Arm_inductor_quantizer for Pt2e quantization

[choudhary-devang]

Title: Enable PyTorch 2 Export Quantization path for ARM CPUs.

Description:

• This PR extends the PyTorch 2 Export Quantization (PT2E Quantization) workflow—originally available only on x86 CPUs—to support ARM platforms. PT2E Quantization is an automated, full-graph quantization solution in PyTorch that improves on Eager Mode Quantization by adding support for functionals and automating the overall process. It is part of the torch.ao module and fully supports quantization when using the compile mode.

Key Changes:

• Introduces ARM-specific support by leveraging oneDNN kernels for matmuls and convolution.

• Integrates pre-defined configuration selection to automatically choose the best quantization settings based on the selected quantization method.

Provides customization options via two flags:

• qat_state: Indicates whether to use Quantization Aware Training (if set to True) or Post Training Quantization (if set to False). The default remains False.
• dynamic_state: Selects between dynamic quantization (if True) and static quantization (if False). The default is also set to False.
  

These options allow users to tailor the quantization process for their specific workload requirements (e.g., using QAT for fine-tuning or PTQ for calibration-based quantization).

Testing and Validation:

The new ARM flow has been thoroughly tested across a range of models with all combinations:
NLP: Models such as BERT and T5.
Vision: Models like ResNet and ViT.
Custom Models: user defined models with various operators.

example script:

import torch
from transformers import BertModel
import copy
import time
from torchao.quantization.pt2e.quantize_pt2e import prepare_pt2e, convert_pt2e
import torchao.quantization.pt2e.quantizer.arm_inductor_quantizer as aiq
from torchao.quantization.pt2e.quantizer.arm_inductor_quantizer import ArmInductorQuantizer
import torch.profiler
import torch._inductor.config as config
# Enable C++ wrapper for Inductor
config.cpp_wrapper = True
config.freezing=True

model_name = "bert-base-uncased"
model = BertModel.from_pretrained(model_name)

# Set the model to eval mode
model = model.eval()

# Create the data, using dummy data here as an example
traced_bs = 32
seq_length = 128
x = torch.randint(0, 10000, (traced_bs, seq_length))
attention_mask = torch.ones((traced_bs, seq_length))
example_inputs = (x, attention_mask)

# Capture the FX Graph to be quantized
with torch.no_grad():
    exported_model = torch.export.export_for_training(model, example_inputs).module()

    # Set up the quantizer and prepare the model for post-training quantization
    quantizer = ArmInductorQuantizer()
    quantizer.set_global(aiq.get_default_arm_inductor_quantization_config(is_dynamic=True))
    prepared_model = prepare_pt2e(exported_model, quantizer)

    # Run the prepared model to apply the quantization
    prepared_model(*example_inputs)

    # Convert the model to the quantized version
    converted_model = convert_pt2e(prepared_model)
    optimized_model = torch.compile(converted_model)
    st = time.time()
    optimized_model(*example_inputs)
    et = time.time()
    print(f"Average time required for inference = {et-st}\n")

cc: @jerryzh168, @fadara01, @Xia-Weiwen

[pytorch-bot]

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[choudhary-devang]

This pr is moved from torch to torchao due to the Pt2e migration to torchao

old pr link
(pytorch/pytorch#146690)