18. Custom Quantization Project
Chapter 18 of 18 · 25 min
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:
- Per-layer quantization configuration
- Custom kernel implementations
- Accuracy calibration pipeline
- 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
EXERCISE
Complete the project by implementing INT4 weight quantization with block-wise scaling for attention projections, deploying to ONNX Runtime, and verifying accuracy within 1% of the floating-point baseline.