18. Custom Quantization Project

This chapter consolidates learned concepts into a complete project: implementing and deploying a mixed-precision quantization scheme for a transformer model.

Project Overview

The project implements:

1. Per-layer quantization configuration
2. Custom kernel implementations
3. Accuracy calibration pipeline
4. Production deployment

Project Structure

# project_structure.py
import dataclasses
from typing import List, Dict, Optional
from enum import Enum

class QuantizationType(Enum):
    FP16 = "fp16"
    INT8 = "int8"
    INT4 = "int4"
    FP8_E4M3 = "fp8_e4m3"
    FP8_E5M2 = "fp8_e5m2"

@dataclasses
class LayerQuantConfig:
    name: str
    weight_quant: QuantizationType
    activation_quant: QuantizationType
    weight_bits: int = 8
    activation_bits: int = 8
    block_size: int = 128
    scale_method: str = "max"

@dataclasses
class ModelQuantConfig:
    layers: List[LayerQuantConfig]
    calibration_samples: int = 512
    accuracy_threshold: float = 0.99

class QuantizationPlanner:
    """Automatically determine quantization strategy per layer."""
    
    def __init__(self, config: ModelQuantConfig):
        self.config = config
        
    def analyze_model(self, model):
        """Determine per-layer quantization based on sensitivity analysis."""
        sensitivity_scores = {}
        
        for name, module in model.named_modules():
            if isinstance(module, (torch.nn.Linear, torch.nn.Conv2d)):
                # Measure output distribution characteristics
                output_stats = self.measure_output_distribution(module)
                sensitivity = self.compute_sensitivity(output_stats)
                sensitivity_scores[name] = sensitivity
                
        return sensitivity_scores
    
    def generate_layer_configs(self, sensitivity_scores) -> List[LayerQuantConfig]:
        """Generate quantization configs based on sensitivity analysis."""
        configs = []
        
        for name, sensitivity in sensitivity_scores.items():
            if sensitivity < 0.1:
                # Low sensitivity - aggressive quantization
                configs.append(LayerQuantConfig(
                    name=name,
                    weight_quant=QuantizationType.INT4,
                    activation_quant=QuantizationType.INT8,
                    weight_bits=4
                ))
            elif sensitivity < 0.3:
                # Medium sensitivity - standard quantization
                configs.append(LayerQuantConfig(
                    name=name,
                    weight_quant=QuantizationType.INT8,
                    activation_quant=QuantizationType.INT8
                ))
            else:
                # High sensitivity - preserve precision
                configs.append(LayerQuantConfig(
                    name=name,
                    weight_quant=QuantizationType.FP16,
                    activation_quant=QuantizationType.FP16
                ))
                
        return configs

Complete Kernel Implementation

// cuda/mixed_precision_transformer.cu

// Mixed precision layer with configurable quantization
template <QuantizationType WeightQ, QuantizationType ActQ>
__global__ void mixed_precision_transformer_kernel(
    const void* __restrict__ input,
    const void* __restrict__ weights,
    const float* __restrict__ scales,
    void* __restrict__ output,
    int batch, int seq_len, int hidden_size,
    int num_heads, int head_dim) {
    
    extern __shared__ float smem[];
    
    int bid = blockIdx.x;
    int tid = threadIdx.x;
    
    // Shared memory layout for cooperative compute
    float* sh_input = smem;
    float* sh_weights = &smem[hidden_size];
    float* sh_output = &smem[hidden_size * 2];
    
    // Grid-stride loop for sequence dimension
    for (int pos = bid * blockDim.y; pos < seq_len; pos += gridDim.y * blockDim.y) {
        int local_pos = pos % blockDim.y;
        
        // Load and dequantize input
        load_input_act<ActQ>(input, sh_input, pos, hidden_size, scales);
        
        // Load and dequantize weights
        load_weight<WeightQ>(weights, sh_weights, scales, hidden_size);
        
        // Compute attention scores: Q @ K^T
        float score_acc = compute_attention_score(
            sh_input, sh_weights, head_dim, num_heads);
        
        // Store intermediate result
        if (local_pos == 0) {
            sh_output[tid] = score_acc;
        }
        __syncthreads();
        
        // Apply softmax and propagate
        apply_attention_kernel(sh_output, sh_input, tid, head_dim);
        
        // Write output
        store_output(output, sh_output, pos, hidden_size);
    }
}

// Calibration kernel to collect statistics
__global__ void calibration_stats_kernel(
    const float* input, float* abs_max, float* abs_min,
    float* histogram, int size, int num_bins, float range) {
    
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    
    __shared__ float local_max[256];
    __shared__ float local_min[256];
    
    local_max[threadIdx.x] = -INFINITY;
    local_min[threadIdx.x] = INFINITY;
    
    for (int i = idx; i < size; i += gridDim.x * blockDim.x) {
        float val = fabsf(input[i]);
        local_max[threadIdx.x] = fmaxf(local_max[threadIdx.x], val);
        local_min[threadIdx.x] = fminf(local_min[threadIdx.x], input[i]);
        
        // Histogram for entropy-based calibration
        int bin = clamp((input[i] + range) / (2 * range) * num_bins, 0, num_bins - 1);
        atomicAdd(&histogram[bin], 1.0f);
    }
    
    __syncthreads();
    
    // Block-level reduction
    if (threadIdx.x == 0) {
        float block_max = -INFINITY;
        for (int i = 0; i < blockDim.x; i++) {
            block_max = fmaxf(block_max, local_max[i]);
        }
        atomicMax((int*)abs_max, float_to_int_bits(block_max));
    }
}

Deployment Pipeline

# deploy/quantized_inference.py

class QuantizedTransformerInference:
    def __init__(self, model_path, quant_config_path, kernel_path):
        self.model = self.load_model(model_path)
        self.quant_config = self.load_quant_config(quant_config_path)
        self.kernels = self.load_kernels(kernel_path)
        self.execution_provider = self.setup_cuda_execution()
        
    def quantize_model(self, calibration_data):
        """Quantize model with custom kernels."""
        calibrator = Calibrator(self.quant_config)
        
        # Collect statistics
        for batch in tqdm(calibration_data, desc="Calibration"):
            calibrator.collect_stats(self.model, batch)
            
        scales = calibrator.compute_scales()
        
        # Replace float operations with quantized kernels
        for layer_config in self.quant_config.layers:
            self.replace_with_quantized_kernel(layer_config, scales)
            
        return self
    
    def benchmark_accuracy(self, test_data):
        """Evaluate quantized model accuracy."""
        correct = 0
        total = 0
        
        with torch.no_grad():
            for inputs, targets in test_data:
                fp32_output = self.model(inputs)
                quantized_output = self.inference(inputs)
                
                # Compare outputs
                max_diff = (fp32_output - quantized_output).abs().max().item()
                if max_diff < 0.1:  # 10% tolerance for mixed precision
                    correct += 1
                total += 1
                
        return correct / total
    
    def export_for_production(self, output_path):
        """Export quantized model for production deployment."""
        export_config = {
            'model_weights': self.serialize_weights(),
            'quantization_scales': self.serialize_scales(),
            'kernel_library': self.kernels.path,
            'execution_provider': 'CUDAExecutionProvider',
            'metadata': {
                'batch_size': self.model.batch_size,
                'sequence_length': self.model.max_seq_len,
                'hidden_size': self.model.hidden_size
            }
        }
        
        with open(output_path, 'wb') as f:
            pickle.dump(export_config, f)
            
        return output_path