The configuration knobs are passed to the split schedule These two knobs are independent, so they span a search space with size 25 = The second one is tile_x with a same list of possible values.
YO YO MINI MOTRIX MODS CODE
Finally we will measure the code generated byĭifferent schedules and pick the best one.ĭefine two tunable knobs. Template with different ConfigEntity in the built space to getĭifferent schedules. Values for all tunable knobs, and we schedule according to these values.ĭuring auto-tuning, we will first call this template with aĬonfigSpace object to build the search space. This is done by makingĬonfigEntity, it will ignore all space definition API (namely,Ĭfg.define_XXXXX(.)). Schedule according to an entity in this space. Tunable knobs in this function and build a search space from it. This is done by makingĬfg a ConfigSpace object. The parameter search space within a single function.ĭefine a search space across a set values. To make the template function more compact, we can do two things to define Aįunction that uses a configuration object like this is called a “template”. Instead, we can passĭifferent configurations to this function and get different schedules. With this argument, thisįunction is no longer a deterministic schedule. This function but we obtain it in a different way.
Get a config object: You can regard this cfg as an argument of Use a decorator to mark this function as a simple template. We can explain the modifications one by one. Here we make four modifications to the previous schedule code and get a reorder ( yo, xo, k, yi, xi ) return s, schedule according to config yo, yi = s. sum ( A * B, axis = k ), name = "C" ) s = te. reduce_axis (( 0, L ), name = "k" ) C = te. placeholder (( L, M ), name = "B", dtype = dtype ) k = te. placeholder (( N, L ), name = "A", dtype = dtype ) B = te. use a decorator def matmul_v1 ( N, L, M, dtype ): A = te. Making your Hardware Accelerator TVM-ready with UMA.Quick Start Tutorial for Compiling Deep Learning Models.Optimizing Operators with Auto-scheduling.Step 2: Use AutoTVM to Optimize the Matrix Multiplication.A Matrix Multiplication Template with the Advanced Parameter API.Optimizing Operators with Schedule Templates and AutoTVM.
Working with Operators Using Tensor Expression.Compiling and Optimizing a Model with the Python Interface (AutoTVM).Getting Starting using TVMC Python: a high-level API for TVM.Compiling and Optimizing a Model with TVMC.An Overview of TVM and Model Optimization.
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