[OpenCLML] Reactor and introduce on chip memory and memory planner #14922
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srkreddy1238
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Introduced thread context with CLMLWorkspace. Organized the code as runtime, utils and memory planners Introcuded recording queue support and on chip memory support. On chip memory allocation planner to acommodate multiple tensors at a time. DDR memory planner introduced to reuse the underlaying memory across multiple tensor descriptors. Dense layer support refactored to use GEMM. CLML binary operators doesn't support broadcasting. Hence introduced an explicite broadcast op as a work around. clml SDK codegen is enhanced accordingly.
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We definitely need few test cases. Let me find a way of exposing the plan to verify externally. |
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Probably you can take a look at the OpenCL tests: https://github.com/apache/tvm/blob/main/tests/cpp-runtime/opencl/opencl_texture_pool_test.cc |
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@echuraev have you ever built gtests (opencl-cpptest bin) for Android ? |
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Yes, I did it. You can build |
| using namespace tvm::runtime; | ||
| using namespace tvm::runtime::cl; | ||
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| void InitMemoryPlan(tvm::runtime::contrib::CachedLayer& layer) { |
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I saw that in class CLMLRuntime you have almost the same methods. Probably it is better to test them? You can change private to protected and inherit CLMLRuntime class in your test class. It should be like a test wrapper above CLMLRuntime class.
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Only InitMemoryPlan can be reused but PlanMemory has dependency with JSONRuntime nodes. initializing CLMLRuntime here requires Json graph and it's dependents. Hence I tried isolating the test environment within "CahcedLayer``` object. Let me see how much I can reuse from CLMLRuntime.
…pache#14922) * Reactor and introduce in chip memory and memory planner Introduced thread context with CLMLWorkspace. Organized the code as runtime, utils and memory planners Introcuded recording queue support and on chip memory support. On chip memory allocation planner to acommodate multiple tensors at a time. DDR memory planner introduced to reuse the underlaying memory across multiple tensor descriptors. Dense layer support refactored to use GEMM. CLML binary operators doesn't support broadcasting. Hence introduced an explicite broadcast op as a work around. clml SDK codegen is enhanced accordingly. * * review comments * * Memory planner cpp_runtime tests. * * gtest build rules while in android environments. * * review comments --------- Co-authored-by: Siva Rama Krishna Reddy B <sivb@blr-ubuntu-ripper.qualcomm.com>
Introduced thread context with CLMLWorkspace.
Organized the code as runtime, utils and memory planners Introcuded recording queue support and on chip memory support. On chip memory allocation planner to acommodate multiple tensors at a time. DDR memory planner introduced to reuse the underlaying memory across multiple tensor descriptors.
Dense layer support refactored to use GEMM.
CLML binary operators doesn't support broadcasting. Hence introduced an explicite broadcast op as a work around.
clml SDK codegen is enhanced accordingly.