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()