TVM: Where Are We GoingRuntimes NPUModule CUDAModule TFModule tvm::runtime::Module GetFunction(string) -> tvm::runtime::PackedFunc SaveToBinary/LoadFromBinary Runtime Module Interface SubclassesUnified Runtime Benefit mod. = tvm.module.load("mylib.so") func = lib["npufunction0"] func(a, b) Automatic RPC Support remote = tvm.rpc.connect(board_url, port) remote.upload("mylib.so") remote_mod = remote.load_module(“mylib Single unified module/pass, type system, with function variants supportCompilation Flow under the New Infra IRModule (relay::Function) IRModule (te::Function, ExternFunc, …) runtime::Module High-level0 码力 | 31 页 | 22.64 MB | 5 月前3
Deploy VTA on Intel FPGAAllocation – Linux Kernel Module DEPLOY VTA ON INTEL FPGA Setup Environment Variables Navigate to 3rdparty/cma and build kernel module Copy kernel module to DE10-Nano and Install Module CMA API Reference©2019 TVM with USE_VTA_FPGA flag ON Step 6: Copy the compiled TVM to the SDCard Step 7: Install kernel module cma.ko and run apps/vta_rpc/start_rpc_server.sh Step 8: Configure vta/config/de10nano_config.json0 码力 | 12 页 | 1.35 MB | 5 月前3
Bring Your Own Codegen to TVMreserved. Option 2: Graph-Level Annotation ● Implement a Relay IR visitor to annotate a subgraph ● Module path: python/tvm/relay/op/contrib//graph_annotator.py ● Apply the annotator to its Affiliates. All rights reserved. Implement the Runtime Dispatcher ● Implement a TVM runtime module to dispatch the subgraph to the generated executable engine ● Runtime path: src/runtime/contri 0 码力 | 19 页 | 504.69 KB | 5 月前3
Google 《Prompt Engineering v7》recent call last): File “/Users/leeboonstra/Documents/test_folder/rename_files.py”, line 7, in <module> text = toUpperCase(prefix) NameError: name ‘toUpperCase’ is not defined Snippet 4. I broke the File "/ Users/leeboonstra/Documents/test_folder/rename_files.py", line 7, in <module> text = toUpperCase(prefix) NameError: name 'toUpperCase' is not defined Debug what's wrong and0 码力 | 68 页 | 6.50 MB | 6 月前3
DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language Model2017), where each Transformer block consists of an attention module and a Feed-Forward Network (FFN). However, for both the attention module and the FFN, we design and employ innovative archi- tectures0 码力 | 52 页 | 1.23 MB | 1 年前3
XDNN TVM - Nov 2019End >> 6 Relay (and NNVM) Graph Parser XIR Compiler Quantizer Partitioner @relay.transform.module_pass(opt_level=4) class AccelModule:© Copyright 2018 Xilinx TVM Partitioning >> 7 Subgraph 1 Parallel0 码力 | 16 页 | 3.35 MB | 5 月前3
Dynamic Model in TVMreserved. Codegen for OpStrategy ● Each implementation defined will be compiled into a kernel in the module ● Dispatch logic will be compiled into another kernel as well # pseudocode for dispatch kernel0 码力 | 24 页 | 417.46 KB | 5 月前3
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