Tianqi Chen
1e1920bcbd
In the past we have been using `DataType` in PrimExpr.dtype field to check type information for PrimExpr while still having BaseExpr.ty for richer type information. DataType is also used both in runtime and compiler. This PR streamlines the boundary: - PrimExpr.ty now carries PrimType that replaces original use of `DataType` - Runtime use will now favor DLPack DLDataType, removing one layer of indirection. - Constants attributes where values are usually runtime values, will use `DLDataType` - DataType will be phased out after this PR We also brings up helper functions in PrimType, but also limits them to a more concise set so the functions do not grow with the data type codes in DLPack. This is a major refactor that changes the IR primitive. It helps to bring possible future benefits: - Unified type mechanism through Expr.ty - Possibility of carry future Type nodes Migration Guide: - Use `PrimType` when code reasons about compiler expression types, tensor element compiler types, or constructs a `PrimExpr`/compiler type. - Use existing source types such as `expr.ty()`, `ExprOp.expr_ty()`, or TE tensor element `dtype` where possible instead of rebuilding a type from dtype text. - Use raw `DLDataType` for runtime constants, ABI paths, dtype-valued attrs, and storage/runtime helper logic. - Prefer direct `PrimType` equality, `MatchesCode(...)`, `MatchesElementType(...)`, and `WithCode(...)` over local wrappers or string dtype checks. Performance: Using Object type instead of DLDataType would indeed bring some performance impact to the IR. We have done the following performance optimizations: - Make sure most of the outputs reuse one of the PrimType from inputs - Cache a thread local PrimType based on input so we don't repeatly realloc We did benchmarks show that rewrite simplify operation stays within +-10% overhead of original one. Which merits the refactor given the benefit the unfication brings
74 lines
2.5 KiB
C++
74 lines
2.5 KiB
C++
/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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#include <gtest/gtest.h>
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#include <tvm/runtime/logging.h>
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#include <tvm/runtime/tensor.h>
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using namespace tvm;
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TEST(TensorTest, IsContiguous_ContiguousStride) {
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auto array = runtime::Tensor::Empty({5, 10}, DLDataType{kDLFloat, 32, 1}, {kDLCPU});
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DLManagedTensor* managed_tensor = array.ToDLPack();
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int64_t strides[] = {10, 1};
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managed_tensor->dl_tensor.strides = strides;
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TVM_FFI_ICHECK(ffi::IsContiguous(managed_tensor->dl_tensor));
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managed_tensor->deleter(managed_tensor);
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}
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TEST(TensorTest, IsContiguous_NullStride) {
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auto array = runtime::Tensor::Empty({5, 10}, DLDataType{kDLFloat, 32, 1}, {kDLCPU});
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DLManagedTensor* managed_tensor = array.ToDLPack();
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managed_tensor->dl_tensor.strides = nullptr;
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TVM_FFI_ICHECK(ffi::IsContiguous(managed_tensor->dl_tensor));
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managed_tensor->deleter(managed_tensor);
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}
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TEST(TensorTest, IsContiguous_AnyStrideForSingular) {
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auto array = runtime::Tensor::Empty({5, 1, 10}, DLDataType{kDLFloat, 32, 1}, {kDLCPU});
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DLManagedTensor* managed_tensor = array.ToDLPack();
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int64_t strides[] = {10, 1, 1}; // strides[1] is normalized to 1 because shape[1] == 1.
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managed_tensor->dl_tensor.strides = strides;
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TVM_FFI_ICHECK(ffi::IsContiguous(managed_tensor->dl_tensor));
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managed_tensor->dl_tensor.strides = nullptr;
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managed_tensor->deleter(managed_tensor);
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}
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TEST(TensorTest, IsContiguous_UncontiguousStride) {
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auto array = runtime::Tensor::Empty({5, 1, 10}, DLDataType{kDLFloat, 32, 1}, {kDLCPU});
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DLManagedTensor* managed_tensor = array.ToDLPack();
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int64_t strides[] = {1, 1, 1};
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managed_tensor->dl_tensor.strides = strides;
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TVM_FFI_ICHECK(!ffi::IsContiguous(managed_tensor->dl_tensor));
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managed_tensor->dl_tensor.strides = nullptr;
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managed_tensor->deleter(managed_tensor);
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}
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