1. 10 Apr, 2020 1 commit
  2. 21 Feb, 2020 1 commit
    • [CODEGEN] Support cuda tensorcore subbyte int data type in auto tensorcore (#4546) · f23ac969
      * support cuda tensorcore subbyte int data type in auto tensorcore
      
      * add lisence
      
      * pass cpplint
      
      * fix code review comments
      
      * merge the int4/int1 codegen tutorial into the existing auto tensorcore tutorial
      
      * using master's new API
      
      * disable tuning when cuda is not enabled
      
      * address cr comment
      
      * do not run the tuning
      
      * fix test failure
      
      * fix cpplint error
      
      * fix bool type reduction bug
      
      * 1. fix a index bug 2. fix returned bytes value of int1/int4/uint4
      
      * fix typo
      Orion34C committed
  3. 19 Jan, 2020 1 commit
    • [REFACTOR] Establish tir (#4740) · cf59b206
      TIR is the new namespace for low-level IR
      for tensor-level optimizations and loop transformations.
      
      This PR establishes the namespace and files.
      
      - lowered_func.h,buffer.h,data_layout.h -> tir/buffer.h,tir/data_layout.h,tir/lowered_func.h
      - ir.h -> tir/expr.h, tir/stmt.h
      - ir_functor_ext.h -> tir/expr_functor.h, tir/stmt_functor.h
      Tianqi Chen committed
  4. 16 Jan, 2020 1 commit
  5. 15 Jan, 2020 1 commit
    • [REFACTOR][IR] Unify IntImm and UIntImm (#4706) · ce807fe8
      * [REFACTOR][IR] Unify IntImm and UIntImm
      
      This PR unifies UIntImm and IntImm to simplify the codebase.
      Unsigned integer constants will also be stored as IntImm.
      
      For uint constant that does not fit into int64(rare case), we introduced
      an intrinsic tvm_big_uint_imm to construct such intgers by its
      lower and higher 32bits.
      
      * [REFACTOR][IR] Remove UIntImm to use IntImm
      
      * rename big->large
      Tianqi Chen committed
  6. 09 Jan, 2020 1 commit
    • [REFACTOR][IR] tvm::Expr -> PrimExpr(Primitive Expr) (#4669) · d6a23cf5
      * [REFACTOR][IR] tvm::Expr -> PrimExpr(Primitive Expr)
      
      As part of unified IR, we will need to unify relay::Expr
      and the current tvm::Expr under the same base type.
      
      From the techinical point of view. tvm::Expr is a "primitive"
      expression that only contains POD types and handles and does
      not do life-cycle management.
      
      This PR renames Expr->PrimExpr to clarify that.
      We will send a subsequent PR to introduce the base expr class.
      
      * Remove legacy VarExpr and ExprHash/Equal
      Tianqi Chen committed
  7. 08 Jan, 2020 1 commit
    • [REFACTOR][IR] Add Node suffix to low-level IR nodes (#4649) · f4c5f93b
      * [REFACTOR][IR] Variable -> VarNode
      
      * [REFACTOR][IR] Add/Sub/Mul/Div -> AddNode/SubNode etc.
      
      * [REFACTOR][IR] Min/Max/FloorDiv/FloorMod -> MinNode/MaxNode etc.
      
      * [REFACTOR][IR] EQ/NE/LT/LE/GT/GE/Select -> EQNode/NENode etc.
      
      * [REFACTOR][IR] Add Node suffix to Select/Call/Load/Ramp/Shuffle/Let
      
      * [REFACTOR][IR] Add node suffix to IntImm/UIntImm/FloatImm/StringImm
      
      * [REFACTOR][IR] Add Node suffix to Any, AttrStmt, AssertStmt
      
      * [REFACTOR][IR] Add Node suffix to Store/Provide/Allocate/Free
      
      * [REFACTOR][IR] Add Node suffix to ProducerConsumer
      
      * Fix lint
      
      * style updates, test fixes
      Tianqi Chen committed
  8. 06 Jan, 2020 1 commit
    • [REFACTOR][IR] Introduce SeqStmt to replace ir::Block (#4627) · 3595cbe0
      * [REFACTOR][IR] Introduce SeqStmt to replace Block
      
      ir::Block was used to represent a sequence of Stmts in the original low-level IR.
      The nested ir::Block structure is not really friendly for recursive visits,
      especially when the statements are unrolled.
      
      This PR introduce a SeqStmt that directly stores a sequence of statements in an Array container.
      The new SeqStmt will be used as a replacement of the original Block structure.
      
      * [REFACTOR] Migrate use of Block to SeqStmt.
      
      * [REFACTOR] Remove Block
      
      * Add more comments per yizhi's comment
      Tianqi Chen committed
  9. 22 Dec, 2019 1 commit
    • [REFACTOR][DTYPE] Isolate dtype to runtime (#4560) · 7fa8aab5
      dtype.h -> runtime/data_type.h
      
      Changes:
      - Rename all old reference of tvm::Type to DataType
      - ExprNode.type -> ExprNode.dtype
      - Expr.type() -> Expr.dtype()
      - Change Expr related functions to expr_operator.
        - DataType::min() -> min_value(DataType)
        - DataType::max() -> max_value(DataType)
      - Move type constructor Int, UInt, Float, Handle, Bool into DataType.
        - Int(bits) -> DataType::Int(bits)
        - UInt(bits) -> DataType::UInt(bits)
      Tianqi Chen committed
  10. 21 Oct, 2019 1 commit
    • [REFACTOR][NODE][RUNTIME] Move Node to the new Object protocol. (#4161) · 7895adb2
      * [REFACTOR][NODE][RUNTIME] Move Node to the new Object protocol.
      
      This PR removes the original node system, and make node as a subclass of Object.
      This is a major refactor towards a better unified runtime object system.
      
      List of changes in the refactor:
      
      - We now hide data_ field, use Downcast explicitly to get a sub-class object.
      - Removed the node system FFI in python.
      - Removed the node C API, instead use PackedFunc for list and get attrs.
      - Change relay::Op::set_attr_type_key(attr_key_name) to relay::Op::set_attr_type<AttrType>().
        - This change was necessary because of the new Object registration mechanism.
        - Subsequent changes to the op registrations
        - The change revealed a few previous problems that is now fixed.
      - Patched up a few missing node type registration.
        - Now we will raise an error if we register object that is not registered.
      - The original node.h and container.h are kept in the same location.
      - Calling convention: kObjectHandle now equals the old kNodeHandle, kNodeHandle is removed.
      - IRFunctor now dispatches on ObjectRef.
      - Update to the new type checking API: is_type, derived_from are replaced by IsInstance.
      - Removed .hash member function, instead use C++ convention hasher functors.
      
      * Address review comments
      Tianqi Chen committed
  11. 25 Sep, 2019 2 commits
    • [ARITH] Refactor to use explicit div/mod functions instead of operators. (#4000) · f0079a57
      * [ARITH] Use explicit div/mod functions instead of operators.
      
      * fix pooling case
      Tianqi Chen committed
    • Changes to make tensorize work. These changes also fix the previously broken test. (#3981) · b410df8c
      * Changes to make tensorize work. These changes also fix the previously
      broken test.
      
      Summary:
      Tensorize was breaking  for a few reasons.
      1)
      Assert at: src/op/tensorize.cc:234 CHECK(is_one(e.region[j]->extent))
      In some cases this cannot be proven, e.g.:
      expected shape=[16, 4], given region=[range(min=((ax1.outer*16)/16), ext=(((((ax1.outer*16) + 15)/16) + 1) - ax1.outer)), range(min=((k.outer*4)/4), ext=(((((k.outer*4) + 3)/4) + 1) - k.outer)), range(min=0, ext=16), range(min=0, ext=4)]
      The unprovable one is: ext=(((((ax1.outer*16) + 15)/16) + 1) - ax1.outer)).
      This can be simplified but it is not because to simplify divide, it must
      prove ax1.outer > 0 and since it is var it cannot. The fix for this to
      just find all the vars in expr in relace them with some const value.
      
      2) Equivalence between tensorized expr and one being asked to tensorize. For example,
      the error would be.
      TVMError: Check failed: Equal(lhs, rhs):
      Failed to match the compute with TensorIntrin tensor_intrin's declaration
      provided= reduce(combiner=comm_reducer(result=[(x + y)], lhs=[x], rhs=[y], identity_element=[(int16)0]), source=[(int16(data(k))*int16(kernel(((((((((k.outer.outer*64) + (k.outer.inner*2)) + k)/2)*128) + i) - (k.outer.inner*128)) - (k.outer.outer*4096)), ((((k.outer.outer*64) + (k.outer.inner*2)) + k) % 2))))], axis=[iter_var(k, range(min=0, ext=2))], where=(bool)1, value_index=0),
      intrin=  reduce(combiner=comm_reducer(result=[(x + y)], lhs=[x], rhs=[y], identity_element=[(int16)0]), source=[(int16(data(k))*int16(kernel(i, k)))], axis=[iter_var(k, range(min=0, ext=2))], where=(bool)1, value_index=0)
      Difference is mainly in the source part:
      source=[(int16(data(k))*int16(kernel(((((((((k.outer.outer*64) + (k.outer.inner*2)) + k)/2)*128) + i) - (k.outer.inner*128)) - (k.outer.outer*4096)), ((((k.outer.outer*64) + (k.outer.inner*2)) + k) % 2))))]
      source=[(int16(data(k))*int16(kernel(i, k)))], axis=[iter_var(k, range(min=0, ext=2))]
      This was not being simpifiled due to compute_intrin_iter_space (map for
      iter var to range) not containing leaf iter vars.
      
      3) Here it fails with:
      Check failed: is_one(Simplify(value->shape[i])): Argument b_buffer shape mismatch[16, 4] vs [(((((ax1.outer*16) + 15)/16) + 1) - ax1.outer), (((((k.outer*4) + 3)/4) + 1) - k.outer), 16, 4]
      This is in buffer binding where it thinks expected and buffer bound
      shape is different. Although if we could simplify expr, this would not
      be the case.
      
      Test Plan:
      On skylake avx512 machine:
      python tests/python/contrib/test_gemm_acc16.py
      
      Reviewers:
      
      Subscribers:
      
      Tasks:
      
      Tags:
      
      * Implemented bounded analyzer which traverses tree and for reduce/for
      statements binds the bound of the analyzer. Later this is used to
      simplify expressions. Inspired from ir_mutator_with_analyzer
      
      Summary:
      
      Test Plan:
      
      Reviewers:
      
      Subscribers:
      
      Tasks:
      
      Tags:
      
      * Addressed comments.
      
      Summary:
      
      Test Plan:
      
      Reviewers:
      
      Subscribers:
      
      Tasks:
      
      Tags:
      
      * Added ASF header + define macro for the header file: TVM_ARITHMETIC_IR_VISITOR_WITH_ANALYZER_H_
      Some lint fixes as well.
      
      * Relax the assumption that dom_map must always contain all leaf itervars.
      
      Summary:
      
      Test Plan:
      
      Reviewers:
      
      Subscribers:
      
      Tasks:
      
      Tags:
      
      * Disable copy constructor and move to raw ptr.
      
      Summary:
      
      Test Plan:
      
      Reviewers:
      
      Subscribers:
      
      Tasks:
      
      Tags:
      Kimish Patel committed
  12. 17 Aug, 2019 1 commit
  13. 06 Jul, 2019 1 commit
  14. 02 Jul, 2019 1 commit
  15. 08 Apr, 2019 1 commit
    • [HEADER] Add Header to Comply with ASF Release Policy (#2982) · cffb4fba
      * [HEADER] ASF header dir=include
      
      * [HEADER] ASF Header dir=src
      
      * [HEADER] ASF Header -dir=python
      
      * [HEADER] ASF header dir=topi
      
      * [HEADER] ASF Header dir=nnvm
      
      * [HEADER] ASF Header -dir=tutorials
      
      * [HEADER] ASF Header dir=tests
      
      * [HEADER] ASF Header -dir=docker
      
      * fix whitespace
      
      * [HEADER] ASF Header -dir=jvm
      
      * [HEADER] ASF Header -dir=web
      
      * [HEADER] ASF Header --dir=apps
      
      * [HEADER] ASF Header --dir=vta
      
      * [HEADER] ASF Header -dir=go
      
      * temp
      
      * [HEADER] ASF Header --dir=rust
      
      * [HEADER] Add ASF Header --dir=cmake
      
      * [HEADER] ASF Header --dir=docs
      
      * [HEADER] Header for Jenkinsfile
      
      * [HEADER] ASF Header to toml and md
      
      * [HEADER] ASF Header to gradle
      
      * Finalize rat cleanup
      
      * Fix permission
      
      * Fix java test
      
      * temporary remove nnvm onnx test
      Tianqi Chen committed
  16. 06 Oct, 2018 1 commit
  17. 23 Aug, 2018 1 commit
  18. 26 Jul, 2018 1 commit
  19. 09 Mar, 2018 1 commit
  20. 04 Dec, 2017 1 commit
  21. 25 Nov, 2017 1 commit
  22. 24 Jul, 2017 1 commit
  23. 06 Jul, 2017 3 commits
  24. 05 Jul, 2017 1 commit
  25. 04 Jul, 2017 1 commit