From 982823fbef649eed79783b03059daed433441cc3 Mon Sep 17 00:00:00 2001 From: shubhambhokare1 Date: Mon, 28 Oct 2024 20:43:14 +0000 Subject: [PATCH] add expanded doc info --- docs/Changelog.md | 40 ++- docs/Operators.md | 247 +++--------------- docs/TestCoverage.md | 197 +------------- .../case/node/scalardotproductattention.py | 178 +------------ .../model.onnx | Bin 12952 -> 13675 bytes .../model.onnx | Bin 6783 -> 7476 bytes .../model.onnx | Bin 14877 -> 15702 bytes .../model.onnx | Bin 7803 -> 8598 bytes .../model.onnx | Bin 7383 -> 8136 bytes .../model.onnx | Bin 7794 -> 8589 bytes .../model.onnx | Bin 549 -> 549 bytes .../test_data_set_0/input_3.pb | Bin 115 -> 308 bytes .../test_data_set_0/input_4.pb | Bin 2327 -> 2327 bytes .../test_data_set_0/input_5.pb | Bin 2905 -> 2905 bytes .../test_data_set_0/output_0.pb | Bin 1941 -> 1941 bytes .../test_data_set_0/output_1.pb | Bin 3482 -> 3482 bytes .../test_data_set_0/output_2.pb | Bin 4348 -> 4348 bytes .../model.onnx | Bin 16081 -> 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onnx/backend/test/data/node/test_scalar_dot_product_attention_4d_with_present_expanded/test_data_set_0/output_2.pb diff --git a/docs/Changelog.md b/docs/Changelog.md index 06345dd0a08..9324c250725 100644 --- a/docs/Changelog.md +++ b/docs/Changelog.md @@ -29034,7 +29034,39 @@ This version of the operator has been available since version 23 of the default ### **ScalarDotProductAttention-23** - Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed + Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed. + + This operator covers self and cross variants of the attention operation based on sequence lengths of K, Q and V. + For self attention, kv_sequence_length equals to q_sequence_length. + For cross attention, query and key might have different lengths. + + This operator also covers the 3 following variants based on the number of heads: + 1) Multi-headed Attention (MHA): Described in the paper https://arxiv.org/pdf/1706.03762, q_num_heads = kv_num_heads. + 2) Group-query Attention (GQA): Described in the paper https://arxiv.org/pdf/2305.13245, q_num_heads > kv_num_heads. + 3) Multi-query Attention (MQA): Described in the paper https://arxiv.org/pdf/1911.02150, q_num_heads > kv_num_heads, q_num_heads=1. + + Attention bias to be added is calculated based on attn_mask input and is_causal attribute, only one of which can be provided. + 1) If is_causal is set to 1, the attention masking is a lower triangular matrix when the mask is a square matrix. The attention masking has the form of the upper left causal bias due to the alignment. + 2) attn_mask: A boolean mask where a value of True indicates that the element should take part in attention or a float mask of the same type as query, key, value that is added to the attention score. + + Both past and present state key/values are optional. They shall be used together, and not allowed to use only one of them. + The following pattern is applied to the Q, K and V inputs after appropriate reshaping of K and V inputs based on sequence lengths and num heads provided: + + Q K V + | | | + | Transpose | + | | | + ---MatMul--- | + | | + scale---Mul | + | | + at_bias---Add | + | | + Softmax | + | | + -----MatMul------ + | + Y #### Version @@ -29064,7 +29096,7 @@ This version of the operator has been available since version 23 of the default
V : T
Value tensor. 4D tensor with shape (batch_size, kv_num_heads, kv_sequence_length, v_head_size) or 3D tensor with shape (batch_size, kv_sequence_length, v_hidden_size). For cases with a 3D input tensor, v_hidden_size = kv_num_heads * v_head_size
attn_mask (optional) : U
-
Attention mask. Shape must be broadcastable to 3D tensor with shape (batch_size, q_sequence_length, kv_sequence_length). Two types of masks are supported. A boolean mask where a value of True indicates that the element should take part in attention. Also supports a float mask of the same type as query, key, value that is added to the attention score.
+
Attention mask. Shape must be broadcastable to 4D tensor with shape (batch_size, q_num_heads, q_sequence_length, total_sequence_length). total_sequence_length is past_sequence_length + kv_sequence_length. Two types of masks are supported. A boolean mask where a value of True indicates that the element should take part in attention. Also supports a float mask of the same type as query, key, value that is added to the attention score.
past_key (optional) : T
past state cache for key with shape (batch_size, kv_num_heads, past_sequence_length, head_size)
past_value (optional) : T
@@ -29077,9 +29109,9 @@ This version of the operator has been available since version 23 of the default
Y : T
The output tensor . 4D tensor with shape (batch_size, q_num_heads, q_sequence_length, v_head_size) or 3D tensor with shape (batch_size, q_sequence_length, hidden_size). For cases with a 3D input tensor, hidden_size = q_num_heads * v_head_size
present_key (optional) : T
-
Updated key cache with shape (batch_size, kv_num_heads, max_sequence_length, head_size).
+
Updated key cache with shape (batch_size, kv_num_heads, total_sequence_length, head_size). total_sequence_length is past_sequence_length + kv_sequence_length.
present_value (optional) : T
-
Updated value cache with shape (batch_size, kv_num_heads, max_sequence_length, v_head_size).
+
Updated value cache with shape (batch_size, kv_num_heads, total_sequence_length, v_head_size). total_sequence_length is past_sequence_length + kv_sequence_length.
#### Type Constraints diff --git a/docs/Operators.md b/docs/Operators.md index 06144841f12..f79604ad656 100644 --- a/docs/Operators.md +++ b/docs/Operators.md @@ -27533,7 +27533,39 @@ expect( ### **ScalarDotProductAttention** - Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed + Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed. + + This operator covers self and cross variants of the attention operation based on sequence lengths of K, Q and V. + For self attention, kv_sequence_length equals to q_sequence_length. + For cross attention, query and key might have different lengths. + + This operator also covers the 3 following variants based on the number of heads: + 1) Multi-headed Attention (MHA): Described in the paper https://arxiv.org/pdf/1706.03762, q_num_heads = kv_num_heads. + 2) Group-query Attention (GQA): Described in the paper https://arxiv.org/pdf/2305.13245, q_num_heads > kv_num_heads. + 3) Multi-query Attention (MQA): Described in the paper https://arxiv.org/pdf/1911.02150, q_num_heads > kv_num_heads, q_num_heads=1. + + Attention bias to be added is calculated based on attn_mask input and is_causal attribute, only one of which can be provided. + 1) If is_causal is set to 1, the attention masking is a lower triangular matrix when the mask is a square matrix. The attention masking has the form of the upper left causal bias due to the alignment. + 2) attn_mask: A boolean mask where a value of True indicates that the element should take part in attention or a float mask of the same type as query, key, value that is added to the attention score. + + Both past and present state key/values are optional. They shall be used together, and not allowed to use only one of them. + The following pattern is applied to the Q, K and V inputs after appropriate reshaping of K and V inputs based on sequence lengths and num heads provided: + + Q K V + | | | + | Transpose | + | | | + ---MatMul--- | + | | + scale---Mul | + | | + at_bias---Add | + | | + Softmax | + | | + -----MatMul------ + | + Y #### Version @@ -27563,7 +27595,7 @@ This version of the operator has been available since version 23 of the default
V : T
Value tensor. 4D tensor with shape (batch_size, kv_num_heads, kv_sequence_length, v_head_size) or 3D tensor with shape (batch_size, kv_sequence_length, v_hidden_size). For cases with a 3D input tensor, v_hidden_size = kv_num_heads * v_head_size
attn_mask (optional) : U
-
Attention mask. Shape must be broadcastable to 3D tensor with shape (batch_size, q_sequence_length, kv_sequence_length). Two types of masks are supported. A boolean mask where a value of True indicates that the element should take part in attention. Also supports a float mask of the same type as query, key, value that is added to the attention score.
+
Attention mask. Shape must be broadcastable to 4D tensor with shape (batch_size, q_num_heads, q_sequence_length, total_sequence_length). total_sequence_length is past_sequence_length + kv_sequence_length. Two types of masks are supported. A boolean mask where a value of True indicates that the element should take part in attention. Also supports a float mask of the same type as query, key, value that is added to the attention score.
past_key (optional) : T
past state cache for key with shape (batch_size, kv_num_heads, past_sequence_length, head_size)
past_value (optional) : T
@@ -27576,9 +27608,9 @@ This version of the operator has been available since version 23 of the default
Y : T
The output tensor . 4D tensor with shape (batch_size, q_num_heads, q_sequence_length, v_head_size) or 3D tensor with shape (batch_size, q_sequence_length, hidden_size). For cases with a 3D input tensor, hidden_size = q_num_heads * v_head_size
present_key (optional) : T
-
Updated key cache with shape (batch_size, kv_num_heads, max_sequence_length, head_size).
+
Updated key cache with shape (batch_size, kv_num_heads, total_sequence_length, head_size). total_sequence_length is past_sequence_length + kv_sequence_length.
present_value (optional) : T
-
Updated value cache with shape (batch_size, kv_num_heads, max_sequence_length, v_head_size).
+
Updated value cache with shape (batch_size, kv_num_heads, total_sequence_length, v_head_size). total_sequence_length is past_sequence_length + kv_sequence_length.
#### Type Constraints @@ -27871,7 +27903,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) @@ -27893,40 +27925,6 @@ expect( -
-scalar_dot_product_attention_3d_diff_head_sizes_with_present - -```python -q_num_heads, kv_num_heads = 3, 3 -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_3d_diff_heads_with_present" -) -``` - -
- -
scalar_dot_product_attention_3d_gqa @@ -28078,7 +28076,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -28100,40 +28098,6 @@ expect(
-
-scalar_dot_product_attention_3d_gqa_with_present - -```python -q_num_heads, kv_num_heads = 9, 3 -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_3d_gqa_with_present" -) -``` - -
- -
scalar_dot_product_attention_3d_scaled @@ -28185,7 +28149,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -28208,40 +28172,6 @@ expect(
-
-scalar_dot_product_attention_3d_with_present - -```python -q_num_heads, kv_num_heads = 3, 3 -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_3d_with_present" -) -``` - -
- -
scalar_dot_product_attention_attn_mask @@ -28439,7 +28369,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) @@ -28460,37 +28390,6 @@ expect(
-
-scalar_dot_product_attention_diff_head_sizes_with_present - -```python -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], -) - -q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_4d_diff_heads_with_present" -) -``` - -
- -
scalar_dot_product_attention_gqa @@ -28626,7 +28525,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -28647,37 +28546,6 @@ expect(
-
-scalar_dot_product_attention_gqa_with_present - -```python -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], -) - -q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_4d_gqa_with_present" -) -``` - -
- -
scalar_dot_product_attention_scaled @@ -28724,7 +28592,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -28746,37 +28614,6 @@ expect(
-
-scalar_dot_product_attention_with_present - -```python -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], -) - -q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_4d_with_present" -) -``` - -
- - ### **Scan** Scan can be used to iterate over one or more scan_input tensors, diff --git a/docs/TestCoverage.md b/docs/TestCoverage.md index a3ebc59178c..8611f6e4e7e 100644 --- a/docs/TestCoverage.md +++ b/docs/TestCoverage.md @@ -19349,7 +19349,7 @@ expect( ### ScalarDotProductAttention -There are 36 test cases, listed as following: +There are 30 test cases, listed as following:
scalar_dot_product_attention @@ -19612,7 +19612,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) @@ -19631,38 +19631,6 @@ expect( ) ``` -
-
-scalar_dot_product_attention_3d_diff_head_sizes_with_present - -```python -q_num_heads, kv_num_heads = 3, 3 -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_3d_diff_heads_with_present" -) -``` -
scalar_dot_product_attention_3d_gqa @@ -19807,7 +19775,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -19826,38 +19794,6 @@ expect( ) ``` -
-
-scalar_dot_product_attention_3d_gqa_with_present - -```python -q_num_heads, kv_num_heads = 9, 3 -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_3d_gqa_with_present" -) -``` -
scalar_dot_product_attention_3d_scaled @@ -19908,7 +19844,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -19928,38 +19864,6 @@ expect( ) ``` -
-
-scalar_dot_product_attention_3d_with_present - -```python -q_num_heads, kv_num_heads = 3, 3 -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_3d_with_present" -) -``` -
scalar_dot_product_attention_attn_mask @@ -20146,7 +20050,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) @@ -20164,35 +20068,6 @@ expect( ) ``` -
-
-scalar_dot_product_attention_diff_head_sizes_with_present - -```python -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], -) - -q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_4d_diff_heads_with_present" -) -``` -
scalar_dot_product_attention_gqa @@ -20321,7 +20196,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -20339,35 +20214,6 @@ expect( ) ``` -
-
-scalar_dot_product_attention_gqa_with_present - -```python -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], -) - -q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_4d_gqa_with_present" -) -``` -
scalar_dot_product_attention_scaled @@ -20413,7 +20259,7 @@ past_sequence_length = 12 q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) +attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -20432,35 +20278,6 @@ expect( ) ``` -
-
-scalar_dot_product_attention_with_present - -```python -node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], -) - -q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) -k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) -attn_mask = np.random.rand(4, 6).astype(np.float32) - -expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, -) - -expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_4d_with_present" -) -``` -
diff --git a/onnx/backend/test/case/node/scalardotproductattention.py b/onnx/backend/test/case/node/scalardotproductattention.py index 498386e1104..a6c8ef7c500 100644 --- a/onnx/backend/test/case/node/scalardotproductattention.py +++ b/onnx/backend/test/case/node/scalardotproductattention.py @@ -60,6 +60,8 @@ def compute_scalar_dot_product_attention( present_value = np.concatenate((past_value, V), axis=2) else: present_value = V + K = present_key + V = present_value # Create attn_bias q_sequence_length = Q.shape[2] @@ -430,7 +432,7 @@ def export_scalar_dot_product_attention_with_past_and_present() -> None: q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) + attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -460,7 +462,7 @@ def export_scalar_dot_product_attention_gqa_with_past_and_present() -> None: q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) + attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -489,7 +491,7 @@ def export_scalar_dot_product_attention_diff_head_sizes_with_past_and_present() q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) + attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) @@ -506,86 +508,6 @@ def export_scalar_dot_product_attention_diff_head_sizes_with_past_and_present() name="test_scalar_dot_product_attention_4d_diff_heads_with_past_and_present" ) - @staticmethod - def export_scalar_dot_product_attention_with_present() -> None: - node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - ) - - q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) - k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) - - expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - ) - - expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_4d_with_present" - ) - - @staticmethod - def export_scalar_dot_product_attention_gqa_with_present() -> None: - node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - ) - - q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) - k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) - - expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - ) - - expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_4d_gqa_with_present" - ) - - @staticmethod - def export_scalar_dot_product_attention_diff_head_sizes_with_present() -> None: - node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - ) - - q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) - k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) - - expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - ) - - expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_4d_diff_heads_with_present" - ) - - - - - - @staticmethod def export_scalar_dot_product_attention_3d() -> None: q_num_heads, kv_num_heads = 3, 3 @@ -933,7 +855,7 @@ def export_scalar_dot_product_attention_3d_with_past_and_present() -> None: q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) + attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -966,7 +888,7 @@ def export_scalar_dot_product_attention_3d_gqa_with_past_and_present() -> None: q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) + attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) @@ -998,7 +920,7 @@ def export_scalar_dot_product_attention_3d_diff_head_sizes_with_past_and_present q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) + attn_mask = np.random.rand(4, 6 + past_sequence_length).astype(np.float32) past_key = np.random.rand(2, 3, past_sequence_length, 8).astype(np.float32) past_value = np.random.rand(2, 3, past_sequence_length, 10).astype(np.float32) @@ -1015,87 +937,3 @@ def export_scalar_dot_product_attention_3d_diff_head_sizes_with_past_and_present outputs=[expected_output, present_key, present_value], name="test_scalar_dot_product_attention_3d_diff_heads_with_past_and_present" ) - - @staticmethod - def export_scalar_dot_product_attention_3d_with_present() -> None: - q_num_heads, kv_num_heads = 3, 3 - node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, - ) - - q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) - k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) - - expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, - ) - - expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_3d_with_present" - ) - - @staticmethod - def export_scalar_dot_product_attention_3d_gqa_with_present() -> None: - q_num_heads, kv_num_heads = 9, 3 - node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, - ) - - q_data = np.random.rand(2, 9, 4, 8).astype(np.float32) - k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - v_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) - - expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, - ) - - expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_3d_gqa_with_present" - ) - - @staticmethod - def export_scalar_dot_product_attention_3d_diff_head_sizes_with_present() -> None: - q_num_heads, kv_num_heads = 3, 3 - node = onnx.helper.make_node( - "ScalarDotProductAttention", - inputs=["Q", "K", "V", "attn_mask"], - outputs=["output", "present_key", "present_value"], - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, - ) - - q_data = np.random.rand(2, 3, 4, 8).astype(np.float32) - k_data = np.random.rand(2, 3, 6, 8).astype(np.float32) - v_data = np.random.rand(2, 3, 6, 10).astype(np.float32) - attn_mask = np.random.rand(4, 6).astype(np.float32) - - expected_output, present_key, present_value = compute_scalar_dot_product_attention( - q_data, k_data, v_data, - attn_mask=attn_mask, - q_num_heads=q_num_heads, kv_num_heads=kv_num_heads, - ) - - expect( - node, - inputs=[q_data, k_data, v_data, attn_mask], - outputs=[expected_output, present_key, present_value], - name="test_scalar_dot_product_attention_3d_diff_heads_with_present" - ) diff --git a/onnx/backend/test/data/node/test_scalar_dot_product_attention_3d_attn_mask_expanded/model.onnx b/onnx/backend/test/data/node/test_scalar_dot_product_attention_3d_attn_mask_expanded/model.onnx index 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Multi-query Attention (MQA): Described in the paper https://arxiv.org/pdf/1911.02150, q_num_heads > kv_num_heads, q_num_heads=1. + +Attention bias to be added is calculated based on attn_mask input and is_causal attribute, only one of which can be provided. +1) If is_causal is set to 1, the attention masking is a lower triangular matrix when the mask is a square matrix. The attention masking has the form of the upper left causal bias due to the alignment. +2) attn_mask: A boolean mask where a value of True indicates that the element should take part in attention or a float mask of the same type as query, key, value that is added to the attention score. + +Both past and present state key/values are optional. They shall be used together, and not allowed to use only one of them. +The following pattern is applied to the Q, K and V inputs after appropriate reshaping of K and V inputs based on sequence lengths and num heads provided: + + Q K V + | | | + | Transpose | + | | | + ---MatMul--- | + | | + scale---Mul | + | | + at_bias---Add | + | | + Softmax | + | | + -----MatMul------ + | + Y )DOC"; @@ -2891,7 +2923,8 @@ ONNX_OPERATOR_SET_SCHEMA( "attn_mask", "Attention mask. " "Shape must be broadcastable to " - "3D tensor with shape (batch_size, q_sequence_length, kv_sequence_length). " + "4D tensor with shape (batch_size, q_num_heads, q_sequence_length, total_sequence_length). " + "total_sequence_length is past_sequence_length + kv_sequence_length. " "Two types of masks are supported. A boolean mask where a value of True indicates that the element should take part in attention. " "Also supports a float mask of the same type as query, key, value that is added to the attention score.", "U", @@ -2918,13 +2951,15 @@ ONNX_OPERATOR_SET_SCHEMA( .Output( 1, "present_key", - "Updated key cache with shape (batch_size, kv_num_heads, max_sequence_length, head_size).", + "Updated key cache with shape (batch_size, kv_num_heads, total_sequence_length, head_size). " + "total_sequence_length is past_sequence_length + kv_sequence_length.", "T", OpSchema::Optional) .Output( 2, "present_value", - "Updated value cache with shape (batch_size, kv_num_heads, max_sequence_length, v_head_size).", + "Updated value cache with shape (batch_size, kv_num_heads, total_sequence_length, v_head_size). " + "total_sequence_length is past_sequence_length + kv_sequence_length.", "T", OpSchema::Optional) .TypeConstraint("T", OpSchema::all_float_types_ir4(), "Constrain input and output types to float tensors.") @@ -3091,20 +3126,23 @@ ONNX_OPERATOR_SET_SCHEMA( if (ctx.hasOutput(1)) { if (ctx.hasInput(4)) { builder.Add("present_key = Concat (past_key, KReshaped)"); - } else { - builder.Add("present_key = Identity (KReshaped)"); } + } else { + builder.Add("present_key = Identity (KReshaped)"); } + if (ctx.hasOutput(2)) { if (ctx.hasInput(5)) { builder.Add("present_value = Concat (past_value, VReshaped)"); - } else { - builder.Add("present_value = Identity (VReshaped)"); } + } else { + builder.Add("present_value = Identity (VReshaped)"); } + // Create a attn_bias filled with zeros of shape (q_sequence_length, kv_sequence_length) - builder.Add("AttnBiasShape = Concat (QSeqLen, KVSeqLen)") + builder.Add("NewKVSeqLen = Shape (present_key)") + .Add("AttnBiasShape = Concat (QSeqLen, NewKVSeqLen)") .Add("AttnBiasZeros = ConstantOfShape(AttnBiasShape)"); // If is_causal set to true, the attention masking is a lower triangular matrix when the mask @@ -3144,10 +3182,10 @@ ONNX_OPERATOR_SET_SCHEMA( .Add("GQACond2 = Equal(RemainderNumHeads, Zero1D)") .Add("GQACond = And(GQACond1, GQACond2)") .Add("InterleaveShape = Concat (One1D, IDivNumHeads, One1D, One1D)") - .Add("KInterleaved = Tile(KReshaped, InterleaveShape)") - .Add("VInterleaved = Tile(VReshaped, InterleaveShape)") - .Add("KAttentionInput = Where(GQACond, KInterleaved, KReshaped)") - .Add("VAttentionInput = Where(GQACond, VInterleaved, VReshaped)"); + .Add("KInterleaved = Tile(present_key, InterleaveShape)") + .Add("VInterleaved = Tile(present_value, InterleaveShape)") + .Add("KAttentionInput = Where(GQACond, KInterleaved, present_key)") + .Add("VAttentionInput = Where(GQACond, VInterleaved, present_value)"); // The following pattern is applied // Q K V