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import tensorflow as tf
import torch
import numpy as np
from typing import Optional, Union, Dict
import logging
from transformers import TFAutoModel, AutoTokenizer
class TensorFlowOptimizer:
"""
TensorFlow integration for model optimization and inference acceleration.
Provides TensorFlow Lite conversion and GPU optimization.
"""
def __init__(self):
self.logger = logging.getLogger(__name__)
# Configure TensorFlow
self._configure_tensorflow()
def _configure_tensorflow(self):
"""Configure TensorFlow for optimal performance."""
try:
# Enable mixed precision
tf.keras.mixed_precision.set_global_policy('mixed_float16')
# Configure GPU memory growth
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
self.logger.info(f"Configured {len(gpus)} GPU(s) for TensorFlow")
else:
self.logger.info("No GPUs found, using CPU")
except Exception as e:
self.logger.warning(f"TensorFlow configuration warning: {e}")
def convert_to_tensorflow_lite(self, model_path: str, output_path: str,
quantize: bool = True) -> bool:
"""
Convert a model to TensorFlow Lite for mobile/edge deployment.
Args:
model_path: Path to the saved model
output_path: Path for the TFLite model
quantize: Whether to apply quantization
Returns:
Success status
"""
try:
# Load the model
converter = tf.lite.TFLiteConverter.from_saved_model(model_path)
if quantize:
# Apply dynamic range quantization
converter.optimizations = [tf.lite.Optimize.DEFAULT]
# For better quantization, use representative dataset
converter.representative_dataset = self._representative_dataset_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
# Convert the model
tflite_model = converter.convert()
# Save the model
with open(output_path, 'wb') as f:
f.write(tflite_model)
self.logger.info(f"Successfully converted model to TFLite: {output_path}")
return True
except Exception as e:
self.logger.error(f"TFLite conversion failed: {e}")
return False
def _representative_dataset_gen(self):
"""Generate representative dataset for quantization."""
# Generate dummy audio data for quantization calibration
for _ in range(100):
# Simulate 16kHz audio for 1 second
dummy_audio = np.random.randn(1, 16000).astype(np.float32)
yield [dummy_audio]
def optimize_for_inference(self, model, input_shape: tuple) -> tf.keras.Model:
"""
Optimize a TensorFlow model for inference.
Args:
model: TensorFlow model to optimize
input_shape: Expected input shape
Returns:
Optimized model
"""
try:
# Create concrete function for optimization
@tf.function
def inference_func(x):
return model(x, training=False)
# Get concrete function
concrete_func = inference_func.get_concrete_function(
tf.TensorSpec(shape=input_shape, dtype=tf.float32)
)
# Apply graph optimization
optimized_func = tf.function(concrete_func)
self.logger.info("Model optimized for inference")
return optimized_func
except Exception as e:
self.logger.error(f"Inference optimization failed: {e}")
return model
def benchmark_model(self, model, input_shape: tuple, num_runs: int = 100) -> Dict:
"""
Benchmark model performance.
Args:
model: Model to benchmark
input_shape: Input shape for testing
num_runs: Number of benchmark runs
Returns:
Performance metrics
"""
try:
# Generate test input
test_input = tf.random.normal(input_shape)
# Warmup runs
for _ in range(10):
_ = model(test_input)
# Benchmark runs
import time
start_time = time.time()
for _ in range(num_runs):
_ = model(test_input)
end_time = time.time()
# Calculate metrics
total_time = end_time - start_time
avg_time = total_time / num_runs
throughput = num_runs / total_time
metrics = {
"total_time": total_time,
"average_inference_time": avg_time,
"throughput_fps": throughput,
"num_runs": num_runs
}
self.logger.info(f"Benchmark results: {metrics}")
return metrics
except Exception as e:
self.logger.error(f"Benchmarking failed: {e}")
return {}
def create_serving_signature(self, model, input_spec: Dict) -> tf.keras.Model:
"""
Create a model with serving signature for deployment.
Args:
model: TensorFlow model
input_spec: Input specification dictionary
Returns:
Model with serving signature
"""
try:
# Define serving function
@tf.function
def serve_fn(audio_input):
# Preprocess input if needed
processed_input = tf.cast(audio_input, tf.float32)
# Run inference
output = model(processed_input)
# Post-process output if needed
return {"transcription": output}
# Create serving signature
signatures = {
"serving_default": serve_fn.get_concrete_function(
audio_input=tf.TensorSpec(
shape=input_spec.get("shape", [None, None]),
dtype=tf.float32,
name="audio_input"
)
)
}
# Save model with signature
model._signatures = signatures
self.logger.info("Created serving signature for model")
return model
except Exception as e:
self.logger.error(f"Serving signature creation failed: {e}")
return model
class TensorFlowSpeechModel:
"""
TensorFlow-native speech-to-text model wrapper.
Provides optimized inference using TensorFlow operations.
"""
def __init__(self, model_name: str, use_mixed_precision: bool = True):
self.model_name = model_name
self.use_mixed_precision = use_mixed_precision
self.logger = logging.getLogger(__name__)
# Initialize TensorFlow optimizer
self.tf_optimizer = TensorFlowOptimizer()
# Load model
self.model = None
self.tokenizer = None
self._load_model()
def _load_model(self):
"""Load TensorFlow version of the model."""
try:
# Try to load TensorFlow version
self.model = TFAutoModel.from_pretrained(
self.model_name,
from_tf=True
)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
# Optimize for inference
if hasattr(self.model, 'config'):
input_shape = (1, self.model.config.max_position_embeddings)
self.model = self.tf_optimizer.optimize_for_inference(
self.model, input_shape
)
self.logger.info(f"Loaded TensorFlow model: {self.model_name}")
except Exception as e:
self.logger.warning(f"TensorFlow model loading failed: {e}")
self.model = None
def transcribe(self, audio_input: np.ndarray) -> str:
"""
Transcribe audio using TensorFlow model.
Args:
audio_input: Audio data as numpy array
Returns:
Transcribed text
"""
if self.model is None:
raise ValueError("Model not loaded")
try:
# Convert to TensorFlow tensor
audio_tensor = tf.convert_to_tensor(audio_input, dtype=tf.float32)
# Add batch dimension if needed
if len(audio_tensor.shape) == 1:
audio_tensor = tf.expand_dims(audio_tensor, 0)
# Run inference
with tf.device('/GPU:0' if tf.config.list_physical_devices('GPU') else '/CPU:0'):
outputs = self.model(audio_tensor)
# Process outputs (this would depend on the specific model)
# For now, return a placeholder
return "TensorFlow transcription result"
except Exception as e:
self.logger.error(f"TensorFlow transcription failed: {e}")
raise
def benchmark(self, audio_shape: tuple = (1, 16000)) -> Dict:
"""Benchmark the TensorFlow model."""
if self.model is None:
return {"error": "Model not loaded"}
return self.tf_optimizer.benchmark_model(self.model, audio_shape)
def save_optimized_model(self, output_path: str) -> bool:
"""Save optimized model for deployment."""
if self.model is None:
return False
try:
# Create serving signature
input_spec = {"shape": [None, 16000]} # Audio input shape
model_with_signature = self.tf_optimizer.create_serving_signature(
self.model, input_spec
)
# Save model
tf.saved_model.save(model_with_signature, output_path)
self.logger.info(f"Saved optimized model to: {output_path}")
return True
except Exception as e:
self.logger.error(f"Model saving failed: {e}")
return False
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