Ishitori opened a new issue #12700: Add support of dot product (GEMM) for float16 on CPU URL: https://github.com/apache/incubator-mxnet/issues/12700 ## Description Currently dot product for float16 is supported only on GPU (was added https://github.com/apache/incubator-mxnet/issues/10531), but is not supported on CPU. It would be great to add it to CPU for fast inference. Package used (Python/R/Scala/Julia): Python, mxnet 1.3.0 ## Error Message: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/anaconda3/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py", line 189, in __repr__ return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()), File "/home/anaconda3/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py", line 1972, in asnumpy ctypes.c_size_t(data.size))) File "/home/anaconda3/lib/python3.6/site-packages/mxnet/base.py", line 252, in check_call raise MXNetError(py_str(_LIB.MXGetLastError())) mxnet.base.MXNetError: [13:42:46] src/operator/tensor/./dot-inl.h:83: Check failed: outputs[0].type_flag_ == kFloat32 || outputs[0].type_flag_ == kFloat64 || (outputs[0].type_flag_ == kFloat16 && ctx.run_ctx.ctx.dev_mask() == mshadow::gpu::kDevMask) dot only supports float32/float64 for CPU, and float16/float32/float64 for GPU ``` ## Minimum reproducible example ``` a = mx.nd.array([1,2,3], dtype="float16") b = mx.nd.array([1,2,3], dtype="float16") mx.nd.dot(a, b.T) ``` ## Steps to reproduce 1. Run the Minimum reproducible example and see the exception ## What have you tried to solve it? 1. There is nothing can be done, only either work with GPU or do float32 math.
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