core/data_architecture/sparse_ops.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import autocast
from torch.optim import Adam
import cupy as cp  # Optional for CUDA kernels
import cudf  # cuDF for GPU-accelerated DataFrames
import flash_attn  # FlashAttention for GPU-optimized attention
import onnx
import onnxruntime as ort
import tensorrt as trt
from nemo.collections.nlp.models import GPTModel
from nemo.collections.tts.models import FastPitchModel
from nemo.collections.asr.models import EncDecCTCModel
from torch2trt import torch2trt  # Convert PyTorch to TensorRT
from transformers import AutoModel, AutoTokenizer
import apex
from apex import amp
from apex.optimizers import FusedAdam

# Assuming fused_ops is compiled and available
import fused_ops  # Custom CUDA extension from fused_ops.cu

class SparseLinear(nn.Module):
    """
    Sparse Linear Layer with Tensor Core Optimizations and Dynamic Pruning.
    Integrates fused GEMM + ReLU CUDA kernel for GPU efficiency.
    """
    def __init__(self, in_features, out_features, sparsity=0.5, use_fp16=True, dynamic_pruning=False):
        super(SparseLinear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.sparsity = sparsity
        self.use_fp16 = use_fp16
        self.dynamic_pruning = dynamic_pruning

        # Initialize dense weight and bias
        self.weight = nn.Parameter(
            torch.randn(out_features, in_features, dtype=torch.float16 if use_fp16 else torch.float32)
        )
        self.bias = nn.Parameter(
            torch.zeros(out_features, dtype=torch.float16 if use_fp16 else torch.float32)
        )

        # Sparse mask
        self.register_buffer("mask", self.generate_mask())

    def generate_mask(self):
        """
        Generates a binary mask based on weight magnitude for structured sparsity.
        """
        if self.dynamic_pruning:
            return torch.ones_like(self.weight)
        weights_abs = self.weight.abs()
        threshold = torch.quantile(weights_abs.flatten(), self.sparsity)
        return (weights_abs > threshold).to(self.weight.dtype)

    def update_mask(self):
        """Update mask dynamically based on current weight magnitudes."""
        if self.dynamic_pruning:
            weights_abs = self.weight.abs()
            threshold = torch.quantile(weights_abs.flatten(), self.sparsity)
            self.mask.data = (weights_abs > threshold).to(self.weight.dtype)

    def forward(self, x):
        if self.dynamic_pruning:
            self.update_mask()

        if self.use_fp16 and x.is_cuda():
            # Use fused CUDA kernel for GEMM + ReLU
            return fused_ops.fused_sparse_gemm_relu(x, self.weight, self.mask, self.bias)
        else:
            # Fallback to PyTorch
            pruned_weight = self.weight.float() * self.mask.float()
            return F.relu(F.linear(x.float(), pruned_weight, self.bias.float()))


class SparseConv2d(nn.Module):
    """
    Sparse 2D Convolution with structured sparsity and block sparsity support.
    Reduces computation by pruning less important weights.
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, 
                 sparsity=0.5, use_fp16=True, block_size=None, dynamic_pruning=False):
        super(SparseConv2d, self).__init__()
        self.use_fp16 = use_fp16
        self.sparsity = sparsity
        self.dynamic_pruning = dynamic_pruning
        self.block_size = block_size

        self.conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            dtype=torch.float16 if use_fp16 else torch.float32,
        )
        self.register_buffer("mask", self.generate_mask())

    def generate_mask(self):
        weights = self.conv.weight
        if self.dynamic_pruning:
            return torch.ones_like(weights)
        weights_abs = weights.abs()
        if self.block_size:
            kh, kw = self.block_size
            weights_reshaped = weights_abs.view(weights_abs.size(0), weights_abs.size(1), 
                                               weights_abs.size(2) // kh, kh, 
                                               weights_abs.size(3) // kw, kw)
            block_magnitudes = weights_reshaped.norm(p=2, dim=(3, 4))
            threshold = torch.quantile(block_magnitudes.flatten(), self.sparsity)
            block_mask = (block_magnitudes > threshold).float()
            mask = block_mask.unsqueeze(-1).unsqueeze(-1).expand_as(weights_reshaped).reshape_as(weights)
        else:
            threshold = torch.quantile(weights_abs.flatten(), self.sparsity)
            mask = (weights_abs > threshold).float()
        return mask

    def update_mask(self):
        if self.dynamic_pruning:
            self.mask.data = self.generate_mask()

    def forward(self, x):
        if self.dynamic_pruning:
            self.update_mask()

        if self.use_fp16:
            with autocast():
                pruned_weight = self.conv.weight * self.mask
                return F.conv2d(x, pruned_weight, self.conv.bias, self.conv.stride, self.conv.padding)
        else:
            pruned_weight = self.conv.weight.float() * self.mask.float()
            return F.conv2d(x.float(), pruned_weight, self.conv.bias.float(), 
                           self.conv.stride, self.conv.padding)


class SparseMLP(nn.Module):
    """
    Sparse MLP with Tensor Core Acceleration and optional dynamic pruning.
    Uses sparse linear layers with fused ops for efficiency.
    """
    def __init__(self, input_dim, hidden_dim, output_dim, sparsity=0.5, 
                 use_fp16=True, dynamic_pruning=False):
        super(SparseMLP, self).__init__()
        self.fc1 = SparseLinear(input_dim, hidden_dim, sparsity, use_fp16, dynamic_pruning)
        self.fc2 = SparseLinear(hidden_dim, output_dim, sparsity, use_fp16, dynamic_pruning)
        self.use_fp16 = use_fp16

    def forward(self, x):
        if self.use_fp16:
            with autocast():
                x = self.fc1(x)  # Already includes ReLU from fused kernel
                x = self.fc2(x)
                return x
        else:
            x = self.fc1(x)  # Includes ReLU from fallback
            return self.fc2(x)

# Example training loop with Apex mixed precision and FusedAdam
def train_sparse_mlp():
    model = SparseMLP(784, 256, 10, sparsity=0.5, use_fp16=True).cuda()
    optimizer = FusedAdam(model.parameters(), lr=0.001)
    
    # Initialize Apex AMP
    model, optimizer = amp.initialize(model, optimizer, opt_level="O1")

    # Dummy data
    inputs = torch.randn(32, 784).cuda()
    targets = torch.randint(0, 10, (32,)).cuda()

    # Training loop
    for _ in range(100):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = F.cross_entropy(outputs, targets)
        
        with amp.scale_loss(loss, optimizer) as scaled_loss:
            scaled_loss.backward()
        optimizer.step()

    # Export to ONNX
    torch.onnx.export(model, inputs, "sparse_mlp.onnx", opset_version=12)

    # Convert to TensorRT
    model_trt = torch2trt(model, [inputs], fp16_mode=True)
    return model_trt

if __name__ == "__main__":
    trt_model = train_sparse_mlp()