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Document generation CI is broken (#13308)
### Description <!-- Describe your changes. --> Fix document generation CI. It's not currently updating the docs as we're skipping the tests, which is the invocation of build.py that would have generated the documentation. Setup specific task to generate documentation for greater clarity. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Operator kernel documentation is not getting updated and is now out of date.
This commit is contained in:
Scott McKay
GitHub
parent
0b29f64dba
commit
ab71c4bbc0
4 changed files with 92 additions and 49 deletions
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@ -758,6 +758,47 @@ Do not modify directly.*
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|Xor|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
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|**Operator Domain:** *com.microsoft*||||
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|Attention|*in* input:**T**<br> *in* weights:**T**<br> *in* bias:**T**<br> *in* mask_index:**M**<br> *in* past:**T**<br> *in* extra_add:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *out* output:**T**<br> *out* present:**T**|1+|**T** = tensor(float), tensor(float16)|
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|BeamSearch|*in* input_ids:**I**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* num_beams:**I**<br> *in* num_return_sequences:**I**<br> *in* length_penalty:**T**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**M**<br> *in* prefix_vocab_mask:**M**<br> *in* attention_mask:**I**<br> *out* sequences:**I**<br> *out* sequences_scores:**T**<br> *out* scores:**T**|1+|**T** = tensor(float), tensor(float16)|
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|BiasDropout|*in* data:**T**<br> *in* bias:**T**<br> *in* residual:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T2**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
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|BiasGelu|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
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|BiasSoftmax|*in* data:**T**<br> *in* bias:**T**<br> *out* output:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
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|BitmaskBiasDropout|*in* data:**T**<br> *in* bias:**T**<br> *in* residual:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T3**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)<br/> **T3** = tensor(uint32)|
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|BitmaskDropout|*in* data:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T3**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)<br/> **T3** = tensor(uint32)|
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|ComplexMul|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(float), tensor(float16)|
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|ComplexMulConj|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(float), tensor(float16)|
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|ConvTransposeWithDynamicPads|*in* X:**T**<br> *in* W:**T**<br> *in* Pads:**tensor(int64)**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
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|DecoderAttention|*in* query:**T**<br> *in* key:**T**<br> *in* q_weight:**T**<br> *in* kv_weight:**T**<br> *in* bias:**T**<br> *in* key_padding_mask:**B**<br> *in* key_cache:**T**<br> *in* value_cache:**T**<br> *in* static_kv:**B**<br> *in* use_past:**B**<br> *in* has_layer_state:**B**<br> *in* has_key_padding_mask:**B**<br> *out* output:**T**<br> *out* new_key_cache:**T**<br> *out* new_value_cache:**T**|1+|**T** = tensor(float), tensor(float16)|
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|DequantizeLinear|*in* x:**T1**<br> *in* x_scale:**T2**<br> *in* x_zero_point:**T1**<br> *out* y:**T2**|1+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(float16)|
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|DequantizeWithOrder|*in* input:**Q**<br> *in* scale_input:**S**<br> *out* output:**F**|1+|**F** = tensor(float), tensor(float16)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
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|EmbedLayerNormalization|*in* input_ids:**T1**<br> *in* segment_ids:**T1**<br> *in* word_embedding:**T**<br> *in* position_embedding:**T**<br> *in* segment_embedding:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* mask:**T1**<br> *in* position_ids:**T1**<br> *out* output:**T**<br> *out* mask_index:**T1**<br> *out* embedding_sum:**T**|1+|**T** = tensor(float), tensor(float16)|
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|FastGelu|*in* X:**T**<br> *in* bias:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(float), tensor(float16)|
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|FusedConv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *in* Z:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
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|FusedMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
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|Gelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
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|GreedySearch|*in* input_ids:**I**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**I**<br> *in* prefix_vocab_mask:**I**<br> *in* attention_mask:**I**<br> *out* sequences:**I**|1+|**T** = tensor(float), tensor(float16)|
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|GridSample|*in* X:**T1**<br> *in* Grid:**T1**<br> *out* Y:**T2**|1+|**T1** = tensor(float)<br/> **T2** = tensor(float)|
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|Inverse|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
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|Irfft|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
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|LongformerAttention|*in* input:**T**<br> *in* weight:**T**<br> *in* bias:**T**<br> *in* mask:**T**<br> *in* global_weight:**T**<br> *in* global_bias:**T**<br> *in* global:**G**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
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|NGramRepeatBlock|*in* input_ids:**Tid**<br> *in* scores:**T**<br> *out* scores_out:**T**|1+|**T** = tensor(float)<br/> **Tid** = tensor(int64)|
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|QAttention|*in* input:**T1**<br> *in* weight:**T2**<br> *in* bias:**T3**<br> *in* input_scale:**T3**<br> *in* weight_scale:**T3**<br> *in* mask_index:**T4**<br> *in* input_zero_point:**T1**<br> *in* weight_zero_point:**T2**<br> *in* past:**T3**<br> *out* output:**T3**<br> *out* present:**T3**|1+|**T1** = tensor(int8)<br/> **T2** = tensor(int8)<br/> **T3** = tensor(float), tensor(float16)<br/> **T4** = tensor(int32)|
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|QOrderedAttention|*in* input:**Q**<br> *in* scale_input:**S**<br> *in* scale_Q_gemm:**S**<br> *in* scale_K_gemm:**S**<br> *in* scale_V_gemm:**S**<br> *in* Q_weight:**Q**<br> *in* K_weight:**Q**<br> *in* V_weight:**Q**<br> *in* scale_Q_weight:**S**<br> *in* scale_K_weight:**S**<br> *in* scale_V_weight:**S**<br> *in* Q_bias:**S**<br> *in* K_bias:**S**<br> *in* V_bias:**S**<br> *in* scale_QKT_gemm:**S**<br> *in* scale_QKT_softmax:**S**<br> *in* scale_values_gemm:**S**<br> *in* mask_index:**G**<br> *in* past:**Q**<br> *in* extra_add:**S**<br> *out* output:**Q**|1+|**G** = tensor(int32)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
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|QOrderedGelu|*in* X:**Q**<br> *in* scale_X:**S**<br> *in* scale_Y:**S**<br> *out* Y:**Q**|1+|**Q** = tensor(int8)<br/> **S** = tensor(float)|
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|QOrderedLayerNormalization|*in* X:**Q**<br> *in* scale_X:**S**<br> *in* scale:**F**<br> *in* B:**F**<br> *in* scale_Y:**S**<br> *out* Y:**Q**|1+|**F** = tensor(float), tensor(float16)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
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|QOrderedLongformerAttention|*in* input:**Q**<br> *in* scale_input:**S**<br> *in* weight:**Q**<br> *in* scale_weight:**S**<br> *in* bias:**S**<br> *in* scale_bias:**S**<br> *in* scale_qkv_gemm:**S**<br> *in* mask:**F**<br> *in* global_weight:**Q**<br> *in* scale_global_weight:**S**<br> *in* global_bias:**S**<br> *in* scale_global_gemm:**S**<br> *in* global:**G**<br> *in* scale_output:**S**<br> *out* output:**Q**|1+|**F** = tensor(float16)<br/> **G** = tensor(int32)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
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|QOrderedMatMul|*in* A:**Q**<br> *in* scale_A:**S**<br> *in* B:**Q**<br> *in* scale_B:**S**<br> *in* scale_Y:**S**<br> *in* bias:**S**<br> *in* C:**Q**<br> *in* scale_C:**S**<br> *out* Y:**Q**|1+|**Q** = tensor(int8)<br/> **S** = tensor(float)|
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|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**T1**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|1+|**T1** = tensor(float16)<br/> **T2** = tensor(int8), tensor(uint8)|
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|QuantizeWithOrder|*in* input:**F**<br> *in* scale_input:**S**<br> *out* output:**Q**|1+|**F** = tensor(float), tensor(float16)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
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|Rfft|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
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|SkipLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**|1+|**T** = tensor(float), tensor(float16)|
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|TransposeMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
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|Trilu|*in* X:**T**<br> *in* k:**tensor(int64)**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
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<a name="dmlexecutionprovider"/>
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## Operators implemented by DmlExecutionProvider
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@ -819,6 +860,7 @@ Do not modify directly.*
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|Crop|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
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|CumSum|*in* x:**T**<br> *in* axis:**T2**<br> *out* y:**T**|14+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
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|||11+|**T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
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|DFT|*in* input:**T1**<br> *in* dft_length:**T2**<br> *out* output:**T1**|17+|**T1** = tensor(float)<br/> **T2** = tensor(int64)|
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|DepthToSpace|*in* input:**T**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
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|||11+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
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|||1+|**T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
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@ -884,6 +926,8 @@ Do not modify directly.*
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|||1+|**T** = tensor(float), tensor(float16)|
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|LSTM|*in* X:**T**<br> *in* W:**T**<br> *in* R:**T**<br> *in* B:**T**<br> *in* sequence_lens:**T1**<br> *in* initial_h:**T**<br> *in* initial_c:**T**<br> *in* P:**T**<br> *out* Y:**T**<br> *out* Y_h:**T**<br> *out* Y_c:**T**|14+|**T** = tensor(float), tensor(float16)|
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|||7+|**T** = tensor(float), tensor(float16)|
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|LayerNormalization|*in* X:**T**<br> *in* Scale:**T**<br> *in* B:**T**<br> *out* Y:**T**<br> *out* Mean:**U**<br> *out* InvStdDev:**U**<br><br>or<br><br>*in* X:**T**<br> *in* Scale:**V**<br> *in* B:**V**<br> *out* Y:**V**<br> *out* Mean:**U**<br> *out* InvStdDev:**U**|17+|**T** = tensor(float), tensor(float16)<br/> **U** = tensor(float)|
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|||1+|**T** = tensor(float), tensor(float16)<br/> **U** = tensor(float)|
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|LeakyRelu|*in* X:**T**<br> *out* Y:**T**|6+|**T** = tensor(float), tensor(float16)|
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|Less|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|13+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
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|||9+|**T** = tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(bool)|
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@ -1059,46 +1103,30 @@ Do not modify directly.*
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|Upsample|*in* X:**T**<br> *in* scales:**tensor(float)**<br> *out* Y:**T**<br><br>or<br><br>*in* X:**T**<br> *out* Y:**T**|10+|**T** = tensor(float), tensor(float16)|
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|||9+|**T** = tensor(float), tensor(float16)|
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|||7+|**T** = tensor(float), tensor(float16)|
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|Where|*in* condition:**B**<br> *in* X:**T**<br> *in* Y:**T**<br> *out* output:**T**|9+|**B** = tensor(bool)<br/> **T** = tensor(bool), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint8)|
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|Where|*in* condition:**B**<br> *in* X:**T**<br> *in* Y:**T**<br> *out* output:**T**|9+|**B** = tensor(bool)<br/> **T** = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
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|Xor|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T1**|7+|**T** = tensor(bool)|
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|**Operator Domain:** *com.microsoft*||||
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|Attention|*in* input:**T**<br> *in* weights:**T**<br> *in* bias:**T**<br> *in* mask_index:**M**<br> *in* past:**T**<br> *in* extra_add:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *out* output:**T**<br> *out* present:**T**|1+|**T** = tensor(float), tensor(float16)|
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|BeamSearch|*in* input_ids:**I**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* num_beams:**I**<br> *in* num_return_sequences:**I**<br> *in* length_penalty:**T**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**M**<br> *in* prefix_vocab_mask:**M**<br> *in* attention_mask:**I**<br> *out* sequences:**I**<br> *out* sequences_scores:**T**<br> *out* scores:**T**|1+|**T** = tensor(float), tensor(float16)|
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|BiasDropout|*in* data:**T**<br> *in* bias:**T**<br> *in* residual:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T2**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
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|BiasGelu|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
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|BiasSoftmax|*in* data:**T**<br> *in* bias:**T**<br> *out* output:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
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|BitmaskBiasDropout|*in* data:**T**<br> *in* bias:**T**<br> *in* residual:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T3**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)<br/> **T3** = tensor(uint32)|
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|BitmaskDropout|*in* data:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T3**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)<br/> **T3** = tensor(uint32)|
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|ComplexMul|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(float), tensor(float16)|
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|ComplexMulConj|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(float), tensor(float16)|
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|ConvTransposeWithDynamicPads|*in* X:**T**<br> *in* W:**T**<br> *in* Pads:**tensor(int64)**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
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|DecoderAttention|*in* query:**T**<br> *in* key:**T**<br> *in* q_weight:**T**<br> *in* kv_weight:**T**<br> *in* bias:**T**<br> *in* key_padding_mask:**B**<br> *in* key_cache:**T**<br> *in* value_cache:**T**<br> *in* static_kv:**B**<br> *in* use_past:**B**<br> *in* has_layer_state:**B**<br> *in* has_key_padding_mask:**B**<br> *out* output:**T**<br> *out* new_key_cache:**T**<br> *out* new_value_cache:**T**|1+|**T** = tensor(float), tensor(float16)|
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|DequantizeLinear|*in* x:**T1**<br> *in* x_scale:**T2**<br> *in* x_zero_point:**T1**<br> *out* y:**T2**|1+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(float16)|
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|DequantizeWithOrder|*in* input:**Q**<br> *in* scale_input:**S**<br> *out* output:**F**|1+|**F** = tensor(float), tensor(float16)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
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|EmbedLayerNormalization|*in* input_ids:**T1**<br> *in* segment_ids:**T1**<br> *in* word_embedding:**T**<br> *in* position_embedding:**T**<br> *in* segment_embedding:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* mask:**T1**<br> *in* position_ids:**T1**<br> *out* output:**T**<br> *out* mask_index:**T1**<br> *out* embedding_sum:**T**|1+|**T** = tensor(float), tensor(float16)|
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|FastGelu|*in* X:**T**<br> *in* bias:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(float), tensor(float16)|
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|FusedConv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *in* Z:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
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|FusedMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
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|Gelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
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|GreedySearch|*in* input_ids:**I**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**I**<br> *in* prefix_vocab_mask:**I**<br> *in* attention_mask:**I**<br> *out* sequences:**I**|1+|**T** = tensor(float), tensor(float16)|
|
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|GridSample|*in* X:**T1**<br> *in* Grid:**T1**<br> *out* Y:**T2**|1+|**T1** = tensor(float)<br/> **T2** = tensor(float)|
|
||||
|Inverse|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Irfft|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|LongformerAttention|*in* input:**T**<br> *in* weight:**T**<br> *in* bias:**T**<br> *in* mask:**T**<br> *in* global_weight:**T**<br> *in* global_bias:**T**<br> *in* global:**G**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|NGramRepeatBlock|*in* input_ids:**Tid**<br> *in* scores:**T**<br> *out* scores_out:**T**|1+|**T** = tensor(float)<br/> **Tid** = tensor(int64)|
|
||||
|QAttention|*in* input:**T1**<br> *in* weight:**T2**<br> *in* bias:**T3**<br> *in* input_scale:**T3**<br> *in* weight_scale:**T3**<br> *in* mask_index:**T4**<br> *in* input_zero_point:**T1**<br> *in* weight_zero_point:**T2**<br> *in* past:**T3**<br> *out* output:**T3**<br> *out* present:**T3**|1+|**T1** = tensor(int8)<br/> **T2** = tensor(int8)<br/> **T3** = tensor(float), tensor(float16)<br/> **T4** = tensor(int32)|
|
||||
|QOrderedAttention|*in* input:**Q**<br> *in* scale_input:**S**<br> *in* scale_Q_gemm:**S**<br> *in* scale_K_gemm:**S**<br> *in* scale_V_gemm:**S**<br> *in* Q_weight:**Q**<br> *in* K_weight:**Q**<br> *in* V_weight:**Q**<br> *in* scale_Q_weight:**S**<br> *in* scale_K_weight:**S**<br> *in* scale_V_weight:**S**<br> *in* Q_bias:**S**<br> *in* K_bias:**S**<br> *in* V_bias:**S**<br> *in* scale_QKT_gemm:**S**<br> *in* scale_QKT_softmax:**S**<br> *in* scale_values_gemm:**S**<br> *in* mask_index:**G**<br> *in* past:**Q**<br> *in* extra_add:**S**<br> *out* output:**Q**|1+|**G** = tensor(int32)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
|
||||
|QOrderedGelu|*in* X:**Q**<br> *in* scale_X:**S**<br> *in* scale_Y:**S**<br> *out* Y:**Q**|1+|**Q** = tensor(int8)<br/> **S** = tensor(float)|
|
||||
|QOrderedLayerNormalization|*in* X:**Q**<br> *in* scale_X:**S**<br> *in* scale:**F**<br> *in* B:**F**<br> *in* scale_Y:**S**<br> *out* Y:**Q**|1+|**F** = tensor(float), tensor(float16)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
|
||||
|QOrderedLongformerAttention|*in* input:**Q**<br> *in* scale_input:**S**<br> *in* weight:**Q**<br> *in* scale_weight:**S**<br> *in* bias:**S**<br> *in* scale_bias:**S**<br> *in* scale_qkv_gemm:**S**<br> *in* mask:**F**<br> *in* global_weight:**Q**<br> *in* scale_global_weight:**S**<br> *in* global_bias:**S**<br> *in* scale_global_gemm:**S**<br> *in* global:**G**<br> *in* scale_output:**S**<br> *out* output:**Q**|1+|**F** = tensor(float16)<br/> **G** = tensor(int32)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
|
||||
|QOrderedMatMul|*in* A:**Q**<br> *in* scale_A:**S**<br> *in* B:**Q**<br> *in* scale_B:**S**<br> *in* scale_Y:**S**<br> *in* bias:**S**<br> *in* C:**Q**<br> *in* scale_C:**S**<br> *out* Y:**Q**|1+|**Q** = tensor(int8)<br/> **S** = tensor(float)|
|
||||
|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**T1**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|1+|**T1** = tensor(float16)<br/> **T2** = tensor(int8), tensor(uint8)|
|
||||
|QuantizeWithOrder|*in* input:**F**<br> *in* scale_input:**S**<br> *out* output:**Q**|1+|**F** = tensor(float), tensor(float16)<br/> **Q** = tensor(int8)<br/> **S** = tensor(float)|
|
||||
|Rfft|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|SkipLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|TransposeMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
|
||||
|Trilu|*in* X:**T**<br> *in* k:**tensor(int64)**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|
||||
|Attention|*in* input:**T**<br> *in* weights:**T**<br> *in* bias:**T**<br> *in* mask_index:**M**<br> *in* past:**T**<br> *in* extra_add:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *out* output:**T**<br> *out* present:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(float), tensor(float16)|
|
||||
|ConvTransposeWithDynamicPads|*in* X:**T**<br> *in* W:**T**<br> *in* Pads:**tensor(int64)**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|DequantizeLinear|*in* x:**T1**<br> *in* x_scale:**T2**<br> *in* x_zero_point:**T1**<br> *out* y:**T2**|1+|**T1** = tensor(float)<br/> **T2** = tensor(uint8)|
|
||||
|FusedMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|Gelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|QLinearAdd|*in* A:**T**<br> *in* A_scale:**tensor(float)**<br> *in* A_zero_point:**T**<br> *in* B:**T**<br> *in* B_scale:**tensor(float)**<br> *in* B_zero_point:**T**<br> *in* C_scale:**tensor(float)**<br> *in* C_zero_point:**T**<br> *out* C:**T**|1+|**T** = tensor(int8), tensor(uint8)|
|
||||
|QLinearSigmoid|*in* X:**T**<br> *in* X_scale:**tensor(float)**<br> *in* X_zero_point:**T**<br> *in* Y_scale:**tensor(float)**<br> *in* Y_zero_point:**T**<br> *out* Y:**T**|1+|**T** = tensor(int8), tensor(uint8)|
|
||||
|QuantizeLinear|*in* x:**T1**<br> *in* y_scale:**T1**<br> *in* y_zero_point:**T2**<br> *out* y:**T2**|1+|**T1** = tensor(float)<br/> **T2** = tensor(uint8)|
|
||||
| |
|
||||
| |
|
||||
|**Operator Domain:** *com.microsoft.dml*||||
|
||||
|DmlFusedAdd|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|DmlFusedBatchNormalization|*in* X:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *in* mean:**T**<br> *in* var:**T**<br> *out* Y:**T**<br> *out* mean:**T**<br> *out* var:**T**<br> *out* saved_mean:**T**<br> *out* saved_var:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|DmlFusedConv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|DmlFusedConvTranspose|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|DmlFusedGemm|*in* A:**T**<br> *in* B:**T**<br> *in* C:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|DmlFusedInstanceNormalization|*in* input:**T**<br> *in* scale:**T**<br> *in* B:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|DmlFusedMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|DmlFusedMeanVarianceNormalization|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
|DmlFusedSum|*in* data_0:**T**<br> *out* sum:**T**|1+|**T** = tensor(float), tensor(float16)|
|
||||
| |
|
||||
| |
|
||||
|
|
|
|||
|
|
@ -2365,7 +2365,7 @@ def generate_documentation(source_dir, build_dir, configs, validate):
|
|||
)
|
||||
log.debug("diff:\n" + str(diff))
|
||||
|
||||
diff_file(opkernel_doc_path, " with CPU and CUDA execution providers enabled")
|
||||
diff_file(opkernel_doc_path, " with CPU, CUDA and DML execution providers enabled")
|
||||
diff_file(contrib_op_doc_path)
|
||||
|
||||
if have_diff:
|
||||
|
|
@ -2386,7 +2386,7 @@ def main():
|
|||
|
||||
# If there was no explicit argument saying what to do, default
|
||||
# to update, build and test (for native builds).
|
||||
if not (args.update or args.clean or args.build or args.test):
|
||||
if not (args.update or args.clean or args.build or args.test or args.gen_doc):
|
||||
log.debug("Defaulting to running update, build [and test for native builds].")
|
||||
args.update = True
|
||||
args.build = True
|
||||
|
|
@ -2788,8 +2788,14 @@ def main():
|
|||
if args.test and args.build_nuget:
|
||||
run_csharp_tests(source_dir, build_dir, args.use_cuda, args.use_openvino, args.use_tensorrt, args.use_dnnl)
|
||||
|
||||
if args.gen_doc and (args.build or args.test):
|
||||
generate_documentation(source_dir, build_dir, configs, args.gen_doc == "validate")
|
||||
if args.gen_doc:
|
||||
# special case CI where we create the build config separately to building
|
||||
if args.update and not args.build:
|
||||
pass
|
||||
else:
|
||||
# assumes build has occurred for easier use in CI where we don't always build via build.py and need to run
|
||||
# documentation generation as a separate task post-build
|
||||
generate_documentation(source_dir, build_dir, configs, args.gen_doc == "validate")
|
||||
|
||||
if args.gen_api_doc and (args.build or args.test):
|
||||
print("Generating Python doc for ORTModule...")
|
||||
|
|
|
|||
|
|
@ -45,8 +45,8 @@ parameters:
|
|||
- name: MachinePool
|
||||
type: string
|
||||
|
||||
- name: DocUpdateNeeded
|
||||
displayName: Run Tests?
|
||||
- name: GenerateDocumentation
|
||||
displayName: Generate updated documentation. Requires build to have occurred and `--gen_doc` to be specified
|
||||
type: boolean
|
||||
default: false
|
||||
|
||||
|
|
@ -58,7 +58,7 @@ jobs:
|
|||
DOTNET_SKIP_FIRST_TIME_EXPERIENCE: true
|
||||
setVcvars: true
|
||||
ALLOW_RELEASED_ONNX_OPSET_ONLY: '0'
|
||||
DocUpdateNeeded: ${{ parameters.DocUpdateNeeded }}
|
||||
DocUpdateNeeded: false # Set to true during document generation if there are diffs
|
||||
skipComponentGovernanceDetection: true
|
||||
workspace:
|
||||
clean: all
|
||||
|
|
@ -106,7 +106,7 @@ jobs:
|
|||
workingDirectory: '$(Build.BinariesDirectory)'
|
||||
displayName: 'Install python modules'
|
||||
|
||||
- ${{ if eq(parameters.RunOnnxRuntimeTests, true) }}:
|
||||
- ${{ if or(eq(parameters.RunOnnxRuntimeTests, true), eq(parameters.GenerateDocumentation, true)) }}:
|
||||
- powershell: |
|
||||
$Env:USE_MSVC_STATIC_RUNTIME=1
|
||||
$Env:ONNX_ML=1
|
||||
|
|
@ -266,7 +266,15 @@ jobs:
|
|||
testRunTitle: 'Unit Test Run'
|
||||
condition: succeededOrFailed()
|
||||
|
||||
# if the validation from --gen_doc failed it sets a variable so we can publish the latest version of the docs
|
||||
- ${{ if eq(parameters.GenerateDocumentation, true) }}:
|
||||
- task: PythonScript@0
|
||||
displayName: 'Generate documentation'
|
||||
inputs:
|
||||
scriptPath: '$(Build.SourcesDirectory)\tools\ci_build\build.py'
|
||||
arguments: '--config ${{ parameters.BuildConfig }} --build_dir $(Build.BinariesDirectory) --gen_doc validate'
|
||||
workingDirectory: '$(Build.BinariesDirectory)'
|
||||
|
||||
# if the validation from --gen_doc failed it sets DocUpdateNeeded so we can publish the latest version of the docs
|
||||
# as an artifact, allowing a developer to download this and replace the current version instead of having to build
|
||||
# and generate the docs locally themselves. handle each of the two md files separately - simpler than copying
|
||||
# them to another location and publishing from there in a single task.
|
||||
|
|
|
|||
|
|
@ -89,12 +89,13 @@ stages:
|
|||
BuildConfig: 'RelWithDebInfo'
|
||||
EnvSetupScript: setup_env_cuda_11.bat
|
||||
buildArch: x64
|
||||
additionalBuildFlags: --gen_doc validate --skip_tests --enable_pybind --use_dml --use_cuda --cuda_version=11.6 --cuda_home="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.6" --enable_cuda_profiling --cmake_extra_defines CMAKE_CUDA_ARCHITECTURES=75 --cmake_extra_defines onnxruntime_BUILD_UNIT_TESTS=OFF
|
||||
# note: need to specify `--gen_doc` when creating the build config so it has to be in additionalBuildFlags
|
||||
additionalBuildFlags: --gen_doc validate --skip_tests --enable_pybind --use_dml --use_cuda --cuda_version=11.6 --cuda_home="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.6" --cmake_extra_defines CMAKE_CUDA_ARCHITECTURES=75 --cmake_extra_defines onnxruntime_BUILD_UNIT_TESTS=OFF
|
||||
msbuildPlatform: x64
|
||||
isX86: false
|
||||
job_name_suffix: x64_RelWithDebInfo
|
||||
RunOnnxRuntimeTests: false
|
||||
RunStaticCodeAnalysis: false
|
||||
GenerateDocumentation: true
|
||||
ORT_EP_NAME: CUDA # It doesn't really matter which EP is selected here since this stage is for documentation.
|
||||
MachinePool: Win-CPU-2019
|
||||
DocUpdateNeeded: true
|
||||
MachinePool: onnxruntime-Win2019-GPU-T4
|
||||
|
|
|
|||
Loading…
Reference in a new issue