from tvm import relay from tvm.relay import testing import numpy as np from infrastructure import get_ref_result
batch_size = 1 num_class = 1000 image_shape = (3, 224, 224) data_shape = (batch_size,) + image_shape out_shape = (batch_size, num_class) dtype=“float32”
mod, params = relay.testing.mobilenet.get_workload( batch_size=batch_size, num_classes=num_class, image_shape=image_shape, layout=‘NCHW’ )
#print(mod.astext(show_meta_data=False))
data = np.random.uniform(size=data_shape).astype(dtype) print(data)
ref_out = get_ref_result(data, mod, params, out_shape, dtype) print(ref_out)
get_ref_result looks like: def get_ref_result(data, mod, params, out_shape, dtype): target = “llvm” with tvm.transform.PassContext(opt_level=3, disabled_pass=[“AlterOpLayout”]): lib = relay.build(mod, target, params=params) cpu_mod = graph_runtime.GraphModule(lib"default") cpu_mod.set_input(“data”, data) cpu_mod.run() cpu_out = cpu_mod.get_output(0, tvm.nd.empty(out_shape, dtype)) return cpu_out
output:
[[[[0.48127168 0.2018001 0.71724653 … 0.0441279 0.57116777 0.1731153 ] [0.40417442 0.3016946 0.74636394 … 0.3417648 0.718218 0.28890228] [0.7683302 0.17131594 0.9016031 … 0.5153679 0.74072677 0.03374053] … [0.6947896 0.6551721 0.85114497 … 0.35421443 0.20508686 0.6471268 ] [0.09923462 0.61146086 0.08773897 … 0.53768474 0.31748652 0.64678025] [0.31008628 0.56266195 0.83621436 … 0.9968801 0.4973068 0.09383171]]
[[0.73113763 0.17166294 0.5789204 … 0.03240918 0.0247721 0.89045954] [0.46058905 0.3739123 0.56078994 … 0.38859197 0.36561185 0.7287658 ] [0.8079502 0.39894798 0.6348208 … 0.56089103 0.58005774 0.52373666] … [0.4517257 0.8520253 0.40640992 … 0.1651029 0.22171977 0.35451823] [0.9394899 0.7759206 0.5117806 … 0.99209446 0.24618751 0.57113916] [0.6102327 0.08231816 0.7101693 … 0.77034265 0.9671634 0.5752965 ]]
[[0.3101213 0.192366 0.22534423 … 0.828487 0.59424293 0.21207647] [0.8794648 0.09954574 0.30758655 … 0.051931 0.03809953 0.3480195 ] [0.81616604 0.92345166 0.36221072 … 0.93277586 0.79536366 0.42082992] … [0.621181 0.4233806 0.83933717 … 0.44883785 0.4910011 0.3370444 ] [0.9489613 0.7982109 0.709624 … 0.6371652 0.5758706 0.6982647 ] [0.36476108 0.2929088 0.49834147 … 0.87037426 0.40084326 0.3614452 ]]]] Cannot find config for target=llvm -keys=cpu, workload=(‘dense_nopack.x86’, (‘TENSOR’, (1, 1024), ‘float32’), (‘TENSOR’, (1000, 1024), ‘float32’), None, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘conv2d_NCHWc.x86’, (‘TENSOR’, (1, 1024, 7, 7), ‘float32’), (‘TENSOR’, (1024, 1024, 1, 1), ‘float32’), (1, 1), (0, 0, 0, 0), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘depthwise_conv2d_NCHWc.x86’, (‘TENSOR’, (1, 1024, 7, 7), ‘float32’), (‘TENSOR’, (1024, 1, 3, 3), ‘float32’), (1, 1), (1, 1, 1, 1), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘conv2d_NCHWc.x86’, (‘TENSOR’, (1, 512, 7, 7), ‘float32’), (‘TENSOR’, (1024, 512, 1, 1), ‘float32’), (1, 1), (0, 0, 0, 0), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘depthwise_conv2d_NCHWc.x86’, (‘TENSOR’, (1, 512, 14, 14), ‘float32’), (‘TENSOR’, (512, 1, 3, 3), ‘float32’), (2, 2), (1, 1, 1, 1), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘conv2d_NCHWc.x86’, (‘TENSOR’, (1, 512, 14, 14), ‘float32’), (‘TENSOR’, (512, 512, 1, 1), ‘float32’), (1, 1), (0, 0, 0, 0), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘depthwise_conv2d_NCHWc.x86’, (‘TENSOR’, (1, 512, 14, 14), ‘float32’), (‘TENSOR’, (512, 1, 3, 3), ‘float32’), (1, 1), (1, 1, 1, 1), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘conv2d_NCHWc.x86’, (‘TENSOR’, (1, 256, 14, 14), ‘float32’), (‘TENSOR’, (512, 256, 1, 1), ‘float32’), (1, 1), (0, 0, 0, 0), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘depthwise_conv2d_NCHWc.x86’, (‘TENSOR’, (1, 256, 28, 28), ‘float32’), (‘TENSOR’, (256, 1, 3, 3), ‘float32’), (2, 2), (1, 1, 1, 1), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘conv2d_NCHWc.x86’, (‘TENSOR’, (1, 256, 28, 28), ‘float32’), (‘TENSOR’, (256, 256, 1, 1), ‘float32’), (1, 1), (0, 0, 0, 0), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘depthwise_conv2d_NCHWc.x86’, (‘TENSOR’, (1, 256, 28, 28), ‘float32’), (‘TENSOR’, (256, 1, 3, 3), ‘float32’), (1, 1), (1, 1, 1, 1), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘conv2d_NCHWc.x86’, (‘TENSOR’, (1, 128, 28, 28), ‘float32’), (‘TENSOR’, (256, 128, 1, 1), ‘float32’), (1, 1), (0, 0, 0, 0), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘depthwise_conv2d_NCHWc.x86’, (‘TENSOR’, (1, 128, 56, 56), ‘float32’), (‘TENSOR’, (128, 1, 3, 3), ‘float32’), (2, 2), (1, 1, 1, 1), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘conv2d_NCHWc.x86’, (‘TENSOR’, (1, 128, 56, 56), ‘float32’), (‘TENSOR’, (128, 128, 1, 1), ‘float32’), (1, 1), (0, 0, 0, 0), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘depthwise_conv2d_NCHWc.x86’, (‘TENSOR’, (1, 128, 56, 56), ‘float32’), (‘TENSOR’, (128, 1, 3, 3), ‘float32’), (1, 1), (1, 1, 1, 1), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘conv2d_NCHWc.x86’, (‘TENSOR’, (1, 64, 56, 56), ‘float32’), (‘TENSOR’, (128, 64, 1, 1), ‘float32’), (1, 1), (0, 0, 0, 0), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘depthwise_conv2d_NCHWc.x86’, (‘TENSOR’, (1, 64, 112, 112), ‘float32’), (‘TENSOR’, (64, 1, 3, 3), ‘float32’), (2, 2), (1, 1, 1, 1), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘conv2d_NCHWc.x86’, (‘TENSOR’, (1, 32, 112, 112), ‘float32’), (‘TENSOR’, (64, 32, 1, 1), ‘float32’), (1, 1), (0, 0, 0, 0), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘depthwise_conv2d_NCHWc.x86’, (‘TENSOR’, (1, 32, 112, 112), ‘float32’), (‘TENSOR’, (32, 1, 3, 3), ‘float32’), (1, 1), (1, 1, 1, 1), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. Cannot find config for target=llvm -keys=cpu, workload=(‘conv2d_NCHWc.x86’, (‘TENSOR’, (1, 3, 224, 224), ‘float32’), (‘TENSOR’, (32, 3, 3, 3), ‘float32’), (2, 2), (1, 1, 1, 1), (1, 1), ‘NCHW’, ‘NCHW’, ‘float32’). A fallback configuration is used, which may bring great performance regression. 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