[GitHub] [incubator-mxnet] Laurawly commented on a change in pull request #16660: [WIP] [Numpy] TVM implementation for binary ops

[GitBox <gi...@apache.org>]

Laurawly commented on a change in pull request #16660: [WIP] [Numpy] TVM implementation for binary ops
URL: https://github.com/apache/incubator-mxnet/pull/16660#discussion_r340226828
 
 

 ##########
 File path: contrib/tvmop/core/umath.py
 ##########
 @@ -120,3 +121,327 @@ def _compute_binary_scalar_logic(op, dtype, ndim):
           **_bin_scalar_logic_cpu_attrs)(_binary_logic_cpu)
     defop(name='{}_gpu'.format(op_name), op=op_name,
           **_bin_scalar_logic_gpu_attrs)(_binary_logic_gpu)
+
+
+_bin_cpu_attrs_base = {
+    'target': 'cpu',
+    'dtype': AllTypes,
+    'ndim': [5],
+    'req': ['kWriteTo', 'kAddTo'],
+    'attrs': ['req'],
+}
+
+_bin_gpu_attrs_base = {
+    'target': 'gpu',
+    'dtype': ["float32", "float64", "uint8", "int8", "int32", "int64"],
+    'ndim': [5],
+    'req': ['kWriteTo', 'kAddTo'],
+    'attrs': ['req'],
+}
+
+def _binary_cpu(compute_func, op, dtype, ndim, req):
+    s, a, b, old, new = compute_func(op, dtype, ndim, req)
+    axes = [axis for axis in new.op.axis]
+    fused = s[new].fuse(*axes)
+    s[new].parallel(fused)
+    return s, [a, b, old, new]
+
+
+def _binary_gpu(compute_func, op, dtype, ndim, req):
+    s, a, b, old, new = compute_func(op, dtype, ndim, req)
+    axes = [axis for axis in new.op.axis]
+    fused = s[new].fuse(*axes)
+    bx, tx = s[new].split(fused, factor=64)
+    s[new].bind(bx, tvm.thread_axis('blockIdx.x'))
+    s[new].bind(tx, tvm.thread_axis('threadIdx.x'))
+    return s, [a, b, old, new]
+
+_bin_op_map = {
 
 Review comment:
   A better method as @reminisce suggested is to add an api to add new binary functions to the dictionary.

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