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[Doc][Relay] Add VM doc (apache#3188)
* [Doc][Relay] Add VM doc * Add Apache header * Apply suggestions from code review Co-Authored-By: Steven S. Lyubomirsky <[email protected]> Co-Authored-By: 雾雨魔理沙 <[email protected]> Co-Authored-By: Logan Weber <[email protected]> Co-Authored-By: Zhi <[email protected]> * Junru's comment * More fix * More fix * More fix * last fix * Apply suggestions from code review Co-Authored-By: 雾雨魔理沙 <[email protected]> * Apply suggestions from code review Co-Authored-By: Logan Weber <[email protected]> * Add code links * Remove unused bp * Update docs/dev/virtual_machine.rst Co-Authored-By: Logan Weber <[email protected]> * Explain TODO * Yong's comment Co-Authored-By: Yong Wu <[email protected]> * Comment
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| .. Licensed to the Apache Software Foundation (ASF) under one | ||
| or more contributor license agreements. See the NOTICE file | ||
| distributed with this work for additional information | ||
| regarding copyright ownership. The ASF licenses this file | ||
| to you under the Apache License, Version 2.0 (the | ||
| "License"); you may not use this file except in compliance | ||
| with the License. You may obtain a copy of the License at | ||
| .. http://www.apache.org/licenses/LICENSE-2.0 | ||
| .. Unless required by applicable law or agreed to in writing, | ||
| software distributed under the License is distributed on an | ||
| "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
| KIND, either express or implied. See the License for the | ||
| specific language governing permissions and limitations | ||
| under the License. | ||
| Putting the VM in TVM: The Relay Virtual Machine | ||
| ================================================ | ||
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| Relay, a new program representation, has enabled the representation and optimization of | ||
| a great breadth of machine learning programs. | ||
| Unfortunately, by supporting a more expressive set of programs, we have | ||
| introduced several new execution challenges. | ||
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| Relay's interpreter can execute the full language but has notable limitations | ||
| that make it unsuited for production deployments. It is structured as an inefficient | ||
| interpreter that performs AST traversal to execute the program. This approach is conceptually | ||
| simple but inefficient, as the AST traversal heavily relies on indirection. | ||
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| There are further challenges in compiling dynamic code, such as dynamic scheduling and allocation, | ||
| fully dynamic tensor shapes, and control flow. The interpreter offers simple solutions | ||
| for these, but none is sufficiently compelling or optimized. | ||
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| The second execution mechanism is the existing graph runtime. In order to target Relay | ||
| programs to this, we compile a small subset of them to the old graph format and execute | ||
| them on the runtime. Graph runtime provides a fast execution experience but only for a very limited | ||
| subset of Relay programs. | ||
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| An alternative but not-standard approach is Relay's ahead-of-time compiler, | ||
| which compiles a Relay program into a shared library containing an ahead- | ||
| of-time implementation. The ahead-of-time compiler provides compelling performance | ||
| but is difficult to extend and instrument, which can only be done by modifying the | ||
| code generation and optimization mechanisms. | ||
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| The Relay virtual machine is intended to be a framework that balances these competing | ||
| approaches, providing a dynamic execution environment which can be extended, instrumented, | ||
| and integrated with other approaches like ahead-of-time compilation via a flexible extension | ||
| mechanism. | ||
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| The virtual machine is designed to strike a balance between performance and flexibility | ||
| when deploying and executing Relay programs, without giving up the benefits of TVM. | ||
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| Virtual machine (VM) design is a well-studied area in programming languages and systems, | ||
| and there have been various virtual machine designs for both full-fledged | ||
| and embedded programing languages. | ||
| Previous language VM designs have been heavily tailored to the execution profile of traditional programs. | ||
| Traditional programs manipulate small scalar values and consist of a large number of low-level instructions. | ||
| The sheer quantity of instructions requires instruction execution and dispatch to be extremely efficient. | ||
| In the context of machine learning we manipulate primarily tensor values, using a (relatively) | ||
| low number of high level instructions. ML programs' cost centers are expensive operator invocations, | ||
| such as GEMM or convolution, over a large input. Due to the execution profile exhibited by ML programs, | ||
| micro-optimizations present in scalar VMs are dramatically less important. | ||
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| TVM has provided strong support for vision models, | ||
| but we want to grow to support a wider variety of models. | ||
| 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, and dynamic | ||
| allocation, we must change how execution works. A virtual machine for Relay is a natural choice. | ||
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| The rest of this document provides a high-level overview of the Relay | ||
| virtual machine design and its instruction set. | ||
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| Design | ||
| ------ | ||
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| The VM's design is focused on simplicity without sacrificing performance. | ||
| In order to accomplish this we have focused on designing a tensor VM rather than a scalar VM. | ||
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| In the tensor VM setting, we optimize for cheap “allocation” of objects (by trying to avoid real allocation), | ||
| reuse of static fragments, and the ability to do dynamic shape (i.e jagged tensors). | ||
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| Instruction Set | ||
| ~~~~~~~~~~~~~~~ | ||
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| The choices of an instruction set and instruction representation are the most critical design decisions for a VM. | ||
| The current representation of the instructions is a tagged union containing the op-code and the data payload. An important design decision is the level of abstraction of the instructions (RISC vs. CISC) and how they take their data (fixed-width instruction encoding vs. variable-length encoding). The current version is closer to CISC, with complex instructions like AllocTensor, and is variable-length due to the inclusion of the shape as part of the instruction. The current instruction set is very high-level and corresponds roughly to high-level operations in Relay. | ||
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| Ret | ||
| ^^^ | ||
| **Arguments**: | ||
| :: | ||
| RegName dst | ||
| RegName result | ||
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| Returns the object in register `result` to caller's register `dst`. | ||
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| InvokePacked | ||
| ^^^^^^^^^^^^ | ||
| **Arguments**: | ||
| :: | ||
| size_t packed_index | ||
| size_t arity | ||
| size_t output_size | ||
| RegName* packed_args | ||
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| Invoke the packed function denoted by `packed_index`. The `arity` | ||
| and `output_size` are used to inform the VM how many inputs and | ||
| outputs to expect. `packed_args` stores the list of argument registers. | ||
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| AllocTensor | ||
| ^^^^^^^^^^^ | ||
| **Arguments**: | ||
| :: | ||
| RegName dst | ||
| RegName shape_register | ||
| size_t ndim | ||
| DLDataType dtype | ||
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| Allocate a tensor value of the appropriate shape (stored in `shape_register`) and `dtype`. The result | ||
| is saved to register `dst`. | ||
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| AllocDatatype | ||
| ^^^^^^^^^^^^^ | ||
| **Arguments**: | ||
| :: | ||
| RegName dst | ||
| size_t tag | ||
| size_t num_fields | ||
| RegName* datatype_fields | ||
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| Allocate a data type with the tag `tag` using the `num_fields` entries | ||
| from registers `datatype_fields`. The result is saved to register `dst`. | ||
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| AllocClosure | ||
| ^^^^^^^^^^^^ | ||
| **Arguments**: | ||
| :: | ||
| RegName dst | ||
| size_t clo_index | ||
| size_t num_freevar | ||
| RegName* free_vars; | ||
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| Allocate a closure with the VMFunction at `clo_index` as | ||
| its code, and the `num_freevar` entries from registers in | ||
| `free_vars`. The result is saved to register `dst`. | ||
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| GetField | ||
| ^^^^^^^^ | ||
| **Arguments**: | ||
| :: | ||
| RegName dst | ||
| RegName object | ||
| size_t field_index | ||
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| Get the field value with index `field_index` from `object`. And saves the result to register `dst`. | ||
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| If | ||
| ^^ | ||
| **Arguments**: | ||
| :: | ||
| RegName if_cond | ||
| size_t true_offset | ||
| size_t false_offset | ||
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| Check if the object at register `if_cond` is `true` or `false`. | ||
| If `true`, relative jump by `true_offset`, else relative | ||
| jump by `false_offset`. | ||
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| Goto | ||
| ^^^^ | ||
| **Arguments**: | ||
| :: | ||
| size_t pc_offset | ||
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| Relative unconditional jump by `pc_offset`. | ||
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| Invoke | ||
| ^^^^^^ | ||
| **Arguments**: | ||
| :: | ||
| size_t func_index | ||
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| Invoke function at `func_index`, consumes the number of arguments contained in the VMFunction's | ||
| arity field. | ||
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| InvokeClosure | ||
| ^^^^^^^^^^^^^ | ||
| **Arguments**: | ||
| :: | ||
| RegName closure | ||
| size_t closure_args_num | ||
| RegName* closure_args | ||
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| Invokes `closure`, consuming the number of arguments declared in the closure's VMFunction. | ||
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| LoadConst | ||
| ^^^^^^^^^ | ||
| **Arguments**: | ||
| :: | ||
| RegName dst | ||
| size_t const_index | ||
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| Load the constant at `const_index` from the constant pool. The result is saved to register `dst`. | ||
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| Object Representation | ||
| ~~~~~~~~~~~~~~~~~~~~~ | ||
| We use a simple object representation that uses shared pointers and tagging. | ||
| There is a huge space of possible object representations trade-offs, but we | ||
| believe micro-optimizing this code has little to no effect on the end-to-end performance. | ||
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| :: | ||
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| struct ObjectCell { | ||
| ObjectTag tag; | ||
| ... | ||
| }; | ||
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| struct Object { | ||
| std::shared_ptr<ObjectCell> ptr; | ||
| ... | ||
| } | ||
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| See `include/tvm/runtime/vm.h` for more details. | ||
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| Currently, we support 3 types of objects: tensors, data types, and closures. | ||
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| :: | ||
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| VMObject VMTensor(const tvm::runtime::NDArray& data); | ||
| VMObject VMDatatype(size_t tag, const std::vector<VMObject>& fields); | ||
| VMObject VMClosure(size_t func_index, std::vector<VMObject> free_vars); | ||
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| Stack and State | ||
| ~~~~~~~~~~~~~~~ | ||
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| The Relay VM maintains a stack frame, which contains information about how to resume the | ||
| previous call. Registers are allocated in a continuous space (virtual register file) for each function. | ||
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| We keep track of a set of Relay functions we have called, a pointer into its bytecode, an offset into the byte code (known as the program counter). | ||
|
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| :: | ||
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| struct VirtualMachine { | ||
| ... | ||
| std::vector<VMFrame> frames; | ||
| ... | ||
| // Current function. | ||
| size_t func_index; | ||
| // Pointer into the current function's instructions. | ||
| const Instruction* code; | ||
| // Current program counter relative to the code pointer. | ||
| size_t pc; | ||
| ... | ||
| }; | ||
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| Dispatch Loop | ||
| ~~~~~~~~~~~~~ | ||
| A critical piece of a VM is the dispatch loop. The dispatch loop usually dominates the execution time of a | ||
| virtual machine, but we have experimentally found this not to be the case for Relay. We have just implemented | ||
| a simple `switch`/`goto` dispatch loop which dispatches based on instruction op code. | ||
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| This loop is implemented by `VirtualMachine::Run()`. | ||
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| VM Compiler | ||
| ~~~~~~~~~~~ | ||
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| An important part of this infrastructure is a compiler from Relay's full IR into a sequence of bytecode. | ||
| The VM compiler transforms a `tvm::relay::Module` into a `tvm::relay::vm::VirtualMachine`. The virtual | ||
| machine contains a set of compiled functions, the compiled functions are contained in `tvm::relay::vm::Function`. The functions contain metadata about the the function as well as its compiled bytecode. For full definitions of the data structures see `vm.h`. | ||
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| Optimizations | ||
| ~~~~~~~~~~~~~ | ||
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| There are quite a few optimizations required by the VM compiler. | ||
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| We have implemented them in the old pass style, but plan to port them to | ||
| the new pass manager (#2546) before merging. | ||
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| Optimizations marked with `TODO` are not implemented yet. | ||
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| - A-Normal Form | ||
| - Lambda Lift (see `src/relay/vm/lambda_lift.cc`) | ||
| - Inline Primitives (see `src/relay/vm/inline_primitives.cc`) | ||
| - Inliner (see `src/relay/pass/inliner.cc`) | ||
| - Constant Pool Layout (see `src/relay/backend/vm/compiler.cc`) | ||
| - ADT Tag Allocation (see `src/relay/backend/vm/compiler.cc`) | ||
| - Tail Call Optimization (TODO) | ||
| - Liveness Analysis (TODO) | ||
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| Serialization | ||
| ~~~~~~~~~~~~~ | ||
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| A final and yet-to-be-implemented part of the VM design is serialization. The accompanying PR will introduce both the bytecode and its serialization, as well as VM-level serialization. The design premise is that a VM can be efficiently stored to disk and resumed at a later time. This would also allow us to efficiently schedule many models on to a single machine in order to obtain good utilization. | ||
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| Unresolved Questions | ||
| ~~~~~~~~~~~~~~~~~~~~ | ||
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| How do we handle dynamic shapes? | ||
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
| TODO | ||
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| How can we modify the VM to support JIT compilation of certain code paths? | ||
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
| In the code generation space there are still many tradeoffs to be analyzed and the VM is designed | ||
| to be very flexible so we can modify it for future experiments. | ||
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| How do we support heterogenous execution? | ||
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
| Heterogenous execution should work out of the box assuming we have annotated the appropriate device copies. | ||
| In order to do this properly we need to run the device annotation and copying passes. |