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未验证 提交 c4abe26c 编写于 作者: J jjyaoao 提交者： GitHub
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[Test Mv] remove infrt tests (#52063)

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paddle/infrt/CMakeLists.txt
浏览文件 @ c4abe26c
...@@ -113,7 +113,6 @@ add_subdirectory(tensor) ...@@ -113,7 +113,6 @@ add_subdirectory(tensor)
add_subdirectory(support) add_subdirectory(support)
add_subdirectory(external_kernels) add_subdirectory(external_kernels)
add_subdirectory(paddle) add_subdirectory(paddle)
add_subdirectory(tests)
# MLIR td file generations # MLIR td file generations
set(infrt_mlir_incs basic_kernels_inc test_kernels_inc tensor_shape_inc set(infrt_mlir_incs basic_kernels_inc test_kernels_inc tensor_shape_inc
......
paddle/infrt/tests/.gitignore 已删除 100644 → 0
浏览文件 @ 66098bff
.DS_Store
.idea
*.log
tmp/
Output
paddle/infrt/tests/CMakeLists.txt 已删除 100644 → 0
浏览文件 @ 66098bff
cc_test_tiny(test_abs_model SRCS models/test_abs.cc DEPS infrt ${MLIR_IR_LIBS})
configure_file(lit.cfg.py.in
"${CMAKE_SOURCE_DIR}/paddle/infrt/tests/lit.cfg.py")
add_test(
NAME test_infrt_by_lit
COMMAND
sh -c
"lit -v ${CMAKE_SOURCE_DIR}/paddle/infrt/tests --filter-out \"disabled_*\""
DEPENDS infrtopt infrtexec)
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/dialect/tensor/tensor_map.mlir.in
${CMAKE_CURRENT_SOURCE_DIR}/dialect/tensor/tensor_map.mlir)
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/dialect/phi/linear_cpu.mlir.in
${CMAKE_CURRENT_SOURCE_DIR}/dialect/phi/linear_cpu.mlir)
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/dialect/phi/resnet50.mlir.in
${CMAKE_CURRENT_SOURCE_DIR}/dialect/phi/resnet50.mlir)
configure_file(
${CMAKE_CURRENT_SOURCE_DIR}/dialect/tensorrt/disabled_linear.mlir.in
${CMAKE_CURRENT_SOURCE_DIR}/dialect/tensorrt/disabled_linear.mlir)
paddle/infrt/tests/dialect/basic.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: @basic_f32
func @basic_f32() -> f32 {
%v0 = infrt.constant.f32 1.0
%v1 = infrt.constant.f32 2.0
%value = "infrt.add.f32"(%v0, %v1) : (f32, f32) -> f32
// CHECK-NEXT: 3
"infrt.print.f32"(%value) : (f32) -> ()
infrt.return %value : f32
}
/// ================================================================
/// @caller call the other function @callee
func @callee.add.f32(%x : f32, %y : f32, %y1 : f32) -> f32 {
%z = "infrt.add.f32"(%x, %y) : (f32, f32) -> f32
%z1 = "infrt.add.f32"(%z, %y1) : (f32, f32) -> f32
infrt.return %z1 : f32
}
// CHECK-LABEL: @caller.add.f32
func @caller.add.f32() -> f32 {
%x = infrt.constant.f32 1.0
%y = infrt.constant.f32 2.0
%y1 = infrt.constant.f32 3.0
%z = infrt.call @callee.add.f32(%x, %y, %y1) : (f32, f32, f32) -> f32
// CHECK-NEXT: 6
"infrt.print.f32"(%z) : (f32) -> ()
infrt.return %z : f32
}
/// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
paddle/infrt/tests/dialect/benchmark.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: @benchmark
func @benchmark() {
// CHECK-LABEL: BM:add.f32:Count: 3
// CHECK-LABEL: BM:add.f32:Duration(ns)
// CHECK-LABEL: BM:add.f32:Time Min(ns)
// CHECK-LABEL: BM:add.f32:Time 50%(ns)
// CHECK-LABEL: BM:add.f32:Time 95%(ns)
// CHECK-LABEL: BM:add.f32:Time 99%(ns)
// CHECK-LABEL: BM:add.f32:CPU Min(ns)
// CHECK-LABEL: BM:add.f32:CPU 50%(ns)
// CHECK-LABEL: BM:add.f32:CPU 95%(ns)
// CHECK-LABEL: BM:add.f32:CPU 99%(ns)
// CHECK-LABEL: BM:add.f32:CPU utilization(percent)
infrt.benchmark "add.f32"() duration_secs = 1, max_count = 3, num_warmup_runs = 3
{
%0 = infrt.constant.f32 1.0
%1 = infrt.constant.f32 2.0
%res = "infrt.add.f32"(%0, %1) : (f32, f32) -> f32
"infrt.print.f32"(%res) : (f32) -> ()
infrt.return %res : f32
}
infrt.return
}
paddle/infrt/tests/dialect/dense_tensor.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: dense_shape0
func @dense_shape0() {
%shape = ts.build_shape [1:i64, 57:i64]
%a = dt.create_uninit_tensor.f32 [12:i64, 23:i64] -> !infrt.dense_tensor<CPU, FP32, NCHW>
infrt.return
}
func @predict(%a: !infrt.dense_tensor<CPU, FP32, NCHW>, %b: !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) {
%a0 = dt.shallow_copy_tensor %a : !infrt.dense_tensor<CPU, FP32, NCHW> -> !infrt.dense_tensor<CPU, FP32, NCHW>
%b0 = dt.shallow_copy_tensor %b : !infrt.dense_tensor<CPU, FP32, NCHW> -> !infrt.dense_tensor<CPU, FP32, NCHW>
infrt.return %a0, %b0: !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>
}
func @main() {
%shape = ts.build_shape [1:i64, 57:i64]
%a = dt.create_uninit_tensor.f32 [12:i64, 23:i64] -> !infrt.dense_tensor<CPU, FP32, NCHW>
%b, %c = infrt.call @predict(%a, %a) : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
paddle/infrt/tests/dialect/disabled_rewrite_conv_bn.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// CHECK-LABEL: @main
func @main(%a:tensor<?x3x256x256xf32>) -> tensor<?xf32> {
%filter = "pd.constant"(){value = dense<1.000000e+00> : tensor<3x64x3x3xf32>} : () -> tensor<3x64x3x3xf32>
%bias = "pd.constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
%scale = "pd.constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
%bias2 = "pd.constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
%mean = "pd.constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
%var = "pd.constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
%c = "pd.conv2d"(%a, %filter, %bias) {} : (tensor<?x3x256x256xf32>, tensor<3x64x3x3xf32>, tensor<64xf32>) -> tensor<?x3x256x256xf32>
%d = "pd.batch_norm"(%c, %scale, %bias2, %mean, %var) {} : (tensor<?x3x256x256xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<?x3x256x256xf32>
infrt.return %d:tensor<?x3x256x256xf32>
}
paddle/infrt/tests/dialect/disabled_tensor_map.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// CHECK-LABEL: @predict
func @predict(%input:!infrt.dense_tensor<CPU, FP32, NCHW>, %map: !infrt.dense_tensor_map) -> (!infrt.dense_tensor<CPU, FP32, NCHW>) {
%w = dt.get_param(%map, "create_parameter_0.w_0") -> !infrt.dense_tensor<CPU, FP32, NCHW>
%bias = dt.get_param(%map, "create_parameter_1.w_0") -> !infrt.dense_tensor<CPU, FP32, NCHW>
%out = dt.create_uninit_tensor.f32 [3, 3] -> !infrt.dense_tensor<CPU, FP32, NCHW>
// fc
"external.matmul"(%input, %w, %out) {}: (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
"external.elementwise_add"(%out, %bias, %out) {axis = -1}: (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
"external.sigmoid"(%out, %out) {}: (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
//dt.print_tensor (%out : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return %out : !infrt.dense_tensor<CPU, FP32, NCHW>
}
// CHECK-LABEL: @main
func @main() {
%input = dt.create_uninit_tensor.f32 [3, 3] -> !infrt.dense_tensor<CPU, FP32, NCHW>
dt.fill_tensor_with_constant.f32 (%input : !infrt.dense_tensor<CPU, FP32, NCHW>) {value=1.0:f32}
// CHECK-LABEL: loading params
%map = dt.load_params() {path="/Infrt/build/paddle/paddle_1.8_fc_model"}
%out = infrt.call @predict(%input, %map): (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor_map) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
dt.print_tensor (%out : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
paddle/infrt/tests/dialect/pd/rewrite.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtopt --pd-op-fuse %s | FileCheck %s
// CHECK-LABEL: @main
func @main(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>, %arg2:tensor<?xf32>, %arg3:tensor<?xf32>, %arg4:tensor<?xf32>, %arg5:tensor<?xf32>, %arg6:tensor<?xf32>) -> tensor<?xf32> {
// CHECK: %0 = "pd.FC"(%arg0, %arg1, %arg4) {in_num_col_dims = 1 : i32} : (tensor<?xf32>, tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
%c = "pd.matmul_v2"(%arg0, %arg1) {transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
%d = "pd.elementwise_add"(%c, %arg4) {axis=1:si32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
%e = "pd.relu6"(%d) {} : (tensor<?xf32>) -> tensor<?xf32>
// CHECK: %2 = "pd.FC"(%1, %arg2, %arg5) {in_num_col_dims = 1 : i32} : (tensor<?xf32>, tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
%c1 = "pd.matmul_v2"(%e, %arg2) {transpose_x=false, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
%d1 = "pd.elementwise_add"(%c1, %arg5) {axis=1:si32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
%e1 = "pd.relu"(%d1) {} : (tensor<?xf32>) -> tensor<?xf32>
// CHECK: %4 = "pd.FC"(%3, %arg3, %arg6) {in_num_col_dims = 1 : i32} : (tensor<?xf32>, tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
%c2 = "pd.matmul_v2"(%e1, %arg3) {transpose_x=true, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
%d2 = "pd.elementwise_add"(%c2, %arg6) {axis=1:si32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
%e2 = "pd.relu"(%d2) {} : (tensor<?xf32>) -> tensor<?xf32>
infrt.return %e2:tensor<?xf32>
}
paddle/infrt/tests/dialect/phi/dense_tensor.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: @sign_any_float32_execute
func @sign_any_float32_execute() {
%ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
%t = "phi_dt.create_dense_tensor.cpu" (%ctx) {
precision=#infrt.precision<FP32>,
layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[1:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
"phi_dt.fill_dense_tensor.f32"(%t) {value=[3.8:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
%e = "phi_cpu.sign.float32.any"(%ctx, %t) : (!phi.context<CPU>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
// CHECK: dense_tensor: shape=shape[1], value=[1]
"phi_dt.print_tensor" (%e) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
infrt.return
}
paddle/infrt/tests/dialect/phi/disabled_phi_test.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s
module {
func @predict(%arg0: !infrt.dense_tensor<CPU, FP32, NCHW>,%filter: !infrt.dense_tensor<CPU, FP32, NCHW>, %arg1: !infrt.dense_tensor<CPU, FP32, NCHW>, %arg2: !infrt.dense_tensor<CPU, FP32, NCHW>, %arg3: !infrt.dense_tensor<CPU, FP32, NCHW>, %arg4: !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW> {
%2 = "pd.abs"(%arg0) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%3 = "pd.matmul_v2"(%arg0, %2) {trans_x = false, trans_y = false} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%4 = "pd.conv2d"(%3, %filter) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y, %MeanOut, %VarianceOut = "pd.batch_norm"(%4, %arg1, %arg2, %arg3, %arg4) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%out = "pd.relu"(%Y) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%5 = "pd.elementwise_add"(%out, %out) {axis = -1:si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%6 = "pd.pool2d"(%5) {adaptive = false, pooling_type = "avg", ceil_mode = false, data_format = "NCHW", exclusive = true, global_pooling = false, ksize = [3 : i32, 3 : i32], padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%7 = "pd.flatten_contiguous_range"(%6) {start_axis = 1 : si32, stop_axis = 3 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
infrt.return %7 : !infrt.dense_tensor<CPU, FP32, NCHW>
}
func @main() {
%ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
%t = "phi_dt.create_inited_dense_tensor.cpu.f32"(%ctx) {value=3.8:f32, layout=#infrt.layout<NCHW>, lod=[1], dims=[1, 3, 8, 8]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%filter = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=3.8:f32, layout=#infrt.layout<NCHW>, lod=[1], dims=[3, 3, 8, 8]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%bias = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=1.5:f32, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%mean = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=3.8:f32, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%scale = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=3.8:f32, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%var = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=3.8:f32, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%2 = infrt.call@predict(%t, %filter, %bias, %mean, %scale, %var) : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>,!infrt.dense_tensor<CPU, FP32, NCHW>,!infrt.dense_tensor<CPU, FP32, NCHW>,!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
phi_dt.print_tensor(%2 : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
}
paddle/infrt/tests/dialect/phi/kernels/disabled_resnet50_ops.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
module {
func @main() {
%ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
%0 = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value = 2.0 : f32, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[1, 3, 6, 6]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%1 = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value = 2.0 : f32, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[1, 3, 3, 3]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%2 = "pd.conv2d"(%0, %1) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [3 : i32, 3 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
// CHECK: dense_tensor: shape=shape[1, 1, 5, 5], value=[0,0,0,0,0,0,48,72,72,24,0,72,108,108,36,0,72,108,108,36,0,24,36,36,12]
phi_dt.print_tensor (%2 : !infrt.dense_tensor<CPU, FP32, NCHW>)
%3 = "pd.relu"(%2) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
// dense_tensor: shape=shape[1, 1, 5, 5], value=[0,0,0,0,0,0,48,72,72,24,0,72,108,108,36,0,72,108,108,36,0,24,36,36,12]
phi_dt.print_tensor (%3 : !infrt.dense_tensor<CPU, FP32, NCHW>)
%4 = "pd.pool2d"(%2) {adaptive = false, ceil_mode = false, data_format = "NCHW", exclusive = true, global_pooling = false, ksize = [2 : i32, 2 : i32], padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], pooling_type = "avg", strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
// CHECK: dense_tensor: shape=shape[1, 1, 3, 3], value=[0,0,0,0,75,60,0,60,48]
phi_dt.print_tensor (%4 : !infrt.dense_tensor<CPU, FP32, NCHW>)
%5 = "pd.flatten_contiguous_range"(%4) {start_axis = 1 : si32, stop_axis = 3 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
// CHECK: dense_tensor: shape=shape[1, 9], value=[0,0,0,0,75,60,0,60,48]
phi_dt.print_tensor (%5 : !infrt.dense_tensor<CPU, FP32, NCHW>)
%6 = "pd.elementwise_add"(%5, %5) {axis = 1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
// CHECK: dense_tensor: shape=shape[1, 9], value=[0,0,0,0,150,120,0,120,96]
phi_dt.print_tensor (%6 : !infrt.dense_tensor<CPU, FP32, NCHW>)
%7 = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value = 4.0 : f32, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[9, 3]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%8 = "pd.matmul_v2"(%5, %7) {trans_x = false, trans_y = false} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
// CHECK: dense_tensor: shape=shape[1, 3], value=[972,972,972]
phi_dt.print_tensor (%8 : !infrt.dense_tensor<CPU, FP32, NCHW>)
%scale = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=1.0:f32, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%bias = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=1.8:f32, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%mean = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=2.0:f32, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%var = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=0.0:f32, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%Y, %MeanOut, %VarianceOut = "pd.batch_norm"(%1, %scale, %bias, %mean, %var) {data_layout = "NCHW", epsilon = 0.01 : f32, momentum = 0.5 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
// CHECK: dense_tensor: shape=shape[1, 3, 3, 3], value=[1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8,1.8]
phi_dt.print_tensor (%Y : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
}
paddle/infrt/tests/dialect/phi/linear_cpu.mlir.in 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s
module {
func @main_graph(%arg0: !phi.dense_tensor_map, %arg1: !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW> {
%0 = phi_dt.tensor_map_get_tensor(%arg0) {name = "linear_0.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%1 = phi_dt.tensor_map_get_tensor(%arg0) {name = "linear_0.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%2 = "phi_dt.create_context.cpu"() : () -> !phi.context<CPU>
%5 = "phi_cpu.matmul.float32.any"(%2, %arg1, %0) {trans_x = false, trans_y = false} : (!phi.context<CPU>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%7 = "phi_cpu.add.float32.any"(%2, %5, %1): (!phi.context<CPU>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
infrt.return %7 : !infrt.dense_tensor<CPU, FP32, NCHW>
}
func @main() {
%ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
%1 = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[16:i64, 784:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%map = phi_dt.load_combined_params(){model_path="@CMAKE_BINARY_DIR@/linear/linear.pdmodel",params_path="@CMAKE_BINARY_DIR@/linear/linear.pdiparams"}
%2 = infrt.call@main_graph(%map, %1) : (!phi.dense_tensor_map, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
phi_dt.print_tensor (%2 : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
}
paddle/infrt/tests/dialect/phi/phi_pass.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtopt -phi-op-convert=valid-targets=CPU-FP32-NCHW -infrt-op-fuse %s
// CHECK-LABEL: @ops
func @ops(%a:!infrt.dense_tensor<CPU, FP32, NCHW>, %b:!infrt.dense_tensor<CPU, FP32, NCHW>) {
%g = "pd.elementwise_add"(%a, %b) {axis=1:si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%h = "pd.abs"(%g):(!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
infrt.return %h:!infrt.dense_tensor<CPU, FP32, NCHW>
}
// CHECK-LABEL: @op_execute
func @op_execute(%a:!infrt.dense_tensor<CPU, FP32, NCHW>, %b:!infrt.dense_tensor<CPU, FP32, NCHW>, %c:!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW> {
%g = "pd.elementwise_add"(%a, %b) {axis=1:si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%h = "pd.abs"(%g):(!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
infrt.return %h:!infrt.dense_tensor<CPU, FP32, NCHW>
}
paddle/infrt/tests/dialect/phi/resnet50.mlir.in 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s
module {
func @main_graph(%arg0: !phi.dense_tensor_map, %arg1: !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW> {
%0 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_37.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%1 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_47.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%2 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_6.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%3 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_13.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%4 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_5.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%5 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_40.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%6 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_6.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%7 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_27.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%8 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_11.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%9 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_40.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%10 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_38.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%11 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_2.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%12 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_21.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%13 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_15.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%14 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_8.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%15 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_29.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%16 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_35.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%17 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_26.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%18 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_50.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%19 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_31.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%20 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_22.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%21 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_27.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%22 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_28.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%23 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_46.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%24 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_37.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%25 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_18.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%26 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_38.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%27 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_39.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%28 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_43.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%29 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_3.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%30 = phi_dt.tensor_map_get_tensor(%arg0) {name = "linear_0.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%31 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_34.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%32 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_49.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%33 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_52.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%34 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_8.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%35 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_45.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%36 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_43.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%37 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_5.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%38 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_29.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%39 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_33.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%40 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_10.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%41 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_43.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%42 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_9.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%43 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_7.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%44 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_7.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%45 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_50.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%46 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_40.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%47 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_42.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%48 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_42.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%49 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_31.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%50 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_7.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%51 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_12.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%52 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_39.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%53 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_30.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%54 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_13.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%55 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_46.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%56 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_36.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%57 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_29.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%58 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_36.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%59 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_49.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%60 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_29.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%61 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_28.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%62 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_51.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%63 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_27.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%64 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_47.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%65 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_30.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%66 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_33.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%67 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_24.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%68 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_22.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%69 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_1.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%70 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_32.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%71 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_20.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%72 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_16.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%73 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_23.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%74 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_11.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%75 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_30.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%76 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_37.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%77 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_16.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%78 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_36.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%79 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_1.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%80 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_31.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%81 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_0.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%82 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_10.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%83 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_1.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%84 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_13.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%85 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_12.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%86 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_0.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%87 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_30.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%88 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_13.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%89 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_3.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%90 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_52.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%91 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_26.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%92 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_48.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%93 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_25.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%94 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_33.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%95 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_30.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%96 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_35.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%97 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_8.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%98 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_18.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%99 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_4.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%100 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_15.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%101 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_16.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%102 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_32.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%103 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_50.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%104 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_44.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%105 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_24.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%106 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_11.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%107 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_2.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%108 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_20.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%109 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_15.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%110 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_44.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%111 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_23.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%112 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_17.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%113 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_25.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%114 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_3.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%115 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_0.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%116 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_38.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%117 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_20.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%118 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_47.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%119 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_50.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%120 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_48.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%121 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_14.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%122 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_47.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%123 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_46.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%124 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_34.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%125 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_45.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%126 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_25.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%127 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_22.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%128 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_21.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%129 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_17.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%130 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_19.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%131 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_1.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%132 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_52.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%133 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_21.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%134 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_9.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%135 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_4.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%136 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_9.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%137 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_45.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%138 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_8.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%139 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_35.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%140 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_39.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%141 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_44.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%142 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_19.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%143 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_27.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%144 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_2.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%145 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_19.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%146 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_23.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%147 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_32.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%148 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_51.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%149 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_17.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%150 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_2.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%151 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_15.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%152 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_23.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%153 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_18.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%154 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_1.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%155 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_21.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%156 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_37.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%157 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_28.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%158 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_31.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%159 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_3.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%160 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_19.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%161 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_38.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%162 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_7.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%163 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_33.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%164 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_44.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%165 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_25.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%166 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_32.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%167 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_26.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%168 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_4.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%169 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_40.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%170 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_17.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%171 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_5.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%172 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_28.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%173 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_27.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%174 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_20.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%175 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_15.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%176 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_18.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%177 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_41.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%178 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_42.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%179 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_25.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%180 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_22.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%181 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_35.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%182 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_24.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%183 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_49.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%184 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_22.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%185 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_26.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%186 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_12.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%187 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_43.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%188 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_38.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%189 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_0.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%190 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_50.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%191 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_10.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%192 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_19.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%193 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_41.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%194 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_10.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%195 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_14.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%196 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_14.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%197 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_12.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%198 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_9.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%199 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_16.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%200 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_29.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%201 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_42.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%202 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_2.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%203 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_48.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%204 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_14.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%205 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_3.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%206 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_6.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%207 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_20.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%208 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_39.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%209 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_34.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%210 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_16.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%211 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_36.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%212 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_48.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%213 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_7.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%214 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_32.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%215 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_52.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%216 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_18.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%217 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_44.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%218 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_6.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%219 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_10.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%220 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_47.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%221 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_51.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%222 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_9.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%223 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_52.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%224 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_45.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%225 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_8.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%226 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_13.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%227 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_46.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%228 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_49.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%229 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_12.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%230 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_4.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%231 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_5.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%232 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_51.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%233 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_33.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%234 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_46.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%235 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_45.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%236 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_6.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%237 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_48.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%238 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_37.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%239 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_14.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%240 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_21.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%241 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_28.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%242 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_26.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%243 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_23.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%244 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_49.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%245 = phi_dt.tensor_map_get_tensor(%arg0) {name = "linear_0.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%246 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_51.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%247 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_41.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%248 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_35.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%249 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_42.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%250 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_43.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%251 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_24.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%252 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_31.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%253 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_41.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%254 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_11.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%255 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_41.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%256 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_34.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%257 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_4.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%258 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_40.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%259 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_0.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%260 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_36.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%261 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_5.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%262 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_11.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%263 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_17.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%264 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_39.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%265 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_34.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%266 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_24.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%267 = "pd.conv2d"(%arg1, %86) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [3 : i32, 3 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y, %MeanOut, %VarianceOut = "pd.batch_norm"(%267, %259, %189, %115, %81) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%268 = "pd.relu"(%Y) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%269 = "pd.pool2d"(%268) {adaptive = false, ceil_mode = false, data_format = "NCHW", exclusive = true, global_pooling = false, ksize = [3 : i32, 3 : i32], padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], pooling_type = "max", strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%270 = "pd.conv2d"(%269, %11) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_0, %MeanOut_1, %VarianceOut_2 = "pd.batch_norm"(%270, %150, %107, %144, %202) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%271 = "pd.relu"(%Y_0) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%272 = "pd.conv2d"(%271, %29) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_3, %MeanOut_4, %VarianceOut_5 = "pd.batch_norm"(%272, %205, %159, %89, %114) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%273 = "pd.relu"(%Y_3) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%274 = "pd.conv2d"(%273, %99) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_6, %MeanOut_7, %VarianceOut_8 = "pd.batch_norm"(%274, %168, %135, %257, %230) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%275 = "pd.conv2d"(%269, %154) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_9, %MeanOut_10, %VarianceOut_11 = "pd.batch_norm"(%275, %79, %131, %69, %83) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%276 = "pd.elementwise_add"(%Y_6, %Y_9) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%277 = "pd.relu"(%276) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%278 = "pd.conv2d"(%277, %231) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_12, %MeanOut_13, %VarianceOut_14 = "pd.batch_norm"(%278, %37, %261, %171, %4) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%279 = "pd.relu"(%Y_12) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%280 = "pd.conv2d"(%279, %2) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_15, %MeanOut_16, %VarianceOut_17 = "pd.batch_norm"(%280, %206, %218, %236, %6) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%281 = "pd.relu"(%Y_15) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%282 = "pd.conv2d"(%281, %44) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_18, %MeanOut_19, %VarianceOut_20 = "pd.batch_norm"(%282, %162, %50, %43, %213) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%283 = "pd.elementwise_add"(%Y_18, %277) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%284 = "pd.relu"(%283) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%285 = "pd.conv2d"(%284, %34) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_21, %MeanOut_22, %VarianceOut_23 = "pd.batch_norm"(%285, %97, %14, %225, %138) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%286 = "pd.relu"(%Y_21) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%287 = "pd.conv2d"(%286, %134) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_24, %MeanOut_25, %VarianceOut_26 = "pd.batch_norm"(%287, %222, %198, %42, %136) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%288 = "pd.relu"(%Y_24) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%289 = "pd.conv2d"(%288, %219) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_27, %MeanOut_28, %VarianceOut_29 = "pd.batch_norm"(%289, %40, %194, %191, %82) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%290 = "pd.elementwise_add"(%Y_27, %284) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%291 = "pd.relu"(%290) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%292 = "pd.conv2d"(%291, %197) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_30, %MeanOut_31, %VarianceOut_32 = "pd.batch_norm"(%292, %229, %85, %51, %186) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%293 = "pd.relu"(%Y_30) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%294 = "pd.conv2d"(%293, %84) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_33, %MeanOut_34, %VarianceOut_35 = "pd.batch_norm"(%294, %3, %54, %88, %226) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%295 = "pd.relu"(%Y_33) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%296 = "pd.conv2d"(%295, %239) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_36, %MeanOut_37, %VarianceOut_38 = "pd.batch_norm"(%296, %196, %121, %204, %195) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%297 = "pd.conv2d"(%291, %74) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_39, %MeanOut_40, %VarianceOut_41 = "pd.batch_norm"(%297, %254, %262, %8, %106) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%298 = "pd.elementwise_add"(%Y_36, %Y_39) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%299 = "pd.relu"(%298) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%300 = "pd.conv2d"(%299, %175) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_42, %MeanOut_43, %VarianceOut_44 = "pd.batch_norm"(%300, %151, %100, %13, %109) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%301 = "pd.relu"(%Y_42) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%302 = "pd.conv2d"(%301, %199) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_45, %MeanOut_46, %VarianceOut_47 = "pd.batch_norm"(%302, %72, %77, %210, %101) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%303 = "pd.relu"(%Y_45) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%304 = "pd.conv2d"(%303, %263) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_48, %MeanOut_49, %VarianceOut_50 = "pd.batch_norm"(%304, %129, %149, %170, %112) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%305 = "pd.elementwise_add"(%Y_48, %299) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%306 = "pd.relu"(%305) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%307 = "pd.conv2d"(%306, %25) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_51, %MeanOut_52, %VarianceOut_53 = "pd.batch_norm"(%307, %98, %176, %153, %216) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%308 = "pd.relu"(%Y_51) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%309 = "pd.conv2d"(%308, %160) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_54, %MeanOut_55, %VarianceOut_56 = "pd.batch_norm"(%309, %145, %130, %192, %142) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%310 = "pd.relu"(%Y_54) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%311 = "pd.conv2d"(%310, %108) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_57, %MeanOut_58, %VarianceOut_59 = "pd.batch_norm"(%311, %117, %207, %174, %71) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%312 = "pd.elementwise_add"(%Y_57, %306) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%313 = "pd.relu"(%312) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%314 = "pd.conv2d"(%313, %155) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_60, %MeanOut_61, %VarianceOut_62 = "pd.batch_norm"(%314, %240, %12, %133, %128) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%315 = "pd.relu"(%Y_60) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%316 = "pd.conv2d"(%315, %20) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_63, %MeanOut_64, %VarianceOut_65 = "pd.batch_norm"(%316, %184, %180, %68, %127) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%317 = "pd.relu"(%Y_63) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%318 = "pd.conv2d"(%317, %111) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_66, %MeanOut_67, %VarianceOut_68 = "pd.batch_norm"(%318, %152, %243, %73, %146) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%319 = "pd.elementwise_add"(%Y_66, %313) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%320 = "pd.relu"(%319) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%321 = "pd.conv2d"(%320, %113) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_69, %MeanOut_70, %VarianceOut_71 = "pd.batch_norm"(%321, %179, %93, %126, %165) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%322 = "pd.relu"(%Y_69) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%323 = "pd.conv2d"(%322, %242) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_72, %MeanOut_73, %VarianceOut_74 = "pd.batch_norm"(%323, %17, %91, %185, %167) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%324 = "pd.relu"(%Y_72) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%325 = "pd.conv2d"(%324, %21) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_75, %MeanOut_76, %VarianceOut_77 = "pd.batch_norm"(%325, %143, %63, %7, %173) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%326 = "pd.conv2d"(%320, %105) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_78, %MeanOut_79, %VarianceOut_80 = "pd.batch_norm"(%326, %182, %266, %251, %67) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%327 = "pd.elementwise_add"(%Y_75, %Y_78) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%328 = "pd.relu"(%327) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%329 = "pd.conv2d"(%328, %157) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_81, %MeanOut_82, %VarianceOut_83 = "pd.batch_norm"(%329, %241, %22, %61, %172) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%330 = "pd.relu"(%Y_81) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%331 = "pd.conv2d"(%330, %15) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_84, %MeanOut_85, %VarianceOut_86 = "pd.batch_norm"(%331, %60, %200, %57, %38) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%332 = "pd.relu"(%Y_84) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%333 = "pd.conv2d"(%332, %75) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_87, %MeanOut_88, %VarianceOut_89 = "pd.batch_norm"(%333, %65, %87, %53, %95) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%334 = "pd.elementwise_add"(%Y_87, %328) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%335 = "pd.relu"(%334) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%336 = "pd.conv2d"(%335, %158) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_90, %MeanOut_91, %VarianceOut_92 = "pd.batch_norm"(%336, %80, %19, %49, %252) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%337 = "pd.relu"(%Y_90) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%338 = "pd.conv2d"(%337, %214) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_93, %MeanOut_94, %VarianceOut_95 = "pd.batch_norm"(%338, %70, %166, %102, %147) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%339 = "pd.relu"(%Y_93) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%340 = "pd.conv2d"(%339, %233) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_96, %MeanOut_97, %VarianceOut_98 = "pd.batch_norm"(%340, %66, %94, %39, %163) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%341 = "pd.elementwise_add"(%Y_96, %335) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%342 = "pd.relu"(%341) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%343 = "pd.conv2d"(%342, %124) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_99, %MeanOut_100, %VarianceOut_101 = "pd.batch_norm"(%343, %256, %265, %31, %209) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%344 = "pd.relu"(%Y_99) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%345 = "pd.conv2d"(%344, %16) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_102, %MeanOut_103, %VarianceOut_104 = "pd.batch_norm"(%345, %139, %248, %96, %181) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%346 = "pd.relu"(%Y_102) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%347 = "pd.conv2d"(%346, %211) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_105, %MeanOut_106, %VarianceOut_107 = "pd.batch_norm"(%347, %260, %78, %56, %58) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%348 = "pd.elementwise_add"(%Y_105, %342) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%349 = "pd.relu"(%348) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%350 = "pd.conv2d"(%349, %24) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_108, %MeanOut_109, %VarianceOut_110 = "pd.batch_norm"(%350, %76, %156, %238, %0) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%351 = "pd.relu"(%Y_108) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%352 = "pd.conv2d"(%351, %26) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_111, %MeanOut_112, %VarianceOut_113 = "pd.batch_norm"(%352, %10, %161, %116, %188) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%353 = "pd.relu"(%Y_111) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%354 = "pd.conv2d"(%353, %27) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_114, %MeanOut_115, %VarianceOut_116 = "pd.batch_norm"(%354, %52, %208, %140, %264) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%355 = "pd.elementwise_add"(%Y_114, %349) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%356 = "pd.relu"(%355) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%357 = "pd.conv2d"(%356, %46) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_117, %MeanOut_118, %VarianceOut_119 = "pd.batch_norm"(%357, %258, %9, %5, %169) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%358 = "pd.relu"(%Y_117) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%359 = "pd.conv2d"(%358, %247) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_120, %MeanOut_121, %VarianceOut_122 = "pd.batch_norm"(%359, %177, %255, %253, %193) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%360 = "pd.relu"(%Y_120) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%361 = "pd.conv2d"(%360, %178) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_123, %MeanOut_124, %VarianceOut_125 = "pd.batch_norm"(%361, %47, %201, %48, %249) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%362 = "pd.elementwise_add"(%Y_123, %356) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%363 = "pd.relu"(%362) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%364 = "pd.conv2d"(%363, %104) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_126, %MeanOut_127, %VarianceOut_128 = "pd.batch_norm"(%364, %164, %217, %110, %141) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%365 = "pd.relu"(%Y_126) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%366 = "pd.conv2d"(%365, %235) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_129, %MeanOut_130, %VarianceOut_131 = "pd.batch_norm"(%366, %137, %125, %224, %35) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%367 = "pd.relu"(%Y_129) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%368 = "pd.conv2d"(%367, %234) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_132, %MeanOut_133, %VarianceOut_134 = "pd.batch_norm"(%368, %227, %55, %123, %23) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%369 = "pd.conv2d"(%363, %28) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_135, %MeanOut_136, %VarianceOut_137 = "pd.batch_norm"(%369, %187, %36, %41, %250) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%370 = "pd.elementwise_add"(%Y_132, %Y_135) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%371 = "pd.relu"(%370) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%372 = "pd.conv2d"(%371, %64) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_138, %MeanOut_139, %VarianceOut_140 = "pd.batch_norm"(%372, %1, %122, %118, %220) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%373 = "pd.relu"(%Y_138) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%374 = "pd.conv2d"(%373, %203) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_141, %MeanOut_142, %VarianceOut_143 = "pd.batch_norm"(%374, %237, %120, %212, %92) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%375 = "pd.relu"(%Y_141) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%376 = "pd.conv2d"(%375, %32) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_144, %MeanOut_145, %VarianceOut_146 = "pd.batch_norm"(%376, %244, %59, %183, %228) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%377 = "pd.elementwise_add"(%Y_144, %371) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%378 = "pd.relu"(%377) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%379 = "pd.conv2d"(%378, %190) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_147, %MeanOut_148, %VarianceOut_149 = "pd.batch_norm"(%379, %119, %103, %18, %45) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%380 = "pd.relu"(%Y_147) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%381 = "pd.conv2d"(%380, %246) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_150, %MeanOut_151, %VarianceOut_152 = "pd.batch_norm"(%381, %148, %232, %221, %62) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%382 = "pd.relu"(%Y_150) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%383 = "pd.conv2d"(%382, %132) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_153, %MeanOut_154, %VarianceOut_155 = "pd.batch_norm"(%383, %215, %90, %33, %223) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%384 = "pd.elementwise_add"(%Y_153, %378) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%385 = "pd.relu"(%384) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%386 = "pd.pool2d"(%385) {adaptive = true, ceil_mode = false, data_format = "NCHW", exclusive = true, global_pooling = false, ksize = [1 : i32, 1 : i32], padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], pooling_type = "avg", strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%387 = "pd.flatten_contiguous_range"(%386) {start_axis = 1 : si32, stop_axis = 3 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%388 = "pd.matmul_v2"(%387, %245) {trans_x = false, trans_y = false} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%389 = "pd.elementwise_add"(%388, %30) {axis = 1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
infrt.return %389 : !infrt.dense_tensor<CPU, FP32, NCHW>
}
func @main() {
%ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
%1 = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value = 12.0 : f32, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[1, 3, 256, 256]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%map = phi_dt.load_combined_params(){model_path="@CMAKE_BINARY_DIR@/models/resnet50/model.pdmodel",params_path="@CMAKE_BINARY_DIR@/models/resnet50/model.pdiparams"}
%2 = infrt.call@main_graph(%map, %1) : (!phi.dense_tensor_map, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
phi_dt.print_tensor (%2 : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
}
paddle/infrt/tests/dialect/tensor/.gitignore 已删除 100644 → 0
浏览文件 @ 66098bff
.DS_Store
.idea
*.log
tmp/
tensor_map.mlir
paddle/infrt/tests/dialect/tensor/dense_tensor.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: dense_shape0
func @dense_shape0() {
%a = dt.create_uninit_tensor.f32 [12:i64, 23:i64] -> !infrt.dense_tensor<CPU, FP32, NCHW>
infrt.return
}
func @predict(%a: !infrt.dense_tensor<CPU, FP32, NCHW>, %b: !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) {
%a0 = dt.shallow_copy_tensor %a : !infrt.dense_tensor<CPU, FP32, NCHW> -> !infrt.dense_tensor<CPU, FP32, NCHW>
%b0 = dt.shallow_copy_tensor %b : !infrt.dense_tensor<CPU, FP32, NCHW> -> !infrt.dense_tensor<CPU, FP32, NCHW>
infrt.return %a0, %b0: !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>
}
func @main() {
%shape = ts.build_shape [1:i64, 57:i64]
%a = dt.create_uninit_tensor.f32 [12:i64, 23:i64] -> !infrt.dense_tensor<CPU, FP32, NCHW>
%b, %c = infrt.call @predict(%a, %a) : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
paddle/infrt/tests/dialect/tensor/naive_kernels.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: naive_elementwise_add
func @naive_elementwise_add() {
// create a
%a = dt.create_uninit_tensor.f32 [2:i64, 8:i64] -> !infrt.dense_tensor<CPU, FP32, NCHW>
dt.fill_tensor_with_constant.f32 (%a : !infrt.dense_tensor<CPU, FP32, NCHW>) {value=1.0:f32}
// create b
%b = dt.create_uninit_tensor.f32 [2:i64, 8:i64] -> !infrt.dense_tensor<CPU, FP32, NCHW>
dt.fill_tensor_with_constant.f32 (%b : !infrt.dense_tensor<CPU, FP32, NCHW>) {value=2.0:f32}
// get c
%c = dt.naive_elementwise_add.f32(%a, %b) {} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
// CHECK: tensor: shape=shape[2,8], values=[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
dt.print_tensor (%c : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: naive_matmul
func @naive_matmul() {
// create a
%a = dt.create_uninit_tensor.f32 [2:i64, 8:i64] -> !infrt.dense_tensor<CPU, FP32, NCHW>
dt.fill_tensor_with_constant.f32 (%a : !infrt.dense_tensor<CPU, FP32, NCHW>) {value=1.0:f32}
// create b
%b = dt.create_uninit_tensor.f32 [8:i64, 4:i64] -> !infrt.dense_tensor<CPU, FP32, NCHW>
dt.fill_tensor_with_constant.f32 (%b : !infrt.dense_tensor<CPU, FP32, NCHW>) {value=2.0:f32}
// get c
%c = dt.naive_matmul.f32(%a, %b) {} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
// CHECK: tensor: shape=shape[2,4], values=[16, 16, 16, 16, 16, 16, 16, 16]
dt.print_tensor (%c : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
paddle/infrt/tests/dialect/tensor/tensor_map.mlir.in 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
func @load_tensor_map() {
%map = dt.load_params(){path="@CMAKE_BINARY_DIR@/multi_fc_model"}
%size = dt.tensor_map_get_size(%map) -> i32
infrt.print.i32 %size
%a = dt.tensor_map_get_tensor(%map) {name="fc_bias"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
// CHECK: tensor: shape=shape[2], values=[0, 0]
dt.print_tensor (%a : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
func @load_phi_tensor_map() {
%map = phi_dt.load_params(){path="@CMAKE_BINARY_DIR@/multi_fc_model"}
%size = phi_dt.tensor_map_get_size(%map) -> i32
infrt.print.i32 %size
%a = phi_dt.tensor_map_get_tensor(%map) {name="fc_bias"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
// CHECK: dense_tensor: shape=shape[2], value=[0,0]
phi_dt.print_tensor (%a : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
func @load_combined_phi_tensor_map() {
%map = phi_dt.load_combined_params(){model_path="@CMAKE_BINARY_DIR@/multi_fc_model/fc.pdmodel",
params_path="@CMAKE_BINARY_DIR@/multi_fc_model/fc.pdiparams"}
%size = phi_dt.tensor_map_get_size(%map) -> i32
infrt.print.i32 %size
%a = phi_dt.tensor_map_get_tensor(%map) {name="fc_bias"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
// CHECK: dense_tensor: shape=shape[2], value=[0,0]
phi_dt.print_tensor (%a : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
paddle/infrt/tests/dialect/tensor/tensor_shape.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: @build_tensor1
func @build_tensor1() {
%a = ts.build_shape [1:i64, 57:i64, 92:i64]
// CHECK: shape[1,57,92]
ts.print_shape %a
infrt.return
}
paddle/infrt/tests/dialect/tensor/tensor_type.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: test_tensor_type
func @test_tensor_type() {
%a = dt.create_uninit_tensor.f32 [3, 4] -> !infrt.dense_tensor<CPU, FP32, NCHW>
dt.fill_tensor_with_constant.f32 (%a : !infrt.dense_tensor<CPU, FP32, NCHW>) {value=1.0:f32}
// CHECK: tensor: shape=shape[3,4], values=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
dt.print_tensor (%a : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
paddle/infrt/tests/dialect/tensor_shape.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: @build_tensor1
func @build_tensor1() {
%a = ts.build_shape [1:i64, 57:i64, 92:i64]
// CHECK: shape[1,57,92]
ts.print_shape %a
infrt.return
}
paddle/infrt/tests/dialect/tensor_type.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: test_tensor_type
func @test_tensor_type() {
%a = dt.create_uninit_tensor.f32 [3, 4] -> !infrt.dense_tensor<CPU, FP32, NCHW>
dt.fill_tensor_with_constant.f32 (%a : !infrt.dense_tensor<CPU, FP32, NCHW>) {value=1.0:f32}
// CHECK: tensor: shape=shape[3,4], values=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
dt.print_tensor (%a : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
paddle/infrt/tests/dialect/tensorrt/disabled_linear.mlir.in 已删除 100644 → 0
浏览文件 @ 66098bff
module {
func @main_graph(%map: !phi.dense_tensor_map, %arg0: !infrt.dense_tensor<CPU, FP32, ANY>) -> !infrt.dense_tensor<CPU, FP32, ANY> {
%0 = "phi_dt.create_context.gpu"() : () -> !phi.context<GPU>
%1 = "phi_dt.memcpy.gpu"(%arg0, %0) {d2h = false} : (!infrt.dense_tensor<CPU, FP32, ANY>, !phi.context<GPU>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%3 = phi_dt.tensor_map_get_tensor(%map) {name = "linear_0.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%4 = phi_dt.tensor_map_get_tensor(%map) {name = "linear_0.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%5 = "trt.create_engine"(%1, %4, %3) ( {
%10 = "trt.FullyConnected"(%1, %4, %3) {out_channel_num = 10 : si32} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
infrt.return %10 : !infrt.dense_tensor<GPU, FP32, NCHW>
}) {run_once = true} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !trt.engine
%6 = "trt.compute"(%5, %0) : (!trt.engine, !phi.context<GPU>) -> !infrt.tensor_list
%7 = "dt.tensor_list_get_tensor"(%6) {id = 0 : i32} : (!infrt.tensor_list) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%8 = "phi_dt.memcpy.gpu"(%7, %0) {d2h = true} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !phi.context<GPU>) -> !infrt.dense_tensor<CPU, FP32, ANY>
infrt.return %8 : !infrt.dense_tensor<CPU, FP32, ANY>
}
func @main() {
%map = phi_dt.load_combined_params(){model_path="@CMAKE_BINARY_DIR@/linear/linear.pdmodel",
params_path="@CMAKE_BINARY_DIR@/linear/linear.pdiparams"}
%ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
%input_tensor = "phi_dt.create_dense_tensor.cpu" (%ctx) {
precision=#infrt.precision<FP32>,
layout=#infrt.layout<NCHW>,
dims=[3:i64, 784:i64, 1:i64, 1:i64], lod=[1:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
"phi_dt.fill_dense_tensor.f32"(%input_tensor) {value=[3.8:f32, 2.4:f32, 1.3:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
%res = infrt.call @main_graph(%map, %input_tensor) {} : (!phi.dense_tensor_map, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
"phi_dt.print_tensor" (%res) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
infrt.return
}
}
paddle/infrt/tests/dialect/tensorrt/disabled_trt_activation.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
module {
func @main_graph(%arg0: !infrt.dense_tensor<CPU, FP32, ANY>) -> !infrt.dense_tensor<CPU, FP32, ANY> {
%0 = "phi_dt.create_context.gpu"() : () -> !phi.context<GPU>
%1 = "phi_dt.memcpy.gpu"(%arg0, %0) {d2h = false} : (!infrt.dense_tensor<CPU, FP32, ANY>, !phi.context<GPU>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%2 = "trt.create_engine"(%1) ( {
%6 = "trt.Activation"(%1) {activation_type = 1 : si32, alpha = 0.000000e+00 : f32, beta = 0.000000e+00 : f32} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
infrt.return %6 : !infrt.dense_tensor<GPU, FP32, NCHW>
}) {run_once = true} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !trt.engine
%3 = "trt.compute"(%2, %0) : (!trt.engine, !phi.context<GPU>) -> !infrt.tensor_list
%4 = "dt.tensor_list_get_tensor"(%3) {id = 0 : i32} : (!infrt.tensor_list) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%5 = "phi_dt.memcpy.gpu"(%4, %0) {d2h = true} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !phi.context<GPU>) -> !infrt.dense_tensor<CPU, FP32, ANY>
infrt.return %5 : !infrt.dense_tensor<CPU, FP32, ANY>
}
func @main() {
%0 = "phi_dt.create_context.cpu"() : () -> !phi.context<CPU>
%1 = "phi_dt.create_inited_dense_tensor.cpu.f32"(%0) {dims = [3, 6, 1, 1], layout = #infrt.layout<NCHW>, lod = [0], value = 1.500000e+00 : f32} : (!phi.context<CPU>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%2 = infrt.call @main_graph(%1) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
phi_dt.print_tensor(%2 : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
}
paddle/infrt/tests/dialect/tensorrt/disabled_trt_conv.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: @run_trt
func @run_trt(%input_tensor : !infrt.dense_tensor<GPU, FP32, NCHW>, %kernel_weight : !infrt.dense_tensor<CPU, FP32, NCHW>, %kernel_bias : !infrt.dense_tensor<CPU, FP32, NCHW>, %gpu_ctx : !phi.context<GPU>) {
%a = "trt.create_engine"(%input_tensor, %kernel_weight, %kernel_bias) ({
%1 = "trt.Activation"(%input_tensor) {activation_type = 1 : si32, alpha = 1.0 : f32, beta = 6.0 : f32} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%2 = "trt.Convolution"(%input_tensor, %kernel_weight, %kernel_bias) {out_channel_num = 3 : si32, kernel_size = [3:i32, 3:i32]} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
"infrt.return"(%1, %2) : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<GPU, FP32, NCHW>) -> ()
}) : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !trt.engine
"trt.inspect_engine"(%a) {} : (!trt.engine) -> ()
%res = "trt.compute"(%a, %gpu_ctx) {} : (!trt.engine, !phi.context<GPU>) -> (!infrt.tensor_list)
%size = "dt.tensor_list_get_size"(%res) {} : (!infrt.tensor_list) -> (i32)
"infrt.print.i32"(%size) {} : (i32) -> ()
%ts0 = "dt.tensor_list_get_tensor"(%res) {id = 0 : i32} : (!infrt.tensor_list) -> (!infrt.dense_tensor<GPU, FP32, NCHW>)
"phi_dt.print_tensor" (%ts0) : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> ()
%ts1 = "dt.tensor_list_get_tensor"(%res) {id = 1 : i32} : (!infrt.tensor_list) -> (!infrt.dense_tensor<GPU, FP32, NCHW>)
"phi_dt.print_tensor" (%ts1) : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> ()
infrt.return
}
// CHECK-LABEL: @main
func @main() {
%gpu_ctx = "phi_dt.create_context.gpu" (): () -> !phi.context<GPU>
%cpu_ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
%input_tensor = "phi_dt.create_dense_tensor.gpu" (%gpu_ctx) {
precision=#infrt.precision<FP32>,
layout=#infrt.layout<NCHW>,
dims=[1:i64, 3:i64, 28:i64, 28:i64], lod=[0:i64]}: (!phi.context<GPU>) -> (!infrt.dense_tensor<GPU, FP32, NCHW>)
"phi_dt.fill_dense_tensor.f32"(%input_tensor) {value=[3.8:f32, 2.4:f32, 1.3:f32]} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> ()
// "phi_dt.print_tensor" (%input_tensor) : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> ()
%kernel_weight = "phi_dt.create_dense_tensor.cpu"(%cpu_ctx) {
precision=#infrt.precision<FP32>,
layout=#infrt.layout<NCHW>,
dims=[3:i64, 3:i64, 3:i64, 3:i64], lod=[0:i64]} : (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
"phi_dt.fill_dense_tensor.f32"(%kernel_weight) {value=[1.:f32, 2.:f32, 3.:f32, 4.:f32, 5.:f32, 6.:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
// "phi_dt.print_tensor" (%kernel_weight) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
%kernel_bias = "phi_dt.create_dense_tensor.cpu"(%cpu_ctx) {
precision=#infrt.precision<FP32>,
layout=#infrt.layout<NCHW>,
dims=[3:i64], lod=[0:i64]} : (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
"phi_dt.fill_dense_tensor.f32"(%kernel_bias) {value=[1.:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
// "phi_dt.print_tensor" (%kernel_bias) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
infrt.call @run_trt(%input_tensor, %kernel_weight, %kernel_bias, %gpu_ctx) : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !phi.context<GPU>) -> ()
infrt.return
}
paddle/infrt/tests/dialect/tensorrt/disabled_trt_fc.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
module {
func @main_graph(%arg0: !infrt.dense_tensor<CPU, FP32, ANY>) -> !infrt.dense_tensor<CPU, FP32, ANY> {
%ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
%0 = "phi_dt.create_context.gpu"() : () -> !phi.context<GPU>
%1 = "phi_dt.memcpy.gpu"(%arg0, %0) {d2h = false} : (!infrt.dense_tensor<CPU, FP32, ANY>, !phi.context<GPU>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%4 = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=1.5:f32, layout=#infrt.layout<NCHW>, lod=[0], dims=[2, 6]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%3 = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=1.5:f32, layout=#infrt.layout<NCHW>, lod=[0], dims=[2]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%5 = "trt.create_engine"(%1, %4, %3) ( {
%10 = "trt.FullyConnected"(%1, %4, %3) {out_channel_num = 2 : si32} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
infrt.return %10 : !infrt.dense_tensor<GPU, FP32, NCHW>
}) {run_once = true} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !trt.engine
%6 = "trt.compute"(%5, %0) : (!trt.engine, !phi.context<GPU>) -> !infrt.tensor_list
%7 = "dt.tensor_list_get_tensor"(%6) {id = 0 : i32} : (!infrt.tensor_list) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%8 = "phi_dt.memcpy.gpu"(%7, %0) {d2h = true} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !phi.context<GPU>) -> !infrt.dense_tensor<CPU, FP32, ANY>
infrt.return %8 : !infrt.dense_tensor<CPU, FP32, ANY>
}
func @main() {
%ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
%input_tensor = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value=1.5:f32, layout=#infrt.layout<NCHW>, lod=[0], dims=[3, 6, 1, 1]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%res = infrt.call @main_graph(%input_tensor) {} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
"phi_dt.print_tensor" (%res) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
infrt.return
}
}
paddle/infrt/tests/dialect/tensorrt/disabled_trt_pool.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
module {
func @main_graph(%arg0: !infrt.dense_tensor<CPU, FP32, ANY>) -> !infrt.dense_tensor<CPU, FP32, ANY> {
%0 = "phi_dt.create_context.gpu"() : () -> !phi.context<GPU>
%1 = "phi_dt.memcpy.gpu"(%arg0, %0) {d2h = false} : (!infrt.dense_tensor<CPU, FP32, ANY>, !phi.context<GPU>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%2 = "trt.create_engine"(%1) ( {
%6 = "trt.Pooling"(%1) {padding_mode = 0 : i32, paddings = [1 : i32, 1 : i32], pool_type = 0 : i32, strides = [2 : i32, 2 : i32], window_size = [3 : i32, 3 : i32], exclusive = false, adaptive = false, padding_algorithm = "EXPLICIT"} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
infrt.return %6 : !infrt.dense_tensor<GPU, FP32, NCHW>
}) {run_once = true} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !trt.engine
%3 = "trt.compute"(%2, %0) : (!trt.engine, !phi.context<GPU>) -> !infrt.tensor_list
%4 = "dt.tensor_list_get_tensor"(%3) {id = 0 : i32} : (!infrt.tensor_list) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%5 = "phi_dt.memcpy.gpu"(%4, %0) {d2h = true} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !phi.context<GPU>) -> !infrt.dense_tensor<CPU, FP32, ANY>
infrt.return %5 : !infrt.dense_tensor<CPU, FP32, ANY>
}
func @main() {
%0 = "phi_dt.create_context.cpu"() : () -> !phi.context<CPU>
%1 = "phi_dt.create_inited_dense_tensor.cpu.f32"(%0) {dims = [1, 3, 10, 10], layout = #infrt.layout<NCHW>, lod = [0], value = 1.500000e+00 : f32} : (!phi.context<CPU>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%2 = infrt.call @main_graph(%1) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
phi_dt.print_tensor(%2 : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
}
paddle/infrt/tests/dialect/trt_ops.mlir 已删除 100644 → 0
浏览文件 @ 66098bff
// RUN: trt-exec %s
// CHECK-LABEL: @main
func @main(%bias:!infrt.dense_tensor<GPU, FP32, NCHW>, %c:!infrt.dense_tensor<GPU, FP32, NCHW>, %b1:!infrt.dense_tensor<GPU, FP32, NCHW>, %b2:!infrt.dense_tensor<GPU, FP32, NCHW>, %bias1:!infrt.dense_tensor<GPU, FP32, NCHW>, %bias2:!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW> {
%d = "pd.elementwise_add"(%c, %bias) {axis=-1:si32} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%e = "pd.relu6"(%d) {} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%c1 = "pd.matmul"(%e, %b1) {transpose_x=false, transpose_y=false} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%d1 = "pd.elementwise_add"(%c1, %bias1) {axis=-1:si32} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%e1 = "pd.relu"(%d1) {} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%c2 = "pd.matmul"(%e1, %b2) {transpose_x=true, transpose_y=false} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%d2 = "pd.elementwise_add"(%c2, %bias2) {axis=-1:si32} : (!infrt.dense_tensor<GPU, FP32, NCHW>, !infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
%e2 = "pd.relu"(%d2) {} : (!infrt.dense_tensor<GPU, FP32, NCHW>) -> !infrt.dense_tensor<GPU, FP32, NCHW>
infrt.return %e2 : !infrt.dense_tensor<GPU, FP32, NCHW>
}
paddle/infrt/tests/lit.cfg.py.in 已删除 100644 → 0
浏览文件 @ 66098bff
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import lit.formats
import os
config.name = "MLIR tests"
config.test_format = lit.formats.ShTest(True)
build_dir = "@CMAKE_BINARY_DIR@"
config.llvm_tools_dir = os.path.join(build_dir, "third_party/install/llvm/bin")
config.llvm_tools_dir = os.path.join(build_dir, "/third_party/install/llvm/lib")
infrtopt_bin = os.path.join(build_dir, "paddle/infrt/dialect/")
trtexec_bin = os.path.join(build_dir, "paddle/infrt/dialect/tensorrt/")
infrtexec_bin = os.path.join(build_dir, "paddle/infrt/host_context/")
phi_ir_exec_bin = os.path.join(build_dir, "paddle/infrt/dialect/phi")
llvm_bin = os.path.join(build_dir, "third_party/install/llvm/bin/")
config.environment['PATH'] = os.path.pathsep.join(
(infrtopt_bin, infrtexec_bin, trtexec_bin, phi_ir_exec_bin, llvm_bin, config.environment['PATH']))
config.suffixes = ['.mlir']
paddle/infrt/tests/models/abs_model.py 已删除 100644 → 0
浏览文件 @ 66098bff
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import paddle
from paddle.jit import to_static
from paddle.static import InputSpec
class AbsNet(paddle.nn.Layer):
def __init__(self):
super().__init__()
def forward(self, x):
x = paddle.abs(x)
return x
if __name__ == '__main__':
# build network
model = AbsNet()
# save inferencing format model
net = to_static(
model, input_spec=[InputSpec(shape=[None, 1, 28, 28], name='x')]
)
paddle.jit.save(net, sys.argv[1])
paddle/infrt/tests/models/efficientnet-b4/model.py 已删除 100644 → 0
浏览文件 @ 66098bff
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
# url: https://aistudio.baidu.com/aistudio/projectdetail/3756986?forkThirdPart=1
from net import EfficientNet
import paddle
from paddle.jit import to_static
from paddle.static import InputSpec
model = EfficientNet.from_name('efficientnet-b4')
net = to_static(
model, input_spec=[InputSpec(shape=[None, 3, 256, 256], name='x')]
)
paddle.jit.save(net, sys.argv[1])
paddle/infrt/tests/models/efficientnet-b4/net/__init__.py 已删除 100644 → 0
浏览文件 @ 66098bff
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .efficientnet import EfficientNet
paddle/infrt/tests/models/resnet50_model.py 已删除 100644 → 0
浏览文件 @ 66098bff
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import paddle
from paddle.jit import to_static
from paddle.static import InputSpec
from paddle.vision.models import resnet50
model = resnet50(True)
net = to_static(
model, input_spec=[InputSpec(shape=[None, 3, 256, 256], name='x')]
)
paddle.jit.save(net, sys.argv[1])
paddle/infrt/tests/models/test_abs.cc 已删除 100644 → 0
浏览文件 @ 66098bff
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <gtest/gtest.h>
#include <llvm/Support/CommandLine.h>
#include <mlir/Pass/PassManager.h>
#include <iostream>
#include <string>
#include "llvm/Support/DynamicLibrary.h"
#include "paddle/infrt/common/global.h"
#include "paddle/infrt/dialect/mlir_loader.h"
#include "paddle/infrt/host_context/core_runtime.h"
#include "paddle/infrt/host_context/kernel_registry.h"
#include "paddle/infrt/host_context/mlir_to_runtime_translate.h"
#include "paddle/infrt/kernel/basic_kernels.h"
#include "paddle/infrt/kernel/control_flow_kernels.h"
#include "paddle/infrt/kernel/phi/infershaped/infershaped_kernel_launchers.h"
#include "paddle/infrt/kernel/phi/registry.h"
#include "paddle/infrt/kernel/tensor_kernels.h"
#include "paddle/infrt/kernel/tensor_shape_kernels.h"
#include "paddle/infrt/kernel/test_kernels.h"
#include "paddle/infrt/kernel/phi/infershaped/infershaped_utils.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/place.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/meta_tensor.h"
#include "paddle/infrt/dialect/infrt/ir/basic_kernels.h"
#include "paddle/infrt/dialect/infrt/ir/infrt_dialect.h"
#include "paddle/infrt/dialect/infrt/pass/infrt_op_fuse_pass.h"
#include "paddle/infrt/dialect/phi/pass/phi_op_convert_pass.h"
#include "paddle/infrt/host_context/paddle_mlir.h"
#include "paddle/infrt/dialect/dense_tensor.h"
#include "paddle/infrt/dialect/phi/ir/infrt_phi_tensor.h"
#include "paddle/infrt/dialect/phi/ir/phi_base.h"
#include "paddle/infrt/dialect/phi/ir/phi_kernels.h"
static llvm::cl::list<std::string> cl_shared_libs( // NOLINT
"shared_libs",
llvm::cl::desc("Specify shared library with kernels."),
llvm::cl::ZeroOrMore,
llvm::cl::MiscFlags::CommaSeparated);
TEST(ABS_MODEL, convert_and_execute) {
std::string model_file_name = "./abs.pdmodel";
std::string params_file_name = "./abs.pdiparams";
// convert model
MLIRModelGenImpl myGen;
auto module_ = myGen.ImportPaddleModel(model_file_name, params_file_name);
module_.dump();
// pick kernel
mlir::MLIRContext* context = infrt::Global::getMLIRContext();
context->allowUnregisteredDialects();
context->getOrLoadDialect<mlir::StandardOpsDialect>();
context->getOrLoadDialect<infrt::InfrtDialect>();
context->getOrLoadDialect<infrt::ts::TensorShapeDialect>();
context->getOrLoadDialect<infrt::InfrtDialect>();
context->getOrLoadDialect<infrt::dt::DTDialect>();
context->getOrLoadDialect<infrt::pd::PaddleDialect>();
context->getOrLoadDialect<infrt::phi::PHIDenseTensorDialect>();
context->getOrLoadDialect<infrt::phi::PHICPUKernelDialect>();
context->getOrLoadDialect<infrt::phi::PHIGPUKernelDialect>();
context->getOrLoadDialect<infrt::phi::PHIDialect>();
context->loadAllAvailableDialects();
mlir::PassManager pm(context);
mlir::OpPassManager& phi_pass_manager = pm.nest<mlir::FuncOp>();
std::vector<infrt::Place> valid_places = {{infrt::TargetType::CPU,
infrt::PrecisionType::FLOAT32,
infrt::LayoutType::NCHW}};
phi_pass_manager.addPass(infrt::CreatePhiOpCvtPass(valid_places));
phi_pass_manager.addPass(infrt::CreateInfrtOpFusePass());
if (mlir::failed(pm.run(module_))) {
std::cout << "\npass failed!\n" << std::endl;
}
module_.dump();
// executate
infrt::host_context::KernelRegistry registry;
infrt::kernel::RegisterBasicKernels(&registry);
infrt::kernel::RegisterTestKernels(&registry);
infrt::kernel::RegisterTensorShapeKernels(&registry);
infrt::kernel::RegisterTensorKernels(&registry);
infrt::kernel::RegisterControlFlowKernels(&registry);
infrt::kernel::RegisterPhiKernels(&registry);
infrt::kernel::RegisterInferShapeLaunchers(&registry);
// load extra shared library
for (const auto& lib_path : cl_shared_libs) {
std::string err;
llvm::sys::DynamicLibrary dynLib =
llvm::sys::DynamicLibrary::getPermanentLibrary(lib_path.c_str(), &err);
if (!dynLib.isValid()) {
llvm::errs() << "Load shared library failed. Error: " << err << "\n";
break;
}
if (auto reg_sym = dynLib.SearchForAddressOfSymbol("RegisterKernels")) {
auto reg_func =
reinterpret_cast<void (*)(infrt::host_context::KernelRegistry*)>(
reg_sym);
reg_func(&registry);
} else {
llvm::outs() << "Symbol \"RegisterKernels\" not found in \"" << lib_path
<< "\". Skip.\n";
}
}
infrt::host_context::TestMlir(module_, &registry);
}
paddle/infrt/tests/timer.h 已删除 100644 → 0
浏览文件 @ 66098bff
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <algorithm>
#include <chrono>
#include <ctime>
#include <sstream>
#include <vector>
namespace infrt {
namespace tests {
template <typename ClockT>
class ChronoTimer {
public:
using TimePoint = std::chrono::time_point<ClockT>;
ChronoTimer() : start_{TimePoint::min()} {}
void Clear() { start_ = TimePoint::min(); }
void Start() { start_ = ClockT::now(); }
double GetMs() {
auto diff = ClockT::now() - start_;
return static_cast<double>(
std::chrono::duration_cast<std::chrono::duration<double>>(diff)
.count()) *
1000.0;
}
private:
TimePoint start_;
};
// To learn more about the difference between system_clock and steady_clock,
// please refer to https://www.cnblogs.com/zhongpan/p/7490657.html.
// To learn more about the difference between Wall Time and CPU Time,
// please refer to https://blog.csdn.net/aganlengzi/article/details/21888351
// and https://blog.csdn.net/filyouzicha/article/details/52447887.
using WallClockTimer = ChronoTimer<std::chrono::system_clock>;
class CpuClockTimer {
public:
CpuClockTimer() = default;
void Clear() { start_ = 0; }
void Start() { start_ = std::clock(); }
double GetMs() {
std::clock_t diff = std::clock() - start_;
return static_cast<double>(diff * 1000.0 / CLOCKS_PER_SEC);
}
private:
std::clock_t start_{0};
};
class BenchmarkStats {
public:
void Start() {
wall_timer_.Start();
cpu_timer_.Start();
}
void Stop() {
wall_time_.push_back(wall_timer_.GetMs());
cpu_time_.push_back(cpu_timer_.GetMs());
}
std::string Summerize(const std::vector<float>& percents) {
std::stringstream ss;
std::sort(wall_time_.begin(), wall_time_.end());
std::sort(cpu_time_.begin(), cpu_time_.end());
auto percentile = [](float p, const std::vector<float>& stats) {
size_t mark = stats.size() * p;
mark = std::max(mark, static_cast<size_t>(0));
mark = std::min(mark, stats.size() - 1);
return stats[mark];
};
for (auto p : percents) {
ss << "=== Wall Time (ms): \n";
ss << " * percent " << std::to_string(static_cast<int>(p * 100));
ss << ": " << percentile(p, wall_time_) << '\n';
}
for (auto p : percents) {
ss << "=== CPU Time (ms): \n";
ss << " * percent " << std::to_string(static_cast<int>(p * 100));
ss << ": " << percentile(p, cpu_time_) << '\n';
}
return ss.str();
}
private:
WallClockTimer wall_timer_;
std::vector<float> wall_time_;
CpuClockTimer cpu_timer_;
std::vector<float> cpu_time_;
};
} // namespace tests
} // namespace infrt
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