From 88d5b128fc86cecfa96aae09a7e7e9095f76b2a4 Mon Sep 17 00:00:00 2001 From: Fredrik Svedberg Date: Fri, 16 Sep 2022 16:24:55 +0200 Subject: MLBEDSW-4075 PACK axis 0 + tanh fails with output diff The test failed since the tanh had batch size > 1. Added checks for batch size for all supported operators. Signed-off-by: Fredrik Svedberg Change-Id: I3570352740c40eb96bd9db965dfa3c91c81ff2ad --- SUPPORTED_OPS.md | 34 +++++++++++----------------------- 1 file changed, 11 insertions(+), 23 deletions(-) (limited to 'SUPPORTED_OPS.md') diff --git a/SUPPORTED_OPS.md b/SUPPORTED_OPS.md index 3ccf3ab5..9488a78b 100644 --- a/SUPPORTED_OPS.md +++ b/SUPPORTED_OPS.md @@ -1,7 +1,7 @@ # Supported Ops This file was automatically generated by Vela using the `--supported-ops-report` parameter. -Vela version: `3.6.0rc1.dev11+gac5e33e` +Vela version: `3.5.1.dev10+gf616c9d6.d20220915` This file complies with [**Gitiles Markdown syntax**](https://github.com/google/gitiles/blob/master/Documentation/markdown.md) @@ -61,19 +61,20 @@ Please check the supported operator list for your chosen runtime for further inf This is a list of constraints most NPU operators must satisfy in order to be scheduled on the NPU. (Operators excluded from certain constraints are shown in brackets [ ] ) -- Input(s) and Output tensors must not be dynamic - [Quantize] -- Input(s) and Output tensors must have a defined shape -- Output tensors cannot be scalar - [Quantize] -- Scalar Input tensors are only valid for op type: ADD, EXPAND_DIMS, MAXIMUM, MEAN, MINIMUM, MUL, QUANTIZE, SPLIT, SPLIT_V, SUB -- Input(s) and Output tensors must not be greater than 4D -- Input(s), Output and Weight tensors must have quantization parameters - [Shape] -- Input(s), Output and Weight tensors with quantization scales must be finite -- Input and Output tensors must have quantization scales that fit within float32 precision -- Constant tensors should not have NoneType-values +- Input(s) and Output tensors must not be dynamic - [QUANTIZE] +- Input(s) and Output tensors must have a defined shape +- Output tensors cannot be scalar - [QUANTIZE] +- Scalar Input tensors are only valid for op type: ADD, EXPAND_DIMS, MAXIMUM, MEAN, MINIMUM, MUL, QUANTIZE, SPLIT, SPLIT_V, SUB +- Input(s) and Output tensors must not be greater than 4D +- Input(s), Output and Weight tensors must have quantization parameters - [SHAPE] +- Input(s), Output and Weight tensors with quantization scales must be finite +- Input and Output tensors must have quantization scales that fit within float32 precision +- Constant tensors should not have NoneType-values - Tensors must be of type: int16, int32, int8, uint8 - Tensors which are int32 are only valid when op type is: ADD, MUL, SHAPE, SUB - Tensor dimensions must be in the range [1, 65535] - Per-axis quantization is only supported for the following op types: CONV_2D, DEPTHWISE_CONV_2D, TRANSPOSE_CONV +- IFM Tensor batch size must be 1 - [FULLY_CONNECTED, RESHAPE, SHAPE, SLICE, SOFTMAX, SPLIT, SPLIT_V, SQUEEZE, STRIDED_SLICE, UNPACK] - The fused activation function (if present) must be one of type: LOGISTIC, RELU, RELU6, RELU_N1_TO_1, TANH - If a fused activation function is present, the Output tensor must be one of type: int16, int8, uint8 @@ -83,7 +84,6 @@ This is a list of constraints that the ABS operator must satisfy in order to be - At least one Input's shape must match the OFM's shape - IFM and OFM data types must match -- Batch size must be 1 for Input tensors with more than 2 dimensions ### TFLite ADD Constraints @@ -93,7 +93,6 @@ This is a list of constraints that the ADD operator must satisfy in order to be - Both Input data types must match - For IFM that are signed, OFM must also be signed - For IFM that are unsigned, OFM must either be the same type or int32 -- Batch size must be 1 for Input tensors with more than 2 dimensions - Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2 ### TFLite AVERAGE_POOL_2D Constraints @@ -103,7 +102,6 @@ This is a list of constraints that the AVERAGE_POOL_2D operator must satisfy in - Stride values for both width and height must be integer types - IFM and OFM data types must match - Kernel filter values for both width and height must be integer types -- IFM Tensor batch size must be 1 - Stride values for both width and height must be in the range [1, 3] - Kernel filter values for both width and height must be in the range [1, 8] - VALID padding: Kernel filter height must be in the range [1, 256] @@ -134,7 +132,6 @@ This is a list of constraints that the CONV_2D operator must satisfy in order to - The sum of the weights cannot exceed 8323072 - Optional Bias tensor must be of type: int32, int64 - Optional Bias tensor values must fit within 40-bits -- IFM Tensor batch size must be 1 ### TFLite DEPTHWISE_CONV_2D Constraints @@ -151,7 +148,6 @@ This is a list of constraints that the DEPTHWISE_CONV_2D operator must satisfy i - The sum of the weights cannot exceed 8323072 - Optional Bias tensor must be of type: int32, int64 - Optional Bias tensor values must fit within 40-bits -- IFM Tensor batch size must be 1 - For depth multipliers > 1, IFM channels must be 1 and OFM channels must be equal to the depth multiplier ### TFLite EXPAND_DIMS Constraints @@ -184,7 +180,6 @@ This is a list of constraints that the LEAKY_RELU operator must satisfy in order - At least one Input's shape must match the OFM's shape - IFM and OFM data types must match -- Batch size must be 1 for Input tensors with more than 2 dimensions ### TFLite MAXIMUM Constraints @@ -192,7 +187,6 @@ This is a list of constraints that the MAXIMUM operator must satisfy in order to - At least one Input's shape must match the OFM's shape - IFM and OFM data types must match -- Batch size must be 1 for Input tensors with more than 2 dimensions - Both Input quantization parameters must match OFM quantization parameters - Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2 @@ -203,7 +197,6 @@ This is a list of constraints that the MAX_POOL_2D operator must satisfy in orde - Stride values for both width and height must be integer types - IFM and OFM data types must match - Kernel filter values for both width and height must be integer types -- IFM Tensor batch size must be 1 - Stride values for both width and height must be in the range [1, 3] - Kernel filter height must be in the range [1, 256] - Product of kernel filter width and height must be in the range [1, 65536] @@ -215,7 +208,6 @@ This is a list of constraints that the MEAN operator must satisfy in order to be - IFM must be int8 or uint8 - Input tensor must be at least 2D - Axis indices must correspond to height and width axes -- IFM Tensor batch size must be 1 - Product of height and width must be no greater than 65536 - Product of height and width must be no greater than 4096 when: IFM and OFM have different scale or zero point; or @@ -236,7 +228,6 @@ This is a list of constraints that the MINIMUM operator must satisfy in order to - At least one Input's shape must match the OFM's shape - IFM and OFM data types must match -- Batch size must be 1 for Input tensors with more than 2 dimensions - Both Input quantization parameters must match OFM quantization parameters - Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2 @@ -248,7 +239,6 @@ This is a list of constraints that the MUL operator must satisfy in order to be - Both Input data types must match - For IFM that are signed, OFM must also be signed - For IFM that are unsigned, OFM must either be the same type or int32 -- Batch size must be 1 for Input tensors with more than 2 dimensions - Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2 ### TFLite PAD Constraints @@ -335,7 +325,6 @@ This is a list of constraints that the SUB operator must satisfy in order to be - Both Input data types must match - For IFM that are signed, OFM must also be signed - For IFM that are unsigned, OFM must either be the same type or int32 -- Batch size must be 1 for Input tensors with more than 2 dimensions - Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2 ### TFLite TRANSPOSE_CONV Constraints @@ -353,7 +342,6 @@ This is a list of constraints that the TRANSPOSE_CONV operator must satisfy in o - The sum of the weights cannot exceed 8323072 - Optional Bias tensor must be of type: int32, int64 - Optional Bias tensor values must fit within 40-bits -- IFM Tensor batch size must be 1 - Stride values for both width and height must be 2 - SAME padding: OFM dimensions must equal IFM dimensions multiplied by stride - VALID padding: OFM dimensions must equal IFM dimensions multiplied by stride, -- cgit v1.2.1