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[examples] Add initial LeNet example.
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| log.mlir | ||
| log.ll | ||
| log.s |
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| module { | ||
| func.func @forward(%arg0: tensor<61706xf32>, %arg1: tensor<1x1x28x28xf32>) -> tensor<1x10xf32> { | ||
| %extracted_slice = tensor.extract_slice %arg0[0] [150] [1] : tensor<61706xf32> to tensor<150xf32> | ||
| %expanded = tensor.expand_shape %extracted_slice [[0, 1, 2, 3]] : tensor<150xf32> into tensor<6x1x5x5xf32> | ||
| %extracted_slice_0 = tensor.extract_slice %arg0[150] [6] [1] : tensor<61706xf32> to tensor<6xf32> | ||
| %extracted_slice_1 = tensor.extract_slice %arg0[156] [2400] [1] : tensor<61706xf32> to tensor<2400xf32> | ||
| %expanded_2 = tensor.expand_shape %extracted_slice_1 [[0, 1, 2, 3]] : tensor<2400xf32> into tensor<16x6x5x5xf32> | ||
| %extracted_slice_3 = tensor.extract_slice %arg0[2556] [16] [1] : tensor<61706xf32> to tensor<16xf32> | ||
| %extracted_slice_4 = tensor.extract_slice %arg0[2572] [48000] [1] : tensor<61706xf32> to tensor<48000xf32> | ||
| %expanded_5 = tensor.expand_shape %extracted_slice_4 [[0, 1, 2, 3]] : tensor<48000xf32> into tensor<120x16x5x5xf32> | ||
| %extracted_slice_6 = tensor.extract_slice %arg0[50572] [120] [1] : tensor<61706xf32> to tensor<120xf32> | ||
| %extracted_slice_7 = tensor.extract_slice %arg0[50692] [10080] [1] : tensor<61706xf32> to tensor<10080xf32> | ||
| %expanded_8 = tensor.expand_shape %extracted_slice_7 [[0, 1]] : tensor<10080xf32> into tensor<84x120xf32> | ||
| %extracted_slice_9 = tensor.extract_slice %arg0[60772] [84] [1] : tensor<61706xf32> to tensor<84xf32> | ||
| %extracted_slice_10 = tensor.extract_slice %arg0[60856] [840] [1] : tensor<61706xf32> to tensor<840xf32> | ||
| %expanded_11 = tensor.expand_shape %extracted_slice_10 [[0, 1]] : tensor<840xf32> into tensor<10x84xf32> | ||
| %extracted_slice_12 = tensor.extract_slice %arg0[61696] [10] [1] : tensor<61706xf32> to tensor<10xf32> | ||
| %0 = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %1 = tosa.transpose %arg1, %0 : (tensor<1x1x28x28xf32>, tensor<4xi32>) -> tensor<1x28x28x1xf32> | ||
| %2 = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %3 = tosa.transpose %expanded, %2 : (tensor<6x1x5x5xf32>, tensor<4xi32>) -> tensor<6x5x5x1xf32> | ||
| %4 = tosa.conv2d %1, %3, %extracted_slice_0 {dilation = array<i64: 1, 1>, pad = array<i64: 2, 2, 2, 2>, stride = array<i64: 1, 1>} : (tensor<1x28x28x1xf32>, tensor<6x5x5x1xf32>, tensor<6xf32>) -> tensor<1x28x28x6xf32> | ||
| %5 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %6 = tosa.transpose %4, %5 : (tensor<1x28x28x6xf32>, tensor<4xi32>) -> tensor<1x6x28x28xf32> | ||
| %7 = "tosa.const"() <{value = dense<0.000000e+00> : tensor<1x6x28x28xf32>}> : () -> tensor<1x6x28x28xf32> | ||
| %8 = tosa.maximum %6, %7 : (tensor<1x6x28x28xf32>, tensor<1x6x28x28xf32>) -> tensor<1x6x28x28xf32> | ||
| %9 = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %10 = tosa.transpose %8, %9 : (tensor<1x6x28x28xf32>, tensor<4xi32>) -> tensor<1x28x28x6xf32> | ||
| %11 = tosa.max_pool2d %10 {kernel = array<i64: 2, 2>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>} : (tensor<1x28x28x6xf32>) -> tensor<1x14x14x6xf32> | ||
| %12 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %13 = tosa.transpose %11, %12 : (tensor<1x14x14x6xf32>, tensor<4xi32>) -> tensor<1x6x14x14xf32> | ||
| %14 = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %15 = tosa.transpose %13, %14 : (tensor<1x6x14x14xf32>, tensor<4xi32>) -> tensor<1x14x14x6xf32> | ||
| %16 = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %17 = tosa.transpose %expanded_2, %16 : (tensor<16x6x5x5xf32>, tensor<4xi32>) -> tensor<16x5x5x6xf32> | ||
| %18 = tosa.conv2d %15, %17, %extracted_slice_3 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x14x14x6xf32>, tensor<16x5x5x6xf32>, tensor<16xf32>) -> tensor<1x10x10x16xf32> | ||
| %19 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %20 = tosa.transpose %18, %19 : (tensor<1x10x10x16xf32>, tensor<4xi32>) -> tensor<1x16x10x10xf32> | ||
| %21 = "tosa.const"() <{value = dense<0.000000e+00> : tensor<1x16x10x10xf32>}> : () -> tensor<1x16x10x10xf32> | ||
| %22 = tosa.maximum %20, %21 : (tensor<1x16x10x10xf32>, tensor<1x16x10x10xf32>) -> tensor<1x16x10x10xf32> | ||
| %23 = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %24 = tosa.transpose %22, %23 : (tensor<1x16x10x10xf32>, tensor<4xi32>) -> tensor<1x10x10x16xf32> | ||
| %25 = tosa.max_pool2d %24 {kernel = array<i64: 2, 2>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 2>} : (tensor<1x10x10x16xf32>) -> tensor<1x5x5x16xf32> | ||
| %26 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %27 = tosa.transpose %25, %26 : (tensor<1x5x5x16xf32>, tensor<4xi32>) -> tensor<1x16x5x5xf32> | ||
| %28 = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %29 = tosa.transpose %27, %28 : (tensor<1x16x5x5xf32>, tensor<4xi32>) -> tensor<1x5x5x16xf32> | ||
| %30 = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %31 = tosa.transpose %expanded_5, %30 : (tensor<120x16x5x5xf32>, tensor<4xi32>) -> tensor<120x5x5x16xf32> | ||
| %32 = tosa.conv2d %29, %31, %extracted_slice_6 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x5x5x16xf32>, tensor<120x5x5x16xf32>, tensor<120xf32>) -> tensor<1x1x1x120xf32> | ||
| %33 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32> | ||
| %34 = tosa.transpose %32, %33 : (tensor<1x1x1x120xf32>, tensor<4xi32>) -> tensor<1x120x1x1xf32> | ||
| %35 = tosa.reshape %34 {new_shape = array<i64: 1, 120>} : (tensor<1x120x1x1xf32>) -> tensor<1x120xf32> | ||
| %36 = "tosa.const"() <{value = dense<[1, 0]> : tensor<2xi32>}> : () -> tensor<2xi32> | ||
| %37 = tosa.transpose %expanded_8, %36 : (tensor<84x120xf32>, tensor<2xi32>) -> tensor<120x84xf32> | ||
| %38 = tosa.reshape %35 {new_shape = array<i64: 1, 1, 120>} : (tensor<1x120xf32>) -> tensor<1x1x120xf32> | ||
| %39 = tosa.reshape %37 {new_shape = array<i64: 1, 120, 84>} : (tensor<120x84xf32>) -> tensor<1x120x84xf32> | ||
| %40 = tosa.matmul %38, %39 : (tensor<1x1x120xf32>, tensor<1x120x84xf32>) -> tensor<1x1x84xf32> | ||
| %41 = tosa.reshape %40 {new_shape = array<i64: 1, 84>} : (tensor<1x1x84xf32>) -> tensor<1x84xf32> | ||
| %42 = tosa.reshape %extracted_slice_9 {new_shape = array<i64: 1, 84>} : (tensor<84xf32>) -> tensor<1x84xf32> | ||
| %43 = tosa.add %42, %41 : (tensor<1x84xf32>, tensor<1x84xf32>) -> tensor<1x84xf32> | ||
| %44 = "tosa.const"() <{value = dense<[1, 0]> : tensor<2xi32>}> : () -> tensor<2xi32> | ||
| %45 = tosa.transpose %expanded_11, %44 : (tensor<10x84xf32>, tensor<2xi32>) -> tensor<84x10xf32> | ||
| %46 = tosa.reshape %43 {new_shape = array<i64: 1, 1, 84>} : (tensor<1x84xf32>) -> tensor<1x1x84xf32> | ||
| %47 = tosa.reshape %45 {new_shape = array<i64: 1, 84, 10>} : (tensor<84x10xf32>) -> tensor<1x84x10xf32> | ||
| %48 = tosa.matmul %46, %47 : (tensor<1x1x84xf32>, tensor<1x84x10xf32>) -> tensor<1x1x10xf32> | ||
| %49 = tosa.reshape %48 {new_shape = array<i64: 1, 10>} : (tensor<1x1x10xf32>) -> tensor<1x10xf32> | ||
| %50 = tosa.reshape %extracted_slice_12 {new_shape = array<i64: 1, 10>} : (tensor<10xf32>) -> tensor<1x10xf32> | ||
| %51 = tosa.add %50, %49 : (tensor<1x10xf32>, tensor<1x10xf32>) -> tensor<1x10xf32> | ||
| return %51 : tensor<1x10xf32> | ||
| } | ||
| } |
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| #!/bin/bash | ||
| BUDDY_OPT := ../../build/bin/buddy-opt | ||
| MLIR_OPT := ../../llvm/build/bin/mlir-opt | ||
| MLIR_TRANSLATE := ../../llvm/build/bin/mlir-translate | ||
| MLIR_CPU_RUNNER := ../../llvm/build/bin/mlir-cpu-runner | ||
| LLC := ../../llvm/build/bin/llc | ||
| OPT_FLAG := -O0 | ||
|
|
||
| ifeq ($(shell uname),Linux) | ||
| MLIR_RUNNER_UTILS := ../../llvm/build/lib/libmlir_runner_utils.so | ||
| MLIR_C_RUNNER_UTILS := ../../llvm/build/lib/libmlir_c_runner_utils.so | ||
| MLIR_ASYNC_RUNTIME := ../../llvm/build/lib/libmlir_async_runtime.so | ||
| MTRIPLE := x86_64-unknown-linux-gnu | ||
| else ifeq ($(shell uname),Darwin) | ||
| MLIR_RUNNER_UTILS := ../../llvm/build/lib/libmlir_runner_utils.dylib | ||
| MLIR_C_RUNNER_UTILS := ../../llvm/build/lib/libmlir_c_runner_utils.dylib | ||
| MLIR_ASYNC_RUNTIME := ./../llvm/build/lib/libmlir_async_runtime.dylib | ||
| MTRIPLE := x86_64-apple-darwin | ||
| endif | ||
|
|
||
| buddy-lenet-lower: | ||
| @${MLIR_OPT} ./lenet.mlir \ | ||
| -pass-pipeline "builtin.module(func.func(tosa-to-linalg-named, tosa-to-linalg, tosa-to-tensor, tosa-to-arith))" | \ | ||
| ${MLIR_OPT} \ | ||
| -eliminate-empty-tensors \ | ||
| -convert-tensor-to-linalg \ | ||
| -linalg-bufferize \ | ||
| -convert-linalg-to-affine-loops \ | ||
| -lower-affine \ | ||
| -func-bufferize \ | ||
| -arith-bufferize \ | ||
| -tensor-bufferize \ | ||
| -buffer-deallocation \ | ||
| -finalizing-bufferize \ | ||
| -convert-vector-to-scf \ | ||
| -expand-strided-metadata \ | ||
| -convert-vector-to-llvm \ | ||
| -convert-arith-to-llvm \ | ||
| -finalize-memref-to-llvm \ | ||
| -convert-scf-to-cf \ | ||
| -llvm-request-c-wrappers \ | ||
| -convert-arith-to-llvm \ | ||
| -convert-func-to-llvm \ | ||
| -reconcile-unrealized-casts \ | ||
| -o ./log.mlir | ||
|
|
||
| buddy-lenet-translate: | ||
| @${MLIR_OPT} ./lenet.mlir \ | ||
| -pass-pipeline "builtin.module(func.func(tosa-to-linalg-named, tosa-to-linalg, tosa-to-tensor, tosa-to-arith))" | \ | ||
| ${MLIR_OPT} \ | ||
| -eliminate-empty-tensors \ | ||
| -convert-tensor-to-linalg \ | ||
| -linalg-bufferize \ | ||
| -convert-linalg-to-affine-loops \ | ||
| -lower-affine \ | ||
| -func-bufferize \ | ||
| -arith-bufferize \ | ||
| -tensor-bufferize \ | ||
| -buffer-deallocation \ | ||
| -finalizing-bufferize \ | ||
| -convert-vector-to-scf \ | ||
| -expand-strided-metadata \ | ||
| -convert-vector-to-llvm \ | ||
| -convert-arith-to-llvm \ | ||
| -finalize-memref-to-llvm \ | ||
| -convert-scf-to-cf \ | ||
| -llvm-request-c-wrappers \ | ||
| -convert-arith-to-llvm \ | ||
| -convert-func-to-llvm \ | ||
| -reconcile-unrealized-casts | \ | ||
| ${MLIR_TRANSLATE} -mlir-to-llvmir -o log.ll |
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