Instructions

The following example shows how to concatenate the preceding APIs. It implements a simple operator that supports the float16 type to obtain an absolute value.

import te.lang.cce 
from te import tvm 
import topi
shape = (28,28)
dtype = "float16"
# Define input.
data = tvm.placeholder(shape, name="data", dtype=dtype)

# Describe the computation process of the operator.
res = te.lang.cce.vabs(data)

with tvm.target.cce():
    # Generate a schedule object.
      sch = topi.generic.auto_schedule(res)

# Define build parameters.
config = {"print_ir" : True,
          "need_build" : True,
          "name" : "abs_28_28_float16",
          "tensor_list" : [data,res]}
# Build operator
te.lang.cce.cce_build_code(sch, config)

If an exception occurs when an API is executed, the error is usually caused by incorrect input parameters. The following example shows the error information when tensor_list is incomplete.

Code example:

data = tvm.placeholder(shape, name="data", dtype=inp_dtype)
with tvm.target.cce():
    res = te.lang.cce.vabs(data)
    sch = generic.auto_schedule(res)
    config = {"print_ir": need_print,
    "need_build": need_build,
    "name": kernel_name,
    "tensor_list": [res]}
te.lang.cce.cce_build_code(sch, config)

The following error information is displayed:

Traceback (most recent call last):
  File "llt/tensor_engine/ut/testcase_python/tf_abs/test_tf_abs_cce.py", line 71, in test_cce_tf_abs_99991_fp16
    tf_abs_cce((99991,), dtype = "Float16", need_build = False, need_print = False, kernel_name = "cce_tf_abs")
  File "/home1/repotvm/tensor_engine/topi/python/topi/cce/tf_abs.py", line 68, in tf_abs_cce
    te.lang.cce.cce_build_code(sch, config)
  File "/home1/repotvm/tensor_engine/python/te/lang/cce/te_schedule/cce_schedule.py", line 381, in cce_build_code
    _build(sch, tensor_list, local_config_map["name"])
  File "/home1/repotvm/tensor_engine/python/te/lang/cce/te_schedule/cce_schedule.py", line 338, in _build
    mod = tvm.build(sch, tensor_list, device, name=name)
  File "/home1/repotvm/tensor_engine/python/te/tvm/build_module.py", line 432, in build
    binds=binds)
  File "/home1/repotvm/tensor_engine/python/te/tvm/build_module.py", line 353, in lower
    stmt = ir_pass.StorageFlatten(stmt, binds, 64)
  File "/home1/repotvm/tensor_engine/python/te/tvm/_ffi/function.py", line 280, in my_api_func
    return flocal(*args)
  File "/home1/repotvm/tensor_engine/python/te/tvm/_ffi/_ctypes/function.py", line 183, in __call__
    ctypes.byref(ret_val), ctypes.byref(ret_tcode)))
  File "/home1/repotvm/tensor_engine/python/te/tvm/_ffi/base.py", line 66, in check_call
    raise TVMError(py_str(_LIB.TVMGetLastError()))
TVMError: [17:12:02] /home1/repotvm/tensor_engine/src/pass/storage_flatten.cc:249: Check failed: it != buf_map_.end() Cannot find allocated buffer for placeholder(data, 0x27d7290)

The problem is solved after the parameter is modified as follows:

"tensor_list" : [data, res]