hello! I am trying to use tensorize on my declared conv2d operator. here is my code.import numpy as np
import tvmfrom tvm import te from tvm import autotvm from tvm import topi from zte.enviroment import get_env from zte.intrin import gemm
data = tvm.te.placeholder((1, 64, 112, 112), dtype=“int16”, name=“data”) kernel = tvm.te.placeholder((64, 64, 3, 3), dtype=“int16”, name=“kernel”)
out_w = 110 out_h = 110 out_dtype = “int32” oshape = (1, 64, 110, 110)
define the conv2d operator over the padded data
c = te.reduce_axis((0, 64), name=“c”) k_h = te.reduce_axis((0, 3), name=“k_h”) k_w = te.reduce_axis((0, 3), name=“k_w”) hstride, wstride = 1, 1 res = te.compute( oshape, lambda n, o, h, w: te.sum( data[n, c, h * hstride + k_h, w * wstride + k_w].astype(out_dtype) * kernel[o, c, k_h, k_w].astype(out_dtype), axis=[c, k_h, k_w], ), name=“res”, tag=“conv2d_dense”, )
s = tvm.te.create_schedule(res.op)
env = get_env()
n_o, o_o, h_o, w_o = s[res].op.axis c_in, h_1, w_1 = s[res].op.reduce_axis
h_w_data = s[res].fuse(h_o, w_o) h_w_kernel = s[res].fuse(k_h, k_w) s[res].reorder(n_o, h_w_kernel, h_w_data, o_o, c_in)
xo, yo, xi, yi = s[res].tile(o_o, c_in, x_factor=16, y_factor=16) x_y_fuse = s[res].fuse(xo, yo) data_out, data_in = s[res].split(h_w_data, 256)
s[res].tensorize(h_w_kernel, gemm(env, 0, 16, 16, 16))
code = tvm.lower(s, [data, kernel, res], simple_mode=True) print(code)
And I got this after I run this program.
Traceback (most recent call last):
File "/home/tonywu/Documents/tvm/zte/test_con2d.py", line 53, in <module>
s[res].tensorize(h_w_kernel, gemm(env, 0, 16, 16, 16))
File "/home/tonywu/Documents/tvm/zte/intrin.py", line 60, in gemm
wgt_layout = tvm.tir.decl_buffer(
File "/home/tonywu/Documents/tvm/python/tvm/tir/buffer.py", line 254, in decl_buffer
return _ffi_api.Buffer(
File "/home/tonywu/Documents/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 237, in __call__
raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
3: TVMFuncCall
2: _ZNSt17_Function_handlerIFvN3
1: tvm::tir::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#3}::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const [clone .isra.0]
0: tvm::runtime::TVMPODValue_::operator int() const
File "/home/tonywu/Documents/tvm/include/tvm/runtime/packed_func.h", line 513
TVMError: ---------------------------------------------------------------
An internal invariant was violated during the execution of TVM.
Please read TVM's error reporting guidelines.
More details can be found here: https://discuss.tvm.ai/t/error-reporting/7793.
---------------------------------------------------------------
Check failed: type_code_ == kDLInt: expected int but got float
Process finished with exit code 1
I’d appreciate it if you could help me solve this problem. Thank you!
