torch.nn.functional` invoked in a module to estimate the flops, thus allowing customized modules in the model (e.g. `ParallelTransformerLayerworks, ParallelSelfAttention, RowParallelLinear, etc.` in [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)). The flops-profiler also supports flops computation at module level (for RNNs).
+
+For models running on multi-node or multi-gpu, only the model parallelism affects the number of flops and parameters (e.g. `--model-parallel-size` in [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)), i.e., model_parallel_size _ flops = total_flops, model_parallel_size _ parameters = total_parameters. The number of gpus or nodes does not affect the output profile.
+
+Below is an example output for LeNet5 with batch size 1024 on a V100 GPU:
+
+```
+LeNet5(
+  61.71 k, 100.00% Params, 439.55 MMACs, 100.00% MACs, 25.62 ms, 100.00% time, 0.034 TFLOPS,
+  (feature_extractor): Sequential(
+    50.69 k, 82.15% Params, 428.37 MMACs, 97.46% MACs, 18.41 ms, 71.85% time, 0.047 TFLOPS,
+    (0): Conv2d(156, 0.25% Params, 125.24 MMACs, 28.49% MACs, 10.56 ms, 41.21% time, 0.024 TFLOPS, 1, 6, kernel_size=(5, 5), stride=(1, 1))
+    (1): Tanh(0, 0.00% Params, 0.0 MACs, 0.00% MACs, 2.25 ms, 8.79% time, 0.0 TFLOPS, )
+    (2): AvgPool2d(0, 0.00% Params, 4.82 MMACs, 1.10% MACs, 2.47 ms, 9.63% time, 0.0039 TFLOPS, kernel_size=2, stride=2, padding=0)
+    (3): Conv2d(2.42 k, 3.92% Params, 247.4 MMACs, 56.28% MACs, 1.08 ms, 4.23% time, 0.46 TFLOPS, 6, 16, kernel_size=(5, 5), stride=(1, 1))
+    (4): Tanh(0, 0.00% Params, 0.0 MACs, 0.00% MACs, 497.39 us, 1.94% time, 0.0 TFLOPS, )
+    (5): AvgPool2d(0, 0.00% Params, 1.64 MMACs, 0.37% MACs, 758.24 us, 2.96% time, 0.0043 TFLOPS, kernel_size=2, stride=2, padding=0)
+    (6): Conv2d(48.12 k, 77.98% Params, 49.27 MMACs, 11.21% MACs, 606.35 us, 2.37% time, 0.16 TFLOPS, 16, 120, kernel_size=(5, 5), stride=(1, 1))
+    (7): Tanh(0, 0.00% Params, 0.0 MACs, 0.00% MACs, 68.86 us, 0.27% time, 0.0 TFLOPS, )
+  )
+  (classifier): Sequential(
+    11.01 k, 17.85% Params, 11.18 MMACs, 2.54% MACs, 7.03 ms, 27.43% time, 0.0032 TFLOPS,
+    (0): Linear(10.16 k, 16.47% Params, 10.32 MMACs, 2.35% MACs, 2.71 ms, 10.57% time, 0.0076 TFLOPS, in_features=120, out_features=84, bias=True)
+    (1): Tanh(0, 0.00% Params, 0.0 MACs, 0.00% MACs, 78.77 us, 0.31% time, 0.0 TFLOPS, )
+    (2): Linear(850, 1.38% Params, 860.16 KMACs, 0.20% MACs, 4.17 ms, 16.27% time, 0.00041 TFLOPS, in_features=84, out_features=10, bias=True)
+  )
+)
+Top 3 modules in flops at depth 2 are {'Conv2d': '421.91 MMACs', 'Linear': '11.18 MMACs', 'AvgPool2d': '6.46 MMACs'}
+Top 3 modules in params at depth 2 are {'Conv2d': '50.69 k', 'Linear': '11.01 k', 'Tanh': '0'}
+Top 3 modules in time at depth 2 are {'Conv2d': '12.25 ms', 'Linear': '6.88 ms', 'AvgPool2d': '3.23 ms'}
+Batch size:                     1024
+Number of multiply-adds:        439.55 MMACs
+Number of parameters:           61.71 k
+Number of steps profiled:       10
+```
+
+## Installation
+
+The profiler is an integral part of DeepSpeed and can be installed by
+
+```
+pip install deepspeed
+```
+
+Refer to the [installaiton of DeepSpeed](https://www.deepspeed.ai/getting-started/#installation) for more information.
+
+## Usage
+
+### With the DeepSpeed runtime
+
+If using DeepSpeed for model training, no explict API calls are needed to use the flops-profiler.
+
+In DeepSpeed config file, specify:
+
+```python
+  ds_config = {
+    ...# other deepspeed configs
+    "flops_profiler": {
+        "enabled": True,
+        "start_step": 2,
+        "end_step": 3,
+        "module_depth": -1,
+        "top_modules": 3,
+    },
+  }
+```
+- `"enabled": true` to enable the flops-profiler.
+- `"start_step": 5` to start the profiler at step 5. Note that warm-up is necessary for getting accurate timing information.
+- `"end_step": 6` to end the profiler at step 6. Note that `end_step > start_step`.
+- `"module_depth": -1` to print aggregated module information at the maximum depth (innermost modules). Can be set to any positive number, caped by the maximum depth of the model.
+- `"top_modules": 3`to set the number of top modules to print aggregated profile
+
+An example is given in [test_flops_profiler](tests/unit/test_flops_profiler.py).
+### Without the DeepSpeed runtime
+
+The flops-profiler can be used as a standalone package outside of the deepspeed runtime.
+
+#### Use the low-level APIs to profile the forward pass in the existing model training workflow
+
+- `start_profile` - starts profiling
+- `get_total_flops` - returns the total number of flops
+- `get_total_params` - returns the total number of params
+- `get_total_duration` - returns the total duration of the model forward pass
+- `get_total_steps` - returns the total number of steps (or input batches) profiled.
+- `print_model_profile` - prints the profile annotated
+- `print_model_aggregated_profile` - prints the aggregated profile for the top modules
+- `end_profile` - ends profiling and cleans up, invoked at the end of the profiling and before any printing method.
+
+`flops_to_string`, `params_to_string`, `duration_to_string` are utility functions to convert the metric number to string.
+
+Below is an example of this usage in a typical training workflow.
+
+```python
+from deepspeed.profiling.flops_profiler.profiler import FlopsProfiler
+
+model = Model()
+profiler = FlopsProfiler(model)
+
+start_step = 5
+end_step = 10
+assert (end_step > start_step), "should end profiling after start profiling"
+print_profile = True
+pring_aggregated_profile = True
+
+for step, batch in enumerate(data_loader):
+  # start profiling at training step "profile_step"
+  if step == start_step:
+    profiler.start_profile()
+
+  # end profiling and print output at training step "profile_step"
+  if model == end_step: # if using multi nodes, check global_rank == 0 as well
+    flops = profiler.get_total_flops()
+    params = profiler.get_total_flops()
+    duration = profiler.get_total_duration()
+    steps = profiler.get_total_steps()
+    if print_profile:
+        profiler.print_model_profile()
+    if print_aggregated_profile:
+        profiler.print_model_aggregated_profile(module_depth=-1, top_modules=3)
+    profiler.end_profile()
+    print(flops, params, duration, step)
+
+  # forward() method
+  loss = model(batch)
+
+  # runs backpropagation
+  loss.backward()
+
+  # weight update
+  optimizer.step()
+```
+
+#### Use the high level-API and run the model inference for profiling purpose
+
+Examples of this usage are given below.
+
+##### Classification model example:
+
+```python
+import argparse
+import sys
+import torch
+import torchvision.models as models
+from deepspeed.profiling.flops_profiler import get_model_profile
+
+pt_models = {
+    'resnet18': models.resnet18,
+    'resnet50': models.resnet50,
+    'alexnet': models.alexnet,
+    'vgg16': models.vgg16,
+    'squeezenet': models.squeezenet1_0,
+    'densenet': models.densenet161,
+    'inception': models.inception_v3
+}
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='flops-profiler example script')
+    parser.add_argument('--device',
+                        type=int,
+                        default=0,
+                        help='Device to store the model.')
+    parser.add_argument('--model',
+                        choices=list(pt_models.keys()),
+                        type=str,
+                        default='resnet18')
+    args = parser.parse_args()
+
+    model = pt_models[args.model]()
+
+    if torch.cuda.is_available():
+        model.cuda(device=args.device)
+
+    batch_size = 256
+    macs, params, steps = get_model_profile(model, # the PyTorch model to be profiled
+                                     input_res=(batch_size, 3, 224, 224), # input shape or input to the input_constructor
+                                     input_constructor=None, # If specified, the constructor is applied to input_res and the constructor output is used as the input to the model
+                                     print_profile=True, # whether to print the model graph with the profile annotated. Defaults to True
+                                     print_aggregated_profile=True, # whether to print the aggregated profile for top modules. Defaults to True
+                                     module_depth=-1, # the depth into the nested modules. Defaults to -1 (the inner most modules)
+                                     top_modules=3, # the number of top modules to print aggregated profile
+                                     warm_up=10, # the number of warm-up steps before measuring the time of each module. Defaults to 5
+                                     num_steps=10, # the number of steps to profile. Defaults to 10
+                                     as_strings=True, # whether to print the output as strings (e.g. 1k). Defaults to True
+                                     ignore_modules=None) # the list of modules to ignore during profiling. Defaults to None
+
+    print("{:<30}  {:<8}".format("Batch size: ", batch_size))
+    print('{:<30}  {:<8}'.format('Number of MACs: ', macs))
+    print('{:<30}  {:<8}'.format('Number of parameters: ', params))
+    print('{:<30}  {:<8}'.format('Number of steps profiled: ', steps))
+
+# Output:
+# Number of MACs:                 466.48 GMACs
+# Number of parameters:           11.69 M
+
+```
+
+##### Bert model example:
+
+```python
+from functools import partial
+
+import torch
+from transformers import BertForSequenceClassification, BertTokenizer
+
+from deepspeed.profiling.flops_profiler import get_model_profile
+
+
+def bert_input_constructor(input_shape, tokenizer):
+    inp_seq = ""
+    for _ in range(input_shape[1] - 2):  # there are two special tokens [CLS] and [SEP]
+        inp_seq += tokenizer.pad_token  # let's use pad token to form a fake
+    # sequence for subsequent flops calculation
+
+    inputs = tokenizer([inp_seq] * input_shape[0],
+                       padding=True,
+                       truncation=True,
+                       return_tensors="pt")
+    labels = torch.tensor([1] * input_shape[0])
+    # Batch size input_shape[0], sequence length input_shape[128]
+    inputs = dict(inputs)
+    inputs.update({"labels": labels})
+    return inputs
+
+
+if __name__ == '__main__':
+    bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+    model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
+    macs, params, steps = get_model_profile(
+        model,
+        (2, 128),
+        input_constructor=partial(bert_input_constructor,
+                                  tokenizer=bert_tokenizer),
+        print_profile=True,
+        print_aggregated_profile=True,
+    )
+    print("{:<30}  {:<8}".format("Number of multiply-adds: ", macs))
+    print("{:<30}  {:<8}".format("Number of parameters: ", params))
+    print("{:<30}  {:<8}".format("Number of steps profiled: ", steps))
+
+# Output:
+# Number of multiply-adds:        21.74 GMACs
+# Number of parameters:           109.48 M
+
+```

deepspeed/profiling/flops_profiler/__init__.py

+from .profiler import *

deepspeed/profiling/flops_profiler/profiler.py

+import time
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from functools import partial
+
+module_flop_count = []
+old_functions = {}
+
+
+class FlopsProfiler(object):
+    """Measures the time, number of estimated flops 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 time, 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 time, flops, and parameters at depth l with k and l specified by the user. The output profile is computed for each batch of input. If multiple forward passes are specified by the user to caputre (in the case where the model have different paths or for more accurate timing), the average profile of the multiple batches is taken.
+
+    Args:
+        object (torch.nn.Module): The PyTorch model to profile.
+    """
+    def __init__(self, model):
+        self.model = model
+
+    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()
+
+        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.clear()
+                if len(input) > 0:
+                    # Can have multiple inputs, getting the first one
+                    input = input[0]
+                module.__steps__ += 1
+
+            module.__pre_hook_handle__ = module.register_forward_pre_hook(pre_hook)
+
+            def post_hook(module, input, output):
+                module.__flops__ += sum([elem[1] for elem in module_flop_count])
+                module_flop_count.clear()
+
+            has_children = len(module._modules.items()) != 0
+            if not has_children:
+                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))
+
+    def end_profile(self):
+        """Ends profiling.
+
+        Added attributes and handles are removed recursively on all the modules and the torch.nn.functionals are restored.
+        """
+        def remove_profile_attrs(module):
+            if hasattr(module, "__steps__"):
+                del module.__steps__
+            if hasattr(module, "__flops__"):
+                del module.__flops__
+            if hasattr(module, "__params__"):
+                del module.__params__
+            if hasattr(module, "__start_time__"):
+                del module.__start_time__
+            if hasattr(module, "__duration__"):
+                del module.__duration__
+            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)
+        _reload_functionals()
+
+    def reset_profile(self):
+        """Resets the profiling.
+
+        Adds or resets the extra attributes.
+        """
+        def add_or_reset_attrs(module):
+            module.__flops__ = 0
+            module.__params__ = sum(p.numel() for p in module.parameters()
+                                    if p.requires_grad)
+            module.__start_time__ = 0
+            module.__duration__ = 0
+            module.__steps__ = 0
+
+        self.model.apply(add_or_reset_attrs)
+
+    def get_total_flops(self, in_str=False):
+        """Returns the total flops of the model.
+
+        Args:
+            in_str (bool, optional): whether to output the flops in string. Defaults to False.
+        """
+        if self.get_total_steps() == 0:
+            return 0
+        sum = 0
+        for module in self.model.modules():
+            sum += module.__flops__
+        total_flops = sum / self.get_total_steps()
+        return flops_to_string(total_flops) if in_str else total_flops
+
+    def get_total_duration(self, in_str=False):
+        """Returns the total duration of the model forward pass.
+
+        Args:
+            in_str (bool, optional): whether to output the duration in string. Defaults to False.
+        """
+        if self.get_total_steps() == 0:
+            return 0
+        total_duration = self.model.__duration__ / self.get_total_steps()
+        return duration_to_string(total_duration) if in_str else total_duration
+
+    def get_total_params(self, in_str=False):
+        """Returns the total parameters of the model.
+
+        Args:
+            in_str (bool, optional): whether to output the parameters in string. Defaults to False.
+        """
+        return params_to_string(
+            self.model.__params__) if in_str else self.model.__params__
+
+    def get_total_steps(self):
+        """Returns the total number of steps (or input batches) profiled.
+        """
+        def get_steps(module):
+            if module.__steps__ == 0:
+                sum = 0
+                for m in module.children():
+                    sum += get_steps(m)
+                module.__steps__ = sum
+            return module.__steps__
+
+        total_steps = get_steps(self.model)
+        if total_steps == 0:
+            print("no step is profiled")
+        return total_steps
+
+    def print_model_profile(self):
+        """Prints the model graph with the measured profile attached to each module.
+        """
+        total_flops = self.get_total_flops()
+        total_duration = self.get_total_duration()
+        total_params = self.get_total_params()
+        total_steps = self.get_total_steps()
+
+        def accumulate_flops(module):
+            has_children = len(module._modules.items()) != 0
+            if not has_children:
+                return module.__flops__
+            else:
+                sum = 0
+                for m in module.children():
+                    sum += m.accumulate_flops()
+            return sum
+
+        def flops_repr(module):
+            params = module.__params__
+            flops = 0 if total_steps == 0 else module.accumulate_flops() / total_steps
+            items = [
+                params_to_string(params),
+                "{:.2%} Params".format(params / total_params),
+                flops_to_string(flops),
+                "{:.2%} MACs".format(0.0 if total_flops == 0 else flops / total_flops),
+            ]
+            duration = 0 if total_steps == 0 else module.__duration__ / total_steps
+            items.append(duration_to_string(duration))
+            items.append("{:.2%} time".format(0.0 if total_duration == 0 else duration /
+                                              total_duration))
+            # flops = 2 * MACs
+            items.append(("{:.2} TFLOPS".format(0.0 if duration == 0 else 2 * flops /
+                                                duration / 10**12)))
+            items.append(str(module.__steps__))
+            items.append(module.original_extra_repr())
+            return ", ".join(items)
+
+        def add_extra_repr(module):
+            module.accumulate_flops = accumulate_flops.__get__(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
+            if hasattr(module, "accumulate_flops"):
+                del module.accumulate_flops
+
+        self.model.apply(add_extra_repr)
+        print(self.model)
+        self.model.apply(del_extra_repr)
+
+    def print_model_aggregated_profile(self, module_depth=-1, top_modules=3):
+        """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 3.
+        """
+        info = {}
+        total_steps = self.get_total_steps()
+        if total_steps == 0:
+            return
+        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,
+                ]  # flops, params, time
+            info[curr_depth][module.__class__.__name__][0] += module.__flops__
+            info[curr_depth][module.__class__.__name__][1] += module.__params__
+            info[curr_depth][module.__class__.__name__][2] += (module.__duration__)
+            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
+
+        num_items = min(top_modules, len(info[depth]))
+
+        sort_flops = {
+            k: flops_to_string(v[0] / total_steps)
+            for k,
+            v in sorted(info[depth].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[depth].items(),
+                        key=lambda item: item[1][1],
+                        reverse=True)[:num_items]
+        }
+        sort_time = {
+            k: duration_to_string(v[2] / total_steps)
+            for k,
+            v in sorted(info[depth].items(),
+                        key=lambda item: item[1][2],
+                        reverse=True)[:num_items]
+        }
+        print(f"Top {num_items} modules in flops at depth {depth} are {sort_flops}")
+        print(f"Top {num_items} modules in params at depth {depth} are {sort_params}")
+        print(f"Top {num_items} modules in time at depth {depth} are {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]
+    return torch.numel(input) * out_features
+
+
+def _relu_flops_compute(input, inplace=False):
+    return torch.numel(input)
+
+
+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)
+
+
+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:])
+
+    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)
+
+    output_dims = [0, 0]
+    output_dims[0] = (input_dims[0] + 2 * paddings[0] -
+                      (dilations[0] * (kernel_dims[0] - 1) + 1)) // strides[0] + 1
+    output_dims[1] = (input_dims[1] + 2 * paddings[1] -
+                      (dilations[1] * (kernel_dims[1] - 1) + 1)) // strides[1] + 1
+
+    filters_per_channel = out_channels // groups
+    conv_per_position_flops = int(_prod(kernel_dims)) * in_channels * filters_per_channel
+    active_elements_count = batch_size * int(_prod(output_dims))
+    overall_conv_flops = conv_per_position_flops * active_elements_count
+
+    bias_flops = 0
+    if bias is not None:
+        bias_flops = out_channels * active_elements_count
+
+    overall_flops = overall_conv_flops + bias_flops
+
+    return int(overall_flops)
+
+
+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:])
+
+    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)
+
+    output_dims = [0, 0]
+    output_dims[0] = (input_dims[0] + 2 * paddings[0] -
+                      (dilations[0] * (kernel_dims[0] - 1) + 1)) // strides[0] + 1
+    output_dims[1] = (input_dims[1] + 2 * paddings[1] -
+                      (dilations[1] * (kernel_dims[1] - 1) + 1)) // strides[1] + 1
+
+    filters_per_channel = out_channels // groups
+    conv_per_position_flops = int(_prod(kernel_dims)) * in_channels * filters_per_channel
+    active_elements_count = batch_size * int(_prod(input_dims))
+    overall_conv_flops = conv_per_position_flops * active_elements_count
+
+    bias_flops = 0
+    if bias is not None:
+        bias_flops = out_channels * batch_size * int(_prod(output_dims))
+
+    overall_flops = overall_conv_flops + bias_flops
+
+    return int(overall_flops)
+
+
+def _batch_norm_flops_compute(
+    input,
+    running_mean,
+    running_var,
+    weight=None,
+    bias=None,
+    training=False,
+    momentum=0.1,
+    eps=1e-05,
+):
+    # assume affine is true
+    flops = 2 * torch.numel(input)
+    return flops
+
+
+def _upsample_flops_compute(input,
+                            size=None,
+                            scale_factor=None,
+                            mode="nearest",
+                            align_corners=None):
+    if size is not None:
+        return int(_prod(size))
+    assert scale_factor is not None
+    flops = torch.numel(input)
+    if len(scale_factor) == len(input):
+        flops * int(_prod(scale_factor))
+    else:
+        flops * scale_factor**len(input)
+    return flops
+
+
+def _softmax_flops_compute(input, dim=None, _stacklevel=3, dtype=None):
+    return torch.numel(input)
+
+
+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
+
+
+def _dropout_flops_compute(input, p=0.5, training=True, inplace=False):
+    return 0
+
+
+def wrapFunc(func, funcFlopCompute):
+    oldFunc = func
+    name = func.__name__
+    old_functions[func.__name__] = oldFunc
+
+    def newFunc(*args, **kwds):
+        flops = funcFlopCompute(*args, **kwds)
+        module_flop_count.append((name, flops))
+        return oldFunc(*args, **kwds)
+
+    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, _relu_flops_compute)
+    F.elu = wrapFunc(F.elu, _relu_flops_compute)
+    F.leaky_relu = wrapFunc(F.leaky_relu, _relu_flops_compute)
+    F.relu6 = wrapFunc(F.relu6, _relu_flops_compute)
+
+    # BatchNorms
+    F.batch_norm = wrapFunc(F.batch_norm, _batch_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 _reload_functionals():
+    # torch.nn.functional does not support importlib.reload()
+    F.linear = old_functions["linear"]
+    F.conv1d = old_functions["conv1d"]
+    F.conv2d = old_functions["conv2d"]
+    F.conv3d = old_functions["conv3d"]
+    F.conv_transpose1d = old_functions["conv_transpose1d"]
+    F.conv_transpose2d = old_functions["conv_transpose2d"]
+    F.conv_transpose3d = old_functions["conv_transpose3d"]
+    F.relu = old_functions["relu"]
+    F.prelu = old_functions["prelu"]
+    F.elu = old_functions["elu"]
+    F.leaky_relu = old_functions["leaky_relu"]
+    F.relu6 = old_functions["relu6"]
+    F.batch_norm = old_functions["batch_norm"]
+    F.avg_pool1d = old_functions["avg_pool1d"]
+    F.avg_pool2d = old_functions["avg_pool2d"]
+    F.avg_pool3d = old_functions["avg_pool3d"]
+    F.max_pool1d = old_functions["max_pool1d"]
+    F.max_pool2d = old_functions["max_pool2d"]
+    F.max_pool3d = old_functions["max_pool3d"]
+    F.adaptive_avg_pool1d = old_functions["adaptive_avg_pool1d"]
+    F.adaptive_avg_pool2d = old_functions["adaptive_avg_pool2d"]
+    F.adaptive_avg_pool3d = old_functions["adaptive_avg_pool3d"]
+    F.adaptive_max_pool1d = old_functions["adaptive_max_pool1d"]
+    F.adaptive_max_pool2d = old_functions["adaptive_max_pool2d"]
+    F.adaptive_max_pool3d = old_functions["adaptive_max_pool3d"]
+    F.upsample = old_functions["upsample"]
+    F.interpolate = old_functions["interpolate"]
+    F.softmax = old_functions["softmax"]
+    F.embedding = old_functions["embedding"]
+
+
+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 flops_to_string(flops, units=None, precision=2):
+    if units is None:
+        if flops // 10**9 > 0:
+            return str(round(flops / 10.0**9, precision)) + " GMACs"
+        elif flops // 10**6 > 0:
+            return str(round(flops / 10.0**6, precision)) + " MMACs"
+        elif flops // 10**3 > 0:
+            return str(round(flops / 10.0**3, precision)) + " KMACs"
+        else:
+            return str(flops) + " MACs"
+    else:
+        if units == "GMACs":
+            return str(round(flops / 10.0**9, precision)) + " " + units
+        elif units == "MMACs":
+            return str(round(flops / 10.0**6, precision)) + " " + units
+        elif units == "KMACs":
+            return str(round(flops / 10.0**3, precision)) + " " + units
+        else:
+            return str(flops) + " MACs"
+
+
+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"
+
+
+def get_model_profile(
+    model,
+    input_res,
+    input_constructor=None,
+    print_profile=True,
+    print_aggregated_profile=True,
+    module_depth=-1,
+    top_modules=3,
+    warm_up=5,
+    num_steps=10,
+    as_strings=True,
+    ignore_modules=None,
+):
+    """Returns the total flops, parameters, and profiled steps of a model.
+
+    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 graph with the profile annotated. Defaults to True.
+        print_aggregated_profile (bool, optional): whether to print the aggregated profile for top modules. 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 time of each module. Defaults to 5.
+        num_steps (int, optional): the number of steps to profile. Defaults to 10.
+        as_strings (bool, optional): whether to print the output as strings. Defaults to True.
+        ignore_modules ([type], optional): the list of modules to ignore during profiling. Defaults to None.
+    """
+    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)
+
+    for _ in range(num_steps):
+        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()
+    params = prof.get_total_params()
+    steps = prof.get_total_steps()
+    if print_profile:
+        prof.print_model_profile()
+    if print_aggregated_profile:
+        prof.print_model_aggregated_profile(module_depth=module_depth,
+                                            top_modules=top_modules)
+    prof.end_profile()
+    if as_strings:
+        return flops_to_string(flops), params_to_string(params), steps
+
+    return flops, params, steps

deepspeed/runtime/config.py

@@ -22,6 +22,8 @@ from ..elasticity.config import ElasticityConfigError
 from ..elasticity.constants import ELASTICITY, IGNORE_NON_ELASTIC_BATCH_INFO, \
     IGNORE_NON_ELASTIC_BATCH_INFO_DEFAULT
 
+from ..profiling.config import DeepSpeedFlopsProfilerConfig
+
 TENSOR_CORE_ALIGN_SIZE = 8
 
 ADAM_OPTIMIZER = 'adam'
@@ -613,6 +615,7 @@ class DeepSpeedConfig(object):
         self.scheduler_params = get_scheduler_params(param_dict)
 
         self.wall_clock_breakdown = get_wall_clock_breakdown(param_dict)
+        self.flops_profiler_config = DeepSpeedFlopsProfilerConfig(param_dict)
         self.memory_breakdown = get_memory_breakdown(param_dict)
         self.tensorboard_enabled = get_tensorboard_enabled(param_dict)
         self.tensorboard_output_path = get_tensorboard_output_path(param_dict)

deepspeed/runtime/engine.py

@@ -39,6 +39,8 @@ from ..ops.op_builder import UtilsBuilder
 from ..ops.adam import DeepSpeedCPUAdam
 from ..ops.adam import FusedAdam
 
+from deepspeed.profiling.flops_profiler.profiler import FlopsProfiler
+
 MEMORY_OPT_ALLREDUCE_SIZE = 500000000
 
 try:
@@ -265,6 +267,21 @@ class DeepSpeedEngine(Module):
     def wall_clock_breakdown(self):
         return self._config.wall_clock_breakdown
 
+    def flops_profiler_enabled(self):
+        return self._config.flops_profiler_config.enabled
+
+    def flops_profiler_start_step(self):
+        return self._config.flops_profiler_config.start_step
+
+    def flops_profiler_end_step(self):
+        return self._config.flops_profiler_config.end_step
+
+    def flops_profiler_module_depth(self):
+        return self._config.flops_profiler_config.module_depth
+
+    def flops_profiler_top_modules(self):
+        return self._config.flops_profiler_config.top_modules
+
     def memory_breakdown(self):
         return self._config.memory_breakdown
 
@@ -764,6 +781,30 @@ class DeepSpeedEngine(Module):
             *inputs: Variable length input list
             **kwargs: variable length keyword arguments
         """
+        if self.flops_profiler_enabled(
+        ) and self.global_steps == self.flops_profiler_start_step(
+        ) and self.global_rank == 0:
+            self.flops_profiler = FlopsProfiler(self.module)
+            self.flops_profiler.start_profile(ignore_list=None)
+
+        if self.flops_profiler_enabled(
+        ) and self.global_steps == self.flops_profiler_end_step(
+        ) and self.global_rank == 0:
+            print('{:<30}  {:<8}'.format(
+                'Number of multiply-adds: ',
+                self.flops_profiler.get_total_flops(in_str=False)))
+            print('{:<30}  {:<8}'.format(
+                'Number of parameters: ',
+                self.flops_profiler.get_total_params(in_str=False)))
+            print('{:<30}  {:<8}'.format('Number of steps profiled: ',
+                                         self.flops_profiler.get_total_steps()))
+            self.flops_profiler.print_model_profile()
+            self.flops_profiler.print_model_aggregated_profile(
+                module_depth=self.flops_profiler_module_depth(),
+                top_modules=self.flops_profiler_top_modules())
+            self.flops_profiler.flops = self.flops_profiler.get_total_flops()
+            self.flops_profiler.params = self.flops_profiler.get_total_params()
+            self.flops_profiler.end_profile()
 
         if self.module.training and self.progressive_layer_drop:
             kwargs.update(self.progressive_layer_drop.get_state())

tests/unit/test_flops_profiler.py

+import torch
+import deepspeed
+import deepspeed.runtime.utils as ds_utils
+from deepspeed.profiling.flops_profiler import FlopsProfiler, get_model_profile
+from simple_model import SimpleModel, SimpleOptimizer, random_dataloader, args_from_dict
+from common import distributed_test
+
+
+def test_flops_profiler_in_ds_trainning(tmpdir):
+    config_dict = {
+        "train_batch_size": 1,
+        "steps_per_print": 1,
+        "optimizer": {
+            "type": "Adam",
+            "params": {
+                "lr": 0.001,
+            }
+        },
+        "zero_optimization": {
+            "stage": 0
+        },
+        "fp16": {
+            "enabled": True,
+        },
+        "flops_profiler": {
+            "enabled": True,
+            "start_step": 2,
+            "end_step": 3,
+            "module_depth": -1,
+            "top_modules": 3,
+        },
+    }
+    args = args_from_dict(tmpdir, config_dict)
+    hidden_dim = 10
+    model = SimpleModel(hidden_dim, empty_grad=False)
+
+    @distributed_test(world_size=[1])
+    def _test_flops_profiler_in_ds_trainning(args, model, hidden_dim):
+        model, _, _, _ = deepspeed.initialize(args=args,
+                                            model=model,
+                                            model_parameters=model.parameters())
+
+        data_loader = random_dataloader(model=model,
+                                        total_samples=50,
+                                        hidden_dim=hidden_dim,
+                                        device=model.device,
+                                        dtype=torch.half)
+        for n, batch in enumerate(data_loader):
+            loss = model(batch[0], batch[1])
+            model.backward(loss)
+            model.step()
+            if n == 3: break
+        assert model.flops_profiler.flops == 100
+        assert model.flops_profiler.params == 110
+
+    _test_flops_profiler_in_ds_trainning(args, model, hidden_dim)
+
+
+class LeNet5(torch.nn.Module):
+    def __init__(self, n_classes):
+        super(LeNet5, self).__init__()
+
+        self.feature_extractor = torch.nn.Sequential(
+            torch.nn.Conv2d(in_channels=1,
+                            out_channels=6,
+                            kernel_size=5,
+                            stride=1),
+            torch.nn.Tanh(),
+            torch.nn.AvgPool2d(kernel_size=2),
+            torch.nn.Conv2d(in_channels=6,
+                            out_channels=16,
+                            kernel_size=5,
+                            stride=1),
+            torch.nn.Tanh(),
+            torch.nn.AvgPool2d(kernel_size=2),
+            torch.nn.Conv2d(in_channels=16,
+                            out_channels=120,
+                            kernel_size=5,
+                            stride=1),
+            torch.nn.Tanh(),
+        )
+
+        self.classifier = torch.nn.Sequential(
+            torch.nn.Linear(in_features=120,
+                            out_features=84),
+            torch.nn.Tanh(),
+            torch.nn.Linear(in_features=84,
+                            out_features=n_classes),
+        )
+
+    def forward(self, x):
+        x = self.feature_extractor(x)
+        x = torch.flatten(x, 1)
+        logits = self.classifier(x)
+        probs = torch.nn.functional.softmax(logits, dim=1)
+        return logits, probs
+
+
+def test_flops_profiler_in_inference():
+    mod = LeNet5(10)
+    batch_size = 1024
+    input = torch.randn(batch_size, 1, 32, 32)
+    macs, params, steps = get_model_profile(
+        mod,
+        tuple(input.shape),
+        print_profile=True,
+        print_aggregated_profile=True,
+        module_depth=-1,
+        top_modules=3,
+        warm_up=5,
+        num_steps=10,
+        as_strings=True,
+        ignore_modules=None,
+    )
+    print(macs, params, steps)
+    assert macs == "439.55 MMACs"
+    assert params == "61.71 k"