Raspbery Pico 2 demo , trying to use quantization to int8 instead and memory utilization doubled from 120KB to 220KB. Why is the memory consumption on the allocator increasing

[landazur]

Hi guys

I am trying to follow along the raspberry pi pico2 demo from last week (https://github.com/pytorch/executorch/tree/main/examples/raspberry_pi/pico2) but with the modification that I want to quantized the model to int8

First below you can see that I first try to get the libquantized_ops_aot_lib.so with the steps provided in the links that are in the README (https://github.com/pytorch/executorch/blob/main/examples/raspberry_pi/pico2/README.md) for quantization

Then I create a new script export_mlp_mnist_int8.py that exports a file called balanced_tiny_mlp_mnist_quantized.pte that I will use to compile with build_firmware_pico.sh. This will be quantized model compiled into a pico2 .uf2 file

Below you are going to see that I can build the original demo and execute on my pico2. However to prove my point I changed the allocator size to 120KB instead of the 200KB that is shown in the git hub main.cpp file (https://github.com/pytorch/executorch/blob/main/examples/raspberry_pi/pico2/main.cpp#L358)

I execute the DEMO with the unquantized model balanced_tiny_mlp_mnist.pte and everything works proving that there is only need for 120KB for allocator size

Then I try to compile the balanced_tiny_mlp_mnist_quantized.pte still with the same allocator size of 120KB and does not load the model because of insufficient
memory error 33 in decimal, 0x21 hex
/// Could not allocate the requested memory.
MemoryAllocationFailed = 0x21,

Then I increase the allocator size to 280KB and recompile the main.cpp and now it works

Why did the memory allocator utilization increased and doubled? I thought with quantization both the ROM size and RAM size will reduce

Below are all my steps. Thanks for helping me understand why the memory usage increase and maybe I am doing some wrong on the quantization steps below

cd pico/executorch

python3 -m venv .pico3 && source .pico3/bin/activate

./install_executorch.sh

examples/arm/setup.sh --i-agree-to-the-contained-eula
source examples/arm/ethos-u-scratch/setup_path.sh

export PATH="~/pico/executorch/examples/arm/ethos-u-scratch/arm-gnu-toolchain-13.3.rel1-x86_64-arm-none-eabi/bin/:$PATH"

TO BUILD the so library for obtaining the quantized model, I run examples/xnnpack/quantization/test_quantize.sh cmake add
This produces cmake-out/kernels/quantized/libquantized_ops_aot_lib.so

Get the .pte file with python examples/raspberry_pi/pico2/export_mlp_mnist.py

Get the .pte file for the quantized model
THE MODEL IS QUANTIZED with this new script I created export_mlp_mnist_int8.py.
It uses torch.ops.load_library("cmake-out/kernels/quantized/libquantized_ops_aot_lib.so")

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import time

	from executorch.exir import EdgeCompileConfig, to_edge
	import logging

	from executorch.backends.xnnpack.quantizer.xnnpack_quantizer import (
		get_symmetric_quantization_config,
		XNNPACKQuantizer,
	)

	from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e

	from torch.export import export
	from enum import Enum
	from executorch.extension.export_util.utils import export_to_edge, save_pte_program
	from executorch.exir.capture._config import ExecutorchBackendConfig
	class QuantType(Enum):
		NONE = 1
		# Used for Operations that don't have weights
		STATIC_PER_TENSOR = 2
		# Used best for CNN/RNN Models with Conv layers
		STATIC_PER_CHANNEL = 3
		# Used for Linear Layers and Transformer Based Models
		DYNAMIC_PER_CHANNEL = 4


	# Constants
	INPUT_SIZE = 784  # 28*28 for MNIST
	HIDDEN1_SIZE = 32
	HIDDEN2_SIZE = 16
	OUTPUT_SIZE = 10
	IMAGE_SIZE = 28


	class TinyMLPMNIST(nn.Module):
		"""A small MLP for MNIST digit classification."""

		def __init__(self):
			super().__init__()
			self.fc1 = nn.Linear(INPUT_SIZE, HIDDEN1_SIZE)
			self.fc2 = nn.Linear(HIDDEN1_SIZE, HIDDEN2_SIZE)
			self.fc3 = nn.Linear(HIDDEN2_SIZE, OUTPUT_SIZE)

		def forward(self, x):
			"""Forward pass through the network."""
			x = x.reshape(x.size(0), -1)
			x = F.relu(self.fc1(x))
			x = F.relu(self.fc2(x))
			x = self.fc3(x)
			return x


	def create_balanced_model():
		"""
		Create a balanced MLP model for MNIST digit classification.

		The model is pre-initialized with specific weights to recognize
		digits 0, 1, 4, and 7 through hand-crafted feature detectors.

		Returns:
			torch.nn.Module: A TinyMLPMNIST model with balanced weights
		"""
		model = TinyMLPMNIST()

		with torch.no_grad():
			# Zero everything first
			for param in model.parameters():
				param.fill_(0.0)

			# Feature 0: Vertical lines (for 1, 4, 7)
			for row in range(IMAGE_SIZE):
				# Middle column
				model.fc1.weight[0, row * IMAGE_SIZE + 14] = 2.0

			# Feature 1: Top horizontal (for 7, 4)
			model.fc1.weight[1, 0:84] = 2.0  # Top 3 rows

			# Feature 2: Bottom horizontal (for 1, 4)
			model.fc1.weight[2, 25 * IMAGE_SIZE :] = 2.0  # Bottom 3 rows

			# Feature 3: STRONGER Oval detector for 0
			# Top and bottom curves
			model.fc1.weight[3, 1 * IMAGE_SIZE + 8 : 1 * IMAGE_SIZE + 20] = 2.0
			model.fc1.weight[3, 26 * IMAGE_SIZE + 8 : 26 * IMAGE_SIZE + 20] = 2.0
			# Left and right sides
			for row in range(4, 24):
				model.fc1.weight[3, row * IMAGE_SIZE + 7] = 2.0  # Left
				model.fc1.weight[3, row * IMAGE_SIZE + 20] = 2.0  # Right
			# Anti-middle (hollow center)
			for row in range(10, 18):
				model.fc1.weight[3, row * IMAGE_SIZE + 14] = -1.5

			# Feature 4: Middle horizontal (for 4) - make it STRONGER
			model.fc1.weight[4, 13 * IMAGE_SIZE : 15 * IMAGE_SIZE] = 3.0

			# Second layer: More decisive detection
			# Digit 0 detector: STRONG oval requirement
			model.fc2.weight[0, 3] = 5.0  # Strong oval requirement
			model.fc2.weight[0, 0] = -2.0  # Anti-vertical
			model.fc2.weight[0, 4] = -3.0  # Anti-middle horizontal

			# Digit 1 detector: vertical + bottom - others
			model.fc2.weight[1, 0] = 3.0  # Vertical
			model.fc2.weight[1, 2] = 2.0  # Bottom
			model.fc2.weight[1, 1] = -1.0  # Anti-top
			model.fc2.weight[1, 3] = -2.0  # Anti-oval

			# Digit 4 detector: REQUIRES middle horizontal
			model.fc2.weight[2, 0] = 2.0  # Vertical
			model.fc2.weight[2, 1] = 1.0  # Top
			model.fc2.weight[2, 4] = 4.0  # STRONG middle requirement
			model.fc2.weight[2, 3] = -2.0  # Anti-oval

			# Digit 7 detector: top + some vertical - bottom
			model.fc2.weight[3, 1] = 3.0  # Top
			model.fc2.weight[3, 0] = 1.0  # Some vertical
			model.fc2.weight[3, 2] = -2.0  # Anti-bottom

			# Output layer
			model.fc3.weight[0, 0] = 5.0  # Digit 0
			model.fc3.weight[1, 1] = 5.0  # Digit 1
			model.fc3.weight[4, 2] = 5.0  # Digit 4
			model.fc3.weight[7, 3] = 5.0  # Digit 7

			# Bias against other digits
			for digit in [2, 3, 5, 6, 8, 9]:
				model.fc3.bias[digit] = -3.0

		return model


	def test_comprehensive(model):
		"""
		Test model with clear digit patterns.

		Args:
			model: The PyTorch model to test
		"""

		# Create clearer test patterns
		def create_digit_1():
			"""Create a test pattern for digit 1."""
			digit = torch.zeros(1, IMAGE_SIZE, IMAGE_SIZE)
			# Thick vertical line in middle
			digit[0, 2:26, 13:16] = 1.0  # Thick vertical line
			# Top part (like handwritten 1)
			digit[0, 2:5, 11:14] = 1.0
			# Bottom base
			digit[0, 24:27, 10:19] = 1.0
			return digit

		def create_digit_7():
			"""Create a test pattern for digit 7."""
			digit = torch.zeros(1, IMAGE_SIZE, IMAGE_SIZE)
			# Top horizontal line
			digit[0, 1:4, 3:26] = 1.0
			# Diagonal line
			for i in range(23):
				row = 4 + i
				col = 23 - i
				if 0 <= row < IMAGE_SIZE and 0 <= col < IMAGE_SIZE:
					digit[0, row, col - 1 : col + 2] = 1.0  # Thick diagonal
			return digit

		def create_digit_0():
			"""Create a test pattern for digit 0."""
			digit = torch.zeros(1, IMAGE_SIZE, IMAGE_SIZE)
			# Oval shape
			for row in range(3, 25):
				for col in range(8, 20):
					condition1 = ((row - 14) ** 2 / 11**2 + (col - 14) ** 2 / 6**2) <= 1
					condition2 = ((row - 14) ** 2 / 8**2 + (col - 14) ** 2 / 3**2) > 1
					if condition1 and condition2:
						digit[0, row, col] = 1.0
			return digit

		patterns = {
			"Digit 1": create_digit_1(),
			"Digit 7": create_digit_7(),
			"Digit 0": create_digit_0(),
		}

		print("🧪 Testing with clear patterns:")
		model.eval()
		with torch.no_grad():
			for name, pattern in patterns.items():
				output = model(pattern)
				pred = output.argmax().item()
				confidence = F.softmax(output, dim=1)[0, pred].item()
				print(f"   {name} → predicted: {pred} (confidence: {confidence:.3f})")

				# Show top 3 predictions
				top3 = output.topk(3, dim=1)
				predictions = [
					(top3.indices[0, i].item(), top3.values[0, i].item()) for i in range(3)
				]
				print(f"      Top 3: {predictions}")


	def quantize(
		model, example_inputs, quant_type: QuantType = QuantType.STATIC_PER_TENSOR
	):
		"""This is the official recommended flow for quantization in pytorch 2.0 export"""
		logging.info(f"Original model: {model}")
		quantizer = XNNPACKQuantizer()
		# if we set is_per_channel to True, we also need to add out_variant of quantize_per_channel/dequantize_per_channel
		is_per_channel = (
			quant_type == QuantType.STATIC_PER_CHANNEL
			or quant_type == QuantType.DYNAMIC_PER_CHANNEL
		)
		is_dynamic = quant_type == QuantType.DYNAMIC_PER_CHANNEL
		operator_config = get_symmetric_quantization_config(
			is_per_channel=True,
			is_dynamic=is_dynamic,
		)
		quantizer.set_global(operator_config)
		m = prepare_pt2e(model, quantizer)
		# calibration
		m(*example_inputs)
		m = convert_pt2e(m)
		logging.info(f"Quantized model: {m}")
		# make sure we can export to flat buffer
		return m



	def main():
		"""Main function to create, test, and export the model."""
		print("🔥 Creating balanced MLP MNIST model...")

		model = create_balanced_model()

		# Test the model
		test_comprehensive(model)

		# Export
		example_inputs = torch.randn(1, IMAGE_SIZE, IMAGE_SIZE)
		param_count = sum(p.numel() for p in model.parameters())
		print(f"\n📊 Model parameters: {param_count:,}")

		print("📦 Exporting...")
		# pre-autograd export. eventually this will become torch.export
		with torch.no_grad():
			model = torch.export.export(model, (example_inputs,), strict=True).module()

		print("📦 Quantizing...")
		start = time.perf_counter()
		quantized_model = quantize(model, example_inputs)
		end = time.perf_counter()
		print(f"Quantize time: {end - start}s")

		print("📦 Exporting Quantized...")
		start = time.perf_counter()
		edge_compile_config = EdgeCompileConfig(_check_ir_validity=False)
		edge_m = export_to_edge(
			quantized_model, (example_inputs,), edge_compile_config=edge_compile_config
		)
		end = time.perf_counter()
		print(f"Export time: {end - start}s")

		#torch.ops.load_library("cmake-out/kernels/quantized/libquantized_ops_lib.so")
		torch.ops.load_library("cmake-out/kernels/quantized/libquantized_ops_aot_lib.so")

		start = time.perf_counter()
		prog = edge_m.to_executorch(
			config=ExecutorchBackendConfig(extract_delegate_segments=False)
		)
		save_pte_program(prog, "balanced_tiny_mlp_mnist_quantized")
		end = time.perf_counter()
		print(f"Save time: {end - start}s")
		print("finished")

		#print("📦 Exporting...")
		#with torch.no_grad():
		#    exported_program = export(model, (example_input,))

		#print("⚙️ Converting to ExecuTorch...")
		#edge_config = EdgeCompileConfig(_check_ir_validity=False)
		#edge_manager = to_edge(exported_program, compile_config=edge_config)
		#et_program = edge_manager.to_executorch()

		# Save with error handling
		#filename = "balanced_tiny_mlp_mnist.pte"
		#print(f"💾 Saving {filename}...")
		#try:
		#    with open(filename, "wb") as f:
		#        f.write(et_program.buffer)

		#    model_size_kb = len(et_program.buffer) / 1024
		#    print("✅ Export complete!")
		#    print(f"📁 Model size: {model_size_kb:.1f} KB")
		#except IOError as e:
		#    print(f"❌ Failed to save model: {e}")


	if __name__ == "__main__":
		main()

NOW We have a quantized model balanced_tiny_mlp_mnist_quantized.pte. Look at the size difference
-rw-r--r-- 1 106216 Oct 19 10:33 balanced_tiny_mlp_mnist.pte
-rw-r--r-- 1 33104 Oct 21 20:09 balanced_tiny_mlp_mnist_quantized.pte

COMPILE THE DEMO with balanced_tiny_mlp_mnist.pte and change the memory allocator in the main.cpp to 120KB
static uint8_t method_allocator_pool[120 * 1024]; // it used to be 200KB - plenty for method metadata
static uint8_t activation_pool[120 * 1024]; // it used to be 200KB - plenty for activations

NOW WE START BUILDING THE RASPBERRY PI PICO FIRMWARE from the demo but with 120KB instead of 200KB. Notice it works

  examples/raspberry_pi/pico2/build_firmware_pico.sh --clean	  
  examples/raspberry_pi/pico2/build_firmware_pico.sh --model=balanced_tiny_mlp_mnist.pte

NOW FLASH

        Loading model data (106216 bytes)...
        ✅ Program loaded successfully
         Program info:
           Method count: 1
           Method 0 name: forward
         Loading method 'forward'...
        offset_bytes (100352) + size_bytes (3136) >= allocator size (122880) for memory_id 0
        offset_bytes (100352) + size_bytes (3136) >= allocator size (122880) for memory_id 0
        offset_bytes (0) + size_bytes (100352) >= allocator size (122880) for memory_id 0
        offset_bytes (103488) + size_bytes (128) >= allocator size (122880) for memory_id 0
        offset_bytes (100352) + size_bytes (128) >= allocator size (122880) for memory_id 0
        offset_bytes (0) + size_bytes (2048) >= allocator size (122880) for memory_id 0
        offset_bytes (103488) + size_bytes (64) >= allocator size (122880) for memory_id 0
        offset_bytes (100352) + size_bytes (64) >= allocator size (122880) for memory_id 0
        offset_bytes (0) + size_bytes (640) >= allocator size (122880) for memory_id 0
        offset_bytes (103488) + size_bytes (40) >= allocator size (122880) for memory_id 0
        ✅ Method 'forward' loaded successfully
         ExecuTorch MLP MNIST Demo (Neural network) on Pico2 (microcontroller) 
        離 Testing all supported digits: 
        ......
        Input stats: 159 white pixels out of 784 total
        Running neural network inference...
        Execute Start time
        Execute End
        ✅ Neural network results:
          Digit 0: 370.000
          Digit 1: 0.000
          Digit 2: -3.000
          Digit 3: -3.000
          Digit 4: 860.000
          Digit 5: -3.000
          Digit 6: -3.000
          Digit 7: 1640.000 ← PREDICTED
          Digit 8: -3.000
          Digit 9: -3.000
        
        🎯 PREDICTED: 7 (Expected: 7) ✅ CORRECT!
        
        ==================================================

NOW Build the quantized version with the same 120KB size for allocator
In build_firmware_pico.sh we add
-DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON
-DEXECUTORCH_BUILD_KERNELS_QUANTIZED_AOT=ON \

Then in we add the quantized libs in CMakeList.txt
-Wl,--whole-archive
${BAREMETAL_BUILD_DIR}/lib/libportable_ops_lib.a
${BAREMETAL_BUILD_DIR}/lib/libquantized_ops_lib.a
-Wl,--no-whole-archive
${BAREMETAL_BUILD_DIR}/lib/libportable_kernels.a
${BAREMETAL_BUILD_DIR}/lib/libquantized_kernels.a

COMPILE
examples/raspberry_pi/pico2/build_firmware_pico.sh --model=balanced_tiny_mlp_mnist_quantized.pte
[SERIAL/DIRECT] CONNECTED TO PORT COM15 (115200-8N1)

    Loading model data (33104 bytes)...
    ✅ Program loaded successfully
    📊 Program info:
       Method count: 1
       Method 0 name: forward
    🔄 Loading method 'forward'...
    offset_bytes (0) + size_bytes (3136) >= allocator size (122880) for memory_id 0
    offset_bytes **(100352) + size_bytes (100352) >= allocator size (122880)** for memory_id 0
    ❌ Failed to load method: error 33
       → **Not enough memory t**o load method
    Failed to load and prepare model    

CHANGE main.cpp allocator size to 280KB. and now it WORKS . BUT Why did the memory needs went up so much from an unquantized model to the quantized model

RECOMPILE the quantized model and it runs now
static uint8_t method_allocator_pool[120 * 1024];
static uint8_t activation_pool[280 * 1024];

        Loading model data (33104 bytes)...
        ✅ Program loaded successfully
         Program info:
           Method count: 1
           Method 0 name: forward
         Loading method 'forward'...
        offset_bytes (0) + size_bytes (3136) >= allocator size (286720) for memory_id 0
        offset_bytes (100352) + size_bytes (100352) >= allocator size (286720) for memory_id 0
        offset_bytes (203840) + size_bytes (2048) >= allocator size (286720) for memory_id 0
        offset_bytes (205888) + size_bytes (640) >= allocator size (286720) for memory_id 0
        offset_bytes (0) + size_bytes (3136) >= allocator size (286720) for memory_id 0
        offset_bytes (3136) + size_bytes (784) >= allocator size (286720) for memory_id 0
        offset_bytes (200704) + size_bytes (3136) >= allocator size (286720) for memory_id 0
        offset_bytes (0) + size_bytes (100352) >= allocator size (286720) for memory_id 0
        offset_bytes (100352) + size_bytes (128) >= allocator size (286720) for memory_id 0
        offset_bytes (0) + size_bytes (128) >= allocator size (286720) for memory_id 0
        offset_bytes (200704) + size_bytes (32) >= allocator size (286720) for memory_id 0
        offset_bytes (100352) + size_bytes (128) >= allocator size (286720) for memory_id 0
        offset_bytes (0) + size_bytes (2048) >= allocator size (286720) for memory_id 0
        offset_bytes (200704) + size_bytes (64) >= allocator size (286720) for memory_id 0
        offset_bytes (0) + size_bytes (64) >= allocator size (286720) for memory_id 0
        offset_bytes (200704) + size_bytes (16) >= allocator size (286720) for memory_id 0
        offset_bytes (100352) + size_bytes (64) >= allocator size (286720) for memory_id 0
        offset_bytes (0) + size_bytes (640) >= allocator size (286720) for memory_id 0
        offset_bytes (200704) + size_bytes (40) >= allocator size (286720) for memory_id 0
        offset_bytes (100352) + size_bytes (10) >= allocator size (286720) for memory_id 0
        offset_bytes (0) + size_bytes (40) >= allocator size (286720) for memory_id 0
        ✅ Method 'forward' loaded successfully
         ExecuTorch MLP MNIST Demo (Neural network) on Pico2 (microcontroller) 
        離 Testing all supported digits: 
        
        Input stats: 159 white pixels out of 784 total
        Running neural network inference...
        Execute Start time
        Execute End
        ✅ Neural network results:
          Digit 0: 367.468
          Digit 1: 0.000
          Digit 2: -2.763
          Digit 3: -2.763
          Digit 4: 436.540 ← PREDICTED
          Digit 5: -2.763
          Digit 6: -2.763
          Digit 7: 375.756
          Digit 8: -2.763
          Digit 9: -2.763
        
        🎯 PREDICTED: 4 (Expected: 7) ❌ WRONG!
        
        ==================================================
        
        🎉 All tests complete! ExecuTorch inference of neural network works on Pico2!

[GregoryComer]

@psiddh Do you have any suggestions?