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[ORT 1.17.0 Release] Cherry-pick Final Round (#19327)
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### Description
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Cherry-pick Final Round


### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->

---------

Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com>
Co-authored-by: Changming Sun <chasun@microsoft.com>
Co-authored-by: Chi Lo <54722500+chilo-ms@users.noreply.github.com>
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
Co-authored-by: aciddelgado <139922440+aciddelgado@users.noreply.github.com>
Co-authored-by: Yufeng Li <liyufeng1987@gmail.com>
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2 changes: 2 additions & 0 deletions .pipelines/OneBranch.Nuget-WindowsAI-Pipeline.Official.yml
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Expand Up @@ -29,6 +29,8 @@ extends:
git:
submodules: false
globalSdl: # https://aka.ms/obpipelines/sdl
asyncSdl:
enabled: false
tsa:
enabled: true
prefast:
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207 changes: 207 additions & 0 deletions ThirdPartyNotices.txt
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Expand Up @@ -6299,3 +6299,210 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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16 changes: 12 additions & 4 deletions docs/ContribOperators.md
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Expand Up @@ -2398,24 +2398,28 @@ This version of the operator has been available since version 1 of the 'com.micr
#### Attributes

<dl>
<dt><tt>do_rotary</tt> : int</dt>
<dd>Whether to use rotary position embedding. Default value is 0.</dd>
<dt><tt>kv_num_heads</tt> : int (required)</dt>
<dd>Number of attention heads for k and v</dd>
<dt><tt>local_window_size</tt> : int</dt>
<dd>left_window_size for local attention (like Mistral). Default value is -1 meaning unused.</dd>
<dt><tt>num_heads</tt> : int (required)</dt>
<dd>Number of attention heads for q</dd>
<dt><tt>rotary_interleaved</tt> : int</dt>
<dd>Rotate using interleaved pattern. Default value is 0 (False).</dd>
<dt><tt>scale</tt> : float</dt>
<dd>Custom scale will be used if specified. Default value is 1/sqrt(head_size)</dd>
</dl>

#### Inputs
#### Inputs (7 - 9)

<dl>
<dt><tt>query</tt> : T</dt>
<dd>Query with shape (batch_size, sequence_length, hidden_size)</dd>
<dt><tt>key</tt> : T</dt>
<dd>Query with shape (batch_size, sequence_length, hidden_size), or packed QKV with shape(batch_size, sequence_length, d) where d is (num_heads * head_size + 2 * kv_num_heads * head_size).</dd>
<dt><tt>key</tt> (optional) : T</dt>
<dd>Key with shape (batch_size, kv_sequence_length, kv_hidden_size) </dd>
<dt><tt>value</tt> : T</dt>
<dt><tt>value</tt> (optional) : T</dt>
<dd>Value with shape (batch_size, kv_sequence_length, kv_hidden_size)</dd>
<dt><tt>past_key</tt> (optional) : T</dt>
<dd>past state key with support for format BNSH. When past_key uses same tensor as present_key(k-v cache), it is of length max_sequence_length... otherwise of length past_sequence_length.</dd>
Expand All @@ -2425,6 +2429,10 @@ This version of the operator has been available since version 1 of the 'com.micr
<dd>1d Tensor of shape (batch_size). Indicates past sequence lengths for token generation case.</dd>
<dt><tt>total_sequence_length</tt> : M</dt>
<dd>Scalar tensor of total sequence length (past + new).</dd>
<dt><tt>cos_cache</tt> (optional) : T</dt>
<dd>2D tensor with shape (max_sequence_length, head_size / 2).</dd>
<dt><tt>sin_cache</tt> (optional) : T</dt>
<dd>2D tensor with shape (max_sequence_length, head_size / 2).</dd>
</dl>

#### Outputs
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2 changes: 1 addition & 1 deletion docs/OperatorKernels.md
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Expand Up @@ -843,7 +843,7 @@ Do not modify directly.*
|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)|
|GridSample|*in* X:**T1**<br> *in* Grid:**T1**<br> *out* Y:**T2**|1+|**T1** = tensor(float)<br/> **T2** = tensor(float)|
|GroupNorm|*in* X:**T**<br> *in* gamma:**M**<br> *in* beta:**M**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
|GroupQueryAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* seqlens_k:**M**<br> *in* total_sequence_length:**M**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(bfloat16), tensor(float16)|
|GroupQueryAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* seqlens_k:**M**<br> *in* total_sequence_length:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(bfloat16), tensor(float16)|
|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)|
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5 changes: 5 additions & 0 deletions onnxruntime/contrib_ops/cpu/bert/attention_common.h
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Expand Up @@ -99,10 +99,15 @@ struct GroupQueryAttentionParameters {
bool is_unidirectional; // causal
int local_window_size;
bool kv_share_buffer;
bool is_packed_qkv;
bool is_prompt; // determines if seqlens_k is past or kv sequence length tensor
bool do_rotary;
bool rotary_interleaved;
float scale;
AttentionQkvFormat qkv_format;
AttentionQkvFormat past_kv_format;
int zeros_count;
int* zero_ptr;
};

namespace attention {
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2 changes: 2 additions & 0 deletions onnxruntime/contrib_ops/cuda/bert/decoder_masked_multihead_attention.cc
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Expand Up @@ -74,6 +74,7 @@ Status DecoderMaskedMultiHeadAttention<T1, T2>::ComputeInternal(OpKernelContext*
parameters.kv_data_in_flight = ParseEnvironmentVariableWithDefault<bool>(
attention::kDecoderMaskedAttentionLoadKVDataInFlight, false);

bool is_unidirectional = false;
bool is_dmmha_packing = (key == nullptr && value == nullptr);
ORT_RETURN_IF_ERROR(multihead_attention_helper::CheckInputs<Tensor>(query,
key,
Expand All @@ -88,6 +89,7 @@ Status DecoderMaskedMultiHeadAttention<T1, T2>::ComputeInternal(OpKernelContext*
num_heads_,
mask_filter_value_,
scale_,
is_unidirectional,
past_present_share_buffer_,
is_dmmha_packing, // dmmha_packing
device_prop.maxThreadsPerBlock));
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