TVM Change Log

This file records the changes in TVM library in reverse chronological order.

On-going version

Refer to the Roadmap issue for complete list on on-going version features. If you check in something that is not reflected in Roadmap issue, please reply to that issue so it can get added.

0.7

v0.7 brings many major features. The community works together to refactor the internal code base to bring an unified IR code structure with a unified IRModule, type system and pass infrastructure. We have also bought many exciting new features, some highlights include:

Initial automatic scheduling support
Initial command line driver interface
WebGPU and webassembly support
Better first class rust support in the codebase
Intial Hexagon support
Bring your own codegen (BYOC) support

The community also continues to bring high quality improvements to the existing modules including, but not limited to: better frontend coverage, performance, quantization, uTVM and dynamic shape support.

New Features

Automatic Scheduling (Experimental)

Phase 0: Ansor minimum system for auto schedule generating #5962
Phase 1: Access Analyzer #6103
Phase 1: Add follow_split and follow_fused_split steps #6142
Phase 1: Add pragma/storage_align/rfactor steps #6141
Phase 1: Add RPC Runner #6077
Phase 1: Add annotation/compute_at/compute_root/compute_inline steps #6073
Phase 1: Add cache_read/cache_write steps #6107
Phase 1: Rename namspace form auto_schedule to auto_scheduler #6059
Phase 1: The base class for cost models #6187
Phase 1: feature extraction for cost models #6190
Phase 1: XGBoost Cost Model #6270
Phase 2: Basic GPU Sketch Search Policy #6269
Phase 2: Evolutionary Search #6310
Phase 2: Update heavy operations with parallel_for #6348
Parallel the InitPopulation (#6512)
Tutorial: Using the template-free auto-scheduler on CPU (#6488)

BYOC

External codegen support in Relay (#4482)，(#4544)
Bring Your Own Codegen Guide -- Part 1 #4602
Bring Your Own Codegen Guide -- Part 2 #4718
Relay annotation and partitioning for external compilers #4570
JSON Runtime with DNNL End-to-End Flow #5919
Handle one symbol for each runtime #5989
Run accelerator specific optimizations #6068
Arm Compute Library integration #5915
Retire the example json runtime #6177
json_node.h should include data_type.h #6224
Improve installation tutorial #6170
Add support for dense (fully connected) layer #6254
Introduce the Ethos-N BYOC integration #6222
Enable remote device via environment variables #6279
Improved pooling support #6248
Add support for quantized convolution #6335
CoreML codegen #5634

Operator Coverage

Add strided_set operation (#4303)
Add support for conv3d (#4400), pool3d (#4478), 3d upsampling ops (#4584)
Add group convolution for VTA (#4421)
Add 1d deconvolution op (#4476)
Allow batch matmul to be fused into injective ops (#4537)
Add native depthtospace and spacetodepth operators (#4566)
Add CUDNN conv3d support (#4418)
Dilation2D operator support #5033
Isfinite operator #4981
Unravel Index operator #5082
Add thrust support for nms #5116
Resize3d, Upsample3d op support #5633
Add operator Correlation #5628
affine_grid and grid_sample #5657
Sparse to dense operator #5447
Conv3d_transpose op support added #5737
add op crop_and_resize #4417
Add bitwise ops #4815
Sparse to dense operator #5447
support dynamic NMS(Non Maximum Suppression), symbolic begin, end, and strides for strided_slice #4312
Conv3d_transpose op support added #5737
ReverseSequence operator #5495
Conv1D #4639
1D Pooling #4663

Quantization

Channel wise quantization - Quantize & Requantize #4629
Support QNN ops. #5066
Adding support for QNN subtract op #5153
TFLite QNN Tutorial #5595
Tutorial: Deploy Quantized Model on CUDA #4667
Support asymmetric per-layer quantized operators #6109

Relay

Add convertlayout pass in Relay (#4335, #4600)
Added Merge Composite pass #4771
Call graph for relay #4922
Add inline pass #4927
Target annotation for external codegen #4933
GradientCell Relay Pass #5039
Add MergeCompilerRegions pass #5134
Non-recursive Graph Vistor and Rewriter (#4886)
[Blocksparse] Pipeline for lowering dense model to sparse-dense (#5377)
Relay op strategy #4644
Static Tensor Array (#5103)
Memory planner (part 1) #5144
ONNX codegen #5052
Add Parser 2.0 #5932, part 2 #6162
Basic block normal form #6152
Convert Layout pass. #4664
Pattern Language, Matcher, Rewriter, and Function Paritioner #5231

Runtime and Backend

Add ADTObject POD container type (#4346)
TFLite RPC runtime (#4439)
Standardized graph runtime export (#4532)
MISRA-C compliant TVM runtime #3934
Add String container #4628
Introduce Virtual Memory Allocator to CRT (#5124)
Initial implementation of Hexagon runtime support (#5252)
FastRPC interface for Hexagon runtime (#5353)
CoreML Runtime (#5283)
AutoTVM + uTVM for Cortex-M7 (#5417)
Windows Support for cpp_rpc (#4857)
Implement TVMDSOOp(TensorFlow custom op) for TVM runtime (#4459)
WebGPU support #5545
TVM WebAssembly JS Runtime #5506
Hexagon driver for offloading kernels to simulator #5492
Introduce runtime::Array #5585
Allow non-nullable ObjectRef, introduce Optional. (#5314)
Introduce static slots for common objects. (#5423)
ntroduce RValue reference(move) support to TypedPackedFunc (#5271)
Introduce MetadataModule to separate code compilation/interpretation and weight initialization #5770
Support module based interface runtime #5753
Add TVM application extension with WASM runtime #5892
Provide guide to user who has difficulty register SEqualReduce (#5300)

Rust Support

Revive the Rust + SGX refactor #4976
Improve Rust bindings: Map, Array, String, various IR nodes #6339
Rust Refactor Stage 4: Rewrite Rust graph runtime to use new APIs #5830
Second stage of Rust Refactor #5527
tvm crate stage 3 of Rust refactor #5769
Add first stage of updating and rewriting Rust bindings. #5526

TIR

Introduce StructuralHash for the Unified IR. #5160
Introduce StructuralEqual Infra for the unified IR. #5154
Introduce ExprDeepEqual, Remove IRDeepCompare #5206
[TIR] Introduce BufferLoad/Store (#5205)
Improved massive build times caused by tir.floormod and tir.floordiv. Fixed Topi testcase. #5666
Buffer logger assert removed #6147
Enhance VerifyGPUCode #6194
HoistIfThenElse added #6066
Hybrid Script Support for TIR #6227
Migrate Low-level Passes to Pass Manager #5198
HoistIfThenElse added #6066
Hybrid Script Support for TIR #6227
Block scope hoisting added #6238

TE

reverse-mode autodiff without any optimization #5121
Tensor Expression Debug Display (TEDD) #4651
Optimize and eliminate the Jacobian tensor for te.autodiff #6078

TVMC(Experimental)

TVMC - A command line driver for TVM (Part 1) #6112
TVMC - Linting error on onnx command line driver frontend #6536
TVMC - Command line driver ‘compile’ (part 2/4) #6302
TVMC - Introduce ‘tune’ subcommand (part 3/4) #6537
TVMC - Introduce ‘run’ subcommand (part 4/4) #6578
TVMC - Getting started tutorial for TVMC #6597

Feature Improvement

Accelerator and Microcontroller Support

Cleanup legacy verilog code (#4576)
uTVM support for ARM STM32F746XX boards (#4274)
Add --runtime=c, remove micro_dev target, enable LLVM backend #6145

Arithmetic Analysis

Linear system and equation solver (#5171)
Inequalities solver #5618
Improve IntervalSet's floormod (#5367)
Remove legacy const pattern functions (#5387)
Handle likely in IRMutatorWithAnalyzer #5665
ExtendedEuclidean merge impl to int_operator #5625
Rewrite simplify fix for Vectorized Cooperative Fetching #5924

AutoTVM and Graph Tuner

Adding ROCM schedules for TOPI (#4507)
NHWC conv2d schedule templates for ARM (#3859)
Use VM compile to extract autotvm tasks #4328
Download fallback schedule file if it does not exist #4671
Ignore error when removing tmpdir #4781
Fix a bug in generating the search space #4779
Minor bug fixes in AutoTVM for QNN graphs #4797
Fix autotvm customized template #5034
Add opt out operator for has_multiple_inputs for graph tuner #5000
Customize SI prefix in logging (#5411)
Update XGBoost verbosity option #5649
Support range in index based tuners #4870
Enable random fill and CPU cache flush for AutoTVM and Ansor (#6391)
Auto-scheduler tutorial for GPU and necessary refactor/fix (#6512)

BYOC

[BYOC] Bind constant tuples in graph partitioner (#5476)
[BYOC] Add support for composite functions in BYOC (#5261)
[BYOC] Register pattern tables from external codegens (#5262)
[BYOC] Enhance partitioning and external codegen (#5310)
[BYOC] Refine AnnotateTarget and MergeCompilerRegion Passes (#5277)
[BYOC] Use Non-Recursive Visitor/Mutator (#5410)
[BYOC] Refine DNNL Codegen (#5288)
[BYOC] Add example of Composite + Annotate for DNNL fused op (#5272)
[BYOC] Prevent duplicate outputs in subgraph Tuple (#5320)
[BYOC] Introduce further operator support (#6355)
[BYOC] Support input nodes with multiple entries (#6368)
[BYOC] Add maximum support for float32 (#6506)

Codegen

Intrinsic dispatching with OCML instead of LLVM for ROCm (#4499)
Make target codegen take IRModule and PrimFunc. #5107
Enhance CUDA codegen for SelectNode #4983
Vectorization for intrinsics #5101
[LLVM] Do not use x86_vcvtph2ps_256 intrinsic with LLVM 11+ (#5267)
[LLVM] Use llvm::ElementCount with LLVM 11+ when creating vectors (#5265)
[LLVM] Use llvm::FunctionCallee in IRBuilder::CreateCall with LLVM 11+ (#5338)
[LLVM] Include Support/Host.h for declaration of getDefaultTargetTriple (#5268)
[LLVM] Replace calls to Type::getVectorNumElements (#5398)
[LLVM] Use ArrayRef in calls to CreateShuffleVector (#5399)
[LLVM] Use llvm::Align with LLVM 11+ to avoid warnings (#5264)
[CodeGen] Cleanup generated code (#5424)
Rename target_id => target_kind #6199
64-bit RPi4b target #6211
Creating Target from JSON-like Configuration #6218
Add python binding to new JSON target construction #6315
Use target class in all codegens #6347
Initial support for Hexagon codegen #6261
Add --runtime=c, remove micro_dev target, enable LLVM backend #6145
Add tvm::support::hexdump() debug utility #6154
Adding AMD codegen unit tests (#4509)
Support cuda tensorcore subbyte int data type in auto tensorcore #4546
Handle empty LLVMModule in GetFunction #5146
Support int4/int8 conv2d tensor core with HWNC layout #6121

Dynamism Support

Add shape function for zero, zeros_like, ones, ones_like (#4448), tile (#4441)
Support symbolic newshape for Reshape #5429
Support symbolic TopK, Ones, Zeros and Full #5459
Add shape_of instruction #5855
symbolic max_output_size #5844
Dynamic TopK Op #6008
Dynamic broadcast_to, zeros, ones #6007
Add dynamic reshape grad #6080
Keep fixed dim when unifying dynamic shape #5795
OneHot operation #6209
Add Dynamic Resize Op #6198
Dynamic full operator #6260
Dynamic upsampling relay op #6273
Dynamic Tile Op #5983

Frontend and User Interface

TFLite parser support for transpose_conv (#4440), unpack (#4447)
LLDB pretty printers for relay (#4453)
ONNX to Relay converter op support: expand op (#4483)
ONNX auto_pad in conv and convtranspose (#4563)
TF to Relay converter op support (#4504) (#4551) (#4484)
Remove unnecessary cast of constants in ONNX converter (#4573)
Add support for tf.Keras networks in Relay Keras frontend #4630
Add conv3d #4604
Fix incorrect calculations in tf SLICE #4518
Dynamically calculate input_stats of any fake_quant range #4789
LSTM Support #4825
Add MIRROR_PAD operator #4822
use qnn helper function in softmax #4840
Add Resize op converter #4838
Add support for TFLite_Detection_PostProcess #4543
Fix tests for tflite unary elemwise operations #4913
GaussianDropout/Noise parsing support #4928
Add parser support for ‘square’ operator #4915
make_loss operator support #4930
Add parser support for l2_normalization #4966
ReadVariableOp operator support #4952
Check graph inputs match expected #4992
support multiply outputs #4980
TFLite: Using real image for QNN testing. #4816
TFLite: FLOOR_MOD & FLOOR_DIV support #4971
PyTorch: Upsampling op support and enable registering a user defined op conversion map #4961
PyTorch: fix unordered dictionary problem for python version under 3.6 #4982
Operator support NonZero #5073
Upsampling op support and enable registering a user defined op conversion map #4961
Check graph inputs match expected #4992
Add support for quantized models via QNN #4977
Add initial control flow support #4964
Remove FP32 piggy back and use QNN add/mul/concatenate #5061
Add missing upcast to uint8 avg_pool conversion #5089
Add initial 3D op support and test on Resnet 3D #5075
Fix conv2d conversion for group conv (group > 1 but != in channels) #5132
Add support for max_pool1d #5142
Add support for split #5174
FLOOR_MOD & FLOOR_DIV support #4971
Activation functions support #4978
Round op parsing support added #5022
DepthToSpace and SpaceToDepth support #5041
TOP_K op parser support #5051
ReadVariableOp operator support #4952
Support multiply outputs #4980
reduce_any op parsing support #4926
TensorFlow Parser Control Flow Enhancement #5020
TensorFlow Frontend support with shared params #5042
Support for AddV2 in Relay Tensorflow frontend converter. #5046
conv3d frontend operator support #5080
max_pool3d and Averagepool3d operator support #5085
Support for Atan/Atan2 in Relay Tensorflow frontend converter. #5104
Use leaky by default for LeakyReLU #5192
Conv3D ONNX support and conv3D_ncdhw x86 schedules #4949
Add support for FusedBatchNormV3 #5065
Activations for pytorch #5194
Dropouts And InstanceNorm support added #5203
[Frontend] Asymmetric padding of convolution support (#4803)
[ONNX]Pool3d & upsample3d op support (#5135)
Add TopK to ONNX Frontend (#5441)
Add RoiAlign to Onnx frontend (#5454)
[PYTORCH]AvgPool3d, MaxPool3d and Squeeze op support (#5220)
[PYTORCH]celu, gelu, selu activations (#5263)
[Pytorch]layernorm bug fix and testcase updated (#5257)
[PYTORCH]LayerNorm support added (#5249)
[PYTORCH]GroupNorm op support added (#5358)
[PYTORCH]Logical & Bitwise operator support (#5341)
[PYTORCH]Tensor creation ops support (#5347)
[PYTORCH]cosh,sinh,log2,log10,log1p op support (#5395)
[PYTORCH]Rsub, Embedded, OneHot ops support (#5434)
[PYTORCH]Abs, Arange, Softplus ops (#5295)
[PYTORCH]isNan, isinf, isfinite, ceil, clamp, round ops (#5316)
[PYTORCH]Activations for pytorch (#5194)
[PYTORCH]Repeat, Reciprocal & Reshape Op support (#5280)
[PYTORCH]Reduce_ops support added (#5308)
[PYTORCH]Take, Topk op support (#5332)
[PYTORCH]Dropouts And InstanceNorm support added (#5203)
[PYTORCH]Unary Ops frontend support. (#5378)
[Torch] Support Python list, more realistic recurrent networks (#5306)
[PYTORCH]where, addcdiv, addcmul op support (#5383)
[Torch] Add support for split (#5174)
[Torch] Fix up graph input handling (#5204)
[TFLITE]Logical not op support (#5475)
[TFLITE]Hard Swish & MobilnetV3 model testing (#5239)
[TFLITE]Gather, StridedSlice op support added (#4788)
[TFLITE] Match TFLite shape for SSD custom op (#5473)
Factor out import of common tflite.Operator in tflite frontend. (#5355)
[TFLite] support for FILL and SPLIT_V operators (#5330)
[TFLite] L2_POOL_2D operator (#5452)
[TFLite] Add config option to specify FlatBuffers location (#5425)
[TFLITE]Logical not op support (#5475)
[TENSORFLOW]reduce ops updated (#5180)
[TENSORFLOW] Fix gather_nd indices (#5279)
[TensorFlow]Improve TensorFlow Static Shape Tensor Array (#5243)
[KERAS]Minimum & AlphaDropout op support (#5380)
[KERAS]Embedding layer (#5444)
[KERAS]Max_pool3d and Averagepool3d operator support (#5085)
[CAFFE2]add Mul and ConvTranspose operator (#5302)
[MXNET]DepthToSpace & SpaceToDepth Operator (#5408)
[MXNET]broadcast and logical op support (#5461)
[MXNET] Use leaky by default for LeakyReLU (#5192)
[MXNET] support elemwise logic ops (#5361)
[Frontend|MXNet] SwapAxis operator support (#5246)
[RELAY] Move frontend utils (#5345)
[Pytorch] Fix translation of transpose when axis argument is as a list (#5451)
LpPool Support added #5696
Skip ADD inside Gemm op when vector is zero #5697
ReduceL1, ReduceL2, ReduceSumSquare, ReduceLogSum ops added #5721
MaxRoiPool, Mod & Xor op support added #5729
Skip multiply with 1.0f constant for GEMM import #5800
StatefulPartitionedCall/PartitionedCall Ops support added #5617
Don‘t add cast for batch norm when type isn’t changing #5731
Conv3d Transpose OP added #5775
expand bug fix #5576
Support max_pool2d_with_indices #5549
Add prim::device op #5584
ImplicitTensorToNum support added #5603
Matmul fix for batch_matmul #5604
ReflectionPad2d op #5624
Padding op support #5638
Minor bug fixes #5683
floor_divide support for squeezenet #5702
ReplicationPad support added #5708
aten::norm support added #5776
broadcast and logical op support #5461
MaxPool3d and AvgPool3d Ops support added #5614
Softmin, trunc op support added #5715
conv3d and conv3d_transpose addedx #5814
Model importer to be compatible with tflite 2.1.0 #5497
Nit: Function names made consistent #5515
Select op support for tflite frontend #5486
GATHER_ND #5508
Quantize & Dequantize op #5394
Fully connected op conversion made in sync with TFLite #5510
ADD_N operator #5474
onnx, mxnet, pytorch mathops added #5561
abs, round, reciprocal, sign, softsign, hard_sigmoid ops support #5587
Gather nd bug fix for one dim support in tensorflow #5588
Add parser support for shape and range #5329
Darknet support batch size for yolo #5688
Improve Control Flow and TensorArray #5699
MXNet: Softmin, trunc op support added #5715
MXNet: conv3d and conv3d_transpose addedx #5814
MXNet: Add parser for contrib.box_decode #5967
Onnx: ReduceL1, ReduceL2, ReduceSumSquare, ReduceLogSum ops added #5721
Onnx: MaxRoiPool, Mod & Xor op support added #5729
Onnx: Skip multiply with 1.0f constant for GEMM import #5800
Onnx: Fix an issue with #5755 and add Batch norm unit tests. #5845
TensorFlow: StatefulPartitionedCall/PartitionedCall Ops support added #5617
TensorFlow: Don’t add cast for batch norm when type isn’t changing #5731
TensorFlow: Conv3d Transpose OP added #5775
Add parser support for shape and range #5329
Darknet support batch size for yolo #5688
Improve Control Flow and TensorArray #5699
Improve TF Parser to keep output nodes for saved_model #5794
Add parser support for relu6, leaky_relu, relu_n1_to_1, log_softmax #4805
Fix TF Dynamic input shape #5825
Support a few contrib ops in mxnet #5819
Improve TF Parser to keep output nodes for saved_model #5794
Add parser support for relu6, leaky_relu, relu_n1_to_1, log_softmax #4805
Check all unsupported ops before raising an exception #5929
Add Pytorch advanced indexing #6318
Support index_select #6295
Fix cast to long #6301
Fix dtype handling for modules with integer parameters #6311
pytorch frontend support conv1d #6203
Add cast to double, fix flatten conversion #6357
Fix aten::max and aten::min conversion #6372
Match pytorch 1.6 googlenet pretrained model (#6201) #6212Add unbiased variance op and corresponding support in pytorch frontend #6232
Implemented PADV2 Operator for TFLite and added support for constant values in PAD. #6167
Implemented ONE_HOT Operator for TFLite. #6223
Implemented EXPAND_DIMS Operator for TFLite. #6243
Implemented REVERSE_V2 Operator for TFLite. #6304
Implemented MATRIX_SET_DIAG Operator for Relay/TOPI and TFLite Frontend. #6303
RESHAPE with dynamic shape arg in TFLite frontend #6208
Constant input attr added to fully connected operation in TFLite frontend #6228
Gather operation with indices as tensor expr in TFLite frontend #6168
Added support for tflite quantized maximum and minimum #6018
Unary ops support added in frontend #6196
Introduce caffe frontend for tvm #6206
Keras softmax and prelu fix under NHWC #6278
add support for MXNET numpy operators #6054
Refine tensorflow frontend 1.x & 2.x compatibility #6240
Reduceops support added to frontend #6252
Update precision in the ONNX strided_slice, update precision of ToScalar #6272
NHWC import support. #4899
Refine tensorflow frontend 1.x & 2.x compatibility #6240
Fix node indices attribute error for tensorflow 2.3 #6288
Support NMSv4 #6085
Support for PyTorch Non-Maximum Suppression #6314
ReplicationPad support added #5708
MXNet pre-quantized BERT #6039
Keep parameter names from PyTorch #5887
Refine LSTMBlockCell to support dynamic rnn #5963

Relay

Add function attributes to IR hash (#4479)
Relay passes lookup overhead optimization (#4594)
Add half_pixel option to Resize op #4610
Skip example json runtime test when config is not set #4614
Test tensor_array in vm #4608
Improve memory_allocation pass to support multiple i/o dynamic kernels #4595
Add unit test for tensor_array_split #4619
Add parses support for unary elemwise ops #4634
Add parses support for SLICE #4502
Added pool autopadding and simplified converters. #4672
Fix meaning of conv2d_transpose output_padding parameter #4318
Use packed func macro for external codegen #4710
Fix _parse_param bug #4711
Add constant input support for elemwise ops #4666
Add parser support for squared difference #4652
Add type check to dense #4724
Invoke tvm::build from relay compile_engine and interpreter #4723
Broadcast condition, x, and y for Where op #4774
Add parser support for relational ops #4695
Remove duplicated BindParamByName function in VM compiler #4793
Use SimplifyInference for L2 Normalization. #4795
Expose vm OptimizeModule to Python #4800
Add parser support for logical operators #4642
Conv2D padding representation #4787
Add support for quantized LOGISTIC #4696
Fix VM compiler for while loop with free vars #4889
Fix bug in re-processing call node in MergeComposite pass #4879
Expose FunctionGetAttr to Python #4905
Add a PyTorch to Relay Parser #4497
Support data types for CSourceModuleCodegen args and output #4934
Clean up and refactor PyTorch frontend #4944
Relay pass to use fast exp/tanh #4873
BatchNorm support with run-time mean and variance calculation #4990
Reduce plevel of conv2d winograd implementation on cuda #4987
Add operation tan to TVM #4938
Outline and inline lifted functions for external codegen #4996
Remove primitive attribute from composite function #5014
Refactor Relay Python to use new FFI #5077
Fix relay node registration after refactor #5083
Codegen_c.h should include relay.function #5093
Move expr.Function to function.py #5087
Propagate constant to subgraphs #5094
Adjust strategy plevel to achieve expected performance by default #5118
Added a AnnotatedRegion utility class #5030
Support TupleGetItem in body of pattern #5106
Partition graph codestyle fixes #5202
Re-wrote the Graph Partitioner to support multiple outputs #5143
Fixes to MergeCompilerRegions #5195
Refactor build module to take IRModule #4988
Separate analysis and transform passes #5035
Relay Node::make to constructor #5128
relay::StructuralHash to tvm::StructuralHash #5166
Conditions updated to cover better user scenarios #5043
Replace UseDefaultCompiler with GetAttr #5088
Return empty CSourceModule when no lowered_funcs exists in Relay mod #4847
Clean up for memory pass to enable heterogenous execution support. (#5324)
Remove re-exports of tvm.transform (#5337)
[Refactor] Add memoized expr translator for use by backend codegen (#5325)
Legalize - Use Non-recursive Rewriter. (#5296)
Add additional check before re-using the cached match #5552
Remove kCompiler attr from external functions #5615
Pattern Language MergeComposite #5656
Support Tuple Output in C/DNNL Codegen #5701
Infer types in MergeComposite #5766
Convert PatternGrouper to do pre-order, non-recursive analysis #5653
Remove constants from partitioned functions #5663
Add a check for null function attributes #5674
Add ConstantPattern #5689
Conditionally Embedding Constants in Partitioned Functions #5693
Simplify Pattern API Implementations #5703
Add ShapePattern and DataTypePattern #5760
Remove unnecessary print #5642
Improve Shape Func handling for Tuple inputs #5467
Relay updated with String #5578
Fix the creation of tuple of tuples in PartitionGraph #5616
Preserve type information in Merge Composite #5640
Move compiler_begin/end_op to local static objects #5622
Fix dataflow_pattern.rewrite() hang if Match in IR #5680
Fix segfault in pretty print when ObjectRef is null #5681
Move fallback_device to config #5690
Replace build_config with PassContext #5698
Clear compile engine after task extraction #5724
Add storage_order ignore in pooling layer. #5781
Tweak cublas/cudnn priority level #5820
Skip Unknown Function Symbols #5888
Allow every runtime module to handle constants #5885
handle Tuple/TupleGetItem in first order gradient #5946
Add resnet-3d & Update network definitions for NHWC layout #5945
Use TargetNode::attrs for Target serialization #5993
each option of target str should only contain one ‘=’ #5988
Rename target_id => target_kind #6199
64-bit RPi4b target #6211
Add resnet-3d & Update network definitions for NHWC layout #5945
Small bug fix for Conv1D imports. #5995
Move invoke_tvm_op and shape_func to vm dialect #5958
GRU Layer Support #6020
Add pass for getting calibration data from a relay module #5997
Merge two consecutive reshape ops #6052
Add operation scatter_add to relay, based on scatter implementation. #6030
i64 indices #5235
Port eliminate_common_subexpr to non-recursive form #6134
Fix interpreter for dyanmic shape input of ndarray_size #6086
Allow to config allocator type and refactor vm code structure #6105
Handle ndarray_size in FoldConstant #6156
when converting constant nodes with types of int64 or float64 #6159
Add ReshapeTensor instruction in the VM to replace the reshape op #6089
Support combine multiple dense op just into dense #6062
Add unbiased variance op and corresponding support in pytorch frontend #6232
Specify additional layouts in convert layout pass #5422
Safe check added for Merge Composite Call Node #5562
Non recursive partitioning #5493
Support combine multiple dense op just into dense #6062
Make the max number of fused ops configurable #6327
Implementation of the dynamic pad operator #6284
change device annotation from post DFS to recursive #6124
Make check stricter: disallow inserting function with free vars into module #6313
Make check stricter by using Feature. Fixed multiple bugs #6326
Resize support for NCHW-convertible layouts #6293
Make AutoDiff thread through global function #6336
Create Interpreter for each constant subgraph #6195
Add Dynamic reshape to a dynamic namespace and add DynamicToStatic Pass #5826
Expose relay BindParamsByName to Python #4751
Implement pass manager tracing API #4782
Move Ops in relay.op.contrib #4942
Conditions updated to cover better user scenarios #4951
[External codegen] Add test cases for fused ops with manual annotation (#4741)
Multiple output support, reshape, split ops added #6296

Operator Coverage

Allow empty tensor for reshape, tile and strided_slice #4618
Fix meaning of conv2d_transpose output_padding parameter"; #4708
Remove cpp upsampling and resize op #4769
upsample operator ‘NCHWinic’ format support. #4791
Injective schedule improvement #4786
Enable vectorization on fp16 type #4867
Support for Int8 schedules - CUDA/x86 #5031
New PR to re-add tan to TVM #5025
Register topi schedule for Relay fast_exp and fast_tanh #5131
Move Dilation2d from nn to image namespace #5110
Use Thrust sort for argsort and topk #5097
Conv2d and Dense ops support on Tensor Core #5099
Setting workload correctly for Depthwise Spatial conv ARM. #5182
Adding a few missing math intrin #5011
Missing vectorize for depthwise conv2d. #5196
[TOPI] Using x86 schedules for ARM conv2d (#5334)
[TOPI-ARM] Do not alter layout if layout is NHWC (#5350)
[TOPI] Setting workload correctly for Depthwise Spatial conv ARM. (#5182)
[OP] Add fast_erf implementation (#5241)
[Topi] Tensorcore support for Conv3D (#5284)
[intrin] a few more math functions (#5468)
[Intrinsic] Add log1p, ldexp, atan2, hypot, nextafter, copysign (#5312)
[topi] Add operation relay.nn.dilate() which calls topi.nn.dilate() (#5331)
[Topi x86] Missing vectorize for depthwise conv2d. (#5196)
[TOPI x86] Adding unroll_kw config option for depthwise conv2d. (#5197)
[Topi] Breakdown topi.cc into smaller files (#5253)
ReduceLogSumExp Operator support #5453
Math ops added #5502
Enable blocking format in x86 conv2d and fold scale axis #5357
Add operation gather to relay. #5716
Add storage_order ignore in pooling layer. #5781
Fix bifrost spatial packing conv2d auto tune #5684
Fix reshape usage in ARM schedule #5732
Block sparse dense on cuda #5746
Improve CUDA softmax scheduling #5600
block sparse dense on cuda #5746
pass-by-value -> pass-by-const-reference #5783
Using MKL blas for quantized dense #6115
topi -> tvm/topi #6186
Use auto-tuner to improve conv2d_gemm performance #6117
Improve CUDA conv2d_transpose_nchw #4762
Add CUDA conv2d for NHWC layout #4737
conv3d_ndhwc schedule #4775
Fast exponent #4790
Add Scatter to Topi/Relay/ONNX via hybrid script #5619
Split MKL from BLAS. #6182
Change the meaning of conv3d_transpose output_padding to match conv{1,2}d_transpose #6065
Gather op support added #6013

Runtime and Backend

Cythonize NDArray.copyto (#4549)
Unified Object System runtime refactor (#4578, #4581, #4603)
VM profiler: sort VM stats by time (#4601)
Update RPC runtime to allow remote module as arg (#4462)
Refactorying system lib and dso lib into library module (#4481)
Improve TSIM virtual memory mapping (#4545)
make adt tag signed #4605
Improve TVMBackendPackedCFunc to allow return val #4637
EdgeTPU runtime for Coral Boards #4698
Fix memory leak when using openMP #4811
Fix memory leakage of TVMByteArray #4856
Fix TVM_DLL_EXPORT_TYPED_FUNC to work on Windows #4955
Fix memory leak when using openMP #4811
Export GraphRuntime in tvm_runtime.dll #5002
MISRA-C compliant TVM runtime #3934
Update the type_keys to reflect the code-org #5074
Fix AttrEqual for Array and StrMap, double #5054
Export GraphRuntime in tvm_runtime.dll #5002
Fix unused-value warning #5140
crt error handling #5147
Bundle deployment with static linking #5158
Implemented kDLCPUPinned (cudaMallocHost) #4985
Explicitly cast min/max operands #5090
ref_counter -> ref_counter_ #5184
Expose runtime::String to Python (#5212)
[FFI] Refactor runtime.String to subclass str (#5426)
[RUNTIME] Auto conversion from str to runtime::String in PackedFUnc (#5251)
[RUNTIME] Improved Packed FFI for optional. (#5478)
[Hexagon] Add hexagon_posix.cc to TVM/RT sources in the right place (#5346)
[FFI] Refactor runtime.String to subclass str (#5426)
Fix workspace #5503
Store nullptr PackedFunc as nullptr for better error propagation #5540
Improve PackedFunc robustness #5517
Seg fault in WorkspacePool's destructor (#5632) #5636
Resolve constexpr issue in debug mode. #5651
Add compile_shared option to linux compile utility fn #5751
Call sync in CopyFromRemote and CopyToRemote #5512
Fix the multihop cpu case #5522
Improve RPCServer AsyncIO support. #5544
Modularize the RPC infra #5484
Add compile_shared option to linux compile utility fn #5751
Overload string operators #5806
Only initialize required module #5926
if a param not in input, we should still consume it’s data #5990
init TVMPackedFunc’s name #6044
Enable auto conversion String->DLDataType #6214
Support random fill #5913
Use new to avoid exit-time de-allocation order #6292
Add parallel_for support to run a loop in parallel #6275
Solve ARM BIG.LITTLE heterogeneous multicores #4747
[RUNTIME] Quick fix PackedFunc String passing (#5266)
Introduce runtime::String::CanConvertFrom #5718
Restore the StrMap behavior in JSON/SHash/SEqual #5719
Support overriding RPCWatchdog termination behavior on Android and other platforms #6216
Set NDArray::Container.shape_ in NDArray::FromDLPack (#5301)
Enable x86 cpu cache flush #5914

Quantization

Conv2D type checking for kernel per-channel scales. #4732
Add missing nullptr check #4773
Doc fix on convolution and dequantize #4799
Conv2D with dilation support. #4796
Making scale/zero_points as expr instead of attrs. #4611
Make calibration faster and more memory usage friendly #4589
Doc fix on convolution and dequantize #4799
Conv2D with dilation support. #4796
Optimize lowering for requantize and FixedPointMultiply. #4798
More doc fix on quantize and convolution #4874
Add support for per channel weight scale in dense op #4880
Add support for quantized models via QNN #4977 #5013
Support 4D padding. #5036
[Requantize] Cleanup and Optimize Lowering (#5286)
[Topi, ARM] Disbale Winograd for quantized tensors. (#5363)
Adding support for TFLite QnnSubtract operator. (#5230)
Remove developer facing api from frontend exports. (#5375)
Add Quantize/Dequantize Partitioning #5940
Add support for quantized models via QNN #5016
Quanitze operation expanded to take const argument #6127
FP32 and Quantized Object Detection Model #5479
Support CallNode inputs in qnn.concatenate #5360
QNN support for TFLite 2.1.0 quantized models #5848

TE

Tighten split's extent #4931
Set split node's range to minimum of ext and split factor or split np… #5044
Support mixing normal and cross-thread reduction (#5193)
Inline -> te/schedule/operation_inline.h (#5386)
Create loops according to storage scope and thread hierarchies (#5190)
Fix import in dump pass ir (#5327)
Scalar support for te.extern #6079

TIR

IR readability enhancement (#4501)
Introduce tir::PrimFunc #5070
Introduce PrimFuncPass. #5139
[TIR] Enhance Substitute, python bindings for Substitute/PostOrderVisit (#5400)
[TIR] Remove ProducerConsumer and AllocateNode::new_expr (#5333)
[TRANSFORM] Enable CopyOnWrite for TIR passes. (#5309)
[REFACTOR] Migrate LowerTVMBuiltin, InferFragment, LowerThreadAllreduce, ThreadSync to Pass Manager (#5213)
[REFACTOR] Remove te::Tensor dependencies from TIR passes. (#5372)
[TIR] Refactor MakePackedAPI to target dependent stage. (#5326)
[REFACTOR] tvm.hybrid -> te.hybrid (#5223)
[REFACTOR] Migrate most of low-level build to use the Pass Manager. (#5225)
[REFACTOR] Migrate low-level passes in tvm.lower to the Pass Manager (#5364)
[TIR] Migrate VTA TIR passes to the new pass manager. (#5397)
[REFACTOR] Migrate all low-level passes to the Pass Manager. (#5233)
[REFACTOR] Introduce ExprDeepEqual, Remove IRDeepCompare (#5206)
[REFACTOR] RewriteForTensorCore -> te/schedule (#5379)
[REFACTOR] Remove ir_pass in favor of analysis/transform. (#5415)
text format printer considering future parsing use #5483
Remove buffer params from pass config. #5652
std::string -> String Migration in TIR nodes #5596
Remove CallNode.call_type in favor of attribute. #5937
Remove legacy HoistIfThenElse #5944
Improve Let/LetStmt support. #5949
Refine side effect analysis. #5954
Provide->ProducerStore, Realize->ProducerRealize. #5750
Migrate the tvm/tir/expr.h to constructor #5773
Migrate tir/stmt.h to use constructor. #5778
Cleanup unused classes #5789
Add tir prefix to type keys #5802
Enhance VerifyGPUCode #6194
Enforce buffer pointer var type to be consistent with dtype. #6317
Create a StringImm reference type #4806
Add init member to ReduceNode #6138
Add dump and print for debugging (NFC) #5207
Streamline Function Attr interface. #5045
alpha_equal to structural_equal #5161
Remove AttrsEqual and AttrsHash related code #5169
[NODE] General serialzation of leaf objects into bytes. (#5299)
[POC] Initial stab at std::string->String upgrade (#5438)
[TIR] Make lower_warp_memory support extent(threadIdx.x) < warp_size (#5307)
[PASS] dtype rewrite for indexing variables (#5092)
[PYTHON] Enhance with_attr API, cleanup MakeAPILegacy in testcases (#5335)
[PYTHON] Make IntImm more like an integer (#5232)
[IR] Move to runtime::String (#5276)
[IR] kExternalSymbol -> kGlobalSymbol (#5211)
[IR] Remove PrimExpr from String (#5311)
IRModule is updated with String #5523
IR is updated with String #5547
Streamline ir/op Registry #5609
Migrate IRModule ObjectRef to not-null #5654
Migrate BuildConfig to PassContext. #5668
relay.op.Op -> tvm.ir.Op #5705
Separate ArgTypeCode from DLDataTypeCode #5730
Remove legacy compute_expr.h #5738
Call::Halide => ProducerLoad, DSL/TIR decouple. #5743
Provide->ProducerStore, Realize->ProducerRealize. #5750
Migrate the tvm/tir/expr.h to constructor #5773
Migrate tir/stmt.h to use constructor. #5778
Migrate all Object construction to constructor. #5784
Cleanup unused classes #5789
Finish std::string->String updates #5793
Add tir prefix to type keys #5802
Change Call.name to Call.op(RelayExpr) #5863
Range/IntSet API style consistency. #5953
Separate ArgTypeCode from DLDataTypeCode #5730
Migrate all Object construction to constructor. #5784
Finish std::string->String updates #5793
Unify StrMapNode and MapNode #5687

Performance Improvements

Int8 GEMM performance enhancement using Cublas (#4550)
Speedup TSIM with multi-threading (#4491)
Support cudnn softmax (#5214)
Add cuDNN grouped convolution support (#5319)
Winograd support for Conv3D (#5186)
Improve get_valid_count and nms performance for CUDA (#5339)
Optimizations of global_ave_pool for NHWC layout (#5450)
Optimization of Conv2d Winograd algorithm on Tensor #5485
Some performance improvement to VM #5901
Optimize x86 conv3d_ndhwc using data packing approach. #4866
Improve NHWC depthwise convolution for AArch64 #6095
Improve quantized convolution performance for armv8 architectures #5754

Documentation

Adding benchmark log format doc (#4366)
Add Ninja build system to installation docs (#4554)
Doc/comment fixes (#4452, #4463, #4469, #4493, #4397, #4580, #4585, #4591)
Fix doc after moving to unified IR #4835
Introduction to module serialization #4564
ConvertLayout - Call RemoveUnunsedFunctions. #4834
Fix bugs that override n_trials #4842
Update the vm doc #4868
Refine the example description of max/min/sum/tag_scope #4974
Fix vta tutorial #4809
Introduce how to add hardware backend to FAQ #4898
Update API docs to reflect the status after the refactor. #4907
Fix sphinx warnings #4917
Fix Sphinx Warnings (RST indent, cross-ref, and image scale) #4920
Fix Sphinx Warning: the target found for cross-reference #4925
Sphinx -- Introduce alias detection. #4954
Fix Warnings from #4942 #4959
Fix sphinx precheck #4967
Move git_howto to rst, add Stage documents to te #5055
Add doc for Relay op strategy #5078
Update relay docs #5112
Include a tarball of docs, add a security faq #5119
Cleanup docs before rebuild #5127
Minimize necessary doc change #5129
Various sphinx related fix. #5168
Point docs to the ASF site. #5178
Use https link #5183
Reduce artifcats generated by sphinx gallery #5208
Refine the example description of max/min/sum/tag_scope #4974
Description updated for pooling attributes #5091
[DOCS] Migrate some markdowns to rst, fix sphinx3 warnings (#5416)
[DOCS] Misc docs improvements (#5222)
[DOCS] Bring relay docs to the top-level flat view (#5343)
[DOCS] Reduce artifcats generated by sphinx gallery (#5208)
[DOCS] Use https link (#5183)
[DOCSTRING]missing function parameters updated (#5228)
[DOCS] Migrate HLS documents from md to rst (#5419)
[Tutorial, QNN] Add tutorial for loading quantized PyTorch model (#5321)
[Docs] VTA install doc migration from md to rst (#5442)
[Docs] compiler version in docs (#5281)
Remove legacy compute_expr.h #5738
TVM_REGISTER_API -> TVM_REGISTER_GLOBAL #4768

Bug Fixes

Add bfloat16 typeflag support (#4525)
MSVC / Windows fixes (#4455, #4569)
Fix Makefile for howto_deploy (#4457)
Fix GCC 4.8 compact (#4461)
Fix search path to build libtvm_topi.so (#4467)
Fix for conv2d_transpose CUDA compilation (#4472)
Fix for LLVM 10.0 codegen (#4480, #4515)
Fix alter op layout when calling global var (#4454)
Fix float2half_rn support for cuda compute capabilities < 53 (#4489)
Fix compile errors for OpenCL backends (#4492)
Fix serialization precision loss (#4503)
Fix hybrid script to support array of tensors (#4494)
Fix annotation for multiply op (#4458)
Fix Dockerfile for linter CI (#4506)
Fix TF resize for dynamic size models (#4510)
Fix bias_add gradient (#4516)
Fix tanH unit test function call (#4517)
Fix extra reshape parameter for ONNX (#4524)
Fix crash caused by empty TOPI config (#4520)
Fix ONNX shape op type to use int64 (#4528)
Fix crash in TSIM virtual memory driver (#4527)
Replace deprecated python library in setup script (#4533)
Fix NMS max_output_size loop (#4541)
Fix style in IR mutator and IR visitor (#4561)
Fix compiler warning (#4559)
Fix to get end to end inference on Chisel VTA (#4574)
Fix LLVM build by adding missing intrinsics headers (#4575)
Fix context creation in quantization (#4582)
Fix NDArray SaveDLTensor signature (#4586)
Fix dense pack schedule for x86 (#4539)
Fix for broadcast tensor of scalar type (#4577)
Datatype refactor (#4513, #4560)
Add const qualifiers for NDArray container (#4590)
Fix TF <= 1.12 compatibility (#4593)
Fix for graph debug runtime (#4598)
Disable copy constructor for external codegen (#4597)
Make ADT tag signed (#4605)
Added declare of aluBits for TensorAlu #4624
Get around limitation of g++-4.8 #4626
Bugfix StmtMutator IfThenElse #4609
Remove unecessary rdynamic #4613
Resolve constexpr related link error in debug mode #4641
Asymmetric padding #4511
Reduce data size of asymmetric padding testcase #4658
Fix Base64OutStream portability issue #4668
Fix topi.nn.global_pool layout=“NHWC” #4656
Also package core.rly #4679
fskip of EliminateCommonSubexpr cannot always return false #4620
Fix Python syntax error in start_rpc_server_to_tracker.py #4682
os.path --> osp to match the import #4681
GitHub actions/checkout@v1 --> v2 #4680
Fix Python syntax error AGAIN in start_rpc_server_to_tracker.py #4685
Use ==/!= to compare str, bytes, and int literals #4686
Rename start_rpc_server_to_tracker.py to start_rpc_server_to_tracker.sh #4689
GitHub Action lint Python code for syntax errors #4688
Generate blob use LLVM directly #4657
Reduce input size to fix oom #4653
Fix RemoveUnusedFunctions pass #4700
Link the math library by default #4713
Update mainline version to 0.7.dev0 #4720
Add SizeVar representing non-neg valued variable in a tensor shape #4684
Fix the compile problem of cpp_rpc #4725
JSON upgrader to upgrade serialized json. #4730
Fallback schedule for Int8 depthwise. #4733
Fix dense x86 schedule #4728
Fix demo dockerfile build failed #4744
Improve CUDA vectorizer #4736
Add .asf.yaml for github info #4761
Fix padding in pooling op #4738
Remove run_infer_type duplicates #4766
pooling.cc improvements #4767
Export builtin_fp16 on Windows #4731
Fix Tensorflow conv3d pad bug, add non-cubic data and kernel tests #4772
Bump prebuilt-image version in demo dockerfile #4770
Update tune_simple_template.py #4778
Explicitly link to cublasLt if it exists #4776
Fix hasattr by extracting Python error type from Windows error message #4780
Replace os.path.exists with try...except...else #4784
Make sure to visit the arguments of inlined functions #4783
Parse additional exception strings #4785
Fix #4670: add bias for fc layer #4801
Change color channel from BGR to RGB for darknet preprocessing #4794
Fix -Wextra #4804
Fix vta tutorial #4809
Minor bug fixes in AutoTVM for QNN graphs #4797
Fixed subprocess creation under windows #4820
Improve tol to resolve flaky case #4836
Fixed process termination routine in windows #4844
test_cuddn flaky #4846
Mxnet parser for Qnn dialect #4714
Enhance cc.cross_compiler #4817
Fixed crash caused by reversing bitwise operations #4852
Reverse some changes made for intel_graphics/conv2d.py in PR #4849 #4853
const auto p -> const auto& p #4861
Fix onnx import bugs #4750
Explicit llvm::StringRef to std::string conversion #4859
Update the runtime PackedFunc for module #4871
Improve antlr import error message #4888
Fix alpha_equal bug for attribute check #4897
Fix issues in cuda codegen #4876
Fixed: Bitwise ops on floats causing wrong code generation and crashes. #4892
Fix tvm.target.generic_func runtime detection #4910
topi/tests/python/test_topi_sort.py::test_argsort #4891
Use opencv reisze method for preprocessing of image in darknet #4883
Fix build breaks with StringRef changes #4923
Remove unnecessary spliting in the cached chunk #4935
Fixing an Infinite Loop case in UnmatchedChecker. #4881
Remove SGX toolchain installation from CI Dockerfile #4948
Fix tedd tutorial after strategy change #4947
Allow customize MKLDNN library location #4814
Added CopyFromBytes and CopyToBytes convenience methods to NDArray. Fixed typos. #4970
Fix gcn tutorial failure #4994
Fix stride default value None in torch.nn.functional.avg_pool #4984
Fix ROCm strategy for winograd conv selection #5001
Fix get_valid_count flaky test for cuda #4901
Change Scala Linter scalafmt => scalastyle #4998
Kill from tvm import te #5007
Chisel fixes and de10nano support #4986
Fix gpu not found when running TVM docker #4975
Fixes for pylint==2.4.4 #4849
Fix unordered dictionary problem for python version under 3.6 #4982
Fix gcn tutorial failure #4994
Fix stride default value None in torch.nn.functional.avg_pool #4984
Fix ROCm strategy for winograd conv selection #5001
Early checking added and new test cases added for schedule fuse #5010
Fixed div by zero core dump. Fixed rounding intrinsics on int crash #5026
Test case modified for int type #5012
Bug Fix for ARM CPUs. Lower strict assumption. #5063
Triage the testcases to fit the the new namespaces #5071
Add colors to compute_at edges and thread/block indices. #5111
Temporary fix to the stack overflow issue in autotvm task extraction #5019
Fix compilation of If-Elses #5040
Fix CompilerAttrs #5109
Fix the existing test cases before refactoring. #5122
Fixed bug where shifting by out-of-bounds value results in no compute code being emitted. #5115
Fix for issue #4831. The data_min_idx and data_max_idx were flipped. #5136
Duplicate likely nodes added when loop axis split unevenly #5084
Fix incorrect name of calibration mode #5150
Remove contrib spatial pack schedule of depthwise convolution #5148
Fix annotate pass static variable #5023
Fixed ConvTranspose2D parsing #5157
Nullptr check #5176
rocm: fix miopen convolutions #5179
rocm: fix dense_rocblas in strategy, topi #5191
Fix CRT static test bug (#5293)
Fix perf regression of tir refactor (#5258)
Bugfix in tensorflow space_to_batch_nd (#5175)
Compilation warnings fixed for 32bit and 64bit compilation (#5349)
Fix hang in MergeCompilerRegions (#5227)
Fixes to MergeCompilerRegions (#5195)
Fix generation of LLVM intrinsics (#5282)
Fix setting up hints for getaddrinfo (#2872)
Add ConstantNode to IsAtomic (#5457)
Fix String SEqual (#5275)
Fix fuse over functions that are handled by external codegen (#5365)
Fix memory leak when accessing NDArray (#5413)
Remove the duplicate PrintIR pass in Relay (#5403)
Fix lower_warp_memory (#5247)
Fix lower_warp_memory when there are >1 warp buffers (#5368)
Fix intel conv2d auto tune (#5200)
Fix FuseBatchNorm output cast error if need_cast is True #4894
Fix an assertion exposed by loop vectorizer #4916
Fix error message #4945
Fix for recursive let #5757
Fix Calibration Pass to Support Modules with Multiple Functions #5768
Fix what looks like bizzare copy-paste issue #6010
Fix bug in transpose_shape_func #6180
Fix bugs in CUDA codegen (#5209)
Don’t remove() TemporaryFile in del. (#5414)
Fix test_ir_type. (#5390)
Fix multiple identical inputs bug (#5389)
Add cuda target check to dense tensorcore schedule. (#5376)
T2 test fixups (#5391)
Fix miopen padding (#5433)
Misc fixes for ROCm (#5431)
Fix copy constructor (#5237)
Corrected TVM autotuning on GPU (#5432)
Fix vector load (#5226)
Minor bugfix in message_passing.cc (#5254)
Fix a bug when vectorized load&store was involved for… (#5428)
Fix to skip node not in graph. (#5238)
Fix #5388 [VULKAN] vkBuffer released before memory copy command se… (#5418)
Fix a minor error in device_annotation (#5291)
Fix scalar’s ndim is 0 (#5344)
Fix the runtime raise error #5586
Fixed bug in attribute parsing for pool layers. #5582
AutoTVM incorrect measurement #5511
fix a min/max simplify bug #5761
Rename tvm_dso_op to libtvm_dso_op #5714
Fix generating types like float44 and float88 #5722
Avoid downloading when TOPHUB_LOCATION is NONE #5720
codegen llvm: move nvptx-specific intrinsic handling into codegen_nvptx #5726
ROCm warp shuffles and reductions #5727
fix small bug about dense_grad #5695
Clarify downstream consistency of TVMArgTypeCode #5742
Fix gelu in PyTorch frontend, tighten numerical checks #5763
Make batch matrix multiplication on GPU tunable #5752
update vulkan build rule #5777
aten::norm support added #5776
Edit onnx parser to infer values in post order #5755
Support symbolic inputs of Fill #5762
support aten::type_as in the pytorch frontend #5787
Temporary disable fp16 type_as test for PyTorch Frontend #5799
Add config switch for nn.dense layer type. #5801
Move cpu-only frontend tests to a CPU stage #5807
Pin hand landmark network to version 0.7.4. #5813
Limit number of threads in all jobs #5815
Error msg update #5818
fix relay.build to not change the module argument in place #5822
Fix InferType when module contains Prelude #5797
Add a combine batch_matmul pass #5791
RepeatVector, Conv3DTranspose op support added #5833
Fix converting serialized quantized models #5839
ffi (Object): make class dict visible in instances #5843
Additional canonicalization added for AddNode #5846
Suppress the warning messages when compile engine selects impls #5821
fix #5849 #5851
Introduce POD-C Compliant tvm::Map #5740
Add bfloat16 #5601
Add Python Classes for all Attrs #5853
Fix map assign issue in CI test #5854
Introduce Target Id Registry #5838
Update has_dtype/has_shape to pattern lang doc #5847
Add nn.batch_flatten as quantizable. #5805
Fail early before running invalid dynamic graphs #5856
Improve type handling in PyTorch frontend #5834
HotFix the python intrin rule #5895
add a few gradients #5899
Add Binary Intrinsic ops to TIR Ops in C++ #5900
Allow implicit conversion in TVM FFI to tvm::Bool #5907
PyTorch frontend: fix handling of duplicate use of a model weight #5897
Don’t multiply by constant 1 uselessly in dense #5911
Support any index matching for TupleGetItem #5909
Add MicroTVM tutorial using the STM32F746 discovery board #5655
Fix serialization of inf float value #5912
Fix CPU Thread Binding for Multiple Sockets #5918
CUDA device API & VerifyGPUCode pass update #5898
Update install.rst #5858
Two small fixes to AMDCPU codegen for LLVM 10+ and ROCm 3.5+ #5920
Add LegalizeInvalidAttach to legalize the compute_at location after split or fuse #591
Don’t rewrite expressions used outside of the pattern #5930
Add TupleGetItem to CSE #5931
Various update for CoreML codegen #5934
Update date in the NOTICE #5943
Raise right error in tensorflow split op #5951
Add rm xla attributes in tf docs #5950
Fix OpenCL get_valid_counts errors due to intrinsic atomic_add #5857
Amendments for gradients #5941
Fix the meaning of conv{1,2}d_transpose output_padding parameter. #5758
Make first order gradient graphs more efficient #5959
Raise an exception when extern function does not return Stmt #5964
Improve docker/bash.sh to handle git worktrees #5970
Install DNNL (OneDNN) to CI Environment #5936
Add Dynamic reshape to a dynamic namespace and add DynamicToStatic Pass #5826
Add meshgrid op in Relay, TOPI, Pytorch frontend #5961
Print right number of parentheses for LoadNode #5965
Migrate data structure of TargetNode #5960
Remove redundant function CreateBufferVecPtr #5982
Fix string argument mismatch in GraphRuntimeCodegen #5933
VectorType::get with two parameters is deprecated in LLVM 11+ #5984
Fix Compilation Error in CRT #5713
Fix runtime::String backward compatibility in JSON #5725
Allow RPCWrappedFunc to rewrite runtime::String as std::string #5796
Fix reshape #5739
Fix building with LLVM-10 on macOS #5859
Add cuda 11 to contrib.nvcc.find_libdevice_path() #5902
Fix sequential cpp test #5745
Infer types in MergeComposite #5766
Fix recursive let for well formed check #5780
Recover global state after test_util.py #5824
Fix bug in rpc ring buffer shrink #5516
Fix remote device sync #5538
Fix bug in rpc ring buffer shrink (#5516) #5537
RPC Server error fix on Pynq FPGA #5607
Fix FloorMod Simplifier #5509
Fix Python debugger segfaults with TVM built with LLVM #5685
Fix Compilation Error in CRT #5713
Fix runtime::String backward compatibility in JSON #5725
Allow RPCWrappedFunc to rewrite runtime::String as std::string #5796
Fix reshape #5739
Make “none” DataType explicit #5491
Change “scalar” and “stack” in IDL from “inrout” to “in” #5487
Link necessary libraries when building runtime for Android #5496
Fixes for wasm32 target #5489
Reset target and wait for runtime initialization on connect. #5499
Bump tophub rocm version #5504
Improve commentary for RingBuffer #5518
Add unit tests for ONNX PRelu and fix importer to pass them. #5521
LRN only supports 4D tensors, remove it from alter_op_layout #5520
Fix an issue with ONNX Upsample #5530
Cache PrimExpr instead of raw pointers in bound analyzer #5533
fix a few bugs with shape inference and types in the ONNX importer #5534
Add Onnx Pad v11 #5539
Changes to cpp_rpc to make it work on Android (+ Hexagon offloading) #5535
Fix to reduce RAM size during loading model #5507
Fix MakeLoopNest for warp memory #5382
Load platform specific lib for tvmdsoop instead of the hard-coded tvm_dso_op.so #5542
Add tests for running micro on native arm hardware #5546
Apparently, ONNX Conv with no ‘pads’ defaults to zero padding #5548
clang-format the h,cc,m files. #5557
Fix conv2d alter op for arm cpu #5532
Fix topi test for non tensorcore CI. #5563
Add clang-format and nodejs to ci-lint #5567
Enable clang-format. #5572
Allow ubuntu_install_darknet.sh to work in both 18.04 and 16.04 #5574
Add a quantized conv2 unit test for the tflite front-end #5558
Fix JSON graph dumping. #5591
Warp level reduction support for CUDA #5498
One more fix for concurrency count #5589
Improve robustness of the docs build #5583
Phase out WebGL #5570
Fix vulkansdk in the ci-gpu and upgrade to 1.2.135 #5566
Update ci-cpu to bionic #5554
Overestimate binary size for microTVM compiled binaries. #5590
Fix bug and re-enable RPC execution test #5436
Add ostream formatters for TargetPtr/TargetVal. #5592
Fix cross thread reduction #5551
Fix TVMArray layout on device #5599
Add debug mode to tempdir() #5581
Represent alignment information in LLVM IR #5598
Fix codegen for warp shuffle intrinsics #5606
Fix Topological Order calculation for DFPattern Language #5612
Global MaxPool3d and AvgPool3d support #5098
Fix build error of iOS RPC #5621
isn't a CallNode sometimes #5623
Introduce config to PassContext. #5631
CMAKE fix #5630
Label Pattern Partitions #5627
Extend AttrPattern to support CallNode and FunctionNode attributes #5637
Increase bss section size. #5660
Add buffer name when creating tensor bindings #5670
µtvm debug improvements #5648
enable amd_apu device on vulkan target #5659
Support TupleWrapper as direct ancestor of control flow ops #5639
add tvm.micro pydoc to sphinx #5661
Add a regression testcase for #5674 #5677
Fix C++ RPC build problem on Linux #5671
Add a check Callback to the Pattern Paritioner #5646
Call previous excepthook in tvm_excepthook. #5675
Fix the shift column for scale_shift_nchw and scale_shift_nhwc in C topi #5679
Support more dtypes for TVMDSOOp #5694
In memory_plan, check if value is not None, instead of just checking value as boolean. #5700
Fix flaky test_topi_pooling.py:test_adaptive_pool #5736
Fix the values for test_fmod since it fails way too often otherwise #5723
fix small bug about dense_grad #5695
Fix sequential cpp test #5745
Add Scatter to Topi/Relay/ONNX via hybrid script #5619
Clean WASM environment before build #5759
Fix gelu in PyTorch frontend, tighten numerical checks #5763
fix #5686: remove a overstrict assert in MakeAllreduce (#5686) #5785
Improve Pattern Language Docs #5676
Add missing expr visitor for any #6082
Remove the tvm web from version update #6122
Clear relay cache after every build & Clear warning message cache after autotvm task extraction #6131
avoid unexpected throw in AttrInitEntry #6128
Verify that tensor reshape is valid. #6215
Use LocalRunner by default in the tutorial tune_relay_cuda.py #6001
Undefined names: import os for line 324 & import re for line 308 #6003
GitHub Actions upgrade to actions/setup-python@v2 #6002
Only pass pythonpath for ci images #6005
Auto-convert shuffle with single index to “extract element” #6006
Cache object refs in loop partitioner instead of object pointers #6004
Fix test_arith_solve_linear_inequality.py::test_multi_equal #6014
MXNet frontend support for AMP cast op #5976
Demo showing how to run a pruned model. #5975
Move compiler related registry items to vta/build_module.py #6012
Pin keras version #6032
Fix in arm_cpu/conv2d_alter_op for NHWC quantized #6027
Add creation of Hexagon device in RPC client #6035
Terminate basic block after “ret” instruction #6036
µTVM CRT modifications for on-device RPC server #5921
Create TBAA information based on the unrelying buffer type #6046
Add support for tflite arg_min and arg_max #5992
Fix fully_connected converter when batch size is not 1 #6038
Fix a primitive check error #5991
Refactor to expose MakeOp functions to C++ #6047
Fix conv2_gemm after target structure update #6037
Remove use of designated initializers from hexagon_module.cc #6055
Build crttest and cpptest separately. #6057
Fix pytorch frontend prim::Constant issue #6051
update frontend tutorials to new model based runtime interface #6063
Remove unnecessary std::cout #6072
Fix error message in Buffer::vstore, NFC #6056
Fix FSIM Compile Error. #6070
Improve vector simplification for float operands #6043
Fix LocalBuilder on macOS with python 3.8. #6083
Add missing test for fast erf #6058
Fixed point multiplication improvements for AArch64 #5980
Fix code generation bugs for C/CUDA & Improve VerifyGPUCode pass #6041
Delete declaration of unused op_node #6102
Load configs even it has no entity #6100
Update SGX example Cargo.toml #6067
Add default value for option USE_DNNL_CODEGEN in the cmake #6099
Update installation doc with minor improvements #6104
lint: add opencl .cl file type #6092
Clean up conversions between TVM and Rust functions #6114
Improve reduction schedule on arm CPUs #6110
Register Shape Func for Some Operators to Handle Dynamic Shapes #5955
Fix variable name conflict with OpenCL keyword #6048
Some rust cleanups #6116
Option to specify alternate directory to output build to #6016
Add get_num_inputs to GraphRuntime #6118
TFLite quantized conv test #6084
Fix autotvm on the conv2d_nchw_winograd.mali operator #6130
add attr option mfloat-abi for arm32 #6123
Fix CUDA Library Tuning #6132
Add missing RPC sources after refactor #6113
Correct runtime.load_module #6161
Improve error messages in graph tuner, graph runtime, and module loader. #6148
Fix some shape mismatches between TF and Relay #6166
Improve doc string #6176
Fix incorrect function signature in header #6172
Fix alignment of note #6181
Implemented PADV2 Operator for TFLite and added support for constant values in PAD. #6167
Unary ops support added in frontend #6196
Change the meaning of conv3d_transpose output_padding to match conv{1,2}d_transpose #6065
Fix compile warnings. #6204
Fix -mfloat-abi=soft compilation for ARM with OpenCL target #6150
Match pytorch 1.6 googlenet pretrained model (#6201) #6212
Mod operator, bug fix #6160
RESHAPE with dynamic shape arg in TFLite frontend #6208
Fix compilation error with cuda 11 #6213
Fix port_end wrong default value 9199 to 9099 for keeping same with source code #6220
Std op without specified dimensions support #6226
fix crt building and running error #6231
Implemented ONE_HOT Operator for TFLite. #6223)
Avoid unexpected throw in AttrInitEntry #6128
Added casting to hybrid script doc and fixed pass infra doc #6174
Fix compile warnings. #6204
Fix -mfloat-abi=soft compilation for ARM with OpenCL target #6150
Mod operator, bug fix #6160
Fix compilation error with cuda 11 #6213
Fix port_end wrong default value 9199 to 9099 for keeping same with source code #6220
Std op without specified dimensions support #6226
Verify that tensor reshape is valid. #6215
Fix crt building and running error #6231
Fix conv2d_transpose output padding #6236
Fix cuda half math function is undefined: hpow, htanh #6225
Fix division range estimation error in simplifier #6244
Fix newer GCC compiler warnings. #6257
Support _contrib_SyncBatchNorm #6245
Fix reduction #6250
Add apt repository for clang-11 and llvm-11 #6256
Update tutorial to new TARGET as micro_dev is no more #6262
Fix clang-format #6264
Trivial fix, up the rodata section for the discovery board to 512 bytes. #6259
Fix cuda half math function is undefined: hpow, htanh #6253
Add dilation in x86 NCHWc depthwise conv support #6267
Decrease test times by introducing testing model #6235
Add support for parsing the any dimension. #6277
Improve error messages for memory verifier and gpu memory verifier #6281
Reflect Compile-Time CMake Options into libtvm.so #6280
Add cmake options into libinfo #6286
Update slice to infer attributes when not graph inputs #6276
Use rpc.LocalSession for simple tests #6294
Fix random fail #6312
Fix resize test #6298
Fix cython FFI compact with np.int64 #6321
Fix relay vm optimize #6322
Changed TVMCTVMContext to TVMContext #6306
Make able to compile with MSVC #6341
ROCm changed name of library and removed the old one in ROCm 3.7 release. #6345
Compatible for ROCm before 3.7 #6359
Use clear name that is separate from ASF brand for cache #6360
Fix Dockerfile.demo_android #6361
Fx sparse dense schedule on cuda #5803
Fix strategy for sparse dense cuda #5782
Fix x86 conv2d template when tuning with unpacked layout #5938
Fix the filter width parameter in depthwise_conv2d #6081
Fix reshape usage in ARM schedule #5732
Missing header #4865
Fix conv2d_transpose output padding #6236
Simplify reduce expression in te.gradient #6611

API Changes

tvm.module -> tvm.runtime.module
tvm.module.load -> tvm.runtime.load_module
tvm.module.enabled -> tvm.runtime.enabled
tvm.module.system_lib -> tvm.runtime.system_lib
tvm.relay.Module -> tvm.IRModule
tvm.create_schedule -> tvm.te.create_schedule
tvm.placeholder -> tvm.te.placeholder
tvm.compute -> tvm.te.compute

Deprecation

Deprecate NNVM (#4535, #4562, #4565, #4571)
Deprecate FreeStmt #5890
Remove legacy compute_expr.h #5738
Deprecate OpenGL #5711, #5712

0.6

Relay in Production

Relay is a functional, differentiable programming language designed to be an expressive intermediate representation for machine learning systems. Relay supports algebraic data types, closures, control flow, and recursion, allowing it to directly represent more complex models than computation graph-based IRs (e.g., NNVM) can. In TVM v0.6, Relay is in stable phase and is ready for production.

Algebraic Data Types (ADT) support (#2442, #2575). ADT provides an expressive, efficient, and safe way to realize recursive computation (e.g., RNN). Refer to https://tvm.apache.org/docs/langref/relay_adt.html for more information.
Pass manager for Relay (#2546, #3226, #3234, #3191)
Most frameworks have been supported in Relay, including ONNX, Keras, Tensorflow, Caffe2, CoreML, NNVMv1, MXNet (#2246).
Explicitly manifest memory and tensor allocations in Relay. (#3560)

Relay Virtual Machine

The Relay Virtual Machine (Relay VM) is the new generation of runtime to strike a balance between performance and flexibility when deploying and executing Relay programs. Previously, the graph runtime is able to utilize the fully static nature of the input graphs to perform aggressive optimization such as fully static allocation, and optimal memory reuse. When we introduce models which make use of control-flow, recursion, dynamic shapes, dynamic allocation we must change how execution works.

Relay VM is now usable and is able to achieve decent performance for a various of models and targets.

Design (#2810 #2915) and a first version of implementation (#2889),
Add VM runtime for Relay and compiler support (#3120, #3121, #2889, #3139)
Relay VM (pattern matching #3470, port to python #3391, serialization #3647)
Relay VM Profiler (#3727)
Support execution on devices for Relay VM (#3678)
[Relay][VM] Add more passes to VMCompiler (#4058)
[relay][vm] Separate VM runtime with executable (#4100)
Port VM, VM compiler, and Object into Python (#3391)
VM: Add AllocTensor instruction and better instruction printer (#3306)
[Relay][VM][Interpreter] Enable first-class constructors in VM and interpreter via eta expansion. (#4218)
[Relay][VM] Clean up the VM and VM profiler code (#4391)

Training

Relay is designed to natively support first-order and higher-order differentiation. The automatic differentiation infrastructure is now usable and a count of operators with gradient support are available in v0.6 release.

Higher order reverse mode automatic differentiation that work with control flow (#2496)
Higher order continuation passing style (#3456, #3485 )
Relay gradient registration (clip #3509, max_pool2d and avg_pool2d #3601)
Relay AD algorithm (#3585)
Relay Training - allow gradient to return a tuple (#3600), numerical gradient check (#3630)
Improve AD for concatenate (#3729)
[Relay][Training] Add missing gradient check to gradient pass (#4169)
As a part of Relay's automatic differentiation system, we are adding primal gradients for Relay operators. Please refer to #2562 for tracking the progress.
Gradient for Conv2d (#3636)
Add gradient operators (#3857, #3894, #3901, #3915)
Add gradient for log-softmax (#4069)
[Relay][Training] Add gradient for Crossentropy (#3925)
[Relay][Training] Add and fix gradients (#4126)

Quantization

Low-bit inference is getting more and more popular as it benefits both the performance and storage usage. TVM now supports two types of quantization. 1. Automatic quantizaion takes floating-point precision model, does per-layer calibration and generates low-bit model. 2. TVM also imports pre-quantized model from Tensorflow and MXNet, a new dialect QNN is introduced to handle further lowering to normal operators.

Automatic Quantization
- Low-bit automatic quantization supported. (#2116). The workflow includes annotation, calibration and transformation.
- Refactor quantization codebase and fix model accuracy. (#3543)
- KL-divergence-based per-layer calibration. (#3538)
- Add option to select which convolution layers are quantized. (#3173)
- [Relay][Quantize] Integrate data-aware calibration into quantization. (#4295)
Pre-quantized model support (QNN operators and legalize pass).
- Add a legalize pass to Relay (#3672)
- Qnn Concatenate, quantize, dequantize and requantize operators (#3819, #3730, #3745, #3531)
- QNNtoRelay & QNNLegalize Pass utility (#3838, #3782)
- Requantize: Optimize lowering for some corner cases. (#3864)
- New quantized operator support: conv2d, add, dense (#3580, #3736, #3896, #3910)
- Do type checking for the input and kernel in the qnn conv2d (#3904)
- Legalize and AlterOpLayout for Intel int8. (#3961)
- Renaming tests to follow the Relay nomenclature. (#3975)
- Fix padding changes due to #3739 (#3989)
- Memorizing quantize node mapping to avoid duplicated simulated quantization (#3233)
- Infrastructure to support pre-quantized models (QNN) (#3971).
- [Relay][AlterOp] NHWC to NCHWc support for Pool, concatenate, sum. (#4059)
- [TOPI][x86] Cascade lake support. (#4123)
- [TOPI][x86] Legalize - Support int8xint8 convolution to use VNNI inst (#4196)
- Qnn dequantize with min max using Mxnet flavor to support Mxnet prequantized models. (#3945)
- Improve the lowering of Qnn Dense (#4213)
- Adding support for dequantizing from int32 to float32. (#4130)
- [QNN] Refactor fixed point multiplication in requantize (#4073)
- [Relay][Quantize] Use fixed point mulplications (#4160)
- Add support for quantized multiply to Relay (#4141)
- Use legalize to handle NHWC layout for arm_cpu (#3754)
- [QNN][Legalize] Specialize for Platforms w/o fast Int8 support (#4307)
- [QNN] Use Int16 upcast in Fallback Conv2D. (#4329)
- Retain input kernel scales in QNN dialect (#4292)
- [QNN] Lowering for Depthwise Convolution. (#4351)
- [QNN][TFLite] Parsing QNN Add op. Adding MobilenetV2. (#4142)
- [QNN][TFLite] Parsing TFLite quantized models. (#3900)
- Added tflite frontend support for quantized mean. (#4339)
- [Relay][Legalize] Legalize conv2d_transpose for NHWC (#4399)

Accelerator and Microcontroller Support

TSIM is introduced to improve software and hardware integration and simulation accuracy. It integrates the hardware development process into the software stack. TSIM enables VTA to provide a more accurate performance feedback, i.e. clock cycles, compared to the traditional functional model of a hardware accelerator. Moreover, Chisel implementation for VTA is availale and it runs on top of TSIM.

There has been a proliferation of resource-constrained and embedded devices that do not have operating systems or a mature software stack. MicroTVM is intended to support TVM on such bare-metal devices.

[TSIM] Enabling Cycle-Accurate Hardware Simulation for VTA (#3010, #3206, #3242)
Chisel implementation for VTA and runs on top of TSIM (#3258, #3347)
MicroTVM (#3227)
Relay Compilation + AutoTVM compatible operator libraries for VTA (#3135)
ChangeBatch pass for batched VTA compilation (#3656, #3660)
VTA fast simulator statistics (#3481)
TSIM improvements and fixes (#3505)
Chisel VTA enhancements and fixes (32bit support #3558, alu instruction generation #3592, coherence support #3593, separate types #3605, tensor issue/commit #3637, uop load request #3643, uop dma requests #3654)
VTA Runtime refactor for non-shared memory FPGAs (#3590)
VTA HLS codebase refactor for Ultra96 (#3496)
VTA support for batched inference (#3661)
VTA bitstream compilation for Intel FPGA (#3494)
TSIM: Introduce Virtual Memory for TSIM Driver (#3686)
Parallel TSIM hardware compilation with macOS and debug support (#3797)
Chisel: scale dram base address in hardware instead of runtime (#3772)
Chisel: run all unittests by default (#3766)
Chisel: improved Data Gen, Added ALU Test (#3743)
Chisel dependencies for TSIM CI (#3721)
Chisel: Added Module Unit Test Infrastructure (#3698)
Add ISA BitPat generation (#3891)
de10-nano driver (#3394)
Extending Vision model coverage compilation for VTA (#3740)
Conv2d transpose (deconvolution) operator support (#3777)
Support TLPP in function simulator. (#3555)
[VTA][Chisel] TSIM VTA Source Refactor (#4163)
[VTA][TSIM] Serial GEMM Application Added (#4082)

Rust Support

Rust language support in TVM includes two parts. 1. The frontend wraps the current C API and exposes a Rust programming model. 2. The backend serves as an alternative to C++ runtime. It privdes a standalone WASM module and security support, e.g., SGX.

Rust frontend (#2292).
Unify types between bindings and pure Rust impl (#2616)
Rust: load syslib modules at compile time (#3274)
Rustify PackedFunc & Friends (#2969)
Rust DSO module (#2976)

Operator Support

A special operator annotation.stop_fusion to prevent it being fused with previous expressions (#2624).
batch_matmul supported (#2561).
reverse_reshape supported (#2503).
Faster-RCNN proposal operator for CUDA (#2420).
Vision operator for YOLO yolo_reorg (#1941).
slice operator for MXNet (#2662).
arange supported (#2621).
Vision operator roi_align (#2618).
where operator for MXNet (#2647).
Deformable conv2d (#2908)
Faster-RCNN Proposal OP (#2725)
ROI Pool operator (#2811)
Gluoncv SSD support on CPU (#2353)
shape, reverse, and sign op (#2749, #2800, #2775)
tile and repeat op (#2720)
logical operators (#2743, #2453)
stack op (#2729)
NCHWc upsampling (#2806)
clip and wrap mode support in take (#2858)
AlterLayout support for intel_graphics conv2d , depthwise conv2d (#2729, #2806)
Add foldr1 operator (#2928)
Add rsqrt operator (#2949)
Add clip and wrap mode support in take (#2858)
Gather_nd exposed to relay (#2945)
bitserial_conv2d move to autotvm template and updates (#2819)
Port x86 NCHWc to AutoTVM for Task Extraction (#2664)
Implement relay nn.bias_add compute in C++ (#3027)
Rename output tensors for better readability (#3006)
int8 dense on CUDA & Dense op quantization (#2877)
Bitserial dense operators for CPU (#3051)
Enhance upsample operator to adapt onnx opset v9 (#2968)
Add adaptive pooling operator (#3085)
Add all operator (#3124)
Add cblas batch_matmul (#3210)
Add packing for int8 1x1 convolution and support the int8 group convolution on X86 (#2991)
Add op size (#3094)
x86 TOPI (roi_align #3475, conv2d_transpose #3491)
Intel INT8 (dilation in conv2d #3510, type checking #3516)
Reinterpretation of tensor elements (#3599)
Spase-Dense for block-sparse multiplication (#3566)
Winograd matrix computation (#3553)
CUDA schedule for pool_grad (#3622), group_conv2d (#3663)
Bitserial operations conv2d, dense and bitpack (#3844)
Improve numeric gradient check (#3856)
Resize rework (3788)
Improve conv2d_transpose CUDA schedule template (#3796)
SpaceToDepth and MirrorPad Operators (#3718)
Add variance and layer norm op (#3700)
Add sparse_transpose for Square CSR matrices (#3707)
TOPI: Memoize winograd matrix (#3687)
New TOPI operators: erf, logical_and, logical_or, logical_not, isnan (#3702, #3929, #3979)
Improve ceil_divide in tile/split (#3842)
[Relay][Frontend][TF] Add tensor array ops (#3798, #4309)
[TF][Op] Op where (#4045)
[TOPI]Add op argwhere (#3994)
[Relay] crossentropy_with_logits and its gradient (#4075)
[Relay][Op] Enhance Upsample Operator to support float scales (#4206)
[Relay][Op] Add instance norm op (#4004)

Frontend and User Interface

Frontend darknet (#2773)
Support tf.gather (#2935)
Support tf.where (#2936)
Adding ADD operator to tflite frontend for compiling the MobileNetV2 (#2919)
Support SpaceToBatchND/BatchToSpaceND in Tensorflow frontend (#2943)
Simplify TF get_output_names (#3025)
TF Tile Round Sign Pow Exp Reverse (#2960)
Gluncv SSD support on the GPU (#2784)
Allow an op as loop var in Tensorflow (#3056)
Add FULLY_CONNECTED op into tflite frontend (#3019)
Add MXNet converter for RNN layer ops (#3125)
Add log op in tf frontend (#3111)
Add SoftPlus Sqrt in Tensorflow frontend (#3187)
Add onnx elemwise greater/less (#3186)
Add PlaceholderWithDefault (limited) implementation in TensorFlow (#3184)
Support tf.math.reduce_prod (#3166)
Better shape inference in TensorFlow Frontend (#3176)
Get list of unsupported ONNX operators (#2995)
Implement ONNX MaxPool-v8 and MaxPool-v10 (#3114)
Convert TFLite NCHW to NHWC (#3141)
Add Crop op converter (#3241)
TFLite frontend operator support: PAD, RESIZE, MUL, Reduce (min, max, mean, prod), LOGISTIC, elemwise operators (Sub, Divide, Power, Max, Min) (#3310, #3370, #3304, #3421, #3313, #3357)
Tensorflow frontend operator support: Abs, FloorDiv, GatherND, LeftShift, LogSoftmax, Max, Min, Mod, RightShift, ZerosLike, TruncateMod, Neg, ClipByValue, ResizeNearestNeighbor (#3270, #3211, #3393)
TFLite: Add fused_activation_function for ADD, SUB, MUL, DIV (#3372)
Support bidirectional RNN layer for MXNet (#3397)
TFLite operator support (pack #3521, split #3520 )
Keras operator support (permute, softmax #3618)
TF operator support (BatchMatMul #3634)
TFLite frontend operator support: tile, transpose (#3814, #3705)
ONNX frontend operator support: PReLU for NNVM, Not, Sign, Equal (#3813, #3836, #3760)
Keras frontend operator support: Dot (#3668)
Add more cases to Keras _convert_reshape (#3846)
TensorFlow frontend operator support: OneHot, log1p, cos, sin (#3781, #3614)
Support BatchMatMul with input dimensions larger than 3 for TensorFlow (#3732)
ONNX new operator support: And, Tile, Erf (#3878, #3941, #3988)
MXNet new operator support: pad, conv1d, deconv1d (#3739)
TFLite new operator support: batch_to_space_nd, space_to_batch_nd, tanh, greater, relu (#3850, #3996, #3963, #4022)
TFLite: Support depthwise convolution multiplier greater than 1 (#3922)
Keras: Fix ReLU in Keras Converter missed the case (#3917)
Keras: frontend upsample and 1 channel conv2d fixes (#3937)
Tensorflow: Convert scalar Const into tvm.relay.const (#3885)
TensorFlow: Add support for SquaredDifference (#3930)
[relay][frontend] clean up tf frontend (#3710)
[Relay][Topi][TensorFlow][ONNX][Lang] Add support for Any op (#4205)
[Relay][Frontend][ONNX] Add support for op Where (#4184)
[Relay][TopHub] Add switch to disable TopHub download (#4015)
Add parser support for CAST tflite operator (#4096)
Add parses support for zeros_like tflite operator (#4042)
Add parser support for SUM tflite operator (#4182)
Add support for tf.assert (as no-op) and tf.no_op to TF Relay frontend. (#4172)
[Relay][Frontend][ONNX] New Operators and Opsets to Support BERT (#4197)
[Relay][Params] Add APIs for storing and retrieving parameters from individual functions. (#4194)
Add build_create_shared_func to tvm/contrib/cc.py (#3840)
Tensorflow saved model for NNVM (#2493 and Relay (#2586).
Introduced HybridModule (#2477) so that normal TVM schedule can be compiled to hybrid target, run and dumped to Hybrid Script.
Relay ][Frontend][Tensorflow] add operator add_n (#4181)
[Relay][Frontend][Tensorflow] StopGradient (#4238)
[Relay][Frontend][ONNX] Add support for broadcasting to Where and MatMul (#4267)
[TFLite] Support PRelu (#4298)
[Frontend][MxNet] support mxnet cond op (#4311)
Add support for quant.mul operator in tflite frontend (#4283)
[Relay][Frontend][ONNX] operator support: DepthToSpace, SpaceToDepth (#4271)
[Relay][Frontend][Tensorflow]Add conv2d_transpose. (#4300)
[Frontend]Add TensorFlow FloorMod (#4308)

Runtime and Backend Support

Make external library extend TVM's NDArray more easily (#2613).
Improvements for NNPACK integratation, includes ci test, winograd (#2846, #2868, #2856, #2721)
Improvements for OpenCL runtime (#2741, #2737)
GraphRuntime: Enable sharing parameters of a model among multiple threads (#3384)
Android runtime argsort support (#3472)
GraphRuntime enhancements (set_input_zero_copy #3416)
A new minimal runtime implementation (~12kb .text on ARMv7/x86) for TVM.
Add AVX512VNNI support for TVM (#3388)
Enable miopen Group Convolution (#3987)
Minimal runtime (~12kb .text on ARMv7/x86) for subset of TVM models (#3567)
[RUNTIME] Separate runtime related contrib into runtime/contrib (#4207)
[topi] add ARM v8.2 udot (uint8) support (#3978)
[codegen] Add multiple operands and function support when using fp16 compilation (#4056)
[TOPI] Added support for Mali Bifrost target (#4047)
[topi] enable fp16 sort for arm (#4084)
Add OpenOCD Low-Level Device (RISC-V Support) (#3756)
Add wave 32 bc for AMD ROCm backend (#3984)
[RUTNIME] Support C++ RPC (#4281)
[TOPI][OP] Support Faster-RCNN Proposal OP on CPU (#4297)
[TVM][RUNTIME] A minimum example to generate external library wrappers for DSOModule (#4280)

Language and Architecture

Support custom datatypes (#2900)
Add the acc16 intrinsic support (#3081)
Handle float16 constants & fix BatchNorm (#3260)
Structural hash - incorporate the var type into its hash (#3267)
Relay C++ Build Module (#3082, #3144, #3174)
Enable decorating python class to be a Relay Pass (#3364)
Make Partial Eval support interprocedural optimization and termination check. (#3033)
Introduce feature manager to Relay. (#3236)
Use Relay parser to define the Relay prelude (#3043)
Mechanism to detect incomplete expression match in Relay (#3203)
EQ/NE operators support for StringImm expressions (#3283)
Mechanism to detect incomplete expression match in Relay (#3203)
Introduce CanonicalizeCast pass to formally reduce memory overhead introduced by fused cast operations (#3280)
Support overloading comparison operations in Relay (#3168)
Mac count: provide a pass to calculate the number of multiply-accumulate operations in a network (#2609).
- support for conv_2d_transpose (#3469)
- [Relay][Pass] Count MAC for BatchMatMul (#4157)
- Detect depthwise conv2d in mac_count pass (#3083)
Add Tuple pattern (#3596)
Text format support for ADTs and prelude (#3863, #3939)
Add new IR pass CombineParallelDense (#3862)
Add support for EQ op in the deduce bound and the loop partition (#3775)
Introduce base-class IRMutatorWithAnalyzer (#3969)
Define more standard global functions in the prelude of relay program, includes foldr1, hd, tl, nth, list update (#2928, #2917, #2771, #2866)
Add SkipVectorize pass (#3222, #3228)
[Relay][Pass] Add pass to remove unused functions in relay module (#4334)

Symbolic shape enhancement

Add shape function for symbolic shape. It enables certain cases for broadcast with symbolic shapes. (#3606)
[tvm][any] broadcast with values other than one (#3967)
Symbolic shape support (broadcast op #3389)
Support reshape for dynamic shape in tf converter (#4185)
Runtime Shape Functions (#4179)

Language and Architecture

An optimization pass to eliminate expressions which have the same functionality and same inputs (#2639).
Refactor text printer to add stream-like API and FunctionType support (#2605, #2882)
Build a scaffold for structured error handling (#2838). The new mechanism detects and rewrites error messages so that c++ and python stack trace are unified and not redundant. Guideslines and conventions for error handling is also discussed.
Higher order reverse mode automatic differentiation that work with control flow (#2496)
Integer arithmetic analyzers, includes modular set analysis, const integer bound analysis and rewrite simplifier (#2904, #2851, #2768, #2722, #2668, #2860)
Improve operator fusion for TupleGetItem in relay (#2914, #2929
Compute FLOP of autotvm template for int8 models (#2776)
Common subexpression elimination pass in Relay (#2639)
Improve quantization in Relay (#2723)
Refactor build_func in measure module of autotvm to better support cross compiler (#2927)
Quantize all fields of concatenate (#2913)
Remove stale verilog generator (#2964)
Improve Relay printing (#2984, #2881, #3030, #3041)
Add min_num_branches option in CombineParallelConv2D (#2961)
Add expr_visitor, fix expr_functor exponential blowup problem (#2988)
Support Deriving channels when it is not provided in AlterLayout. (#2972)
Enhance BoundDeduce algorithm (#2795)
Enhance loop partition algorithm (#2956)
Better tuple fusion implementation (#3092)
Enhance fusion rule that starts from elemwise and broadcast (#2932)
Remove on_device op after annotation in heterogeneous pass (#3204)
Improve canonical and rewrite simplifier (#3132, #3149)
Capture constant external python variables in hybrid script (#3157)
Remove Peano nats from the prelude (#3045)
Macro to define NodeRef methods, constructor style example (#3224)
Consistent RAII scoping API (#3231)
Register all operators' attributes in Python (#3175)
Add module supoort in relay.build (#3424)
Relay pass infrastructure improvement (#3319, #3336, #3430, #3353)
Migrate Relay passes to pass manager (#3323, #3289, #3251, #3406)
Improve heterogeneous annotation by using visitor (#3261)
Support export ADT value in Python (#3299)
Extend TensorComputeOp to allow scalar inputs (#3300)
Transitioning low-level IR away from HalideIR (#3533, #3535)
Tags for ADT constructors (#3369)
IR dumping for debugging (#3493)
Pretty printer and parser roundtrip (#3460, #3536)
Relay type checking (conv2d weight dimension #3511, any shape #3221)
Relay Module enhancements (remove free variables #3476)
LLVM DWARF debug information (#3420)
Printer for Layout/BijectiveLayout (#3582)
Type inference escape hatch (#3571)
Making iterators compatible with constructors of STL containers (#3624)
Moving Conv, Dense, Concatenate InferTypes to header (#3783)
Simplify casts of constants 0 and 1 (#3758)
Conditionally replace reduction init axis. (#3408)
Improve Partial Evaluator (#3749, #3703)
Strict mode in Relay pattern matching (#3620)
Quit and clean when TVM is interrupted (#3640)
Make Type Relation catch more errors (#3899, #3699)
Refactor the way we interface between different modules of Relay (#3906)
Introduce schedule_injective_from_existing and unify external schedules for all targets (#3983)
[NODE][REFACTOR] Refactor reflection system in node. (#4189)
Unify node system and object (#4161, #4115, #4128)
[Relay][Refactor] Rename Datatype to ADT (#4156)
[Relay] fix exponential blowup in interpreter (#3559)
[Relay] Fix memory leak in the interpreter (#4155)
[rpc] use callback func to do send & recv (#4147)
Add lift_if_then_else pass to improve loop partitioning (#3865)
Decrease the complexity of CalcDep from exponential to linear (#4053)
[IR] Make iterators compatible with constructors of STL containers (#3624)
[Relay][Pass] Avoid FoldConstant folding some ops (#4245)
[Relay][Prelude] More dtypes support in tensor_t (#4233)
[NODE][REFACTOR] Rename IRFunctor->NodeFunctor, use func pointer (#4247)
[RUNTIME][REFACTOR] Use object protocol to support runtime::Module (#4289)
[CodeGen] Add build config option disable_assert to control whether to generate assert. (#4340)

Arithmetic Analysis

Formalize Integer Arithmetic Analysis (RFC: #2588). It is aiming to perform better context-dependent analysis, bound analysis, centralized arithmetic logic and arithmetic simplification. (#3272, #3463, #3464, #3368, #3503, #3504 , #3502, #3479 , #3568)
Introduce FloorDiv/Mod, TruncDiv/Mod, and IndexDiv/Mod for better arithmetic simplification (#3976, #3986, #4000, #4014, #4008, #4028)
[ARITH] Use floordiv for the deduce bound (#4025)
[Simplifier] Rewrite simplification rule to eliminate unnecessary conditionals. (#4076)

Runtime and Backend Support

Provide error msg for failure function call in tvm4j (#2967)
Expose backtrace symbols in Debug mode (#3001)
C++ GraphRuntimeCodegen, Deprecate Python2 (#2986)
Ensure interpreted functions can take values that are not TensorValues (#3015)
Make OpenCL runtime Compatible with OpenCL2.0 (#2897)
Handle INF and NAN in CUDA and OpenCL (#3194)
Update debug graph runtime for more precise layerwise timing (#3232)
ROCM support (llvm printing #3662, ld.lld finding #3664, save to file #3665)
Threadpool: make spin_count configurable (#3577)
RPC worker children termination (#3669)
Vulkan runtime reimplementation (stream approach) (#3849)
Vulkan backend supports Call::reinterpret and vectorized comparison (#3795)
Support MKL on Windows (#3837)
Vulkan IR builder (bool to float #3513)
Force code_object_v2 for amd gpu backend (#4099)
[Codegen][cuda-fp16] fallback to fp32 simulation when cuda arch < sm53 (#4268)
Fix and refactoring for AMD gpu backend (#4305, #4321, #4341, #4342)
[Debugger] Sorting op-time breakdown for quicker analysis. (#4352)
[nvcc] enable multiple arch in one fatbin (#4377)
[RUNTIME] Move module export to the function level. (#4405)

Frontend and User Interface

Relay now supports saving and loading parameter dictionaries. (#2620)
Add max_num_threads to Hybrid Script, which allows users to get max number of threads for GPU targets (#2672).
Improvements for tensorflow frontend (#2830, #2757, #2586), includes decompiling tf control flow (#2830)
Improvements for mxnet frontend (#2844, #2777, #2772, #2706, #2704, #2709,, #2739)
Improvements for keras frontend (#2842, #2854)
Improvements for DarkNet frontend (#2673)
Improvements for ONNX frontend (#2843, #2840)
Better profile result dump in Chrome Tracing format (#2922, #2863)
Unified error handling in NNVM and Relay frontends (#2828)
Improve NNVM to Relay conversion (#2734)
Remove input_0d_mismatch special handling for TF Frontend(#3087)
Bumped ONNX version from 1.1.0 to 1.4.1 (#3286)
Simplify parameter handling in Tensorflow frontend (#2993)
CoreML improvement for image scaler and padding (#3800)
Clean up TensorFlow frontend (#3710)
Darknet: Solve tvm parsing darknet resnext failure bug (#3778)
Frontend changes get_workload - (#3483)
[TF][Relay][Op] Pass module when infer shape (#4287)

AutoTVM

Support override in register_topi_compute and register_topi_schedule. (#3292)
Improve graph tuner dealing with Tuple. (#3649)
Add AutoTVM template for conv2d Intel int8. (#3955)
Add AutoTVM template for dense on CUDA. (#3923)
Add AutoTVM template for conv2d on Intel graphics. (#3839)
Optimizing autotvm task extraction speed. (#4138)
[AutoTVM] Add batch_matmul to tunable operations. (#4242)
Selecting tuning templates when extracting task. (#4338)

Performance Improvements

Enable AlterOpLayout pass for x86 on Relay (#2585). It is essential to get decent performance for CNN-based model on Intel CPUs.
Better intrinsic matching for x86 CPU and ARM CPU, includes variants of vcvtph2ps and vmlal.s16 (#2925, #2748).
Improve injective schedule for ARM CPU(#2801)
Core functionality for Graph tuner (#2184)
Fast tanh implementation (#3255)
Improve multi-batch conv2d on x86 (#3308)
Improve non_max_suppression and get_valid_counts for CPU (#3305)
Improve roi_align performance for CPU (#3296)
Improve nms and get_valid_count performance (#3282)
Graph tuner for multiple subgraph (#3490)
For sparsity, fast transpose for square CSR matrices has been now merged, which is a good start point for more general sparse type support.
Reduce set_input and set_input_zero_copy overhead (#3805)
Parallelize batch axis for ARM (#3931)
Support cuBLAS BatchMatMul (#3936)
Add AVX512VNNI support for TVM (#3388)
Enhance tuning space of split (#3949)
Enable miopen transpose convolution and fp16 support (#3952)
Improve conv2d_transpose schedule on X86 and CUDA (#3948)
Expose llvm.nearbyint intrinsic (#4001)
[TOPI][X86] Pool operator parallel support. (#4090)
Improve layout for several operators (#4103, #4040, #4080)
[Relay][VM] Fix constant folding issue in VM compiler (#4077)
[relay][vm] Reuse allocated device memory (#4170)
[Runtime] Enable option to use OpenMP thread pool (#4089)
[PERF] Parallelize reduction for CPU (#4158)
[TOPI] Tunable Template for Conv2D HWCN on CUDA (#4168)
[TOPI] Add valid auto tvm for Intel Graphics (#4078)
[TOPI] FIFO buffer op, to accelerate sequence modeling with dilated convolutions (#4039)
TensorCore Support using Intrinsic (#4136)
Auto TensorCore CodeGen (#4234)
Use cblas for dense and batch_matmul (#3787)
Update TOPI softmax compute and CPU schedule (#3680)
[VTA] Performance optimize, remove unnecessary contigious memory use. (#4246)
[TOPI][AlterOpLayout][ARM] Enabling NHWC to NCHW layout transformation. (#4249)
[PERF] Parallelize reduction for CPU (#4158)
[ThreadPool] Solve thread transitions issue (#4344)

Documentation

Tutorials for deep learning frameworks support in Relay.
Tutorial for running AutoTVM with Relay (#2594).
Document for Algebraic Data Types (#2575).
Move NNVM tutorials to Relay (#2783, #2785, #2766, #2693)
Documentation on operators (#2761)
Add gradient operator tutorial docs (#2751)
Add compiler pass tutorial docs (#2746)
Add Android Tutorial (#2977)
Developer documentation for InferBound pass (#3126)
Add missing targets to target_name documentation (#3128)
Various documentation improvements (#3133)
Add VM doc (#3188)
Update documents for TSim (#3409, #3318, #3302, #3343, #3206)
Improve tvm4j document describing LLVM support (#3404)
Tutorial migration to Python3 (#3498)
Android RPC README (#3500)
Documentation for Relay opcode (#3522)
Tutorial for pass manager (#3515)
Minimum version of Python in docs (#3588)
Relay pass infra (#3583)
X86 Autotune tutorial improvements (#3609)
YOLOv3 tiny Darknet tutorial (#3674)
SSD doc to avoid confusion (#3677)
Tutorial: Build a Graph Convolutional Network on TVM (#3681)
Add docs for analysis namespace (#3985)
[tutorial] Relay pass infra tutorial (#4083)
[DOCS] Add TensorFlow frontend docs (#4154)
Tutorial: update Building a Graph Convolutional Network tutorial (#4060)
[Docs] Add dependency of compilation with LLVM (#4117)
[Documentation]Fix example code in comment of tvm.build_module.build() (#4195)
TSIM: add virtual memory support to examples (#3868)
Relay pass infra tutorial (#4083)
Fix the TF tutorial to run against TF2.0 and TF1.x (#4104)
Add topi.nn.fifo_buffer to TVM doc (#4343)
License statement (#4345, #4359, #4401, #4402, #4408, #4409, #4410, #4414, #4431)

Build and Test

Increate the robuteness of CI test (#2841, #2798, #2793, #2788, #2781, #2727, #2710, #2711, #2923)
Improve conda build (#2742)
Add caffe2 nnvm frontend to CI (#3018)
Use bridge network and expose port on macOS when launch docker image (#3086）
Run DarkNet tests (#2673)
Add file type check (#3116)
Always run cpptest during build to ensure library correctness (#3147)
Handle more file types in ASF header (#3235)
Add test_forward_ssd_mobilenet_v1 to tflite/test_forward (#3350)
Add Azure build pipeline (#3458, #3459)
Update ci-gpu to v0.52 (#3374)
Enable more visible symbols by default (#3365)
Separate out legacy as a stage in CI (#3337)
Simplify build script, remove python 2 support (#3419)
Ignore rust cargo lock files in rat (#3314)
Improve CUDA Conda package build (#3281)
Update CMakeLists.txt to be more flexible to find the third parties libraries (#3354)
Docker update conda package (#3344), requests and pillow (#3495), Android demo (#3499), rat install (#3527), ARM support (#3546), LLVM (#3590)
Relay-to-Python testing (#3156)
Code refactoring/remove (#3523, #3667)
Zero-rank testing (#3612)
CMake compilation (#3611, #3650, google test #3628)
Standalone wheel build for TOPI (#3657)
Fixing performance issues in PassUpDomain when fusing and splitting axes (#3073)
conda recipe (#3791)
Allow users to specify download directory (#3803)
Update docs for installation for CUDA (#3832)
Update hybrid_script.rst (#3799)
Acknowledge Halide attributions (#3824)
Add psutil dependency (#3780)
Temporary disable rust test (#3809)
Solve occasional CI issue when pad value is all 0 (#3801)
Towards TSIM CI testing (#3704)
Use pip3 for python3 (#3742)
Update docker image ci_cpu,i386 to include verilator (#3738)
Remove sccache from Rust install (#3728)
Update dmlc-core to the latest commit (#3716)
Update GPU docker (#3709)
Add an option to build with -pthread (#3671)
Add DGL to {ci_gpu, demo_cpu, demo_gpu} docker images (#3692)
Use pytest instead of nosetest (#3524)
Enable NHWC of relay.testing.mobilenet (#3886)
Add .hsaco save/load for tesnor_expr Tutorial (#3852)
Support LLVM trunk (#3907)
Remove GTest cmake flag from install docs (#3953)
Allow USE_LLVM to take extra arguments (#3954)
[CI] Pin NNPack pthreadtools version (#4152)
[TOPI] Fix flaky testcase for check round (#4211)
[CI] Move gpu docker binary to cuda10 (#4229)
[CI] use llvm9 for the gpu tests (#4224)
[CI] Update GPU docker to cuda10 (#4228)
[Relay] Install Relay Prelude program in package install (#4227)
[relay] use time_evaluator for measurement (#4191)
[Relay] Improve build error when no lowered funcs are produced (#4132)
[llvm] switch to use Align for llvm trunk (#4051)
[CUDA] Update have_int8 condition to run on compute capability 7.x devices (#4214)
[DOCKER] Pin torchvision==0.4.1 (#4140)
[DOCKER] torch install depends on future package (#4098)
[CodeGen] Disable -mfloat-abi hard option for LLVM < 6.0 (#4071)
Add a python how to example of deploying tvm module with tvm runtime only (#4094)
Hide symbols from dependent libraries if HIDE_PRIVATE_SYMBOLS is ON. (#4041)
[BUILD] Disable utvm standalone runtime by default (#4240)
Fix TSIM compile error in Linux (add missing -fPIC flag) (#3876)
Add scalafmt and format existing scala codebase (#3880)
Update TFLite wheel version to 1.13.1 (#3435)
Remove PEP498 f-string new feature for support python3.5 (#4250)
Require LLVM >= 9 for AMDGPU backend (#4253)
Rename ml.dmlc.tvm to org.apache.tvm (#4290)
[Test][TF][Relay] Fix argument preparation for vm test mode (#4296)
Add test for the qnn_add operator (#4282)
[CI][DOCKER] Add ONNX runtime dep (#4314)
[CI][DOCKER] Upgrade image to include onnx runtime (#4313)
[CI] Set workspace to be per executor (#4336)
[Build][Windows] Fix Windows build by including cctype (#4319)
[Contrib] Add MKL DNN option (#4323)
[Test][Relay][Pass] Add test case for lambda lift (#4317)
Remove Python imp module as it is deprecated (#4275)
Bump up CUDA log version in tophub.py (#4347)
Add rule for clean in APPs (#4364)
[Relay tests] Temporary Attr Update for Order-Independent Testing (#4357)
[CI] Avoid content-length request in test data download (#4375)
Compare all outputs in TFLite test_forward_ssd_mobilenet_v1 (#4373)

Bug Fixes

[RELAY] Fix get_int_tuple. (#2691)
[ARITH] Select support for integer set analysis. (#2687)
[Relay] Fix error in ANF (too agressively inline atomic expression and create free variable). (#2665)
[Hybrid Script] Fix name conflict and attached scope problem. (#2649)
[Relay] Fix ANF for reference and pattern matching. (#2637)
[Relay] Fix fusion bug when call symbol that is not an operator. (#2630)
Fix missing header file. (#2629)
[Relay]Fix the bug in heterogeneous annotation which mistakenly steps into the fused op. (#2622)
[AutoTVM] Fix incorrect localhost usage in RPC mode. (#2619)
[NNVM] Fix incorrectly getting layout attribute as a tuple. (#2610)
[Relay] Fix mutating IF expression. (#2601)
[Tutorial] Fix downloaded file path. (#2590)
[Storage] Fix int32 overflow bug when input is big. (#2580)
[NNVM] Fix non-identity problem for FInplaceIdentity. (#2572)
[Golang] Fix compilation error. (#2558)
[Tensor Expression] Fix missing reduction init predicates. (#2495)
[Relay] Fix missing argument for NCHWc in Relay. (#2627)
[TOPI] Fix Nms_ir data race. (#2600)
Fix compute_inline with multiple outputs (#2934)
[TEXPR][PASS] Fix thread all reduce to avoid write after read hazzard (#2937)
[FRONTEND][TENSORFLOW] bug fix for tensorflow official slim models. (#2864)
[FRONTEND][ONNX] Some bug fixes and Shape operator fixed for relay. (#2850)
Turn on USE_SORT by default (#2916)
[DOCKER] Upgrade ci-cpu to latest v0.50 (#2901)
[TESTS] Import script robustness (set -u) (#2896)
[Relay] Fix name of bias in testing.mlp (#2892)
[TESTS] Improve script robustness (#2893)
Add dense schedules to __init__ for cpu (#2855)
[Apps] [howto_deploy] fix cxx-flags order and build directory (#2888)
[Relay] Add TVM_DLL for ANF/GNF conversion #2883
[Relay] Fix Relay ARM CPU depthwise spatial pack schedule alter op layout issue. (#2861)
Fix setting up hints for getaddrinfo (#2872)
Add missing sgx includes (#2878)
Fix error reporting for missing axis (#2835)
Fix an OrderDict initilization bug. (#2862)
Fix Xcode 10 metal compile error (#2836)
tvmrpc: Fix includes (#2825)
Fix init_proj.py: Team ID expected (#2824)
[DOCKER] Fix git clone failure. (#2816)
upgrade java style-check due to CVE-2019-9658 (#2817)
[Relay][Quantization] Fix duplicated simulated quantization (#2803)
[Bugfix] Repeat and tile bug fixed, relay tests added (#2804)
Fix caffe2 relay frontend (#2733)
Fix a bug in nnvm to relay converter. (#2756)
Ensure loop count is a constant before trying to unroll. (#2797)
xcode.py: Decode bytes before output #2833
[WIN] Fix a bug in find_llvm when specify llvm-config (#2758)
[DLPACK] fix flaky ctypes support (#2759)
[Bugfix][Relay][Frontend] Fix bug in mxnet converter for slick_like (#2744)
[DOCS] Fix tutorial (#2724)
[TOPI][Relay] Fix default out_dtype for conv2d_NCHWc and Relay (#2702)
[Relay] fix checkwellform (#2705)
fix prelu, now can use on 2d input and add one test (#2875)
[CODEGEN][OPENCL] Fix compile error about ternary expression. (#2821)
Fix Placeholder issue (#2834)
Fix makedirs() condition in contrib (#2942)
Add missing #!/bin/bash directive (#2951)
Bilinear resize bug fix from PR #2777 (#2857)
Fix bias_add default axis (#2829)
Remove empty ty.rs (#2958)
fix undefined reference to dlopen, etc (#2957)
Removed deprecated std::unary_function (#2962)
Add output format to ndk build func (#2999)
Fix java checkstyle version (#2998)
Fix relay invariant error message (#3011)
Fix for caffe2 nnvm frontend (#2996)
Fix rust resnet example (#3000)
Fix x||!x for comparisons in rewrite simplifier (#3029)
Fix BatchMatMulRel typerelation (#3032)
Update dmlc-core, fix default ctors of NodeEntry (#3017)
Fix Fuse (#3035)
Fix PostOrderVisit signature (#3048)
Fix winograd nnpack fp16 (#3046)
Fix some typos (#3063, #3112)
Fix group_conv2d unit test (#3113)
Fix bug in ONNX importer (#3084)
Fixing a doc nit (#3123)
Fix type code error for StringImm (#3050)
Fix bug of wrongly generated device_map (#2990)
use unordered_map instead of map in ANF (#3024)
Fix PRelu layout in Relay (#3013)
Minor addition to graph runtime debug (#3129)
Fix mali conv2d performance regression (#3131)
Fix dense autotvm template registration in ROCm (#3136)
Fix conv2d_transpose (#3138)
Fix python lint warnings (#3145)
Some fixes for golang latest version compiler #3119 (#3182)
Add more syncs to fix flaky test caused by get_valid_counts (#3151)
Fix AlterLayout Pass (#3155)
Fix a multithreaded bug in llvm LazyInitJIT (#3158)
Fix a tensorflow test bug. (#3165)
Fix concat for ARM (#3061)
Handle vectorize for LE statement (#3137)
Raise exception group_conv2d_nchw not supported (#3195)
Quick fix of VTA FPGA Toolchain Installation documentation (#3196)
Check file exists before removing it (#3178)
Fix a bug of flatten in ONNX to Relay converter (#3180)
Fix converter where initializers were not registered as nodes (#3143)
Fix bug in cast to bool (#3207)
Hotfix build_module creation (#3198)
Fix sort changing original input data issue (#3212)
Fix bug in vta runtime DepPop function (#3208)
Fix resize nearest with fractional scaling (#3244)
Fix vta_conv2d crash issue after change vta_config.json (#3213)
Fix a memory leak in OpManager (#3263)
PkgConfig cause crash in PYNQ board due to link library (#3257)
Fix Error messages in tflite.py (#3320)
Fix typos in docs and comments (#3309, #3376)
Bugfix min/max const canonicalize rule (#3386)
Return module from frontend for autotvm (#3401)
Fix constant and reshape in ONNX (#3387)
Default verilator location fix (#3324)
Fix autodiff for conditional expression (#3453)
Gramatical improvements to tensor_expr_get_started (#3330)
Fix AutoTVM data structure bug (#3462)
Fix MXNet RNN without providing state initialization as input (#3326)
Fix flaky test on topk and quantize pass (#3362)
Add VTA PYNQ metal_test bitstream program logic and fix compilation issue. (#3400)
Fix VTA function Vivado Compile Error. (#3375)
Fix VTA DRAM functionality issue. (#3278)
Fix reshape precompute and type error in ONNX frontend (#3230)
Fix interpreter argument conversion for tuples. (#3349)
Fix code generation for packed functions + tuples in VM (#3287)
Fix memory leak in Relay interpreter (#3448)
Fix x86 depthwise conv2d alter_op_layout (#3264)
Create closure object for GlobalVar (#3411)
Fix getting global var in prelude (#3405)
Fix rfactor bugs which related to predicate and loop partition (#3382, #3444)
Fix the bug in AutoTVM where SimulatedAnnealingOptimizer sometimes finds useless candidate (#3413)
Fix name conflict in PartialEval (#3402)
Fix int bound analysis bug for modular (#3288)
Check arg positiveness for modular rules (#3279)
Fixes failure of sum and all on axis=0 (#3422)
Fix package path in tflite test (#3427)
Fix Windows build (#3429)
Fix LSTMBlockCell in Tensorflow frontend (#3410)
TF fix where output index is ignored (#3622)
Runtime fix for custom datatypes (#3471)
Relay build module warnings (#3452)
Relay partial evaluator (#3482)
Pynq AutoTVM tracker (#3497, #3578)
A normal form test (#3525)
Lint issue (#3519, #3615 )
Any shape testing (#3528)
Android posix_memalign (#3532)
Quantization add_rewrite and UnifyDTypeScale (#3534)
Bound inference fix (#3526)
Tensorflow NCHW data format (#3514)
First order gradient (#3550)
JS load module example (#3556)
Build error (#3552)
Relay VM debug statements (#3565)
C++ lambda expr (#3570)
Handling of tempdir if subprocess is killed (#3574)
Remove tabs in Chisel source (#3603)
Relay VM DataTypeObject (#3604)
Removing prints (#3616)
Average Pool2D Bug (#3607)
Missing header in cuda_device_api.cc (#3621)
Tensorflow frontend fix where output_shape is None (#3632)
Winograd accuracy fix (#3644)
Fix comment (#3646)
Zero-input op fix for recursive traversals (#3623)
Python 3.5 compatibility (#3675)
Fix infinite recursive device_api.ext_dev call in VTA. (#3843)
Fix depth_mult for TensorFlow frontend (#3676)
Fix database APIs for AutoTVM (#3821)
Fix axis of softmax in Keras (#3834)
Fix VTA TensorLoad module (#3841)
Fix inconsistent python/cpp API behavior for if_then_else, power (#3829)
Fix code comment of operators in ONNX frontend (#3830)
Added repo for llvm-9 to fix missing dependency issue (#3826)
Fix typo in Relay text parser (#3785)
Fix tvm const warnings (#3817)
Add gfx906 bc (#3808)
Fixed onnx test failures when run on a cpu backend (#3764)
Fix ArgBinder assert order (#3794)
Fix for NoneType Target for quantization (#3792)
Fix out-of-date quantization realize (#3790)
Fix Qnn concatenate InferType (#3779)
Fix dense tuning (#3768)
Fix visit_pattern in ExprMutator (#3769)
Fix Chisel Scala style (#3765)
Fix some pass docs (#3767)
Fix mistype in rpc tutorial (#3763)
Fix tvm.scan follow by tvm.compute segfault (#3723)
Fix the potential index overflow in where operator (#3751)
Revert compile_cmd kwarg name change (#3746)
Update tophub (#3752)
Fix typo in ir_pass.h (#3741)
Bug fix for VME Shell (#3737)
Fix missing apt https transport support (#3735)
Take zero extent loops as NoOp and remove it (#3724)
Fix mxnet converter for hybridblock and add div_sqrt_dim (#3701)
Fix partial eval unit test name (#3719)
Fix conv2d schedule code (#3648, #3717)
Remove thread related headers (#3713)
Fix FunctionPass (#3712)
Export tvm::relay::OpRegistry::OpRegistry (#3711)
Fix Metal reinterpret (#3706)
Fix gather_nd in Relay (#3442)
Fix error in partial evaluator (#3693)
Align the naming rule for OpAttributeUnImplemented (#3695)
Enable the sparse schedule (#3651)
Fix typo names in Caffe2 frontend (#3685)
Make tests multi-process friendly. (#3683)
Fix typo in README.md (#3684)
Fix doc rendering (#3897)
Add test script starter command to document (#3993)
Add type solver unit tests for unifying quantified funcs (#3947)
Change Vivado install instructions to version 2018.3 (#4003)
Add a link to the defining network description of auto-tuning tutorial (#4023)
Additional MXNet Convolution and Deconvolution tests (#4026)
Adding support to check if an attribute is present or not without having to get the value (#3957)
Fix parser for cast. (#3873)
Fix operator fusion for multiple output (#3871)
Remove extern C warpper for cuBLAS (#3877)
Fix int32 range overflow by using int64 (#3870)
Remove duplicate resize (#3902)
Fix blas cmake for mac os (#3898)
Add another MKL name alias for MKL installed through pypi (#3853)
Numpy compatible dtype inference for tvm.convert and tvm.const (#3861)
Remove incorrect check for LLVM in C codegen test (#3921)
Fix exponential blowup in interpreter (#3559)
Fix CUDA int8x4 vectorize (#3928)
Make buffer auto broadcast independent to the order of input args (#3956)
Fix benchmark layout in graph tuner (#3926)
Fix Android Demo LLVM version (#3962)
Cast filepath arguments to string (#3968)
Fixes “common” sub crate using nightly and master (#3965)
Changes to make tensorize work. These changes also fix the previously broken test. (#3981)
Remove FLOP computation when calling 3rd party library (#4005)
Use a more intuitive way to limit the #ops in a group (#4018)
Add more pad_mode support for onnx converter (#4029)
Impose a max op limit to the op fusion pass (#4002)
Fixes issue with CPP enums (#4019)
Int64 shape handling for outputs. (#4031)
[PYTHON] Fix installation for generated grammar (#4223)
[Bugfix] Fix target host for vm compiler (#4057)
[Fix][VM] Fix VM invoke with set_params (#4079)
[Fix] Fix a few bugs when dtype is fp16 (#4088)
[Relay][Frontend][TF] Fix Size operator (#4175)
[cmake][ANTLR] Support setting path to ANTLR jar (#4176)
Fix infer type of kernel in dense. (#4125)
[Relay] Fix match case in Python-side expr functor (#4037)
Split adaptive_pool2d_avg into sum and div (#4186)
[AutoTVM] Fix Split Factors when no_tail is off (#4044)
Fix extent one for the post_stmt in loop partition (#3734)
[TOPI] Fix bug in intel graphics auto tune (#4093)
[ARITH] Fix lowering of floormod(x, y) != 0 (#4127)
[ARITH] Fix the rule y < x && x <= y (#4220)
[Bugfix][TF] reset graph after getting tag of savedmodel (#4055)
[Fix] Fix the logic of the number of nodes checking in op fusion (#4074)
[VTA] hotfix for de10-nano driver (#4081)
Fixing tensor not found issue in bitserial operator (#4095)
Fix wrong n_trial number in autotvm tutorials' progress bar if n_trial is larger then config space. (#4070)
[PATCH] Fix undefined __floatdihf in libtvmruntime.so on aarch64. (#4119)
[ARITH] Fix lowering of FloorMod (#4236)
[Relay][Frontend][Tensorflow] Fix GatherV2 (#4238)
Fix typing.Deque import error for Python 3.5 (#4254)
[VTA] Hotfix for padded load test in Chisel VTA (#4264)
[Contrib] Fix error message at callback_get_section_size() (#4221)
[TOPI] Fix bug in Winograd on CUDA (#4260)
AutoTVM: Fix hang/crash issues on feature extraction (#3689)
[TOPI][CUDA] Fix Winograd Kernel Size Support (#4276)
[Relay][Frontend][Tensorflow] Fix type assignment for ‘tf.range’ operator (#4294)
Fix incorrect call to Unicode Win32 InetPton (#4306)
[Relay][Frontend][Keras] handle batch_norm op params well (#4310)
[VTA] fix error when memory_id is VTA_MEM_ID_OUT (#4330)
[Doc][fix] fix sphinx parsing for pass infra tutorial (#4337)
[Codegen] remove fp16 function override for cuda (#4331)
[TFLite] Fix Prelu unified shape error (#4326)
[Relay][Frontend][TF] Fix transpose when axes is not a param (#4327)
[VTA] Bug fix for padded load with large inputs (#4293)
Fix inconsistent operator tag name (#4134)
Fix for a specific case when loop partitioning with indivisble. (#4243)
Send list as argument to schedule_conv2d (#4358)
[Docker] Fix TVM folder name for installing on Android and OpenCL. (#4363)
Fix TFLite Reshape assert (#4320)
[Relay][Frontend][TF] Fix slice when begin or size is not Const (#4372)
Fix compilaton of bfloat16 on Windows (#4415)

Known Issues

The performance of Relay VM is not good enough on GPU, due to memeory allocation overhead which will be resolved later.
TFlite rounding vs tvm rounding causing differences in accuracy and potentially off by 1 errors. For reference #3900
TFlite pre-quantized network support is still a work in progress and the project would welcome further contributions.
TSIM build requires python command exist on the host. See forum discussion for details.
Tensorflow control flow has not been fully supported in the frontend converter.
topi.floor_div is inconsistent with floor division semantic when result number is close to an integer.

Depreciations

Deprecating python2 support in the master branch and following release (v0.6). (#2994, #2986)
NNVM is deprecated and will be removed in a future version. (#4333, #4368)

0.5

This release features several major improvements. Some of the highlights are: Arbitrary bits quantization algorithm; High-level auto-differentiable programming IR -- Relay.

Fully featured 8-bit network support
- 8bit quantizer
- Arbitrary bits quantization algorithm
- Intel cpu support
- ARM cpu support
NVidia GPU 8-bit kernel
- int8 gemm recipe
- int8 conv2d
- Autotvm integration
Automated tuning and scheduling
- AutoTVM optimizations for mobile GPUs
- AutoTVM optimizations for CUDA
- AutoTVM optimizations for x86
Initial release of the differentiable programming IR, Relay
- Generic & informative Relay error reporting #2408
- Relay IR text format support #1781
- Support control flows
- A Normal Form Canonicalization #2251
- Type system support
- End to end compilation
  - Frontend support: Caffe2 #2507 , CoreML #2476 , Keras #2376 , MXNet #2163 , ONNX, TFLite #2365
  - Operator coverage #1799 #2051
- FoldScaleAxis #2020
- SimplifyInference #2033
- CombineParallelConv2D #2089
- InstrumentBoundCheckers pass #2079
- Bind & FoldConstant #2100
- Alter Op Layout #2150
- General OpFusion #2090
CodeGen
- Gcc / g++ compatible C code generator for TVM #2161
- Device type annotation for heterogeneous compilation #2361
- Cache packed func ptr, lift alloca #2070
- Generalize compute to tensor region #1476
Runtime
- Relay interpreter and compiler #1954
- Heterogeneous runtime #1695
- Language bindings: Golang runtime #1470 , Rust runtime #1597
- Add min_repeat_ms to time_evaluator #2200
- Bundled interpreter demonstration #2297
- Enable PlanMemory in the graph runtime #2120
Language Binding
- Rust frontend #2292
VTA
- Improved RPC for VTA #2043
Hybrid python programming model
- Support for scheduling #2416
- Support for Inter-function call #2287
- Backend support #2477
TOPI
- Initial support for sparse tensor computation
- Improve ARM CPU depthwise convolution performance #2345
- Port winograd ops to relay #2356
- Add faster-rcnn proposal op #2420
Tutorials and docs
- Relay language docs #2232
- Tutorials on how to use SGX backend
- How to write a pass in python
- General lowering flow of TVM
- How to do tensorize
- TFLite frontend tutorial #2508
- Keras seq2seq model for translation tutorial #1815
- Committer guide and tips #2468
- Code review guideline on API designs #2459

0.4

This release features several major improvements. The high-level graph optimizer is now part of TVM repo. Some of the highlights are: Initial support of AutoTVM for automated optimization; customized accelerator backend VTA.

Tensor operator primitives
- Introduce attrs field to operator primitives(e.g. compute) to store additional metadata, the attrs can be used as hint for scheduling
Enable embedding of asm micro-kernels
Hybrid python programming model
- python AST based IR builder interface
- support GPU programs
AutoTVM, Automated tuning, and scheduling
- basic autotvm infra
- GPU IR verifier
- basic autotuning tutorial
- topi integration
ARM support
- winograd support
- initial support of ARM autotuning records
TOPI Vision
- Generic GPU sort support(useful for vision)
- SSD operator support
TOPI numpy consistency
- Rename all binary operators for numpy consistecy: broadcast_add-> add, broadcast_sub -> substract, broadcast_mul -> multiply, broadcast_div->divide
- New operators: slice, LRN, equal, not_equal, less, greater
- tutorials on topi
Initial low-bit operator support support
- Optimized popcount generation on ARM
- general bit-serial convolution and GEMM
- optimized low bit kernels
- parallel optimization
New topi backend optimization for intel graphics
Adapt AVX schedules for SSE target
VTA: customized accelerator backend
- custom hardware backend example
- tutorials on how to use customized accelerator
Initial experimental support for HLS backend
Bugfix in SPIRV code generator for vulkan
libdevice support, enable NVPTX backend
Introduce NDArrayContainer for managed NDarray
RPC and Device API
- Support communication between big/small endian machines.
- RPC and device API protocol upgrade (this is a non-backward compatible change) to support big-small endian communication. This is a non-backward compatible change, need to use the latest version of TVM runtime with the RPC
- graduate rpc from contrib, tvm.contrib.rpc->tvm.rpc -Support tracker in Android RPC, add fault tolerance for AutoTVM
BIG.LITTLE aware threadpool
tvm4j graph runtime that runs end to end workload in java
DLPack support
- Support from_dlpack and to_dlpack
- Enables bridges to pytorch
Enable link of stackvm in runtime
Tensorflow graphdef frontend
Keras frontend
- improved to support reuse layers, add activations
ONNX
- gather, LRN
CoreML frontend
- Support C-RNN and activation functions
Fix grads for sum and expand_like
Enhanced operator fusion for multiple elemwise branches
Separate nnvm fusion and compilation pass
Unified build system to cmake, customizable cmake path for vulkan, rocm, cuda

0.3

This release features numerous improvements in TOPI and backends. We make the first step toward object detection support in TOPI, featuring operators necessary for YOLO and SSDs. The topi now supports numpy-style API and operator overloading. RPC is significantly improved to support resource allocation and using a pool of devices. We are adding two new backends: WebGL for running GPUs on the browser, and Vulkan for running on next-generation graphics API.

TOPI Vision operators
- SSD support
- YOLO support
- NMS operator support in vision
TOPI general numpy-style operators
- numpy style operator overload in topi
- more operators: flip, take
- dilation support on conv2d and depthwise
8bit support
- ARM 8bit gemm
- ARM 8bit conv
Low bit operator support
- popcount intrinsics
- 1-bit fully connected
Contrib: MPSDNN fully-connected and conv2d support
Better RPC support
- RPC Tracker support to allow centralized resource management
- RPC protocol upgrade (this is a non-backward compatible change) to support timeout in the proxy
  - This is a breaking change, need to use the latest version of TVM runtime with the RPC
- Fault-tolerant to early server termination with correct exception propagated
- RPC support enabled for ROCm AMDGPUs
Tutorials and docs
- How to deploy to android devices.
Optimizations for hardware backends
- intel CPU (AVX and AVX512)
Schedule Primitives
- rfactor now support factor_axis to specify the factored dimension in the result
- cache_write now support multiple output operators
- enable warp memory which generates shuffle instructions
Framework bridge
- MXNet bridge supported
C++ compiler API support
- build migration
- topi migration to c++
- Target system in c++
WebGL backend
- runtime and codegen
- topi integration
- end to end pipeline on the browser
Vulkan backend
- vulkan runtime
- spirv code generator
Security
- intel SGX runtime support
- multi-threaded SGX runtime
LLVM 7.0 support
Robustness
- VerifyMemory to verify incorrect GPU schedules that writes into GPU memory from cpu
- Verify compute formulas
Better CPU parallel runtime

0.2

This release comes with a complete set of TOPI support for NNVM compiler, which allows compilation of end to end workloads. We also make major improvements in supporting new backends: ROCm for AMDGPUs and ARM GPU.

Backend support
- Support LLVM mainline(4.0, 5.0, 6.0)
- Support ROCM stack for AMD GPUs
- More robust OpenCL support for ARM GPUs
Android RPC runtime
Multi-threading optimization for ARM
- multi-threaded depthwise
- multi-threaded conv2d
New schedule primitives
- storage_align for shared memory alignment
- double_buffer
UnrollLoop : more robust version of unroll loop, count maximum steps that can be unrolled.
Full set of TOPI operators
- Introduce tvm.target to specify target options for compilation better.
- broadcast/ reduction operators
- pooling and global pooling
- Generic target support for topi
- schedule with external libraries
End to end deep learning pipelines for CPU, GPU, ARM GPU
Tutorials
- How to load compiled module in any language runtime
- How to use java runtime
Contrib library: MIOpen, CuDNN
Ongoing items that contains functioning pieces
- WebGL backend
- C++ compiler support
- MPS DNN
- low bit support, introduced popcount

0.1

Language runtime
- python
- javascript
- java
- c++
Backend
- arm, x86
- javascript, wasm
- CUDA
- opencl
- Metal
DNN Library integration
RPC runtime
TOPI operator pipeline python
TOPI operator pipeline in C++
Rough perf of the TOPI GPU pipeline
Rough pref of TOPI CPU pipeline
End to end graph executors

Initial version

Pack libary into shared library.
External function and contrib libraries
DLPack integration support
AOT and module system
Basic code structure ready.