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185 changes: 184 additions & 1 deletion docs/ContribOperators.md
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* <a href="#com.microsoft.DequantizeWithOrder">com.microsoft.DequantizeWithOrder</a>
* <a href="#com.microsoft.DynamicQuantizeLSTM">com.microsoft.DynamicQuantizeLSTM</a>
* <a href="#com.microsoft.DynamicQuantizeMatMul">com.microsoft.DynamicQuantizeMatMul</a>
* <a href="#com.microsoft.DynamicTimeWarping">com.microsoft.DynamicTimeWarping</a>
* <a href="#com.microsoft.EPContext">com.microsoft.EPContext</a>
* <a href="#com.microsoft.EmbedLayerNormalization">com.microsoft.EmbedLayerNormalization</a>
* <a href="#com.microsoft.ExpandDims">com.microsoft.ExpandDims</a>
Expand Down Expand Up @@ -98,7 +99,9 @@ Do not modify directly.*
* <a href="#com.microsoft.TorchEmbedding">com.microsoft.TorchEmbedding</a>
* <a href="#com.microsoft.TransposeMatMul">com.microsoft.TransposeMatMul</a>
* <a href="#com.microsoft.Trilu">com.microsoft.Trilu</a>
* <a href="#com.microsoft.UnfoldTensor">com.microsoft.UnfoldTensor</a>
* <a href="#com.microsoft.Unique">com.microsoft.Unique</a>
* <a href="#com.microsoft.WhisperBeamSearch">com.microsoft.WhisperBeamSearch</a>
* <a href="#com.microsoft.WordConvEmbedding">com.microsoft.WordConvEmbedding</a>
* <sub>experimental</sub> <a href="#com.microsoft.IsAllFinite">com.microsoft.IsAllFinite</a>
* <sub>experimental</sub> <a href="#com.microsoft.QEmbedLayerNormalization">com.microsoft.QEmbedLayerNormalization</a>
Expand Down Expand Up @@ -1132,6 +1135,8 @@ This version of the operator has been available since version 1 of the 'com.micr
<dd>The value to be filled in the attention mask. Default value is -10000.0f</dd>
<dt><tt>num_heads</tt> : int (required)</dt>
<dd>Number of attention heads</dd>
<dt><tt>output_qk</tt> : int</dt>
<dd>Need output the cross attention MatMul(Q, K)</dd>
<dt><tt>past_present_share_buffer</tt> : int</dt>
<dd>Corresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)</dd>
<dt><tt>scale</tt> : float</dt>
Expand Down Expand Up @@ -1165,7 +1170,7 @@ This version of the operator has been available since version 1 of the 'com.micr
<dd>Bias tensor with shape (hidden_size + hidden_size + v_hidden_size) from input projection</dd>
</dl>

#### Outputs (1 - 3)
#### Outputs (1 - 4)

<dl>
<dt><tt>output</tt> : T</dt>
Expand All @@ -1174,11 +1179,15 @@ This version of the operator has been available since version 1 of the 'com.micr
<dd>present state for key with shape (batch_size, num_heads, total_sequence_length, head_size). If past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size), while effective_seq_length = (past_sequence_length + kv_sequence_length).</dd>
<dt><tt>present_value</tt> (optional) : T</dt>
<dd>present state for value with shape (batch_size, num_heads, total_sequence_length, head_size). If past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size), while effective_seq_length = (past_sequence_length + kv_sequence_length).</dd>
<dt><tt>qk</tt> (optional) : V</dt>
<dd>normalized Q * K, of shape (batch_size, num_heads, 1, head_size). </dd>
</dl>

#### Type Constraints

<dl>
<dt><tt>V</tt> : tensor(float)</dt>
<dd>Constrain qk output types to float32 tensors.</dd>
<dt><tt>T</tt> : tensor(float), tensor(float16)</dt>
<dd>Constrain input and output types to float tensors.</dd>
<dt><tt>M</tt> : tensor(int32)</dt>
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</dl>


### <a name="com.microsoft.DynamicTimeWarping"></a><a name="com.microsoft.dynamictimewarping">**com.microsoft.DynamicTimeWarping**</a>

Input is cost matrix where each value in input[r][c] is the cost for pass the point (r, c). From current point(r, c), points (r+1, c), (r+1, c+1) or (r, c+1) could be arrived in next move. Given such cost matrix, return dynamic time wrapping of shape [2, x], where the path made by all points (output[0][t], output[1][t])have the lowest cost among all paths from (0, 0) to (M-1, N-1).

#### Version

This version of the operator has been available since version 1 of the 'com.microsoft' operator set.

#### Inputs

<dl>
<dt><tt>input</tt> : F</dt>
<dd>Input cost tensor, it must be 2D tensor of shape M x N, or 1 x M x N</dd>
</dl>

#### Outputs

<dl>
<dt><tt>output</tt> : I</dt>
<dd>Output tensor. shape is [2, x], where max(M, N) <= x < M + N</dd>
</dl>

#### Type Constraints

<dl>
<dt><tt>F</tt> : tensor(float)</dt>
<dd>Constrain to float tensors.</dd>
<dt><tt>I</tt> : tensor(int32)</dt>
<dd>Constrain to integer types.</dd>
</dl>


### <a name="com.microsoft.EPContext"></a><a name="com.microsoft.epcontext">**com.microsoft.EPContext**</a>

Onnx node container for EP context.
Expand Down Expand Up @@ -5192,6 +5233,47 @@ This version of the operator has been available since version 1 of the 'com.micr
</dl>


### <a name="com.microsoft.UnfoldTensor"></a><a name="com.microsoft.unfoldtensor">**com.microsoft.UnfoldTensor**</a>

Returns a tensor which contains all slices of size size from input tensor in the dimension dim. Step between two slices is given by step. If sizedim is the size of dimension dim for input tensor, the size of dimension dim in the returned tensor will be (sizedim - size) / step + 1. An additional dimension of size size is appended in the returned tensor.

#### Version

This version of the operator has been available since version 1 of the 'com.microsoft' operator set.

#### Attributes

<dl>
<dt><tt>dim</tt> : int</dt>
<dd>specify the dimension to unfold</dd>
<dt><tt>size</tt> : int (required)</dt>
<dd>specify the size</dd>
<dt><tt>step</tt> : int</dt>
<dd>specify the step.</dd>
</dl>

#### Inputs

<dl>
<dt><tt>input</tt> : T</dt>
<dd>input tensor</dd>
</dl>

#### Outputs

<dl>
<dt><tt>output</tt> : T</dt>
<dd>Output tensor.</dd>
</dl>

#### Type Constraints

<dl>
<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(bfloat16), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
<dd>Allow inputs and outputs to be any kind of tensor.</dd>
</dl>


### <a name="com.microsoft.Unique"></a><a name="com.microsoft.unique">**com.microsoft.Unique**</a>

Finds all the unique values (deduped list) present in the given input tensor.
Expand Down Expand Up @@ -5238,6 +5320,107 @@ This version of the operator has been available since version 1 of the 'com.micr
</dl>


### <a name="com.microsoft.WhisperBeamSearch"></a><a name="com.microsoft.whisperbeamsearch">**com.microsoft.WhisperBeamSearch**</a>

Beam Search for whisper model, especiall with cross_qk features etc.

#### Version

This version of the operator has been available since version 1 of the 'com.microsoft' operator set.

#### Attributes

<dl>
<dt><tt>decoder</tt> : graph (required)</dt>
<dd>Decoder subgraph to execute in a loop.</dd>
<dt><tt>decoder_output_cross_qk</tt> : int</dt>
<dd>If nozero, decoder subgraph contains output Q*K from cross attentions. Default 0.</dd>
<dt><tt>decoder_start_token_id</tt> : int</dt>
<dd>The id of the token that indicates decoding starts.</dd>
<dt><tt>early_stopping</tt> : int</dt>
<dd>early stop or not</dd>
<dt><tt>encoder</tt> : graph</dt>
<dd>The subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.</dd>
<dt><tt>eos_token_id</tt> : int (required)</dt>
<dd>The id of the end-of-sequence token</dd>
<dt><tt>init_decoder</tt> : graph</dt>
<dd>The subgraph for the first decoding run. It will be called once before `decoder` subgraph. This is relevant only for the GPT2 model. If this attribute is missing, the `decoder` subgraph will be used for all decoding runs</dd>
<dt><tt>model_type</tt> : int</dt>
<dd>Must be 2 for whisper</dd>
<dt><tt>no_repeat_ngram_size</tt> : int</dt>
<dd>no repeat ngrams size</dd>
<dt><tt>no_speech_token</tt> : int</dt>
<dd>The token in whisper model that mark all sequence empty. With this model, whisper could output no_speech_prob after Default -1.</dd>
<dt><tt>pad_token_id</tt> : int (required)</dt>
<dd>The id of the padding token</dd>
<dt><tt>vocab_size</tt> : int</dt>
<dd>Size of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shape</dd>
</dl>

#### Inputs (5 - 14)

<dl>
<dt><tt>input_ids</tt> : F</dt>
<dd>The sequence used as a prompt for the generation in the encoder subgraph. Shape is (batch_size, sequence_length)</dd>
<dt><tt>max_length</tt> : I</dt>
<dd>The maximum length of the sequence to be generated. Shape is (1)</dd>
<dt><tt>min_length</tt> (optional) : I</dt>
<dd>The minimum length below which the score of eos_token_id is set to -Inf. Shape is (1)</dd>
<dt><tt>num_beams</tt> : I</dt>
<dd>Number of beams for beam search. 1 means no beam search. Shape is (1)</dd>
<dt><tt>num_return_sequences</tt> : I</dt>
<dd>The number of returned sequences in the batch. Shape is (1)</dd>
<dt><tt>length_penalty</tt> (optional) : T</dt>
<dd>Exponential penalty to the length. Default value 1.0 means no penalty.Value > 1.0 encourages longer sequences, while values < 1.0 produces shorter sequences.Shape is (1,)</dd>
<dt><tt>repetition_penalty</tt> (optional) : T</dt>
<dd>The parameter for repetition penalty. Default value 1.0 means no penalty. Accepts value > 0.0. Shape is (1)</dd>
<dt><tt>vocab_mask</tt> (optional) : M</dt>
<dd>Mask of vocabulary. Words that masked with 0 are not allowed to be generated, and 1 is allowed. Shape is (vacab_size)</dd>
<dt><tt>prefix_vocab_mask</tt> (optional) : M</dt>
<dd>Mask of vocabulary for first step. Words that masked with 0 are not allowed to be generated, and 1 is allowed. Shape is (batch_size, vocab_size)</dd>
<dt><tt>attention_mask</tt> (optional) : I</dt>
<dd>Custom attention mask. Shape is (batch_size, sequence_length)</dd>
<dt><tt>decoder_input_ids</tt> (optional) : I</dt>
<dd>The forced input id sequence for the decoder subgraph. Shape is (batch_size, initial_sequence_length)</dd>
<dt><tt>logits_processor</tt> (optional) : I</dt>
<dd>Specific logits processor for different types of beamsearch models. Default value 0 means no specific logit processor. Accepts value >= 0. Shape is (1)</dd>
<dt><tt>cross_qk_layer_head</tt> (optional) : I</dt>
<dd>Only keep this list of (layer, head) of QK in the final cross_qk output when use_cross_qk is set. Default collect allits shape is (number of (layer, head) to keep, 2), i.e., [[layer_id1, head_id1], [layer_id2, head_id2]......]</dd>
<dt><tt>extra_decoding_ids</tt> (optional) : I</dt>
<dd>Part of the decoder_input_ids that we need cross qk for it. it is of shape (batch_size, extra_decoding_ids_len).In such case, we should remove this from the tail of the decoder_input_ids, and put it here. ids < 0 in it (for multiple batch) are treated as stop of the extra_decoding_ids for corresponding batch.</dd>
</dl>

#### Outputs (1 - 5)

<dl>
<dt><tt>sequences</tt> : I</dt>
<dd>Word IDs of generated sequences. Shape is (batch_size, num_return_sequences, max_sequence_length)</dd>
<dt><tt>sequences_scores</tt> (optional) : T</dt>
<dd>Final beam score of the generated sequences. Shape is (batch_size, num_return_sequences)</dd>
<dt><tt>scores</tt> (optional) : T</dt>
<dd>Processed beam scores for each vocabulary token at each generation step.Beam scores consisting of log softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam.Shape is (max_length - sequence_length, batch_size, num_beams, vocab_size)</dd>
<dt><tt>cross_qk</tt> (optional) : V</dt>
<dd>Output the accumulated stacked Q*K in cross attentions. Let H = number of Head of cross attention, F = the frames or kv-seq-len of the cross attention input, T = real decoded token length, L = number of layers,B = batch size, R = num_return_sequences. It then should return tensor of shape [B, R, L*H, T, F].If cross_qk_layer_head is given, shape is [B, R, cross_qk_layer_head.shape[0], T, F]</dd>
<dt><tt>non_speech_probs</tt> (optional) : T</dt>
<dd>For whisper model, output the probabilities from logits after encoder and context decoding for the no_speech_token.Currently we treat the last token's logits is what we need, in future extra graph logic may be add to the encoder/context-decoder subgraph.The prob is save before logits may be updated by extra-decoding-ids. The shape of non_speech_probs is [B]</dd>
</dl>

#### Type Constraints

<dl>
<dt><tt>T</tt> : tensor(float), tensor(float16)</dt>
<dd>Constrain to float tensors.</dd>
<dt><tt>F</tt> : tensor(float), tensor(int32), tensor(float16)</dt>
<dd>Constrain input type to float or int tensors.</dd>
<dt><tt>I</tt> : tensor(int32)</dt>
<dd>Constrain to integer types</dd>
<dt><tt>M</tt> : tensor(int32)</dt>
<dd>Constrain mask to integer types</dd>
<dt><tt>V</tt> : tensor(float)</dt>
<dd>Constrain cross_qk to float32 tensors.</dd>
</dl>


### <a name="com.microsoft.WordConvEmbedding"></a><a name="com.microsoft.wordconvembedding">**com.microsoft.WordConvEmbedding**</a>

The WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.
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6 changes: 5 additions & 1 deletion docs/OperatorKernels.md
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|TransposeMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
|Trilu|*in* X:**T**<br> *in* k:**tensor(int64)**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(int64)|
|Unique|*in* x:**T**<br> *out* y:**T**<br> *out* idx:**tensor(int64)**<br> *out* counts:**tensor(int64)**|1+|**T** = tensor(float)|
|WhisperBeamSearch|*in* input_ids:**F**<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> *in* decoder_input_ids:**I**<br> *in* logits_processor:**I**<br> *in* cross_qk_layer_head:**I**<br> *in* extra_decoding_ids:**I**<br> *out* sequences:**I**<br> *out* sequences_scores:**T**<br> *out* scores:**T**<br> *out* cross_qk:**V**<br> *out* non_speech_probs:**T**|1+|**T** = tensor(float)|
|WordConvEmbedding|*in* Sequence:**T**<br> *in* W:**T1**<br> *in* B:**T1**<br> *in* C:**T1**<br> *out* Y:**T1**|1+|**T** = tensor(int32)<br/> **T1** = tensor(float)|
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Expand Down Expand Up @@ -827,10 +828,11 @@ Do not modify directly.*
|ComplexMulConj|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**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)|
|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)|
|DecoderMaskedMultiHeadAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* mask_index:**M**<br> *in* relative_position_bias:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* past_sequence_length:**M**<br> *in* beam_width:**M**<br> *in* cache_indirection:**M**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**T** = tensor(float), tensor(float16)|
|DecoderMaskedMultiHeadAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* mask_index:**M**<br> *in* relative_position_bias:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* past_sequence_length:**M**<br> *in* beam_width:**M**<br> *in* cache_indirection:**M**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**<br> *out* qk:**V**|1+|**T** = tensor(float), tensor(float16)|
|DecoderMaskedSelfAttention|*in* input:**T**<br> *in* weights:**T**<br> *in* bias:**T**<br> *in* mask_index:**M**<br> *in* past:**T**<br> *in* relative_position_bias:**T**<br> *in* past_sequence_length:**M**<br> *in* beam_width:**M**<br> *in* cache_indirection:**M**<br> *out* output:**T**<br> *out* present:**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(int8), tensor(uint8)<br/> **T2** = tensor(float16)|
|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)|
|DynamicTimeWarping|*in* input:**F**<br> *out* output:**I**|1+|**F** = tensor(float)<br/> **I** = tensor(int32)|
|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)|
|FastGelu|*in* X:**T**<br> *in* bias:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(float), tensor(float16)|
|FusedConv|*in* X:**T**<br> *in* W:**T**<br> *in* B:**T**<br> *in* Z:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
Expand Down Expand Up @@ -867,6 +869,8 @@ Do not modify directly.*
|SkipSimplifiedLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**<br> *out* input_skip_bias_sum:**T**|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)|
|UnfoldTensor|*in* input:**T**<br> *out* output:**T**|1+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|WhisperBeamSearch|*in* input_ids:**F**<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> *in* decoder_input_ids:**I**<br> *in* logits_processor:**I**<br> *in* cross_qk_layer_head:**I**<br> *in* extra_decoding_ids:**I**<br> *out* sequences:**I**<br> *out* sequences_scores:**T**<br> *out* scores:**T**<br> *out* cross_qk:**V**<br> *out* non_speech_probs:**T**|1+|**T** = tensor(float), tensor(float16)|
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