Create sparse_ops.py

core/data_architecture/sparse_ops.py

@@ -0,0 +1,163 @@
+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
+
+class SparseLinear(nn.Module):
+    """
+    Sparse Linear Layer with Tensor Core Optimizations and Dynamic Pruning.
+    Prunes weights based on magnitude to improve efficiency on GPU.
+    """
+    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  # Toggle dynamic vs static 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 (static unless dynamic_pruning is enabled)
+        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:
+            # Dynamic pruning will recompute this in forward pass
+            return torch.ones_like(self.weight)
+        weights_abs = self.weight.abs()
+        threshold = torch.quantile(weights_abs.flatten(), self.sparsity)
+        return (weights_abs > threshold).float()
+
+    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).float()
+
+    def forward(self, x):
+        if self.dynamic_pruning:
+            self.update_mask()  # Recompute mask if dynamic pruning is enabled
+
+        if self.use_fp16:
+            with autocast():
+                pruned_weight = self.weight * self.mask
+                return F.linear(x, pruned_weight, self.bias)
+        else:
+            pruned_weight = self.weight.float() * self.mask.float()
+            return 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  # Optional block sparsity (e.g., (2, 2))
+
+        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):
+        """
+        Generate a mask based on weight magnitude, optionally with block sparsity.
+        """
+        weights = self.conv.weight
+        if self.dynamic_pruning:
+            return torch.ones_like(weights)
+
+        weights_abs = weights.abs()
+        if self.block_size:  # Block sparsity
+            # Reshape weights into blocks and compute block-wise magnitude
+            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()
+            # Expand block mask back to full weight shape
+            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):
+        """Update mask dynamically based on current weight magnitudes."""
+        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 to reduce computation.
+    """
+    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 = F.relu(self.fc1(x))
+                x = self.fc2(x)
+                return x
+        else:
+            x = F.relu(self.fc1(x.float()))
+            return self.fc2(x.float())