Optimizing ResNet50 Model#

In this article we will experiment with various ways to compress a convolutional neural network (CNN) for meeting different performance objectives while staying within a specified accuracy loss budget. In particular, we will consider two scenarios - one with the goal to reduce the model size, the other with the goal to reduce runtime latency.

For this exercise, we will use the pretrained ResNet50 model from torchvision. Baseline ResNet50 model has a top 1% accuracy of 76.13%, mlpackage size of 49MB (float16 precision) and latency of ~1.63ms[1].

Scenario 1 : Minimizing model size#

In this scenario, our goal would be to minimize the disk size of the model, while trying to retain as much accuracy as possible of the float16 model (within 5%).

Palettization using data free compression#

Let’s start with the quickest workflow, which is to apply data free compression. We will take the model and apply palettization with different bit precisions and see how the accuracy behaves.

from coremltools.optimize.torch.palettization import (
    PostTrainingPalettizer, 
    PostTrainingPalettizerConfig
)

config_dict = {"global_config": {"n_bits": 4}}
config = PostTrainingPalettizerConfig.from_dict(config_dict)
palettizer = PostTrainingPalettizer(model, config)

# Compress model
palettized_model = palettizer.compress()

In the above code snippet, we apply data free compression directly to the PyTorch model. However, you can also use ct.optimize.coreml.palettize_weights if working with a Core ML model.

We apply n_bits equal to 8, 6, 4 & 3 bits to get accuracies of 76.09%, 75.55%, 66.81% and 23.09% respectively. We see that there is marginal loss of accuracy with 8 and 6 bit palettized models, whereas there is a big drop with 4 bits, and the model becomes unusable for 3 bits.

Let’s try to regain the accuracy loss with 4 bit palletization by applying the per_grouped_channel palettization, which increases the number of LUTs (look up tables) per weight tensor.

from coremltools.optimize.torch.palettization import (
    PostTrainingPalettizer, 
    PostTrainingPalettizerConfig
)

config_dict = {
    "global_config": {
        "n_bits": 4,
        "granularity": "per_grouped_channel",
        "group_size": 8,
    }
}
config = PostTrainingPalettizerConfig.from_dict(config_dict)
palettizer = PostTrainingPalettizer(model, config)

# Compress model
palettized_model = palettizer.compress()

We try group_size equal to 16, 8 & 4 and see accuracy improve to 69.29%, 72.26% and 73.05% respectively.

We summarize our results below:

Config

Accuracy

Model Size

Latency

6-bit (per tensor)

75.55%

18.6 MB

1.25ms

4-bit (per-tensor)

66.81%

12.5 MB

1.12ms

4-bit (group_size=16)

69.29%

15.5 MB

1.38ms

4 bit (group_size=8)

72.26%

12.6 MB

1.34ms

4 bit (group_size=4)

73.05%

12.7 MB

1.71ms

Note that while higher granularity achieved with grouped channel palettization helps improve accuracy, we may lose runtime performance.
For this model 4-bit palettization with group_size=8 configuration is a good sweet spot for good accuracy and runtime performance for minimal model size.

Palettization using fine tuning#

For this particular model, we do not see any benefits of using calibration data based compression w.r.to accuracy. So we move on to training time compression worflow, where we will fine tune the model as we compress it. We can do so by using the DKM algorithm, as follows:

from coremltools.optimize.torch.palettization import (
    DKMPalettizer,
    DKMPalettizerConfig,
    ModuleDKMPalettizerConfig
)

global_config = ModuleDKMPalettizerConfig(n_bits=2)
config = DKMPalettizerConfig().set_global(global_config)

palettizer = DKMPalettizer(model, config)

palettizer.prepare(inplace=True)

for epoch in range(num_epochs):
    model.train()
    for data, label in enumerate(train_loader):
        train_step(model, optimizer, train_loader, data, label, epoch)
        palettizer.step()

model.eval()
palettized_model = palettizer.finalize()

With training time compression we can go up to 1-bit palettized model while still being within our accuracy budget. 2-bit palettized model has accuracy of 75.51% and size of 6.3 MB, while the 1-bit palettized model has accuracy of 71.22% and size of 3.4 MB, giving over 15x model size reduction over baseline.

Summary#

Below we summarize results from above experiments as well as note the order of time taken to apply each of these compression workflows. Post training algorithms are easier to set up and take less time, while training time techniques can provide better accuracy to compression trade-off. Note also that while the goal here was to reduce the model size, compressing the model also helps reduce latency (results may vary based on model).

Optimization API

Best config

Accuracy

Model Size

Latency

Time to compress

Baseline

-

76.13%

48.8 MB

1.63ms

-

PostTrainingPalettizer

4-bit (group_size=8)

72.26%

13.1 MB

1.34ms

O(minutes)

DKMPalettizer

1-bit (per-tensor)

71.22%

3.4 MB

1.14ms

O(hours) (300 epochs)

Scenario 2: Minimizing latency#

Next, we will try to minimize latency of our model with less than 5% accuracy loss. Let’s say our latency target is < 1ms.

Latency reduction with pruning#

We start out by pruning the model using data free compression, to see which sparsity configuration gives us the desired latency.

from coremltools.optimize.torch.pruning import (
    MagnitudePruner,
    MagnitudePrunerConfig,
    ModuleMagnitudePrunerConfig
)

global_config = ModuleMagnitudePrunerConfig(initial_sparsity=0.5, target_sparsity=0.5)
config = MagnitudePrunerConfig().set_global(global_config)

pruner = MagnitudePruner(model, config)
pruner.prepare(inplace=True)

# Skip training 

pruned_model = pruner.finalize()

In the above code snippet, we apply data free compression directly to the PyTorch model. However, you can also use ct.optimize.coreml.prune_weights if working with a Core ML model.

Trying with 50%, 75% and 90% target_sparsity we get 1.23ms, 1.05ms and 0.86ms latency respectively.

Since we meet our latency goals with 90% sparsity, we will now fine-tune the pretrained PyTorch model to see if we can get good enough accuracy.

from coremltools.optimize.torch.pruning import (
    MagnitudePruner,
    MagnitudePrunerConfig,
    ModuleMagnitudePrunerConfig
)
from coremltools.optimize.torch.pruning.pruning_scheduler import (
    PolynomialDecayScheduler
)

# Setup scheduler for applying sparsity during fine-tuning
scheduler = PolynomialDecayScheduler(update_steps=list(range(25000, 62500, 100)))
global_config = ModuleMagnitudePrunerConfig(target_sparsity=0.9, scheduler=scheduler)
config = MagnitudePrunerConfig().set_global(global_config)

pruner = MagnitudePruner(model, config)

pruner.prepare(inplace=True)

# Train the model
for epoch in range(num_epochs):
    model.train()
    for data, label in enumerate(train_loader):
        train_step(model, optimizer, train_loader, data, label, epoch)
        pruner.step()

model.eval()
pruned_model = pruner.finalize()

With fine-tuning, we observe an accuracy of 74.6% for 90% sparse ResNet50 model. This is quite good for this model, the results will vary based on the model.

Latency reduction with activation quantization#

In this section, we explore activation quantization on ResNet50 model, where we quantize both model weights and activations to 8-bit (W8A8). This can give latency gains by leveraging int8-int8 compute that is available on the Neural Engine (NE) on newer chips (A17 pro, M4).

You can apply activation quantization with calibration data using LinearQuantizer API on the PyTorch model. We use the calibration data to measure statistics of activations and weights without actually simulating quantization during model’s forward pass, and without needing to perform a backward pass. Learn more about this workflow in the API Overview section.

import torch
from coremltools.optimize.torch.quantization import (
    LinearQuantizer,
    LinearQuantizerConfig,
    ModuleLinearQuantizerConfig
)

config = LinearQuantizerConfig(
    global_config=ModuleLinearQuantizerConfig(
        quantization_scheme="symmetric",
        milestones=[0, 1000, 1000, 0],
    )
)

quantizer = LinearQuantizer(model, config)

quantizer.prepare(example_inputs=[1, 3, 224, 224], inplace=True)

# Only step through quantizer once to enable statistics collection (milestone 0),
# and turn batch norm to inference mode (milestone 3) 
quantizer.step()

# Do a forward pass through the model with calibration data
for idx, data in enumerate(dataloader):
    with torch.no_grad():
        model(data)

model.eval()
quantized_model = quantizer.finalize()

With 128 calibration samples, this gives us an accuracy of 76.1% and a latency of 1.07ms.

Note that if you have a Core ML model, you can use the linear_quantize_activations method to quantize the activations.

Summary#

Below we summarize results from above experiments as well as note the order of time taken to apply each of these compression workflows. With calibration data based activation quantization workflow we are able to achieve good speedup for minimal loss in accuracy, and it is much quicker to set up and apply. On the other hand, by pruning the model with fine-tuning we are able to get even better speedup and a much smaller model for a slightly higher loss in accuracy, but with a more extensive set up.

Optimization API

Best config

Accuracy

Model Size

Latency

Time to compress

Baseline

-

76.13%

48.8 MB

1.63ms

-

MagnitudePruner

90% sparsity

74.60%

8.6 MB

0.86ms

O(hours) (200 epochs)

LinearQuantizer

W8A8 symmetric per-channel

76.09%

25.8 MB

1.07ms

O(minutes)