Kernels:

amd
/

aiter-flash-attn

Trusted publisher

Uploaded using `kernel-builder`.

by kernels-bot - opened 6 days ago

base: refs/heads/v2

←

from: refs/pr/2

Discussion Files changed

+22

-20

Files changed (6) hide show

README.md +2 -2
build/torch-rocm/_kernels/flash_attn_triton_amd/bwd.py +5 -3
build/torch-rocm/_ops.py +1 -1
build/torch-rocm/metadata.json +6 -6
build/torch-rocm/metadata.json.sigstore +1 -1
build/torch-rocm/symbols.json +7 -7

README.md CHANGED Viewed

@@ -3,7 +3,7 @@ library_name: kernels
 license: mit
 ---
-This is the repository card of kernels-community/aiter-flash-attn that has been pushed on the Hub. It was built to be used with the [`kernels` library](https://github.com/huggingface/kernels). This card was automatically generated.
 ## How to use
@@ -11,7 +11,7 @@ This is the repository card of kernels-community/aiter-flash-attn that has been
 # make sure `kernels` is installed: `pip install -U kernels`
 from kernels import get_kernel
-kernel_module = get_kernel("kernels-community/aiter-flash-attn", version=2)
 flash_attn_func = kernel_module.flash_attn_func
 flash_attn_func(...)

 license: mit
 ---
+This is the repository card of amd/aiter-flash-attn that has been pushed on the Hub. It was built to be used with the [`kernels` library](https://github.com/huggingface/kernels). This card was automatically generated.
 ## How to use
 # make sure `kernels` is installed: `pip install -U kernels`
 from kernels import get_kernel
+kernel_module = get_kernel("amd/aiter-flash-attn", version=2)
 flash_attn_func = kernel_module.flash_attn_func
 flash_attn_func(...)

build/torch-rocm/_kernels/flash_attn_triton_amd/bwd.py CHANGED Viewed

@@ -16,7 +16,7 @@ from .utils import (
 PREPROCESS_AUTOTUNE_KEYS = [
     "max_seqlen_q",
-    "ACTUAL_HEAD_DIM",
     "IS_VARLEN",
 ]
@@ -24,7 +24,8 @@ CAUSAL_AUTOTUNE_KEYS = [
     "dropout_p",
     "max_seqlen_q",
     "max_seqlen_k",
-    "ACTUAL_HEAD_DIM",
     "IS_VARLEN",
     "HQ",
     "HK",
@@ -34,7 +35,8 @@ NONCAUSAL_AUTOTUNE_KEYS = [
     "dropout_p",
     "max_seqlen_q",
     "max_seqlen_k",
-    "ACTUAL_HEAD_DIM",
     "IS_VARLEN",
     "HQ",
     "HK",

 PREPROCESS_AUTOTUNE_KEYS = [
     "max_seqlen_q",
+    "ACTUAL_HEAD_DIM_V",
     "IS_VARLEN",
 ]
     "dropout_p",
     "max_seqlen_q",
     "max_seqlen_k",
+    "ACTUAL_HEAD_DIM_QK",
+    "ACTUAL_HEAD_DIM_V",
     "IS_VARLEN",
     "HQ",
     "HK",
     "dropout_p",
     "max_seqlen_q",
     "max_seqlen_k",
+    "ACTUAL_HEAD_DIM_QK",
+    "ACTUAL_HEAD_DIM_V",
     "IS_VARLEN",
     "HQ",
     "HK",

build/torch-rocm/_ops.py CHANGED Viewed

@@ -25,7 +25,7 @@ def get_backend() -> str:
 def _find_ops_name() -> str:
     kernel_name = "aiter_flash_attn"
-    unique_id = "fe2bb66"
     backend = get_backend()
     return f"_{kernel_name}_{backend}_{unique_id}"

 def _find_ops_name() -> str:
     kernel_name = "aiter_flash_attn"
+    unique_id = "5fe9dbb"
     backend = get_backend()
     return f"_{kernel_name}_{backend}_{unique_id}"

build/torch-rocm/metadata.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "aiter-flash-attn",
-  "id": "_aiter_flash_attn_rocm_fe2bb66",
   "version": 2,
   "kernels-minver": "0.14.0",
   "license": "MIT",
@@ -15,7 +15,7 @@
       "__init__.py": "V8KdHVvK1+C2G2Ji5P8jHcFXRJcFb6I+OvrP0qHheD4=",
       "_kernels/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "_kernels/flash_attn_triton_amd/__init__.py": "j/n4XyqMKr0Xcl2QVHY+/ZRLW9CT46EsHblfZfZRW8E=",
-      "_kernels/flash_attn_triton_amd/bwd.py": "DX8+Ad+qnlCxv5P56EznW/TW01uwORim3ZWT0g/n3ok=",
       "_kernels/flash_attn_triton_amd/common.py": "Za22tFtoBkGxRqpEBvIexRBlNsVdPxMeL8WnRD3ZRXw=",
       "_kernels/flash_attn_triton_amd/fwd_decode.py": "8F/WwZpTCyFdy6ITf0Pe+HzUxnI4UmXt3c53RT5p34w=",
       "_kernels/flash_attn_triton_amd/fwd_prefill.py": "G1InUnNUtHevT2YInshAlAiEEyloDTJQqqsWxsLMkX0=",
@@ -25,7 +25,7 @@
       "_kernels/mha.py": "aXpxPRtrBPAPc9MXFPIMhZnGYddcd1+MlfrtrOBaKWQ=",
       "_kernels/mha_fused_bwd.py": "eXvVWoS0ysazZttrPfoDA14hDcL9zcjlpwyLVbeVxeU=",
       "_kernels/mha_onekernel_bwd.py": "DOWu0L/MqMNwGpHoBlObMtcnx+txk7juFlPF3FZ8U4I=",
-      "_ops.py": "PsT9QAAJ8peP0WeFSwUb//GFxu/jMT7h+anpaJnOjKw=",
       "configs/gfx1150-MHA-DEFAULT.json": "fY8XJia4+bo/qBUZhODLcWdN8ic+0n0ZUWmxAbcdxjk=",
       "configs/gfx1151-MHA-DEFAULT.json": "fY8XJia4+bo/qBUZhODLcWdN8ic+0n0ZUWmxAbcdxjk=",
       "configs/gfx1250-MHA-DEFAULT.json": "MC9NGs+jRZ0ppvwjszTaoc6YYMsaHKdXiP1K/cUZTSg=",
@@ -34,7 +34,7 @@
       "mha.py": "tdH71c+PZ0vWTC6N5C79i0BzcmdUz3s6f2JuRhrrDSM=",
       "mha_fused_bwd.py": "80rk+DlUC52j/c1hHym8xryXll/xGUtaYxn29YgCSRs=",
       "mha_onekernel_bwd.py": "OsmmrfXuYtr/dwyZkC0beRZ+UM59lOoDQBQH4knQrGg=",
-      "symbols.json": "7UweiWWUtK8f0BxFcmh5IOHuk0yMvShLxE6UJGQT6RQ=",
       "utils/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "utils/_triton/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "utils/_triton/arch_info.py": "D6l6BbeGTMMp48tMcti9Q2mLFsXBnbwoYeoMVyGyy6w=",
@@ -50,11 +50,11 @@
   "provenance": {
     "kernel-builder": {
       "version": "0.18.0-dev0",
-      "commit": "d054906a13710b811b96697b4635f03020842733",
       "dirty": false
     },
     "kernel": {
-      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
       "dirty": false
     }
   }

 {
   "name": "aiter-flash-attn",
+  "id": "_aiter_flash_attn_rocm_5fe9dbb",
   "version": 2,
   "kernels-minver": "0.14.0",
   "license": "MIT",
       "__init__.py": "V8KdHVvK1+C2G2Ji5P8jHcFXRJcFb6I+OvrP0qHheD4=",
       "_kernels/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "_kernels/flash_attn_triton_amd/__init__.py": "j/n4XyqMKr0Xcl2QVHY+/ZRLW9CT46EsHblfZfZRW8E=",
+      "_kernels/flash_attn_triton_amd/bwd.py": "lGsltjiLtKO4vRMUmv5IzUWw2CPi+uG7AfTKVuGqI3w=",
       "_kernels/flash_attn_triton_amd/common.py": "Za22tFtoBkGxRqpEBvIexRBlNsVdPxMeL8WnRD3ZRXw=",
       "_kernels/flash_attn_triton_amd/fwd_decode.py": "8F/WwZpTCyFdy6ITf0Pe+HzUxnI4UmXt3c53RT5p34w=",
       "_kernels/flash_attn_triton_amd/fwd_prefill.py": "G1InUnNUtHevT2YInshAlAiEEyloDTJQqqsWxsLMkX0=",
       "_kernels/mha.py": "aXpxPRtrBPAPc9MXFPIMhZnGYddcd1+MlfrtrOBaKWQ=",
       "_kernels/mha_fused_bwd.py": "eXvVWoS0ysazZttrPfoDA14hDcL9zcjlpwyLVbeVxeU=",
       "_kernels/mha_onekernel_bwd.py": "DOWu0L/MqMNwGpHoBlObMtcnx+txk7juFlPF3FZ8U4I=",
+      "_ops.py": "lyeSILMGFhP3Wo3ytnmz7wH6TRXcd9jOcJ+2e0J96x8=",
       "configs/gfx1150-MHA-DEFAULT.json": "fY8XJia4+bo/qBUZhODLcWdN8ic+0n0ZUWmxAbcdxjk=",
       "configs/gfx1151-MHA-DEFAULT.json": "fY8XJia4+bo/qBUZhODLcWdN8ic+0n0ZUWmxAbcdxjk=",
       "configs/gfx1250-MHA-DEFAULT.json": "MC9NGs+jRZ0ppvwjszTaoc6YYMsaHKdXiP1K/cUZTSg=",
       "mha.py": "tdH71c+PZ0vWTC6N5C79i0BzcmdUz3s6f2JuRhrrDSM=",
       "mha_fused_bwd.py": "80rk+DlUC52j/c1hHym8xryXll/xGUtaYxn29YgCSRs=",
       "mha_onekernel_bwd.py": "OsmmrfXuYtr/dwyZkC0beRZ+UM59lOoDQBQH4knQrGg=",
+      "symbols.json": "MjzLzV945d3X064nIEtc5ahqIpcXhLErlJ77ooNLO+A=",
       "utils/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "utils/_triton/__init__.py": "47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=",
       "utils/_triton/arch_info.py": "D6l6BbeGTMMp48tMcti9Q2mLFsXBnbwoYeoMVyGyy6w=",
   "provenance": {
     "kernel-builder": {
       "version": "0.18.0-dev0",
+      "commit": "21bdf69d97a289b9c581a0c60f63deed131647d5",
       "dirty": false
     },
     "kernel": {
+      "commit": "5fe9dbb3d45f6f2652958c1f70e5381891fa7aa0",
       "dirty": false
     }
   }

build/torch-rocm/metadata.json.sigstore CHANGED Viewed

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build/torch-rocm/symbols.json CHANGED Viewed

@@ -1,11 +1,11 @@
 {
   "schema_version": 1,
-  "module": "_aiter_flash_attn_rocm_fe2bb66",
   "functions": [
     {
       "name": "flash_attn_func",
       "qualname": "flash_attn_func",
-      "module": "_aiter_flash_attn_rocm_fe2bb66.mha",
       "kind": "function",
       "doc": "dropout_p should be set to 0.0 during evaluation\nSupports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads\nthan Q. Note that the number of heads in Q must be divisible by the number of heads in KV.\nFor example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head\n0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.\n\nIf causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.\nFor example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:\n    1 1 1 1 0\n    1 1 1 1 1\nIf seqlen_q = 5 and seqlen_k = 2, the causal mask is:\n    0 0\n    0 0\n    0 0\n    1 0\n    1 1\nIf the row of the mask is all zero, the output will be zero.\n\nIf window_size != (-1, -1), implements sliding window local attention. Query at position i\nwill only attend to keys between\n[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.\n\nArguments:\n    q: (batch_size, seqlen, nheads, headdim)\n    k: (batch_size, seqlen, nheads_k, headdim)\n    v: (batch_size, seqlen, nheads_k, headdim)\n    dropout_p: float. Dropout probability.\n    softmax_scale: float. The scaling of QK^T before applying softmax.\n        Default to 1 / sqrt(headdim_q).\n    causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).\n    window_size: (left, right). If not (-1, -1), implements sliding window local attention.\n    bias: (seqlen_q, seqlen_k)\n    alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of\n        (-alibi_slope * |i + seqlen_k - seqlen_q - j|)\n        is added to the attention score of query i and key j.\n    deterministic: bool. Whether to use the deterministic implementation of the backward pass,\n        which is slightly slower and uses more memory. The forward pass is always deterministic.\n    return_attn_probs: bool. Whether to return the attention probabilities. This option is for\n       testing only. The returned probabilities are not guaranteed to be correct\n       (they might not have the right scaling).\n    s_aux: (nheads,), attention sink scores (one per Q head), or None\nReturn:\n    out: (batch_size, seqlen, nheads, headdim).\n    softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The\n        logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax\n        normalization factor).\n    S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).\n        The output of softmax (possibly with different scaling). It also encodes the dropout\n        pattern (negative means that location was dropped, nonnegative means it was kept).",
       "signature": {
@@ -101,7 +101,7 @@
     {
       "name": "flash_attn_varlen_func",
       "qualname": "flash_attn_varlen_func",
-      "module": "_aiter_flash_attn_rocm_fe2bb66.mha",
       "kind": "function",
       "doc": "dropout_p should be set to 0.0 during evaluation\nSupports multi-query and grouped-query attention (MQA/GQA) by passing in K, V with fewer heads\nthan Q. Note that the number of heads in Q must be divisible by the number of heads in KV.\nFor example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head\n0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.\n\nIf causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.\nFor example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:\n    1 1 1 1 0\n    1 1 1 1 1\nIf seqlen_q = 5 and seqlen_k = 2, the causal mask is:\n    0 0\n    0 0\n    0 0\n    1 0\n    1 1\nIf the row of the mask is all zero, the output will be zero.\n\nIf window_size != (-1, -1), implements sliding window local attention. Query at position i\nwill only attend to keys between\n[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.\n\nArguments:\n    q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.\n    k: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.\n    v: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.\n    cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths\n       of the sequences in the batch, used to index into q.\n    cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths\n       of the sequences in the batch, used to index into kv.\n    max_seqlen_q: int. Maximum query sequence length in the batch.\n    max_seqlen_k: int. Maximum key sequence length in the batch.\n    dropout_p: float. Dropout probability.\n    softmax_scale: float. The scaling of QK^T before applying softmax.\n        Default to 1 / sqrt(headdim).\n    causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).\n    window_size: (left, right). If not (-1, -1), implements sliding window local attention.\n    bias: (seqlen_q, seqlen_k)\n    alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of\n        (-alibi_slope * |i + seqlen_k - seqlen_q - j|)\n        is added to the attention score of query i and key j.\n    deterministic: bool. Whether to use the deterministic implementation of the backward pass,\n        which is slightly slower and uses more memory. The forward pass is always deterministic.\n    return_attn_probs: bool. Whether to return the attention probabilities. This option is for\n       testing only. The returned probabilities are not guaranteed to be correct\n       (they might not have the right scaling).\n    s_aux: (nheads,), attention sink scores (one per Q head), or None\nReturn:\n    out: (total, nheads, headdim).\n    softmax_lse [optional, if return_attn_probs=True]: (nheads, total_q_seqlen). The\n        logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax\n        normalization factor).\n    S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).\n        The output of softmax (possibly with different scaling). It also encodes the dropout\n        pattern (negative means that location was dropped, nonnegative means it was kept).",
       "signature": {
@@ -233,7 +233,7 @@
     {
       "name": "flash_attn_with_kvcache",
       "qualname": "flash_attn_with_kvcache",
-      "module": "_aiter_flash_attn_rocm_fe2bb66.mha",
       "kind": "function",
       "doc": "This mirrors the public flash_attn v2 interface for KV cache using the AMD Triton backend.\n\nArgs:\n    q: (batch, seqlen_q, nheads_q, headdim)\n    k_cache / v_cache: Either contiguous (batch, seqlen_cache, nheads_k, headdim) or paged\n        (num_blocks, page_block_size, nheads_k, headdim) when block_table provided.\n    k, v: Optional incremental tokens to append in-place (appended logically after existing cache).\n    cache_seqlens: int or (batch,) current valid lengths per batch entry.\n    softmax_scale: Optional override; defaults to 1/sqrt(headdim).\n    causal: Apply causal masking.\n    window_size: (left, right) local attention window; (-1,-1) = full.\n    softcap: (float) currently must be 0.0 (backend limitation).\n    num_splits: 0 or 1 only (backend limitation >1).\n    rotary_cos/rotary_sin: Optional rotary embeddings (applied if provided) - interleaving flag unused here.\n    cache_batch_idx/cache_leftpad: Optional indexing / left padding metadata.\n        block_table: Optional paging table mapping logical blocks for paged KV cache.\n    alibi_slopes: (nheads,) or (batch,nheads) bias slopes (currently ignored if provided - placeholder).\n    rotary_interleaved: Flag kept for parity (currently forwarded as True constant to backend which ignores it).\n        return_softmax_lse: If True returns (out, lse) else out.\n\nReturns:\n    out (and optionally softmax_lse): (batch, seqlen_q, nheads_q, headdim)",
       "signature": {
@@ -359,7 +359,7 @@
     {
       "name": "mha_set_impl",
       "qualname": "mha_set_impl",
-      "module": "_aiter_flash_attn_rocm_fe2bb66.mha",
       "kind": "function",
       "doc": "Set MHA forward implementation: 'default' (_attn_fwd) or 'dao_ai' (flash_attn_triton_amd).",
       "signature": {
@@ -377,7 +377,7 @@
     {
       "name": "mha_set_use_fused_bwd_kernel",
       "qualname": "mha_set_use_fused_bwd_kernel",
-      "module": "_aiter_flash_attn_rocm_fe2bb66.mha",
       "kind": "function",
       "doc": "Set whether to use fused backward kernel (with atomics) or one-kernel backward (without atomics).\nFused backward is faster but doesn't support positional encoding.",
       "signature": {
@@ -395,7 +395,7 @@
     {
       "name": "mha_set_use_int64_strides",
       "qualname": "mha_set_use_int64_strides",
-      "module": "_aiter_flash_attn_rocm_fe2bb66.mha",
       "kind": "function",
       "doc": "Use 64-bit integer strides to prevent integer overflows with very large tensors.",
       "signature": {

 {
   "schema_version": 1,
+  "module": "_aiter_flash_attn_rocm_5fe9dbb",
   "functions": [
     {
       "name": "flash_attn_func",
       "qualname": "flash_attn_func",
+      "module": "_aiter_flash_attn_rocm_5fe9dbb.mha",
       "kind": "function",
       "doc": "dropout_p should be set to 0.0 during evaluation\nSupports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads\nthan Q. Note that the number of heads in Q must be divisible by the number of heads in KV.\nFor example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head\n0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.\n\nIf causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.\nFor example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:\n    1 1 1 1 0\n    1 1 1 1 1\nIf seqlen_q = 5 and seqlen_k = 2, the causal mask is:\n    0 0\n    0 0\n    0 0\n    1 0\n    1 1\nIf the row of the mask is all zero, the output will be zero.\n\nIf window_size != (-1, -1), implements sliding window local attention. Query at position i\nwill only attend to keys between\n[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.\n\nArguments:\n    q: (batch_size, seqlen, nheads, headdim)\n    k: (batch_size, seqlen, nheads_k, headdim)\n    v: (batch_size, seqlen, nheads_k, headdim)\n    dropout_p: float. Dropout probability.\n    softmax_scale: float. The scaling of QK^T before applying softmax.\n        Default to 1 / sqrt(headdim_q).\n    causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).\n    window_size: (left, right). If not (-1, -1), implements sliding window local attention.\n    bias: (seqlen_q, seqlen_k)\n    alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of\n        (-alibi_slope * |i + seqlen_k - seqlen_q - j|)\n        is added to the attention score of query i and key j.\n    deterministic: bool. Whether to use the deterministic implementation of the backward pass,\n        which is slightly slower and uses more memory. The forward pass is always deterministic.\n    return_attn_probs: bool. Whether to return the attention probabilities. This option is for\n       testing only. The returned probabilities are not guaranteed to be correct\n       (they might not have the right scaling).\n    s_aux: (nheads,), attention sink scores (one per Q head), or None\nReturn:\n    out: (batch_size, seqlen, nheads, headdim).\n    softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The\n        logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax\n        normalization factor).\n    S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).\n        The output of softmax (possibly with different scaling). It also encodes the dropout\n        pattern (negative means that location was dropped, nonnegative means it was kept).",
       "signature": {
     {
       "name": "flash_attn_varlen_func",
       "qualname": "flash_attn_varlen_func",
+      "module": "_aiter_flash_attn_rocm_5fe9dbb.mha",
       "kind": "function",
       "doc": "dropout_p should be set to 0.0 during evaluation\nSupports multi-query and grouped-query attention (MQA/GQA) by passing in K, V with fewer heads\nthan Q. Note that the number of heads in Q must be divisible by the number of heads in KV.\nFor example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head\n0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.\n\nIf causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.\nFor example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:\n    1 1 1 1 0\n    1 1 1 1 1\nIf seqlen_q = 5 and seqlen_k = 2, the causal mask is:\n    0 0\n    0 0\n    0 0\n    1 0\n    1 1\nIf the row of the mask is all zero, the output will be zero.\n\nIf window_size != (-1, -1), implements sliding window local attention. Query at position i\nwill only attend to keys between\n[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.\n\nArguments:\n    q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.\n    k: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.\n    v: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.\n    cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths\n       of the sequences in the batch, used to index into q.\n    cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths\n       of the sequences in the batch, used to index into kv.\n    max_seqlen_q: int. Maximum query sequence length in the batch.\n    max_seqlen_k: int. Maximum key sequence length in the batch.\n    dropout_p: float. Dropout probability.\n    softmax_scale: float. The scaling of QK^T before applying softmax.\n        Default to 1 / sqrt(headdim).\n    causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).\n    window_size: (left, right). If not (-1, -1), implements sliding window local attention.\n    bias: (seqlen_q, seqlen_k)\n    alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of\n        (-alibi_slope * |i + seqlen_k - seqlen_q - j|)\n        is added to the attention score of query i and key j.\n    deterministic: bool. Whether to use the deterministic implementation of the backward pass,\n        which is slightly slower and uses more memory. The forward pass is always deterministic.\n    return_attn_probs: bool. Whether to return the attention probabilities. This option is for\n       testing only. The returned probabilities are not guaranteed to be correct\n       (they might not have the right scaling).\n    s_aux: (nheads,), attention sink scores (one per Q head), or None\nReturn:\n    out: (total, nheads, headdim).\n    softmax_lse [optional, if return_attn_probs=True]: (nheads, total_q_seqlen). The\n        logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax\n        normalization factor).\n    S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).\n        The output of softmax (possibly with different scaling). It also encodes the dropout\n        pattern (negative means that location was dropped, nonnegative means it was kept).",
       "signature": {
     {
       "name": "flash_attn_with_kvcache",
       "qualname": "flash_attn_with_kvcache",
+      "module": "_aiter_flash_attn_rocm_5fe9dbb.mha",
       "kind": "function",
       "doc": "This mirrors the public flash_attn v2 interface for KV cache using the AMD Triton backend.\n\nArgs:\n    q: (batch, seqlen_q, nheads_q, headdim)\n    k_cache / v_cache: Either contiguous (batch, seqlen_cache, nheads_k, headdim) or paged\n        (num_blocks, page_block_size, nheads_k, headdim) when block_table provided.\n    k, v: Optional incremental tokens to append in-place (appended logically after existing cache).\n    cache_seqlens: int or (batch,) current valid lengths per batch entry.\n    softmax_scale: Optional override; defaults to 1/sqrt(headdim).\n    causal: Apply causal masking.\n    window_size: (left, right) local attention window; (-1,-1) = full.\n    softcap: (float) currently must be 0.0 (backend limitation).\n    num_splits: 0 or 1 only (backend limitation >1).\n    rotary_cos/rotary_sin: Optional rotary embeddings (applied if provided) - interleaving flag unused here.\n    cache_batch_idx/cache_leftpad: Optional indexing / left padding metadata.\n        block_table: Optional paging table mapping logical blocks for paged KV cache.\n    alibi_slopes: (nheads,) or (batch,nheads) bias slopes (currently ignored if provided - placeholder).\n    rotary_interleaved: Flag kept for parity (currently forwarded as True constant to backend which ignores it).\n        return_softmax_lse: If True returns (out, lse) else out.\n\nReturns:\n    out (and optionally softmax_lse): (batch, seqlen_q, nheads_q, headdim)",
       "signature": {
     {
       "name": "mha_set_impl",
       "qualname": "mha_set_impl",
+      "module": "_aiter_flash_attn_rocm_5fe9dbb.mha",
       "kind": "function",
       "doc": "Set MHA forward implementation: 'default' (_attn_fwd) or 'dao_ai' (flash_attn_triton_amd).",
       "signature": {
     {
       "name": "mha_set_use_fused_bwd_kernel",
       "qualname": "mha_set_use_fused_bwd_kernel",
+      "module": "_aiter_flash_attn_rocm_5fe9dbb.mha",
       "kind": "function",
       "doc": "Set whether to use fused backward kernel (with atomics) or one-kernel backward (without atomics).\nFused backward is faster but doesn't support positional encoding.",
       "signature": {
     {
       "name": "mha_set_use_int64_strides",
       "qualname": "mha_set_use_int64_strides",
+      "module": "_aiter_flash_attn_rocm_5fe9dbb.mha",
       "kind": "function",
       "doc": "Use 64-bit integer strides to prevent integer overflows with very large tensors.",
       "signature": {