000 enterprises and digital natives – from Atomicwork, to Epic, Fujitsu, and Gainsight, to H&R Block and LG Electronics – to design, customize, and manage their AI apps and agents. We processed over typing a query into a search engine but instead talking to a machine that talks back. Imagine skipping the traditional application layer entirely, with an agent-driven interface managing disparate regions, the next wave of internet users will likely come online through AI-native experiences – skipping traditional app ecosystems and jumping straight into conversational, multimodal agents. Similarly

Invokes a Relay closure. InvokePacked Invokes a TVM compiled kernel. AllocStorage Allocates a storage block. AllocTensor Allocates a tensor value of a certain shape. AllocTensorReg Allocates a tensor based = [tvm.relay.Any(), 3, 224, 224] dtype = "float32" block = get_model('resnet50_v1', pretrained=True) mod, params = relay.frontend.from_mxnet(block, shape={input_name: input_shape}, dtype=dtype) tvm

and model co-design - PyTorch operator overhead makes interpreter infeasible - Reduce FLOPs with block-sparsified weight matrices - not a new idea, cf WaveRNN, Sparse Transformers, etc - Reduce precision Related work in Gibiansky (2017), Gray (2019), et al. Image from OpenAI- Add relay.nn.sparse_dense for block-sparse matrix multiplication (~50 lines of TVM IR) - Add relay.reinterpret to implement rational

ce 2 |sep Cooperative Fetching lets threads in the same thread block cooperatively fetch dependent data out_channel WwWly, pm Bly zx) https://docstvm ] Cooperative Fetching Lets threads (work item) in the same thread block (work group) cooperatively fetch dependent data https/www khronos.org/registry/DpenCLspecs/opencl-1

??????????????????????? Attention Feed-Forward Network … 3 4 RMS Norm RMS Norm Transformer Block ×???????????? DeepSeekMoE 0 Input Hidden ???????????????????????? Multi-Head Latent Attention DeepSeek-V2 is still in the Transformer architecture (Vaswani et al., 2017), where each Transformer block consists of an attention module and a Feed-Forward Network (FFN). However, for both the attention

during the renaming process. It would be better to wrap the `shutil.move` call in a `try...except` block to catch any potential errors. Here is the improved code with these suggestions: ```python import