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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() |