Gemm - 1 vs 9

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text. Green means an addition to the newer version, red means a deletion. Anything else is unchanged.

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  1. Gemm1 → Gemm9 +17 -18
Gemm1 → Gemm9 RENAMED
@@ -1 +1 @@
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  General Matrix multiplication:
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  https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
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+
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+ A' = transpose(A) if transA else A
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+
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+ B' = transpose(B) if transB else B
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+
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- Compute Y = alpha * A * B + beta * C, where input tensor A has
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+ Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
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+ input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
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+ and output tensor Y has shape (M, N). A will be transposed before doing the
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- dimension (M X K), input tensor B has dimension (K X N), input tensor C and
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- output tensor Y have dimension (M X N).
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- If attribute broadcast is non-zero, input tensor C will be broadcasted to match
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- the dimension requirement. A will be transposed before doing the computation
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- if attribute transA is non-zero, same for B and transB.
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+ computation if attribute transA is non-zero, same for B and transB.
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+ This operator supports **unidirectional broadcasting** (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check [Broadcasting in ONNX](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).
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  ### Attributes
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  * **alpha - FLOAT** (default is '1.0'):
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- Scalar multiplier for the product of input tensors A * B, the default value is 1.0.
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+ Scalar multiplier for the product of input tensors A * B.
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  * **beta - FLOAT** (default is '1.0'):
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- Scalar multiplier for input tensor C, the default value is 1.0.
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+ Scalar multiplier for input tensor C.
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-
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- * **broadcast - INT** (default is '0'):
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-
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- Whether C should be broadcasted
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  * **transA - INT** (default is '0'):
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  Whether A should be transposed
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  * **transB - INT** (default is '0'):
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  Whether B should be transposed
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  ### Inputs
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  - **A** (heterogeneous) - **T**:
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- Input tensor A
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+ Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
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  - **B** (heterogeneous) - **T**:
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- Input tensor B
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+ Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
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  - **C** (heterogeneous) - **T**:
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- Input tensor C, can be inplace.
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+ Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).
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  ### Outputs
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  - **Y** (heterogeneous) - **T**:
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- Output tensor.
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+ Output tensor of shape (M, N).
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  ### Type Constraints
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- * **T** in ( tensor(double), tensor(float), tensor(float16) ):
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+ * **T** in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ):
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- Constrain input and output types to float tensors.+ Constrain input and output types to float/int tensors.? ++++