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Test (#14)
Summary:
Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/14
This DIFF triggered a concurrent bug in the unit test.
It is weird that there are no errors for "SpMDMTest", while errors are reported for "NoRequantizeTest".
Update 1:
There might be problems with "memCopy" function. Then I change "Cint32_buffer.data()" to "Cint32_fb.data()" (see my inline comment) so that the accumulation buffer and the output buffer are the same. It appears that we can output the correct result.
I have a discussion with Daya. Now I understand the reason for the failure of this unit test
- For the purpose of this unit test, we should just use the same buffer "Cint32_fb.data()" for the accumulation and output. Not sure why this issue is not found in the original code.
- If the thread number is not 1, and we we use different buffers: "Cint32_buffer" for the accumulation buffer and "Cint32_fb" for the output buffer, then the pointers of "Cint32_buffer.data()" is actually shared by different threads. When doing the accumulation inside "ExecuteKernelU8S8.cc", different threads will just write to the same memory location: Check the code below
int32_t* C_buffer_row_start = C_buffer_ +
((C_buffer_ == reinterpret_cast<int32_t*>(matC_)) ? row_start_A * ldc_
: 0);
- If the thread number is not 1, and we use the same buffers: "Cint32_fb.data()" for the accumulation and output. According to the above code, different threads will write to different memory locations.
Update 2:
I add a new test case "{1024, 512, 258}" in Acc16 and Acc32 unit tests. "PackedRequantizeAcc16Test" runs well, but "PackedRequantizeTest" is broken.
Update 3:
I change the above code snippet to
int32_t* C_buffer_row_start = C_buffer_ + row_start_A * ldc_;
Finally we get both Acc16 and Acc32 tests passed. Now different threads will always write to different memory locations.
Update 4:
Jongsoo comments that reusing the first row block of C_buffer_ is mostly to optimize for cache not for memory allocation size (this was making a big difference in xray ocr perf. don't remember exact number). A right thing to do is to have each thread to use different portion of C_buffer_.
So I optimize the above code snippet to
// If the accumulation buffer C_buffer_ is the same as matC_ (inplace output
// processing), then each thread use the different parts of output buffer
// matC_;
// Otherwise, each thread uses different portions of the accumulation
// buffer C_buffer_. Note that each thread can use at most MC * n portion of
// C_buffer_. If the number of threads is 1, the only thread (thread 0) will
// always reuse the first rowblock of C_buffer_.
int32_t* C_buffer_row_start = C_buffer_ +
((C_buffer_ == reinterpret_cast<int32_t*>(matC_)) ? row_start_A * ldc_
: std::min(thread_id_ * mbSize_ * ldc_, row_start_A * ldc_));
Note that `thread_id` and `num_threads` is passed as the arguments into `ExecuteKernel`.
Update 5:
Rebase, Also add the parts of D12937408 to remove the dependency.
Reviewed By: jspark1105
Differential Revision: D13001149
fbshipit-source-id: b16c20863dc467de6faaefcaf1134cf1036f8a65
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