Closed
Description
Please try to reproduce the issue on PR #161
This issue may share the same root cause with #278, but not the same phenomena
reproduce command
python -m benchgc --verbose 1 --driver mlir --case /path/to/mlir
python: /home/jovyan/graph-compiler/lib/gc/ExecutionEngine/CPURuntime/MemoryPool.cpp:213: void (anonymous namespace)::FILOMemoryPool::dealloc(void *): Assertion `chunk->canary == MemoryChunk::magicCheckNum && "Corrupt stack detected"' failed.Please save the following module as an mlir file
module {
func.func @entry(%arg0: tensor<1x32x4096xbf16>, %arg1: tensor<4096x4096xbf16>, %arg2: tensor<1x32x4096xbf16>, %arg3: tensor<1xf32>, %arg4: tensor<4096xbf16>, %arg5: tensor<4096x11008xbf16>, %arg6: tensor<4096x11008xbf16>, %arg7: tensor<11008x4096xbf16>, %arg8: tensor<1xf32>, %arg9: tensor<4096xbf16>) -> tensor<1x32x4096xbf16> attributes {llvm.emit_c_interface} {
%collapsed = tensor.collapse_shape %arg0 [[0, 1], [2]] : tensor<1x32x4096xbf16> into tensor<32x4096xbf16>
%cst = arith.constant 0.000000e+00 : bf16
%0 = tensor.empty() : tensor<32x4096xbf16>
%1 = linalg.fill ins(%cst : bf16) outs(%0 : tensor<32x4096xbf16>) -> tensor<32x4096xbf16>
%2 = linalg.matmul ins(%collapsed, %arg1 : tensor<32x4096xbf16>, tensor<4096x4096xbf16>) outs(%1 : tensor<32x4096xbf16>) -> tensor<32x4096xbf16>
%expanded = tensor.expand_shape %2 [[0, 1], [2]] output_shape [1, 32, 4096] : tensor<32x4096xbf16> into tensor<1x32x4096xbf16>
%3 = tensor.empty() : tensor<1x32x4096xbf16>
%4 = linalg.add ins(%arg2, %expanded : tensor<1x32x4096xbf16>, tensor<1x32x4096xbf16>) outs(%3 : tensor<1x32x4096xbf16>) -> tensor<1x32x4096xbf16>
%5 = tensor.empty() : tensor<1x32x4096xf32>
%6 = linalg.copy ins(%4 : tensor<1x32x4096xbf16>) outs(%5 : tensor<1x32x4096xf32>) -> tensor<1x32x4096xf32>
%cst_0 = arith.constant dense<2.000000e+00> : tensor<1x32x4096xf32>
%7 = tensor.empty() : tensor<1x32x4096xf32>
%8 = linalg.powf ins(%6, %cst_0 : tensor<1x32x4096xf32>, tensor<1x32x4096xf32>) outs(%7 : tensor<1x32x4096xf32>) -> tensor<1x32x4096xf32>
%cst_1 = arith.constant 0.000000e+00 : f32
%9 = tensor.empty() : tensor<1x32xf32>
%10 = linalg.fill ins(%cst_1 : f32) outs(%9 : tensor<1x32xf32>) -> tensor<1x32xf32>
%reduced = linalg.reduce ins(%8 : tensor<1x32x4096xf32>) outs(%10 : tensor<1x32xf32>) dimensions = [2]
(%in: f32, %init: f32) {
%26 = arith.addf %in, %init : f32
linalg.yield %26 : f32
}
%cst_2 = arith.constant dense<4.096000e+03> : tensor<1x32xf32>
%11 = tensor.empty() : tensor<1x32xf32>
%12 = linalg.div ins(%reduced, %cst_2 : tensor<1x32xf32>, tensor<1x32xf32>) outs(%11 : tensor<1x32xf32>) -> tensor<1x32xf32>
%expanded_3 = tensor.expand_shape %12 [[0], [1, 2]] output_shape [1, 32, 1] : tensor<1x32xf32> into tensor<1x32x1xf32>
%13 = tensor.empty() : tensor<1x32x1xf32>
%broadcasted = linalg.broadcast ins(%arg8 : tensor<1xf32>) outs(%13 : tensor<1x32x1xf32>) dimensions = [0, 1]
%14 = tensor.empty() : tensor<1x32x1xf32>
%15 = linalg.add ins(%expanded_3, %broadcasted : tensor<1x32x1xf32>, tensor<1x32x1xf32>) outs(%14 : tensor<1x32x1xf32>) -> tensor<1x32x1xf32>
%cst_4 = arith.constant dense<-5.000000e-01> : tensor<1x32x1xf32>
%16 = tensor.empty() : tensor<1x32x1xf32>
%17 = linalg.powf ins(%15, %cst_4 : tensor<1x32x1xf32>, tensor<1x32x1xf32>) outs(%16 : tensor<1x32x1xf32>) -> tensor<1x32x1xf32>
%collapsed_5 = tensor.collapse_shape %17 [[0], [1, 2]] : tensor<1x32x1xf32> into tensor<1x32xf32>
%18 = tensor.empty() : tensor<1x32x4096xf32>
%broadcasted_6 = linalg.broadcast ins(%collapsed_5 : tensor<1x32xf32>) outs(%18 : tensor<1x32x4096xf32>) dimensions = [2]
%19 = tensor.empty() : tensor<1x32x4096xf32>
%20 = linalg.mul ins(%6, %broadcasted_6 : tensor<1x32x4096xf32>, tensor<1x32x4096xf32>) outs(%19 : tensor<1x32x4096xf32>) -> tensor<1x32x4096xf32>
%21 = tensor.empty() : tensor<1x32x4096xbf16>
%22 = linalg.copy ins(%20 : tensor<1x32x4096xf32>) outs(%21 : tensor<1x32x4096xbf16>) -> tensor<1x32x4096xbf16>
%23 = tensor.empty() : tensor<1x32x4096xbf16>
%broadcasted_7 = linalg.broadcast ins(%arg4 : tensor<4096xbf16>) outs(%23 : tensor<1x32x4096xbf16>) dimensions = [0, 1]
%24 = tensor.empty() : tensor<1x32x4096xbf16>
%25 = linalg.mul ins(%broadcasted_7, %22 : tensor<1x32x4096xbf16>, tensor<1x32x4096xbf16>) outs(%24 : tensor<1x32x4096xbf16>) -> tensor<1x32x4096xbf16>
return %25 : tensor<1x32x4096xbf16>
}
}