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intermediate/inductor_debug_cpu

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Inductor CPU backend debugging and profiling#

Created On: Jul 01, 2023 | Last Updated: Jan 08, 2025 | Last Verified: Nov 05, 2024

Authors:Xuan Liao,Haozhe Zhu,Jiong Gong,Weihan Wang

Overview#

PyTorch 2.0 introduced the compilation API calledtorch.compile.This new feature offers a significant speedup over eager mode execution through graph-level optimization powered by the default Inductor backend.

This tutorial is intended to provide an in-depth introduction on the debuggingand performance profiling on Inductor CPU backend by delving into the intricacies oftorch.compile.

Meanwhile, you may also find related tutorials abouttorch.compilearoundbasic usage,comprehensivetroubleshootingand GPU-specific knowledge likeGPU performance profiling.

We will start debugging with a motivating example that triggers compilation issues and accuracy problemsby demonstrating the process of debugging to pinpoint the problems.

By enabling logging and exploring the underlying generated code,you can learn how to narrow down the failure step by step and finally figure out the route cause.

Following that, we will proceed to discuss how to profile the compiled code and,through a performance comparison with eager mode,elaborate on the reasons whytorch.compile can provide an additional performance boost compared to its eager counterpart.

Debugging#

Here is a simple example to run thetorch.compile using Inductor and compare its result with eager mode:

importtorchdeffoo1(x1,x2):a=torch.neg(x1)b=torch.maximum(x2,a)y=torch.cat([b],dim=0)returnyx1=torch.randint(256,(1,8),dtype=torch.uint8)x2=torch.randint(256,(8390,8),dtype=torch.uint8)compiled_foo1=torch.compile(foo1)result=compiled_foo1(x1,x2)

The correct implementation ofneg in thecpp codegen is as follows:

defneg1(x):returnf"decltype({x})(-{x})"

In order to demonstrate the debugging, we will modify the function to a wrong one later.

Get more logging information#

No debugging information would be provided if you run this simple example by default. In order to get more useful debugging and logging information, we usually add aTORCH_COMPILE_DEBUG environment variable like below:

TORCH_COMPILE_DEBUG=1pythonxx.py

This would print more debug information in the output logs and also dump the intermediate IRs generated during the codegen process. You can find the dumped file paths in the log like below:

torch._inductor.debug:[WARNING]model___20debugtrace:/tmp/torchinductor_root/rx/crxfi2ybd7yp5sbj2pnhw33wfhtdw7wumvrobyp5sjvdui5ktjc2.debug

In this directory, the following files are saved for debugging purposes:

File	Description
`fx_graph_runnable.py`	Executable FX graph, after decomposition, before pattern match
`fx_graph_transformed.py`	Transformed FX graph, after pattern match
`ir_pre_fusion.txt`	Inductor IR before fusion
`ir_post_fusion.txt`	Inductor IR after fusion
`output_code.py`	Generated Python code for graph, with C++/Triton kernels

Note thatfx_graph_runnable.py andoutput_code.py are both runnable and editable in order to make debugging easier.Here are the main parts of code extracted from the files and we correlate the C++ generated line with the FX code line.

fx_graph_runnable:

defforward1(self,arg0_1,arg1_1):neg=torch.ops.aten.neg.default(arg0_1);arg0_1=Nonemaximum=torch.ops.aten.maximum.default(arg1_1,neg);arg1_1=neg=Noneclone=torch.ops.aten.clone.default(maximum);maximum=Nonereturn(clone,)

C++ kernel inoutput_code:

importtorchfromtorch._inductor.async_compileimportAsyncCompileasync_compile=AsyncCompile()cpp_fused_cat_maximum_neg_0=async_compile.cpp('''#include "/tmp/torchinductor_root/gv/cgv6n5aotqjo5w4vknjibhengeycuattfto532hkxpozszcgxr3x.h"extern "C" void kernel(const unsigned char* in_ptr0,                       const unsigned char* in_ptr1,                       unsigned char* out_ptr0){    {        #pragma GCC ivdep        for(long i0=static_cast<long>(0L); i0<static_cast<long>(8390L); i0+=static_cast<long>(1L))        {            #pragma GCC ivdep            for(long i1=static_cast<long>(0L); i1<static_cast<long>(8L); i1+=static_cast<long>(1L))            {                auto tmp0 = in_ptr0[static_cast<long>(i1 + (8L*i0))];                auto tmp1 = in_ptr1[static_cast<long>(i1)];                // Corresponding FX code line: neg = torch.ops.aten.neg.default(arg0_1);  arg0_1 = None                auto tmp2 = decltype(tmp1)(-tmp1);                // Corresponding FX code line: maximum = torch.ops.aten.maximum.default(arg1_1, neg);  arg1_1 = neg = None                auto tmp3 = max_propagate_nan(tmp0, tmp2);                // Corresponding FX code line: clone = torch.ops.aten.clone.default(maximum);  maximum = None                out_ptr0[static_cast<long>(i1 + (8L*i0))] = tmp3;            }        }    }}''')

Determine component of error#

When encountering errors or accuracy problems, a straightforward solution to find the bug is to narrow down the problem. The first thing to do is to determine the component where the error occurs. Luckily, it can be simply achieved by changing the backend oftorch.compile.

Code	Description
`torch.compile(fn,backend="eager")`	Enable Dynamo
`torch.compile(fn,backend="aot_eager")`	Enable Dynamo + AOT Autograd
`torch.compile(fn,backend="inductor")`	Enable Dynamo + AOT Autograd + Inductor

If the model can successfully run when the backend is set toeager oraot_eager while it fails withinductor, we can narrow down the failure to Inductor.

Compilation error#

As we know, the evolved chain of graph-level optimization is like:

torch.neg(Python)->torch.ops.aten.neg.default(withinFXgraph)->ops.neg(withinIRnode)->tmp2=-tmp1(withinC++kernel)

If you encounter a compilation error, there is something wrong when compiling C++ kernels in the output code.This type of error indicates that bugs are introduced when lowering IR nodes to output code.The root cause of compilation error is usually shown in the traceback log.

For example, theneg function is modified like this:

defneg2(x):returnf"-{x}"

The logging gives the following compile error with a rather clear reason.

 torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised: CppCompileError: C++ compile error /tmp/torchinductor_root/xg/cxga5tk3b4lkwoxyigrtocjp5s7vc5cg2ikuscf6bk6pjqip2bhx.cpp: In function ‘void kernel(const unsigned char*, const unsigned char*, unsigned char*)’: /tmp/torchinductor_root/xg/cxga5tk3b4lkwoxyigrtocjp5s7vc5cg2ikuscf6bk6pjqip2bhx.cpp:17:57: error: no matching function for call to ‘max_propagate_nan(unsigned char&, int&)’   17 |                 auto tmp3 = max_propagate_nan(tmp0, tmp2);        |                                                         ^ In file included from /tmp/torchinductor_root/xg/cxga5tk3b4lkwoxyigrtocjp5s7vc5cg2ikuscf6bk6pjqip2bhx.cpp:2: /tmp/torchinductor_root/gv/cgv6n5aotqjo5w4vknjibhengeycuattfto532hkxpozszcgxr3x.h:27:17: note: candidate: ‘template<class scalar_t> scalar_t max_propagate_nan(scalar_t, scalar_t)’ 27 | inline scalar_t max_propagate_nan(scalar_t a, scalar_t b) {      |                 ^~~~~~~~~~~~~~~~~ /tmp/torchinductor_root/gv/cgv6n5aotqjo5w4vknjibhengeycuattfto532hkxpozszcgxr3x.h:27:17: note:   template argument deduction/substitution failed:/tmp/torchinductor_root/xg/cxga5tk3b4lkwoxyigrtocjp5s7vc5cg2ikuscf6bk6pjqip2bhx.cpp:17:57: note:   deduced conflicting types for parameter ‘scalar_t’ (‘unsigned char’ and ‘int’) 17 |                 auto tmp3 = max_propagate_nan(tmp0, tmp2);      |                                                         ^

Let us also see the corresponding C++ kernel in output code and IR node.

C++ kernel:

include"/tmp/torchinductor_root/gv/cgv6n5aotqjo5w4vknjibhengeycuattfto532hkxpozszcgxr3x.h"extern"C"voidkernel(constunsignedchar*in_ptr0,constunsignedchar*in_ptr1,unsignedchar*out_ptr0){{#pragma GCC ivdepfor(longi0=static_cast<long>(0L);i0<static_cast<long>(8390L);i0+=static_cast<long>(1L)){#pragma GCC ivdepfor(longi1=static_cast<long>(0L);i1<static_cast<long>(8L);i1+=static_cast<long>(1L)){autotmp0=in_ptr0[static_cast<long>(i1+(8L*i0))];autotmp1=in_ptr1[static_cast<long>(i1)];autotmp2=-tmp1;autotmp3=max_propagate_nan(tmp0,tmp2);out_ptr0[static_cast<long>(i1+(8L*i0))]=tmp3;}}}}

IR node:

buf0:SchedulerNode(ComputedBuffer)buf0.writes=[MemoryDep('buf0',c0,{c0:67120})]buf0.unmet_dependencies=[]buf0.met_dependencies=[MemoryDep('arg0_1',c1,{c0:8390,c1:8}),MemoryDep('arg1_1',c0,{c0:67120})]buf0.users=[NodeUser(node=OUTPUT,can_inplace=False)]buf0.group.device=cpubuf0.group.iteration=((8390,8),())buf0.sizes=([8390,8],[])classbuf0_loop_body:var_ranges={z0:8390,z1:8}index0=8*z0+z1index1=z1defbody(self,ops):get_index=self.get_index('index0')load=ops.load('arg1_1',get_index)get_index_1=self.get_index('index1')load_1=ops.load('arg0_1',get_index_1)neg=ops.neg(load_1)maximum=ops.maximum(load,neg)get_index_2=self.get_index('index0')store=ops.store('buf0',get_index_2,maximum,None)returnstore

According to the traceback logging, the compilation error is caused by the data type inconsistency ofmax_propagate_nan’s inputs.By checking the C++ kernel, we know thattmp2 is no longerlong after doing- astmp0 islong.We can easily match- andmax_propagate_nan in C++ kernel withops.neg andops.maximum in IR node respectively.

Now we successfully find that the root cause is the implementation ofops.neg incpp codegen, which silently changes the data type when doingneg.

Accuracy debugging#

Otherwise, if the model runs with other errors or accuracy problem, you can use the PyTorch debugging tool calledMinifier.

The core idea ofMinifier is to keep removing the nodes and inputs of graph until finding the minimal graph with problem.It helps to automatically generate a minified problematic graph through 4 strategies: truncating suffix, delta debugging, eliminating dead code and removing unused inputs.

We will now show the debugging process for the accuracy problem with the help ofMinifer.The accuracy problem refers to the case where the outputs of backends eager and inductor are different.

For instance, we modify the example like this:

fromtorch._dynamo.utilsimportsamedeffoo2(x1,x2):a=torch.neg(x1)b=torch.maximum(x2,a)y=torch.cat([b],dim=0)returnyx1=torch.randn((1,8),dtype=torch.float32)x2=torch.randn((8390,8),dtype=torch.float32)expected_result=foo2(x1,x2)compiled_foo2=torch.compile(foo2)actual_result=compiled_foo2(x1,x2)assertsame(expected_result,actual_result)==True

And also modify theneg function:

defneg3(x):returnf"decltype({x})(2 *{x})"

An accuracy problem would be raised as follows:

torch._dynamo.utils:[ERROR]Accuracyfailed:allclosenotwithintol=0.0001Traceback(mostrecentcalllast):File"test_script.py",line18,in<module>assertsame(expected_result,actual_result)==TrueAssertionError

To debug an accuracy problem with Minifier, two environment variables are needed:

TORCHDYNAMO_REPRO_AFTER="aot"TORCHDYNAMO_REPRO_LEVEL=4pythonxx.py

Which gives us logging information that demonstrates the steps of minifying:

Startedoffwith6nodesTryinggranularity2Strategy:Truncatesuffix(G:2)(6nodes,2inputs)SUCCESS:Wentfrom6to4nodesTryinggranularity4Strategy:Removeunusedinputs(G:4)(4nodes,2inputs)SUCCESS:Wentfrom4to3nodes

After running, we get the final minified graph with the target nodeneg:

defforward2(self,arg0_1):neg=torch.ops.aten.neg.default(arg0_1);arg0_1=Nonereturn(neg,)

For more usage details about Minifier, please refer toTroubleshooting.

Performance profiling#

Within this section, we will demonstrate the process of conducting performance analysis for a model that has been compiled using the Inductor CPU backend.In the example below, we benchmark a Hugging Face Transformer modelMobileBertForQuestionAnswering with both the eager mode and the Inductor graph mode.The execution time and the speedup ratio of Inductor are printed after the benchmark.We use Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz and run benchmark on the first socket to demonstrate the optimization within this section.We set following environment variable as a best practice to benchmark on Intel(R) CPU.

exportKMP_BLOCKTIME=1exportKMP_SETTINGS=1exportKMP_AFFINITY=granularity=fine,compact,1,0exportLD_PRELOAD=${CONDA_PREFIX:-"$(dirname$(whichconda))/../"}/lib/libiomp5.so:${CONDA_PREFIX:-"$(dirname$(whichconda))/../"}/lib/libjemalloc.soexportMALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:-1"numactl-C0-31-m0pythonbench.py

# bench.pyfromtransformersimportMobileBertForQuestionAnswering# Initialize an eager modelmodel=MobileBertForQuestionAnswering.from_pretrained("csarron/mobilebert-uncased-squad-v2")seq_length=128bs=128vocab_size=model.config.vocab_sizeinput=torch.randint(0,vocab_size,(bs,seq_length),dtype=torch.int64)input_dict={"input_ids":input}# Initialize the inductor modelcompiled_model=torch.compile(model)withtorch.no_grad():compiled_model(**input_dict)NUM_ITERS=50importtimeitwithtorch.no_grad():# warmupfor_inrange(10):model(**input_dict)eager_t=timeit.timeit("model(**input_dict)",number=NUM_ITERS,globals=globals())withtorch.no_grad():# warmupfor_inrange(10):compiled_model(**input_dict)inductor_t=timeit.timeit("compiled_model(**input_dict)",number=NUM_ITERS,globals=globals())# print(f"eager use: {eager_t * 1000 / NUM_ITERS} ms/iter")# print(f"inductor use: {inductor_t * 1000 / NUM_ITERS} ms/iter")# print(f"speed up ratio: {eager_t / inductor_t}")

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Output:

eageruse:802.1023553796113ms/iterinductoruse:339.95180135127157ms/iterspeedupratio:2.359459053287382

In our own testing, we find the Inductor CPU backend speed up the model by around 2.355x.

Next, let’s dive deep into the performance at the operation level to understand where the speed-up comes from.Pytorch Profiler is a good tool to help us.Inductor CPU backend has the support to report the time of the fusion kernels to the profiler with theenable_kernel_profile configuration option:

fromtorch._inductorimportconfigconfig.cpp.enable_kernel_profile=True

Following the steps inPytorch ProfilerWe are able to get the profiling table and trace files.

# bench.pyfromtorch.profilerimportprofile,schedule,ProfilerActivityRESULT_DIR="./prof_trace"my_schedule=schedule(skip_first=10,wait=5,warmup=5,active=1,repeat=5)deftrace_handler(p):output=p.key_averages().table(sort_by="self_cpu_time_total",row_limit=20)# print(output)p.export_chrome_trace(f"{RESULT_DIR}/{p.step_num}.json")for_inrange(10):model(**input_dict)# compiled_model(**input_dict) to get inductor model profilingtotal=0withprofile(activities=[ProfilerActivity.CPU],schedule=my_schedule,on_trace_ready=trace_handler)asp:for_inrange(50):model(**input_dict)# compiled_model(**input_dict) to get inductor model profilingp.step()

/usr/local/lib/python3.10/dist-packages/torch/profiler/profiler.py:217: UserWarning:Warning: Profiler clears events at the end of each cycle.Only events from the current cycle will be reported.To keep events across cycles, set acc_events=True.

We get the following performance profiling table for the eager-mode model (omitting some columns):

-------------------------------------------------------------NameCPUtotal%CPUtotal# of Calls-------------------------------------------------------------aten::addmm45.73%370.814ms362aten::add19.89%161.276ms363aten::copy_14.97%121.416ms488aten::mul9.02%73.154ms194aten::clamp_min8.81%71.444ms96aten::bmm5.46%44.258ms48ProfilerStep*100.00%810.920ms1aten::div2.89%23.447ms24aten::_softmax1.00%8.087ms24aten::linear46.48%376.888ms362aten::clone2.77%22.430ms98aten::t0.31%2.502ms362aten::view0.14%1.161ms850aten::transpose0.17%1.377ms386aten::index_select0.12%952.000us3aten::expand0.12%986.000us458aten::matmul8.31%67.420ms48aten::cat0.09%703.000us1aten::as_strided0.08%656.000us963aten::relu8.86%71.864ms96-------------------------------------------------------------SelfCPUtimetotal:810.920ms

Similarly, we also get the table for the compiled model with Inductor (omitting some columns):

-----------------------------------------------------------------------------------NameCPUtotal%CPUtotal# of Calls-----------------------------------------------------------------------------------mkl::_mkl_linear68.79%231.573ms362aten::bmm8.02%26.992ms48ProfilerStep*100.00%336.642ms1graph_0_cpp_fused_constant_pad_nd_embedding_00.27%915.000us1aten::empty0.27%911.000us362graph_0_cpp_fused__mkl_linear_add_mul_relu_1510.27%901.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_2260.27%899.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_3610.27%898.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_1210.27%895.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_310.27%893.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_760.26%892.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_2560.26%892.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_3460.26%892.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_2410.26%891.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_3160.26%891.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_910.26%890.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_1060.26%890.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_2110.26%890.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_610.26%889.000us1graph_0_cpp_fused__mkl_linear_add_mul_relu_2860.26%889.000us1-----------------------------------------------------------------------------------SelfCPUtimetotal:336.642ms

From the profiling table of the eager model, we can see the most time consumption ops are [aten::addmm,aten::add,aten::copy_,aten::mul,aten::clamp_min,aten::bmm].Comparing with the inductor model profiling table, we notice anmkl::_mkl_linear entry and multiple fused kernels in the formgraph_0_cpp_fused_*. They are the majoroptimizations that the inductor model is doing. Let us discuss them separately.

(1) Regardingmkl::_mkl_linear: You may notice the number of calls to this kernel is 362, which is exactly the same asaten::linear in the eager model profiling table.The CPU total ofaten::linear is 376.888ms, while it is 231.573ms formkl::_mkl_linear. This suggests a ~1.63x for the “linear” part.The speedup mainly comes frompacking the weight tensor to block memory formatand invokingcblas_sgemm_compute within the Inductor CPU backendto have a better cache behavior during GEMM computation.

(2) Regarding other memory-intensive ops: The end-to-end latency for the eager/inductor model is 802/339ms in our testing. So we can roughly infer that the speed up for the other memory-intensive ops is around 3.94x.Let’s read the generated code to understand how the inductor achieves this impressive optimization. You can find the generated code bysearchingcpp_fused__mkl_linear_add_mul_relu_151 inoutput_code.py

cpp_fused__mkl_linear_add_mul_relu_151=async_compile.cpp('''#include <ATen/record_function.h>#include "/tmp/torchinductor_root/lr/clrlgu27q4ggd472umdzwsu6qcpqxcuusjxqvx2hwitjbujiiz7z.h"extern "C" void kernel(float* in_out_ptr0,                       const float* in_ptr0,                       const float* in_ptr1,                       const float* in_ptr2,                       const float* in_ptr3){    RECORD_FUNCTION("graph_0_cpp_fused__mkl_linear_add_mul_relu_151", c10::ArrayRef<c10::IValue>({}));    #pragma omp parallel num_threads(32)    {        {            #pragma omp for            for(long i0=static_cast<long>(0L); i0<static_cast<long>(16384L); i0+=static_cast<long>(1L))            {                for(long i1=static_cast<long>(0L); i1<static_cast<long>(512L); i1+=static_cast<long>(8L))                {                    auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + static_cast<long>(i1 + (512L*i0)));                    auto tmp1 = at::vec::Vectorized<float>::loadu(in_ptr1 + static_cast<long>(i1));                    auto tmp3 = at::vec::Vectorized<float>::loadu(in_out_ptr0 + static_cast<long>(i1 + (512L*i0)));                    auto tmp5 = at::vec::Vectorized<float>::loadu(in_ptr2 + static_cast<long>(i1));                    auto tmp7 = at::vec::Vectorized<float>::loadu(in_ptr3 + static_cast<long>(i1));                    auto tmp2 = tmp0 + tmp1;                    auto tmp4 = tmp2 + tmp3;                    auto tmp6 = tmp4 * tmp5;                    auto tmp8 = tmp6 + tmp7;                    tmp8.store(in_out_ptr0 + static_cast<long>(i1 + (512L*i0)));                }            }        }    }}''')

From the generated code above, we can see this kernel has done a typicalLoop Fusion on[add,add,mul,add].This is a memory-bound bottle neck preventing good performance. To get a more intuitive feeling about this optimization,we can infer the sizes and stride of the inputs and further benchmark this[add,add,mul,add] pattern.

# bench.pydeffunc(arg_0,arg_1,arg_2,arg_3,arg_4):add_0=arg_0+arg_1add_1=add_0+arg_2mul_1=add_1*arg_3add_2=mul_1+arg_4arg_2=add_2returnarg_2arg_0=torch.rand(16384,512)arg_1=torch.rand(1,512)arg_2=torch.zeros(16384,512)arg_3=torch.rand(1,512)arg_4=torch.rand(1,512)input=(arg_0,arg_1,arg_2,arg_3,arg_4)inductor_func=torch.compile(func)withtorch.no_grad():inductor_func(*input)importtimeitNUM_ITERS=100withtorch.no_grad():# warmupfor_inrange(10):func(*input)eager_t=timeit.timeit("func(*input)",number=NUM_ITERS,globals=globals())withtorch.no_grad():# warmupfor_inrange(10):inductor_func(*input)inductor_t=timeit.timeit("inductor_func(*input)",number=NUM_ITERS,globals=globals())# print(f"eager use: {eager_t * 1000 / NUM_ITERS} ms/iter")# print(f"inductor use: {inductor_t * 1000 / NUM_ITERS} ms/iter")# print(f"speed up ratio: {eager_t / inductor_t}")

Output:

eageruse:5.780875144992024ms/iterinductoruse:0.9588955780491233ms/iterspeedupratio:6.0286805751604735

This is just an example. The profiling table shows all element-wise op are fused within the inductor automatically in this model. You can read more kernels inoutput_code.py

Conclusion#

The document gives an in-depth tutorial for the Inductor CPU backend.

With motivating examples, we walk through the process of debugging and profiling.The main idea is to narrow down the problem.

We demonstrate step by step the way to delve deeper the issue and find the root cause of failures, with the help of debugging logging and the tool Minifier.Firstly determine which component the failure occurs in and then try to generate the smallest snippet of code that can reproduce the failure.

When the performance with Inductor is better than that of eager mode, we provide a solid analytical method for performance profiling.We show how to find the time-consuming hotspot with PyTorch Profiler and figure out the operator-level or kernel-level reason to explain the phenomenon.

Total running time of the script: (10 minutes 28.969 seconds)

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