Support model debugging (#138)
* Call llc, ld from within onnx-mlir. * Rename EmitLLVMBC -> EmitLib., reorder header files * Checkpoint, debug.py works. * Automatically generate inputs in debug.py. * Use float. * Fix merge conflict, remove RapidCheck from this patch. * Remove submodule rapidcheck properly. * Reformat code. * More comments. * Add documentation. * Add documentation to navigation. * Account for the fact that some initializers may also appear as input.
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Tian Jin
GitHub
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# Debugging Numerical Error
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Use `util/debug.py` python script to debug numerical errors, when onnx-mlir-compiled inference executable produces
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numerical results that are inconsistent with those produced by the training framework.
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This python script will run the model through onnx-mlir and a reference backend, and compare
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the intermediate results produced by these two backends layer by layer.
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## Rrerequisite
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- Set `ONNX_MLIR_HOME` environment variable to be the path to
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the HOME directory for onnx-mlir. The HOME directory for onnx-mlir refers to
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the parent folder containing the `bin`, `lib`, etc sub-folders in which ONNX-MLIR
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executables and libraries can be found.
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- Install an ONNX backend, by default onnx-runtime is used as testing backend. Install by
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running `pip install onnxruntime`. To use a different testing backend, simply replace code
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importing onnxruntime to some other ONNX-compliant backend.
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## Usage
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`util/debug.py` supports the following command-line options:
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```bash
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usage: debug.py [-h] model_path
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positional arguments:
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model_path Path to the model to debug.
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```
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@ -29,5 +29,7 @@ toc:
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# url: /piece1.html
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- title: Tools
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subfolderitems:
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- page: debug.py - Debug Numerical Errors
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url: /DebuggingNumericalError.html
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- page: DocCheck - Handling Necessary Code Duplication
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url: /doc_check/
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@ -10,12 +10,13 @@
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#include <cstdio>
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#include <fcntl.h>
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#include "llvm/Support/Program.h"
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#include <llvm/Support/Program.h>
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#include "src/ExternalUtil.hpp"
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#include "src/MainUtils.hpp"
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#include "MainUtils.hpp"
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#ifdef _WIN32
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#include <io.h>
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#else
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@ -387,36 +387,57 @@ public:
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staticInputs.emplace_back(ptrToMemRef);
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}
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// If more than one output exists, the struct becomes a nested struct,
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// the unpacking logic can be more involved, so no support for now.
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assert(numOutputs == 1 && "only support 1 output tensor now.");
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// Call static entry point with the memref ptrs created, and get output.
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auto outputMemRefs = rewriter.create<LLVM::CallOp>(loc,
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auto outMemRefs =
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rewriter
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.create<LLVM::CallOp>(loc,
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staticEntryPointTy.getFunctionResultType(),
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rewriter.getSymbolRefAttr(wrappedStaticEntryPointFuncName),
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staticInputs);
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staticInputs)
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.getResult(0);
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auto outMemRefsType = outMemRefs.getType().dyn_cast<LLVMType>();
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std::vector<mlir::Value> outMemRefList;
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if (numOutputs == 1) {
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// If only one output tensor exists, the tensor's corresponding memref
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// descriptor will be returned as is.
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outMemRefList.emplace_back(outMemRefs);
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} else {
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// Otherwise, if multiple tensors are to be returned, the returned value
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// is a struct. Multiple tensors' memref descriptors are packed into the
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// same struct. So we unpack them iteratively to outMemRefList.
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for (int i = 0; i < numOutputs; i++) {
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auto position = rewriter.getArrayAttr({rewriter.getI64IntegerAttr(i)});
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auto type = outMemRefsType.getStructElementType(i);
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auto extractOp = rewriter.create<LLVM::ExtractValueOp>(loc,
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/*res=*/type,
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/*type=*/outMemRefs,
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/*position=*/position);
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outMemRefList.emplace_back(extractOp.getResult());
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}
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}
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// Create wrapped output.
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auto wrappedOutput = callApi(
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rewriter, loc, apiRegistry, API::CREATE_ORDERED_DYN_MEM_REF_DICT, {});
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// Get the first memref returned, convert to a dynamic memref and store
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// it in the wrapped Output.
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auto outMemRef = outputMemRefs.getResult(0);
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auto outMemRefTy = outMemRef.getType().dyn_cast<LLVMType>();
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for (decltype(numOutputs) i = 0; i < outMemRefList.size(); i++) {
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// Get the i-th memref returned, convert to a dynamic memref and store it
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// in the wrappedOutput.
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auto memRef = outMemRefList.at(i);
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auto outMemRefTy = memRef.getType().dyn_cast<LLVMType>();
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auto outMemRefRank = getRankFromMemRefType(outMemRefTy);
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auto outMemRefRankVal = rewriter.create<LLVM::ConstantOp>(
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loc, int32Ty, rewriter.getI32IntegerAttr(outMemRefRank));
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auto outDynMemRef = callApi(rewriter, loc, apiRegistry,
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API::CREATE_DYN_MEM_REF, {outMemRefRankVal});
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fillDynMemRefWithMemRef(
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outMemRef, outDynMemRef, rewriter, loc, apiRegistry, llvmDialect);
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auto zero = rewriter.create<LLVM::ConstantOp>(
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loc, int32Ty, rewriter.getI32IntegerAttr(0));
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memRef, outDynMemRef, rewriter, loc, apiRegistry, llvmDialect);
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auto idx = rewriter.create<LLVM::ConstantOp>(
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loc, int32Ty, rewriter.getI32IntegerAttr(i));
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callApi(rewriter, loc, apiRegistry, API::SET_DYN_MEM_REF,
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{wrappedOutput, zero, outDynMemRef});
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{wrappedOutput, idx, outDynMemRef});
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}
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// Return wrapped output.
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rewriter.create<LLVM::ReturnOp>(
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loc, SmallVector<Value, 1>({wrappedOutput}));
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@ -613,7 +634,6 @@ void KrnlToLLVMLoweringPass::runOnOperation() {
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ConversionTarget target(getContext());
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target.addLegalDialect<LLVM::LLVMDialect>();
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target.addLegalOp<ModuleOp, ModuleTerminatorOp>();
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// target.addLegalOp<KrnlEntryPointOp>();
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// Lower the MemRef types to a representation in LLVM.
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LLVMTypeConverter typeConverter(&getContext());
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@ -626,7 +646,6 @@ void KrnlToLLVMLoweringPass::runOnOperation() {
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populateStdToLLVMConversionPatterns(typeConverter, patterns,
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/*emitCWrapperS=*/true,
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/*useAlignedAlloc=*/false);
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patterns.insert<KrnlGlobalOpLowering>(&getContext(), typeConverter);
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// Lower from the `krnl` dialect i.e. the Reshape operation.
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@ -0,0 +1,112 @@
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import os
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import sys
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import argparse
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import onnx
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import subprocess
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import numpy as np
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import tempfile
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from collections import OrderedDict
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# Reference backend, use onnxruntime by default
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import onnxruntime
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prepare = onnxruntime.InferenceSession
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if (not os.environ.get('ONNX_MLIR_HOME', None)):
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raise RuntimeError(
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"Environment variable ONNX_MLIR_HOME is not set, please set it to the path to "
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"the HOME directory for onnx-mlir. The HOME directory for onnx-mlir refers to "
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"the parent folder containing the bin, lib, etc sub-folders in which ONNX-MLIR "
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"executables and libraries can be found.")
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VERBOSE = os.environ.get('VERBOSE', False)
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ONNX_MLIR = os.path.join(os.environ['ONNX_MLIR_HOME'], "bin/onnx-mlir")
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# Include runtime directory in python paths, so pyruntime can be imported.
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RUNTIME_DIR = os.path.join(os.environ['ONNX_MLIR_HOME'], "lib")
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sys.path.append(RUNTIME_DIR)
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try:
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from pyruntime import ExecutionSession
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except ImportError:
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raise ImportError(
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"Looks like you did not build the pyruntime target, build it by running `make pyruntime`."
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)
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def execute_commands(cmds):
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if (VERBOSE):
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print(" ".join(cmds))
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subprocess.run(cmds, stdout=subprocess.PIPE, check=True)
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def extend_model_output(model, intermediate_outputs):
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# onnx-mlir doesn't care about manually specified output types & shapes.
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DUMMY_TENSOR_TYPE = onnx.TensorProto.FLOAT
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while (len(model.graph.output)):
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model.graph.output.pop()
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for output_name in intermediate_outputs:
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output_value_info = onnx.helper.make_tensor_value_info(
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output_name, DUMMY_TENSOR_TYPE, None)
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model.graph.output.extend([output_value_info])
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return model
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def main(model_path):
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model = onnx.load(model_path)
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intermediate_outputs = sum(
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[list(node.output) for node in model.graph.node], [])
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intermediate_outputs = list(OrderedDict.fromkeys(intermediate_outputs))
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model = extend_model_output(model, intermediate_outputs)
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with tempfile.TemporaryDirectory() as temp_dir:
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print("Temporary directory has been created at {}".format(temp_dir))
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# Save modified model & invoke onnx-mlir to compile it.
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temp_model_path = os.path.join(temp_dir, "model.onnx")
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onnx.save(model, temp_model_path)
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execute_commands([ONNX_MLIR, temp_model_path])
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# Use the generated shared library to create an execution session.
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temp_shared_lib_path = os.path.join(temp_dir, "model.so")
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sess = ExecutionSession(temp_shared_lib_path,
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"_dyn_entry_point_main_graph")
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# Generate random data as input.
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inputs = []
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input_names = []
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initializers = list(map(lambda x: x.name, model.graph.initializer))
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np.random.seed(42)
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for input_proto in model.graph.input:
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if input_proto.name not in initializers:
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input_names.append(input_proto.name)
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shape_proto = input_proto.type.tensor_type.shape
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explicit_shape = []
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for dim in shape_proto.dim:
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assert dim.dim_value, "Can only debug models with inputs that have explicit shapes."
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explicit_shape.append(dim.dim_value)
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inputs.append(
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np.random.uniform(-1.0, 1.0, explicit_shape).astype(np.float32))
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# Run the compiled inference function on the randomly generated data.
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outs = sess.run(inputs)
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# Run the model with reference backend and get results.
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ref_session = prepare(temp_model_path)
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output_names = list(map(lambda x: x.name, model.graph.output))
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input_feed = dict(zip(input_names, inputs))
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ref_outs = ref_session.run(output_names, input_feed)
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# For each intermediate output tensor, compare results.
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for i, name in enumerate(intermediate_outputs):
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print("Verifying value of {}".format(name))
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np.testing.assert_array_almost_equal(ref_outs[i], outs[i], decimal=5)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('model_path', type=str, help="Path to the model to debug.")
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args = parser.parse_args()
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main(**vars(args))
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