Gemm - 7 vs 11#

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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.

Files changed (1) hide show
  1. Gemm7 → Gemm11 +4 -12
Gemm7 → Gemm11 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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  A' = transpose(A) if transA else A
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  B' = transpose(B) if transB else B
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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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  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/master/docs/Broadcasting.md>_.
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- This operator has **optional** inputs/outputs. See ONNX <https://github.com/onnx/onnx/blob/master/docs/IR.md>_ for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
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  **Attributes**
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  * **alpha**:
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  Scalar multiplier for the product of input tensors A * B.
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  * **beta**:
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  Scalar multiplier for input tensor C.
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  * **transA**:
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  Whether A should be transposed
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  * **transB**:
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  Whether B should be transposed
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  **Inputs**
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- Between 2 and 3 inputs.
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-
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  * **A** (heterogeneous) - **T**:
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  Input tensor A. The shape of A should be (M, K) if transA is 0, or
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  (K, M) if transA is non-zero.
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  * **B** (heterogeneous) - **T**:
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  Input tensor B. The shape of B should be (K, N) if transB is 0, or
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  (N, K) if transB is non-zero.
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- * **C** (optional, heterogeneous) - **T**:
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+ * **C** (heterogeneous) - **T**:
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- Optional input tensor C. If not specified, the computation is done
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- as if C is a scalar 0. The shape of C should be unidirectional
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+ Input tensor C. The shape of C should be unidirectional
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  broadcastable to (M, N).
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  **Outputs**
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  * **Y** (heterogeneous) - **T**:
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  Output tensor of shape (M, N).
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  **Type Constraints**
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  * **T** in (
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  tensor(double),
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  tensor(float),
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- tensor(float16),
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+ tensor(float16)
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- tensor(int32),
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- tensor(int64),
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- tensor(uint32),
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- tensor(uint64)
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  ):
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- Constrain input and output types to float/int tensors.? ----
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+ Constrain input and output types to float tensors.