fix(op_graph): 5 个算子原型头补 OPS_PROTO_DEF 隔离宏-ops-math-AtomGit

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已合并

fix(op_graph): 5 个算子原型头补 OPS_PROTO_DEF 隔离宏 #5066

zl_hw创建于 8月25日从zl_hw/ops-math： fix/proto-isolation-macro 合入到 cann/ops-math： master

fix(op_graph): 5 个算子原型头补 OPS_PROTO_DEF 隔离宏 #5066

已合并

zl_hw创建于 8月25日

从zl_hw/ops-math fix/proto-isolation-macro合入到cann/ops-math master

共 5 个文件变更+15-0

Mmath/cumulative_logsumexp/op_graph/cumulative_logsumexp_proto.h+3-0

	@@ -35,6 +35,8 @@ namespace ge {
35	* @par Outputs:	35	* @par Outputs:
36	* y: A Tensor. Has the same type and shape as "x".	36	* y: A Tensor. Has the same type and shape as "x".
37	*/	37	*/
		38	+#ifndef OPS_PROTO_DEF_CUMULATIVELOGSUMEXP
		39	+#define OPS_PROTO_DEF_CUMULATIVELOGSUMEXP
38	REG_OP(CumulativeLogsumexp)	40	REG_OP(CumulativeLogsumexp)
39	.INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16}))	41	.INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16}))
40	.INPUT(axis, TensorType({DT_INT32, DT_INT64, DT_INT16}))	42	.INPUT(axis, TensorType({DT_INT32, DT_INT64, DT_INT16}))
	@@ -42,6 +44,7 @@ REG_OP(CumulativeLogsumexp)
42	.ATTR(exclusive, Bool, false)	44	.ATTR(exclusive, Bool, false)
43	.ATTR(reverse, Bool, false)	45	.ATTR(reverse, Bool, false)
44	.OP_END_FACTORY_REG(CumulativeLogsumexp)	46	.OP_END_FACTORY_REG(CumulativeLogsumexp)
		47	+#endif // OPS_PROTO_DEF_CUMULATIVELOGSUMEXP
45	} // namespace ge	48	} // namespace ge
46		49
47	#endif	50	#endif

Mmath/fused_mul_add/op_graph/fused_mul_add_proto.h+3-0

	@@ -35,12 +35,15 @@ namespace ge {
35	* @par Third-party framework compatibility	35	* @par Third-party framework compatibility
36	* Compatible with TensorFlow/PyTorch graph fusion of Mul followed by Add.	36	* Compatible with TensorFlow/PyTorch graph fusion of Mul followed by Add.
37	*/	37	*/
		38	+#ifndef OPS_PROTO_DEF_FUSEDMULADD
		39	+#define OPS_PROTO_DEF_FUSEDMULADD
38	REG_OP(FusedMulAdd)	40	REG_OP(FusedMulAdd)
39	.INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))	41	.INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
40	.INPUT(x2, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))	42	.INPUT(x2, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
41	.INPUT(x3, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))	43	.INPUT(x3, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
42	.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))	44	.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
43	.OP_END_FACTORY_REG(FusedMulAdd)	45	.OP_END_FACTORY_REG(FusedMulAdd)
		46	+#endif // OPS_PROTO_DEF_FUSEDMULADD
44		47
45	} // namespace ge	48	} // namespace ge
46		49

Mmath/fused_mul_add_add/op_graph/fused_mul_add_add_proto.h+3-0

	@@ -44,6 +44,8 @@ namespace ge {
44	* Compatible with the graph fusion of Mul followed by two Add operators	44	* Compatible with the graph fusion of Mul followed by two Add operators
45	* (e.g. BatchMatmul + bias + residual patterns).	45	* (e.g. BatchMatmul + bias + residual patterns).
46	*/	46	*/
		47	+#ifndef OPS_PROTO_DEF_FUSEDMULADDADD
		48	+#define OPS_PROTO_DEF_FUSEDMULADDADD
47	REG_OP(FusedMulAddAdd)	49	REG_OP(FusedMulAddAdd)
48	.INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))	50	.INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
49	.INPUT(x2, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))	51	.INPUT(x2, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
	@@ -51,6 +53,7 @@ REG_OP(FusedMulAddAdd)
51	.INPUT(x4, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))	53	.INPUT(x4, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
52	.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))	54	.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))
53	.OP_END_FACTORY_REG(FusedMulAddAdd)	55	.OP_END_FACTORY_REG(FusedMulAddAdd)
		56	+#endif // OPS_PROTO_DEF_FUSEDMULADDADD
54		57
55	} // namespace ge	58	} // namespace ge
56		59

Mmath/fused_mul_addn_l2loss/op_graph/fused_mul_addn_l2loss_proto.h+3-0

	@@ -36,6 +36,8 @@ namespace ge {
36	* @par Third-party framework compatibility	36	* @par Third-party framework compatibility
37	* Compatible with the fused Mul + AddN + L2Loss subgraph.	37	* Compatible with the fused Mul + AddN + L2Loss subgraph.
38	*/	38	*/
		39	+#ifndef OPS_PROTO_DEF_FUSEDMULADDNL2LOSS
		40	+#define OPS_PROTO_DEF_FUSEDMULADDNL2LOSS
39	REG_OP(FusedMulAddNL2loss)	41	REG_OP(FusedMulAddNL2loss)
40	.INPUT(x1, TensorType::NumberType())	42	.INPUT(x1, TensorType::NumberType())
41	.INPUT(x2, TensorType::NumberType())	43	.INPUT(x2, TensorType::NumberType())
	@@ -43,6 +45,7 @@ REG_OP(FusedMulAddNL2loss)
43	.OUTPUT(y1, TensorType::NumberType())	45	.OUTPUT(y1, TensorType::NumberType())
44	.OUTPUT(y2, TensorType::NumberType())	46	.OUTPUT(y2, TensorType::NumberType())
45	.OP_END_FACTORY_REG(FusedMulAddNL2loss)	47	.OP_END_FACTORY_REG(FusedMulAddNL2loss)
		48	+#endif // OPS_PROTO_DEF_FUSEDMULADDNL2LOSS
46		49
47	} // namespace ge	50	} // namespace ge
48		51

Mmath/mul_no_nan/op_graph/mul_no_nan_proto.h+3-0

	@@ -38,11 +38,14 @@ namespace ge {
38	* @par Third-party framework compatibility	38	* @par Third-party framework compatibility
39	* Compatible with the TensorFlow operator MulNoNan (tf.math.multiply_no_nans).	39	* Compatible with the TensorFlow operator MulNoNan (tf.math.multiply_no_nans).
40	*/	40	*/
		41	+#ifndef OPS_PROTO_DEF_MULNONAN
		42	+#define OPS_PROTO_DEF_MULNONAN
41	REG_OP(MulNoNan)	43	REG_OP(MulNoNan)
42	.INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_BF16}))	44	.INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_BF16}))
43	.INPUT(x2, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_BF16}))	45	.INPUT(x2, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_BF16}))
44	.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_BF16}))	46	.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32, DT_BF16}))
45	.OP_END_FACTORY_REG(MulNoNan)	47	.OP_END_FACTORY_REG(MulNoNan)
		48	+#endif // OPS_PROTO_DEF_MULNONAN
46		49
47	} // namespace ge	50	} // namespace ge
48		51