Let me analyze this task carefully. The task is to replace a PyTorch model that uses `AutoModelForSequenceClassification` from Hugging Face transformers with a custom CUDA kernel. The model is "LongSafari/hyenadna-small-32k-seqlen-hf", which is a HyenaDNA model for sequence classification. Looking at the reference: 1. It loads a pre-trained model from HuggingFace 2. The forward pass takes input tokens (integer tensor) and returns logits 3. The model is for sequence classification with 2 labels This is a complex task because: 1. HyenaDNA is a sophisticated architecture with Hyena operators (which are alternatives to attention) 2. The model has embeddings, multiple layers, and a classification head 3. We need to match the exact weights and computation The key insight here is that I can't just write a CUDA kernel for the entire model - the model has millions of parameters loaded from HuggingFace. Instead, I need to: 1. Keep the model loading in `ModelNew.__init__` 2. Replace specific computationally intensive parts with custom CUDA kernels Let me think about what can be optimized: - The embedding layer (simple lookup) - The Hyena blocks (the core of the model) - The classification head Since this is a HyenaDNA model, the main computation involves: 1. Token embeddings 2. Hyena operators (which involve convolutions and gating) 3. Layer normalization 4. Classification head For a practical approach, I should: 1. Keep the model structure but replace forward pass with optimized CUDA kernels 2. Or, extract weights and implement a custom forward with CUDA kernels Actually, looking at this more carefully, the challenge is that the model is loaded from HuggingFace and has complex architecture. The most practical approach would be to: 1. In `ModelNew.__init__`, load the model and extract weights 2. In `ModelNew.forward`, use custom CUDA kernels for the main computations But implementing the full HyenaDNA architecture from scratch is extremely complex. Let me think about a simpler approach - maybe I can use the model's forward hook to inject custom kernels for specific layers, or I can rewrite the forward method to call the model's forward but with certain operations replaced by CUDA kernels. Actually, looking at this problem more carefully, the challenge is that HyenaDNA uses complex Hyena operators. A reasonable approach is to load the model, extract the weights, and implement a custom forward pass using PyTorch operations that can then be optimized with CUDA kernels. Or I could try to speed up the model by using torch.compile or other optimizations. The core architecture involves token embeddings, position embeddings, multiple Hyena layers, and a classification head with pooling and projection layers. Since implementing the full HyenaDNA from scratch isn't practical, I should leverage the existing model for inference while replacing critical operations like embedding lookups, layer normalization, and linear projections with custom CUDA kernels. These are the most straightforward to optimize and will likely provide meaningful speedups without requiring a complete rewrite of the architecture. My approach would be to load the pre-trained model weights and strategically apply custom kernels to the most compute-intensive components. The token embedding step is particularly well-suited for optimization since it's essentially a lookup operation, and layer norm and linear layers follow predictable patterns that CUDA handles efficiently. I'll build this incrementally: start with the embedding layer using a simple kernel, then add layer norm and linear projections as separate optimized kernels. The HyenaDNA architecture's core operations—embeddings, convolutions with gating mechanisms, and the classification head—are all candidates for acceleration, though I should prioritize the parts that execute most frequently during inference. For the embedding kernel, I'm setting up a straightforward CUDA implementation that handles the lookup of token indices and retrieval of their corresponding embedding vectors. The kernel takes the input indices and embedding weight matrix, then for each position in the output, it retrieves the appropriate embedding row based on the token index. I need to ensure bounds checking on the indices to prevent out-of-range access, then copy the embedding data to the output tensor. The host function validates the input dimensions and launches the kernel with an appropriate grid and block configuration, calculating the total number of output elements to determine the launch parameters. For a practical implementation approach, I should load the model in ModelNew's constructor and then either leverage torch.compile for optimization, apply fused kernels selectively to bottleneck operations, or create custom forward methods that target specific intensive computations. Rather than implementing the full HyenaDNA architecture from scratch, I can extract the weights and focus optimization efforts on the most computationally demanding components. My approach involves initializing the model from the pretrained checkpoint, identifying the heaviest operations through profiling, and creating targeted CUDA kernels for those specific sections. I'd keep the original model accessible but implement a streamlined forward pass that uses the custom kernels where they provide the most benefit, such as embedding lookups and other high-frequency operations. For the classification head, I'm setting up weight and bias parameters that will be registered as model parameters, then applying them directly in a fused linear layer. The full model call processes the input through the main architecture, and then I extract and reshape the output before applying the classification transformation to produce the final logits. = torch.empty({batch_size, num_embeddings}, opts); int threads = 256; int blocks = (batch_size * num_embeddings + threads - 1) / threads; fused_embed_kernel<<<blocks, threads>>>( input.data_ptr<long>(), weight.data_ptr<float>(), bias.data_ptr<float>(), output.data_ptr<float>(), batch_size, seq_len, num_embeddings ); return output; } ''' The host function allocates output tensor space and launches the CUDA kernel with calculated grid and block dimensions to process the embedding lookup and addition in a single fused operation. For this transformer-based model, I should consider which operations would actually benefit from kernel fusion. Since it's HyenaDNA running at fp32 precision with a batch size of 32 and sequence length of 8192, the real opportunities for optimization likely lie elsewhere. The embedding layer processes (32, 8192) tokens and produces (32, 8192, 256) embeddings—that's 67M elements, which is substantial. But more importantly, I need to identify the actual bottlenecks in the model architecture itself. For HyenaDNA specifically, the dominant operations are convolutions and FFT operations, not just embeddings. A practical strategy would be to load the pre-trained weights, then focus on fusing operations that frequently occur together—like combining embedding lookups with layer normalization, or fusing linear layers with activations. Rather than trying to optimize everything, I should target the operations that execute most frequently or process the largest tensors. The model's structure suggests I should look at how the embeddings flow into the transformer blocks and whether I can combine operations there. I'm setting up the model to extract and potentially cache weights from the pre-trained checkpoint, then implement a forward pass that could use custom kernels for the embedding and early transformations. The challenge is that the HuggingFace model's internal structure makes it difficult to cleanly separate individual operations for custom CUDA implementation, so I might need to work with what's accessible through the model's public interface while adding targeted optimizations where possible. Looking at this more carefully, the actual bottleneck isn't the model invocation itself but rather the underlying operations HyenaDNA performs—embeddings, Hyena blocks with FFT-based convolutions and gating, plus normalization layers. Since I need to call the pretrained model's forward method to get correct outputs, I should identify where I can inject custom kernels into those internal operations. I could fuse the embedding lookup with bias addition, or optimize the final linear projection, but these are relatively minor gains. For something like HyenaDNA with its FFT-based approach, the real speedup would require replacing the Hyena operators themselves. Given the constraints though, I'll focus on what's practical: embedding fusion and post-processing optimizations. Actually, there's a more direct path—I can extract the model weights and implement a custom forward pass using optimized operations. For a sequence classification model, the core components are embeddings, the backbone processing, pooling over the sequence dimension, and a classifier head. Let me build this out with a custom CUDA kernel that fuses embedding lookup with bias addition, then traces through each layer with fused kernels where possible. The approach loads a pretrained model, extracts its weights, and reimplements forward with optimized CUDA kernels for critical operations. I'll define the kernel inline and load it as a PyTorch extension. ```python import torch from torch.utils.cpp_extension import load_inline from transformers import AutoModelForSequenceClassification, AutoConfig # Define CUDA kernel code cuda_source = """ #include <torch/extension.h> // Fused embedding + layer norm kernel __global__ void fused_embed_norm_kernel( const long* __restrict__ indices, const float* __restrict__ embed_weight, const float* __restrict__ ln_weight, const float* __restrict__ ln_bias, float* __restrict__ output, int batch_size, int seq_len, int embed_dim, float eps) { int idx = blockIdx.x * blockDim.x + threadIdx.x; int total = batch_size * seq_len; if (idx < total) { int b = idx / seq_len; int s = idx % seq_len; int token = indices[idx]; // Compute mean and variance for layer norm float mean = 0.0f; for (int d = 0; d < embed_dim; d++) { mean += embed_weight[token * embed_dim + d]; } mean /= embed_dim; float var = 0.0f; for (int d = 0; d < embed_dim; d++) { float diff = embed_weight[token * embed_dim + d] - mean; var += diff * diff; } var /= embed_dim; float inv_std = rsqrtf(var + eps); // Normalize and apply affine transformation for (int d = 0; d < embed_dim; d++) { float val = embed_weight[token * embed_dim + d]; output[(b * seq_len + s) * embed_dim + d] = (val - mean) * inv_std * ln_weight[d] + ln_bias[d]; } } } torch::Tensor fused_embed_norm_cuda( torch::Tensor indices, torch::Tensor embed_weight, torch::Tensor ln_weight, torch::Tensor ln_bias, int embed_dim, float eps) { int batch_size = indices.size(0); int seq_len = indices.size(1); auto output = torch::empty({batch_size, seq_len, embed_dim}, torch::TensorOptions().dtype(torch::kFloat32).device(indices.device())); ``` Computing mean across the embedding dimension, then variance, normalizing with the inverse standard deviation, and applying the learned affine transformation from layer norm parameters. The kernel threads are organized to process each sequence position independently, computing the layer norm statistics and writing normalized values to the output tensor. After launching the kernel with sufficient blocks and threads, I return the result. However, I'm realizing that reimplementing the entire HyenaDNA architecture requires handling embeddings, position encodings, and multiple Hyena blocks with FFTs, gates, and projections—far more involved than just a fused embedding and layer norm kernel. A more practical strategy would be to leverage the pretrained model directly while selectively optimizing the most computationally demanding components. Rather than rebuilding everything from scratch, I can apply torch.compile for automatic optimization and insert custom kernels only where they'd provide meaningful speedups, keeping the original model's weights and structure intact. out = torch.mean(hidden, dim=1) # Mean pooling logits = self.classifier(out) return logits ``` Since the input is integer tokens, I need to process them through the model first before applying any custom kernels. The hidden states become available after the forward pass, so optimization opportunities exist in the pooling and classification steps. I'm considering whether PyTorch's built-in optimizations or torch.compile might handle this more efficiently than manual kernel fusion. I need to extract the model's weights and understand its architecture to implement efficient kernels. For HyenaDNA, the structure follows: embeddings convert tokens, Hyena blocks process the sequence, pooling condenses it, and a classification head produces logits. I can leverage this by caching the model and potentially applying torch.compile for performance gains, while also developing custom CUDA kernels for operations where PyTorch's default implementation is suboptimal. The practical approach combines loading the pretrained model with targeted CUDA optimizations—specifically a fused mean pooling and linear projection kernel that handles the sequence aggregation and classification steps in one operation. The kernel normalizes the output and applies the linear transformation with the classifier weights and bias. After computing the mean pooling across the sequence dimension for each batch item, it performs the matrix multiplication to produce the logits. The host function validates the input dimensions, allocates the output tensor, and launches the kernel with appropriate grid and block dimensions based on the batch size and number of classes. back to the model's output return self.model(input_ids).logits This approach should work, but I'm overcomplicating the setup. I should streamline the CUDA kernel implementation—loading the model, compiling the kernel once, and then applying it in the forward pass. Let me consolidate this into a cleaner single-file version that handles everything together.