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git

How to show the tracking relationship between local and remote branches?

git branch -vv

How to change remote tracking branch?

git branch --set-upstream-to origin/somebranch

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Knowledge

Endianness

• Big-endian: the most significant byte is stored in the smallest address
• Small-endian: the least sigificant byte is stored in the smallest address

safety and liveness

Any specification can be expressed as the conjunction of a safety property and a liveness property.

• safety: something bad will never happen

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blog about these tools

Setup Zsh and Prezto

• Change default shell to Zsh

chsh -s $(which zsh)

• Clone Presto and install it according to README.md

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cmake_minimum_required(VERSION 2.8)

function(use_llvm TARGET)
message("ARGC=\"${ARGC}\"")
message("ARGN=\"${ARGN}\"")
message("ARGV=\"${ARGV}\"")
message("ARGV0=\"${ARGV0}\"")
message("ARGV1=\"${ARGV1}\"")
endfunction()

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// *** IR Dump After Canonicalizer ***


module {
  func @while() attributes {iree.module.export} {
    %cst = constant dense<1> : tensor<i32>
    %cst_0 = constant dense<3> : tensor<i32>
    %cst_1 = constant dense<4> : tensor<i32>
    %0 = iree.do_not_optimize(%cst) : tensor<i32>
    %1 = iree.do_not_optimize(%cst_0) : tensor<i32>

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// *** IR Dump After mlir::mhlo::(anonymous namespace)::LegalizeControlFlowPass ***
func @pad_test() attributes {iree.module.export} {
  %0 = iree.unfoldable_constant dense<[[1, 2, 3], [4, 5, 6]]> : tensor<2x3xi32>
  %1 = iree.unfoldable_constant dense<0> : tensor<i32>
  %2 = "mhlo.pad"(%0, %1) {edge_padding_high = dense<[1, 5]> : tensor<2xi64>, edge_padding_low = dense<[0, 1]> : tensor<2xi64>, interior_padding = dense<0> : tensor<2xi64>} : (tensor<2x3xi32>, tensor<i32>) -> tensor<3x9xi32>
  check.expect_eq_const(%2, dense<[[0, 1, 2, 3, 0, 0, 0, 0, 0], [0, 4, 5, 6, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]> : tensor<3x9xi32>) : tensor<3x9xi32>
  return
}

// *** IR Dump After mlir::iree_compiler::IREE::Flow::(anonymous namespace)::HLOToHLOPreprocessing ***

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// *** IR Dump After mlir::mhlo::(anonymous namespace)::LegalizeControlFlowPass ***
func @pad_test() attributes {iree.module.export} {
  %0 = iree.unfoldable_constant dense<[[1, 2, 3], [4, 5, 6]]> : tensor<2x3xi32>
  %1 = iree.unfoldable_constant dense<0> : tensor<i32>
  %2 = "mhlo.pad"(%0, %1) {edge_padding_high = dense<[1, 5]> : tensor<2xi64>, edge_padding_low = dense<[0, 1]> : tensor<2xi64>, interior_padding = dense<0> : tensor<2xi64>} : (tensor<2x3xi32>, tensor<i32>) -> tensor<3x9xi32>
  check.expect_eq_const(%2, dense<[[0, 1, 2, 3, 0, 0, 0, 0, 0], [0, 4, 5, 6, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]> : tensor<3x9xi32>) : tensor<3x9xi32>
  return
}

// *** IR Dump After mlir::iree_compiler::IREE::Flow::(anonymous namespace)::HLOToHLOPreprocessing ***

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// *** IR Dump After mlir::iree_compiler::IREE::SIP::MaterializeReflectionAttrsPass ***
func @conv(%arg0: tensor<1x225x225x3xf32>, %arg1: tensor<3x3x3x32xf32>) -> tensor<1x112x112x32xf32> attributes {iree.module.export, iree.reflection = {f = "I30!B13!d1d225d225d3B10!d3d3d3d32R18!B14!d1d112d112d32", fv = "1"}} {
  %0 = "mhlo.convolution"(%arg0, %arg1) {batch_group_count = 1 : i64, dimension_numbers = {input_batch_dimension = 0 : i64, input_feature_dimension = 3 : i64, input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>, kernel_input_feature_dimension = 2 : i64, kernel_output_feature_dimension = 3 : i64, kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>, output_batch_dimension = 0 : i64, output_feature_dimension = 3 : i64, output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>}, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x225x225x3xf32>, tensor<3x3x3x32xf32>) -> tensor<1x112x

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// *** IR Dump After CSE ***
func @call(%arg0: tensor<1x224x224x3xf32> {tf._user_specified_name = "x"}) -> tensor<1x1000xf32> attributes {iree.module.export, iree.reflection = {abi = "sip", abiv = 1 : i32, f = "I17!B13!d1d224d224d3R11!B8!d1d1000", fv = "1", sip = "I8!S5!k0_0R3!_0"}, tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf.shape<1x224x224x3>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #t

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Vulkan 1.1 compute API

This Gist contains a PDF file that are derived from the Khronos Group's Vulkan 1.1 API reference card. It removes graphics specific APIs to show the compute subset.