7.1 The Framework Landscape

Modern deep learning development relies heavily on high-level frameworks that abstract low-level hardware details. PyTorch, TensorFlow, JAX, and ONNX Runtime dominate the ecosystem, each with distinct philosophies and architectures. Successfully integrating WIA-AI-011 with these frameworks requires understanding their internal designs, extension mechanisms, and performance characteristics.

The framework landscape presents both opportunities and challenges. Each framework targets different use cases: PyTorch excels in research flexibility, TensorFlow in production deployment, JAX in high-performance computing, and ONNX in cross-framework interoperability. WIA-AI-011's integration strategy must respect these differences while providing consistent accelerator access across all frameworks.

Framework	Primary Strength	Execution Model	Integration Approach
PyTorch	Research flexibility	Eager (dynamic graph)	Custom device backend
TensorFlow	Production deployment	Graph + Eager	Device plugin + XLA
JAX	HPC and composability	JIT compilation	Custom backend
ONNX Runtime	Interoperability	Optimizing runtime	Execution provider

7.2 PyTorch Integration

PyTorch's dynamic computation graph and imperative programming model make it popular for research and rapid prototyping. The framework provides backend extension points through custom device types and dispatcher mechanisms. WIA-AI-011 integration creates a new device type "wia" that routes operations to WIA-compliant accelerators while maintaining PyTorch's ergonomic API.

import torch
import torch_wia  # WIA-AI-011 PyTorch extension

# Discover WIA devices
print(f"WIA devices available: {torch_wia.device_count()}")  # Output: 4
print(f"Current device: {torch_wia.current_device()}")
print(f"Device name: {torch_wia.get_device_name(0)}")  # "WIA NPU-X1"

# Move model to WIA device
device = torch.device('wia:0')
model = ResNet50().to(device)

# Verify device placement
print(f"Model on device: {next(model.parameters()).device}")  # wia:0

# Run inference
input_tensor = torch.randn(32, 3, 224, 224, device=device)
with torch.inference_mode():
    output = model(input_tensor)

print(f"Output shape: {output.shape}")  # torch.Size([32, 1000])
print(f"Output device: {output.device}")  # wia:0

# Training example
model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(num_epochs):
    for batch in dataloader:
        # Move batch to WIA device
        images, labels = batch
        images = images.to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # Backward pass
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        print(f"Epoch {epoch}, Loss: {loss.item():.4f}")

7.2.1 Custom Operators

PyTorch's operator registration system allows frameworks to provide implementations for built-in operations. WIA-AI-011 registers optimized kernels for all standard torch operations, automatically dispatching to appropriate accelerators. The registration system supports both C++ and Python implementations.

// C++ operator implementation
#include 
#include 

// WIA matmul implementation
at::Tensor wia_matmul(const at::Tensor& self, const at::Tensor& other) {
    // Convert PyTorch tensors to WIA tensors
    wia_tensor_t a = convert_to_wia(self);
    wia_tensor_t b = convert_to_wia(other);

    // Allocate output tensor
    auto output_shape = infer_matmul_shape(self, other);
    wia_tensor_t c = wia_allocate_tensor(output_shape, WIA_FLOAT32);

    // Execute WIA matmul kernel
    wia_matmul(get_wia_context(), a, b, c, nullptr, get_wia_stream());

    // Convert back to PyTorch tensor
    return convert_from_wia(c);
}

// Register operator with PyTorch dispatcher
TORCH_LIBRARY_IMPL(aten, WIA, m) {
    m.impl("matmul", wia_matmul);
    m.impl("addmm", wia_addmm);
    m.impl("conv2d", wia_conv2d);
    m.impl("linear", wia_linear);
    m.impl("relu", wia_relu);
    m.impl("softmax", wia_softmax);
    m.impl("layer_norm", wia_layer_norm);
    m.impl("batch_norm", wia_batch_norm);
    // ... hundreds more operators
}

// Advanced operator with tuning
at::Tensor wia_conv2d(
    const at::Tensor& input,
    const at::Tensor& weight,
    const c10::optional& bias,
    at::IntArrayRef stride,
    at::IntArrayRef padding,
    at::IntArrayRef dilation,
    int64_t groups)
{
    // Select optimal algorithm based on input characteristics
    wia_conv_algorithm_t algo = select_conv_algorithm(
        input.sizes(), weight.sizes(), stride, padding);

    // Execute with optimal configuration
    wia_conv_config_t config = {
        .algorithm = algo,
        .workspace_size = compute_workspace_size(algo, input.sizes()),
        .use_tensor_cores = should_use_tensor_cores(input.dtype()),
        .fusion = detect_fusion_opportunity(input)
    };

    return execute_wia_conv2d(input, weight, bias, config);
}

7.2.2 Autograd Support

PyTorch's automatic differentiation requires backward pass implementations for all operations. WIA-AI-011 provides gradient formulas for all operations, enabling seamless training on WIA devices. The autograd engine automatically calls backward implementations during backpropagation.

// Autograd function for custom WIA operator
class WIAMatmul : public torch::autograd::Function {
public:
    static torch::Tensor forward(
        torch::autograd::AutogradContext* ctx,
        torch::Tensor A,
        torch::Tensor B)
    {
        // Save tensors for backward
        ctx->save_for_backward({A, B});

        // Forward computation
        return wia_matmul_forward(A, B);
    }

    static std::vector backward(
        torch::autograd::AutogradContext* ctx,
        std::vector grad_outputs)
    {
        // Retrieve saved tensors
        auto saved = ctx->get_saved_variables();
        auto A = saved[0];
        auto B = saved[1];
        auto grad_output = grad_outputs[0];

        // Compute gradients
        // d(AB)/dA = grad_output @ B^T
        // d(AB)/dB = A^T @ grad_output
        torch::Tensor grad_A = torch::matmul(grad_output, B.t());
        torch::Tensor grad_B = torch::matmul(A.t(), grad_output);

        return {grad_A, grad_B};
    }
};

// Python wrapper
torch::Tensor wia_matmul_autograd(torch::Tensor A, torch::Tensor B) {
    return WIAMatmul::apply(A, B);
}

7.2.3 Memory Management Integration

PyTorch's caching allocator must integrate with WIA's memory management for optimal performance.

// Custom allocator for WIA devices
class WIAAllocator : public c10::Allocator {
public:
    c10::DataPtr allocate(size_t nbytes) const override {
        void* ptr = nullptr;
        wia_malloc(&ptr, nbytes, get_current_wia_device());

        return {ptr, ptr, &WIAAllocator::deleter, c10::Device(c10::DeviceType::WIA, 0)};
    }

    static void deleter(void* ptr) {
        wia_free(ptr);
    }

    c10::DeleterFnPtr raw_deleter() const override {
        return &WIAAllocator::deleter;
    }
};

// Register allocator with PyTorch
at::globalContext().lazyInitWIA();
at::DeviceGuard device_guard(at::Device(at::DeviceType::WIA, 0));
c10::SetAllocator(c10::DeviceType::WIA, std::make_unique());

7.3 TensorFlow Integration

TensorFlow uses static computation graphs (in graph mode) compiled into optimized executables. WIA-AI-011 integrates through custom devices and kernel libraries, with XLA (Accelerated Linear Algebra) compiler integration for optimal performance. The integration supports both TensorFlow 2.x eager execution and graph mode.

import tensorflow as tf
import tensorflow_wia

# List available WIA devices
wia_devices = tensorflow_wia.list_physical_devices('WIA')
print(f"Available WIA devices: {len(wia_devices)}")
for i, device in enumerate(wia_devices):
    print(f"  Device {i}: {device.name}")

# Configure WIA device
tf.config.experimental.set_visible_devices(wia_devices[0], 'WIA')
tf.config.experimental.set_memory_growth(wia_devices[0], True)

# Build model with explicit device placement
with tf.device('/WIA:0'):
    model = tf.keras.applications.ResNet50(weights=None, classes=1000)
    model.compile(
        optimizer=tf.keras.optimizers.Adam(0.001),
        loss=tf.keras.losses.SparseCategoricalCrossentropy(),
        metrics=['accuracy'])

# Train model
history = model.fit(
    train_dataset,
    epochs=10,
    validation_data=val_dataset,
    callbacks=[
        tf.keras.callbacks.ModelCheckpoint('best_model.h5'),
        tf.keras.callbacks.TensorBoard(log_dir='./logs')
    ])

# Inference with explicit device
with tf.device('/WIA:0'):
    sample_input = tf.random.normal([1, 224, 224, 3])
    predictions = model(sample_input, training=False)
    print(f"Predictions shape: {predictions.shape}")

7.3.1 XLA Integration

XLA compiles TensorFlow graphs into optimized kernels. WIA-AI-011 provides an XLA backend that generates WIA-compatible code, enabling graph-level optimizations like fusion and layout optimization.

// XLA WIA backend implementation
#include "tensorflow/compiler/xla/service/custom_call_target_registry.h"
#include 

// Custom call implementation for WIA matmul
void WiaXlaMatmul(void* stream, void** buffers, const char* opaque, size_t opaque_len) {
    // Parse configuration from opaque data
    WiaMatmulConfig config;
    deserialize_config(opaque, opaque_len, &config);

    // Extract input/output buffers
    float* a = static_cast(buffers[0]);
    float* b = static_cast(buffers[1]);
    float* c = static_cast(buffers[2]);

    // Execute WIA matmul
    wia_context_t* ctx = get_wia_context();
    wia_stream_t* wia_stream = static_cast(stream);

    wia_matmul_strided(ctx, a, b, c, &config.dims, wia_stream);
}

// Register custom calls
XLA_REGISTER_CUSTOM_CALL_TARGET(WiaXlaMatmul);
XLA_REGISTER_CUSTOM_CALL_TARGET(WiaXlaConv2d);
XLA_REGISTER_CUSTOM_CALL_TARGET(WiaXlaBatchNorm);

// XLA compilation with WIA target
auto xla_computation = BuildMatmulComputation();
auto wia_executable = wia_xla_backend.Compile(xla_computation);

// Execute compiled XLA program
auto result = wia_executable.Execute(arguments);

7.3.2 Kernel Implementation

TensorFlow kernels implement operations for specific devices. WIA kernels leverage the standard TensorFlow kernel interface.

// TensorFlow WIA kernel registration
#include "tensorflow/core/framework/op_kernel.h"

class WiaMatMulOp : public OpKernel {
public:
    explicit WiaMatMulOp(OpKernelConstruction* context) : OpKernel(context) {
        // Parse attributes
        OP_REQUIRES_OK(context, context->GetAttr("transpose_a", &transpose_a_));
        OP_REQUIRES_OK(context, context->GetAttr("transpose_b", &transpose_b_));
    }

    void Compute(OpKernelContext* context) override {
        // Get input tensors
        const Tensor& a = context->input(0);
        const Tensor& b = context->input(1);

        // Validate shapes
        OP_REQUIRES(context, a.dims() == 2, errors::InvalidArgument("A must be 2D"));
        OP_REQUIRES(context, b.dims() == 2, errors::InvalidArgument("B must be 2D"));

        // Allocate output
        TensorShape output_shape({a.dim_size(0), b.dim_size(1)});
        Tensor* output = nullptr;
        OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));

        // Execute WIA matmul
        wia_tensor_t wia_a = convert_tf_to_wia(a);
        wia_tensor_t wia_b = convert_tf_to_wia(b);
        wia_tensor_t wia_c = convert_tf_to_wia(*output);

        wia_matmul_config_t config = {
            .transpose_a = transpose_a_,
            .transpose_b = transpose_b_,
            .alpha = 1.0f,
            .beta = 0.0f
        };

        wia_matmul(get_wia_context(), wia_a, wia_b, wia_c, &config,
                   context->eigen_device().stream());
    }

private:
    bool transpose_a_;
    bool transpose_b_;
};

// Register kernel
REGISTER_KERNEL_BUILDER(Name("MatMul").Device("WIA"), WiaMatMulOp);

7.4 JAX Integration

JAX brings functional programming and composable transformations (jit, grad, vmap, pmap) to numerical computing. Its design around pure functions and explicit transformations aligns well with WIA-AI-011's explicit device model. JAX's XLA integration provides a natural path for WIA backend support.

import jax
import jax.numpy as jnp
from jax_wia import wia_backend

# Set WIA as default platform
jax.config.update('jax_platform_name', 'wia')
jax.config.update('jax_wia_device', 0)

# Verify WIA backend
print(f"JAX backend: {jax.default_backend()}")  # wia
print(f"Available devices: {jax.devices()}")

# Define model
def model(params, x):
    w1, b1, w2, b2 = params
    h = jnp.tanh(jnp.dot(x, w1) + b1)
    return jnp.dot(h, w2) + b2

# JIT compile for WIA
@jax.jit
def train_step(params, batch):
    def loss_fn(params):
        predictions = model(params, batch['image'])
        return jnp.mean((predictions - batch['label'])**2)

    loss, grads = jax.value_and_grad(loss_fn)(params)
    return loss, grads

# Automatically compiled and optimized for WIA device
params = initialize_params()
for epoch in range(num_epochs):
    for batch in data_iter:
        loss, grads = train_step(params, batch)
        params = update_params(params, grads)
        print(f"Epoch {epoch}, Loss: {loss:.4f}")

# Vectorized map (vmap) for batch processing
@jax.vmap
def process_single_image(image):
    return model(params, image)

# Processes batch in parallel on WIA device
batch_predictions = process_single_image(image_batch)

# Parallel map (pmap) for multi-device
@jax.pmap
def parallel_train_step(params, batch):
    loss, grads = train_step(params, batch)
    # Automatically uses WIA all-reduce for gradient synchronization
    return loss, grads

# Runs on multiple WIA devices
multi_device_loss, multi_device_grads = parallel_train_step(
    replicated_params, sharded_batch)

7.4.1 Primitive Operations

JAX's primitives (lax operations) form the basis of all computations. WIA-AI-011 implements these primitives directly, enabling optimal performance without high-level translation overhead.

// JAX WIA primitive implementation
#include "jax/wia_primitives.h"

// Implement dot_general primitive
void wia_dot_general(
    void* out, void** args,
    const WiaDotDimensionNumbers* dimension_numbers) {

    wia_tensor_t lhs = static_cast(args[0]);
    wia_tensor_t rhs = static_cast(args[1]);
    wia_tensor_t result = static_cast(out);

    // Convert JAX dimension numbers to WIA matmul config
    wia_matmul_config_t config = convert_dot_dimensions(dimension_numbers);

    // Execute optimized WIA matmul
    wia_matmul(get_wia_context(), lhs, rhs, result, &config, get_stream());
}

// Register WIA implementations for all JAX primitives
RegisterWiaPrimitive("dot_general", wia_dot_general);
RegisterWiaPrimitive("conv_general_dilated", wia_conv_general);
RegisterWiaPrimitive("reduce_sum", wia_reduce_sum);
RegisterWiaPrimitive("reduce_max", wia_reduce_max);
RegisterWiaPrimitive("broadcast_in_dim", wia_broadcast);
RegisterWiaPrimitive("transpose", wia_transpose);
RegisterWiaPrimitive("reshape", wia_reshape);
// ... all JAX lax primitives

7.5 ONNX Runtime Integration

ONNX Runtime executes models in the standardized ONNX format from any framework. Adding a WIA execution provider enables ONNX models to run on any WIA-compliant accelerator without modification, providing true framework-agnostic deployment.

import onnxruntime as ort
import numpy as np

# Create session with WIA provider
session_options = ort.SessionOptions()
session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL

session = ort.InferenceSession(
    "model.onnx",
    sess_options=session_options,
    providers=[
        ('WiaExecutionProvider', {
            'device_id': 0,
            'enable_fusion': True,
            'enable_quantization': True,
            'enable_fp16': True
        }),
        'CPUExecutionProvider'  # Fallback
    ])

# Query provider information
print(f"Active provider: {session.get_providers()}")

# Run inference
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name

input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)
outputs = session.run([output_name], {input_name: input_data})

print(f"Output shape: {outputs[0].shape}")
print(f"Predictions: {outputs[0][:5]}")

# Benchmark performance
import time

warmup_runs = 10
benchmark_runs = 100

# Warmup
for _ in range(warmup_runs):
    session.run([output_name], {input_name: input_data})

# Benchmark
start = time.time()
for _ in range(benchmark_runs):
    session.run([output_name], {input_name: input_data})
end = time.time()

avg_time = (end - start) / benchmark_runs * 1000  # ms
throughput = 1000.0 / avg_time  # inferences/sec

print(f"Average inference time: {avg_time:.2f} ms")
print(f"Throughput: {throughput:.1f} inferences/sec")

7.5.1 Execution Provider Implementation

ONNX execution providers implement the IExecutionProvider interface, handling operator compilation and execution. The WIA provider translates ONNX operators to WIA operations, leveraging operator fusion and kernel caching.

// WIA Execution Provider implementation
class WIAExecutionProvider : public IExecutionProvider {
public:
    WIAExecutionProvider(const WIAExecutionProviderInfo& info)
        : IExecutionProvider("WIAExecutionProvider"),
          device_id_(info.device_id),
          enable_fusion_(info.enable_fusion) {

        wia_create_context(&context_, device_id_);
        wia_create_stream(&stream_, context_);
    }

    std::vector>
    GetCapability(const GraphViewer& graph,
                  const std::vector& registries) const override {

        std::vector> capabilities;

        // Identify subgraphs that can run on WIA
        for (auto& node : graph.Nodes()) {
            if (IsWIASupportedOp(node.OpType())) {
                capabilities.push_back(
                    CreateCapability(node, graph));
            }
        }

        // Fuse compatible subgraphs
        if (enable_fusion_) {
            capabilities = FuseSubgraphs(capabilities, graph);
        }

        return capabilities;
    }

    Status Compile(const std::vector& nodes,
                   std::vector& node_compute_funcs) override {

        for (auto* node : nodes) {
            // Compile ONNX op to WIA kernel
            auto kernel = CompileNodeToWIA(node);

            // Create compute function
            NodeComputeInfo compute_info;
            compute_info.create_state_func = [kernel](ComputeContext* ctx,
                                                       FunctionState* state) {
                *state = kernel;
                return Status::OK();
            };

            compute_info.compute_func = [](FunctionState state,
                                           const OrtApi* api,
                                           OrtKernelContext* context) {
                auto* kernel = static_cast(state);
                return kernel->Execute(api, context);
            };

            node_compute_funcs.push_back(compute_info);
        }

        return Status::OK();
    }

private:
    wia_context_t* context_;
    wia_stream_t* stream_;
    int device_id_;
    bool enable_fusion_;
};

7.6 Model Deployment and Serving

Production deployment requires efficient model serving infrastructure. WIA-AI-011 integrates with TorchServe, TensorFlow Serving, and Triton Inference Server, enabling high-throughput low-latency inference with features like dynamic batching, model versioning, and A/B testing.

// Triton model configuration for WIA backend
name: "resnet50_wia"
platform: "wia"
max_batch_size: 128
default_model_filename: "model.onnx"

input [
  {
    name: "input"
    data_type: TYPE_FP32
    dims: [ 3, 224, 224 ]
  }
]

output [
  {
    name: "output"
    data_type: TYPE_FP32
    dims: [ 1000 ]
  }
]

instance_group [
  {
    count: 4
    kind: KIND_WIA
    gpus: [ 0, 1, 2, 3 ]
  }
]

optimization {
  priority: PRIORITY_MAX
  graph: {
    level: 1  # Enable graph optimizations
  }

  execution_accelerators {
    wia_execution_accelerator {
      parameters { key: "precision" value: "fp16" }
      parameters { key: "enable_fusion" value: "true" }
    }
  }
}

dynamic_batching {
  preferred_batch_size: [ 8, 16, 32, 64, 128 ]
  max_queue_delay_microseconds: 1000
  preserve_ordering: false
}

model_warmup [{
  name: "random_sample"
  batch_size: 32
  inputs {
    key: "input"
    value: {
      data_type: TYPE_FP32
      dims: [ 3, 224, 224 ]
      random_data: true
    }
  }
}]

7.6.1 TorchServe Integration

TorchServe provides a flexible serving solution for PyTorch models with WIA backend support.

# TorchServe handler for WIA models
import torch
import torch_wia
from ts.torch_handler.base_handler import BaseHandler

class WIAHandler(BaseHandler):
    def initialize(self, context):
        properties = context.system_properties
        self.device = torch.device('wia:0')

        # Load model
        model_dir = properties.get("model_dir")
        model_pt_path = os.path.join(model_dir, "model.pt")

        self.model = torch.jit.load(model_pt_path, map_location=self.device)
        self.model.eval()

        # Optimize for inference
        self.model = torch.jit.optimize_for_inference(self.model)

        self.initialized = True

    def preprocess(self, data):
        images = []
        for row in data:
            image = row.get("data") or row.get("body")
            images.append(preprocess_image(image))

        return torch.stack(images).to(self.device)

    def inference(self, data):
        with torch.inference_mode():
            outputs = self.model(data)
        return outputs

    def postprocess(self, data):
        # Convert to probabilities
        probs = torch.softmax(data, dim=1)
        return probs.cpu().numpy().tolist()

7.7 Quantization and Model Optimization

Framework integration includes support for quantization-aware training and post-training quantization. WIA-AI-011 provides quantization schemes compatible with each framework's approach, enabling efficient INT8/FP16 inference.

# PyTorch quantization for WIA
import torch
import torch.quantization as quant
import torch_wia

# Define model
model = ResNet50()
model.eval()

# Set quantization configuration for WIA
model.qconfig = torch.quantization.get_default_qconfig('wia')

# Or custom configuration
custom_qconfig = torch.quantization.QConfig(
    activation=torch.quantization.observer.MovingAverageMinMaxObserver.with_args(
        dtype=torch.qint8, qscheme=torch.per_tensor_affine),
    weight=torch.quantization.observer.MovingAveragePerChannelMinMaxObserver.with_args(
        dtype=torch.qint8, qscheme=torch.per_channel_symmetric))

model.qconfig = custom_qconfig

# Prepare model for quantization
quant.prepare(model, inplace=True)

# Calibration
with torch.no_grad():
    for batch in calibration_dataloader:
        model(batch)

# Convert to quantized model
quant.convert(model, inplace=True)

# Move to WIA device
model = model.to('wia:0')

# Verify quantization
print(f"Model quantized: {next(model.parameters()).dtype}")  # qint8

# Benchmark
input_tensor = torch.randn(1, 3, 224, 224, device='wia:0')

# FP32 baseline
model_fp32 = ResNet50().to('wia:0')
fp32_time = benchmark_model(model_fp32, input_tensor)

# INT8 quantized
int8_time = benchmark_model(model, input_tensor)

print(f"FP32 latency: {fp32_time:.2f} ms")
print(f"INT8 latency: {int8_time:.2f} ms")
print(f"Speedup: {fp32_time / int8_time:.2f}x")

7.8 Distributed Training Support

Framework integration extends to distributed training APIs. PyTorch's DistributedDataParallel (DDP), TensorFlow's MultiWorkerMirroredStrategy, and JAX's pmap all work seamlessly with WIA devices, leveraging WIA-AI-011's collective communication primitives.

# PyTorch DDP with WIA backend
import torch
import torch.distributed as dist
import torch_wia

# Initialize process group with WIA backend
def setup(rank, world_size):
    dist.init_process_group(
        backend='wia',  # Use WIA collective operations
        init_method='env://',
        world_size=world_size,
        rank=rank)

    # Set device for this process
    torch.wia.set_device(rank)

def cleanup():
    dist.destroy_process_group()

def train(rank, world_size):
    setup(rank, world_size)

    # Create model and move to WIA device
    model = ResNet50().to(rank)

    # Wrap with DDP
    model = torch.nn.parallel.DistributedDataParallel(
        model,
        device_ids=[rank],
        output_device=rank,
        gradient_as_bucket_view=True)  # Optimization

    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

    # Training loop
    for epoch in range(num_epochs):
        for batch in distributed_sampler:
            images, labels = batch
            images = images.to(rank)
            labels = labels.to(rank)

            outputs = model(images)
            loss = criterion(outputs, labels)

            optimizer.zero_grad()
            loss.backward()  # Automatically does all-reduce of gradients
            optimizer.step()

    cleanup()

# Launch distributed training
if __name__ == '__main__':
    world_size = torch.wia.device_count()
    torch.multiprocessing.spawn(
        train,
        args=(world_size,),
        nprocs=world_size,
        join=True)

7.8.1 TensorFlow Distributed Strategy

TensorFlow's distribution strategies abstract multi-device training.

import tensorflow as tf
import tensorflow_wia

# Multi-worker mirrored strategy with WIA
strategy = tf.distribute.MultiWorkerMirroredStrategy(
    communication_options=tf.distribute.CommunicationOptions(
        implementation=tf.distribute.CommunicationImplementation.WIA,
        timeout_seconds=300))

print(f"Number of devices: {strategy.num_replicas_in_sync}")

# Define model within strategy scope
with strategy.scope():
    model = create_model()
    model.compile(
        optimizer='adam',
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy'])

# Distributed training
model.fit(distributed_dataset, epochs=10)

7.9 Profiling and Debugging Integration

Framework profilers integrate with WIA-AI-011's profiling infrastructure, providing unified views of performance across framework and hardware layers.

# PyTorch profiling with WIA
import torch
import torch.profiler
import torch_wia

model = ResNet50().to('wia:0')
input_tensor = torch.randn(32, 3, 224, 224, device='wia:0')

# Profile with WIA support
with torch.profiler.profile(
    activities=[
        torch.profiler.ProfilerActivity.CPU,
        torch.profiler.ProfilerActivity.WIA  # WIA device activity
    ],
    schedule=torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=2),
    on_trace_ready=torch.profiler.tensorboard_trace_handler('./log/wia_profile'),
    record_shapes=True,
    profile_memory=True,
    with_stack=True
) as prof:
    for _ in range(10):
        output = model(input_tensor)
        prof.step()

# Print profiling results
print(prof.key_averages().table(sort_by="wia_time_total", row_limit=10))

# Profiler output:
# -------------------------  ------------  ------------  ------------
# Name                       CPU time      WIA time      Memory
# -------------------------  ------------  ------------  ------------
# aten::conv2d              2.34ms         15.67ms       128.5 MB
# aten::batch_norm          0.89ms         3.21ms        64.2 MB
# aten::relu                0.12ms         0.54ms        32.1 MB
# aten::adaptive_avg_pool2d 0.08ms         0.31ms        4.0 MB
# aten::linear              0.45ms         2.12ms        8.0 MB
# -------------------------  ------------  ------------  ------------

7.10 Migration Tools and Best Practices

Migrating existing models to WIA devices should be straightforward. WIA-AI-011 provides migration tools that automatically detect device placement, suggest optimizations, and verify numerical accuracy after migration.

# Migration tool usage
wia-migrate --input model.py \\
            --output model_wia.py \\
            --source-device cuda:0 \\
            --target-device wia:0 \\
            --verify \\
            --benchmark \\
            --optimize

# Tool output:
# Analyzing model...
# Found 152 operations
# - 145 operations supported on WIA
# - 7 operations fallback to CPU
#
# Applying transformations:
# - Replacing torch.device('cuda:0') with torch.device('wia:0')
# - Updating model.to() calls
# - Adding WIA-specific optimizations
#
# Verifying numerical accuracy:
# - Running test inputs on both devices
# - Max absolute difference: 1.2e-5 (within tolerance)
# - Max relative difference: 3.4e-6 (within tolerance)
#
# Benchmarking performance:
# - CUDA latency: 12.5 ms
# - WIA latency: 8.3 ms
# - Speedup: 1.5x
#
# Migration successful! Output written to model_wia.py