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"""
NPU Export Module for MiniMind Max2
Export to TFLite, QNN (Qualcomm), and other NPU formats.
"""
from dataclasses import dataclass
from typing import List, Optional, Dict, Any, Tuple, Union
from pathlib import Path
import torch
import torch.nn as nn
import json
@dataclass
class NPUExportConfig:
"""Configuration for NPU export."""
# Target platforms
target_platform: str = "tflite" # tflite, qnn, coreml, nnapi
# Quantization
quantization: str = "int8" # float16, int8, int4
calibration_samples: int = 100
# Optimization
optimize_for_inference: bool = True
enable_xnnpack: bool = True # TFLite XNNPACK delegate
# Model settings
max_sequence_length: int = 2048
batch_size: int = 1
# QNN specific
qnn_target: str = "gpu" # cpu, gpu, dsp, htp
# Output
include_metadata: bool = True
class TFLiteExporter:
"""Export MiniMind models to TensorFlow Lite format."""
def __init__(self, config: NPUExportConfig):
self.config = config
def export(
self,
model: nn.Module,
output_path: str,
sample_input: Optional[torch.Tensor] = None,
) -> str:
"""
Export model to TFLite format.
Args:
model: PyTorch model to export
output_path: Path for output .tflite file
sample_input: Sample input for tracing
Returns:
Path to exported model
"""
try:
import tensorflow as tf
except ImportError:
print("TensorFlow not installed. Install with: pip install tensorflow")
return self._export_via_onnx(model, output_path, sample_input)
model.eval()
# Get model config
if hasattr(model, 'config'):
vocab_size = model.config.vocab_size
hidden_size = model.config.hidden_size
else:
vocab_size = 102400
hidden_size = 1024
# Create sample input if not provided
if sample_input is None:
sample_input = torch.randint(
0, vocab_size,
(self.config.batch_size, self.config.max_sequence_length),
)
# Export via ONNX as intermediate
onnx_path = output_path.replace('.tflite', '.onnx')
self._export_to_onnx(model, onnx_path, sample_input)
# Convert ONNX to TFLite
try:
import onnx
from onnx_tf.backend import prepare
# Load ONNX model
onnx_model = onnx.load(onnx_path)
tf_rep = prepare(onnx_model)
# Save as SavedModel
saved_model_path = output_path.replace('.tflite', '_saved_model')
tf_rep.export_graph(saved_model_path)
# Convert to TFLite
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
# Quantization settings
if self.config.quantization == "int8":
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.int8]
elif self.config.quantization == "float16":
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]
# Enable optimizations
if self.config.optimize_for_inference:
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
# Save
with open(output_path, 'wb') as f:
f.write(tflite_model)
print(f"Exported TFLite model to: {output_path}")
return output_path
except Exception as e:
print(f"TFLite conversion failed: {e}")
return onnx_path
def _export_to_onnx(
self,
model: nn.Module,
output_path: str,
sample_input: torch.Tensor,
) -> str:
"""Export to ONNX as intermediate format."""
torch.onnx.export(
model,
sample_input,
output_path,
export_params=True,
opset_version=14,
do_constant_folding=True,
input_names=['input_ids'],
output_names=['logits'],
dynamic_axes={
'input_ids': {0: 'batch_size', 1: 'sequence_length'},
'logits': {0: 'batch_size', 1: 'sequence_length'},
},
)
return output_path
def _export_via_onnx(
self,
model: nn.Module,
output_path: str,
sample_input: torch.Tensor,
) -> str:
"""Fallback: export to ONNX only."""
onnx_path = output_path.replace('.tflite', '.onnx')
return self._export_to_onnx(model, onnx_path, sample_input)
class QNNExporter:
"""Export MiniMind models to Qualcomm QNN format."""
def __init__(self, config: NPUExportConfig):
self.config = config
def export(
self,
model: nn.Module,
output_path: str,
sample_input: Optional[torch.Tensor] = None,
) -> Dict[str, str]:
"""
Export model to QNN format for Qualcomm NPUs.
Returns:
Dictionary with paths to exported files
"""
model.eval()
# Get model config
if hasattr(model, 'config'):
vocab_size = model.config.vocab_size
else:
vocab_size = 102400
if sample_input is None:
sample_input = torch.randint(
0, vocab_size,
(self.config.batch_size, self.config.max_sequence_length),
)
output_dir = Path(output_path).parent
output_dir.mkdir(parents=True, exist_ok=True)
# Step 1: Export to ONNX
onnx_path = str(output_dir / "model.onnx")
torch.onnx.export(
model,
sample_input,
onnx_path,
export_params=True,
opset_version=14,
do_constant_folding=True,
input_names=['input_ids'],
output_names=['logits'],
)
outputs = {"onnx": onnx_path}
# Step 2: Generate QNN conversion script
qnn_script = self._generate_qnn_script(onnx_path, output_path)
script_path = str(output_dir / "convert_to_qnn.sh")
with open(script_path, 'w') as f:
f.write(qnn_script)
outputs["conversion_script"] = script_path
# Step 3: Generate model config for QNN
config_path = str(output_dir / "qnn_config.json")
qnn_config = {
"model_name": "minimind_max2",
"input_tensors": [{
"name": "input_ids",
"dims": [self.config.batch_size, self.config.max_sequence_length],
"data_type": "int32"
}],
"output_tensors": [{
"name": "logits",
"data_type": "float32"
}],
"backend": self.config.qnn_target,
"quantization": self.config.quantization,
}
with open(config_path, 'w') as f:
json.dump(qnn_config, f, indent=2)
outputs["config"] = config_path
print(f"QNN export prepared. Run {script_path} with QNN SDK installed.")
return outputs
def _generate_qnn_script(self, onnx_path: str, output_path: str) -> str:
"""Generate shell script for QNN conversion."""
return f'''#!/bin/bash
# QNN Conversion Script for MiniMind Max2
# Requires Qualcomm QNN SDK
# Check QNN SDK
if [ -z "$QNN_SDK_ROOT" ]; then
echo "Error: QNN_SDK_ROOT not set. Please install Qualcomm QNN SDK."
exit 1
fi
# Convert ONNX to QNN
$QNN_SDK_ROOT/bin/x86_64-linux-clang/qnn-onnx-converter \\
--input_network {onnx_path} \\
--output_path {output_path}.cpp
# Compile model library
$QNN_SDK_ROOT/bin/x86_64-linux-clang/qnn-model-lib-generator \\
-c {output_path}.cpp \\
-b {output_path}.bin \\
-t {self.config.qnn_target}
echo "QNN model exported to {output_path}.bin"
'''
class CoreMLExporter:
"""Export MiniMind models to Apple Core ML format."""
def __init__(self, config: NPUExportConfig):
self.config = config
def export(
self,
model: nn.Module,
output_path: str,
sample_input: Optional[torch.Tensor] = None,
) -> str:
"""Export model to Core ML format for Apple Neural Engine."""
try:
import coremltools as ct
except ImportError:
print("coremltools not installed. Install with: pip install coremltools")
return ""
model.eval()
# Get model config
if hasattr(model, 'config'):
vocab_size = model.config.vocab_size
else:
vocab_size = 102400
if sample_input is None:
sample_input = torch.randint(
0, vocab_size,
(self.config.batch_size, self.config.max_sequence_length),
)
# Trace model
traced = torch.jit.trace(model, sample_input)
# Convert to Core ML
mlmodel = ct.convert(
traced,
inputs=[ct.TensorType(
name="input_ids",
shape=sample_input.shape,
dtype=int,
)],
compute_units=ct.ComputeUnit.ALL, # Use Neural Engine when available
)
# Quantization
if self.config.quantization == "float16":
mlmodel = ct.models.neural_network.quantization_utils.quantize_weights(
mlmodel, nbits=16
)
elif self.config.quantization == "int8":
mlmodel = ct.models.neural_network.quantization_utils.quantize_weights(
mlmodel, nbits=8
)
# Save
mlmodel.save(output_path)
print(f"Core ML model exported to: {output_path}")
return output_path
class NPUExporter:
"""Unified NPU export interface."""
def __init__(self, config: Optional[NPUExportConfig] = None):
self.config = config or NPUExportConfig()
self.exporters = {
"tflite": TFLiteExporter(self.config),
"qnn": QNNExporter(self.config),
"coreml": CoreMLExporter(self.config),
}
def export(
self,
model: nn.Module,
output_path: str,
target_platform: Optional[str] = None,
sample_input: Optional[torch.Tensor] = None,
) -> Union[str, Dict[str, str]]:
"""
Export model to specified NPU format.
Args:
model: PyTorch model
output_path: Output file path
target_platform: Target platform (tflite, qnn, coreml)
sample_input: Sample input for tracing
Returns:
Path(s) to exported model(s)
"""
platform = target_platform or self.config.target_platform
if platform not in self.exporters:
raise ValueError(f"Unknown platform: {platform}. Supported: {list(self.exporters.keys())}")
exporter = self.exporters[platform]
return exporter.export(model, output_path, sample_input)
def export_all(
self,
model: nn.Module,
output_dir: str,
sample_input: Optional[torch.Tensor] = None,
) -> Dict[str, Any]:
"""Export to all supported formats."""
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
results = {}
for platform, exporter in self.exporters.items():
try:
if platform == "tflite":
path = str(output_dir / "model.tflite")
elif platform == "qnn":
path = str(output_dir / "qnn" / "model")
elif platform == "coreml":
path = str(output_dir / "model.mlpackage")
else:
continue
result = exporter.export(model, path, sample_input)
results[platform] = {"success": True, "path": result}
except Exception as e:
results[platform] = {"success": False, "error": str(e)}
return results
def export_for_mobile(
model: nn.Module,
output_dir: str,
platforms: Optional[List[str]] = None,
config: Optional[NPUExportConfig] = None,
) -> Dict[str, Any]:
"""
High-level function to export model for mobile devices.
Args:
model: PyTorch model
output_dir: Output directory
platforms: List of target platforms (default: all)
config: Export configuration
Returns:
Dictionary with export results for each platform
"""
config = config or NPUExportConfig()
exporter = NPUExporter(config)
if platforms is None:
return exporter.export_all(model, output_dir)
results = {}
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for platform in platforms:
try:
if platform == "tflite":
path = str(output_dir / "model.tflite")
elif platform == "qnn":
path = str(output_dir / "qnn" / "model")
elif platform == "coreml":
path = str(output_dir / "model.mlpackage")
else:
continue
result = exporter.export(model, path, target_platform=platform)
results[platform] = {"success": True, "path": result}
except Exception as e:
results[platform] = {"success": False, "error": str(e)}
return results
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