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antiagainst / KnowledgeTips.md
Last active August 29, 2015 13:57

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
@antiagainst
antiagainst / DevToolTips.md
Last active October 7, 2017 10:54
Tips for Development Tools

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

@antiagainst
antiagainst / while-conversion.mlir
Created August 5, 2020 16:21
// *** 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>
@antiagainst
antiagainst / pad-before.mlir
Created March 14, 2021 12:26
// *** 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 ***
@antiagainst
antiagainst / pad-after.mlir
Created March 14, 2021 12:28
// *** 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 ***
@antiagainst
antiagainst / tile-vectorize-conv.mlir
Created April 2, 2021 00:37
// *** 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
@antiagainst
antiagainst / mobile-fusion.mlir
Last active April 16, 2021 17:30
This file has been truncated, but you can view the full file.
// *** 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
@antiagainst
antiagainst / vulkan-1.1-compute.md
Last active June 26, 2021 21:17
vulkan-1.1-compute

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.

@antiagainst
antiagainst / MobileBertSquad-2021-08-09.mlir
Last active August 9, 2021 21:46
This file has been truncated, but you can view the full file.
// -----// IR Dump After mlir::iree_compiler::IREE::ABI::WrapEntryPointsPass //----- //
builtin.module {
flow.variable @"__iree_flow_bert/embeddings/FakeLayerNorm/beta" opaque<"_", "0xDEADBEEF"> : tensor<512xf32> attributes {sym_visibility = "private"}
flow.variable @"__iree_flow_bert/embeddings/FakeLayerNorm/gamma" opaque<"_", "0xDEADBEEF"> : tensor<512xf32> attributes {sym_visibility = "private"}
flow.variable @"__iree_flow_bert/embeddings/embedding_transformation/bias" opaque<"_", "0xDEADBEEF"> : tensor<512xf32> attributes {sym_visibility = "private"}
flow.variable @"__iree_flow_bert/embeddings/embedding_transformation/kernel" opaque<"_", "0xDEADBEEF"> : tensor<384x512xf32> attributes {sym_visibility = "private"}
flow.variable @"__iree_flow_bert/embeddings/position_embeddings" opaque<"_", "0xDEADBEEF"> : tensor<512x512xf32> attributes {sym_visibility = "private"}
flow.variable @"__iree_flow_bert/embeddings/token_type_embeddings" opaque<"_", "0xDEADBEEF"> : tensor<2x512xf32> attributes {sym_vis
@antiagainst
antiagainst / MobileNetV2-2021-08-09.mlir
Last active August 9, 2021 21:48
This file has been truncated, but you can view the full file.
// -----// IR Dump After mlir::iree_compiler::IREE::ABI::WrapEntryPointsPass //----- //
builtin.module {
flow.variable @"__iree_flow___sm_node163__m.layer-1.kernel" opaque<"_", "0xDEADBEEF"> : tensor<3x3x3x32xf32> attributes {sym_visibility = "private"}
flow.variable @"__iree_flow___sm_node169__m.layer-2.gamma" opaque<"_", "0xDEADBEEF"> : tensor<32xf32> attributes {sym_visibility = "private"}
flow.variable @"__iree_flow___sm_node170__m.layer-2.beta" opaque<"_", "0xDEADBEEF"> : tensor<32xf32> attributes {sym_visibility = "private"}
flow.variable @"__iree_flow___sm_node171__m.layer-2.moving_mean" opaque<"_", "0xDEADBEEF"> : tensor<32xf32> attributes {sym_visibility = "private"}
flow.variable @"__iree_flow___sm_node172__m.layer-2.moving_variance" opaque<"_", "0xDEADBEEF"> : tensor<32xf32> attributes {sym_visibility = "private"}
flow.variable @"__iree_flow___sm_node181__m.layer-4.depthwise_kernel" opaque<"_", "0xDEADBEEF"> : tensor<3x3x32x1xf32> attributes {sym_visibility = "private"}
flow.variable